bluuebunny/crossref_metadata_2025_split huggingface dataset:
Landscape genetics across the Andes Mountains: Environmental variation drives genetic divergence in the leaf-cutting ant Atta cephalotes
Authors: Muñoz-Valencia Vanessa , Montoya-Lerma James , Seppä Perttu , Diaz Fernando 
Published: December 2021

ABSTRACTDisentangling the mechanisms underlying spatial distribution of genetic variation into those due to environment or physical barriers from mere geographic distance is challenging in complex landscapes. The Andean uplift represents one of the most heterogeneous habitats where these questions remain unexplored as multiple mechanisms might interact, confounding their relative roles. We explore this broad question in the leaf-cutting ant Atta cephalotes, a species distributed across the Andes Mountains using nuclear microsatellite markers and mtCOI gene sequences. We investigate spatial genetic divergence across the Western range of Northern Andes in Colombia by testing the relative role of alternative scenarios of population divergence, including geographic distance (IBD), climatic conditions (IBE), and physical barriers due to the Andes Mountains (IBB). Our results reveal substantial genetic differentiation among A. cephalotes populations for both types of markers, but only nuclear divergence followed a hierarchical pattern with multiple models of genetic divergence imposed by the Western range. Model selection showed that IBD, IBE (temperature and precipitation), and IBB (Andes mountains) models, often proposed as individual drivers of genetic divergence, interact, and explain up to 33% of genetic divergence in A. cephalotes. IBE models remained significant after accounting for IBD, suggesting that environmental factors play a more prominent role when compared to IBB. These factors in combination with idiosyncratic dispersal patterns of ants appear to determine hierarchical patterns of gene flow. This study enriches our understanding of the forces shaping population divergence in complex habitat landscapes.
Health impact assessment of salt reduction in bread in Portugal: a pilot study
Authors: Santos Joana , Alves Joana , Braz Paula , Brazão Roberto , Costa Alexandra , Santos Mariana , Assunção Ricardo , Caldas de Almeida Teresa , Costa Luciana 
Published: December 2021

AbstractHypertension is a risk factor for cardiovascular diseases, which can be caused by excessive salt intake. In Portugal, one of the main foods to contribute to ingestion of salt is bread. Thus, a voluntary Protocol was signed between stakeholders with the aim to reduce salt content in bread by 2021. Herein, a retrospective HIA was carried out to assess the impact in blood pressure (BP) after this agreement. In order to find average values of salt intake and BP in Portuguese population, national surveys were used. Also, estimates of BP reduction and its size effects were calculated based upon meta-analysis data. It is expected that salt intake will be reduced mostly in individuals with low educational level, men, aged between 65-74 years old and residents in South region of Portugal. Results in hypertensive patients indicate that a higher effect on BP will occur in the same profile of individuals, except age (between 55 and 64 years old). However, the estimated effect is very low for all groups, suggesting that the Protocol will contribute to modest health gains. Complementary measures supported by HIA studies need to be adopted to actively promote salt intake reduction and effectively prevent hypertension.
Validation of Genomic and Transcriptomic Models of Homologous Recombination Deficiency in a Real-World Pan-Cancer Cohort
Authors: Leibowitz Benjamin , Dougherty Bonnie V , Bell Joshua SK , Kapilivsky Joshuah , Michuda Jackson , Sedgwick Andrew , Munson Wesley , Chandra Tushar , Dry Jonathan R , Beaubier Nike , Igartua Catherine , Taxter Timothy 
Published: December 2021

