bluuebunny/crossref_metadata_2025_split huggingface dataset:
Occupational risk of SARS-CoV-2 infection: a nationwide register-based study of the Danish workforce during the Covid-19 pandemic 2020-21
Authors: Bonde Jens Peter Ellekilde , Mølenberg Begtrup Luise , Høy Jensen Johan , Flachs Esben Meulengracht , Schlünssen Vivi , Kolstad Henrik Albert , Jakobsson Kristina , Nielsen Christel , Nilsson Kerstin , Rylander Lars , Vilhelmsson Andreas , Petersen Kajsa Ugelvig , Tøttenborg Sandra Søgaard 
Published: October 2022

ABSTRACTObjectivesMost earlier studies on occupational risk of Covid-19 covering the entire workforce are based on relatively rare outcomes such as hospital admission and mortality. This study examines the incidence of SARS-CoV-2 infection by occupational group based upon real-time polymerase chain reaction tests (RT-PCR).MethodsThe cohort includes 2.4 million Danish employees, 20-69 years of age. All data were retrieved from public registries. The sex-specific incidence rate ratios (IRR) of first-occurring positive RT-PCR test from week 8 of 2020 through week 50 of 2021 were computed by Poisson regression for each 4-digit DISCO-08 job code with more than 100 employees (337 in men; 297 in women). Occupational groups with low risk of workplace infection according to a job exposure matrix constituted the reference group. Risk estimates were adjusted by demographic, social and health characteristics including household size, completed Covid-19 vaccination, pandemic wave and occupation-specific frequency of testing.ResultsIRR’s of SARS-CoV-2 infection were elevated in 34 occupations comprising 12 % of male employees and 45 occupations comprising 41 % of female employees. All IRR estimates were below 2.0. Decreased IRRs were observed in 85 occupations in men but none in women.DiscussionWe observed a modestly increased risk of SARS-CoV-2 infection among employees in numerous occupations indicating a large potential for preventive actions, especially in the female workforce. Cautious interpretation of observed risk in specific occupations is needed because of methodological issues inherent in analyses of RT-PCR-test results and because of multiple statistical tests.WHAT IS ALREADY KNOW ABOUT THIS TOPIC?Epidemiological studies suggest that the workplace contribute to the Covid-19 pandemicResults are mostly based upon studies of less frequent outcomes as Covid-19 morbidity or mortality which limits inference about risk in specific occupationsWHAT THIS STUDY ADDSThe risk of Covid-19 infection was increased in 34 of 337 occupations in men and in 45 of 297 occupations in womenSome 12% of the Danish male workforce and 41% of the female workforce are at increased risk of Covid-19 infectionHOW THIS RESEARCH MIGHT AFFECT RESEARCH, PRACTICE OR POLICY?Preventive actions targeting the workplace may contribute substantially to alleviate disease occurrence in the ongoing Covid-19 and similar future pandemics.
Pilot Clinical Trial to test the function of a Diagnostic Sensor in predicting Impending Urinary Catheter Blockage in Long-term Catheterized Patients
Authors: Heylen Rachel A. , Mercer-Chalmers June , Morton Annette , Urie James , Jefferies Edward , Patenall Bethany L. , Laabei Maisem , Jenkins A. Toby. A. 
Published: October 2022

