bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrating Phenotypic Data For Depression
Authors: Butler Amy W. , Cohen-Woods Sarah , Farmer Anne , McGuffin Peter , Lewis Cathryn M.
Published: December 2017

The golden era of molecular genetic research brings about an explosion of phenotypic, genotypic and sequencing data. Building on the common aims to exploit understanding of human diseases, it also opens up an opportunity for scientific communities to share and combine research data. Genome-wide association studies (GWAS) have been widely used to locate genetic variants, which are susceptible for common diseases. In the field of medical genetics, many international collaborative consortiums have been established to conduct meta-analyses of GWAS results and to combine large genotypic data sets to perform mega genetic analyses. Having an integrated phenotype database is significant for exploiting the full potential of extensive genotypic data. In this paper, we aim to share our experience gained from integrating four heterogeneous sets of major depression phenotypic data onto the MySQL platform. These data sets constitute clinical data which had been gathered for various genetic studies for the past decade. We also highlight in this report some generic data handling techniques, the costs and benefits regarding the use of integrated phenotype database within our own institution and under the consortium framework.
Noise tolerance of Multiple Classifier Systems in data integration-based gene function prediction
Authors: Rè Matteo , Valentini Giorgio
Published: December 2017

The availability of various high-throughput experimental and computational methods developed in the last decade allowed molecular biologists to investigate the functions of genes at system level opening unprecedented research opportunities. Despite the automated prediction of genes functions could be included in the most difficult problems in bioinformatics, several recently published works showed that consistent improvements in prediction performances can be obtained by integrating heterogeneous data sources. Nevertheless, very few works have been dedicated to the investigation of the impact of noisy data on the prediction performances achievable by using data integration approaches.

In this contribution we investigated the tolerance of multiple classifier systems (MCS) to noisy data in gene function prediction experiments based on data integration methods. The experimental results show that performances of MCS do not undergo a significant decay when noisy data sets are added. In addition, we show that in this task MCS are competitive with kernel fusion, one of the most widely applied technique for data integration in gene function prediction problems.
Analysis and construction of pathogenicity island regulatory pathways in Salmonella enterica serovar Typhi
Authors: Ong Su Yean , Ng Fui Ling , Badai Siti Suriawati , Yuryev Anton , Alam Maqsudul
Published: December 2017

Signal transduction through protein-protein interactions and protein modifications are the main mechanisms controlling many biological processes. Here we described the implementation of MedScan information extraction technology and Pathway Studio software (Ariadne Genomics Inc.) to create a Salmonella specific molecular interaction database. Using the database, we have constructed several signal transduction pathways in Salmonella enterica serovar Typhi which causes Typhoid Fever, a major health threat especially in developing countries. S. Typhi has several pathogenicity islands that control rapid switching between different phenotypes including adhesion and colonization, invasion, intracellular survival, proliferation, and biofilm formation in response to environmental changes. Understanding of the detailed mechanism for S. Typhi survival in host cells is necessary for development of efficient detection and treatment of this pathogen. The constructed pathways were validated using publically available gene expression microarray data for Salmonella.
Reconstruction of biological networks based on life science data integration
Authors: Kormeier Benjamin , Hippe Klaus , Arrigo Patrizio , Töpel Thoralf , Janowski Sebastian , Hofestädt Ralf
Published: December 2017

For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH - an integration toolkit for building life science data warehouses, CardioVINEdb - a information system for biological data in cardiovascular-disease and VANESA- a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.
Towards Prediction and Prioritization of disease genes by the modularity of human phenome-genome assembled network
Authors: Jiang Jeffrey Q. , Dress Andreas W. M. , Chen Ming
Published: December 2017

Empirical clinical studies on the human interactome and phenome not only illustrates prevalent phenotypic overlap and genetic overlap between diseases, but also reveals a modular organization of the genetic landscape of human disease, provding new opportunities to reduce the complexity in dissecting the phenotype-genotype association. We here introduce a network-module based method towards phenotype-genotype association inference and disease gene identification. This approach incorporates protein-protein interaction network, phenotype similarity network and known phenotype-genotype associations into an assembled network. We then decomposes the resulted network into modules (or communities) wherein we identified and prioritized the disease genes from the candidates within the loci associated with the query disease using a linear regression model and concordance score. For the known phenotype-gene associations in the OMIM database, we used the leave-one-out validation to evaluate the feasibility of our method, and successfully ranked known disease genes at top 1 in 887 out of 1807 cases. Moreover, applying this approach on 850 OMIMloci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases.
Stochastic simulation of the spatio-temporal dynamics of reaction-diffusion systems: the case for the bicoid gradient
Authors: Lecca Paola , Ihekwaba Adaoha E. C. , Dematté Lorenzo , Priami Corrado
Published: December 2017

