bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mathematical Modeling and a Month Ahead Forecast of the Coronavirus Disease 2019 (COVID-19) Pandemic: An Indian Scenario
Authors: Ganiny Suhail , Nisar Owais 
Published: September 2020

AbstractIndia, the second-most populous country in the world, has been lately witnessing a daily surge in the COVID-19 infected cases. India is currently among the worst-hit nations worldwide, due to the COVID-19 pandemic, and ranks just behind Brazil and USA. In order to prevent the further worsening of the situation, predicting the future course of the pandemic is of utmost importance. In this paper, we model the past trajectory (March 01, 2020 - July 25, 2020) and make a month-long (July 26, 2020 - August 24, 2020) forecast of the future evolution of the COVID-19 pandemic in India using an autoregressive integrated moving average (ARIMA) model. According to our forecasting results, India is likely to have 3,800,989 cumulative infected cases, 1,634,142 cumulative active cases, 2,110,697 cumulative recoveries and 56,150 cumulative deaths by August 24, 2020, if the current trend of the pandemic continues to prevail. The implications of these forecasts are that in the upcoming month the infection rate of COVID-19 in India is going to escalate, while as the rate of recovery and the case-fatality rate is likely to reduce. In order to avert these possible scenarios, the administration and health-care personnel need to formulate and implement robust control measures, while the general public needs to be more responsible and strictly adhere to the established and newly formulated guidelines to slow down the spread of the pandemic and prevent it from transforming into a catastrophe.
Relationship between nursing home COVID-19 outbreaks and staff neighborhood characteristics
Authors: Shen Karen 
Published: September 2020

AbstractThe COVID-19 pandemic has taken a significant toll on nursing homes in the US, with upwards of a third of deaths occurring in nursing homes, and more in long-term care facilities. By combining data on facility-level COVID-19 deaths with facility-level data on the neighborhoods where nursing home staff reside for a sample of eighteen states, this paper finds that staff neighborhood characteristics are a large and significant predictor of COVID-19 outbreaks. One standard deviation increases in average staff tract population density, public transportation use, and non-white share were associated with 1.3 (SE .33), 1.4 (SE .35), and 0.9 (SE .24) additional deaths per 100 beds, respectively. These effects are larger than all facility management or quality variables, and larger than the effect of the nursing home’s own neighborhood characteristics. These results suggest that staff communities are likely to be an important source of infection, and that disparities in nursing home outbreaks may be related to differences in the types of neighborhoods nursing home staff live in.
A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts
Authors: Ni Guiyan , Zeng Jian , Revez Joana A , Wang Ying , Zheng Zhili , Ge Tian , Restuadi Restuadi , Kiewa Jacqueline , Nyholt Dale R , Coleman Jonathan R I , Smoller Jordan W ,   Schizophrenia Working Group of the Psychiatric Genomics Consortium,   Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Yang Jian , Visscher Peter M , Wray Naomi R 
Published: September 2020

AbstractBackgroundPolygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies (GWASs). PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, e.g., phenotype definition or technical factors.MethodsThe Psychiatric Genomics Consortium working groups for schizophrenia (SCZ) and major depressive disorder (MDD) bring together many independently collected case- control cohorts. We used these resources (31K SCZ cases, 41K controls; 248K MDD cases, 563K controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and nine methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) are compared.ResultsCompared to PC+T, the other nine methods give higher prediction statistics, MegaPRS, LDPred2 and SBayesR significantly so, up to 9.2% variance in liability for SCZ across 30 target cohorts, an increase of 44%. For MDD across 26 target cohorts these statistics were 3.5% and 59%, respectively.ConclusionsAlthough the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparison and are recommended in applications to psychiatric disorders.
On the reliability of predictions on Covid-19 dynamics: a systematic and critical review of modelling techniques
Authors: Gnanvi Janyce , Salako Valère Kolawolé , Kotanmi Brezesky , Kakaï Romain Glèlè 
Published: September 2020

