bluuebunny/crossref_metadata_2025_split huggingface dataset:
Orientation processing by synaptic integration across first-order tactile neurons
Authors: Hay Etay , Pruszynski J Andrew 
Published: August 2018

AbstractOur ability to manipulate objects relies on tactile inputs from first-order tactile neurons that innervate the glabrous skin of the hand. The distal axon of these neurons branches in the skin and innervates many mechanoreceptors, yielding spatially-complex receptive fields. Here we show that synaptic integration across the complex signals from the first-order neuronal population could underlie human ability to accurately (< 3°) and rapidly process the orientation of edges moving across the fingertip. We first derive spiking models of human first-order tactile neurons that fit and predict responses to moving edges with high accuracy. We then use the model neurons in simulating the peripheral neuronal population that innervates a fingertip. We use machine learning to train classifiers performing synaptic integration across the neuronal population activity, and show that synaptic integration across first-order neurons can process edge orientations with high acuity and speed. In particular, our models suggest that integration of fast-decaying (AMPA-like) synaptic inputs within short timescales is critical for discriminating fine orientations, whereas integration of slow-decaying (NMDA-like) synaptic inputs refine discrimination and maintain robustness over longer timescales. Taken together, our results provide new insight into the computations occurring in the earliest stages of the human tactile processing pathway and how they may be critical for supporting hand function.Author SummaryOur ability to manipulate objects relies on tactile inputs signaled by first-order neurons that innervate mechanoreceptors in the skin of the hand and have spatially-complex receptive fields. Here we show how synaptic integration across the rich inputs from first-order neurons can rapidly and accurately process the orientation of edges moving across the fingertip. We derive spiking models of human first-order tactile neurons, then use the models to simulate the peripheral neuronal population that innervates a fingertip. We use machine learning to train classifiers performing synaptic integration across the neuronal population activity, and show that synaptic integration across first-order neurons could underlie human ability to process edge orientations with high acuity and speed.
Confidence in predicted position error explains saccadic decisions during pursuit
Authors: Coutinho Jonathan D , Lefèvre Philippe , Blohm Gunnar 
Published: August 2018

AbstractA fundamental problem in motor control is the coordination of complementary movement types to achieve a common goal. As a common example, humans view moving objects through coordinated pursuit and saccadic eye movements. Pursuit is initiated and continuously controlled by retinal image velocity. During pursuit, eye position may lag behind the target. This can be compensated by the discrete execution of a catch-up saccade. The decision to trigger a saccade is influenced by both position and velocity errors and the timing of saccades can be highly variable. The observed distributions of saccade occurrence and trigger time remain poorly understood and this decision process remains imprecisely quantified. Here we propose a predictive, probabilistic model explaining the decision to trigger saccades during pursuit to foveate moving targets. In this model, expected position error and its associated uncertainty are predicted through Bayesian inference across noisy, delayed sensory observations (Kalman filtering). This probabilistic prediction is used to estimate the confidence that a saccade is needed (quantified through log-probability ratio), triggering a saccade upon accumulating to a fixed threshold. The model qualitatively explains behavioural observations on the probability of occurrence and trigger time distributions of saccades during pursuit over a range of target motion trajectories. Furthermore, this model makes novel predictions about the influence of sensory uncertainty and target motion parameters on saccade decisions. We suggest that this predictive, confidence-based decision making strategy represents a fundamental principle for the probabilistic neural control of coordinated movements.New & NoteworthyThis is the first stochastic dynamical systems model of pursuit-saccade coordination accounting for noise and delays in the sensorimotor system. The model uses Bayesian inference to predictively estimate visual motion, triggering saccades when confidence in predicted position error accumulates to a threshold. This model explains saccade probability and trigger time distributions across target trajectories and makes novel predictions about the influence of sensory uncertainty in saccade decisions during pursuit.
Antibacterial activity ofActinomycetesisolated from the soil sample of South India and polyketide synthase gene identification
Authors: Srivastava Ashu , Shanmugaiah Vellasamy 
Published: August 2018

