bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison of BOLD and CBV using 3D EPI and 3D GRASE for cortical layer fMRI at 7T
Authors: Beckett Alexander JS , Dadakova Tetiana , Townsend Jennifer , Huber Laurentius , Park Suhyung , Feinberg David A 
Published: September 2019

AbstractPurposeFunctional MRI (fMRI) at the mesoscale of cortical layers and columns requires both sensitivity and specificity, which can be compromised if the imaging method is affected by vascular artifacts, particularly cortical draining veins at the pial surface. Recent studies have shown that cerebral blood volume (CBV) imaging is more specific to the actual laminar locus of neural activity than BOLD imaging when using standard gradient-echo (GE) EPI sequences. Gradient and Spin Echo (GRASE) BOLD imaging has also shown greater specificity when compared with GE-BOLD.MethodsHere we directly compare CBV and BOLD contrasts in high-resolution imaging of the primary motor cortex for laminar fMRI in four combinations of signal labeling, VASO (CBV) and BOLD with 3D GE-EPI and zoomed 3D GRASE image readouts.ResultsWe find that both CBV imaging using EPI-VASO and BOLD imaging using GRASE-BOLD, show similar specificity and sensitivity and are thus useful tools for mesoscopic fMRI in the human cortex.ConclusionThese techniques demonstrate sufficient sensitivity and specificity to allow layer-fMRI to be used by neuroscientists in a wide range of investigations of depth-dependent neural circuitry in the human brain.
Active transcription regulates ORC/MCM distribution whereas replication timing correlates with ORC density in human cells
Authors: Kirstein Nina , Buschle Alexander , Wu Xia , Krebs Stefan , Blum Helmut , Hammerschmidt Wolfgang , Hyrien Olivier , Audit Benjamin , Schepers Aloys 
Published: September 2019

AbstractEukaryotic replication initiates during S phase from origins that have been licensed in the preceding G1 phase. Here, we compare ChIP-seq profiles of the licensing factors Orc2, Orc3, Mcm3, and Mcm7 with replication initiation events obtained by Okazaki fragment sequencing. We demonstrate that MCM is displaced from early replicating, actively transcribed gene bodies, while ORC is mainly enriched at active TSS. Late replicating, H4K20me3 containing initiation zones display enhanced ORC and MCM levels. Furthermore, we find early RTDs being primarily enriched in ORC, compared to MCM, indicating that ORC levels are involved in organizing the temporal order of DNA replication. The organizational connection between active transcription and replication competence directly links changes in the transcriptional program to flexible replication patterns, which ensures the cell’s flexibility to respond to environmental cues.
High-dimensional Bayesian phenotype classification and model selection using genomic predictors
Authors: Linder Daniel F. , Panchal Viral 
Published: September 2019

AbstractMotivationIn this paper we describe a Bayesian hierarchical model termed ‘PMMLogit’ for classification and model selection in high-dimensional settings with binary phenotypes as outcomes. Posterior computation in the logistic model is known to be computationally demanding due to its non-conjugacy with common priors. We combine a Polya-Gamma based data augmentation strategy and use recent results on Markov chain Monte-Carlo (MCMC) techniques to develop an efficient and exact sampling strategy for the posterior computation. We use the resulting MCMC chain for model selection and choose the best combination(s) of genomic variables via posterior model probabilities. Further, a Bayesian model averaging (BMA) approach using the posterior mean, which averages across visited models, is shown to give superior prediction of phenotypes given genomic measurements.ResultsUsing simulation studies, we compared the performance of the proposed method with other popular methods. Simulation results show that the proposed method is quite effective in selecting the true model and has better estimation and prediction accuracy than other methods. These observations are consistent with theoretical results that have been developed in the statistics literature on optimality for this class of priors. Application to two well-known datasets on colon cancer and leukemia identified genes that have been previously reported in the clinical literature to be related to the disease outcomes.AvailabilitySource code is publicly available on GitHub athttps://github.com/v-panchal/PMML.Contactdlinder@augusta.eduSupplementary informationSupplementary data are available online.
High throughput small molecule screening reveals NRF2-dependent and - independent pathways of cellular stress resistance
Authors: Lombard David B. , Kohler William , Guo Angela H. , Gendron Christi , Han Melissa , Ding Weiqiao , Lyu Yang , Li Xinna , Shi Xiaofang , Nikolovska-Coleska Zaneta , Duan Yuzhu , Girke Thomas , Hsu Ao-Lin , Pletcher Scott D. , Miller Richard A. 
Published: September 2019

