bluuebunny/crossref_metadata_2025_split huggingface dataset:
Real-time estimation of R0 for supporting public-health policies against COVID-19
Authors: Contreras Sebastián , Villavicencio H. Andrés , Medina-Ortiz David , Saavedra Claudia P. , Olivera-Nappa Álvaro 
Published: April 2020

AbstractBackgroundIn the absence of a consensus protocol to slow down the current SARS-CoV2 spread, policy makers are in need of real-time indicators to support decisions in public health matters. The Basic Reproduction Number (R0) represents viral spread rate and can be dramatically modified by the application of effective public control measures. However, current methodologies to calculate R0 from data remain cumbersome and unusable during an outbreak.ObjectiveTo provide a simple mathematical formulation for obtaining R0 in Real-Time, and apply it to assess the effectiveness of public-health policies in different iconic countries.Study designBy modifying the equations describing the spread of the virus, we derived a real-time R0 estimator that can be readily calculated from daily official case reports.ResultsWe show the application of a time trend analysis of the R0 estimator to assess the efficacy and promptness of public health measures that impacted on the development of the COVID-19 epidemic in iconic countries.ConclusionsWe propose our simple estimator and method as useful tools to follow and assess in real time the effectiveness of public health policies on COVID-19 evolution.
An age-structured epidemiological model of the Belgian COVID-19 epidemic
Authors: Deforche Koen 
Published: April 2020

AbstractCOVID-19 has prompted many countries to implement extensive social distancing to stop the rapid spread of the virus, in order to prevent over-loading health care systems. Yet, the main epidemic parameters of this virus are not well understood. In the absence of broad testing or serological surveillance, it is hard to evaluate or predict the impact of different strategies to exit implemented lock-down measures. An age-structured epidemiological model was developed, which distinguishes between the younger versus older population (e.g. < 65 and ≥ 65). Because the illness severity is markedly different for these two populations, such a separation is necessary when estimating the model based on death and hospitalization incidence data. The model was applied to data of the Belgian epidemic and used to predict how the epidemic would react to a relaxing of social distancing measures.
A Model for SARS-CoV-2 Infection with Treatment
Authors: Chatterjee Amar Nath , Basir Fahad Al 
Published: April 2020

AbstractThe current emergence of coronavirus (SARS-CoV-2) puts the world in threat. The structural research on the receptor recognition by SARS-CoV-2 has identified the key interactions between SARS-CoV-2 spike protein and its host (epithelial cell) receptor, also known as angiotensin-converting enzyme 2 (ACE2). It controls both the crossspecies and human-to-human transmissions of SARS-CoV-2. In view of this, we propose and analyze a mathematical model for investigating the effect of CTL responses over the viral mutation to control the viral infection when a postinfection immunostimulant drug (pidotimod) is administered at regular intervals. Dynamics of the system with and without impulses have been analyzed using the basic reproduction number. This study shows that the proper dosing interval and drug dose both are important to eradicate the viral infection.
Sputum microbiota profiles of treatment-naïve TB patients in Uganda before and during first-line therapy
Authors: Kateete David Patrick , Mbabazi Monica M , Nakazzi Faith , Katabazi Fred A , Kigozi Edgar , Ssengooba Willy , Nakiyingi Lydia , Namiiro Sharon , Okwera Alphonse , Joloba Moses L , Muwonge Adrian 
Published: April 2020

AbstractThere is limited information on microbiota dynamics in tuberculosis (TB) in Africa. Here, we investigated changes in microbiota composition, abundance, co-occurrence and community remodelling relative to clinical parameters, among treatment-naïve pulmonary TB patients at Mulago National Referral Hospital in Kampala, Uganda. We sequenced 205 sputum samples from 120 patients before initiating anti-TB therapy (baseline) and during treatment follow-up (at months 2 and 5). A total of 8.6 million high quality sequences were generated, yielding 8,180 operational taxonomic units (OTUs), 18 phyla and 333 genera. A sputum sample on average generated 44,992 sequences, yielding 6,580 OTUs, 4 phyla and 36 genera. The sputum microbiota core comprised of 34 genera and it was remarkably stable during treatment. Month 2 was characterized by a significant mean reduction in core microbiota biomass, limited variance changes and general lack of entropy. However, variance and entropy recovered at month 5. Co-occurrence patterns were predominated by accessory genera at baseline but their abundance significantly reduced during treatment. Our findings reveal discernible sputum microbiota signals associated with first-line anti-TB therapy, with potential to inform treatment response monitoring in developing countries.
Concentration-dependent mortality of chloroquine in overdose
Authors: Watson James A , Tarning Joel , Hoglund Richard M , Baud Frederic J , Mégarbane Bruno , Clemessy Jean-Luc , White Nicholas J 
Published: April 2020