AbstractTrial designPilot feasibility trial.MethodsParticipantsadults attending the Outpatient Urology Clinic, having a long-term indwelling urinary catheter and have the mental capacity to consent. Consent for the donation of the urinary catheter and drainage bag were gained at the Urology Clinic, Royal United Hospital (RUH) Bath, alongside a quality-of-life questionnaire.Interventionsthere was no direct intervention to the participants, the trial was to test the functionality of the diagnostic sensor, there was no change to the participant’s medical care.Objectivesrecruitment of 48 participants to donate catheter and drainage bags (including re-recruits); assess the functionality of the sensor to predict impending catheter blockage in human urine; assess the functionality to release at pH > 7; and assess the microbiological profile of the patients with long-term catheters.Outcomedetermination of whether the participant had a blockage event 3 weeks post catheter change, and whether this matched with the result from the sensor. Measurements of the participant’s urine to assess whether the sensor could detect human urine at a pH > 7. Determine the microbial species present in the drainage bag of the participants.ResultsRecruitmentreceived 35 samples from 28 individuals.Outcometwo participants reported blockage events which were successfully detected by the sensor. However, the sensor also predicted blockage in participants that did not block within the 3-week report time period, sensitivity = 100%, specificity = 58.06%. The functionality of the sensor to detect urine above pH > 7 had a sensitivity = 78.75% and a specificity = 96.77%, which gave ap= 2.06×10−24(χ2test). Inclusion of the maintenance solution prescribed to participants, to test the predictability of the sensor, gave a sensitivity = 100%, and a specificity = 62.95%,p= 0.029 (Fisher Exact test). Microbiological analysis indicated thatProteusspp. andPseudomonasspp. were the most commonly isolated microbes.HarmNo adverse events.ConclusionsThe sensor can predict participants more prone to catheter blockage, and it is accurate in detecting urine with a pH >7. Owing to the small sample number of this trial, the results are not statistically powered. However, the data can be used to improve the design of the sensor and inform the design of a larger, randomized clinical trial.Trial registrationTrial was ethically approved by the Research Ethics Committee (REC) number: 20/LO/0094. Integrated Research Application System (IRAS) number: 261095.Fundingtrial was funded by the Urology Foundation and an IAA seed grant, University of Bath.
It’s not just about pads! Adolescent reproductive health in Kenya:A qualitative secondary analysis
Authors: Ayieko Sylvia , Nguku Angela , Kidula Nancy 
Published: October 2022

AbstractMany adolescents face barriers to accessing reproductive health care even though quality reproductive health care is a fundamental human right. This qualitative analysis describes quality reproductive health requests among adolescent high school girls in Kenya. We conducted a secondary analysis of qualitative data from a sub-sample of adolescent girls in Kenya who participated in theWhat Women Wantglobal campaign. The campaign utilized one open-ended survey question. We also analyzed interview data from key informants involved in the survey. We used pre-existing codes and current literature to design the coding framework and thematic analysis to describe emerging themes. Atlas. ti 8 was used to organize and analyze codes. Over 4,500 high school girls, ages 12 and 19 years, were included in the analysis, with 61.6% from all-girls boarding schools and 13.8% from mixed-day schools. Data from nine key informants complemented findings from the survey. Emerging themes included: 1) The need for improved menstrual health and hygiene: Sanitary towels and cleaner toilets; 2) Prevention of adolescent pregnancy: Access to contraception; 3) Respect and dignity: Participants want privacy and confidentiality; and 4) The need to address social determinants of health: Economic stability and a safe physical environment. This study indicated that adolescent high school girls have varied requests for reproductive health care and services. While menstrual health and hygiene are key issues, reproductive needs are beyond just sanitary products. The results suggest a need for targeted reproductive health interventions using a multi-sectoral approach.
Accurate detection and identification of insects from camera trap images with deep learning
Authors: Bjerge Kim , Alison Jamie , Dyrmann Mads , Frigaard Carsten Eie , Mann Hjalte M. R. , Høye Toke Thomas 
Published: October 2022

AbstractReported insect declines have dramatically increased the global demand for standardized insect monitoring data. Image-based monitoring can generate such data cost-efficiently and non-invasively. However, extracting ecological data from images is more challenging for insects than for vertebrates because of their small size and great diversity. Deep learning facilitates fast and accurate insect detection and identification, but the lack of training data for coveted deep learning models is a major obstacle for their application. We present a large annotated image dataset of functionally important insect taxa. The primary dataset consists of 29,960 annotated insects representing nine taxa including bees, hoverflies, butterflies and beetles across more than two million images recorded with ten time-lapse cameras mounted over flowers during the summer of 2019. The insect image dataset was extracted using an iterative approach: First, a preliminary detection model identified candidate insects. Second, candidate insects were manually screened by users of an online citizen science platform. Finally, all annotations were quality checked by experts. We used the dataset to train and compare the performance of selected You Only Look Once (YOLO) deep learning algorithms. We show that these models detect and classify small insects in complex scenes with unprecedented accuracy. The best performing YOLOv5 model consistently identifies nine dominant insect species that play important roles in pollination and pest control across Europe. The model reached an average precision of 92.7% and recall of 93.8 % in detection and classification across species. Importantly, when presented with uncommon or unclear insects not seen during training, our model detects 80% of individuals and usually interprets them as closely related species. This is a useful property to (1) detect rare insects for which training data are absent, and (2) generate new training data to correctly identify those insects in future. Our camera system, dataset and deep learning framework show promising results in non-destructive monitoring of insects. Furthermore, resulting data are useful to quantify phenology, abundance, and foraging behaviour of flower-visiting insects. Above all, this dataset represents a critical first benchmark for future development and evaluation of deep learning models for insect detection and identification.
bRaw: an R package for digital raw canopy imagery
Authors: Chianucci F. 
Published: October 2022

