bluuebunny/crossref_metadata_2025_split huggingface dataset:
The age-regulated zinc finger factor ZNF367 is a new modulator of embryonic neurogenesis
Authors: Naef Valentina , Monticelli Sara , Corsinovi Debora , Mazzetto Maria Teresa , Cellerino Alessandro , Ori Michela 
Published: February 2018

AbstractGlobal population aging is one of the major social and economic challenges of contemporary society. During aging the progressive decline in physiological functions has serious consequences for all organs including brain. The age-related incidence of neurodegenerative diseases coincides with the sharp decline of the amount and functionality of adult neural stem cells. Recently, we identified a short list of brain age-regulated genes by means of next-generation sequencing. Among themznf367codes for a transcription factor that represents a central node in gene coregulation networks during aging but its function, in the central nervous system (CNS), is completely unknown. As proof of concept we analyzed the role ofznf367during neurogenesis. By means of a gene loss of function approach limited to the CNS, we suggested thatznf367might act as a key controller of the neuroblasts cell cycle, particularly in the progression of mitosis and spindle check-point. Using a candidate gene approach, based on a weighted-gene co-expression network analysis, we suggested possible targets of znf367 such asfancd2andska3. The age-related decline ofznf367 well correlated with its role during embryonic neurogenesis opening new lines of investigation to improved maintenance and even repair of neuronal function.
In vivotopology converts competition for cell-matrix adhesion into directional migration
Authors: Bajanca Fernanda , Gouignard Nadège , Colle Charlotte , Parsons Maddy , Mayor Roberto , Theveneau Eric 
Published: February 2018

AbstractWhen migratingin vivo, cells are exposed to numerous, and somewhat conflicting, signals: chemokines, repellents, extracellular matrix, growth factors. The roles of several of these molecules have been studied individuallyin vitroorin vivobut we have yet to understand how cells integrate them. To start addressing this question, we used the cephalic neural crest as a model system and looked at the roles of its best examples of positive and negative signals: stromal-cell derived factor 1 (Sdf1/Cxcl12) and class3-Semaphorins. Our results indicate that Sdf1 and Sema3A antagonistically control cell-matrix adhesion via opposite effects on Rac1 activity at the single cell level. Directional migration at the population level emerges as a result of global Semaphorin-dependent confinement and broad activation of adhesion by Sdf1 in the context of a biased Fibronectin distribution. These results indicate that unevenin vivotopology renders the need for precise distribution of secreted signals mostly dispensable.
Detecting within-host interactions from genotype combination prevalence data
Authors: Alizon Samuel , Murall Carmen Lía , Saulnier Emma , Sofonea Mircea 
Published: February 2018

AbstractParasite genetic diversity can provide information on disease transmission dynamics but most methods ignore the exact combinations of genotypes in infections. We introduce and validate a new method that combines explicit epidemiological modelling of coinfections and regression Approximate Bayesian Computing (ABC) to detect within-host interactions. Using genital infections by different types of Human Papillomaviruses (HPVs) as a test case, we show that, if sufficiently strong, within-host parasite interactions can be detected from epidemiological data and that this detection is robust even in the face of host heterogeneity in behaviour. These results suggest that the combination of mathematical modelling and sophisticated inference techniques is promising to extract additional epidemiological information from existing datasets.
Arabidopsis Lunapark proteins are involved in ER cisternae formation
Authors: Kriechbaumer Verena , Breeze Emily , Pain Charlotte , Tolmie Frances , Frigerio Lorenzo , Hawes Chris 
Published: February 2018

SummaryThe plant endoplasmic reticulum (ER) is crucial to the maintenance of cellular homeostasis. The ER consists of a dynamic and continuously remodelling network of tubules and cisternae. Several conserved membrane proteins have been implicated in formation and maintenance of the ER network in plants, such as RHD3 and the reticulon family of proteins.Despite the recent work in mammalian and yeast cells, the detailed molecular mechanisms of ER network organisation in plants still remain largely unknown. Recently novel ER network-shaping proteins called Lunapark have been identified in yeast and mammalian cells.Here we identify two arabidopsis LNP homologues and investigate their subcellular localisation via confocal microscopy and potential function in shaping the ER network using protein-protein interaction assays and mutant analysis.We show that AtLNP1 overexpression in tobacco leaf epidermal cells mainly labels the three-way junctions (trivia) of the ER network whereas AtLNP2 labels the whole ER. Overexpression of LNP proteins results in an increased abundance of ER cisternae and an lnp1lnp2 amiRNA line displays a less structured ER network.Thus, we hypothesize that AtLNP1 and AtLNP2 are involved in determining the dynamic morphology of the plant ER, possibly by regulating the formation of ER cisternae.
PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes
Authors: Papadopoulos Nikolaos , Parra R. Gonzalo , Söding Johannes 
Published: February 2018