Reaction-diffusion systems are mathematical models that describe how the concentrations of substances distributed in space change under the influence of local chemical reactions, and diffusion which causes the substances to spread out in space. The classical representation of a reaction-diffusion system is given by semi-linear parabolic partial differential equations, whose solution predicts how diffusion causes the concentration field to change with time. This change is proportional to the diffusion coefficient. If the solute moves in a homogeneous system in thermal equilibrium, the diffusion coefficients are constants that do not depend on the local concentration of solvent and solute. However, in nonhomogeneous and structured media the assumption of constant intracellular diffusion coefficient is not necessarily valid, and, consequently, the diffusion coefficient is a function of the local concentration of solvent and solutes. In this paper we propose a stochastic model of reaction-diffusion systems, in which the diffusion coefficients are function of the local concentration, viscosity and frictional forces. We then describe the software tool Redi (REaction-DIffusion simulator) which we have developed in order to implement this model into a Gillespie-like stochastic simulation algorithm. Finally, we show the ability of our model implemented in the Redi tool to reproduce the observed gradient of the bicoid protein in the Drosophila Melanogaster embryo. With Redi, we were able to simulate with an accuracy of 1% the experimental spatio-temporal dynamics of the bicoid protein, as recorded in time-lapse experiments obtained by direct measurements of transgenic bicoidenhanced green fluorescent protein.
CASSys: an integrated software-system for the interactive analysis of ChIP-seq data
Authors: Alawi Malik , Kurtz Stefan , Beckstette Michael
Published: December 2017

The mapping of DNA-protein interactions is crucial for a full understanding of transcriptional regulation. Chromatin-immunoprecipitation followed bymassively parallel sequencing (ChIP-seq) has become the standard technique for analyzing these interactions on a genome-wide scale. We have developed a software system called CASSys (ChIP-seq data Analysis Software System) spanning all steps of ChIP-seq data analysis. It supersedes the laborious application of several single command line tools. CASSys provides functionality ranging from quality assessment and -control of short reads, over the mapping of reads against a reference genome (readmapping) and the detection of enriched regions (peakdetection) to various follow-up analyses. The latter are accessible via a state-of-the-art web interface and can be performed interactively by the user. The follow-up analyses allow for flexible user defined association of putative interaction sites with genes, visualization of their genomic context with an integrated genome browser, the detection of putative binding motifs, the identification of over-represented Gene Ontology-terms, pathway analysis and the visualization of interaction networks. The system is client-server based, accessible via a web browser and does not require any software installation on the client side. To demonstrate CASSys’s functionality we used the system for the complete data analysis of a publicly available Chip-seq study that investigated the role of the transcription factor estrogen receptor-α in breast cancer cells.
Protein analysis tools and services at IBIVU
Authors: Brandt Bernd W. , Heringa Jaap
Published: December 2017

During the last years several new tools applicable to protein analysis have made available on the IBIVU web site. Recently, a number of tools, ranging from multiple sequence alignment construction to domain prediction, have been updated and/or extended with services for programmatic access using SOAP. We provide an overview of these tools and their application.
Oncology In Vivo Data Integration for Hypothesis Generation
Authors: Wei Jia , Chen Ming
Published: December 2017

AstraZeneca’s Oncology in vivo data integration platform brings multidimensional data from animal model efficacy, pharmacokinetic and pharmacodynamic data to animal model profiling data and public in vivo studies. Using this platform, scientists can cluster model efficacy and model profiling data together, quickly identify responder profiles and correlate molecular characteristics to pharmacological response. Through meta-analysis, scientists can compare pharmacology between single and combination treatments, between different drug scheduling and administration routes.
Nutrilyzer: A Tool for Deciphering Atomic Stoichiometry of Differentially Expressed Paralogous Proteins
Authors: Lotz Katrin , Schreiber Falk , Wünschiers Röbbe
Published: December 2017

Organisms try to maintain homeostasis by balanced uptake of nutrients from their environment. From an atomic perspective this means that, for example, carbon:nitrogen:sulfur ratios are kept within given limits. Upon limitation of, for example, sulfur, its acquisition is triggered. For yeast it was shown that transporters and enzymes involved in sulfur up- take are encoded as paralogous genes that express different isoforms. Sulfur deprivation leads to up-regulation of isoforms that are poor in sulfur-containing amino acids, that is, methinone and cysteine. Accordingly, sulfur-rich isoforms are down-regulated.

