bluuebunny/crossref_metadata_2025_split huggingface dataset:
Influence of data sampling methods on the representation of neural spiking activity in vivo
Authors: van der Heijden Meike E. , Brown Amanda M. , Sillitoe Roy V. 
Published: January 2022

AbstractIn vivo single-unit recordings distinguish the basal spiking properties of neurons in different experimental settings and disease states. Here, we examined over 300 spike trains recorded from Purkinje cells and cerebellar nuclei neurons to test whether data sampling approaches influence the extraction of rich descriptors of firing properties. Our analyses included neurons recorded in awake and anesthetized control mice, as well as disease models of ataxia, dystonia, and tremor. We find that recording duration circumscribes overall representations of firing rate and pattern. Notably, shorter recording durations skew estimates for global firing rate variability towards lower values. We also find that only some populations of neurons in the same mouse are more similar to each other than to neurons recorded in different mice. These data reveal that recording duration and approach are primary considerations when interpreting task-independent single-neuron firing properties. If not accounted for, group differences may be concealed or exaggerated.
Cd59 and inflammation orchestrate Schwann cell development
Authors: Wiltbank Ashtyn T. , Steinson Emma R. , Criswell Stacey J. , Piller Melanie , Kucenas Sarah 
Published: January 2022

AbstractEfficient neurotransmission is essential for organism survival and is enhanced by myelination. However, the genes that regulate myelin and myelinating glial cell development have not been fully characterized. Data from our lab and others demonstrates thatcd59, which encodes for a small GPI-anchored glycoprotein, is highly expressed in developing zebrafish, rodent, and human oligodendrocytes (OLs) and Schwann cells (SCs), and that patients with CD59 dysfunction develop neurological dysfunction during early childhood. Yet, the function of CD59 in the developing nervous system is currently undefined. In this study, we demonstrate thatcd59is expressed in a subset of developing SCs. Usingcd59mutant zebrafish, we show that developing SCs proliferate excessively, which leads to reduced myelin volume, altered myelin ultrastructure, and perturbed node of Ranvier assembly. Finally, we demonstrate that complement activity is elevated incd59mutants and that inhibiting inflammation restores SC proliferation, myelin volume, and nodes of Ranvier to wildtype levels. Together, this work identifies Cd59 and developmental inflammation as key players in myelinating glial cell development, highlighting the collaboration between glia and the innate immune system to ensure normal neural development.
Two modes of evolution shape bacterial strain diversity in the gut for thousands of generations
Authors: Frazão N. , Konrad A. , Güleresi D. , Lässig M. , Gordo I. 
Published: January 2022

AbstractHow and at what pace bacteria evolve when colonizing healthy hosts remains unclear. Here, by monitoring evolution for more than six thousand generations in the mouse gut, we show that the successful colonization of an invader Escherichia coli depends on the diversity of the existing microbiota and the presence of a closely related strain. Following colonization, two modes of evolution were observed: one in which diversifying selection leads to long-term coexistence of ecotypes and a second in which directional selection propels selective sweeps. These modes can be quantitatively distinguished by the statistics of mutation trajectories. In our experiments, diversifying selection was marked by the emergence of metabolic mutations, and directional selection by acquisition of prophages, which bring their own benefits and costs. In both modes, we observed parallel evolution, with mutation accumulation rates comparable to those typically observed in vitro on similar time scales. Our results show that gut environments can rapidly generate diversifying selection and ecotype formation.
Brain responses in aggression-prone individuals: A systematic review and meta-analysis of functional magnetic resonance imaging (fMRI) studies of anger- and aggression-eliciting tasks
Authors: Nikolic Maja , Pezzoli Patrizia , Jaworska Natalia , Seto Michael C. 
Published: January 2022

AbstractBackgroundWhile reactive aggression (in response to a perceived threat or provocation) is part of humans’ adaptive behavioral repertoire, it can violate social and legal norms. Understanding brain function in individuals with high levels of reactive aggression as they process anger- and aggression-eliciting stimuli is critical for refining interventions. Three neurobiological models of reactive aggression–the limbic hyperactivity, prefrontal hypoactivity, and dysregulated limbic-prefrontal connectivity models–have been proposed. However, these models are based on neuroimaging studies involving mainly healthy individuals, leaving it unclear which model best describes brain function in aggression-prone individuals.MethodsWe conducted a systematic literature search (PubMed and Psycinfo) and Multilevel Kernel Density meta-analysis (MKDA) of nine functional magnetic resonance imaging (fMRI) studies of brain responses to tasks putatively eliciting anger and aggression in aggression-prone individuals alone, and relative to healthy controls.ResultsAggression-prone individuals exhibited greater activity during reactive aggression relative to baseline in the superior temporal gyrus and in regions comprising the cognitive control and default mode networks (right posterior cingulate cortex, precentral gyrus, precuneus, right inferior frontal gyrus). Compared to healthy controls, aggression-prone individuals exhibited increased activity in limbic regions (left hippocampus, left amygdala, left parahippocampal gyrus) and temporal regions (superior, middle, inferior temporal gyrus), and reduced activity in occipital regions (left occipital cortex, left calcarine cortex).ConclusionsThese findings lend support to the limbic hyperactivity model and further indicate altered temporal and occipital activity in anger- and aggression-eliciting situations that involve face and speech processing.
Machine-learning fromPseudomonas putidaTranscriptomes Reveals Its Transcriptional Regulatory Network
Authors: Lim Hyun Gyu , Rychel Kevin , Sastry Anand V. , Mueller Joshua , Niu Wei , Feist Adam M. , Palsson Bernhard O. 
Published: January 2022

