bluuebunny/crossref_metadata_2025_split huggingface dataset:
DeepGenGrep: a general deep learning-based predictor for multiple genomic signals and regions
Authors: Liu Quanzhong , Fang Honglin , Wang Xiao , Wang Miao , Li Shuqin , Coin Lachlan J M , Li Fuyi , Song Jiangning 
Published: July 2022

AbstractMotivationAccurate annotation of different genomic signals and regions (GSRs) from DNA sequences is fundamentally important for understanding gene structure, regulation and function. Numerous efforts have been made to develop machine learning-based predictors for in silico identification of GSRs. However, it remains a great challenge to identify GSRs as the performance of most existing approaches is unsatisfactory. As such, it is highly desirable to develop more accurate computational methods for GSRs prediction.ResultsIn this study, we propose a general deep learning framework termed DeepGenGrep, a general predictor for the systematic identification of multiple different GSRs from genomic DNA sequences. DeepGenGrep leverages the power of hybrid neural networks comprising a three-layer convolutional neural network and a two-layer long short-term memory to effectively learn useful feature representations from sequences. Benchmarking experiments demonstrate that DeepGenGrep outperforms several state-of-the-art approaches on identifying polyadenylation signals, translation initiation sites and splice sites across four eukaryotic species including Homo sapiens, Mus musculus, Bos taurus and Drosophila melanogaster. Overall, DeepGenGrep represents a useful tool for the high-throughput and cost-effective identification of potential GSRs in eukaryotic genomes.Availability and implementationThe webserver and source code are freely available at http://bigdata.biocie.cn/deepgengrep/home and Github (https://github.com/wx-cie/DeepGenGrep/).Supplementary informationSupplementary data are available at Bioinformatics online.
Gfastats: conversion, evaluation and manipulation of genome sequences using assembly graphs
Authors: Formenti Giulio , Abueg Linelle , Brajuka Angelo , Brajuka Nadolina , Gallardo-Alba Cristóbal , Giani Alice , Fedrigo Olivier , Jarvis Erich D 
Published: July 2022

AbstractMotivationWith the current pace at which reference genomes are being produced, the availability of tools that can reliably and efficiently generate genome assembly summary statistics has become critical. Additionally, with the emergence of new algorithms and data types, tools that can improve the quality of existing assemblies through automated and manual curation are required.ResultsWe sought to address both these needs by developing gfastats, as part of the Vertebrate Genomes Project (VGP) effort to generate high-quality reference genomes at scale. Gfastats is a standalone tool to compute assembly summary statistics and manipulate assembly sequences in FASTA, FASTQ or GFA [.gz] format. Gfastats stores assembly sequences internally in a GFA-like format. This feature allows gfastats to seamlessly convert FAST* to and from GFA [.gz] files. Gfastats can also build an assembly graph that can in turn be used to manipulate the underlying sequences following instructions provided by the user, while simultaneously generating key metrics for the new sequences.Availability and implementationGfastats is implemented in C++. Precompiled releases (Linux, MacOS, Windows) and commented source code for gfastats are available under MIT licence at https://github.com/vgl-hub/gfastats. Examples of how to run gfastats are provided in the GitHub. Gfastats is also available in Bioconda, in Galaxy (https://assembly.usegalaxy.eu) and as a MultiQC module (https://github.com/ewels/MultiQC). An automated test workflow is available to ensure consistency of software updates.Supplementary informationSupplementary data are available at Bioinformatics online.
Response best-subset selector for multivariate regression with high-dimensional response variables
Authors: Hu Jianhua , Huang Jian , Liu Xiaoqian , Liu Xu 
Published: July 2022

