bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of CSF biomarkers and microRNA-mediated disease mechanisms in SMA
Authors: Welby Emily , Rehborg Rebecca J. , Harmelink Matthew , Ebert Allison D. 
Published: May 2021

AbstractObjectiveCerebral spinal fluid (CSF) is a promising biospecimen for the detection of central nervous system (CNS) biomarkers to monitor therapeutic efficacy at the cellular level in neurological diseases. Spinal muscular atrophy (SMA) patients receiving intrathecal antisense oligonucleotide (nusinersen) therapy tend to show improved motor function, but the treatment effect on cellular function remains unknown. The objective of this study was to assess the potential of extracellular RNAs and microRNAs in SMA patient CSF as indicators of neuron and glial health following nusinersen treatment.MethodsCSF samples from SMA Type 1-3 patients were screened using quantitative RT-PCR to assess expression of extracellular RNAs associated with inflammation and cellular stress, and microRNAs previously implicated in SMA pathogenesis. We also used mRNA sequencing and multi-electrode array approaches to assess the transcriptional and functional effects of astrocyte-associated miR-146a on healthy and SMA induced pluripotent stem cell (iPSC)-derived motor neurons.ResultsExtracellular RNA analysis is suggestive of ongoing cellular stress, even in nusinersen treated samples. microRNAs previously associated with SMA pathology tended to show improvement in expression levels in nusinersen treated samples, with the exception of the astrocyte-secreted miR-146a. miR-146a treated iPSC-derived motor neurons showed a downregulation of extracellular matrix genes found in the synaptic perineuronal net and decreased spontaneous activity.InterpretationExtracellular RNAs and microRNAs can be detected in SMA patient CSF samples, potentially serving as useful biomarkers to monitor cellular health during nusinersen treatment. Astrocyte health and response to nusinersen are important aspects to address in SMA pathogenesis and treatment strategies.
Synergistic coevolution accelerates genome evolution
Authors: Preussger Daniel , Herbig Alexander , Kost Christian 
Published: May 2021

AbstractEcological interactions are key drivers of evolutionary change. Although it is well-documented that antagonistic coevolution can accelerate molecular evolution, the evolutionary consequences of synergistic coevolution remain poorly understood. Here we show experimentally that also synergistic coevolution can speed up the rate of molecular evolution. Pairs of auxotrophic genotypes of the bacterium Escherichia coli, whose growth depended on a reciprocal exchange of amino acids, were experimentally coevolved, and compared to two control groups of independently growing cells. Coevolution drove the rapid emergence of a strong metabolic cooperation that correlated with a significantly increased number of mutations in coevolved auxotrophs as compared to monoculture controls. These results demonstrate that synergistic coevolution can cause rapid evolution that in the long run may drive diversification of mutualistically interacting species.One-Sentence SummarySynergistic coevolution among obligate mutualists increases the rate of molecular evolution relative to independent types.
Large-scale field phenotyping using backpack LiDAR and GUI-based CropQuant-3D to measure structural responses to different nitrogen treatments in wheat
Authors: Zhu Yulei , Sun Gang , Ding Guohui , Zhou Jie , Wen Mingxing , Jin Shichao , Zhao Qiang , Colmer Joshua , Ding Yanfeng , Ober Eric S. , Zhou Ji 
Published: May 2021

