bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cellular resolution imaging of vestibular processing across the larval zebrafish brain
Authors: Favre-Bulle Itia A. , Vanwalleghem Gilles , Taylor Michael A. , Rubinsztein-Dunlop Halina , Scott Ethan K. 
Published: April 2018

SummaryThe vestibular system, which reports on motion and gravity, is essential to postural control, balance, and egocentric representations of movement and space. The motion needed to stimulate the vestibular system complicates studying its circuitry, so we previously developed a method for fictive vestibular stimulation in zebrafish, using optical trapping to apply physical forces to the otoliths. Here, we combine this fictive stimulation with whole-brain calcium imaging at cellular resolution, delivering a comprehensive map of the brain regions and cellular responses involved in basic vestibular processing. We find these responses to be broadly distributed across the brain, with unique profiles of cellular responses and topography in each brain region. The most widespread and abundant responses involve excitation that is rate coded to the stimulus strength. Other responses, localized to the telencephalon and habenulae, show excitation that is only weakly rate coded and that is sensitive to weak stimuli. Finally, numerous brain regions contain neurons that are inhibited by vestibular stimuli, and these inhibited neurons are often tightly localised spatially within their regions. By exerting separate control over the left and right otoliths, we explore the laterality of brain-wide vestibular processing, distinguishing between neurons with unilateral and bilateral vestibular sensitivity, and revealing patterns by which conflicting vestibular signals from the two ears can be mutually cancelling. Our results show a broader and more extensive network of vestibular responsive neurons than has previously been described in larval zebrafish, and provides a framework for more targeted studies of the underlying functional circuits.
Reproducible functional connectivity alterations are associated with autism spectrum disorder
Authors: Holiga Štefan , Hipp Joerg F. , Chatham Christopher H. , Garces Pilar , Spooren Will , D’Ardhuy Xavier Liogier , Bertolino Alessandro , Bouquet Céline , Buitelaar Jan K , Bours Carsten , Rausch Annika , Oldehinkel Marianne , Bouvard Manuel , Amestoy Anouck , Caralp Mireille , Gueguen Sonia , Moal Myriam Ly-Le , Houenou Josselin , Beckmann Christian F. , Loth Eva , Murphy Declan , Charman Tony , Tillmann Julian , Laidi Charles , Delorme Richard , Beggiato Anita , Gaman Alexandru , Scheid Isabelle , Leboyer Marion , d’Albis Marc-Antoine , Sevigny Jeff , Czech Christian , Bolognani Federico , Honey Garry D. , Dukart Juergen 
Published: April 2018

AbstractDespite the high clinical burden little is known about pathophysiology underlying autism spectrum disorder (ASD). Recent resting state functional magnetic resonance imaging (rs-fMRI) studies have found atypical synchronization of brain activity in ASD. However, no consensus has been reached on the nature and clinical relevance of these alterations. Here we address these questions in the most comprehensive, large-scale effort to date comprising evaluation of four large ASD cohorts. We followed a strict exploration and replication procedure to identify core rs-fMRI functional connectivity (degree centrality) alterations associated with ASD as compared to typically developing (TD) controls (ASD: N=841, TD: N=984). We then tested for associations of these imaging phenotypes with clinical and demographic factors such as age, sex, medication status and clinical symptom severity. We find reproducible patterns of ASD-associated functional hyper- and hypo-connectivity with hypo-connectivity being primarily restricted to sensory-motor regions and hyper-connectivity hubs being predominately located in prefrontal and parietal cortices. We establish shifts in between-network connectivity from outside to within the identified regions as a key driver of these abnormalities. The magnitude of these alterations is linked to core ASD symptoms related to communication and social interaction and is not affected by age, sex or medication status. The identified brain functional alterations provide a reproducible pathophysiological phenotype underlying the diagnosis of ASD reconciling previous divergent findings. The large effect sizes in standardized cohorts and the link to clinical symptoms emphasize the importance of the identified imaging alterations as potential treatment and stratification biomarkers for ASD.
Network analysis reveals differential metabolic functionality in antibiotic-resistantPseudomonas aeruginosa
Authors: Dunphy Laura J. , Yen Phillip , Papin Jason A. 
Published: April 2018

