bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving Fabric Face Masks: Impact of Design Features on the Protection Offered by Fabric Face Masks
Authors: O’Kelly Eugenia , Arora Anmol , Pirog Sophia , Ward James , Clarkson P John 
Published: January 2021

AbstractObjectiveWith much of the public around the world depending on fabric face masks to protect themselves and others, it is essential to understand how the protective ability of fabric masks can be enhanced. This study evaluated the protection offered by eighteen fabric masks designs. In addition, it assessed the benefit of including three design features: insert filters, surgical mask underlayers, and nose wires.MethodsQuantitative fit tests were conducted on different masks and with some additional design features. An array of fabric masks were tested on a single participant to account for variability in face shapes. The effects of insert filters, surgical mask underlayers and nose wires were also assessed.ResultsAs expected, the fabric masks offered low degrees of protection; however, alterations in design showed significant increase in their protective ability. The most effective designs were multi-layered masks that fit tightly to the face and lacked dead space between the user and mask. Also, low air-resistance insert filters and surgical mask underlays provided the greatest increase in protection.ConclusionsOur findings indicate substantial heterogeneity in the protection offered by various fabric face masks. We also note some design features which may enhance the protection these masks offer.
Longitudinal trends and risk factors for depressed mood among Canadian adults during the first wave of COVID-19
Authors: Betini Gustavo S. , Hirdes John P. , Adekpedjou Rhéda , Perlman Christopher M. , Huculak Nathan , Hébert Paul 
Published: January 2021

AbstractBackgroundThe COVID-19 pandemic has raised serious concerns about the mental health impact of people directed and indirectly affected by the virus. Because this is a rapidly evolving situation, our goal was to explore potential risk factors and trends in feelings of anxiety and depression among the general population in Canada over the first five months of the pandemic.MethodsWe completed on-line surveys of 3,127 unique individuals representative of the Canadian general population at 4 discreet periods every 6 weeks from April 15thto July 28th2020. We assessed feelings of anxiety, depression and loss of interest with the interRAI self-reported mood scale using a multivariable generalized estimating equation model to examine factors associated with having a 5+ score on the scale (indicating potentially depressed mood). We also investigated potential longitudinal trends to examine temporal changes in mood scores.ResultsMore than 30% of participants felt highly anxious, depressed, and disinterested in everyday activities in the first survey (April), but this number decreased to about 20% over 4 months. Feeling lonely, younger age, feeling overwhelmed by one’s health needs, having financial concerns, and living outside of Québec were significantly associated with depressed mood.InterpretationThe prevalence of depressed mood during the pandemic was between 2 and 3 times the pre-pandemic rate (especially among young people), but it can change rapidly in response to social changes. Thus, monitoring of psychological distress among vulnerable groups that may benefit from additional supports should be a priority.
Predicting Prognosis and IDH Mutation Status for Patients with Lower-Grade Gliomas Using Whole Slide Images
Authors: Jiang Shuai , Zanazzi George J. , Hassanpour Saeed 
Published: January 2021

ABSTRACTWe developed end-to-end deep learning models using whole slide images of adults diagnosed with diffusely infiltrating, World Health Organization (WHO) grade 2 gliomas to predict prognosis and the mutation status of a somatic biomarker, isocitrate dehydrogenase (IDH) 1/2. The models, which utilize ResNet-18 as a backbone, were developed and validated on 296 patients from The Cancer Genome Atlas (TCGA) database. To account for the small sample size, repeated random train/test splits were performed for hyperparameter tuning, and the out-of-sample predictions were pooled for evaluation. Our models achieved a concordance- (C-) index of 0.715 (95% CI: 0.569, 0.830) for predicting prognosis and an area under the curve (AUC) of 0.667 (0.532, 0.784) for predicting IDH mutations. When combined with additional clinical information, the performance metrics increased to 0.784 (95% CI: 0.655, 0.880) and 0.739 (95% CI: 0.613, 0.856), respectively. When evaluated on the grade 3 gliomas TCGA dataset, which was not used for training, our models were able to predict survival with a C-index of 0.654 (95% CI: 0.537, 0.768) and IDH mutations with an AUC of 0.814 (95% CI: 0.721, 0.897). If validated in a prospective study, our method could potentially assist clinicians in managing and treating patients with diffusely infiltrating gliomas.
Using body temperature and variables commonly available in the EHR to predict acute infection: A proof-of-concept study showing improved pretest probability estimates for acute COVID-19 infection among discharged emergency department patients
Authors: Berdahl Carl T , Nguyen An T , Diniz Marcio A , Henreid Andrew J , Nuckols Teryl K , Libby Christopher P , Pevnick Joshua M 
Published: January 2021

