bluuebunny/crossref_metadata_2025_split huggingface dataset:
Socio-spatial heterogeneity in participation in mass dog vaccination campaigns, Arequipa, Peru
Authors: Castillo-Neyra Ricardo , Toledo Amparo M. , Arevalo-Nieto Claudia , MacDonald Hannelore , De la Puente Micaela , Naquira-Velarde Cesar , Paz-Soldan Valerie A. , Buttenheim Alison M. , Levy Michael Z. 
Published: February 2019

AbstractTo control and prevent rabies in Latin America, mass dog vaccination campaigns (MDVC) are implemented mainly through fixed-location vaccination points: owners have to bring their dogs to the vaccination points where they receive the vaccination free of charge. Dog rabies is still endemic in some Latin-American countries and high overall dog vaccination coverage and coverage evenness are desired attributes of MDVC to halt rabies virus transmission. In Arequipa, Peru, we conducted a door-to-door post-campaign survey on >6,000 houses to assess the placement of vaccination points on these two attributes. We found that the odds of participating in the campaign decreased by 16% for every 100 m from the owner’s house to the nearest vaccination point (p=0.041) after controlling for potential covariates. We found social determinants associated with participating in the MDVC: for each child under 5 in the household the odds of participating in the MDVC decreased by 13% (p=0.032), and for every ten more years living in the area, the odds of participating in the MDVC increased by 9% (p<0.001), after controlling for distance and other covariates. We also found significant spatial clustering of unvaccinated dogs over 500m from the vaccination points, which created pockets of unvaccinated dogs that may sustain rabies virus transmission. Understanding the barriers to dog owners’ participation in community-based dog-vaccination programs will be crucial to implement effective zoonotic disease preventive activities. Spatial and social elements of urbanization play an important role in coverage of MDVCs and should be considered during their planning and evaluation.
State or trait? MRS-measured GABA and Glutamate concentrations are not modulated by task demand and do not robustly predict task performance
Authors: Talsma Lotte , Loon Anouk van , Scholte H. Steven , Slagter Heleen A. 
Published: February 2019

AbstractOver the past few years, Magnetic Resonance Spectroscopy (MRS) has become a popular method to non-invasively study the relationship between in-vivo concentrations of neurotransmitters such as GABA and Glutamate and cognitive functions in the human brain. However, currently, it is unclear to what extent MRS measures reflect stable trait-like neurotransmitter levels, or may be sensitive to the brain’s activity state as well. Therefore, this study investigated if cortical GABA (GABA+/Cr) and Glutamate (Glx/Cr) levels differ as a function of task demand, and if so, in which activity state these measures may best predict behavioral performance. We acquired 3T-MRS data from thirty healthy men in two brain areas during different task demands: the medial occipital cortex (OC), at rest (eyes closed) and while subjects watched a movie (on-task); and the left dorsolateral prefrontal cortex (lDLPFC), at rest, during an easy working memory (WM) task, and during a challenging WM task. Task demand had no effect on the concentration of GABA or Glutamate in either brain region. Moreover, we observed no correlations between GABA and Glutamate concentrations and behavioral performance; occipital neurotransmitter concentrations did not predict visual discrimination nor did those in lDLPFC predict WM updating accuracy, capacity or maintenance. These null findings were supported by Bayesian statistics. In conclusion, these results suggest that with 3T-MRS we measure relatively stable trait-like neurotransmitter concentrations, but at the same time question the validity of 3T-MRS as a method to relate GABA and Glutamate concentrations to behavior.
Self-Attention based model forde-novoantibiotic resistant gene classification with enhanced reliability for Out of Distribution data detection
Authors: Hamid Md-Nafiz , Friedberg Iddo 
Published: February 2019

AbstractAntibiotic resistance monitoring is of paramount importance in the face of this ongoing global epidemic. Using traditional alignment based methods to detect antibiotic resistant genes results in huge number of false negatives. In this paper, we introduce a deep learning model based on a self-attention architecture that can classify antibiotic resistant genes into correct classes with high precision and recall by just using protein sequences as input. Additionally, deep learning models trained with traditional optimization algorithms (e.g. Adam, SGD) provide poor posterior estimates when tested against Out-of-Distribution (OoD) antibiotic resistant/non-resistant genes. We train our model with an optimization method called Preconditioned Stochastic Gradient Langevin Dynamics (pSGLD) which provides reliable uncertainty estimates when tested against OoD data.
Unraveling Diagnostic Biomarkers of Schizophrenia Through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data
Authors: Acar Evrim , Schenker Carla , Levin-Schwartz Yuri , Calhoun Vince , Adalı Tülay 
Published: February 2019

