bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network
Authors: Hu Jian , Li Xiangjie , Coleman Kyle , Schroeder Amelia , Irwin David J. , Lee Edward B. , Shinohara Russell T. , Li Mingyao 
Published: December 2020

AbstractRecent advances in spatial transcriptomics technologies have enabled comprehensive characterization of gene expression patterns in the context of tissue microenvironment. To elucidate spatial gene expression variation, we present SpaGCN, a graph convolutional network approach that integrates gene expression, spatial location and histology in spatial transcriptomics data analysis. Through graph convolution, SpaGCN aggregates gene expression of each spot from its neighboring spots, which enables the identification of spatial domains with coherent expression and histology. The subsequent domain guided differential expression analysis then detects genes with enriched expression patterns in the identified domains. Analyzing five spatially resolved transcriptomics datasets using SpaGCN, we show it can detect genes with much more enriched spatial expression patterns than existing methods. Furthermore, genes detected by SpaGCN are transferrable and can be utilized to study spatial variation of gene expression in other datasets. SpaGCN is computationally fast, making it a desirable tool for spatial transcriptomics studies.
Spatial population genomics of a recent mosquito invasion
Authors: Schmidt Thomas L , Swan T. , Chung Jessica , Karl Stephan , Demok Samuel , Yang Qiong , Field Matt A , Muzari Mutizwa Odwell , Ehlers Gerhard , Brugh Mathew , Bellwood Rodney , Horne Peter , Burkot Thomas R , Ritchie Scott , Hoffmann Ary A 
Published: December 2020

AbstractPopulation genomic approaches can characterise dispersal across a single generation through to many generations in the past, bridging the gap between individual movement and intergenerational gene flow. These approaches are particularly useful when investigating dispersal in recently altered systems, where they provide a way of inferring long-distance dispersal between newly established populations and their interactions with existing populations. Human-mediated biological invasions represent such altered systems which can be investigated with appropriate study designs and analyses. Here we apply temporally-restricted sampling and a range of population genomic approaches to investigate dispersal in a 2004 invasion ofAedes albopictus(the Asian tiger mosquito) in the Torres Strait Islands (TSI) of Australia. We sampled mosquitoes from 13 TSI villages simultaneously and genotyped 373 mosquitoes at genome-wide single nucleotide polymorphisms (SNPs): 331 from the TSI, 36 from Papua New Guinea (PNG), and 4 incursive mosquitoes detected in uninvaded regions. Within villages, spatial genetic structure varied substantially but overall displayed isolation by distance and a neighbourhood size of 232–577. Close kin dyads revealed recent movement between islands 31–203 km apart, and deep learning inferences showed incursiveAe. albopictushad travelled to uninvaded regions from both adjacent and non-adjacent islands. Private alleles and a coancestry matrix indicated direct gene flow from PNG into nearby islands. Outlier analyses also detected four linked alleles introgressed from PNG, with the alleles surrounding 12 resistance-associated cytochrome P450 genes. By treating dispersal as both an intergenerational process and a set of discrete events, we describe a highly interconnected invasive system.
Visual perceptual learning of a primitive feature in human V1/V2 as a result of unconscious processing, revealed by Decoded fMRI Neurofeedback (DecNef)
Authors: Wang Zhiyan , Tamaki Masako , Shibata Kazuhisa , Worden Michael S. , Yamada Takashi , Kawato Mitsuo , Sasaki Yuka , Watanabe Takeo 
Published: December 2020

