bluuebunny/crossref_metadata_2025_split huggingface dataset:
A TRANSPARENT, OPEN-SOURCE SIRD MODEL FOR COVID19 DEATH PROJECTIONS IN INDIA
Authors: Agarwal Ananye , Tyagi Utkarsh 
Published: June 2020

AbstractAs India emerges from the lockdown with ever higher COVID19 case counts and a mounting death toll, reliable projections of case numbers and deaths counts are critical in informing policy decisions. We examine various existing models and their shortcomings. Given the amount of uncertainty surrounding the disease we choose a simple SIRD model with minimal assumptions enabling us to make robust predictions. We employ publicly available mobility data from Google to estimate social distancing covariates which influence how fast the disease spreads. We further present a novel method for estimating the uncertainty in our predictions based on first principles. To demonstrate, we fit our model to three regions (Spain, Italy, NYC) where the peak has passed and obtain predictions for the Indian states of Delhi and Maharashtra where the peak is desperately awaited.
Viral load dynamics in transmissible symptomatic patients with COVID-19
Authors: Kawasuji Hitoshi , Takegoshi Yusuke , Kaneda Makito , Ueno Akitoshi , Miyajima Yuki , Kawago Koyomi , Fukui Yasutaka , Yoshida Yoshihiko , Kimura Miyuki , Yamada Hiroshi , Sakamaki Ippei , Tani Hideki , Morinaga Yoshitomo , Yamamoto Yoshihiro 
Published: June 2020

AbstractTo investigate the relationship between viral load and secondary transmission in novel coronavirus disease 2019 (COVID-19), we reviewed epidemiological and clinical data obtained from immunocompetent laboratory-confirmed patients with COVID-19 at Toyama University Hospital. In total, 28 patients were included in the analysis. Median viral load at the initial sample collection was significantly higher in adults than in children and in symptomatic than in asymptomatic patients. Among symptomatic patients, non-linear regression models showed that the estimated viral load at onset was higher in the index (patients who transmitted the disease to at least one other patient) than in the non-index patients (patients who were not the cause of secondary transmission; median [95% confidence interval]: 6.6 [5.2–8.2] vs. 3.1 [1.5–4.8] log copies/μL, respectively). High nasopharyngeal viral loads around onset may contribute to secondary transmission of COVID-19.Article Summary LineHigh nasopharyngeal viral load around the onset may contributes to secondary transmission of COVID-19.
Increased serum levels of sCD14 and sCD163 indicate a preponderant role for monocytes in COVID-19 immunopathology
Authors: Gómez-Rial J , Currás-Tuala MJ , Rivero-Calle I , Gómez-Carballa A , Cebey-López M , Rodríguez-Tenreiro C , Dacosta-Urbieta A , Rivero-Velasco C , Rodríguez-Núñez N , Trastoy-Pena R , Rodríguez-García J , Salas A , Martinón-Torres F 
Published: June 2020

AbstractBackgroundEmerging evidence indicates a potential role for monocyte in COVID-19 immunopathology. We investigated two soluble markers of monocyte activation, sCD14 and sCD163, in covid19 patients with the aim of characterizing their potential role in monocyte-macrophage disease immunopathology. To the best of our knowledge, this is the first study of its kind.MethodsFifty-nine SARS-Cov-2 positive hospitalized patients, classified according to ICU or non-ICU admission requirement, were prospectively recruited and analyzed by ELISA for levels of sCD14 and sCD163, along with other laboratory parameters, and compared to a healthy control group.ResultssCD14 and sCD163 levels were significantly higher among COVID-19 patients, independently of ICU admission requirement, compared to the control group. We found a significant correlation between sCD14 levels and other inflammatory markers, particularly Interleukin-6, in the non-ICU patients’ group. sCD163 showed a moderate positive correlation with the time at sampling from admission, increasing its value over time, independently of severity group.ConclusionsMonocyte-macrophage activation markers are increased and correlate with other inflammatory markers in SARS-Cov-2 infection, in association to hospital admission. These data suggest a potentially preponderant role for monocyte-macrophage activation in the development of immunopathology of covid19 patients.
An MRI-based, data-driven model of cortical laminar connectivity
Authors: Shamir Ittai , Assaf Yaniv 
Published: June 2020