ABSTRACTFusing complementary information from different modalities can lead to the discovery of more accurate diagnostic biomarkers for psychiatric disorders. However, biomarker discovery through data fusion is challenging since it requires extracting interpretable and reproducible patterns from data sets, consisting of shared/unshared patterns and of different orders. For example, multi-channel electroencephalography (EEG) signals from multiple subjects can be represented as a third-order tensor with modes:subject,time, andchannel, while functional magnetic resonance imaging (fMRI) data may be in the form ofsubjectbyvoxelmatrices. Traditional data fusion methods rearrange higher-order tensors, such as EEG, as matrices to use matrix factorization-based approaches. In contrast, fusion methods based on coupled matrix and tensor factorizations (CMTF) exploit the potential multi-way structure of higher-order tensors. The CMTF approach has been shown to capture underlying patterns more accurately without imposing strong constraints on the latent neural patterns,i.e., biomarkers. In this paper, EEG, fMRI and structural MRI (sMRI) data collected during an auditory oddball task (AOD) from a group of subjects consisting of patients with schizophrenia and healthy controls, are arranged as matrices and higher-order tensors coupled along thesubjectmode, and jointly analyzed using structure-revealing CMTF methods (also known as advanced CMTF (ACMTF)) focusing on unique identification of underlying patterns in the presence of shared/unshared patterns. We demonstrate that joint analysis of the EEG tensor and fMRI matrix using ACMTF reveals significant and biologically meaningful components in terms of differentiating between patients with schizophrenia and healthy controls while also providing spatial patterns with high resolution and improving the clustering performance compared to the analysis of only the EEG tensor. We also show that these patterns are reproducible, and study reproducibility for different model parameters. In comparison to the joint independent component analysis (jICA) data fusion approach, ACMTF provides easier interpretation of EEG data by revealing a single summary map of the topography for each component. Furthermore, fusion of sMRI data with EEG and fMRI through an ACMTF model provides structural patterns; however, we also show that when fusing data sets from multiple modalities, hence of very different nature, preprocessing plays a crucial role.
Measurement and self-operating computer of the leukocyte continuum as a fixed space–time continuum in inflammation
Authors: Nonomura Yuusuke 
Published: February 2019

AbstractMotivationNo biomarkers and systems, including leukocyte count and flow cytometry, can be used to measure tissue injury for diagnosing inflammation. A fixed space–time continuum (SτC) biomarker can address this issue. A leukocyte continuum (LC) is a biomarker forming a SτC capable of measuring injury by operators and equations for a self-operating computation.ResultsA self-operating computer (SOC) LC as a water treatment for leukocyte(s) was generated using leukocyte(s). String leukocyte continuum (StrLC), single-layer leukocyte (SLL) and multilayer leukocyte continuum (MLC) were demonstrated in various LCs using an equation with a primitive-operator. In the SOC, the LC is the inflammation graph of the operation result. The relative differential equation (RDE) shows how to recognize the LC not as a ‘model’ in the conventional-other-operating-computer (cOOC), but as an actual arithmetic unit with a display unit. The SOC shows the essential nature in real time.
Maternal RhD heterozygous genotype is associated with male biased secondary sex ratio
Authors: Kaňková Šárka , Flegr Jaroslav , Toman Jan , Calda Pavel 
Published: February 2019

