bluuebunny/crossref_metadata_2025_split huggingface dataset:
A comprehensive knowledge base of synaptic electrophysiology in the rodent hippocampal formation
Authors: Moradi Keivan , Ascoli Giorgio A. 
Published: May 2019

AbstractThe cellular and synaptic architecture of the rodent hippocampus has been described in thousands of peer-reviewed publications. However, no human- or machine-readable public catalog of synaptic electrophysiology data exists for this or any other neural system. Harnessing state of the art information technology, we have developed a cloud-based toolset for identifying empirical evidence from the scientific literature pertaining to synaptic electrophysiology, for extracting the experimental data of interest, and for linking each entry to relevant text or figure excerpts. Mining more than 1,200 published journal articles, we have identified eight different signal modalities quantified by 68 different methods to measure synaptic amplitude, kinetics, and plasticity in hippocampal neurons. We have designed a data structure that both reflects these variabilities and maintains the existing relations among experimental modalities. Moreover, we mapped every annotated experiment to identified “synapse types,” i.e. specific pairs of presynaptic and postsynaptic neuron types. To this aim, we leveraged Hippocampome.org, an open-access knowledge base of morphologically, electrophysiologically, and molecularly characterized neuron types in the rodent hippocampal formation. Specifically, we have implemented a computational pipeline to systematically translate neuron type properties into formal queries in order to find all compatible synapse types. With this system, we have collected nearly 40,000 synaptic data entities covering 88% of the 3,120 potential connections in Hippocampome.org. Correcting membrane potentials with respect to liquid junction potentials significantly reduced the difference between theoretical and experimental reversal potentials, thereby enabling the accurate conversion of all synaptic amplitudes to conductance. This dataset allows for large-scale hypothesis testing of the general rules governing synaptic signals. To illustrate these applications, we confirmed several expected correlations between synaptic measurements and their covariates while suggesting previously unreported ones. We release all data open source at Hippocampome.org in order to further research across disciplines.
Quantifying genetic regulatory variation in human populations improves transcriptome analysis in rare disease patients
Authors: Mohammadi Pejman , Castel Stephane E. , Cummings Beryl B. , Einson Jonah , Sousa Christina , Hoffman Paul , Donkervoort Sandra , Mohassel Payam , Foley Reghan , Wheeler Heather E. , Im Hae Kyung , Bonnemann Carsten G. , MacArthur Daniel G. , Lappalainen Tuuli 
Published: May 2019

AbstractTranscriptome data holds substantial promise for better interpretation of rare genetic variants in basic research and clinical settings. Here, we introduce ANalysis of Expression VAriation (ANEVA) to quantify genetic variation in gene dosage from allelic expression (AE) data in a population. Application to GTEx data showed that this variance estimate is robust across datasets and is correlated with selective constraint in a gene. We next used ANEVA variance estimates in a Dosage Outlier Test (ANEVA-DOT) to identify genes in an individual that are affected by a rare regulatory variant with an unusually strong effect. Applying ANEVA-DOT to AE data form 70 Mendelian muscular disease patients showed high accuracy in detecting genes with pathogenic variants in previously resolved cases, and lead to one confirmed and several potential new diagnoses in cases previously unresolved. Using our reference estimates from GTEx data, ANEVA-DOT can be readily incorporated in rare disease diagnostic pipelines to better utilize RNA-seq data.One Sentence SummaryNew statistical framework for modelling allelic expression characterizes genetic regulatory variation in populations and informs diagnosis in rare disease patients
Transcriptome Profile Analysis of Twisted Leaf Disease Response in Susceptible Sugarcane with Narenga porphyrocoma Genetic Background
Authors: Wei Jinju , Xiu Zhihui , Ou Huiping , Chen Junhui , Jiang Huayan , Zhang Xiaoqiu , Zhang Ronghua , Zhou Hui , Gui Yiyun , Li Haibi , Li Yangrui , Yang Rongzhong , Huang Dongliang , Tan Hongwei , Liu Xihui 
Published: May 2019

