bluuebunny/crossref_metadata_2025_split huggingface dataset:
Olfactory Projection Neurons From the Moth Antennal Lobe Lateral Cluster exhibit Diverse Morphological and Neurophysiological Characteristics
Authors: Lee Seong-Gyu , Celestino Christine Fogarty , Stagg Jeffrey , Kleineidam Christoph , Vickers Neil J. 
Published: March 2018

AbstractOlfactory projection neurons convey information from the insect antennal lobe (AL) to higher centers in the brain. Many studies on moths have reported excitatory projection neurons with cell bodies in the medial cell cluster (mcPNs) that predominantly send an axon from the AL to calyces of the mushroom body (CA) via the medial antennal lobe tract (mALT) and then to the lateral horn (LH) of the protocerebrum. These neurons tend to have dendritic arbors restricted to a single glomerulus (i.e. they are uniglomerular). In this study, we report on the physiological and morphological properties of a group of pheromone-responsive olfactory projection neurons with cell bodies in the moth AL lateral cell cluster (lcPNs) of two heliothine moth species. While mcPNs typically exhibit a narrow odor tuning range related to the restriction of their dendritic arbors within a single glomerulus, lcPNs exhibited an array of morphological and physiological configurations. Pheromone-responsive lcPNs varied in their associations with glomeruli (uniglomerular and multiglomerular), dendritic arborization structure and connections to higher brain centers with projections primarily through the lateral antennal lobe tract and to a lesser extent the mediolateral antennal lobe tract to a variety of protocerebral targets including ventrolateral and superior neuropils as well as LH. Physiological characterization of lcPNs also revealed a diversity of response profiles including those either enhanced by or reliant upon presentation of a pheromone blend. These responses manifested themselves as higher maximum firing rates and/or improved temporal resolution of pulsatile stimuli. lcPNs therefore participate in conveying a variety of olfactory information relating to qualitative and temporal facets of the pheromone stimulus to a more expansive number of protocerebral targets than their mcPN counterparts. The role of lcPNs in the overall scheme of olfactory processing is discussed.
A Cross-Platform Comparison of Student’s Perceptions of the Learning Environment in an Introductory Microbiology Course
Authors: Ramirez Melissa V. , Lee Virginia S. , Hamm Lindsay , Taveirne Michael E. , Lee Alice M. 
Published: March 2018

AbstractStudents’ positive perceptions of the learning environment increase retention and persistence in STEM disciplines. This study presents results from a Learning Environment Questionnaire administered at the beginning and end of the semester in a redesigned general/introductory microbiology course offered in traditional lecture, flipped and online sections. Split-sample t-tests and chi-squared tests were used for cross-section and within section analyses, respectively. The findings support the study hypothesis that student perceptions of the learning environment will vary as a function of platform. This work demonstrates the additional effect of the time in the semester when students complete the questionnaire and this effect on students’ perceptions over the course of the semester relative to their initial perceptions. The results of this study offer insight on student perceptions of the learning environment as universities embrace online education in introductory and gateway courses as a response to rising student enrollments and diminishing resources.
The Induction of Larval Resource Preference in Heterogeneous Habitats
Authors: Kumar Vrinda Ravi , Issar Swastika , Agashe Deepa 
Published: March 2018