SummaryThis article investigates the statistical problem of response-variable selection with high-dimensional response variables and a diverging number of predictor variables with respect to the sample size in the framework of multivariate linear regression. A response best-subset selection model is proposed by introducing a 0-1 selection indicator for each response variable, and then a response best-subset selector is developed by introducing a separation parameter and a novel penalized least-squares function. The proposed procedure can perform response-variable selection and regression-coefficient estimation simultaneously, and the response best-subset selector has the property of model consistency under mild conditions for both fixed and diverging numbers of predictor variables. Also, consistency and asymptotic normality of regression-coefficient estimators are established for cases with a fixed dimension, and it is found that the Bonferroni test is a special response best-subset selector. Finite-sample simulations show that the response best-subset selector has strong advantages over existing competitors in terms of the Matthews correlation coefficient, a criterion that aims to balance accuracies for both true and false response variables. An analysis of real data demonstrates the effectiveness of the response best-subset selector in an application involving the identification of dosage-sensitive genes.
Hug and hop: a discrete-time, nonreversible Markov chain Monte Carlo algorithm
Authors: Ludkin M , Sherlock C 
Published: July 2022

Summary
This article introduces the hug and hop Markov chain Monte Carlo algorithm for estimating expectations with respect to an intractable distribution. The algorithm alternates between two kernels, referred to as hug and hop. Hug is a nonreversible kernel that repeatedly applies the bounce mechanism from the recently proposed bouncy particle sampler to produce a proposal point that is far from the current position yet on almost the same contour of the target density, leading to a high acceptance probability. Hug is complemented by hop, which deliberately proposes jumps between contours and has an efficiency that degrades very slowly with increasing dimension. There are many parallels between hug and Hamiltonian Monte Carlo using a leapfrog integrator, including the order of the integration scheme, but hug is also able to make use of local Hessian information without requiring implicit numerical integration steps, and its performance is not terminally affected by unbounded gradients of the log-posterior. We test hug and hop empirically on a variety of toy targets and real statistical models, and find that it can, and often does, outperform Hamiltonian Monte Carlo.
Improved prediction of early cognitive impairment in multiple sclerosis combining blood and imaging biomarkers
Authors: Brummer Tobias , Muthuraman Muthuraman , Steffen Falk , Uphaus Timo , Minch Lena , Person Maren , Zipp Frauke , Groppa Sergiu , Bittner Stefan , Fleischer Vinzenz 
Published: July 2022

Abstract
Disability in multiple sclerosis is generally classified by sensory and motor symptoms, yet cognitive impairment has been identified as a frequent manifestation already in the early disease stages. Imaging- and more recently blood-based biomarkers have become increasingly important for understanding cognitive decline associated with multiple sclerosis. Thus, we sought to determine the prognostic utility of serum neurofilament light chain levels alone and in combination with MRI markers by examining their ability to predict cognitive impairment in early multiple sclerosis. A comprehensive and detailed assessment of 152 early multiple sclerosis patients (Expanded Disability Status Scale: 1.3 ± 1.2, mean age: 33.0 ± 10.0 years) was performed, which included serum neurofilament light chain measurement, MRI markers (i.e. T2-hyperintense lesion volume and grey matter volume) acquisition and completion of a set of cognitive tests (Symbol Digits Modalities Test, Paced Auditory Serial Addition Test, Verbal Learning and Memory Test) and mood questionnaires (Hospital Anxiety and Depression scale, Fatigue Scale for Motor and Cognitive Functions). Support vector regression, a branch of unsupervised machine learning, was applied to test serum neurofilament light chain and combination models of biomarkers for the prediction of neuropsychological test performance. The support vector regression results were validated in a replication cohort of 101 early multiple sclerosis patients (Expanded Disability Status Scale: 1.1 ± 1.2, mean age: 34.4 ± 10.6 years). Higher serum neurofilament light chain levels were associated with worse Symbol Digits Modalities Test scores after adjusting for age, sex Expanded Disability Status Scale, disease duration and disease-modifying therapy (B = −0.561; SE = 0.192; P = 0.004; 95% CI = −0.940 to −0.182). Besides this association, serum neurofilament light chain levels were not linked to any other cognitive or mood measures (all P-values &gt; 0.05). The tripartite combination of serum neurofilament light chain levels, lesion volume and grey matter volume showed a cross-validated accuracy of 88.7% (90.8% in the replication cohort) in predicting Symbol Digits Modalities Test performance in the support vector regression approach, and outperformed each single biomarker (accuracy range: 68.6–75.6% and 68.9–77.8% in the replication cohort), as well as the dual biomarker combinations (accuracy range: 71.8–82.3% and 72.6–85.6% in the replication cohort). Taken together, early neuro-axonal loss reflects worse information processing speed, the key deficit underlying cognitive dysfunction in multiple sclerosis. Our findings demonstrate that combining blood and imaging measures improves the accuracy of predicting cognitive impairment, highlighting the clinical utility of cross-modal biomarkers in multiple sclerosis.
Real-World Effectiveness of Vedolizumab Dose Escalation in Patients With Inflammatory Bowel Disease: A Systematic Literature Review
Authors: Patel Dipen , Martin Stephan , Luo Michelle , Ursos Lyann , Lirio Richard A , Kamble Pravin , Wang Song 
Published: July 2022

