bluuebunny/crossref_metadata_2025_split huggingface dataset:
Incidence and outcomes of healthcare-associated COVID-19 infections: significance of delayed diagnosis and correlation with staff absence
Authors: Khonyongwa Kirstin , Taori Surabhi K. , Soares Ana , Desai Nergish , Sudhanva Malur , Bernal Will , Schelenz Silke , Curran Lisa A. 
Published: July 2020

AbstractBackgroundThe sudden increase in COVID-19 admissions in hospitals during the SARS-CoV2 pandemic of 2020 has led to onward transmissions among vulnerable inpatients.AimsThis study was performed to evaluate the prevalence and clinical outcomes of Healthcare-associated COVID-19 infections (HA-COVID-19) during the 2020 epidemic and study factors which may promote or correlate with its incidence and transmission in a London Teaching Hospital Trust.MethodsElectronic laboratory, patient and staff self-reported sickness records were interrogated for the period 1st March to 18th April 2020. HA-COVID-19 was defined as symptom onset >14d of admission. Test performance of a single combined throat and nose swab (CTNS) for patient placement and the effect of delayed RNA positivity (DRP, defined as >48h delay) on patient outcomes was evaluated. The incidence of staff self-reported COVID-19 sickness absence, hospital bed occupancy, community incidence and DRP was compared HA-COVID-19. The incidence of other significant hospital-acquired bacterial infections (OHAI) was compared to previous years.Results58 HA-COVID-19 (7.1%) cases were identified. As compared to community-acquired cases, significant differences were observed in age (p=0.018), ethnicity (p<0.001) and comorbidity burden (p<0.001) but not in 30d mortality. CTNS negative predictive value was 60.3%. DRP was associated with greater mortality (p=0.034) and 34.5% HA-COVID-19 cases could be traced to delayed diagnosis in CA-COVID-19. Incidence of HA-COVID-19 correlated positively with DRP (R=0.7108) and staff sickness absence (R=0.7815). OHAI rates were similar to previous 2 years.ConclusionEarly diagnosis and isolation of COVID-19 would help reduce transmission. A single CTNS has limited value in segregating patients into positive and negative pathways.
Dense reconstruction of elongated cell lineages: overcoming suboptimum lineage encoding and sparse cell sampling
Authors: Sugino Ken , Miyares Rosa L. , Espinosa-Medina Isabel , Chen Hui-Min , Potter Christopher J , Lee Tzumin 
Published: July 2020

AbstractAcquiring both lineage and cell-type information during brain development could elucidate transcriptional programs underling neuronal diversification. This is now feasible with single-cell RNA-seq combined with CRISPR-based lineage tracing, which generates genetic barcodes with cumulative CRISPR edits. This technique has not yet been optimized to deliver high-resolution lineage reconstruction of protracted lineages.Drosophilaneuronal lineages are an ideal model to consider, as multiple lineages have been morphologically mapped at single-cell resolution. Here we find the parameter ranges required to encode a representative neuronal lineage emanating from 100 stem cell divisions. We derive the optimum editing rate to be inversely proportional to lineage depth, enabling encoding to persist across lineage progression. Further, we experimentally determine the editing rates of a Cas9-deaminase in cycling neural stem cells, finding near ideal rates to map elongatedDrosophilaneuronal lineages. Moreover, we propose and evaluate strategies to separate recurring cell-types for lineage reconstruction. Finally, we present a simple method to combine multiple experiments, which permits dense reconstruction of protracted cell lineages despite suboptimum lineage encoding and sparse cell sampling.
Thermodynamics and kinetics of the amyloid-β peptide revealed by Markov state models based on MD data in agreement with experiment
Authors: Paul Arghadwip , Samantray Suman , Anteghini Marco , Strodel Birgit 
Published: July 2020

