bluuebunny/crossref_metadata_2025_split huggingface dataset:
I had to leave. I had to leave my clinic, my city, leave everything behind in Syria. Qualitative accounts from Syrian health care workers migrating from the war-torn country
Authors: Kallström Agneta , Orwa Al Abdulla , Jan Parkki , Häkkinen Mikko , Juusola Hannu , Jussi Kauhanen 
Published: September 2020

ABSTRACTObjectivesTo explore the experiences of Syrian health care workers of violence since 2011 and reasons behind migration from the conflict-affected countryDesignA qualitative study using semi-structured interviews and inductive content analysis. Interviews were conducted in Turkey and Europe in 2016 - 2017.SettingInterviewees were Syrian health care workers who had been working in the country after the conflict started in 2011, but at some point, migrated from Syria to Turkey or Europe.ParticipantsWe studied data from 20 semi-structured in-depth interviews collected with a snowball sampling method.ResultsOur findings show that health care workers migrated from Syria only because of security concerns. In most cases, the decision to leave was a result of the generalised violence by different warring parties. Targeted attacks against health care was one of the main reasons for leaving. Some participants had a specific notable trigger event before they left; such as a colleague being detained or killed. Many just grew tired of living under constant threat and fear, with their families also at risk.ConclusionsThis research adds to the body of literature on violence in Syria. It helps to understand the reasons why health care workers, even though realising that it will leave their population without proper health care provision, nevertheless decide to flee the country. Understanding the motives of the health care workers will help to find new ways to protect them.
Health Impact Assessment of PM2.5attributable mortality from the September 2020 Washington State Wildfire Smoke Episode
Authors: Liu Yisi , Austin Elena , Xiang Jianbang , Gould Tim , Larson Tim , Seto Edmund 
Published: September 2020

AbstractThe historic wildfires of August and September 2020, up and down the west coast have made Western cities in the U.S. rank among the highest in the world for dirtiest air. Washington State was impacted by active wildfire smoke in the state as well as transported aged wood smoke from Oregon and California. Particulate matter smaller than 2.5 μm in aerodynamic diameter (PM2.5) is the primary pollutant from wildfire smoke of health concern for the public. This study aims to assess the potential population health impact of increased PM2.5concentrations attributable to the wildfire smoke. Daily PM2.5concentrations for each county before and during the 2020 Washington state wildfire smoke episode was obtained from the WA Department of Ecology. Utilizing previously established associations of short-term mortality and PM2.5concentrations we estimated increases in all-cause, respiratory, and cardiovascular mortality for Washington state attributable to the increased PM2.5levels during the wildfire episode. During the episode, PM2.5concentrations increased 99.4 μg/m3on average in Washington. Each week of wildfire smoke exposures are estimated to cause 91.0 (95% CI: 73.8, 107.1) cases of increased all-cause mortality, 19.9 (95% CI: 10.4, 29.3) increased cardiovascular disease deaths, and 9.7 (95% CI: 5.4, 14.0) increased respiratory disease deaths. This is without considering the coincidence with the COVID-19 pandemic, since wildfire smoke exposure may exacerbate the effects of SARS-CoV-2 infection based on some early evidence. Because wildfire smoke episodes are likely to continue impacting the Pacific Northwest in years to come, continued preparedness and mitigations to reduce exposures to wildfire smoke are necessary to avoid this excess health burden.
A comparison of five epidemiological models for transmission of SARS-CoV-2 in India
Authors: Purkayastha Soumik , Bhattacharyya Rupam , Bhaduri Ritwik , Kundu Ritoban , Gu Xuelin , Salvatore Maxwell , Mishra Swapnil , Mukherjee Bhramar 
Published: September 2020