AbstractWhile numerous studies have shown that visual perceptual learning (VPL) occurs as a result of exposure to a visual feature in a task-irrelevant manner, the underlying neural mechanism is poorly understood. In a previous psychophysical study, subjects were repeatedly exposed to a task-irrelevant global motion display that induced the perception of not only the local motions but also a global motion moving in the direction of the spatiotemporal average of the local motion vectors. As a result, subjects enhanced their sensitivity only to the local moving directions, suggesting that early visual areas (V1/V2) that process local motions are involved in task-irrelevant VPL. However, this hypothesis has never been examined by directly examining the involvement of early visual areas (V1/V2). Here, we employed a decoded neurofeedback technique (DecNef) using functional magnetic resonance imaging. During the DecNef training, subjects were trained to induce the activity patterns in V1/V2 that were similar to those evoked by the actual presentation of the global motion display. The DecNef training was conducted with neither the actual presentation of the display nor the subjects’ awareness of the purpose of the experiment. As a result, subjects increased the sensitivity to the local motion directions but not specifically to the global motion direction. The training effect was strictly confined to V1/V2. Moreover, subjects reported that they neither perceived nor imagined any motion during the DecNef training. These results together suggest that that V1/V2 are sufficient for exposure-based task-irrelevant VPL to occur unconsciously.Significance StatementWhile numerous studies have shown that visual perceptual learning (VPL) occurs as a result of exposure to a visual feature in a task-irrelevant manner, the underlying neural mechanism is poorly understood. Previous psychophysical experiments suggest that early visual areas (V1/V2) are involved in task-irrelevant VPL. However, this hypothesis has never been examined by directly examining the involvement of early visual areas (V1/V2). Here, using decoded fMRI neurofeedback, the activity patterns similar to those evoked by the presentation of a complex motion display were repeatedly induced only in early visual areas. The training sensitized only the local motion directions and not the global motion direction, suggesting that V1/V2 are involved in task-irrelevant VPL.
A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics
Authors: Zhou Geyu , Zhao Hongyu 
Published: December 2020

AbstractGenetic prediction of complex traits has great promise for disease prevention, monitoring, and treatment. The development of accurate risk prediction models is hindered by the wide diversity of genetic architecture across different traits, limited access to individual level data for training and parameter tuning, and the demand for computational resources. To overcome the limitations of the most existing methods that make explicit assumptions on the underlying genetic architecture and need a separate validation data set for parameter tuning, we develop a summary statistics-based nonparametric method that does not rely on validation datasets to tune parameters. In our implementation, we refine the commonly used likelihood assumption to deal with the discrepancy between summary statistics and external reference panel. We also leverage the block structure of the reference linkage disequilibrium matrix for implementation of a parallel algorithm. Through simulations and applications to twelve traits, we show that our method is adaptive to different genetic architectures, statistically robust, and computationally efficient. Our method is available athttps://github.com/eldronzhou/SDPR.
eSCAN: Scan Regulatory Regions for Aggregate Association Testing using Whole Genome Sequencing Data
Authors: Yang Yingxi , Yang Yuchen , Huang Le , Broome Jai G. , Correa Adolfo , Reiner Alexander ,   NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Raffield Laura M. , Li Yun 
Published: December 2020

AbstractMultiple statistical methods for aggregate association testing have been developed for whole genome sequencing (WGS) data. Many aggregate variants in a given genomic window and ignore existing knowledge to define test regions, resulting in many identified regions not clearly linked to genes and thus limiting biological understanding. Functional information from new technologies (such as Hi-C and its derivatives), which can help link enhancers to their effector genes, can be leveraged to predefine variant sets for aggregate testing in WGS data. Here we propose the eSCAN (Scan the Enhancers) method for genome-wide assessment of enhancer regions in sequencing studies, combining the advantages of dynamic window selection in SCANG, a previously developed method, with the advantages of incorporating putative regulatory regions from annotation. eSCAN, by searching in putative enhancer, increases statistical power and aids mechanistic interpretation, as demonstrated by extensive simulation studies. We also apply eSCAN for blood cell traits using TOPMed WGS data. Results from real data analysis show that eSCAN is able to capture more significant signals, and these signals are of shorter length (indicating higher resolution fine-mapping capability) and drive association of larger regions detected by other methods.
Immunoglobulin A Antibody Composition Is Sculpted to Bind the Self Gut Microbiome
Authors: Yang Chao , Chen-Liaw Alice , Moran Thomas M. , Cerutti Andrea , Faith Jeremiah J. 
Published: December 2020

