bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detection of COVID-19 in smartphone-based breathing recordings using CNN-BiLSTM: a pre-screening deep learning tool
Authors: Alkhodari Mohanad , Khandoker Ahsan H. 
Published: September 2021

AbstractThis study was sought to investigate the feasibility of using smartphone-based breathing sounds within a deep learning framework to discriminate between COVID-19, including asymptomatic, and healthy subjects. A total of 480 breathing sounds (240 shallow and 240 deep) were obtained from a publicly available database named Coswara. These sounds were recorded by 120 COVID-19 and 120 healthy subjects via a smartphone microphone through a website application. A deep learning framework was proposed herein the relies on hand-crafted features extracted from the original recordings and from the mel-frequency cepstral coefficients (MFCC) as well as deep-activated features learned by a combination of convolutional neural network and bi-directional long short-term memory units (CNN-BiLSTM). Analysis of the normal distribution of the combined MFCC values showed that COVID-19 subjects tended to have a distribution that is skewed more towards the right side of the zero mean (shallow: 0.59±1.74, deep: 0.65±4.35). In addition, the proposed deep learning approach had an overall discrimination accuracy of 94.58% and 92.08% using shallow and deep recordings, respectively. Furthermore, it detected COVID-19 subjects successfully with a maximum sensitivity of 94.21%, specificity of 94.96%, and area under the receiver operating characteristic (AUROC) curves of 0.90. Among the 120 COVID-19 participants, asymptomatic subjects (18 subjects) were successfully detected with 100.00% accuracy using shallow recordings and 88.89% using deep recordings. This study paves the way towards utilizing smartphone-based breathing sounds for the purpose of COVID-19 detection. The observations found in this study were promising to suggest deep learning and smartphone-based breathing sounds as an effective pre-screening tool for COVID-19 alongside the current reverse-transcription polymerase chain reaction (RT-PCR) assay. It can be considered as an early, rapid, easily distributed, time-efficient, and almost no-cost diagnosis technique complying with social distancing restrictions during COVID-19 pandemic.
The Delta Variant Had Negligible Impact on COVID-19 Vaccine Effectiveness in the USA
Authors: Blaiszik Ben , Graziani Carlo , Olds James L. , Foster Ian 
Published: September 2021

The SARS-CoV-2 Delta variant (B.1.617.2) was initially identified in India in December 2020. Due to its high transmissibility, its prevalence in the U.S.A. grew from a near-zero baseline in early May 2021 to nearly 100% by late August 2021, according to CDC tracking. We accessed openly available data sources from the public health authorities of seven U.S. states, five U.S. counties, and the District of Columbia on RT-PCR COVID-19 tests split by the COVID-19 vaccination status of individuals tested during this period. Together, these time series enable estimation and tracking of COVID-19 vaccine effectiveness (VE∗) (against RT-PCR diagnosed infection) concurrently with the growth of Delta variant prevalence in those locations. Our analyses reveal that in each locality the VE∗ for the combined set of all three US vaccines remained relatively stable and quite well-performing, despite the dramatic concurrent rise of Delta variant prevalence. We conclude that the Delta variant does not significantly evade vaccine-induced immunity. The variations in our measured VE∗ appear to be driven by demographic factors affecting the composition of the vaccinated cohorts, particularly as pertains to age distribution. We report that the measured VE∗, aggregated across the collected sites, began at a value of about 0.9 in mid-May, declined to about 0.76 by mid-July, and recovered to about 0.9 by mid-September.SummaryWe estimated local COVID-19 vaccine effectiveness using RT-PCR COVID-19 test data broken out by vaccination status from select localities in the U.S.A. between 15 May and 15 September 2021 while the SARS-CoV-2 Delta variant (B.1.617.2) was ascending from essentially zero prevalence to total dominance of the genome, and showed that the rise of the Delta variant had negligible effect on vaccine effectiveness.
Shared decision making and specific informed consent in patients with aortic aneurysms
Authors: Silva Cunha Marcela Juliano , Teivelis Marcelo Passos , de Almeida Mendes Cynthia , Annicchino Baptistella Conrado Dias Pacheco , Henry Sant’Anna Pedro Vasconcelos , Wolosker Nelson 
Published: September 2021