ABSTRACTAnimals often have to evaluate and choose between multiple food sources in their habitat, and these potentially complex decisions can have a large impact on their fitness. Among other factors, previous experience with an alternative resource can significantly increase subsequent preference for the resource ("induction of preference"). Such induction of resource preference is particularly relevant in spatially or temporally heterogeneous habitats. Although most mobile species – especially generalists – probably frequently encounter habitat heterogeneity, the impact of preference induction on individual behaviour and fitness in heterogeneous habitats is poorly understood. We analysed larval preference induction in wheat-adapted generalist red flour beetles (Tribolium castaneum) under three types of habitat heterogeneity. We first analysed the induction of larval preference for novel resources (other cereal flours) under temporal heterogeneity, exposing larvae to new resources during development. We found that larvae preferred a new resource if they experienced it recently, but that the magnitude of induction varied across resources. Interestingly, we also observed specific induction for a homogenous mix of wheat and a novel resource, with larvae preferring the mix over either pure resource. To analyse induction under spatial heterogeneity, we placed beetle eggs in one of two alternative resource patches and tested the preference of emerged larvae. Unexpectedly, hatching into a novel resource did not always induce preference. Finally, we found that induction of preference for new resources could be maladaptive for larval development. Together, our work demonstrates that experience-based plasticity of larval resource choice may strongly impact larval preference and fitness in heterogeneous habitats.
Muramyl endopeptidase Spr contributes to intrinsic vancomycin resistance inSalmonella entericaserovar Typhimurium
Authors: Vestö Kim , Huseby Douglas L , Iina Snygg , Wang Helen , Hughes Diarmaid , Rhen Mikael 
Published: March 2018

ABSTRACTThe impermeability barrier provided by the outer membrane of enteric bacteria, a feature lacking in Gram-positive bacteria, plays a major role in maintaining resistance to numerous antimicrobial compounds and antibiotics. Here we demonstrate that mutational inactivation ofspr, coding for a muramyl endopeptidase, significantly sensitizesSalmonella entericaserovar Typhimurium to vancomycin without any accompanying apparent growth defect or outer membrane destabilization. A similar phenotype was not achieved by deleting themepA, pbpG, nlpC, yebAoryhdOgenes coding for functional homologues to Spr. Thesprmutant revealed increased autolytic behavior in response to not only vancomycin, but also to penicillin G, an antibiotic for which the mutant displayed a wild-type MIC. A screen for suppressor mutations of thesprmutant phenotype revealed that deletion oftsp(prc), encoding a periplasmic carboxypeptidase involved in processing Spr and PBP3, restored intrinsic resistance to vancomycin and reversed the autolytic phenotype of ansprmutant. Our data suggest that Spr contributes to intrinsic antibiotic resistance in S. Typhimurium without directly affecting the outer membrane permeability barrier. Furthermore, our data suggests that compounds that target specific cell wall endopeptidases might have the potential to expand the activity spectrum of traditional Gram-positive antibiotics.
Metabolic and signalling network map integration: application to cross-talk studies and omics data analysis in cancer
Authors: Sompairac Nicolas , Modamio Jennifer , Barillot Emmanuel , Fleming Ronan M. T. , Zinovyev Andrei , Kuperstein Inna 
Published: March 2018

AbstractBackgroundThe interplay between metabolic processes and signalling pathways remains poorly understood. Global, detailed and comprehensive reconstructions of human metabolism and signalling pathways exist in the form of molecular maps, but they have never been integrated together. We aim at filling in this gap by creating an integrated resource of both signalling and metabolic pathways allowing a visual exploration of multi-level omics data and study of cross-regulatory circuits between these processes in health and in disease.ResultsWe combined two comprehensive manually curated network maps. Atlas of Cancer Signalling Network (ACSN), containing mechanisms frequently implicated in cancer; and ReconMap 2.0, a comprehensive reconstruction of human metabolic network. We linked ACSN and ReconMap 2.0 maps via common players and represented the two maps as interconnected layers using the NaviCell platform for maps exploration. In addition, proteins catalysing metabolic reactions in ReconMap 2.0 were not previously visually represented on the map canvas. This precluded visualisation of omics data in the context of ReconMap 2.0. We suggested a solution for displaying protein nodes on the ReconMap 2.0 map in the vicinity of the corresponding reaction or process nodes. This permits multi-omics data visualisation in the context of both map layers. Exploration and shuttling between the two map layers is possible using Google Maps-like features of NaviCell. The integrated ACSN-ReconMap 2.0 resource is accessible online and allows data visualisation through various modes such as markers, heat maps, bar-plots, glyphs and map staining. The integrated resource was applied for comparison of immunoreactive and proliferative ovarian cancer subtypes using transcriptomic, copy number and mutation multi-omics data. A certain number of metabolic and signalling processes specifically deregulated in each of the ovarian cancer sub-types were identified.ConclusionsAs knowledge evolves and new omics data becomes more heterogeneous, gathering together existing domains of biology under common platforms is essential. We believe that an integrated ACSN-ReconMap 2.0 resource will help in understanding various disease mechanisms and discovery of new interactions at the intersection of cell signalling and metabolism. In addition, the successful integration of metabolic and signalling networks allows broader systems biology approach application for data interpretation and retrieval of intervention points to tackle simultaneously the key players coordinating signalling and metabolism in human diseases.
LIPEA: Lipid Pathway Enrichment Analysis
Authors: Acevedo Aldo , Durán Claudio , Ciucci Sara , Gerl Mathias , Cannistraci Carlo Vittorio 
Published: March 2018

AbstractMotivationAnalyzing associations among multiple omic variables to infer mechanisms that meaningfully link them is a crucial step in systems biology. Gene Set Enrichment Analysis (GSEA) was conceived to pursue this aim in computational genomics, unveiling significant pathways associated to certain gene signatures under investigation. Lipidomics is a rapidly growing omic field, and absolute quantification of lipid abundance by shotgun mass spectrometry is generating high-throughput datasets that depict lipid metabolism in a plethora of conditions and organisms. In addition, high-throughput lipidomics represents a new important ally to develop personalized medicine approaches, investigate the causes and predict effective biomarkers in metabolic diseases, and not only.ResultsHere, we present Lipid Pathway Enrichment Analysis (LIPEA), a web-tool for over-representation analysis of lipid signatures and detection of the biological pathways in which they are enriched. LIPEA is a new valid resource for biologists and physicians to mine pathways significantly associated to a set of lipids, helping them to discover whether common and collective mechanisms are hidden behind those lipids. LIPEA was extensively tested and we provide two examples where our system gave successfully results related with Major Depression Disease (MDD) and insulin re-sistance.AvailabilityThe tool is available as web platform at https://lipea.biotec.tu-dresden.de.
Predicting improved protein conformations with a temporal deep recurrent neural network
Authors: Pfeiffenberger Erik , Bates Paul A. 
Published: March 2018

AbstractAccurate protein structure prediction from amino acid sequence is still an unsolved problem. The most reliable methods centre on template based modelling. However, the accuracy of these models entirely depends on the availability of experimentally resolved homologous template structures. In order to generate more accurate models, extensive physics based molecular dynamics (MD) refinement simulations are performed to sample many different conformations to find improved conformational states. In this study, we propose a deep recurrent network model, called DeepTrajectory, that is able to identify these improved conformational states, with high precision, from a variety of different MD based sampling protocols. The proposed model learns the temporal patterns of features computed from the MD trajectory data in order to classify whether each recorded simulation snapshot is an improved conformational state, decreased conformational state or a none perceivable change in state with respect to the starting conformation. The model is trained and tested on 904 trajectories from 42 different protein systems with a cumulative number of more than 1.7 million snapshots. We show that our model outperforms other state of the art machine-learning algorithms that do not consider temporal dependencies. To our knowledge, DeepTrajectory is the first implementation of a time-dependent deep-learning protocol that is re-trainable and able to adapt to any new MD based sampling procedure, thereby demonstrating how a neural network can be used to learn the latter part of the protein folding funnel.
reactIDR: Evaluation of the statistical reproducibility of high-throughput structural analyses for a robust RNA reactivity classification
Authors: Kawaguchi Risa , Kiryu Hisanori , Iwakiri Junichi , Sese Jun 
Published: March 2018

AbstractMotivationRecently, next-generation sequencing techniques have been applied for the detection of RNA secondary structures called high-throughput RNA structural (HTS) analy- sis, and dozens of different protocols were used to detect comprehensive RNA structures at single-nucleotide resolution. However, the existing computational analyses heavily depend on experimental data generation methodology, which results in many difficulties associated with statistically sound comparisons or combining the results obtained using different HTS methods.ResultsHere, we introduced a statistical framework, reactIDR, which is applicable to the experimental data obtained using multiple HTS methodologies, and it classifies the nucleotides into three structural categories, stem, loop, and unmapped. reactIDR uses the irreproducible discovery rate (IDR) with a hidden Markov model (HMM) to discriminate accurately between the true and spurious signals obtained in the replicated HTS experiments. In reactIDR, IDR and HMM parameters are efficiently optimized by using an expectation-maximization algorithm. Furthermore, if known reference structures are given, a supervised learning can be applicable in a semi-supervised manner. The results of our analyses for real HTS data showed that reactIDR achieved the highest accuracy in the classification problem of stem/loop structures of rRNA using both individual and integrated HTS datasets as well as the best correspondence with the three-dimensional structure. Because reactIDR is the first method to compare HTS datasets obtained from multiple sources in a single unified model, it has a great potential to increase the accuracy of RNA secondary structure prediction at transcriptome-wide level with further experiments performed.AvailabilityreactIDR is implemented in Python. Source code is publicly available at https://github.com/carushi/reactIDRhttps://github.com/carushi/reactIDR.Contactkawaguchi-rs@aist.go.jpSupplementary informationSupplementary data are available at online.
Protease target prediction via matrix factorization
Authors: Marini Simone , Vitali Francesca , Rampazzi Sara , Demartini Andrea , Akutsu Tatsuya 
Published: March 2018

AbstractMotivationProtein cleavage is an important cellular event, involved in a myriad of processes, from apoptosis to immune response. Bioinformatics provides in silico tools, such as machine learning-based models, to guide target discovery. State-of-the-art models have a scope limited to specific protease families (such as Caspases), and do not explicitly include biological or medical knowledge (such as the hierarchical protein domain similarity, or gene-gene interactions). To fill this gap, we present a novel approach for protease target prediction based on data integration.ResultsBy representing protease-protein target information in the form of relational matrices, we design a model that: (a) is general, i.e., not limited to a single protease family; and (b) leverages on the available knowledge, managing extremely sparse data from heterogeneous data sources, including primary sequence, pathways, domains, and interactions from nine databases. When compared to other algorithms on test data, our approach provides a better performance even for models specifically focusing on a single protease family.Availabilityhttps://gitlab.com/smarini/MaDDA/ (Matlab code and utilized data.)Contactsmarini@med.umich.edu, or takutsu@kuicr.kyoto-u.ac.jp
Deep learning predicts tuberculosis drug resistance status from genome sequencing data
Authors: Chen Michael L. , Doddi Akshith , Royer Jimmy , Freschi Luca , Schito Marco , Ezewudo Matthew , Kohane Isaac S. , Beam Andrew , Farhat Maha 
Published: March 2018

AbstractBackgroundThe diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of clinicalMycobacteriumtuberculosis isolates promises to circumvent the long wait times and limited scope of conventional phenotypic antimicrobial susceptibility, but gaps remain for predicting phenotype accurately from genotypic data.Methods and FindingsUsing targeted or whole genome sequencing and conventional drug resistance phenotyping data from 3,601Mycobacterium tuberculosisstrains, 1,228 of which were multidrug resistant, we investigated the use of machine learning to predict phenotypic drug resistance to 10 anti-tuberculosis drugs. The final model, a multitask wide and deep neural network (MD-WDNN), achieved improved high predictive performance: the average AUCs were 0.979 for first-line drugs and 0.936 for second-line drugs during repeated cross-validation. On an independent validation set, the MD-WDNN showed average AUCs, sensitivities, and specificities, respectively, of 0.937, 87.9%, and 92.7% for first-line drugs and 0.891, 82.0% and 90.1% for second-line drugs. In addition to being able to learn from samples that have only been partially phenotyped, our proposed multidrug architecture shares information across different anti-tuberculosis drugs and genes to provide a more accurate phenotypic prediction. We uset-distributed Stochastic Neighbor Embedding (t-SNE) visualization and feature importance analyses to examine inter-drug similarities.ConclusionsMachine learning is capable of accurately predicting resistant status using genomic information and holds promise in bringing sequencing technologies closer to the bedside.
Interplay of size and shape in miniaturized land snails
Authors: Örstan Aydin 
Published: March 2018