BackgroundSingle-cell RNA sequencing (scRNA-seq) is an enabling technology for the study of cellular differentiation and heterogeneity. From snapshots of the transcriptomic profiles of differentiating single cells, the cellular lineage tree that leads from a progenitor population to multiple types of differentiated cells can be derived. The underlying lineage trees of most published datasets are linear or have a single branchpoint, but many studies with more complex lineage trees will soon become available. To test and further develop tools for lineage tree reconstruction, we need test datasets with known trees.ResultsPROSSTT can simulate scRNA-seq datasets for differentiation processes with lineage trees of any desired complexity, noise level, noise model, and size. PROSSTT also provides scripts to quantify the quality of predicted lineage trees.Availabilityhttps://github.com/soedinglab/prossttContactsoeding@mpibpc.mpg.de
Learning to read increases the informativeness of distributed ventral temporal responses
Authors: Nordt Marisa , Gomez Jesse , Natu Vaidehi , Jeska Brianna , Barnett Michael , Grill-Spector Kalanit 
Published: February 2018

AbstractBecoming a proficient reader requires substantial learning over many years. However, it is unknown how learning to read affects development of distributed visual representations across human ventral temporal cortex (VTC). Using fMRI and a data-driven, computational approach, we quantified the development of distributed VTC responses to characters (pseudowords and numbers) vs. other domains in children, preteens, and adults. Results reveal anatomical- and hemisphere-specific development. With development, distributed responses to words and characters became more distinctive and informative in lateral but not medial VTC, and in the left but not right hemisphere. While development of voxels with both positive (that is, word-selective) and negative preference to words affected distributed information, only development of word-selective voxels predicted reading ability. These data show that developmental increases in informativeness of distributed left lateral VTC responses enable proficient reading and have important implications for both developmental theories and for elucidating neural mechanisms of reading disabilities.
Using river temperature to optimize fish incubation metabolism and survival: a case for mechanistic models
Authors: Anderson James J. 
Published: February 2018

AbstractAllocating reservoir flows to societal and ecosystem needs under increasing demands for water and increasing variability in climate presents challenges to resource managers. In the past, managers have operated reservoirs to achieve flow and temperature compliance points based on qualitative predictions of competing needs. Because it is difficult, if not impossible, to assess whether meeting such compliance points is efficient or equitable, new strategies for regulation are being advanced. Critical to these strategies is the need for new models with sufficient biological details to identify the effects of reservoir operations on organism growth and survival in real time. This paper evaluates the adequacy of three models of differing complexity for managing the Sacramento River temperature during the incubation of winter-run Chinook salmon. The models similarly characterize temperature-and density-dependent mortality from egg through fry survival, but use different spatial and temporal resolutions. The models all fit survival data reasonably well, but predict different reservoir operations to protect fish. Importantly, the models with the finer spatial/temporal resolution predict reservoir operations that require less flow and better protect fish when water resources are limited. The paper illustrates that shifting the focus of management from meeting compliance points to meeting the metabolic needs of the organisms’ yields efficiencies and identifies when water is needed and when it can be saved.
Association of a lincRNA postmortem with suicide by violent means and in vivo with aggressive phenotypes
Authors: Punzi Giovanna , Ursini Gianluca , Viscanti Giovanna , Radulescu Eugenia , Shin Joo Heon , Quarto Tiziana , Catanesi Roberto , Blasi Giuseppe , Jaffe Andrew E. , Deep-Soboslay Amy , Hyde Thomas M. , Kleinman Joel E. , Bertolino Alessandro , Weinberger Daniel R. 
Published: February 2018

ABSTRACTObjectivePrevious findings suggest that differences in brain expression of a human-specific long intergenic non-coding RNA (LINC01268; GRCh37/hg19: LOC285758) may be linked to aggressive behavior and suicide. The authors sought to replicate and extend these findings in a new sample, and translate the results to the behavioral level in living healthy subjects.MethodThe authors examined RNA sequencing data in human brain to confirm the prior postmortem association of the lincRNA specifically with suicide by violent means. In addition, they used a genetic variant associated with LINC01268 expression to detect association with in vivo prefrontal physiology related to behavioral control. They finally performed weighted gene co-expression network analysis (WGCNA) and gene-ontology analysis to identify biological processes associated with a LINC01268 co-expression network.ResultsIn the replication sample, prefrontal expression of LINC01268 was again higher in suicides by violent means (N=65) than both non-suicides (N=78; 1.29e-06) and suicides by non-violent means (N=46; p=1.4e-06). In a living cohort, carriers of the minor allele of a SNP associated with increased LINC01268 expression in brain scored higher on a lifetime aggression questionnaire and show diminished engagement of prefrontal cortex (BA10) when viewing angry faces during fMRI. WGCNA highlighted the immune response.ConclusionsThese results suggest that LINC01268 influences emotional regulation, aggressive behavior and suicide by violent means; the underlying biological dynamics may include modulation of genes potentially engaged in the immune response.
A Single Dose Of Ayahuasca Modulates Salivary Cortisol In Treatment-Resistant Depression
Authors: de Menezes Galvão Ana Cecília , de Almeida Raíssa Nóbrega , dos Santos Silva Erick Allan , de Morais Freire Fúlvio Aurélio , Palhano-Fontes Fernanda , Onias Heloisa , Arcoverdee Emerson , Maia-de-Oliveira João Paulo , Araújo Draúlio B , Lobão-Soares Bruno , Galvão-Coelho Nicole Leite 
Published: February 2018

