bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling and Forecasting Trend of COVID-19 Epidemic in Iran
Authors: Ahmadi Ali , Fadaei Yasin , Shirani Majid , Rahmani Fereydoon 
Published: March 2020

Background an objectiveCOVID-19 is an emerging disease and precise data on its epidemiological profile are not available in the world and Iran. this study aimed to model and determine the epidemic trend and prediction of COVID-19 in Iran.MethodsThis study is a secondary data analysis and mathematical modeling. We used the daily reports of definitive COVID-19 patients released by Iran Ministry of Health and Medical Education. Estimated are based on current trends, Sampling of severe cases, hospitalization and tip of iceberg spread disease and asymptomatic, mild and moderate cases could not be calculated in forecasting. Epidemic projection models of logistic growth differential equations, Gompertz, Von Bertalanffy and least squared error (LSE) method were used to predict the number of cases definitive until April 3, 2020 and April28,2020.ResultsR0in Iran was estimated to be 4.7 that has now fallen to below 2. Given the assumptions in Models, and three different scenarios, the prediction of the patients on April 3, 2020 using three growth models of Von Bertalanffy, Gompertz and LSE were estimated at 19,500, 27,000, and 48,830, respectively. The number of deceased COVID-19 patients was also estimated to be 1707 individuals using the logistic growth model, 3165 ones by Von’s model and 6300 ones according to the LSE method. Assuming continuation of the predicted trend until April 3, 2020, The prediction of the number of patients based on the Gompertz’s and number of dead based on the Von’s model until control the epidemic are estimated about 31000 and 5000 near April 28, 2020 respectively.ConclusionThe process of controlling the epidemic is tangible. The most ideal scenario is the Von’s model, but it is hard to fulfill and unattainable. If enforcement and public behavior interventions continue with current trends, the control and reduction of the COVID-19 epidemic in Iran will be flat from April 28, until July, 2020 by Gompertz’s model and new cases are expected to decline from the following Iranian new year.
Intensive Care Unit Resource Planning During COVID-19 Emergency at the Regional Level: the Italian case
Authors: Guzzi Pietro Hiram , Tradigo Giuseppe , Veltri Pierangelo 
Published: March 2020

AbstractSevere acute respiratory syndrome COVID-19 (SARS-CoV-2) has been declared a worldwide emergency and a pandemic disease by the World Health Organisation (WHO). It started in China in December 2019, and it is currently rapidly spreading throughout Italy, which is now the most affected country after China. There is great attention for the diffusion and evolution of the COVID-19 infection which started from the north (particularly in the Lombardia region) and it is now rapidly affecting other Italian regions. We investigate on the impact of patients hospitalisation in Intensive Care Units (ICUs) at a regional and subregional granularity. We propose a model derived from well-known models in epidemic to estimate the needed number of places in intensive care units. The model will help decision-makers to plan resources in the short and medium-term in order to guarantee appropriate treatments to all patients needing it. We analyse Italian data at regional level up to March 15th aiming to: (i) support health and government decision-makers in gathering rapid and efficient decisions on increasing health structures capacities (in terms of ICU slots) and (ii) define a scalable geographic model to plan emergency and future COVID-19 patients management using reallocating them among health structures. Finally, the here proposed model can be useful in countries where COVID-19 is not yet strongly diffused.
Pan-cancer analysis identified inflamed microenvironment associated multi-omics signatures
Authors: Wang Ben , Liu Mengmeng , Ran Zhujie , Li Xin , Li Jie , Ou Yunsheng 
Published: March 2020

