bluuebunny/crossref_metadata_2025_split huggingface dataset:
Foxq2 determines blue cone identity in zebrafish
Authors: Ogawa Yohey , Shiraki Tomoya , Fukada Yoshitaka , Kojima Daisuke 
Published: March 2021

AbstractMost vertebrate lineages retain a tetrachromatic visual system, which is supported by a functional combination of spectrally distinct multiple cone photoreceptors, UV, blue, green, and red cones. The blue cone identity is ensured by selective expression of blue (sws2) opsin, and the mechanism is poorly understood becauseSWS2gene has been lost in mammalian species such as mouse, whose visual system has been extensively studied. Here we pursued loss-of-function studies on transcription factors expressed predominantly in zebrafish cone photoreceptors and identified Foxq2 as a core factor drivingsws2gene expression.foxq2is expressed only in the blue cone, and loss offoxq2selectively abolishessws2expression. Comparative genomic analysis revealed that a wide range of vertebrate species retain bothFOXQ2andSWS2genes. We propose that FOXQ2-dependentSWS2expression is a prevalent regulatory mechanism that was acquired at the early stage of vertebrate evolution
Understanding Convergence Between Non-Hispanic Black and White COVID-19 Mortality: A County-Level Approach
Authors: Lawton Ralph , Zheng Kevin , Zheng Daniel , Huang Erich 
Published: March 2021

BackgroundNon-Hispanic Black populations have suffered greater per capita COVID-19 mortality at more than 1.5 times that of White populations. Previous work has established that, over time, rates of Black and White mortality have converged; however, some studies suggest that regional shifts in COVID-19 prevalence may play a role in the relative change between racial groups. This study’s objective was to investigate changes in Black and White COVID-19 mortality over time and uncover potential mechanisms driving these changes.Methods and FindingsUsing county-level COVID-19 mortality data stratified by race, we investigate the trajectory of non-Hispanic Black mortality, White mortality, and the Black/White per capita mortality ratio from June 2020–January 2021. Over this period, in the counties studied, cumulative mortality rose by 56.7% and 82.8% for Black and White populations respectively, resulting in a decrease in mortality ratio of 0.369 (23.8%). These trends persisted even when a county-level fixed-effects model was used to estimate changes over time within counties (controlling for all time-invariant county level characteristics and removing the effects of changes in regional distribution of COVID-19). Next, we leverage county-level variation over time in COVID-19 prevalence to show that the decline in the Black/White mortality ratio can be explained by changes in COVID-19 prevalence. Finally, we study heterogeneity in the time trend, finding that convergence occurs most significantly in younger populations, areas with less dense populations, and outside of the Northeast. Limitations include suppressed data in counties with fewer than 10 deaths in a racial category, and the use of provisional COVID-19 death data that may be incomplete.ConclusionsThe results of this study suggest that convergence in Black/White mortality is not driven by county-level characteristics or changes in the regional dispersion of COVID-19, but instead by changes within counties. Further, declines in the Black/White mortality ratio appear strongly linked to changes in COVID-19 prevalence, rather than a time-specific effect. Further studies on changes in exposure by race over time, or on the vulnerability of individuals who died at different points in the pandemic, may provide crucial insight on mechanisms and strategies to further reduce COVID-19 mortality disparities.
Evaluation of RNA extraction free method for detection of SARS-COV-2 in salivary samples for mass screening for COVID-19
Authors: Mahmoud Sally A. , Ibrahim Esra , Ganesan Subhashini , Thakre Bhagyashree , Teddy Juliet G , Raheja Preeti , Abbas Walid Z 
Published: March 2021