AbstractBiological aging is the dominant risk factor for most chronic diseases. Development of anti-aging interventions offers the promise of preventing many such illnesses simultaneously. Cellular stress resistance is an evolutionarily conserved feature of longevity. Here, we identify compounds that induced resistance to the superoxide generator paraquat (PQ), the heavy metal cadmium (Cd), and the DNA alkylator methyl methanesulfonate (MMS). Some rescue compounds conferred resistance to a single stressor, while others provoked multiplex resistance. Induction of stress resistance in fibroblasts was predictive of longevity extension in a published large-scale longevity screen inC. elegans. Transcriptomic analysis implicated Nrf2 signaling in stress resistance provided by two protective compounds, cardamonin and AEG 3482. Molecules that conferred stress resistance also induced cellular inflammatory pathways, and other core pathways such as AMPK signaling. Small molecules identified in this work may represent attractive candidates to evaluate for their potential pro-health and pro-longevity effects in mammals.
Using Parsimony-Guided Tree Proposals to Accelerate Convergence in Bayesian Phylogenetic Inference
Authors: Zhang Chi , Huelsenbeck John P. , Ronquist Fredrik 
Published: September 2019

AbstractSampling across tree space is one of the major challenges in Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) algorithms. Standard MCMC tree moves consider small random perturbations of the topology, and select from candidate trees at random or based on the distance between the old and new topologies. MCMC algorithms using such moves tend to get trapped in tree space, making them slow in finding the globally most probable trees (known as ‘convergence’) and in estimating the correct proportions of the different types of them (known as ‘mixing’). Here, we introduce a new class of moves, which propose trees based on their parsimony scores. The proposal distribution derived from the parsimony scores is a quickly computable albeit rough approximation of the conditional posterior distribution over candidate trees. We demonstrate with simulations that parsimony-guided moves correctly sample the uniform distribution of topologies from the prior. We then evaluate their performance against standard moves using six challenging empirical datasets, for which we were able to obtain accurate reference estimates of the posterior using long MCMC runs, a mix of topology proposals, and Metropolis coupling. On these datasets, ranging in size from 357 to 934 taxa and from 1,740 to 5,681 sites, we find that single chains using parsimony-guided moves usually converge an order of magnitude faster than chains using standard moves. They also exhibit better mixing, that is, they cover the most probable trees more quickly. Our results show that tree moves based on quick and dirty estimates of the posterior probability can significantly outperform standard moves. Future research will have to show to what extent the performance of such moves can be improved further by finding better ways of approximating the posterior probability, taking the trade-off between accuracy and speed into account.
Oscillatory Multi-Timescale Mechanisms Underlying Multisensory Sequence Prediction
Authors: Wang Peng , Maÿe Alexander , Daume Jonathan , Xue Gui , Engel Andreas K. 
Published: September 2019