Abstract

Background
Vedolizumab is a gut-selective anti-lymphocyte trafficking agent approved for the treatment of moderate to severely active inflammatory bowel disease (IBD: ulcerative colitis [UC] and Crohn’s disease [CD]).

Methods
A systematic literature review (SLR) of real-world studies was conducted to assess the effectiveness of dose escalation of vedolizumab every 8 weeks (Q8W) during maintenance treatment to achieve a response in patients who were either vedolizumab responders experiencing secondary loss of response (SLOR) or non-responders. MEDLINE and EMBASE databases were searched from January 2014 to August 2021.

Results
Screening of SLR outputs identified 72 relevant real-world study publications featuring dose escalation of vedolizumab maintenance therapy. After qualitative review, ten eligible studies (9 articles, 1 abstract) were identified as reporting clinical response and/or clinical remission rates following escalation of intravenous vedolizumab 300 mg Q8W maintenance dosing to every 4 weeks (Q4W) maintenance dosing in adult patients with UC/CD (≥10 patients per study). Overall, 196/395 (49.6%) patients with IBD had a response within 54 weeks of vedolizumab maintenance dose escalation. Although definitions for clinical response/remission varied across the 10 studies, clinical response rates after escalated vedolizumab Q8W maintenance dosing ranged from 40.0% to 73.3% (9 studies) and from 30.0% to 55.8% for remission (4 studies) over a range of 8 to &lt;58 weeks’ follow-up.

Conclusions
This synthesis of real-world effectiveness data in vedolizumab-treated patients with IBD indicates that approximately half were able to achieve or recapture clinical response after escalating vedolizumab maintenance dosing.
Altitudinal variation in life-history features of a Qinghai-Tibetan Plateau lizard
Authors: Yu Wei , Zhu Zeyu , Zhao Xiaolong , Cui Shuang , Liu Zhensheng , Zeng Zhigao 
Published: July 2022

Abstract
Environmental changes along an altitudinal gradient can facilitate the differentiation of life-history features in ectothermic species, but little attention has been devoted to the reciprocal influence of altitude and alpine slope directionality on life-history variation. According to life-history theory, increased environmental stress causes a change in reproductive allocation from number to quality of offspring, as well as a stronger trade-off between size and number of offspring. To clarify the influence of environmental pressures on the life-history features of the Qinghai toad-headed lizard Phrynocephalus vlangalii along an altitudinal cline, we surveyed late pregnant females from 3 populations of low (2,600 m), middle (3,400 m), and high (3,600 m) elevations in the Dangjin Mountain of Gansu, China from July to October 2019, and compared their inter-population differences in maternal body size, reproductive characteristics, offspring growth, and locomotor performance. Because of lower temperatures, higher humidity, and lower light intensity caused by slope aspect and altitude, the middle-altitude region experienced stronger environmental stress than the high- and low-altitude regions. Our results showed that females were larger at middle- and high-altitude sites and smaller at the low-altitude site, following Bergmann’s rule. We also found that females from low-altitude population gave birth earlier than those from the middle and high altitudes. Our results showed a shift in the offspring size-number trade-off of P. vlangalii in response to colder and harsher environments, with lizards from the alpine steppe (i.e. the middle- and high-altitude habitats) producing fewer but larger offspring than those from the warm steppe (i.e. the low-altitude habitat). Low-altitude juveniles grew faster than high-altitude ones, but at the same rates as middle-altitude juveniles. This result demonstrates that the growth of P. vlangalii was associated with temperature and light intensity. Our findings contribute to enhancing our understanding of the altitudinal variation in life-history features of plateau ectotherms and their phenotypic plasticity or local adaptation.
Mismatch between calf paternity and observed copulations between male and female reindeer: Multiple mating in a polygynous ungulate?
Authors: Coombs Keenin R , Weladji Robert B , Holand Øystein , Røed Knut H 
Published: July 2022

