bluuebunny/crossref_metadata_2025 huggingface dataset:
Temporal Variations in the Intensity of Care Provided to Community and Nursing Home Residents Who Died of COVID-19 in Ontario, Canada
Authors: Brown Kevin A. , Daneman Nick , Buchan Sarah A. , Chan Adrienne K. , Stall Nathan M.
Published: November 2020

Introduction - Worldwide, nursing home residents have experienced disproportionately high COVID-19 mortality due to the intersection of congregate living, multimorbidity, and advanced age. Among 12 OECD countries, Canada has had the highest proportion of COVID-19 deaths in nursing home residents (78%), raising concerns about a skewed pandemic response that averted much transmission and mortality in community-dwelling residents, but did not adequately protect those in nursing homes. To investigate this, we measured temporal variations in hospitalizations among community and nursing home-dwelling decedents with COVID-19 during the first and second waves of the pandemic.

Methods - We conducted a population-based cohort study of residents of Ontario, Canada with COVID-19 who died between March 11, 2020 (first COVID-19 death in Ontario) and October 28, 2020. We examined hospitalization prior to death as a function of 4 factors: community (defined as all non-nursing home residents) vs. nursing home residence, age in years (<70, 70-79, 80-89, ≥90), gender, and month of death (1st wave: March-April [peak], May, June-July 2020 [nadir], 2nd wave: August-October 2020).

Results - A total of 3,114 people with confirmed COVID-19 died in Ontario from March to October, 2020 (Table 1), of whom 1,354 (43.5%) were hospitalized prior to death (median: 9 days before death, interquartile range: 4-19). Among nursing home decedents (N=2000), 22.4% were admitted to hospital prior to death, but this varied substantially from a low of 15.5% in March-April (peak of wave 1) to a high of 41.2% in June-July (nadir of wave 1). Among community-dwelling decedents (N=1,114), admission to acute care was higher (81.4%) and remained relatively stable throughout the first and second waves. Similar temporal trends for nursing home versus community decedents were apparent in age-stratified analyses (Figure 1). Women who died were less likely to have been hospitalized compared to men in both community (80% women vs 84% men) and nursing home (21% women vs 24% men) settings.

Discussion - Only a minority of Ontario nursing home residents who died of COVID-19 were hospitalized prior to death, and that there were substantial temporal variations, with hospitalizations reaching their lowest point when overall COVID-19 incidence peaked in mid-April, 2020. While many nursing home residents had pre-pandemic advance directives precluding hospitalization, the low admission rate observed in March-April 2020 (15.5%) was inconsistent with both higher admission rates in subsequent months (>30%), and comparatively stable rates among community-dwelling adults. Our findings substantiate reports suggesting that hospitalizations for nursing home residents with COVID-19 were low during the peak of the pandemic’s first wave in Canada, which may have contributed to the particularly high concentration of COVID-19 mortality in Ontario’s nursing homes.
ConceptWAS: a high-throughput method for early identification of COVID-19 presenting symptoms
Authors: Zhao Juan , Grabowska Monika E , Kerchberger Vern Eric , Smith Joshua C. , Eken H. Nur , Feng QiPing , Peterson Josh F. , Rosenbloom S. Trent , Johnson Kevin B. , Wei Wei-Qi
Published: November 2020

Objective

Identifying symptoms highly specific to COVID-19 would improve the clinical and public health response to infectious outbreaks. Here, we describe a high-throughput approach – Concept-Wide Association Study (ConceptWAS) that systematically scans a disease’s clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic.

Methods

Using the Vanderbilt University Medical Center (VUMC) EHR, we parsed clinical notes through a natural language processing pipeline to extract clinical concepts. We examined the difference in concepts derived from the notes of COVID-19-positive and COVID-19-negative patients on the PCR testing date. We performed ConceptWAS using the cumulative data every two weeks for early identifying specific COVID-19 symptoms.

Results

We processed 87,753 notes 19,692 patients (1,483 COVID-19-positive) subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020. We found 68 clinical concepts significantly associated with COVID-19. We identified symptoms associated with increasing risk of COVID-19, including “absent sense of smell” (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21–7.50), “fever” (OR = 1.43, 95% CI = 1.28–1.59), “with cough fever” (OR = 2.29, 95% CI = 1.75–2.96), and “ageusia” (OR = 5.18, 95% CI = 3.02–8.58). Using ConceptWAS, we were able to detect loss sense of smell or taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC).

Conclusion

ConceptWAS is a high-throughput approach for exploring specific symptoms of a disease like COVID-19, with a promise for enabling EHR-powered early disease manifestations identification.
Impact of Indolent Schistosomiasis on Morbidity and Mortality from Respiratory Tract Infections in Preschool Age Children from a Schistosomiasis Endemic Area
Authors: Mduluza-Jokonya Tariro L. , Vengesai Arthur , Jokonya Luxwell , Thakataka Amanda , Midzi Herald , Mduluza Takafira , Sibanda Elopy , Naicker Thajasvarie
Published: November 2020

Introduction

Pneumonia is the biggest child killer, after the neonatal period. This is especially so in children from developing countries who are exposed to other infections simultaneously. In this article we investigated the impact of indolentSchistosoma haematobiuminfection on background of a respiratory infection

Method

A cross sectional study with 237 preschool age children with a respiratory infection, was performed during winter months in a schistosomiasis endemic area. Participants were clinically examined and investigated appropriately. Upper respiratory tract infection (URTI) and pneumonia were defined and classified as per IMCI and WHO guidelines, respectively.S. haematobiuminfection diagnosis was by urine filtration on urine collected over three consecutive days. Data was analysed using SPSS.