AbstractHydroxychloroquine and chloroquine are used extensively in malaria and rheumatological conditions, and now in COVID-19 prevention and treatment. Although generally safe they are potentially lethal in overdose. In-vitro data suggest that high concentrations and thus high doses are needed for COVID-19 infections, but as yet there is no convincing evidence they are clinically effective. Bayesian regression models were fitted to survival outcomes and electrocardiograph QRS durations from 302 prospectively studied French patients who had taken intentional chloroquine overdoses, of whom 33 died (11%), and 16 healthy volunteers who took 620 mg base chloroquine single doses. Whole blood concentrations of 13.5 μmol/L (95% credible interval 10.1-17.7) were associated with 1% mortality. Prolongation of ventricular depolarisation is concentration-dependent with a QRS duration >150 msec independently highly predictive of mortality. Pharmacokinetic modelling combined with these lethality data predicts that the majority of chloroquine regimens trialled in COVID-19 should not cause serious cardiovascular toxicity.
Laboratory Findings of COVID-19 Infection are Conflicting in Different Age Groups and Pregnant Women: A Literature Review
Authors: Vakili Sina , Savardashtaki Amir , Jamalnia Sheida , Tabrizi Reza , Nematollahi Mohammad Hadi , Jafarinia Morteza , Akbari Hamed 
Published: April 2020

AbstractCoronavirus disease 2019 (COVID-19), a new type and rapidly spread viral pneumonia, is now producing an outbreak of pandemic proportions. The clinical features and laboratory results of different age groups are different due to the general susceptibility of the disease. The laboratory findings of COVID-19 in pregnant women are also conflicting. Para-clinical investigations including laboratory tests and radiologic findings play an important role in early diagnosis and treatment monitoring of severe acute respiratory syndrome and coronavirus-2 (SARS-CoV-2). The majority of previous reports on the SARS-CoV-2 laboratory results were based on data from the general population and limited information is available based on age difference and pregnancy status. This review aimed to describe the COVID-19 laboratory findings in neonates, children, adults, elderly and pregnant women altogether for the first time. The most attracting and reliable markers of COVID-19 in patients were: normal C-reactive protein (CRP) and very different and conflicting laboratory results regardless of clinical symptoms in neonates, normal or temporary elevated CRP, conflicting WBC count results and procalcitonin elevation in children, lymphopenia and elevated lactate dehydrogenase (LDH) in adult patients, lymphopenia and elevated CRP and LDH in the elderly people and high CRP, leukocytosis and elevated neutrophil ratio in pregnant women.
Lopinavir-ritonavir alone or combined with arbidol in the treatment of 73 hospitalized patients with COVID-19: a pilot retrospective study
Authors: Lan Xiu , Shao Chuxiao , Zeng Xu , Wu Zhenbo , Xu Yanyan 
Published: April 2020

