bluuebunny/crossref_metadata_2025_split huggingface dataset:
Curation of over 10,000 transcriptomic studies to enable data reuse
Authors: Lim Nathaniel , Tesar Stepan , Belmadani Manuel , Poirier-Morency Guillaume , Mancarci Burak Ogan , Sicherman Jordan , Jacobson Matthew , Leong Justin , Tan Patrick , Pavlidis Paul 
Published: July 2020

AbstractVast amounts of transcriptomic data reside in public repositories, but effective reuse remains challenging. Issues include unstructured dataset metadata, inconsistent data processing and quality control, and inconsistent probe-gene mappings across microarray technologies. Thus, extensive curation and data reprocessing is necessary prior to any reuse. The Gemma bioinformatics system was created to help address these issues. Gemma consists of a database of curated transcriptomic datasets, analytical software, a web interface, and web services. Here we present an update on Gemma’s holdings, data processing and analysis pipelines, our curation guidelines, and software features. As of June 2020, Gemma contains 10,811 manually curated datasets (primarily human, mouse, and rat), over 395,000 samples and hundreds of curated transcriptomic platforms (both microarray and RNA-sequencing). Dataset topics were represented with 10,215 distinct terms from 12 ontologies, for a total of 54,316 topic annotations (mean topics/dataset = 5.2). While Gemma has broad coverage of conditions and tissues, it captures a large majority of available brain-related datasets, accounting for 34% of its holdings. Users can access the curated data and differential expression analyses through the Gemma website, RESTful service, and an R package.Database URL: https://gemma.msl.ubc.ca/home.html
Stress, Burnout and Depression in Women in Healthcare during COVID-19 Pandemic: Rapid Scoping Review
Authors: Sriharan Abi , Ratnapalan Savithiri , Tricco Andrea C. , Lupea Doina , Ayala Ana Patricia , Pang Hilary , Lee Dongjoo Daniel 
Published: July 2020

ABSTRACTObjectivesThe overall objectives of this rapid scoping review are to (a) synthesize the common triggers of stress, burnout, and depression faced by women in health care during the COVID-19 pandemic, and (b) identify individual-, organizational-, and systems-level interventions that can support the well-being of women HCWs during a pandemic.DesignThis scoping review is registered on Open Science Framework (OSF) and was guided by the JBI guide to scoping reviews and reported using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) extension to scoping reviews. A systematic search of literature databases (Medline, EMBASE, CINAHL, PsycInfo and ERIC) was conducted from 2003 until June 12, 2020. Two reviewers independently assessed full-text articles according to predefined criteria.InterventionsWe included review articles and primary studies that reported on stress, burnout, and depression in HCWs; that primarily focused on women; and that included the percentage or number of women included. All English language studies from any geographical setting where COVID-19 has affected the population were reviewed.Primary and secondary outcome measuresStudies reporting on mental health outcomes (e.g., stress, burnout, and depression in HCWs), interventions to support mental health well-being were included.ResultsOf the 2,803 papers found, 31 were included. The triggers of stress, burnout and depression are grouped under individual-, organizational-, and systems-level factors. There is a limited amount of evidence on effective interventions that prevents anxiety, stress, burnout and depression during a pandemic.ConclusionsOur preliminary findings show that women HCWs are at increased risk for stress, burnout, and depression during the COVID-19 pandemic. These negative outcomes are triggered by individual level factors such as lack of social support; family status; organizational factors such as access to personal protective equipment or high workload; and systems-level factors such as prevalence of COVID-19, rapidly changing public health guidelines, and a lack of recognition at work.Strengths and limitations of this studyA rapid scoping review was conducted to identify stress, burnout and depression faced by women HCWs during COVID-19.To ensure the relevance of our review, representatives from the women HCWs were engaged in defining the review scope, developing review questions, approving the protocol and literature search strategies, and identifying key messages.It provides a descriptive synthesis of current evidence on interventions to prevent mental health for women HCWs.Most studies used cross-sectional surveys, making it difficult to determine the longitudinal impact.There was significant variability in the tools used to measure mental health.
Predictive performance of international COVID-19 mortality forecasting models
Authors: Friedman Joseph , Liu Patrick , Gakidou Emmanuela ,   the IHME COVID19 Model Comparison Team
Published: July 2020