Results

S haematobiuminfection prevalence was 29% (69). Prevalence of repiratory infections were as follows: common cold 79% (188), pneumonia 15% (36) and severe pneumonia 6% (15). Eighty-one percent of participants with the common cold wereS. haematobiumnegative, whilst 80 % of those with severe pneumonia were infected. Schistosomiasis infected children were at greater odds of developing; pneumonia (aOR=3.61 (95% CI 1.73-7.55) and severe pneumonia (aOR=21.13 (95% CI 4.65-95.89). High intensity S. haematobium infection was associated with an increased risk of severe pneumonia RR= 23.78(95% CI 6.86-82.32). Mortality from coinfection emanated from severe pneumonia and severeS. haematobiuminfection intensity (RR= 26.56 (95% CI 1.49 to 473.89). Number needed to harm (NNH) forS. haematobiuminfected children who develop respiratory tract infection was 4:1 for pneumonia and 5:1 for severe pneumonia.

Conclusion

The study demonstrated that coinfection with Schistosomiasis increases morbidity and mortality from respiratory tract infections by up to 20 times in children less than five years old. There is need to cover schistosomiasis screening and treatment in children under 5 years old to avert mortality and morbidity due to coinfection with respiratory infections.
Seasonal human coronavirus antibodies are boosted upon SARS-CoV-2 infection but not associated with protection
Authors: Anderson Elizabeth M. , Goodwin Eileen C. , Verma Anurag , Arevalo Claudia P. , Bolton Marcus J. , Weirick Madison E. , Gouma Sigrid , McAllister Christopher M. , Christensen Shannon R. , Weaver JoEllen , Hicks Phillip , Manzoni Tomaz B. , Oniyide Oluwatosin , Ramage Holly , Mathew Divij , Baxter Amy E. , Oldridge Derek A. , Greenplate Allison R. , Wu Jennifer E. , Alanio Cécile , D’Andrea Kurt , Kuthuru Oliva , Dougherty Jeanette , Pattekar Ajinkya , Kim Justin , Han Nicholas , Apostolidis Sokratis A. , Huang Alex C. , Vella Laura A. ,   The UPenn COVID Processing Unit, Wherry E. John , Meyer Nuala J. , Cherry Sara , Bates Paul , Rader Daniel J. , Hensley Scott E.
Published: November 2020

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread within the human population. Although SARS-CoV-2 is a novel coronavirus, most humans had been previously exposed to other antigenically distinct common seasonal human coronaviruses (hCoVs) before the COVID-19 pandemic. Here, we quantified levels of SARS-CoV-2-reactive antibodies and hCoV-reactive antibodies in serum samples collected from 204 humans before the COVID-19 pandemic. We then quantified pre-pandemic antibody levels in serum from a separate cohort of 252 individuals who became PCR-confirmed infected with SARS-CoV-2. Finally, we longitudinally measured hCoV and SARS-CoV-2 antibodies in the serum of hospitalized COVID-19 patients. Our studies indicate that most individuals possessed hCoV-reactive antibodies before the COVID-19 pandemic. We determined that ∼23% of these individuals possessed non-neutralizing antibodies that cross-reacted with SARS-CoV-2 spike and nucleocapsid proteins. These antibodies were not associated with protection against SARS-CoV-2 infections or hospitalizations, but paradoxically these hCoV cross-reactive antibodies were boosted upon SARS-CoV-2 infection.
Asymptomatic Employee Screening for SARS-CoV-2: Implementation of and Reactions to an Employer-Based Testing Program
Authors: Goetz Laura H. , DeLaughder Tyler L. , Kennedy Kathleen L. , Schork Nicholas J. , McDaniel Timothy K. , Trent Jeffrey M. , Engelthaler David M.
Published: November 2020

Introduction

Asymptomatic testing for SARS-CoV-2 among healthcare workers or other essential personnel could remove infected carriers from the workforce, decreasing chances for transmission and workplace outbreaks. Results from one-time testing programs have been reported but data regarding longitudinal testing, including information about employee’s reactions to such programs, is not readily available.

Methods

To identify asymptomatic carriers of SARS-CoV-2, we implemented a longitudinal screening program for critical on-site employees within our research institute in early April 2020. We conducted a survey of both on-site employees and those working from home in order to measure their reactions to the testing program. Statistical analysis of the survey was conducted with general linear regression and Pearson’s Chi-Square tests.

Results

Despite an ongoing high community prevalence rate of COVID-19, to date only two asymptomatic employees tested positive out of 1050 tests run during 7 months of the program. However, 12 symptomatic employees not participating in the program have tested positive. The employee survey was completed by 132/306 (43%) employees, with 93% agreeing that asymptomatic employee screening led to a better and safer working environment and 75% agreeing with on-site public health measures to help contain the virus, but only 58% feeling COVID-19 was a serious threat to their health.

