bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigating spatial variability in COVID-19 pandemic severity across 19 geographic areas, Spain, 2020
Authors: Dahal Sushma , Mizumoto Kenji , Rothenberg Richard , Chowell Gerardo 
Published: April 2020

AbstractIntroductionSpain has been disproportionately affected by the COVID-19 pandemic, ranking fifth in the world in terms of both total cases and total deaths due to COVID-19 as of May 20, 2020. Here we derived estimates of pandemic severity and assessed its relationship with socio-demographic and healthcare factors.MethodsWe retrieved the daily cumulative numbers of laboratory-confirmed COVID-19 cases and deaths in Spain from February 20, 2020 to May 20, 2020. We used statistical methods to estimate the time-delay adjusted case fatality risk (aCFR) for 17 autonomous communities and 2 autonomous cities of Spain. We then assessed how transmission and sociodemographic variables were associated with the aCFR across areas using multivariate regression analysis.ResultsWe estimated the highest aCFR for Madrid (25.9%) and the average aCFR in Spain (18.2%). Our multivariate regression analysis revealed three statistically significant predictor variables: population size, population density, and the unemployment rate.ConclusionsThe estimated aCFR for 10 autonomous communities/cities in Spain are significantly higher than those previously estimated for other geographic regions including China and Korea. Our results suggest that public health interventions focused on densely populated areas and low socioeconomic groups can ameliorate the mortality burden of the COVID-19 pandemic in Spain.
The Longevity-Frailty Hypothesis: Evidence from COVID-19 Death Rates in Europe
Authors: Zahran Sammy , Altringer Levi , Prasad Ashok 
Published: April 2020

AbstractBy the end of spring (May 31st), the COVID-19 death rate was remarkably unevenly distributed across the countries Europe. While the risk of COVID-19 mortality is known to increase with age, age-specific COVID-19 death rates across Europe were similarly aberrantly distributed, implying that differences in age structure is an unlikely source of European variation in COVID-19 mortality. To explain these mortality distributions, we present a simple model where more favorable survival environments promote longevity and the accumulation of health frailty among the elderly while less favorable survival environments induce a mortality selection process that results in lower health frailty. Because the age-related conditions of frailty render the elderly less resistant to SARS-CoV-2, pre-existing survival environments may be non-obviously positively related to the COVID-19 death rate. To quantify thesurvival environmentparameter of our model, we collected historic cohort- and period-based age-specific probabilities of death across Europe. We find strong positive relationships between survival indicators and COVID-19 death rates across Europe, a result that is robust to statistical control for the capacity of a healthcare system to treat and survive infected persons, the timing and stringency of non-pharmaceutical interventions, and the volume of inbound international travelers, among other factors. To address possible concerns over reporting heterogeneity across countries, we show that results are robust to the substitution of our response variable for a measure of cumulative excess mortality. Consistent with the intuition of our model, we also show a strong negative association between age-specific COVID-19 death rates and pre-existing all-cause age-specific mortality rates for a subset of European countries. Overall, results support the notion that variation in pre-existing frailty, resulting from heterogeneous survival environments, partially caused striking differences in COVID-19 death during the first wave of the pandemic.
Using Feedback on Symptomatic Infections to Contain the Coronavirus Epidemic: Insight from a SPIR Model
Authors: Nikolaou Michael 
Published: April 2020

AbstractA study is presented on the use of real-time information about symptomatic infectious individuals to adjust restrictions of human contacts at two basic levels, the stricter being on the symptomatic infectious group. Explicit analytical formulas as well as numerical results are presented to rapidly elucidate what-if questions on averting resurgence of the coronavirus epidemic after the first wave wanes. Implementation of related ideas would rely on a mix of several factors, including personal initiative and sophisticated technology for monitoring and testing. For robust decision making on the subject, detailed multidisciplinary studies remain indispensable.
CoroNet: A Deep Network Architecture for Semi-Supervised Task-Based Identification of COVID-19 from Chest X-ray Images
Authors: Khobahi Shahin , Agarwal Chirag , Soltanalian Mojtaba 
Published: April 2020