AbstractBackgroundForecasts and alternative scenarios of the COVID-19 pandemic have been critical inputs into a range of important decisions by healthcare providers, local and national government agencies and international organizations and actors. Hundreds of COVID-19 models have been released. Decision-makers need information about the predictive performance of these models to help select which ones should be used to guide decision-making.MethodsWe identified 383 published or publicly released COVID-19 forecasting models. Only seven models met the inclusion criteria of: estimating for five or more countries, providing regular updates, forecasting at least 4 weeks from the model release date, estimating mortality, and providing date-versioned sets of previously estimated forecasts. These models included those produced by: a team at MIT (Delphi), Youyang Gu (YYG), the Los Alamos National Laboratory (LANL), Imperial College London (Imperial) the USC Data Science Lab (SIKJalpha), and three models produced by the Institute for Health Metrics and Evaluation (IHME). For each of these models, we examined the median absolute percent error—compared to subsequently observed trends—for weekly and cumulative death forecasts. Errors were stratified by weeks of extrapolation, world region, and month of model estimation. For locations with epidemics showing a clear peak, each model’s accuracy was also evaluated in predicting the timing of peak daily mortality.ResultsAcross models, the median absolute percent error (MAPE) on cumulative deaths for models released in June rose with increased weeks of extrapolation, from 2.3% at one week to 32.6% at ten weeks. Globally, ten-week MAPE values were lowest for IHME-MS-SEIR (20.3%) and YYG (22.1). Across models, MAPE at six weeks were the highest in Sub-Saharan Africa (55.6%), and the lowest in high-income countries (7.7%). Median absolute errors (MAE) for peak timing also rose with increased forecasting weeks, from 14 days at one week to 30 days at eight weeks. Peak timing MAE at eight weeks ranged from 24 days for the IHME Curve Fit model, to 48 days for LANL.InterpretationFive of the models, from IHME, YYG, Delphi, SIKJalpha and LANL, had less than 20% MAPE at six weeks. Despite the complexities of modelling human behavioural responses and government interventions related to COVID-19, predictions among these better-performing models were surprisingly accurate. Forecasts and alternative scenarios can be a useful input to decision-makers, although users should be aware of increasing errors with a greater amount of extrapolation time, and corresponding steadily widening uncertainty intervals further in the future. The framework and publicly available codebase presented can be routinely used to evaluate the performance of all publicly released models meeting inclusion criteria in the future, and compare current model predictions.
Discrete simulation analysis of COVID-19 and prediction of isolation bed numbers
Authors: Li Xinyu , Cai Yufeng , Ding Yinghe , Li Jia-da , Huang Guoqing , Liang Ye , Xu Linyong 
Published: July 2020

AbstractThe outbreak of COVID-19 has caused tremendous pressure on medical systems. Adequate isolation facilities are essential to control outbreaks, so this study aims to quickly estimate the demand and number of isolation beds. We established a discrete simulation model for epidemiology. By adjusting or fitting necessary epidemic parameters, the effects of the following indicators on the development of the epidemic and the occupation of medical resources were explained: (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease cure time, and (4) population mobility. Finally, a method for predicting the reasonable number of isolation beds was summarized through multiple linear regression. The prediction equation can be easily and quickly applied to estimate the demanded number of isolation beds in a COVID-19-affected city. A detailed explanation is given for the specific measurement of each parameter in the article.
O Group is a protective factor for COVID19 in Basque population
Authors: Muñoz-Culla M , Roncancio-Clavijo A , Martínez B , Gorostidi M , Piñeiro L , Azkune A , Alberro A , Monge-Ruiz J , Castillo-Trivino T , Prada A , Otaegui D 
Published: July 2020

