With the growth of protein structure data, the analysis of molecular interactions between ligands and their target molecules is gaining importance. PLIP, the protein-ligand interaction profiler, detects and visualises these interactions and provides data in formats suitable for further processing. PLIP has proven very successful in applications ranging from the characterisation of docking experiments to the assessment of novel ligand-protein complexes. Besides ligand-protein interactions, interactions with DNA and RNA play a vital role in many applications, such as drugs targeting DNA or RNA-binding proteins. To date, over 7% of all 3D structures in the Protein Data Bank include DNA or RNA. Therefore, we extended PLIP to encompass these important molecules. We demonstrate the power of this extension with examples of a cancer drug binding to a DNA target, and an RNA-protein complex central to a neurological disease. PLIP is available online at https://plip-tool.biotec.tu-dresden.de and as open source code. So far, the engine has served over a million queries and the source code has been downloaded several thousand times.

Masked language models have recently been interpreted as energy-based sequence models that can be generated from using a Metropolis--Hastings sampler. This short paper demonstrates how this can be instrumentalized for constrained composition and explores the poetics implied by such a usage. Our focus on constraints makes it especially apt to understand the generated text through the poetics of the OuLiPo movement.

For optimal pancreatic cancer treatment, early and accurate diagnosis is vital. Blood-derived biomarkers and genetic predispositions can contribute to early diagnosis, but they often have limited accuracy or applicability. Here, we seek to exploit the synergy between them by combining the biomarker CA19-9 with RNA-based variants. We use deep sequencing and deep learning to improve differentiating pancreatic cancer and chronic pancreatitis. We obtained samples of nucleated cells found in peripheral blood from 268 patients suffering from resectable, non-resectable pancreatic cancer, and chronic pancreatitis. We sequenced RNA with high coverage and obtained millions of variants. The high-quality variants served as input together with CA19-9 values to deep learning models. Our model achieved an area under the curve (AUC) of 96% in differentiating resectable cancer from pancreatitis using a test cohort. Moreover, we identified variants to estimate survival in resectable cancer. We show that the blood transcriptome harbours variants, which can substantially improve noninvasive clinical diagnosis.

Abstract The development of artificial intelligence, e. g. for Computer Vision, through supervised learning requires the input of large amounts of annotated or labeled data objects as training data. Usually, the creation of high-quality training data is done manually which can be repetitive and tiring. Gamification, the use of game elements in a non-game context, is one method to make such tedious tasks more interesting. We propose a multi-step process for gamifying the manual creation of training data for machine learning purposes. In this article, we give an overview of related concepts and existing implementations and present a user-centered approach for a real-life use case. Based on a survey within the target user group we identified annotation use cases and dominant player characteristics. The results served as a foundation for designing the gamification concepts which were then discussed with the participants. The final concept includes levels of increasing difficulty, tutorials, progress indicators and a narrative built around a robot character which at the same time is a user assistant. The implemented prototype is an extension of an existing annotation tool at an AI product company and serves as a basis for further observations.

The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code. This tech report describes the progress of the collaboration until December 2022, outlining the current state of the Personally Identifiable Information (PII) redaction pipeline, the experiments conducted to de-risk the model architecture, and the experiments investigating better preprocessing methods for the training data. We train 1.1B parameter models on the Java, JavaScript, and Python subsets of The Stack and evaluate them on the MultiPL-E text-to-code benchmark. We find that more aggressive filtering of near-duplicates can further boost performance and, surprisingly, that selecting files from repositories with 5+ GitHub stars deteriorates performance significantly. Our best model outperforms previous open-source multilingual code generation models (InCoder-6.7B and CodeGen-Multi-2.7B) in both left-to-right generation and infilling on the Java, JavaScript, and Python portions of MultiPL-E, despite being a substantially smaller model. All models are released under an OpenRAIL license at https://hf.co/bigcode.

AbstractLogic-based approaches to AI have the advantage that their behavior can in principle be explained to a user. If, for instance, a Description Logic reasoner derives a consequence that triggers some action of the overall system, then one can explain such an entailment by presenting a proof of the consequence in an appropriate calculus. How comprehensible such a proof is depends not only on the employed calculus, but also on the properties of the particular proof, such as its overall size, its depth, the complexity of the employed sentences and proof steps, etc. For this reason, we want to determine the complexity of generating proofs that are below a certain threshold w.r.t. a given measure of proof quality. Rather than investigating this problem for a fixed proof calculus and a fixed measure, we aim for general results that hold for wide classes of calculi and measures. In previous work, we first restricted the attention to a setting where proof size is used to measure the quality of a proof. We then extended the approach to a more general setting, but important measures such as proof depth were not covered. In the present paper, we provide results for a class of measures called recursive, which yields lower complexities and also encompasses proof depth. In addition, we close some gaps left open in our previous work, thus providing a comprehensive picture of the complexity landscape.

Key point analysis is the task of extracting a set of concise and high-level statements from a given collection of arguments, representing the gist of these arguments. This paper presents our proposed approach to the Key Point Analysis shared task, collocated with the 8th Workshop on Argument Mining. The approach integrates two complementary components. One component employs contrastive learning via a siamese neural network for matching arguments to key points; the other is a graph-based extractive summarization model for generating key points. In both automatic and manual evaluation, our approach was ranked best among all submissions to the shared task.

Text generation has received a lot of attention in computational argumentation research as of recent. A particularly challenging task is the generation of counter-arguments. So far, approaches primarily focus on rebutting a given conclusion, yet other ways to counter an argument exist. In this work, we go beyond previous research by exploring argument undermining, that is, countering an argument by attacking one of its premises. We hypothesize that identifying the argument's weak premises is key to effective countering. Accordingly, we propose a pipeline approach that first assesses the premises' strength and then generates a counter-argument targeting the weak ones. On the one hand, both manual and automatic evaluation proves the importance of identifying weak premises in counter-argument generation. On the other hand, when considering correctness and content richness, human annotators favored our approach over state-of-the-art counter-argument generation.

Surrogate models, or machine learning based emulators of simulators, have been shown to be a powerful tool for accelerating simulations. However, capturing the system response of general nonlinear systems is still an open area of investigation. In this paper we propose a new surrogate architecture which is capable of capturing the input/output response of causal models to automatically replace large aspects of block model diagrams with neural-accelerated forms. We denote this technique the Nonlinear Response Continuous-Time Echo State Network (NR-CTESN) and describe a training mechanism for it to accurately predict the simulation response to exogenous inputs. We then describe a science-guided or physics-informed surrogate architecture based on Cellular Neural Networks to enable the NR-CTESN to accurately reproduce discontinuous output signals. We demonstrate this architecture on an inverter circuit and a Sky130 Digital to Analog Converter (DAC), showcasing a 9x and 300x acceleration of the respective simulations. These results showcase that the NR-CTESN can learn emulate the behavior of components within composable modeling frameworks and thus be reused in new applications without requiring retraining. Together this showcases a machine learning technique that can be used to generate nonlinear model order reductions of model components in SPICE simulators, Functional Markup Interface (FMI) representations of causal model components, and beyond.

BACKGROUND: Small Proteins have received increasing attention in recent years. They have in particular been implicated as signals contributing to the coordination of bacterial communities. In genome annotations they are often missing or hidden among large numbers of hypothetical proteins because genome annotation pipelines often exclude short open reading frames or over-predict hypothetical proteins based on simple models. The validation of novel proteins, and in particular of small proteins (sProteins), therefore requires additional evidence. Proteogenomics is considered the gold standard for this purpose. It extends beyond established annotations and includes all possible open reading frames (ORFs) as potential sources of peptides, thus allowing the discovery of novel, unannotated proteins. Typically this results in large numbers of putative novel small proteins fraught with large fractions of false-positive predictions. RESULTS: We observe that number and quality of the peptide-spectrum matches (PSMs) that map to a candidate ORF can be highly informative for the purpose of distinguishing proteins from spurious ORF annotations. We report here on a workflow that aggregates PSM quality information and local context into simple descriptors and reliably separates likely proteins from the large pool of false-positive, i.e., most likely untranslated ORFs. We investigated the artificial gut microbiome model SIHUMIx, comprising eight different species, for which we validate 5114 proteins that have previously been annotated only as hypothetical ORFs. In addition, we identified 37 non-annotated protein candidates for which we found evidence at the proteomic and transcriptomic level. Half (19) of these candidates have close functional homologs in other species. Another 12 candidates have homologs designated as hypothetical proteins in other species. The remaining six candidates are short (< 100 AA) and are most likely bona fide novel proteins. CONCLUSIONS: The aggregation of PSM quality information for predicted ORFs provides a robust and efficient method to identify novel proteins in proteomics data. The workflow is in particular capable of identifying small proteins and frameshift variants. Since PSMs are explicitly mapped to genomic locations, it furthermore facilitates the integration of transcriptomics data and other sources of genome-level information.

Hyperbolic space has become a popular choice of manifold for representation learning of various datatypes from tree-like structures and text to graphs. Building on the success of deep learning with prototypes in Euclidean and hyperspherical spaces, a few recent works have proposed hyperbolic prototypes for classification. Such approaches enable effective learning in low-dimensional output spaces and can exploit hierarchical relations amongst classes, but require privileged information about class labels to position the hyperbolic prototypes. In this work, we propose Hyperbolic Busemann Learning. The main idea behind our approach is to position prototypes on the ideal boundary of the Poincaré ball, which does not require prior label knowledge. To be able to compute proximities to ideal prototypes, we introduce the penalised Busemann loss. We provide theory supporting the use of ideal prototypes and the proposed loss by proving its equivalence to logistic regression in the one-dimensional case. Empirically, we show that our approach provides a natural interpretation of classification confidence, while outperforming recent hyperspherical and hyperbolic prototype approaches.

We provide a formal definition for a class of algorithms known as "particle methods". Particle methods are used in scientific computing. They include popular simulation methods, such as Discrete Element Methods (DEM), Molecular Dynamics (MD), Particle Strength Exchange (PSE), and Smoothed Particle Hydrodynamics (SPH), but also particle-based image processing methods, point-based computer graphics, and computational optimization algorithms using point samples. All of these rest on a common concept, which we here formally define. The presented definition of particle methods makes it possible to distinguish what formally constitutes a particle method, and what not. It also enables us to define different sub-classes of particle methods that differ with respect to their computational complexity and power. Our definition is purely formal, independent of any application. After stating the definition, we therefore illustrate how several well-known particle methods can be formalized in our framework, and we show how the formal definition can be used to formulate novel particle methods for non-canonical problems.

Semantics based on weak admissibility were recently introduced to overcome a problem with self-defeating arguments that has not been solved for more than 25 years. The recursive definition of weak admissibility mainly relies on the notion of a reduct regarding a set E which only contains arguments which are neither in E, nor attacked by E. At first glance the reduct seems to be tailored for the weaker versions of Dung-style semantics only. In this paper we show that standard Dung semantics can be naturally reformulated using the reduct revealing that this concept is already implicit. We further identify a new abstract principle for semantics, so-called modularization describing how to obtain further extensions given an initial one. Its importance for the study of abstract argumentation semantics is shown by its ability to alternatively characterize classical and non-classical semantics.

Abstract argumentation as defined by Dung in his seminal 1995 paper is by now a major research area in knowledge representation and reasoning. Dynamics of abstract argumentation frameworks (AFs) as well as syntactical consequences of semantical facts of them are the central issues of this paper. The first main part is engaged with the systematical study of the influence of attackers and supporters regarding the acceptability status of whole sets and/or single arguments. In particular, we investigate the impact of addition or removal of arguments, a line of research that has been around for more than a decade. Apart from entirely new results, we revisit, generalize and sum up similar results from the literature. To gain a comprehensive formal and intuitive understanding of the behavior of AFs we put special effort in comparing different kind of semantics. We concentrate on classical admissibility-based semantics and also give pointers to semantics based on naivity and weak admissibility, a recently introduced mediating approach. In the second main part we show how to infer syntactical information from semantical one. For instance, it is well-known that if a finite AF possesses no stable extension, then it has to contain an odd-cycle. In this paper, we even present a characterization of this issue. Moreover, we show that the change of the number of extensions if adding or removing an argument allows to conclude the existence of certain even or odd cycles in the considered AF without having further information.

We develop a notion of explanations for acceptance of arguments in an abstract argumentation framework. To this end we show that extensions returned by Dung's standard semantics can be decomposed into i) non-deterministic choices made on even cycles of the given argumentation graph and then ii) deterministic iteration of the so-called characteristic function. Naturally, the choice made in i) can be viewed as an explanation for the corresponding extension and thus the arguments it contains. We proceed to propose desirable criteria a reasonable notion of an explanation should satisfy. We present an exhaustive study of the newly introduced notion w.r.t. these criteria. Finally some interesting decision problems arise from our analysis and we examine their computational complexity, obtaining some surprising tractability results.

AbstractIn this article, a special case of two coupled M/G/1-queues is considered, where two servers are exposed to two types of jobs that are distributed among the servers via a random switch. In this model, the asymptotic behavior of the workload buffer exceedance probabilities for the two single servers/both servers together/one (unspecified) server is determined. Hereby, one has to distinguish between jobs that are either heavy-tailed or light-tailed. The results are derived via the dual risk model of the studied coupled M/G/1-queues for which the asymptotic behavior of different ruin probabilities is determined.

AbstractAlignments, i.e., position-wise comparisons of two or more strings or ordered lists are of utmost practical importance in computational biology and a host of other fields, including historical linguistics and emerging areas of research in the Digital Humanities. The problem is well-known to be computationally hard as soon as the number of input strings is not bounded. Due to its practical importance, a huge number of heuristics have been devised, which have proved very successful in a wide range of applications. Alignments nevertheless have received hardly any attention as formal, mathematical structures. Here, we focus on the compositional aspects of alignments, which underlie most algorithmic approaches to computing alignments. We also show that the concepts naturally generalize to finite partially ordered sets and partial maps between them that in some sense preserve the partial orders. As a consequence of this discussion we observe that alignments of even more general structure, in particular graphs, are essentially characterized by the fact that the restriction of alignments to a row must coincide with the corresponding input graphs. Pairwise alignments of graphs are therefore determined completely by common induced subgraphs. In this setting alignments of alignments are well-defined, and alignments can be decomposed recursively into subalignments. This provides a general framework within which different classes of alignment algorithms can be explored for objects very different from sequences and other totally ordered data structures.

Invertible neural networks are a recent technique in machine learning promising neural network architectures that can be run in forward and reverse mode. In this paper, we will be introducing invertible surrogate models that approximate complex forward simulation of the physics involved in laser plasma accelerators: iLWFA. The bijective design of the surrogate model also provides all means for reconstruction of experimentally acquired diagnostics. The quality of our invertible laser wakefield acceleration network will be verified on a large set of numerical LWFA simulations.

MOTIVATION: Many diseases have a metabolic background, which is increasingly investigated due to improved measurement techniques allowing high-throughput assessment of metabolic features in several body fluids. Integrating data from multiple cohorts is of high importance to obtain robust and reproducible results. However, considerable variability across studies due to differences in sampling, measurement techniques and study populations needs to be accounted for. RESULTS: We present Metabolite-Investigator, a scalable analysis workflow for quantitative metabolomics data from multiple studies. Our tool supports all aspects of data pre-processing including data integration, cleaning, transformation, batch analysis as well as multiple analysis methods including uni- and multivariable factor-metabolite associations, network analysis and factor prioritization in one or more cohorts. Moreover, it allows identifying critical interactions between cohorts and factors affecting metabolite levels and inferring a common covariate model, all via a graphical user interface. AVAILABILITY AND IMPLEMENTATION: We constructed Metabolite-Investigator as a free and open web-tool and stand-alone Shiny-app. It is hosted at https://apps.health-atlas.de/metabolite-investigator/, the source code is freely available at https://github.com/cfbeuchel/Metabolite-Investigator. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Web search and other large-scale web data analytics rely on processing archives of web pages stored in a standardized and efficient format. Since its introduction in 2008, the IIPC's Web ARCive (WARC) format has become the standard format for this purpose. As a list of individually compressed records of HTTP requests and responses, it allows for constant-time random access to all kinds of web data via off-the-shelf open source parsers in many programming languages, such as WARCIO, the de-facto standard for Python. When processing web archives at the terabyte or petabyte scale, however, even small inefficiencies in these tools add up quickly, resulting in hours, days, or even weeks of wasted compute time. Reviewing the basic components of WARCIO and analyzing its bottlenecks, we proceed to build FastWARC, a new high-performance WARC processing library for Python, written in C++/Cython, which yields performance improvements by factors of 1.6-8x.

