bluuebunny/crossref_metadata_2025_split huggingface dataset:
Forecasting COVID-19 cases: A comparative analysis between Recurrent and Convolutional Neural Networks
Authors: Nabi Khondoker Nazmoon , Tahmid Md Toki , Rafi Abdur , Kader Muhammad Ehsanul , Haider Md. Asif 
Published: November 2020

AbstractWhen the entire world is waiting restlessly for a safe and effective COVID-19 vaccine that could soon become a reality, numerous countries around the globe are grappling with unprecedented surges of new COVID-19 cases. As the number of new cases is skyrocketing, pandemic fatigue and public apathy towards different intervention strategies are posing new challenges to the government officials to combat the pandemic. Henceforth, it is indispensable for the government officials to understand the future dynamics of COVID-19 flawlessly in order to develop strategic preparedness and resilient response planning. In light of the above circumstances, probable future outbreak scenarios in Brazil, Russia and the United kingdom have been sketched in this study with the help of four deep learning models: long short term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN) and multivariate convolutional neural network (MCNN). In our analysis, CNN algorithm has outperformed other deep learning models in terms of validation accuracy and forecasting consistency. It has been unearthed in our study that CNN can provide robust long term forecasting results in time series analysis due to its capability of essential features learning, distortion invariance and temporal dependence learning. However, the prediction accuracy of LSTM algorithm has been found to be poor as it tries to discover seasonality and periodic intervals from any time series dataset, which were absent in our studied countries. Our study has highlighted the promising validation of using convolutional neural networks instead of recurrent neural networks when it comes to forecasting with very few features and less amount of historical data.
Amplicon and metagenomic analysis of MERS-CoV and the microbiome in patients with severe Middle East respiratory syndrome (MERS)
Authors: Aljabr Waleed , Alruwaili Muhannad , Penrice-Randal Rebekah , Alrezaihi Abdulrahman , Harrison Abbie Jasmine , Ryan Yan , Bentley Eleanor , Jones Benjamin , Alhatlani Bader Y. , AlShahrani Dayel , Mahmood Zana , Rickett Natasha J. , Alosaimi Bandar , Naeem Asif , Alamri Saad , Alsran Hadel , Hamed Maaweya , Dong Xiaofeng , Assiri Abdullah , Alrasheed Abdullah R. , Hamza Muaawia , Carroll Miles W. , Gemmell Matthew , Darby Alistair , Donovan-Banfield I’ah , Stewart James P. , Matthews David A. , Davidson Andrew D. , Hiscox Julian A. 
Published: November 2020

AbstractMiddle East Respiratory Syndrome coronavirus (MERS-CoV) is a zoonotic infection that emerged in the Middle East in 2012. Symptoms range from mild to severe and include both respiratory and gastrointestinal illnesses. The virus is mainly present in camel populations with occasional spill overs into humans. The severity of infection in humans is influenced by numerous factors and similar to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) underlying health complications can play a major role. Currently, MERS-CoV and SARS-CoV-2 are co-incident in the Middle East and a rapid way is required of sequencing MERS-CoV to derive genotype information for molecular epidemiology. Additionally, complicating factors in MERS-CoV infections are co-infections that require clinical management. The ability to rapidly characterise these infections would be advantageous. To rapidly sequence MERS-CoV, we developed an amplicon-based approach coupled to Oxford Nanopore long read length sequencing. The advantage of this approach is that insertions and deletions can be identified – which are the major drivers of genotype change in coronaviruses. This and a metagenomic approach were evaluated on clinical samples from patients with MERS. The data illustrated that whole genome or near whole genome information on MERS-CoV could be rapidly obtained. This approach provided data on both consensus genomes and the presence of minor variants including deletion mutants. Whereas, the metagenomic analysis provided information of the background microbiome.
REALDIST: Real-valued protein distance prediction
Authors: Adhikari Badri 
Published: November 2020