We developed a web-based software, doped Nutrilyzer, that extracts paralogous protein coding sequences from an annotated genome sequence and evaluates their atomic compo- sition. When fed with gene-expression data for nutrient limited and normal conditions, Nutrilyzer provides a list of genes that are significantly differently expressed and simul- taneously contain significantly different amounts of the limited nutrient in their atomic composition. Its intended use is in the field of ecological stoichiometry. Nutrilyzer is available at http://nutrilyzer.hs-mittweida.de.

Here we describe the work flow and results with an example from a whole-genome Ara- bidopsis thaliana gene-expression analysis upon oxygen deprivation. 43 paralogs distributed over 37 homology clusters were found to be significantly differently expressed while containing significantly different amounts of oxygen.
Computer and Statistical Analysis of Transcription Factor Binding and Chromatin Modifications by ChIP-seq data in Embryonic Stem Cell
Authors: Orlov Yuriy , Xu Han , Afonnikov Dmitri , Lim Bing , Heng Jian-Chien , Yuan Ping , Chen Ming , Yan Junli , Clarke Neil , Orlova Nina , Huss Mikael , Gunbin Konstantin , Podkolodnyy Nikolay , Ng Huck-Hui
Published: December 2017

Advances in high throughput sequencing technology have enabled the identification of transcription factor (TF) binding sites in genome scale. TF binding studies are important for medical applications and stem cell research. Somatic cells can be reprogrammed to a pluripotent state by the combined introduction of factors such as Oct4, Sox2, c-Myc, Klf4. These reprogrammed cells share many characteristics with embryonic stem cells (ESCs) and are known as induced pluripotent stem cells (iPSCs). The signaling requirements for maintenance of human and murine embryonic stem cells (ESCs) differ considerably. Genome wide ChIP-seq TF binding maps in mouse stem cells include Oct4, Sox2, Nanog, Tbx3, Smad2 as well as group of other factors. ChIP-seq allows study of new candidate transcription factors for reprogramming. It was shown that Nr5a2 could replace Oct4 for reprogramming. Epigenetic modifications play important role in regulation of gene expression adding additional complexity to transcription network functioning. We have studied associations between different histone modification using published data together with RNA Pol II sites. We found strong associations between activation marks and TF binding sites and present it qualitatively. To meet issues of statistical analysis of genome ChIP-sequencing maps we developed computer program to filter out noise signals and find significant association between binding site affinity and number of sequence reads. The data provide new insights into the function of chromatin organization and regulation in stem cells.
Microbase2.0: A Generic Framework for Computationally Intensive Bioinformatics Workflows in the Cloud
Authors: Flanagan Keith , Nakjang Sirintra , Hallinan Jennifer , Harwood Colin , Hirt Robert P. , Pocock Matthew R. , Wipat Anil
Published: December 2017

As bioinformatics datasets grow ever larger, and analyses become increasingly complex, there is a need for data handling infrastructures to keep pace with developing technology. One solution is to apply Grid and Cloud technologies to address the computational requirements of analysing high throughput datasets. We present an approach for writing new, or wrapping existing applications, and a reference implementation of a framework, Microbase2.0, for executing those applications using Grid and Cloud technologies. We used Microbase2.0 to develop an automated Cloud-based bioinformatics workflow executing simultaneously on two different Amazon EC2 data centres and the Newcastle University Condor Grid. Several CPU years’ worth of computational work was performed by this system in less than two months. The workflow produced a detailed dataset characterising the cellular localisation of 3,021,490 proteins from 867 taxa, including bacteria, archaea and unicellular eukaryotes. Microbase2.0 is freely available from http://www.microbase.org.uk/.
Widespread Recruitment of Ancient Domain Structures in Modern Enzymes during Metabolic Evolution
Authors: Kim Hee Shin , Mittenthal Jay E. , Caetano-Anollés Gustavo
Published: December 2017

Protein domains sometime combine to form multidomain proteins and are acquired or lost in lineages of organisms. These processes are ubiquitous in modern metabolism. To sort out evolutionary patterns of domain recruitment, we developed an algorithm that derives the most plausible ancestry of an enzyme from structural and evolutionary annotations in the MANET database. We applied this algorithm to the analysis of 1,163 enzymes with structural assignments. We then counted the number of enzymes along a time series and analyzed enzyme distribution in organisms belonging to superkingdoms Archaea, Bacteria, and Eukarya. The generated timelines described the evolution of modern metabolic networks and showed an early build-up of metabolic activities associated with metabolism of nucleotides, cofactors, and vitamins, followed by enzymes involved in carbohydrate and amino acid metabolism. More importantly, we find that existing domain structures were pervasively co-opted to perform more modern enzymatic tasks, either singly or in combination with other domains. This occurred differentially in lineages of the superkingdoms as the world diversified and organisms adapted to various environments. Our results highlight the important role of recruitment and domain organization in metabolic evolution.
Space-time dynamics of Stem Cell Niches: a unified approach for Plants
Authors: Pérez Maria del Carmen , López Alejandro , Padilla Pablo
Published: December 2017

Many complex systems cannot be analyzed using traditional mathematical tools, due to their irreducible nature. This makes it necessary to develop models that can be implemented computationally to simulate their evolution. Examples of these models are cellular automata, evolutionary algorithms, complex networks, agent-based models, symbolic dynamics and dynamical systems techniques.