AbstractMetabolic adaptations accompanying the development of antibiotic resistance in bacteria remain poorly understood. To interrogate this relationship, we profiled the growth of lab-evolved antibiotic-resistant lineages of the opportunistic pathogenPseudomonas aeruginosaacross 190 unique carbon sources. We semi-automatically calculated growth dynamics (maximum growth density, growth rate, and time to mid-exponential phase) of over 2,800 growth curves. These data revealed that the evolution of antibiotic resistance resulted in systems-level changes to growth dynamics and metabolic phenotype. Drug-resistant lineages predominantly displayed decreased growth relative to the ancestral lineage; however, resistant lineages occasionally displayed enhanced growth on certain carbon sources, indicating that adaption to drug can provide a growth advantage in certain environments. A genome-scale metabolic network reconstruction (GENRE) ofP. aeruginosastrain UCBPP-PA14 was paired with whole-genome sequencing data of one of the drug-evolved lineages to predict genes contributing to observed changes in metabolism. Finally, we experimentally validatedin silicopredictions to identify genes mutated in resistantP. aeruginosaaffecting loss of catabolic function. Our results build upon previous mechanistic knowledge of drug-induced metabolic adaptation and provide a framework for the identification of metabolic limitations in antibiotic-resistant pathogens. Robust drug-driven changes in bacterial metabolism have the potential to be exploited to select against antibiotic-resistant populations in chronic infections.
A Coupled Network of Growth Transform Neurons for Spike-Encoded Auditory Feature Extraction
Authors: Gangopadhyay Ahana , Aono Kenji , Mehta Darshit , Chakrabartty Shantanu 
Published: April 2018

AbstractThis paper builds upon our previously reported growth transform based optimization framework to present a novel spiking neuron model and demonstrate its application for spike-based auditory signal processing. Unlike conventional neuromorphic approaches, the proposed Growth Transform (GT) neuron model is tightly coupled to a system objective function, which results in network dynamics that are always stable and interpretable; and the process of spike generation and population dynamics is the result of minimizing an energy functional. We then extend the model to include axonal propagation delays in a manner that the optimized solution of the system or network objective function remains unaffected. The paper characterizes the model for different types of stimuli, and explores how changing different aspects of the cost function can reproduce known single neuron dynamics. We then investigate the properties of a coupled GT neural network that can generate spike-encoded auditory features corresponding to the output of a gammatone filterbank. We show that the discriminatory information is not only encoded in the traditional spike-rates and interspike-interval statistics, but is also encoded in the subthreshold response of GT neurons for inputs that are not strong enough to elicit spikes. We also demonstrate that while different forms of coupling between the neurons could produce compact and energy-efficient representation of the auditory features, the classification performance for a speaker recognition task remains invariant across different types of coupling. Thus, we believe that the proposed GT neuron model provides a flexible neuromorphic framework to systematically design large-scale spiking neural networks with stable and interpretable dynamics.
Counting with DNA in metabarcoding studies: how should we convert sequence reads to dietary data?
Authors: Deagle Bruce E. , Thomas Austen C. , McInnes Julie C. , Clarket Laurence J. , Vesterinen Eero J. , Clare Elizabeth L. , Kartzinel Tyler R. , Eveson J. Paige 
Published: April 2018