AbstractABO blood groups have been related to COVID19 infection. ABO blood groups are slightly differently distributed in the populations and therefore these results should be replicated in the specific areas with a proper control population. In this work, we present data from 412 COVID19 patients and 17796 blood donors from Gipuzkoa, a region in Northern Spain. Our data shows the importance of group O as a protective factor.
Prevalence of Nonsuppressed Viral Load and Associated Factors Among Adults Receiving Antiretroviral Therapy in Eswatini, Lesotho, Malawi, Zambia, and Zimbabwe (2015-2017): Results from Population-Based Nationally-Representative Surveys
Authors: Haas Andreas D. , Radin Elizabeth , Hakim Avi J. , Jahn Andreas , Philip Neena , Jonnalagadda Sasi , Saito Suzue , Low Andrea , Patel Hetal , Schwitters Amee , Rogers John , Frederix Koen , Kim Evelyn , Bello George , Williams Daniel B , Parekh Bharat , Sachathep Karampreet , Barradas Danielle T. , Kalua Thokozani , Birhanu Sehin , Musuka Godfrey , Mugurungi Owen , Tippett Barr Beth A. , Sleeman Katrina , Mulenga Lloyd B , Thin Kyaw , Ao Trong , Brown Kristin , Voetsch Andrew C. , Justman Jessica J. 
Published: July 2020

AbstractIntroductionThe Joint United Nations Programme on HIV/AIDS (UNAIDS) has set a target of ≥90% of people living with HIV (PLHIV) receiving antiretroviral therapy (ART) to have viral load suppression (VLS). We examined factors associated with nonsuppressed viral Load (NVL).MethodsWe included PLHIV receiving ART aged 15–59 years from Eswatini, Lesotho, Malawi, Zambia, and Zimbabwe. Blood samples from PLHIV were analyzed for HIV RNA and recent exposure to antiretroviral drugs (ARVs). Outcomes were NVL (viral load ≥1000 copies/mL), virologic failure (VF; ARVs present and viral load ≥1000 copies/mL), interrupted ART (ARVs absent and viral load ≥1000 copies/mL), and receiving second-line ART. We calculated odds ratios and incidence rate ratios for factors associated with NVL, VF, interrupted ART, and switching to second-line ART.ResultsThe prevalence of NVL was 11.2%: 8.2% experienced VF, and 3.0% interrupted ART. Younger age, male gender, less education, suboptimal adherence, receiving nevirapine, HIV non-disclosure, never having married, and residing in Zimbabwe, Lesotho, or Zambia were associated with higher odds of NVL. Among people with NVL, marriage, female gender, shorter ART duration, higher CD4 count, and alcohol use were associated with higher odds for interrupted ART and lower odds for VF. Many people with VF (44.8%) had CD4 counts <200 cells/µL, but few (0.31% per year) switched to second-line ART.ConclusionsCountries are approaching UNAIDS VLS targets for adults. Treatment support for people initiating ART with asymptomatic HIV infection, scale-up of viral load monitoring, and optimized ART regimens may further reduce NVL prevalence.
Linear epitope landscape of SARS-CoV-2 Spike protein constructed from 1,051 COVID-19 patients
Authors: Li Yang , Ma Ming-liang , Lei Qing , Wang Feng , Lai Dan-yun , Hou Hongyan , Xu Zhao-wei , Zhang Bo , Chen Hong , Yu Caizheng , Xue Jun-biao , Zheng Yun-xiao , Wang Xue-ning , Jiang He-wei , Zhang Hai-nan , Qi Huan , Guo Shu-juan , Zhang Yandi , Lin Xiaosong , Yao Zongjie , Wu Jiaoxiang , Sheng Huiming , Sun Ziyong , Fan Xionglin , Tao Sheng-ce 
Published: July 2020

