bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biofunctionalized Two-dimensional MoS2 Receptors for Rapid Response Modular Electronic SARS-CoV-2 and Influenza A Antigen Sensors
Authors: Muratore C. , Muratore M.K. , Austin D.R. , Look P. , Benton A.K. , Beagle L.K. , Motala M.J. , Moore D.C. , Brothers M.C. , Kim S.S. , Krupa K. , Back T.A. , Grant J.T. , Glavin N. R. 
Published: November 2020

AbstractMultiplex electronic antigen sensors for detection of SARS-Cov-2 spike glycoproteins or hemagglutinin from Influenza A in liquid samples with characteristics resembling extracted saliva were fabricated using scalable processes with potential for economical mass-production. The sensors utilize the sensitivity and surface chemistry of a two-dimensional MoS2 transducer for attachment of antibody fragments in a conformation favorable for antigen binding. Ultra-thin layers (3 nm) of amorphous MoS2 were directly sputtered over the entire sensor chip at room temperature and laser annealed to create an array of semiconducting 2H-MoS2 active sensor regions between metal contacts. The semiconducting region was functionalized with monoclonal antibody Fab (fragment antigen binding) fragments derived from whole antibodies complementary to either SARS-CoV-2 S1 spike protein or Influenza A hemagglutinin using a papain digestion to cleave the antibodies at the disulfide hinges. The high affinity for the MoS2 transducer surface with some density of sulfur vacancies for the antibody fragment base promoted chemisorption with antigen binding regions oriented for interaction with the sample. The angiostatin converting enzyme 2 (ACE2) receptor protein for the SARS-CoV-2 spike glycoprotein, was tethered to a hexa-histidine (his6) tag at its c-terminus both for purification purposes, as well as a motif for binding to MoS2. This modified protein was also investigated as a bio-recognition element. Electrical resistance measurements of sensors functionalized with antibody fragments and exposed to antigen concentrations ranging from 2-20,000 picograms per milliliter revealed selective responses in the presence of complementary antigens with sensitivity to SARS-CoV-2 or influenza A on the order of pg/mL and comparable to gold-standard diagnostics such as Polymerase Chain Reaction (PCR) analysis. Lack of antigen sensitivity for the larger ACE2 BRE further demonstrates the utility of the engineered antibody fragment/transducer interface in bringing the target antigen closer to the transducer surface for sensitivity required for early detection viral diagnostics.
RangeShiftR: an R package for individual-based simulation of spatial eco-evolutionary dynamics and species’ responses to environmental change
Authors: Malchow Anne-Kathleen , Bocedi Greta , Palmer Stephen C. F. , Travis Justin M. J. , Zurell Damaris 
Published: November 2020

AbstractReliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species’ distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses.We present RangeShiftR, an R package that provides flexible and fast simulations of spatial eco-evolutionary dynamics and species’ responses to environmental changes. It implements the individual-based simulation software RangeShifter for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package’s functionality, describe the underlying model structure with its main components and present a short example.RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with comprehensive documentation and elaborate tutorials to provide a low entry level. Thanks to the implementation of the core code in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible.The RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification, and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.
Midbrain and lateral nucleus accumbens dopamine depletion affects free-choice high-fat high-sugar diet preference in male rats
Authors: Joshi Anil , Faivre Fanny , la Fleur Susanne Eva , Barrot Michel 
Published: November 2020

ABSTRACTDopamine influences food intake behavior. Reciprocally, food intake, especially of palatable dietary items, can modulate dopamine-related brain circuitries. Among these reciprocal impacts, it has been observed that an increased intake of dietary fat results in blunted dopamine signaling and, to compensate this lowered dopamine function, caloric intake may subsequently increase. To determine how dopamine regulates food preference we did 6-hydroxydopamine (6-OHDA) lesions, depleting dopamine in specific brain regions in male Sprague Dawley rats. The food preference was assessed by providing the rats with free choice access to control diet, fat, 20% sucrose and tap water. Rats with midbrain lesions targeting the substantia nigra (which is also a model of Parkinson’s disease) consumed fewer calories, as reflected by a decrease in control diet intake, but they surprisingly displayed an increase in fat intake, without change in the sucrose solution intake compared to sham animals. To determine which of the midbrain dopamine projections may contribute to this effect, we next compared the impact of 6-OHDA lesions of terminal fields, targeting the dorsal striatum, the lateral nucleus accumbens and the medial nucleus accumbens. We found that 6-OHDA lesion of the lateral nucleus accumbens, but not of the dorsal striatum or the medial nucleus accumbens, led to increased fat intake. These findings indicate a role for lateral nucleus accumbens dopamine in regulating food preference, in particularly the intake of fat.HIGHLIGHTSDopamine influences fat intakeAnimal model of Parkinson’s disease display lower kcal intake but increased fat choiceDopamine depletion in the lateral nucleus accumbens favors fat intake
Artificial Joint Speed Feedback for Myoelectric Prosthesis Controls
Authors: Earley Eric J. , Johnson Reva E. , Sensinger Jonathon W. , Hargrove Levi J. 
Published: November 2020