ABSTRACTMany popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM). Using COVID-19 data for India from March 15 to June 18 to train the models, we generate predictions from each of the five models from June 19 to July 18. To compare prediction accuracy with respect to reported cumulative and active case counts and cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) for each of the five models. For active case counts, SMAPE values are 0.72 (SEIR-fansy) and 33.83 (eSIR). For cumulative case counts, SMAPE values are 1.76 (baseline) 23.10 (eSIR), 2.07 (SAPHIRE) and 3.20 (SEIR-fansy). For cumulative death counts, the SMAPE values are 7.13 (SEIR-fansy) and 26.30 (eSIR). For cumulative cases and deaths, we compute Pearson’s and Lin’s correlation coefficients to investigate how well the projected and observed reported COVID-counts agree. Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) counts as well. We compute underreporting factors as of June 30 and note that the SEIR-fansy model reports the highest underreporting factor for active cases (6.10) and cumulative deaths (3.62), while the SAPHIRE model reports the highest underreporting factor for cumulative cases (27.79).
An improved method to estimate the effective reproduction number of the COVID-19 pandemic: lessons from its application in Greece
Authors: Lytras Theodore , Sypsa Vana , Panagiotakos Demosthenes , Tsiodras Sotirios 
Published: September 2020

AbstractIntroductionMonitoring the time-varying effective reproduction number Rt is crucial for assessing the evolution of the COVID-19 pandemic. We present an improved method to estimate Rt and its application to routine surveillance data from Greece.MethodsOur method extends that of Cori et al (2013), adding Bayesian imputation of missing symptom onset dates, imputation of infection times using an external estimate of the incubation period, and an adjustment for reporting delay. To facilitate its use, we provide an R software package named “bayEStim”. We applied the method to COVID-19 surveillance data from Greece, and examined the resulting Rt estimates in relation to control measures applied, in order to assess their effectiveness. We also associated Rt, as a measure of transmissibility, to population mobility as recorded in Google data and to ambient temperature. We used a serial interval between 4 and 7.5 days, and a median incubation period of 5.1 days.ResultsIn Greece Rt fell rapidly as the first control measures were introduced, dropping below 1 at least a week before a full lockdown came into effect. In mid-July Rt started increasing again, as increased mobility associated with tourism activity was observed. Each 10% of increase in relative mobility increased Rt by 8.1% (95% CrI 6.1–10.2%), whereas each unit celsius of temperature increase decreased Rt by 4.6% (95% CrI 5.4–13.7%).ConclusionsMobility patterns significantly affect Rt. Most of the reduction in COVID-19 transmissibility in Greece occurred already before the lockdown, likely as a result of decreased population mobility. Lower viral transmissibility in summer does not appear sufficient to counterbalance the increased mobility due to tourism. Monitoring Rt is an essential component of COVID-19 surveillance, and it is crucial for correctly assessing the effect of control measures.
Association of genetic variants in enamel-formation genes with dental caries: A meta- and gene-cluster analysis
Authors: Li Xueyan , Liu Di , Sun Yang , Yang Jingyun , Yu Youcheng 
Published: September 2020

AbstractPrevious studies have reported the association between multiple genetic variants in enamel formation-related genes and the risk of dental caries with inconsistent results. We performed a systematic literature search of the PubMed, Cochrane Library, HuGE and Google Scholar databases for studies published before March 21, 2020 and conducted meta-, gene-based and gene-cluster analysis on the association between genetic variants in enamel-formation-related genes and the risk of dental caries. Our systematic literature search identified 21 relevant publications including a total of 24 studies for analysis. The genetic variant rs17878486 in AMELX was significantly associated with dental caries risk (OR=1.40, 95% CI: 1.02-1.93, P=0.037). We found no significant association between the risk of dental caries with rs12640848 in ENAM (OR=1.15, 95% CI: 0.88-1.52, P=0.310), rs1784418 in MMP20 (OR=1.07, 95% CI: 0.76-1.49, P=0.702) and rs3796704 in ENAM (OR=1.06, 95% CI: 0.96-1.17, P=0.228). Gene-based analysis indicated that multiple genetic variants in AMELX showed joint association with the risk of dental caries (6 variants; P<10−5), so did genetic variants in MMP13 (3 variants; P=0.004), MMP2 (3 variants; P<10−5), MMP20 (2 variants; P<10−5) and MMP3 (2 variants; P<10−5). The gene-cluster analysis indicated a significant association between the genetic variants in this enamel-formation gene cluster and the risk of dental caries (P<10−5). The present meta-analysis revealed that genetic variant rs17878486 in AMELX were associated with dental caries, and multiple genetic variants in enamel-formation-related genes jointly contribute to the risk of dental caries, supporting the role of genetic variants in the enamel-formation genes in the etiology of dental caries.
COVID-19: Risks of Re-emergence, Re-infection, and Control Measures – A Long Term Modeling Study
Authors: Ghosh Subhas Kumar , Ghosh Sachchit 
Published: September 2020