AbstractThe amlyoid-β peptide (Aβ) is closely linked to the development of Alzheimer’s disease. Molecular dynamics (MD) simulations have become an indispensable tool for studying the behavior of this peptide at the (sub)molecular level, thereby providing insight into the molecular basis of Alzheimer’s disease. General key aspects of MD simulations are the force field used for modeling the peptide or protein and its environment, which is important for accurate modeling of the system of interest, and the length of the simulations, which determines whether or not equilibrium is reached. In this study we address these points by analyzing 30-µs MD simulations acquired for Aβ40 using seven different force fields. We assess the convergence of these simulations based on the convergence of various structural properties and of NMR and fluorescence spectroscopic observables. Moreover, we calculate Markov state models for each of the seven MD simulations, which provide an unprecedented view of the thermodynamics and kinetics of the amyloid-β peptide. This further allows us to provide answers for pertinent questions, like:Which force fields are suitable for modeling Aβ?(a99SB-UCB and a99SB-ILDN/TIP4P-D);What does Aβ peptide really look like?(mostly extended and disordered) and;How long does it take MD simulations of Aβ to attain equilibrium?(20–30 µs). We believe the analyses presented in this study will provide a useful reference guide for important questions relating to the structure and dynamics of Aβin particular, and by extension other similar disordered peptides.
Microplastic shape, concentration and polymer type affect soil properties and plant biomass
Authors: Lozano Yudi M. , Lehnert Timon , Linck Lydia T. , Lehmann Anika , Rillig Matthias C. 
Published: July 2020

ABSTRACTMicroplastics are an increasing concern in terrestrial systems. These particles can be incorporated into the soil in a wide range of shapes and polymers, reflecting the fact that manufacturers produce plastics in a variety of physical and chemical properties matching their intended use.Despite of this, little is known about the effects that the addition into the soil of microplastics of different shapes, polymer type and concentration levels may have on soil properties and plant performance.To fill this gap, we selected four microplastic shapes: fibers, films, foams and fragments; and for each shape we selected three microplastics made of one of the following polymers: polyester, polyamide, polypropylene, polyethylene, polyethylenterephthalat, polyurethane, polystyrene and polycarbonate. In a glasshouse experiment, each microplastic was added to a soil from a dry grassland at a concentration of 0.1%, 0.2%, 0.3% and 0,4%. A carrot (Daucus carota) plant grew in each pot during four weeks. At harvest, shoot and root mass, soil aggregation and microbial activity were measured.Our results showed that all microplastic shapes increased shoot and root masses. As concentration increased, microfibers increased plant biomass probably as fibers may hold water in the soil for longer. In contrast, microfilms decreased biomass with concentration, likely because they can create channels in the soil that promote water evaporation affecting plant performance. All microplastic shapes decreased soil aggregation, probably since microplastics may introduce fracture points in the aggregates affecting their stability and also due to potential negative effects on soil biota. The latter may also explain the decrease in microbial activity with, for example, polyethylene films. Similar to plant biomass, microfilms decreased soil aggregation with increasing concentration.Our study tested the microplastic shape mediation and dissimilarity hypotheses, highlighting the importance of microplastic shape, polymer type and concentration when studying the effects of microplastics on terrestrial systems.
Exploring the quality of protein structural models from a Bayesian perspective
Authors: Arroyuelo Agustina , Vila Jorge A. , Martin Osvaldo A. 
Published: July 2020

AbstractWe explore how ideas and practices common in Bayesian modeling can be applied to help assess the quality of 3D protein structural models. As the wordmodelis used in both Bayesian Statistics and Protein Science, throughout this article we deliberately use the wordmodelto discuss statistical models andstructureto discuss protein 3D models, thus avoiding potential confusions. The basic premise of our approach, is that the evaluation of a Bayesian statistical model’s fit may reveal aspects of the quality of a structure, when the fitted data are related to protein structural properties. Therefore, we fit a Bayesian hierarchical linear model to experimental and theoretical13CαChemical Shifts. Then, we propose two complementary approaches for the evaluation of such fitting: 1) in terms of theexpected differencesbetween experimental and posterior predicted values; 2) in terms of theleave-one-out cross validation point-wise predictive accuracy. Finally, we present visualizations that can help interpret these evaluations. The analyses presented in this article are aimed to aid in detecting problematic residues in protein structures. The code developed for this work is available on:https://github.com/BIOS-IMASL/Hierarchical-Bayes-NMR-Validation.
UNCG RNA tetraloop as a formidable force-field challenge for MD simulations
Authors: Mráziková Klaudia , Mlýnský Vojtěch , Kührová Petra , Pokorná Pavlína , Kruse Holger , Krepl Miroslav , Otyepka Michal , Banáš Pavel , šponer Jiří 
Published: July 2020