I.AbstractAccurate control of human limbs involves both feedforward and feedback signals. For prosthetic arms, feedforward control is commonly accomplished by recording myoelectric signals from the residual limb to predict the user’s intent, but augmented feedback signals are not explicitly provided in commercial devices. Previous studies have demonstrated inconsistent results when artificial feedback was provided in the presence of vision. We hypothesized that negligible benefits in past studies may have been due to artificial feedback with low precision compared to vision, which results in heavy reliance on vision during reaching tasks. Furthermore, we anticipated more reliable benefits from artificial feedback when providing information that vision estimates with high uncertainty – joint speed. In this study, we test an artificial sensory feedback system providing joint speed information and how it impacts performance and adaptation during a hybrid positional-and-myoelectric ballistic reaching task. We found modest improvement in overall reaching errors after perturbed control, and that high prosthesis control noise was compensated for by strategic overreaching with the positional control and underreaching with the myoelectric control. These results provide insights into the relevant factors influencing the improvements conferred by artificial sensory feedback.
U-Net Model for Brain Extraction: Trained on Humans for Transfer to Non-human Primates
Authors: Wang Xindi , Li Xin-Hui , Cho Jae Wook , Russ Brian E. , Rajamani Nanditha , Omelchenko Alisa , Ai Lei , Korchmaros Annachiara , Sawiak Stephen , Benn R. Austin , Garcia-Saldivar Pamela , Wang Zheng , Kalin Ned H. , Schroeder Charles E. , Craddock R. Cameron , Fox Andrew S. , Evans Alan C. , Messinger Adam , Milham Michael P. , Xu Ting 
Published: November 2020

AbstractBrain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20s~10min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community athttps://github.com/HumanBrainED/NHP-BrainExtraction.
The road to sorghum domestication: evidence from nucleotide diversity and gene expression patterns
Authors: Burgarella Concetta , Berger Angélique , Glémin Sylvain , David Jacques , Terrier Nancy , Deu Monique , Pot David 
Published: November 2020

AbstractNative African cereals (sorghum, millets) ensure food security to millions of low-income people from low fertility and drought-prone regions of Africa and Asia. In spite of their agronomic importance, the genetic bases of their phenotype and adaptations are still not well understood. Here we focus onSorghum bicolor, which is the fifth cereal worldwide for grain production and constitutes the staple food for around 500 million people. We leverage transcriptomic resources to address the adaptive consequences of the domestication process. Gene expression and nucleotide variability were analyzed in 11 domesticated and 9 wild accessions. We documented a downregulation of expression and a reduction of diversity both in nucleotide polymorphism (30%) and gene expression levels (18%) in domesticated sorghum. These findings at the genome-wide level support the occurrence of a genetic bottleneck in the domestication history of sorghum, although several genes showed also patterns consistent with the action of selection. Nine hundred and forty-nine genes were significantly differentially expressed between wild and domesticated gene pools. Their functional annotation points to metabolic pathways most likely contributing to the sorghum domestication syndrome, such as photosynthesis and auxin metabolism. Coexpression network analyses revealed 21 clusters of genes sharing similar expression patterns. Four clusters (totalizing 2449 genes) were significantly enriched in differentially expressed genes between the wild and domesticated pools and two were also enriched in domestication and improvement genes previously identified in sorghum. These findings reinforce the evidence that domestication and improvement do not only affect the behaviors of a few genes but led to a large rewiring of the transcriptome during the domestication event and the improvement process. Overall, these analyses pave the way towards the identification of key domestication genes valuable for genetic resources characterization and breeding purposes.
The role of macrophage scavenger receptor 1 (Msr1) in prion pathogenesis
Authors: Li Bei , Chen Meiling , Aguzzi Adriano , Zhu Caihong 
Published: November 2020