AbstractDigital photography is an increasingly popular tool to estimate forest canopy attributes. However, estimates of gap fraction, upon which calculations of canopy attributes are based, are sensitive to photographic exposure in upward-facing images. Recent studies have indicated that analyzing RAW imagery, rather than other inbuilt camera format (e.g. jpeg, png, tiff) allows to obtain largely-insensitive gap fraction retrieval from digital photography. The package bRaw implemented the method proposed by Macfarlane et al. (2014). They found that shooting raw with one stop of underexposure and applying a linear contrast stretch yielded largely insensitive results, thus providing a way for standardizing and optimizing photographic exposure. The package replicate the methodology and thus it provides an effective tool to use raw imagery in canopy photography.
Lmx1a is a master regulator of the cortical hem
Authors: Iskusnykh Igor Y. , Fattakhov Nikolai , Li Yiran , Bihannic Laure , Kirchner Matthew K. , Steshina Ekaterina Y. , Northcott Paul A. , Chizhikov Victor V. 
Published: October 2022

AbstractDevelopment of the nervous system depends on signaling centers – specialized cellular populations that produce secreted molecules to regulate neurogenesis in the neighboring neuroepithelium. Some signaling centers also generate key types of neurons. The formation of a signaling center involves its induction, the maintenance of expression of its secreted molecules, and cell differentiation and migration events. How these distinct processes are coordinated during signaling center development remains unknown. Here we show that Lmx1a acts as a master regulator to orchestrate the formation and function of the cortical hem (CH), a critical signaling center that controls hippocampus development. Lmx1a co-regulates CH induction, its Wnt signaling, and the differentiation and migration of CH-derived Cajal-Retzius neurons. Combining RNAseq, genetic, and rescue experiments, we identified major downstream genes that mediate distinct Lmx1a-dependent processes. Our work revealed that signaling centers in the mammalian brain employ master regulatory genes and established a framework for analyzing signaling center development.
Benchmarking of protein interaction databases for integration with manually reconstructed signaling network models
Authors: Van de Graaf Matthew W. , Eggertsen Taylor G. , Zeigler Angela C. , Tan Philip M. , Saucerman Jeffrey J. 
Published: October 2022

AbstractProtein interaction databases are critical resources for network bioinformatics and integrating molecular experimental data. Interaction databases may also enable construction of predictive computational models of biological networks, although their fidelity for this purpose is not clear. Here, we benchmark protein interaction databases X2K, Reactome, Pathway Commons, Omnipath, and Signor for their ability to recover manually curated edges from three logic-based network models of cardiac hypertrophy, mechano-signaling, and fibrosis. Pathway Commons performed best at recovering interactions from manually reconstructed hypertrophy (137 of 193 interactions, 71%), mechano-signaling (85 of 125 interactions, 68%), and fibroblast networks (98 of 142 interactions, 69%). While protein interaction databases successfully recovered central, well-conserved pathways, they performed worse at recovering tissue-specific and transcriptional regulation. This highlights a knowledge gap where manual curation is critical. Finally, we tested the ability of Signor and Pathway Commons to identify new edges that improve model predictions, revealing important roles of PKC autophosphorylation and CaMKII phosphorylation of CREB in cardiomyocyte hypertrophy. This study provides a platform for benchmarking protein interaction databases for their utility in network model construction, as well as providing new insights into cardiac hypertrophy signaling.
Quantitative profiling of pseudouridylation landscape in the human transcriptome
Authors: Zhang Meiling , Jiang Zhe , Ma Yichen , Liu Wenqing , Zhuang Yuan , Lu Bo , Li Kai , Yi Chengqi 
Published: October 2022