AbstractAdvances in DNA sequencing technology have revolutionised the field of molecular analysis of trophic interactions and it is now possible to recover counts of food DNA barcode sequences from a wide range of dietary samples. But what do these counts mean? To obtain an accurate estimate of a consumer’s diet should we work strictly with datasets summarising the frequency of occurrence of different food taxa, or is it possible to use the relative number of sequences? Both approaches are applied in the dietary metabarcoding literature, but occurrence data is often promoted as a more conservative and reliable option due to taxa-specific biases in recovery of sequences. Here, we point out that diet summaries based on occurrence data overestimate the importance of food consumed in small quantities (potentially including low-level contaminants) and are sensitive to the count threshold used to define an occurrence. Our simulations indicate that even with recovery biases incorporated, using relative read abundance (RRA) information can provide a more accurate view of population-level diet in many scenarios. The ideas presented here highlight the need to consider all sources of bias and to justify the methods used to interpret count data in dietary metabarcoding studies. We encourage researchers to continue to addressing methodological challenges, and acknowledge unanswered questions to help spur future investigations in this rapidly developing area of research.
Efficient sorting of single-unit activity from midbrain cells using KiloSort is as accurate as manual sorting
Authors: Allen Madeleine , Chowdhury Tara , Wegener Meredyth , Moghaddam Bita 
Published: April 2018

AbstractExtracting single-unit activity from in vivo extracellular neural electrophysiology data requires sorting spikes from background noise and action potentials from multiple cells in order to identify the activity of individual neurons. Typically this has been achieved by algorithms that employ principal component analyses followed by manual allocation of spikes to individual clusters based on visual inspection of the waveform shape. This method of manual sorting can give varying results between human operators and is highly time-consuming, especially in recordings with higher levels of background noise. To address these problems, automatic sorting algorithms have begun to gain popularity as viable methods for sorting electrophysiological data, although little is known about the use of these algorithms with neural data from midbrain recordings. KiloSort is a relatively new algorithm that automatically clusters raw data which can then be manually curated. In this report, we compare results of manually-sorted and KiloSort-processed recordings from the ventral tegmental area (VTA) and substantia nigra pars compacta (SNc). Sorting with KiloSort required substantially less time to complete, while yielding comparable and consistent results. We conclude that the use of KiloSort to identify single units from multi-channel recording in the VTA and SNc is accurate and efficient.
Deep learning of quality control for stereotaxic registration of human brain MRI
Authors: Fonov Vladimir S. , Dadar Mahsa ,   The PREVENT-AD Research Group, Collins D. Louis 
Published: April 2018

AbstractLinear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is crucial for the success of the following image-processing steps. Several well-established algorithms are commonly used in the field of neuroimaging for this task, but none of them has a 100% success rate. Manual assessment of the registration is commonly used as part of quality control.We propose a completely automatic quality control method based on deep learning that replaces human rater and accurately performs quality control assessment for stereotaxic registration of T1w brain scans.In a recently published study from our group comparing linear registration methods, we used a database of 9693 MRI scans from several publically available datasets and applied five linear registration tools. In this study, the resulting images that were assessed and labeled by a human rater are used to train a deep neural network to detect cases when registration failed.Our method was able to achieve 88% accuracy and 11% false positive rate in detecting scans that should pass quality control, better than a manual QC rater.
Targeting glutamine metabolism and redox state for leukemia therapy
Authors: Gregory Mark A. , Nemkov Travis , Zaberezhnyy Vadym , Park Hae J. , Gehrke Sarah , Hansen Kirk C. , D’Alessandro Angelo , DeGregori James 
Published: April 2018

AbstractAcute myeloid leukemia (AML) is a hematological malignancy characterized by the accumulation of immature myeloid precursor cells. AML is poorly responsive to conventional genotoxic chemotherapy and a diagnosis of AML is usually fatal. More effective and less toxic forms of therapy are desperately needed. AML cells are known to be highly dependent on the amino acid glutamine for their survival. Here, we show that blocking glutamine metabolism through the use of a glutaminase inhibitor (CB-839) significantly impairs antioxidant glutathione production in multiple types of AML, resulting in accretion of mitochondrial reactive oxygen species (mitoROS) and apoptotic cell death. Moreover, glutaminase inhibition makes AML cells susceptible to adjuvant drugs that further perturb mitochondrial redox state, such as arsenic trioxide (ATO) and homoharringtonine (HHT). Indeed, the combination of ATO or HHT with CB-839 exacerbates mitoROS and apoptosis, and leads to more complete cell death in AML cell lines, primary AML patient samples andin vivousing mouse models of AML. In addition, these redox-targeted combination therapies are effective in eradicating acute lymphoblastic leukemia cellsin vitroandin vivo. Thus, targeting glutamine metabolism in combination with drugs that perturb mitochondrial redox state represents an effective and potentially widely applicable therapeutic strategy for treating multiple types of leukemia.Key PointsGlutaminase inhibition commonly impairs glutathione metabolism and induces mitochondrial oxidative stress in acute myeloid leukemia cellsA glutaminase inhibitor synergizes with pro-oxidant drugs in inducing apoptosis and eliminating leukemia cellsin vitroandin vivo
Comparative analysis of IgG Responses to recombinant Qβ phage displayed MSP3 and UB05 in Dual HIV-malaria infected adults living in areas differing in Malaria transmission intensities
Authors: Lissom Abel , Ouambo Herve F. , Megnekou Rosette , Okeke Malachy I. , Ngu Loveline N. , Netongo Palmer M. , Ngoh Apeh A. , Sanders Carrie A. , Bawage Swapnil , Tchouangueu Thibau F. , Tchadji Colince J. , Okoli Arinze S. , Priso Ghislain D. Njambe , Garcia Rosario , Gutiérrez Anna , Chukwuma George O. , Esimone Charles O. , Achidi Eric A. , Mbacham Wilfred F. , Kaptue Lazare , Leke Rose FG , Park Chae Gyu , Waffo Alain Bopda , Nchinda Godwin W. 
Published: April 2018

AbstractImmunoglobulin G specific responses againstPlasmodium falciparummerozoite antigens such as the merozoite surface protein 3 (MSP3) and UB05 are known to play critical roles in parasitemia control and protection from symptomatic illness. However when there is intense perennial malaria transmission coupled with concurrent infection with the human immunodeficiency virus type 1 (HIV), knowledge of IgG antibody response profiles is limited. In this study we assessed the impact of dual HIV-Malaria infections on IgG subclass responses to MSP3 (QβMSP3) and UB05 (QβUB05) in individuals living in two areas of Cameroon differing in transmission intensity. We observed differences in antigen specific IgG and IgG subclass responses which was dependent upon the antigen type, malaria transmission intensity, HIV infection, malaria infection and dual HIV-malaria infections. Individuals living in high malaria transmission areas irrespective of HIV or malaria status had significantly higher IgG responses to both antigens (P=0.0001 for QβMSP3, P=0.0001 for QβUB05) than their counterpart from low transmission areas. When dual HIV-Malaria infection is considered significantly higher QβMSP3 specific IgG1 (P=0.0001) and IgG3 (P=0.04) responses in double negative individuals was associated with protection against malaria in low transmission areas. Superior QβUBO5 specific IgG1 responses (P=0.0001) in double negative individuals were associated with protection in high transmission areas in contrast to significantly higher IgG3 responses to QβUB05 (P=0.0001) which were more relevant to protection in low malaria transmission areas in the same population. Thus, understanding immune responses to QβUB05 and QβMSP3 could facilitate the development of immunotherapeutic strategies suitable for areas differing in malaria transmission intensity.
Nisin penetratesStaphylococcus aureusbiofilms but shows differences in killing effects against sessile and planktonic cells
Authors: Godoy-Santos Fernanda , Pitts Betsey , Stewart Philip S. , Mantovani Hilário Cuquetto 
Published: April 2018