AbstractOver the past two centuries, great scientific efforts have been spent on deciphering the structure and function of the cerebral cortex using a wide variety of methods. Since the advent of MRI neuroimaging, significant progress has been made in imaging of global white matter connectivity (connectomics), followed by promising new studies regarding imaging of grey matter laminar compartments. Despite progress in both fields, there still lacks mesoscale information regarding cortical laminar connectivity that could potentially bridge the gap between the current resolution of connectomics and the relatively higher resolution of cortical laminar imaging. Here, we systematically review a sample of prominent published articles regarding cortical laminar connectivity, in order to offer a simplified data-driven model that integrates white and grey matter MRI datasets into a novel way of exploring whole-brain tissue-level connectivity. Although it has been widely accepted that the cortex is exceptionally organized and interconnected, studies on the subject display a variety of approaches towards its structural building blocks. Our model addresses three principal cortical building blocks: cortical layer definitions (laminar grouping), vertical connections (intraregional, within the cortical microcircuit and subcortex) and horizontal connections (interregional, including connections within and between the hemispheres). While cortical partitioning into layers is more widely accepted as common knowledge, certain aspects of others such as cortical columns or microcircuits are still being debated. This study offers a broad and simplified view of histological and microscopical knowledge in laminar research that is applicable to the limitations of MRI methodologies, primarily regarding specificity and resolution.
Climatic influence on the magnitude of COVID-19 outbreak: a stochastic model-based global analysis
Authors: Pramanik Malay , Chowdhury Koushik , Juel Rana Md , Bisht Praffulit , Pal Raghunath , Szabo Sylvia , Pal Indrajit , Behera Bhagirath , Liang Qiuhua , Padmadas Sabu S. , Udmale Parmeshwar 
Published: June 2020

AbstractThis study examines the association between community transmission of COVID-19 cases and climatic predictors, considering travel information and annual parasite index across the three climatic zones, i.e., tropical, subtropical, and temperate. A Boosted Regression Tree model has been employed to understand the association between the COVID-19 cases. The results show that average temperature and average relative humidity are the major contributors in explaining the differentials of COVID-19 transmission in temperate and subtropical regions whereas the mean diurnal temperature range and temperature seasonality are the most significant determinants in tropical regions. The average temperature is the most influential factor affecting the number of COVID-19 cases in France, Turkey, the US, the UK, and Germany, and the cases decrease sharply above 10°C. Among the tropical countries, India found to be most affected by mean diurnal temperature, and Brazil fazed by temperature seasonality. Most of the temperate countries like France, USA, Turkey, UK, and Germany with an average temperature between 5–12°C had high number of COVID-19 cases. The findings are expected to add to the ongoing debates on the influence of climatic factors influencing the number of COVID-19 cases and could help researchers and policymakers to make appropriate decisions for preventing the spread.HighlightsAnalyzed influence of climatic & bioclimatic factors on the spread of COVID-19First to analyze COVID-19 cases in 228 cities globally across three climatic zonesTemperature & humidity influenced COVID-19 cases in temperate & sub-tropicsMean diurnal temperature & temperature seasonality had effects in tropicsLow temperature elicits COVID-19 cases in France, Turkey, the US, the UK, & GermanyGraphical abstract
SARS-CoV-2 receptor is co-expressed with elements of the kinin-kallikrein, renin-angiotensin and coagulation systems in alveolar cells
Authors: Sidarta-Oliveira Davi , Jara Carlos Poblete , Ferruzzi Adriano J. , Skaf Munir S. , Velander William H. , Araujo Eliana P. , Velloso Licio A. 
Published: June 2020

