bluuebunny/crossref_metadata_2025_split huggingface dataset:
Age-Related Changes in the Neural Dynamics of Bottom-Up and Top-Down Processing During Visual Object Recognition: An Electrophysiological Investigation
Authors: Lai Leslie Y. , Frömer Romy , Festa Elena K. , Heindel William C. 
Published: April 2019

ABSTRACTWhen recognizing objects in our environments, we rely on both what we see and what we know. While elderly adults have been found to display increased sensitivity to top-down influences of contextual information during object recognition, the locus of this increased sensitivity remains unresolved. To address this issue, we examined the effects of aging on the neural dynamics of bottom-up and top-down visual processing during rapid object recognition. Specific EEG ERP components indexing bottom-up and top-down processes along the visual processing stream were assessed while systematically manipulating the degree of object ambiguity and scene context congruity. An increase in early attentional feedback mechanisms (as indexed by N1) as well as a functional reallocation of executive attentional resources (as indexed by P200) prior to object identification were observed in elderly adults, while post-perceptual semantic integration (as indexed by N400) remained intact. These findings suggest that compromised bottom-up perceptual processing of visual input in healthy aging leads to an increased involvement of top-down processes to resolve greater perceptual ambiguity during object recognition.
A Pandas complex adapted for piRNA-guided transposon silencing
Authors: Zhao Kang , Cheng Sha , Miao Na , Xu Ping , Lu Xiaohua , Zhang Yuhan , Wang Ming , Ouyang Xuan , Yuan Xun , Liu Weiwei , Lu Xin , Zhou Peng , Gu Jiaqi , Zhang Yiqun , Qiu Ding , Jin Zhaohui , Su Chen , Peng Chao , Wang Jian-Hua , Dong Meng-Qiu , Wan Youzhong , Ma Jinbiao , Cheng Hong , Huang Ying , Yu Yang 
Published: April 2019

AbstractThe repression of transposons by the Piwi-interacting RNA (piRNA) pathway is essential to protect animal germ cells. In Drosophila ovaries, Panoramix (Panx) enforces transcriptional silencing by binding to the target-engaged Piwi-piRNA complex, although the precise mechanisms by which this occurs remain elusive. Here, we show that Panx functions together with a germline specific paralogue of a nuclear export factor, dNxf2, and its cofactor dNxt1 (p15), as a ternary complex to suppress transposon expression. Structural and functional analyses demonstrate that dNxf2 binds Panx via its UBA domain, which plays an important role in transposon silencing. Unexpectedly, dNxf2 interacts directly with dNxf1 (TAP), a general nuclear export factor. As a result, dNxf2 prevents dNxf1 from binding to the FG repeats of the nuclear pore complex, a process required for proper RNA export. Transient tethering of dNxf2 to nascent transcripts leads to their nuclear retention. Therefore, we propose that dNxf2 may function as a Pandas (Panoramix-dNxf2 dependent TAP/p15 silencing) complex, which counteracts the canonical RNA exporting machinery and restricts transposons to the nuclear peripheries. Our findings may have broader implications for understanding how RNA metabolism modulates epigenetic gene silencing and heterochromatin formation.
Partial FMRP expression is sufficient to normalize neuronal hyperactivity in Fragile X neurons
Authors: Graef John D. , Wu Hao , Ng Carrie , Sun Chicheng , Villegas Vivian , Qadir Deena , Jesseman Kimberly , Warren Stephen T. , Jaenisch Rudolf , Cacace Angela , Wallace Owen 
Published: April 2019