AbstractIn late 2019, a new Coronavirus disease, referred to as Corona virus disease 2019 (COVID-19), emerged in Wuhan city, Hubei, China, and resulted in a global pandemic—claiming a large number of lives and affecting billions all around the world. The current global standard used in diagnosis of COVID-19 in suspected cases is the real-time polymerase chain reaction (RT-PCR) test. Although the RT-PCR remains the standard reference for diagnosis purposes, it is a time-consuming and expensive test, and moreover, it usually suffers from high rates of false-negatives. Several early works have reported that the sensitivity of the chest Computed Tomography (CT) and the chest X-ray imaging are noticeably greater than that of the RT-PCR test at the initial representations of the disease, making them great candidates for developing new and sophisticated methodologies for analysis and classification of COVID-19 cases. In this paper, we establish the use of a rapid, non-invasive and cost-effective X-ray-based method as a key diagnosis and screening tool for COVID-19 at early and intermediate stages of the disease. To this end, we develop a novel and sophisticated deep learning-based signal and image processing technique as well as classification methodology for analyzing X-ray images specific to COVID-19 disease. Specifically, we consider a semi-supervised learning methodology based on AutoEncoders to first extract the infected legions in chest X-ray manifestation of COVID-19 and other Pneumonia-like diseases (as well as healthy cases). Then, we utilize this highly-tailored deep architecture to extract the relevant features specific to each class (i.e., healthy, non-COVID pneumonia, and COVID-19) and train a powerful yet efficient classifier to perform the task of automatic diagnosis. Furthermore, the semi-supervised nature of the proposed framework enables us to efficiently exploit the limited available dataset on COVID-19 while exploiting the vast amount of available X-ray dataset for healthy and non-COVID classes. Moreover, such a semi-supervised approach does not require an expert-annotated lesion area for each class. Our numerical investigations demonstrate that the proposed framework outperforms the state-of-the-art methods for COVID-19 identification while employing approximately ten times fewer training parameters as compared to other existing methodologies for classification of the COVID-19 from X-ray images (facilitating efficient training in a limited data regime). We further develop explainable artificial intelligence tools that can explain the diagnosis by using attribution maps while providing an indispensable tool for the radiologist in triage state. We have made the codes of our proposed framework publicly available to the research and healthcare community1.
Forecasting Hospital Staff Availability During The COVID-19 Epidemic
Authors: Schooling Chloe N. , Gyenge Norbert , Kadirkamanathan Visakan , Alix James J.P. 
Published: April 2020

AbstractThe COVID-19 pandemic poses two challenges to healthcare providers. Firstly, a high number of patients require hospital admission. Second, a high number of healthcare staff are either falling ill with the infection, or self-isolating. This poses significant problems for the staffing of busy hospital departments. We have created a simple model which allows users to stress test their rota. The model provides plots of staff availability over time using either a constant infection rate, or a changing infection rate fitted to population-based infection curves. It allows users to gauge the extent and timing of dips in staff availability. The basic constant infection rate model is available within an on-line web application (https://covid19.shef.ac.uk). As for any model, our work is imperfect. However, it allows a range of infection rates to be simulated quickly across different work patterns. We hope it will be useful to those planning staff deployment and will stimulate debate on the most effective patterns of work during the COVID-19 epidemic.
Amyloid precursor protein causes fusion of promyelocytic leukemia nuclear bodies in human hippocampal areas with high plaque load
Authors: Marks David , Heinen Natalie , Bachmann Lisa , Meermeyer Sophia , Werner Michelle , Gallego Lucia , Nolte Stephanie , Hemmerich Peter , Bader Verian , Winklhofer Konstanze , Schröder Elisabeth , Knauer Shirley K. , Müller Thorsten 
Published: April 2020