AbstractSARS-CoV-2, the pathogenic agent of COVID-19, employs angiotensin converting enzyme-2 (ACE2) as its cell entry receptor. Clinical data reveal that in severe COVID-19, SARS-CoV-2 infects the lung, leading to a frequently lethal triad of respiratory insufficiency, acute cardiovascular failure, and coagulopathy. Physiologically, ACE2 plays a role in the regulation of three systems that could potentially be involved in the pathogenesis of severe COVID-19: the kinin-kallikrein system, resulting in acute lung inflammatory edema; the renin-angiotensin system, promoting cardiovascular instability; and the coagulation system, leading to thromboembolism. Here we analyzed ~130,000 human lung single-cell transcriptomes and show that key elements of the kinin-kallikrein, renin-angiotensin and coagulation systems are co-expressed with ACE2 in alveolar cells, which could explain how changes in ACE2 promoted by SARS- CoV-2 cell entry result in the development of the three most severe clinical components of COVID-19.
Infectivity of symptomatic malaria patients to Anopheles farauti colony mosquitoes in Papua New Guinea
Authors: Timinao Lincoln , Vinit Rebecca , Katusele Michelle , Koleala Tamarah , Nate Elma , Czeher Cyrille , Schofield Louis , Felger Ingrid , Mueller Ivo , Laman Moses , Robinson Leanne J. , Karl Stephan 
Published: June 2020

AbstractDespite being a weak point in their life cycle, transmission of Plasmodium parasites from humans to mosquitoes is an understudied field of research. Direct membrane feeding assays (DMFA) are an important tool, allowing detailed mechanistic malaria transmission studies from humans to mosquitoes. Especially for Plasmodium vivax, which cannot be cultured long-term under laboratory conditions, implementation of DMFAs requires proximity to P. vivax endemic areas. In the present study, we investigated the infectivity of symptomatic Plasmodium infections to Anopheles farauti colony mosquitoes in Papua New Guinea (PNG), a country with one of the highest rates of Plasmodium vivax in the world. A total of 182 DMFAs were performed with venous blood collected from symptomatic malaria patients positive by rapid diagnostic test (RDT). DMFAs resulted in mosquito infection in 20.9% (38/182) of cases. The parasite species in the blood feeds were determined retrospectively by expert light microscopy and quantitative real-time qPCR. Based on light microscopy, 9.2% of P. falciparum and 42% of P. vivax human infections resulted in mosquito infections. Infections containing gametocytes detected by microscopy led to mosquito infections in 58.8% of P. vivax and 8.7% of P. falciparum infections. Based on qPCR, 10% of P. falciparum and 43.6% of P. vivax lead to a successful mosquito infection. Venous blood samples from symptomatic P. vivax patients were more infectious to An. farauti mosquitoes in DMFAs compared to P. falciparum infected patients. The capacity to perform DMFAs in a high-burden P. vivax setting creates a unique opportunity to address critical gaps in our understanding of P. vivax human-tomosquito transmission.
Decreased plasma levels of the survival factor renalase are associated with worse outcomes in COVID-19
Authors: Wang Melinda , Guo Xiaojia , Chun Hyung J , Lee Alfred Ian , Cha Charles , Gorelick Fred , Desir Gary V 
Published: June 2020

AbstractIntroductionRenalase (RNLS), a novel secreted plasma flavoprotein, has anti-inflammatory effects in a variety of disease processes. Severe COVID-19 disease is associated with disordered inflammatory responses. We hypothesized that reduced plasma RNLS levels could be a marker of COVID-19 disease severity.MethodsPlasma was collected from 51 hospitalized COVID-19 patients and 15 uninfected non-hospitalized controls. Plasma RNLS and cytokine levels were measured and sociodemographic and clinical data were collected from chart review. Data were analyzed using nonparametric analyses and Kaplan Meir curve log rank analysis.ResultsPlasma RNLs levels were negatively correlated with inflammatory markers, including IL-1β, IL-6, and TNFα (p = 0.04, p = 0.03, p = 0.01, respectively). Patients with COVID-19 disease had lower levels of RNLS than controls. Lower levels of RNLS were associated with more severe disease among COVID-19 patients. Low RNLS was also associated with worse survival among COVID-19 patients (HR = 4.54; 95% CI: 1.06-19.43; p = 0.005).ConclusionLow plasma RNLS levels are associated with severe COVID-19 disease and may be a useful additional biomarker when identifying patients with severe COVID-19 disease. Given RNLS’ antiinflammatory properties and negative correlation with inflammatory markers, these findings also suggest evidence of a potential pathophysiological mechanism for severe COVID-19 disease.
In situ measurement of intracellular thermal conductivity using heater-thermometer hybrid diamond nanosensor
Authors: Sotoma Shingo , Zhong Chongxia , Kah James Chen Yong , Yamashita Hayato , Plakhotnik Taras , Harada Yoshie , Suzuki Madoka 
Published: June 2020