AbstractBackgroundWith the introduction of DNA-damaging therapies into standard of care cancer treatment, there is a growing need for predictive diagnostics assessing homologous recombination deficiency (HRD) status across tumor types. Following the strong clinical evidence for the utility of DNA-sequencing-based HRD testing in ovarian cancer, and growing evidence in breast cancer, we present analytical validation of the Tempus|HRD-DNA test. We further developed, validated, and explored the Tempus|HRD-RNA model, which uses gene expression data from 16,470 RNA-seq samples to predict HRD status from formalin-fixed paraffin-embedded (FFPE) tumor samples across numerous cancer types.MethodsGenomic and transcriptomic profiling was performed using next-generation sequencing from Tempus|xT, Tempus|xO, Tempus|xE, Tempus|RS, and Tempus|RS.v2 assays on 48,843 samples. Samples were labeled based on their BRCA1, BRCA2 and selected Homologous Recombination Repair (HRR) pathway gene (CDK12, PALB2, RAD51B, RAD51C, RAD51D) mutational status to train and validate HRD-DNA, a genome-wide loss-of-heterozygosity biomarker, and HRD-RNA, a logistic regression model trained on gene expression, using several performance metrics and statistical tests.ResultsIn a sample of 2,058 breast and 1,216 ovarian tumors, BRCA status was predicted by HRD-DNA with F1-scores of 0.98 and 0.96, respectively. Across an independent set of 1,363 samples across solid tumor types, the HRD-RNA model was predictive of BRCA status in prostate, pancreatic, and non-small cell lung cancer, with F1-scores of 0.88, 0.69, and 0.62, respectively.ConclusionsWe predict HRD-positive patients across many cancer types and believe both HRD models may generalize to other mechanisms of HRD outside of BRCA loss. HRD-RNA complements DNA-based HRD detection methods, especially for indications with low prevalence of BRCA alterations.
Neuropsychiatric symptoms are associated with exacerbated cognitive impairment in covert cerebral small vessel disease
Authors: Anne Arola , Tuuli Levänen , Laakso Hanna M. , Johanna Pitkänen , Juha Koikkalainen , Jyrki Lötjönen , Antti Korvenoja , Timo Erkinjuntti , Susanna Melkas , Hanna Jokinen 
Published: December 2021

AbstractBackground and purposeNeuropsychiatric symptoms are related to disease progression and cognitive decline over time in cerebral small vessel disease (SVD) but their significance is poorly understood in covert SVD. We investigated neuropsychiatric symptoms and their relationships between cognitive and functional abilities in subjects with varying degrees of white matter hyperintensities (WMH), but without clinical diagnosis of stroke, dementia or significant disability.MethodsThe Helsinki Small Vessel Disease Study consisted of 152 subjects, who underwent brain magnetic resonance imaging (MRI) and comprehensive neuropsychological evaluation of global cognition, processing speed, executive functions and memory. Neuropsychiatric symptoms were evaluated with the Neuropsychiatric Inventory Questionnaire (NPI-Q, n=134) and functional abilities with the Amsterdam Instrumental Activities of Daily Living questionnaire (A-IADL, n=132), both filled in by a close informant.ResultsNPI-Q total score correlated significantly with WMH volume (rs=0.20, p=0.019) and inversely with A-IADL score (rs=-0.41, p<0.001). In total, 38% of the subjects had one or more informant evaluated neuropsychiatric symptoms. Linear regressions adjusted for age, sex and education revealed no direct associations between neuropsychiatric symptoms and cognitive performance. However, there were significant synergistic interactions between neuropsychiatric symptoms and WMH volume on cognitive outcomes. Neuropsychiatric symptoms were also associated with A-IADL score irrespective of WMH volume.ConclusionsNeuropsychiatric symptoms are associated with an accelerated relationship between WMH and cognitive impairment. Furthermore, the presence of neuropsychiatric symptoms is related to worse functional abilities. Neuropsychiatric symptoms should be routinely assessed in covert SVD as they are related to worse cognitive and functional outcomes.
HeartNet: Self Multi-Head Attention Mechanism via Convolutional Network with Adversarial Data Synthesis for ECG-based Arrhythmia Classification
Authors: Rafi Taki Hasan , Woong-Ko Young 
Published: December 2021

AbstractCardiovascular disease is now one of the leading causes of morbidity and mortality in humans. Electrocardiogram (ECG) is a reliable tool for monitoring the health of the cardiovascular system. Currently, there has been a lot of focus on accurately categorizing heartbeats. There is a high demand on automatic ECG classification systems to assist medical professionals. In this paper we proposed a new deep learning method called HeartNet for developing an automatic ECG classifier. The proposed deep learning method is compressed by multi-head attention mechanism on top of CNN model. The main challenge of insufficient data label is solved by adversarial data synthesis adopting generative adversarial network (GAN) with generating additional training samples. It drastically improves the overall performance of the proposed method by 5-10% on each insufficient data label category. We evaluated our proposed method utilizing MIT-BIH dataset. Our proposed method has shown 99.67 ± 0.11 accuracy and 89.24 ± 1.71 MCC trained with adversarial data synthesized dataset. However, we have also utilized two individual datasets such as Atrial Fibrillation Detection Database and PTB Diagnostic Database to see the performance of our proposed model on ECG classification. The effectiveness and robustness of proposed method are validated by extensive experiments, comparison and analysis. Later on, we also highlighted some limitations of this work.
Field evaluation of Rapid SARS-Cov2 Antigen screening test on self-collected deep throat saliva samples in Malaysia
Authors: Noordin Noorliza Mohamad , Loon Lim Steven Chee , Lim Zhuo-zhi , Lim Teck-Onn 
Published: December 2021