Conclusion

Our results suggest that a longitudinal asymptomatic employee screening program for SARS-CoV-2 can be accepted by employees and can be used to maintain the health of the workforce, potentially keeping positivity rates below community levels in the face of the ongoing COVID-19 pandemic.
Direct regulation of cell cycle regulatory gene expression by NtrX to promoteSinorhizobium meliloticell division
Authors: Xing Shenghui , An Fang , Huang Leqi , Yang Xinwei , Zeng Shuang , Li Ningning , Zhang Lanya , Zheng Wenjia , Ouenzar Khadidja , Yan Junhui , Yu Liangliang , Luo Li
Published: November 2020

Cell division of the alfalfa symbiont,Sinorhizobium meliloti, is regulated by a signaling network centered on CtrA. The gene expression of regulatory proteins in the network can be regulated by nutrient signaling systems. In this study, we found that NtrX, one of the regulators of nitrogen metabolic response, can directly regulate the expression of several regulatory genes in the CtrA signaling network. Three groups ofS. meliloti ntrXmutants showed similar cell division defects, such as slow growth, abnormal morphology of some cells and delayed DNA synthesis. Quantitative RT-PCR assays indicated that in these mutants, the transcription of genes such asctrAandgcrAwas up-regulated, while the transcription of genes such asdnaAandftsZ1was down-regulated. Western blotting showed that the CtrA and GcrA proteins were apparently increased in the mutants. The 53rdaspartate conserved in NtrX homologs can be phosphorylatedin vitroandin vivo. The phosphorylated NtrX protein can bind directly to the promoter regions ofctrA, gcrA, dnaAandftsZ1by recognizing the characteristic sequence CAAN1-5TTG. Therefore, phosphorylation of NtrX is essential for cell cycle regulation ofS. meliloti. We expressed the NtrX protein carrying a phosphorylation site substitution inAgrobacterium tumefaciensand found that the expressed strains had different growth phenotypes, suggesting that NtrX also regulates cell division in this bacterial species. Our findings reveal that NtrX acts as a transcriptional regulator that positively affects bacterial cell division, associated with nitrogen metabolism.

IMPORTANCE

Sinorhizobium melilotiinfects the host legume alfalfa and induces the formation of nitrogen-fixing nodules. The proliferation of rhizobia in plant tissues and cells is strictly controlled in the early stage of the interaction between symbiotic partners. However, the control mechanism is not very clear. Cell division ofS. melilotiin the free-living state is regulated by the CtrA signaling network, but the molecular mechanisms by which the CtrA system is associated with environmental nutrient signals (e.g., ammonia nitrogen) need to be further explored. This study demonstrates that NtrX, a regulator of nitrogen metabolism, required for symbiotic nodulation and nitrogen fixation byS. meliloti1021, can act as a transcriptional regulator of the CtrA signaling system. It may link nitrogen signaling to cell cycle regulation inRhizobiumspecies.
A zebrafish model for COVID-19 recapitulates olfactory and cardiovascular pathophysiologies caused by SARS-CoV-2
Authors: Kraus Aurora , Casadei Elisa , Huertas Mar , Ye Chunyan , Bradfute Steven , Boudinot Pierre , Levraud Jean-Pierre , Salinas Irene
Published: November 2020

The COVID-19 pandemic has prompted the search for animal models that recapitulate the pathophysiology observed in humans infected with SARS-CoV-2 and allow rapid and high throughput testing of drugs and vaccines. Exposure of larvae to SARS-CoV-2 Spike (S) receptor binding domain (RBD) recombinant protein was sufficient to elevate larval heart rate and treatment with captopril, an ACE inhibitor, reverted this effect. Intranasal administration of SARS-CoV-2 S RBD in adult zebrafish recombinant protein caused severe olfactory and mild renal histopathology. Zebrafish intranasally treated with SARS-CoV-2 S RBD became hyposmic within minutes and completely anosmic by 1 day to a broad-spectrum of odorants including bile acids and food. Single cell RNA-Seq of the adult zebrafish olfactory organ indicated widespread loss of expression of olfactory receptors as well as inflammatory responses in sustentacular, endothelial, and myeloid cell clusters. Exposure of wildtype zebrafish larvae to SARS-CoV-2 in water did not support active viral replication but caused a sustained inhibition oface2expression, triggered type 1 cytokine responses and inhibited type 2 cytokine responses. Combined, our results establish adult and larval zebrafish as useful models to investigate pathophysiological effects of SARS-CoV-2 and perform pre-clinical drug testing and validation in an inexpensive, high throughput vertebrate model.
Nanopanel2 calls phased low-frequency variants in Nanopore panel sequencing data
Authors: Popitsch Niko , Preuner Sandra , Lion Thomas
Published: November 2020

