bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exposure Determinants in the French Database COLCHIC (1987–2019): Statistical Modeling across 77 Chemicals
Authors: Mater Gautier , Sauvé Jean-François , Sarazin Philippe , Lavoué Jérôme 
Published: October 2021

Abstract

Objectives
The COLCHIC database contains workplace exposure results of chemical samples collected by the French prevention network since 1987. We aimed to investigate potential associations between exposure levels and ancillary variables in COLCHIC across a broad range of chemical agents in order to provide insight into how to best interpret and exploit the information in this national database.

Methods
We selected personal and area measurements in COLCHIC and collected outside respiratory personal protective equipment (PPE), restricted to chemical agents that had at least 1000 samples available. We used Tobit models to estimate associations between exposure concentrations and sample year, sampling duration, PPE, workforce size, collective protective equipment, origin of request, and reason for request for each chemical agent for the period 1987–2019. Models for period 2002–2019 also included type of process (open/closed) and exposure frequency. We used separate models for each combination of agent, period, and personal or area samples. We then applied Bayesian meta-analytical methods to assess average effects and effect heterogeneity of exposure factors across agents.

Results
COLCHIC contained 720 282 exposure results (62% personal and 38% area samples) to 77 agents, including 346 766 results for the more recent period 2002–2019 (67% personal and 33% area samples). Sample year and duration, PPE, and process type had the strongest and most consistent associations with exposure levels across agents. Personal and area exposure levels decreased yearly (6% for the entire period and 9% since 2002), and 30-min samples were approximately twice as high as 240-min samples. Workers wearing PPE were exposed to levels 1.7 times higher on average than those without PPE for both area and personal samples. Personal exposure levels associated with enclosed or semi-enclosed processes were approximately 20–30% lower compared with open processes. The associations for the other exposure variables were weaker and more inconsistent between agents. Between-agent heterogeneity of estimated effects, based on 80% prediction intervals, was lowest for sampling duration, time trends, and the presence of PPE.

Conclusions
Sampling duration, time trends, and the presence of PPE are important factors to take into account when analyzing COLCHIC and had similar associations with exposure levels across agents. Other variables generally showed weaker associations or variable effects. These results will be used to adjust exposure estimates for the French working population from measurements stored in COLCHIC.
Identifying correlations driven by influential observations in large datasets
Authors: Bu Kevin , Wallach David S , Wilson Zach , Shen Nan , Segal Leopoldo N , Bagiella Emilia , Clemente Jose C 
Published: October 2021

AbstractAlthough high-throughput data allow researchers to interrogate thousands of variables simultaneously, it can also introduce a significant number of spurious results. Here we demonstrate that correlation analysis of large datasets can yield numerous false positives due to the presence of outliers that canonical methods fail to identify. We present Correlations Under The InfluencE (CUTIE), an open-source jackknifing-based method to detect such cases with both parametric and non-parametric correlation measures, and which can also uniquely rescue correlations not originally deemed significant or with incorrect sign. Our approach can additionally be used to identify variables or samples that induce these false correlations in high proportion. A meta-analysis of various omics datasets using CUTIE reveals that this issue is pervasive across different domains, although microbiome data are particularly susceptible to it. Although the significance of a correlation eventually depends on the thresholds used, our approach provides an efficient way to automatically identify those that warrant closer examination in very large datasets.
CCWeights: an R package and web application for automated evaluation and selection of weighting factors for accurate quantification using linear calibration curve
Authors: Dong Yonghui , Wachsman Tal , Morgan Liat , Gazit Ehud , Birkler Rune Isak Dupont 
Published: October 2021

Abstract

Summary
The accuracy of any analytical method is highly dependent on the selection of an appropriate calibration model. Here, we present CCWeights, an R package for automated assessment and selection of weighting factors for accurate quantification using linear calibration curve. Additionally, CCWeights includes a web application that allows users to analyze their data using an interactive graphical user interface, without any programming requirements. The workflow and features of CCWeights are illustrated by the analyses of two datasets acquired by liquid chromatography-mass spectrometry (LC-MS). The resulting quantification table can be directly utilized for further model assessment and subsequent data analysis.