ABSTRACTMajor depression is a highly prevalent mood disorder, affecting about 350 million people, and around 30% of the patients are resistant to currently available antidepressant medications. Recent evidence from a randomized placebo-controlled trial supports the rapid antidepressant effects of the psychedelic ayahuasca in treatment-resistant depression. The aim of this study was to explore the effect of ayahuasca on plasma cortisol and awakening salivary cortisol response, in the same group of treatment-resistant patients and in healthy volunteers. Subjects received a single dose of ayahuasca or placebo, and both plasma and awakening salivary cortisol response were measured at baseline (before dosing) and 48h after the dosing session. Baseline assessment (D0) showed blunted awakening salivary cortisol response and hypocortisolemia in patients (DM), both with respect to healthy controls group (C). Salivary cortisol also was measured during dosing session and we observed a large increased for both C and DM that ingested ayahuasca, than placebo groups. After 48h of the dosing session (D2) with ayahuasca, awakening salivary cortisol response (for both sexes) of treated patients became similar to levels detected in controls. This was not observed in patients that ingested placebo. No changes in plasma cortisol were observed after 48 hours of ayahuasca or placebo ingestion for both groups and sexes. Therefore, these findings point to new evidence of modulation of ayahuasca on salivary cortisol levels, as cortisol acts in regulation of distinct physiological pathways, emotional and cognitive processes related to etiology of depression, this modulation could be an important part of the antidepressant effects observed with ayahuasca. Moreover, this study highlights the importance of psychedelics in the treatment of human mental disorders.
Predatory Behavior is Primary Predictor of Movement of Wildland-Urban Cougars
Authors: Buderman Frances E. , Hooten Mevin B. , Alldredge Mat W. , Hanks Ephraim M. , Ivan Jacob S. 
Published: February 2018

ABSTRACTWhile many species have suffered from the detrimental impacts of increasing human population growth, some species, such as cougars (Puma concolor), have been observed using human-modified landscapes. However, human-modified habitat can be a source of both increased risk and increased food availability, particularly for large carnivores. Assessing preferential use of the landscape is important for managing wildlife and can be particularly useful in transitional habitats, such as at the wildland-urban interface. Preferential use is often evaluated using resource selection functions (RSFs), but RSFs do not adequately account for the habitat available to an individual at a given time and may mask conflict or avoidance behavior. Contemporary approaches to incorporate landscape availability into the assessment of habitat preference include spatio-temporal point process models, step-selection functions, and continuous-time Markov chain (CTMC) models; in contrast with the other methods, the CTMC model allows for explicit inference on animal movement. We used the CTMC framework to model speed and directionality of movement by a population of cougars inhabiting the Front Range of Colorado, U.S.A., an area exhibiting rapid population growth and increased recreational use, as a function of individual variation and time-varying responses to landscape covariates. The time-varying framework allowed us to detect temporal variability that would be masked in a generalized linear model. We found evidence for individual- and daily temporal-variability in cougar response to landscape characteristics. Distance to nearest kill site emerged as the most important driver of movement at a population-level. We also detected seasonal differences in average response to elevation, heat loading, and distance to roads. Motility was also a function of amount of development, with cougars moving faster in developed areas than in undeveloped areas.
Motor Cortex Encodes A Temporal Difference Reinforcement Learning Process
Authors: Tarigoppula Venkata S Aditya , Choi John S , Hessburg John P , McNiel David B , Marsh Brandi T , Francis Joseph T 
Published: February 2018