AbstractPlant phenomics is widely recognised as a key area to bridge the gap between traits of agricultural importance and genomic information. A wide range of field-based phenotyping solutions have been developed, from aerial-based to ground-based fixed gantry platforms and handheld devices. Nevertheless, several disadvantages of these current systems have been identified by the research community concerning mobility, affordability, throughput, accuracy, scalability, as well as the ability to analyse big data collected. Here, we present a novel phenotyping solution that combines a commercial backpack LiDAR device and our graphical user interface (GUI) based software called CropQuant-3D, which has been applied to phenotyping of wheat and associated 3D trait analysis. To our knowledge, this is the first use of backpack LiDAR for field-based plant research, which can acquire millions of 3D points to represent spatial features of crops. A key feature of the innovation is the GUI software that can extract plot-based traits from large, complex point clouds with limited computing time and power. We describe how we combined backpack LiDAR and CropQuant-3D to accurately quantify crop height and complex 3D traits such as variation in canopy structure, which was not possible to measure through other approaches. Also, we demonstrate the methodological advance and biological relevance of our work in a case study that examines the response of wheat varieties to three different levels of nitrogen fertilisation in field experiments. The results indicate that the combined solution can differentiate significant genotype and treatment effects on key morphological traits, with strong correlations with conventional manual measurements. Hence, we believe that the combined solution presented here could consistently quantify key traits at a larger scale and more quickly than heretofore possible, indicating the system could be used as a reliable research tool in large-scale and multi-location field phenotyping for crop research and breeding activities. We exhibit the system’s capability in addressing challenges in mobility, throughput, and scalability, contributing to the resolution of the phenotyping bottleneck. Furthermore, with the fast maturity of LiDAR technologies, technical advances in image analysis, and open software solutions, it is likely that the solution presented here has the potential for further development in accuracy and affordability, helping us fully exploit available genomic resources.
Polyelectrolyte brush bilayer under shear at linear and nonlinear response regimes: A combination of the density functional theory framework and the scaling theory
Authors: Edwards Mike J. 
Published: May 2021

ABSTRACTThe density functional theory framework and the scaling theory are employed to approach the problem of the Polyelectrolyte brush bilayer under shear. It turns out that, the system at shear rates larger than a critical shear rate undergo a global restructuring during which chains stretch in the shear direction. In the absence of the electrostatic interactions as well as the hydrodynamic interactions, this global restructuring causes a sublinear scaling of the shear stress with the shear rate which makes the shear thinning effect. Nevertheless, in the presence of the hydrodynamic interactions, not only there is no sublinear regime but also a weak superlinear regime which makes a weak shear thickening effect. In the presence of the electrostatic interactions, the stress tensor components change by their second Virial coefficients, however, their shear rate power law are untouched. Nonetheless, the kinetic friction coefficient is independent of the electrostatic interactions. This suggests that the lubrication is not very much different than the neutral bilayers and the electrostatic interactions do not change that. The results of this study offers that maybe nature uses another mechanism to reduce friction coefficient in synovial joint and other biological systems.SIGNIFICANCEThe significance of this study is that it strongly criticizes the theoretical approach to the same system which is already published (T. Kreer,Soft Matter,12, 3479 (2016)). Moreover, the results of this study may help our understanding from Biological systems and optimization of artificial synovial joint which is the core of this study.
The cellular architecture of microvessels, pericytes and neuronal cell types in organizing regional brain energy homeostasis in mice
Authors: Wu Yuan-ting , Bennett Hannah C. , Chon Uree , Vanselow Daniel J. , Zhang Qingguang , Muñoz-Castañeda Rodrigo , Cheng Keith C. , Osten Pavel , Drew Patrick J. , Kim Yongsoo 
Published: May 2021

SummaryCerebrovasculature and its mural cells must meet dynamic energy demands of different neuronal cell types across the brain, but their spatial relationship is largely unknown. Here, we apply brain-wide mapping methods to create a comprehensive cellular-resolution resource comprising the distribution of and quantitative relationship between cerebrovasculature, pericytes, and glutamatergic and GABAergic neurons, including neuronal nitric oxide synthase-positive (nNOS+) neurons and their subtypes, as well as simulation-ready vascular tracing data in mice. We discover strikingly high densities of vasculature and pericytes with high blood perfusion in primary motor-sensory cortices compared to association cortices that show significant positive and negative correlation with parvalbumin+ and nNOS+ neurons, respectively. Thalamo-striatal areas linked to primary motor-sensory cortices also contain high densities of vasculature and pericytes compared to association areas. Collectively, our results unveil a finely tuned spatial relationship between cerebrovascular network and neuronal cell composition in meeting regional energy needs of the brain.
Network-based integration of epigenetic landscapes unveils molecular programs underlying human T follicular helper cell differentiation
Authors: Mahajan Vinay S , Rahman Syed A , Viswanadham Vinayak V , Yuen Grace J , Sun Na , Mattoo Hamid , Pillai Shiv S , Das Jishnu 
Published: May 2021

