bluuebunny/crossref_metadata_2025_split huggingface dataset:
Temporal single cell transcriptome atlas of zebrafish anterior segment development reveals high degree of conservation between the trabecular meshwork and the annular ligament
Authors: Vöcking Oliver , Famulski J.K. 
Published: October 2022

ABSTRACTAnterior segment dysgenesis (ASD), resulting in vision impairment, stems from maldevelopment of anterior segment (AS) tissues. Incidence of ASD has been linked to malfunction of periocular mesenchyme cells (POM). POM cells specify into anterior segment mesenchyme (ASM) cells which colonize and produce AS tissues. In this study we uncover ASM developmental trajectories associated with formation of the AS. Using a transgenic line of zebrafish that fluorescently labels the ASM throughout development, Tg[foxc1b:GFP], we isolated GFP+ ASM cells at several developmental timepoints (48-144hpf) and performed single cell RNA sequencing. Clustering analysis indicates subdifferentiation of ASM as early as 48hpf and subsequent diversification into corneal, epithelium/endothelium/stroma, or annular ligament (AL) lineages. Tracking individual clusters reveals common developmental pathways, up to 72hpf, for the AL and corneal endothelium/stroma, and distinct pathways for corneal epithelium starting at 48hpf. Spatiotemporal validation of over 80 genes found associated AS development demonstrates high degree of conservation with mammalian trabecular meshwork and corneal tissues. In addition, we characterize thirteen novel genes associated with AL and seven with corneal development. Overall, the data provide a molecular verification of the long-standing hypothesis that POM derived ASM give rise to AS tissues and highlight the high degree of conservation between zebrafish and mammals.
Evaluation of a structured screening assessment to detect patients with isolated REM Sleep Behavior Disorder
Authors: Seger Aline , Ophey Anja , Heitzmann Wiebke , Doppler Christopher E. J. , Lindner Marie-Sophie , Brune Corinna , Kickartz Johanna , Dafsari Haidar S. , Oertel Wolfgang H. , Fink Gereon R. , Jost Stefanie T. , Sommerauer Michael 
Published: October 2022

AbstractBackgroundIsolated rapid eye movement (REM) sleep behavior disorder (iRBD) cohorts have provided novel insights in the earliest neurodegenerative processes in α-synucleinopathies. Even though polysomnography remains the gold standard for diagnosis, an accurate questionnaire-based algorithm to identify eligible subjects could facilitate efficient recruitment in research.ObjectivesThis study aimed to optimize the identification of subjects with iRBD from the general population.MethodsBetween June 2020 and July 2021, we placed newspaper advertisements including the single-question screen for RBD (RBD1Q). Participants’ evaluations included a structured telephone screening consisting of the RBD screening questionnaire (RBDSQ) and additional sleep-related questionnaires. We examined anamnestic information predicting polysomnography-proven iRBD using logistic regressions and receiver operating characteristic curves.Results543 participants answered the advertisements and 185 subjects fulfilling in- and exclusion criteria were screened. Of these, 124 received polysomnography after expert selection and 78 (62.9%) were diagnosed with iRBD. Selected items of the RBDSQ, the Pittsburgh Sleep Quality Index, the STOP-Bang questionnaire, and age predicted iRBD with high accuracy in a multiple logistic regression model (area under the curve >80%). Comparing the algorithm to the sleep expert decision, 77 instead of 124 polysomnographies (62.1%) would have been carried out, while 63 (80.8%) of iRBD patients would have been identified. 32 of 46 (69.6%) unnecessary polysomnography examinations could have been avoided.ConclusionsOur proposed algorithm displayed high diagnostic accuracy for polysomnography-proven iRBD in a cost-effective manner and may be a convenient tool for application in research and clinical settings. External validation sets are warranted to prove its reliability.
A SEIRD+V Model for the Effect of Vaccination and Social Distancing on SARS-CoV-2 Infection and Mortality
Authors: Jin Alexander , Sharifi Husham 
Published: October 2022