AbstractTheStreptomycesgenus is well studied owing to its capacity in producing more than 70% of antibiotics. This study was undertaken to characterizeStreptomycesstrains, occurring in soils of different districts of Tamilnadu, India as well as to evaluate their potential to produce antimicrobial compounds. Samples were collected from rice rhizosphere from different district of Tamilnadu agricultural zone. In primary screening by cross streak method,Streptomyces strainswere assessed for antibiotic production and activity against different human bacterial. Then active isolates were selected for secondary screening by agar well diffusion method. Solvent extraction method was used to identify the best crude samples which are exhibiting better antibacterial activity. Then, 16S rRNA PCR was carried out for confirmation of isolates. In primary screening among all of the isolates, 50% isolates were active against at least one of the test organisms and 21.31% strains exhibited a broad-spectrum activity against almost all of the test bacteria. The minimum inhibitory concentrations (MICs) of the ethyl acetate extracts measured. Out of 26 positive strains two of the most active isolates SVG-07-15 and TK-01-05 were taken for further studies. These results highlight the importance ofStreptomycesisolates in antibiotic production. Together with antibacterial activity, the PKS gene based approach can be applied for efficient screening of isolated strains of pharmaceutical value and related compounds.HighlightsIsolation of actinomycetes from soil samples collected from South India.Screening of isolated actinomycetes for antibacterial activityDetails study of potent strains against human pathogensIdentification of PKS gene from the active strains
Dynamic regulation of HYL1 provides new insights into its multifaceted role in Arabidopsis
Authors: Bhagat Prakash Kumar , Verma Deepanjali , Badmi Raghuram , Sinha Alok Krishna 
Published: August 2018

SummaryMicroRNAs (miRNAs) are 21 to 24 nucleotide non-coding RNAs that regulate gene expression. Biogenesis of miRNAs is fine-tuned by specialized microprocessor complex, the regulation of which is being continuously understood. Recruitment of HYL1 to the microprocessor complex is crucial for accurate primary-miRNA (pri-miRNA) processing and accumulation of mature miRNA inArabidopsis thaliana. HYL1 is a double-stranded RNA binding protein also termed as DRB1, has two double-stranded RNA binding domain at N-terminal and a highly disordered C-terminal region. Also, the biological activity of HYL1 is dynamically regulated through transition from hyperphosphorylation to hypophosphorylation state. HYL1 is known to be phosphorylated by a MAP kinase MPK3 and SnRK2. However, the precise role of its phosphorylation are still unknown. Recently, the stability of HYL1 protein has been shown to be regulated by an unknown protease X. However, the identity of the protease and its molecular mechanisms are poorly understood. Here, we describe, three functionally important facets of HYL1, which provide a better picture of its association with molecular processes. First, we identified a conserved MPK3 phosphorylation site on HYL1 and its possible role in the miRNA biogenesis. Secondly, the C-terminal region of HYL1 displays tendencies to bind dsDNA. Lastly, the role of C-terminal region of HYL1 in the regulation of its protein stability and the regulation of miRNA biogenesis is documented. We show the unexplored role of C-terminal and hypothesize the novel functions of HYL1 in addition to miRNA biogenesis. We anticipate that the data presented in this study, will open a new dimension of understanding the role of double stranded RNA binding proteins in diverse biological processes of plants and animal.
A Latent Allocation Model for the Analysis of Microbial Composition and Disease
Authors: Abe Ko , Hirayama Masaaki , Ohno Kinji , Shimamura Teppei 
Published: August 2018