We review some representative approaches to model the stem cell niche in Arabidopsis thaliana and the basic biological mechanisms that underlie its formation and maintenance. We propose a mathematical model based on cellular automata for describing the space-time dynamics of the stem cell niche in the root. By making minimal assumptions on the cell communication process documented in experiments, we classify the basic developmental features of the stem-cell niche, including the basic structural architecture, and suggest that they could be understood as the result of generic mechanisms given by short and long range signals. This could be a first step in understanding why different stem cell niches share similar topologies, not only in plants. Also the fact that this organization is a robust consequence of the way information is being processed by the cells and to some extent independent of the detailed features of the signaling mechanism.
Reducing the n-gram feature space of class C GPCRs to subtype-discriminating patterns
Authors: König Caroline , Alqézar Renè , Vellido Alfredo , Giraldo Jesús
Published: December 2017

G protein-coupled receptors (GPCRs) are a large and heterogeneous superfamily of receptors that are key cell players for their role as extracellular signal transmitters. Class C GPCRs, in particular, are of great interest in pharmacology. The lack of knowledge about their full 3-D structure prompts the use of their primary amino acid sequences for the construction of robust classifiers, capable of discriminating their different subtypes. In this paper, we investigate the use of feature selection techniques to build Support Vector Machine (SVM)-based classification models from selected receptor subsequences described as n-grams. We show that this approach to classification is useful for finding class C GPCR subtype-specific motifs.
Systems Biology Graphical Notation: Entity Relationship language Level 1 Version 2
Authors: Sorokin Anatoly , Le Novère Nicolas , Luna Augustin , Czauderna Tobias , Demir Emek , Haw Robin , Mi Huaiyu , Moodie Stuart , Schreiber Falk , Villéger Alice
Published: December 2017

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail.

The SBGN Entity Relationship language (ER) represents biological entities and their interactions and relationships within a network. SBGN ER focuses on all potential relationships between entities without considering temporal aspects. The nodes (elements) describe biological entities, such as proteins and complexes. The edges (connections) provide descriptions of interactions and relationships (or influences), e.g., complex formation, stimulation and inhibition. Among all three languages of SBGN, ER is the closest to protein interaction networks in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.
Systems Biology Graphical Notation: Activity Flow language Level 1 Version 1.2
Authors: Mi Huaiyu , Schreiber Falk , Moodie Stuart , Czauderna Tobias , Demir Emek , Haw Robin , Luna Augustin , Le Novère Nicolas , Sorokin Anatoly , Villéger Alice
Published: December 2017

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail.

The SBGN Activity Flow language represents the influences of activities among various entities within a network. Unlike SBGN PD and ER that focus on the entities and their relationships with others, SBGN AF puts the emphasis on the functions (or activities) performed by the entities, and their effects to the functions of the same or other entities. The nodes (elements) describe the biological activities of the entities, such as protein kinase activity, binding activity or receptor activity, which can be easily mapped to Gene Ontology molecular function terms. The edges (connections) provide descriptions of relationships (or influences) between the activities, e.g., positive influence and negative influence. Among all three languages of SBGN, AF is the closest to signaling pathways in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.
Multi-agent System for Obtaining Relevant Genes in Expression Analysis between Young and Older Women with Triple Negative Breast Cancer
Authors: González-Briones Alfonso , Ramos Juan , De Paz Juan F. , Corchado Juan M.
Published: December 2017

Triple negative breast cancer is an aggressive form of breast cancer. Despite treatment with chemotherapy, relapses are frequent and response to these treatments is not the same in younger women as in older women. Therefore, the identification of genes that cause this difference is required. The identification of therapeutic targets is one of the sought after goals to develop new drugs. Within the range of different hybridization techniques, the developed system uses expression array analysis to measure the expression of the signal levels of thousands of genes in a given sample. Probesets of Gene 1.0 ST GeneChip arrays provide categorical genome transcript coverage, providing a measurement of the expression level of the sample. This paper proposes a multi-agent system to manage information of expression arrays, with the goal of providing an intuitive system that is also extensible to analyze and interpret the results. The roles of agent integrate different types of techniques, statistical and data mining methods that select a set of genes, searching techniques that find pathways in which such genes participate, and an information extraction procedure that applies a CBR system to check if these genes are involved in the disease.
UV-visible scanning spectrophotometry and chemometric analysis as tools for carotenoids analysis in cassava genotypes (Manihot esculenta Crantz)
Authors: Moresco Rodolfo , Uarrota Virgílio G. , Pereira Aline , Tomazzoli Maíra , Nunes Eduardo da C. , Martins Peruch Luiz Augusto , Gazzola Jussara , Costa Christopher , Rocha Miguel , Maraschin Marcelo
Published: December 2017