AbstractProtein structure prediction continues to stand as an unsolved problem in bioinformatics and biomedicine. Deep learning algorithms and the availability of metagenomic sequences have led to the development of new approaches to predict inter-residue distances—the key intermediate step. Different from the recently successful methods which frame the problem as a multi-class classification problem, this article introduces a real-valued distance prediction method REALDIST. Using a representative set of 43 thousand protein chains, a variant of deep ResNet is trained to predict real-valued distance maps. The contacts derived from the real-valued distance maps predicted by this method, on the most difficult CASP13 free-modeling protein datasets, demonstrate a long-range top-L precision of 52%, which is 17% higher than the top CASP13 predictor Raptor-X and slightly higher than the more recent trRosetta method. Similar improvements are observed on the CAMEO ‘hard’ and ‘very hard’ datasets. Three-dimensional (3D) structure prediction guided by real-valued distances reveals that for short proteins the mean accuracy of the 3D models is slightly higher than the top human predictor AlphaFold and server predictor Quark in the CASP13 competition.
Reduced access to care among older American adults during CoVID-19 pandemic: results from a prospective cohort study
Authors: Das Ashis Kumar , Mishra Devi Kalyan , Gopalan Saji Saraswathy 
Published: November 2020

AbstractBackgroundDue to preexisting conditions, older adults are at higher risk of COVID-19 related severe complications. Current evidence is limited on access to care for older adults during the COVID-19 pandemic.ObjectivesTo examine the extent of reduced access to care among older American adults during the COVID-19 pandemic, identify predictors and reasons of reduced access.Materials and methodsUsing publicly available data from the COVID-19 module (interim release) of the Health and Retirement Study, we undertook descriptive analyses of older adults stratified by sex, age group, race, education, marital status, employment, receipt of social security benefits, health insurance, number of limitations in activities of daily living and pre-existing conditions. Associations between reduced access to care and predictors were estimated using a multivariable logistic regression model.ResultsAbout 30% of respondents delayed or avoided care during the pandemic. Reduced access was more likely to be reported by respondents that were female, younger, educated, not receiving social security benefits, with limitations in daily activities and three preexisting conditions. In terms of the reasons, the majority of the respondents (45.9%) reported that their visit was either cancelled or rescheduled by the provider; 13.9% thought they could wait, 10.9% could not get an appointment, 9.1% found it unaffordable, and 7.4% were afraid to visit the provider. Respondents reported of reduced access to doctor’s visits, surgery, prescription filling, and dental care.ConclusionsWe suggest urgent attention on improving access to care for older adults during the pandemic. For nonemergency conditions and routine care that can be delivered virtually, telehealth services can be strengthened. Additionally, health messaging can reemphasize that neglecting medical care might lead to increased morbidity and mortality among older adults from preexisting illnesses.
Nationwide SARS-CoV-2 Surveillance Study for Sewage and Sludges of Wastewater Treatment Plants in Turkey
Authors: Kocamemi Bilge Alpaslan , Kurt Halil , Sait Ahmet , Kadi Hamza , Sarac Fahriye , Aydın Ismail , Saatci Ahmet Mete , Pakdemirli Bekir 
Published: November 2020

1.AbstractSince the announcement of the pandemic of Covid-19 by WHO on March 11, 2020, the countries have started to monitor surveillance of SARS-CoV-2 through medical tests. However, people with no and very light symptoms are usually not medically tested or never hospitalized and they are missed. In the study of Wu et al. [1], it was realized that the urine and faeces of all infected people contain SARS-CoV-2. After that, sewage, and sludge-based SARS-CoV-2 surveillance studies have gained significant importance around the world (Fig.1). SARS-CoV-2 was detected in wastewaters in The Netherlands [2,3,4], USA [1,5,6,7, 8, 9, 10], Australia [11], France [12, 13, 14], China [15], Spain [16,17,18,19,20], Italy [21, 22,23], Israel [24], Turkey[25], Germany[26], Japan [27,28], India [29,30], Pakistan [31], Brazil [32,33], Chile [34], Denmark, France, Belgium[35], Equator [36] and Sweden [37] using different virus concentration techniques. Published data show that high concentrations of the SARS-CoV-2 RNA reaches to wastewater treatment plants (WWTPs). On 7th of May 2020, Turkey took its place among a few country which have been started wastewater based surveillance studies at the early stages of pandemic by reporting SARS-CoV-2 RT-qPCR levels of major WWTPs of Istanbul [25]. Turkey [38] first detected SARS-CoV-2 in both primary and waste activated sludges of Istanbul WWTPs. Later, USA [39] and Spain [40] were also studied on sludge samples. There are also studies evaluating the SARS-CoV-2 in WWTPs effluents [10,13,14, 28, 29,30, 34, 36].This study aimed to scan distribution of Covid-19 through Turkey by SARS-CoV-2 measurements in influent, effluent and sludge samples of WWTPs. The influent, effluent and sludge samples were collected from main WWTPs located in 81 cities of Turkey through May 2020-July 2020. Among those 81 cities, Istanbul metropole with 15.5 million inhabitants was chosen as the pilot city since 65% of all cases in Turkey were present here. Hence, all treatment plants in Istanbul were scanned through the study. The viral activity tests were also conducted for the influent, effluent and sludge samples resulting high qPCR.
Coronavirus (COVID-19) Outbreak Prediction Using Epidemiological Models of Richards Gompertz Logistic Ratkowsky and SIRD
Authors: Sedaghat Ahmad , Abbas Oloomi Seyed Amir , Malayer Mahdi Ashtian , Rezaei Nima , Mosavi Amir 
Published: November 2020