ABSTRACTExplicit solvent atomistic molecular dynamics (MD) simulations represent an established technique to study structural dynamics of RNA molecules and an important complement for diverse experimental methods. However, performance of molecular mechanical (MM) force fields (ffs) remains far from satisfactory even after decades of development, as apparent from a problematic structural description of some important RNA motifs. Actually, some of the smallest RNA molecules belong to the most challenging systems for MD simulations and, among them, the UNCG tetraloop is saliently difficult. We report a detailed analysis of UNCG MD simulations, depicting the sequence of events leading to the loss of the UNCG native state during MD simulations. We identify molecular interactions, backbone conformations and substates that are involved in the process. The total amount of MD simulation data analyzed in this work is close to 1.3 millisecond. Then, we unravel specificffdeficiencies using diverse quantum mechanical/molecular mechanical (QM/MM) and QM calculations. Comparison between the MM and QM methods shows discrepancies in the description of the 5’-flanking phosphate moiety and both signature sugar-base interactions. Our work indicates that poor behavior of the UNCG tetraloop in simulations is a complex issue that cannot be attributed to one dominant and straightforwardly correctable factor. Instead, there is a concerted effect of multipleffinaccuracies that are coupled and amplifying each other. We attempted to improve the simulation behavior by some carefully-tailored interventions but the results are still far from satisfactory, underlying the difficulties in development of accurate nucleic acidsffs.
Breathing Behaviors in Common Marmoset (Callithrix jacchus)
Authors: Bishop Mitchell , Turk Ariana , Adeck Afuh , SheikhBahaei Shahriar 
Published: July 2020

AbstractThe respiratory system maintains homeostatic levels of oxygen (O2) and carbon dioxide (CO2) in the body through rapid and efficient regulation of frequency and depth (tidal volume) of breathing. Many studies on homeostatic control of breathing have been done on rodent animal models, in which they have relatively reduced chemosensitivities when compared with primates. The use of common marmoset (Callithrix jacchus), a New World non-human primate model, in neuroscience is increasing, however, the data on their breathing is limited and their respiratory behaviors have yet to be characterized. Using Whole-body Plethysmography in room air as well as in hypoxic (low O2) and hypercapnic (high CO2) conditions, we defined breathing behaviors in awake, freely behaving marmosets. Additionally, we optimized an analysis toolkit for unsupervised analysis of respiratory activities in common marmoset. Our data indicate that marmosets’ exposure to hypoxia decreased metabolic rate and increased sigh rate. However, the hypoxic condition did not augment the ventilatory response. Hypercapnia, on the other hand, increased both the frequency and tidal volume. In this study, we described breathing behaviors of common marmosets in a variety of O2and CO2conditions.
Comparative Performance of Popular Methods for Hybrid Detection using Genomic Data
Authors: Kong Sungsik , Kubatko Laura S. 
Published: July 2020

AbstractInterspecific hybridization is an important evolutionary phenomenon that generates genetic variability in a population and fosters species diversity in nature. The availability of large genome scale datasets has revolutionized hybridization studies to shift from the examination of the presence or absence of hybrids in nature to the investigation of the genomic constitution of hybrids and their genome-specific evolutionary dynamics. Although a handful of methods have been proposed in an attempt to identify hybrids, accurate detection of hybridization from genomic data remains a challenging task. The available methods can be classified broadly as site pattern frequency based and population genetic clustering approaches, though the performance of the two classes of methods under different hybridization scenarios has not been extensively examined. Here, we use simulated data to comparatively evaluate the performance of four tools that are commonly used to infer hybridization events: the site pattern frequency based methodsHyDeand theD-statistic (i.e., the ABBA-BABA test), and the population clustering approachesstructureand ADMIXTURE. We consider single hybridization scenarios that vary in the time of hybridization and the amount of incomplete lineage sorting (ILS) for different proportions of parental contributions (γ); introgressive hybridization; multiple hybridization scenarios; and a mixture of ancestral and recent hybridization scenarios. We focus on the statistical power to detect hybridization, the false discovery rate (FDR) for theD-statistic andHyDe, and the accuracy of the estimates ofγas measured by the mean squared error forHyDe, structure, and ADMIXTURE. BothHyDeand theD-statistic demonstrate a high level of detection power in all scenarios except those with high ILS, although theD-statistic often has an unacceptably high FDR. The estimates ofγinHyDeare impressively robust and accurate whereasstructureand ADMIXTURE sometimes fail to identify hybrids, particularly when the proportional parental contributions are asymmetric (i.e., whenγis close to 0). Moreover, the posterior distribution estimated usingstructureexhibits multimodality in many scenarios, making interpretation difficult. Our results provide guidance in selecting appropriate methods for identifying hybrid populations from genomic data.
Residence Time Analysis of RNA Polymerase Transcription Dynamics: A Bayesian Sticky HMM Approach
Authors: Kilic Zeliha , Sgouralis Ioannis , Pressé Steve 
Published: July 2020