BACKGROUND: Obesity is of complex origin, involving genetic and neurobehavioral factors. Genetic polymorphisms may increase the risk for developing obesity by modulating dopamine-dependent behaviors, such as reward processing. Yet, few studies have investigated the association of obesity, related genetic variants, and structural connectivity of the dopaminergic reward network. METHODS: We analyzed 347 participants (age range: 20-59 years, BMI range: 17-38 kg/m2) of the LIFE-Adult Study. Genotyping for the single nucleotid polymorphisms rs1558902 (FTO) and rs1800497 (near dopamine D2 receptor) was performed on a microarray. Structural connectivity of the reward network was derived from diffusion-weighted magnetic resonance imaging at 3 T using deterministic tractography of Freesurfer-derived regions of interest. Using graph metrics, we extracted summary measures of clustering coefficient and connectivity strength between frontal and striatal brain regions. We used linear models to test the association of BMI, risk alleles of both variants, and reward network connectivity. RESULTS: Higher BMI was significantly associated with lower connectivity strength for number of streamlines ($beta$ = -0.0025, 95%-C.I.: [-0.004, -0.0008], p = 0.0042), and, to lesser degree, fractional anisotropy ($beta$ = -0.0009, 95%-C.I. [-0.0016, -0.00008], p = 0.031), but not clustering coefficient. Strongest associations were found for left putamen, right accumbens, and right lateral orbitofrontal cortex. As expected, the polymorphism rs1558902 in FTO was associated with higher BMI (F = 6.9, p < 0.001). None of the genetic variants was associated with reward network structural connectivity. CONCLUSIONS: Here, we provide evidence that higher BMI correlates with lower reward network structural connectivity. This result is in line with previous findings of obesity-related decline in white matter microstructure. We did not observe an association of variants in FTO or near DRD2 receptor with reward network structural connectivity in this population-based cohort with a wide range of BMI and age. Future research should further investigate the link between genetics, obesity and fronto-striatal structural connectivity.

Stress and strain tensors, two well-known quantities in mechanical engineering, are linked through a fourth-order stiffness tensor, which is not considered by many visualizations due to its complexity. Considering an orthotropic material, the tensor naturally decomposes into nine known material properties.We used fiber surfaces to analyze a data set representing a biological tissue. A sphere is pushed into the material to confirm the mathematical link as well as the possibility to extract highly deformed regions even if only the stiffness tensor is available.

AbstractFeeding exclusively on blood, vampire bats represent the only obligate sanguivorous lineage among mammals. To uncover genomic changes associated with adaptations to this unique dietary specialization, we generated a new haplotype-resolved reference-quality genome of the common vampire bat (Desmodus rotundus) and screened 26 bat species for genes that were specifically lost in the vampire bat lineage. We discovered previously-unknown gene losses that relate to metabolic and physiological changes, such as reduced insulin secretion (FFAR1,SLC30A8), limited glycogen stores (PPP1R3E), and a distinct gastric physiology (CTSE). Other gene losses likely reflect the biased nutrient composition (ERN2,CTRL) and distinct pathogen diversity of blood (RNASE7). Interestingly, the loss ofREP15likely helped vampire bats to adapt to high dietary iron levels by enhancing iron excretion and the loss of the 24S-hydroxycholesterol metabolizing enzymeCYP39A1could contribute to their exceptional cognitive abilities. Finally, losses of key cone phototransduction genes (PDE6H,PDE6C) suggest that these strictly-nocturnal bats completely lack cone-based vision. These findings enhance our understanding of vampire bat biology and the genomic underpinnings of adaptations to sanguivory.

We characterise the sentences in Monadic Second-order Logic (MSO) that are over finite structures equivalent to a Datalog program, in terms of an existential pebble game. We also show that for every class C of finite structures that can be expressed in MSO and is closed under homomorphisms, and for all ��,k $in$ , there exists a canonical Datalog program $Pi$ of width (��,k), that is, a Datalog program of width (��,k) which is sound for C (i.e., $Pi$ only derives the goal predicate on a finite structure �� if �� $in$ C) and with the property that $Pi$ derives the goal predicate whenever some Datalog program of width (��,k) which is sound for C derives the goal predicate. The same characterisations also hold for Guarded Second-order Logic (GSO), which properly extends MSO. To prove our results, we show that every class C in GSO whose complement is closed under homomorphisms is a finite union of constraint satisfaction problems (CSPs) of $omega$-categorical structures.

Mutations in leucine-rich repeat kinase 2 (LRRK2) are a frequent cause of autosomal dominant Parkinson's disease (PD) and have been associated with familial and sporadic PD. Reducing the kinase activity of LRRK2 is a promising therapeutic strategy since pathogenic mutations increase the kinase activity. Several small-molecule LRRK2 inhibitors are currently under investigation for the treatment of PD. However, drug discovery and development are always accompanied by high costs and a risk of late failure. The use of already approved drugs for a new indication, which is known as drug repositioning, can reduce the cost and risk. In this study, we applied a structure-based drug repositioning approach to identify new LRRK2 inhibitors that are already approved for a different indication. In a large-scale structure-based screening, we compared the protein-ligand interaction patterns of known LRRK2 inhibitors with protein-ligand complexes in the PDB. The screening yielded 6 drug repositioning candidates. Two of these candidates, Sunitinib and Crizotinib, demonstrated an inhibition potency (IC50) and binding affinity (Kd) in the nanomolar to micromolar range. While Sunitinib has already been known to inhibit LRRK2, Crizotinib is a novel LRRK2 binder. Our results underscore the potential of structure-based methods for drug discovery and development. In light of the recent breakthroughs in cryo-electron microscopy and structure prediction, we believe that structure-based approaches like ours will grow in importance.

The use of unnatural fluorogenic molecules widely expands the pallet of available genetically encoded fluorescent imaging tools through the design of fluorogen activating proteins (FAPs). While there is already a handful of such probes available, each of them went through laborious cycles of in vitro screening and selection. Computational modeling approaches are evolving incredibly fast right now and are demonstrating great results in many applications, including de novo protein design. It suggests that the easier task of fine-tuning the fluorogen-binding properties of an already functional protein in silico should be readily achievable. To test this hypothesis, we used Rosetta for computational ligand docking followed by protein binding pocket redesign to further improve the previously described FAP DiB1 that is capable of binding to a BODIPY-like dye M739. Despite an inaccurate initial docking of the chromophore, the incorporated mutations nevertheless improved multiple photophysical parameters as well as the overall performance of the tag. The designed protein, DiB-RM, shows higher brightness, localization precision, and apparent photostability in protein-PAINT super-resolution imaging compared to its parental variant DiB1. Moreover, DiB-RM can be cleaved to obtain an efficient split system with enhanced performance compared to a parental DiB-split system. The possible reasons for the inaccurate ligand binding pose prediction and its consequence on the outcome of the design experiment are further discussed.

Degradation of large forest areas such as the Brazilian Amazon due to logging and fires can increase the human footprint way beyond deforestation. Monitoring and quantifying such changes on a large scale has been addressed by several research groups (eg Souza et al. 2013) by making use of freely available remote sensing data such as the Landsat archive. However, fully automatic large-scale land cover/land use mapping is still one of the great challenges in remote sensing. One problem is the availability of reliable" ground truth" labels for training supervised learning algorithms. For the Amazon area, several landcover maps with 22 classes are available from the MapBiomas project that were derived by semi-automatic classification and verified by extensive fieldwork (Project MapBiomas). These labels cannot be considered real ground-truth as they were derived from Landsat data themselves but can still be …

RESUMO O estudo propôs a elaboração de um índice de vulnerabilidade à propagação da COVID-19 utilizando análise multivariada associada à análise geoespacial. O método empregado considerou a vulnerabilidade como uma combinação de três fatores: exposição, susceptibilidade e capacidade de resposta. A metodologia foi composta de seis etapas: seleção e agrupamento de variáveis; definição dos indicadores; normalização; atribuição de pesos via análise dos componentes principais; estimativa e normalização do índice; e classificação por meio das técnicas Jenks, Kmeans, quantis e clusterização hierárquica (Hclust). Foi realizada uma caracterização da exposição da cidade de Fortaleza, Brasil, à COVID-19 por meio da densidade populacional, da quantidade de habitações subnormais e precárias, da porcentagem de idosos por residência e da proximidade a terminais de ônibus. O estudo procurou sobrepor fatores socioeconômicos e índices de abastecimento público e de esgotamento sanitário, para a classificação de setores censitários em cinco níveis de vulnerabilidade. Estes apresentaram, em sua maioria, classe de alta (Jenks e quantis) e moderada (K-means e Hclust) vulnerabilidade. As regiões de maior vulnerabilidade estão localizadas no sul e no oeste da cidade, onde há maior concentração de aglomerados subnormais. Os resultados podem auxiliar no desenvolvimento de estratégias de enfrentamento direcionadas para os grupos mais expostos aos riscos associados à COVID-19, assim como na preparação para futuras crises de saúde pública. A metodologia pode ser replicada para outras cidades e serve …

Climate variability highly influences water availability and demand in urban areas, but medium-term predictive models of residential water demand usually do not include climate variables. This study proposes a method to predict monthly residential water demand using temperature and precipitation, by combining a novel decomposition technique and gradient boost regression. The variational mode decomposition (VMD) was used to filter the water demand time series and remove the component associated with the socioeconomic characteristics of households. VMD was also used to extract the relevant signal from precipitation and maximum temperature series which could explain water demand. The results indicate that by filtering the water demand and climate signals we can obtain accurate predictions at least four months in advance. These results suggest that the climate information can be used to …

The relative scarcity of water resources has encouraged cities to create mechanisms to control water demand and avoid water stress. In the decision-making process, water companies need to assess the price influence on water demand predictions to design better policies. The aim of this study is to estimate the medium-term effectiveness of the implementation of a contingent tariff and its consequences for water demand elasticity to price. A novel model that requires only secondary data is proposed, that can be useful for guiding the drought planning process. The methodology consists of a framework that provides monthly predictions of water demand at the household level, considering price, seasonality, and previous water use. The results indicated that the contingent tariff promoted a reduction of 11–17% in water demand, but at a higher cost for low income households. Also, reduction in water demand was found …

As mudanças climáticas têm sérias implicações para a ocorrência de eventos climáticos extremos, com efeitos sociais, econômicos e ambientais significativos. A previsão de eventos extremos de seca é fundamental para o planejamento eficiente dos recursos hídricos, assim como para a adoção de medidas preventivas. Além disso, compreender os padrões climáticos que resultam em anos secos pode ser importante para os tomadores de decisão. Nesse trabalho, propomos um modelo de classificação de secas utilizando a técnica de aprendizado de máquina de máquinas de vetores de suporte. O modelo foi aplicado para o estado do Ceará, frequentemente atingido por secas longas e severas. As variáveis explicativas consistem em dados globais de temperatura em ponto de grade, o que permitiu identificar as regiões que mais influenciam a ocorrência de secas no Ceará. O modelo apresentou uma acurácia de 77%, ou seja, dos 61 anos avaliados, 47 foram classificados corretamente como anos secos ou não secos. A avaliação dos pesos atribuídos pelo modelo confirma a influência da temperatura da superfície do Oceano Pacífico e do Atlântico Sul sobre o regime de secas no Ceará.

Despite recent efforts to apply machine learning (ML) for water demand modeling, overcoming the black-box nature of these techniques to extract practical information remains a challenge, especially in developing countries. This study integrated random forest (RF), self-organizing map (SOM), and artificial neural network (ANN) techniques to assess water demand patterns and to develop a predictive model for the city of Fortaleza, Brazil. We performed the analysis at two spatial scales, with different level of information: census tracts (CTs) at the fine scale, and census blocks (CBs) at the coarse scale. At the CB scale, demand was modeled with socioeconomic, demographic, and household characteristics. The RF technique was applied to rank these variables, and the most relevant were used to cluster census blocks with SOMs. RFs and ANNs were used in an iterative approach to define the input variables for the …

BACKGROUND: Cardiogenic shock (CS) complicating acute myocardial infarction (AMI) still reaches excessively high mortality rates. This analysis is aimed to develop a new easily applicable biomarker-based risk score. METHODS AND RESULTS: A biomarker-based risk score for 30-day mortality was developed from 458 patients with CS complicating AMI included in the randomized CULPRIT-SHOCK trial. The selection of relevant predictors and the coefficient estimation for the prognostic model were performed by a penalized multivariate logistic regression analysis. Validation was performed internally, internally externally as well as externally in 163 patients with CS included in the randomized IABP-SHOCK II trial. Blood samples were obtained at randomization. The two trials are registered with ClinicalTrials.gov (NCT01927549 and NCT00491036), are closed to new participants, and follow-up is completed. Out of 58 candidate variables, the four strongest predictors for 30-day mortality were included in the CLIP score (cystatin C, lactate, interleukin-6, and N-terminal pro-B-type natriuretic peptide). The score was well calibrated and yielded high c-statistics of 0.82 [95% confidence interval (CI) 0.78-0.86] in internal validation, 0.82 (95% CI 0.75-0.89) in internal-external (temporal) validation, and 0.73 (95% CI 0.65-0.81) in external validation. Notably, it outperformed the Simplified Acute Physiology Score II and IABP-SHOCK II risk score in prognostication (0.83 vs 0.62; P < 0.001 and 0.83 vs. 0.76; P = 0.03, respectively). CONCLUSIONS: A biomarker-only score for 30-day mortality risk stratification in infarct-related CS was developed, extensively validated and calibrated in a prospective cohort of contemporary patients with CS after AMI. The CLIP score outperformed other clinical scores and may be useful as an early decision tool in CS.

O crescimento populacional, as variações climáticas e o desenvolvimento dos padrões de consumo tendem a acentuar os períodos de escassez hídrica em diversas conjunturas globais. Nesse viés, as previsões relacionadas ao consumo de água são fundamentais para a elaboração de projetos capazes de mitigar as complicações das secas e aumentar a resiliência dos sistemas de abastecimento. Assim, o presente trabalho realiza uma previsão da demanda hídrica urbana através de modelagem dinâmica baseada em Autômatos Celulares (AC’s). A cidade de Fortaleza (CE) foi utilizada como estudo de caso a fim de prospectar o consumo de água para o ano de 2040. Os resultados apontam que o prolongamento das tendências atuais de crescimento dos padrões de consumo de Fortaleza, devem gerar uma taxa de crescimento médio anual de, aproximadamente, 0, 96%, propiciando um aumento de 33% de 2010 (ano base) a 2040 (horizonte de previsão). Diante de tal conjuntura, é extremamente necessário desenvolver estratégias capazes de reduzir os padrões de consumo e conceber balanços hídricos favoráveis.Population growth, climatic variations and the development of consumption patterns tend to accentuate periods of water scarcity in several global conjunctures. In this bias, forecasts related to water consumption are fundamental for the elaboration of projects capable of mitigating the complications of droughts and increasing the resilience of supply systems. Thus, the present work makes a forecast of urban water demand through dynamic modeling based on Cellular Automata (AC’s). The city of Fortaleza (CE …

Os eventos recorrentes de escassez hídrica ocasionados pelo desequilíbrio entre a oferta e a demanda, revelam a importância de compreender o consumo atual e prospectar as demandas futuras. Nessa perspectiva, o presente trabalho realiza uma análise bibliométrica sobre as principais características do acervo científico recentemente publicado acerca da previsão de demanda hídrica urbana. Para tanto, foi utilizado o software VosViewer como ferramenta de mapeamento gráfico e a plataforma Scopus como base de dados. Os resultados demonstraram, principalmente, a carência de estudos sobre a previsão da demanda hídrica urbana nos horizontes de médio e longo prazo. Portanto, é extremamente necessário ampliar a produção bibliográfica referente ao tema de forma a auxiliar os gestores de recursos hídricos na previsão e mitigação de estresses hídricos.The recurring events of water scarcity caused by the imbalance between supply and demand, reveal the importance of understanding current consumption and prospecting future demands. In this perspective, the present work conducts a bibliometric analysis on the main characteristics of the recently published scientific collection about the forecast of urban water demand. For this, the VosViewer software was used as a graphical mapping tool and the Scopus platform as a database. The results demonstrate, mainly, the lack of studies that develop the forecast of urban water demand across the global sphere, especially for the medium and long term horizons. Therefore, it is extremely necessary to expand the bibliographic production related to the theme in order to …

We describe an approach for integrating distance restraints from Double Electron-Electron Resonance (DEER) spectroscopy into Rosetta with the purpose of modeling alternative protein conformations from an initial experimental structure. Fundamental to this approach is a multilateration algorithm that harnesses sets of interconnected spin label pairs to identify optimal rotamer ensembles at each residue that fit the DEER decay in the time domain. Benchmarked relative to data analysis packages, the algorithm yields comparable distance distributions with the advantage that fitting the DEER decay and rotamer ensemble optimization are coupled. We demonstrate this approach by modeling the protonation-dependent transition of the multidrug transporter PfMATE to an inward facing conformation with a deviation to the experimental structure of less than 2AA C$alpha$ RMSD. By decreasing spin label rotamer entropy, this approach engenders more accurate Rosetta models that are also more closely clustered, thus setting the stage for more robust modeling of protein conformational changes.

