bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cervical Whole Slide Histology Image Analysis Toolbox
Authors: Sornapudi Sudhir , Addanki Ravitej , Stanley R. Joe , Stoecker William V. , Long Rodney , Zuna Rosemary , Frazier Shellaine R. , Antani Sameer 
Published: July 2020

AbstractCervical intraepithelial neoplasia (CIN) is regarded as a potential precancerous state of the uterine cervix. Timely and appropriate early treatment of CIN can help reduce cervical cancer mortality. Accurate estimation of CIN grade correlated with human papillomavirus (HPV) type, which is the primary cause of the disease, helps determine the patient’s risk for developing the disease. Colposcopy is used to select women for biopsy. Expert pathologists examine the biopsied cervical epithelial tissue under a microscope. The examination can take a long time and is prone to error and often results in high inter- and intra-observer variability in outcomes. We propose a novel image analysis toolbox that can automate CIN diagnosis using whole slide image (digitized biopsies) of cervical tissue samples. The toolbox is built as a four-step deep learning model that detects the epithelium regions, segments the detected epithelial portions, analyzes local vertical segment regions, and finally classifies each epithelium block with localized attention. We propose an epithelium detection network in this study and make use of our earlier research on epithelium segmentation and CIN classification to complete the design of the end-to-end CIN diagnosis toolbox. The results show that automated epithelium detection and segmentation for CIN classification yields comparable results to manually segmented epithelium CIN classification. This highlights the potential as a tool for automated digitized histology slide image analysis to assist expert pathologists.
The answer lies in the energy: how simple atomistic molecular dynamics simulations may hold the key to epitope prediction on the fully glycosylated SARS-CoV-2 spike protein
Authors: Serapian Stefano , Marchetti Filippo , Triveri Alice , Morra Giulia , Meli Massimiliano , Moroni Elisabetta , Sautto Giuseppe A. , Rasola Andrea , Colombo Giorgio 
Published: July 2020

AbstractBetacoronavirus SARS-CoV-2 is posing a major threat to human health and its diffusion around the world is having dire socioeconomical consequences. Thanks to the scientific community’s unprecedented efforts, the atomic structure of several viral proteins has been promptly resolved. As the crucial mediator of host cell infection, the heavily glycosylated trimeric viral Spike protein (S) has been attracting the most attention and is at the center of efforts to develop antivirals, vaccines, and diagnostic solutions.Herein, we use an energy-decomposition approach to identify antigenic domains and antibody binding sites on the fully glycosylated S protein. Crucially, all that is required by our method are unbiased atomistic molecular dynamics simulations; no prior knowledge of binding properties or ad hoc combinations of parameters/measures extracted from simulations is needed. Our method simply exploits the analysis of energy interactions between all intra-protomer aminoacid and monosaccharide residue pairs, and cross-compares them with structural information (i.e., residueresidue proximity), identifying potential immunogenic regions as those groups of spatially contiguous residues with poor energetic coupling to the rest of the protein.Our results are validated by several experimentally confirmed structures of the S protein in complex with anti- or nanobodies. We identify poorly coupled sub-domains: on the one hand this indicates their role in hosting (several) epitopes, and on the other hand indicates their involvement in large functional conformational transitions. Finally, we detect two distinct behaviors of the glycan shield: glycans with stronger energetic coupling are structurally relevant and protect underlying peptidic epitopes; those with weaker coupling could themselves be poised for antibody recognition. Predicted Immunoreactive regions can be used to develop optimized antigens (recombinant subdomains, synthetic (glyco)peptidomimetics) for therapeutic applications.
Platinum resistance induces diverse evolutionary trajectories in high grade serous ovarian cancer
Authors: Hoare J.I. , Hockings H. , Saxena J. , Silva V.L. , Maniati E. , Mirza H.B. , Huang W. , Wood G.E. , Nicolini F. , Graham T.A. , McNeish I.A. , Lockley M. 
Published: July 2020

