bluuebunny/crossref_metadata_2025_split huggingface dataset:
The correspondence between EMG and EEG measures of changes in cortical excitability following transcranial magnetic stimulation
Authors: Biabani Mana , Fornito Alex , Coxon James P. , Fulcher Ben D. , Rogasch Nigel C. 
Published: September 2019

AbstractTranscranial magnetic stimulation (TMS) is a powerful tool to investigate cortical circuits. Changes in cortical excitability following TMS are typically assessed by measuring changes in either conditioned motor-evoked potentials (MEPs) following paired-pulse TMS over motor cortex or evoked potentials measured with electroencephalography following single-pulse TMS (TEPs). However, it is unclear whether these two measures of cortical excitability index the same cortical response. Twenty-four healthy participants received local and interhemispheric paired-pulse TMS over motor cortex with eight inter-pulse intervals, suband suprathreshold conditioning intensities, and two different pulse waveforms, while MEPs were recorded from a hand muscle. TEPs were also recorded in response to single-pulse TMS using the conditioning pulse alone. The relationships between TEPs and conditioned-MEPs were evaluated using metrics sensitive to both their magnitude at each timepoint and their overall shape across time. The impacts of undesired sensory potentials resulting from TMS pulse and muscle contractions were also assessed on both measures. Both conditioned-MEPs and TEPs were sensitive to re-afferent somatosensory activity following motor-evoked responses, but over different post-stimulus timepoints. Moreover, the amplitude of low-frequency oscillations in TEPs was strongly correlated with the sensory potentials, whereas early and local high-frequency responses showed minimal relationships. Accordingly, conditioned-MEPs did not correlate with TEPs in the time domain but showed high shape similarity with the amplitude of high-frequency oscillations in TEPs. Therefore, despite the effects of sensory confounds, the TEP and MEP measures share a response component, suggesting that they index a similar cortical response and perhaps the same neuronal populations.
LncRNA-UCA1 inhibits the astrocyte activation in the temporal lobe epilepsy via regulating JAK/STAT signaling pathway
Authors: Hongxin Wang MM , Guangyan Yao BM , Lei Li MM , Zhaoyin Ma MM , Jing Chen MM , Wen Chen DM 
Published: September 2019

AbstractThis article aimed to reveal the mechanism of Urothelial cancer associated 1 (UCA1) regulated astrocyte activation in temporal lobe epilepsy (TLE) rats via JAK/STAT signaling pathway. A model of TLE was established based on rats via kainic acid (KA) injection. All rats were divided into sham group, KA group, normal control (NC) + KA group and UCA1 + KA group. The Morris water maze was used to test the learning and memory ability of rats, and the expression of UCA1 in hippocampus was determined by qRT-PCR. Surviving neurons were counted by Nissl staining, and expression of glial cells glial fibrillary acidic protein, p-JAK1, and p-STAT and glutamate/aspartate transporter (GLAST) was analyzed by immunofluorescence and Western blot. A rat model of TLE was established by intraperitoneal injection of KA. QRT-PCR and fluorescence study showed that UCA1 inhibited astrocyte activation in hippocampus of epileptic rats. Meanwhile, the MWM analysis indicated that UCA1 improved the learning and memory in epilepsy rats. Moreover, the Nissl staining showed that UCA1 might has protective effect on neuronal injury induced by KA injection. Furthermore, the immunofluorescence and Western blot analysis revealed that the overexpression of UCA1 inhibited KA-induced abnormal elevation of GLAST, astrocyte activation of JAK/STAT signaling pathway, as well as hippocampus of epilepsy rats. UCA1 inhibited hippocampal astrocyte activation and GLAST expression in TLE rats via regulating JAK/STAT signaling, and improved the adverse reactions caused by epilepsy.
Independent Recruitment of the Mediator Tail Module to theHOPromoter Suggests Mediator Core Limits Coactivator Recruitment inSaccharomyces cerevisiae
Authors: Yarrington Robert M. , Yu Yaxin , Yan Chao , Bai Lu , Stillman David J. 
Published: September 2019