AbstractIntroductionStudies show that vascular surgery patients have the desire to participate more actively in their treatment, but that they find it difficult to express themselves. Patients prefer to know all the therapeutic options available, not just those that the surgeon considers appropriate. With this knowledge, patients can begin to choose the most appropriate therapeutic modality for themselves, also becoming responsible for the therapeutic decision. Therefore, the objective of this paper was to analyze the refusal rate of elective aortic aneurysm surgery in asymptomatic patients after the presentation of a detailed IC form followed by a meeting where the patient and his or her family could analyze each item;MethodsData from 49 patients who had aneurysms and were offered surgical treatment were retrospectively collected and analyzed.ResultsAfter reading the IC and the described meeting, among the 49 patients, 13 (26.5%) refused surgery. We observed that patients who refused surgery had statistically smaller aneurysms than patients who accepted surgery (9% versus 26%).ConclusionOne-quarter of patients who were indicated for elective surgical correction of aortic aneurysms rejected surgery after SDM, which consisted of the presentation of an IC form followed by a clarification meeting for the patient and his or her family to analyze each item. The only factor that significantly influenced a rejection of the procedure was the size of the aneurysm, so patients who rejected surgery had smaller aneurysms than those who accepted surgery.
Qatar Genome: Insights on Genomics from the Middle East
Authors: Mbarek Hamdi , Gandhi Geethanjali Devadoss , Selvaraj Senthil , Al-Muftah Wadha , Badji Radja , Al-Sarraj Yasser , Saad Chadi , Darwish Dima , Alvi Muhammad , Fadl Tasnim , Yasin Heba , Alkuwari Fatima , Razali Rozaimi , Aamer Waleed , Abbaszadeh Fatemeh , Ahmed Ikhlak , Mokrab Younes , Suhre Karsten , Albagha Omar , Fakhro Khalid , Badii Ramin , Ismail Said I. , Althani Asma ,   for the Qatar Genome Program Research Consortium
Published: September 2021

AbstractDespite recent biomedical breakthroughs and large genomic studies growing momentum, the Middle Eastern population, home to over 400 million people, is under-represented in the human genome variation databases. Here we describe insights from phase 1 of the Qatar Genome Program which whole genome sequenced 6,045 individuals from Qatar. We identified more than 88 million variants of which 24 million are novel and 23 million are singletons. Consistent with the high consanguinity and founder effects in the region, we found that several rare deleterious variants were more common in the Qatari population while others seem to provide protection against diseases and have shaped the genetic architecture of adaptive phenotypes. Insights into the genetic structure of the Qatari population revealed five non-admixed subgroups. Based on sequence data, we also reported the heritability and genetic marker associations for 45 clinical traits. These results highlight the value of our data as a resource to advance genetic studies in the Arab and neighbouring Middle Eastern populations and will significantly boost the current efforts to improve our understanding of global patterns of human variations, human history and genetic contributions to health and diseases in diverse populations.
Evaluation of sleep pattern due to stress in undergraduate medical students and its impact on health and academic performance: A cross-sectional study from tertiary health center of Central India
Authors: Gandhi Kusum , Godaria Yashvardhan , Revadi G 
Published: September 2021