AbstractT follicular helper (Tfh) cells play a critical role in T-dependent humoral immune responses. While genetic programs controlling Tfh cell differentiation have been extensively studied using murine models, studies in humans have been hampered by the lack of a robust in vitro differentiation system for Tfh cells. We characterized epigenomic landscapes across stages of Tfh cell differentiation in a healthy human tonsil using ATAC-Seq and CUT&RUN for selected histone modifications. We combined these epigenomic datasets and integrated them with the reference human protein interactome using a novel network propagation approach. Our approach uncovered subnetworks integral to Tfh cell differentiation. These subnetworks captured known Tfh cell drivers to a greater extent than conventional gene-centric analyses would, and also revealed novel modules that may be required for Tfh cell differentiation. We find that human Tfh cell subnetworks are functionally associated with specific immune signaling cascades including cytokine receptor driven pathways. Analyses of transcriptomic data revealed that in addition to these immune pathways being significantly dysregulated in severe COVID-19, the corresponding Tfh cell subnetworks are also transcriptionally perturbed to a similar extent. This provides a molecular mechanistic basis for the previously observed impaired Tfh cell differentiation and loss of germinal centers in severe COVID-19.
Predicting Moisture Content During Maize Nixtamalization Using Machine Learning with NIR Spectroscopy
Authors: Burns Michael J. , Renk Jonathan S. , Eickholt David P. , Gilbert Amanda M. , Hattery Travis J. , Holmes Mark , Anderson Nickolas , Waters Amanda J. , Kalambur Sathya , Flint-Garcia Sherry A. , Yandeau-Nelson Marna D. , Annor George A. , Hirsch Candice N. 
Published: May 2021

ABSTRACTLack of high throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels. Machine learning was utilized to develop models based on the combination of NIR spectra and moisture content determined from a scaled-down benchtop cook method. A linear support vector machine (SVM) model with a Spearman’s rank correlation coefficient of 0.852 between wet lab and predicted values was developed from 100 diverse temperate genotypes grown in replicate across two environments. This model was applied to NIR data from 501 diverse temperate genotypes grown in replicate in five environments. Analysis of variance revealed environment explained the highest percent of the variation (51.5%), followed by genotype (15.6%) and genotype-by-environment interaction (11.2%). A genome-wide association study identified 26 significant loci across five environments that explained between 5.04% and 16.01% (average = 10.41%). However, genome-wide markers explained 10.54% to 45.99% (average = 31.68%) of the variation, indicating the genetic architecture of this trait is likely complex and controlled by many loci of small effect. This study provides a high-throughput method to evaluate moisture content during nixtamalization that is feasible at the scale of a breeding program and provides important information about the factors contributing to variation of this trait for breeders and food companies to make future strategies to improve this important processing trait.Key MessageMoisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture.
In vitro cell cycle oscillations exhibit a robust and hysteretic response to changes in cytoplasmic density
Authors: Jin Minjun , Tavella Franco , Wang Shiyuan , Yang Qiong 
Published: May 2021