AbstractFragile X Syndrome (FXS) is the most common genetic form of intellectual disability caused by a CGG repeat expansion in the 5’-UTR of the Fragile X mental retardation gene FMR1, triggering epigenetic silencing and the subsequent absence of the protein, FMRP. Reactivation of FMR1 represents an attractive therapeutic strategy targeting the genetic root cause of FXS. However, largely missing in the FXS field is an understanding of how much FMR1 reactivation is required to rescue FMRP-dependent mutant phenotypes. Here, we utilize FXS patient derived excitatory neurons to model FXS in vitro and confirm that the absence of FMRP leads to neuronal hyperactivity. We further determined the levels of FMRP and the percentage of FMRP positive cells necessary to correct this phenotype utilizing a mixed and mosaic neuronal culture system and a combination of CRISPR, antisense and expression technologies to titrate FMRP in FXS and WT neurons. Our data demonstrate that restoration of greater than 5% of overall FMRP expression levels or greater than 20% FMRP expressing neurons in a mosaic pattern is sufficient to normalize a FMRP-dependent, hyperactive phenotype in FXS iPSC-derived neurons.HighlightsCRISPR gene editing to generate FMRP KO and CGG-deleted isogenic iPSCsMEA as an approach to identify FMR1 dependent phenotype in NGN2 neurons derived from FXS and FMRP KO iPSCsCell mixing paradigm as mosaicism in a dish to rescue phenotypeMinimal level of FMRP determined by FMR1 mRNA and targeted demethylation of CGG repeats to correct the hyperactive phenotype in FXS neuronsASO titration-validated partial expression of FMRP is sufficient to normalize increased neuronal activity
Reducing false positives in tractography with microstructural and anatomical priors
Authors: Schiavi Simona , Barakovic Muhamed , Ocampo-Pineda Mario , Descoteaux Maxime , Thiran Jean-Philippe , Daducci Alessandro 
Published: April 2019

AbstractTractography is a family of algorithms that use diffusion-weighted magnetic resonance imaging data to reconstruct the white matter pathways of the brain. Although it has been proven to be particularly effective for studying non-invasively the neuronal architecture of the brain, recent studies have highlighted that the large incidence of false positive connections retrieved by these techniques can significantly bias any connectivity analysis. Some solutions have been proposed to overcome this issue and the ones relying on convex optimization framework showed a significant improvement. Here we propose an evolution of the Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT) framework, that combines basic prior knowledge about brain anatomy with group-sparsity regularization into the optimization problem. We show that the new formulation dramatically reduces the incidence of false positives in synthetic DW-MRI data.
VariCarta: a comprehensive database of harmonized genomic variants found in ASD sequencing studies
Authors: Belmadani Manuel , Jacobson Matthew , Holmes Nathan , Phan Minh , Pavlidis Paul , Rogic Sanja 
Published: April 2019

AbstractBackgroundRecent years has seen a boom in the application of the next-generation sequencing technology to the study of human diseases, including Autism Spectrum Disorder (ASD), where the focus has been on identifying rare, possibly causative genomic variants in ASD individuals. Because of the high genetic heterogeneity of ASD, a large number of subjects is needed to establish evidence for a variant or gene ASD-association, thus aggregating data across cohorts and studies is necessary. However, methodological inconsistencies and subject overlap across studies complicate data aggregation.DescriptionHere we present VariCarta, a web-based database developed to address these challenges by collecting, reconciling and consistently cataloguing literature-derived genomic variants found in ASD subjects using ongoing semi-manual curation. The careful manual curation combined with a robust data import pipeline rectifies errors, converts variants into a standardized format, identifies and harmonizes cohort overlaps and documents data provenance. The harmonization aspect is especially important since it prevents the potential double-counting of variants which can lead to inflation of gene-based evidence for ASD-association.ConclusionVariCarta is the largest collection of systematically curated, harmonized and comprehensively annotated literature-derived ASD-associated variants. The database currently contains 35,615 variant events from 8,044 subjects, collected across 50 publications, and reconciles 6,057 variants that have been reported in literature multiple times. VariCarta is freely accessible at http://varicarta.msl.ubc.ca.
Designer DNA architecture offers precise and multivalent spatial pattern-recognition for viral sensing and inhibition
Authors: Kwon Paul S. , Ren Shaokang , Kwon Seok-Joon , Kizer Megan E. , Kuo Lili , Zhou Feng , Zhang Fuming , Kim Domyoung , Fraser Keith , Kramer Laura D. , Seeman Nadrian C. , Dordick Jonathan S. , Linhardt Robert J. , Chao Jie , Wang Xing 
Published: April 2019