Clinical decision making is increasingly guided by accurate and recurrent determination of presence and frequency of (somatic) variants and their haplotype through panel sequencing of disease-relevant genomic regions. Haplotype calling (phasing), however, is difficult and error prone unless variants are located on the same read which limits the ability of short-read sequencing to detect, e.g., co-occurrence of drug-resistance variants. Long-read panel sequencing enables direct phasing of amplicon variants besides having multiple other benefits, however, high error rates of current technologies prevented their applicability in the past. We have developed nanopanel2 (np2), a variant caller for Nanopore panel sequencing data. Np2 works directly on base-called FAST5 files and uses allele probability distributions and several other filters to robustly separate true from false positive calls. It effectively calls SNVs and INDELs with variant allele frequencies (VAF) as low as 1% and 5% respectively and produces only few low-frequency false-positive calls. Haplotype compositions are then determined by direct phasing. Np2 is the first somatic variant caller for Nanopore data, enabling accurate, fast (turnaround <48h) and cheap (sequencing costs ~10$/sample) diagnostic workflows.
High-quality genome-scale metabolic model ofAurantiochytriumsp. T66
Authors: Simensen Vetle , Voigt André , Almaas Eivind
Published: November 2020

The long-chain,ω-3 polyunsaturated fatty acids (PUFAs) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are essential for humans and animals, including marine fish species. Presently, the primary source of these PUFAs is fish oils. As the global production of fish oils appears to be reaching its limits, alternative sources of high-qualityω-3 PUFAs is paramount to support the growing aquaculture industry. Thraustochytrids are a group of heterotrophic protists able to synthesize and accrue large amounts of essentialω-3 PUFAs, including EPA and DHA. Thus, the thraustochytrids are prime candidates to solve the increasing demand forω-3 PUFAs using microbial cell factories. However, a systems-level understanding of their metabolic shift from cellular growth into lipid accumulation is, to a large extent, unclear. Here, we reconstructed a high-quality genome-scale metabolic model of the thraustochytridAurantiochytriumsp. T66 termed iVS1191. Through iterative rounds of model refinement and extensive manual curation, we significantly enhanced the metabolic scope and coverage of the reconstruction from that of previously published models, making considerable improvements with stoichiometric consistency, metabolic connectivity, and model annotations. We show that iVS1191 is highly consistent with experimental growth data, reproducingin vivogrowth phenotypes as well as specific growth rates on minimal carbon media. The availability of iVS1191 provides a solid framework for further developing our understanding of T66’s metabolic properties, as well as exploring metabolic engineering and process-optimization strategiesin silicofor increasedω-3 PUFA production.
Bioactivity assessment of natural compounds using machine learning models based on drug target similarity
Authors: Periwal Vinita , Bassler Stefan , Andrejev Sergej , Gabrielli Natalia , Patil Kaustubh Raosaheb , Typas Athanasios , Patil Kiran Raosaheb
Published: November 2020

Natural compounds constitute a rich resource of potential small-molecule therapeutics. While experimental access to this resource is limited due to its vast diversity and difficulties in systematic purification, computational assessment of structural similarity with known therapeutic molecules offers a scalable approach. Here, we assessed functional similarity between natural compounds and approved drugs by combining multiple chemical similarity metrics and physicochemical properties through a random forest model. As a training set, we used pair-wise similarity between 1410 drugs in terms of their shared protein targets. The resulting model featured high performance metrics (matthews correlation coefficient of 0.81, and balanced accuracy of 0.91) suggesting that it well-captured the structure-activity relation. The model was then used to predict protein targets of circa 11k natural compounds by comparing them with the drugs. This revealed therapeutic potential of several natural compounds, including those with support from previously published sources as well as those hitherto unexplored. We experimentally validated one of the predicted link’s activities, viz., Cox-1 inhibition by 5-methoxysalicylic acid, a molecule commonly found in tea, herbs and spices. In contrast, another natural compound, 4-isopropylbenzoic acid, which showed a higher similarity when considering the most weighted similarity metric but was not picked by the random forest model, did not inhibit Cox-1. Our results demonstrate the utility of a machine-learning approach combining multiple chemical features for uncovering protein binding potential of natural compounds.
PyBindingCurve, simulation and curve fitting to complex binding systems at equilibrium
Authors: Shave Steven , Chen Yan-Kai , Pham Nhan T. , Auer Manfred
Published: November 2020

Understanding multicomponent binding interactions in protein-ligand, protein-protein and competition systems is essential for fundamental biology and drug discovery. Hand deriving equations quickly becomes unfeasible when the number of components is increased, and direct analytical solutions only exist to a certain complexity. To address this problem and allow easy access to simulation, plotting and parameter fitting to complex systems at equilibrium, we present the Python package PyBindingCurve. We apply this software to explore homodimer and heterodimer formation culminating in the discovery that under certain conditions, homodimers are easier to break with an inhibitor than heterodimers and may also be more readily depleted. This is a potentially valuable and overlooked phenomenon of great importance to drug discovery. PyBindingCurve may be expanded to operate on any equilibrium binding system and allows definition of custom systems using a simple syntax. PyBindingCurve is available under the MIT license at:https://github.com/stevenshave/pybindingcurveas Python source code accompanied by examples and as an easily installable package within the Python Package Index.
NASA GeneLab RNA-Seq Consensus Pipeline: Standardized Processing of Short-Read RNA-Seq Data
Authors: Overbey Eliah G. , Saravia-Butler Amanda M. , Zhang Zhe , Rathi Komal S. , Fogle Homer , da Silveira Willian A. , Barker Richard J. , Bass Joseph J. , Beheshti Afshin , Berrios Daniel C. , Blaber Elizabeth A. , Cekanaviciute Egle , Costa Helio A. , Davin Laurence B. , Fisch Kathleen M. , Gebre Samrawit G. , Geniza Matthew , Gilbert Rachel , Gilroy Simon , Hardiman Gary , Herranz Raúl , Kidane Yared H. , Kruse Colin P.S. , Lee Michael D. , Liefeld Ted , Lewis Norman G. , McDonald J. Tyson , Meller Robert , Mishra Tejaswini , Perera Imara Y. , Ray Shayoni , Reinsch Sigrid S. , Rosenthal Sara Brin , Strong Michael , Szewczyk Nathaniel J , Tahimic Candice G.T. , Taylor Deanne M. , Vandenbrink Joshua P. , Villacampa Alicia , Weging Silvio , Wolverton Chris , Wyatt Sarah E. , Zea Luis , Costes Sylvain V. , Galazka Jonathan M.
Published: November 2020