ABSTRACTObjectivesObtaining body temperature is a quick and easy method to screen for acute infection such as COVID-19. Currently, the predictive value of body temperature for acute infection is inhibited by failure to account for other readily available variables that affect temperature values. In this proof-of-concept study, we sought to improve COVID-19 pretest probability estimation by incorporating covariates known to be associated with body temperature, including patient age, sex, comorbidities, month, time of day.MethodsFor patients discharged from an academic hospital emergency department after testing for COVID-19 in March and April of 2020, we abstracted clinical data. We reviewed physician documentation to retrospectively generate estimates of pretest probability for COVID-19. Using patients’ COVID-19 PCR test results as a gold standard, we compared AUCs of logistic regression models predicting COVID-19 positivity that used: 1) body temperature alone; 2) body temperature and pretest probability; 3) body temperature, pretest probability, and body temperature-relevant covariates. Calibration plots and bootstrap validation were used to assess predictive performance for model #3.ResultsData from 117 patients were included. The models’ AUCs were: 1) 0.69 2) 0.72, and 3) 0.76, respectively. The absolute difference in AUC was 0.029 (95%CI −0.057 to 0.114, p=0.25) between model 2 and 1 and 0.038 (95%CI −0.021 to 0.097, p=0.10) between model 3 and 2.ConclusionsBy incorporating covariates known to affect body temperature, we demonstrated improved pretest probability estimates of acute COVID-19 infection. Future work should be undertaken to further develop and validate our model in a larger, multi-institutional sample.
Single-cell deconvolution of 3,000 post-mortem brain samples for eQTL and GWAS dissection in mental disorders
Authors: Park Yongjin , He Liang , Davila-Velderrain Jose , Hou Lei , Mohammadi Shahin , Mathys Hansruedi , Peng Zhuyu , Bennett David , Tsai Li-Huei , Kellis Manolis 
Published: January 2021

AbstractThousands of genetic variants acting in multiple cell types underlie complex disorders, yet most gene expression studies profile only bulk tissues, making it hard to resolve where genetic and non-genetic contributors act. This is particularly important for psychiatric and neurodegenerative disorders that impact multiple brain cell types with highly-distinct gene expression patterns and proportions. To address this challenge, we develop a new framework, SPLITR, that integrates single-nucleus and bulk RNA-seq data, enabling phenotype-aware deconvolution and correcting for systematic discrepancies between bulk and single-cell data. We deconvolved 3,387 post-mortem brain samples across 1,127 individuals and in multiple brain regions. We find that cell proportion varies across brain regions, individuals, disease status, and genotype, including genetic variants in TMEM106B that impact inhibitory neuron fraction and 4,757 cell-type-specific eQTLs. Our results demonstrate the power of jointly analyzing bulk and single-cell RNA-seq to provide insights into cell-type-specific mechanisms for complex brain disorders.
Low adenovirus vaccine doses administered to skin using microneedle patches induce better functional antibody immunogenicity as compared to systemic injection
Authors: Flynn Olivia , Dillane Kate , Lanza Juliane Sousa , Marshall Jennifer M. , Jin Jing , Silk Sarah E. , Draper Simon J. , Moore Anne C. 
Published: January 2021