AbstractSugarcane is an important industrial crop with a high sugar yield that has become a leading energy crop worldwide. It is widely cultivated in tropical and subtropical regions. Various diseases beset the cultivation of sugarcane. The molecular study of disease resistance in sugarcane is limited by its complicated genome. In our study, RNA-seq was employed to detect the mechanism of twisted leaf disease tolerance in modern cultivar sugarcane, which derived from Narenga porphyrocoma. We completed high-throughput transcriptomic sequencing of 12 samples, including three stages of a susceptible (NSBC1_T “H3-8”) and an unsusceptible cultivar (NSBC1_CK “H-19”) with two biological repeats, respectively. Using the Saccharum spontaneum genome as reference, the average mapping ratio of the clean data was over 70%. Among the differentially expressed genes between H3-8 and H3-19, we focused on the analysis of hormone pathways and resistance (R) genes. The results showed that twisted leaf disease triggers hormone networks and around 40% of R genes conditioned lower expression in the susceptible cultivar. One of the possible reasons for H3-8 being susceptible to twisted leaf disease might be the null/retarded response of R genes, especially in pre-onset stage (46% down-regulated) of pathogens infection.
Protein secondary structure prediction with context convolutional neural network
Authors: Long Shiyang , Tian Pu 
Published: May 2019

AbstractProtein secondary structure (SS) prediction is important for studying protein structure and function. Both traditional machine learning methods and deep learning neural networks have been utilized and great progress has been achieved in approaching the theoretical limit. Convolutional and recurrent neural networks are two major types of deep leaning architectures with comparable prediction accuracy but different training procedures to achieve optimal performance. We are interested in seeking novel architectural style with competitive performance and in understanding performance of different architectures with similar training procedures.ResultsWe constructed a context convolutional neural network (Contextnet) and compared its performance with popular models (e.g. convolutional neural network, recurrent neural network, conditional neural fields …) under similar training procedures on Jpred dataset. the Contextnet was proven to be highly competitive. Additionally, we retrained the network with the Cullpdb data set and compared with Jpred, ReportX and Spider3 server, the Contextnet was found to be more accurate on CASP13 dataset. Training procedures were found to have significant impact on the accuracy of the Contextnet.AvailabilityThe full source code and dataset have been uploaded at https://github.com/qzlshy/second_structure_model and https://github.com/qzlshy/ss_pssm_hhm.Contacttianpu@jlu.edu.cn
Environmental niche forecasts of globally threatened yellow-throated bulbul, Pycnonotus xantholaemus for conservation prospects in the Deccan peninsula, India
Authors: Jha Ashish , Vasudevan Karthikeyan 
Published: May 2019

AbstractYellow-throated Bulbul,Pycnonotus xantholaemusis an endemic, rare and threatened species with discontinuous distribution across Deccan peninsula. IUCN has enlisted surveys for newer populations as one of the conservation measures. We used maxent algorithm to generate environmental niche model for further surveys. We looked into climatic envelope at occurrence points and contrast it with background climatic envelope. We compared the model for current scenario and future scenario to assess change in extent of predicted niche over time. We used six variables: climatic, topographical and vegetation layers, and a final set of 102 verified presence locations to generate the model. Topographic ruggedness index and precipitation of wettest month (Bio13) were the strong predictors for Yellow-throated Bulbul niche. Model predicts highly discontinuous and small fragments totalling 7% area of peninsular India as suitable niche. Only 10 % of predicted niche falls within India’s protected area network. Loss of habitat due to granite quarrying and anthropogenic pressure will be a bigger threat than climate change.
A customizable tablet app for hand movement research outside the lab
Authors: Matic Adam , Gomez-Marin Alex 
Published: May 2019