AbstractThe results of previous studies overwhelmingly suggest that RhD positive heterozygotes express better health status than Rh positive homozygous, especially in RhD negative subjects. This also applies to pregnant women. According to the Trivers-Willard hypothesis, women in better physical condition should have a male-skewed sex ratio. The aim of the present study was to test the hypothesis that RhD positive heterozygous mothers give birth to more sons than daughters. In the present cross-sectional study, we analysed data from 5,655 women who have given birth in the General University Hospital in Prague, Czech Republic between 2008-2012. Clinical records comprised maternal weight before pregnancy, number of previous deliveries, sex of the newborn, maternal RhD phenotype, and RhD phenotype of the newborn. Secondary sex ratio was significantly higher (P=0.028) in RhD positive mothers who had RhD negative newborns, i.e. in heterozygotes (SR=1.23), than in RhD positive mothers who had RhD positive newborns, i.e. in a mixed population of heterozygotes and homozygotes (SR=1.00), especially in primiparous women (P=0.013; SR=1.37 and 0.99 resp.). In line with the Trivers-Willard effect, RhD maternal heterozygous genotype is associated with male biased secondary sex ratio. The results supported the hypothesis that RhD polymorphism may be maintained due to heterozygote health advantages.
Pubertal Testosterone Correlates with Adolescent Impatience and Dorsal Striatal Activity
Authors: Laube Corinna , Lorenz Robert , van den Bos Wouter 
Published: February 2019

AbstractRecent self-report and behavioral studies have demonstrated that pubertal testosterone is related to an increase in risky and impulsive behavior. Yet, the mechanisms underlying such a relationship are poorly understood. Findings from both human and rodent studies point towards distinct striatal pathways including the ventral and dorsal striatum as key target regions for pubertal hormones. In this study we investigated task-related impatience of boys between 10 and 15 years of age (N= 75), using an intertemporal choice task combined with measures of functional magnetic resonance imaging and hormonal assessment. Increased levels of testosterone were associated with a greater response bias towards choosing the smaller sooner option. Furthermore, our results show that testosterone specifically modulates the dorsal, not ventral, striatal pathway. These results provide novel insights into our understanding of adolescent impulsive and risky behaviors and how pubertal hormones are related to neural processes.
Ecological speciation in European whitefish is driven by a large-gaped predator
Authors: Öhlund Gunnar , Bodin Mats , Nilsson Karin A. , Öhlund Sven-Ola , Mobley Kenyon B. , Hudson Alan G. , Peedu Mikael , Brännström Åke , Bartels Pia , Præbel Kim , Hein Catherine L. , Johansson Petter , Englund Göran 
Published: February 2019

AbstractLake-dwelling fish that form species pairs/flocks characterized by body size divergence are important model systems for speciation research. While several sources of divergent selection have been identified in these systems, their importance for driving the speciation process remains elusive. A major problem is that in retrospect, we cannot distinguish selection pressures that initiated divergence from those acting later in the process. To address this issue, we reconstructed the initial stages of speciation in European whitefish (Coregonus lavaretus) using data from 357 populations of varying age (26-10 000 years). We find that whitefish speciation is driven by a large-growing predator, the northern pike (Esox lucius). Pike initiates divergence by causing a largely plastic differentiation into benthic giants and pelagic dwarfs; ecotypes that will subsequently develop partial reproductive isolation and heritable differences in gill raker number. Using an eco-evolutionary model, we demonstrate how pike’s habitat specificity and large gape size are critical for imposing a between-habitat trade-off, causing prey to mature in a safer place or at a safer size. Thereby, we propose a novel mechanism for how predators may cause dwarf/giant speciation in lake-dwelling fish species.
Neural Field Models for Latent State Inference: Application to Large-Scale Neuronal Recordings
Authors: Rule M. E. , Schnoerr D. , Hennig M. H. , Sanguinetti G. 
Published: February 2019