AbstractOn 30 July 2020, a total number of 301,530 diagnosed COVID-19 cases were reported in Iran, with 261,200 recovered and 16,569 dead. The COVID-19 pandemic started with 2 patients in Qom city in Iran on 20 February 2020. Accurate prediction of the end of the COVID-19 pandemic and the total number of populations affected is challenging. In this study, several widely used models, including Richards, Gompertz, Logistic, Ratkowsky, and SIRD models, are used to project dynamics of the COVID-19 pandemic in the future of Iran by fitting the present and the past clinical data. Iran is the only country facing a second wave of COVID-19 infections, which makes its data difficult to analyze. The present study’s main contribution is to forecast the near-future of COVID-19 trends to allow non-pharmacological interventions (NPI) by public health authorities and/or government policymakers. We have divided the COVID-19 pandemic in Iran into two waves, Wave I, from February 20, 2020 to May 4, 2020, and Wave II from May 5, 2020, to the present. Two statistical methods, i.e., Pearson correlation coefficient (R) and the coefficient of determination (R2), are used to assess the accuracy of studied models. Results for Wave I Logistic, Ratkowsky, and SIRD models have correctly fitted COVID-19 data in Iran. SIRD model has fitted the first peak of infection very closely on April 6, 2020, with 34,447 cases (The actual peak day was April 7, 2020, with 30,387 active infected patients) with the re-production number R0=3.95. Results of Wave II indicate that the SIRD model has precisely fitted with the second peak of infection, which was on June 20, 2020, with 19,088 active infected cases compared with the actual peak day on June 21, 2020, with 17,644 cases. In Wave II, the re-production number R0=1.45 is reduced, indicating a lower transmission rate. We aimed to provide even a rough project future trends of COVID-19 in Iran for NPI decisions. Between 180,000 to 250,000 infected cases and a death toll of between 6,000 to 65,000 cases are expected in Wave II of COVID-19 in Iran. There is currently no analytical method to project more waves of COVID-19 beyond Wave II.
A Retrospective Longitudinal Study of COVID-19 as Seen by a Large Urban Hospital in Chicago
Authors: Chen Haotian , Varatharajah Yogatheesan , de Ramirez Sarah Stewart , Arnold Paul , Frankenberger Casey , Hota Bala , Iyer Ravishankar 
Published: November 2020

AbstractThe rapid spread of the novel coronavirus disease 2019 (COVID-19) has created high demand for medical resources, including personnel, intensive care unit beds, and ventilators. As thousands of patients are hospitalized, the disease has shown remarkable diversity in its manifestation; many patients with mild to no symptoms recover from the disease requiring minimal care, but some patients with severe disease progression require mechanical ventilation support in intensive care units (ICU) with an increased risk of death. Studying the characteristics of patients in these various strata can help us understand the varied progression of this disease, enable earlier interventions for at-risk patients, and help manage medical resources more efficiently. This paper presents a retrospective analysis of 10,123 COVID-19 patients treated at the Rush University Medical Center in Chicago, including their demographics, symptoms, comorbidities, laboratory values, vital signs, and clinical history. Specifically, we present a staging scheme based on discrete clinical events (i.e., admission to the hospital, admission to the ICU, mechanical ventilation, and death), and investigate the temporal trend of clinical variables and the effect of comorbidities in each of those stages. We then developed a prognostic model to predict ventilation demands at an individual patient level by analyzing baseline clinical variables, which entails (1) a least absolute shrinkage and selection operator (LASSO) regression and a decision tree model to identify predictors for mechanical ventilation; and (2) a logistic regression model based on these risk factors to predict which patients will eventually need ventilatory support. Our results indicate that the prognostic model achieves an AUC of 0.823 (95% CI: 0.765–0.880) in identifying patients who will eventually require mechanical ventilation.
AI4CoV: Matching COVID-19 Patients to Treatment Options Using Artificial Intelligence
Authors: Hsu Andrew I. , Yeh Amber S. , Chen Shao-Lang , Yeh Jerry J. , Lv DongQing , Hsu Jane Y.-J. , Huang Pai Jung 
Published: November 2020

