bluuebunny/crossref_metadata_2025_split huggingface dataset:
Animal models for auditory streaming
Authors: Itatani Naoya , Klump Georg M. 
Published: January 2017

Sounds in the natural environment need to be assigned to acoustic sources to evaluate complex auditory scenes. Separating sources will affect the analysis of auditory features of sounds. As the benefits of assigning sounds to specific sources accrue to all species communicating acoustically, the ability for auditory scene analysis is widespread among different animals. Animal studies allow for a deeper insight into the neuronal mechanisms underlying auditory scene analysis. Here, we will review the paradigms applied in the study of auditory scene analysis and streaming of sequential sounds in animal models. We will compare the psychophysical results from the animal studies to the evidence obtained in human psychophysics of auditory streaming, i.e. in a task commonly used for measuring the capability for auditory scene analysis. Furthermore, the neuronal correlates of auditory streaming will be reviewed in different animal models and the observations of the neurons’ response measures will be related to perception. The across-species comparison will reveal whether similar demands in the analysis of acoustic scenes have resulted in similar perceptual and neuronal processing mechanisms in the wide range of species being capable of auditory scene analysis.
This article is part of the themed issue ‘Auditory and visual scene analysis’.
Surveillance to Establish Elimination of Transmission and Freedom from Dog-mediated Rabies
Authors: Hampson Katie , Abela-Ridder Bernadette , Brunker Kirstyn , Bucheli S. Tamara M. , Carvalho Mary , Caldas Eduardo , Changalucha Joel , Cleaveland Sarah , Dushoff Jonathan , Gutierrez Veronica , Fooks Anthony R , Hotopp Karen , Haydon Daniel T , Lugelo Ahmed , Lushasi Kennedy , Mancy Rebecca , Marston Denise A , Mtema Zac , Rajeev Malavika , Dourado Lúcia R. Montebello P , Gonzalez Roldan J. F. , Rysava Kristyna , Rocha Silene Manrique , Sambo Maganga , Sikana Lwitiko , Vigilato Marco , Del Rio Vilas Victor 
Published: January 2017

AbstractBackgroundWith a global target set for zero human deaths from dog-mediated rabies by 2030 and some regional programmes close to eliminating canine rabies, there is an urgent need for enhanced surveillance strategies suitable for declaring freedom from disease and elimination of transmission with known confidence.MethodsUsing exhaustive contact tracing across settings in Tanzania we generated detailed data on rabies incidence, rabid dog biting behaviour and health-seeking behaviour of bite victims. Using these data we compared case detection of sampling-based and enhanced surveillance methodologies and investigated elimination verification procedures.FindingsWe demonstrate that patients presenting to clinics with bite injuries are sensitive sentinels for identifying dog rabies cases. Triage of patients based on bite history criteria and investigation of suspicious incidents can confirm >10% of dog rabies cases and is an affordable approach that will enable validation of disease freedom following two years without case detection. Approaches based on sampling the dog population without using bite-injury follow-up were found to be neither sensitive nor cost-effective.InterpretationThe low prevalence of rabies, and short window in which disease can be detected, preclude sampling-based surveillance. Instead, active case finding guided by bite-patient triage is needed as elimination is approached. Our proposed methodology is affordable, practical and supports the goal of eliminating human rabies deaths by improving administration of lifesaving post-exposure prophylaxis for genuinely exposed but untreated contacts. Moreover, joint investigations by public health and veterinary workers will strengthen intersectoral partnerships and capacity for control of emerging zoonoses.
α7 nicotinic acetylcholine receptor signaling modulates the inflammatory and iron homeostasis in fetal brain microglia
Authors: Cortes M. , Cao M. , Liu H.L. , Moore C.S. , Durosier L.D. , Burns P. , Fecteau G. , Desrochers A. , Barreiro L.B. , Antel J.P. , Frasch M.G. 
Published: January 2017