AbstractNeural oscillations have been proposed to be involved in predictive processing, however less addressed in the multisensory context. In the present study, we recorded cortical activity using magnetoencephalography during an audiovisual serial prediction task in which participants had to acquire stimulus sequences and to monitor whether subsequent probe items complied with the sequence. In a given trial, either visual or auditory features of the bimodal stimuli were task-relevant. Task-related changes of power and coupling of neural oscillations were analyzed using a data-driven clustering approach. We observed prediction-related theta-band coupling in a network involving the cingulate, premotor, prefrontal, and superior temporal regions. The behavioral performance of participants varied as a function of the phase delay of the coupling between different regions in this network. Two additional clusters were detected in which beta power was stronger if the previous or the following stimuli were the same as the current one in the sequence. These clusters interacted with the theta-band prediction network through cross-frequency coupling. Our results offer novel insights into the multi-timescale neural dynamics underlying multisensory predictions, suggesting that oscillations in multiple frequency ranges, as well as coupling within and across frequencies, may be critical for multisensory sequence processing.
A streamlined mass spectrometry-based proteomics workflow for large scale FFPE tissue analysis
Authors: Coscia Fabian , Doll Sophia , Bech Jacob Mathias , Mund Andreas , Lengyel Ernst , Lindebjerg Jan , Madsen Gunvor Iben , Moreira José M. A. , Mann Matthias 
Published: September 2019

ABSTRACTFormalin fixation and paraffin-embedding (FFPE) is the most common method to preserve human tissue for clinical diagnosis and FFPE archives represent an invaluable resource for biomedical research. Proteins in FFPE material are stable over decades but their efficient extraction and streamlined analysis by mass spectrometry (MS)-based proteomics has so far proven challenging. Here, we describe an MS-based proteomic workflow for quantitative profiling of large FFPE tissue cohorts directly from pathology glass slides. We demonstrate broad applicability of the workflow to clinical pathology specimens and variable sample amounts, including less than 10,000 cancer cells isolated by laser-capture microdissection. Using state-of-the-art data dependent acquisition (DDA) and data independent (DIA) MS workflows, we consistently quantify a large part of the proteome in 100 min single-run analyses. In an adenoma cohort comprising more than 100 samples, total work up took less than a day. We observed a moderate trend towards lower protein identifications in long-term stored samples (>15 years) but clustering into distinct proteomic subtypes was independent of archival time. Our results underline the great promise of FFPE tissues for patient phenotyping using unbiased proteomics and prove the feasibility of analyzing large tissue cohorts in a robust, timely and streamlined manner.
Sleeve Gastrectomy enhances glucose utilization and remodels adipose tissue independent of weight loss
Authors: Harris David A. , Mina Amir , Cabarkapa Dimitrije , Heshmati Keyvan , Subramaniam Renuka , Banks Alexander S. , Tavakkoli Ali , Sheu Eric G. 
Published: September 2019

ABSTRACTObjectiveSleeve gastrectomy (SG) induces weight-loss independent improvements in glucose homeostasis by unknown mechanisms. We sought to identify the metabolic adaptations responsible for these improvements.MethodsNon-obese C57Bl6/J mice on standard chow underwent SG or sham surgery. Functional testing and indirect calorimetry were used to capture metabolic phenotypes. Tissue-specific glucose uptake was assessed by 18-FDG PET/CT and RNA sequencing was used for gene expression analysis.ResultsIn this model, SG induced durable improvements in glucose tolerance despite not causing lasting changes in weight, fat/lean mass, or food intake. Indirect calorimetry revealed post-SG animals had respiratory exchange ratios (RER) nearing 1.0 on average and had daily RER excursions above 1.0, indicating preferential glucose utilization and increased energy demand, respectively. Sham operated mice demonstrate normal RER feeding/fasting excursions. PET/CT showed increased avidity within white adipose depots. Finally, SG led to an upregulation in the transcriptional pathways involved in energy metabolism, adipocyte maturation, and adaptive and innate immune cell chemotaxis and differentiation within the visceral adipose tissue.ConclusionsSG induces a rapid, weight-loss independent shift towards glucose utilization and transcriptional remodeling of metabolic and immune pathways in visceral adipose tissue.
Microbiota Assembly, Structure, and Dynamics Among Tsimane Horticulturalists of the Bolivian Amazon
Authors: Sprockett Daniel D. , Martin Melanie , Costello Elizabeth K. , Burns Adam , Holmes Susan P. , Gurven Michael , Relman David A. 
Published: September 2019