AbstractBackgroundGood quality sleep is essential for good health and well-being. Medical students are at no exception to this and are prone to greater risk for sleep deprivation. The major reason being challenges to maintain a high level of academic achievement and constant thirst to acquire new learning skills and knowledge. However, in this process they are circumstanced to various levels of stress that might cause potential damage to their cognitive functioning and mental exhaustion to a certain extent.ObjectivesThus, our study objectives were to evaluate the sleep pattern in first- and second-year medical students. To understand how the stress levels and academic performance are related to sleep pattern and to explore the copying strategies of stress in our study participants.MethodologyThis cross-sectional study was conducted using a self-reported, web-based, questionnaire that included questions on sleep quality and deprivation through Pittsburgh sleep quality index. All the eligible students of first and second year who were part of a premiere teaching hospital during February and March 2021 were included. Data was analysed using IBM SPSS version 24.ResultsOut of 180 participants, 91(50.55%) had their initiation of sleeping time from 12-2 am and also, majority of students 112 (62.22%) had a sleep duration of six to eight hours. However, 119 (66.1%) students had self-reported change in sleeping pattern which was found to be significantly associated with relatively greater number of academic factors as compared to social factors. Most of the students scored between 50-60% score in their four assessments amongst which their first assessment was significantly associated with change in sleep pattern (P 0.040). Also, these individual assessment score was found to significantly affect their duration of sleep. The common coping strategies adopted by students under study were talking to family members/ friends, music/ book reading (hobby).ConclusionMajority of students in our study had reported change in sleeping pattern. Also, association between stress factors and change in sleeping pattern were observed with academic stress factors proving to be more significantly associated than social stress factors. The academic performance of students was also found to be associated with change in sleeping pattern and duration of sleep.
The apportionment of citations: A scientometric analysis of Lewontin, 1972
Authors: Carlson Jedidiah , Harris Kelley 
Published: September 2021

Abstract“The Apportionment of Human Diversity” (1972) is the most highly cited research article published by geneticist Richard Lewontin in his career. This study’s primary result—that most genetic diversity in humans can be accounted for by within-population differences, not between-population differences—along with Lewontin’s outspoken, politically-charged interpretations thereof, has become foundational to the scientific and cultural discourse pertaining to human genetic variation. The article has an unusual bibliometric trajectory in that it is much more salient in the bibliographic record today compared to the first 20 years after its publication. Here, we show how the paper’s fame was shaped by four factors: 1) citations in influential publications across several disciplines; 2) Lewontin’s own popular books and media appearances; 3) the renaissance of population genetics research of the early 1990s; and 4) the serendipitous collision of scientific progress, influential books/papers, and heated controversies in the year 1994. We conclude with an analysis of Twitter data to characterize the communities and conversations that continue to keep this study at the epicenter of discussions about race and genetics, prompting new challenges for scientists who have inherited Lewontin’s legacy.
deCS: A Tool for Systematic Cell Type Annotations of Single-cell RNA Sequencing Data among Human Tissues
Authors: Pei Guangsheng , Yan Fangfang , Simon Lukas M. , Dai Yulin , Jia Peilin , Zhao Zhongming 
Published: September 2021

AbstractSingle-cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex and dynamic cellular mechanisms. However, cell-type annotation remains a main challenge as it largely relies on a priori knowledge and manual curation, which is cumbersome and less accurate. The increasing number of scRNA-seq data sets, as well as numerous published genetic studies, motivated us to build a comprehensive human cell type reference atlas. Here, we present deCS (decoding Cell type-Specificity), an automatic cell type annotation method augmented by a comprehensive collection of human cell type expression profiles and marker genes. We used deCS to annotate scRNA-seq data from various tissue types and systematically evaluated the annotation accuracy under different conditions, including reference panels, sequencing depth and feature selection strategies. Our results demonstrated that expanding the references is critical for improving annotation accuracy. Compared to many existing state-of-the-art annotation tools, deCS significantly reduced computation time and increased accuracy. deCS can be integrated into the standard scRNA-seq analytical pipeline to enhance cell type annotation. Finally, we demonstrated the broad utility of deCS to identify trait-cell type associations in 51 human complex traits, providing deeper insights into the cellular mechanisms of disease pathogenesis. All documents, including source code, user manual, demo data, and tutorials, are freely available at https://github.com/bsml320/deCS.
Mathematical modelling of activation-induced heterogeneity reveals cell state transitions underpinning macrophage responses to LPS
Authors: Dey S. , Boucher D. , Pitchford J. W. , Lagos D. 
Published: September 2021