AbstractBackgroundImmunotherapy has revolutionized cancer therapy. However, responses are not universal. The inflamed tumor microenvironment has been reported to correlate with response in tumor patients. However, how different tumors shape their tumor microenvironment remains a critical unsolved problem. A deeper insight into the molecular characteristics of inflamed tumor microenvironment may be needed.Materials and methodsHere, based on single-cell RNA sequencing technology and TCGA pan-cancer cohort, we investigated multi-omics molecular features of tumor microenvironment phenotypes. Based on single-cell RNA-seq analysis, we classified pan-cancer tumor samples into inflamed or non-inflamed tumor and identified molecular features of these tumors. Analysis of integrating identified gene signatures with a drug-genomic perturbation database identified multiple drugs which may be helpful for converting non-inflamed tumors to inflamed tumors.ResultsOur results revealed several inflamed/non-inflamed tumor microenvironments-specific molecular characteristics. For example, inflamed tumors highly expressed miR-650 and lncRNA including MIR155HG and LINC00426, these tumors showed activated cytokines-related signaling pathways. Interestingly, non-inflamed tumors tended to express several genes related to neurogenesis. Multi-omics analysis demonstrated the neuro phenotype transformation may be induced by hypomethylated promoters of these genes and down-regulated miR-650. Drug discovery analysis revealed histone deacetylase inhibitors may be a potential choice for helping favorable tumor microenvironment phenotype transformation and aiding current immunotherapy.ConclusionOur results provide a comprehensive molecular-level understanding of tumor cell-immune cell interaction and may have profound clinical implications.
The tempo of trophic evolution in small-bodied primates
Authors: Scott Jeremiah E. 
Published: March 2020

AbstractObjectivesAs a primary trophic strategy, insectivory is uncommon and unevenly distributed across extant primates. This pattern is partly a function of the challenges that insectivory poses for large-bodied primates. In this study, I demonstrate that the uneven distribution is also a consequence of variation in the rate of trophic evolution among small-bodied lineages.MethodsThe sample consisted of 307 species classified by primary trophic strategy and body size, creating an ordered three-state character: small-insectivorous, small-herbivorous, and large-herbivorous. I tested for rate heterogeneity by partitioning major clades from the rest of the primate tree and estimating separate rates of transition between herbivory and insectivory for small-bodied lineages in each partition.ResultsBayesian analysis of rate estimates indicates that a model with two rates of trophic evolution provides the best fit to the data. According to the model, lorisiforms have a trophic rate that is 4–6 times higher than the rate for other small-bodied lineages.ConclusionsThe rate heterogeneity detected here suggests that lorisiforms are characterized by traits that give them greater trophic flexibility than other primates. Previous discussions of trophic evolution in small-bodied primates focused on the low frequency of insectivory among anthropoids and the possibility that diurnality makes insectivory unlikely to evolve or persist. The present study challenges this idea by showing that a common transition rate can explain the distribution of insectivory in small-bodied anthropoids and nocturnal lemurs and tarsiers. The results of this study offer important clues for reconstructing trophic evolution in early primates.
Epidemiology of seasonal coronaviruses: Establishing the context for COVID-19 emergence
Authors: Nickbakhsh Sema , Ho Antonia , Marques Diogo F.P. , McMenamin Jim , Gunson Rory N. , Murcia Pablo R. 
Published: March 2020

AbstractPublic health preparedness for coronavirus disease 2019 (COVID-19) is challenging in the absence of setting-specific epidemiological data. Here we describe the epidemiology of seasonal human coronaviruses (sCoVs) and other cocirculating viruses in the West of Scotland, UK. We analyzed routine diagnostic data for >70,000 episodes of respiratory illness tested molecularly for multiple respiratory viruses between 2005 and 2017. Statistical associations with patient age and sex differed between CoV-229E, CoV-OC43 and CoV-NL63. Furthermore, the timing and magnitude of sCoV outbreaks did not occur concurrently and coinfections were not reported. With respect to other cocirculating respiratory viruses, we found evidence of positive, rather than negative, interactions with sCoVs. These findings highlight the importance of considering cocirculating viruses in the differential diagnosis of COVID-19. Further work is needed to establish the occurrence/degree of cross-protective immunity conferred across sCoVs and with COVID-19, as well as the role of viral coinfection in COVID-19 disease severity.
Variants inSTAU2associate with metformin response in a type 2 diabetes cohort: a pharmacogenomics study using real-world electronic health record data
Authors: Zhang Yanfei , Hu Ying , Ho Kevin , Hartzel Dustin N. , Abedi Vida , Zand Ramin , Williams Marc S. , Lee Ming Ta M. 
Published: March 2020

