bluuebunny/crossref_metadata_2025_split huggingface dataset:
SARS-CoV-2 naïve and recovered individuals show qualitatively different antibody responses following mRNA vaccination
Authors: Vaquero Sonia Tejedor , de Campos-Mata Leire , María Ramada José , Díaz Pilar , Navarro-Barriuso Juan , Ribas-Llaurado Clara , Melero Natalia Rodrigo , Carolis Carlo , Cerutti Andrea , Gimeno Ramon , Magri Giuliana 
Published: May 2021

SummarymRNA-based vaccines effectively induce protective neutralizing antibody responses against SARS-CoV-2, the etiological agent of COVID-19. The specific compositional patterns of these responses remain largely unknown. We found that SARS-CoV-2-naïve individuals receiving the first dose of an mRNA vaccine developed a SARS-CoV-2-specific antibody response with a subclass profile comparable to that induced by the natural infection, except IgA2, which did not increase. SARS-CoV-2-naïve subjects also mounted a robust virus-specific recall response after receiving the second dose. This response increased all IgG subclasses, but boosted neither IgM nor IgA1 and IgA2 subclasses. In contrast, individuals recovered from COVID-19 mounted peak virus-specific antibody responses upon primary immunization and did not further augment such responses following secondary immunization. Remarkably, compared to SARS-CoV-2-naïve subjects, individuals with pre-existing immunity showed increased levels of all virus-specific antibodies but IgG3 following primary vaccination. By dissecting the heterogeneity of mRNA vaccine-induced humoral responses to SARS-CoV-2, our findings indicate that the induction of optimal immune protection may require the development of personalized vaccination programs.
First comparison of conventional activated sludge versus root-zone treatment for SARS-CoV-2 RNA removal from wastewaters: statistical and temporal significance
Authors: Kumar Manish , Kuroda Keisuke , Joshi Madhvi , Bhattacharya Prosun , Barcelo Damia 
Published: May 2021

ABSTRACTIn the initial pandemic phase, effluents from wastewater treatment facilities were reported mostly free from Severe Acute Respiratory Coronavirus 2 (SARS-CoV-2) RNA, and thus conventional wastewater treatments were generally considered effective. However, there is a lack of first-hand data on i) comparative efficacy of various treatment processes for SARS-CoV-2 RNA removal; and ii) temporal variations in the removal efficacy of a given treatment process in the backdrop of active COVID-19 cases. This work provides a comparative account of the removal efficacy of conventional activated sludge (CAS) and root zone treatments (RZT) based on weekly wastewater surveillance data, consisting of forty-four samples, during a two-month period. The average genome concentration was higher in the inlets of CAS-based wastewater treatment plant in the Sargasan ward (1.25 x 103 copies/ L), than that of RZT plant (7.07 x 102 copies/ L) in an academic institution campus of Gandhinagar, Gujarat, India. ORF 1ab and S genes appeared to be more sensitive to treatment i.e., significantly reduced (p <0.05) than N genes (p>0.05). CAS treatment exhibited better RNA removal efficacy (p=0.014) than RZT (p=0.032). Multivariate analyses suggested that the effective genome concentration should be calculated based on the presence/absence of multiple genes. The present study stresses that treated effluents are not always free from SARS-CoV-2 RNA, and the removal efficacy of a given WWTPs is prone to exhibit temporal variability owing to variations in active COVID-19 cases in the vicinity and genetic material accumulation over time. Disinfection seems less effective than the adsorption and coagulation processes for SARS-CoV-2 removal. Results stress the need for further research on mechanistic insight on SARS-CoV-2 removal through various treatment processes taking solid-liquid partitioning into account.Graphical AbstractHighlightsWastewater treatments may not completely remove the SARS-CoV-2 RNA.The activated sludge process exhibited better RNA removal efficacy than root-zone treatment.ORF 1ab and S genes appeared more sensitive to treatment than N genes.Temporal variability is observed in the removal efficacy of wastewater treatment plants.
A linear Mixed Model to Estimate COVID-19-induced Excess Mortality
Authors: Verbeeck Johan , Faes Christel , Neyens Thomas , Hens Niel , Verbeke Geert , Deboosere Patrick , Molenberghs Geert 
Published: May 2021