AbstractSince the beginning of the new coronavirus 2019-nCoV disease (Covid-19) in December 2019, there has been an exponential number of studies using diverse modelling techniques to assess the dynamics of transmission of the disease, predict its future course and determine the impact of different control measures. In this study, we conducted a global systematic literature review to summarize trends in the modelling techniques used for Covid-19 from January 1st 2020 to June 30th 2020. We further examined the reliability and correctness of predictions by comparing predicted and observed values for cumulative cases and deaths. From an initial 2170 peer-reviewed articles and preprints found with our defined keywords, 148 were fully analyzed. We found that most studies on the modelling of Covid-19 were from Asia (52.70%) and Europe (25%). Most of them used compartmental models (SIR and SEIR) (57%) and statistical models (growth models and time series) (28%) while few used artificial intelligence (5%) and Bayesian approach (3%). For cumulative cases, the ratio predicted/observed values and the ratio of the amplitude of confidence interval (CI) or credibility interval (CrI) of predictions and the central value were on average larger than 1 (4.49 ± 9.98 and 1.10 ± 1.94, respectively) indicating cases of incorrect predictions, large uncertainty on predictions, and large variation across studies. There was no clear difference among models used for these two ratios. However, the ratio predicted/observed values was relatively smaller for SIR models than for SEIR models, indicating that more complex models might not be more accurate for predictions. We further found that values of both ratios decreased with the number of days covered by studies, indicating that the wider the time covered by the data, the higher the correctness and accuracy of predictions. In 21.62% of studies, observed values fall within the CI or CrI of the cumulative cases predicted by studies. Only six of the 148 selected studies (4.05%) predicted the number of deaths. For 33.3% of these predictions, the ratio of predicted to actual number of deaths was close to 1. We also found that the Bayesian model made predictions closer to reality than the compartmental and the statistical models, although these differences are only suggestive due to the small size of the data. Our findings suggest that while predictions made by the different models are useful to understand the pandemic course and guide policy-making, there should be cautious in their usage.
Racial disparities in COVID-19 mortality are driven by unequal infection risks
Authors: Zelner Jon , Trangucci Rob , Naraharisetti Ramya , Cao Alex , Malosh Ryan , Broen Kelly , Masters Nina , Delamater Paul 
Published: September 2020

BackgroundAs of August 5, 2020, there were more than 4.8M confirmed and probable cases and 159K deaths attributable to SARS-CoV-2 in the United States, with these numbers undoubtedly reflecting a significant underestimate of the true toll. Geographic, racial-ethnic, age and socioeconomic disparities in exposure and mortality are key features of the first and second wave of the U.S. COVID-19 epidemic.MethodsWe used individual-level COVID-19 incidence and mortality data from the U.S. state of Michigan to estimate age-specific incidence and mortality rates by race/ethnic group. Data were analyzed using hierarchical Bayesian regression models, and model results were validated using posterior predictive checks.FindingsIn crude and age-standardized analyses we found rates of incidence and mortality more than twice as high than Whites for all groups other than Native Americans. Of these, Blacks experienced the greatest burden of confirmed and probable COVID-19 infection (Age-standardized incidence = 1,644/100,000 population) and mortality (age-standardized mortality rate 251/100,000). These rates reflect large disparities, as Blacks experienced age-standardized incidence and mortality rates 5.6 (95% CI = 5.5, 5.7) and 6.9 (6.5, 7.3) times higher than Whites, respectively. We also found that the bulk of the disparity in mortality between Blacks and Whites is driven by dramatically higher rates of COVID-19 infection across all age groups, particularly among older adults, rather than age-specific variation in case-fatality rates.InterpretationThis work suggests that well-documented racial disparities in COVID-19 mortality in hard-hit settings, such as the U.S. state of Michigan, are driven primarily by variation in household, community and workplace exposure rather than case-fatality rates.FundingThis work was supported by a COVID-PODS grant from the Michigan Institute for Data Science (MIDAS) at the University of Michigan. The funding source had no role in the preparation of this manuscript.
Exploring the binding of resveratrol to an oncogene promoter DNA sequence d(CCAATTGG)2 through multispectroscopic, nuclear magnetic resonance and molecular dynamics studies
Authors: Kumar Shailendra , Kumar Peeyush , Nair Maya S. 
Published: September 2020