With the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility and reusability of pipeline data, to provide a template for data processing of future spaceflight-relevant datasets, and to encourage cross-analysis of data from other databases with the data available in GeneLab.
Characterization and Engineering ofStreptomyces griseofuscusDSM 40191 as a Potential Host for Heterologous Expression of Biosynthetic Gene Clusters
Authors: Gren Tetiana , Whitford Christopher M. , Mohite Omkar S. , Jørgensen Tue S. , Kontou Eftychia E. , Nielsen Julie B. , Lee Sang Yup , Weber Tilmann
Published: November 2020

Streptomyces griseofuscusDSM 40191 is a fast growingStreptomycesstrain that remains largely underexplored as a heterologous host. Here, we report the genome mining ofS. griseofuscus, followed by the detailed exploration of its phenotype, including production of native secondary metabolites and ability to utilise carbon, nitrogen, sulphur and phosphorus sources. Furthermore, several routes for genetic engineering ofS. griseofuscuswere explored, including use of GusA-based vectors, CRISPR-Cas9 and CRISPR-cBEST-mediated knockouts. Using CRISPR-BEST technology, core genes of 4 biosynthetic gene clusters (BGCs) that are situated on the chromosome arms were inactivated and the outcomes of the inactivations were tested. Two out of the three native plasmids were cured using CRISPR-Cas9 technology, leading to the generation of strainS. griseofuscusDEL1. DEL1 was further modified by full deletion of a pentamycin BGC and an unknown NRPS BGC, leading to the generation of strain DEL2, lacking approx. 500 kbp of the genome, which corresponds to a 5,19% genome reduction. Sequencing confirmed that DEL2 does not bear any crucial off-target effects or rearrangements in its genome. It can be characterized by faster growth and inability to produce three main native metabolites ofS. griseofuscus: lankacidin, lankamycin, pentamycin and their derivatives. To test the ability of DEL2 to heterologously produce secondary metabolites, the actinorhodin BGC was used. We were able to confirm the production of actinorhodin by bothS. griseofuscuswild type and DEL2. We believe that this strain will serve as a good chassis for heterologous expression of BGCs.

Importance

The rise of antibacterial resistance calls on the development of the next generation of antibiotics, majority of which are derived from natural compounds, produced by actinomycetes. The manipulation, refactoring and expression of BGCs coding for such natural products is a promising approach in secondary metabolite discovery. Thus, the development of a versatile panel of heterologous hosts for the expression of BGCs is essential. We believe that first-to-date systematic, detailed characterisation ofS. griseofuscus, a highly promising chassis strain, will not only facilitate the further development of this particular strain, but also will set a blueprint for characterisation of other potential hosts.
Universal epidemic curve for COVID-19 and its usage for forecasting
Authors: Sharma Aryan , Sapkal Srujan , Verma Mahendra K
Published: November 2020

We construct a universal epidemic curve for COVID-19 using the epidemic curves of eight nations that have reached saturation for the first phase, and then fit an eight-degree polynomial that passes through the universal curve. We take India’s epidemic curve up to September 22, 2020 and overlap it with the universal curve by minimizing square-root error. The constructed curve is used to forecast epidemic evolution up to January 1, 2021. The predictions of our model and those of supermodel for India are reasonably close to each other considering the uncertainties in data fitting.
A National Multicenter Evaluation of the Clinical Utility of Optical Genome Mapping for Assessment of Genomic Aberrations in Acute Myeloid Leukemia
Authors: Levy Brynn , Baughn Linda B. , Chartrand Scott , LaBarge Brandon , Claxton David , Lennon Alan , Akkari Yassmine , Cujar Claudia , Kolhe Ravindra , Kroeger Kate , Pitel Beth , Sahajpal Nikhil , Sathanoori Malini , Vlad George , Zhang Lijun , Fang Min , Kanagal-Shamanna Rashmi , Broach James
Published: November 2020