ABSTRACTMediator is an essential, multisubunit complex that functions as a transcriptional coactivator in yeast and other eukaryotic organisms. Mediator has four conserved modules, Head, Middle, Tail, and Kinase, and has been implicated in nearly all aspects of gene regulation. The Tail module has been shown to recruit the Mediator complex to the enhancer or UAS regions of genes via interactions with transcription factors, and the Kinase domain facilitates the transition of Mediator from the UAS/enhancer to the preinitiation complex via protein phosphorylation. Here we analyze expression of theSaccharomyces cerevisiae HOgene using asin4Mediator Tail mutation that separates the Tail module from the rest of the complex; thesin4mutation permits independent recruitment of the Tail module to promoters without the rest of Mediator. Significant increases in recruitment of the SWI/SNF and SAGA coactivators to theHOpromoter UAS were observed in asin4mutant, along with increased gene activation. These results are consistent with recent studies that have suggested the Kinase module functions negatively to inhibit activation by the Tail. However, we found that Kinase module mutations did not mimic the effect of asin4mutation onHOexpression. This suggests that atHOthe core Mediator complex (Middle and Head modules) must play a role in limiting Tail binding to the promoter UAS and gene activation. We propose that the core Mediator complex helps modulate Mediator binding to the UAS regions of genes to limit coactivator recruitment and ensure proper regulation of gene transcription.
The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants
Authors: Fitzgibbon Sean P. , Harrison Samuel J. , Jenkinson Mark , Baxter Luke , Robinson Emma C. , Bastiani Matteo , Bozek Jelena , Karolis Vyacheslav , Grande Lucilio Cordero , Price Anthony N. , Hughes Emer , Makropoulos Antonios , Passerat-Palmbach Jonathan , Schuh Andreas , Gao Jianliang , Farahibozorg Seyedeh-Rezvan , O’Muircheartaigh Jonathan , Ciarrusta Judit , O’Keeffe Camilla , Brandon Jakki , Arichi Tomoki , Rueckert Daniel , Hajnal Joseph V. , Edwards A. David , Smith Stephen M. , Duff Eugene , Andersson Jesper 
Published: September 2019

AbstractThe developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20 to 45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates.HighlightsAn automated and robust pipeline to minimally pre-process highly confounded neonatal fMRI dataIncludes integrated dynamic distortion and slice-to-volume motion correctionA robust multimodal registration approach which includes custom neonatal templatesIncorporates an automated and self-reporting QC framework to quantify data quality and identify issues for further inspectionData analysis of 538 infants imaged at 26-45 weeks post-menstrual age
Bayesian hierarchical models for disease mapping applied to contagious pathologies
Authors: Coly Sylvain , Garrido Myriam , Abrial David , Yao Anne-Françoise 
Published: September 2019