AbstractIn this current COVID - 19 pandemic, there is a dire need for cost effective and less time-consuming alternatives for SARS-COV-2 testing. The RNA extraction free method for detecting SARS-COV-2 in saliva is a promising option, this study found that it has high sensitivity (85.34%), specificity (95.04%) and was comparable to the gold standard nasopharyngeal swab. The method showed good percentage of agreement (kappa coefficient) 0.797 between salivary and NPS samples. However, there are variations in the sensitivity and specificity based on the RT-PCR kit used. The Thermo Fischer-Applied biosystems showed high sensitivity, PPV and NPV but also showed higher percentage of invalid reports. Whereas the BGI kit showed high specificity, better agreement (kappa coefficient) between the results of saliva and NPS samples and higher correlation between the Ct values of saliva and NPS samples. Thus, the RNA extraction free method for salivary sample serves as an effective alternative for SARS-CoV 2-testing.
Safety of administering biologics to IBD patients at an outpatient infusion center In New York City during the COVID-19 pandemic: Sars-CoV-2 seroprevalence and clinical and social characteristics
Authors: Wong Serre-Yu , Gold Stephanie , Accorsi Emma K. , Cowger Tori L. , Wiseman Dean , Navalurkar Reema , Dixon Rebekah , Helmus Drew S. ,   CiTI Study Group, Firpo-Betancourt Adolfo , Mendu Damodara Rao , Zolla-Pazner Susan , Cadwell Ken , Colombel Jean-Frederic 
Published: March 2021

AbstractPatients with immune-mediated inflammatory diseases (IMIDs) and acquired and genetic immunodeficiencies receiving therapeutic infusions are considered high risk for SARS-CoV-2 infection. However, the seroprevalance in this group and the safety of routine administrations at outpatient infusion centers are unknown. To determine the infection rate and clinical-social factors related to SARS-CoV-2 in asymptomatic patients with IMIDs and immunodeficiencies receiving routine non-cancer therapeutic infusions, we conducted a seroprevalence study at our outpatient infusion center. We report the first prospective SARS-CoV-2 sero-surveillance of 444 IBD/IMID, immunodeficiency, and immune competent patients at an outpatient infusion center in the U.S. showing lower seroprevalence in patients compared with the general population and provide clinical and social characteristics associated with seroprevalence in this group. These data suggest that patients can safely continue infusions at outpatient centers.
COVID-19 with early neurological and cardiac thromboembolic phenomena—timeline of incidence and clinical features
Authors: Sundar Uma , Merchant Sanah , Shah Meera , Mukhopadhyay Amita , Kolte Shaonak , Darole Pramod , Mahajan Sharvari , Bansal Ashank , Gosavi Satish , Asole Dnaneshwar , Karnik Niteen D , Mahajan Ajay , Joshi Anagha 
Published: March 2021

AbstractBackgroundAt our tertiary care public hospital, we saw COVID-19 presenting with thromboembolic phenomena, indicating a possible early thrombo-inflammatory pathology.ObjectivesWe documented patients with cardiac and neurological thromboembolic phenomena as a primary presentation of COVID-19, and compared a subset of COVID associated strokes against COVID-19 patients without thrombotic manifestations.MethodsWe included all COVID-Stroke and COVID-ACS (COVID-19, with ischemic arterial stroke/Acute Coronary Syndrome presenting prior to/simultaneous with/within 72 hours of systemic/respiratory COVID manifestations) admitted from April to November 2020. In the nested case control analysis, we used unpaired T-test and chi-square test to study differences between COVID-Strokes (case group) and non-thrombotic COVID controls.Results and ConclusionsWe noted 68 strokes and 122 ACS associated with COVID-19. ACS peaked in May-June, while stroke admissions peaked later in September-October, possibly because severe strokes may have expired at home during the lockdown.In the case-control analysis, cases (n=43; 12F:31M; mean age 51.5 years) had significantly higher D-Dimer values than controls (n=50; 9F:41M; mean age 51.6 years). Mortality was significantly higher in cases (51.2% vs. 26.0%; p = 0.018). We noted 7.5 times higher mortality in cases versus controls even among patients needing minimal oxygen support. Imaging in 37 patients showed both anterior and posterior circulation territories affected in seven, with almost half of Carotid territory strokes being large hemispherical strokes. Additionally, CT/MRI angiography in 28 strokes showed large vessel occlusions in 19 patients. Death in cases thus probably occurred before progression to intense respiratory support, due to severe central nervous system insult.Binary logistic regression analysis showed respiratory support intensity to be the sole independent predictor of mortality among cases. Respiratory distress could have been due to COVID-19 lung infection or aspiration pneumonia resulting from obtunded sensorium. In controls, mortality was predicted by increasing age, female sex, and respiratory support intensity.
Cognitive impairment and functional change in COVID-19 patients undergoing inpatient rehabilitation
Authors: Patel Ruchi , Savrides Irene , Cahalan Christine , Doulatani Gargi , O’Dell Michael W. , Toglia Joan , Jaywant Abhishek 
Published: March 2021