AbstractLarge-scale neural recordings are becoming increasingly better at providing a window into functional neural networks in the living organism. Interpreting such rich data sets, however, poses fundamental statistical challenges. The neural field models of Wilson, Cowan and colleagues remain the mainstay of mathematical population modeling owing to their interpretable, mechanistic parameters and amenability to mathematical analysis. We developed a method based on moment closure to interpret neural field models as latent state-space point-process models, making mean field models amenable to statistical inference. We demonstrate that this approach can infer latent neural states, such as active and refractory neurons, in large populations. After validating this approach with synthetic data, we apply it to high-density recordings of spiking activity in the developing mouse retina. This confirms the essential role of a long lasting refractory state in shaping spatio-temporal properties of neonatal retinal waves. This conceptual and methodological advance opens up new theoretical connections between mathematical theory and point-process state-space models in neural data analysis.SignificanceDeveloping statistical tools to connect single-neuron activity to emergent collective dynamics is vital for building interpretable models of neural activity. Neural field models relate single-neuron activity to emergent collective dynamics in neural populations, but integrating them with data remains challenging. Recently, latent state-space models have emerged as a powerful tool for constructing phenomenological models of neural population activity. The advent of high-density multi-electrode array recordings now enables us to examine large-scale collective neural activity. We show that classical neural field approaches can yield latent statespace equations and demonstrate inference for a neural field model of excitatory spatiotemporal waves that emerge in the developing retina.
Fan cells in layer 2 of lateral entorhinal cortex are critical for episodic-like memory
Authors: Vandrey Brianna , Garden Derek L. F. , Ambrozova Veronika , McClure Christina , Nolan Matthew F. , Ainge James A. 
Published: February 2019

AbstractThe lateral entorhinal cortex (LEC) is a critical structure for episodic memory, but the roles of discrete neuronal populations within LEC are unclear. Here, we establish an approach for selectively targeting fan cells in layer 2 (L2) of LEC. Whereas complete lesions of the LEC were previously found to abolish associative recognition memory, we find that after selective suppression of synaptic output from fan cells mice still recognise novel object-context configurations, but are impaired in recognition of novel object-place-context associations. Our experiments suggest a segregation of memory functions within LEC networks and indicate that specific inactivation of fan cells leads to behavioural deficits reminiscent of early stages of Alzheimer’s disease.
Molecular Mechanisms Driving Bistable Switch Behavior in Xylem Cell Differentiation
Authors: Turco Gina M , Rodriguez-Medina Joel , Siebert Stefan , Han Diane , Vahldick Hannah , Shulse Christine N , Cole Benjamin J , Juliano Celina , Dickel Diane E , Savageau Michael A , Brady Siobhan M 
Published: February 2019

SummaryPlant xylem cells conduct water and mineral nutrients. Although most plant cells are totipotent, xylem cells are unusual and undergo terminal differentiation. Many genes regulating this process are well characterized, including theVASCULAR-RELATED NAC DOMAIN7 (VND7), MYB46andMYB83transcription factors which are proposed to act in interconnected feed-forward loops. Much less is known regarding the dynamic behavior underlying the terminal transition to xylem cell differentiation. Here we utilize whole root and single cell data to mathematically model this relationship. These provide evidence forVND7regulating bistable switching of cells in the root to a xylem cell identity, with additional features of hysteresis. We further determine that althoughMYB46responds toVND7induction, it is not inherently involved in executing the binary switch. A novel regulatory architecture is proposed that involves four downstream targets of VND7 that act in a cycle. These data provide an important model to study the emergent properties that may give rise to totipotency relative to terminal differentiation and reveal novel xylem cell subtypes.
The MarR-type regulator MalR is involved in stress-responsive cell envelope remodeling inCorynebacterium glutamicum
Authors: Hünnefeld Max , Persicke Marcus , Kalinowski Jörn , Frunzke Julia 
Published: February 2019