AbstractDisease mapping aims to determine the underlying disease risk from scattered epidemiological data and to represent it on a smoothed colored map. This methodology is based on Bayesian inference and is classically dedicated to non-infectious diseases whose incidence is low and whose cases distribution is spatially (and eventually temporally) structured. Over the last decades, disease mapping has received many major improvements to extend its scope of application: integrating the temporal dimension, dealing with missing data, taking into account various a prioris (environmental and population covariates, assumptions concerning the repartition and the evolution of the risk), dealing with overdispersion, etc. We aim to adapt this approach to rare infectious diseases. In the context of a contagious disease, the outcome of a primary case can in addition generate secondary occurrences of the pathology in a close spatial and temporal neighborhood; this can result in local overdispersion and in higher spatial and temporal dependencies due to direct and/or indirect transmission. We have proposed and tested 60 Bayesian hierarchical models on 400 simulated datasets and bovine tuberculosis real data. This analysis shows the relevance of the CAR (Conditional AutoRegressive) processes to deal with the structure of the risk. We can also conclude that the negative binomial models outperform the Poisson models with a Gaussian noise to handle overdispersion. In addition our study provided relevant maps which are congruent with the real risk (simulated data) and with the knowledge concerning bovine tuberculosis (real data).Author summaryDisease mapping is dedicated to non-infectious diseases whose incidence is low and whose distribution is spatially (and eventually temporally) structured. In this paper, we aim to adapt this approach to rare infectious pathologies. In the context of a contagious disease, the outcome of a primary case can in addition generate secondary occurrences of the pathology in a close spatial and temporal neighborhood, resulting in local overdispersion and in high spatial and temporal dependencies. We thus explored different adapted spatial, temporal and spatiotemporal links and highlight the most adapted to likely risk structures for infectious diseases. We also conclude that the negative binomial models outperform the Poisson models with a Gaussian noise to handle overdispersion. Our study also provided relevant maps which are congruent with the real risk (in case of simulated data) and with the knowledge concerning bovine tuberculosis (when applying to real data). Thus disease mapping appears as a promising way to investigate rare infectious diseases.
Modeling Mayaro and Chikungunya Control Strategies in Rio de Janeiro Outbreaks
Authors: Dodero-Rojas Esteban , Ferreira Luiza G. , Leite Vitor B. P. , Onuchic José N. , Contessoto Vinícius G. 
Published: September 2019

AbstractMosquito-borne diseases have become a significant health issue in many regions around the world. For tropical countries, diseases such as Dengue, Zika, and Chikungunya, became epidemic in the last decades. Health surveillance reports during this period were crucial in providing scientific-based information to guide decision making and resources allocation to control outbreaks. In this work, we perform data analysis of last Chikungunya epidemics in the city of Rio de Janeiro by applying a compartmental mathematical model. We estimate the “basic reproduction number” for those outbreaks and predict the potential epidemic outbreak of Mayaro virus. We also simulated several scenarios with different public interventions to decrease the number of infected people. Such scenarios should provide insights about possible strategies to control future outbreaks.
Resolving the fibrotic niche of human liver cirrhosis using single-cell transcriptomics
Authors: Ramachandran P , Dobie R , Wilson-Kanamori JR , Dora EF , Henderson BEP , Taylor RS , Matchett KP , Portman JR , Efremova M , Vento-Tormo R , Luu NT , Weston CJ , Newsome PN , Harrison EM , Mole DJ , Wigmore SJ , Iredale JP , Tacke F , Pollard JW , Ponting CP , Marioni JC , Teichmann SA , Henderson NC 
Published: September 2019

AbstractCurrently there are no effective antifibrotic therapies for liver cirrhosis, a major killer worldwide. To obtain a cellular resolution of directly-relevant pathogenesis and to inform therapeutic design, we profile the transcriptomes of over 100,000 primary human single cells, yielding molecular definitions for the major non-parenchymal cell types present in healthy and cirrhotic human liver. We uncover a novel scar-associated TREM2+CD9+ macrophage subpopulation with a fibrogenic phenotype, that has a distinct differentiation trajectory from circulating monocytes. In the endothelial compartment, we show that newly-defined ACKR1+ and PLVAP+ endothelial cells expand in cirrhosis and are topographically located in the fibrotic septae. Multi-lineage ligand-receptor modelling of specific interactions between the novel scar-associated macrophages, endothelial cells and collagen-producing myofibroblasts in the fibrotic niche, reveals intra-scar activity of several major pathways which promote hepatic fibrosis. Our work dissects unanticipated aspects of the cellular and molecular basis of human organ fibrosis at a single-cell level, and provides the conceptual framework required to discover rational therapeutic targets in liver cirrhosis.
Bicistronic expression and differential localization of proteins in insect cells andDrosophila suzukiiusing picornaviral 2A peptides
Authors: Schwirz Jonas , Yan Ying , Franta Zdenek , Schetelig Marc F. 
Published: September 2019