Detection of hallmark genomic aberrations in acute myeloid leukemia (AML) is essential for prognosis and patient management. Clinical practice guidelines for identifying such structural variants (SVs), established by the World Health Organization (WHO), European Leukemia Net (ELN) and National Comprehensive Cancer Network (NCCN), rely substantially on cytogenetic/cytogenomic techniques such as karyotyping, fluorescence in situ hybridization (FISH) or chromosomal microarray analysis (CMA). However, these techniques are limited by the need for skilled personnel as well as significant time and labor, making them cost-prohibitive for some patients. Optical genome mapping (OGM) addresses these limitations and allows for the accurate identification of clinically significant SVs using a novel, high throughput, inexpensive methodology. In a single assay, OGM offers a significantly higher resolution than karyotyping with comprehensive genome-wide analysis comparable to CMA and the added unique ability to detect balanced SVs that are missed by microarray. Here, we report the performance of OGM in a cohort of 100 AML cases, which were previously characterized by karyotype alone or karyotype and FISH. CMA was performed as an additional test in some cases. OGM identified all the clinically relevant SVs and CNVs reported by these standard cytogenetic methods. Moreover, OGM identified clinically relevant SVs in 11% of cases that had been missed by the routine methods. In 24% of cases, OGM refined the underlying genomic structure reported by traditional cytogenomic testing (13%), identified additional clinically relevant variants (7%) or both (4%). Three of 48 (6.25%) cases reported with normal karyotypes were shown to have cryptic translocations involving gene fusions. Two of these cases included fusion between NSD1-NUP98. Based on the comprehensive genomic profiling of the AML patients in this multi-institutional study, we recommend that OGM be considered as a first-line test for detection and identification of clinically relevant SVs.
Differentiation independent neuroprotective role of Vcan+oligodendrocyte precursor cells in poststroke cognitive impairment recovery
Authors: Shen Shanghang , Wang Yongjie , Xia Ruixue , Zeng Fanning , Cao Jun , Liu Xiaolei , Zhang Yunbin , Wang Zhanxiang , Tao Tao
Published: November 2020

The cell type-specific molecular pathology of post-stroke cognitive impairment (PSCI) in the hippocampus has not been thoroughly elucidated. We analyzed 27,069 cells by using single-cell RNA sequencing, and four oligodendrocyte precursor cell (OPC) subtypes were identified, Vcan+OPCs, which were determined to be the primary cluster among them. Additionally, we examined the features of endothelial cells (ECs) and found that Lcn2+ECs might play neuroprotective roles via Vwf after stroke. These results may facilitate further studies attempting to identify new avenues of research and novel targets for PSCI treatment.
Water-immersion finger-wrinkling improves grip efficiency in handling wet objects
Authors: Davis Nick J.
Published: November 2020

For most people, immersing their hands in water leads to wrinkling of the skin of the fingertips. This phenomenon is very striking, yet we know little about why it occurs. It has been proposed that the wrinkles act to distribute water away from the contact surfaces of the fingertip, meaning that wet objects can be grasped more readily. This study examined the coordination between the grip force used to hold an object and the load force exerted on it, when participants used dry or wrinkly fingers, or fingers that were wet but not wrinkly. The results showed that wrinkly fingers reduce the grip force needed to grip a wet object, bringing that force in line with what is needed for handling a dry object. The results suggest that enhancing grip force efficiency in watery environments is a possible adaptive reason for the development of wrinkly fingers.
Detecting Misannotated Long Non-coding RNAs with Training Dynamics of Deep Sequence Classification
Authors: Nabi Afshan , Adebali Ogun , Tastan Oznur
Published: November 2020

Long non-coding RNAs (lncRNAs) are the largest class of non-coding RNAs (ncRNAs). However, recent experimental evidence has shown that some lncRNAs contain small open reading frames (sORFs) that are translated into functional micropeptides. Current methods to detect misannotated lncRNAs rely on ribosome-profiling (ribo-seq) experiments, which are expensive and cell-type dependent. In addition, while very accurate machine learning models have been trained to distinguish between coding and non-coding sequences, little attention has been paid to the increasing evidence about the incorrect ground-truth labels of some lncRNAs in the underlying training datasets. We present a framework that leverages deep learning models’ training dynamics to determine whether a given lncRNA transcript is misannotated. Our models achieve AUC scores > 91% and AUPR > 93% in classifying non-coding vs. coding sequences while allowing us to identify possible misannotated lncRNAs present in the dataset. Our results overlap significantly with a set of experimentally validated misannotated lncRNAs as well as with coding sORFs within lncRNAs found by a ribo-seq dataset. The general framework applied here offers promising potential for use in curating datasets used for training coding potential predictors and assisting experimental efforts in characterizing the hidden proteome encoded by misannotated lncRNAs. Source code is available athttps://github.com/nabiafshan/DetectingMisannotatedLncRNAs.
NBR: Network-based R-statistics for (unbalanced) longitudinal samples
Authors: Gracia-Tabuenca Zeus , Alcauter Sarael
Published: November 2020