ABSTRACTLittle is known about the relative contributions of selective and neutral forces on human-associated microbiota assembly. Here, we characterize microbial community assembly in 52 Tsimane infant-mother pairs, using longitudinally collected stool and tongue swab samples profiled with 16S rRNA gene amplicon sequencing. The Tsimane are an indigenous Bolivian population who practice infant care associated behaviors expected to increase mother-infant dispersal. Infant consumption of dairy products, vegetables, and chicha (a fermented drink inoculated with oral microbes) was significantly associated with gut microbiota composition. At both body sites, maternal microbes at higher relative abundance were more likely to be shared. Shared microbes were also higher in abundance in infants at both body sites, but decreased in average relative abundance with age and were not significantly higher by 12 months of age. Infant microbiotas were modeled using a neutral community model of assembly, which showed that the prevalence of more than two thirds of infant-colonizing microbes could be explained using neutral processes alone. The same method was applied to datasets from Finnish and Bangladeshi infants, confirming that the majority of microbes colonizing infants from different countries were neutrally distributed. Among the Tsimane infant and adult gut microbiota samples, neutral processes were less prominent in villages with more market access. These results underscore the importance of neutral processes during infant microbiota assembly, and suggest that cultural changes associated with market integration may be affecting traditional modes of microbiota assembly by decreasing the role of these neutral processes, perhaps through changes in diet, sanitation, or access to medical care.
Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neurons
Authors: Sederberg Audrey , Nemenman Ilya 
Published: September 2019

AbstractAdvances in neural recording methods enable sampling from populations of thousands of neurons during the performance of behavioral tasks, raising the question of how recorded activity relates to the theoretical models of computations underlying performance. In the context of decision making in rodents, patterns of functional connectivity between choice-selective cortical neurons, as well as broadly distributed choice information in both excitatory and inhibitory populations, were recently reported [1]. The straightforward interpretation of these data suggests a mechanism relying on specific patterns of anatomical connectivity to achieve selective pools of inhibitory as well as excitatory neurons. We investigate an alternative mechanism for the emergence of these experimental observations using a computational approach. We find that a randomly connected network of excitatory and inhibitory neurons generates single-cell selectivity, patterns of pairwise correlations, and indistinguishable excitatory and inhibitory readout weight distributions, as observed in recorded neural populations. Further, we make the readily verifiable experimental predictions that, for this type of evidence accumulation task, there are no anatomically defined sub-populations of neurons representing choice, and that choice preference of a particular neuron changes with the details of the task. This work suggests that distributed stimulus selectivity and patterns of functional organization in population codes could be emergent properties of randomly connected networks.Author summaryWhat can we learn about neural circuit organization and function from recordings of large populations of neurons? For example, in population recordings in the posterior parietal cortex of mice performing an evidence integration task, particular patterns of selectivity and correlations between cells were observed. One hypothesis for an underlying mechanism generating these patterns is that they follow from intricate rules of connectivity between specific neurons, but this raises the question of how such intricate patterns arise during learning or development. An alternative hypothesis, which we explore here, is that such patterns emerge from networks with broad spectra of eigenvalues, which is a generic property of certain random networks. We find that a random network model matches many features of experimental recordings, from single cells to populations. We suggest that such emergent selectivity could be an important principle in brain areas, in which a broad distribution of selectivity is observed.
Accumulated degeneration of transcriptional regulation contributes to disease development and detrimental clinical outcomes of Alzheimer’s disease
Authors: Meng Guofeng , Lu Dong , Yu Feng , Sun Jijia , Ding Chong , Sun Yan , Liu Xuan , Dai Jiapei , Jin Wenfei , Zhang Weidong 
Published: September 2019