AbstractBackgroundEstablishing the relationship between microbiota and specific disease is important but requires appropriate statistical methodology. A specialized feature of microbiome count data is the presence of a large number of zeros, which makes it difficult to analyze in case-control studies. Most existing approaches either add a small number called a pseudo-count or use probability models such as the multinomial and Dirichlet-multinomial distributions to explain the excess zero counts, which may produce unnecessary biases and impose a correlation structure taht is unsuitable for microbiome data.ResultsThe purpose of this article is to develop a new probabilistic model, called BERMUDA (BERnoulli and MUltinomial Distribution-based latent Allocation), to address these problems. BERMUDA enables us to describe the differences in bacteria composition and a certain disease among samples. We also provide a simple and efficient learning procedure for the proposed model using an annealing EM algorithm.ConclusionWe illustrate the performance of the proposed method both through both the simulation and real data analysis. BERMUDA is implemented with R and is available from GitHub (https://github.com/abikoushi/Bermuda).
Pyrite formation from FeS and H2S is mediated by a novel type of microbial energy metabolism
Authors: Thiel Joana , Byrne James , Kappler Andreas , Schink Bernhard , Pester Michael 
Published: August 2018

AbstractThe exergonic reaction of FeS with H2S to form FeS2(pyrite) and H2was postulated to have operated as an early form of energy metabolism on primordial Earth. Since the Archean, sedimentary pyrite formation played a major role in the global iron and sulfur cycles, with direct impact on the redox chemistry of the atmosphere. To date, pyrite formation was considered a purely geochemical reaction. Here, we present microbial enrichment cultures, which grew with FeS, H2S, and CO2as their sole substrates to produce FeS2and CH4. Cultures grew over periods of three to eight months to cell densities of up to 2–9×106cells mL−1. Transformation of FeS with H2S to FeS2was followed by57Fe Mössbauer spectroscopy and showed a clear biological temperature profile with maximum activity at 28°C and decreasing activities towards 4°C and 60°C. CH4was formed concomitantly with FeS2and exhibited the same temperature dependence. Addition of either penicillin or 2-bromoethanesulfonate inhibited both FeS2and CH4production, indicating a syntrophic coupling of pyrite formation to methanogenesis. This hypothesis was supported by a 16S rRNA gene-based phylogenetic analysis, which identified at least one archaeal and five bacterial species. The archaeon was closely related to the hydrogenotrophic methanogenMethanospirillum stamsiiwhile the bacteria were most closely related to sulfate-reducingDeltaproteobacteria, as well as unculturedFirmicutesandActinobacteria. We identified a novel type of microbial metabolism able to conserve energy from FeS transformation to FeS2, which may serve as a model for a postulated primordial iron-sulfur world.Significance statementPyrite is the most abundant iron-sulfur mineral in sediments. Over geological times, its burial controlled oxygen levels in the atmosphere and sulfate concentrations in seawater. Its formation in sediments is so far considered a purely geochemical process that is at most indirectly supported by microbial activity. We show that lithotrophic microorganisms can directly transform FeS and H2S to FeS2and use this exergonic reaction as a novel form of energy metabolism that is syntrophically coupled to methanogenesis. Our results provide insights into a syntrophic relationship that could sustain part of the deep biosphere and lend support to the iron-sulfur-world theory that postulated FeS transformation to FeS2as a key energy-delivering reaction for life to emerge.
Fast Batch Alignment of Single Cell Transcriptomes Unifies Multiple Mouse Cell Atlases into an Integrated Landscape
Authors: Park Jong-Eun , Polański Krzysztof , Meyer Kerstin , Teichmann Sarah A. 
Published: August 2018

AbstractIncreasing numbers of large scale single cell RNA-Seq projects are leading to a data explosion, which can only be fully exploited through data integration. Therefore, efficient computational tools for combining diverse datasets are crucial for biology in the single cell genomics era. A number of methods have been developed to assist data integration by removing technical batch effects, but most are computationally intensive. To overcome the challenge of enormous datasets, we have developed BBKNN, an extremely fast graph-based data integration method. We illustrate the power of BBKNN for dimensionalityreduced visualisation and clustering in multiple biological scenarios, including a massive integrative study over several murine atlases. BBKNN successfully connects cell populations across experimentally heterogeneous mouse scRNA-Seq datasets, which reveals global markers of cell type and organspecificity and provides the foundation for inferring the underlying transcription factor network. BBKNN is available athttps://github.com/Teichlab/bbknn.
ENIGMA: An Enterotype-Like Unigram Mixture Model for Microbial Association Analysis
Authors: Abe Ko , Hirayama Masaaki , Ohno Kinji , Shimamura Teppei 
Published: August 2018