AbstractResistance to therapy is an enduring challenge in cancer care. Here we interrogate this critical unmet need using high grade serous ovarian cancer (HGSC) as a disease model. We have generated a unique panel of platinum-resistant HGSC models and shown that they share multiple transcriptomic features with relapsed human HGSC. Moreover, they evolve diversein vivophenotypes reflecting the human disease. We previously characterised copy number signatures in HGSC that correlate with patient survival and now provide the first evidence that these signatures undergo recurrent alterations during platinum therapy. Furthermore, specific, resistance-associated signature change is associated with functionally relevant gene expression differences. For example, reduced signature 3 (BRCA1/2-related homologous recombination deficiency) is associated with increased expression of homologous recombination repair genes (Rad51C, Rad51D, BRCA1) and DNA recombination pathway enrichment. Our mechanistic examination therefore provides new and clinically relevant insights into the genomic evolution of platinum-resistant cancers.
Infectivity, susceptibility, and risk factors associated with SARS-CoV-2 transmission under intensive contact tracing in Hunan, China
Authors: Hu Shixiong , Wang Wei , Wang Yan , Litvinova Maria , Luo Kaiwei , Ren Lingshuang , Sun Qianlai , Chen Xinghui , Zeng Ge , Li Jing , Liang Lu , Deng Zhihong , Zheng Wen , Li Mei , Yang Hao , Guo Jinxin , Wang Kai , Chen Xinhua , Liu Ziyan , Yan Han , Shi Huilin , Chen Zhiyuan , Zhou Yonghong , Sun Kaiyuan , Vespignani Alessandro , Viboud Cécile , Gao Lidong , Ajelli Marco , Yu Hongjie 
Published: July 2020

AbstractBackgroundSeveral parameters driving the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain unclear, including age-specific differences in infectivity and susceptibility, and the contribution of inapparent infections to transmission. Robust estimates of key time-to-event distributions remain scarce as well.MethodsWe collected individual records for 1,178 SARS-CoV-2 infected individuals and their 15,648 contacts identified by contact tracing and monitoring over the period from January 13 to April 02, 2020 in Hunan Province, China. We provide descriptive statistics of the characteristics of cases and their close contacts; we fitted distributions to time-to-key-events distributions and infectiousness profile over time; and we used generalized linear mixed model to estimate risk factors for susceptibility and transmissibility of SARS-CoV-2.ResultsWe estimated the mean serial interval at 5.5 days (95%CI −5.0, 19.9) and the mean generation time at 5.5 days (95%CI 1.7, 11.6). The infectiousness was estimated to peak 1.8 days before symptom onset, with 95% of transmission events occurring between 7.6 days before and 7.3 days after the date of symptom onset. The proportion of pre-symptomatic transmission was estimated to be 62.5%. We estimated that at least 3.5% of cases were generated asymptomatic individuals. SARS-CoV-2 transmissibility was not significantly different between working-age adults (15-59 years old) and other age groups (0-14 years old: p-value=0.16; 60 years and over: p-value=0.33), whilst susceptibility to SARS-CoV-2 infection was estimated to increase with age (p-value=0.03). In addition, transmission risk was higher for household contacts (p-value<0.001), decreased for higher generations within a cluster (second generation: odds ratio=0.13, p-value<0.001; generations 3-4: odds ratio=0.05, p-value<0.001, relative to generation 1), and decreased for infectors with a larger number of contacts (p-value=0.04).InterpretationOur findings warn of the possible relevant contribution of children to SARS-CoV-2 transmission. When lockdown interventions are in place, we found that odds of transmission are highest in the household setting but, with the relaxation of interventions, other settings (including schools) could bear a higher risk of transmission. Moreover, the estimated relevant fraction of pre-symptomatic and asymptomatic transmission highlight the importance of large-scale testing, contact tracing activities, and the use of personnel protective equipment during the COVID-19 pandemic.
SARS-CoV-2 serosurvey in Health Care Workers of the Veneto Region
Authors: Mario Plebani , Andrea Padoan , Ugo Fedeli , Elena Schievano , Elena Vecchiato , Giuseppe Lippi , Giuliana Lo Cascio , Stefano Porru , Giorgio Palù 
Published: July 2020