SummaryThe Corona Virus Disease (COVID-19) pandemic has increased mortality in countries worldwide. To evaluate the impact of the pandemic on mortality, excess mortality has been suggested rather than reported COVID-19 deaths. Excess mortality, however, requires estimation of mortality under non-pandemic conditions. Although many methods exist to forecast mortality, they are either complex to apply, require many sources of information, ignore serial correlation, and/or are influenced by historical excess mortality. We propose a linear mixed model that is easy to apply, requires only historical mortality data, allows for serial correlation, and down-weighs the influence of historical excess mortality. Appropriateness of the linear mixed model is evaluated with fit statistics and forecasting accuracy measures for Belgium and the Netherlands. Unlike the commonly used 5-year weekly average, the linear mixed model is forecasting the subject-specific mortality, and as a result improves the estimation of excess mortality for Belgium and the Netherlands.
Transmission roles of symptomatic and asymptomatic COVID-19 cases: a modeling study
Authors: Tan Jianbin , Ge Yang , Martinez Leonardo , Sun Jimin , Li Changwei , Westbrook Adrianna , Chen Enfu , Pan Jinren , Li Yang , Ling Feng , Chen Zhiping , Huang Hui , Shen Ye 
Published: May 2021

Key PointsQuestionWhat is the transmissibility of COVID-19 asymptomatic and symptomatic cases, respectively? To date, they have not been well quantified in existing literature.FindingsThe transmissibility of asymptomatic and symptomatic COVID-19 increases with patient age. The asymptomatic cases had a 66.72% lower transmissibility rate than symptomatic cases.MeaningThe transmissibility of asymptomatic COVID-19 cases is not low. Asymptomatic cases are harder to detect compared to symptomatic cases. Consequently, the burden of asymptomatic transmission could potentially dominate the spreading in certain scenarios.
Genetic estimates of correlation and causality between blood-based biomarkers and psychiatric disorders
Authors: Reay William R. , Kiltschewskij Dylan J. , Geaghan Michael P. , Atkins Joshua R. , Carr Vaughan J. , Green Melissa J. , Cairns Murray J. 
Published: May 2021

ABSTRACTThere is a long-standing interest in exploring the relationship between blood-based biomarkers of biological exposures and psychiatric disorders, despite their causal role being difficult to resolve in observational studies. In this study, we leverage genome-wide association study data for a large panel of heritable biochemical traits measured from serum to refine our understanding of causal effect in biochemical-psychiatric trait parings. In accordance with expectation we observed widespread evidence of positive and negative genetic correlation between psychiatric disorders and biochemical traits. We then implemented causal inference to distinguish causation from correlation and found strong evidence that C-reactive protein (CRP) exerts a causal effect on psychiatric disorders, along with other putatively causal relationships involving urate and glucose. Strikingly, these analyses suggested CRP has a protective effect on three disorders including anorexia nervosa, obsessive-compulsive disorder, and schizophrenia, whilst being a risk factor for major depressive disorder. Multivariable models that conditioned CRP effects on interleukin-6 signalling and body mass index suggested that CRP-schizophrenia relationship was not likely mediated by those factors. Collectively, these data suggest that there are shared pathways that influence both biochemical traits and psychiatric illness, including factors such as CRP that are likely to constitute a causal effect and could be targets for therapeutic intervention and precision medicine.
Cardiovascular vulnerability predicts hospitalisation in primary care clinically suspected and confirmed COVID-19 patients: a model development and validation study
Authors: van Royen Florien S. , Joosten Linda P.T. , van Smeden Maarten , Slottje Pauline , Rutten Frans H. , Geersing Geert-Jan , van Doorn Sander 
Published: May 2021

AbstractObjectivesCardiovascular conditions were shown to be predictive of clinical deterioration in hospitalised patients with coronavirus disease 2019 (COVID-19). Whether this also holds for outpatients managed in primary care is yet unknown. The aim of this study was to determine the incremental value of cardiovascular vulnerability in predicting the risk of hospital referral in primary care COVID-19 outpatients.DesignAnalysis of anonymised routine care data extracted from electronic medical records from three large Dutch primary care registries.SettingPrimary care.ParticipantsConsecutive adult patients seen in primary care for COVID-19 symptoms in the ‘first wave’ of COVID-19 infections (March 1 2020 to June 1 2020) and in the ‘second wave’ (June 1 2020 to April 15 2021) in the Netherlands.Outcome measuresA multivariable logistic regression model was fitted to predict hospital referral within 90 days after first COVID-19 consultation in primary care. Data from the ‘first wave’ was used for derivation (n=5,475 patients). Age, sex, the interaction between age and sex, and the number of cardiovascular conditions and/or diabetes (0, 1, or ≥2) were pre-specified as candidate predictors. This full model was (i) compared to a simple model including only age and sex and its interaction, and (ii) externally validated in COVID-19 patients during the ‘second wave’ (n=16,693).ResultsThe full model performed better than the simple model (likelihood ratio test p<0.001). Older male patients with multiple cardiovascular conditions and/or diabetes had the highest predicted risk of hospital referral, reaching risks above 15-20%, whereas on average this risk was 5.1%. The temporally validated c-statistic was 0.747 (95%CI 0.729-0.764) and the model showed good calibration upon validation.ConclusionsFor patients with COVID-19 symptoms managed in primary care, the risk of hospital referral was on average 5.1%. Older, male and cardiovascular vulnerable COVID-19 patients are more at risk for hospital referral.
The effect of recent habitat change on genetic diversity at putatively adaptive and neutral loci inPrimula verisin semi-natural grasslands
Authors: Träger S. , Rellstab C. , Reinula I. , Zemp N. , Helm A. , Holderegger R. , Aavik T. 
Published: May 2021