ABSTRACTDNA, when folded into nanostructures of customizable shapes, is capable of spacing and arranging external ligands in a desired geometric pattern with nanometer-precision. This allows DNA to serve as an excellent, biocompatible scaffold for complex spatial pattern-recognizing displays. In this report, we demonstrate that a templated designer DNA nanostructure achieves multi-ligand display with precise spatial pattern-recognition, representing a unique strategy in synthesizing potent viral sensors and inhibitors. Specifically, a star-shaped DNA architecture, carrying five molecular beacon-like motifs, was constructed to display ten dengue virus envelope protein domain-III (ED3)-binding aptamers into a 2D pattern precisely matching the pentagonal arrangement of ED3 clusters on the dengue viral surface. The resulting spatial pattern recognition and multivalent interactions achieve high dengue-binding avidity, conferring direct, highly-sensitive, facile, low-cost, and rapid sensing as well as potent viral inhibition capability. Our molecular-platform design strategy could be adapted to detect and combat other disease-causing pathogens, including bacteria and microbial-toxins, by generating the requisite ligand patterns on customized DNA nanoarchitectures.
Genomic selection for lentil breeding: empirical evidence
Authors: Haile Teketel A. , Heidecker Taryn , Wright Derek , Neupane Sandesh , Ramsay Larissa , Vandenberg Albert , Bett Kirstin E. 
Published: April 2019

AbstractGenomic selection (GS) is a type of marker-based selection which was initially suggested for livestock breeding and is being encouraged for crop breeding. Several statistical models and approaches have been developed to implement GS; however, none of these methods have been tested for use in lentil breeding. This study was conducted to evaluate different GS models and prediction scenarios based on empirical data and to make recommendations for designing genomic selection strategies for lentil breeding. We evaluated nine single-trait models, two multiple-trait models, and models that account for population structure and genotype-by-environment interaction (GEI) using a lentil diversity panel and two recombinant inbred lines (RIL) populations that were genotyped using a custom exome capture assay. Within-population, across-population and across-environment predictions were made for five phenology traits. Prediction accuracy varied among the evaluated models, populations, prediction scenarios, traits, and statistical models. Single-trait models showed similar accuracy for each trait in the absence of large effect QTL but BayesB outperformed all models when there were QTL with relatively large effects. Models that accounted for GEI and multiple-trait (MT) models increased prediction accuracy for a low heritability trait by up to 66% and 14% but accuracy did not improve for traits of high heritability. Moderate to high accuracies were obtained for within-population and across-environment predictions but across-population prediction accuracy was very low. This suggests that GS can be implemented in lentil to make predictions within populations and across environments, but across-population prediction should not be considered when the population size is small.
CAncer bioMarker Prediction Pipeline (CAMPP) - A standardised and user-friendly framework for the analysis of quantitative biological data
Authors: Terkelsen Thilde , Krogh Anders , Papaleo Elena 
Published: April 2019

AbstractMotivationRecent improvements in -omics and next-generation sequencing (NGS) technologies, and the lowered costs associated with generating these types of data, have made the analysis of high-throughput datasets standard, both for forming and testing biomedical hypotheses. Alongside new wet-lab methodologies, our knowledge of how to normalise bio-data has grown extensively. By removing latent undesirable variances, we obtain standardised datasets, which can be more easily compared between studies. These advancements mean that non-experts in bioinformatics are now faced with the challenge of performing computational data analysis, pre-processing and visualisation. One example could be the analysis of biological data to pinpoint disease-related biomarkers for experimental validation. In this case, bio-researchers will desire an easy and standardised way of analysing high-throughput datasets.ResultsHere we present the CAncer bioMarker Prediction Pipeline (CAMPP), an open-source R-based wrapper intended to aid non-experts in bioinformatics with data analyses. CAMPP is called from a terminal command line and is supported by a user-friendly manual. The pipeline may be run on a local computer and requires little or no knowledge of programming. CAMPP performs missing value imputation and normalisation followed by (I) k-means clustering, (II) differential expression/abundance analysis, (III) elastic-net regression, (IV) correlation and co-expression network analyses, (V) survival analysis and (IV) protein-protein/miRNA-gene interaction networks. The pipeline returns tabular files and graphical representations of the results. We hope that CAMPP will assist biomedical researchers in the analysis of quantitative biological data, whilst ensuring an appropriate biostatistical framework.Availability and ImplementationCAMPP is available athttps://github.com/ELELAB/CAMPP
Efficient enzyme coupling algorithms identify functional pathways in genome-scale metabolic models
Authors: Pradhan Dikshant , Papin Jason A. , Jensen Paul A. 
Published: April 2019