AbstractPseudouridine (Ψ) is an abundant post-transcriptional RNA modification in ncRNA and mRNA. However, transcriptome-wide measurement of individual Ψ sites remains unaddressed. Here, we develop “PRAISE”, via selective chemical labeling of Ψ by bisulfite to induce nucleotide deletion signature during reverse transcription, to realize quantitative assessment of the Ψ landscape in the human transcriptome. Unlike traditional RNA/DNA bisulfite treatment, our approach is based on quaternary base mapping and revealed a ~10% median modification level for 2,714 confident Ψ sites in HEK293T cells. By perturbing pseudouridine synthases, we obtained differential mRNA targets of PUS1, PUS7 and TRUB1, with TRUB1 mRNA targets showing the highest modification stoichiometry. In addition, we identified and quantified known and novel Ψ sites in mitochondrial mRNA, catalyzed by a mitochondria-localized isoform of PUS1. Collectively, we provide a sensitive and convenient method to measure transcriptome-wide Ψ; we envision this quantitative approach would facilitate emerging efforts to elucidate the function and mechanism of mRNA pseudouridylation.
Reading negative action verbs: one or two-step processing within the primary motor cortex?
Authors: Dupont W , Papaxanthis C , Lurquin L , Lebon F , Madden-Lombardi C 
Published: October 2022

AbstractControversy persists regarding the representation of negated actions, specifically concerning activation and inhibitory mechanisms in the motor system, and whether this occurs in one or two steps. We conducted two experiments probing corticospinal excitability (CSE) and short-interval intracortical inhibition (SICI) in the primary motor cortex at different latencies while reading affirmative and negative action sentences.Twenty-six participants read action and non-action sentences in affirmative or negative forms. Using transcranial magnetic stimulation, we probed CSE in hand muscles at rest and at several latencies after verb presentation. We observed a greater CSE for action sentences compared to non-action sentences, regardless of verb form.In experiment two, nineteen participants read affirmative and negative action sentences. We measured CSE and SICI at short and long latencies after verb presentation. CSE was greater for affirmative and negative action sentences at both latencies compared to rest. SICI did not change at the short latency but increased at longer latencies, regardless of verb form.Our results lend partial support for a two-step model, as negated actions showed the same motor excitability as affirmed actions with no additional inhibition at early latencies. Later neural differences between affirmative and negative actions may occur outside the primary motor cortex.Significant statementIn two TMS experiments, we probed corticospinal excitability and short-interval intracortical inhibition in the primary motor cortex at different latencies while subjects read affirmative and negative action sentences. Consistent with an embodied view of language comprehension, our results demonstrate that reading about actions indeed activates the motor system, and this for both negative and affirmative sentences. Our results lend partial support for a two-step model of negation, as negated actions showed the same increase in motor excitability as affirmed actions, with no additional inhibition at early latencies. This suggests that the motor system contributes to comprehension by simulating the negated or affirmed action. Later neural differences between affirmative and negative actions may occur outside the primary motor cortex.
‘Same same but different’ Exploring evolutionary and ecological causes of eye diversification in bumblebees
Authors: Tichit Pierre , Bodey Andrew , Rau Christoph , Baird Emily 
Published: October 2022

AbstractBees rely heavily on vision during most of their interaction with the environment, but so far, visual abilities have not been included into functional investigations of these crucial pollinators. This is probably due to the lack of comprehensive and phylogenetically-controlled quantification of visual traits across species. In the present study, we used high-throughput micro-CT tools to quantify, compare and understand the diversity of visual traits of compound eyes in bumblebees. Visual systems of bumblebees were far from identical, with variations across sizes, castes and species. While phylogenetic proximity poorly supported interspecific variations, these were better explained by two ecological factors: social parasitism and habitat. The eye parameter – a metric that measures the relative investment of a compound eye into resolution or sensitivity – was lower in queens of social parasitic species than of non-parasitic species. Workers of species associated with forested habitat had distinct visual traits, including a higher eye parameter, than those of species living in open landscapes. These diverse visual traits are likely to provide selective advantages to bumblebees given their specific ecological requirements. We thus propose that social parasitism and forest habitat are drivers of the diversification of compound eyes in bumblebees. Finally, we discuss how the present study can inspire trait-based approaches in ecology and conservation biology.
Evaluation of metabolic and functional properties of cholesterol-reducing and GABA-producerLimosilactobacillus fermentumstrain AGA52 isolated from lactic acid fermented Shalgam by usingin vitroandin silicoprobiogenomic approaches
Authors: Yetiman Ahmet , Horzum Mehmet , Akbulut Mikail 
Published: October 2022