AbstractNeutralization antibodies and vaccines for treating COVID-19 are desperately needed. For precise development of antibodies and vaccines, the key is to understand which part of SARS-CoV-2 Spike protein is highly immunogenic on a systematic way. We generate a linear epitope landscape of Spike protein by analyzing serum IgG response of 1,051 COVID-19 patients with a peptide microarray. We reveal two regions that rich of linear epitopes,i.e., CTD and a region close to the S2’ cleavage site and fusion peptide. Unexpectedly, we find RBD is lack of linear epitope. Besides 3 moderate immunogenic peptides from RBD, 16 highly immunogenic peptides from other regions of Spike protein are determined. These peptides could serve as the base for precise development of antibodies and vaccines for COVID-19 on a systematic level.One sentence summaryA linear epitope landscape of SARS-CoV-2 Spike protein is generated by analyzing serum IgG response of 1,051 COVID-19 patients.
Excess mortality in mental health service users during the COVID-19 pandemic described by ethnic group: South London and Maudsley data.
Authors: Stewart Robert , Broadbent Matthew , Das-Munshi Jayati 
Published: July 2020

AbstractThe COVID-19 pandemic in the UK was accompanied by excess all-cause mortality at a national level, only part of which was accounted for by known infections. Excess mortality has previously been described in people who had received care from the South London and Maudsley NHS Foundation Trust (SLaM), a large mental health service provider for 1.2m residents in south London. SLaM’s Clinical Record Interactive Search (CRIS) data resource receives 24-hourly updates from its full electronic health record, including regularly sourced national mortality on all past and present SLaM service users. SLaM’s urban catchment has high levels of deprivation and is ethnically diverse, so the objective of the descriptive analyses reported in this manuscript was to compare mortality in SLaM service users from 16th March to 15th May 2020 to that for the same period in 2019 within specific ethnic groups: i) White British, ii) Other White, iii) Black African/Caribbean, iv) South Asian, v) Other, and vi) missing/not stated. For Black African/Caribbean patients (the largest minority ethnic group) this ratio was 3.33, compared to 2.47 for White British patients. Considering premature mortality (restricting to deaths below age 70), these ratios were 2.74 and 1.96 respectively. Ratios were also high for those from Other ethnic groups (2.63 for all mortality, 3.07 for premature mortality).
Improved virus isoelectric point estimation by exclusion of known and predicted genome-binding regions
Authors: Heffron Joe , Mayer Brooke K. 
Published: July 2020

AbstractAccurate prediction of the isoelectric point (pI) of viruses is beneficial for modeling virus behavior in environmental transport and physical/chemical treatment applications. However, the empirically measured pIs of many viruses have thus far defied simple explanation, let alone prediction, based on the ionizable amino acid composition of the virus capsid. Here, we suggest an approach for predicting virus pI by excluding capsid regions that stabilize the virus polynucleotide via electrostatic interactions. This method was applied first to viruses with known polynucleotide-binding regions (PBRs) and/or 3D structures. Then, PBRs were predicted in a group of 32 unique viral capsid proteome sequences via conserved structures and sequence motifs. Removing predicted PBRs resulted in a significantly better fit to empirical pI values. After modification, mean differences between theoretical and empirical pI values were reduced from 2.1 ± 2.4 to 0.1 ± 1.7 pH units.ImportanceThis model is the first to fit predicted pIs to empirical values for a diverse set of viruses. The results suggest that many previously-reported discrepancies between theoretical and empirical virus pIs can be explained by coulombic neutralization of PBRs of the inner capsid. Given the diversity of virus capsid structures, this nonarbitrary, heuristic approach to predicting virus pI offers an effective alternative to a simplistic, one-size-fits-all charge model of the virion. The accurate, structure-based prediction of PBRs of the virus capsid employed here may also be of general interest to structural virologists.
Intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge
Authors: Yang Hongjuan , Chen Jiwen , Wang Chen , Cui Jiajia , Wei Wensheng 
Published: July 2020

Purpose
The implied assembly constraints of a computer-aided design (CAD) model (e.g. hierarchical constraints, geometric constraints and topological constraints) represent an important basis for product assembly sequence intelligent planning. Assembly prior knowledge contains factual assembly knowledge and experience assembly knowledge, which are important factors for assembly sequence intelligent planning. This paper aims to improve monotonous assembly sequence planning for a rigid product, intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge is proposed.

Design/methodology/approach
A spatio-temporal semantic assembly information model is established. The internal data of the CAD model are accessed to extract spatio-temporal semantic assembly information. The knowledge system for assembly sequence intelligent planning is built using an ontology model. The assembly sequence for the sub-assembly and assembly is generated via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge. The optimal assembly sequence is achieved via a fuzzy comprehensive evaluation.