AbstractFlux coupling identifies sets of reactions whose fluxes are “coupled" or correlated in genome-scale models. By identified sets of coupled reactions, modelers can 1.) reduce the dimensionality of genome-scale models, 2.) identify reactions that must be modulated together during metabolic engineering, and 3.) identify sets of important enzymes using high-throughput data. We present three computational tools to improve the efficiency, applicability, and biological interpretability of flux coupling analysis.The first algorithm (cachedFCF) uses information from intermediate solutions to decrease the runtime of standard flux coupling methods by 10-100 fold. Importantly, cachedFCF makes no assumptions regarding the structure of the underlying model, allowing efficient flux coupling analysis of models with non-convex constraints.We next developed a mathematical framework (FALCON) that incorporates enzyme activity as continuous variables in genome-scale models. Using data from gene expression and fitness assays, we verified that enzyme sets calculated directly from FALCON models are more functionally coherent than sets of enzymes collected from coupled reaction sets.Finally, we present a method (delete-and-couple) for expanding enzyme sets to allow redundancies and branches in the associated metabolic pathways. The expanded enzyme sets align with known biological pathways and retain functional coherence. The expanded enzyme sets allow pathway-level analyses of genome-scale metabolic models.Together, our algorithms extend flux coupling techniques to enzymatic networks and models with transcriptional regulation and other non-convex constraints. By expanding the efficiency and flexibility of flux coupling, we believe this popular technique will find new applications in metabolic engineering, microbial pathogenesis, and other fields that leverage network modeling.
Imaging Membrane Curvature inside a Model Immunological Synapse in RBL-2H3 Cells using TIRF Microscopy with Polarized Excitation
Authors: Machado Rosa , Bendesky Justin , Brown Madison , Spendier Kathrin , Hagen Guy M. 
Published: April 2019

AbstractTotal internal reflection fluorescence microscopy with polarized excitation (P-TIRF) can be used to image nanoscale curvature phenomena in live cells. We used P-TIRF to visualize rat basophilic leukemia cells (RBL-2H3 cells) coming into contact with a supported lipid bilayer, modeling an immunological synapse. These studies help correlate the dynamics of cell surface molecules with the mechanical properties of the plasma membrane during synapse formation.
A shared core microbiome in soda lakes separated by large distances
Authors: Zorz Jackie K. , Sharp Christine , Kleiner Manuel , Gordon Paul M.K. , Pon Richard T. , Dong Xiaoli , Strous Marc 
Published: April 2019