AbstractUnderstanding heat dissipation processes at nanoscale during cellular thermogenesis is essential to clarify the relationships between the heat and biological processes in cells and organisms. A key parameter determining the heat flux inside a cell is the local thermal conductivity, a factor poorly investigated both experimentally and theoretically. Here, using a nanoheater/nanothermometer hybrid based on a polydopamine shell encapsulating a fluorescent diamond nanocrystal, we measured the intracellular thermal conductivity of HeLa cell with a spatial resolution of about 200 nm. Its mean value of 0.11 Wm-1K-1 determined for the first time is significantly smaller than that of water. Bayesian analysis of the data strongly supports the existence of variation of the intracellular thermal conductivity of about 40%. These results present a major milestone towards understanding the intracellular heat transfer phenomena at nanoscale.
Asymmetric phospholipids impart novel biophysical properties to lipid bilayers allowing environmental adaptation
Authors: Smith Paul , Owen Dylan M. , Lorenz Christian D. , Makarova Maria 
Published: June 2020

AbstractPhospholipids are a diverse group of biomolecules consisting of a hydrophilic head group and two hydrophobic acyl tails. The nature of the head and length and saturation of the acyl tails are important for defining the biophysical properties of lipid bilayers. It has recently been shown that the membranes of certain yeast species contain high levels of unusual asymmetric phospholipids, consisting of one long and one medium chain acyl moiety – a configuration not common in mammalian cells or other well studied model yeast species. This raises the possibility that structurally asymmetric phospholipids impart novel biophysical properties to the yeast membranes. Here, we use atomistic molecular dynamics simulations (MD) and environmentally-sensitive fluorescent membrane probes to characterize key biophysical parameters of membranes formed from asymmetric lipids for the first time. Interestingly, we show that saturated, but asymmetric phospholipids maintain membrane lipid order across a wider range of temperatures and do not require acyl tail unsaturation or sterols to maintain their properties. This may allow cells to maintain membrane fluidity even in environments which lack the oxygen required for the synthesis of unsaturated lipids and sterols.
SPOTlight: Seeded NMF regression to Deconvolute Spatial Transcriptomics Spots with Single-Cell Transcriptomes
Authors: Elosua Marc , Nieto Paula , Mereu Elisabetta , Gut Ivo , Heyn Holger 
Published: June 2020

AbstractThe integration of orthogonal data modalities greatly supports the interpretation of transcriptomic landscapes in complex tissues. In particular, spatially resolved gene expression profiles are key to understand tissue organization and function. However, spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes, and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots). Using synthetic spots, simulating varying reference quantities and qualities, we confirmed high prediction accuracy also with shallowly sequenced or small-sized scRNA-seq reference datasets. We trained the NMF regression model with sample-matched or external datasets, resulting in accurate and sensitive spatial predictions. SPOTlight deconvolution of the mouse brain correctly mapped subtle neuronal cell states of the cortical layers and the defined architecture of the hippocampus. In human pancreatic cancer, we successfully segmented patient sections into healthy and cancerous areas, and further fine-mapped normal and neoplastic cell states. Trained on an external pancreatic tumor immune reference, we charted the localization of clinical-relevant and tumor-specific immune cell states. Using SPOTlight to detect regional enrichment of immune cells and their co-localization with tumor and adjacent stroma provides an illustrative example in its flexible application spectrum and future potential in digital pathology.
Leaf reflectance can surrogate foliar economics better than physiological traits across macrophyte species
Authors: Villa Paolo , Bolpagni Rossano , Pinardi Monica , Tóth Viktor R. 
Published: June 2020