ABSTRACTAdenovirus-based vaccines are demonstrating promising clinical potential for multiple infectious diseases including COVID-19. However the immunogenicity of the vector itself decreases its effectiveness as a boosting vaccine due to the induction of strong anti-vector neutralising immunity. Here we determined how dissolvable microneedle patches (DMN) for skin immunization can overcome this issue, using a clinically-relevant adenovirus-basedPlasmodium falciparummalaria vaccine, AdHu5-PfRH5, in mice. Incorporation of vaccine into patches significantly enhanced its thermostability compared to the liquid form. Conventional high dose repeated immunization by the intramuscular (IM) route induced low antigen-specific IgG titres and high anti-vector immunity. A low priming dose of vaccine, by the IM route but more so using DMN patches, induced the most efficacious immune responses, assessed by parasite growth inhibitory activity (GIA) assays. Administration of low dose AdHu5-PfRH5 using patches to the skin, boosted by high dose IM, induced the highest antigen-specific serum IgG response after boosting, the greatest skewing of the antibody response towards the antigen and away from the vector and the highest efficacy. This study therefore demonstrates that repeated use of the same adenovirus vaccine can be highly immunogenic towards the transgene if a low dose is used to prime the response. It also provides a method of stabilising adenovirus vaccine, in easy-to-administer dissolvable microneedle patches, permitting storage and distribution out of cold chain.
Publication practices during the COVID-19 pandemic: Biomedical preprints and peer-reviewed literature
Authors: Sevryugina Yulia V. , Dicks Andrew J. 
Published: January 2021

AbstractThe coronavirus pandemic introduced many changes to our society, and deeply affected the established in biomedical sciences publication practices. In this article, we present a comprehensive study of the changes in scholarly publication landscape for biomedical sciences during the COVID-19 pandemic, with special emphasis on preprints posted on bioRxiv and medRxiv servers. We observe the emergence of a new category of preprint authors working in the fields ofimmunology, microbiology,infectious diseases, andepidemiology, who extensively used preprint platforms during the pandemic for sharing their immediate findings. The majority of these findings were works-in-progress unfitting for a prompt acceptance by refereed journals. The COVID-19 preprints that became peer-reviewed journal articles were often submitted to journals concurrently with the posting on a preprint server, and the entire publication cycle, from preprint to the online journal article, took on average 63 days. This included an expedited peer-review process of 43 days and journal’s production stage of 15 days, however there was a wide variation in publication delays between journals. Only one third of COVID-19 preprints posted during the first nine months of the pandemic appeared as peer-reviewed journal articles. These journal articles display high Altmetric Attention Scores further emphasizing a significance of COVID-19 research during 2020. This article will be relevant to editors, publishers, open science enthusiasts, and anyone interested in changes that the 2020 crisis transpired to publication practices and a culture of preprints in life sciences.
Activity in perirhinal and entorhinal cortex predicts perceived visual similarities among category exemplars with highest precision
Authors: Ferko Kayla M. , Blumenthal Anna , Martin Chris B. , Proklova Daria , Minos Alexander , Saksida Lisa M. , Bussey Timothy J. , Khan Ali R. , Köhler Stefan 
Published: January 2021

AbstractVision neuroscience has made great strides in understanding the hierarchical organization of object representations along the ventral visual stream (VVS). How VVS representations capture fine-grained visual similarities between objects that observers subjectively perceive has received limited examination so far. In the current study, we addressed this question by focusing on perceived visual similarities among subordinate exemplars of real world-categories. We hypothesized that these perceived similarities are reflected with highest fidelity in neural activity patterns downstream from inferotemporal regions, namely in perirhinal and anterolateral entorhinal cortex in the medial temporal-lobe. To address this issue with fMRI, we administered a modified 1-Back task that required discrimination between category exemplars as well as categorization. Further, we obtained observer-specific ratings of perceived visual similarities, which predicted behavioural performance during scanning. As anticipated, we found that activity patterns in perirhinal and anterolateral entorhinal cortex predicted the structure of perceived visual similarity relationships among category exemplars, including its observer-specific component, with higher precision than any other VVS region. Our findings provide new evidence that subjective aspects of object perception that rely on fine-grained visual differentiation are reflected with highest fidelity in the medial temporal lobe.
The Pif1 helicase is actively inhibited during meiotic recombination which restrains gene conversion tract length
Authors: Vernekar Dipti Vinayak , Reginato Giordano , Adam Céline , Ranjha Lepakshi , Dingli Florent , Marsolier Marie-Claude , Loew Damarys , Guérois Raphaël , Llorente Bertrand , Cejka Petr , Borde Valérie 
Published: January 2021