ABSTRACTThe distribution of the mean greater shell dimensions of 2446 genera of stylommatophoran land snails consists of two groups peaking at 3 mm and 15.3 mm. The 3-mm group includes the miniature snails whose adult shell dimensions range from almost 0.8 mm to less than 1.5 mm. Relative surface area, shell thickness and egg size are discussed as potential factors that may limit the minimum shell dimensions. To obtain uniform distributions, the shell shape is expressed as the expanse angle, defined as the apex angle of a right triangle the height and the base of which are the shell height and diameter, respectively. In terms of expanse angles there are three shell shapes: tall (< 40°), flat (> 50°) and roughly equiaxial (40°-50°). The variation of shell shape with size was analyzed within the morphospace they form. At shell dimensions above 10 mm the lowest expanse angle is about 10°. Below 10 mm, the lower limit of the expanse angle increases as shells get smaller. As a result, no miniature species has a tall shell. It is proposed that two evolutionary functional constraints render small and narrow shells of miniaturized snails nonadaptive. These are the requirements to reduce surface areas to decrease water loss and to have enough volume and a wide enough body whorl to accommodate at least one egg.
Channelrhodopsin variants engage distinct patterns of network activity
Authors: Jun Na Young , Cardin Jessica A. 
Published: March 2018

AbstractChannelrhodopsins (ChRs) are light-gated ion channels that enable cell type-specific activation of neurons or neural circuits. Channelrhodopsin-2 has been widely used as a tool to probe circuit function in vitro and in vivo. Several recently developed ChR variants are characterized by faster kinetics and reduced desensitization. However, little is known about how their varying properties may regulate their interaction with local network dynamics. We compared ChR-evoked patterns of multi-unit activity and local field potentials in primary visual cortex of mice expressing three ChR variants with distinct temporal profiles: Chronos, Chrimson, and ChR2. We assessed overall activation of by measuring the amplitude and temporal progression of evoked spiking. Using gamma-range (30-80Hz) LFP power as an assay for local network engagement, we examined the recruitment of cortical network activity by each tool. All ChR variants caused light-evoked increases in firing in vivo, but each demonstrated different temporal patterning of evoked activity. In addition, the three ChRs had distinct effects on cortical gamma-band activity. Our findings suggest that variations in the kinetics of optogenetic tools can substantially affect their efficacy in neural networks in vivo, as well as the manner in which their activation engages circuit resonance.
Strong Episodic Selection for Natural Competence for Transformation Due to Host-Pathogen Dynamics
Authors: Palmer Nathan D. , Cartwright Reed A. 
Published: March 2018

AbstractSexual recombination only occurs in eukaryotes; however, many bacteria can actively recombine with environmental DNA. This behavior, referred to as transformation, has been described in many species from diverse taxonomic backgrounds. Transformation is hypothesized to carry some selective advantages similar to those postulated for meiotic sex in eukaryotes. However, the accumulation of loss-of-function alleles at transformation loci and an increased mutational load from recombining with DNA from dead cells create additional costs to transformation. These costs have been shown to outweigh many of the benefits of recombination under a variety of likely parameters. We investigate an additional proposed benefit of sexual recombination, the Red Queen hypothesis, as it relates to bacterial transformation. Here we describe a model showing that host-pathogen coevolution may provide a large selective benefit to transformation and allow transforming cells to invade an environment dominated by otherwise equal non-transformers. Furthermore, we observe that host-pathogen dynamics cause the selection pressure on transformation to vary extensively in time, potentially explaining the tight regulation and wide variety of rates observed in naturally competent bacteria. Host-pathogen dynamics may explain the evolution and maintenance of natural competence despite its associated costs.