AbstractCognitive impairment is increasingly recognized as a sequela of COVID-19. It is unknown how cognition changes and relates to functional gain during inpatient rehabilitation. We administered the Montreal Cognitive Assessment (MoCA) at admission to 77 patients undergoing inpatient rehabilitation for COVID-19 in a large U.S. academic medical center. 45 patients were administered the MoCA at discharge. Functional gain was assessed by change in the Quality Indicator for Self-Care (QI-SC). In the full sample, 80.5% of patients exhibited cognitive impairment on admission, which was associated with prior delirium. Among 45 patients with retest data, there were significant improvements in MoCA and QI-SC. QI-SC score gain was higher in patients who made clinically meaningful changes on the MoCA, an association that persisted after accounting for age and delirium history. Cognitive impairment is frequent among COVID-19 patients, but improves over time and is associated with functional gain during inpatient rehabilitation.
Identifying hidden Zika hotspots in Pernambuco, Brazil: A spatial analysis
Authors: Freitas Laís Picinini , Lowe Rachel , Koepp Andrew E. , Alves Sandra Valongueiro , Dondero Molly , Marteleto Letícia J. 
Published: March 2021

AbstractNortheast Brazil has the world’s highest rate of Zika-related microcephaly. Yet, in this hard-hit region, traditional case counts of Zika cannot accurately describe Zika risk. Reporting of Zika cases only became mandatory after its association with microcephaly in neonates, when the Zika epidemic was already declining in the region. To advance the study of the Brazilian Zika epidemic and its impacts, we identified hotspots of Zika in Pernambuco state, Northeast Brazil, usingAedes-borne diseases (dengue, chikungunya and Zika) and microcephaly data. We used the Kulldorff’s Poisson purely spatial scan statistic to detect low- and high-risk clusters and combined the results to identify the municipalities most affected by the Zika epidemic. Municipalities were classified as hotspots if they were part of any high-risk cluster, and classified according to a gradient of Zika burden during the epidemic, considering the strength of the evidence. In Pernambuco, officials confirmed 123,934 dengue cases, 167 Zika cases, and 32,983 chikungunya cases between 2014-2017, and 800 microcephaly cases between 2015-2017. We identified 26Aedes-borne diseases clusters (11 high-risk), and 5 microcephaly cases clusters (3 high-risk). Combining the results, sixty-three out of 184 municipalities were identified as hotspots for Zika. The northeast of Pernambuco and the Sertão region were hit hardest by the Zika epidemic. The first is the most populous area, while the second has one of the highest rates of social and economic inequality in Brazil. The identification of Sertão as a Zika hotspot was only possible because the clusters results were combined. The under-reporting of acute infectious diseases is expected to be higher in poor areas. Therefore, using onlyAedes-borne data does not correctly identify the high-risk areas. We successfully identified hidden Zika hotspots using a simple methodology combiningAedes-borne diseases and microcephaly information.
Age specific COVID-19 undercount in Spain from the regional breakdown of 52–week accumulated mortality rates
Authors: Martín-Olalla José María 
Published: March 2021

Spanish NUTS3 region records of age specific weekly deaths since the year 2020, records of age specific COVID–19 deaths and age specific population since the year 2020 are used to estimate first age specific all–cause death rates and age specific COVID–19 undercount.Results shows a statistically significant impact of the pandemic in excess deaths for population aged 40 and elder. Statistically significant COVID–19 undercount is identified only for population aged 80 and elder. Very likely this is the result of the impact of the pandemic in institutionalized population at the early stages of the pandemic.
A Comprehensive Analysis of Allostery in 14-3-3 ζ Docking Proteins using the Spatial Convolution Model (SCM)
Authors: Davis Leroy K. 
Published: March 2021