ABSTRACTBackgroundPrecise behavioral measurements allow the discovery of movement constraints that provide insights into sensory-motor processes and their underlying neural mechanisms. For instance, when humans draw an ellipse on a piece of paper, the instantaneous speed of the pen co-varies tightly with the local curvature of the path. Known as the speed-curvature power law, this phenomenon relates to fundamental questions of motor control.New MethodWe have developed a software app for displaying static curves or dynamic targets while recording finger or stylus movements on Android touch-screen tablets. Designed for human hand movement research, the app is free, ready-to-use, open-source and customizable.ResultsWe provide a template experimental protocol, and detailed explanations to use it and flexibly modify the code for different kinds of tasks. Our validation of the app demonstrates laboratory-quality results outside the laboratory. We also provide raw data and analysis scripts.Comparison with Existing MethodsCommonly used laboratory devices for recording hand movement trajectories are large, heavy and expensive. In turn, software apps are often not published, nor customizable. Our app running on tablets becomes an affordable, flexible, and portable tool suited for quantitative and robust behavioral studies with large number of participants and outside the laboratory (e.g. in a classroom, a hospital, or at home).ConclusionsThe affordability, flexibility, and resolution of our tablet app provide an effective tool to study behavior quantitatively in the real world.HighlightsA free, ready-to-use, open source, and customizable app for Android tablets.High-resolution measuring of finger movement during tracing, tracking & scribbling.Fast and easy data collection and experimental design with affordable hardware.Allowing for high-throughput experiments outside the lab (classroom, hospital, home).Validated for state-of-the-art research (speed-curvature power law, drawing accuracy).
Operations Research Methods for Estimating the Population Size of Neuron Types
Authors: Attili Sarojini M. , Mackesey Sean , Ascoli Giorgio A. 
Published: May 2019

AbstractUnderstanding brain computation requires assembling a complete catalog of its architectural components. Although the brain is organized into several anatomical and functional regions, it is ultimately the neurons in every region that are responsible for cognition and behavior. Thus, classifying neuron types through-out the brain and quantifying the population sizes of distinct classes in different regions is a key subject of research in the neuroscience community. Although the total number of neurons in the brain has been estimated for multiple species, the definition and population size of each neuron type are still open questions even in common model organisms: the so called cell census problem. We propose a methodology that uses operations research principles to estimate the number of neurons in each type based on available information on their distinguishing properties. Thus, assuming a set of neuron type definitions, we provide a solution to the issue of assessing their relative proportions. Specifically, we present a three-step approach that includes literature search, equation generation, and numerical optimization. Solving numerically the set of equations generated by literature mining yields best estimates or most likely ranges for the number of neurons in each type. While this strategy can be applied to any neural system, we illustrate its usage on the rodent hippocampus.
A comparison of eDNA to camera trapping for assessment of terrestrial mammal diversity
Authors: Leempoel Kevin , Hebert Trevor , Hadly Elizabeth A. 
Published: May 2019

AbstractEnvironmental DNA (eDNA) is one of the most promising approaches to meet the demand for the fast and frequent monitoring of ecosystems needed to tackle the current decline in biodiversity. However, before eDNA can establish itself as a robust alternative for mammal monitoring, comparison with existing approaches is necessary, yet has not been done. Moreover, much is unknown regarding the nature, spread and persistence of DNA shed by animals into terrestrial environments, or the optimal experimental design for understanding these potential biases.To address some of these challenges, we compared the detection of terrestrial mammals using eDNA analysis of soil samples against confirmed species observations from a long-term (∼9-yr) camera trapping study. At the same time, we considered multiple experimental parameters, including two sampling designs, two DNA extraction kits and two metabarcodes of different sizes.All mammals consistently recorded with cameras were detected in eDNA. In addition, eDNA reported many small mammals not recorded by camera traps, but whose presence in the study area is otherwise documented. A long metabarcode (≈220bp) offering a high taxonomic resolution, achieved a similar efficiency as a shorter one (≈70bp) and a phosphate buffer-based extraction gave similar results as a total DNA extraction method for a fraction of the price. Our results support that eDNA-based monitoring should become a valuable part of terrestrial mammal surveys. Yet, the lack of coverage of mammal mitochondrial genomes in public databases must be addressed before eDNA can be used to its full potential.
The portrayal of early accountants in nineteenth century Portuguese literature
Authors: Leão Fernanda , Gomes Delfina , Carnegie Garry D. 
Published: May 2019

Purpose
The purpose of this paper is to study the portrayal of early accountants in the unfamiliar site of Portugal by examining Portuguese-language realist literature from the second half of the nineteenth century.