AbstractRecent habitat change in semi-natural grasslands due to a lack of management has been shown to affect the genetic diversity of grassland plants. However, it is unknown how a change in local environment affects genetic diversity at adaptive loci. We applied RADseq (restriction-site associated DNA sequencing) to extract > 3,000 SNPs across 568 individuals from 32 Estonian populations ofPrimula veris, a plant species common to semi-natural grasslands. We evaluated the effect of recent grassland overgrowth following management abandonment on the genetic diversity at putatively neutral and adaptive loci, which we distinguished by applying three methods, i.e., linear and categorical environmental association analyses, and anFSToutlier test. For validation, we randomised the genotype to sample assignments. Effects of recent habitat change on genetic diversity differed between neutral and adaptive SNP sets. Genetic diversity assessed at putatively neutral loci was similar in open and overgrown habitats but showed a significant difference between these habitat types at putatively adaptive loci: overgrown (i.e. newly established) habitats exhibited higher genetic diversity at putatively adaptive loci than open (i.e. old) habitats, likely due to the exertion of novel selection pressures imposed by new habitat conditions. This increase in genetic diversity at putatively adaptive loci in the new environment points to currently ongoing selection processes where genetic adaptation to the old habitat is potentially lost through altered allele frequencies. Our study suggests that a recent change in local habitat conditions may not be reflected in neutral loci whereas putatively adaptive loci can inform about potential selection processes.
Generalizability in White Blood Cells’ Classification Problem
Authors: Tavakoli Sajad , Ghaffari Ali , Kouzehkanan Zahra Mousavi 
Published: May 2021

AbstractCounting and classifying white blood cells (WBCs) in blood samples helps the early diagnosis of the disease. Many works have been done to develop machine learning-based methods to count WBCs. However, most of these works have low generalizability, and their accuracy decreases sharply as the dataset changes. In this paper, a new method is presented that helps to increase the generalization power. In this method, first, the WBC’s nucleus is segmented, and then its convex hull is obtained. By subtracting the nucleus from the convex hull, a new image is created called the representative of the convex hull (ROC). Then, by Training a convolutional neural network (CNN) with the cells’ RGB image as well as the binary images of the nucleus and ROC, the generalization power is increased. The proposed method was first trained on the Raabin-WBC dataset, then its performance was evaluated on the LISC dataset without retraining. The proposed method’s accuracy on the Raabin-WBC and LISC datasets is 93.97% and 51.57 %, respectively. Besides, the generalization power of four well-known CNNs named VGG16, ResNext50, MobileNet-V2, and MnasNet1 was investigated. It was found that VGG16 has more generalization power among these models.
Selective ablation of Nfix in Macrophages preserves Muscular Dystrophy by inhibiting FAPs-dependent fibrosis
Authors: Saclier Marielle , Temponi Giulia , Bonfanti Chiara , Messina Graziella 
Published: May 2021

AbstractMuscular dystrophies are genetic diseases characterized by chronic inflammation and fibrosis. Macrophages are immune cells that sustain muscle regeneration upon acute injury but seem deleterious in the context of chronic muscle injury such as muscular dystrophies. Here we observed that the number of macrophages expressing the transcription factor Nfix increases in two distinct murine models of muscular dystrophies. Plus, we showed that the deletion of Nfix in macrophages in dystrophic mice delays fibrosis establishment and muscle wasting until 6 months of life. Indeed, macrophages lacking Nfix express more TNFα and less TGFβ1 thus promoting apoptosis of fibro-adipogenic progenitors. Moreover, pharmacological treatment of dystrophic mice with ROCK inhibitor accelerates fibrosis through the increase of Nfix expression by macrophages. Thus, we identify Nfix as a macrophage profibrotic actor in muscular dystrophies, whose inhibition could be a therapeutic way to rescue the dystrophic disease.
LaminaRGeneVis: a tool to visualize gene expression across the laminar architecture of the human neocortex
Authors: Kim Ethan , Howard Derek , Chen Yuxiao , Tripathy Shreejoy J. , French Leon 
Published: May 2021

