bluuebunny/crossref_metadata_2025_split huggingface dataset:
Semaphorin3F reduces vascular endothelial and smooth muscle cell PI3K activation and decreases neointimal plaque formation
Authors: Rattanasopa Chutima , Castano-Mayan David , Su Chengxun , Farrugia Aaron J. , Corlianò Maria , Nilcham Pakhwan , Pang Crystal , Hota Monalisa , Mei Koh Ser , Lee Wendy , Cibi Dasan Mary , Aiba Atsu , Singh Manvendra K. , Wong Siew Cheng , Rotzschke Olaf , Bershadsky Alexander , Hou Han Wei , Liehn Elisa A. , Ghosh Sujoy , Singaraja Roshni R. 
Published: March 2022

AbstractWe previously conducted genetic analyses, and identified semaphorin signaling as associating with coronary artery disease. Of the semaphorins, human vascular expression profiling suggestedSEMA3Fas potentially linked to atherogenesis. In hyperlipidemic mice, SEMA3F reduced aortic lesion area, and increased fibrous cap endothelial content, leading to plaque stability. In a disturbed-flow-mediated endothelial dysfunction-driven lesion model, the absence ofSema3fincreased plaques, further implicating SEMA3F in endothelial function. Monocyte adhesion toSema3f-/-vascular endothelial cells (VECs) was elevated, driven by increased PI3K activity, leading to increased NF-κB-mediated elevation in VCAM1 and ICAM1 expression, suggesting that SEMA3F reduces VEC PI3K activity. Increased permeability led to increased monocyte transmigration throughSema3f-/-VECs, and mTOR phosphorylation was decreased, suppressing VE-cadherin expression and cell-cell adherens junction stability. Actomyosin fiber formation was decreased inSema3f-/-VECs, which was reversed by PI3K inhibition, further implicating SEMA3F in adherens junction stability. InSema3f-/-vascular smooth muscle cells (VSMCs), active PI3K was also increased. PI3K facilitates VSMC proliferation, migration, and pro-atherogenic phenotype switching, which were reduced by SEMA3F. In agreement, in a model of VSMC proliferation and migration-induced neointima formation, SEMA3F reduced plaques. Semaphorin3F is causally atheroprotective. SEMA3F’s suppression of VEC and VSMC PI3K activation may contribute to its atheroprotection.
MRSCloud: a Cloud-based MR Spectroscopy Tool for Basis Set Simulation
Authors: Hui Steve C.N. , Saleh Muhammad G. , Zöllner Helge J. , Oeltzschner Georg , Fan Hongli , Li Yue , Song Yulu , Jiang Hangyi , Near Jamie , Lu Hanzhang , Mori Susumu , Edden Richard A. E. 
Published: March 2022

AbstractBackgroundAccurate quantification of in vivo proton magnetic resonance spectra involves modeling with a linear combination of known metabolite basis functions. Basis sets can be generated by numerical simulation using the quantum mechanical density-matrix formalism. Accurate simulations for a basis set require correct sequence timings, and pulse shapes and durations.PurposeTo present a cloud-based spectral simulation tool ‘MRSCloud’. It allows community users of MRS to simulate a vendor- and sequence-specific basis set online in a convenient and timeefficient manner. This tool can simulate basis sets for 3 major MR scanner vendors (GE, Philips, Siemens), including conventional acquisitions and spectral editing schemes (MEGA, HERMES, HERCULES) with PRESS and semi-LASER localization.Study TypeProspective.SpecimenN/AField Strength/SequenceSimulations of 3T basis sets for conventional and spectral-editing sequences (MEGA, HERMES, HERCULES) with PRESS and sLASER localizations.AssessmentSimulated metabolite basis functions generated by MRSCloud are compared to those generated by FID-A and MARSS, and a phantom-acquired basis-set from LCModel.Statistical TestsIntraclass correlation coefficients (ICC) were calculated to measure the agreement between individual metabolite basis functions generated using different packages. Statistical analysis was performed using R in RStudio.ResultsSimulation time for a full basis set is approximately 1 hour. ICCs between MRSCloud and FID-A were at least 0.98 and ICCs between MRSCloud and MARSS were at least 0.96. ICCs between simulated MRSCloud basis spectra and acquired LCModel basis spectra were lowest for Gln at 0.68 and highest for NAA at 0.96.Data ConclusionSubstantial reductions in runtime have been achieved by implementing the 1D projection method, coherence-order filtering, and pre-calculation of propagators. High ICC values indicated that the accelerating features are running correctly and produce comparable and accurate basis sets. The generated basis set has been successfully used with LCModel.
flashfm-ivis: interactive visualisation for fine-mapping of multiple quantitative traits
Authors: Zhou Feng , Butterworth Adam S , Asimit Jennifer L 
Published: March 2022