AbstractAlzheimer’s disease (AD) is extremely complex for both causal mechanism and clinical manifestation, requiring efforts to uncover its diversity and the corresponding mechanisms. Here, we applied a modelling analysis to investigate the regulation divergence among a large-scale cohort of AD patients. We found that transcription regulation tended to get degenerated in AD patients, which contributed to disease development and the detrimental clinical outcomes, mainly by disrupting protein degradation, neuroinflammation, mitochondrial and synaptic functions. To measure the accumulated effects, we came up with a new concept, regulation loss burden, which better correlated with AD related clinical manifestations and the ageing process. The epigenetic studies to multiple active regulation marks also supported a tendency of regulation loss in AD patients. Our finding can lead to a unified model as AD causal mechanism, where AD and its diversity are contributed by accumulated degeneration of transcriptional regulation.The significance of this study is that: (1) it is the first system biology investigation to transcription regulation divergence among AD patients; (2) we observed an accumulated degeneration of transcription regulation, which well correlates with detrimental clinical outcomes; (3) transcriptional degeneration also contributes to the ageing process, where its correlation with ages is up to 0.78.
Defining data-driven primary transcript annotations with primaryTranscriptAnnotation in R
Authors: Anderson Warren D. , Duarte Fabiana M. , Civelek Mete , Guertin Michael J. 
Published: September 2019

Nascent transcript measurements derived from run-on sequencing experiments are critical for the investigation of transcriptional mechanisms and regulatory networks. However, conventional gene annotations specify the boundaries of mRNAs, which significantly differ from the boundaries of primary transcripts. Moreover, transcript isoforms with distinct transcription start and end coordinates can vary between cell types. Therefore, new primary transcript annotations are needed to accurately interpret run-on data. We developed the primaryTranscriptAnnotation R package to infer the transcriptional start and termination sites of annotated genes from genomic run-on data. We then used these inferred co-ordinates to annotate transcriptional units identified de novo. Hence, this package provides the novel utility to integrate data-driven primary transcript annotations with transcriptional unit coordinates identified in an unbiased manner. Our analyses demonstrated that this new methodology increases the sensitivity for detecting differentially expressed transcripts and provides more accurate quantification of RNA polymerase pause indices, consistent with the importance of using accurate primary transcript coordinates for interpreting genomic nascent transcription data.Availabilityhttps://github.com/WarrenDavidAnderson/genomicsRpackage/tree/master/primaryTranscriptAnnotation
Establishing the concurrent validity of the Clinical Outcome in Routine Evaluation-Learning Disabilities scale
Authors: Briscoe Henry , Ashworth Sarah , Shelton Lyn 
Published: September 2019

PurposeIndividuals with an intellectual disability (ID) develop mental health difficulties at similar rates to individuals in the general population. Using Patient Reported Outcome Measures can help track deterioration and improve the outcomes of individuals seeking help for their difficulties. The Clinical Outcome in Routine Evaluation-Learning Disabilities (CORE-LD) is a multi-trait measure of psychological distress which has shown moderate test-rest reliability. However, the CORE-LD is yet to be validated for the population it was designed for. Therefore the purpose of this paper is to establish the concurrent validity of the CORE-LD in a population of individuals with a diagnosis of mild–moderate ID.Design/methodology/approachParticipants with a diagnosis of mild–moderate ID, as well as other co-morbidities, were recruited from two UK inpatient hospitals and asked to complete the CORE-LD and its general population counterpart the Clinical Outcome in Routine Evaluation-Outcome Measure (CORE-OM).FindingsStatistically significant differences were found regarding the CORE-LD across gender, with females scoring higher on the CORE-LD than males. There was no significant difference between security levels. The overall mean scores on each measure were moderately correlated. The data from this analysis suggest a significant positive correlation (rs=0.68).Originality/valueThis initial study’s findings have demonstrated the CORE-LD may have concurrent validity, and further replication studies in larger and more diverse samples are needed.