AbstractThe progression of prion diseases is accompanied by the accumulation of prions in the brain. Ablation of microglia enhances prion accumulation and accelerates disease progression, suggesting that microglia play a neuroprotective role by clearing prions. However, the mechanisms underlying the phagocytosis and clearance of prion are largely unknown. The macrophage scavenger receptor 1 (Msr1) is an important phagocytic receptor expressed by microglia in the brain, and is involved in the uptake and clearance of soluble amyloid-β. We therefore asked whether Msr1 might play a role in prion clearance, and assessed the scavenger function of Msr1 in prion pathogenesis. We found that Msr1 expression was upregulated in prion-infected mouse brains. However, Msr1 deficiency did not change prion disease progression or lesion patterns. Prion deposition in Msr1 deficient mice was similar to their wild type littermates. In addition, prion-induced neuroinflammation was not affected by Msr1 ablation. We conclude that Msr1 does not play a major role in prion pathogenesis.
Long-term stability of avalanche scaling and integrative network organization in prefrontal and premotor cortex
Authors: Miller Stephanie R. , Yu Shan , Pajevic Sinisa , Plenz Dietmar 
Published: November 2020

AbstractOngoing neuronal activity in the cortex establishes functional networks of synchronization that reflect normal and pathological brain function. The reconstruction of these networks typically suffers from the use of indirect measures of neuronal activity at low spatiotemporal resolution and a lack of longitudinal tracking. Accordingly, the precise nature of the underlying synchronization dynamics and its translation into robust graph theoretical markers are not well characterized. Here, we studied the stability of cortical dynamics and reconstructed functional networks over many weeks in prefrontal and premotor cortex of awake nonhuman primates. We monitored neuronal population activity directly in the ongoing local field potential (LFP) at high spatial and temporal resolution using chronically implanted high-density microelectrode arrays. Ongoing activity was composed of neuronal avalanches exhibiting stable, inverted parabolic profiles with the collapse exponent of 2 in line with a critical branching process. Avalanche-based functional networks, reconstructed using a Normalized Count estimator, revealed robust integrative properties characterized by high neighborhood overlap between strongly connected nodes and robustness to weak-link pruning. “Entropy of mixing” analysis demonstrated progressive link reorganization over weeks. The long-term stability of avalanche scaling and integrative network organization should support the development of robust biomarkers to characterize normal and abnormal brain function.
HTTsilencing delays onset and slows progression of Huntington’s disease like phenotype: Monitoring with a novel neurovascular biomarker
Authors: Liu Hongshuai , Zhang Chuangchuang , Xu Jiadi , Jin Jing , Cheng Liam , Wu Qian , Wei Zhiliang , Liu Peiying , Lu Hanzhang , van Zijl Peter C. M. , Ross Christopher A. , Hua Jun , Duan Wenzhen 
Published: November 2020

AbstractHuntington’s disease (HD) is a dominantly inherited, fatal neurodegenerative disorder caused by a CAG expansion in theHuntingtin(HTT) gene, coding for pathologic mutant HTT protein (mHTT). Because of its gain-of-function mechanism and monogenic etiology, strategies to lower HTT are being actively investigated as disease-modifying therapies. Most approaches are currently targeted at the manifest HD stage, when clinical outcomes are used to evaluate the effectiveness of therapy. However, as almost 50% of striatal volume has been lost at the time of onset of manifest HD it would be preferable to begin therapy in the premanifest period. An unmet challenge is how to evaluate therapeutic efficacy before the presence of clinical symptoms as outcome measures. To address this, we have been developing more sensitive biomarkers such as functional neuroimaging with the goal of identifying noninvasive biomarkers that provide insight into the best time to introduce HTT-lowering treatment. In this study, we mapped the temporal trajectories of arteriolar cerebral blood volumes (CBVa) using inflow-based vascular-space-occupancy (iVASO) MRI technique in an HD mouse model. Significantly elevated CBVa was evident in premanifest zQ175 HD mice prior to motor deficits and striatal atrophy, recapitulating altered CBVa in human premanifest HD. CRISPR/Cas9-mediated non-allele-specificHTTsilencing in striatal neurons restored altered CBVa in premanifest zQ175 mice, delayed onset of striatal atrophy, and slowed the progression of motor phenotype and brain pathology. This study showed the potential of CBVa as a noninvasive fMRI biomarker for premanifest HD clinical trials and demonstrates long-term benefits of introducing an HTT lowering treatment in the premanifest HD.
Complex trait–environment relationships underlie the structure of Wisconsin forest plant communities
Authors: Rolhauser Andres G. , Waller Donald M. , Tucker Caroline M. 
Published: November 2020