AbstractWe present a deterministic, calibrated Susceptible-Exposed-Infected-Recovered-Dead + Vaccinated (SEIRD+V) model that simulates the spread and containment of COVID-19. We use the model to compare the effectiveness of vaccination vs. social distancing on death prevention and total and peak infection reduction. We find that vaccination drastically reduces total deaths from COVID-19, as well as total and peak infections with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). We find that social distancing plays a role in reducing total COVID-19 deaths, but its impact is less pronounced when vaccine efficacy and vaccination fraction are both high. Social distancing also plays a role in reducing total and peak infections, which is attenuated in the presence of vaccination. We employed a thresholding methodology to assess the requirements of vaccine efficacy and the vaccination fraction to limit total COVID-19 deaths and peak infections to a 5% threshold. Our thresholding results quantify the impact of social distancing on total COVID-19 deaths and peak infections and are significant in their ability to inform public health policy for future outbreaks, as well as for SARS-CoV-2 itself as it continues to mutate and alter its transmissibility.
Prevalence and associated factors of depression among junior healthcare professionals of public teaching hospitals of Bangladesh: An analytical cross-sectional study
Authors: Mamun Gazi Md. Salahuddin , Hassan Md. Zakiul , Khatun Aysha , Kabir Md. Farhad , Shaima Shamsun Nahar , Afrin Sadia , Seoty Nuhad Raisa 
Published: October 2022

AbstractDue to the significant number and its effects on quality of life, depression is becoming a major concern worldwide. Though its prevalence among junior healthcare professionals is also increasing day by day, still very few data are available regarding this. So, we’ve conducted a study to find out the prevalence and associated factors of depression among this vulnerable population. A total of 218 participants were enrolled from two public teaching tertiary-level hospitals in Dhaka, Bangladesh from October 2018 to April 2019. Data were collected by using a self-administered questionnaire including the WHO-5 well-being index. Prevalence of major depression was found at 17.9% and poor-emotional well-being was 25.2%. Factors associated independently with major depression were those thinking to be a doctor as the wrong decision (aRRR: 6.85, 95% CI: 1.40-33.45, p=0.017) and taking sedative or anxiolytic drugs (aRRR: 4.54, 95% CI: 1.50-13.73, p=0.007). On the other hand, doing physical exercise (aRRR: 0.32, 95% CI: 0.12-0.89, p=0.028) and being satisfied in their current job position (aRRR: 0.07, 95% CI: 0.02-0.29, p<0.001) had significantly less chance of being suffering of major depression. Suicidal and self-hurting ideation was also found among 23.4% of participants. If these modifiable factors can be addressed properly and by taking necessary steps against these simply identifiable factors, unwanted incidences can be prevented especially in low- and middle-income countries.What is already known on this topicDepression is common among healthcare professionals but is still neglected especially in low- and middle-income countries.What this study addsThinking of being a doctor as the wrong decision, taking sleeping pills, not doing physical exercise, and being not satisfied in their current job position are associated with depression among junior healthcare professionals. Suicidal and self-hurting ideation were also found high among the participants.How this study might affect research, practice or policyEarly identification of major depression by simple factors may help to initiate prompt strategies that will reduce the burden of depression among junior healthcare professionals and may improve the healthcare services of low- and middle-income countries.
Multiomic single-cell lineage tracing to dissect fate-specific gene regulatory programs
Authors: Jindal Kunal , Adil Mohd Tayyab , Yamaguchi Naoto , Yang Xue , Wang Helen C. , Kamimoto Kenji , Rivera-Gonzalez Guillermo C. , Morris Samantha A. 
Published: October 2022

AbstractComplex gene regulatory mechanisms underlie differentiation and reprogramming. Contemporary single-cell lineage tracing (scLT) methods use expressed, heritable DNA barcodes to combine cell lineage readout with single-cell transcriptomics enabling high-resolution analysis of cell states while preserving lineage relationships. However, reliance on transcriptional profiling limits their adaptation to an ever-expanding tool kit of multiomic single-cell assays. With CellTag-multi, we present a novel approach for independently profiling lineage barcodes with single-cell chromatin accessibility without relying on co-assay of transcriptional state, paving the way for truly multiomic lineage tracing. We validate CellTag-multi in mouse hematopoiesis, characterizing transcriptional and epigenomic lineage priming across progenitor cell populations. In direct reprogramming of fibroblasts to endoderm progenitors, we use CellTag-multi to comprehensively link early cell state with reprogramming outcomes, identifying core regulatory programs underlying on-target and off-target reprogramming. Further, we reveal the Transcription Factor (TF) Zfp281 as a novel regulator of reprogramming outcome, biasing cells towards an off-target mesenchymal fate via its regulation of TGF-β signaling. Together, these results establish CellTag-multi as a novel lineage tracing method compatible with multiple single-cell modalities and demonstrate its utility in revealing fate-specifying gene regulatory changes across diverse paradigms of differentiation and reprogramming.
Inferring time-aware models of cancer progression using Timed Hazard Networks
Authors: Chen Jian 
Published: October 2022

