bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting Genes Involved in Human Cancer Using Network Contextual Information
Authors: Rahmani Hossein , Blockeel Hendrik , Bender Andreas
Published: December 2017

Protein-Protein Interaction (PPI) networks have been widely used for the task of predicting proteins involved in cancer. Previous research has shown that functional information about the protein for which a prediction is made, proximity to specific other proteins in the PPI network, as well as local network structure are informative features in this respect. In this work, we introduce two new types of input features, reflecting additional information: (1) Functional Context: the functions of proteins interacting with the target protein (rather than the protein itself); and (2) Structural Context: the relative position of the target protein with respect to specific other proteins selected according to a novel ANOVA (analysis of variance) based measure. We also introduce a selection strategy to pinpoint the most informative features. Results show that the proposed feature types and feature selection strategy yield informative features. A standard machine learning method (Naive Bayes) that uses the features proposed here outperforms the current state-of-the-art methods by more than 5% with respect to F-measure. In addition, manual inspection confirms the biological relevance of the top-ranked features.
Can MiRBase Provide Positive Data for Machine Learning for the Detection of MiRNA Hairpins?
Authors: Saçar Müşerref Duygu , Hamzeiy Hamid , Allmer Jens
Published: December 2017

Experimental detection and validation of miRNAs is a tedious, time-consuming, and expensive process. Computational methods for miRNA gene detection are being developed so that the number of candidates that need experimental validation can be reduced to a manageable amount. Computational methods involve homology-based and ab inito algorithms. Both approaches are dependent on positive and negative training examples. Positive examples are usually derived from miRBase, the main resource for experimentally validated miRNAs. We encountered some problems with miRBase which we would like to report here. Some problems, among others, we encountered are that folds presented in miRBase are not always the fold with the minimum free energy; some entries do not seem to conform to expectations of miRNAs, and some external accession numbers are not valid. In addition, we compared the prediction accuracy for the same negative dataset when the positive data came from miRBase or miRTarBase and found that the latter led to more precise prediction models. We suggest that miRBase should introduce some automated facilities for ensuring data quality to overcome these problems.
Excemplify: A Flexible Template Based Solution, Parsing and Managing Data in Spreadsheets for Experimentalists
Authors: Shi Lei , Jong Lenneke , Wittig Ulrike , Lucarelli Philippe , Stepath Markus , Mueller Stephanie , D‘Alessandro Lorenza Alice , Klingmüller Ursula , Müller Wolfgang
Published: December 2017

In systems biology, quantitative experimental data is the basis of building mathematical models. In most of the cases, they are stored in Excel files and hosted locally. To have a public database for collecting, retrieving and citing experimental raw data as well as experimental conditions is important for both experimentalists and modelers. However, the great effort needed in the data handling procedure and in the data submission procedure becomes the crucial limitation for experimentalists to contribute to a database, thereby impeding the database to deliver its benefit. Moreover, manual copy and paste operations which are commonly used in those procedures increase the chance of making mistakes. Excemplify, a web-based application, proposes a flexible and adaptable template-based solution to solve these problems. Comparing to the normal template based uploading approach, which is supported by some public databases, rather than predefining a format that is potentiall impractical, Excemplify allows users to create their own experiment-specific content templates in different experiment stages and to build corresponding knowledge bases for parsing. Utilizing the embedded knowledge of used templates, Excemplify is able to parse experimental data from the initial setup stage and generate following stages spreadsheets automatically. The proposed solution standardizes the flows of data traveling according to the standard procedures of applying the experiment, cuts down the amount of manual effort and reduces the chance of mistakes caused by manual data handling. In addition, it maintains the context of meta-data from the initial preparation manuscript and improves the data consistency. It interoperates and complements RightField and SEEK as well.
Discovery of miR-mRNA interactions via simultaneous Bayesian inference of gene networks and clusters using sequence-based predictions and expression data
Authors: Godsey Brian
Published: December 2017

MicroRNAs (miRs) are known to interfere with mRNA expression, and much work has been put into predicting and inferring miR-mRNA interactions. Both sequence-based interaction predictions as well as interaction inference based on expression data have been proven somewhat successful; furthermore, models that combine the two methods have had even more success. In this paper, I further refine and enrich the methods of miR-mRNA interaction discovery by integrating a Bayesian clustering algorithm into a model of prediction-enhanced miR-mRNA target inference, creating an algorithm called PEACOAT, which is written in the R language. I show that PEACOAT improves the inference of miR-mRNA target interactions using both simulated data and a data set of microarrays from samples of multiple myeloma patients. In simulated networks of 25 miRs and mRNAs, our methods using clustering can improve inference in roughly two-thirds of cases, and in the multiple myeloma data set, KEGG pathway enrichment was found to be more significant with clustering than without. Our findings are consistent with previous work in clustering of non-miR genetic networks and indicate that there could be a significant advantage to clustering of miR and mRNA expression data as a part of interaction inference.
Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data
Authors: List Markus , Hauschild Anne-Christin , Tan Qihua , Kruse Torben A. , Baumbach Jan , Batra Richa
Published: December 2017