AbstractApplication of RNA sequencing has enabled the characterization of genome-wide gene expression in the human brain, including distinct layers of the neocortex. Neuroanatomically, the molecular patterns that underlie the laminar organization of the neocortex can help link structure to circuitry and function. To advance our understanding of cortical architecture, we created LaminaRGeneVis, a web application that displays across-layer cortical gene expression from multiple datasets. These datasets were collected using bulk, single-nucleus, and spatial RNA sequencing methodologies and these data were harmonized to facilitate comparisons between datasets. The online resource facilitates single- and multi-gene analyses by providing figures and statistics for user-friendly assessment of laminar gene expression patterns in the adult human neocortex.Availability and implementationLaminaRGeneVis is available at https://ethanhkim.shinyapps.io/laminargenevis. The source code and data is accessible at https://github.com/ethanhkim/laminargenevis.
Genome-wide Imputation Using the Practical Haplotype Graph in the Heterozygous Crop Cassava
Authors: Long Evan M , Bradbury Peter J. , Romay M. Cinta , Buckler Edward S. , Robbins Kelly R 
Published: May 2021

ABSTRACTGenomic applications such as genomic selection and genome-wide association have become increasingly common since the advent of genome sequencing. Genotype imputation makes it possible to infer whole genome information from limited input data, making large sampling for genomic applications more feasible, especially in non-model species where resources are less abundant. Imputation becomes increasingly difficult in heterozygous species where haplotypes must be phased. The Practical Haplotype Graph is a recently developed tool that can accurately impute genotypes, using a reference panel of haplotypes. The Practical Haplotype Graph is a haplotype database that implements a trellis graph to predict haplotypes using minimal input data. Genotyping information is aligned to the database and missing haplotypes are predicted from the most likely path through the graph. We showcase the ability of the Practical Haplotype Graph to impute genomic information in the highly heterozygous crop cassava (Manihot esculenta). Accurately phased haplotypes were sampled from runs of homozygosity across a diverse panel of individuals to populate the graph, which proved more accurate than relying on computational phasing methods. At 1X input sequence coverage, the Practical Haplotype Graph achieves a high concordance between predicted and true genotypes (R=0.84), as compared to the standard imputation tool Beagle (R=0.69). This improved accuracy was especially visible in the prediction of rare and heterozygous alleles. We validate the Practical Haplotype Graph as an accurate imputation tool in the heterozygous crop cassava, showing its potential for application in heterozygous species.
Chromosome length and gene density contribute to micronuclear membrane stability
Authors: Mammel Anna E , Huang Heather Z , Gunn Amanda L , Choo Emma , Hatch Emily M 
Published: May 2021

AbstractMicronuclei are derived from missegregated chromosomes and frequently lose membrane integrity, leading to DNA damage, innate immune activation, and metastatic signaling. Here we demonstrate that two characteristics of the trapped chromosome, length and gene density, are key contributors to micronuclei membrane stability in human cells. Chromosome length is proportional to micronuclei size, and gene density has an additive effect with micronucleus size on membrane stability. We demonstrate that these results are not due to chromosome-specific differences in spindle position or initial nuclear pore complex recruitment during post-mitotic nuclear envelope assembly. We find that chromosome length and micronuclei size strongly correlate with lamin B1 and nuclear pore density in intact micronuclei. Unexpectedly, lamin B1 levels do not predict nuclear lamina organization and membrane stability. Instead, small gene-dense micronuclei have decreased nuclear lamina gaps compared to large micronuclei, despite very low levels of lamin B1. Our data strongly suggest that nuclear envelope composition defects previously correlated with membrane rupture only partly explain membrane stability in micronuclei. We propose that an unknown factor linked to gene density has a separate function that inhibits the appearance of nuclear lamina gaps and delays membrane rupture until late in the cell cycle.
Live-Cell Imaging of (p)ppGpp with RNA-based Fluorescent Sensors
Authors: Sun Zhining , Wu Rigumula , Zhao Bin , Zeinert Rilee , Chien Peter , You Mingxu 
Published: May 2021

AbstractGuanosine tetra- and pentaphosphate, (p)ppGpp, are important alarmone nucleotides that regulate bacterial survival in stressful environment. A direct detection of (p)ppGpp in living cells is critical for our understanding of the mechanism of bacterial stringent response. However, it is still challenging to directly image and measure cellular (p)ppGpp. Here, we report a type of RNA-based fluorescent sensors for live-cell imaging of (p)ppGpp. Our sensor is engineered by conjugating a recently identified (p)ppGpp-specific riboswitch with a fluorogenic RNA aptamer, Broccoli. These sensors can be genetically encoded and enable direct monitoring of cellular (p)ppGpp accumulation. Unprecedented information on cell-to-cell variation and cellular dynamics of (p)ppGpp levels can now be obtained under different nutritional conditions. We predict that these RNA-based sensors will be broadly adapted to study bacterial stringent response.
Rapid Estimation of SNP Heritability using Predictive Process approximation in Large scale Cohort Studies
Authors: Seal Souvik , Datta Abhirup , Basu Saonli 
Published: May 2021

AbstractWith the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM motivated by the concepts of genetic coalescence and gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with extensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort.