bluuebunny/crossref_metadata_2025_split huggingface dataset:
Divided attention for feature-based selection in visual cortex
Authors: Moreland James C. , Boynton Geoffrey M. 
Published: February 2020

AbstractFeature-based attention can select relevant features such as colors or directions of motion from the visual field irrespective of the spatial position. In visual cortex not only do we see feature-specific attention affecting responses in neurons with receptive fields at an attended location, but that effect also spreads to neurons with receptive fields beyond the spatially relevant location. When only one feature is task relevant, the spread of activity across space can act to facilitate perception of behaviorally relevant stimuli. However, when multiple features are relevant, what is the effect on behavior and brain activity? We tested this question by having observers divide attention between two patches (left and right of fixation) of moving dot stimuli, each containing overlapping upward and downward motion fields. In one condition, subjects performed a task on motion fields moving in the same direction in both patches. In another condition attention was divided between opposite directions of motion. Replicating a previous behavioral study, we found that observers showed better performance when dividing attention to the same directions of motion than opposing directions of motion. We analyzed the BOLD responses while observers performed this task using an inverted encoding model approach that provides estimates of responses to each of the four component dot fields. We found larger responses to the attended components, replicating previous studies of spatial and feature-based attention. However, these effects were much larger when attention was divided to between the same directions of motion than to opposing directions. Our fMRI results in area hMT+ predict our behavioral results by extending the normalization model of attention to include a global feature-based attention component in that leads to suppressed responses to attended stimulus components when attention is directed to opposing directions of motion.
Detecting strawberry cultivar misidentification in the Philippines using single nucleotide polymorphism markers from the anthocyanin reductase gene
Authors: Ledesma Nadine Adellia , Matulac John Mark , Sevilleja Jesus Emmanuel , Enriquez Maria Luisa 
Published: February 2020

ABSTRACTBACKGROUNDCommercial strawberry production in the Philippines is done by small-holder farmers in La Trinidad, Benguet, where the climate is conducive for optimal growth of this temperate crop. However, these farmers are not cognizant of the importance of proper cultivar identification, particularly during runner propagation, distribution, and transplanting. Thus, there is a high likelihood that misidentification of commonly grown cultivars has taken place.OBJECTIVEThe study aimed to develop single nucleotide polymorphism markers and use them to detect possible misidentification among strawberry cultivars.METHODSLeaf samples from several cultivars were obtained from farmers and the germplasm collection of a local university in La Trinidad, Benguet, Philippines. Expressed sequence tags from theANRgene were screened for putative SNPs. Eleven SNP markers were developed and used to discriminate among the collected samples.RESULTSThe SNP markers grouped the cultivars into five genotypic clusters with seven distinct genotypic identities. Clustering analysis revealed inconsistencies between the farmers’ identification and the molecular classification. ‘Sweet Charlie’ samples were assigned to four genotypic clusters and ‘Strawberry Festival’ samples were grouped into three separate clusters.CONCLUSIONThere is a high probability that cultivar misidentification has indeed occurred. The molecular markers developed in this study could assist in future cultivar verification efforts, germplasm management, and breeding programs.
TRACE: An unbiased method to permanently tag transiently activated inputs
Authors: Krauth Nathalie , Khalil Valentina , Jariwala Meet , Mermet-Joret Noëmie , Vestergaard Anne-Katrine , Capogna Marco , Nabavi Sadegh 
Published: February 2020

AbstractA fundamental interest in circuit analysis is to parse out the synaptic inputs underlying a behavioral experience. Toward this aim, we have devised an unbiased strategy that specifically labels the afferent inputs that are activated by a defined stimulus in an activity-dependent manner. We validated this strategy in four brain areas receiving known sensory inputs. This strategy, as demonstrated here, accurately identifies these inputs.
Finding recurrent RNA structural networks with fast maximal common subgraphs of edge-colored graphs
Authors: Soulé Antoine , Reinharz Vladimir , Sarrazin-Gendron Roman , Denise Alain , Waldispühl Jérôme 
Published: February 2020

AbstractMotivationsRNA tertiary structure is crucial to its many non-coding molecular functions. RNA architecture is shaped by its secondary structure composed of stems, stacked canonical base pairs, enclosing loops. While stems are captured by free-energy models, loops composed of non-canonical base pairs are not. Nor are distant interactions linking together those secondary structure elements (SSEs). Databases of conserved 3D geometries (a.k.a. modules) not captured by energetic models are lever-aged for structure prediction and design, but the computational complexity has limited their study to local elements, loops, and recently to those covering pairs of SSEs. Systematically capturing recurrent patterns on a large scale is a main challenge in the study of RNA structures.ResultsIn this paper, we present an efficient algorithm to compute maximal isomorphisms in edge colored graphs. This framework is well suited to RNA structures and allows us to generalize previous approaches. In particular, we apply our techniques to find for the first time modules spanning more than 2 SSEs, while improving speed a hundredfold. We extract all recurrent base pair networks among all non-redundant RNA tertiary structures and identify a module connecting 36 different SSEs common to the 23S ribosome ofE. ColiandThermus thermophilus. We organize this information as a hierarchy of modules sharing similarities in their structure, which can serve as a basis for future research on the emergence of structural patterns.Availabilityhttp://csb.cs.mcgill.ca/carnaval2
Optimal anticipatory control as a theory of motor preparation: a thalamo-cortical circuit model
Authors: Kao Ta-Chu , Sadabadi Mahdieh S. , Hennequin Guillaume 
Published: February 2020

SummaryAcross a range of motor and cognitive tasks, cortical activity can be accurately described by low-dimensional dynamics unfolding from specific initial conditions on every trial. These “preparatory states” largely determine the subsequent evolution of both neural activity and behaviour, and their importance raises questions regarding how they are — or ought to be — set. Here, we formulate motor preparation as optimal anticipatory control of future movements, and show that the solution requires a form of internal feedback control of cortical circuit dynamics. In contrast to a simple feedforward strategy, feedback control enables fast movement preparation and orthogonality between preparatory and movement activity, a distinctive feature of peri-movement activity in reaching monkeys. We propose a circuit model in which optimal preparatory control is implemented as a thalamo-cortical loop gated by the basal ganglia.
The Oft-Overlooked Massively Parallel Reporter Assay: Where, When, and Which Psychiatric Genetic Variants are Functional?
Authors: Mulvey Bernard , Lagunas Tomas , Dougherty Joseph D. 
Published: February 2020

AbstractNeuropsychiatric phenotypes have been long known to be influenced by heritable risk factors. The past decade of genetic studies have confirmed this directly, revealing specific common and rare genetic variants enriched in disease cohorts. However, the early hope for these studies—that only a small set of genes would be responsible for a given disorder—proved false. The picture that has emerged is far more complex: a given disorder may be influenced by myriad coding and noncoding variants of small effect size, and/or by rare but severe variants of large effect size, manyde novo. Noncoding genomic sequences harbor a large portion of these variants, the molecular functions of which cannot usually be inferred from sequence alone. This creates a substantial barrier to understanding the higher-order molecular and biological systems underlying disease risk. Fortunately, a proliferation of genetic technologies—namely, scalable oligonucleotide synthesis, high-throughput RNA sequencing, CRISPR, and CRISPR derivatives—have opened novel avenues to experimentally identify biologically significant variantsen masse. These advances have yielded an especially versatile technique adaptable to large-scale functional assays of variation in both untranscribed and untranslated regulatory features: Massively Parallel Reporter Assays (MPRAs). MPRAs are powerful molecular genetic tools that can be used to screen tens of thousands of predefined sequences for functional effects in a single experiment. This approach has several ideal features for psychiatric genetics, but remains underutilized in the field to date. To emphasize the opportunities MPRA holds for dissecting psychiatric polygenicity, we review here its applications in the literature, discuss its ability to test several biological variables implicated in psychiatric disorders, illustrate this flexibility with a proof-of-principle,in vivocell-type specific implementation of the assay, and envision future outcomes of applying MPRA to both computational and experimental neurogenetics.
Unsupervised Topological Alignment for Single-Cell Multi-Omics Integration
Authors: Cao Kai , Bai Xiangqi , Hong Yiguang , Wan Lin 
Published: February 2020