Availability and implementation
CCWeights is publicly available on CRAN repository (https://cran.r-project.org/web/packages/CCWeights), with source code available on GitHub (https://github.com/YonghuiDong/CCWeights) under a GPL-3 license. The web application can be run locally from R console using a simple command “runGui()”. Alternatively, the web application can be freely accessed for direct online use at https://bcdd.shinyapps.io/CCWeights/.

Supplementary information
Supplementary data are available at Bioinformatics Advances online.
CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks
Authors: Feng Shi-Hao , Xia Chun-Qiu , Shen Hong-Bin 
Published: October 2021

Abstract

Motivation
Coiled-coil is composed of two or more helices that are wound around each other. It widely exists in proteins and has been discovered to play a variety of critical roles in biology processes. Generally, there are three types of structural features in coiled-coil: coiled-coil domain (CCD), oligomeric state and register. However, most of the existing computational tools only focus on one of them.

Results
Here, we describe a new deep learning model, CoCoPRED, which is based on convolutional layers, bidirectional long short-term memory, and attention mechanism. It has three networks, i.e. CCD network, oligomeric state network, and register network, corresponding to the three types of structural features in coiled-coil. This means CoCoPRED has the ability of fulfilling comprehensive prediction for coiled-coil proteins. Through the 5-fold cross-validation experiment, we demonstrate that CoCoPRED can achieve better performance than the state-of-the-art models on both CCD prediction and oligomeric state prediction. Further analysis suggests the CCD prediction may be a performance indicator of the oligomeric state prediction in CoCoPRED. The attention heads in CoCoPRED indicate that registers a, b and e are more crucial for the oligomeric state prediction.

Availability and implementation
CoCoPRED is available at http://www.csbio.sjtu.edu.cn/bioinf/CoCoPRED. The datasets used in this research can also be downloaded from the website.

Supplementary information
Supplementary data are available at Bioinformatics online.
AOP-helpFinder webserver: a tool for comprehensive analysis of the literature to support adverse outcome pathways development
Authors: Jornod Florence , Jaylet Thomas , Blaha Ludek , Sarigiannis Denis , Tamisier Luc , Audouze Karine 
Published: October 2021

Abstract

Motivation
Adverse outcome pathways (AOPs) are a conceptual framework developed to support the use of alternative toxicology approaches in the risk assessment. AOPs are structured linear organizations of existing knowledge illustrating causal pathways from the initial molecular perturbation triggered by various stressors, through key events (KEs) at different levels of biology, to the ultimate health or ecotoxicological adverse outcome.

Results
Artificial intelligence can be used to systematically explore available toxicological data that can be parsed in the scientific literature. Recently, a tool called AOP-helpFinder was developed to identify associations between stressors and KEs supporting thus documentation of AOPs. To facilitate the utilization of this advanced bioinformatics tool by the scientific and the regulatory community, a webserver was created. The proposed AOP-helpFinder webserver uses better performing version of the tool which reduces the need for manual curation of the obtained results. As an example, the server was successfully applied to explore relationships of a set of endocrine disruptors with metabolic-related events. The AOP-helpFinder webserver assists in a rapid evaluation of existing knowledge stored in the PubMed database, a global resource of scientific information, to build AOPs and Adverse Outcome Networks supporting the chemical risk assessment.

Availability and implementation
AOP-helpFinder is available at http://aop-helpfinder.u-paris-sciences.fr/index.php

Supplementary information
Supplementary data are available at Bioinformatics online.
Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs
Authors: Leng Jiacheng , Wu Ling-Yun 
Published: October 2021

Abstract

Motivation
Differential network inference is a fundamental and challenging problem to reveal gene interactions and regulation relationships under different conditions. Many algorithms have been developed for this problem; however, they do not consider the differences between the importance of genes, which may not fit the real-world situation. Different genes have different mutation probabilities, and the vital genes associated with basic life activities have less fault tolerance to mutation. Equally treating all genes may bias the results of differential network inference. Thus, it is necessary to consider the importance of genes in the models of differential network inference.