ABSTRACTCells control the properties of the cytoplasm to ensure the proper functioning of biochemical processes. Recent studies showed that the density of the cytoplasm varies in both physiological and pathological states of cells undergoing growth, division, differentiation, apoptosis, senescence, and metabolic starvation. Little is known about how cellular processes cope with these cytoplasmic variations. Here we study how a cell cycle oscillator comprising cyclin -dependent kinase (CDK1) responds to cytoplasmic density changes by systematically diluting or concentrating a cycling Xenopus egg cytoplasm in cell-like microfluidic droplets. We found that the cell cycle maintains robust oscillations over a wide range of deviations from the endogenous density by as low as 0.2x to more than 1.22x. A further dilution or concentration from these values will arrest the system in a low or high steady-state of CDK1 activity, respectively. Interestingly, diluting a concentrated arrested cytoplasm recovers its oscillatory behavior but requires a significantly lower concentration than 1.22x. Thus, the cell cycle switches reversibly between oscillatory and stable steady states at distinct thresholds depending on the direction of density tuning, forming a hysteresis loop. We recapitulated these observations by a mathematical model. The model predicted that Wee1 and Cdc25 positive feedback do not contribute to the observed robustness, confirmed by experiments. Nevertheless, modulating these feedback strengths and cytoplasmic density changes the total number of cycles, revealing a new role of Wee1 and Cdc25 in controlling the cycle number of early embryonic extracts. Our system can be applied to study how cytoplasmic density affects other cellular processes.
Cross-species identification of cancer-resistance associated genes uncovers their relevance to human cancer risk
Authors: Nair Nishanth Ulhas , Cheng Kuoyuan , Naddaf Lamis , Sharon Elad , Pal Lipika R. , Rajagopal Padma S. , Unterman Irene , Aldape Kenneth , Hannenhalli Sridhar , Day Chi-Ping , Tabach Yuval , Ruppin Eytan 
Published: May 2021

ABSTRACTCancer is an evolutionarily conserved disease that occurs in a wide variety of species. We applied a comparative genomics approach to systematically characterize the genes whose conservation levels significantly correlates positively (PC) or negatively (NC) with a broad spectrum of cancer-resistance estimates, computed across almost 200 vertebrate species. PC genes are enriched in pathways relevant to tumor suppression including cell cycle, DNA repair, and immune response, while NC genes are enriched with a host of metabolic pathways. The conservation levels of the PC and NC genes in a species serve to build the first genomics-based predictor of its cancer resistance score. We find that PC genes are less tolerant to loss of function (LoF) mutations, are enriched in cancer driver genes and are associated with germline mutations that increase human cancer risk. Furthermore, their expression levels are associated with lifetime cancer risk across human tissues. Finally, their knockout in mice results in increased cancer incidence. In sum, we find that many genes associated with cancer resistance across species are implicated in human cancers, pointing to several additional candidate genes that may have a functional role in human cancer.
Efficacy Estimates for Various COVID-19 Vaccines: What we Know from the Literature and Reports
Authors: Shapiro Julia , Dean Natalie E. , Madewell Zachary J. , Yang Yang , Halloran M.Elizabeth , Longini Ira 
Published: May 2021

AbstractIn this report, we provide summary estimates, from publications and reports, of vaccine efficacy (VE) for the COVID-19 vaccines that are being rolled out on a global scale. We find that, on average, the efficacy against any disease with infection is 85% (95% CI: 71 - 93%) after a fully course of vaccination. The VE against severe disease, hospitalization or death averages close to 100%. The average VE against infection, regardless of symptoms, is 84% (95% CI: 70 - 91%). We also find that the average VE against transmission to others for infected vaccinated people is 48% (95% CI: 45 - 52%). Finally, we prove summary estimates of the VE against any disease with infection for some of the variants of concern (VOC). The average VE for the VOCγ(P1) is 61% (95% CI: 43 - 73%). The average VE for the VOCα(B.1.1.7),β(B.1.351), andδ(B.1.617.2) after dose 1 are 48% (95% CI: 44 - 51%), 35% (95% CI: -11 - 62%), and 33% (95% CI: 21 - 43%), respectively. The average VE for the VOCα(B.1.1.7),β(B.1.351), andδ(B.1.617.2) after dose 2 are 85% (95% CI: 25 - 97%), 57% (95% CI: 14 - 78%), and 78% (95% CI: 28 - 93%), respectively.
Early Detection of COVID-19 Outbreaks Using Human Mobility Data
Authors: Guan Grace , Dery Yotam , Yechezkel Matan , Ben-Gal Irad , Yamin Dan , Brandeau Margaret L. 
Published: May 2021

