bluuebunny/crossref_metadata_2025_split huggingface dataset:
Coronavirus Disease 2019 (COVID-19) Candidate Chest CT Features: A Systematic Review of Extracted Imaging Features from 7571 Individuals
Authors: Zahiri Javad , Afsharinia Mohammad Hossein , Hekmati Zhaleh , Khodarahmi Mohsen , Hekmati Shahrzad , Pourghorban Ramin 
Published: November 2020

AbstractSince the outbreak of Coronavirus Disease 2019 (COVID-19) causing novel coronavirus (2019-nCoV)-infected pneumonia (NCIP), over 45 million affected cases have been reported worldwide. Many patients with COVID-19 have involvement of their respiratory system. According to studies in the radiology literature, chest computed tomography (CT) is recommended in suspected cases for initial detection, evaluating the disease progression and monitoring the response to therapy. The aim of this article is to review the most frequently reported imaging features in COVID-19 patients in order to provide a reliable insight into expected CT imaging manifestations in patients with positive reverse-transcription polymerase chain reaction (RT-PCR) test results, and also for the initial detection of patients with suspicious clinical presentation whose RT-PCR test results are false negative. A total of 60 out of 173 initial COVID-19 studies, comprising 7571 individuals, were identified by searching PubMed database for articles published between the months of January and June 2020. The data of these studies were related to patients from China, Japan, Italy, USA, Iran and Singapore. Among 40 reported features, presence of ground glass opacities (GGO), consolidation, bilateral lung involvement and peripheral distribution are the most frequently observed ones, reported in 100%, 91.7%, 85%, and 83.3% of articles, respectively. In a similar way, we extracted CT imaging studies of similar pulmonary syndromes outbreaks caused by other strains of coronavirus family: Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). For MERS and SARS, 2 out of 21 and 5 out of 153 initially retrieved studies had CT findings, respectively. Herein, we have indicated the most common coronavirus family related and COVID-19 specific features. Presence of GGO, consolidation, bilateral lung involvement and peripheral distribution were the features reported in at least 83% of COVID-19 articles, while air bronchogram, multi-lobe involvement and linear opacity were the three potential COVID-19 specific CT imaging findings. This is necessary to recognize the most promising imaging features for diagnosis and follow-up of patients with COVID-19. Furthermore, we identified co-existed CT imaging features.
Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona
Authors: Cao Kai , Hong Yiguang , Wan Lin 
Published: November 2020

AbstractSingle-cell multi-omics sequencing data can provide a comprehensive molecular view of cells. However, effective approaches for the integrative analysis of such data are challenging. Although achieved state-of-the-art performance on single-cell multi-omics data integration and did not require any correspondence information, either among cells or among features, current manifold alignment based integrative methods are often limited by requiring that single-cell datasets be derived from the same underlying cellular structure. To overcome this limitation, we present Pamona, an algorithm that integrates heterogeneous single-cell multi-omics datasets with the aim of delineating and representing the shared and dataset-specific cellular structures. We formulate this task as a partial manifold alignment problem and develop a partial Gromov-Wasserstein optimal transport framework to solve it. Pamona identifies both shared and dataset-specific cells based on the computed probabilistic couplings of cells across datasets, and it aligns cellular modalities in a common low-dimensional space, while simultaneously preserving both shared and dataset-specific structures. Our framework can easily incorporate prior information, such as cell type annotations or cell-cell correspondence, to further improve alignment quality. Simulation studies and applications to four real data sets demonstrate that Pamona can accurately identify shared and dataset-specific cells, as well as faithfully recover and align cellular structures of heterogeneous single-cell modalities in the common space. Pamona software is available athttps://github.com/caokai1073/Pamona.
Genetic evidence for a large overlap and potential bidirectional causal effects between resilience and well-being
Authors: de Vries L.P. , Baselmans B.M.L. , Luykx J.J. , de Zeeuw E.L. , Minică C. , de Geus E.J.C. , Vinkers C.H. , Bartels M. 
Published: November 2020