AbstractPlant species shift in abundance as environmental conditions change because traits adapt species to particular conditions. As a result, trait values shift along environmental gradients—the so-called trait–environment relationships. These relationships are often assessed by regressing community-weighted mean (CWM) traits on environmental gradients. Such regressions (CWMr) assume that local communities exhibit centered optimum trait–abundance relationships and that traits are not independent from one another. However, the shape of trait–abundance relationships can vary widely along environmental gradients—reflecting the interaction between traits and gradients—and traits are usually interrelated. Accounting for these complexities should improve our ability to accurately describe trait–environment relationships. We tested these ideas by analyzing how abundances of 185 herbaceous understory species distributed among 189 forested sites in Wisconsin, USA, varied in response to four functional traits (vegetative height-VH, leaf size-LS, leaf mass per area-LMA, and leaf carbon content) and six soil and climate variables. A generalized linear mixed model (GLMM) allowed us to assess how the shape of trait–abundance relationships changed along environmental gradients for the 24 trait–environment combinations simultaneously. We then compared the resulting trait–environment relationships to those estimated via CWMr. The GLMM identified five significant trait–environment relationships that together explained ∼40% of variation in species abundances across sites. Temperature played important roles with warmer and more seasonal sites favoring taller plants. Soil texture and temperature seasonality affected LS and LMA more modestly; these seasonality effects declined at more seasonal sites. Only some traits under certain conditions showed centered optimum trait– abundance relationships. Concomitantly, CWMr identified 17 significant trait–environment relationships including effects of temperature, precipitation, and soil on LMA as often reported in other studies. Despite this overidentification, CWMr failed to detect significant temperature-seasonality effects found in the GLMM. Modeling the complexity of how traits and environments interact to affect plant abundance allows us to identify and rank key trait– environment relationships. Although the GLMM model was more complex compared to single CWM regressions, it identified a simpler hierarchy of trait–environment relationships that accurately and reliably predicted responses of forest understory species to gradients in environmental conditions.
Universal annotation of the human genome through integration of over a thousand epigenomic datasets
Authors: Vu Ha , Ernst Jason 
Published: November 2020

AbstractBackgroundGenome-wide maps of chromatin marks such as histone modifications and open chromatin sites provide valuable information for annotating the non-coding genome, including identifying regulatory elements. Computational approaches such as ChromHMM have been applied to discover and annotate chromatin states defined by combinatorial and spatial patterns of chromatin marks within the same cell type. An alternative ‘stacked modeling’ approach was previously suggested, where chromatin states are defined jointly from datasets of multiple cell types to produce a single universal genome annotation based on all datasets. Despite its potential benefits for applications that are not specific to one cell type, such an approach was previously applied only for small-scale specialized purposes. Large-scale applications of stacked modeling have previously posed scalability challenges.ResultsUsing a version of ChromHMM enhanced for large-scale applications, we applied the stacked modeling approach to produce a universal chromatin state annotation of the human genome using over 1000 datasets from more than 100 cell types, with the learned model denoted as the full-stack model. The full-stack model states show distinct enrichments for external genomic annotations, which we used in characterizing each state. Compared to per-cell-type annotations, the full-stack annotations directly differentiate constitutive from cell type specific activity and is more predictive of locations of external genomic annotations.ConclusionsThe full-stack ChromHMM model provides a universal chromatin state annotation of the genome and a unified global view of over 1000 datasets. We expect this to be a useful resource that complements existing per-cell-type annotations for studying the non-coding human genome.
Evaluating the Effects of Propiconazole on Hard Fescue (Festuca brevipila) via RNA sequencing and Liquid Chromatography–Mass Spectrometry
Authors: Qiu Yinjie , Petrella Dominic , Sessoms Florence , Yang Ya , Esler Mark , Hirsch Cory D. , Heineck Garett , Hegeman Adrian , Watkins Eric 
Published: November 2020

AbstractPropiconazole is often used to remove fungal endophytes from turfgrass to study the effects ofEpichloëendophytes. However, besides a fungicidal effect, propiconazole can bind to the genes in the cytochrome P450 family and affect the biosynthesis of brassinosteroids. For this reason, outside of fungicidal application, propiconazole has also been used as plant growth regulator. In this study, we used a combination of RNA sequencing and liquid chromatography–mass spectrometry (LC-MS) to study how hard fescue (Festuca brevipila) responded to the high dose of propiconazole treatment. To test the long-term effect of the heavy use of propiconazole on plants, we inoculated withMicrodochium nivale(causal agent of pink snow mold) half year post the last fungicide application. Propiconazole-treated plants showed enhanced pink snow mold resistance. This study suggested that the high dose use of propiconazole fungicide resulted in phenotypic and physiological changes in the plant such as slow growth and change in disease resistance. Genes and pathways affected by propiconazole identified in this study provide turfgrass breeders new information for genetic improvement of hard fescue and also provide turfgrass management new ways to control turfgrass diseases.