AbstractThe amyloid precursor protein (APP) is a type I transmembrane protein with unknown physiological function but potential impact in neurodegeneration. The current study demonstrates that APP signals to the nucleus causing the generation of aggregates comprising its adapter protein FE65 and the tumour suppressor proteins p53 and PML. The PML nuclear body generation, known to be of relevance in virus defence and cell division, is induced and fusion occurs over time depending on APP signalling. We further show that the nuclear aggregates of APP C-terminal (APP-CT) fragments together with PML and FE65 are present in the aged human brain but not in cerebral organoids differentiated from iPS cells. Notably, human Alzheimer’s disease brains reveal a highly significant loss of these nuclear aggregates in areas with high plaque load compared to plaque-free areas of the same individual. Based on these results we conclude that APP-CT signalling to the nucleus takes place in the aged human brain and is potentially involved in the pathophysiology of AD. Taken the current knowledge on PML bodies into account, we hypothesize a new role for APP as a twofold virus response protein. The APP-dependent defence strategy includes Aß-virus interaction at the extracellular matrix and APP-CT driven PML aggregation in the nucleus to encapsulate the viral nucleic acid. This defence strategy preferentially occurs in high-plaque regions of the human brain and overstimulation of this pathway results in a pyrrhic victory.
ASolanum lycopersicoidesreference genome facilitates biological discovery in tomato
Authors: Powell Adrian F. , Courtney Lance E. , Schmidt Maximilian H.-W. , Feder Ari , Vogel Alexander , Xu Yimin , Lyon David A. , Dumschott Kathryn , McHale Marcus , Sulpice Ronan , Bao Kan , Duhan Asha , Hallab Asis , Denton Alisandra K. , Mueller Lukas A. , Alseekh Saleh , Lie Jie , Martin Cathie , Fernie Alisdair R. , Hind Sarah R. , Martin Gregory B. , Fei Zhangjun , Giovannoni James J. , Strickler Susan R. , Usadel Björn 
Published: April 2020

SummaryWild relatives of tomato are a valuable source of natural variation in tomato breeding, as many can be hybridized to the cultivated species (Solanum lycopersicum). Several, includingSolanum lycopersicoides, have been crossed toS. lycopersicumfor the development of ordered introgression lines (ILs). Despite the utility of these wild relatives and their associated ILs, limited finished genomes have been produced to aid genetic and genomic studies. We have generated a chromosome-scale genome assembly forSolanum lycopersicoidesLA2951 using PacBio sequencing, Illumina, and Hi-C. We identified 37,938 genes based on Illumina and Isoseq and compared gene function to the available cultivated tomato genome resources, in addition to mapping the boundaries of theS. lycopersicoidesintrogressions in a set of cv. VF36 x LA2951 introgression lines (IL). The genome sequence and IL map will support the development ofS. lycopersicoidesas a model for studying fruit nutrient/quality, pathogen resistance, and environmental stress tolerance traits that we have identified in the IL population and are known to exist inS. lycopersicoides.
Big dairy data to disentangle the effect of geo-environmental, physiological and morphological factors on milk production of mountain-pastured Braunvieh cows
Authors: Duruz Solange , Vajana Elia , Burren Alexander , Flury Christine , Joost Stéphane 
Published: April 2020

1.SummaryThe transhumance system, which consists in moving animals to high mountain pastures during summer, plays a considerable role in preserving both local biodiversity and traditions, as well as protecting against natural hazard. In cows, particularly, milk production is observed to decline as a response to food shortage and climatic stress, leading to atypical lactation curves that are barely described by current lactation models. Here, we relied on five million monthly milk records from over 200,000 Braunvieh and Original Braunvieh cows to devise a new model accounting for transhumance, and test the influence of environmental, physiological, and morphological factors on cattle productivity. Counter to expectations, environmental conditions in the mountain showed a globally limited impact on milk production during transhumance, with cows in favourable conditions producing only 10% less compared to cows living in adverse conditions, and with precipitation in spring and altitude revealing to be the most production-affecting variables. Conversely, physiological factors as lactation number and pregnancy stage presented an important impact over the whole lactation cycle with 20% difference in milk production, and may therefore alter the way animals respond to transhumance. Finally, the considered morphological factors (cow height and foot angle) presented a smaller impact during the whole lactation cycle (10% difference in milk production). The present findings can help farmers to establish sustainable strategies for alleviating the negative effects of transhumance on productivity and preserving this important livestock practice.
Investigating the genomic landscape of novel coronavirus (2019-nCoV) to identify non-synonymous mutations for use in diagnosis and drug design
Authors: Tiwari Manish , Mishra Divya 
Published: April 2020