AbstractThe objective of this study is characterization of the genomic and metabolic properties of a novelLb. fermentumstrain AGA52 which was isolated from a lactic acid fermented beverage called “Shalgam”. The genome size of AGA52 was 2,001,084 bp, which is predicted to carry 2,024 genes, including 50 tRNAs, 3 rRNAs, 3ncRNAs, 15 CRISPR repeats, 14 CRISPR spacers, and 1 CRISPR array. The genome has a GC content of 51.82% including 95 predicted pseudogenes, 56 complete or partial transposases and 2 intact prophages. The similarity of the clusters of orthologous groups (COG) was analysed by comparison with the otherLb. fermentumstrains. The detected resistome on the genome of AGA52 was found to be intrinsically originated. Based on genome analyses many CAZYmes were identified that are responsible for carbohydrate degradation and adaptation to gastrointestinal conditions. Besides, it has been determined that AGA52 has an obligate heterofermentative carbohydrate metabolism due to the absence of the 1-phosphofructokinase (pfK) enzyme. Additionally, it is determined that the strain has a higher antioxidant capacity and is adaptable to gastrointestinal simulated environments. It was also observed that the AGA52 has antimicrobial activity againstY. EnterocoliticaATCC9610,B. cereusATCC33019,S. entericasv. Typhimurium,E.coliO157:h7 ATCC43897,L.monocytogenesATCC7644,K.pneumoniaeATCC13883, andP. vulgarisATCC8427. Cholesterol assimilation (33.9±0.005%) and GABA production capacities were also confirmed by “in silico” and “in vitro”. Overall, investigation of genomic and metabolic features of the AGA52 revealed that is a potential psychobiotic and probiotic dietary supplement candidate and can bring functional benefits to the host.
Classifying directed and diffusive transport in short, noisy single-molecule trajectories with wMSD
Authors: Danné Noémie , Zhang Zhiqing , Peterman Erwin J. G. 
Published: October 2022

AbstractFluorescence imaging in combination with single-particle tracking analysis has emerged as a powerful tool to study and characterize the motion of proteins moving in biological media. One of the main challenges in this approach is to reliably distinguish between directed and diffusive transport, especially for short and often noisy trajectories showing distinct, time- and place-dependent modes of motility. In this contribution, we present a windowed Mean-Square Displacements classifier (wMSDc) that is able to reliably (i) identify periods of diffusive and directed transport, (ii) extract position-dependent diffusion coefficients and velocities, and (iii) identify the location of switches in direction or motility mode in short (< 50 time points) and noisy single-molecule trajectories. We compare the performance of this approach to a Hidden Markov Model (HMM) method and a Moment Scaling Spectrum based method (DC-MSS) previously published and show that, in most cases, its performance is superior. We present a wide range of applications: from the movement of whole organisms and cells to protein-DNA interactionsin vitroand motor-protein dynamicsin vivo.Statement of SignificanceExtracting quantitative parameters from single-particle trajectories is a challenging task, especially in biological samples, which often show a high degree of heterogeneity. Trajectories can reveal switches between different types of motion; directed, diffusive and sub-diffusive motion. Usually, the length of these trajectories and their localization precision are limited by the experimental conditions. Here, we present a novel approach to analyse single molecule trajectories, windowed Mean-Square Displacement classifier (wMSDc) to reliably distinguish between directed and diffusive transport in the short trajectories with a finite precision of localization and integration time typically obtained when imaging single fluorescent proteins in living cells or organisms. We show that, using simulated and a wide range of experimental trajectories, wMSDc is a reliable method to extract motility parameters such as diffusion coefficient and velocity.