ABSTRACTObjectivesThis study aimed to evaluate the antiviral efficacy of lopinavir/ritonavir alone or combined with arbidol in the treatment of hospitalized patients with common coronavirus disease-19 (COVID-19).MethodsIn this retrospective observational study, COVID-19 hospitalized patients were identified and divided into two groups based on the antiviral agents used during their hospitalization. Group-LR patients were treated with single antiviral drug of lopinavir-ritonavir. Group-LR+Ar patients were treated with lopinavir-ritonavir combined with arbidol for antiviral therapy at least 3 days. Patients were assessed for different clinical outcomes.ResultsA total of 34 and 39 patients were identified for Group-LR and Group-LR+Ar, respectively. Treatment with lopinavir–ritonavir alone was not difference from lopinavir-ritonavir combined with arbidol in overall cure rate of COVID-19 hospitalized patients (92.3% and 97.1%, respectively). In a modified intention-to-treat analysis, lopinavir–ritonavir combined with abidol led to a median time of hospital stay that was shorter by 1.5 days than group-LR (12.5 days vs. 14 days). The percentages of COVID-19 RNA clearance was 92.3 in group-LR and 97.1 in group-LR+Ar. The mean time of virus turning negative was 11.5±9.0 days in group-LR+Ar that were longer than group-LR. Treatment of lopinavir-ritonavir combined with arbidol did not significantly accelerate main symptoms improvement and promote the image absorption of pulmonary inflammation.ConclusionNo benefit was observed in the anti-virus effect of lopinavir–ritonavir combined with arbidol compared with lopinavir-ritonavir alone in the hospitalized patients with COVID-19. More clinical observations in COVID-19 patients may help to confirm or exclude the effect of antiviral agents.
FIRST DETECTION OF SARS-COV-2 IN UNTREATED WASTEWATERS IN ITALY
Authors: La Rosa Giuseppina , Iaconelli Marcello , Mancini Pamela , Ferraro Giusy Bonanno , Veneri Carolina , Bonadonna Lucia , Lucentini Luca , Suffredini Elisabetta 
Published: April 2020

ABSTRACTSeveral studies have demonstrated the advantages of environmental surveillance through the monitoring of sewer systems for the assessment of viruses circulating in a given community (wastewater-based epidemiology, WBE).During the COVID-19 public health emergency, many reports have described the presence of SARS-CoV-2 RNA in stools from COVID-19 patients, and a few studies reported the occurrence of SARS-CoV-2 in wastewaters worldwide. Italy is among the world’s worst-affected countries in the COVID-19 pandemic, but so far there are no studies assessing the presence of SARS-CoV-2 in Italian wastewaters. To this aim, twelve influent sewage samples, collected between February and April 2020 from Wastewater Treatment Plants in Milan and Rome, were tested adapting, for concentration, the standard WHO procedure for Poliovirus surveillance. Molecular analysis was undertaken with three nested protocols, including a newly designed SARS-CoV-2 specific primer set.SARS-CoV-2 RNA detection occurred in volumes of 250 mL of wastewaters collected in both areas at high (Milan) and low (Rome) epidemic circulation, according to clinical data. Overall, 6 out of 12 samples were positive. One of the positive results was obtained in a Milan wastewater sample collected a few days after the first notified Italian case of autochthonous SARS-CoV-2.The study shows that WBE has the potential to be applied to SARS-CoV-2 as a sensitive tool to study spatial and temporal trends of virus circulation in the population.
Effects of latency and age structure on the dynamics and containment of COVID-19
Authors: Blyuss K.B. , Kyrychko Y.N. 
Published: April 2020

AbstractIn this paper we develop an SEIR-type model of COVID-19, with account for two particular aspects: non-exponential distribution of incubation and recovery periods, as well as age structure of the population. For the mean-field model, which does not distinguish between different age groups, we demonstrate that including a more realistic Gamma distribution of incubation and recovery periods may not have an effect on the total number of deaths and the overall size of an epidemic, but it has a major effect in terms of increasing the peak numbers of infected and critical care cases, as well as on changing the timescales of an epidemic, both in terms of time to reach the peak, and the overall duration of an outbreak. In order to obtain more accurate estimates of disease progression and investigate different strategies for introducing and lifting the lockdown, we have also considered an age-structured version of the model, which has allowed us to include more accurate data on age-specific rates of hospitalisation and COVID-19 related mortality. Applying this model to three comparable neighbouring regions in the UK has delivered some fascinating insights regarding the effect of quarantine in regions with different population structure. We have discovered that for a fixed quarantine duration, the timing of its start is very important in the sense that the second epidemic wave after lifting the quarantine can be significantly smaller or larger depending on the specific population structure. Also, the later the fixed-duration quarantine is introduced, the smaller is the resulting final number of deaths at the end of the outbreak. When the quarantine is introduced simultaneously for all regions, increasing quarantine duration postpones and slightly reduces the epidemic peak, though without noticeable differences in peak magnitude between different quarantine durations.
HRIBO- High-throughput analysis of bacterial ribosome profiling data
Authors: Gelhausen Rick , Heyl Florian , Svensson Sarah L. , Froschauer Kathrin , Hadjeras Lydia , Sharma Cynthia M. , Eggenhofer Florian , Backofen Rolf 
Published: April 2020