ABSTRACTDespite being the most abundantly secreted immunoglobulin isotype, the reactivity of IgA antibodies towards each individual’s own gut commensal bacteria still remains elusive. By colonizing germ-free mice with defined commensal bacteria, we found the binding specificity of bulk fecal and serum IgA towards resident gut bacteria resolves well at the species level and has modest strain level specificity. IgA hybridomas generated from lamina propria B cells of gnotobiotic mice showed that most IgA clones recognized a single bacterial species, while a small portion displayed polyreactivity. Species-specific IgAs had a range of strain specificities. Given the unique bacterial species and strain composition in each individual’s gut, our findings suggest the IgA repertoire is uniquely shaped to bind our self gut bacteria.
A novel coalescent method insensitive to population structure
Authors: Yao Zeqi , Liu Kehui , Deng Shanjun , He Xionglei 
Published: December 2020

AbstractConventional coalescent inferences of population history make the critical assumption that the population under examination is panmictic. However, most populations are structured. This complicates the prevailing coalescent analyses and sometimes leads to inaccurate estimates. To develop a coalescent method unhampered by population structure, we perform two analyses. First, we demonstrate that the coalescent probability of two randomly sampled alleles from the immediate preceding generation (one generation back) is independent of population structure. Second, motivated by this finding, we propose a new coalescent method:i-coalescent analysis.i-coalescent analysis computes the instantaneous coalescent rate (iCR) by using a phylogenetic tree of sampled alleles. Using simulated data, we broadly demonstrate the capability ofi-coalescent analysis to accurately reconstruct population size dynamics of highly structured populations, although we find this method often requires larger sample sizes for structured populations than for panmictic populations. Overall, our results indicatei-coalescent analysis to be a useful tool, especially for the inference of population histories with intractable structure such as the developmental history of cell populations in the organs of complex organisms.
Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla
Authors: Stucynski Joseph A. , Schott Amanda L. , Baik Justin , Chung Shinjae , Weber Franz 
Published: December 2020

ABSTRACTThe two major stages of mammalian sleep – rapid eye movement sleep (REMs) and non-REM sleep (NREMs) – are characterized by distinct brain rhythms ranging from millisecond to minute-long (infraslow) oscillations. The mechanisms controlling transitions between sleep stages and how they are synchronized with infraslow rhythms remain poorly understood. Using opto- and chemogenetic manipulation, we show that GABAergic neurons in the dorsomedial medulla (dmM) promote the initiation and maintenance of REMs, in part through their projections to the dorsal and median raphe nuclei. Fiber photometry revealed that dmM GABAergic neurons are strongly activated during REMs. During NREMs, their activity fluctuated in close synchrony with infraslow oscillations in the spindle band of the electroencephalogram, and the phase of this rhythm modulated the latency of optogenetically induced REMs episodes. Thus, dmM inhibitory neurons powerfully promote REMs, and their slow activity fluctuations may coordinate transitions from NREMs to REMs with infraslow brain rhythms.
Validation testing to determine the effectiveness of lateral flow testing for asymptomatic SARS-CoV-2 detection in low prevalence settings
Authors: Ferguson Jack , Dunn Steven , Best Angus , Mirza Jeremy , Percival Benita , Mayhew Megan , Megram Oliver , Ashford Fiona , White Thomas , Moles-Garcia Emma , Crawford Liam , Plant Tim , Bosworth Andrew , Kidd Michael , Richter Alex , Deeks Jonathan , McNally Alan 
Published: December 2020