Mitarbeiterbefragungen sind wichtige strategische Führungsinstrumente in der Organisationsentwicklung und messen systematisch die Einstellungen der Mitarbeiter*innen (Borg, 2007). In der vorliegenden Arbeit wird die Mitarbeitermeinungsumfrage (Employee Opinion Survey, kurz: EOS) der Magna International teststatistisch überprüft und die Validität des Fragenbogens untersucht. Hierfür werden N = 44 007 personenbezogene Daten aus den Erhebungen im Zeitraum 2016 bis 2019 an österreichischen und deutschen Magna-Standorten herangezogen. Mit diesen Daten werden zwei konfirmatorische Faktorenanalysen zur Kreuzvalidierung des gefundenen Modells aus der explorativen Faktorenanalyse berechnet. Die Analysen ergeben eine 3-Faktorenstruktur mit den Faktoren ‚wertschätzende, faire und respektvolle Behandlungtextquoteleft (Faktor 1), ‚Faire Behandlung durch direkte Vorgesetztetextquoteleft (Faktor 2) und ‚Information und Kommunikationtextquoteleft (Faktor 3). Ergänzend zu den quantitativen Daten werden mit N = 15 Mitarbeiter*innen kognitive Interviews durchgeführt, um Gründe für die Nicht-Beantwortung von Items zu identifizieren. Entsprechend den quantitativen und qualitativen Erkenntnissen werden handlungspraktische Implikationen für konkrete inhaltliche Verbesserungen und Optimierungen auf Item- und Strukturebene abgeleitet.

We study the computational complexity of abstract argumentation semantics based on weak admissibility, a recently introduced concept to deal with arguments of self-defeating nature. Our results reveal that semantics based on weak admissibility are of much higher complexity (under typical assumptions) compared to all argumentation semantics which have been analysed in terms of complexity so far. In fact, we show PSPACE-completeness of all non-trivial standard decision problems for weak-admissible based semantics. We then investigate potential tractable fragments and show that restricting the frameworks under consideration to certain graph-classes significantly reduces the complexity. As a strategy for implementation we also provide a polynomial-time reduction to DATALOG with stratified negation.

The development and evaluation of In-Vehicle Information Systems (IVISs) is strongly based on insights from qualitative studies conducted in artificial contexts (e.g., driving simulators or lab experiments). However, the growing complexity of the systems and the uncertainty about the context in which they are used, create a need to augment qualitative data with quantitative data, collected during real-world driving. In contrast to many digital companies that are already successfully using data-driven methods, Original Equipment Manufacturers (OEMs) are not yet succeeding in releasing the potentials such methods offer. We aim to understand what prevents automotive OEMs from applying data-driven methods, what needs practitioners formulate, and how collecting and analyzing usage data from vehicles can enhance UX activities. We adopted a Multiphase Mixed Methods approach comprising two interview studies with more than 15 UX practitioners and two action research studies conducted with two different OEMs. From the four studies, we synthesize the needs of UX designers, extract limitations within the domain that hinder the application of data-driven methods, elaborate on unleveraged potentials, and formulate recommendations to improve the usage of vehicle data. We conclude that, in addition to modernizing the legal, technical, and organizational infrastructure, UX and Data Science must be brought closer together by reducing silo mentality and increasing interdisciplinary collaboration. New tools and methods need to be developed and UX experts must be empowered to make data-based evidence an integral part of the UX design process.

BACKGROUND: The overall survival of patients with advanced and refractory oesophageal squamous cell carcinoma, mostly aged 65 years and older, is poor. Treatment with PD-1 antibodies showed improved progression-free survival and overall survival. We assessed the safety and efficacy of combined nivolumab and ipilimumab therapy in this population. METHODS: This multicentre, open-label, phase 2 trial done in 32 sites in Germany included patients aged 65 years and older with oesophageal squamous cell carcinoma and disease progression or recurrence following first-line therapy. Patients were treated with nivolumab (240 mg fixed dose once every 2 weeks, intravenously) in the safety run-in phase and continued with nivolumab and ipilimumab (nivolumab 240 mg fixed dose once every 2 weeks and ipilimumab 1 mg/kg once every 6 weeks, intravenously). The primary endpoint was overall survival, which was compared with a historical cohort receiving standard chemotherapy in the intention-to-treat population. This study is registered with ClinicalTrials.gov, NCT03416244. FINDINGS: Between March 2, 2018, and Aug 20, 2020, we screened 75 patients with advanced oesophageal squamous cell carcinoma. We enrolled 66 patients (50 [76%] men and 16 [24%] women; median age 70·5 years [IQR 67·0-76·0]), 44 (67%) of whom received combined nivolumab and ipilimumab therapy and 22 (33%) received nivolumab alone. Median overall survival time at the prespecified data cutoff was 7·2 months (95% CI 5·7-12·4) and significantly higher than in a historical cohort receiving standard chemotherapy (p=0·0063). The most common treatment-related adverse events were fatigue (12 [29%] of 42), nausea (11 [26%]), and diarrhoea (ten [24%]). Grade 3-5 treatment-related adverse events occurred in 13 (20%) of 66 patients. Treatment-related death occurred in one patient with bronchiolitis obliterans while on nivolumab and ipilimumab treatment. INTERPRETATION: Patients aged at least 65 years, with advanced oesophageal squamous cell carcinoma might benefit from combined nivolumab and ipilimumab therapy in second-line treatment. FUNDING: Bristol Myers Squibb.

The burden of fungal infections for humans, animals and plants is widely underestimated and comprises deadly infections as well as great economic costs. Despite that, antifungal drugs are scarce and emergence of resistance in fungal strains contributes to a high mortality. To overcome this shortage, we propose toxic intermediates and their controlling enzymes in metabolic pathways as a resource for new targets and provide a web-service, FunTox-Networks to explore the landscape of toxic intermediates in the metabolic networks of fungal pathogens. The toxicity of metabolites is predicted by a new random forest regression model and is available for over one hundred fungal species. Further, for major fungal pathogens, metabolic networks from the KEGG database were enriched with data of toxicity and regulatory effort for each enzyme to support identification of targets. We determined several toxic intermediates in fungal-specific pathways like amino acid synthesis, nitrogen and sulfur assimilation, and the glyoxylate bypass. For the latter, we show experimentally that growth of the pathogen Candida albicans is inhibited when the detoxifying enzymes Mls1 and Hbr2 are deleted and toxic glyoxylate accumulates in the cell. Thus, toxic pathway intermediates and their controlling enzymes represent an untapped resource of antifungal targets.

Aspergillus fumigatus is an important human fungal pathogen and its conidia are constantly inhaled by humans. In immunocompromised individuals, conidia can grow out as hyphae that damage lung epithelium. The resulting invasive aspergillosis is associated with devastating mortality rates. Since infection is a race between the innate immune system and the outgrowth of A. fumigatus conidia, we use dynamic optimization to obtain insight into the recruitment and depletion of alveolar macrophages and neutrophils. Using this model, we obtain key insights into major determinants of infection outcome on host and pathogen side. On the pathogen side, we predict in silico and confirm in vitro that germination speed is an important virulence trait of fungal pathogens due to the vulnerability of conidia against host defense. On the host side, we found that epithelial cells, which have been underappreciated, play a role in fungal clearance and are potent mediators of cytokine release. Both predictions were confirmed by in vitro experiments on established cell lines as well as primary lung cells. Further, our model affirms the importance of neutrophils in invasive aspergillosis and underlines that the role of macrophages remains elusive. We expect that our model will contribute to improvement of treatment protocols by focusing on the critical components of immune response to fungi but also fungal virulence traits.

The AGM postulates by Alchourrón, Gärdenfors, and Makinson continue to represent a cornerstone in research related to belief change. Katsuno and Mendelzon (K&M) adopted the AGM postulates for changing belief bases and characterized AGM belief base revision in propositional logic over finite signatures. We generalize K&M's approach to the setting of (multiple) base revision in arbitrary Tarskian logics, covering all logics with a classical model-theoretic semantics and hence a wide variety of logics used in knowledge representation and beyond. Our generic formulation applies to various notions of ``base'' (such as belief sets, arbitrary or finite sets of sentences, or single sentences). The core result is a representation theorem showing a two-way correspondence between AGM base revision operators and certain ``assignments'': functions mapping belief bases to total - yet not transitive - ``preference'' relations between interpretations. Alongside, we present a companion result for the case when the AGM postulate of syntax-independence is abandoned. We also provide a characterization of all logics for which our result can be strengthened to assignments producing transitive preference relations (as in K&M's original work), giving rise to two more representation theorems for such logics, according to syntax dependence vs. independence.

During the epithelial-to-mesenchymal transition, the intracellular cytoskeleton undergoes severe reorganization which allows epithelial cells to transition into a motile mesenchymal phenotype. Among the different cytoskeletal elements, the intermediate filaments keratin (in epithelial cells) and vimentin (in mesenchymal cells) have been demonstrated to be useful and reliable histological markers. In this study, we assess the potential invasiveness of six human breast carcinoma cell lines and two mouse fibroblasts cells lines through single cell migration assays in confinement. We find that the keratin and vimentin networks behave mechanically the same when cells crawl through narrow channels and that vimentin protein expression does not strongly correlate to single cells invasiveness. Instead, we find that what determines successful migration through confining spaces is the ability of cells to mechanically switch …

The Webis MS MARCO Anchor Text 2022 dataset enriches Version 1 and 2 of the document collection of MS MARCO with anchor text extracted from six Common Crawl snapshots. The six Common Crawl snapshots cover the years 2016 to 2021 (between 1.7-3.4 billion documents each). We sampled 1,000 anchor texts for documents with more than 1,000 anchor texts at random and all anchor texts for documents with less than 1,000 anchor texts (this sampling yields that all anchor text is included for 94% of the documents in Version 1 and 97% of documents for Version 2). Overall, the MS MARCO Anchor Text 2022 dataset enriches 1,703,834 documents for Version 1 and 4,821,244 documents for Version 2 with anchor text. Cleaned versions of the MS MARCO Anchor Text 2022 dataset are available in ir_datasets, Zenodo and Hugging Face. The raw dataset with additional information and all metadata for the extracted anchor texts (roughly 100GB) is available on Hugging Face and files.webis.de. The details of the construction of the Webis MS MARCO Anchor Text 2022 dataset are described in the associated paper. If you use this dataset, please cite @InProceedingsfroebe:2022a, address = Berlin Heidelberg New York, author = Maik Fr``obe and Sebastian G''unther and Maximilian Probst and Martin Potthast and Matthias Hagen, booktitle = Advances in Information Retrieval. 44th European Conference on IR Research (ECIR 2022), editor = Matthias Hagen and Suzan Verberne and Craig Macdonald and Christin Seifert and Krisztian Balog and Kjetil Norvrag and Vinay Setty, month = apr, publisher = Springer, series = Lecture Notes in Computer Science, site = Stavanger, Norway, title = The Power of Anchor Text in the Neural Retrieval Era, year = 2022

Commercial web search engines employ near-duplicate detection to ensure that users see each relevant result only once, albeit the underlying web crawls typically include (near-)duplicates of many web pages. We revisit the risks and potential of near-duplicates with an information retrieval focus, motivating that current efforts toward an open and independent European web search infrastructure should maintain metadata on duplicate and near-duplicate documents in its index. Near-duplicate detection implemented in an open web search infrastructure should provide a suitable similarity threshold, a difficult choice since identical pages may substantially differ in parts of a page that are irrelevant to searchers (templates, advertisements, etc.). We study this problem by comparing the similarity of pages for five (main) content extraction methods in two studies on the ClueWeb crawls. We find that the full content of pages serves precision-oriented near-duplicate-detection, while main content extraction is more recall-oriented.

AbstractSuperbubbles are acyclic induced subgraphs of a digraph with single entrance and exit that naturally arise in the context of genome assembly and the analysis of genome alignments in computational biology. These structures can be computed in linear time and are confined to non-symmetric digraphs. We demonstrate empirically that graph parameters derived from superbubbles provide a convenient means of distinguishing different classes of real-world graphical models, while being largely unrelated to simple, commonly used parameters.

Abstract dialectical frameworks (ADFs) are a recently introduced powerful generalization of Dung's popular abstract argumentation frameworks (AFs). Inspired by similar work for AFs, we introduce a decomposition scheme for ADFs, which proceeds along the ADF's strongly connected components. We find that, for several semantics, the decompositionbased version coincides with the original semantics, whereas for others, it gives rise to a new semantics. These new semantics allow us to deal with pertinent problems such as odd-length negative cycles in a more general setting, that for instance also encompasses logic programs. We perform an exhaustive analysis of the computational complexity of these new, so-called naive-based semantics. The results are quite interesting, for some of them involve little-known classes of the so-called Boolean hierarchy (another hierarchy in between classes of the polynomial hierarchy). Furthermore, in credulous and sceptical entailment, the complexity can be different depending on whether we check for truth or falsity of a specific statement.

Abstract Motivation Accurate assembly of RNA-seq is a crucial step in many analytic tasks such as gene annotation or expression studies. Despite ongoing research, progress on traditional single sample assembly has brought no major breakthrough. Multi-sample RNA-Seq experiments provide more information than single sample datasets and thus constitute a promising area of research. Yet, this advantage is challenging to utilize due to the large amount of accumulating errors. Results We present an extension to Ry=ut=o enabling the reconstruction of consensus transcriptomes from multiple RNA-seq datasets, incorporating consensus calling at low level features. We report stable improvements already at three replicates. Ry=ut=o outperforms competing approaches, providing a better and user-adjustable sensitivity-precision trade-off. Ry=ut=o's unique ability to utilize a (incomplete) reference for multi sample assemblies greatly increases precision. We demonstrate benefits for differential expression analysis. Ry=ut=o consistently improves assembly on replicates of the same tissue independent of filter settings, even when mixing conditions or time series. Consensus voting in Ry=ut=o is especially effective at high precision assembly, while Ry=ut=o's conventional mode can reach higher recall. Availability and implementation Ry=ut=o is available at https://github.com/studla/RYUTO. Supplementary information Supplementary data are available at Bioinformatics online.