Results
Based on the Gaussian graphical model with adaptive gene importance regularization, we develop a novel Importance-Penalized Joint Graphical Lasso method (IPJGL) for differential network inference. The presented method is validated by the simulation experiments as well as the real datasets. Furthermore, to precisely evaluate the results of differential network inference, we propose a new metric named APC2 for the differential levels of gene pairs. We apply IPJGL to analyze the TCGA colorectal and breast cancer datasets and find some candidate cancer genes with significant survival analysis results, including SOST for colorectal cancer and RBBP8 for breast cancer. We also conduct further analysis based on the interactions in the Reactome database and confirm the utility of our method.

Availability and implementation
R source code of Importance-Penalized Joint Graphical Lasso is freely available at https://github.com/Wu-Lab/IPJGL.

Supplementary information
Supplementary data are available at Bioinformatics online.
Association of painful human immunodeficiency virus distal sensory polyneuropathy with aberrant expectation of pain relief: functional magnetic resonance imaging evidence
Authors: Strigo Irina A , Keltner John R , Ellis Ronald J , Simmons Alan N 
Published: October 2021

Abstract
Mechanisms underlying chronic neuropathic pain associated with HIV-associated distal sensory polyneuropathy are poorly understood, yet 40% of those with distal neuropathy (or 20% of all people with HIV) suffer from this debilitating condition. Central pain processing mechanisms are thought to contribute to the development of HIV neuropathic pain, yet studies investigating central mechanisms for HIV neuropathic pain are few. Considering the motivational nature of pain, we aimed to examine the degree to which expectation of pain onset and expectation of pain offset are altered in sixty-one male patients with HIV-related distal sensory polyneuropathy with (N = 30) and without (N = 31) chronic neuropathic pain. By contrasting painful (foot) and non-painful (hand) sites between those with and without neuropathic pain, we could identify unique neural structures that showed altered activation during expectation of pain offset or relief. Our results showed no evidence for peripheral mechanisms evidenced by lack of significant between group differences in thermo-sensation, subjective pain response or epidermal nerve fibre density. Likewise, we found no significant differences between groups in subjective or brain mechanisms underlying the expectation of pain onset. Conversely, we found significant interaction within right anterior insula during expectation of pain offset in our study in that individuals in the pain group compared to the no-pain group exhibited increased anterior insula activation on the painful compared to the non-painful site. Our findings are consistent with abnormal processing of expectation of pain offset or abnormal pain relief-related mechanisms potentially due to increased emotional distress regarding the experience of chronic endogenous pain.
Pantomime of tool use: looking beyond apraxia
Authors: Osiurak François , Reynaud Emanuelle , Baumard Josselin , Rossetti Yves , Bartolo Angela , Lesourd Mathieu 
Published: October 2021

Abstract
Pantomime has a long tradition in clinical neuropsychology of apraxia. It has been much more used by researchers and clinicians to assess tool-use disorders than real tool use. Nevertheless, it remains incompletely understood and has given rise to controversies, such as the involvement of the left inferior parietal lobe or the nature of the underlying cognitive processes. The present article offers a comprehensive framework, with the aim of specifying the neural and cognitive bases of pantomime. To do so, we conducted a series of meta-analyses of brain-lesion, neuroimaging, and behavioral studies about pantomime and other related tasks (i.e., real tool use, imitation of meaningless postures, and semantic knowledge). The first key finding is that the area PF (Area PF complex) within the left inferior parietal lobe is crucially involved in both pantomime and real tool use as well as in the kinematics component of pantomime. The second key finding is the absence of a well-defined neural substrate for the posture component of pantomime (both grip errors and body-part-as-tool responses). The third key finding is the role played by the intraparietal sulcus in both pantomime and imitation of meaningless postures. The fourth key finding is that the left angular gyrus seems to be critical in the production of motor actions directed toward the body. The fifth key finding is that performance on pantomime is strongly correlated with the severity of semantic deficits. Taken together, these findings invite us to offer a neurocognitive model of pantomime, which provides an integrated alternative to the two hypotheses that dominate the field: The gesture-engram hypothesis and the communicative hypothesis. More specifically, this model assumes that technical reasoning (notably the left area PF), the motor-control system (notably the intraparietal sulcus), body structural description (notably the left angular gyrus), semantic knowledge (notably the polar temporal lobes) and potentially theory of mind (notably the middle prefrontal cortex) work in concert to produce pantomime. The original features of this model open new avenues for understanding the neurocognitive bases of pantomime, emphasizing that pantomime is a communicative task that nevertheless originates in specific tool-use (not motor-related) cognitive processes.
Bayesian slip inversion with automatic differentiation variational inference
Authors: Zhang Chendong , Chen Ting 
Published: October 2021