AbstractBiofilms may restrict antimicrobial penetration and contribute to the recalcitrance of bacterial infections. In this work, we investigated the penetration of nisin intoS. aureusbiofilms and compared the susceptibility ofS. aureusplanktonic and sessile cells to this lantibiotic. Biofilms were grown under continuous flow in CDC reactors and calcein fluorescence was used to monitor the effect of nisin on the cytoplasmic membrane ofS. aureuscells. Confocal scanning laser microscopy (CLSM) showed that calcein was lost within approximately 20 min in CDC biofilms, demonstrating that nisin penetrated to the bottom of the biofilm and caused membrane permeabilization. Viability analysis using PI staining showed that nisin was bactericidal againstS. aureussessile cells. Time-kill assays were performed againstS. aureusin the following conditions: homogenized exponential planktonic (HEP), homogenized stationary planktonic (HSP), homogenized CDC biofilm (HB) and intact CDC biofilm (IB). The mean viability reduction of HEP and HSP were 6.71 and 1.64 log CFU.ml-1, respectively, confirming that stationaryS. aureuscells were much less susceptible than exponential cells. The HB and IB treatments showed mean viability reductions of 1.25 and 0.50 log CFU.ml-1, respectively. Nisin activity againstS. aureuswas not limited by its ability to penetrate the bacterial biofilm, but the killing efficacy of the antimicrobial peptide was reduced by the physiological status of the biofilm-grown cells.ImportanceBiofilms represent a major problem to control microorganisms in industrial environments and medical devices. We developed a direct real-time microscopic visualization technique to demonstrate experimentally that the antimicrobial peptide nisin is able to penetrateS. aureusbiofilms. Our results confirmed that nisin caused membrane permeabilization of sessile bacteria and revealed qualitative agreement between viability loss and membrane integrity loss. This approach could improve the evaluation of antibacterial susceptibility breakpoints when testing the efficacy of standard and novel antimicrobials.
Stimulus Vignetting and Orientation Selectivity in Human Visual Cortex
Authors: Roth Zvi N. , Heeger David J. , Merriam Elisha P. 
Published: April 2018

AbstractNeural selectivity to orientation is one of the simplest and most thoroughly-studied cortical sensory features. Here, we show that a large body of research that purported to measure orientation tuning may have in fact been inadvertently measuring sensitivity to second-order changes in luminance, a phenomenon we term ‘vignetting’. Using a computational model of neural responses in primary visual cortex (V1), we demonstrate the impact of vignetting on simulated V1 responses. We then used the model to generate a set of predictions, which we confirmed with functional MRI experiments in human observers. Our results demonstrate that stimulus vignetting can wholly determine the orientation selectivity of responses in visual cortex measured at a macroscopic scale, and suggest a reinterpretation of a well-established literature on orientation processing in visual cortex.
DCARS: Differential correlation across ranked samples
Authors: Ghazanfar Shila , Strbenac Dario , Ormerod John T. , Yang Jean Y. H. , Patrick Ellis 
Published: April 2018

ABSTRACTGenes act as a system and not in isolation. Thus, it is important to consider coordinated changes of gene expression rather than single genes when investigating biological phenomena such as the aetiology of cancer. We have developed an approach for quantifying how changes in the association between pairs of genes may inform patient prognosis called Differential Correlation across Ranked Samples (DCARS). Modelling gene correlation across a continuous sample ranking does not require the classification of patients into ‘good’ or ‘poor’ prognosis groups and can identify differences in gene correlation across early, mid or late stages of survival outcome. When we evaluated DCARS against the typical Fisher Z-transformation test for differential correlation, as well as a typical approach testing for interaction within a linear model, on real TCGA data, DCARS significantly ranked gene pairs containing known cancer genes more highly across a number of cancers. Similar results are found with our simulation study. DCARS was applied to 13 cancers datasets in TCGA, revealing a number of distinct relationships for which survival ranking was found to be associated with a change in correlation between genes. Furthermore, we demonstrated that DCARS can be used in conjunction with network analysis techniques to extract biological meaning from multilayered and complex data.Availability: https://github.com/shazanfar/DCARS.