AbstractIn polygynous systems, such as that exhibited by reindeer Rangifer tarandus, mate choice can be difficult to disentangle from male intrasexual competition because male behavior may constrain female choice. Multiple mating may provide an avenue for female mate choice, though it is difficult to identify using behavioral estimators alone. Molecular techniques address this issue by affording ecologists an opportunity to reassess mating systems from a genetic perspective. We assessed the frequency and possible explanations for multiple mating in reindeer using a genetic approach to determine the success of observed copulations in a semi-domesticated herd in Kaamanen, Finland. Behavioral and genetic data were synthesized with population characteristics over a 7-year period to test the hypothesis that, if present, polyandry in reindeer is driven by sexual harassment from sub-dominant males. We observed multiple mating in 42% of females, with as many as 60% exhibiting multiple mating in certain years. We found no evidence that multiple mating resulted from sexual harassment by sub-dominant males, suggesting that it is likely a deliberate strategy among females. Conversion rate of copulations into paternities varied with male size, with smaller males more likely to experience mismatch than larger males. Female preference for larger males persisted despite the occurrence of multiple mating, possibly suggesting a mechanism for cryptic post-copulatory selection. We suggest further research to delineate the possible influence of cryptic post-copulatory selection and multiple mating to defend against infertility in exhausted males.
Stochastic Dynamic Optimization for Forest Rotation with Uncertain Stumpage Prices
Authors: Huang Yu-Kai , Bawa Ranjit , Dwivedi Puneet , Zhao Dehai 
Published: July 2022

Abstract
This study investigates the effect of uncertain stumpage prices on the optimal forest rotation decision and related profitability under various silvicultural scenarios. The study applies loblolly pine (Pinus taeda L.) growth and yield models with distinct silvicultural scenarios in the Piedmont and Upper Coastal Plain region of the southeastern United States. It then applies chance-constrained dynamic optimization to derive optimal rotation age and related profitability on per acre basis. The growth and yield models show that using a combination of herbicide and fertilizer silvicultural treatment leads to a higher timber yield and profit at a lower optimal rotation age than other treatment scenarios. Furthermore, the stochastic optimization model shows that low risk tolerance results in low returns. Within a particular risk tolerance level, silvicultural treatment options with higher standard deviations almost always produce higher returns. The findings of this article contribute to the literature on optimizing forest management when considering uncertain stumpage prices over time, given different risk tolerance levels.
Imaging-associated stress causes divergent phase transitions of RNA-binding proteins in the Caenorhabditis elegans germ line
Authors: Elaswad Mohamed T , Munderloh Chloe , Watkins Brooklynne M , Sharp Katherine G , Breton Elizabeth , Schisa Jennifer A 
Published: July 2022

Abstract
One emerging paradigm of cellular organization of RNA and RNA-binding proteins is the formation of membraneless organelles. Examples of membraneless organelles include several types of ribonucleoprotein granules that form via phase separation. A variety of intracellular pH changes and posttranslational modifications, as well as extracellular stresses, can stimulate the condensation of proteins into granules. For example, the assembly of stress granules induced by oxidative stress, osmotic stress, and heat stress has been well characterized in a variety of somatic cell types. In the germ line, similar stress-induced condensation of proteins occurs; however, less is known about the role of phase separation during gamete production. Researchers who study phase transitions often make use of fluorescent reporters to study the dynamics of RNA-binding proteins during live cell imaging. In this report, we demonstrate that common conditions of live-imaging Caenorhabditis elegans can cause an inadvertent stress and trigger phase transitions of RNA-binding proteins. We show that this imaging-associated stress stimulates decondensation of multiple germ granule proteins and condensation of several P-body proteins. Proteins within larger ribonucleoprotein granules in meiotically arrested oocytes do not appear to be as sensitive to the stress as proteins in diakinesis oocytes of young hermaphrodites, with the exception of the germ granule protein PGL-1. Our results have important methodological implications for all researchers using live-cell imaging techniques. The data also suggest that the RNA-binding proteins within large ribonucleoprotein granules of arrested oocytes may have distinct phases, which we characterize in our companion article.
Genomic and gene expression associations to morphology of a sexual ornament in the chicken
Authors: Bakovic Vid , Höglund Andrey , Martin Cerezo Maria Luisa , Henriksen Rie , Wright Dominic 
Published: July 2022