AbstractBackgroundThe ongoing outbreak of coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses formidable challenges to all health care systems. Serological assays may improve disease management when appropriately used, for better understanding the antibody responses mounted upon SARS-CoV-2 infection and for assessing its real prevalence. Although testing the whole population is impratical, well-designed serosurveys in selected subpopulations in specific risk groups may provide valuable information.Aimwe evaluated the prevalence of SARS-CoV-2 infection in health care workers who underwent molecular testing with reverse transcription real-time polymerase chain reaction (rRT-PCR) in the main hospitals of the Veneto Region by measuring specific antibodies (Abs).Methodsboth IgM and IgG antibodies against SARS-Cov-2 S-antigen and N-protein were measured using a validated chemiluminescent analytical system (CLIA) called Maglumi™ 2000 Plus (New Industries Biomedical EngineeringCo., Ltd [Snibe], Shenzhen, China)ResultsA total of 8285 health care workers were tested. SARS-CoV-2 specific antibodies (IgM, IgG or both) were detectable in 378 cases (4.6%, 95% CI 4.1-5.0%). Seroconversion was observed in 4.4% women and 5% men, but the difference was not significant. Although detectable antibodies were found in all severe COVID-19 patients (100%), lower seropositivity was found in mild disease (83%) and the lowest prevalence (58%) was observed in asymptomatic subjects.ConclusionSeroprevalence surveys are of utmost importance for understanding the rate of population that has already developed antibodies against SARS-CoV-2. The present study has the statistical power to define precisely the circulation of SARS-CoV-2 in a cohort of health workers in our region, with its prevalence (4.6%) reflecting a relatively low circulation. Symptomatic individuals or those hospitalized for medical care were 100% antibody positive, whilst Abs were only detectable in 58% of asymptomatic carriers.
An novel epidemiological model for COVID-19
Authors: Gaspari Mauro 
Published: July 2020

AbstractCOVID-19 is characterized by a large number of asymptomatic and mild cases that are difficult to detect; most of them remain unknown, still having an important role in the transmission of the disease, this make the pandemic difficult to control. The purpose of this research is to develop an epidemiological model that allow to estimate the number of unknown/asymptomatic cases in a given area.The SEIAMPR system, a novel simulation based model for COVID-19 is designed and implemented in Python. The intuition of the model is simple: about 80% of COVID-19 infected people evolve as asymptomatic or with a mild clinical course, many of them remain unknown to the authorities, some of them including those in critical conditions are eventually detected and classified as positive cases. The simulator reproduces this process using an adaptive method integrated with official data.The simulator has been used for modelling the outbreak in 21 regions in Italy. The positive effects of lockdown policies are demonstrated: unknown active cases 12 days after the lockdown (March the 21th) ranged from 284101 to 374038, e.g. many more than all the official cases in Italy, reducing to 10213/20949 the reopening day. The number of unknown active cases at the beginning of June in the Lombardia region ranged from 6813 to 13390 demanding particular attention.SEIAMPR is simple to tune and integrate with official data, it emerges as an up-and-coming tool for reporting the effect of lockdown measures, the impact of the disease on the population, and the remaining unknown active cases for evaluating the timing of exit strategies.
A machine learning aided global diagnostic and comparative tool to assess effect of quarantine control in Covid-19 spread
Authors: Dandekar Raj , Rackauckas Chris , Barbastathis George 
Published: July 2020

1SUMMARYWe have developed a globally applicable diagnostic Covid-19 model by augmenting the classical SIR epidemiological model with a neural network module. Our model does not rely upon previous epidemics like SARS/MERS and all parameters are optimized via machine learning algorithms employed on publicly available Covid-19 data. The model decomposes the contributions to the infection timeseries to analyze and compare the role of quarantine control policies employed in highly affected regions of Europe, North America, South America and Asia in controlling the spread of the virus. For all continents considered, our results show a generally strong correlation between strengthening of the quarantine controls as learnt by the model and actions taken by the regions’ respective governments. Finally, we have hosted our quarantine diagnosis results for the top 70 affected countries worldwide, on a public platform, which can be used for informed decision making by public health officials and researchers alike.Article Summary LineData-driven epidemiological model to quantify and compare quarantine control policies in controlling COVID-19 spread in Europe, North America, South America and Asia.
Relative Contributions of Family History and a Polygenic Risk Score on COPD and Related Outcomes: COPDGene and ECLIPSE studies
Authors: Moll Matthew , Lutz Sharon M. , Ghosh Auyon J , Sakornsalkopat Phuwanat ,   International COPD Genetics Consortium, Hersh Craig , Beaty Terri H , Dudbridge Frank , Tobin Martin D , Mittleman Murray A , Silverman Edwin K. , Hobbs Brian D. , Cho Michael H. 
Published: July 2020