AbstractTemporal difference reinforcement learning (TDRL) accurately models associative learning observed in animals, where they learn to associate outcome predicting environmental states, termed conditioned stimuli (CS), with the value of outcomes, such as rewards, termed unconditioned stimuli (US). A component of TDRL is the value function, which captures the expected cumulative future reward from a given state. The value function can be modified by changes in the animal’s knowledge, such as by the predictability of its environment. Here we show that primary motor cortical (M1) neurodynamics reflect a TD learning process, encoding a state value function and reward prediction error in line with TDRL. M1 responds to the delivery of reward, and shifts its value related response earlier in a trial, becoming predictive of an expected reward, when reward is predictable due to a CS. This is observed in tasks performed manually or observed passively, as well as in tasks without an explicit CS predicting reward, but simply with a predictable temporal structure, that is a predictable environment. M1 also encodes the expected reward value associated with a set of CS in a multiple reward level CS-US task. Here we extend the Microstimulus TDRL model, reported to accurately capture RL related dopaminergic activity, to account for M1 reward related neural activity in a multitude of tasks.Significance statementThere is a great deal of agreement between aspects of temporal difference reinforcement learning (TDRL) models and neural activity in dopaminergic brain centers. Dopamine is know to be necessary for sensorimotor learning induced synaptic plasticity in the motor cortex (M1), and thus one might expect to see the hallmarks of TDRL in M1, which we show here in the form of a state value function and reward prediction error during. We see these hallmarks even when a conditioned stimulus is not available, but the environment is predictable, during manual tasks with agency, as well as observational tasks without agency. This information has implications towards autonomously updating brain machine interfaces as others and we have proposed and published on.
Big data and single cell transcriptomics: implications for ontological representation
Authors: Aevermann Brian D. , Novotny Mark , Bakken Trygve , Miller Jeremy A. , Diehl Alexander D. , Osumi-Sutherland David , Lasken Roger S. , Lein Ed S. , Scheuermann Richard H. 
Published: February 2018

AbstractCells are fundamental functional units of multicellular organisms, with different cell types playing distinct physiological roles in the body. The recent advent of single cell transcriptional profiling using RNA sequencing is producing “big data”, enabling the identification of novel human cell types at an unprecedented rate. In this review, we summarize recent work characterizing cell types in the human central nervous and immune systems using single cell and single nuclei RNA sequencing, and discuss the implications that these discoveries are having on the representation of cell types in the reference Cell Ontology (CL). We propose a method based on random forest machine learning for identifying sets of necessary and sufficient marker genes that can be used to assemble consistent and reproducible cell type definitions for incorporation into the CL. The representation of defined cell type classes and their relationships in the CL using this strategy will make the cell type classes findable, accessible, interoperable, and reusable (FAIR), allowing the CL to serve as a reference knowledgebase of information about the role that distinct cellular phenotypes play in human health and disease.
GEOMetaCuration: A web-based application for accurate manual curation of Gene Expression Omnibus metadata
Authors: Li Zhao , Li Jin , Yu Peng 
Published: February 2018

AbstractMetadata curation has become increasingly important for biological discovery and biomedical research because a large amount of heterogeneous biological data is currently freely available. To facilitate efficient metadata curation, we developed an easy-to-use web-based curation application, GEOMetaCuration, for curating the metadata of Gene Expression Omnibus datasets. It can eliminate mechanical operations that consume precious curation time and can help coordinate curation efforts among multiple curators. It improves the curation process by introducing various features that are critical to metadata curation, such as a back-end curation management system and a curator-friendly front-end. The application is based on a commonly used web development framework of Python/Django and is open-sourced under the GNU General Public License V3. GEOMetaCuration is expected to benefit the biocuration community and to contribute to computational generation of biological insights using large-scale biological data. An example use case can be found at the demo website: http://geometacuration.yubiolab.org. Source code URL: https://bitbucket.com/yubiolab/GEOMetaCuration
Reproducible model development in the Cardiac Electrophysiology Web Lab
Authors: Daly Aidan C. , Clerx Michael , Beattie Kylie A. , Cooper Jonathan , Gavaghan David J. , Mirams Gary R. 
Published: February 2018

AbstractThe modelling of the electrophysiology of cardiac cells is one of the most mature areas of systems biology. This extended concentration of research effort brings with it new challenges, foremost among which is that of choosing which of these models is most suitable for addressing a particular scientific question. In a previous paper, we presented our initial work in developing an online resource for the characterisation and comparison of electrophysiological cell models in a wide range of experimental scenarios. In that work, we described how we had developed a novel protocol language that allowed us to separate the details of the mathematical model (the majority of cardiac cell models take the form of ordinary differential equations) from the experimental protocol being simulated. We developed a fully-open online repository (which we termed the Cardiac Electrophysiology Web Lab) which allows users to store and compare the results of applying the same experimental protocol to competing models. In the current paper we describe the most recent and planned extensions of this work, focused on supporting the process of model building from experimental data. We outline the necessary work to develop a machine-readable language to describe the process of inferring parameters from wet lab datasets, and illustrate our approach through a detailed example of fitting a model of the hERG channel using experimental data. We conclude by discussing the future challenges in making further progress in this domain towards our goal of facilitating a fully reproducible approach to the development of cardiac cell models.