Abstract
How sexual selection affects the genome ultimately relies on the strength and type of selection, and the genetic architecture of the involved traits. While associating genotype with phenotype often utilizes standard trait morphology, trait representations in morphospace using geometric morphometric approaches receive less focus in this regard. Here, we identify genetic associations to a sexual ornament, the comb, in the chicken system (Gallus gallus). Our approach combined genome-wide genotype and gene expression data (&gt;30k genes) with different aspects of comb morphology in an advanced intercross line (F8) generated by crossing a wild-type Red Junglefowl with a domestic breed of chicken (White Leghorn). In total, 10 quantitative trait loci were found associated to various aspects of comb shape and size, while 1,184 expression QTL were found associated to gene expression patterns, among which 98 had overlapping confidence intervals with those of quantitative trait loci. Our results highlight both known genomic regions confirming previous records of a large effect quantitative trait loci associated to comb size, and novel quantitative trait loci associated to comb shape. Genes were considered candidates affecting comb morphology if they were found within both confidence intervals of the underlying quantitative trait loci and eQTL. Overlaps between quantitative trait loci and genome-wide selective sweeps identified in a previous study revealed that only loci associated to comb size may be experiencing on-going selection under domestication.
Monte Carlo and molecular dynamics simulations of CH4 molecules adsorption behavior in bituminous coal
Authors: Zhao Dan , Liu Xiaoqing 
Published: July 2022

Abstract
In this study, we established a macromolecular structure model of the Wiser bituminous coal (C192H166N4O19S9) to study the adsorption characteristics of CH4 molecules in bituminous coal. We then combined the grand canonical Monte Carlo and molecular dynamics simulation methods to study the adsorption behavior of CH4 molecules in coal at different temperatures and moisture contents with fugacity values of 0–12 MPa. The simulation results showed that the adsorption isotherms of CH4 gas are consistent with those of the Langmuir model, and the adsorption of CH4 molecules is negatively correlated with temperature and positively correlated with fugacity. The adsorption amount and rate of CH4 molecules in bituminous coal decreased as the moisture content in the coal molecules increased. The presence of H2O molecules in coal reduced the interaction between the coal and CH4 molecules. H2O molecules exhibited a stronger interaction with the heteroatomic groups in coal molecules than that of the CH4 molecules. Finally, the main factors causing a linear negative correlation between moisture content and the adsorption of CH4 molecules were the preferential occupation of H2O molecules in effective adsorption sites in coal molecules and the linear reduction of free volume in coal.
Applications of intelligent techniques in modeling geothermal heat pumps: an updated review
Authors: Almutairi Khalid 
Published: July 2022

AbstractRegarding the significance of energy efficiency in cooling and heating systems, it is crucial to apply proper technologies. Heat pumps are among the systems with ability of efficient performance applicable in different operating conditions. These technologies can be coupled with renewable energy sources such as solar and geothermal, which cause lower energy consumption and emission of greenhouse gases. In the present work, studies considered utilization of intelligent techniques in modeling performance of geothermal heat pumps (GHPs) are reviewed. The main findings of the reviewed works reveal that intelligent techniques are able to model heat pumps output with significant and remarkable exactness; for instance, in some cases, R2 of the models proposed that the coefficient of performance of the ground sources heat pumps is around 0.9999, revealing closeness of the predicted data and actual quantities. The precision of the models, based on the intelligent methods, is affected by different elements including the used function, algorithm and architecture. Furthermore, it is observed that using optimization algorithms for tuning the hyperparameters of intelligent techniques cause higher estimation exactness. In addition to performance prediction, some other parameters related to the GHPs such as well temperature and thermal conductivity of the soil layers could be predicted by utilization of intelligent methods.