ABSTRACTIn this work we define a modified SEIR model that accounts for the spread of infection during the latent period, infections from asymptomatic or pauci-symptomatic infected individuals, potential loss of acquired immunity, people’s increasing awareness of social distancing and the use of vaccination as well as non-pharmaceutical interventions like social confinement. We estimate model parameters in three different scenarios - in Italy, where there is a growing number of cases and re-emergence of the epidemic, in India, where there are significant number of cases post confinement period and in Victoria, Australia where a re-emergence has been controlled with severe social confinement program. Our result shows the benefit of long term confinement of 50% or above population and extensive testing. With respect to loss of acquired immunity, our model suggests higher impact for Italy. We also show that a reasonably effective vaccine with mass vaccination program can be successful in significantly controlling the size of infected population. We show that for India, a reduction in contact rate by 50% compared to a reduction of 10% in the current stage can reduce death from 0.0268% to 0.0141% of population. Similarly, for Italy we show that reducing contact rate by half can reduce a potential peak infection of 15% population to less than 1.5% of population, and potential deaths from 0.48% to 0.04%. With respect to vaccination, we show that even a 75% efficient vaccine administered to 50% population can reduce the peak number of infected population by nearly 50% in Italy. Similarly, for India, a 0.056% of population would die without vaccination, while 93.75% efficient vaccine given to 30% population would bring this down to 0.036% of population, and 93.75% efficient vaccine given to 70% population would bring this down to 0.034%.
Determining the effects of preseasonal climate factors toward dengue early warning system in Bangladesh
Authors: Hossain M. Pear , Zhou Wen , Ren Chao , Marshall John , Yuan Hsiang-Yu 
Published: September 2020

AbstractThe incidence of dengue has increased rapidly in Bangladesh since 2010 with an outbreak in 2018 reaching a historically high number of cases, 10,148. Bangladesh is located to the northeast of India and spans both tropical and subtropical regions. A better understanding of the effects of preseasonal climate variability on the increasing incidence of dengue in Bangladesh may provide insight into whether dengue has been expanding from tropical to temperate regions through the subtropics in Southeast Asia and can enable early warning for preventative measures.We developed a generalized linear model to predict the number of annual dengue cases based on monthly temperature, rainfall and sunshine prior to dengue season. Variable selection and leave-one-out cross-validation were performed to identify the best prediction model and to evaluate the model’s performance.Our model successfully predicted the largest outbreak in 2018, with 10,077 cases (95% CI: [9,912–10,276]), in addition to smaller outbreaks in five different years (2003, 2006, 2010, 2012 and 2014) and successfully identified the increasing trend in cases between 2010 and 2018. We found that the temperature has an apparent dual effect on the annual incidence of dengue, with a positive effect seen prior to April and negative effect seen between April and June. Our results suggest that the optimal minimum temperature for mosquito population expansion is 21–23°C. This study has implications for understanding how climate variability has affected recent dengue expansion in northern India and Southeast Asia.Author summaryBangladesh has experienced an unprecedented outbreak of dengue in recent years. Climate factors are believed to have played a significant role in the increased mosquito population, the primary vector of dengue. We have observed an upward trend in the number of dengue cases since 2010, which has continued until recent years. However, in previous studies of dengue–climate association, this link was not investigated. Therefore, we developed a model that uses this information to assess the association of annual dengue incidence with climate factors such as temperature, rainfall and sunshine duration. Our findings suggest that warmer springs, with minimum monthly temperatures of 21–23°C, are more favorable for mosquito population expansion and subsequent dengue transmission. Shorter duration of sunshine increase the risk of dengue, whereas abundant rainfall reduces the incidence of dengue. This research will help increase understanding of the effect of climate variability on dengue expansion, not only in Bangladesh but also in northern India and Southeast Asia.
An automatic pipeline for the design of irreversible derivatives identifies a potent SARS-CoV-2 Mproinhibitor
Authors: Zaidman Daniel , Gehrtz Paul , Filep Mihajlo , Fearon Daren , Prilusky Jaime , Duberstein Shirly , Cohen Galit , Owen David , Resnick Efrat , Strain-Damerell Claire , Lukacik Petra ,   Covid-Moonshot Consortium, Barr Haim , Walsh Martin A. , von Delft Frank , London Nir 
Published: September 2020