AbstractMotivationRibosome profiling (Ribo-seq) is a powerful approach based on ribosome-protected RNA fragments to explore the translatome of a cell, and is especially useful for the detection of small proteins (<=70 amino acids) that are recalcitrant to biochemical andin silicoapproaches. While pipelines are available to analyze Ribo-seq data, none are designed explicitly for the analysis of Ribo-seq data from prokaryotes, nor are they focused on the discovery of unannotated open reading frames (ORFs) in bacteria.ResultsWe presentHRIBO(High-throughput annotation by Ribo-seq), a workflow to enable reproducible and high-throughput analysis of bacterial Ribo-seq data. The workflow performs all required pre-processing and quality control steps. Importantly,HRIBOoutputs annotation-independent ORF predictions based on two complementary bacteria-focused tools, and integrates them with additional features. This facilitates the rapid discovery of novel ORFs and their prioritization for functional characterization.AvailabilityHRIBOis a free and open source project available under the GPL-3 license at:https://github.com/RickGelhausen/HRIBO
Geometric model incorporating prey’s turn and predator attack endpoint explains multiple preferred escape trajectories
Authors: Kawabata Yuuki , Akada Hideyuki , Shimatani Ken-ichiro , Nishihara Gregory N. , Kimura Hibiki , Nozomi Nishiumi , Domenici Paolo 
Published: April 2020

AbstractThe escape trajectory (ET) of prey – measured as the angle relative to the predator’s approach path – plays a major role in avoiding predation. Previous geometric models predict a single ET; however, many species show highly variable ETs with multiple preferred directions. Although such a high ET variability may confer unpredictability to avoid predation, the reasons why animals prefer specific multiple ETs remain unclear. Here, we constructed a novel geometric model that incorporates the time required for prey to turn and the predator’s position at the end of its attack. The optimal ET was determined by maximizing the time difference of arrival at the edge of the safety zone between the prey and predator. By fitting the model to the experimental data of fishPagrus majorwe show that the model can clearly explain the observed multiple preferred ETs. By using the same model, we were able to explain different patterns of ETs empirically observed in other species (e.g., insects and frogs): a single preferred ET and multiple preferred ETs at small (20–50°) and large (150–180°) angles from the predator. Our results open new avenues of investigation for understanding how animals choose their ETs from behavioral and neurosensory perspectives.
Drosophila suzukiioxidative stress response involvesJhehgene cluster but not transposable elements
Authors: Marin Pierre , Jacquet Angelo , Henri Hélène , Gibert Patricia , Vieira Cristina 
Published: April 2020

ABSTRACTThe study of the mechanisms involved in adaptation remains a timely issue, particularly in the context of global changes. To better understand these mechanisms of rapid adaptation, invasive species are a good model because they are subjected to new and/or different environmental factors. Using different lines of different geographical origin of the invasive pestDrosophila suzukii, we characterized the phenotypic response to oxidative stress. Subsequently, we tested the involvement of theJhehgene cluster in this response and the possible role of transposable elements. We show that the resistance to oxidative stress of the lines appears to be related to their invasive status and we confirm the role of theJhehgene cluster in this response. We have not identified any transposable elements in this gene region that could influence the expression of the gene.Summary statementThe responses to oxidative stress of the invasive species, Drosophila suzukii, show variability between genotypes related to their invasion status. The genes of the juvenile hormone epoxide hydrolase cluster are involved in this response.
Predicting microbiomes through a deep latent space
Authors: García-Jiménez Beatriz , Muñoz Jorge , Cabello Sara , Medina Joaquín , Wilkinson Mark D. 
Published: April 2020