ABSTRACTNeuroinflammationin uteromay result in life-long neurological disabilities. Microglia play a pivotal role, but the mechanisms are poorly understood. No early postnatal treatment strategies exist to enhance neuroprotective potential of microglia. We hypothesized that agonism on α7 nicotinic acetylcholine receptor (α7nAChR) in fetal microglia will augment their neuroprotective transcriptome profile, while the antagonistic stimulation of α7nAChR will achieve the opposite. Using anin vivo-in vitromodel of developmental programming of neuroinflammation induced by lipopolysaccharide (LPS), we validated this hypothesis in primary fetal sheep microglia cultures re-exposed to LPS in presence of a selective α7nAChR agonist or antagonist. Our RNAseq and protein level findings show that a pro-inflammatory microglial phenotype acquiredin vitroby LPS stimulation is reversed with α7nAChR agonistic stimulation. Conversely, antagonistic α7nAChR stimulation potentiates the pro-inflammatory microglial phenotype. Surprisingly, under conditions of LPS double-hit an interference of a postulated α7nAChR - ferroportin signaling pathway may impede this mechanism. These results suggest a therapeutic potential of α7nAChR agonists in early re-programming of microglia in neonates exposed toin uteroinflammation via an endogenous cerebral cholinergic anti-inflammatory pathway. Future studies will assess the role of interactions between inflammation-triggered microglial iron sequestering and α7nAChR signaling in neurodevelopment.
Modeling HIV disease progression and transmission at population-level: The potential impact of modifying disease progression in HIV treatment programs
Authors: Ross Jennifer M. , Ying Roger , Celum Connie L. , Baeten Jared M. , Thomas Katherine K. , Murnane Pamela M. , van Rooyen Heidi , Hughes James P , Barnabas Ruanne 
Published: January 2017

AbstractIntroductionMathematical models of HIV transmission that incorporate the dynamics of disease progression can estimate the potential impact of adjunctive strategies to antiretroviral therapy (ART) for HIV treatment and prevention. Suppressive treatment of HIV-positive persons co-infected with herpes simplex virus-2 (HSV-2) with valacyclovir, a medication directed against HSV-2, can lower HIV viral load, but the impact of valacyclovir on population HIV transmission has not been estimated.MethodsWe applied data on CD4 and viral load progression in ART-naïve persons studied in two HIV clinical trials to a novel, discrete-time Markov model. We validated our disease progression estimates using data from a trial of home-based HIV counseling and testing in KwaZulu-Natal, South Africa. Finally, we applied our disease progression estimates to a dynamic transmission model estimating the impact of providing valacyclovir to ART-naïve individuals to reduce onward transmission of HIV in three scenarios of different ART and valacyclovir population coverage. We assumed that valacyclovir reduced HIV viral load by 1.23 log copies/μL, and that persons treated with valacyclovir initiated ART more rapidly when their CD4 fell below 500 due to improved retention in pre-ART care.ResultsThe average duration of HIV infection following acute infection was 9.5 years. The duration of disease after acute infection and before reaching CD4 200 cells/μL was 2.53 years longer for females than males. Relative to a baseline of community HIV testing and counseling and ART initiation at CD4 <=500 cells/μL, valacyclovir with increased linkage to care resulted in 166,000 fewer HIV infections over ten years, with an incremental cost-effectiveness ratio (ICER) of $4,696 per HIV infection averted. The Test and Treat scenario with 70% ART coverage and no valacyclovir resulted in 202,000 fewer HIV infections at an ICER of $6,579.ConclusionEven when compared with initiation of valacyclovir, a safe drug that reduces HIV viral load, universal treatment for HIV is the optimal strategy for averting new infections and increasing public health benefit. Universal HIV treatment should be pursued by all countries to most effectively and efficiently reduce the HIV burden.
Joint genetic analysis using variant sets reveals polygenic gene-context interactions
Authors: Casale Francesco Paolo , Horta Danilo , Rakitsch Barbara , Stegle Oliver 
Published: January 2017