AbstractLow cost Rapid Antigen Tests are widely used in Malaysia and the government has also mandated worksite screening as a condition for reopening. Numerous RAT kits have been approved by the Malaysian Medical Device Authority. However, it remains uncertain how these kits would perform in the field.We enrolled workers between June and September 2021 from 23 worksites. They were trained and experienced in performing RAT selftest by virtue of their worksite participation in routine screening program. These workers also had reverse transcriptase polymerase chain reaction tests in the course of mass screening or contact tracing. We also enrolled patients with PCR confirmed Covid19 from a quarantine centre. These patients were instructed on selftesting and then immediately perform RAT under supervision. Two manufacturers donated RAT for this study.A total of 340 participants were enrolled, 130 were from quarantine centre and 210 from worksites. The overall sensitivity of RAT compared to PCR was 70 percent. The specificity was 91 percent. Sensitivity decreased with increasing PCR cycle threshold values. Sensitivity is also lower among untrained subjects at each level of Ct. Logistic regression analysis confirmed false negative result is associated with Ct and participants prior training and experience.This study shows that in the real world, RAT performance were markedly lower than that reported by the manufacturers. The test sensitivity is dependent on the operator training and experience, as well as on viral load as measured by Ct. User training and repeated testing for screening purpose is necessary to mitigate the low sensitivity of RAT.
MSRCall: A Multi-scale Deep Neural Network to Basecall Oxford Nanopore Sequences
Authors: Yeh Yang-Ming , Lu Yi-Chang 
Published: December 2021

AbstractMinION, a third-generation sequencer from Oxford Nanopore Technologies, is a portable device that can provide long nucleotide read data in real-time. It primarily aims to deduce the makeup of nucleotide sequences from the ionic current signals generated when passing DNA/RNA fragments through nanopores charged with a voltage difference. To determine the nucleotides from the measured signals, a translation process known as basecalling is required. However, compared to NGS basecallers, the calling accuracy of MinION still needs to be improved.In this work, a simple but powerful neural network architecture called MSRCall is proposed. MSRCall comprises a multi-scale structure, recurrent layers, a fusion block, and a CTC decoder. To better identify both short-range and long-range dependencies, the recurrent layer is redesigned to capture various time-scale features with a multi-scale structure. The results show that MSRCall outperforms other basecallers in terms of both read and consensus accuracies.
Urine and Fecal Microbiota in a Canine Model of Bladder Cancer
Authors: Mrofchak Ryan , Madden Christopher , Evans Morgan V. , Kisseberth William C. , Dhawan Deepika , Knapp Deborah W. , Hale Vanessa L. 
Published: December 2021

AbstractIntroductionUrothelial carcinoma (UC) is the tenth most diagnosed cancer in humans worldwide. Dogs are a robust model for invasive UC as tumor development and progression is similar in humans and dogs. Recent studies on urine microbiota in humans revealed alterations in microbial diversity and composition in individuals with UC; however, the potential role of microbiota in UC has yet to be elucidated. Dogs could be valuable models for this research, but microbial alterations in dogs with UC have not been evaluated.ObjectiveThe objective of this this pilot study was to compare the urine and fecal microbiota of dogs with UC (n = 7) and age-, sex-, and breed-matched healthy controls (n = 7).MethodsDNA was extracted from mid-stream free-catch urine and fecal samples using Qiagen Bacteremia and PowerFecal kits, respectively. 16S rRNA gene sequencing was performed followed by sequence processing and analyses (QIIME 2 and R).ResultsCanine urine and fecal samples were dominated by taxa similar to those found in humans. Significantly decreased microbial diversity (Kruskal-Wallis: Shannon,p= 0.048) and altered bacterial composition were observed in the urine but not feces of dogs with UC (PERMANOVA: Unweighted UniFrac,p= 0.011). The relative abundances ofFusobacteriumwas also increased, although not significantly, in the urine and feces of dogs with UC.ConclusionThis study characterizes urine and fecal microbiota in dogs with UC, and it provides a foundation for future work exploring host-microbe dynamics in UC carcinogenesis, prognosis, and treatment.
Assessing the impact of fire on spiders through a global comparative analysiss
Authors: McLean Claire A. , Melville Jane , Schubert Joseph , Rose Rebecca , Medina Iliana 
Published: December 2021