BACKGROUND: Advances in genome sequencing over the last years have lead to a fundamental paradigm shift in the field. With steadily decreasing sequencing costs, genome projects are no longer limited by the cost of raw sequencing data, but rather by computational problems associated with genome assembly. There is an urgent demand for more efficient and and more accurate methods is particular with regard to the highly complex and often very large genomes of animals and plants. Most recently, ``hybrid'' methods that integrate short and long read data have been devised to address this need. RESULTS: LazyB is such a hybrid genome assembler. It has been designed specificially with an emphasis on utilizing low-coverage short and long reads. LazyB starts from a bipartite overlap graph between long reads and restrictively filtered short-read unitigs. This graph is translated into a long-read overlap graph G. Instead of the more conventional approach of removing tips, bubbles, and other local features, LazyB stepwisely extracts subgraphs whose global properties approach a disjoint union of paths. First, a consistently oriented subgraph is extracted, which in a second step is reduced to a directed acyclic graph. In the next step, properties of proper interval graphs are used to extract contigs as maximum weight paths. These path are translated into genomic sequences only in the final step. A prototype implementation of LazyB, entirely written in python, not only yields significantly more accurate assemblies of the yeast and fruit fly genomes compared to state-of-the-art pipelines but also requires much less computational effort. CONCLUSIONS: LazyB is new low-cost genome assembler that copes well with large genomes and low coverage. It is based on a novel approach for reducing the overlap graph to a collection of paths, thus opening new avenues for future improvements. AVAILABILITY: The LazyB prototype is available at https://github.com/TGatter/LazyB .

We present the Webis-STEREO-21 dataset, a massive collection of Scientific Text Reuse in Open-access publications. It contains more than 91 million cases of reused text passages found in 4.2 million unique open-access publications. Featuring a high coverage of scientific disciplines and varieties of reuse, as well as comprehensive metadata to contextualize each case, our dataset addresses the most salient shortcomings of previous ones on scientific writing. Webis-STEREO-21 allows for tackling a wide range of research questions from different scientific backgrounds, facilitating both qualitative and quantitative analysis of the phenomenon as well as a first-time grounding on the base rate of text reuse in scientific publications.

Given a large graph, graph sampling determines a subgraph with similar characteristics for certain metrics of the original graph. The samples are much smaller thereby accelerating and simplifying the analysis and visualization of large graphs. We focus on the implementation of distributed graph sampling for Big Data frameworks and in-memory dataflow systems such as Apache Spark or Apache Flink and evaluate the scalability of the new implementations. The presented methods will be open source and be integrated into Gradoop, a system for distributed graph analytics.

Recent years have witnessed a growing interest in EEG-based wearable classifiers of emotions, which could enable the real-time monitoring of patients suffering from neurological disorders such as Amyotrophic Lateral Sclerosis (ALS), Autism Spectrum Disorder (ASD), or Alzheimer's. The hope is that such wearable emotion classifiers would facilitate the patients' social integration and lead to improved healthcare outcomes for them and their loved ones. Yet in spite of their direct relevance to neuro-medicine, the hardware platforms for emotion classification have yet to fill up some important gaps in their various approaches to emotion classification in a healthcare context. In this paper, we present the first hardware-focused critical review of EEG-based wearable classifiers of emotions and survey their implementation perspectives, their algorithmic foundations, and their feature extraction methodologies. We further provide a neuroscience-based analysis of current hardware accelerators of emotion classifiers and use it to map out several research opportunities, including multi-modal hardware platforms, accelerators with tightly-coupled cores operating robustly in the near/supra-threshold region, and pre-processing libraries for universal EEG-based datasets.

Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m2/year or more (``Rapid3''; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m2 at follow-up among those with eGFRcrea 60 mL/min/1.73m2 or more at baseline (``CKDi25''; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or LARP4B. Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function.

Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m2/year or more (“Rapid3”; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m2 at follow-up among those with eGFRcrea 60 mL/min/1.73m2 or more at baseline (“CKDi25”; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or LARP4B. Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function.

Amphipathic helices have hydrophobic and hydrophilic/charged residues situated on opposite faces of the helix. They can anchor peripheral membrane proteins to the membrane, be attached to integral membrane proteins, or exist as independent peptides. Despite the widespread presence of membrane-interacting amphipathic helices, there is no computational tool within Rosetta to model their interactions with membranes. In order to address this need, we developed the AmphiScan protocol with PyRosetta, which runs a grid search to find the most favorable position of an amphipathic helix with respect to the membrane. The performance of the algorithm was tested in benchmarks with the RosettaMembrane, ref2015_memb, and franklin2019 score functions on six engineered and 44 naturally-occurring amphipathic helices using membrane coordinates from the OPM and PDBTM databases, OREMPRO server, and MD simulations for comparison. The AmphiScan protocol predicted the coordinates of amphipathic helices within less than 3AA of the reference structures and identified membrane-embedded residues with a Matthews Correlation Constant (MCC) of up to 0.57. Overall, AmphiScan stands as fast, accurate, and highly-customizable protocol that can be pipelined with other Rosetta and Python applications.

AbstractThe network Laplacian spectral density calculation is critical in many fields, including physics, chemistry, statistics, and mathematics. It is highly computationally intensive, limiting the analysis to small networks. Therefore, we present two efficient alternatives: one based on the network's edges and another on the degrees. The former gives the exact spectral density of locally tree-like networks but requires iterative edge-based message-passing equations. In contrast, the latter obtains an approximation of the spectral density using only the degree distribution. The computational complexities are ��(|E|log(n)) and ��(n), respectively, in contrast to ��(n3) of the diagonalization method, where n is the number of vertices and |E| is the number of edges.

We propose and validate a novel car following model based on deep reinforcement learning. Our model is trained to maximize externally given reward functions for the free and car-following regimes rather than reproducing existing follower trajectories. The parameters of these reward functions such as desired speed, time gap, or accelerations resemble that of traditional models such as the Intelligent Driver Model (IDM) and allow for explicitly implementing different driving styles. Moreover, they partially lift the black-box nature of conventional neural network models. The model is trained on leading speed profiles governed by a truncated Ornstein-Uhlenbeck process reflecting a realistic leader's kinematics. This allows for arbitrary driving situations and an infinite supply of training data. For various parameterizations of the reward functions, and for a wide variety of artificial and real leader data, the model turned out to be unconditionally string stable, comfortable, and crash-free. String stability has been tested with a platoon of five followers following an artificial and a real leading trajectory. A cross-comparison with the IDM calibrated to the goodness-of-fit of the relative gaps showed a higher reward compared to the traditional model and a better goodness-of-fit.

We introduce a novel approach to dynamic obstacle avoidance based on Deep Reinforcement Learning by defining a traffic type independent environment with variable complexity. Filling a gap in the current literature, we thoroughly investigate the effect of missing velocity information on an agent's performance in obstacle avoidance tasks. This is a crucial issue in practice since several sensors yield only positional information of objects or vehicles. We evaluate frequently-applied approaches in scenarios of partial observability, namely the incorporation of recurrency in the deep neural networks and simple frame-stacking. For our analysis, we rely on state-of-the-art model-free deep RL algorithms. The lack of velocity information is found to significantly impact the performance of an agent. Both approaches - recurrency and frame-stacking - cannot consistently replace missing velocity information in the observation space. However, in simplified scenarios, they can significantly boost performance and stabilize the overall training procedure.

AIMS: While most patients with myocardial infarction (MI) have underlying coronary atherosclerosis, not all patients with coronary artery disease (CAD) develop MI. We sought to address the hypothesis that some of the genetic factors which establish atherosclerosis may be distinct from those that predispose to vulnerable plaques and thrombus formation. METHODS AND RESULTS: We carried out a genome-wide association study for MI in the UK Biobank (n∼472 000), followed by a meta-analysis with summary statistics from the CARDIoGRAMplusC4D Consortium (n∼167 000). Multiple independent replication analyses and functional approaches were used to prioritize loci and evaluate positional candidate genes. Eight novel regions were identified for MI at the genome wide significance level, of which effect sizes at six loci were more robust for MI than for CAD without the presence of MI. Confirmatory evidence for association of a locus on chromosome 1p21.3 harbouring choline-like transporter 3 (SLC44A3) with MI in the context of CAD, but not with coronary atherosclerosis itself, was obtained in Biobank Japan (n∼165 000) and 16 independent angiography-based cohorts (n∼27 000). Follow-up analyses did not reveal association of the SLC44A3 locus with CAD risk factors, biomarkers of coagulation, other thrombotic diseases, or plasma levels of a broad array of metabolites, including choline, trimethylamine N-oxide, and betaine. However, aortic expression of SLC44A3 was increased in carriers of the MI risk allele at chromosome 1p21.3, increased in ischaemic (vs. non-diseased) coronary arteries, up-regulated in human aortic endothelial cells treated with interleukin-1$beta$ (vs. vehicle), and associated with smooth muscle cell migration in vitro. CONCLUSIONS: A large-scale analysis comprising ∼831 000 subjects revealed novel genetic determinants of MI and implicated SLC44A3 in the pathophysiology of vulnerable plaques.

AIMS: Artificial intelligence (AI) provides a powerful tool to extract information from digitised histopathology whole slide images. During the last 5 years, academic and commercial actors have developed new technical solutions for a diverse set of tasks, including tissue segmentation, cell detection, mutation prediction, prognostication and prediction of treatment response. In the light of limited overall resources, it is presently unclear for researchers, practitioners and policymakers which of these topics are stable enough for clinical use in the near future and which topics are still experimental, but worth investing time and effort into. METHODS AND RESULTS: To identify potentially promising applications of AI in pathology, we performed an anonymous online survey of 75 computational pathology domain experts from academia and industry. Participants enrolled in 2021 were queried about their subjective opinion on promising and appealing subfields of computational pathology with a focus upon solid tumours. The results of this survey indicate that the prediction of treatment response directly from routine pathology slides is regarded as the most promising future application. This item was ranked highest in the overall analysis and in subgroups by age and professional background. Furthermore, prediction of genetic alterations, gene expression and survival directly from routine pathology images scored consistently high throughout subgroups. CONCLUSIONS: Together, these data demonstrate a possible direction for the development of computational pathology systems in clinical, academic and industrial research in the near future.

This paper introduces the processing element architecture of the second generation SpiNNaker chip, implemented in 22nm FDSOI. On circuit level, the chip features adaptive body biasing for near-threshold operation, and dynamic voltage-and-frequency scaling driven by spiking activity. On system level, processing is centered around an ARM M4 core, similar to the processor-centric architecture of the first generation SpiNNaker. To speed operation of subtasks, we have added accelerators for numerical operations of both spiking (SNN) and rate based (deep) neural networks (DNN). PEs communicate via a dedicated, custom-designed network-on-chip. We present three benchmarks showing operation of the whole processor element on SNN, DNN and hybrid SNN/DNN networks.

Risk stratification and treatment decisions for leukemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improve the predictions for patient-specific treatment responses. We designed a synthetic experiment simulating response kinetics of 5,000 patients to compare different computational methods with respect to their ability to accurately predict relapse for chronic and acute myeloid leukemia treatment. Technically, we used clinical reference data to first fit a model and then generate de novo model simulations of individual patients' time courses for which we can systematically tune data quality (i.e. measurement error) and quantity (i.e. number of measurements). Based hereon, we compared the prediction accuracy of three different computational methods, namely mechanistic models, generalized linear models, and deep neural networks that have been fitted to the reference data. Reaching prediction accuracies between 60 and close to 100%, our results indicate that data quality has a higher impact on prediction accuracy than the specific choice of the particular method. We further show that adapted treatment and measurement schemes can considerably improve the prediction accuracy by 10 to 20%. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukemia patients.

BACKGROUND: The coexistence of low muscle mass and high fat mass, two interrelated conditions strongly associated with declining health status, has been characterized by only a few protein biomarkers. High-throughput proteomics enable concurrent measurement of numerous proteins, facilitating the discovery of potentially new biomarkers. METHODS: Data derived from the prospective population-based Cooperative Health Research in the Region of Augsburg S4/FF4 cohort study (median follow-up time: 13.5 years) included 1478 participants (756 men and 722 women) aged 55-74 years in the cross-sectional and 608 participants (315 men and 293 women) in the longitudinal analysis. Appendicular skeletal muscle mass (ASMM) and body fat mass index (BFMI) were determined through bioelectrical impedance analysis at baseline and follow-up. At baseline, 233 plasma proteins were measured using proximity extension assay. We implemented boosting with stability selection to enable false positives-controlled variable selection to identify new protein biomarkers of low muscle mass, high fat mass, and their combination. We evaluated prediction models developed based on group least absolute shrinkage and selection operator (lasso) with 100$times$ bootstrapping by cross-validated area under the curve (AUC) to investigate if proteins increase the prediction accuracy on top of classical risk factors. RESULTS: In the cross-sectional analysis, we identified kallikrein-6, C-C motif chemokine 28 (CCL28), and tissue factor pathway inhibitor as previously unknown biomarkers for muscle mass [association with low ASMM: odds ratio (OR) per 1-SD increase in log2 normalized protein expression values (95% confidence interval (CI)): 1.63 (1.37-1.95), 1.31 (1.14-1.51), 1.24 (1.06-1.45), respectively] and serine protease 27 for fat mass [association with high BFMI: OR (95% CI): 0.73 (0.61-0.86)]. CCL28 and metalloproteinase inhibitor 4 (TIMP4) constituted new biomarkers for the combination of low muscle and high fat mass [association with low ASMM combined with high BFMI: OR (95% CI): 1.32 (1.08-1.61), 1.28 (1.03-1.59), respectively]. Including protein biomarkers selected in $geq$90% of group lasso bootstrap iterations on top of classical risk factors improved the performance of models predicting low ASMM, high BFMI, and their combination [delta AUC (95% CI): 0.16 (0.13-0.20), 0.22 (0.18-0.25), 0.12 (0.08-0.17), respectively]. In the longitudinal analysis, N-terminal prohormone brain natriuretic peptide (NT-proBNP) was the only protein selected for loss in ASMM and loss in ASMM combined with gain in BFMI over 14 years [OR (95% CI): 1.40 (1.10-1.77), 1.60 (1.15-2.24), respectively]. CONCLUSIONS: Proteomic profiling revealed CCL28 and TIMP4 as new biomarkers of low muscle mass combined with high fat mass and NT-proBNP as a key biomarker of loss in muscle mass combined with gain in fat mass. Proteomics enable us to accelerate biomarker discoveries in muscle research.

We present a design and implementation of distributed sparse block grids that transparently scale from a single CPU to multi-GPU clusters. We support dynamic sparse grids as, e.g., occur in computer graphics with complex deforming geometries and in multi-resolution numerical simulations. We present the data structures and algorithms of our approach, focusing on the optimizations required to render them computationally efficient on CPUs and GPUs alike. We provide a scalable implementation in the OpenFPM software library for HPC. We benchmark our implementation on up to 16 Nvidia GTX 1080 GPUs and up to 64 Nvidia A100 GPUs showing state-of-the-art scalability (68% to 96% parallel efficiency) on three benchmark problems. On a single GPU, our implementation is 14 to 140-fold faster than on a multi-core CPU.

The ImageCLEF evaluation campaign was integrated with CLEF (Conference and Labs of the Evaluation Forum) for more than 20 years and represents a Multimedia Retrieval challenge aimed at evaluating the technologies for annotation, indexing, and retrieval of multimodal data. Thus, it provides information access to large data collections in usage scenarios and domains such as medicine, argumentation and content recommendation. ImageCLEF 2024 has four main tasks: (i) a Medical task targeting automatic image captioning for radiology images, synthetic medical images created with Generative Adversarial Networks (GANs), Visual Question Answering and medical image generation based on text input, and multimodal dermatology response generation; (ii) a joint ImageCLEF-Touché task Image Retrieval/Generation for Arguments to convey the premise of an argument, (iii) a Recommending task addressing cultural heritage content-recommendation, and (iv) a joint ImageCLEF-ToPicto task aiming to provide a translation in pictograms from natural language. In 2023, participation increased by 67% with respect to 2022 which reveals its impact on the community.