SUMMARYThe Bayesian slip inversion offers a powerful tool for modelling the earthquake source mechanism. It can provide a fully probabilistic result and thus permits us to quantitatively assess the inversion uncertainty. The Bayesian problem is usually solved with Monte Carlo methods, but they are computationally expensive and are inapplicable for high-dimensional and large-scale problems. Variational inference is an alternative solver to the Bayesian problem. It turns Bayesian inference into an optimization task and thus enjoys better computational performances. In this study, we introduce a general variational inference algorithm, automatic differentiation variational inference (ADVI), to the Bayesian slip inversion and compare it with the classic Metropolis–Hastings (MH) sampling method. The synthetic test shows that the two methods generate nearly identical mean slip distributions and standard deviation maps. In the real case study, the two methods produce highly consistent mean slip distributions, but the ADVI-derived standard deviation map differs from that produced by the MH method, possibly because of the limitation of the Gaussian approximation in the ADVI method. In both cases, ADVI can give comparable results to the MH method but with a significantly lower computational cost. Our results show that ADVI is a promising and competitive method for the Bayesian slip inversion.
A rapid evidence assessment exploring whether antimicrobial resistance complicates non-infectious health conditions and healthcare services, 2010–20
Authors: Hocking Lucy , Ali Gemma-Claire , d’Angelo Camilla , Deshpande Advait , Stevenson Cagla , Virdee Mann , Guthrie Susan 
Published: October 2021

AbstractAntimicrobial resistance (AMR) is one of the greatest public health threats at this time. While there is a good understanding of the impacts of AMR on infectious diseases, an area of less focus is the effects AMR may be having on non-communicable health conditions (such as cancer) and healthcare services (such as surgery). Therefore, this study aimed to explore what impact AMR is currently having on non-communicable health conditions, or areas of health services, where AMR could be a complicating factor impacting on the ability to treat the condition and/or health outcomes. To do this, a rapid evidence assessment of the literature was conducted, involving a systematic approach to searching and reviewing the evidence. In total, 101 studies were reviewed covering surgery, organ transplants, cancer, ICUs, diabetes, paediatric patients, immunodeficiency conditions, liver and kidney disease, and physical trauma. The results showed limited research in this area and studies often use a selective population, making the results difficult to generalize. However, the evidence showed that for all health conditions and healthcare service areas reviewed, at least one study demonstrated a higher risk of death for patients with resistant infections, compared with no or drug-susceptible infections. Poor health outcomes were also associated with resistant infections in some instances, such as severe sepsis and failure of treatments, as well as a greater need for invasive medical support. While there are gaps in the evidence base requiring further research, efforts are also needed within policy and practice to better understand and overcome these challenges.
Individual snowshoe hares manage risk differently: integrating stoichiometric distribution models and foraging ecology
Authors: Richmond Isabella C , Balluffi-Fry Juliana , Vander Wal Eric , Leroux Shawn J , Rizzuto Matteo , Heckford Travis R , Kennah Joanie L , Riefesel Gabrielle R , Wiersma Yolanda F 
Published: October 2021