AbstractWe report the interaction of resveratrol with an octamer DNA sequence d(CCAATTGG)2, present in the promoter region of many oncogenes, using a combination of absorption, fluorescence, calorimetric and nuclear magnetic resonance techniques to probe the binding. Resveratrol binds to the duplex sequence with a binding constant 2.20×106 M−1 in absorption studies. A ligandduplex stoichiometry of 2.2:1 was obtained with binding constant varying from 109 to 106M−1 with the concentration of DNA varied in fluorescence titration measurements. Spectral changes indicated external binding of resveratrol to duplex DNA. Circular dichroism data displayed minimal variation suggesting external binding. Melting temperatures of DNA and its 1:1 complex showed a difference of approximately 2.25°C, which supports the external binding. Nuclear magnetic resonance data showed resveratrol binds to the minor groove region near the AT basepair from the nuclear Overhauser effect spectroscopic cross peaks. Distance restrained molecular dynamics was employed in explicit solvent condition to obtain the lowest energy structure. The complex was stable and retained the B-DNA conformation. Findings in this study identify resveratrol as a minor groove binder to the AT region of DNA and pave the way for exploring resveratrol and its analogues as promising anticancer/antibacterial drug.
In vivoandin vitromechanistic characterization of a clinically relevant PolγA mutation
Authors: Silva-Pinheiro Pedro , Pardo-Hernández Carlos , Reyes Aurelio , Tilokani Lisa , Mishra Anup , Cerutti Raffaele , Li Shuaifeng , Ho Dieu Hien , Valenzuela Sebastian , Dogan Anil Sukru , Bradley Peter , Fernandez-Silva Patricio , Trifunovic Aleksandra , Prudent Julien , Minczuk Michal , Bindoff Laurence , Macao Bertil , Zeviani Massimo , Falkenberg Maria , Viscomi Carlo 
Published: September 2020

AbstractMutations inPOLG, encoding POLγA, the catalytic subunit of the mitochondrial DNA polymerase, cause a spectrum of disorders characterized by mtDNA instability. However, the molecular pathogenesis ofPOLG-related diseases is poorly understood and efficient treatments are missing. Here, we generated aPOLGA449T/A449Tmouse model, which reproduces the most common human recessive mutation ofPOLG, encoding the A467T change, and dissected the mechanisms underlying pathogenicity. We show that the A449T mutation impairs DNA binding and mtDNA synthesis activities of POLγin vivoandin vitro. Interestingly, the A467T mutation also strongly impairs interactions with POLγB, the homodimeric accessory subunit of holo-POLγ. This allows the free POLγA to become a substrate for LONP1 protease degradation, leading to dramatically reduced levels of POLγA, which in turn exacerbates the molecular phenotypes ofPolgA449T/A449Tmice. Importantly, we validated this mechanism for other mutations affecting the interaction between the two POLγ subunits. We suggest that LONP1 dependent degradation of POLγA can be exploited as a target for the development of future therapies.
The temporal and spectral characteristics of expectations and prediction errors in pain and thermoception
Authors: Strube Andreas , Rose Michael , Fazeli Sepideh , Büchel Christian 
Published: September 2020

AbstractIn the context of a generative model, such as predictive coding, pain and heat perception can be construed as the integration of expectation and input with their difference denoted as a prediction error. In a previous neuroimaging study (Geuter et al., 2017) we observed an important role of the insula in such a model, but could not establish its temporal aspects. Here we employed electroencephalography to investigate neural representations of predictions and prediction errors in heat and pain processing. Our data show that alpha-to-beta activity was associated with stimulus intensity expectation, followed by a negative modulation of gamma band activity by absolute prediction errors. This is in contrast to prediction errors in visual and auditory perception, which are associated with increased gamma band activity, but is in agreement with observations in working memory and word matching, which show gamma band activity for correct, rather than violated predictions.
A COVID-19 antibody curbs SARS-CoV-2 nucleocapsid protein-induced complement hyper-activation
Authors: Kang Sisi , Yang Mei , He Suhua , Wang Yueming , Chen Xiaoxue , Chen Yao-Qing , Hong Zhongsi , Liu Jing , Jiang Guanmin , Chen Qiuyue , Zhou Ziliang , Zhou Zhechong , Huang Zhaoxia , Huang Xi , He Huanhuan , Zheng Weihong , Liao Hua-Xin , Xiao Fei , Shan Hong , Chen Shoudeng 
Published: September 2020