Lake Baikal is inhabited by more than 300 endemic amphipod species, which are narrowly adapted to certain thermal niches due to the high interspecific competition. In contrast, the surrounding freshwater fauna is commonly represented by species with large-scale distribution and high phenotypic thermal plasticity. Here, we investigated the thermal plasticity of the energy metabolism in two closely-related endemic amphipod species from Lake Baikal (Eulimnogammarus verrucosus; stenothermal and Eulimnogammarus cyaneus; eurythermal) and the ubiquitous Holarctic amphipod Gammarus lacustris (eurythermal) by exposure to a summer warming scenario (6-23.6 °C; 0.8 °C d-1). In concert with routine metabolic rates, activities of key metabolic enzymes increased strongly with temperature up to 15 °C in E. verrucosus, whereupon they leveled off (except for lactate dehydrogenase). In contrast, exponential increases were seen in E. cyaneus and G. lacustris throughout the thermal trial (Q10-values: 1.6-3.7). Cytochrome-c-oxidase, lactate dehydrogenase, and 3-hydroxyacyl-CoA dehydrogenase activities were found to be higher in G. lacustris than in E. cyaneus, especially at the highest experimental temperature (23.6 °C). Decreasing gene expression levels revealed some thermal compensation in E. cyaneus but not in G. lacustris. In all species, shifts in enzyme activities favored glycolytic energy generation in the warmth. The congruent temperature-dependencies of enzyme activities and routine metabolism in E. verrucosus indicate a strong feedback-regulation of enzymatic activities by whole organism responses. The species-specific thermal reaction norms reflect the different ecological niches, including the spatial distribution, distinct thermal behavior such as temperature-dependent migration, movement activity, and mating season.

Metagenomics became a standard strategy to comprehend the functional potential of microbial communities, including the human microbiome. Currently, the number of metagenomes in public repositories is increasing exponentially. The Sequence Read Archive (SRA) and the MG-RAST are the two main repositories for metagenomic data. These databases allow scientists to reanalyze samples and explore new hypotheses. However, mining samples from them can be a limiting factor, since the metadata available in these repositories is often misannotated, misleading, and decentralized, creating an overly complex environment for sample reanalysis. The main goal of the HumanMetagenomeDB is to simplify the identification and use of public human metagenomes of interest. HumanMetagenomeDB version 1.0 contains metadata of 69 822 metagenomes. We standardized 203 attributes, based on standardized ontologies, describing host characteristics (e.g. sex, age and body mass index), diagnosis information (e.g. cancer, Crohn's disease and Parkinson), location (e.g. country, longitude and latitude), sampling site (e.g. gut, lung and skin) and sequencing attributes (e.g. sequencing platform, average length and sequence quality). Further, HumanMetagenomeDB version 1.0 metagenomes encompass 58 countries, 9 main sample sites (i.e. body parts), 58 diagnoses and multiple ages, ranging from just born to 91 years old. The HumanMetagenomeDB is publicly available at https://webapp.ufz.de/hmgdb/.

AbstractBased on data from the European banking stress tests of 2014, 2016 and the transparency exercise of 2018 we construct networks of European banks and demonstrate that the latent geometry of these financial networks can be well-represented by geometry of negative curvature, i.e., by hyperbolic geometry. Using two different hyperbolic embedding methods, hydra+ and Mercator, this allows us to connect the network structure to the popularity-vs-similarity model of Papdopoulos et al., which is based on the Poincaré disc model of hyperbolic geometry. We show that the latent dimensions of `popularity' and `similarity' in this model are strongly associated to systemic importance and to geographic subdivisions of the banking system, independent of the embedding method that is used. In a longitudinal analysis over the time span from 2014 to 2018 we find that the systemic importance of individual banks has remained rather stable, while the peripheral community structure exhibits more (but still moderate) variability. Based on our analysis we argue that embeddings into hyperbolic geometry can be used to monitor structural change in financial networks and are able to distinguish between changes in systemic relevance and other (peripheral) structural changes.

textcopyright 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).Idiosyncrasies in human writing styles make it difficult to develop systems for authorship identification that scale well across individuals. In this year's edition of PAN, the authorship identification track focused on open-set authorship verification, so that systems are applied to unknown documents by previously unseen authors in a new domain. As in the previous year, the sizable materials for this campaign were sampled from English-language fanfiction. The calibration materials handed out to the participants were the same as last year, but a new test set was compiled with authors and fandom domains not present in any of the previous datasets. The general setup of the task did not change, i.e., systems still had to estimate the probability of a pair of documents being authored by the same person. We attracted 13 submissions by 10 international teams, which were compared to three complementary baselines, using five diverse evaluation metrics. Post-hoc analyses show that systems benefitted from the abundant calibration materials and were well-equipped to handle the open-set scenario: Both the top-performing approach and the highly competitive cohort of runner-ups presented surprisingly strong verifiers. We conclude that, at least within this specific text variety, (large-scale) open-set authorship verification is not necessarily or inherently more difficult than a closed-set setup, which offers encouraging perspectives for the future of the field.

The exchange of meta-information has always formed part of information behavior. In this article, we show that this rule also extends to conversational search. Information about the user's information need, their preferences, and the quality of search results are only some of the most salient examples of meta-information that are exchanged as a matter of course in a search conversation. To understand the importance of meta-information for conversational search, we revisit its definition and survey how meta-information has been taken into account in the past in information retrieval. Meta-information has gone by many names, about which a concise overview is provided. An in-depth analysis of the role of meta-information in search and conversation theories reveals that they provide significant support for the importance of meta-information in conversational search. We further identify conversational search datasets are suitable for a deeper inspection with regard to meta-information, namely, Spoken Conversational Search and Microsoft Information-Seeking Conversations. A quantitative data analysis demonstrates the practical significance of meta-information in information-seeking conversations, whereas a qualitative analysis shows the effects of exchanging different types. Finally, we discuss practical applications and challenges of meta-information in conversational search, including a case study of VERSE, an existing search system for the visually impaired.

Long non-coding RNAs (lncRNAs) are widely recognized as important regulators of gene expression. Their molecular functions range from miRNA sponging to chromatin-associated mechanisms, leading to effects in disease progression and establishing them as diagnostic and therapeutic targets. Still, only a few representatives of this diverse class of RNAs are well studied, while the vast majority is poorly described beyond the existence of their transcripts. In this review we survey common in silico approaches for lncRNA annotation. We focus on the well-established sets of features used for classification and discuss their specific advantages and weaknesses. While the available tools perform very well for the task of distinguishing coding sequence from other RNAs, we find that current methods are not well suited to distinguish lncRNAs or parts thereof from other non-protein-coding input sequences. We conclude that the distinction of lncRNAs from intronic sequences and untranslated regions of coding mRNAs remains a pressing research gap.

Antibody-antigen co-crystal structures are a valuable resource for the fundamental understanding of antibody-mediated immunity. Determination of structures with antibodies in complex with their antigens, however, is a laborious task without guarantee of success. Therefore, homology modeling of antibodies and docking to their respective antigens has become a very important technique to drive antibody and vaccine design. The quality of the antibody modeling process is critical for the success of these endeavors. Here, we compare different computational protocols for predicting antibody structure from sequence in the biomolecular modeling software Rosetta-all of which use multiple existing antibody structures to guide modeling. Specifically, we compare protocols developed solely to predict antibody structure (RosettaAntibody, AbPredict) with a universal homology modeling protocol (RosettaCM). Following recent advances in homology modeling with multiple templates simultaneously, we propose that the use of multiple templates over the same antibody regions may improve modeling performance. To evaluate whether multi-template comparative modeling with RosettaCM can improve the modeling accuracy of antibodies over existing methods, this study compares the performance of the three modeling algorithms when modeling human antibodies taken from antibody-antigen co-crystal structures. In these benchmarking experiments, RosettaCM outperformed other methods when modeling antibodies with long HCDR3s and few available templates.

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

AIMS: Recent evidence points towards a distinct obese phenotype among patients with heart failure with preserved ejection fraction (HFpEF). We aimed to identify differentially expressed circulating biomarkers in obese HFpEF patients and link them to disease severity and outcomes. METHODS AND RESULTS: From the LIFE-Heart study, 999 patients with HFpEF and 999 patients without heart failure (no-HF) were selected and 92 circulating serum biomarkers were measured using a proximity extension assay. Elevation of identified biomarkers was validated in 220 patients from the Aldo-DHF trial with diagnosed HFpEF. HFpEF patients were older and had more comorbidities including coronary artery disease and type 2 diabetes as compared to no-HF patients (P < 0.05 for all). After adjusting for covariates, adrenomedullin (ADM), galectin-9 (Gal-9), thrombospondin-2 (THBS-2), CD4, and tumour necrosis factor-related apoptosis-inducing ligand receptor 2 (TRAIL-R2) were significantly higher in obese HFpEF patients [body mass index (BMI) $geq$30 kg/m2 , n = 464] as compared to lean HFpEF (BMI <30 kg/m2 , n = 535) and obese no-HF patients (BMI $geq$30 kg/m2 , n = 387) (P < 0.001 for both); these findings were verified in the Aldo-DHF validation cohort (P < 0.001). Except for CD4 these proteins were associated with increased estimates of left atrial pressure in a linear fashion. Importantly, ADM and CD4 were associated with increased mortality in obese HFpEF patients after adjusting for covariates. CONCLUSION: Obese HFpEF patients exhibit higher circulating biomarkers of volume expansion (ADM), myocardial fibrosis (THBS-2) and systemic inflammation (Gal-9, CD4) compared to obese non-HFpEF or lean HFpEF patients. These findings support the clinical definition of a distinct obese HFpEF phenotype and might merit further investigation.

Entity resolution is the data integration task of identifying matching entities (e.g. products, customers) in one or several data sources. Previous approaches for matching and clustering entities between multiple (>2) sources either treated the different sources as a single source or assumed that the individual sources are duplicate-free, so that only matches between sources have to be found. In this work we propose and evaluate a general Multi-Source Clean Dirty (MSCD) scheme with an arbitrary combination of clean (duplicate-free) and dirty sources. For this purpose, we extend a constraint-based clustering algorithm called Affinity Propagation (AP) for entity clustering with clean and dirty sources (MSCD-AP). We also consider a hierarchical version of it for improved scalability. Our evaluation considers a full range of datasets containing 0% to 100% of clean sources. We compare our proposed algorithms with other clustering schemes in terms of both match quality and runtime.

``Artificial Intelligence`` (AI) is experiencing a boom which has undoubtedly impacted copyright law. ``AI``-generated works such as ``The Next Rembrandt'' raise the question whether, against the conventional anthropocentric focus of copyright law, such works are eligible for protection de lege lata. Moreover, the question regarding the protectability of ``AI`` has not yet been answered conclusively. Taking an interdisciplinary approach, this thesis examines these questions as well as how creative ``AI`` is and what is understood by ``AI``. This thesis also considers options for action and the need for adaptation whilst offering a comparative analysis by examining the legal situation in the UK and the US.

In the last four years, advances in Deep Learning technology have enabled the inference of selected mutational alterations directly from routine histopathology slides. In particular, recent studies have shown that genetic changes in clinically relevant driver genes are reflected in the histological phenotype of solid tumors and can be inferred by analysing routine Haematoxylin and Eosin (H&E) stained tissue sections with Deep Learning. However, these studies mostly focused on selected individual genes in selected tumor types. In addition, genetic changes in solid tumors primarily act by changing signaling pathways that regulate cell behaviour. In this study, we hypothesized that Deep Learning networks can be trained to directly predict alterations of genes and pathways across a spectrum of solid tumors. We manually outlined tumor tissue in H&E-stained tissue sections from 7,829 patients with 23 different tumor types from The Cancer Genome Atlas. We then trained convolutional neural networks in an end-to-end way to detect alterations in the most clinically relevant pathways or genes, directly from histology images. Using this automatic approach, we found that alterations in 12 out of 14 clinically relevant pathways and numerous single gene alterations appear to be detectable in tissue sections, many of which have not been reported before. Interestingly, we show that the prediction performance for single gene alterations is better than that for pathway alterations. Collectively, these data demonstrate the predictability of genetic alterations directly from routine cancer histology images and show that individual genes leave a stronger morphological signature than genetic pathways.

Due to the low corrugation of the Au(111) surface, 1,4-bis(phenylethynyl)-2,5-bis(ethoxy)benzene (PEEB) molecules can form quasi interlocked lateral patterns, which are observed in scanning tunneling microscopy experiments at low temperatures. We demonstrate a multi-dimensional clustering approach to quantify the anisotropic pair-wise interaction of molecules and explain these patterns. We perform high-throughput calculations to evaluate an energy function, which incorporates the adsorption energy of single PEEB molecules on the metal surface and the intermolecular interaction energy of a pair of PEEB molecules. The analysis of the energy function reveals, that, depending on coverage density, specific types of pattern are preferred which can potentially be exploited to form one-dimensional molecular wires on Au(111).

We propose a statistical learning framework based on group-sparse regression that can be used to (i) enforce conservation laws, (ii) ensure model equivalence, and (iii) guarantee symmetries when learning or inferring differential-equation models from data. Directly learning interpretable mathematical models from data has emerged as a valuable modeling approach. However, in areas such as biology, high noise levels, sensor-induced correlations, and strong intersystem variability can render data-driven models nonsensical or physically inconsistent without additional constraints on the model structure. Hence, it is important to leverage prior knowledge from physical principles to learn biologically plausible and physically consistent models rather than models that simply fit the data best. We present the group iterative hard thresholding algorithm and use stability selection to infer physically consistent models with minimal parameter tuning. We show several applications from systems biology that demonstrate the benefits of enforcing priors in data-driven modeling.

Numerical methods for approximately solving partial differential equations (PDE) are at the core of scientific computing. Often, this requires high-resolution or adaptive discretization grids to capture relevant spatio-temporal features in the PDE solution, e.g., in applications like turbulence, combustion, and shock propagation. Numerical approximation also requires knowing the PDE in order to construct problem-specific discretizations. Systematically deriving solution-adaptive discrete operators, however, is a current challenge. Here we present an artificial neural network architecture for data-driven learning of problem-and resolution-specific local discretizations of nonlinear PDEs. Our proposed method achieves numerically stable discretization of the operators in an unknown nonlinear PDE by spatially and temporally adaptive parametric pooling on regular Cartesian grids, and by incorporating knowledge about discrete time integration. Knowing the actual PDE is not necessary, as solution data is sufficient to train the network to learn the discrete operators. A once-trained network can be used to predict solutions of the PDE on larger spatial domains and for longer times than it was trained for, addressing the problem of PDE-constrained extrapolation from data. We present examples on long-term forecasting of hard numerical problems including equation-free forecasting of the nonlinear dynamics of the forced Burgers problem on coarse spatio-temporal grids.

Numerical methods for approximately solving partial differential equations (PDE) are at the core of scientific computing. Often, this requires high-resolution or adaptive discretization grids to capture relevant spatio-temporal features in the PDE solution, e.g., in applications like turbulence, combustion, and shock propagation. Numerical approximation also requires knowing the PDE in order to construct problem-specific discretizations. Systematically deriving such solution-adaptive discrete operators, however, is a current challenge. Here we present STENCIL-NET, an artificial neural network architecture for data-driven learning of problem- and resolution-specific local discretizations of nonlinear PDEs. STENCIL-NET achieves numerically stable discretization of the operators in an unknown nonlinear PDE by spatially and temporally adaptive parametric pooling on regular Cartesian grids, and by incorporating knowledge about discrete time integration. Knowing the actual PDE is not necessary, as solution data is sufficient to train the network to learn the discrete operators. A once-trained STENCIL-NET model can be used to predict solutions of the PDE on larger spatial domains and for longer times than it was trained for, hence addressing the problem of PDE-constrained extrapolation from data. To support this claim, we present numerical experiments on long-term forecasting of chaotic PDE solutions on coarse spatio-temporal grids. We also quantify the speed-up achieved by substituting base-line numerical methods with equation-free STENCIL-NET predictions on coarser grids with little compromise on accuracy.