ABSTRACTIn many regions fire regimes are changing due to anthropogenic factors. Understanding the responses of species to fire can help to develop predictive models and inform fire management decisions. Spiders are a diverse and ubiquitous group and can offer important insights into the impacts of fire on invertebrates and whether these depend on environmental factors, phylogenetic history, or functional traits. We conducted phylogenetic comparative analyses of data from studies investigating the impacts of fire on spiders. We investigated whether fire affects spider abundance or presence and whether ecologically relevant traits or site-specific factors influence species’ responses to fire. Although difficult to make broad generalisations about the impacts of fire due to variation in site- and fire-specific factors, we find evidence that short fire intervals may be a threat to some spiders, and that fire affects abundance and species compositions in forests relative to other vegetation types. Orb and sheet web weavers were also more likely to be absent after fire than ambush hunters, ground hunters, and other hunters suggesting functional traits may affect responses. Finally, we show that analyses of published data can be used to detect broad scale patterns and provide an alternative to traditional meta-analytical approaches.
The African swine fever modelling challenge: objectives, model description and synthetic data generation
Authors: Picault Sébastien , Vergne Timothée , Mancini Matthieu , Bareille Servane , Ezanno Pauline 
Published: December 2021

AbstractAfrican swine fever (ASF) is an emerging disease currently spreading at the interface between wild boar and pig farms in Europe and Asia. Current disease control regulations, which involve massive culling with significant economic and animal welfare costs, need to be improved. Modelling enables relevant control measures to be explored, but conducting the exercise during an epidemic is extremely difficult. Modelling challenges enhance modellers’ ability to timely advice policy makers, improve their readiness when facing emerging threats, and promote international collaborations. The ASF-Challenge, which ran between August 2020 and January 2021, was the first modelling challenge in animal health. In this paper, we describe the objectives and rules of the challenge. We then demonstrate the mechanistic multi-host model that was used to mimic as accurately as possible an ASF-like epidemic, provide a detailed explanation of the surveillance and intervention strategies that generated the synthetic data, and describe the different management strategies that were assessed by the competing modelling teams. We then outline the different technical steps of the challenge as well as its environment. Finally, we synthesize the lessons we learnt along the way to guide future modelling challenges in animal health.HighlightsThe 1st modelling challenge in animal health mimics ASF spread with synthetic dataA mechanistic spatially-explicit stochastic model was developed to represent ASF spread and controlSynthetic data concerned date and location of detected cases in pigs and wild boarModelling ASF at the wildlife-livestock interface illustrates the reservoir role of wild fauna
Identification of upstream transcription factor binding sites in orthologous genes using mixed Student’s t-test statistics
Authors: Huang Tinghua , Xiao Hong , Tian Qi , He Zhen , Yuan Cheng , Lin Zezhao , Gao Xuejun , Yao Min 
Published: December 2021