AbstractSummaryflashfm-ivis provides a suite of interactive visualisation plots to view potential causal genetic variants that underlie associations that are shared or distinct between multiple quantitative traits and compares results between single- and multi-trait fine-mapping. Unique features include network diagrams that show joint effects between variants for each trait and regional association plots that integrate fine-mapping results, all with user-controlled zoom features for an interactive exploration of potential causal variants across traits.Availability and Implementationflashfm-ivis is an open-source software under the MIT license. It is available as an interactive web-based tool (http://shiny.mrc-bsu.cam.ac.uk/apps/flashfm-ivis/) and as an R package format. Code and documentation are available at https://github.com/fz-cambridge/flashfm-ivis and https://zenodo.org/record/6376244#.YjnarC-l2X0. Additional features can be downloaded as standalone R libraries to encourage reuse.Contactfeng.zhou@mrc-bsu.cam.ac.uk or jennifer.asimit@mrc-bsu.cam.ac.uk
Cellular Mechanisms Underlying Central Sensitization in a Mouse Model of Chronic Muscle Pain
Authors: Lin Yu-Ling , Yang Zhu-Sen , Wong Wai-Yi , Lin Shih-Che , Wang Shuu-Jiun , Chen Shih-Pin , Cheng Jen-Kun , Lu Hui , Lien Cheng-Chang 
Published: March 2022

AbstractChronic pain disorders are often associated with psychiatric symptoms. The central nucleus of the amygdala (CeA) has emerged as an integrative hub for nociceptive and affective components during the development of central pain. Although the exact cause for this process remains unknown, prior adverse injuries are precipitating factors and thought to transform nociceptors into a primed state for chronic pain. However, the cellular basis underlying the primed state and the subsequent pain chronification remains unknown. Here, we investigated cellular and synaptic alterations of the CeA in a mouse model of chronic muscle pain. In these mice, local infusion of pregabalin, a clinically approved drug for fibromyalgia and other chronic pain disorders, into the CeA or selective inactivation of somatostatin-expressing CeA (CeA-SST) neurons during the priming phase prevented pain chronification. Further, electrophysiological recording revealed that CeA-SST neurons received increased excitatory transmission and showed enhanced excitability in chronic pain states. In line with the possible role of CeA-SST neurons in central sensitization, chemogenetic inactivation of CeA-SST neurons or pharmacological suppression of nociceptive afferents from the brainstem to CeA-SST neurons by pregabalin after the development of chronic muscle pain alleviated pain and negative emotions.
Reproducible acquisition, management, and meta-analysis of nucleotide sequence (meta)data using q2-fondue
Authors: Ziemski Michal , Adamov Anja , Kim Lina , Flörl Lena , Bokulich Nicholas A. 
Published: March 2022

AbstractThe volume of public nucleotide sequence data has blossomed over the past two decades, enabling novel discoveries via re-analysis, meta-analyses, and comparative studies for uncovering general biological trends. However, reproducible re-use and management of sequence datasets remains a challenge. We created the software pluginq2-fondueto enable user-friendly acquisition, re-use, and management of public nucleotide sequence (meta)data while adhering to open data principles. The software allows fully provenance-tracked programmatic access to and management of data from the Sequence Read Archive (SRA). Sequence data and accompanying metadata retrieved withq2-fonduefollow a validated format, which is interoperable with the QIIME 2 ecosystem and its multiple user interfaces. To highlight the manifold capabilities ofq2-fondue, we present several demonstration analyses using amplicon, whole genome, and shotgun metagenome datasets. These use cases demonstrate howq2-fondueincreases analysis reproducibility and transparency from data download to final visualizations by including source details in the integrated provenance graph. We believeq2-fonduewill lower existing barriers to comparative analyses of nucleotide sequence data, enabling more transparent, open, and reproducible conduct of meta-analyses.q2-fondueis a Python 3 package released under the BSD 3-clause license athttps://github.com/bokulich-lab/q2-fondue.
An in-silico analysis of experimental designs to study right ventricular function and pulmonary hypertension
Authors: Colebank M. J. , Chesler N.C. 
Published: March 2022