AbstractIntroductionFamily history is a risk factor for chronic obstructive pulmonary disease (COPD). We previously developed a COPD risk score from genome-wide genetic markers (polygenic risk score, or PRS). Whether the PRS and family history provide complementary or redundant information for predicting COPD and related outcomes is unknown.MethodsWe assessed the predictive capacity of family history and PRS on COPD and COPD-related outcomes in European ancestry subjects from the COPDGene and ECLIPSE studies. We also performed interaction and mediation analyses.ResultsIn COPDGene, family history and PRS were significantly associated with COPD in a single model (PFamHx = 1.6e-12; PPRS = 5.0e-92). Similar trends were seen in ECLIPSE. Area-under-the-receiver-operator-characteristic-curves (AUCs) for family history, PRS, and the combined predictors for COPD were 0.752, 0.798, and 0.803, respectively. The AUC for a model containing both family history and the PRS was significantly higher than models with PRS (p = 0.00035) or family history (p = 6.1e-29) alone. Both family history and PRS were significantly associated with BODE, SGRQ, and multiple measures of quantitative emphysema and airway thickness. There was a weakly positive interaction between family history and the PRS under the additive, but not the multiplicative scale (RERI = 0.48, p=0.04). Mediation analysis found 16.5% of the effect of family history on risk for COPD was mediated through the PRS [95% CI: 9.4%-24.3%].ConclusionFamily history and the PRS provide complementary information for predicting COPD and related outcomes. Future studies can address the impact of obtaining both measures in clinical practice.
COVID-19: Data-Driven Mean-Field-Type Game Perspective
Authors: Tembine Hamidou 
Published: July 2020

AbstractIn this article, a class of mean-field-type games with discrete-continuous state spaces is considered. We establish Bellman systems which provide sufficiency conditions for mean-field-type equilibria in state-and-mean-field-type feedback form. We then derive unnormalized master adjoint systems (MASS). The methodology is shown to be flexible enough to capture multi-class interaction in epidemic propagation in which multiple authorities are risk-aware atomic decision-makers and individuals are risk-aware non-atomic decision-makers. Based on MASS, we present a data-driven modelling and analytics for mitigating Coronavirus Disease 2019 (COVID-19). The model integrates untested cases, age-structure, decision-making, gender, pre-existing health conditions, location, testing capacity, hospital capacity, mobility map on local areas, in-city, inter-cities, and international. It shown that the data-driven model can capture most of the reported data on COVID-19 on confirmed cases, deaths, recovered, number of testing and number of active cases in 66+ countries. The model also reports non-Gaussianity and non-exponential properties in 15+ countries.
Weather, Social Distancing, and the Spread of COVID-19*
Authors: Wilson Daniel J. 
Published: July 2020

AbstractUsing high-frequency panel data for U.S. counties, I estimate the full dynamic response of COVID-19 cases and deaths to exogenous movements in mobility and weather. I find several important results. First, holding mobility fixed, temperature is found to have a negative and significant effect on COVID-19 cases from 1 to 8 weeks ahead and on deaths from 2 to 8 weeks ahead. Second, holding weather fixed, mobility is found to have a large positive effect on subsequent growth in COVID-19 cases and deaths. The impact on cases becomes significant 3 to 4 weeks ahead and continues through 8 to 10 weeks ahead. The impact on deaths becomes significant around 4 weeks ahead and persists for at least 10 weeks. Third, I find that the deleterious effects of mobility on COVID-19 outcomes are far greater when the local virus transmission rate is above one – evidence supportive of public health policies aiming to reduce mobility specifically in places experiencing high transmission rates while relaxing restrictions elsewhere. Fourth, I find that the dynamic effects of mobility on cases are generally similar across counties, but the effects on deaths are higher for counties with older populations and, surprisingly, counties with lower black or hispanic population shares. Lastly, I find that while the marginal impact of mobility changes has been stable over recent weeks for cases, it has come down for deaths.
Lifting mobility restrictions and the induced short-term dynamics of COVID-19
Authors: Santana-Cibrian Mario , Acuña-Zegarra Manuel A. , Velasco-Hernandez Jorge X. 
Published: July 2020