AbstractWe developed AI4CoV, a novel AI system to match thousands of COVID-19 clinical trials to patients based on each patient’s eligibility to clinical trials in order to help physicians select treatment options for patients. AI4CoV leveraged Natural Language Processing (NLP) and Machine Learning to parse through eligibility criteria of trials and patients’ clinical manifestations in their clinical notes, both presented in English text, to accomplish 92.76% AUROC on a cross-validation test with 3,156 patient-trial pairs labeled with ground truth of suitability. Our retrospective multiple-site review shows that according to AI4CoV, severe patients of COVID-19 generally have less treatment options suitable for them than mild and moderate patients and that suitable and unsuitable treatment options are different for each patient. Our results show that the general approach of AI4CoV is useful during the early stage of a pandemic when the best treatments are still unknown.
Genome-wide CRISPR screens reveal APR-246 (Eprenetapopt) triggers ferroptosis and inhibits iron-sulfur cluster biogenesis
Authors: Fujihara Kenji M. , Zhang Bonnie , Jackson Thomas D. , Nijiagel Brunda , Ang Ching-Seng , Nikolic Iva , Sutton Viv , Trapani Joe , Simpson Kaylene J. , Stojanovski Diana , Leimuehler Silke , Haupt Sue , Phillips Wayne A. , Clemons Nicholas J. 
Published: November 2020

ABSTRACTThe mechanisms by which cells respond and adapt to oxidative stress are largely unknown but are key to developing a rationale for cancer therapies that target antioxidant pathways. APR-246 is a mutant-p53 targeted therapeutic currently under clinical investigation in myeloid dysplastic syndrome (MDS) and acute myeloid leukemia1. Whilst the mechanism of action of APR-246 is thought to be reactivation of wild-type p53 activity through covalent modification of cysteine residues in the core domain of mutant-p53 protein2,3, here we report that the anti-neoplastic capacity of APR-246 lies predominantly in the conjugation of free cysteine. Genome-wide CRISPR perturbation screening, metabolite profiling and proteomics in response to APR-246 treatment in mutant-p53 cancer cells highlighted the role of GSH and mitochondrial metabolism in determining APR-246 efficacy. APR-246 sensitivity was increased through loss of key enzymes in mitochondrial one-carbon metabolism,SHMT2andMTHFD1L, due to diminished glycine supply forde novoGSH synthesis. Critically, we show that APR-246 induces iron-dependent, apoptotic machinery-independent cell death, ferroptosis. Whole-cell proteomics analyses indicated an upregulation of proteins involved in iron-sulfur cluster biogenesis (eg. FDX1). GSH, acetyl-CoA and NADH levels were also depleted in APR-246 treated cells. Importantly, we found that APR-246 inhibits iron-sulfur cluster biogenesis in the mitochondria of cancer cells through cysteine conjugation. This work not only details novel determinants of APR-246 activity in cancer cells, but also provides a clinical roadmap for targeting antioxidant pathways in tumours - beyond targeting mutant-p53 tumours.
Differentiation of human induced pluripotent stem cells into functional airway epithelium
Authors: Ahmed Engi , Fieldes Mathieu , Bourguignon Chloé , Mianné Joffrey , Petit Aurélie , Vernisse Charlotte , Jory Myriam , Cazevieille Chantal , Boukhaddaoui Hassan , Garnett James P. , Massiera Gladys , Vachier Isabelle , Assou Said , Bourdin Arnaud , De Vos John 
Published: November 2020