AbstractBacterial gene expression is orchestrated by numerous transcription factors (TFs). Elucidating how gene expression is regulated is fundamental to understanding bacterial physiology and engineering it for practical use. In this study, a machine-learning approach was applied to uncover the genome-scale transcriptional regulatory network (TRN) inPseudomonas putida, an important organism for bioproduction. We performed independent component analysis of a compendium of 321 high-quality gene expression profiles, which were previously published or newly generated in this study. We identified 84 groups of independently modulated genes (iModulons) that explain 75.7% of the total variance in the compendium. With these iModulons, we (i) expand our understanding of the regulatory functions of 39 iModulon associated TFs (e.g., HexR, Zur) by systematic comparison with 1,993 previously reported TF-gene interactions; (ii) outline transcriptional changes after the transition from the exponential growth to stationary phases; (iii) capture group of genes required for utilizing diverse carbon sources and increased stationary response with slower growth rates; (iv) unveil multiple evolutionary strategies of transcriptome reallocation to achieve fast growth rates; and (v) define an osmotic stimulon, which includes the Type VI secretion system, as coordination of multiple iModulon activity changes. Taken together, this study provides the first quantitative genome-scale TRN forP. putidaand a basis for a comprehensive understanding of its complex transcriptome changes in a variety of physiological states.
SARS-CoV-2 drives NLRP3 inflammasome activation in human microglia through spike-ACE2 receptor interaction
Authors: Albornoz Eduardo , Amarilla Alberto A , Modhiran Naphak , Parker Sandra , Li Xaria X. , Wijesundara Danushka K. , Zamora Adriana Pliego , McMillan Christopher LD , Liang Benjamin , Peng Nias Y.G. , Sng Julian D.J. , Saima Fatema Tuj , Paramitha Devina , Parry Rhys , Avumegah Michael S. , Isaacs Ariel , Lo Martin , Miranda-Chacon Zaray , Bradshaw Daniella , Salinas-Rebolledo Constanza , Rajapakse Niwanthi W. , Munro Trent , Rojas-Fernandez Alejandro , Young Paul R. , Stacey Katryn J , Khromykh Alexander A. , Chappell Keith J. , Watterson Daniel , Woodruff Trent M. 
Published: January 2022

ABSTRACTCoronavirus disease-2019 (COVID-19) is primarily a respiratory disease, however, an increasing number of reports indicate that SARS-CoV-2 infection can also cause severe neurological manifestations, including precipitating cases of probable Parkinson’s disease. As microglial NLRP3 inflammasome activation is a major driver of neurodegeneration, here we interrogated whether SARS-CoV-2 can promote microglial NLRP3 inflammasome activation utilising a model of human monocyte-derived microglia. We identified that SARS-CoV-2 isolates can bind and enter microglia, triggering inflammasome activation in the absence of viral replication. Mechanistically, microglial NLRP3 could be both primed and activated with SARS-CoV-2 spike glycoprotein in a NF-κB and ACE2-dependent manner. Notably, virus- and spike protein-mediated inflammasome activation in microglia was significantly enhanced in the presence of α-synuclein fibrils, which was entirely ablated by NLRP3-inhibition. These results support a possible mechanism of microglia activation by SARS-CoV-2, which could explain the increased vulnerability to developing neurological symptoms akin to Parkinson’s disease in certain COVID-19 infected individuals, and a potential therapeutic avenue for intervention.SIGNIFICANCE STATEMENTSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) principally affects the lungs, however there is evidence that the virus can also reach the brain and lead to chronic neurological symptoms. In this study, we examined the interaction SARS-CoV-2 with brain immune cells, by using an ex-vivo model of human monocyte-derived microglia. We identified robust activation of the innate immune sensor complex, NLRP3 inflammasome, in cells exposed to SARS-CoV-2. This was dependent on spike protein-ACE2 receptor interaction and was potentiated in the presence of α-synuclein. We therefore identify a possible mechanism for SARS-CoV-2 and increased vulnerability to developing neurological dysfunction. These findings support a potential therapeutic avenue for treatment of SARS-CoV-2 driven neurological manifestations, through use of NLRP3 inflammasome or ACE2 inhibitors.
Hydrodynamic and Polyelectrolyte Properties of Actin Filaments: Theory and Experiments
Authors: Alva Ernesto , George Annitta , Brancaleon Lorenzo , Marucho Marcelo 
Published: January 2022