AbstractDespite extensive work on macrophage heterogeneity, the mechanisms driving activation induced heterogeneity (AIH) in macrophages remain poorly understood. Here, we use two in vitro cellular models of LPS-induced tolerance (bone marrow-derived macrophages or BMDMs and RAW 264.7 cells), single-cell protein measurements, and mathematical modelling to explore how AIH underpins primary and secondary responses to LPS. We measure expression of TNF, IL-6, pro-IL-1β, and NOS2 and demonstrate that macrophage community AIH is dependent on LPS dose. We show that altered AIH kinetics in macrophages responding to a second LPS challenge underpin hypo-responsiveness to LPS. These empirical data can be explained by a mathematical 3-state model including negative, positive, and non-responsive states (NRS), but they are also compatible with a 4-state model that includes distinct reversibly NRS and non-responsive permanently states (NRPS). Our mathematical model, termed NoRM (Non-Responsive Macrophage) model identifies similarities and differences between BMDM and RAW 264.7 cell responses. In both cell types, transition rates between states in the NoRM model are distinct for each of the tested proteins and, crucially, macrophage hypo-responsiveness is underpinned by changes in transition rates to and from NRS. Overall, our findings provide support for a critical role for phenotypically negative macrophage populations as an active component of AIH and primary and secondary responses to LPS. This reveals unappreciated aspects of cellular ecology and community dynamics associated with LPS-driven training of macrophages.
Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings
Authors: Millán Ana. P. , van Straaten Elisabeth C.W. , Stam Cornelis J. , Nissen Ida A. , Idema Sander , Baayen Johannes C. , Van Mieghem Piet , Hillebrand Arjan 
Published: September 2021

AbstractBackgroundEpilepsy surgery is the treatment of choice for drug-resistant epilepsy patients. However, seizure-freedom is currently achieved in only 2/3 of the patients after surgery. In this study we have developed an individualized computational model based on functional brain networks to explore seizure propagation and the efficacy of different virtual resections. Eventually, the goal is to obtain individualized models to optimize resection strategy and outcome.MethodsWe have modelled seizure propagation as an epidemic process using the susceptible-infected (SI) model on individual functional networks derived from presurgical MEG. We included 10 patients who had received epilepsy surgery and for whom the surgery outcome at least one year after surgery was known. The model parameters were tuned in order to reproduce the patient-specific seizure propagation patterns as recorded with invasive EEG. We defined a personalized search algorithm that combined structural and dynamical information to find resections that maximally decreased seizure propagation for a given resection size. The optimal resection for each patient was defined as the smallest resection leading to at least a 90% reduction in seizure propagation.ResultsThe individualized model reproduced the basic aspects of seizure propagation for 9 out of 10 patients when using the resection area as the origin of epidemic spreading, and for 10 out of 10 patients with an alternative definition of the seed region. We found that, for 7 patients, the optimal resection was smaller than the resection area, and for 4 patients we also found that a resection smaller than the resection area could lead to a 100% decrease in propagation. Moreover, for two cases these alternative resections included nodes outside the resection area.ConclusionEpidemic spreading models fitted with patient specific data can capture the fundamental aspects of clinically observed seizure propagation, and can be used to test virtual resectionsin silico. Combined with optimization algorithms, smaller or alternative resection strategies, that are individually targeted for each patient, can be determined with the ultimate goal to improve surgery outcome.
Mind the gap: Mapping Variation between National and Local Clinical Practice Guidelines for Acute Paediatric Asthma from the United Kingdom and the Netherlands
Authors: Koldeweij Charlotte , Clarke Jonathan , Rodriguez Gonzalvez Carmen , Nijman Joppe , Sinha Ruchi , Maconochie Ian , Appelbaum Nicholas 
Published: September 2021