AbstractThe Spatial Convolution Model (SCM) analyzes allostery based on the spatial evolution of the docking protein elastic media, whereby convolution of the media in response to wave propagation is solved as a function of Z fluctuations and backbone vibration modes. We show that although the elastic media is a complex three-dimensional structure allostery behaves as if it occurs along a stretched oscillating string, where inhomogeneities along the string effect local entropies responsible for ligand binding and transduction of allosteric waves. To identify inhomogeneities along the string, we ignored local density and tension changes during wave propagation and resolved helix wave and physical properties by applying molecular string and beam bending theories. Importantly, we show that allostery occurs at three major scales and that propagation of standing waves create a rolling entropy which drives entropy transfers between fields. Conversion of resonance energy to quantum harmonic oscillators allowed us to consider effects of damping and interactions with the surrounding media as well as to model effects of residue interaction strength on entropy transfer.Abstract Figure
Cross-species prediction of essential genes in insects through machine learning and sequence-based attributes
Authors: de Castro Giovanni Marques , Hastenreiter Zandora , Monteiro Thiago Augusto Silva , Lobo Francisco Pereira 
Published: March 2021

AbstractInsects are organisms with a vast phenotypic diversity and key ecological roles. Several insect species also have medical, agricultural and veterinary importance as parasites and vectors of diseases. Therefore, strategies to identify potential essential genes in insects may reduce the resources needed to find molecular players in central processes of insect biology. Furthermore, the detection of essential genes that occur only in certain groups within insects, such as lineages containing insect pests and vectors, may provide a more rational approach to select essential genes for the development of insecticides with fewer off-target effects. However, most predictors of essential genes in multicellular eukaryotes using machine learning rely on expensive and laborious experimental data to be used as gene features, such as gene expression profiles or protein-protein interactions. This information is not available for the vast majority of insect species, which prevents this strategy to be effectively used to survey genomic data from non-model insect species for candidate essential genes. Here we present a general machine learning strategy to predict essential genes in insects using only sequence-based attributes (statistical and physicochemical data). We validate our strategy using genomic data for the two insect species where large-scale gene essentiality data is available: Drosophila melanogaster (fruit fly, Diptera) and Tribolium castaneum (red flour beetle, Coleoptera). We used publicly available databases plus a thorough literature review to obtain databases of essential and non-essential genes for D. melanogaster and T. castaneum, and proceeded by computing sequence-based attributes that were used to train statistical models (Random Forest and Gradient Boosting Trees) to predict essential genes for each species. Both models are capable of distinguishing essential from non-essential genes significantly better than zero-rule classifiers. Furthermore, models trained in one insect species are also capable of predicting essential genes in the other species significantly better than expected by chance. The Random Forest D. melanogaster model can also distinguish between essential and non-essential T. castaneum genes with no known homologs in the fly significantly better than a zero-rule model, demonstrating that it is possible to use our models to predict lineage-specific essential genes in a phylogenetically distant insect order. Here we report, to the best of our knowledge, the development and validation of the first general predictor of essential genes in insects using sequence-based attributes that can, in principle, be computed for any insect species where genomic information is available. The code and data used to predict essential genes in insects are freely available at https://github.com/g1o/GeneEssentiality/.
dGPredictor: Automated fragmentation method for metabolic reaction free energy prediction andde novopathway design
Authors: Wang Lin , Upadhyay Vikas , Maranas Costas D. 
Published: March 2021