AbstractPolycistronic expression systems in insects can be used for applications such as recombinant protein production in cells, enhanced transgenesis methods, and the development of novel pest-control strategies based on the sterile insect technique (SIT). Here we tested the performance of four picornaviral 2A self-cleaving peptides (TaV-2A, DrosCV-2A, FMDV 2A1/31 and FMDV 2A1/32) for the co-expression and differential subcellular targeting of two fluorescent marker proteins in cell lines (Anastrepha suspensaAsE01 andDrosophila melanogasterS2 cells) andin vivoin the pest insectDrosophila suzukii. We found that all four 2A peptides showed comparable activity in cell lines, leading to the production of independent upstream and downstream proteins that were directed to the nucleus or membrane by a C-terminal nuclear localization signal (NLS) on the upstream protein and a poly-lysine/CAAX membrane anchor on the downstream protein. Two of the 2A peptides were inserted intopiggyBacconstructs to create transgenicD. suzukiistrains, confirming efficient ribosomal skippingin vivo. Interestingly, we found that the EGFP-CAAX protein was distributed homogeneously in the membrane whereas the DsRed-CAAX protein formed clumps and aggregates that induced extensive membrane blebbing. Accordingly, only flies expressing the EGFP-CAAX protein could be bred to homozygosity whereas the DsRed-CAAX construct was lethal in the homozygous state. Our results therefore demonstrate that four different 2A constructs and two novel targeting motifs are functional inD. suzukii, and that DsRed-CAAX shows dosage-dependent lethality. These molecular elements could be further used to improve expression systems in insects and generate novel pest control strains.HighlightsFour picornaviral 2A peptides have been studied for their self-cleaving ability in cell lines andin vivoin the pest insectDrosophila suzukii.All tested 2A peptides showed comparable activity that resulted in the production of independent upstream and downstream proteins.The proteins co-expressed by 2A peptides were either directed to the cell nucleus by a C-terminal nuclear localization signal (NLS), or to the cell membrane by a poly-lysine/CAAX membrane anchor.The combination of optimized membrane localization signals fused to DsRed generated an intrinsically lethal phenotype, which can be used to develop novel pest control strains.
ERM-Dependent Assembly of T-Cell Receptor Signaling and Co-stimulatory Molecules on Microvilli Prior to Activation
Authors: Ghosh Shirsendu , Di Bartolo Vincenzo , Tubul Liron , Shimoni Eyal , Kartvelishvily Elena , Dadosh Tali , Feigelson Sara W. , Alon Ronen , Alcover Andres , Haran Gilad 
Published: September 2019

SummaryT-cell surfaces are covered with microvilli, actin-rich and flexible protrusions. We use super-resolution microscopy to show that ≥90% T-cell receptor (TCR) complex molecules TCRαβ and TCRζ, as well as the co-receptor CD4 and the co-stimulatory molecule CD2 reside on microvilli of human T cells. Furthermore, TCR proximal signaling molecules involved in the initial stages of the immune response, such as the protein tyrosine kinase Lck and the key adaptor molecule LAT, are also enriched on microvilli. Notably, phosphorylated proteins of the ERM (ezrin, radixin, moesin) family colocalize with these heterodimers as well as with actin filaments within the microvilli of resting T cells. This finding implies a role for one or more phosphorylated ERMs in linking the TCR complex to the actin cytoskeleton within microvilli. Indeed, expression of a dominant-negative ezrin fragment effectively redistributes TCR molecules over the whole T cell surface. Our results establish microvilli as key signaling hubs, in which the TCR complex and its proximal signaling molecules and adaptors are pre-assembled prior to activation in an ERM-dependent manner. The preformed positioning of these actin-binding TCR assemblies on individual microvilli can facilitate the local transmission of TCR signals seconds after TCR occupancy and impacts the slower subsequent events that lead to the assembly of immunological synapses.
Right on time: prediction-errors bidirectionally bias time perception
Authors: Toren Ido , Aberg Kristoffer , Paz Rony 
Published: September 2019