Design/methodology/approach
Two popular literary works – Uma Família Inglesa (An English Family), written by Júlio Dinis and published in 1867, and Singularidades de uma Rapariga Loura (The Idiosyncrasies of a Young Blonde Woman), written by Eça de Queirós and published in 1873 – were examined through a qualitative content analysis.

Findings
The dimensions of the accounting stereotype discerned for the two early accounting practitioners featured in these works are portrayed as: modest; on-the-job trained practitioner; uncreative, conservative and unenergetic; honest financial manager; servant of the capitalist (i.e. merchant), and warm and sentimental. The accountant stereotype depicted from 1860s to 1870s period is similar to the conventional accountant stereotype, identified as the “traditional accountant” stereotype. Variations from this stereotype, however, are identified in the local, time-specific settings of Lisbon and Oporto.

Originality/value
The study’s portrayal of early accounting practitioners occurs during a period of transformation marked by liberalism. It augments an understanding of the image of early accounting practitioners, reflecting their social positioning at a time of significant social, economic, political and cultural changes, thereby contributing to an appreciation of the historical legacy of the accountant stereotype in continental Europe. Importantly, a taxonomy is proposed for content analysis that can be used and developed by future researchers.
An empirical analysis of stock market interdependence and volatility spillover in the stock markets of Africa and Middle East region
Authors: Panda Ajaya Kumar , Nanda Swagatika , Paital Rashmi Ranjan 
Published: May 2019

PurposeThe purpose of this paper is to examine the short-term and long-term interdependence among the stock markets of Africa and Middle East region. It also attempts to analyze the pattern of volatility spillover among the regional stock markets.Design/methodology/approachThe study has used Granger causality test, variance decomposition test of vector auto-regression (VAR) model, vector error correction model (VECM), multivariate generalized conditional heteroskedasticity (MGARCH-BEKK) models and Johansen and Juselius multivariate cointegration techniques.FindingsThe study finds that the interlinkages of the stock markets are not uniform across all the countries of the region. The stock market of Israel, South Africa and Jordan are found to be highly connected stock market of the region followed by Egypt and Botswana. The study also finds significant spillover of lagged standardized volatility across the stock markets of the region. But the magnitude of the response of volatility spillover and its persistence is very minimum. However, the stock markets are found to be co-integrated and expected to share long-run equilibrium relationships among each other.Research limitations/implicationsThe study has the scope to be extended to capture the time-varying integration of market returns with transmission of monetary policy and exchange rate changes within the region. The results obtained from this study may assist the firm managers and international investors to understand the key drivers of market connectedness.Originality/valueEmpirically investigating the pattern of stock market connectedness in Africa and Middle East region with advanced methodology over a long study period is the originality of this study.
Do prestige and trust sustain knowledge-based communities? Evidence from Medical Ethics co-authorship networks in France
Authors: Kumar Sameer 
Published: May 2019

Purpose
The purpose of this paper is to identify embedded communities among Medical Ethics scholars in France based on their position and strength of association in the social networks they form.

Design/methodology/approach
The author constructed a co-authorship network by linking two authors if they co-authored a paper. Once the co-authorship network was constructed, the author investigated the entire network quantitatively using standard research methods known as social network analysis. The author’s mechanism of community detection was via identification, i.e. communities were user-defined based on topological analysis.

Findings
The study identified that communities formed by trust (bonding or repeat connections) or by prestige (connection of well-connected scholars with those that are already well-connected) overlap each other, suggesting that trust and prestige may go hand in hand in sustaining communities. Structural holes within trust networks indicated that some actors played more of a broker role than did other actors.