AbstractAlthough human antibodies elicited by severe acute respiratory distress syndrome coronavirus-2 (SARS-CoV-2) nucleocapsid (N) protein are profoundly boosted upon infection, little is known about the function of N-directed antibodies. Herein, we isolated and profiled a panel of 32 N protein-specific monoclonal antibodies (mAb) from a quick recovery coronavirus disease-19 (COVID-19) convalescent, who had dominant antibody responses to SARS-CoV-2 N protein rather than to Spike protein. The complex structure of N protein RNA binding domain with the highest binding affinity mAb nCoV396 reveals the epitopes and antigen’s allosteric changes. Functionally, a virus-free complement hyper-activation analysis demonstrates that nCoV396 specifically compromises N protein-induced complement hyper-activation, a risk factor for morbidity and mortality in COVID-19, thus paving the way for functional anti-N mAbs identification.One Sentence SummaryB cell profiling, structural determination, and protease activity assays identify a functional antibody to N protein.
Induction of AmpC-mediated β-lactam resistance requires a single lytic transglycosylase inAgrobacterium tumefaciens
Authors: Figueroa-Cuilan Wanda M. , Howell Matthew , Richards Christopher , Randich Amelia , Yadav Akhilesh K. , Cava Felipe , Brown Pamela J.B. 
Published: September 2020

ABSTRACTThe remarkable ability ofAgrobacterium tumefaciensto transfer DNA to plant cells has allowed the generation of important transgenic crops. One challenge ofA. tumefaciens-mediated transformation is eliminating the bacteria after plant transformation to prevent detrimental effects to plants and the release of engineered bacteria to the environment. Here we use a reverse genetics approach to identify genes involved in ampicillin resistance with the goal of utilizing these antibiotic-sensitive strains for plant transformations. We show that treatingA. tumefaciensC58 with ampicillin led to increased β-lactamase production, a response dependent on the broad-spectrum β-lactamase AmpC and its transcription factor AmpR. Loss of the putativeampDorthologue,atu2113, led to constitutive production of AmpC-dependent β-lactamase activity and ampicillin resistance. Finally, one cell wall remodeling enzyme, MltB3, was necessary for the AmpC-dependent β-lactamase activity and its loss elicited ampicillin and carbenicillin sensitivity in theA. tumefaciensC58 and GV3101 strains. Furthermore, GV3101ΔmltB3transforms plants with comparable efficiency to wildtype but can be cleared with sub-lethal concentrations of ampicillin. The functional characterization of the genes involved in the inducible ampicillin resistance pathway ofA. tumefaciensconstitutes a major step forward in efforts to reduce the intrinsic antibiotic resistance of this bacterium.IMPORTANCEAgrobacterium tumefaciens, a significant biotechnological tool for production of transgenic plant lines, is highly resistant to a wide variety of antibiotics, posing challenges for various applications. One challenge is the efficient elimination ofA. tumefaciensfrom transformed plant tissue without using levels of antibiotics that are toxic to the plants. Here, we present the functional characterization of genes involved in β-lactam resistance inA. tumefaciens.Knowledge about proteins that promote or inhibit β-lactam resistance will enable the development of strains to improve the efficiency ofAgrobacterium-mediated plant genetic transformations. Effective removal ofAgrobacteriumfrom transformed plant tissue has the potential to maximize crop yield and food production, improving the outlook for global food security.
No association between circulating levels of testosterone and sex hormone-binding globulin and risk of COVID-19 mortality in UK biobank
Authors: Fan Xikang , Yang Jing , Wang Jiayu , Yin Cheng , Zhu Meng , Ma Hongxia , Jin Guangfu , Hu Zhibin , Shen Hongbing , Hang Dong 
Published: September 2020