AbstractBackgroundOne of the major challenges in microbial studies is to discover associations between microbial communities and a specific disease. A specialized feature of microbiome count data is that intestinal bacterial communities have clusters reffered as enterotype characterized by differences in specific bacterial taxa, which makes it difficult to analyze these data under health and disease conditions. Traditional probabilistic modeling cannot distinguish dysbiosis of interest with the individual differences.ResultsWe propose a new probabilistic model, called ENIGMA (Enterotype-like uNIGram mixture model for Microbial Association analysis), to address these problems. ENIGMA enables us to simultaneously estimate enterotype-like clusters characterized by the abundances of signature bacterial genera and environmental effects associated with the disease.ConclusionWe illustrate the performance of the proposed method both through the simulation and clinical data analysis. ENIGMA is implemented with R and is available from GitHub (https://github.com/abikoushi/enigma).
Expected differences in diversity and rarity between communities containing sexually versus asexually reproducing taxa
Authors: Herren Cristina M. 
Published: August 2018

AbstractRecent studies evaluating the community structures of microorganisms and macro-organisms have found greater diversity and rarity within micro-scale communities, compared to macro-scale communities. However, reproductive method has been a confounding factor in these comparisons; the microbes considered generally reproduce asexually, while the macro-organisms considered generally reproduce sexually. Sexual reproduction imposes the constraint of mate finding, which can have significant demographic consequences by depressing birth rates at low population sizes. Here, I examine theoretically how the effects of mate finding in sexual populations translate to the emergent community properties of diversity, rarity, and dominance. Using continuous-time Markov chain models, I compare communities with and without constraints of mate limitation. In mate-limited sexual populations, the decreased growth rates at low population densities translates to a much higher extinction rate. In communities consisting of sexually reproducing taxa, the increased extinction rate due to mate limitation decreases expected diversity. Furthermore, mate limitation has a disproportionately strong effect on taxa with low population density, leading to fewer rare taxa. These shifts in community structure mirror recent empirical studies of micro versus macro communities, indicating that reproductive method may contribute to observed differences in emergent properties between communities at these two scales.
A better way to define and describe Morlet wavelets for time-frequency analysis
Authors: Cohen Michael X 
Published: August 2018

AbstractMorlet wavelets are frequently used for time-frequency analysis of non-stationary time series data, such as neuroelectrical signals recorded from the brain. The crucial parameter of Morlet wavelets is the width of the Gaussian that tapers the sine wave. This width parameter controls the trade-off between temporal precision and frequency precision. It is typically defined as the “number of cycles,” but this parameter is opaque, and often leads to uncertainty and suboptimal analysis choices, as well as being difficult to interpret and evaluate. The purpose of this paper is to present alternative formulations of Morlet wavelets in time and in frequency that allow parameterizing the wavelets directly in terms of the desired temporal and spectral smoothing (as full-width at half-maximum). This formulation provides clarity on an important data analysis parameter, and should facilitate proper analyses, reporting, and interpretation of results. MATLAB code is provided.
RNAi screen inTriboliumreveals involvement of F-BAR proteins in myoblast fusion and visceral muscle morphogenesis in insects
Authors: Schultheis Dorothea , Schwirz Jonas , Frasch Manfred 
Published: August 2018