BACKGROUND: Identification of lung parenchyma on computer tomographic (CT) scans in the research setting is done semi-automatically and requires cumbersome manual correction. This is especially true in pathological conditions, hindering the clinical application of aeration compartment (AC) analysis. Deep learning based algorithms have lately been shown to be reliable and time-efficient in segmenting pathologic lungs. In this contribution, we thus propose a novel 3D transfer learning based approach to quantify lung volumes, aeration compartments and lung recruitability. METHODS: Two convolutional neural networks developed for biomedical image segmentation (uNet), with different resolutions and fields of view, were implemented using Matlab. Training and evaluation was done on 180 scans of 18 pigs in experimental ARDS (u2Net Pig ) and on a clinical data set of 150 scans from 58 ICU patients with lung conditions varying from healthy, to COPD, to ARDS and COVID-19 (u2Net Human ). One manual segmentations (MS) was available for each scan, being a consensus by two experts. Transfer learning was then applied to train u2Net Pig on the clinical data set generating u2Net Transfer . General segmentation quality was quantified using the Jaccard index (JI) and the Boundary Function score (BF). The slope between JI or BF and relative volume of non-aerated compartment (S JI and S BF , respectively) was calculated over data sets to assess robustness toward non-aerated lung regions. Additionally, the relative volume of ACs and lung volumes (LV) were compared between automatic and MS. RESULTS: On the experimental data set, u2Net Pig resulted in JI = 0.892 [0.88 : 091] (median [inter-quartile range]), BF = 0.995 [0.98 : 1.0] and slopes S JI = -0.2 95% conf. int. -0.23 : -0.16 and S BF = -0.1 -0.5 : -0.06. u2Net Human showed similar performance compared to u2Net Pig in JI, BF but with reduced robustness S JI = -0.29 -0.36 : -0.22 and S BF = -0.43 -0.54 : -0.31. Transfer learning improved overall JI = 0.92 [0.88 : 0.94], P < 0.001, but reduced robustness S JI = -0.46 -0.52 : -0.40, and affected neither BF = 0.96 [0.91 : 0.98] nor S BF = -0.48 -0.59 : -0.36. u2Net Transfer improved JI compared to u2Net Human in segmenting healthy (P = 0.008), ARDS (P < 0.001) and COPD (P = 0.004) patients but not in COVID-19 patients (P = 0.298). ACs and LV determined using u2Net Transfer segmentations exhibited < 5% volume difference compared to MS. CONCLUSION: Compared to manual segmentations, automatic uNet based 3D lung segmentation provides acceptable quality for both clinical and scientific purposes in the quantification of lung volumes, aeration compartments, and recruitability.

We previously described BCL::Conf, a knowledge-based conformation sampling algorithm utilizing a small molecule fragment rotamer library derived from the Cambridge Structural Database (CSD, license required), as a component of the BioChemical Library (BCL) cheminformatics toolkit. This paper describes substantial improvements made to the BCL::Conf algorithm and a transition to a rotamer library derived from molecules in the Crystallography Open Database (COD, no license required). We demonstrate the performance of the new BCL::Conf on native conformer recovery in the Platinum dataset of high-quality protein-ligand complexes. This set of 2859 structures has previously been used to assess the performance of over a dozen conformer generation algorithms, including the Conformator, Balloon, RDKit DG, ETKDG, Confab, Frog2, MultiConf-DOCK, CSD conformer generator, ConfGenX-OPSL3 force field, Omega, excalc, iCon, and MOE. These benchmarks suggest that the CSD conformer generator is at the state of the art of reported conformer generators. Our results indicate that the improved BCL::Conf significantly outperforms the CSD conformer generation algorithm at binding conformer recovery across a range of ensemble sizes and with similarly fast rates of conformer generation. BCL::Conf is now distributed with the COD-derived rotamer library and is free for academic use. The BCL can be downloaded at http://meilerlab.org/bclcommons for Windows, Linux, or Apple operating systems. BCL::Conf can now also be accessed via webserver at http://meilerlab.org/bclconf.

Each year the International Semantic Web Conference accepts a set of Semantic Web Challenges to establish competitions that will advance the state of the art solutions in any given problem domain. The SeMantic AnsweR Type prediction task (SMART) was part of ISWC 2020 challenges. Question type and answer type prediction can play a key role in knowledge base question answering systems providing insights that are helpful to generate correct queries or rank the answer candidates. More concretely, given a question in natural language, the task of SMART challenge is, to predict the answer type using a target ontology (e.g., DBpedia or Wikidata).

The process for automatically creating 3D city models from contemporary photographs and visualizing them on mobile devices is well established. 4D city models that can display a temporal dimension are far more complex to generate automatically. In this article, we focus on major challenges in the process of developing an automated pipeline, starting from content-based image retrieval applied to historical images, via automatic historical image orientation, up to visualization of the 4D data in Virtual Reality (VR). The result is an interactive browser-based device-rendered 4D visualization and information system for mobile devices. This pipeline has been in development since 2015. In this article, we present initial results and early-stage findings in the process of overcoming three major challenges on the way to 4D city models: (1) to identify photographs with corresponding views, (2) to reconstruct the position and orientation of photographs and (3) to design a user-centered, browser-based 4D mobile application.

Loss of FLG causes ichthyosis vulgaris. Reduced FLG expression compromises epidermal barrier function and is associated with atopic dermatitis, allergy, and asthma. The flaky tail mouse harbors two mutations that affect the skin barrier, Flgft, resulting in hypomorphic FLG expression, and Tmem79ma, inactivating TMEM79. Mice defective only for TMEM79 featured dermatitis and systemic atopy, but also Flgft/ft BALB/c congenic mice developed eczema, high IgE, and spontaneous asthma, suggesting that FLG protects from atopy. In contrast, a targeted Flg-knockout mutation backcrossed to BALB/c did not result in dermatitis or atopy. To resolve this discrepancy, we generated FLG-deficient mice on pure BALB/c background by inactivating Flg in BALB/c embryos. These mice feature an ichthyosis phenotype, barrier defect, and facilitated percutaneous sensitization. However, they do not develop dermatitis or atopy. Whole-genome sequencing of the atopic Flgft BALB/c congenics revealed that they were homozygous for the atopy-causing Tmem79matted mutation. In summary, we show that FLG deficiency does not cause atopy in mice, in line with lack of atopic disease in a fraction of patients with ichthyosis vulgaris carrying two Flg null alleles. However, the absence of FLG likely promotes and modulates dermatitis caused by other genetic barrier defects.

Artificial microswimmers that can replicate the complex behavior of active matter are often designed to mimic the self-propulsion of microscopic living organisms. However, compared with their living counterparts, artificial microswimmers have a limited ability to adapt to environmental signals or to retain a physical memory to yield optimized emergent behavior. Different from macroscopic living systems and robots, both microscopic living organisms and artificial microswimmers are subject to Brownian motion, which randomizes their position and propulsion direction. Here, we combine real-world artificial active particles with machine learning algorithms to explore their adaptive behavior in a noisy environment with reinforcement learning. We use a real-time control of self-thermophoretic active particles to demonstrate the solution of a simple standard navigation problem under the inevitable influence of Brownian motion at these length scales. We show that, with external control, collective learning is possible. Concerning the learning under noise, we find that noise decreases the learning speed, modifies the optimal behavior, and also increases the strength of the decisions made. As a consequence of time delay in the feedback loop controlling the particles, an optimum velocity, reminiscent of optimal run-and-tumble times of bacteria, is found for the system, which is conjectured to be a universal property of systems exhibiting delayed response in a noisy environment.

Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.

Conservation is broadly used to identify biologically important (epi)genomic regions. In the case of tumor growth, preferential conservation of DNA methylation can be used to identify areas of particular functional importance to the tumor. However, reliable assessment of methylation conservation based on multiple tissue samples per patient requires the decomposition of methylation variation at multiple levels.We developed a Bayesian hierarchical model that allows for variance decomposition of methylation on three levels: between-patient normal tissue variation, between-patient tumor-effect variation and within-patient tumor variation. We then defined a model-based conservation score to identify loci of reduced within-tumor methylation variation relative to between-patient variation. We fit the model to multi-sample methylation array data from 21 colorectal cancer (CRC) patients using a Monte Carlo Markov Chain algorithm (Stan). Sets of genes implicated in CRC tumorigenesis exhibited preferential conservation, demonstrating the model’s ability to identify functionally relevant genes based on methylation conservation. A pathway analysis of preferentially conserved genes implicated several CRC relevant pathways and pathways related to neoantigen presentation and immune evasion. Our findings suggest that preferential methylation conservation may be used to identify novel gene targets that are not consistently mutated in CRC. The flexible structure makes the model amenable to the analysis of more complex multi-sample data structures.The data underlying this article are available in the NCBI GEO Database, under accession code GSE166212. The R analysis code is available at https://github.com/kevin-murgas/DNAmethylation-hierarchicalmodel.Supplementary data are available at Bioinformatics online.

Many computer science disciplines (e.g., combinatorial optimization, natural language processing, and information retrieval) use standard or established test suites for evaluating algorithms. In visualization, similar approaches have been adopted in some areas (e.g., volume visualization), while user testimonies and empirical studies have been the dominant means of evaluation in most other areas, such as designing colormaps. In this paper, we propose to establish a test suite for evaluating the design of colormaps. With such a suite, the users can observe the effects when different continuous colormaps are applied to planar scalar fields that may exhibit various characteristic features, such as jumps, local extrema, ridge or valley lines, different distributions of scalar values, different gradients, different signal frequencies, different levels of noise, and so on. The suite also includes an expansible collection of real-world data sets including the most popular data for colormap testing in the visualization literature. The test suite has been integrated into a web-based application for creating continuous colormaps (https://ccctool.com/), facilitating close inter-operation between design and evaluation processes. This new facility complements traditional evaluation methods such as user testimonies and empirical studies.

Continuous colormaps are integral parts of many visualization techniques, such as heat-maps, surface plots, and flow visualization. Despite that the critiques of rainbow colormaps have been around and well-acknowledged for three decades, rainbow colormaps are still widely used today. One reason behind the resilience of rainbow colormaps is the lack of tools for users to create a continuous colormap that encodes semantics specific to the application concerned. In this paper, we present a web-based software system, CCC-Tool (short for Charting Continuous Colormaps) under the URL https://ccctool.com, for creating, editing, and analyzing such application-specific colormaps. We introduce the notion of ``colormap specification (CMS)'' that maintains the essential semantics required for defining a color mapping scheme. We provide users with a set of advanced utilities for constructing CMS's with various levels of complexity, examining their quality attributes using different plots, and exporting them to external application software. We present two case studies, demonstrating that the CCC-Tool can help domain scientists as well as visualization experts in designing semantically-rich colormaps.

This study used explainable artificial intelligence for data-driven identification of extrastriatal brain regions that can contribute to the interpretation of dopamine transporter SPECT with 123I-FP-CIT in parkinsonian syndromes. A total of 1306 123I-FP-CIT-SPECT were included retrospectively. Binary classification as 'reduced' or 'normal' striatal 123I-FP-CIT uptake by an experienced reader served as standard-of-truth. A custom-made 3-dimensional convolutional neural network (CNN) was trained for classification of the SPECT images with 1006 randomly selected images in three different settings: ``full image'', ``striatum only'' (3-dimensional region covering the striata cropped from the full image), ``without striatum'' (full image with striatal region removed). The remaining 300 SPECT images were used to test the CNN classification performance. Layer-wise relevance propagation (LRP) was used for voxelwise quantification of the relevance for the CNN-based classification in this test set. Overall accuracy of CNN-based classification was 97.0%, 95.7%, and 69.3% in the ``full image'', ``striatum only'', and ``without striatum'' setting. Prominent contributions in the LRP-based relevance maps beyond the striatal signal were detected in insula, amygdala, ventromedial prefrontal cortex, thalamus, anterior temporal cortex, superior frontal lobe, and pons, suggesting that 123I-FP-CIT uptake in these brain regions provides clinically useful information for the differentiation of neurodegenerative and non-neurodegenerative parkinsonian syndromes.

Whole genome bisulfite sequencing is currently at the forefront of epigenetic analysis, facilitating the nucleotide-level resolution of 5-methylcytosine (5mC) on a genome-wide scale. Specialized software have been developed to accommodate the unique difficulties in aligning such sequencing reads to a given reference, building on the knowledge acquired from model organisms such as human, or Arabidopsis thaliana. As the field of epigenetics expands its purview to non-model plant species, new challenges arise which bring into question the suitability of previously established tools. Herein, nine short-read aligners are evaluated: Bismark, BS-Seeker2, BSMAP, BWA-meth, ERNE-BS5, GEM3, GSNAP, Last and segemehl. Precision-recall of simulated alignments, in comparison to real sequencing data obtained from three natural accessions, reveals on-balance that BWA-meth and BSMAP are able to make the best use of the data during mapping. The influence of difficult-to-map regions, characterized by deviations in sequencing depth over repeat annotations, is evaluated in terms of the mean absolute deviation of the resulting methylation calls in comparison to a realistic methylome. Downstream methylation analysis is responsive to the handling of multi-mapping reads relative to mapping quality (MAPQ), and potentially susceptible to bias arising from the increased sequence complexity of densely methylated reads.

Thlaspi arvense (field pennycress) is being domesticated as a winter annual oilseed crop capable of improving ecosystems and intensifying agricultural productivity without increasing land use. It is a selfing diploid with a short life cycle and is amenable to genetic manipulations, making it an accessible field-based model species for genetics and epigenetics. The availability of a high quality reference genome is vital for understanding pennycress physiology and for clarifying its evolutionary history within the Brassicaceae. Here, we present a chromosome-level genome assembly of var. MN106-Ref with improved gene annotation, and use it to investigate gene structure differences between two accessions (MN108 and Spring32-10) that are highly amenable to genetic transformation. We describe small RNAs, pseudogenes, and transposable elements, and highlight tissue specific expression and methylation patterns. Resequencing of forty wild accessions provides insights into genome-wide genetic variation as well as QTL regions for flowering time and a seedling color phenotype. Altogether, these data will serve as a tool for pennycress improvement in general and for translational research across the Brassicaceae.Competing Interest StatementThe authors declare potential competing interests as intellectual property applications have been submitted on some of the genes discussed in this study.

Entity Resolution (ER) is a constitutional part for integrating different knowledge graphs in order to identify entities referring to the same real-world object. A promising approach is the use of graph embeddings for ER in order to determine the similarity of entities based on the similarity of their graph neighborhood. The similarity computations for such embeddings translates to calculating the distance between them in the embedding space which is comparatively simple. However, previous work has shown that the use of graph embeddings alone is not sufficient to achieve high ER quality. We therefore propose a more comprehensive ER approach for knowledge graphs called EAGER (Embedding-Assisted Knowledge Graph Entity Resolution) to flexibly utilize both the similarity of graph embeddings and attribute values within a supervised machine learning approach. We evaluate our approach on 23 benchmark datasets with differently sized and structured knowledge graphs and use hypothesis tests to ensure statistical significance of our results. Furthermore we compare our approach with state-of-the-art ER solutions, where our approach yields competitive results for table-oriented ER problems and shallow knowledge graphs but much better results for deeper knowledge graphs.

AbstractTranscription Factors (TFs) are proteins that control the flow of genetic information by regulating cellular gene expression. Here we describe PredicTF, a first platform supporting the prediction and classification of novel bacterial TF in complex microbial communities. We evaluated PredicTF using a two-step approach. First, we tested PredictTF's ability to predict TFs for the genome of an environmental isolate. In the second evaluation step, PredicTF was used to predict TFs in a metagenome and 11 metatranscriptomes recovered from a community performing anaerobic ammonium oxidation (anammox) in a bioreactor. PredicTF is open source pipeline available at https://github.com/mdsufz/PredicTF.

Self-cleaving ribozymes are catalytically active RNAs that cleave themselves into a 5'-fragment with a 2',3'-cyclic phosphate and a 3'-fragment with a 5'-hydroxyl. They are widely applied for the construction of synthetic RNA devices and RNA-based therapeutics. However, the targeted discovery of self-cleaving ribozymes remains a major challenge. We developed a transcriptome-wide method, called cyPhyRNA-seq, to screen for ribozyme cleavage fragments in total RNA extract. This approach employs the specific ligation-based capture of ribozyme 5'-fragments using a variant of the Arabidopsis thaliana tRNA ligase we engineered. To capture ribozyme 3'-fragments, they are enriched from total RNA by enzymatic treatments. We optimized and enhanced the individual steps of cyPhyRNA-seq in vitro and in spike-in experiments. Then, we applied cyPhyRNA-seq to total RNA isolated from the bacterium Desulfovibrio vulgaris and detected self-cleavage of the three predicted type II hammerhead ribozymes, whose activity had not been examined to date. cyPhyRNA-seq can be used for the global analysis of active self-cleaving ribozymes with the advantage to capture both ribozyme cleavage fragments from total RNA. Especially in organisms harbouring many self-cleaving RNAs, cyPhyRNA-seq facilitates the investigation of cleavage activity. Moreover, this method has the potential to be used to discover novel self-cleaving ribozymes in different organisms. [Figure: see text].