AbstractIn-vivostudies of pulmonary hypertension (PH) have provided key insight into the progression of the disease and right ventricular (RV) dysfunction. Additionalin-silicoexperiments using multiscale computational models have provided further details into biventricular mechanics and hemodynamic function in the presence of PH, yet few have assessed whether model parameters are identifiable prior to data collection. Moreover, none have used modeling to devise synergistic experimental designs. To address this knowledge gap, we conduct an identifiability analysis of a multiscale cardiovascular model across four simulated experimental designs. We determine a set of parameters using a combination of Morris screening and local sensitivity analysis, and test for identifiability using profile likelihood based confidence intervals. We employ Markov chain Monte Carlo (MCMC) techniques to quantify parameter and model forecast uncertainty in the presence of noise corrupted data. Our results show that model calibration to only RV pressure suffers from identifiability issues and suffers from large forecast uncertainty in output space. In contrast, parameter and model forecast uncertainty is substantially reduced once additional left ventricular (LV) pressure and volume data is included. A comparison between single point systolic and diastolic LV data and continuous, time-dependent LV pressure volume data reveals that even basic, functional data from the LV remedies identifiability issues and provides substantial insight into biventricular interactions.Author SummaryComputational models of cardiac dynamics are becoming increasingly useful in understanding the underlying mechanisms of disease.In-silicoanalyses are especially insightful in understanding PH and eventual RV dysfunction, as these conditions are diagnosed months to years after disease onset. Many researchers couple computational models within-vivoexperimental models of PH, yet few ever assess what data might be necessary or sufficient for parameter inference prior to designing their experiments. Here, we considered a multiscale computational model including sarcomere dynamics, biventricular interactions, and vascular hemodynamics, and assessed whether parameters could be inferred accurately given limited cardiac data. We utilized sensitivity analyses, profile likelihood confidence intervals, and MCMC to quantify parameter influence and uncertainty. We observed that RV pressure alone is not sufficient to infer the influential parameters in the model, whereas combined pressure and volume data in both the RV and LV reduced uncertainty in model parameters and in model forecasts. We conclude that synergistic PH studies utilizing computational modeling include these data to reduce issues with parameter identifiability and minimize uncertainty.
Enhanced intrathecal drug dispersion achieved by high volume injection and natural micromixing
Authors: Ayansiji Ayankola O. , Gehrke Daniel S. , Baralle Bastien , Nozain Ariel , Singh Meenesh R. , Linninger Andreas A. 
Published: March 2022

AbstractBackgroundTraditionally, there is a widely held belief that drug dispersion after intrathecal (IT) delivery is confined locally near the injection site. We posit that high volume infusions can overcome this perceived limitation of IT administration.MethodsTo test our hypothesis, subject-specific deformable phantom models of the human central nervous system were manufactured so that tracer infusion could be realistically replicated in vitro over the entire physiological range of pulsating cerebrospinal fluid (CSF) amplitudes and frequencies. Dispersion of IT injected tracers was studied systematically with high-speed optical methods to determine the relative impact of injection parameters including infusion volume, flow rate, catheter configurations and natural CSF oscillations.ResultsOptical imaging analysis of high-volume infusion experiments showed that tracer spreads quickly throughout the spinal subarachnoid space (SAS), reaching the cervical region in less than ten minutes. The experimentally observed biodispersion is much faster than suggested by prior theories (Taylor-Aris-Watson TAW dispersion). Our experiments indicate that micro-mixing patterns induced by oscillatory CSF flow around microanatomical features such as nerve roots significantly accelerate solute transport. Strong micro mixing effects due to anatomical features in the spinal subarachnoid space were found to be active in intrathecal drug administration but were not considered in prior dispersion theories. Their omission explains why prior models developed in the engineering community are poor predictors for IT delivery.ConclusionOur experiments support the feasibility of targeting large sections of the neuroaxis or brain utilizing high-volume IT injection protocols. The experimental tracer dispersion profiles acquired with an in vitro human CNS analog informed a new predictive model of tracer dispersion as a function of physiological CSF pulsations and adjustable infusion parameters. The ability to predict spatiotemporal dispersion patterns is an essential prerequisite for exploring new indications of IT drug delivery that targets specific regions in the central nervous system (CNS) or the brain.
Quadripulse Stimulation: A Replication Study with A Newly Developed Stimulator
Authors: Kimura Ikko , Ugawa Yoshikazu , Hayashi Masamichi J , Amano Kaoru 
Published: March 2022