1AbstractIt is the enormous adaptive capacity of microorganisms, which is key to their competitive success in nature, but also challenges antibiotic treatment of human diseases. To deal with a diverse set of stresses, bacteria are able to reprogram gene expression using a wide variety of transcription factors. Here, we focused on the MarR-type regulator MalR conserved in theCorynebacterineae, including the prominent pathogensCorynebacterium diphtheriaeandMycobacterium tuberculosis. In several corynebacterial species, themalRgene forms an operon with a gene encoding a universal stress protein (uspA). Chromatin-affinity purification and sequencing (ChAP-Seq) analysis revealed that MalR binds more than 60 target promoters in theC. glutamicumgenome as well as in the large cryptic prophage CGP3. Overproduction of MalR caused severe growth defects and an elongated cell morphology. ChAP-Seq data combined with a global transcriptome analysis of themalRoverexpression strain emphasized a central role of MalR in cell envelope remodeling in response to environmental stresses. Prominent MalR targets are for example involved in peptidoglycan biosynthesis and synthesis of branched-chain fatty acids. Phenotypic microarrays suggest an altered sensitivity of a ΔmalRmutant towards several β-lactam antibiotics. We furthermore revealed MalR as a repressor of several prophage genes suggesting that MalR may be involved in the control of stress-responsive induction of the large CGP3 element. In conclusion, our results emphasize MalR as a regulator involved in stress-responsive remodeling of the cell envelope ofC. glutamicumand suggest a link between cell envelope stress and the control of phage gene expression.ImportanceBacteria live in changing environments that force the cells to be highly adaptive. The cell envelope represents both, a barrier against harsh external conditions and an interaction interface. The dynamic remodeling of the cell envelope as a response towards, e.g. antibiotic treatment represents a major challenge in the treatment of diseases. Members of the MarR family of regulators are known to contribute to an adaptation of bacterial cells towards antibiotic stress. However, our knowledge on this adaptive response was so far restricted to a small number of well-described target genes. In this study, we performed a genome-wide profiling of DNA-binding of the MarR-type regulator MalR ofC. glutamicum, which is conserved in several coryne- and mycobacterial species. By binding to more than 60 different target promoters, MalR is shaping a global reprogramming of gene expression conferring a remodeling of the cell envelope in response to stress.
Distinct roles for the Anterior Temporal Lobe and Angular Gyrus in the spatio-temporal cortical semantic network
Authors: Farahibozorg Seyedeh-Rezvan , Henson Richard N. , Woollams Anna M. , Hauk Olaf 
Published: February 2019

AbstractIt is now well recognised that human semantic knowledge is supported by a large neural network distributed over multiple brain regions, but the dynamic organisation of this network remains unknown. Some studies have proposed that a central semantic hub coordinates this network. We explored the possibility of different types of semantic hubs; namely “representational hubs”, whose neural activity is modulated by semantic variables, and “connectivity hubs”, whose connectivity to distributed areas is modulated by semantic variables. We utilised the spatio-temporal resolution of source-estimated Electro-/Magnetoencephalography data in a word-concreteness task (17 participants, 12 female) in order to: (i) find representational hubs at different timepoints based on semantic modulation of evoked brain activity in source space; (ii) identify connectivity hubs among left Anterior Temporal Lobe (ATL), Angular Gyrus (AG), Middle Temporal Gyrus and Inferior Frontal Gyrus based on their functional connectivity to the whole cortex, in particular sensory-motor-limbic systems; and (iii) explicitly compare network models with and without an intermediate hub linking sensory input to other candidate hub regions using Dynamic Causal Modelling (DCM) of evoked responses. ATL’s activity was modulated as early as 150ms post-stimulus, while both ATL and AG showed modulations of functional connectivity with sensory-motor-limbic areas from 150-450ms. DCM favoured models with one intermediate hub, namely ATL in an early time window and AG in a later time-window. Our results support ATL as a single representational hub with an early onset, but suggest that both ATL and AG function as connectivity hubs depending on the stage of semantic processing.
MDiNE: A model to estimate differential co-occurrence networks in microbiome studies
Authors: McGregor Kevin , Labbe Aurélie , Greenwood Celia MT 
Published: February 2019

AbstractMotivationThe human microbiota is the collection of microorganisms colonizing the human body, and plays an integral part in human health. A growing trend in microbiome analysis is to construct a network to estimate the co-occurrence patterns among taxa though precision matrices. Existing methods do not facilitate investigation into how these networks change with respect to covariates.ResultsWe propose a new model called Microbiome Differential Network Estimation (MDiNE) to estimate network changes with respect to a binary covariate. The counts of individual taxa in the samples are modelled through a multinomial distribution whose probabilities depend on a latent Gaussian random variable. A sparse precision matrix over all the latent terms determines the co-occurrence network among taxa.The model fit is obtained and evaluated using Hamiltonian Monte Carlo methods. The performance of our model is evaluated through an extensive simulation study, and is shown to outperform existing methods in terms of estimation of network parameters. We also demonstrate an application of the model to estimate changes in the intestinal microbial network topology with respect to Crohn’s disease.Availability and ImplementationMDiNE is implemented in a freely available R package: https://github.com/kevinmcgregor/mdine.Supplementary informationA file containing supplemental material has been submitted with this manuscript.