AbstractMotivationMicrobial communities influence their environment by modifying the availability of compounds such as nutrients or chemical elicitors. Knowing the microbial composition of a site is therefore relevant to improving productivity or health. However, sequencing facilities are not always available, or may be prohibitively expensive in some cases. Thus, it would be desirable to computationally predict the microbial composition from more accessible, easily-measured features.ResultsIntegrating Deep Learning techniques with microbiome data, we propose an artificial neural network architecture based on heterogeneous autoencoders to condense the long vector of microbial abundance values into a deep latent space representation. Then, we design a model to predict the deep latent space and, consequently, to predict the complete microbial composition using environmental features as input. The performance of our system is examined using the rhizosphere microbiome of Maize. We reconstruct the microbial composition (717 taxa) from the deep latent space (10 values) with high fidelity (¿0.9 Pearson correlation). We then successfully predict microbial composition from environmental variables such as plant age, temperature or precipitation (0.73 Pearson correlation, 0.42 Bray-Curtis). We extend this to predict microbiome composition under hypothetical scenarios, such as future climate change conditions. Finally, via transfer learning, we predict microbial composition in a distinct scenario with only a hundred sequences, and distinct environmental features. We propose that our deep latent space may assist microbiome-engineering strategies when technical or financial resources are limited, through predicting current or future microbiome compositions.AvailabilitySoftware, results, and data are available athttps://github.com/jorgemf/DeepLatentMicrobiome
C. elegans BRC-1-BRD-1 functions at an early step of DSB processing and inhibits supernumerary crossovers during male meiosis
Authors: Li Qianyan , Hariri Sara , Engebrecht JoAnne 
Published: April 2020

AbstractMeiosis is regulated in a sex-specific manner to produce two distinct gametes, sperm and oocytes, for sexual reproduction. To determine how meiotic recombination is regulated in spermatogenesis, we analyzed the meiotic phenotypes of mutants in the tumor suppressor E3 ubiquitin ligase BRC-1-BRD-1 complex inCaenorhabditis elegansmale meiosis. Unlike in mammals, this complex is not required for meiotic sex chromosome inactivation, the process whereby hemizygous sex chromosomes are transcriptionally silenced. Interestingly,brc-1andbrd-1mutants showed meiotic recombination phenotypes that are largely opposing to those previously reported for female meiosis. Fewer meiotic recombination foci marked by the recombinase RAD-51 were observed inbrc-1andbrd-1mutants, and the reduction in RAD-51 foci can be suppressed by mutation of nonhomologous end joining proteins. We show that concentration of BRC-1-BRD-1 to sites of meiotic recombination is dependent on DNA end resection, suggesting that BRC-1-BRD-1 regulates the processing of meiotic double strand breaks to promote repair by homologous recombination, similar to a role for the complex in somatic cells. We also show that BRC-1-BRD-1 is important to promote progeny viability when male meiosis is perturbed by mutations that block the pairing and synapsis of different chromosome pairs, although the complex is not required to stabilize the RAD-51 filament as in female meiosis under the same conditions. Analyses of crossover designation and formation reveal that BRC-1-BRD-1 inhibits supernumerary crossovers when meiosis is perturbed. Together, our findings suggest that BRC-1-BRD-1 regulates different aspects of meiotic recombination in male and female meiosis.
Permutation-based Identification of Important Biomarkers for Complex Diseases via Black-box Models
Authors: Mi Xinlei , Zou Baiming , Zou Fei , Hu Jianhua 
Published: April 2020

AbstractStudy of human disease remains challenging due to convoluted disease etiologies and complex molecular mechanisms at genetic, genomic, and proteomic levels. Many machine learning-based methods, including deep learning and random forest, have been developed and widely used to alleviate some analytic challenges in complex human disease studies. While enjoying the modeling flexibility and robustness, these model frameworks suffer from non-transparency and difficulty in interpreting the role of each individual feature due to their intrinsic black-box natures. However, identifying important biomarkers associated with complex human diseases is a critical pursuit towards assisting researchers to establish novel hypotheses regarding prevention, diagnosis and treatment of complex human diseases. Herein, we propose a Permutation-based Feature Importance Test (PermFIT) for estimating and testing the feature importance, and for assisting interpretation of individual feature in various black-box frameworks, including deep neural networks, random forests, and support vector machines. PermFIT (available athttps://github.com/SkadiEye/deepTL) is implemented in a computationally efficient manner, without model refitting for each permuted data. We conduct extensive numerical studies under various scenarios, and show that PermFIT not only yields valid statistical inference, but also helps to improve the prediction accuracy of black-box models with top selected features. With the application to the Cancer Genome Atlas (TCGA) kidney tumor data and the HITChip atlas BMI data, PermFIT clearly demonstrates its practical usage in identifying important biomarkers and boosting performance of black-box predictive models.