In this study, the metabolomics characterization focusing on the carotenoid composition of ten cassava (Manihot esculenta) genotypes cultivated in southern Brazil by UV-visible scanning spectrophotometry and reverse phase-high performance liquid chromatography was performed. Cassava roots rich in β-carotene are an important staple food for populations with risk of vitamin A deficiency. Cassava genotypes with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin. The data set was used for the construction of a descriptive model by chemometric analysis. The genotypes of yellow-fleshed roots were clustered by the higher concentrations of cis- β-carotene and lutein. Inversely, cream-fleshed roots genotypes were grouped precisely due to their lower concentrations of these pigments, as samples rich in lycopene (redfleshed) differed among the studied genotypes. The analytical approach (UV-Vis, HPLC, and chemometrics) used showed to be efficient for understanding the chemodiversity of cassava genotypes, allowing to classify them according to important features for human health and nutrition.
PetriScape - A plugin for discrete Petri net simulations in Cytoscape
Authors: Almeida Diogo , Azevedo Vasco , Silva Artur , Baumbach Jan
Published: December 2017

Systems biology plays a central role for biological network analysis in the post-genomic era. Cytoscape is the standard bioinformatics tool offering the community an extensible platform for computational analysis of the emerging cellular network together with experimental omics data sets. However, only few apps/plugins/tools are available for simulating network dynamics in Cytoscape 3. Many approaches of varying complexity exist but none of them have been integrated into Cytoscape as app/plugin yet. Here, we introduce PetriScape, the first Petri net simulator for Cytoscape. Although discrete Petri nets are quite simplistic models, they are capable of modeling global network properties and simulating their behaviour. In addition, they are easily understood and well visualizable. PetriScape comes with the following main functionalities: (1) import of biological networks in SBML format, (2) conversion into a Petri net, (3) visualization as Petri net, and (4) simulation and visualization of the token flow in Cytoscape. PetriScape is the first Cytoscape plugin for Petri nets. It allows a straightforward Petri net model creation, simulation and visualization with Cytoscape, providing clues about the activity of key components in biological networks.
Web-based hybrid-dimensional Visualization and Exploration of Cytological Localization Scenarios
Authors: Kovanci Gökhan , Ghaffar Mehmood , Sommer Björn
Published: December 2017

The CELLmicrocosmos 4.2 PathwayIntegration (CmPI) is a tool which provides hybriddimensional visualization and analysis of intracellular protein and gene localizations in the context of a virtual 3D environment. This tool is developed based on Java/Java3D/JOGL and provides a standalone application compatible to all relevant operating systems. However, it requires Java and the local installation of the software. Here we present the prototype of an alternative web-based visualization approach, using Three.js and D3.js. In this way it is possible to visualize and explore CmPI-generated localization scenarios including networks mapped to 3D cell components by just providing a URL to a collaboration partner. This publication describes the integration of the different technologies - Three.js, D3.js and PHP - as well as an application case: a localization scenario of the citrate cycle. The CmPI web viewer is available at: http://CmPIweb.CELLmicrocosmos.org.
Clustering of Biological Datasets in the Era of Big Data
Authors: Röttger Richard
Published: December 2017

Clustering is a long-standing problem in computer science and is applied in virtually any scientific field for exploring the inherent structure of datasets. In biomedical research, clustering tools have been utilized in manifold areas, among many others in expression analysis, disease subtyping or protein research. A plethora of different approaches have been developed but there is only little guideline what approach is the optimal in what particular situation. Furthermore, a typical cluster analysis is an entire process with several highly interconnected steps; from preprocessing, proximity calculation, the actual clustering to evaluation and optimization. Only when all steps seamlessly work together, an optimal result can be achieved. This renders a cluster analyses tiresome and error-prone especially for non-experts. A mere trial-and-error approach renders increasingly infeasible when considering the tremendous growth of available datasets; thus, a strategic and thoughtful course of action is crucial for a cluster analysis. This manuscript provides an overview of the crucial steps and the most common techniques involved in conducting a state-of-the-art cluster analysis of biomedical datasets.
Computational miRNomics
Authors: Allmer Jens , Yousef Malik
Published: December 2017