AbstractQuadripulse stimulation (QPS) is a promising patterned repetitive transcranial magnetic stimulation protocol, which allows the modulation of brain activity for over one hour after the stimulation. Recently, Deymed Diagnostic developed a new stimulator specifically designed to deliver QPS (DuoMAG MP-Quad, https://deymed.com/duomag-qps). The properties of the after-effect with this new stimulator were expected to parallel those obtained with another stimulator, the Magstim stimulator, which is currently used in the psychological and clinical research but not commercially available anymore. Nevertheless, experimental validation of DuoMAG MP-Quad was still warranted. We thus studied the QPS after-effect induced by this stimulator. As a result, motor evoked potentials were found to be bidirectionally modulated by the QPS for more than one hour, consistent with previous studies using the Magstim stimulator. Moreover, the degree of the after-effect was comparable to the after-effect induced by the other stimulator. Taken together, we conclude that the newly developed QPS stimulator is as effective as the Magstim stimulator.
Comparative Analysis of Right Ventricular Metabolic Reprogramming in Pre-clinical Rat Models of Severe Pulmonary Hypertension-induced Right Ventricular Failure
Authors: Banerjee Somanshu , Hong Jason , Umar Soban 
Published: March 2022

AbstractBackgroundPulmonary hypertension (PH) leads to right ventricular (RV) hypertrophy and failure (RVF). The precise mechanisms of the metabolic basis of maladaptive PH-induced RVF (PH-RVF) are yet to be fully elucidated. Here we performed a comparative analysis of RV-metabolic reprogramming in MCT and Su/Hx rat models of severe PH-RVF using targeted metabolomics and multi-omics.MethodsMale Sprague Dawley rats (250-300gm; n=15) were used. Rats received subcutaneous monocrotaline (60mg/kg; MCT; n=5) and followed for ∼30-days or Sugen (20mg/kg; Su/Hx; n=5) followed by hypoxia (10%O2; 3-weeks) and normoxia (2-weeks). Controls received saline (Control; n=5). Serial echocardiography was performed to assess cardiopulmonary hemodynamics. Terminal RV-catheterization was performed to assess PH. Targeted metabolomics was performed on RV tissue using UPLC-MS. RV multi-omics analysis was performed integrating metabolomic and transcriptomic datasets using Joint Pathway Analysis (JPA).ResultsMCT and Su/Hx rats developed severe PH, RV-hypertrophy and decompensated RVF. Targeted metabolomics of RV of MCT and Su/Hx rats detected 126 and 125 metabolites respectively. There were 28 and 24 metabolites significantly altered in RV of MCT and Su/Hx rats, respectively, including 11 common metabolites. Common significantly upregulated metabolites included aspartate and GSH, whereas downregulated metabolites included phosphate,α-ketoglutarate, inositol, glutamine, 5-Oxoproline, hexose phosphate, creatine, pantothenic acid and acetylcarnitine. JPA highlighted common genes and metabolites from key pathways such as glycolysis, fatty acid metabolism, oxidative phosphorylation, TCA cycle etc.ConclusionsComparative analysis of metabolic reprogramming of RV from MCT and Su/Hx rats reveals common and distinct metabolic signatures which may serve as RV-specific novel therapeutic targets for PH-RVF.
Seqrutinator: Non-Functional Homologue Sequence Scrutiny for the Generation of large Datatsets for Protein Superfamily Analysis
Authors: Amalfitano Agustín , Stocchi Nicolás , Atencio Hugo Marcelo , Villarreal Fernando , ten Have Arjen 
Published: March 2022