SUMMARY: Many biochemical processes in living organisms take place inside compartments that can interact with each other and remodel over time. In a recent work (Duso and Zechner, 2020), we have shown how the stochastic dynamics of a compartmentalized biochemical system can be effectively studied using moment equations. With this technique, the time evolution of a compartment population is summarized using a finite number of ordinary differential equations, which can be analyzed very efficiently. However, the derivation of moment equations by hand can become time-consuming for systems comprising multiple reactants and interactions. Here we present Compartor, a toolbox that automatically generates the moment equations associated with a user-defined compartmentalized system. Through the moment equation method, Compartor renders the analysis of stochastic population models accessible to a broader scientific community. AVAILABILITY AND IMPLEMENTATION: Compartor is provided as a Python package and is available at https://pypi.org/project/compartor/. Usage tutorials for Compartor are provided as Jupyter notebooks at https://github.com/zechnerlab/Compartor.

The research presents a multisite annual streamflow generation model that combines the Generalized Linear Model (GLM), for determining the temporal structure, with copulas, for modelling the spatial dependence joint distributions. The performance of the GLM-Copula model was verified by comparing its ability to preserve historical features and simulate drought events with the multivariate auto regressive moving average (ARMA) model and the copula autoregressive (COPAR) model. The statistical measures adopted for the models’ performance evaluation include summary statistics (mean, standard deviation, maximum, minimum and skewness coefficient), temporal and spatial correlation, simulation of drought conditions (maximum number of years under drought condition) and copula entropy as a nonlinear measure of total association. The combined GLM-Copula model’s main advantages are that (i) it does …

No Web technology has undergone such an impressive evolution as Web search engines did and still do. Starting with the promise of "Bringing order to the Web"1 by compiling information sources matching a query, retrieval technology has been evolving to a kind of "oracle machinery", being able to recommend a single source, and even to provide direct answers extracted from that source. Notwithstanding the remarkable progress made and the apparent user preferences for direct answers, this paradigm shift comes at a price which is higher than one might expect at first sight, affecting both users and search engine developers in their own way. We call this tradeoff "the dilemma of the direct answer"; it deserves an analysis which has to go beyond system-oriented aspects but scrutinize the way our society deals with both their information needs and means to information access. The paper in hand contributes to this analysis by putting the evolution of retrieval technology and the expectations at it in the context of information retrieval history. Moreover, we discuss the trade offs in information behavior and information system design that users and developers may face in the future.

The Information Retrieval Anthology, IR Anthology for short, is an endeavor to create a comprehensive collection of metadata and full texts of IR-related publications. We report on its first release, the use cases it can serve, as well as the challenges lying ahead to develop it towards a resource that serves the IR community for years to come. The IR Anthology's metadata browser and full text search engine are available at IR.webis.de.

The potential of machine learning for modeling spatio-temporal properties of water isotopologue distributions in precipitation - NASA/ADS Now on home page ads icon ads Enable full ADS view NASA/ADS The potential of machine learning for modeling spatio-temporal properties of water isotopologue distributions in precipitation Rehfeld, Kira ; Wider, Jonathan ; Theisen, Nadine ; Werner, Martin ; Köthe, Ullrich ; Weitzel, Nils Abstract Tracing the spatio-temporal distribution of water isotopologues (eg, H216O, H218O,HD16O, D216O), in the atmosphere allows insights in to the hydrological cycle and surface-atmosphere interactions. Strong relationships between atmospheric circulation and isotopologue variability exist, mitigated by fractionation during phase transitions of water. Isotopic gradients correlate with precipitation amount, temperature, with distance to source areas of evaporation and often follow topographic …

High-quality and complete reference genome assemblies are fundamental for the application of genomics to biology, disease, and biodiversity conservation. However, such assemblies are available for only a few non-microbial species1-4. To address this issue, the international Genome 10K (G10K) consortium5,6 has worked over a five-year period to evaluate and develop cost-effective methods for assembling highly accurate and nearly complete reference genomes. Here we present lessons learned from generating assemblies for 16 species that represent six major vertebrate lineages. We confirm that long-read sequencing technologies are essential for maximizing genome quality, and that unresolved complex repeats and haplotype heterozygosity are major sources of assembly error when not handled correctly. Our assemblies correct substantial errors, add missing sequence in some of the best historical reference genomes, and reveal biological discoveries. These include the identification of many false gene duplications, increases in gene sizes, chromosome rearrangements that are specific to lineages, a repeated independent chromosome breakpoint in bat genomes, and a canonical GC-rich pattern in protein-coding genes and their regulatory regions. Adopting these lessons, we have embarked on the Vertebrate Genomes Project (VGP), an international effort to generate high-quality, complete reference genomes for all of the roughly 70,000 extant vertebrate species and to help to enable a new era of discovery across the life sciences.

AbstractConcerns have been raised about the use of relative abundance data derived from next generation sequencing as a proxy for absolute abundances. For example, in the differential abundance setting, compositional effects in relative abundance data may give rise to spurious differences (false positives) when considered from the absolute perspective. In practice however, relative abundances are often transformed by renormalization strategies intended to compensate for these effects and the scope of the practical problem remains unclear. We used simulated data to explore the consistency of differential abundance calling on renormalized relative abundances versus absolute abundances and find that, while overall consistency is high, with a median sensitivity (true positive rates) of 0.91 and specificity (1 - false positive rates) of 0.89, consistency can be much lower where there is widespread change in the abundance of features across conditions. We confirm these findings on a large number of real data sets drawn from 16S metabarcoding, expression array, bulk RNA-seq, and single-cell RNA-seq experiments, where data sets with the greatest change between experimental conditions are also those with the highest false positive rates. Finally, we evaluate the predictive utility of summary features of relative abundance data themselves. Estimates of sparsity and the prevalence of feature-level change in relative abundance data give reasonable predictions of discrepancy in differential abundance calling in simulated data and can provide useful bounds for worst-case outcomes in real data.

BACKGROUND: Data analysis for biomedical research often requires a record linkage step to identify records from multiple data sources referring to the same person. Due to the lack of unique personal identifiers across these sources, record linkage relies on the similarity of personal data such as first and last names or birth dates. However, the exchange of such identifying data with a third party, as is the case in record linkage, is generally subject to strict privacy requirements. This problem is addressed by privacy-preserving record linkage (PPRL) and pseudonymization services. Mainzelliste is an open-source record linkage and pseudonymization service used to carry out PPRL processes in real-world use cases. METHODS: We evaluate the linkage quality and performance of the linkage process using several real and near-real datasets with different properties w.r.t. size and error-rate of matching records. We conduct a comparison between (plaintext) record linkage and PPRL based on encoded records (Bloom filters). Furthermore, since the Mainzelliste software offers no blocking mechanism, we extend it by phonetic blocking as well as novel blocking schemes based on locality-sensitive hashing (LSH) to improve runtime for both standard and privacy-preserving record linkage. RESULTS: The Mainzelliste achieves high linkage quality for PPRL using field-level Bloom filters due to the use of an error-tolerant matching algorithm that can handle variances in names, in particular missing or transposed name compounds. However, due to the absence of blocking, the runtimes are unacceptable for real use cases with larger datasets. The newly implemented blocking approaches improve runtimes by orders of magnitude while retaining high linkage quality. CONCLUSION: We conduct the first comprehensive evaluation of the record linkage facilities of the Mainzelliste software and extend it with blocking methods to improve its runtime. We observed a very high linkage quality for both plaintext as well as encoded data even in the presence of errors. The provided blocking methods provide order of magnitude improvements regarding runtime performance thus facilitating the use in research projects with large datasets and many participants.

This work is a summarized view on the results of a one-year cooperation between Oracle Corp. and the University of Leipzig. The goal was to research the organization of relationships within multi-dimensional time-series data, such as sensor data from the IoT area. We showed in this project that temporal property graphs with some extensions are a prime candidate for this organizational task that combines the strengths of both data models (graph and time-series). The outcome of the cooperation includes four achievements: (1) a bitemporal property graph model, (2) a temporal graph query language, (3) a conception of continuous event detection, and (4) a prototype of a bitemporal graph database that supports the model, language and event detection.

The study of cellular migration dynamics and strategies plays a relevant role in the understanding of both physiological and pathological processes. An important example could be the link between cancer cell motility and tumor evolution into metastatic stage. These strategies can be strongly influenced by the extracellular environment and the consequent mechanical constrains. In this framework, the possibility to study the behavior of single cells when subject to specific topological constraints could be an important tool in the hands of biologists. Two-photon polymerization is a sub-micrometric additive manufacturing technique that allows the fabrication of 3D structures in biocompatible resins, enabling the realization of ad hoc biochips for cell motility analyses, providing different types of mechanical stimuli. In our work, we present a new strategy for the realization of multilayer microfluidic lab-on-a-chip constructs for the study of cell motility which guarantees complete optical accessibility and the possibility to freely shape the migration area, to tailor it to the requirements of the specific cell type or experiment. The device includes a series of micro-constrictions that induce different types of mechanical stress on the cells during their migration. We show the realization of different possible geometries, in order to prove the versatility of the technique. As a proof of concept, we present the use of one of these devices for the study of the motility of murine neuronal cancer cells under high physical confinement, highlighting their peculiar migration mechanisms.

The research field of augmented reality (AR) is of increasing popularity, as seen, among others, in several recently published surveys. To produce further advancements in AR, it is not only necessary to create new systems or applications, but also to evaluate them. One important aspect in regards to the evaluation is the general understanding of how users experience a given AR application, which can also be seen by the increased number of papers focusing on this topic that were published in the last years. With the steadily growing understanding and development of AR in general, it is only a matter of time until AR devices make the leap into the consumer market where such an in-depth user understanding is even more essential. Thus, a better understanding of factors that could influence the design and results of user experience studies can help us to make them more robust and dependable in the future. In this position paper, we describe three challenges which researchers face while designing and conducting AR users studies. We encountered these challenges in our past and current research, including papers that focus on perceptual studies of visualizations, interaction studies, and studies exploring the use of AR applications and their design spaces.

Besides the ability to utilize visualizations, the process of creating and authoring them is of equal importance. However, for visualization environments beyond the desktop, like multi-display or immersive analytics environments, this process is often decoupled from the place where the visualization is actually used. This separation makes it hard for authors, developers, or users of such systems to understand, what consequences different choices they made will have for the created visualizations. We present an extended visualization authoring model for Post-WIMP interfaces, which support designers by a more seamless approach of developing and utilizing visualizations. With it, our emphasis is on the iterative nature of creating and configuring visualizations, the existence of multiple views in the same system, and requirements for the data analysis process.

BACKGROUND: The supertree problem, i.e., the task of finding a common refinement of a set of rooted trees is an important topic in mathematical phylogenetics. The special case of a common leaf set L is known to be solvable in linear time. Existing approaches refine one input tree using information of the others and then test whether the results are isomorphic. RESULTS: An O(k|L|) algorithm, LinCR, for constructing the common refinement T of k input trees with a common leaf set L is proposed that explicitly computes the parent function of T in a bottom-up approach. CONCLUSION: LinCR is simpler to implement than other asymptotically optimal algorithms for the problem and outperforms the alternatives in empirical comparisons. AVAILABILITY: An implementation of LinCR in Python is freely available at https://github.com/david-schaller/tralda .

Computational protein design has the ambitious goal of crafting novel proteins that address challenges in biology and medicine. To overcome these challenges, the computational protein modeling suite Rosetta has been tailored to address various protein design tasks. Recently, statistical methods have been developed that identify correlated mutations between residues in a multiple sequence alignment of homologous proteins. These subtle inter-dependencies in the occupancy of residue positions throughout evolution are crucial for protein function, but we found that three current Rosetta design approaches fail to recover these co-evolutionary couplings. Thus, we developed the Rosetta method ResCue (residue-coupling enhanced) that leverages co-evolutionary information to favor sequences which recapitulate correlated mutations, as observed in nature. To assess the protocols via recapitulation designs, we compiled a benchmark of ten proteins each represented by two, structurally diverse states. We could demonstrate that ResCue designed sequences with an average sequence recovery rate of 70%, whereas three other protocols reached not more than 50%, on average. Our approach had higher recovery rates also for functionally important residues, which were studied in detail. This improvement has only a minor negative effect on the fitness of the designed sequences as assessed by Rosetta energy. In conclusion, our findings support the idea that informing protocols with co-evolutionary signals helps to design stable and native-like proteins that are compatible with the different conformational states required for a complex function.

Damage identification of composite structures is a major ongoing challenge for a secure operational life-cycle due to the complex, gradual damage behaviour of composite materials. Especially for composite rotors in aero-engines and wind-turbines, a cost-intensive maintenance service has to be performed in order to avoid critical failure. A major advantage of composite structures is that they are able to safely operate after damage initiation and under ongoing damage propagation. Therefore, a robust, efficient diagnostic damage identification method would allow monitoring the damage process with intervention occurring only when necessary. This study investigates the structural vibration response of composite rotors by applying machine learning methods and the ability to identify, localise and quantify the present damage. To this end, multiple fully connected neural networks and convolutional neural networks were trained on vibration response spectra from damaged composite rotors with barely visible damage, mostly matrix cracks and local delaminations using dimensionality reduction and data augmentation. A databank containing 720 simulated test cases with different damage states is used as a basis for the generation of multiple data sets. The trained models are tested using k-fold cross validation and they are evaluated based on the sensitivity, specificity and accuracy. Convolutional neural networks perform slightly better providing a performance accuracy of up to 99.3% for the damage localisation and quantification.

We introduce small-text, an easy-to-use active learning library, which offers pool-based active learning for single- and multi-label text classification in Python. It features numerous pre-implemented state-of-the-art query strategies, including some that leverage the GPU. Standardized interfaces allow the combination of a variety of classifiers, query strategies, and stopping criteria, facilitating a quick mix and match, and enabling a rapid and convenient development of both active learning experiments and applications. With the objective of making various classifiers and query strategies accessible for active learning, small-text integrates several well-known machine learning libraries, namely scikit-learn, PyTorch, and Hugging Face transformers. The latter integrations are optionally installable extensions, so GPUs can be used but are not required. Using this new library, we investigate the performance of the recently published SetFit training paradigm, which we compare to vanilla transformer fine-tuning, finding that it matches the latter in classification accuracy while outperforming it in area under the curve. The library is available under the MIT License at https://github.com/webis-de/small-text, in version 1.3.0 at the time of writing.

Active learning is the iterative construction of a classification model through targeted labeling, enabling significant labeling cost savings. As most research on active learning has been carried out before transformer-based language models (``transformers'') became popular, despite its practical importance, comparably few papers have investigated how transformers can be combined with active learning to date. This can be attributed to the fact that using state-of-the-art query strategies for transformers induces a prohibitive runtime overhead, which effectively nullifies, or even outweighs the desired cost savings. For this reason, we revisit uncertainty-based query strategies, which had been largely outperformed before, but are particularly suited in the context of fine-tuning transformers. In an extensive evaluation, we connect transformers to experiments from previous research, assessing their performance on five widely used text classification benchmarks. For active learning with transformers, several other uncertainty-based approaches outperform the well-known prediction entropy query strategy, thereby challenging its status as most popular uncertainty baseline in active learning for text classification.