AbstractAnalysis of the sequential accumulation of genetic events, such as mutations and copy number alterations, is key to understanding disease dynamics and may provide insights into the design of targeted therapies. Oncogenetic graphical models are computational methods that use genetic event profiles from cross-sectional genomic data to infer the statistical dependencies between events and thereby deduce their temporal order of occurrence. Existing research focuses mainly on the development of graph structure learning algorithms. However, no algorithm explicitly links the oncogenetic graph with the temporal differences of samples in an analytic way. In this paper, we propose a novel statistical framework Timed Hazard Networks (TimedHN), that treat progression times as hidden variables and jointly infers oncogenetic graph and pseudo-time order of samples. We modeled the accumulation process as a continuous-time Markov chain and developed an efficient gradient computation algorithm for the optimization. Experiment results using synthetic data showed that our method outperforms the state-of-the-art in graph reconstruction. We highlighted the differences between TimedHN and competing methods on a luminal breast cancer dataset and illustrated the potential utility of the proposed method. Implementation and data are available athttps://github.com/puar-playground/TimedHN
Which curve are we flattening? The disproportionate impact of COVID-19 among economically marginalized communities in Ontario, Canada, was unchanged from wild-type to omicron
Authors: Ma Huiting , Chan Adrienne K. , Baral Stefan D. , Fahim Christine , Straus Sharon , Sander Beate , Mishra Sharmistha 
Published: October 2022

ABSTRACTEconomically marginalized communities have faced disproportionately higher risks for infection and death from COVID-19 across Canada. It was anticipated that health disparities would dissipate over time and during subsequent waves. We used person-level surveillance and neighbourhood-level income data to explore, using Lorenz curves and Gini coefficients, magnitude of inequalities in COVID-19 hospitalizations and deaths over five waves of COVID-19 in Ontario, Canada (population 14 million) between February 26, 2020 and February 28, 2022. We found that despite attempts at equity-informed policies alongside fluctuating levels of public health measures, inequalities in hospitalizations and deaths by income remained at levels observed during the first wave - prior to vaccination, discussion or implementation of equity-informed policies - and despite rising levels of hybrid immunity. There was no change in the magnitude of inequalities across all waves evaluated. Our findings indicate that interventions did not sufficiently address differential exposure risks amplified at the intersections of household crowding and size, workplace exposures, and systemic barriers to prevention and care (including access to therapeutics). Equity and effectiveness of programs are inherently linked and ongoing evaluation of both is central to inform the public health response to future waves of COVID-19 and other rapidly emergent pandemics.
A deep learning approach for metabolic rate prediction
Authors: Kontopoulos Ioannis , Valagkouti Christina-Ariadni , Kontopoulos Panagiotis 
Published: October 2022

AbstractThe sudden increase of wearable devices has led to the generation of an abundance of data. As a result, researchers can use such data to perform analyses and generate recommendations. A crucial factor in the research field of nutrition and dietetics is the accurate measurement of metabolic rate, as it can be used to estimate several other variables, e.g. calorie expenditure. Nonetheless, limited studies have been conducted to examine the use of machine learning models for metabolic rate prediction based on data generated from wearable devices. Therefore, in this paper, a neural network architecture is proposed, able to predict a subject’s metabolic rate exploiting only such data. Experimental results demonstrated that the proposed methodology can outperform conventional algorithms in prediction accuracy of real-world data. Furthermore, results indicated that the trivial time taken for the network to predict the metabolic rate makes it suitable for wearable devices deployment.
Exploring Digital Speech Biomarkers of Hypokinetic Dysarthria in a Multilingual Cohort
Authors: Kovac Daniel , Mekyska Jiri , Aharonson Vered , Harar Pavol , Galaz Zoltan , Rapcsak Steven , Orozco-Arroyave Juan Rafael , Brabenec Lubos , Rektorova Irena 
Published: October 2022