AbstractType 2 diabetes mellitus (T2DM) is a major health and economic burden because of the seriousness of the disease and its complications. Improvements in short- and long-term glycemic control is the goal of diabetes treatment. To investigate the longitudinal management of T2DM at Geisinger, we interrogated the electronic health record (EHR) information and identified a T2DM cohort including 125,477 patients using the Electronic Medical Records and Genomics Network (eMERGE) T2DM phenotyping algorithm. We investigated the annual anti-diabetic medication usage and the overall glycemic control using hemoglobin A1c (HbA1c). Metformin remains the most frequently medication despite the availability of the new classes of anti-diabetic medications. Median value of HbA1c decreased to 7% in 2002 and since remained stable, indicating a good glycemic management in Geisinger population. Using metformin as a pilot study, we identified three groups of patients with distinct HbA1c trajectories after metformin treatment. The variabilities in metformin response is mainly explained by the baseline HbA1c. The pharmacogenomic analysis of metformin identified a missense variant rs75740279 (Leu/Val) for STAU2 associated with the metformin response. This strategy can be applied to study other anti-diabeticmedications. Such research will facilitate the translational healthcare for better T2DM management.
Predicting the epidemic trend of COVID-19 in China and across the world using the machine learning approach
Authors: Li Mengyuan , Zhang Zhilan , Jiang Shanmei , Liu Qian , Chen Canping , Zhang Yue , Wang Xiaosheng 
Published: March 2020

AbstractBackgroundAlthough COVID-19 has been well controlled in China, it is rapidly spreading outside the country and may have catastrophic results globally without implementation of necessary mitigation measures. Because the COVID-19 outbreak has made comprehensive and profound impacts on the world, an accurate prediction of its epidemic trend is significant. Although many studies have predicted the COVID-19 epidemic trend, most have used early-stage data and focused on Chinese cases.MethodsWe first built models to predict daily numbers of cumulative confirmed cases (CCCs), new cases (NCs), and death cases (DCs) of COVID-19 in China based on data from January 20, 2020, to March 1, 2020. Based on these models, we built models to predict the epidemic trend across the world (outside China). We also built models to predict the epidemic trend in Italy, Spain, Germany, France, UK, and USA where COVID-19 is rapidly spreading.ResultsThe COVID-19 outbreak will have peaked on February 22, 2020, in China and will peak on May 22, 2020, across the world. It will be basically under control in early April 2020 in China and late August 2020 across the world. The total number of COVID-19 cases will reach around 89,000 in China and 6,126,000 across the world during the epidemic. Around 4,000 and 290,000 people will die of COVID-19 in China and across the world, respectively. The COVID-19 outbreak will have peaked recently in Italy and will peak in Spain, Germany, France, UK, and USA within two weeks.ConclusionThe COVID-19 outbreak is controllable in the foreseeable future if comprehensive and stringent control measures are taken.
Prediction of protein mutational free energy: benchmark and sampling improvements increase classification accuracy
Authors: Frenz Brandon , Lewis Steven , King Indigo , Park Hahnbeom , DiMaio Frank , Song Yifan 
Published: March 2020

AbstractSoftware to predict the change in protein stability upon point mutation is a valuable tool for a number of biotechnological and scientific problems. To facilitate the development of such software and provide easy access to the available experimental data, the ProTherm database was created. Biases in the methods and types of information collected has led to disparity in the types of mutations for which experimental data is available. For example, mutations to alanine are hugely overrepresented whereas those involving charged residues, especially from one charged residue to another, are underrepresented. ProTherm subsets created as benchmark sets that do not account for this often underrepresented certain mutational types. This issue introduces systematic biases into previously published protocols’ ability to accurately predict the change in folding energy on these classes of mutations. To resolve this issue, we have generated a new benchmark set with these problems corrected. We have then used the benchmark set to test a number of improvements to the point mutation energetics tools in the Rosetta software suite.
The p53-p21 axis plays a central role in lymphatic homeostasis and disease
Authors: Mylavarapu Rohan , Kulikauskas Molly R. , Dierkes Cathrin , Sobhani Nema , Mangette Michelle , Finlon Jeffrey , Stevens Wanida , Arbab Farinaz , Box Neil F. , Lovell Mark , Muley Ajit , Shawber Carrie J. , Tamburini Beth , Kiefer Friedemann , Terzian Tamara 
Published: March 2020