Abstract
Herbivores making space use decisions must consider the trade-off between perceived predation risk and forage quality. Herbivores, specifically snowshoe hares (Lepus americanus), must constantly navigate landscapes that vary in predation risk and food quality, providing researchers with the opportunity to explore the factors that govern their foraging decisions. Herein, we tested predictions that intersect the risk allocation hypothesis (RAH) and optimal foraging theory (OFT) in a spatially explicit ecological stoichiometry framework to assess the trade-off between predation risk and forage quality. We used individual and population estimates of snowshoe hare (n = 29) space use derived from biotelemetry across three summers. We evaluated resource forage quality for lowbush blueberry (Vaccinium angustifolium), a common and readily available forage species within our system, using carbon:nitrogen and carbon:phosphorus ratios. We used habitat complexity to proxy perceived predation risk. We analyzed how forage quality of blueberry, perceived predation risk, and their interaction impact the intensity of herbivore space use. We used generalized mixed effects models, structured to enable us to make inferences at the population and individual home range level. We did not find support for RAH and OFT. However, variation in the individual-level reactions norms in our models showed that individual hares have unique responses to forage quality and perceived predation risk. Our finding of individual-level responses indicates that there is fine-scale decision-making by hares, although we did not identify the mechanism. Our approach illustrates spatially explicit empirical support for individual behavioral responses to the food quality–predation risk trade-off.
Two newly identified eclipsing binaries in open cluster NGC 3532
Authors: Özdarcan O 
Published: October 2021

ABSTRACT
We present light-curve analyses of two newly identified detached eclipsing binaries, namely HD 96609 and HD 303734, in the region of the richly populated open cluster NGC 3532. HD 96609 is composed of two main sequence stars (B9-A0V + A2V) with masses and radii of M1 = 2.66 ± 0.02 M⊙, M2 = 1.84 ± 0.01 M⊙, R1 = 2.740 ± 0.006 R⊙, and R2 = 1.697 ± 0.005 R⊙. The positions of the components on logM − logR plane suggest log(age/yr) 8.55, corresponding 350 ± 40 Myr of age, which agrees with the 300 ± 100 Myr age of NGC 3532 estimated in previous studies. We find the distance of HD 96609 as 460 ± 17 pc, which is consistent with the $484^{+35}_{-30}$ pc distance of NGC 3532, estimated from Gaia parallaxes. HD 303734 is an interesting totally eclipsing binary with a quite shallow secondary eclipse. Using photometric properties of the system in conjunction with theoretical calibrations, we estimate that HD 303734 consists of A6V + K3V components. HD 96609 and HD 303734 are the second and third eclipsing binaries discovered in the region of NGC 3532, after the first one, GV Car.
The homogeneity scale and the growth rate of cosmic structures
Authors: Avila Felipe , Bernui Armando , Nunes Rafael C , de Carvalho Edilson , Novaes Camila P 
Published: October 2021

ABSTRACT
We propose a novel approach to obtain the growth rate of cosmic structures, f(z), from the evolution of the cosmic homogeneity scale, RH(z). Our methodology needs two ingredients in a specific functional form: RH(z) data and the matter two-point correlation function today, i.e. ξ(r, z = 0). We use a Gaussian Process approach to reconstruct the function RH. In the absence of suitable observational information of the matter correlation function in the local Universe, z ≃ 0, we assume a fiducial cosmology to obtain ξ(r, z = 0). For this reason, our final result turns out to be a consistency test of the cosmological model assumed. Our results show a good agreement between: (i) the growth rate $f^{R_{\text{H}}}(z)$ obtained through our approach, (ii) the fΛCDM(z) expected in the fiducial model, and (iii) the best-fitting f(z) from data compiled in the literature. Moreover, using this data compilation, we perform a Gaussian Process to reconstruct the growth rate function fdata(z) and compare it with the function $f^{R_{\text{H}}}(z)$ finding a concordance of $\lt \!2 \, \sigma$, a good result considering the few data available for both reconstruction processes. With more accurate RH(z) data, from forthcoming surveys, the homogeneity scale function might be better determined and would have the potential to discriminate between ΛCDM and alternative scenarios as a new cosmological observable.