ABSTRACTThe time spent by a single RNA polymerase (RNAP) at specific locations along the DNA, termed “residence time”, reports on the initiation, elongation and termination stages of transcription. At the single molecule level, this information can be obtained from dual ultra-stable optical trapping experiments, revealing a transcriptional elongation of RNAP interspersed with residence times of variable duration. Successfully discriminating between long and short residence times was used by previous approaches to learn about RNAP’s transcription elongation dynamics. Here, we propose an approach based on the Bayesian sticky hidden Markov models that treats all residence times, for an E. Coli RNAP, on an equal footing without a priori discriminating between long and short residence times. In addition, our method has two additional advantages, we provide: full distributions around key point statistics; and directly treat the sequence-dependence of RNAP’s elongation rate.By applying our approach to experimental data, we find: no emergent separation between long and short residence times warranted by the data; force dependent average residence time transcription elongation dynamics; limited effects of GreB on average backtracking durations and counts; and a slight drop in the average residence time as a function of applied force in RNaseA’s presence.STATEMENT OF SIGNIFICANCEMuch of what we know about RNA Polymerase, and its associated transcription factors, relies on successfully discriminating between what are believed to be short and long residence times in the data. This is achieved by applying pause-detection algorithms to trace analysis. Here we propose a new method relying on Bayesian sticky hidden Markov models to interpret time traces provided by dual optical trapping experiments associated with transcription elongation of RNAP. Our method does not discriminate between short and long residence times from the offset in the analysis. It allows for DNA site-dependent transition probabilities of RNAP to neighboring sites (thereby accounting for chemical variability in site to site transitions) and does not demand any time trace pre-processing (such as denoising).
Brazilian dental students and COVID-19: a survey on knowledge and perceptions
Authors: Aragao Maria Gerusa Brito , Gomes Francisco Isaac Fernandes , de Melo Letícia Pinho Maia Paixão , Corona Silmara Aparecida Milori 
Published: July 2020

AbstractThis study evaluated the knowledge and perception of Brazilian dental students about COVID-19 and the undergraduate clinical practice during the outbreak by a self-administered web-based questionnaire. A social network campaign on Instagram was raised to approach the reach population. The survey covered demographic and academic profile, general knowledge, preventive measures, and perception about the COVID-19. Descriptive statistics were used to identify frequencies and distributions of variables, which were compared by type of institution and current year of enrollment using Chi-square or Fisher’s exact tests (α=0.05). A total of 833 valid responses were received over 10 days. Students were able to identify the incubation period, main symptoms, and contagious routes of the disease but struggled in recognizing the name of the virus responsible for the pandemics. Hand washing before and after a dental appointment with a patient (97.7%) followed by use of barriers to protect mucosa (97.2%) were the more frequently recognized measures to prevent COVID-19 spread in the dental office. As for the perception of COVID-19, 73.2% of the dental students perceive the disease as severe, while only 11.1% of them think that COVID-19 is severe only for people presenting risk factors. Dental student’s knowledge and perception were associated with the type of institution and year of enrollment. In summary, the dental students demonstrated an acceptable general knowledge about COVID-19, but dental schools will need to address gaps in knowledge and control measures and perceptions to ensure a safer return to presential activities.
Stochastic modelling of the effects of human-mobility restriction and viral infection characteristics on the spread of COVID-19
Authors: Ando Shiho , Matsuzawa Yuki , Tsurui Hiromichi , Mizutani Testuya , Hall Damien , Kuroda Yutaka 
Published: July 2020