AbstractNovel coronavirus has wrecked medical and health care facilities claiming ~5% death tolls globally. All efforts to contain the pathogenesis either using inhibitory drugs or vaccines largely remained futile due to a lack of better understanding of the genomic feature of this virus. In the present study, we compared the 2019-nCoV with other coronaviruses, which indicated that bat-SARS like coronavirus could be a probable ancestor of the novel coronavirus. The protein sequence similarity of pangolin-hCoV and bat-hCoV with human coronavirus was higher as compared to their nucleotide similarity denoting the occurrence of more synonymous mutations in the genome. Phylogenetic and alignment analysis of 591 novel coronaviruses of different clades from Group I to Group V revealed several mutations and concomitant amino acid changes. Detailed investigation on nucleotide substitution unfolded 100 substitutions in the coding region of which 43 were synonymous and 57 were of non-synonymous type. The non-synonymous substitutions resulting into 57 amino acid changes were found to be distributed over different hCoV proteins with maximum on spike protein. An important diamino acid change RG to KR was observed in ORF9 protein. Additionally, several interesting features of the novel coronavirus genome have been highlighted in respect to various other human infecting viruses which may explain extreme pathogenicity, infectivity and simultaneously the reason behind failure of the antiviral therapies.SummaryThis study presents a comprehensive phylogenetic analysis of SARS-CoV2 isolates to understand discrete mutations that are occurring between patient samples. This analysis will provide an explanation for varying treatment efficacies of different inhibitory drugs and a future direction towards a combinatorial treatment therapies based on the kind of mutation in the viral genome.
G3BP1 tethers the TSC complex to lysosomes and suppresses mTORC1 in the absence of stress granules
Authors: Prentzell Mirja T. , Rehbein Ulrike , Sandoval Marti Cadena , De Meulemeester Ann-Sofie , Baumeister Ralf , Brohée Laura , Berdel Bianca , Bockwoldt Mathias , Carroll Bernadette , von Deimling Andreas , Demetriades Constantinos , Figlia Gianluca , Heberle Alexander M. , Heiland Ines , Holzwarth Birgit , Huber Lukas A. , Jaworski Jacek , Kern Katharina , Kopach Andrii , Korolchuk Viktor I. , van ’t Land-Kuper Ineke , Macias Matylda , Nellist Mark , Pusch Stefan , Reil Michele , Reintjes Anja , Reuter Friederike , Scheldeman Chloë , Stefan Eduard , Teleman Aurelio , Torres-Quesada Omar , Trump Saskia , de Witte Peter , Yordanov Teodor , Opitz Christiane A. , Thedieck Kathrin 
Published: April 2020

SummaryG3BP1 (Ras GTPase-activating protein-binding protein 1) is widely recognized as a core component of stress granules (SG), non-membranous RNA-protein-assemblies required for cellular survival under stress. We report that in the absence of SG, G3BP1 acts as lysosomal anchor of the Tuberous Sclerosis Complex (TSC) protein complex. By tethering the TSC complex to lysosomes, G3BP1 suppresses signaling through the metabolic master regulator mTORC1 (mechanistic target of rapamycin complex 1). Like the known TSC complex subunits, G3BP1 suppresses phenotypes related to mTORC1 hyperactivity in the context of tumors and neuronal dysfunction. Thus, G3BP1 is not only a core component of SG but also a key element of lysosomal TSC-mTORC1 signaling.HighlightsThebona fidestress granule component G3BP1is a key element of the TSC-mTORC1 signaling axis.tethers the TSC complex to lysosomes.prevents mTORC1 hyperactivation by metabolic stimuli.suppresses mTORC1-driven cancer cell motility and epileptiform activity.Graphical Abstract
Metagenomic Next-Generation Sequencing of Rectal Swabs for the Surveillance of Antimicrobial Resistant Organisms on the Illumina Miseq and Oxford MinION Platforms
Authors: Yee Rebecca , Breitwieser Florian P. , Hao Stephanie , Opene Belita N.A. , Workman Rachael E. , Tamma Pranita D. , Dien-Bard Jennifer , Timp Winston , Simner Patricia J. 
Published: April 2020