AbstractJoint genetic models for multiple traits have helped to enhance association analyses. Most existing multi-trait models have been designed to increase power for detecting associations, whereas the analysis of interactions has received considerably less attention. Here, we propose iSet, a method based on linear mixed models to test for interactions between sets of variants and environmental states or other contexts. Our model generalizes previous interaction tests and in particular provides a test for local differences in the genetic architecture between contexts. We first use simulations to validate iSet before applying the model to the analysis of genotype-environment interactions in an eQTL study. Our model retrieves a larger number of interactions than alternative methods and reveals that up to 20% of cases show context-specific configurations of causal variants. Finally, we apply iSet to test for sub-group specific genetic effects in human lipid levels in a large human cohort, where we identify a gene-sex interaction for C-reactive protein that is missed by alternative methods.Author summaryGenetic effects on phenotypes can depend on external contexts, including environment. Statistical tests for identifying such interactions are important to understand how individual genetic variants may act in different contexts. Interaction effects can either be studied using measurements of a given phenotype in different contexts, under the same genetic backgrounds, or by stratifying a population into subgroups. Here, we derive a method based on linear mixed models that can be applied to both of these designs. iSet enables testing for interactions between context and sets of variants, and accounts for polygenic effects. We validate our model using simulations, before applying it to the genetic analysis of gene expression studies and genome-wide association studies of human blood lipid levels. We find that modeling interactions with variant sets offers increased power, thereby uncovering interactions that cannot be detected by alternative methods.
ArabidopsisHAP2/GCS1 is a gamete fusion protein homologous to somatic and viral fusogens
Authors: Valansi Clari , Moi David , Leikina Evgenia , Matveev Elena , Graña Martín , Chernomordik Leonid V , Romero Héctor , Aguilar Pablo S. , Podbilewicz Benjamin 
Published: January 2017

AbstractCell-cell fusion is inherent to any form of sexual reproduction. Loss of HAPLESS 2/GENERATIVE CELL SPECIFIC 1 (HAP2/GCS1) proteins results in gamete fusion failure in different organisms but their exact role is unclear. Here we show thatArabidopsisHAP2/GCS1 expression in mammalian cells is sufficient to promote cell-cell fusion. Hemifusion and complete fusion depend on HAP2/GCS1 presence in both fusing cells. Furthermore, expression of HAP2 on the surface of pseudotyped vesicular stomatitis virus and on the target cells results in HAP2-dependent virus-cell fusion. This bilateral requirement can be bypassed by replacing the plant gene withC. elegansEFF-1 somatic cell fusogen in one of the fusing cells or the virus, indicating that HAP2/GCS1 and EFF-1 share a similar fusion mechanism. Structural modeling of the HAP2/GCS1 protein family predicts that they are homologous to EFF-1 and class II fusion proteins from enveloped viruses (e.g. dengue and Zika viruses). We name this superfamily FUSEXINS: FUSion proteins essential for sexual reproduction and EXoplasmic merger of plasma membranes. Thus, Fusexins unify the origin and evolution of sexual reproduction, enveloped virus entry into cells and somatic cell fusion.
Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis
Authors: Baglietto Gabriel , Gigante Guido , Del Giudice Paolo 
Published: January 2017

AbstractSimultaneous recordings from N electrodes generate N-dimensional time series that call for efficient representations to expose relevant aspects of the underlying dynamics.Binning the time series defines a sequence of neural activity vectors that populate the N-dimensional space as a density distribution, especially informative when the neural dynamics proceeds as a noisy path through metastable states (often a case of interest in neuroscience); this makes clustering in the N-dimensional space a natural choice.We apply a variant of the ‘mean-shift’ algorithm to perform such clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memory attractors.The neural states identified as clusters’ centroids are then used to define a parsimonious parametrization of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities.We next consider the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects; we develop a procedure, inspired by Boltzmann learning but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations.After clustering the activity generated by such multi-modular spiking networks, we cast their multi-dimensional dynamics in the form of the symbolic sequence of the clusters’ centroids; this representation naturally lends itself to complexity estimates that provide compact information on memory effects like those induced by spike-frequency adaptation. Specifically, to obtain a relative complexity measure we compare the Lempel-Ziv complexity of the actual centroid sequence to the one of Markov processes sharing the same transition probabilities between centroids; as an illustration, we show that the dependence of such relative complexity on the characteristic time scale of spike-frequency adaptation.
In Vitro Reconstitution of T Cell Receptor–Mediated Segregation of the CD45 Phosphatase
Authors: B. Carbone Catherine , kerm Nadja , A. Fernandes Ricardo , Hui Enfu , Su Xiaolei , Garcia K. Christopher , Vale Ronald D. 
Published: January 2017