AbstractSingle-cell multi-omics data provide a comprehensive molecular view of cells. However, single-cell multi-omics datasets consist of unpaired cells measured with distinct unmatched features across modalities, making data integration challenging. In this study, we present a novel algorithm, termed UnionCom, for the unsupervised topological alignment of single-cell multi-omics integration. UnionCom does not require any correspondence information, either among cells or among features. It first embeds the intrinsic low-dimensional structure of each single-cell dataset into a distance matrix of cells within the same dataset and then aligns the cells across single-cell multi-omics datasets by matching the distance matrices via a matrix optimization method. Finally, it projects the distinct unmatched features across single-cell datasets into a common embedding space for feature comparability of the aligned cells. To match the complex nonlinear geometrical distorted low-dimensional structures across datasets, UnionCom proposes and adjusts a global scaling parameter on distance matrices for aligning similar topological structures. It does not require one-to-one correspondence among cells across datasets, and it can accommodate samples with dataset-specific cell types. UnionCom outperforms state-of-the-art methods on both simulated and real single-cell multi-omics datasets. UnionCom is robust to parameter choices, as well as subsampling of features. UnionCom software is available athttps://github.com/caokai1073/UnionCom.
Individual differences in social reward and threat expectancies linked to grey matter volumes in key regions of the social brain
Authors: Crawford Bonni , Muhlert Nils , MacDonald Geoff , Lawrence Andrew D. 
Published: February 2020

AbstractProspection (mentally simulating future events) generates emotionally charged mental images that guide social decision-making. Positive and negative social expectancies – imagining new social interactions to be rewarding vs. threatening – are core components of social approach and avoidance motivation, respectively. Stable individual differences in such positive and negative future-related cognitions may be underpinned by distinct neuroanatomical substrates. Here, we asked 100 healthy adults to vividly imagine themselves in a novel self-relevant social scenario that was ambiguous with regards to possible social acceptance or rejection. During this task we measured their expectancies for social reward (e.g. anticipated feelings of social connection) or threat (e.g. anticipated feelings of rejection). On a separate day they underwent structural MRI; voxel-based morphometry (VBM) was used to explore the relation between their social reward and threat expectancies and regional grey matter volumes (rGMV). Increased rGMV in key regions involved in prospection, subjective valuation and emotion regulation (including ventromedial prefrontal cortex), correlated with both higher social reward and lower social threat expectancies. In contrast, social threat expectancies were uniquely linked with rGMV of regions involved in social attention (posterior superior temporal sulcus) and interoception (somatosensory cortex). These findings provide novel insight into the neurobiology of future-oriented cognitive-affective processes critical to adaptive social functioning.
Proteome Exploration of Legionella pneumophila for Identifying Novel Therapeutics: A Hierarchical Subtractive Genomics and Reverse Vaccinology Approach
Authors: Khan Md Tahsin , Mahmud Araf , Hasan Mahmudul , Azim Kazi Faizul , Begum Musammat Kulsuma , Akter Arzuba , Mondal Shakhinur Islam 
Published: February 2020

AbstractLegionella pneumophila, the causative agent of a serious type of pneumonia (lung infection) called Legionnaires’ disease. It is emerging as an antibacterial resistant strain day by day. Hence, the identification of novel drug targets and vaccine candidates is essential to fight against this pathogen. Herein attempts were taken through subtractive genomics approach on complete proteome of L. pneumophila to address the challenges of multidrug resistance. A total 2930 proteins from L. pneumophila proteome were investigated through diverse subtractive proteomics approaches, e.g., identification of human non-homologous and pathogen-specific essential proteins, druggability and ‘anti-target’ analysis, prediction of subcellular localization, human microbiome non-homology screening, protein-protein interactions studies in order to find out effective drug and vaccine targets. Only 3 were identified that fulfilled all these criteria and proposed as novel drug targets against L. pneumophila. Furthermore, outer membrane protein TolB was identified as potential vaccine target with better antigenicity score and allowed for further in silico analysis to design a unique multiepitope subunit vaccine against it. Antigenicity and transmembrane topology screening, allergenicity and toxicity assessment, population coverage analysis, and molecular docking approach were adopted to generate the most potent epitopes. The final vaccine was constructed by the combination of highly immunogenic epitopes along with suitable adjuvant and linkers. The designed vaccine construct showed higher binding interaction with different MHC molecules and human immune TLR2 receptors with minimum deformability at molecular level. The translational potency and microbial expression of the vaccine protein was also analyzed using pET28a(+) vector. The present study aids in the development of novel therapeutics and vaccine candidates for efficient treatment of the infections caused by Legionella pneumophila. However, further wet lab-based investigations and in vivo trials are highly recommended to experimentally validate our prediction.
aNy-way Independent Component Analysis
Authors: Duan Kuaikuai , Silva Rogers F. , Calhoun Vince D. , Liu Jingyu 
Published: February 2020