AbstractAfter several weeks of “lockdown” as the sole answer to the COVID-19 pandemic, many countries are restarting their economic and social activities. However, balancing the re-opening of society against the implementation of non-pharmaceutical measures needed for minimizing interpersonal contacts requires a careful assessment of the risks of infection as a function of the confinement relaxation strategies. Here, we present a stochastic coarse grained model that examines this problem. In our model, people are allowed to move between discrete positions on a one-dimensional grid with viral infection possible when two people are collocated at the same site. Our model features three sets of adjustable parameters, which characterize (i) viral transmission, (ii) viral detection, and (iii) degree of personal mobility, and as such, it is able to provide a qualitative assessment of the potential for second-wave infection outbreaks based on the timing, extent, and pattern of the lockdown relaxation strategy. In line with general expectations, our model predicts that a full lockdown yields the best results, namely, the lowest number of total infections. A less anticipated result was that when personal mobility is increased beyond a critical level, the risk of infection rapidly reaches a constant value, which depends solely on the population density. Furthermore, according to our model, confinement alone is not effective if it is not accompanied by a detection capacity (coupled with quarantine) that surpasses 40% of the patients during their symptomatic phase. The results of our simulation also showed that keeping the virus transmission probability to less than 0.4, which can be achieved in real life by respecting social distancing or wearing masks, is as effective as imposing a mild lockdown. Finally, we note that detection and quarantine of pre-symptomatic patients, even with a probability as low as 0.2, would reduce the final numbers of infections by a factor of ten or more.Availabilityhttp://domserv.lab.tuat.ac.jp/COVID19.html (under preparation)
Initial evaluation of a mobile SARS-CoV-2 RT-LAMP testing strategy
Authors: Newman Christina M. , Ramuta Mitchell D. , McLaughlin Matthew T. , Wiseman Roger W. , Karl Julie A. , Dudley Dawn M. , Stauss Miranda R. , Maddox Robert J. , Weiler Andrea M. , Bliss Mason I. , Fauser Katrina N. , Haddock Luis A. , Shortreed Cecilia G. , Haj Amelia K. , Accola Molly A. , Heffron Anna S. , Bussan Hailey E. , Reynolds Matthew R. , Harwood Olivia E. , Moriarty Ryan V. , Stewart Laurel M. , Crooks Chelsea M. , Prall Trent M. , Neumann Emma K. , Somsen Elizabeth D. , Burmeister Corrie B. , Hall Kristi L. , Rehrauer William M. , Friedrich Thomas C. , O’Connor Shelby L. , O’Connor David H. 
Published: July 2020

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) control in the United States remains hampered, in part, by testing limitations. We evaluated a simple, outdoor, mobile, colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay workflow where self-collected saliva is tested for SARS-CoV-2 RNA. From July 16 to November 19, 2020, 4,704 surveillance samples were collected from volunteers and tested for SARS-CoV-2 at 5 sites. A total of 21 samples tested positive for SARS-CoV-2 by RT-LAMP; 12 were confirmed positive by subsequent quantitative reverse-transcription polymerase chain reaction (qRT-PCR) testing, while 8 were negative for SARS-CoV-2 RNA, and 1 could not be confirmed because the donor did not consent to further molecular testing. We estimated the RT-LAMP assay’s false-negative rate from July 16 to September 17, 2020 by pooling residual heat-inactivated saliva that was unambiguously negative by RT-LAMP into groups of 6 or less and testing for SARS-CoV-2 RNA by qRT-PCR. We observed a 98.8% concordance between the RT-LAMP and qRT-PCR assays, with only 5 of 421 RT-LAMP negative pools (2,493 samples) testing positive in the more sensitive qRT-PCR assay. Overall, we demonstrate a rapid testing method that can be implemented outside the traditional laboratory setting by individuals with basic molecular biology skills and can effectively identify asymptomatic individuals who would not typically meet the criteria for symptom-based testing modalities.