AbstractGroup contribution (GC) methods are conventionally used in thermodynamics analysis of metabolic pathways to estimate the standard Gibbs free energy change (ΔrG′o) of enzymatic reactions from limited experimental measurements. However, these methods are limited by their dependence on manually curated groups and inability to capture stereochemical information, leading to low reaction coverage. Herein, we introduce an automated molecular fingerprint-based thermodynamic analysis tool called dGPredictor that enables the consideration of stereochemistry within metabolite structures and thus increases reaction coverage. dGPredictor has a higher prediction accuracy as compared to existing GC methods and can capture free energy changes for isomerase and transferase reactions, which exhibit no overall group changes. We also demonstrate dGPredictor’s ability to predict the Gibbs free energy change for novel reactions and seamless integration withinde novometabolic pathway design tools such as novoStoic. This enables performing a thermodynamic analysis for synthetic pathways, thus safeguarding against the inclusion of reaction steps with infeasible directionalities. To facilitate easy access to dGPredictor, we developed a graphical user interface to predict the standard Gibbs free energy change for reactions at various pH and ionic strengths. The tool allows customized user input of known metabolites as KEGG IDs and novel metabolites as InChI strings (https://github.com/maranasgroup/dGPredictor).Author summaryThe genome-scale metabolic networks consist of a large number of biochemical reactions interconnected in a complex system. The standard Gibbs free energy change is commonly used to check for the feasibility of enzyme-catalyzed reactions as thermodynamics plays a crucial role in pathway design for biochemical synthesis. The group contribution methods using expert-defined functional groups have been extensively used for estimating standard Gibbs free energy change with limited experimental measurements. However, current methods using functional groups have major issues that limit its ability to cover all the metabolites and reactions as well as the inability to consider stereochemistry leads to erroneous estimation of free energy that undergoes only stereochemical change such as isomerases. Here, we introduce a molecular fingerprint-based thermodynamic tool dGPredictor that enables stereochemistry in metabolites and thus improves the reaction coverage with higher prediction accuracy compared to current GC methods. It also allows the ability to predict free energy change for novel reactions which can aid thede novometabolic pathway design tool to ensure the reaction feasibility. We apply and test our method on reactions in the KEGG database and isobutanol synthesis pathway. In addition, we provide an open-source user-friendly web interface to facilitate easy access for standard Gibbs free energy change of reactions at different physiological states.
Indication-specific tumor evolution and its impact on neoantigen targeting and biomarkers for individualized cancer immunotherapies
Authors: Lo Amy A. , Wallace Andrew , Oreper Daniel , Lounsbury Nicolas , Havnar Charles , Pechuan-Jorge Ximo , Wu Thomas D. , Bourgon Richard , Jones Ryan , Krogh Katrina , Yang Guang-Yu , Zill Oliver A. 
Published: March 2021

AbstractIndividualized neoantigen specific immunotherapy (iNeST) requires robustly expressed clonal neoantigens for efficacy, but tumor mutational heterogeneity, loss of neoantigen expression, and variable tissue sampling present challenges. To characterize these potential obstacles, we combined multi-region sequencing (MR-seq) analysis of five untreated, synchronously sampled metastatic solid tumors with re-analysis of published MR-seq data from 103 patients. Branching evolution in colorectal cancer and renal cell carcinoma led to fewer clonal neoantigens and to clade-specific neoantigens (those shared across a subset of tumor regions but not fully clonal), with the latter not being readily distinguishable in single tumor samples. Prioritizing mutations with higher purity- and ploidy-adjusted variant allele frequency enriched for globally clonal neoantigens (those found in all tumor regions), whereas estimated cancer cell fraction derived from clustering-based tools, surprisingly, did not. Neoantigen quality was associated with loss of neoantigen expression in the bladder cancer case, and HLA-allele loss was observed in the renal and non-small cell lung cancer cases. Our results show that indication type, multi-lesion sampling, neoantigen expression, and HLA allele retention are important factors for iNeST targeting and patient selection.
Leakiness at the human-animal interface in Southeast Asia and implications for the spread of antibiotic resistance
Authors: Nadimpalli Maya L. , Stegger Marc , Viau Roberto , Yith Vuthy , de Lauzanne Agathe , Sem Nita , Borand Laurence , Huynh Bich-tram , Brisse Sylvain , Passet Virginie , Overballe-Petersen Søren , Aziz Maliha , Gouali Malika , Jacobs Jan , Phe Thong , Hungate Bruce A. , Leshyk Victor O. , Pickering Amy J. , Gravey François , Liu Cindy M. , Johnson Timothy J. , Hello Simon Le , Price Lance B. 
Published: March 2021

AbstractInternational efforts to curb antimicrobial resistance have focused on drug development and limiting unnecessary use. However, in areas where water, sanitation, and hygiene infrastructure is lacking, and where biosecurity in food-animal production is poor, pathogen-flow between humans and animals could exacerbate the emergence and spread of resistant pathogens. Here, we compared mobile resistance elements amongEscherichia colirecovered from humans and meat in Cambodia, a country with substantial connectivity between humans and animals, unregulated antibiotic use, and poor environmental controls. We identified multiple resistance-encoding plasmids and a novel,blaCTX-MandqnrS1-encoding transposon that were widely dispersed in both humans and animals, a phenomenon rarely observed in high-income settings. Our findings indicate that plugging leaks at human-animal interfaces should be a critical part of addressing antimicrobial resistance in low and middle-income countries.