AbstractBackgroundTranscription factor (TF) regulates the transcription of DNA to messenger RNA by binding to upstream sequence motifs. Identifying the locations of known motifs in whole genomes is computationally intensive.Methodology/Principal FindingsThis study presents a computational tool, named “Grit”, for screening TF-binding sites (TFBS) by coordinating transcription factors to their promoter sequences in orthologous genes. This tool employs a newly developed mixed Student’s t-test statistical method that detects high-scoring conserved and non-conserved binding sites among species. The program performs sequence scanning at a rate of 3.2 Mb/s on a quad-core Amazon server and has been benchmarked by the well-established ChIP-Seq datasets, putting Grit amongst the top-ranked TFBS predictors. It marginally outperforms the well-known transcription factor motif scanning tools, Pscan (4.8%) and FIMO (17.8%), in analyzing well-documented ChIP-Atlas human genome Chip-Seq datasets.SignificanceGrit is a good alternative to current available motif scanning tools and is publicly available at http://www.thua45.cn/grit under an academic free license.Author SummaryLocating transcription factor-binding (TF-binding) site in the genome and identification their function is fundamental in understanding various biological processes. Improve the performance of the prediction tools is important because accurate TF-binding site prediction can save cost and time for wet-lab experiments. Also, genome wide TF-binding site prediction can provide new insights for transcriptome regulation in system biology perspective. This study developed a new TF-binding site prediction tool based on mixed Student’s t-test statistical method. The tool is amongst the top-ranked TF-binding site predictors, as such, it can help the researchers in TF-binding site identification and transcriptional regulation mechanism interpretation of genes.
Single-Cell Atlas of Common Variable Immunodeficiency reveals germinal center-associated epigenetic dysregulation in B cell responses
Authors: Rodríguez-Ubreva Javier , Arutyunyan Anna , Bonder Marc Jan , Pino-Molina Lucía Del , Clark Stephen J. , de la Calle-Fabregat Carlos , Garcia-Alonso Luz , Handfield Louis-François , Ciudad Laura , Andrés-León Eduardo , Krueger Felix , Català-Moll Francesc , Rodríguez-Cortez Virginia C. , Polanski Krzysztof , Mamanova Lira , van Dongen Stijn , Kiselev Vladimir Yu. , Martínez-Saavedra María T. , Heyn Holger , Martín Javier , Warnatz Klaus , López-Granados Eduardo , Rodríguez-Gallego Carlos , Stegle Oliver , Kelsey Gavin , Vento-Tormo Roser , Ballestar Esteban 
Published: December 2021

ABSTRACTCommon variable immunodeficiency (CVID), the most prevalent symptomatic primary immunodeficiency, is characterized by impaired terminal B-cell differentiation and defective antibody responses. Incomplete genetic penetrance and a wide range of phenotypic expressivity in CVID suggest the participation of additional pathogenic mechanisms. Monozygotic (MZ) twins discordant for CVID are uniquely valuable for studying the contribution of epigenetics to the disease. We used single-cell epigenomics and transcriptomics to create a cell census of naïve-to-memory B cell differentiation in a pair of CVID-discordant MZ twins. Our analysis identifies DNA methylation, chromatin accessibility and transcriptional defects in memory B cells that mirror defective cell-cell communication defects following activation. These findings were validated in a cohort of CVID patients and healthy donors. Our findings provide a comprehensive multi-omics map of alterations in naïve-to-memory B-cell transition in CVID and reveal links between the epigenome and immune cell cross-talk. Our resource, publicly available at the Human Cell Atlas, paves the way for future diagnosis and treatments of CVID patients.
Genomes of keystone Mortierella species lead to better in silico prediction of soil mycobiome functions from Taiwan’s offshore islands
Authors: Lin Yu-fei , Liu Wei-An , Liu Yu-Ching , Lee Hsin-Han , Lin Yen-Ju , Chang Ed-Haun , Lu Meiyeh J , Chiu Chih-Yu , Tsai Isheng Jason 
Published: December 2021

AbstractThe ability to correlate the functional relationship between microbial communities and their environment is critical to understanding microbial ecology. There is emerging knowledge on island biogeography of microbes but how island characteristics influence functions of microbial community remain elusive. Here, we explored soil mycobiomes from nine islands adjacent to Taiwan using ITS2 amplicon sequencing. Geographical distances and island size were positively correlated to dissimilarity in mycobiomes, and we identified 56 zero-radius operational taxonomic units (zOTUs) that were ubiquitously present across all islands, and as few as five Mortierella zOTUs dominate more than half of mycobiomes. Correlation network analyses revealed that seven of the 45 hub species were part of the ubiquitous zOTUs belonging to Mortierella, Trichoderma, Aspergillus, Clonostachys and Staphylotrichum. We sequenced and annotated the genomes of seven Mortierella isolates, and comparative predictions of KEGG orthologues using PICRUSt2 database updated with new genomes increased sequence reads coverage by 62.9% at the genus level. In addition, genes associated with carbohydrate and lipid metabolisms were differentially abundant between islands which remained undetected in the original database. Predicted functional pathways were similar across islands despite their geographical separation, difference in differentially abundant genes and composition. Our approach demonstrated the incorporation of the key taxa genomic data can improve functional gene prediction results and can be readily applied to investigate other niches of interests.