AbstractDesigning covalent inhibitors is a task of increasing importance in drug discovery. Efficiently designing irreversible inhibitors, though, remains challenging. Here, we presentcovalentizer, a computational pipeline for creating irreversible inhibitors based on complex structures of targets with known reversible binders. For each ligand, we create a custom-made focused library of covalent analogs. We use covalent docking, to dock these tailored covalent libraries and to find those that can bind covalently to a nearby cysteine while keeping some of the main interactions of the original molecule. We found ~11,000 cysteines in close proximity to a ligand across 8,386 protein-ligand complexes in the PDB. Of these, the protocol identified 1,553 structures with covalent predictions. In prospective evaluation against a panel of kinases, five out of nine predicted covalent inhibitors showed IC50between 155 nM - 4.2 μM. Application of the protocol to an existing SARS-CoV-1 Mproreversible inhibitor led to a new acrylamide inhibitor series with low micromolar IC50against SARS-CoV-2 Mpro. The docking prediction was validated by 11 co-crystal structures. This is a promising lead series for COVID-19 antivirals. Together these examples hint at the vast number of covalent inhibitors accessible through our protocol.
Rapid detection of inter-clade recombination in SARS-CoV-2 with Bolotie
Authors: Varabyou Ales , Pockrandt Christopher , Salzberg Steven L. , Pertea Mihaela 
Published: September 2020

AbstractThe ability to detect recombination in pathogen genomes is crucial to the accuracy of phylogenetic analysis and consequently to forecasting the spread of infectious diseases and to developing therapeutics and public health policies. However, previous methods for detecting recombination and reassortment events cannot handle the computational requirements of analyzing tens of thousands of genomes, a scenario that has now emerged in the effort to track the spread of the SARS-CoV-2 virus. Furthermore, the low divergence of near-identical genomes sequenced in short periods of time presents a statistical challenge not addressed by available methods. In this work we present Bolotie, an efficient method designed to detect recombination and reassortment events between clades of viral genomes. We applied our method to a large collection of SARS-CoV-2 genomes and discovered hundreds of isolates that are likely of a recombinant origin. In cases where raw sequencing data was available, we were able to rule out the possibility that these samples represented co-infections by analyzing the underlying sequence reads. Our findings further show that several recombinants appear to have persisted in the population.
Insertions of codons encoding basic amino acids in H7 hemagglutinins of influenza A viruses occur by recombination with RNA at hotspots near snoRNA binding sites
Authors: Gultyaev Alexander P. , Spronken Monique I. , Funk Mathis , Fouchier Ron A.M. , Richard Mathilde 
Published: September 2020

ABSTRACTThe presence of multiple basic amino acids in the protease cleavage site of the hemagglutinin (HA) protein is the main molecular determinant of virulence of highly pathogenic avian influenza (HPAI) viruses. Recombination of HA RNA with other RNA molecules of host or virus origin is a dominant mechanism of multi basic cleavage site (MBCS) acquisition for H7 subtype HA. Using alignments of HA RNA sequences from documented cases of MBCS insertion due to recombination, we show that such recombination with host RNAs is most likely to occur at particular hotspots in ribosomal RNAs (rRNAs), transfer RNAs (tRNAs) and viral RNAs. The locations of these hotspots in highly abundant RNAs indicate that RNA recombination is facilitated by the binding of small nucleolar RNA (snoRNA) near the recombination points.
Dissolved organic carbon (DOC) is essential to balance the metabolic demands of North-Atlantic deep-sea sponges
Authors: Bart Martijn C. , Mueller Benjamin , Rombouts Titus , van de Ven Clea , Tompkins Gabrielle J. , Osinga Ronald , Brussaard Corina P.D. , MacDonald Barry , Engel Anja , Rapp Hans Tore , de Goeij Jasper M. 
Published: September 2020