AbstractMacrophytes are key players in aquatic ecosystems diversity, but knowledge on variability of their functional traits, among and within species, is still limited. Remote sensing is a high-throughput, feasible option for characterizing plant traits at different scales, provided that reliable spectroscopy models are calibrated with congruous empirical data.We sampled leaves from six floating and emergent macrophyte species common in temperate areas, covering different phenological stages, seasons, and environmental conditions, and measured leaf reflectance (400-2500 nm) and leaf traits (dealing with photophysiology, pigments and structure). We explored optimal spectral bands combinations and established non-parametric reflectance-based models for selected traits, eventually showing how airborne hyperspectral data can capture spatial-temporal macrophyte variability.Our key finding is that structural - leaf dry matter content, leaf mass per area - and biochemical - chlorophyll-a content and chlorophylls to carotenoids ratio - traits can be surrogated by leaf reflectance with relative error under 20% across macrophyte species, while performance of reflectance-based models for photophysiological traits depends on species.This finding shows the link between leaf reflectance and leaf economics (structure and biochemistry) for aquatic plants, thus supporting the use of remote sensing for enhancing the level of detail of macrophyte functional diversity analysis, to intra-site and intra-species scales.
Designer protein assemblies with tunable phase diagrams in living cells
Authors: Heidenreich Meta , Georgeson Joseph M. , Locatelli Emanuele , Rovigatti Lorenzo , Nandi Saroj Kumar , Steinberg Avital , Nadav Yotam , Shimoni Eyal , Safran Samuel A. , Doye Jonathan P. K. , Levy Emmanuel D. 
Published: June 2020

AbstractThe self-organization of proteins into specific assemblies is a hallmark of biological systems. Principles governing protein-protein interactions have long been known. However, principles by which such nanoscale interactions generate diverse phenotypes of mesoscale assemblies, including phase-separated compartments, remains challenging to characterize and understand. To illuminate such principles, we create a system of two proteins designed to interact and form mesh-like assemblies in living cells. We devise a novel strategy to map high-resolution phase diagramsin vivo, which provide mesoscale self-assembly signatures of our system. The structural modularity of the two protein components allows straightforward modification of their molecular properties, enabling us to characterize how point mutations that change their interaction affinity impact the phase diagram and material state of the assembliesin vivo. Both, the phase diagrams and their dependence on interaction affinity were captured by theory and simulations, including out-of-equilibrium effects seen in growing cells. Applying our system to interrogate biological mechanisms of self-assembly, we find that co-translational protein binding suffices to recruit an mRNA to the designed micron-scale structures.
MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data
Authors: Cmero Marek , Schmidt Breon , Majewski Ian J. , Ekert Paul G. , Oshlack Alicia , Davidson Nadia M. 
Published: June 2020

AbstractGenomic rearrangements can modify gene function by altering transcript sequences, and have been shown to be drivers in both cancer and rare diseases. Although there are now many methods to detect structural variants from Whole Genome Sequencing (WGS), RNA sequencing (RNA-seq) remains under-utilised as a technology for the detection of gene altering structural variants. Calling fusion genes from RNA-seq data is well established, but other transcriptional variants such as fusions with novel sequence, tandem duplications, large insertions and deletions, and novel splicing are difficult to detect using existing approaches.To identify all types of variants in transcriptomes, we developed MINTIE, an integrated pipeline for RNA-seq data. We take a reference free approach, which combines de novo assembly of transcripts with differential expression analysis, to identify up-regulated novel variants in a case sample.We validated MINTIE on simulated and real data sets and compared it with eight other approaches for finding novel transcriptional variants. We found MINTIE was able to detect >85% of variants while no other method was able to achieve this.We applied MINTIE to RNA-seq data from a cohort of acute lymphoblastic leukemia (ALL) patient samples and identified several clinically relevant variants, including a recurrent unpartnered fusion involving the tumour suppressor gene RB1, and variants in ALL-associated genes: tandem duplications in IKZF1 and PAX5, and novel splicing in ETV6. We further demonstrate the utility of MINTIE to identify rare disease variants using RNA-seq, including the discovery of an inter-chromosomal translocation in the DMD gene in a patient with muscular dystrophy. We posit that MINTIE will be able to identify new disease variants across a range of cancers and other disease types.