AbstractBackgroundSex-disaggregated data suggest that men with coronavirus disease 2019 (COVID-19) are more likely to die than women. Whether circulating testosterone or sex hormone-binding globulin (SHBG) contributes to such sex differences remains unknown.ObjectiveTo evaluate the associations of circulating total testosterone (TT), free testosterone (FT), and SHBG with COVID-19 mortality.DesignProspective analysis.SettingUK Biobank.ParticipantsWe included 1306 COVID-19 patients (678 men and 628 women) who had serum TT and SHBG measurements and were free of cardiovascular disease or cancer at baseline (2006-2010).Main outcome measuresThe death cases of COVID-19 were identified from National Health Service death records updated at 31 July 2020. Unconditional logistic regression was performed to estimate the odds ratio (OR) and 95% confidence intervals (CI) for mortality.ResultsWe documented 315 deaths of COVID-19 (194 men and 121 women). After adjusting for potential confounders, we did not find any statistically significant associations for TT (OR per 1-SD increase = 1.03, 95% CI: 0.85-1.25), FT (OR per 1-SD increase = 0.95, 95% CI: 0.77-1.17), or SHBG (OR per 1-SD increase = 1.09, 95% CI: 0.87-1.37) with COVID-19 mortality in men. Similar null results were observed in women (TT: OR per 1-SD increase = 1.10, 95% CI: 0.85-1.42; FT: OR per 1-SD increase = 1.10, 95% CI: 0.82-1.46; SHBG: OR per 1-SD increase = 1.16, 95% CI: 0.89-1.53).ConclusionsOur findings do not support a significant role of circulating testosterone or SHBG in COVID-19 prognosis.
Early effect of the Covid-19 epidemic on vaccine coverage of major antigens in Guinea: an analysis of the interrupted time series of national immunization coverage
Authors: Dabo Moustapha , Samah Yombounaud , Kande Mouctar , Sompare Djenou , Camara Aly , Dian Bah Mamadou , Solano Sekou , Baldé Idrissa , Fabrice Latou , Sylla Younoussa , Condé Iya saidou , Kuotu Gerard Cristhian , Touré Almamy Amara , Sidiki Magassouba Aboubacar 
Published: September 2020

SummaryIntroductionSince the declaration of the first case of Covid-19 on March 12, 2020, in Guinea, the number of COVID-19 cases has been increasing day by day despite the state of health emergency and the barrier measures decreed by the Guinean government. This study aimed to assess the early impact of COVID-19 on immunization activities by comparing current trends to trends over the past year when vaccine coverage of major antigens (BCG, OPV, DTC, MMR, IPV, and Td) had improved considerably.MethodsThe study was carried out at the Expanded Vaccination Program (EPI) of the Republic of Guinea from February 2019 to June 2019. It was a comparative retrospective cohort study on the trends in administrative coverage of the different antigens used in the framework of vaccination.We performed interrupted time series (STI) analysis using the delayed dependent variable model ANCOVA type II Sum Squares with significance for a p-value less than 0.05 to confirm the link between the occurrence of Covid-19 and the collapse of vaccine coverage. These analyzes were performed on global vaccine-preventable disease surveillance data extracted from the District Immunization Data Management Tool (DVD-MT) designed by WHO. Estimates of the target population were obtained from the National Health Information System (SNIS), and surveillance data for Covid-19 patients were obtained from the National Health Security Agency (ANSS).ResultsOverall, the EPI recorded a median vaccination coverage of less than 80% for all the vaccines introduced and the analysis of the interrupted time series shows that the interruption of the vaccination program was significant for all the vaccines. This finding is factual at both the national and district levels. However, there are disparities at this level, even though some districts have yet to report cases of COVID-19 have experienced drop-in vaccination coverage. The comparison of vaccination coverage for DTP3, for example, shows a sharp drop in the prefectures of Yomou, N’Nzérékoré, Macenta, Kankan, Mandiana, Dinguiraye, Mamou, Koubia, Mali, and Conakry, where it varies between 0 and 80% compared to 2019 where it was above 80%.ConclusionOur results express the need for a resilient health system that could adapt quickly and effectively to pandemics and which in turn makes it possible to strengthen EPI activities in Guinea during this period of a health crisis, in particular for children.