ABSTRACTT cell signaling initiates upon binding of peptide-major histocompatibility complex (pMHC) on an antigen-presenting cell (APC) to the T cell receptor (TCR) on a T cell. TCR phosphorylation in response to pMHC binding is accompanied by segregation of the transmembrane phosphatase CD45 away from TCR-pMHC complexes. The kinetic segregation hypothesis proposes that CD45 exclusion shifts the local kinase-phosphatase balance to favor TCR phosphorylation. Spatial partitioning may arise from the size difference between the large CD45 extracellular domain and the smaller TCR-pMHC complex, although parsing potential contributions of extracellular protein size, actin activity, and lipid domains is difficult in living cells. Here, we reconstitute segregation of CD45 from bound receptor-ligand pairs using purified proteins on model membranes. Using a model receptor-ligand pair (FRB-FKBP), we first test physical and computational predictions for protein organization at membrane interfaces. We then show that the TCR-pMHC interaction causes partial exclusion of CD45. Comparing two developmentally-regulated isoforms of CD45, the larger RABCvariant is excluded more rapidly and efficiently (~50%) than the smaller R0isoform (~20%), suggesting that CD45 isotypes could regulate signaling thresholds in different T cell subtypes. Similar to the sensitivity of T cell signaling, TCR-pMHC interactions with Kds of ≤15 μM were needed to exclude CD45. We further show that the co-receptor PD-1 with its ligand PD-L1, immunotherapy targets that inhibit T cell signaling, also exclude CD45. These results demonstrate that the binding energies of physiological receptor-ligand pairs on the T cell are sufficient to create spatial organization at membrane-membrane interfaces.SIGNIFICANCE STATEMENTThe interface between a T cell and an antigen-presenting cell (APC) results in the formation of biochemically distinct plasma membrane domains that initiate signaling cascades. Here, using biochemical reconstitution and microscopy, we show that the binding energies of the TCRpMHC and PD-1-PD-L1 complexes are sufficient to create spatial organization at a model membrane-membrane interface. We show that spatial organization depends upon receptor-ligand binding affinity and the relative sizes of the extracellular domains. These biophysical parameters may be used to fine-tune signaling cascades in T cells.
Evolution of genetic variance during adaptive radiation
Authors: Walter Greg M. , Aguirre J. David , Blows Mark W. , Ortiz-Barrientos Daniel 
Published: January 2017

AbstractGenetic correlations between traits can bias adaptation away from optimal phenotypes and constrain the rate of evolution. If genetic correlations between traits limit adaptation to contrasting environments, rapid adaptive divergence across a heterogeneous landscape may be difficult. However, if genetic variance can evolve and align with the direction of natural selection, then abundant allelic variation can promote rapid divergence during adaptive radiation. Here, we explored adaptive divergence among ecotypes of an Australian native wildflower by quantifying divergence in multivariate phenotypes of populations that occupy four contrasting environments. We investigated differences in multivariate genetic variance underlying morphological traits and examined the alignment between divergence in phenotype and divergence in genetic variance. We found that divergence in mean multivariate phenotype has occurred along two major axes represented by different combinations of plant architecture and leaf traits. Ecotypes also showed divergence in the level of genetic variance in individual traits, and the multivariate distribution of genetic variance among traits. Divergence in multivariate phenotypic mean aligned with divergence in genetic variance, with most of the divergence in phenotype among ecotypes associated with a change in trait combinations that had substantial levels of genetic variance in each ecotype. Overall, our results suggest that divergent natural selection acting on high levels of standing genetic variation might fuel ecotypic differentiation during the early stages of adaptive radiation.
Wiring variations that enable and constrain neural computation in a sensory microcircuit
Authors: Tobin William F. , Wilson Rachel I. , Lee Wei-Chung Allen 
Published: January 2017