AbstractHypokinetic dysarthria, a motor speech disorder characterized by reduced movement and control in the speech-related muscles, is mostly associated with Parkinson’s disease. Acoustic speech features thus offer the potential for early digital biomarkers to diagnose and monitor the progression of this disease. However, the influence of language on the successful classification of healthy and dysarthric speech remains crucial. This paper explores the analysis of acoustic speech features, both established and newly proposed, in a multilingual context to support the diagnosis of PD. The study aims to identify language-independent and highly discriminative digital speech biomarkers using statistical analysis and machine learning techniques. The study analyzes thirty-three acoustic features extracted from Czech, American, Israeli, Columbian, and Italian PD patients, as well as healthy controls. The analysis employs correlation and statistical tests, descriptive statistics, and the XGBoost classifier. Feature importances and Shapley values are used to provide explanations for the classification results. The study reveals that the most discriminative features, with reduced language dependence, are those measuring the prominence of the second formant, monopitch, and the frequency of pauses during text reading. Classification accuracies range from 67 % to 85 %, depending on the language. This paper introduces the concept of language robustness as a desirable quality in digital speech biomarkers, ensuring consistent behaviour across languages. By leveraging this concept and employing additional metrics, the study proposes several language-independent digital speech biomarkers with high discrimination power for diagnosing PD.
BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures
Authors: Geistlinger Ludwig , Mirzayi Chloe , Zohra Fatima , Azhar Rimsha , Elsafoury Shaimaa , Grieve Claire , Wokaty Jennifer , Gamboa-Tuz Samuel David , Sengupta Pratyay , Hecht Isaac , Ravikrishnan Aarthi , Gonçalves Rafael , Franzosa Eric , Raman Karthik , Carey Vincent , Dowd Jennifer B. , Jones Heidi E. , Davis Sean , Segata Nicola , Huttenhower Curtis , Waldron Levi 
Published: October 2022

AbstractThe literature of human and other host-associated microbiome studies is expanding rapidly, but systematic comparisons among published results of host-associated microbiome signatures of differential abundance remain difficult. We present BugSigDB, a community-editable database of manually curated microbial signatures from published differential abundance studies, accompanied by information on study geography, health outcomes, host body site, and experimental, epidemiological, and statistical methods using controlled vocabulary. The initial release of the database contains >2,500 manually curated signatures from >600 published studies on three host species, enabling high-throughput analysis of signature similarity, taxon enrichment, co-occurrence and co-exclusion, and consensus signatures. These data allow assessment of microbiome differential abundance within and across experimental conditions, environments, or body sites. Database-wide analysis reveals experimental conditions with the highest level of consistency in signatures reported by independent studies and identifies commonalities among disease-associated signatures including frequent introgression of oral pathobionts into the gut.
Oncogenic context shapes the fitness landscape of tumor suppression
Authors: Blair Lily M. , Juan Joseph M. , Sebastian Lafia , Tran Vy B. , Nie Wensheng , Wall Gregory D. , Gerceker Mehmet , Lai Ian K. , Apilado Edwin A. , Grenot Gabriel , Amar David , Foggetti Giorgia , Do Carmo Mariana , Ugur Zeynep , Deng Debbie , Chenchik Alex , Paz Zafra Maria , Dow Lukas E. , Politi Katerina , MacQuitty Jonathan J. , Petrov Dmitri A. , Winslow Monte M. , Rosen Michael J. , Winters Ian P. 
Published: October 2022

