bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel olfactometer for efficient and flexible odorant delivery
Authors: Burton Shawn D. , Wipfel Mia , Guo Michael , Eiting Thomas P. , Wachowiak Matt 
Published: November 2018

ABSTRACTUnderstanding how sensory space maps to neural activity in the olfactory system requires efficiently and flexibly delivering numerous odorants within single experimental preparations. Such delivery is difficult with current olfactometer designs, which typically include limited numbers of stimulus channels and are subject to inter-trial and inter-channel contamination of odorants. Here, we present a novel olfactometer design that is easily constructed, modular, and capable of delivering an unlimited number of odorants with temporal precision and no detectable inter-trial or inter-channel contamination. The olfactometer further allows for flexible generation of odorant mixtures and flexible timing of odorant sequences. Odorant delivery from the olfactometer is turbulent but reliable from trial-to-trial, supporting operant conditioning of mice in an odorant discrimination task and permitting odorants and concentrations to be mapped to neural activity with a level of precision equivalent to that obtained with a flow dilution olfactometer. This novel design thus provides several unique advantages for interrogating olfactory perception and for mapping sensory space to neural activity in the olfactory system.
Focus on the breath: Brain decoding reveals internal states of attention during meditation
Authors: Weng H.Y. , Lewis-Peacock J.A. , Hecht F.M. , Uncapher M.R. , Ziegler D.A. , Farb N.A.S. , Goldman V. , Skinner S. , Duncan L.G. , Chao M.T. , Gazzaley A. 
Published: November 2018

AbstractMeditation practices are used to cultivate internally-oriented attention to bodily sensations, which may improve health via cognitive and emotion regulation of bodily signals. However, it remains unclear how meditation impacts internal attention states due to lack of measurement tools that can objectively assess mental states during meditation practice itself, and produce time estimates of internal focus at individual or group levels. To address these measurement gaps, we tested the feasibility of applying multi-voxel pattern analysis (MVPA) to single-subject fMRI data to (1) learn and recognize internal attentional (IA) states relevant for meditation during a directed IA task, and (2) decode or estimate the presence of those IA states during an independent meditation session. Within a mixed sample of experienced meditators and novice controls (N=16), we first used MVPA to develop single-subject brain classifiers for 5 modes of attention during an IA task in which subjects were specifically instructed to engage in one of five states (i.e., meditation-related states: breath attention, mind wandering, and self-referential processing, and control states: attention to feet and sounds). Using standard cross-validation procedures, MVPA classifiers were trained in five of six IA blocks for each subject, and predictive accuracy was tested on the independent sixth block (iterated until all block volumes were tested, N=2160). Across participants, all five IA states were significantly recognized well above chance (>41% vs. 20% chance). At the individual level, IA states were recognized in most participants (87.5%), suggesting that recognition of IA neural patterns may be generalizable for most participants, particularly experienced meditators. Next, for those who showed accurate IA neural patterns, the originally trained classifiers were then applied to a separate meditation run (10-min) to make an inference about the percentage time engaged in each IA state (breath attention, mind wandering, or self-referential processing). Preliminary group-level analyses demonstrated that during meditation practice, participants spent more time attending to breath compared to mind wandering or self-referential processing. This paradigm established the feasibility of using MVPA classifiers to objectively assess mental states during meditation at the participant level, which holds promise for improved measurement of internal attention states cultivated by meditation.
Experience-specific anterograde amnesia: memory reacquisition deficit phenomenon and its characterization in the vertebrate learning model
Authors: Tiunova A. A. , Bezryadnov D. V. , Gaeva D.R. , Solodovnikov V.S. , Anokhin K.V. 
Published: November 2018

AbstractA common assumption from experiments that interfere with memory consolidation is that the resultant amnesia returns the brain of an animal to a tabula rasa state with respect to disturbed experience. However, recent studies in terrestrial snail classical conditioning revealed an odd phenomenon: animals were unable to relearn conditioned avoidance of specific food after this memory had been impaired by protein-synthesis inhibitors or N-methyl-D-aspartate (NMDA) receptor antagonists. Here we examined whether such specific memory reacquisition deficit can also be observed in vertebrate learning. We trained day-old chicks in a one-trial passive avoidance task by presenting them a bead of a specific color covered with a repellent substance, methyl anthranilate. Training was preceded by administration of the protein synthesis inhibitor anisomycin or the NMDA receptor antagonist MK-801. Both drugs produced permanent amnesia, and no spontaneous recovery of memory was observed. A second training was given to the amnestic animals either using a bead of the same color (retraining) or a new color (novel training). The interval between the first and second training was 2 or 24 h, and the retention test was given from 30 min to 48 h after the second training. Retraining of the amnestic chicks with the bead that was presented during the initial training failed to produce new avoidance memory for this stimulus at all the between-training and training-to-test intervals. This memory reacquisition deficit was specific and was not transferred to a new conditioned stimulus, which was readily learned. We suggest that such pharmacologically induced experience-specific anterograde amnesia might reflect general properties of normal memory allocation, and we discuss its possible neural bases.
SHARP: Single-cell RNA-seq Hyper-fast and Accurate Processing via Ensemble Random Projection
Authors: Wan Shibiao , Kim Junil , Won Kyoung Jae 
Published: November 2018