AbstractActivation of the transcription factor p53 has been associated with several developmental syndromes. In normal tissues, p53 is kept at very low undetectable physiological levels. When triggered by cellular stressors, p53 prompts important anti-proliferative and apoptotic programs part of its tumor suppressor activity or as the guardian of tissue homeostasis.We generated two murine models that display cutaneous hemorrhaging, severe edema, and distended blood-filled lymphatic vessels at late-gestation due to overactive p53 uniquely affecting lymphatic endothelial cells during development. Overactive p53 operated distinctively through anti-proliferative route in this tissue resulting in a decrease in initial lymphatics that normally absorb interstitial fluid. Remarkably, genetic or pharmacologic normalization of p53 restored lymphatic homeostasis and reversed lymphatic phenotypes. In parallel, several human lymphatic disease tissues exhibited high p53 levels exclusively in the lymphatic endothelium while p53 remained undetectable in surrounding arterial or venous vessels.We report here, for the first time, an extended role that the p53 pathway plays in the genesis of lymphatic homeostasis deficiencies opening the way for new therapeutic avenues for these rare, poorly understood, and incurable lymphatic maladies.
Polycyclic Aromatic Hydrocarbon, Heavy Metal, and Derivable Metabolic Water and Energy of Catle Hides Processed by Singeing
Authors: Woko Eze C. , Ibegbulem C. O. , Alisi Chinwe S. 
Published: March 2020

AbstractMeat is consumed as source of protein, processed cattle hide popularly known asKandain southeastern Nigeria is consumed as a substitute for meat. The commercial method of processing this very food delicacy through singeing with scrap tyre, or firewood has posed question marks over the contamination status of theKanda. This study therefore investigated the heavy metal, polycyclic aromatic hydrocarbon (PAHs) content, proximate composition, and also calculated the derivable metabolic water and energy content ofKandasinged with scrap tyre, and firewood. Singeing significantly reduced (p < 0.05) the moisture, ash, fibre and carbohydrate contents of the hide while it increased the protein content; increased (p < 0.05) the iron (Fe) concentration of the hide though the concentration of iron detected was far below the average lethal dose allowed in meat and meat products and elevated (p < 0.05) the PAH concentration of the hides. However, the hides singed with firewood showed higher (p < 0.05) concentrations of benzo[a]pyrene. With the exception of naphthalene, 2-methylnaphthalene, acenenaphthalene and indeno [1,2,3-cd] pyrene all the 16 Environmental Protection Agency (EPA) priority PAHs evaluated showed concentration above 0.0001 ppm (0.1 μg/kg) in at least one of the singed samples. Also, Singeing significantly increased (p < 0.05) the derivable metabolic water and energy contents of the cattle hides. In conclusion, while consuming singed cattle hide (Kanda) may offer higher energy content and derivable metabolic water, it may in the long term expose the consumer to health hazards associated with heavy metal and PAH contamination.
Profiling microglia from AD donors and non-demented elderly in acute human post-mortem cortical tissue
Authors: Alsema Astrid M. , Jiang Qiong , Kracht Laura , Gerrits Emma , Dubbelaar Marissa L. , Miedema Anneke , Brouwer Nieske , Woodbury Maya , Wachter Astrid , Xi Hualin S. , Möller Thomas , Biber Knut P. , Kooistra Susanne M. , Boddeke Erik W.G.M , Eggen Bart J.L. 
Published: March 2020