ABSTRACTTumors acquire alterations in oncogenes and tumor suppressor genes in an adaptive walk through the fitness landscape of tumorigenesis. However, the features of this landscape remain poorly understood and cannot be revealed by human cancer genotyping alone. Here, we use a multiplexed, autochthonous mouse platform to model and quantify the initiation and growth of more than one hundred genotypes of lung tumors across four oncogenic contexts: KRAS G12D, KRAS G12C, BRAF V600E, and EGFR L858R. The resulting fitness landscape is rugged (the effect of tumor suppressor inactivation often switches between beneficial and deleterious depending on the oncogenic context), shows no evidence of diminishing-returns epistasis within variants of the same oncogene, and is inconsistent with expectations of a simple linear signaling relationship among these three oncogenes. Our findings suggest that tumor suppressor effects are strongly context-specific, which limits the set of evolutionary paths that can be taken through the fitness landscape.
A scalable, reproducible and open-source pipeline for morphologically profiling image cytometry data
Authors: Lippeveld Maxim , Peralta Daniel , Filby Andrew , Saeys Yvan 
Published: October 2022

AbstractDue to high resolution and throughput of modern image cytometry platforms, morphologically profiling generated datasets poses a significant computational challenge. Here, we present Scalable Cytometry Image Processing (SCIP), an image processing software aimed at running on distributed high performance computing infrastructure. SCIP is scalable, flexible, open-source and enables reproducible image processing. It performs projection, illumination correction, segmentation, background masking and extensive morphological profiling on various imaging types.We showcase SCIP’s capabilities on three large-scale image cytometry datasets. First, we process an imaging flow cytometry (IFC) dataset of human white blood cells and show how the obtained features are used to classify cells into 8 cell types based on bright- and darkfield imagery. Secondly, we process an automated microscopy dataset of human white blood cells to divide them into cell types in an unsupervised manner. Finally, a high-content screening dataset of breast cancer cells is processed to predict the mechanism-of-action of a large set of compound treatments.The software can be installed from the PyPi repository. Its source code is available athttps://github.com/ScalableCytometryImageProcessing/SCIPunder the GNU General Public License version 3. It has been tested on Unix operating systems. Issues with the software can be submitted athttps://github.com/ScalableCytometryImageProcessing/SCIP/issues.1Author SummaryCytometry is a field of biology that studies cells by measuring their characteristics. In image cytometry, this is done by acquiring images of cells. In order to gain biological insight from a set of images, an extensive amount of measurements are derived from them describing the cells they contain. These measurements include, for instance, a cell’s area, diameter, or the average brightness of the cell image. These measurements can then be analyzed using automated software tools to understand, for example, how cells respond to drug treatments, or how cells differ between a healthy and a diseased person. In this work, we present a novel software tool that is able to efficiently compute image measurements on large datasets of images. We do this by harnessing the power of high performance computing infrastructure. By enabling image cytometry researchers to make use of more computational power, they can more efficiently process complex and large datasets, paving the way to novel, fascinating biological discoveries.
A frameshift inYersinia pestis rcsDleads to expression of a small HPt variant that alters canonical Rcs signalling to preserve flea-mammal plague transmission cycles
Authors: Guo Xiao-Peng , Yan Hai-Qin , Yang Wenhui , Yin Zhe , Vadyvaloo Viveka , Zhou Dongsheng , Sun Yi-Cheng 
Published: October 2022

AbstractMultiple genetic changes in the enteric pathogenYersinia pseudotuberculosishave driven the emergence ofYesinia pestis, the arthropod-borne, etiological agent of plague. These include developing the capacity for biofilm-dependent blockage of the flea foregut to enable transmission by flea bite. Previously, we showed that pseudogenisation ofrcsA, encoding a component of the Rcs signalling pathway, is an important evolutionary step facilitatingY. pestisflea-borne transmission. Additionally,rcsD, another important gene in the Rcs system, harbours a frameshift mutation. Here, we demonstrated that thisrcsDmutation resulted in predominant production of a small protein composing the C-terminal RcsD histidine-phosphotransferase domain (designated RcsD-Hpt) and low levels of full-length RcsD. Genetic analysis revealed that thercsDframeshift mutation followed the emergence ofrcsApseudogenisation. It further altered the canonical Rcs phosphorylation signal cascade, fine-tuning biofilm production to be conducive with retention of thepgmlocus in modern lineages ofY. pestis. Taken together, our findings suggest that a frameshift mutation inrcsD, is an important evolutionary step that fine-tuned biofilm production to ensure perpetuation of flea-mammal plague transmission cycles.