ABSTRACTNeural network function can be shaped by varying the strength of synaptic connections. One way to achieve this is to vary connection structure. To investigate how structural variation among synaptic connections might affect neural computation, we examined primary afferent connections in theDrosophilaolfactory system. We used large-scale serial section electron microscopy to reconstruct all the olfactory receptor neuron (ORN) axons that target a left-right pair of glomeruli, as well as all the projection neurons (PNs) postsynaptic to these ORNs. We found three variations in ORN→PN connectivity. First, we found a systematic co-variation in synapse number and PN dendrite size, suggesting total synaptic conductance is tuned to postsynaptic excitability. Second, we discovered that PNs receive more synapses from ipsilateral than contralateral ORNs, providing a structural basis for odor lateralization behavior. Finally, we found evidence of imprecision in ORN→PN connections and show how this can diminish network performance.
Chemical composition and the potential for proteomic transformation in cancer, hypoxia, and hyperosmotic stress
Authors: Dick Jeffrey M. 
Published: January 2017

ABSTRACTModels of chemical composition and thermodynamic potential emphasize the microenvironmental context for proteomic transformations. Here, data from 71 comparative proteomics studies were analyzed using elemental ratios and chemical components as measures of oxidation and hydration state. Experimental lowering of oxygen availability (hypoxia) and water activity (hyperosmotic stress) are reflected in decreased oxidation and hydration states of proteomes, but up-expressed proteins in colorectal and pancreatic cancer most often have higher average oxidation or hydration state. Calculations of chemical affinity were used to quantify the thermodynamic potentials for transformation of proteomes as a function of fugacity of O2and activity of H2O, which serve as scales of oxidation and hydration potential. Potential diagrams for the overall reactions between down- and up-expressed proteins in various datasets have predicted equipotential lines that cluster near unit activity of H2O, suggesting that protein expression is sensitive to local variations in water activity. A redox balance calculation indicates that an increase in the lipid to protein ratio in cancer cells by 20% over hypoxic cells would generate an electron sink of sufficient size for oxidation of the cancer proteomes. These findings demonstrate consistent, condition-dependent chemical differences between proteomes, identify primary microenvironmental constraints on proteomic transformations, and help to quantify the redox balance among cellular macromolecules. The datasets and computer code used here are supplied in a new R package,canprot.
Multi-Region Neural Representation: A novel model for decoding visual stimuli in human brains
Authors: Yousefnezhad Muhammad , Zhang Daoqiang 
Published: January 2017

AbstractMultivariate Pattern (MVP) classification holds enormous potential for decoding visual stimuli in the human brain by employing task-based fMRI data sets. There is a wide range of challenges in the MVP techniques, i.e. decreasing noise and sparsity, defining effective regions of interest (ROIs), visualizing results, and the cost of brain studies. In overcoming these challenges, this paper proposes a novel model of neural representation, which can automatically detect the active regions for each visual stimulus and then utilize these anatomical regions for visualizing and analyzing the functional activities. Therefore, this model provides an opportunity for neuroscientists to ask this question: what is the effect of a stimulus on each of the detected regions instead of just study the fluctuation of voxels in the manually selected ROIs. Moreover, our method introduces analyzing snapshots of brain image for decreasing sparsity rather than using the whole of fMRI time series. Further, a new Gaussian smoothing method is proposed for removing noise of voxels in the level of ROIs. The proposed method enables us to combine different fMRI data sets for reducing the cost of brain studies. Experimental studies on 4 visual categories (words, consonants, objects and nonsense photos) confirm that the proposed method achieves superior performance to state-of-the-art methods.