AbstractResilience and well-being are strongly related. People with higher levels of well-being are more resilient after stressful life events or trauma and vice versa. Less is known about the underlying sources of overlap and causality between the constructs. In a sample of 11.304 twins and 2.572 siblings from the Netherlands Twin Register, we investigated the overlap and possible direction of causation between resilience (i.e. the absence of psychiatric symptoms despite negative life events) and well-being (i.e. satisfaction with life) using polygenic score (PGS) prediction, twin-sibling modelling, and the Mendelian Randomization Direction of Causality (MR-DoC) model. Longitudinal twin-sibling models showed significant phenotypic correlations between resilience and well-being (.41/.51 at time 1 and 2). Well-being PGS were predictive for both well-being and resilience, indicating that genetic factors influencing well-being also predict resilience. Twin-sibling modeling confirmed this genetic correlation (.71) and showed a strong environmental correlation (.93). In line with causality, both genetic (51%) and environmental (49%) factors contributed significantly to the covariance between resilience and well-being. Furthermore, the results of within-subject and MZ twin differences analyses were in line with bidirectional causality. Additionally, we used the MR-DoC model combining both molecular and twin data to test causality, while correcting for pleiotropy. We confirmed the causal effect from well-being to resilience, with the direct effect of well-being explaining 11% (T1) and 20% (T2) of the variance in resilience. Data limitations prevented us to test the directional effect from resilience to well-being with the MR-DoC model. To conclude, we showed a strong relation between well-being and resilience. A first attempt to quantify the direction of this relationship points towards a bidirectional causal effect. If replicated, the potential mutual effects can have implications for interventions to lower psychopathology vulnerability, as resilience and well-being are both negatively related to psychopathology.
Identifying temporal and spatial patterns of variation from multi-modal data using MEFISTO
Authors: Velten Britta , Braunger Jana M. , Arnol Damien , Argelaguet Ricard , Stegle Oliver 
Published: November 2020

AbstractFactor analysis is among the most-widely used methods for dimensionality reduction in genome biology, with applications from personalized health to single-cell studies. Existing implementations of factor analysis assume independence of the observed samples, an assumption that fails in emerging spatio-temporal profiling studies. Here, we present MEFISTO, a flexible and versatile toolbox for modelling high-dimensional data when spatial or temporal dependencies between the samples are known. MEFISTO maintains the established benefits of factor analysis for multi-modal data, but enables performing spatio-temporally informed dimensionality reduction, interpolation and separation of smooth from non-smooth patterns of variation. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. We demonstrate MEFISTO through applications to an evolutionary atlas of mammalian organ development, where the model reveals conserved and evolutionary diverged developmental programs. In applications to a longitudinal microbiome study in infants, birth mode and diet were highlighted as major causes for heterogeneity in the temporally-resolved microbiome over the first years of life. Finally, we demonstrate that the proposed framework can also be applied to spatially resolved transcriptomics.
Fluvial seeding of cyanobacterial blooms in oligotrophic Lake Superior
Authors: Reinl Kaitlin L. , Sterner Robert W. , Lafrancois Brenda Moraska , Brovold Sandra 
Published: November 2020

AbstractLake Superior has recently begun experiencing cyanobacterial blooms comprised ofDolichospermum lemmermanniinear the Apostle Islands and along the southern shore of the western arm. Little is known about the origin of these blooms. Experiments were conducted during the summers of 2017 and 2018 to identify sources of propagules and characteristics of sites that were potential sources. The 2017 experiments were conducting using a factorial design with three source zones (Harbor, River, and Lake), two nutrient conditions (high and low N:P), and three temperatures (15, 20, and 25 °C). At the end of the experiment, cyanobacteria were most abundant from the ‘River’ and ‘Harbor’ zones at low N:P and 20 and 25 °C, withD. lemmermanniimost abundant at 20 °C. Subsequently in 2018 we evaluated 26 specific inland locations from three waterbody types (Coastal, Lake/Pond, and River) and explored similarities among those sites that produced cyanobacteria in high abundance when samples were incubated under optimal conditions (low N:P and 25 °C). Under these growing conditions, we found high cyanobacteria abundance developed in samples from river sites with low ambient temperatures and high conductivity. Field monitoring showed that Lake Superior nearshore temperatures were higher than rivers. These observations suggest that blooms ofD. lemmermanniiin Lake Superior are initiated by fluvial seeding of propagules and highlight the importance of warmer temperatures and favorable nutrient and light conditions for subsequent extensive cyanobacterial growth. We argue that the watershed is an important source of biological loading ofD. lemmermanniito Lake Superior, and that when those cells reach the nearshore where there is warmer water temperatures and increased light, they can grow in abundance to produce blooms.
Nitrate inhibits nodule organogenesis through inhibition of cytokinin biosynthesis inLotus japonicus
Authors: Lin Jieshun , Roswanjaya Yuda Purwana , Kohlen Wouter , Stougaard Jens , Reid Dugald 
Published: November 2020