AbstractRationaleHighly reproduciblein vitrogeneration of human bronchial epithelium from pluripotent stem cells is an unmet key goal for drug screening to treat lung diseases. The possibility of using induced pluripotent stem cells (hiPSC) to model normal and diseased tissuein vitrofrom a simple blood sample will reshape drug discovery for chronic lung, monogenic and infectious diseases.MethodsWe devised a simple and reliable method that drives a blood sample reprogrammed into hiPSC subsequently differentiated within 45 days into air-liquid interface bronchial epithelium (iALI), through key developmental stages, definitive-endoderm (DE) and Ventralized-Anterior-Foregut-Endoderm (vAFE) cells.ResultsReprogramming blood cells from one healthy and 3 COPD patients, and from skin-derived fibroblasts obtained in one PCD patient, succeeded in 100% of samples using Sendai viruses. Mean cell purity at DE and vAFE stages was greater than 80%, assessed by expression of CXCR4 and NKX2.1, avoiding the need of cell sorting. When transferred to ALI conditions, vAFE cells reliably differentiated within 4 weeks into bronchial epithelium with large zones covered by beating ciliated, basal, goblets, club cells and neuroendocrine cells as foundin vivo. Benchmarking all culture conditions including hiPSCs adaptation to single-cell passaging, cell density and differentiation induction timing allowed for consistently producing iALI bronchial epithelium from the five hiPSC lines.ConclusionsReliable reprogramming and differentiation of blood-derived hiPSCs into mature and functional iALI bronchial epithelium is ready for wider use and this will allow better understanding lung disease pathogenesis and accelerating the development of novel gene therapies and drug discovery.
Cell Layers: Uncovering clustering structure and knowledge in unsupervised single-cell transcriptomic analysis
Authors: Blair Andrew P. , Hu Robert K. , Farah Elie N. , Chi Neil C. , Pollard Katherine S. , Przytycki Pawel F. , Kathiriya Irfan S. , Bruneau Benoit G. 
Published: November 2020

AbstractMotivationUnsupervised clustering of single-cell transcriptomics is a powerful method for identifying cell populations. Static visualization techniques for single-cell clustering only display results for a single resolution parameter. Analysts will often evaluate more than one resolution parameter, but then only report one.ResultsWe developed Cell Layers, an interactive Sankey tool for the quantitative investigation of gene expression, coexpression, biological processes, and cluster integrity across clustering resolutions. Cell Layers enhances the interpretability of single-cell clustering by linking molecular data and cluster evaluation metrics, to provide novel insight into cell populations.Availability and implementationUpon request
Bringing BOS to light: Uncovering the key enzyme in the biosynthesis of the neurotoxin β-ODAP in Grass Pea (Lathyrus sativusL.)
Authors: Goldsmith Moshe , Barad Shiri , Knafo Maor , Savidor Alon , Ben-Dor Shifra , Brandis Alexander , Mehlman Tevie , Peleg Yoav , Albeck Shira , Dym Orly , Ben-Zeev Efrat , Reich Ziv 
Published: November 2020

AbstractGrass pea (Lathyrus sativusL.) is a grain legume commonly grown in parts of Asia and Africa for food and forage. While being a highly nutritious and robust crop, able to survive both drought and floods, it produces a neurotoxic compound, β-N-oxalyl-L-α,β-diaminopropionic acid (β-ODAP), which can cause a severe neurological disorder if consumed as a main diet component. So far, the enzyme that catalyzes the formation of β-ODAP has not been identified. By combining protein purification and enzymatic assays with transcriptomic and proteomic analyses, we were able to identify the enzyme β-ODAP synthetase (BOS) from grass pea. We show that BOS is an HXXXD-type acyltransferase of the BAHD superfamily and that its crystal structure is highly similar to that of plant hydroxycinnamoyl transferases. The identification of BOS, more than 50 years after it was proposed, paves the way towards the generation of non-toxic grass pea cultivars safe for human and animal consumption.
Separase cleaves the kinetochore protein Meikin to direct the meiosis I/II transition
Authors: Maier Nolan K , Ma Jun , Lampson Michael A , Cheeseman Iain M 
Published: November 2020

SummaryTo generate haploid gametes, germ cells undergo two consecutive meiotic divisions requiring key changes to the cell division machinery. Here, we explore the regulatory mechanisms that differentially control meiotic events. We demonstrate that the protease Separase rewires key cell division processes at the meiosis I/II transition by cleaving the meiosis-specific protein Meikin. In contrast to cohesin, which is inactivated by Separase proteolysis, cleaved Meikin remains functional, but results in a distinct activity state. Full-length Meikin and the C-terminal Meikin Separase-cleavage product both localize to kinetochores, bind to Plk1 kinase, and promote Rec8 cleavage, but our results reveal distinct roles for these proteins in controlling meiosis. Mutations that prevent Meikin cleavage or that conditionally inactivate Meikin at anaphase I both result in defective meiosis II chromosome alignment. Thus, Separase cleavage of Meikin creates an irreversible molecular switch to rewire the cell division machinery at the meiosis I/II transition.