ABSTRACTTo process large-scale single-cell RNA-sequencing (scRNA-seq) data effectively without excessive distortion during dimension reduction, we present SHARP, an ensemble random projection-based algorithm which is scalable to clustering 10 million cells. Comprehensive benchmarking tests on 17 public scRNA-seq datasets demonstrate that SHARP outperforms existing methods in terms of speed and accuracy. Particularly, for large-size datasets (>40,000 cells), SHARP’s running speed far excels other competitors while maintaining high clustering accuracy and robustness. To the best of our knowledge, SHARP is the only R-based tool that is scalable to clustering scRNA-seq data with 10 million cells.
Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based methods
Authors: Zhang Song-Yao , Zhang Shao-Wu , Fan Xiaonan , Meng Jia , Chen Yidong , Gao Shoujiang , Huang Yufei 
Published: November 2018

AbstractN6-methyladenosine (m6A) is the most abundant methylation, existing in >25% of human mRNAs. Exciting recent discoveries indicate the close involvement of m6A in regulating many different aspects of mRNA metabolism and diseases like cancer. However, our current knowledge about how m6A levels are controlled and whether and how regulation of m6A levels of a specific gene can play a role in cancer and other diseases is mostly elusive. We propose in this paper a computational scheme for predicting m6A-regulated genes and m6A-associated disease, which includes Deep-m6A, the first model for detecting condition-specific m6A sites from MeRIP-Seq data with a single base resolution using deep learning and a new network-based pipeline that prioritizes functional significant m6A genes and its associated diseases using the Protein-Protein Interaction (PPI) and gene-disease heterogeneous networks. We applied Deep-m6A and this pipeline to 75 MeRIP-seq human samples, which produced a compact set of 709 functionally significant m6A-regulated genes and nine functionally enriched subnetworks. The functional enrichment analysis of these genes and networks reveal that m6A targets key genes of many critical biological processes including transcription, cell organization and transport, and cell proliferation and cancer-related pathways such as Wnt pathway. The m6A-associated disease analysis prioritized five significantly associated diseases including leukemia and renal cell carcinoma. These results demonstrate the power of our proposed computational scheme and provide new leads for understanding m6A regulatory functions and its roles in diseases.Author summaryThe goal of this work is to identify functional significant m6A-regulated genes and m6A-associated diseases from analyzing an extensive collection of MeRIP-seq data. To achieve this, we first developed Deep-m6A, a CNN model for single-base m6A prediction. To our knowledge, this is the first condition-specific single-base m6A site prediction model that combines mRNA sequence feature and MeRIP-Seq data. The 10-fold cross-validation and test on an independent dataset showthat Deep-m6A outperformed two sequence-based models. We applied Deep-m6A followed by network-based analysis using HotNet2 and RWRH to 75 human MeRIP-Seq samples from various cells and tissue under different conditions to globally detect m6A-regulated genes and further predict m6A mediated functions and associated diseases. This is also to our knowledge the first attempt to predict m6A functions and associated diseases using only computational methods in a global manner on a large number of human MeRIP-Seq samples. The predicted functions and diseases show considerable consistent with those reported in the literature, which demonstrated the power of our proposed pipeline to predict potential m6A mediated functions and associated diseases.
Partitioned coalescence support reveals biases in species-tree methods and detects gene trees that determine phylogenomic conflicts
Authors: Gatesy John , Sloan Daniel B. , Warren Jessica M. , Baker Richard H. , Simmons Mark P. , Springer Mark S. 
Published: November 2018