Selecting the most promising treatment strategy for breast cancer crucially depends on determining the correct subtype. In recent years, gene expression profiling has been investigated as an alternative to histochemical methods. Since databases like TCGA provide easy and unrestricted access to gene expression data for hundreds of patients, the challenge is to extract a minimal optimal set of genes with good prognostic properties from a large bulk of genes making a moderate contribution to classification. Several studies have successfully applied machine learning algorithms to solve this so-called gene selection problem. However, more diverse data from other OMICS technologies are available, including methylation. We hypothesize that combining methylation and gene expression data could already lead to a largely improved classification model, since the resulting model will reflect differences not only on the transcriptomic, but also on an epigenetic level. We compared so-called random forest derived classification models based on gene expression and methylation data alone, to a model based on the combined features and to a model based on the gold standard PAM50. We obtained bootstrap errors of 10-20% and classification error of 1-50%, depending on breast cancer subtype and model. The gene expression model was clearly superior to the methylation model, which was also reflected in the combined model, which mainly selected features from gene expression data. However, the methylation model was able to identify unique features not considered as relevant by the gene expression model, which might provide deeper insights into breast cancer subtype differentiation on an epigenetic level.
VANESA - A Software Application for the Visualization and Analysis of Networks in Systems Biology Applications
Authors: Brinkrolf Christoph , Janowski Sebastian Jan , Kormeier Benjamin , Lewinski Martin , Hippe Klaus , Borck Daniela , Hofestädt Ralf
Published: December 2017

VANESA is a modeling software for the automatic reconstruction and analysis of biological networks based on life-science database information. Using VANESA, scientists are able to model any kind of biological processes and systems as biological networks. It is now possible for scientists to automatically reconstruct important molecular systems with information from the databases KEGG, MINT, IntAct, HPRD, and BRENDA. Additionally, experimental results can be expanded with database information to better analyze the investigated elements and processes in an overall context. Users also have the possibility to use graph theoretical approaches in VANESA to identify regulatory structures and significant actors within the modeled systems. These structures can then be further investigated in the Petri net environment of VANESA. It is platform-independent, free-of-charge, and available at http://vanesa.sf.net.
Integrating Sequence Analysis with Biophysical Modelling for Accurate Transcription Start Site Prediction
Authors: Djordjevic Marko
Published: December 2017

Promoter prediction in bacteria is a classical bioinformatics problem, where available methods for regulatory element detection exhibit a very high number of false positives. We here argue that accurate transcription start site (TSS) prediction is a complex problem, where available methods for sequence motif discovery are not in itself well adopted for solving the problem. We here instead propose that the problem requires integration of quantitative understanding of transcription initiation with careful description of promoter sequence specificity. We review evidence for this viewpoint, and discuss a current progress on these issues on the example of sigma70 transcription start sites in E. coli.
A distributed computational search strategy for the identification of diagnostics targets: Application to finding aptamer targets for methicillin-resistant staphylococci
Authors: Flanagan Keith , Cockell Simon , Harwood Colin , Hallinan Jennifer , Nakjang Sirintra , Lawry Beth , Wipat Anil
Published: December 2017

The rapid and cost-effective identification of bacterial species is crucial, especially for clinical diagnosis and treatment. Peptide aptamers have been shown to be valuable for use as a component of novel, direct detection methods. These small peptides have a number of advantages over antibodies, including greater specificity and longer shelf life. These properties facilitate their use as the detector components of biosensor devices. However, the identification of suitable aptamer targets for particular groups of organisms is challenging. We present a semi-automated processing pipeline for the identification of candidate aptamer targets from whole bacterial genome sequences. The pipeline can be configured to search for protein sequence fragments that uniquely identify a set of strains of interest. The system is also capable of identifying additional organisms that may be of interest due to their possession of protein fragments in common with the initial set. Through the use of Cloud computing technology and distributed databases, our system is capable of scaling with the rapidly growing genome repositories, and consequently of keeping the resulting data sets up-to-date. The system described is also more generically applicable to the discovery of specific targets for other diagnostic approaches such as DNA probes, PCR primers and antibodies.
A harmonised vocabulary for communicating and interchanging Biofilms experimental results
Authors: Sousa Ana Margarida , Pereira Maria Olívia , Azevedo Nuno F. , Lourenço Anália
Published: December 2017