Network neuroscience models the brain as interacting elements. However, a large number of elements imply a vast number of interactions, making it difficult to assess which connections are relevant and which are spurious. Zalesky et al. (2010) proposed the Network-Based Statistics (NBS), which identifies clusters of connections and tests their likelihood via permutation tests. This framework shows a better trade-off of Type I and II errors compared to conventional multiple comparison corrections. NBS uses General Linear Hypothesis Testing (GLHT), which may underestimate the within-subject variance structure when dealing with longitudinal samples with a varying number of observations (unbalanced samples). We implemented NBR, an R-package that extends the NBS framework adding (non)linear mixed-effects (LME) models. LME models the within-subject variance in more detail, and deals with missing values more flexibly. To illustrate its advantages, we used a public dataset of 333 human participants (188/145 females/males; age range: 17.0-28.4 y.o.) with two (n=212) or three (n=121) sessions each. Sessions include a resting-state fMRI scan and psychometric data. State anxiety scores and connectivity matrices between brain lobes were extracted. We tested their relationship using GLHT and LME models for balanced and unbalanced datasets, respectively. Only the LME approach found a significant association between state anxiety and a subnetwork that includes the cingulum, frontal, parietal, occipital, and cerebellum. Given that missing data is very common in longitudinal studies, we expect that NBR will be very useful to explore unbalanced samples.

Significant Statement

Longitudinal studies are increasing in neuroscience, providing new insights into the brain under treatment, development, or aging. Nevertheless, missing data is highly frequent in those studies, and conventional designs may discard incomplete observations or underestimate the within-subject variance. We developed a publicly available software (R package: NBR) that implements mixed-effect models into every possible connection in a sample of networks, and it can find significant subsets of connections using non-parametric permutation tests. We demonstrate that using NBR on larger unbalanced samples has higher statistical power than when exploring the balanced subsamples. Although this method is applicable in general network analysis, we anticipate this method being potentially useful in systems neuroscience considering the increase of longitudinal samples in the field.
Mathematical modeling of the SARS-CoV-2 epidemic in Qatar and its impact on the national response to COVID-19
Authors: Ayoub Houssein H. , Chemaitelly Hiam , Seedat Shaheen , Makhoul Monia , Kanaani Zaina Al , Khal Abdullatif Al , Kuwari Einas Al , Butt Adeel A. , Coyle Peter , Jeremijenko Andrew , Kaleeckal Anvar Hassan , Latif Ali Nizar , Shaik Riyazuddin Mohammad , Yassine Hadi M. , Al Kuwari Mohamed G. , Al Romaihi Hamad Eid , Al-Thani Mohamed H. , Bertollini Roberto , Abu Raddad Laith J.
Published: November 2020

Background

Mathematical modeling constitutes an important tool for planning robust responses to epidemics. This study was conducted to guide the Qatari national response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic. The study investigated the time course of the epidemic, forecasted healthcare needs, predicted the impact of social and physical distancing restrictions, and rationalized and justified easing of restrictions.

Methods

An age-structured deterministic model was constructed to describe SARS-CoV-2 transmission dynamics and disease progression throughout the population.

Results

The enforced social and physical distancing interventions flattened the epidemic curve, reducing the peaks for incidence, prevalence, acute-care hospitalization, and intensive care unit (ICU) hospitalizations by 87%, 86%, 76%, and 78%, respectively. The daily number of new infections was predicted to peak at 12,750 on May 23, and active-infection prevalence was predicted to peak at 3.2% on May 25. Daily acute-care and ICU-care hospital admissions and occupancy were forecast accurately and precisely. By October 15, 2020, the basic reproduction numberR0had varied between 1.07-2.78, and 50.8% of the population were estimated to have been infected (1.43 million infections). The proportion of actual infections diagnosed was estimated at 11.6%. Applying the concept ofRttuning, gradual easing of restrictions was rationalized and justified to start on June 15, 2020, whenRtdeclined to 0.7, to buffer the increased interpersonal contact with easing of restrictions and to minimize the risk of a second wave. No second wave has materialized as of October 15, 2020, five months after the epidemic peak.

Conclusions

Use of modeling and forecasting to guide the national response proved to be a successful strategy, reducing the toll of the epidemic to a manageable level for the healthcare system.
Continuously-Encoded Deep Recurrent Networks for Interpretable Knowledge Tracing in Speech-Language and Cognitive Therapy
Authors: Dadgar-Kiani Ehsan , Anantha Veera
Published: November 2020

Intelligent Tutoring Systems (ITS), developed over the last few decades, have been especially important in delivering online education. These systems use Knowledge Tracing (KT) to model a student’s understanding of concepts as they perform exercises. Recently, there have been several advancements using Recurrent Neural Networks (RNNs) to develop Deep Knowledge Tracing (DKT) that eliminates the need for manually encoding the student knowledge space. In online education, these models are crucial for predicting student performance and designing personalized curricula (sequence of courses and exercises). In this paper we develop a novel Knowledge Tracing model, called Continuously-encoded Deep Knowledge Tracing (CE-DKT) to automatically encode the user’s knowledge space, when the user’s skill in a given task is continuous-valued instead of binary. We then apply Knowledge tracing, specifically CE-DKT, to the context of digital therapy. Specifically, patients suffering from various neurological disorders such as aphasia, traumatic brain injury, or dementia are often prescribed speech, language and cognitive therapy exercises to perform from a set of predefined workbooks that are not personalized for the patient. We use CE-DKT to automatically encode a patient’s skill level across different tasks, and predict how the patient will perform on unseen tasks. We use data from the digital therapy platform, Constant Therapy, to train a CE-DKT model and demonstrate its high degree of accuracy in predicting a patient’s performance in a digital therapy application. We also demonstrate how to extract interpretable confidence intervals from this model and how to trace predictions to previous tasks using time-step level feature importance. Finally, we describe how this model can be applied to significantly enhance future digital therapy platforms and online student learning systems.
Therapeutic use of convalescent plasma in COVID-19 patients with immunodeficiency
Authors: Senefeld Jonathon W. , Klassen Stephen A. , Ford Shane K. , Wiggins Chad C. , Bostrom Bruce C. , Thompson Michael A. , Baker Sarah E. , Nicholson Wayne T. , Johnson Patrick W. , Carter Rickey E. , Henderson Jeffrey P. , Hartman William R. , Pirofski Liise-anne , Wright R. Scott , Fairweather DeLisa , Bruno Katelyn A. , Paneth Nigel S. , Casadevall Arturo , Joyner Michael J.
Published: November 2020