AbstractGenomic datasets sometimes support unconventional or conflicting phylogenetic relationships when different tree-building methods are applied. Coherent interpretations of such results are enabled by partitioning support for controversial relationships among the constituent genes of a phylogenomic dataset. For the supermatrix (= concatenation) approach, several simple methods that measure the distribution of support and conflict among loci were introduced over 15 years ago. More recently, partitioned coalescence support (PCS) was developed for phylogenetic coalescence methods that account for incomplete lineage sorting and use the summed fits of gene trees to estimate the species tree. Here, we automate computation of PCS to permit application of this index to genome-scale matrices that include hundreds of loci. Reanalyses of four phylogenomic datasets for amniotes, land plants, skinks, and angiosperms demonstrate how PCS scores can be used to: 1) compare conflicting results favored by alternative coalescence methods, 2) identify outlier gene trees that have a disproportionate influence on the resolution of contentious relationships, 3) assess the effects of missing data in species-trees analysis, and 4) clarify biases in commonly-implemented coalescence methods and support indices. We show that key phylogenomic conclusions from these analyses often hinge on just a few gene trees and that results can be driven by specific biases of a particular coalescence method and/or the extreme weight placed on gene trees with high taxon sampling. Attributing exceptionally high weight to some gene trees and very low weight to other gene trees counters the basic logic of phylogenomic coalescence analysis; even clades in species trees with high support according to commonly used indices (likelihood-ratio test, bootstrap, Bayesian local posterior probability) can be unstable to the removal of only one or two gene trees with high PCS. Computer simulations cannot adequately describe all of the contingencies and complexities of empirical genetic data. PCS scores complement simulation work by providing specific insights into a particular dataset given the assumptions of the phylogenetic coalescence method that is applied. In combination with standard measures of nodal support, PCS provides a more complete understanding of the overall genomic evidence for contested evolutionary relationships in species trees.
Prefrontal cortical ChAT-VIP interneurons provide local excitation by cholinergic synaptic transmission and control attention
Authors: Obermayer Joshua , Luchicchi Antonio , de Kloet Sybren F. , Terra Huub , Bruinsma Bastiaan , Heistek Tim S. , Mnie-Filali Oissame , Kortleven Christian , Kroon Tim , Jonker Allert J. , Khalil Ayoub J. , de Haan Roel , Goriounova Natalia A. , van den Berg Wilma D.J. , de Kock Christiaan P.J. , Pattij Tommy , Mansvelder Huibert D. 
Published: November 2018

SummaryNeocortical choline acetyltransferase (ChAT)-expressing interneurons are a subclass of vasoactive intestinal peptide (ChAT-VIP) neurons of which circuit and behavioural function are unknown. It has also not been addressed whether these neurons release both neurotransmitters acetylcholine (ACh) and GABA. Here, we find that in the medial prefrontal cortex (mPFC), ChAT-VIP neurons directly excite interneurons in layers (L)1-3 as well as pyramidal neurons in L2/3 and L6 by fast cholinergic transmission. Dual recordings of presynaptic ChAT-VIP neurons and postsynaptic L1 interneurons show fast nicotinic receptor currents strictly time-locked to single presynaptic action potentials. A fraction (10-20%) of postsynaptic neurons that received cholinergic input from ChAT-VIP interneurons also received GABAergic input from these neurons. In contrast to regular VIP interneurons, ChAT-VIP neurons did not disinhibit pyramidal neurons, but instead depolarized fast spiking and low threshold spiking interneurons. Finally, we find that ChAT-VIP neurons control attention behaviour distinctly from basal forebrain ACh inputs to mPFC. Our findings show that ChAT-VIP neurons are a local source of cortical ACh, that directly excite pyramidal and interneurons throughout cortical layers.
Fast, sensitive, and accurate integration of single cell data with Harmony
Authors: Korsunsky Ilya , Fan Jean , Slowikowski Kamil , Zhang Fan , Wei Kevin , Baglaenko Yuriy , Brenner Michael , Loh Po-Ru , Raychaudhuri Soumya 
Published: November 2018

AbstractThe rapidly emerging diversity of single cell RNAseq datasets allows us to characterize the transcriptional behavior of cell types across a wide variety of biological and clinical conditions. With this comprehensive breadth comes a major analytical challenge. The same cell type across tissues, from different donors, or in different disease states, may appear to express different genes. A joint analysis of multiple datasets requires the integration of cells across diverse conditions. This is particularly challenging when datasets are assayed with different technologies in which real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Unlike available single-cell integration methods, Harmony can simultaneously account for multiple experimental and biological factors. We develop objective metrics to evaluate the quality of data integration. In four separate analyses, we demonstrate the superior performance of Harmony to four single-cell-specific integration algorithms. Moreover, we show that Harmony requires dramatically fewer computational resources. It is the only available algorithm that makes the integration of ∼ 106 cells feasible on a personal computer. We demonstrate that Harmony identifies both broad populations and fine-grained subpopulations of PBMCs from datasets with large experimental differences. In a meta-analysis of 14,746 cells from 5 studies of human pancreatic islet cells, Harmony accounts for variation among technologies and donors to successfully align several rare subpopulations. In the resulting integrated embedding, we identify a previously unidentified population of potentially dysfunctional alpha islet cells, enriched for genes active in the Endoplasmic Reticulum (ER) stress response. The abundance of these alpha cells correlates across donors with the proportion of dysfunctional beta cells also enriched in ER stress response genes. Harmony is a fast and flexible general purpose integration algorithm that enables the identification of shared fine-grained subpopulations across a variety of experimental and biological conditions.
Genome-wide strategies reveal target genes of Npas4l associated with cardiovascular development in zebrafish
Authors: Marass Michele , Beisaw Arica , Gerri Claudia , Luzzani Francesca , Fukuda Nana , Günther Stefan , Kuenne Carsten , Reischauer Sven , Stainier Didier Y. R. 
Published: November 2018