AbstractMultimodal data fusion is a topic of great interest. Several fusion methods have been proposed to investigate coherent patterns and corresponding linkages across modalities, such as joint independent component analysis (jICA), multiset canonical correlation analysis (mCCA), mCCA+jICA, disjoint subspace using ICA (DS-ICA) and parallel ICA. JICA exploits source independence but assumes shared loading parameters. MCCA maximizes correlation linkage across modalities directly but is limited to orthogonal features. While there is no theoretical limit to the number of modalities analyzed together by jICA, mCCA, or the two-step approach mCCA+jICA, these approaches can only extract common features and require thesamenumber of sources/components for all modalities. On the other hand, DS-ICA and parallel ICA can identify both common and distinct features but are limited to two modalities. DS-ICA assumes shared loading parameters among common features, which works well when links are strong. Parallel ICA simultaneously maximizes correlation between modalities and independence of sources, while allowing different number of sources for each modality. However, only a very limited number of modalities and linkage pairs can be optimized. To overcome these limitations, we propose aNy-way ICA, a new model to simultaneously maximize the independence of sources and correlations across modalities. aNy-way ICA combines infomax ICA and Gaussian independent vector analysis (IVA-G) via a shared weight matrix model without orthogonality constraints. Simulation results demonstrate that aNy-way ICA not only accurately recovers sources and loadings, but also the true covariance/linkage patterns, whether different modalities have the same or different number of sources. Moreover, aNy-way ICA outperforms mCCA and mCCA+jICA in terms of source and loading recovery accuracy, especially under noisy conditions.Clinical RelevanceThis establishes a model for N-way data fusion of any number of modalities and linkage pairs, allowing different number of non-orthogonal sources for different modalities.
UFMylation regulates translational homeostasis and cell cycle progression
Authors: Gak Igor A. , Vasiljevic Djordje , Zerjatke Thomas , Yu Lu , Brosch Mario , Roumeliotis Theodoros I. , Horenburg Cindy , Klemm Nancy , Bakos Gábor , Herrmann Alexander , Hampe Jochen , Glauche Ingmar , Choudhary Jyoti S. , Mansfeld Jörg 
Published: February 2020

SummaryUFMylation, the posttranslational modification of proteins with ubiquitin fold modifier 1 (UFM1) is essential for metazoan life and is associated with multiple human diseases. Although UFMylation has been linked to endoplasmic reticulum (ER) stress, its biological functions and relevant cellular targets beyond the ER are obscure. Here, we show that UFMylation directly controls translation and cell division in a manner otherwise known for cellular homeostasis-sensing pathways such as mTOR. By combining cell cycle analyses, mass spectrometry and ribosome profiling we demonstrate that UFMylation is required for eIF4F translation initiation complex assembly and recruitment of the ribosome. Interference with UFMylation shuts down global translation, which is sensed by cyclin D1 and halts the cell cycle independently of integrated stress response signalling. Our findings establish UFMylation as a key regulator of translation and uncover a pathway that couples translational homeostasis to cell cycle progression via a ubiquitin-like modification.
Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories
Authors: Lali Ricky , Chong Michael , Omidi Arghavan , Mohammadi-Shemirani Pedrum , Le Ann , Paré Guillaume 
Published: February 2020