AbstractLateral flow devices are quickly being implemented for use in large scale population surveillance programs for SARS-CoV-2 infection in the United Kingdom. These programs have been piloted in city wide screening in the city of Liverpool, and are now being rolled out to support care home visits and the return home of University students for the Christmas break. Here we present data on the performance of Lateral Flow devices to test almost 8,000 students at the University of Birmingham between December 2nd and December 9th 2020. The performance is validated against almost 800 samples using PCR performed in the University Pillar 2 testing lab, and theoretically validated on thousands of Pillar 2 PCR testing results performed on low-prevalence care home testing samples. Our data shows that Lateral Flow Devices do not detect infections presenting with PCR Ct values over 29-30, meaning that only 3.2% (95% CI 0.6% to 15.6%) of total cases in the student population were detected, but that as many of 85% of cases tested in the Pillar 2 PCR lab would have been detected theoretically
Impact of the Covid-19 pandemic on the mental health and wellbeing of adults with mental health conditions in the UK: A qualitative interview study
Authors: Burton Alexandra , McKinlay Alison , Aughterson Henry , Fancourt Daisy 
Published: December 2020

AbstractBackgroundPeople with mental health conditions have been identified as particularly vulnerable to poor mental health during the coronavirus disease 2019 (COVID-19) pandemic. However, why this population have faced these adverse effects, how they have experienced them and how they have coped remains under-explored.AimsTo explore how the COVID-19 pandemic affected the mental health of people with existing mental health conditions, and to identify coping strategies for positive mental health.MethodsSemi-structured qualitative interviews with 22 people with mental health conditions. Participants were purposively recruited via social media, study newsletters and third sector mental health organisations. Data were analysed using reflexive thematic analysis.ResultsParticipants were aged 23-70 (mean age 43), predominantly female (59.1%) and of white ethnicity (68.2%). Fifty percent were unable to work due to illness and the most frequently reported mental health condition was depression. Five pandemic related factors contributed to deteriorating mental health: i) feeling safe but isolated at home ii) disruption to mental health services, iii) cancelled plans and changed routines iv) uncertainty and lack of control, v) rolling media coverage. Five coping strategies were identified for maintaining mental health: i) previous experience of adversity ii) social comparison and accountability iii) engaging in hobbies and activities, iv) staying connected with others, v) perceived social support.ConclusionsChallenges were identified as a direct result of the pandemic and people with severe mental illnesses were particularly negatively affected. However, some found this period a time of respite, drew upon reserves of resilience and adapted their coping strategies to maintain positive wellbeing.
Impact of social distancing regulations and epidemic risk perception on social contact and SARS-CoV-2 transmission potential in rural South Africa: analysis of repeated cross-sectional surveys
Authors: McCreesh Nicky , Dlamini Vuyiswa , Edwards Anita , Olivier Stephen , Dayi Njabulo , Dikgale Keabetswe , Nxumalo Siyabonga , Dreyer Jaco , Baisley Kathy , Siedner Mark J. , White Richard G. , Herbst Kobus , Grant Alison D. , Harling Guy 
Published: December 2020

ABSTRACTBackgroundSouth Africa implemented rapid and strict physical distancing regulations to minimize SARS-CoV-2 epidemic spread. Evidence on the impact of such measures on interpersonal contact in rural and lower-income settings is limited.MethodsWe compared population-representative social contact surveys conducted in the same rural KwaZulu-Natal location once in 2019 and twice in mid-2020. Respondents reported characteristics of physical and conversational (‘close interaction’) contacts over 24 hours. We built age-mixing matrices and estimated the proportional change in the SARS-CoV-2 reproduction number (R0). Respondents also reported counts of others present at locations visited and transport used, from which we evaluated change in potential exposure to airborne infection due to shared indoor space (‘shared air’).ResultsRespondents in March-December 2019 (n=1704) reported a mean of 7.4 close interaction contacts and 196 shared air person-hours beyond their homes. Respondents in June-July 2020 (n=216), as the epidemic peaked locally, reported 4.1 close interaction contacts and 21 shared air person-hours outside their home, with significant declines in others’ homes and public spaces. Adults aged over 50 had fewer close contacts with others over 50, but little change in contact with 15-29 year olds, reflecting ongoing contact within multigenerational households. We estimate potential R0fell by 42% (95% plausible range 14-59%) between 2019 and June-July 2020.DiscussionExtra-household social contact fell substantially following imposition of Covid-19 distancing regulations in rural South Africa. Ongoing contact within intergenerational households highlighted the limitation of social distancing measures in protecting older adults.FundingWellcome Trust, UKRI, DFID, European Union
Social relationships and depression during the COVID-19 lockdown: longitudinal analysis of the COVID-19 social study
Authors: Sommerlad Andrew , Marston Louise , Huntley Jonathan , Livingston Gill , Lewis Gemma , Steptoe Andrew , Fancourt Daisy 
Published: December 2020