AbstractLegumes balance nitrogen acquisition from soil nitrate with symbiotic nitrogen fixation. Nitrogen fixation requires establishment of a new organ, which is a cytokinin dependent developmental process in the root. We found cytokinin biosynthesis is a central integrator, balancing nitrate signalling with symbiotic acquired nitrogen. Low nitrate conditions provide a permissive state for induction of cytokinin by symbiotic signalling and thus nodule development. In contrast, high nitrate is inhibitory to cytokinin accumulation and nodule establishment in the root zone susceptible to nodule formation. This reduction of symbiotic cytokinin accumulation was further exacerbated in cytokinin biosynthesis mutants, which display hypersensitivity to nitrate inhibition of nodule development, maturation and nitrogen fixation. Consistent with this, cytokinin application can rescue nodulation and nitrogen fixation of biosynthesis mutants in a concentration dependent manner. These inhibitory impacts of nitrate on symbiosis occur in aNlp1andNlp4dependent manner and contrast with the positive influence of nitrate on cytokinin biosynthesis that occurs in non-symbiotic species. Altogether this shows that legumes, as exemplified byLotus japonicus, have evolved a different cytokinin response to nitrate compared to non-legumes.One sentence summaryCytokinin biosynthesis is suppressed by nitrate inLotus japonicus, providing a mechanism for nitrate inhibition of symbiotic nodule organogenesis.
Osteosarcoma tumors maintain intra-tumoral transcriptional heterogeneity during bone and lung colonization
Authors: Rajan Sanjana , Franz Emily , McAloney Camille A. , Vetter Tatyana A. , Cam Maren , Gross Amy C. , Taslim Cenny , Wang Meng , Cannon Matthew V. , Oles Alexander , Roberts Ryan D. 
Published: November 2020

AbstractBackgroundTumors are complex tissues containing collections of phenotypically diverse malignant and nonmalignant cells. We know little of the mechanisms that govern heterogeneity of tumor cells nor of the role heterogeneity plays in overcoming stresses, such as adaptation to different microenvironments. Osteosarcoma is an ideal model for studying these mechanisms—it exhibits widespread inter- and intra-tumoral heterogeneity, predictable patterns of metastasis, and a lack of clear targetable driver mutations. Understanding the processes that facilitate adaptation to primary and metastatic microenvironments could inform the development of therapeutic targeting strategies.ResultsWe investigated single-cell RNA-sequencing profiles of 47,977 cells obtained from cell line and patient-derived xenograft models as cells adapted to growth within primary bone and metastatic lung environments. Tumor cells maintained phenotypic heterogeneity as they responded to the selective pressures imposed during bone and lung colonization. Heterogenous subsets of cells defined by distinct transcriptional profiles were maintained within bone- and lung-colonizing tumors, despite high-level selection. One prominent heterogenous feature involving glucose metabolism was clearly validated using immunofluorescence staining. Finally, using concurrent lineage tracing and single-cell transcriptomics, we found that lung colonization enriches for multiple clones with distinct transcriptional profiles that are preserved across cellular generations.ConclusionsResponse to environmental stressors occurs through complex and dynamic phenotypic adaptations. Heterogeneity is maintained, even in conditions that enforce clonal selection. These findings likely reflect the influences of developmental processes promoting diversification of tumor cell subpopulations, which are retained, even in the face of selective pressures.
Yeast synthetic minimal biosensors for evaluating protein production
Authors: Peng Kai , Koukamp Heinrich , Pretorius Isak S. , Paulsen Ian T. 
Published: November 2020

AbstractThe unfolded protein response (UPR) is a highly conserved cellular response in eukaryotic cells to counteract endoplasmic reticulum (ER) stress, typically triggered by unfolded protein accumulation. In addition to its relevance to human diseases like cancer cell development, the induction of the UPR has a significant impact on recombinant protein production yields in microbial cell factories, including the industrial workhorseSaccharomyces cerevisiae.Being able to accurately detect and measure this ER stress response in single cells, enables the rapid optimisation of protein production conditions and high-throughput strain selection strategies. Current methodologies to monitor the UPR inS. cerevisiaeare often temporally and spatially removed from the cultivation stage, or lack updated systematic evaluation. To this end we constructed and systematically evaluated a series of high-throughput UPR sensors by different designs, incorporating either yeast native UPR promoters or novel synthetic minimal UPR promoters. The native promoters ofDER1andERO1were identified to have suitable UPR biosensor properties and served as an expression level guide for orthogonal sensor benchmarking. Our best synthetic minimal sensor, SM1, was only 98 bp in length, had minimal homology to other native yeast sequences and displayed superior sensor characteristics. Using this synthetic minimal UPR sensor, we demonstrate its ability to accurately discriminate between cells expressing different heterologous proteins and at varying production levels. Our sensor is thus a novel high-throughput tool for determining expression/engineering strategies for optimal heterologous protein production.
Higher risk of mental health deterioration during the Covid-19 lockdown among students rather than non-students. The French Confins study
Authors: Arsandaux Julie , Montagni Ilaria , Macalli Mélissa , Texier Nathalie , Pouriel Mathilde , Germain Raphaël , Mebarki Adel , Kinouani Shérazade , Tournier Marie , Schück Stéphane , Tzourio Christophe 
Published: November 2020