AbstractSponges are ubiquitous components of various deep-sea habitats, including cold water coral reefs and deep-sea sponge grounds. Despite being surrounded by oligotrophic waters, these ecosystems are known to be hotspots of biodiversity and carbon cycling. To assess the role of sponges in the carbon cycling of deep-sea ecosystems, we studied the energy budgets of six dominant deep-sea sponges (the hexactinellid speciesVazella pourtalesi, and demosponge speciesGeodia barretti, Geodia atlantica, Craniella zetlandica, Hymedesmia paupertasandAcantheurypon spinispinosum)in an ex situ aquarium setup. Additionally, we determined morphological metrics for all species (volume, dry weight (DW), wet weight (WW), carbon (C) content, and ash-free dry weight (AFDW)) and provide species-specific conversion factors. Oxygen (O2) removal rates averaged 3.3 ± 2.8µmol O2DWspongeh−1(all values mean ± SD), live particulate (bacterial and phytoplankton) organic carbon (LPOC) removal rates averaged 0.30 ± 0.39µmol C DWspongeh−1and dissolved organic carbon (DOC) removal rates averaged 18.70 ± 25.02µmol C DWspongeh−1. Carbon mass balances were calculated for four species (V. pourtalesi, G. barretti, G. atlanticaandH. paupertas) and revealed that the sponges acquired 1.3–6.6 times the amount of carbon needed to sustain their minimal respiratory demands. These results indicate that irrespective of taxonomic class, growth form, and abundance of microbial symbionts, DOC is responsible for over 90 % of the total net organic carbon removal of deep-sea sponges and allows them to sustain in otherwise food-limited environments on the ocean floor.
Distinct tooth regeneration systems deploy a conserved battery of genes
Authors: Square Tyler A. , Sundaram Shivani , Mackey Emma J. , Miller Craig T. 
Published: September 2020

AbstractBackgroundVertebrate teeth exhibit a wide range of regenerative systems. Many species, including most mammals, reptiles, and amphibians, form replacement teeth at a histologically distinct location called the successional dental lamina, while other species do not employ such a system. Notably, a ‘lamina-less’ tooth replacement condition is found in a paraphyletic array of ray-finned fishes, such as stickleback, trout, cod, medaka, and bichir. Furthermore, the position, renewal potential, and latency times appear to vary drastically across different vertebrate tooth regeneration systems. The progenitor cells underlying tooth regeneration thus present highly divergent arrangements and potentials. Given the spectrum of regeneration systems present in vertebrates, it is unclear if morphologically divergent tooth regeneration systems deploy an overlapping battery of genes in their naïve dental tissues.ResultsIn the present work, we aimed to determine whether or not tooth progenitor epithelia could be composed of a conserved cell type between vertebrate dentitions with divergent regeneration systems. To address this question, we compared the tooth regeneration processes in two ray-finned fishes: zebrafish (Danio rerio) and threespine stickleback (Gasterosteus aculeatus). These two teleost species diverged approximately 250 million years ago, and demonstrate some stark differences in dental morphology and regeneration. Here we find that the successional dental lamina in zebrafish sharply upregulates Wnt signaling andLef1expression during early morphogenesis stages of tooth development. Additionally, the naïve zebrafish successional dental lamina expresses a battery of nine genes (Bmpr1a, Bmp6, CD34, Gli1, Igfbp5a, Lgr4, Lgr6, Nfatc1, andPitx2). We also find that, despite the absence of a histologically distinct successional dental lamina in stickleback tooth fields, new tooth germs also sharply upregulate Wnt signaling andLef1expression, and additionally express the same battery of nine genes in the basalmost endodermal cell layer from which replacement tooth epithelia arise. Thus, two fish systems that either have an organized successional dental lamina (zebrafish) or lack a morphologically distinct successional dental lamina (sticklebacks) deploy similar genetic programs during tooth regeneration.ConclusionsWe propose that the expression domains described here delineate a highly conserved “successional dental epithelium” (SDE). Furthermore, a set of orthologous genes is known to mark hair follicle epithelial stem cells in mice, suggesting that regenerative systems in other epithelial appendages may utilize a related epithelial progenitor cell type, despite the highly derived nature of the resulting functional organs.