AbstractBackgroundIn recent years protein bioinformatics has resulted in many good algorithms for multiple sequence alignment (MSA) and phylogeny. Little attention has been paid to sequence selection whereas notably recently published complete proteomes often have many sequences that are partial or derive from pseudogenes. Not only do these sequences add noise to the MSA, phylogeny and other downstream computational analyses, they also instigate many errors in the processing of the MSAs and downstream analyses, including the phylogeny.ObjectiveThis work aims to provide and test an objective, automated but flexible pipeline for the scrutiny of sequence sets from large, complex, eukaryotic protein superfamilies. The pipeline should classify sequences with high precision and recall as either functional or non-functional. The pipeline should classify no or only a few SwissProt sequences as non-functional (high precision) and sequences from other related superfamilies as non-functional (high recall) and result in a demonstrably much improved MSA (high performance).ResultsSeqrutinator is a pipeline that consists of five modules written in Python3 that identify and remove sequences that are likely Non-Functional Homologues (NFH). Here we tested the pipeline using three complex plant superfamilies (BAHD, CYP and UGT) that act in specialized metabolism, using the complete proteomes of 16 plant species as input and SwissProt as a control. Only 1.94% of SwissProt sequences with wetlab evidence were identified as NFH and all sequences from other related superfamilies were removed. Most NFH sequences are partial but, interestingly, their removal results in highly improved MSAs. a few but significant sequences that instigate large gaps were found. The five modules show similar behaviour when applied to the 16 sequence sets of the three analysed superfamilies. Pipelines with different module orders result in similar classifications and, moreover, show that different modules often detect the same sequences.Conclusion and perspectiveSeqrutinator forms a consistent pipeline for sequence scrutiny that does result in sequence sets that generate high fidelity MSAs. Recovery analyses show the method has high precision and recall.
Particle-Swarm Based Modelling Reveals Two Distinct Classes of CRHPVNNeurons
Authors: Lameu Ewandson L. , Rasiah Neilen P. , Baimoukhametova Dinara V. , Loewen Spencer , Bains Jaideep S. , Nicola Wilten 
Published: March 2022

AbstractElectrophysiological recordings can provide detailed information of single neurons’ dynamical features and shed light into their response to stimuli. Unfortunately, rapidly modeling electrophysiological data for inferring network-level behaviours remains challenging. Here, we investigate how modeled single neuron dynamics lead to network-level responses in the paraventricular nucleus of the hypothalamus (PVN), a critical nucleus for the mammalian stress response. Recordings of corticotropinreleasing hormone neurons from the PVN (CRHPVN) were performed using whole-cell current-clamp. These, neurons, which initiate the endocrine response to stress, were rapidly and automatically fit to a modified Adaptive Exponential Integrate and Fire model (AdEx) with Particle Swarm Optimization (PSO). All CRHPVNneurons were accurately fit by the AdEx model with PSO. Multiple sets of parameters were found that reliably reproduced current-clamp traces for any single neuron. Despite multiple solutions, the dynamical features of the models such as the rheobase current levels, fixed points, and bifurcations, were shown to be stable across fits. We found that CRHPVNneurons can be divided into two sub-types according to their bifurcation at the onset of firing: saddles (integrators) and sub-critical Hopf (resonators). We constructed networks of these fit CRHPVNmodel neurons to investigate the network level responses of CRHPVNneurons. We found that CRHPVN-resonators maintain baseline firing in networks even when all inputs are inhibitory. The dynamics of a small subset of CRHPVNneurons may be critical to maintaining a baseline firing tone in the PVN.Key PointsCorticotropin-releasing hormone neurons (CRHPVN) in the paraventricular nucleus of the hypothalamus act as the final neural controllers of the stress response.We developed a rapid computational modeling platform that uses Particle-Swarm Optimization to rapidly and accurately fit biophysical neuron models.A model was fit to each patched neuron without the use of dynamic clamping, or other procedures requiring sophisticated inputs and fitting procedures. Any neuron undergoing standard current clamping for a few minutes can be fit by this procedureThe dynamical analysis of the modeled neurons shows thatCRHPVNcomes in two specific ‘flavours’: CRHPVN-resonators and CRHPVN-integrators.Network simulations show thatCRHPVN-resonators are critical to retaining the baseline firing rate of the entire network of CRHPVNneurons as these cells can fire rebound spikes and bursts in the presence of strong inhibitory synaptic input.