AbstractIn alkaline soda lakes, high concentrations of dissolved carbonates establish an environment favouring productive phototrophic microbial mat communities. Here we show how different species of microbial phototrophs and autotrophs contribute to this exceptional productivity. Four years of amplicon and shotgun DNA sequencing data from microbial mats from four different lakes indicated the presence of over 2,000 different species of Bacteria and Eukaryotes. Metagenome-assembled-genomes were obtained for a core microbiome of <100 abundant bacteria, which was shared among lakes and accounted for half of the extracted DNA throughout the four year sampling period. Most of the associated species were related to similar microbes previously detected in sediments of Central Asian alkaline soda lakes, showing that common selection principles drive community assembly from a globally distributed reservoir of alkaliphile biodiversity. Dispersal events between the two distant lake systems were shown to be extremely rare, with dispersal rates a function of abundance in microbial mats, but not sediments. Detection of more than 7,000 expressed proteins showed how phototrophic populations allocated resources to specific processes and occupied complementary niches. Carbon fixation only proceeded by the Calvin-Benson-Bassham cycle, detected in Cyanobacteria, Alphaproteobacteria, and, suprisingly, Gemmatomonadetes. Our study not only provides new fundamental insight into soda lake ecology, but also provides a template, guiding future efforts to engineer robust and productive biotechnology for carbon dioxide conversion.ImportanceAlkaline soda lakes are among the most productive ecosystems worldwide, despite their high pH. This high productivity leads to growth of thick “mats” of filamentous cyanobacteria. Here, we show that such mats have very high biodiversity, but at the same time contain a core, shared set of only approximately 100 different bacteria that perform key functions, such as photosynthesis. This “core microbiome” occurs both in Canadian and Central Asian soda lakes, >8,000 km apart. We present evidence for (very rare) dispersion of some core microbiome members from Canadian mats to Central Asian soda lake sediments. The close similarity between distant microbial communities indicates that these communities share common design principles, that reproducibly lead to a high and robust productivity. We unravel a few examples of such principles and speculate that these might be applied to create robust biotechnology for carbon dioxide conversion, to mitigate of global climate change.
Lytic transglycosylases RlpA and MltC assist inVibrio choleraedaughter cell separation
Authors: Weaver Anna I. , Jiménez-Ruiz Valeria , Tallavajhalla Srikar R. , Ransegnola Brett P. , Wong Kimberly Q. , Dörr Tobias 
Published: April 2019

ABSTRACTThe cell wall is a crucial structural feature in the vast majority of bacteria and comprises a rigid, covalently closed, mesh-like network of peptidoglycan (PG) strands. While PG synthesis is important for bacterial survival under many conditions, the cell wall is also a dynamic structure, undergoing degradation and remodeling by so-called “autolysins”, enzymes that break bonds in the PG network. Cell division, for example, requires extensive PG remodeling and separation of daughter cells, which depends primarily upon the activity of amidases. However, inV. cholerae, we have found that amidase activity alone is insufficient for daughter cell separation and that the lytic transglycosylases RlpA and MltC both contribute to this process. MltC and RlpA both localize to the septum and are functionally redundant under normal laboratory conditions; however, only RlpA can support normal cell separation in low salt media. The division-specific activity of lytic transglycosylases has implications for the local structure of septal PG, suggesting that there may be glycan bridges between daughter cells that cannot be resolved by amidases. We propose that lytic transglycosylases at the septum serve as a back-up mechanism to cleave rare, stochastically produced PG strands that are crosslinked beyond the reach of the highly spatio-temporally limited activity of the amidase and to clear PG debris that may block the completion of outer-membrane invagination.
Experienced meditators exhibit no differences to demographically-matched controls in theta phase synchronisation, P200, or P300 during an auditory oddball task
Authors: Payne JR , Baell O , Geddes H , Fitzgibbon B , Emonson M , Hill AT , Van Dam NT , Humble G , Fitzgerald PB , Bailey NW 
Published: April 2019

AbstractObjectivesLong-term meditation practice affects the brain’s ability to sustain attention. However, how this occurs is not well understood. Electroencephalography (EEG) studies have found that during dichotic oddball listening tasks, experienced meditators displayed altered attention-related neural markers including theta phase synchronisation (TPS) and event-related potentials (ERP; P200 and P300) to target tones while meditating compared to resting, and compared to non-meditators after intensive meditation interventions. Research is yet to establish whether the changes in the aforementioned neural markers are trait changes which may be observable in meditators irrespective of practice setting.MethodThe present study expanded on previous research by comparing EEG measures from a dichotic oddball task in a sample of community-based mindfulness meditators (n=22) to healthy controls with no meditation experience (n=22). To minimise state effects, neither group practiced meditation during / immediately prior to the EEG session.ResultsNo group differences were observed in behavioural performance or either the global amplitude or distribution of theta phase synchronisation, P200 or P300. Bayes Factor analysis suggested evidence against group differences for the P200 and P300.ConclusionsThe results suggest that increased P200, P300 and TPS do not reflect trait-related changes in a community sample of mindfulness meditators. The present study used a larger sample size than previous research and power analayses suggested the study was suficiently powered to detect differences. These results add nuance to our understanding of which processes are affected by meditation and the amount of meditation required to generate differences in specific neural processes.