ABSTRACTRare variants are collectively numerous and may underlie a considerable proportion of complex disease risk. However, identifying genuine rare variant associations is challenging due to small effect sizes, presence of technical artefacts, and heterogeneity in population structure. We hypothesized that rare variant burden over a large number of genes can be combined into predictive rare variant genetic risk score (RVGRS). We propose a novel method (RV-EXCALIBER) that leverages summary-level data from a large public exome sequencing database (gnomAD) as controls and robustly calibrates rare variant burden to account for the aforementioned biases. A RVGRS was found to strongly associate with coronary artery disease (CAD) in European and South Asian populations. Calibrated RVGRS capture the aggregate effect of rare variants through a polygenic model of inheritance, identifies 1.5% of the population with substantial risk of early CAD, and confers risk even when adjusting for known Mendelian CAD genes, clinical risk factors, and common variant gene scores.
Designing an occupancy framework to monitor an endemic rainforest duck: Methodological and modelling considerations
Authors: Jelil Shah Nawaz , Parasar Murchana , Cancino Laura , Cook Kimberly 
Published: February 2020

AbstractUnderstanding species trend, decline or growth, is vital to further conservation efforts. Species-habitat relationship studies are equally important for conservation as it helps in understanding the habitat a particular species depends upon, i.e. habitat conservation. However, rare and endemic species are inherently difficult to study and occupancy models are especially useful in such cases. We conducted the first detection, non-detection survey for the white winged duck in Dehing Patkai Wildlife Sanctuary, India to assess site occupancy and test habitat factors that explain its occupancy. We found that white winged duck occupancy was low (0.27 ± 0.21 SE) and detection probability was 0.44 ± 0.30 SE. We found that increasing tree richness and decreasing elevation increased species occupancy. Detection probability was influenced by our effort in that detection increased with increasing number of survey hours. Using two standard approaches, we estimated the optimal number of sites and replicate surveys for future occupancy studies. We further present considerations for future surveys. Considering the sporadic and fragmented information available, we recommend long-term ecological research to better understand the present and future population trends of the species.
RAS activation via CRLF2 signaling is a widespread mechanism in Down syndrome acute lymphoblastic leukemia regardless of RAS mutations
Authors: Koschut David , Ray Debleena , Li Zhenhua , Giarin Emanuela , Groet Jürgen , Alić Ivan , Kham Shirley Kow-Yin , Chng Wee Joo , Ariffin Hany , Weinstock David M. , Yeoh Allen Eng-Juh , Basso Giuseppe , Nižetić Dean 
Published: February 2020

AbstractBackgroundDown syndrome acute lymphoblastic leukemia (DS-ALL) is characterized by the high frequency of CRLF2-rearrangements, JAK2-mutations, or RAS-pathway mutations. Intriguingly, JAK2 and RAS mutations are mutually exclusive in leukemic sub-clones, causing dichotomy in therapeutic target choices.ResultsHere we show that in primary leukemic cells from DS-ALL, in the absence of RAS-mutations, wild-type (wt)RAS is active, and/or can be induced by the physiological ligand TSLP of the transmembrane-receptor CRLF2. We show active/inducible RAS in 14/20 (70%) of primary DS-ALL samples analyzed, 8 of which had no RAS-mutations, but 75% of those had either mutated or hyperphosphorylated JAK2. No wtRAS cases with mutated/hyperphosphorylated JAK2 were observed that lacked activated RAS protein. We prove in a cell model that elevated CRLF2 in combination with constitutionally active JAK2 is sufficient to activate wtRAS. We show that TSLP boosts the direct binding of active PTPN11 to wtRAS. Pre-inhibition of RAS or PTPN11, but not of PI3K or JAK signaling, prevented TSLP-induced RAS-GTP boost.Using multivariate-clustering based on RAS-activity/inducibility we show significant separation between standard-risk and high-risk DS-ALL groups. Cox proportional-hazards model showed protein-activity (but not mutation status) as independently predictive of outcome.ConclusionsOur data indicate that RAS protein activity levels (and not JAK2/RAS mutation profiles), are predictive of outcome. Importantly, our data suggest that inhibition of RAS and direct RAS-pathway components should be combined with PI3K/mTOR and/or JAK2 inhibitors for high-risk cases. Therapeutically this is relevant for >75% of DS-ALL and our additional data suggest that it warrants further investigation in high-risk non-DS-ALL.