Editorial

The term MicroRNA or its contraction miRNA currently appears in 21,215 titles of abstracts, published between 1997 and now, available on Pubmed (2016-21-22:12:59 EET). 4,108 of these were published in 2016 alone which signifies the importance of miRNA-related research. MicroRNAs can be detected experimentally using various techniques like directional cloning of endogenous small RNAs but they are time consuming [1]. Additionally, it is necessary for the miRNA and its mRNA target(s) to be co-expressed to infer a functional relationship which is difficult, if not impossible, to achieve [2]. Since experimental approaches are facing such difficulties, they have been complemented by computational approaches [3] thereby defining the field of computational miRNomics. Due to the rapid development in the discipline, it is important to assess the state-of-the-art. In this special issue, several areas of the field are investigated ranging from pre-miRNA detection via machine learning to application of differential expression analysis in plants. First, Saçar Demirci et al. discuss an approach to virus pre-miRNA detection using machine learning [4]. Such approaches are based on parameterization of miRNAs and Yousef et al. discuss how to select among such features [5]. A different computational perspective is provided by Kotipalli et al. who model the kinetics of miRNA genesis and targeting [6]. To fuel more refined future models for genesis and targeting, it is important to establish miRNA and target expression under varying conditions. Zhang et al. [7] and Kanke et al. [8] discuss two approaches to quantify miRNAs and other non-coding short RNAs. Diler et al., finally, discuss actual biological implications of differentially expressed miRNAs [9]. This special issue on computational miRNomics, thus, provides a trajectory from detection of pre-miRNAs to biological implications of differentially expressed miRNAs. Additional topics will be covered in the upcoming second volume of the special issue on computational miRNomics.
Ensemble Clustering Classification Applied to Competing SVM and One-Class Classifiers Exemplified by Plant MicroRNAs Data
Authors: Yousef Malik , Khalifa Waleed , AbdAllah Loai
Published: December 2017

The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN). In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that EC-kNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.
Differential Expression of Hyperhydricity Responsive Peach MicroRNAs
Authors: Diler Ebru , Unver Turgay , Karakülah Gökhan
Published: December 2017

Hyperhydricity is a syndrome that causes morpho-physiological malformations in tissue culture plantlets. Micro-RNAs (miRNA) are small non-coding RNAs that play important regulatory roles in plant development, stress response, and adaptation to environmental conditions. In this study, differential expression analysis indicated that miRNAs play an underlying role in the responses to the hyperhydricity syndrome in peach Prunus persica (L.) leaves. 24 known and three novel potential miRNAs were characterized in hyperhydric and non-hyperhydric transcriptome libraries. The miRNA-target transcript analyses indicated that transport, plant cuticle development, intracellular part, and stress response are regulated by miRNAs in hyperhydric leaves. It is also suggested that miR5021 and miRnovel2 might play critical regulatory roles in hyperhydricity regarding miRNA-based response to stress. This study went one step further to advance understanding of molecular miRNA-based regulatory mechanisms regarding responses to hyperhydricity in peach.
Diversity Challenges and Organizational Cynicism: A study on Egyptian Physicians
Authors: Mousa Mohamed
Published: December 2017

As a result of globalization and interactions of people with various identities, cultural diversity has become a noticeable norm in today’s business life. Accordingly, managing such cultural diversity requires a dependence on a much more tolerant culture in which employees seek to attain both career and organizational objectives without being hurdled by irrelevant factors of religion, race, ethnicity and so on. During the last two decades, organizational cynicism has found a place in academic management literature. It has a vital role in interpreting many organizational unwanted behaviors such as: absenteeism, day dreaming and the state of being careless when doing work. This study investigates the association between cultural diversity challenges and organizational cynicism dimensions by conducting a quantitative study of physicians in Kasr El Eini public hospital in Egypt. Upon using correlation and regression analysis for the collected data, it appears that not all cultural diversity challenges have a negative impact on organizational cynicism dimensions.
Nonautonomous partial functional differential equations; existence and regularity
Authors: Kpoumiè Moussa El-Khalil , Ezzinbi Khalil , Békollè David
Published: December 2017