AbstractActin filament’s polyelectrolyte and hydrodynamic properties, their interactions with the biological environment, and external force fields play an essential role in their biological activities in eukaryotic cellular processes. In this article, we introduce a unique approach that combines dynamics and electrophoresis light scattering experiments, an extended semiflexible worm-like chain model, and an asymmetric polymer length distribution theory to characterize the polyelectrolyte and hydrodynamic properties of actin filaments in aqueous electrolyte solutions. A fitting approach was used to optimize the theories and filament models for hydrodynamic conditions. We used the same sample and experimental conditions and considered several g-actin and polymerization buffers to elucidate the impact of their chemical composition, reducing agents, pH values, and ionic strengths on the filament translational diffusion coefficient, electrophoretic mobility, structure factor, asymmetric length distribution, effective filament diameter, electric charge, zeta potential, and semiflexibility. Compared to those values obtained from molecular structure models, our results revealed a lower value of the effective G-actin charge and a more significant value of the effective filament diameter due to the formation of the double layer of the electrolyte surrounding the filaments. Contrary to the data usually reported from electron micrographs, the lower values of our results for the persistence length and average contour filament length agree with the significant difference in the association rates at the filament ends that shift to sub-micro lengths, the maximum of the length distribution.
Benchmarking taxonomic classifiers with Illumina and Nanopore sequence data for clinical metagenomic diagnostic applications
Authors: Govender Kumeren N. , Eyre David W. 
Published: January 2022

AbstractCulture-independent metagenomic detection of microbial species has the potential to provide rapid and precise real-time diagnostic results. However, it is potentially limited by sequencing and classification errors. We use simulated and real-world data to benchmark rates of species misclassification using 100 reference genomes for each of ten common bloodstream pathogens and six frequent blood culture contaminants (n=1600). Simulating both with and without sequencing error for both the Illumina and Oxford Nanopore platforms, we evaluated commonly used classification tools including Kraken2, Bracken, and Centrifuge, utilising mini (8GB) and standard (30-50GB) databases. Bracken with the standard database performed best, the median percentage of reads across both sequencing platforms identified correctly to the species level was 98.46% (IQR 93.0:99.3) [range 57.1:100]. For Kraken2 with a mini database, a commonly used combination, median species-level identification was 79.3% (IQR 39.1:88.8) [range 11.2:100]. Classification performance varied by species, with E. coli being more challenging to classify correctly (59.4% to 96.4% reads with correct species, varying by tool used). By filtering out shorter Nanopore reads (<3500bp) we found performance similar or superior to Illumina sequencing, despite higher sequencing error rates. Misclassification was more common when the misclassified species had a higher average nucleotide identity to the true species. Our findings highlight taxonomic misclassification of sequencing data occurs and varies by sequencing and analysis workflow. This “bioinformatic contamination” should be accounted for in metagenomic pipelines to ensure accurate results that can support clinical decision making.ImportanceMetagenomics may transform clinical microbiology by enabling more rapid species detection in a potentially unbiased manner and reducing reliance on culture-based approaches. However, it is still limited by ongoing challenges such as sequencing and classification software errors. In this study, we use simulated and real-world data to define the intrinsic rates of species misclassification that occur using Illumina and Oxford Nanopore sequencing platforms with commonly used taxonomic classification tools and databases. We quantify the extent of “bioinformatic contamination” arising from the classification process. This enables us to identify the best performing tools that maximize classification accuracy, and to suggest how taxonomic misclassification can be formally accounted for in clinical diagnostic workflows. Specifically, we specify thresholds for identifying or excluding polymicrobial infections in metagenomic samples, based on rates of misclassification of similar species, which might have clinical implications when treating infection.
Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm
Authors: Pooley Christopher M. , Doeschl-Wilson Andrea B. , Marion Glenn 
Published: January 2022