SummaryThe brain updates internal representation of the environment by using the mismatch between the predicted state/outcome and the actual one, termed prediction-error. In parallel, time perception in the sub-second range is crucial for many behaviors such as movement, learning, memory, attention and speech. Both time-perception and prediction-errors are essential for everyday life function of an organism, and interestingly, the striatum was shown to be independently involved in both functions. We therefore hypothesized that the putative shared circuitry might induce behavioral interaction, namely that prediction-errors might bias time perception. To examine this, participants performed a time-duration discrimination task in the presence of positive and negative prediction-errors that were irrelevant and independent of the main task. We find that positive/negative prediction-errors induce a bias in time perception by increasing/decreasing the perceived time, respectively. Using functional imaging, we identify an interaction in Putamen activity between encoding of prediction-error and performance in the discrimination task. A model that accounts for the behavioral and physiological observations confirms that the interaction in regional activations for prediction-errors and time-estimation underlies the observed bias. Our results demonstrate that these two presumably independent roles of the striatum can actually interfere or aid one another in specific scenarios.
Yeast single cell protein production from a biogas co-digestion substrate
Authors: Ohlsson Jonas A. , Olstorpe Matilda , Passoth Volkmar , Leong Su-lin L. 
Published: September 2019

AbstractBiogas plants serve as hubs for the collection and utilization of highly nutritious waste streams from households and agriculture. However, their outputs (biogas and digestate) are of relatively low economic value. Here, we explore the co-production of yeast single cell protein, a potentially valuable feed ingredient for aquaculture and other animal producing industries, with biogas on substrate collected at a co-digestion biogas plant, using three yeast species well suited for this purpose (Wickerhamomyces anomalus,Pichia kudriavzevii, andBlastobotrys adeninivorans). All yeasts grew rapidly on the substrate, yielding 7.0–14.8 g l−1biomass after 12–15 The biomass crude protein contents were 22.6–32.7 %, with relatively favorable amino acid compositions mostly deficient in methionine and cysteine. Downstream biomethanation potential was significantly different between yeast species, with the highest product yielding species (Blastobotrys adeninivorans) also yielding the highest biomethanation potential.HighlightsAll yeasts grew well on the biogas substrate, with high growth rates.Produced biomass was of high nutritional value for use in fish feed formulations.Downstream effects on methane potential were strain-dependent.Yeast biomass may be a viable biogas co-product.
A new local covariance matrix estimation for the classification of gene expression profiles in RNA-Seq data
Authors: Koçhan Necla , Tütüncü Gözde Yazgı , Giner Göknur 
Published: September 2019

AbstractBackground and ObjectiveRecent developments in the next-generation sequencing (NGS) based on RNA-sequencing (RNA-Seq) allow researchers to measure the expression levels of thousands of genes for multiple samples simultaneously. In order to analyze these kind of data sets, many classification models have been proposed in the literature. Most of the existing classifiers assume that genes are independent; however, this is not a realistic approach for real RNA-Seq classification problems. For this reason, some other classification methods, which incorporates the dependence structure between genes into a model, are proposed. qtQDA proposed by Koçhan et al. [1] is one of those classifiers, which estimates covariance matrix by Maximum Likelihood Estimator.MethodsIn this study, we use a another approach based on local dependence function to estimate the covariance matrix to be used in the qtQDA classification model. We investigate the impact of different covariance estimates on RNA-Seq data classification.ResultsThe performances of qtQDA classifier based on two different covariance matrix estimates are compared over two real RNA-Seq data sets, in terms of classification error rates. The results show that using local dependence function approach yields a better estimate of covariance matrix and increases the performance of qtQDA classifier.ConclusionIncorporating the true/accurate covariance matrix into the classification model is an important and crucial step particularly for cancer prediction. The local covariance matrix estimate allows researchers to classify cancer patients based on gene expression profiles more accurately. R code for local dependence function is available at https://github.com/Necla/LocalDependence.