ABSTRACTBackgroundCovid-19 pandemic and its consequences have raised fears of its psychological impact. The objective of this study was to estimate the effect of student status on mental health conditions during Covid-19 general lockdown among adults in France.MethodsUsing cross-sectional data of the Confins cohort, we estimated the effect of student status on depressive and anxiety symptoms, suicidal thoughts and perceived stress using multivariate logistic regression analyses. Stratified models for college students and non- students were performed to identify associated population-specific factors.ResultsAmong the 2260 included participants, students represented 59% (n=1335 vs 925 non- students) and 78% of the total sample were female. Student status was more frequently associated with depressive symptoms (adjusted OR(aOR)=1.58; 95%CI 1.17;2.14), anxiety symptoms (aOR=1.51; 95%CI 1.10;2.07), perceived stress (n=1919, aOR=1.70, 95%CI 1.26;2.29) and frequent suicidal thoughts (n=1919, aOR=1.57, 95%CI 0.97;2.53). Lockdown conditions that could be potentially aggravating on mental health like isolation had a higher impact on students than non-students.LimitationsParticipants were volunteers, which could limit generalisation of the findings. The cross-sectional design did not allow determining if lockdown impacted directly mental health or if there is another cause. However, we adjusted analyses with the history of psychiatric disorders, and factors related to lockdown conditions were associated with mental health disturbances.ConclusionsCollege student’s mental health is of great importance in the context of the general lockdown set up during the pandemic. Follow-up and interventions should be implemented especially for those at high-risk (younger people and those with history of psychiatric disorders).
Hex1, the Major Component of Woronin Bodies, is Required for Normal Development, Pathogenicity and Stress Response in the Plant Pathogenic FungusVerticillium dahliae
Authors: Vangalis Vasileios , Papaioannou Ioannis A. , Markakis Emmanouil A. , Knop Michael , Typas Milton A. 
Published: November 2020

AbstractWoronin bodies are membrane-bound organelles of filamentous ascomycetes that mediate hyphal compartmentalization by plugging septal pores upon hyphal damage. Their major component is the peroxisomal protein Hex1, which has also been implicated in additional cellular processes in fungi. Here, we analyzed the Hex1 homolog ofVerticillium dahliae, an important asexual plant pathogen, and we report its pleiotropic involvement in fungal growth, physiology, stress response and pathogenicity. Alternative splicing of theVdhex1gene can lead to the production of two Hex1 isoforms, which are structurally similar to theirNeurospora crassahomolog. We show thatVdHex1 is targeted to the septum, consistently with its demonstrated function in sealing hyphal compartments to prevent excessive cytoplasmic bleeding upon injury. Furthermore, our investigation provides direct evidence for significant contributions of Hex1 in growth and morphogenesis, as well as in asexual reproduction capacity. We discovered that Hex1 is required both for normal responses to osmotic stress and factors that affect the cell wall and plasma membrane integrity, and for normal resistance to oxidative stress and ROS homeostasis. TheVdhex1mutant exhibited diminished ability to colonize and cause disease on eggplant. Overall, we show that Hex1 has fundamentally important multifaceted roles in the biology ofV. dahliae.
Identification of genetic variants regulating the abundance of clinically relevant plasma proteins using the Diversity Outbred mouse model
Authors: Philtjens Stéphanie , Acri Dominic J. , Kim Byungwook , Kim Hyewon , Kim Jungsu 
Published: November 2020

AbstractAlthough there have been numerous expression quantitative trait loci (eQTL) studies, the effect of genetic variants on the levels of multiple plasma proteins still warrants more systematic investigation. To identify genetic modifiers that influence the levels of clinically relevant plasma proteins, we performed protein quantitative trait locus (pQTL) mapping on 92 proteins using the Diversity Outbred (DO) mouse population and identified 12 significantcisand 6transpQTL. Among them, we discovered coding variants in acis-pQTL inAhrand atrans-pQTL inRfx1for the IL-17A protein. Our study reports an innovative pipeline for the identification of genetic modifiers that may be targeted for drug development.Author SummaryBlood plasma is a body fluid that can be collected in a noninvasive way to detect diseases, such as autoimmune disease. However, it is known that plasma protein levels are affected by both the environment and genetic background. To determine the effect of genetics on plasma protein levels in human, one needs a rather large sample size. To overcome this critical issue, a mouse model, the Diversity Outbred (DO), was established that is genetically as diverse as the human population. In this study, we used N=140 DO mice and genotyped over 140,000 variants. In addition, we measured the levels of 92 proteins in plasma of these DO mice using Olink Proteomics technology. The proteins detected in this panel are known to be detectable in human plasma, making our study translatable to human. We identified 18 significant protein quantitative trait loci. Furthermore, we describe an analysis pipeline that allows for the detection of a single gene in the locus that is responsible for the differences in protein levels. We identified how variants in the Regulatory Factor X1 (Rfx1) gene regulates Interleukin-17A (IL-17A) plasma levels. Our study reports an innovative approach to identify genetic modifiers that may be targeted for drug development.