AbstractPurposeAntimicrobial resistance (AMR) is a public health threat where efficient surveillance is needed to prevent outbreaks. Existing methods for detection of gastrointestinal colonization of multidrug-resistant organisms (MDRO) are limited to specific organisms or resistance mechanisms. Metagenomic next-generation sequencing (mNGS) is a more rapid and agnostic diagnostic approach for microbiome and resistome investigations. We determined if mNGS can detect MDRO from rectal swabs in concordance with standard microbiology results.MethodsWe performed and compared mNGS performance on short-read Illumina MiSeq (N=10) and long-read Nanopore MinION (N=4) platforms directly from peri-rectal swabs to detect vancomycin-resistant enterococci (VRE) and carbapenem-resistant Gram-negative organisms (CRO).ResultsWe detectedE. faecium(N=8) andE. faecalis(N=2) with associatedvangenes (9/10) in concordance with VRE culture-based results. We studied the microbiome and identified CRO organisms,P. aeruginosa(N=1),E. cloacae(N=1), and KPC-producingK. pneumoniae(N=1). Nanopore real-time detection detected theblaKPCgene in 2.5 minutes and provided genetic context (blaKPCharbored on pKPC_Kp46 IncF plasmid). Illumina sequencing provided accurate allelic variant determination (i.e.,blaKPC-2) and strain typing of theK. pneumoniae(ST-15). Conclusions: We demonstrated an agnostic approach for surveillance of MDRO, examining advantages of both short and long-read mNGS methods for AMR detection.
The white matter network in cognitive normal elderly predict the rate of cognitive decline
Authors: Huang Weijie , Shu Ni 
Published: April 2020

AbstractWhite matter degradation has been proposed as one possible explanation for age-related cognitive decline. The human brain is, however, a network and it may be more appropriate to relate cognitive functions to properties of the network rather than specific brain regions. Cognitive domains were measured annually (mean follow-up = 1.25 ± 0.61 years), including processing speed, memory, language, visuospatial, and executive functions. Diffusion tensor imaging was performed at baseline in 90 clinically normal older adults (aged 54–86). We report on graph theory-based analyses of diffusion tensor imaging tract-derived connectivity. The machine learning approach was used to predict the rate of cognitive decline from white matter connectivity data. The reduced efficacy of white matter networks could predict the performance of these cognitive domains except memory. The predicted scores were significantly correlated with the real scores. For the local regions for predicting the cognitive changes, the right precuneus, left inferior parietal lobe and cuneus are the most important regions for predicting monthly change of executive function; some left partial and occipital regions are the most important for the changed of attention; the right frontal and temporal regions are the most important for the changed of language. Our findings suggested that the global white matter connectivity characteristics are the valuable predictive index for the longitudinal cognitive decline. For the first time, topological efficiency of white matter connectivity maps which related to special domains of cognitive decline in the elderly are identified.
A mathematical model-based approach to optimize loading schemes of isometric resistance training sessions
Authors: Herold Johannes L. , Sommer Andreas 
Published: April 2020

AbstractIndividualized resistance training (RT) is necessary to optimize training results. A model-based optimization of loading schemes could provide valuable impulses for practitioners and complement the predominant manual program design by customizing the loading schemes to the trainee and the training goals. We compile a literature overview of model-based approaches used to simulate or optimize the response to single RT sessions or to longterm RT plans in terms of strength, power, muscle mass, or local muscular endurance by varying the loading scheme. To the best of our knowledge, contributions employing a predictive model to algorithmically optimize loading schemes for different training goals are nonexistent in the literature. Thus, we propose to set up optimal control problems as follows. For the underlying dynamics, we use a phenomenological model of the time course of maximum voluntary isometric contraction force. Then, we provide mathematical formulations of key performance indicators for loading schemes identified in sport science and use those as objective functionals or constraints. We then solve those optimal control problems using previously obtained parameter estimates for the elbow flexors. We discuss our choice of training goals, analyze the structure of the computed solutions, and give evidence of their real-life feasibility. The proposed optimization methodology is independent from the underlying model and can be transferred to more elaborate physiological models once suitable ones become available.