AbstractIn a large-scale RNAi screen inTribolium castaneumfor genes with knock-down phenotypes in the larval somatic musculature, one recurring phenotype was the appearance of larval muscle fibers that were significantly thinner than those in control animals. Several of the genes producing this knock-down phenotype corresponded to orthologs ofDrosophilagenes that are known to participate in myoblast fusion, particularly via their effects on actin polymerization. A new gene previously not implicated in myoblast fusion but displaying a similar thin-muscle knock-down phenotype was theTriboliumortholog ofNostrin, which encodes an F-BAR and SH3 domain protein. Our genetic studies ofNostrinandCip4, a gene encoding a structurally related protein, inDrosophilashow that the encoded F-BAR proteins jointly contribute to efficient myoblast fusion during larval muscle development. Together with the F-Bar protein Syndapin they are also required for normal embryonic midgut morphogenesis. In addition,Cip4is required together withNostrinduring the profound remodeling of the midgut visceral musculature during metamorphosis. We propose that these F-Bar proteins help govern proper morphogenesis particularly of the longitudinal midgut muscles during metamorphosis.
Plug-and-Play Metabolic Transducers Expand the Chemical Detection Space of Cell-Free Biosensors
Authors: Voyvodic Peter L , Pandi Amir , Koch Mathilde , Faulon Jean-Loup , Bonnet Jerome 
Published: August 2018

ABSTRACTCell-free transcription-translation systems have great potential for biosensing, yet the range of detectable chemicals is limited. Here we provide a framework to expand the range of molecules detectable by cell-free biosensors by combining synthetic metabolic cascades with transcription factor-based networks. These hybrid cell-free biosensors are highly-sensitive and have a fast response and high-dynamic range. This work provides a foundation to engineer modular cell-free biosensors tailored for many applications.
Hierarchical paradigm for automated optimal design of dual-band wearable antenna with simplified human body models
Authors: Januszkiewicz Lukasz , Di Barba Paolo , Hausman Slawomir 
Published: August 2018

Purpose
The purpose of this study is to develop a method to reduce the computation time necessary for the automated optimal design of dual-band wearable antennas. In particular, the authors investigated if this can be achieved by the use of a hierarchical optimization paradigm combined with a simplified human body model. The geometry of the antenna under consideration is described via eight geometrical parameters which are automatically adjusted with the use of an evolutionary algorithm to improve the impedance matching of an antenna located in the proximity of a human body. Specifically, the antennas were designed to operate in the ISM band which covers two frequency ranges: 2.4-2.5 GHz and 5.7-5.9 GHz.

Design/methodology/approach
During the studies on the automated design of wearable antennas using evolutionary computing, the authors observed that not all design parameters exhibit equal influence on the objective function. Therefore, it was hypothesized that to reduce the computation effort, the design parameters can be activated sequentially based on their influence. Accordingly, the authors’ computer code has been modified to include this feature.

Findings
The authors’ novel hierarchical multi-parameter optimization method was able to converge to a better solution within a shorter time compared to an equivalent method not exploiting automatic activation of an increasing number of design parameters. Considering a significant computational cost involved in the calculation of the objective function, this exhibits a convincing advantage of their hierarchical approach, at least for the considered class of antennas.

Research limitations/implications
The described method has been developed for the design of single- or dual-band wearable antennas. Its application to other classes of antennas and antenna environments may require some adjustments of the objective functions or parameter values of the evolutionary algorithm. It follows from the well-recognized fact that all optimization methods are to some extent application-specific.

Practical implications
Computation load involved in the automated design and optimization can be significantly reduced compared to the non-hierarchical approach with a heterogeneous human body model.

Originality/value
To the best of the authors’ knowledge, the described application of hierarchical paradigm to the optimization of wearable antennas is fully original, as well as is its combination with simplified body models.
Fractional derivatives and the laws of electrical engineering
Authors: Sikora Ryszard , Pawłowski Stanislaw 
Published: August 2018

Purpose
This paper aims to evaluate the possibilities of fractional calculus application in electrical circuits and magnetic field theories.

Design/methodology/approach
The analysis of mathematical notation is used for physical phenomena description. The analysis aims to challenge or prove the correctness of applied notation.

Findings
Fractional calculus is sometimes applied correctly and sometimes erroneously in electrical engineering.

Originality/value
This paper provides guidelines regarding correct application of fractional calculus in description of electrical circuits’ phenomena. It can also inspire researchers to find new applications for fractional calculus in the future.