AbstractMeiotic recombination ensures proper chromosome segregation to form viable gametes and results in gene conversions events between homologs. Conversion tracts are shorter in meiosis than in mitotically dividing cells. This results at least in part from the binding of a complex, containing the Mer3 helicase and the MutLβ heterodimer, to meiotic recombination intermediates. The molecular actors inhibited by this complex are elusive. The Pif1 DNA helicase is known to stimulate DNA polymerase delta (Pol δ) -mediated DNA synthesis from D-loops, allowing long synthesis required for break-induced replication. We show that Pif1 is also recruited genome wide to meiotic DNA double-strand break (DSB) sites. We further show that Pif1, through its interaction with PCNA, is required for the long gene conversions observed in the absence of MutLβ recruitment to recombination sites.In vivo, Mer3 interacts with the PCNA clamp loader RFC, andin vitro, Mer3-MutLβ ensemble inhibits Pif1-stimulated D-loop extension by Pol δ and RFC-PCNA. Mechanistically, our results suggest that Mer3-MutLβ may compete with Pif1 for binding to RFC-PCNA. Taken together, our data show that Pif1’s activity that promotes meiotic DNA repair synthesis is restrained by the Mer3-MutLβ ensemble which in turn prevents long gene conversion tracts and possibly associated mutagenesis.
Braiding Braak and Braak: Staging patterns and model selection in network neurodegeneration
Authors: Putra Prama , Thompson Travis B. , Chaggar Pavanjit , Goriely Alain ,   for the Alzheimer’s Disease Neuroimaging Initiative
Published: January 2021

AbstractA hallmark of Alzheimer’s disease is the aggregation of insoluble amyloid-beta plaques and tau protein neurofibrillary tangles. A key histopathological observation is that tau protein aggregates follow a structured progression pattern through the brain. Mathematical network models of prion-like propagation have the ability to capture such patterns but a number of factors impact the observed staging result, thus introducing questions regarding model selection. Here, we introduce a novel approach, based on braid diagrams, for studying the structured progression of a marker evolving on a network. We apply this approach to a six-stage ‘Braak pattern’of tau proteins, in Alzheimer’s disease, motivated by a recent observation that seed-competent tau precedes tau aggregation. We show that the different modeling choices, from the model parameters to the connectome resolution, play a significant role in the landscape of observable staging patterns. Our approach provides a systematic way to approach model selection for network propagation of neurodegenerative diseases that ensures both reproducibility and optimal parameter fitting.Author summaryNetwork diffusion models of neurodegenerative diseases are a class of dynamical systems that simulate the evolution of toxic proteins on the connectome. These models predict, from an initial seed, a pattern of invasion called staging. The generalized staging problem seeks to systematically study the effect of various model choices on staging. We introduce methods based on braid diagrams to test the possible staging landscape of a model and how it depends on the choice of connectome, as well as the model parameters. Our primary finding is that connectome construction, the choice of the graph Laplacian, and transport models all have an impact on staging that should be taken into account in any study.
A complex genetic architecture in zebrafish relativesDanio quaggaandD. kyathitunderlies development of stripes and spots
Authors: McCluskey Braedan M. , Uji Susumu , Mancusi Joseph L. , Postlethwait John H. , Parichy David M. 
Published: January 2021