The aim of this work is to establish several results on the existence and regularity of solutions for some nondensely nonautonomous partial functional differential equations with finite delay in a Banach space. We assume that the linear part is not necessarily densely defined and generates an evolution family under the conditions introduced by N. Tanaka.We show the local existence of the mild solutions which may blow up at the finite time. Secondly,we give sufficient conditions ensuring the existence of the strict solutions. Finally, we consider a reaction diffusion equation with delay to illustrate the obtained results.
Programmable optical processor chips: toward photonic RF filters with DSP-level flexibility and MHz-band selectivity
Authors: Xie Yiwei , Geng Zihan , Zhuang Leimeng , Burla Maurizio , Taddei Caterina , Hoekman Marcel , Leinse Arne , Roeloffzen Chris G.H. , Boller Klaus-J. , Lowery Arthur J.
Published: December 2017

Integrated optical signal processors have been identified as a powerful engine for optical processing of microwave signals. They enable wideband and stable signal processing operations on miniaturized chips with ultimate control precision. As a promising application, such processors enables photonic implementations of reconfigurable radio frequency (RF) filters with wide design flexibility, large bandwidth, and high-frequency selectivity. This is a key technology for photonic-assisted RF front ends that opens a path to overcoming the bandwidth limitation of current digital electronics. Here, the recent progress of integrated optical signal processors for implementing such RF filters is reviewed. We highlight the use of a low-loss, high-index-contrast stoichiometric silicon nitride waveguide which promises to serve as a practical material platform for realizing high-performance optical signal processors and points toward photonic RF filters with digital signal processing (DSP)-level flexibility, hundreds-GHz bandwidth, MHz-band frequency selectivity, and full system integration on a chip scale.
Optofluidics for handling and analysis of single living cells
Authors: Perozziello Gerardo , Candeloro Patrizio , Coluccio Maria Laura , Di Fabrizio Enzo
Published: December 2017

Optofluidics is a field with important applications in areas such as biotechnology, chemical synthesis and analytical chemistry. Optofluidic devices combine optical elements into microfluidic devices in ways that increase portability and sensitivity of analysis for diagnostic or screening purposes .In fact in these devices fluids give fine adaptability, mobility and accessibility to nanoscale photonic devices which otherwise could not be realized using conventional devices. This review describes several cases inwhich optical or microfluidic approaches are used to trap single cells in proximity of integrated optical sensor for being analysed.
Selected health behaviours in professionally active men and women
Authors: Chuchra Maria , Gorbaniuk Julia
Published: December 2017

Introduction. Women suffer from health problems more often and for a longer time, however it is generally men who die earlier.

Aim. The aim of this work is to compare the selected health behaviours of working men and women.

Material and methods. The research included 100 men and 100 women. The respondents were tested with the matched pair-selection method, taking into consideration four variables: place of residence, level of education, marital status, and age. The survey questionnaire was used.

Results. Men and women show similarities with respect to some health behaviours, showing significant differences (amount and kind of alcoholic beverages drunk, regularity of preventive checks) in relation to others.

Conclusions. Women’s health behaviours are to be evaluated more positively than men’s. Both men and women require intensive educational activities concerning the effects of anti-health behaviours, especially bad eating habits and irregularity of preventive checks.
Progress in the research and applications of natural fiber-reinforced polymer matrix composites
Authors: Sun Zhanying
Published: December 2017

The natural and fiber-reinforced resin matrix composites have been increasingly and widely applied in society in order to improve the environment and promote sustainable development. In the first part of this article, the major foci of recent research on natural fiber composites are reviewed in detail, including fiber surface treatment, fiber agglomeration and dispersion, interfacial transcrystallinity, impact strength, foaming technique, inflaming retardance, biodegradable resin matrix and nanofiber reinforcement techniques. The fiber surface treatment and fiber dispersion are important factors in improving the mechanical properties of the natural fiber composites. The interfacial transcrystallinity can be generated on natural fibers owing to the rough surfaces. The natural fiber composites have a low impact strength. In order to reduce the density and improve the flame retardant effect of the composites, both the foaming technique and flame-retardant technology are studied in detail. The biodegradable resin matrix and natural fibers can be mixed to prepare fully-degradable composite materials. Using the natural nano fiber reinforcements in reinforcing resins is another important research area. In the second part of this article, the applications of these composites are summarized systematically, especially those involving the automotive industry, household appliances, construction materials, packaging materials and boards of musical instruments. Currently, the automotive industry and the production of construction materials are the most important application areas of these composites.
“What is essential is invisible to the eye”
Authors: Mayr Beate , Frick Eckhard , Hvidt Niels Christian , Breidenbach Thomas , Erbe Nicole , Seidel Dorothee , Paal Piret
Published: December 2017