0

In the absence of effective countermeasures, human convalescent plasma has been widely used to treat severe acute respiratory syndrome coronavirus 2 including among patients with innate or acquired immunodeficiency. However, the association between COVID-19-associated mortality in patients with immunodeficiency and therapeutic use of convalescent plasma is unknown. We review clinical features and treatment protocols of COVID-19 patients with immunodeficiency after treatment with human convalescent plasma. We also discuss the time course and clinical features of recovery. These insights provide evidence for the need to develop a clear treatment protocol for COVID-19 patients with immunodeficiency and support the efficacy of convalescent plasma in patients with primary or secondary immunodeficiency.
Spatial dynamics of SARS-Cov-2 and reduced risk of contagion: evidence from the second Italian epidemic wave*
Authors: Buonanno Paolo , Galletta Sergio , Puca Marcello
Published: November 2020

We highlight a negative association between the severity of the first-wave of SARS-Cov-2 and the spread of the virus during the second-wave. Analyzing data of a sample of municipalities from the Italian region of Lombardy, we find that a one standard deviation increase in excess of mortality during the first-wave is associated with a reduction of approximately 30% in the number of detected infected individuals in the initial phase of the second-wave. Our findings may reflect a behavioral response in more severely hit areas as well as a cross-protection between successive waves.

JEL Classification

I10; I18
cblaster: a remote search tool for rapid identification and visualisation of homologous gene clusters
Authors: Gilchrist Cameron L.M. , Booth Thomas J. , van Wersch Bram , van Grieken Liana , Medema Marnix H. , Chooi Yit-Heng
Published: November 2020

Genes involved in coordinated biological pathways, including metabolism, drug resistance and virulence, are often collocalised as gene clusters. Identifying homologous gene clusters aids in the study of their function and evolution, however existing tools are limited to searching local sequence databases. Tools for remotely searching public databases are necessary to keep pace with the rapid growth of online genomic data. Here, we presentcblaster, a Python based tool to rapidly detect collocated genes in local and remote databases.cblasteris easy to use, offering both a command line and a user-friendly graphical user interface (GUI). It generates outputs that enable intuitive visualisations of large datasets, and can be readily incorporated into larger bioinformatic pipelines.cblasteris a significant update to the comparative genomics toolbox.cblastersource code and documentation is freely available from GitHub under the MIT license (github.com/gamcil/cblaster).
clinker & clustermap.js: Automatic generation of gene cluster comparison figures
Authors: Gilchrist Cameron L.M. , Chooi Yit-Heng
Published: November 2020

Genes involved in biological pathways are often collocalised in gene clusters, the comparison of which can give valuable insights into their function and evolutionary history. However, comparison and visualisation of gene cluster homology is a tedious process, particularly when many clusters are being compared. Here, we presentclinker, a Python based tool, andclustermap.js, a companion JavaScript visualisation library, which used together can automatically generate accurate, interactive, publication-quality gene cluster comparison figures directly from sequence files.

Availability and Implementation

Source code and documentation forclinkerandclustermap.jsis available on GitHub (github.com/gamcil/clinkerandgithub.com/gamcil/clustermap.js, respectively) under the MIT license.clinkercan be installed directly from the Python Package Index viapip.

Contact

E-mail:cameron.gilchrist@research.uwa.edu.au,yitheng.chooi@uwa.edu.au
Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity
Authors: Liu Belle , White Alexander James , Lo Chung-Chuan
Published: November 2020

Rapid, flexible response to an ever-changing environment is critical for an organism’s survival. Recently, multicellular recordings have shown that this rapid, flexible switching between activity patterns is present in neural microcircuits. However, the underlying neural mechanism is not clear. Strikingly, we show in a neural circuit model that mutually inhibitory connections are crucial for rapid and flexible switching between distinct functions without synaptic plasticity. Here, we develop a theoretical framework to explain how inhibitory recurrent circuits give rise to this flexibility and show that mutual inhibition doubles the number of cusp bifurcations in small neural circuits. As a concrete example, we study a commonly observed class of functional motifs we call Coupled Recurrent Inhibitory and Recurrent Excitatory Loops (CRIRELs). These CRIRELs have the advantage of being both multifunctional and controllable, performing a plethora of unique functions. Finally, we demonstrate how mutual inhibition maximizes storage capacity for larger networks.