AbstractMicroglia are the tissue-resident macrophages of the central nervous system (CNS). Recent studies based on bulk and single-cell RNA sequencing in mice indicate high relevance of microglia with respect to risk genes and neuro-inflammation in Alzheimer’s disease. Here, we investigated microglia transcriptomes at bulk and single cell level in non-demented elderly and AD donors using acute human post-mortem cortical brain samples. We identified 9 human microglial subpopulations with heterogeneity in gene expression. Notably, gene expression profiles and subcluster composition of microglia did not differ between AD donors and non-demented elderly in bulk RNA sequencing nor in single-cell sequencing.
Revisiting the Association between Human Leukocyte Antigen and End-Stage Renal Disease
Authors: Noureen Naila , Shah Farhad Ali , Lisec Jan , Usman Hina , Khalid Mohammad , Munir Rimsha , Zaidi Nousheen 
Published: March 2020

AbstractMultiple works have studied possible associations between human leukocyte antigen (HLA) alleles and end stage renal disease (ESRD) showing, however, contradictory and inconsistent results. Here, we revisit the association between ESRD and HLA antigens, comparing HLA polymorphism (at HLA-A, -B, -C, -DRB1, -DQB1 and DQA1 loci) in ESRD patients (n=497) and controls (n=672). Our data identified several HLA alleles that displayed a significant positive or negative association with ESRD. We also determined whether heterozygosity or homozygosity of the ESRD-associated HLA alleles at different loci could modify the prevalence of the disease. Few HLA allele combinations displayed significant associations with ESRD among which HLA-A*3–HLA-A*26 combination showed the highest strength of association (OR= 4.488, P≤ 0.05) with ESRD. Interestingly, the age of ESRD onset was not affected by HLA allele combinations at different loci. We also performed an extensive literature analysis to determine whether the association of HLA to ESRD can be similar across different ethnic groups. Our analysis showed at least for certain alleles, HLA-A*11, HLA-DRB1*11, and HLA-DRB1*4, a significant association of HLA to ESRD in different ethnic groups. The findings of our study will help in determining possible protective or susceptible roles of various HLA alleles in ESRD.
OrganoidTracker: efficient cell tracking using machine learning and manual error correction
Authors: Kok Rutger N.U. , Hebert Laetitia , Huelsz-Prince Guizela , Goos Yvonne J. , Zheng Xuan , Bozek Katarzyna , Stephens Greg J. , Tans Sander J. , van Zon Jeroen S. 
Published: March 2020

AbstractTime-lapse microscopy is routinely used to follow cells within organoids, allowing direct study of division and differentiation patterns. There is an increasing interest in cell tracking in organoids, which makes it possible to study their growth and homeostasis at the single-cell level. As tracking these cells by hand is prohibitively time consuming, automation using a computer program is required. Unfortunately, organoids have a high cell density and fast cell movement, which makes automated cell tracking difficult. In this work, a semi-automated cell tracker has been developed. To detect the nuclei, we use a machine learning approach based on a convolutional neural network. To form cell trajectories, we link detections at different time points together using a min-cost flow solver. The tracker raises warnings for situations with likely errors. Rapid changes in nucleus volume and position are reported for manual review, as well as cases where nuclei divide, appear and disappear. When the warning system is adjusted such that virtually error-free lineage trees can be obtained, still less than 2% of all detected nuclei positions are marked for manual analysis. This provides an enormous speed boost over manual cell tracking, while still providing tracking data of the same quality as manual tracking.
An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning
Authors: Bjerge Kim , Nielsen Jakob Bonde , Sepstrup Martin Videbæk , Helsing-Nielsen Flemming , Høye Toke Thomas 
Published: March 2020

AbstractInsect monitoring methods are typically very time consuming and involves substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5,675 images per night. A customized convolutional neural network was trained on 2,000 labelled images of live moths represented by eight different species, achieving a high validationF1-score of 0.93. The algorithm measured an average classification and trackingF1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.