AbstractBackgroundContact mixing plays a key role in the spread of COVID-19. Thus, mobility restrictions of varying degrees up to and including nationwide lockdowns have been implemented in over 200 countries. To appropriately target the timing, location, and severity of measures intended to encourage social distancing at a country level, it is essential to predict when and where outbreaks will occur, and how widespread they will be.MethodsWe analyze aggregated, anonymized health data and cell phone mobility data from Israel. We develop predictive models for daily new cases and the test positivity rate over the next 7 days for different geographic regions in Israel. We evaluate model goodness of fit using root mean squared error (RMSE). We use these predictions in a five-tier categorization scheme to predict the severity of COVID-19 in each region over the next week. We measure magnitude accuracy (MA), the extent to which the correct severity tier is predicted.ResultsModels using mobility data outperformed models that did not use mobility data, reducing RMSE by 17.3% when predicting new cases and by 10.2% when predicting the test positivity rate. The best set of predictors for new cases consisted of 1-day lag of past 7-day average new cases, along with a measure of internal movement within a region. The best set of predictors for the test positivity rate consisted of 3-days lag of past 7-day average test positivity rate, along with the same measure of internal movement. Using these predictors, RMSE was 4.812 cases per 100,000 people when predicting new cases and 0.79% when predicting the test positivity rate. MA in predicting new cases was 0.775, and accuracy of prediction to within one tier was 1.0. MA in predicting the test positivity rate was 0.820, and accuracy to within one tier was 0.998.ConclusionsUsing anonymized, macro-level data human mobility data along with health data aids predictions of when and where COVID-19 outbreaks are likely to occur. Our method provides a useful tool for government decision makers, particularly in the post-vaccination era, when focused interventions are needed to contain COVID-19 outbreaks while mitigating the collateral damage of more global restrictions.
Co-designing an online COmmunity suPporting familiEs after Sudden Cardiac Death (COPE-SCD) in the young
Authors: Yeates Laura , Gardner Karen , Do Judy , van den Heuvel Lieke , Fleming Gabrielle , Semsarian Christopher , McEwen Alison , Adlard Leesa , Ingles Jodie 
Published: May 2021

ABSTRACTObjectiveTo co-design an online support intervention for families after sudden cardiac death (SCD) in the young (<35 years).DesignCo-design of a SCD family intervention by stakeholder focus groups.SettingFamilies and healthcare professionals with experience in SCD in the young.ParticipantsSemi-structured online focus groups were held with key stakeholders, i.e. family members who had experienced young SCD, healthcare professionals and researchers. Guided discussions were used to develop an online support intervention. Thematic analysis of discussions and iterative feedback on draft materials guided content development.ResultsFour focus groups were held (10-12 participants per group). Stakeholder involvement facilitated development of high-level ideas and priority issues. Creative content and materials were developed based on user preference for stories, narratives and information reflecting everyday experience of families navigating the legal and medical processes surrounding SCD, normalising and supporting grief responses in the context of family relationships, and fostering hope. Emphasis on accessibility led to the overarching need for digital information and online engagement. These insights allowed development of an online intervention - COPE-SCD: A COmmunity suPporting familiEs after Sudden Cardiac Death - which includes a website and online support program.ConclusionUsing co-design with stakeholders we have developed a support intervention that directly addresses the needs of SCD families and fills a large gap in existing health care. We will evaluate COPE-SCD to determine whether this is an effective intervention for support of families following a young SCD.Strengths and limitations of this studyHealthcare providers and consumer representatives participated as stakeholders in support intervention design.Co-design allowed development of a support intervention incorporating innovative ideas to meet user needs.Focus groups were limited in size and may not fully represent the needs of the wider community affected by sudden cardiac death in the young.