AbstractSARS-CoV-2 has now infected 15 million people and produced more than six hundred thousand deaths around the world. Due to high transmission levels, many governments implemented social-distancing measures and confinement with different levels of required compliance to mitigate the COVID-19 epidemic. In several countries, these measures were effective, and it was possible to flatten the epidemic curve and control it. In others, this objective was not or has not been achieved. In far to many cities around the world rebounds of the epidemic are occurring or, in others, plateau-like states have appeared where high incidence rates remain constant for relatively long periods of time. Nonetheless, faced with the challenge of urgent social need to reactivate their economies, many countries have decided to lift mitigation measures at times of high incidence. In this paper, we use a mathematical model to characterize the impact of short duration transmission events within the confinement period previous but close to the epidemic peak. The model describes too, the possible consequences on the disease dynamics after mitigation measures are lifted. We use Mexico City as a case study. The results show that events of high mobility may produce either a later higher peak, a long plateau with relatively constant but high incidence or the same peak as in the original baseline epidemic curve, but with a post-peak interval of slower decay. Finally, we also show the importance of carefully timing the lifting of mitigation measures. If this occurs during a period of high incidence, then the disease transmission will rapidly increase, unless the effective contact rate keeps decreasing, which will be very difficult to achieve once the population is released.
barcodetrackR: an R package for the interrogation of clonal tracking data
Authors: Espinoza Diego A. , Mortlock Ryland D. , Koelle Samson J. , Wu Chuanfeng , Dunbar Cynthia E. 
Published: July 2020

ABSTRACTClonal tracking methods provide quantitative insights into the cellular output of genetically labelled progenitor cells across time and cellular compartments. In the context of gene and cell therapies, clonal tracking methods have enabled the tracking of progenitor cell output both in humans receiving cellular therapies and in corresponding animal models, providing valuable insight into lineage reconstitution, clonal dynamics, and vector genotoxicity. However, the absence of a toolbox by which to interrogate these data has precluded the development of standardized analytical frameworks within the field. Thus, we developedbarcodetrackR, an R package that provides users with tools for the analysis and visualization of clonal dynamics across time and cellular compartments in clonal tracking experiments. Here, we demonstrate the utility ofbarcodetrackRin exploring longitudinal clonal patterns and lineage relationships in the context of a number of clonal tracking studies of hematopoietic stem and progenitor cells (HSPCs) in humans receiving HSPC gene therapy and in animals receiving lentivirally transduced HSPC transplants.
Differential Mast Cell Phenotypes in Benign versus Cancer Tissues and Prostate Cancer Oncologic Outcomes
Authors: Sullivan Heidi Hempel , Maynard Janielle P. , Heaphy Christopher M. , Lu Jiayun , De Marzo Angelo M. , Lotan Tamara L. , Joshu Corinne E. , Sfanos Karen S. 
Published: July 2020

AbstractWe previously reported that high numbers of mast cells in benign (extra-tumoral) regions of the prostate are associated with worse outcomes after radical prostatectomy including biochemical recurrence and the development of metastases. Herein, on a cohort of 384 men, we performed mast cell subtyping and report that higher minimum number of the tryptase-only (MCT) subset of extra-tumoral mast cells is associated with increased risk of biochemical recurrence (comparing highest to lowest tertiles: HR 2.20, 95% CI 1.32-3.65; P-trend 0.004), metastases (HR 3.60, 95% CI 1.77-7.36; P-trend 0.001), and death from prostate cancer (HR 2.96, 95% CI 1.23-7.08; P-trend 0.02). RNAsequencing of benign versus cancer tissue mast cells revealed differential expression of additional site-specific genes. We demonstrate that genes more highly expressed in tumor-infiltrating mast cells, such as CXCR4 and TFE3, represent an altered tumor microenvironment. C-kit variants were also differentially expressed in benign versus cancer tissue mast cells, with C-kit variant 1 (GNNK+) mast cells identified as more prevalent in extra-tumoral regions of the prostate. Finally, using an established mouse model, we found that mast cells do not infiltrate Hi-Myc tumors, providing a model to specifically examine the role of extra-tumoral mast cells in tumorigenesis. Hi-Myc mice crossed to mast cell knockout (Wsh) mice and aged to one year revealed a higher degree of pre-invasive lesions and invasive cancer in wildtype mice versus heterozygous and knockout mice. This suggests a dosage effect where higher numbers of extra-tumoral mast cells resulted in higher cancer invasion. Overall, our studies provide further evidence for a role of extra-tumoral mast cells in driving adverse prostate cancer outcomes.