AIMS: Inflammation plays an important role in cardiovascular disease (CVD) development. The NOD-like receptor protein-3 (NLRP3) inflammasome contributes to the development of atherosclerosis in animal models. Components of the NLRP3 inflammasome pathway such as interleukin-1$beta$ can therapeutically be targeted. Associations of genetically determined inflammasome-mediated systemic inflammation with CVD and mortality in humans are unknown. METHODS AND RESULTS: We explored the association of genetic NLRP3 variants with prevalent CVD and cardiovascular mortality in 538 167 subjects on the individual participant level in an explorative gene-centric approach without performing multiple testing. Functional relevance of single-nucleotide polymorphisms on NLRP3 inflammasome activation has been evaluated in monocyte-enriched peripheral blood mononuclear cells (PBMCs). Genetic analyses identified the highly prevalent (minor allele frequency 39.9%) intronic NLRP3 variant rs10754555 to affect NLRP3 gene expression. rs10754555 carriers showed significantly higher C-reactive protein and serum amyloid A plasma levels. Carriers of the G allele showed higher NLRP3 inflammasome activation in isolated human PBMCs. In carriers of the rs10754555 variant, the prevalence of coronary artery disease was significantly higher as compared to non-carriers with a significant interaction between rs10754555 and age. Importantly, rs10754555 carriers had significantly higher risk for cardiovascular mortality during follow-up. Inflammasome inducers (e.g. urate, triglycerides, apolipoprotein C3) modulated the association between rs10754555 and mortality. CONCLUSION: The NLRP3 intronic variant rs10754555 is associated with increased systemic inflammation, inflammasome activation, prevalent coronary artery disease, and mortality. This study provides evidence for a substantial role of genetically driven systemic inflammation in CVD and highlights the NLRP3 inflammasome as a therapeutic target.

We propose and evaluate several approaches for multi-party privacy-preserving record linkage (MP-PPRL) for multiple data sources. To reduce the number of comparisons for scalability we propose a new pivot-based metric space approach that dynamically adapts the selection of pivots for additional sources and growing data volume. We investigate so-called early and late clustering schemes that either cluster matching records per additional source or holistically for all sources. A comprehensive evaluation for different datasets confirms the high effectiveness and efficiency of the proposed methods.

The importance of Variational Autoencoders reaches far beyond standalone generative models -- the approach is also used for learning latent representations and can be generalized to semi-supervised learning. This requires a thorough analysis of their commonly known shortcomings: posterior collapse and approximation errors. This paper analyzes VAE approximation errors caused by the combination of the ELBO objective and encoder models from conditional exponential families, including, but not limited to, commonly used conditionally independent discrete and continuous models. We characterize subclasses of generative models consistent with these encoder families. We show that the ELBO optimizer is pulled away from the likelihood optimizer towards the consistent subset and study this effect experimentally. Importantly, this subset can not be enlarged, and the respective error cannot be decreased, by considering deeper encoder/decoder networks.

Faced by the abundant use of machine learning in industry and academia, the effective and efficient teaching of core concepts in this field becomes of high importance. For this, we organized a workshop on teaching methods in the field of machine learning. In this document, we summarize the current standing of the community as by our workshop and their methods. We touch on existing working concepts in machine learning didactics, what methods present initiatives use and cover open teaching resources available to date. With this, we hope to provide a starting point for future collaborations on this central topic given the expanding use of machine learning in science, industry and our daily lives.

In this paper, we provide asymptotic results concerning (generalized) Bayesian inference for certain dynamical systems based on a large deviation approach. Given a sequence of observations $y$, a class of model processes parameterized by $varthetain Theta$ which can be characterized as a stochastic process $X^vartheta$ or a measure $mu_vartheta$, and a loss function $L$ which measures the error between $y$ and a realization of $X^vartheta$, we specify the generalized posterior distribution $pi_t(varthetamid y)$. The goal of this paper is to study the asymptotic behavior of $pi_t(varthetamid y)$ as $t to infty.$ In particular, we state conditions on the model family $mu_vartheta_varthetain Theta$ and the loss function $L$ such that the posterior distribution converges. The two conditions we require are: (1) a conditional large deviation behavior for a single $X^vartheta$, and (2) an exponential continuity condition over the model family for the map from the parameter $vartheta$ to the loss incurred between $X^vartheta$ and the observation sequence $y$. The proposed framework is quite general, we apply it to two very different classes of dynamical systems: continuous time hypermixing processes and Gibbs processes on shifts of finite type. We also show that the generalized posterior distribution concentrates asymptotically on those parameters that minimize the expected loss and a divergence term, hence proving posterior consistency.

Abstract argumentation frameworks are by now a major research area in knowledge representation and reasoning. Various aspects of AFs have been extensively studied over the last 25 years. Contributing to understanding the expressive power of AFs, researchers found lower and upper bounds for the maximal number of extensions, that is, acceptable points of view, in AFs. One of the classical and most important concepts in AFs are so-called complete extensions. Surprisingly, the exact bound for the maximal number of complete extensions in an AF has not yet been formally established, although there is a reasonable conjecture tracing back at least to 2015. Recently the notion of modularization was introduced and it was shown that this concept plays a key role for the understanding of relations between semantics as well as intrinsic properties. In this paper, we will use this property to give a formal proof of the conjecture regarding complete semantics.

Monitoring the status of large computing systems is essential to identify unexpected behavior and improve their performance and uptime. However, due to the large-scale and distributed design of such computing systems as well as a large number of monitoring parameters, automated monitoring methods should be applied. Such automatic monitoring methods should also have the ability to adapt themselves to the continuous changes in the computing system. In addition, they should be able to identify behavioral anomalies in useful time, to perform appropriate reactions. This work proposes a general lightweight and unsupervised method for near real-time anomaly detection using operational data measurement on large computing systems. The proposed model requires as little as 4 hours of data and 50 epochs for each training process to accurately resemble the behavioral pattern of computing systems.

Web archive analytics is the exploitation of publicly accessible web pages and their evolution for research purposes---to the extent organizationally possible for researchers. In order to better understand the complexity of this task, the first part of this paper puts the entirety of the world's captured, created, and replicated data (the ``Global Datasphere'') in relation to other important data sets such as the public internet and its web pages, or what is preserved thereof by the Internet Archive. Recently, the Webis research group, a network of university chairs to which the authors belong, concluded an agreement with the Internet Archive to download a substantial part of its web archive for research purposes. The second part of the paper in hand describes our infrastructure for processing this data treasure: We will eventually host around 8 PB of web archive data from the Internet Archive and Common Crawl, with the goal of supplementing existing large scale web corpora and forming a non-biased subset of the 30 PB web archive at the Internet Archive.

Wikidata, now a decade old, is the largest public knowledge graph, with data on more than 100 million concepts contributed by over 560,000 editors. It is widely used in applications and research. At its launch in late 2012, however, it was little more than a hopeful new Wikimedia project, with no content, almost no community, and a severely restricted platform. Seven years earlier still, in 2005, it was merely a rough idea of a few PhD students, a conceptual nucleus that had yet to pick up many important influences from others to turn into what is now called Wikidata. In this paper, we try to recount this remarkable journey, and we review what has been accomplished, what has been given up on, and what is yet left to do for the future.

In biology, we are often confronted with information-rich, large-scale trajectory data, but exploring and communicating patterns in such data can be a cumbersome task. Ideally, the data should be wrapped with an interactive visualisation in one concise packet that makes it straightforward to create and test hypotheses collaboratively. To address these challenges, we have developed a tool, linus, which makes the process of exploring and sharing 3D trajectories as easy as browsing a website. We provide a python script that reads trajectory data, enriches them with additional features such as edge bundling or custom axes, and generates an interactive web-based visualisation that can be shared online. linus facilitates the collaborative discovery of patterns in complex trajectory data.

Abstract. Compiling and disseminating information about incidents and disasters are key to disaster management and relief. But due to inherent limitations of the acquisition process, the required information is often incomplete or missing altogether. To fill these gaps, citizen observations spread through social media are widely considered to be a promising source of relevant information, and many studies propose new methods to tap this resource. Yet, the overarching question of whether and under which circumstances social media can supply relevant information (both qualitatively and quantitatively) still remains unanswered. To shed some light on this question, we review 37 disaster and incident databases covering 27 incident types, compile a unified overview of the contained data and their collection processes, and identify the missing or incomplete information. The resulting data collection reveals six major use cases for social media analysis in incident data collection: (1) impact assessment and verification of model predictions, (2) narrative generation, (3) recruiting citizen volunteers, (4) supporting weakly institutionalized areas, (5) narrowing surveillance areas, and (6) reporting triggers for periodical surveillance. Furthermore, we discuss the benefits and shortcomings of using social media data for closing information gaps related to incidents and disasters.

In this work we propose a deep neural network based surrogate model for a plasma shadowgraph - a technique for visualization of perturbations in a transparent medium. We are substituting the numerical code by a computationally cheaper projection based surrogate model that is able to approximate the electric fields at a given time without computing all preceding electric fields as required by numerical methods. This means that the projection based surrogate model allows to recover the solution of the governing 3D partial differential equation, 3D wave equation, at any point of a given compute domain and configuration without the need to run a full simulation. This model has shown a good quality of reconstruction in a problem of interpolation of data within a narrow range of simulation parameters and can be used for input data of large size.

Hyperparameter optimization is a crucial task in numerous applications of numerical modelling techniques. Methods as diverse as classical simulations and the great variety of machine learning techniques used nowadays, require an appropriate choice of their hyperparameters (HPs). While for classical simulations, calibration to measured data by numerical optimization techniques has a long tradition, the HPs of neural networks are often chosen by a mixture of grid search, random search and manual tuning. In the present study the expert tool “OmniOpt” is introduced, which allows to optimize the HPs of a wide range of problems, ranging from classical simulations to different kinds of neural networks. Thereby, the emphasis is on versatility and flexibility for the user in terms of the applications and the choice of its HPs to be optimized. Moreover, the optimization procedure – which is usually a very time-consuming task – should be performed in a highly parallel way on the HPC system Taurus at TU Dresden. To this end, a Bayesian stochastic optimization algorithm (TPE) has been implemented on the Taurus system and connected to a user-friendly graphical user interface (GUI). In addition to the automatic optimization service, there is a variety of tools for analyzing and graphically displaying the results of the optimization. The application of OmniOpt to a practical problem from material science is presented as an example.

Objective Ghrelin is an orexigenic peptide hormone involved in the regulation of energy homeostasis, food intake and glucose metabolism. Serum levels increase anticipating a meal and fall afterwards. Underlying genetic mechanisms of the ghrelin secretion are unknown. Methods Total serum ghrelin was measured in 1501 subjects selected from the population-based LIFE-ADULT-sample after an overnight fast. A genome-wide association study (GWAS) was performed. Gene-based expression association analyses (transcriptome-wide association study (TWAS)) are statistical tests associating genetically predicted expression to a certain trait and were done using MetaXcan. Results In the GWAS, three loci reached genome-wide significance: the WW-domain containing the oxidoreductase-gene (WWOX; P = 1.80E-10) on chromosome 16q23.3-24.1 (SNP: rs76823993); the contactin-associated protein-like 2 gene (CNTNAP2; P = 9.0E-9) on chromosome 7q35-q36 (SNP: rs192092592) and the ghrelin And obestatin prepropeptide gene (GHRL; P = 2.72E-8) on chromosome 3p25.3 (SNP: rs143729751). In the TWAS, the three genes where the expression was strongest associated with serum ghrelin levels was the ribosomal protein L36 (RPL36; P = 1.3E-06, FDR = 0.011, positively correlated), AP1B1 (P = 1.1E-5, FDR = 0.048, negatively correlated) and the GDNF family receptor alpha like (GFRAL), receptor of the anorexigenic growth differentiation factor-15 (GDF15), (P = 1.8E-05, FDR = 0.15, also negatively correlated). Conclusions The three genome-wide significant genetic loci from the GWA and the genes identified in the TWA are functionally plausible and should initiate further research.

In this demo, we present PostCENN , an enhanced PostgreSQL database system with an end-to-end integration of machine learning (ML) models for cardinality estimation. In general, cardinality estimation is a topic with a long history in the database community. While traditional models like histograms are extensively used, recent works mainly focus on developing new approaches using ML models. However, traditional as well as ML models have their own advantages and disadvantages. With PostCENN , we aim to combine both to maximize their potentials for cardinality estimation by introducing ML models as a novel means to increase the accuracy of the cardinality estimation for certain parts of the database schema. To achieve this, we integrate ML models as first class citizen in PostgreSQL with a well-defined end-to-end life cycle. This life cycle consists of creating ML models for different sub-parts of the database schema, triggering the training, using ML models within the query optimizer in a transparent way, and deleting ML models.

This study aimed to understand the perception of drought among farmers, in order to support decision-making in the water allocation process. This study was carried out in the Tabuleiro de Russas irrigated perimeter, in northeast Brazil, over the drought period of 2012–2018. Two analyses were conducted: (i) drought characterization, using the Standardized Precipitation Index (SPI) based on drought duration and frequency criteria; and (ii) analysis of farmers’ perceptions of drought via selection of explanatory variables using the Random Forest (RF) and the Decision Tree (DT) methods. The 2012–2018 drought period was defined as a meteorological phenomenon by local farmers; however, an SPI evaluation indicated that the drought was of a hydrological nature. According to the RF analysis, four of the nine study variables were more statistically important than the others in influencing farmers’ perception of drought: number of cultivated land plots, farmer’s age, years of experience in the agriculture sector, and education level. These results were confirmed using DT analysis. Understanding the relationship between these variables and farmers’ perception of drought could aid in the development of an adaptation strategy to water deficit scenarios. Farmers’ perception can be beneficial in reducing conflicts, adopting proactive management practices, and developing a holistic and efficient early warning drought system.

Abstract Factors ranging from ecological opportunity to genome composition might explain why only some lineages form adaptive radiations. While being rare, particular systems can provide natural experiments within an identical ecological setting where species numbers and phenotypic divergence in two closely related lineages are notably different. We investigated one such natural experiment using two de novo assembled and 40 resequenced genomes and asked why two closely related Neotropical cichlid fish lineages, the Amphilophus citrinellus species complex (Midas cichlids; radiating) and Archocentrus centrarchus (Flyer cichlid; nonradiating), have resulted in such disparate evolutionary outcomes. Although both lineages inhabit many of the same Nicaraguan lakes, whole-genome inferred demography suggests that priority effects are not likely to be the cause of the dissimilarities. Also, genome-wide levels of selection, transposable element dynamics, gene family expansion, major chromosomal rearrangements and the number of genes under positive selection were not markedly different between the two lineages. To more finely investigate particular subsets of the genome that have undergone adaptive divergence in Midas cichlids, we also examined if there was evidence for ‘molecular pre-adaptation’ in regions identified by QTL mapping of repeatedly diverging adaptive traits. Although most of our analyses failed to pinpoint substantial genomic differences, we did identify functional categories containing many genes under positive selection that provide candidates for future studies on the propensity of Midas cichlids to radiate. Our results point to a disproportionate role of local, rather than genome-wide factors underlying the propensity for these cichlid fishes to adaptively radiate.

Abstract We implemented two neural network based benchmark tasks on a prototype chip of the second-generation SpiNNaker (SpiNNaker 2) neuromorphic system: keyword spotting and adaptive robotic control. Keyword spotting is commonly used in smart speakers to listen for wake words, and adaptive control is used in robotic applications to adapt to unknown dynamics in an online fashion. We highlight the benefit of a multiply-accumulate (MAC) array in the SpiNNaker 2 prototype which is ordinarily used in rate-based machine learning networks when employed in a neuromorphic, spiking context. In addition, the same benchmark tasks have been implemented on the Loihi neuromorphic chip, giving a side-by-side comparison regarding power consumption and computation time. While Loihi shows better efficiency when less complicated vector-matrix multiplication is involved, with the MAC array, the SpiNNaker 2 prototype shows better efficiency when high dimensional vector-matrix multiplication is involved.

The accurate and complete assembly of both haplotype sequences of a diploid organism is essential to understanding the role of variation in genome functions, phenotypes and diseases1. Here, using a trio-binning approach, we present a high-quality, diploid reference genome, with both haplotypes assembled independently at the chromosome level, for the common marmoset (Callithrix jacchus), an primate model system that is widely used in biomedical research2,3. The full spectrum of heterozygosity between the two haplotypes involves 1.36% of the genome-much higher than the 0.13% indicated by the standard estimation based on single-nucleotide heterozygosity alone. The de novo mutation rate is 0.43 $times$ 10-8 per site per generation, and the paternal inherited genome acquired twice as many mutations as the maternal. Our diploid assembly enabled us to discover a recent expansion of the sex-differentiation region and unique evolutionary changes in the marmoset Y chromosome. In addition, we identified many genes with signatures of positive selection that might have contributed to the evolution of Callithrix biological features. Brain-related genes were highly conserved between marmosets and humans, although several genes experienced lineage-specific copy number variations or diversifying selection, with implications for the use of marmosets as a model system.