ABSTRACTThe development of a vascular network is essential to nourish tissues and sustain organ function throughout life. Endothelial cells (ECs) are the building blocks of blood vessels, yet our understanding of EC specification remains incomplete. zebrafishcloche/npas4lmutants have been used broadly as an avascular model, but little is known about the molecular mechanisms of action of the Npas4l transcription factor. Here, to identify its direct and indirect target genes, we combined complementary genome-wide approaches including transcriptome analyses and chromatin immunoprecipitation (ChIP). The cross-analysis of these datasets indicates that Npas4l functions as a master regulator by directly inducing a group of transcription factor genes crucial for hematoendothelial specification such asetv2,tal1andlmo2. We also identified new targets of Npas4l and investigated the function of a subset of them using the CRISPR/Cas9 technology. Phenotypic characterization oftspan18bmutants reveals a novel player in developmental angiogenesis, confirming the reliability of the datasets generated. Collectively, these data represent a useful resource for future studies aimed to better understand EC fate determination and vascular development.
Parameter Identifiability of the Generalized Lotka-Volterra Model for Microbiome Studies
Authors: Remien Christopher H , Eckwright Mariah J , Ridenhour Benjamin J 
Published: November 2018

AbstractBackgroundPopulation dynamic models can be used in conjunction with time series of species abundances to infer interactions. Understanding microbial interactions is a prerequisite for numerous goals in microbiome research; predicting how populations change over time, determining how manipulations of microbiomes affect dynamics, and designing synthetic microbiomes to perform tasks are just a few examples. As such, there is great interest in adapting population dynamic theory for microbial systems. Despite the appeal, numerous hurdles exist. One hurdle is that the data commonly obtained from DNA sequencing yield estimates of relative abundances, while population dynamic models such as the generalized Lotka-Volterra model track absolute abundances or densities. It is not clear whether relative abundance data alone can be used to infer parameters of population dynamic models such as the Lotka-Volterra model.ResultsWe used structural identifiability analyses to determine the extent to which time series of relative abundances can be used to parameterize the generalized Lotka-Volterra model. We found that only with absolute abundance data to accompany relative abundance estimates from sequencing can all parameters be uniquely identified. However, relative abundance data alone do contain information on relative interaction strengths, which is sufficient for many studies where the goal is to estimate key interactions and their effects on dynamics. Our results also indicate that the relative interaction rates that can be estimated using relative abundance data provide ample information to estimate relative changes of absolute abundance over time. Using synthetic data for which we know the underlying structure, we found our results to be robust to modest amounts of both process and measurement error.ConclusionsFitting the generalized Lotka-Volterra model to time-series sequencing data typically requires either assuming a constant population size or performing additional measurements to obtain absolute abundances. We have found that these assumptions are not strictly necessary because relative abundance data alone contain sufficient information to estimate relative rates of interaction, and thus to infer key drivers of microbial population dynamics.
Simultaneous mesoscopic Ca2+ imaging and fMRI: Neuroimaging spanning spatiotemporal scales
Authors: Lake Evelyn MR , Ge Xinxin , Shen Xilin , Herman Peter , Hyder Fahmeed , Cardin Jessica A , Higley Michael J , Scheinost Dustin , Papademetris Xenophon , Crair Michael C , Constable R Todd 
Published: November 2018

ABSTRACTTo achieve a more comprehensive understanding of brain function requires simultaneous measurement of activity across a range of spatiotemporal scales. However, the appropriate tools to perform such studies are largely unavailable. Here, we present a novel approach for concurrent wide-field optical and functional magnetic resonance imaging (fMRI). By merging these two modalities, we are for the first time able to simultaneously acquire whole-brain blood-oxygen-level-dependent and whole-cortex calcium-sensitive fluorescent measures of brain activity. We describe the developments that allow us to combine these modalities without compromising the fidelity of either technique. In a transgenic murine model, we examine correspondences between activity measured using these modalities and identify unique and complementary features of each. Our approach links cell-type specific optical measurements of neural activity to the most widely used method for assessing human brain function. These data and approach directly establish the neural basis for the macroscopic connectivity patterns observed with fMRI.