AbstractVertebrate pigmentation is a fundamentally important, multifaceted phenotype. Zebrafish,Danio rerio, has been a valuable model for understanding genetics and development of pigment pattern formation due to its genetic and experimental tractability, advantages that are shared across severalDaniospecies having a striking array of pigment patterns. Here, we use the sister speciesD. quaggaandD. kyathit, with stripes and spots, respectively, to understand how natural genetic variation impacts phenotypes at cellular and organismal levels. We first show thatD. quaggaandD. kyathitphenotypes resemble those of wild-typeD. rerioand several single locus mutants ofD. rerio, respectively, in a morphospace defined by pattern variation along dorsoventral and anteroposterior axes. We then identify differences in patterning at the cellular level betweenD. quaggaandD. kyathitby repeated daily imaging during pattern development and quantitative comparisons of adult phenotypes, revealing that patterns are similar initially but diverge ontogenetically. To assess the genetic architecture of these differences, we employ reduced-representation sequencing of second-generation hybrids. Despite the similarity ofD. quaggatoD. rerio, andD. kyathitto someD. reriomutants, our analyses reveal a complex genetic basis for differences betweenD. quaggaandD. kyathit, with several quantitative trait loci contributing to variation in overall pattern and cellular phenotypes, epistatic interactions between loci, and abundant segregating variation within species. Our findings provide a window into the evolutionary genetics of pattern-forming mechanisms inDanioand highlight the complexity of differences that can arise even between sister species. Further studies of natural genetic diversity underlying pattern variation inD. quaggaandD. kyathitshould provide insights complementary to those from zebrafish mutant phenotypes and more distant species comparisons.Author SummaryPigment patterns of fishes are diverse and function in a wide range of behaviors. Common pattern themes include stripes and spots, exemplified by the closely related minnowsDanio quaggaandD. kyathit, respectively. We show that these patterns arise late in development owing to alterations in the development and arrangements of pigment cells. In the closely related model organism zebrafish (D. rerio) single genes can switch the pattern from stripes to spots. Yet, we show that pattern differences betweenD. quaggaandD. kyathithave a more complex genetic basis, depending on multiple genes and interactions between these genes. Our findings illustrate the importance of characterizing naturally occuring genetic variants, in addition to laboratory induced mutations, for a more complete understanding of pigment pattern development and evolution.
Conserved and divergent features of neuronal CaMKII holoenzyme structure, function, and high-order assembly
Authors: Buonarati Olivia R. , Miller Adam P. , Coultrap Steven J. , Bayer K. Ulrich , Reichow Steve L. 
Published: January 2021

SUMMARYNeuronal CaMKII holoenzymes (α- and β-isoforms) enable molecular signal computation underlying learning and memory, but also mediate excitotoxic neuronal death. Here, we provide a comparative analysis of these signaling devices, using single particle EM in combination with biochemical and live-cell imaging studies. In the basal state, both isoforms assembled mainly as 12-mers (but also 14-mers, and even 16-mers for the β-isoform). CaMKIIα and β-isoforms adopted an ensemble of extended activatable states (with average radius of 12.6 versus 16.8 nm, respectively), characterized by multiple transient intra- and inter-holoenzyme interactions associated with distinct functional properties. The extended state of CaMKIIβ allowed EM analysis to directly resolve intra-holoenzyme kinase-domain dimers that could enable the cooperative activation mechanism by calmodulin, which was found for both isoforms. Surprisingly, high-order CaMKII clustering mediated by inter-holoenzyme kinase-domain dimerization was reduced for the β isoform for both basal and excitotoxicity-induced clusters, bothin vitroand in neurons.
DYNAMITE: a phylogenetic tool for identification of dynamic transmission epicenters
Authors: Magalis Brittany Rife , Marini Simone , Salemi Marco , Prosperi Mattia 
Published: January 2021

AbstractMolecular data analysis is invaluable in understanding the overall behavior of a rapidly spreading virus population when epidemiological surveillance is problematic. It is also particularly beneficial in describing subgroups within the population, often identified as clades within a phylogenetic tree, that represent individuals connected via direct transmission or transmission via differing risk factors in viral spread. However, transmission patterns or viral dynamics within these smaller groups should not be expected to exhibit homogeneous behavior over time. As such, standard phylogenetic approaches that identify clusters based on summary statistics (e.g., median genetic distance over the clade) would not be expected to capture dynamic clusters of transmission. For this purpose, we have developed DYNAMITE (DYNAMic Identification of Transmission Epicenters), a cluster identification algorithm based on a branch-wise (rather than traditional clade-wise) search for cluster criteria, allowing partial clades to be recognized as clusters. Using simulated viral outbreaks with varying cluster types and dynamics, we show that DYNAMITE is consistently more sensitive than existing tools in detecting both static and dynamic transmission clusters. DYNAMITE has been implemented in R and released as open source at:github.com/ProsperiLab/DYNAMITE.