Findings
The proposed spatio-temporal semantic information model and knowledge system can simultaneously express CAD model knowledge and prior knowledge for intelligent planning of product assembly sequences. Attribute retrieval and rule reasoning of spatio-temporal semantic knowledge can be used to generate product assembly sequences.

Practical implications
The assembly sequence intelligent planning example of linear motor highlights the validity of intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge.

Originality/value
The spatio-temporal semantic information model and knowledge system are built to simultaneously express CAD model knowledge and assembly prior knowledge. The generation algorithm via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge is given for intelligent planning of product assembly sequences in this paper. The proposed method is efficient because of the small search space.
Knowledge-based society and emerging disciplines: a correlation of academic performance
Authors: Rafi Muhammad , Ahmad Khurshid , Naeem Salman Bin , Khan Asad Ullah , JianMing Zheng 
Published: July 2020

Purpose
Digital libraries promote and accelerate scientific research in academic institutions. The subscribed database resources of digital libraries have become an increasingly valuable asset for researchers. Database resources help generate new ideas, determine research directions and promote productive academic interaction between teachers and students in the information age. The purpose of this study is to examine the use of electronic resources by students in various databases, the research productivity of the faculty in the science network and the number of students who graduate each year.

Design/methodology/approach
This study uses a quantitative method to collect secondary data from the central database of the Higher Education Commission (HEC) for the population of 26 universities for 2 years (2015–2016). In addition to the HEC digital library, data was also collected from the Web of Science to determine the quality academic performance of faculty and researchers. Moreover, in the study, the total strength of teaching staff and doctoral faculty was extracted from the HEC website for investigation. The authors applied the Spearman’s correlation test to the secondary data using Statistical Package for Social Sciences version 25.

Findings
The correlation results of the enrolled students and the downloaded papers from various databases were statistically insignificant (p > 0.05). However, the result showed a positive correlation (p < 0.05) between the use of selected/known databases from a number of databases accessed by the HEC. More importantly, it turns out that the faculty’s productivity in the scientific network and the number of students who graduated from public and private universities are found to be insignificant (p > 0.05). However, the authors found a positive correlation (p < 0.05) between doctoral and non-doctoral faculties, which show that a significant number of non-doctoral faculties are still actively involved in teaching and research.

Originality/value
Research based on academic activities by faculties and students, performed for the first time on the basis of secondary data, will help the HEC and university management to determine the right direction and develop plans to improve academic performance and research quality.
A conceptual model for university-society research collaboration facilitating societal impact for local innovation
Authors: Olsson Anna Karin , Bernhard Iréne , Arvemo Tobias , Lundh Snis Ulrika 
Published: July 2020

PurposeThe purpose is to develop a work-integrated learning (WIL) model for university-society research collaboration facilitating societal impact toward short lag yet sustainable societal impact for local innovation.Design/methodology/approachThe methodology applied was engaged scholarship based on a WIL approach involving a network of collaborating partners from different sectors of society and cross-disciplinary university researchers. Mixed data collection methods were applied.FindingsConceptualization of university-society research collaboration for local innovation is presented as a WIL model including the elements of continuity and commitment, coordination, communication and relationships, trust, courage and creativity and co-creation opportunities. Short lag societal impact as local innovation was identified as product and process innovations.Research limitations/implicationsFurther validation of the model is encouraged for the model to be viable in various contexts and to generate different kinds of societal impact.Practical implicationsThe model may act as a governing tool for project management to facilitate co-creative and short lag societal impact for local innovation to ensure that engaged and learning activities are embedded in the collaborative process.Social implicationsThe model has implications for inclusiveness and co-creation fostering transparency, respect and mutuality in university-society research collaboration and to equate both academic and practice knowledge.Originality/valueThe conclusions drawn support the understanding of a WIL approach practicing engaged scholarship in research collaborations. The main theoretical and practical contributions of the article are the conceptual model for university-society research collaboration generating short lag societal implications and local innovation.