Organ donation and transplantation (ODT) is a complex multi-professional clinical procedure. Healthcare professionals’ attitudes and concerns towards the procedure influence the entire process. A 3-hour interview with ODT staff (13 participants) was performed in order to explore ODT professionals’ spiritual attitudes and concerns. The interview was recorded and transcribed verbatim. All (sub)themes were directly derived from the data. The discussants expressed the following issues: (1) ethical considerations and barriers in ODT; (2) individual beliefs about death and afterlife; (3) dealings with patients, relatives and close ones; (4) lack of support for ODT staff; and (5) spiritual facilitators. The interview provided a list of themes that either explicitly or implicitly demonstrate spiritual attitudes and concerns which may influence procedures in ODT. The themes and subthemes formed the basis to design a questionnaire in order to initiate a more detailed investigation of a larger group of professionals.
A New Application of Beta Gompertz Distribution in Reliability
Authors: Hassan M. KH.
Published: December 2017

In this paper, we considered the problem of the estimation R = P(Y < X) that was defined as a life of a component, and which has strength X and is subjected to stress Y. We derive R = P(Y < X) when X and Y are independent beta Gompertz random variables. The reliability is estimated using two estimation methods, they were the maximum likelihood and Bayes estimator. A simulation study was used to compare the two different estimators. Real data was used as a practical application of the proposed procedure.
Production Yield for Multiple Line Processes: Product Acceptance Determination
Authors: Pearn W. L. , Tai Y. T. , Chiu Y. T.
Published: December 2017

Product inspection and quality testing are very important, particularly for product sentencing in product acceptance determination. Because the production yield requirements for most high-tech processes are very stringent, processes with a very low fraction of defectives are common. In such cases, process capability indices have been widely applied to provide numerical evaluations in process capability, which are effective tools for quality assurance. In product acceptance determination, methods of evaluating yield for processes with a single manufacturing line have been investigated extensively, but processes with multiple lines have been comparatively neglected. However, multiple manufacturing lines are popular in many factory applications because of high demand. Consequently, in this paper, we consider the product acceptance sampling plan based on CMpk to deal with product acceptance determination for processes with multiple lines. The required sample sizes and the critical acceptance values for various levels of producers’ and customers’ risks with various values of acceptable quality level (AQL) and lot tolerance percent defective (LTPD) are tabulated. In addition, a Monte Carlo simulation is conducted and the coverage rates of producers’ and customers’ risks for various values of AQL and LTPD are provided. The product acceptance determination based on CMpu for processes with one-sided specification limits and multiple lines are also discussed. The computational results are useful to the practitioners for their in-plant applications.
Non-Newly Generated, “Immature” Neurons in the Sheep Brain Are Not Restricted to Cerebral Cortex
Authors: Piumatti Matteo , Palazzo Ottavia , La Rosa Chiara , Crociara Paola , Parolisi Roberta , Luzzati Federico , Lévy Frederic , Bonfanti Luca
Published: December 2017

A newly proposed form of brain structural plasticity consists of non-newly generated, “immature” neurons of the adult cerebral cortex. Similar to newly generated neurons, these cells express the cytoskeletal protein Doublecortin (DCX), yet they are generated prenatally and then remain in a state of immaturity for long periods. In rodents, the immature neurons are restricted to the paleocortex, whereas in other mammals, they are also found in neocortex. Here, we analyzed the DCX-expressing cells in the whole sheep brain of both sexes to search for an indicator of structural plasticity at a cellular level in a relatively large-brained, long-living mammal. Brains from adult and newborn sheep (injected with BrdU and analyzed at different survival times) were processed for DCX, cell proliferation markers (Ki-67, BrdU), pallial/subpallial developmental origin (Tbr1,Sp8), and neuronal/glial antigens for phenotype characterization. We found immature-like neurons in the whole sheep cortex and in large populations of DCX-expressing cells within the external capsule and the surrounding gray matter (claustrum and amygdala). BrdU and Ki-67 detection at neonatal and adult ages showed that all of these DCX+cells were generated during embryogenesis, not after birth. These results show that the adult sheep, unlike rodents, is largely endowed with non-newly generated neurons retaining immature features, suggesting that such plasticity might be particularly important in large-brained, long-living mammals.

SIGNIFICANCE STATEMENTBrain plasticity is important in adaptation and brain repair. Structural changes span from synaptic plasticity to adult neurogenesis, the latter being highly reduced in large-brained, long-living mammals (e.g., humans). The cerebral cortex contains “immature” neurons, which are generated prenatally and then remain in an undifferentiated state for long periods, being detectable with markers of immaturity. We studied the distribution and developmental origin of these cells in the whole brain of sheep, relatively large-brained, long-living mammals. In addition to the expected cortical location, we also found populations of non-newly generated neurons in several subcortical regions (external capsule, claustrum, and amygdala). These results suggests that non-neurogenic, parenchymal structural plasticity might be more important in large mammals with respect to adult neurogenesis.