SummaryWell parameterised epidemiological models including accurate representation of contacts, are fundamental to controlling epidemics. However, age-stratified contacts are typically estimated from pre-pandemic/peace-time surveys, even though interventions and public response likely alter contacts. Here we fit age-stratified models, including re-estimation of relative contact rates between age-classes, to public data describing the 2020-21 COVID-19 outbreak in England. This data includes age-stratified population size, cases, deaths, hospital admissions, and results from the Coronavirus Infection Survey (almost 9000 observations in all). Fitting stochastic compartmental models to such detailed data is extremely challenging, especially considering the large number of model parameters being estimated (over 150). An efficient new inference algorithm ABC-MBP combining existing Approximate Bayesian Computation (ABC) methodology with model-based proposals (MBP) is applied. Modified contact rates are inferred alongside time-varying reproduction numbers that quantify changes in overall transmission due to pandemic response, and age-stratified proportions of asymptomatic cases, hospitalisation rates and deaths. These inferences are robust to a range of assumptions including the values of parameters that cannot be estimated from available data. ABC-MBP is shown to enable reliable joint analysis of complex epidemiological data yielding consistent parametrisation of dynamic transmission models that can inform data-driven public health policy and interventions.
Indoor Air Quality Intervention in Schools; Effectiveness of a Portable HEPA Filter Deployment in Five Schools Impacted by Roadway and Aircraft Pollution Sources
Authors: Carmona Nancy , Seto Edmund , Gould Timothy , Shirai Jeffry H. , Cummings B.J. , Hayward Lisa , Larson Timothy , Austin Elena 
Published: January 2022

AbstractThe Healthy Air, Healthy Schools Study was established in January 2020 to better understand the impact of ultrafine particles (UFP) on indoor air quality in communities surrounding Seattle-Tacoma (Sea-Tac) International Airport. The study team took multipollutant measurements indoor and outdoor air pollution at five participating school locations to infiltration indoors. The schools participating in this project were located within a 7-mile radius of Sea-Tac Airport and within 0.5 miles of an active flight path. Based on experimental measures in an unoccupied classroom, infiltration rates of a) Ultrafine particles of aircraft origin b) Ultrafine particles of traffic origin and c) Wildfire smoke or other outdoor pollutants were characterized before and after the introduction of a classroom based portable HEPA filter intervention. The portable HEPA cleaners were an effective short-term intervention to improve the air quality in classroom environments, reducing the ultrafine particles to approximately 1/10th of that measured outside. Before the HEPA filter deployment, approximately one-half of all outdoor UFPs were measured indoors. This study is unique in focusing on UFP in school settings and demonstrating through multivariate methods that the UFP measured in the classroom space is primarily of outdoor origin. Although existing research suggests that improvements to indoor air quality in homes can significantly improve asthma outcomes, further research is necessary to establish the benefit to student health and academic performance of improved air quality in schools.
A Precision Functional Atlas of Network Probabilities and Individual-Specific Network Topography
Authors: Hermosillo Robert J.M. , Moore Lucille A. , Fezcko Eric , Dworetsky Ally , Pines Adam , Conan Gregory , Mooney Michael A. , Randolph Anita , Adeyemo Babatunde , Earl Eric , Perrone Anders , Carrasco Cristian Morales , Uriarte-Lopez Johnny , Snider Kathy , Doyle Olivia , Cordova Michaela , Nagel Bonnie J. , Feldstein Ewing Sarah W. , Satterthwaite Theodore , Dosenbach Nico , Gratton Caterina , Petersen Steven , Miranda-Domínguez Óscar , Fair Damien A. 
Published: January 2022

SUMMARYThe brain is organized into a broad set of functional neural networks. These networks and their various characteristics have been described and scrutinized through in vivo resting state functional magnetic resonance imaging (rs-fMRI). While the basic properties of networks are generally similar between healthy individuals, there is vast variability in the precise topography across the population. These individual differences are often lost in population studies due to population averaging which assumes topographical uniformity. We leveraged precision brain mapping methods to establish a new open-source, method-flexible set of precision functional network atlases: the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. Using participants from the Adolescent Brain Cognitive Development (ABCD) study, single subject precision network maps were generated with two supervised network-matching procedures (template matching and non-negative matrix factorization), an overlapping template matching method for identifying integration zones, as well as an unsupervised community detection algorithm (Infomap). From these individualized maps we also generated probabilistic network maps and integration zones for two demographically-matched groups of n∼3000 each. We demonstrate high reproducibility between groups (Pearson’s r >0.999) and between methods (r=0.96), revealing both regions of high invariance and high variability. Compared to using parcellations based on groups averages, the MIDB Precision Brain Atlas allowed us to derive a set of brain regions that are largely invariant in network topography across populations, which provides more reproducible statistical maps of executive function in brain-wide associations. We also explore an example use case for probabilistic maps, highlighting their potential for use in targeted neuromodulation. The MIDB Precision Brain Atlas is expandable to alternative datasets and methods and is provided open-source with an online web interface to encourage the scientific community to experiment with probabilistic atlases and individual-specific topographies to more precisely relate network phenomenon to functional organization of the human brain.