AbstractBackgroundClinical practice guidelines (CPGs) aim to standardize clinical care. Increasingly, hospitals rely on locally produced guidelines alongside national guidance. This study examines variation between national and local CPGs, using the example of acute paediatric asthma guidance from the United Kingdom and the Netherlands.MethodsFifteen British and Dutch local CPGs were collected with the matching national guidance for the management of acute asthma in children under 18 years old. The drug sequences, routes and methods of administration recommended for patients with severe asthma and the tone of recommendation across both types of CPGs were schematically represented. Deviations from national guidance were measured. Variation in recommended doses of intravenous salbutamol was examined.ResultsBritish and Dutch national CPGs differed in the recommended drug choices, sequences, routes and methods of administration for severe asthma. Dutch national guidance was more rigidly defined. Local British CPGs diverged from national guidance for 23% of their recommended interventions compared to 8% for Dutch local CPGs. Five British local guidelines and two Dutch local guidelines differed from national guidance for multiple treatment steps. Variation in second-line recommendations was greater than for first-line recommendations across local CPGs from both countries. Recommended starting doses for salbutamol infusions varied by more than tenfold.ConclusionsLocal CPGs for the management of severe acute paediatric asthma featured substantial variation and frequently diverged from national guidance. Although limited to one condition, this study suggests that unmeasured variation across local CPGs may contribute to variation of care more broadly, with possible effects on healthcare quality.
Can we distinguish the roles of demographic and temporal changes in the incidence and prevalence of musculoskeletal disorders? A systematic search and review
Authors: Bouziri Hanifa , Descatha Alexis , Roquelaure Yves , Dab William , Jean Kévin 
Published: September 2021

ABSTRACTBackgroundMusculoskeletal disorders (MSDs) represent a major public health issue, affecting more than 40 million European workers in 2017. The overall ageing of the working population is expected to increase the burden of disease, but temporal changes in exposures or diagnosis may also drive global trends in MSDs. We, therefore, sought to review and summarize evidence describing the role of demographic and temporal changes in the occurrence of MSDs.MethodsWe conducted a systematic search and review of articles reporting temporal trends in MSDs in the general working-age population. Only articles controlling for age in the analysis were included. The risk of bias was assessed. The main indicators extracted were age-controlled time trends in MSD incidence or prevalence.ResultsAmong 2,680 articles, 16 fulfilled the inclusion criteria, representing 23 results according to the indicators extracted. No study was found with a high risk of bias. Twelve results reported time trends in prevalence and 11 in incidence. After controlling for age, the reported temporal trends varied, mostly between non-monotonic changes (n=12/23) and increases (n=10/23); one article also highlighted an increase among women and non-monotonic changes among men (n = 1/23). Several factors other than ageing were suggested to explain temporal trends in MSDs, main trends in obesity, changing occupational exposures, and cultural factors regarding pain tolerance.ConclusionThis review shows that different kinds of factors in addition to ageing may contribute to varying or increasing trends in MSDs. This review also highlighted the scarcity of evidence regarding time trends in the burden of MSDs and their underlying causes.
Quantifying Epistemic and Aleatoric Uncertainty in 3D U-Net Segmentation
Authors: Jones Craig K , Wang Guoqing , Yedavalli Vivek , Sair Haris 
Published: September 2021

AbstractThis work shows a derivation of a multinomial probability function and quantitative measures of the data and epistemic uncertainty as direct output of a 3D U-Net segmentation network. A set of T1 brain MRI images were downloaded from the Connectome Project and segmented using FMRIB’s FAST algorithm to be used as ground truth. A 3D U-Net neural network was trained with sample sizes of 200, 500, and 898 T1 brain images using a loss function defined as the negative logarithm of the likelihood based on a derivation of the definition of the multinomial probability function. From this definition, the epistemic (model) and aleatoric (data) uncertainty equations were derived and used to quantify maps of the uncertainty in data prediction. The epistemic and aleatoric uncertainty decreased based on the increasing number of training data used to train the neural network. The neural network trained with 898 volumes resulted in uncertainty maps that were high primarily in the tissue boundary regions. The uncertainty was averaged over all test data (connectome and tumor separately) and the epistemic uncertainty showed a decreasing trend, as expected, with increasing numbers of data used to train the model. The aleatoric uncertainty showed a similar trend, but it was less obvious, which was also expected as the aleatoric uncertainty is not expected to be as dependent on the number of training data. The derived data and epistemic uncertainty equations from a multinomial probability distribution are applicable for all 2D and 3D neural networks.