Research limitations/implications
The study uses Web of Science field based category to extract data. Although thorough cleaning of data has been performed, author disambiguation over a three decade period remains a limitation.

Originality/value
Although there have been numerous studies on mapping co-authorship networks, there has been limited work investigating the underlying communities formed by author associations. The results of this study should be of immense benefit to those studying the science of science and S&T policy.
Exploring the impact of game framing and task framing on user participation in citizen science projects
Authors: Tang Jian , Prestopnik Nathan R. 
Published: May 2019

Purpose
The purpose of this paper is to examine how game framing and task framing influence experienced meaningfulness (eudaimonia) and perceived enjoyment (hedonia), which, in turn, can account for user participation behavior in citizen science projects.

Design/methodology/approach
The authors designed and implemented a citizen science system, Citizen Sort, and used a survey method to investigate to what extent game framing and task framing influence participation behavior. PLS–SEM was used to test research hypotheses with 76 Citizen Sort participants.

Findings
Analysis confirmed that game framing and task framing have a significant impact on perceived enjoyment, but showed that only task framing has a direct effect on experienced meaningfulness. The effects of experienced meaningfulness on participation were fully mediated by perceived enjoyment. Content analysis of qualitative data revealed additional insights.

Research limitations/implications
This research is limited due to its sample size and considered as an exploratory study, in which PLS–SEM was used to identify the impact of game framing and task framing as well as support the theory development regarding the dual nature of citizen science games.

Practical implications
This research provided suggestions for scientists, designers and project initiators that game framing and task framing should be effectively integrated to provide enjoyable and meaningful experiences so as to promote user contribution.

Originality/value
This research is one of initial studies which explored the impact of dual nature of citizen science games. The findings of this study provide the groundwork for guidelines and strategies to facilitate user contribution in citizen science projects.
Application of Whitney elements for the reconstruction of electric arc current density in low-voltage circuit breakers
Authors: Dong Jinlong , Di Rienzo Luca , Chadebec Olivier , Wang Jianhua 
Published: May 2019

Purpose
This paper aims to present the mathematical formulations of a magnetic inverse problem for the electric arc current density reconstruction in a simplified arc chamber of a low-voltage circuit breaker.

Design/methodology/approach
Considering that electric arc current density is a zero divergence vector field, the inverse problem can be solved in Whitney space W2 in terms of electric current density J with the zero divergence condition as a constraint or can be solved in Whitney space W1 in terms of electric vector potential T where the zero divergence condition naturally holds. Moreover, the tree gauging condition is applied to ensure a unique solution when solving for the vector potential in space W1. Tikhonov regularization is used to treat the ill-posedness of the inverse problem complemented with L-curve method for the selection of regularization parameters. A common mode approach is proposed, which solves for the reduced electric vector potential representing the internal current loops instead of solving for the total electric vector potential. The proposed inversion approaches are numerically tested starting from simulated magnetic field values.

Findings
With the common mode approach, the reconstruction of current density is significantly improved for both formulations using face elements in space W2 and using edge elements in space W1. When solving the inverse problem in space W1, the choice of the regularization operator has a key role to obtain a good reconstruction, where the discrete curl operator is a good option. The standard Tikhonov regularization obtains a good reconstruction with J-formulation, but fails in the case of T-formulation. The use of edge elements requires a tree-cotree gauging to ensure the uniqueness of T. Moreover, additional efforts have to be taken to find an optimal regularization operator and an optimal tree when using edge elements. In conclusion, the J-formulation is to be preferred.

Originality/value
The proposed approaches are able to reconstruct the three-dimensional electric arc current density from its magnetic field in a non-intrusive manner. The formulations enable us to incorporate a priori knowledge of the unknown current density into the solution of the inverse problem, including the zero divergence condition and the boundary conditions. A common mode approach is proposed, which can significantly improve the current density reconstruction.