AbstractBackgroundThe COVID-19 pandemic led to social and physical distancing measures that reduced social contact and support. We explored whether people with more frequent and supportive social contact had fewer depressive symptoms during the UK Spring 2020 ‘lockdown’, whether this applied to face-to-face and remote electronic contact, and whether people with higher empathy levels, or more frequent pre-COVID social contact with others were more protected.MethodsUK dwelling participants aged ≥18 in the internet-based longitudinal COVID-19 Social Study completed up to 22 weekly questionnaires about frequency of face-to-face and phone/video social contact, perceived social support, and depressive symptoms assessed with the patient health questionnaire (PHQ-9). Mixed linear models examined associations between social contact and support, and depressive symptoms. We examined for interaction by empathic concern and perspective taking and pre-COVID social contact frequency.ResultsIn 71,117 people with mean age 49 years (standard deviation 15) we found that daily face-to-face or phone/video contact was associated with lower PHQ-9 scores (mean difference 0.258 (95% confidence interval 0.225, 0.290) and 0.117 (0.080, 0.154) respectively) compared to having no contact. Those with high social support scored 1.836 (1.801, 1.871) PHQ-9 points lower than those with low support. The odds ratio for depression for those with daily face-to-face social contact compared to no face-to-face contact was 0.712 (0.678, 0.747). Daily compared to no phone/video contact was associated with odds ratio for depression 0.825 (0.779, 0.873). And reporting high, compared to low, social support was associated with 0.145 (95%CI 0.138, 0.152) odds ratio for depression. The negative association between social relationships and depressive symptoms was stronger for those with high empathic concern, perspective taking and usual sociability.ConclusionsThose who had more face-to-face contact during lockdown had fewer depressive symptoms. Phone or video communication were beneficial but less so. People who were usually more sociable or had higher empathy were more likely to have depressive symptoms during enforced reduced social contact. Results have implications both for our management of COVID-19 and potential future pandemics, and for our understanding of the relationship between social factors and mental health.
A Bayesian Susceptible-Infectious-Hospitalized-Ventilated-Recovered Model to Predict Demand for COVID-19 Inpatient Care in a Large Healthcare System
Authors: Self Stella Coker Watson , Huang Rongjie , Amin Shrujan , Ewing Joseph , Rudisill Caroline , McLain Alexander C. 
Published: December 2020

SummaryThe COVID-19 pandemic strained healthcare systems in many parts of the United States. During the early months of the pandemic, there was substantial uncertainty about whether the large number of COVID-19 patients requiring hospitalization would exceed healthcare system capacity. This uncertainty created an urgent need for accurate predictions about the number of COVID-19 patients requiring inpatient and ventilator care at the local level. In this work, we develop a Bayesian Susceptible-Infectious-Hospitalized-Ventilated-Recovered (SIHVR) model to predict the burden of COVID-19 at the healthcare system level. The Bayesian SIHVR model provides daily estimates of the number of new COVID-19 patients admitted to inpatient care, the total number of non-ventilated COVID-19 inpatients, and the total number of ventilated COVID-19 patients at the healthcare system level. The model also incorporates county-level data on the number of reported COVID-19 cases, and county-level social distancing metrics, making it locally customizable. The uncertainty in model predictions is quantified with 95% credible intervals. The Bayesian SIHVR model is validated with an extensive simulation study, and then applied to data from two regional healthcare systems in South Carolina. This model can be adapted for other healthcare systems to estimate local resource needs.