Biofilm studies are at the crossroads of Biology, Chemistry, Medicine, Material Science and Engineering, among other fields. Data harmonisation in Biofilms is therefore crucial to allow for researchers to collaborate, interchange, understand, and replicate studies at an inter-laboratory and inter-domain scale. The international Minimum Information About a Biofilms Experiment initiative has prepared a set of guidelines for documenting biofilms experiments and data, namely the minimum information checklist. This paper goes a step forward and describes a new ontology for the broad description of biofilm experiments and data. In such an interdisciplinary context we chose to rely on a common integration framework provided by a foundational ontology that facilitates the addition and extension of various sub-domain modules, and the consistent integration of terminology extracted from several existing ontologies, e.g. EXPO and ChEBI. The community is participating actively in the production of this resource, and it is already used by public biofilms-centred databases, such as BiofOmics, and bioinformatics tools, such as the Biofilms Experiment Workbench. This practical validation serves the purpose of disseminating the controlled vocabulary among researchers and identifying current limitations, glitches, and inconsistencies. Information branches will be added, extended or refactored according to user feedback and group discussions.
High Performance Pattern Matching on Heterogeneous Platform
Authors: Soroushnia Shima , Daneshtalab Masoud , Plosila Juha , Pahikkala Tapio , Liljeberg Pasi
Published: December 2017

Pattern discovery is one of the fundamental tasks in bioinformatics and pattern recognition is a powerful technique for searching sequence patterns in the biological sequence databases. Fast and high performance algorithms are highly demanded in many applications in bioinformatics and computational molecular biology since the significant increase in the number of DNA and protein sequences expand the need for raising the performance of pattern matching algorithms. For this purpose, heterogeneous architectures can be a good choice due to their potential for high performance and energy efficiency. In this paper we present an efficient implementation of Aho-Corasick (AC) which is a well known exact pattern matching algorithm with linear complexity, and Parallel Failureless Aho-Corasick (PFAC) algorithm which is the massively parallelized version of AC algorithm without failure transitions, on a heterogeneous CPU/GPU architecture. We progressively redesigned the algorithms and data structures to fit on the GPU architecture. Our results on different protein sequence data sets show that the new implementation runs 15 times faster compared to the original implementation of the PFAC algorithm.
A web application for automatic prediction of gene translation elongation efficiency
Authors: Sokolov Vladimir S. , Zuraev Bulat S. , Lashin Sergei A. , Matushkin Yury G.
Published: December 2017

Expression efficiency is one of the major characteristics describing genes in various modern investigations. Expression efficiency of genes is regulated at various stages: transcription, translation, posttranslational protein modification and others. In this study, a special EloE (Elongation Efficiency) web application is described. The EloE sorts the organism’s genes in a descend order on their theoretical rate of the elongation stage of translation based on the analysis of their nucleotide sequences. Obtained theoretical data have a significant correlation with available experimental data of gene expression in various organisms. In addition, the program identifies preferential codons in organism’s genes and defines distribution of potential secondary structures energy in 5´ and 3´ regions of mRNA. The EloE can be useful in preliminary estimation of translation elongation efficiency for genes for which experimental data are not available yet. Some results can be used, for instance, in other programs modeling artificial genetic structures in genetically engineered experiments. The EloE web application is available at http://www-bionet.sscc.ru:7780/EloE.
Shared Bioinformatics Databases within the Unipro UGENE Platform
Authors: Protsyuk Ivan V. , Grekhov German A. , Tiunov Alexey V. , Fursov Mikhail Yu.
Published: December 2017

Unipro UGENE is an open-source bioinformatics toolkit that integrates popular tools along with original instruments for molecular biologists within a unified user interface. Nowadays, most bioinformatics desktop applications, including UGENE, make use of a local data model while processing different types of data. Such an approach causes an inconvenience for scientists working cooperatively and relying on the same data. This refers to the need of making multiple copies of certain files for every workplace and maintaining synchronization between them in case of modifications. Therefore, we focused on delivering a collaborative work into the UGENE user experience. Currently, several UGENE installations can be connected to a designated shared database and users can interact with it simultaneously. Such databases can be created by UGENE users and be used at their discretion. Objects of each data type, supported by UGENE such as sequences, annotations, multiple alignments, etc., can now be easily imported from or exported to a remote storage. One of the main advantages of this system, compared to existing ones, is the almost simultaneous access of client applications to shared data regardless of their volume. Moreover, the system is capable of storing millions of objects. The storage itself is a regular database server so even an inexpert user is able to deploy it. Thus, UGENE may provide access to shared data for users located, for example, in the same laboratory or institution. UGENE is available at: http://ugene.net/download.html.
SBML Level 3 package: Qualitative Models, Version 1, Release 1
Authors: Chaouiya Claudine , Keating Sarah M. , Berenguier Duncan , Naldi Aurélien , Thieffry Denis , Iersel Martijn P. van , Le Novère Nicolas , Helikar Tomáš
Published: December 2017

Quantitative methods for modelling biological networks require an in-depth knowledge of the biochemical reactions and their stoichiometric and kinetic parameters. In many practical cases, this knowledge is missing. This has led to the development of several qualitative modelling methods using information such as, for example, gene expression data coming from functional genomic experiments. The SBML Level 3 Version 1 Core specification does not provide a mechanism for explicitly encoding qualitative models, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactical constructs.

The SBML Qualitative Models package for SBML Level 3 adds features so that qualitative models can be directly and explicitly encoded. The approach taken in this package is essentially based on the definition of regulatory or influence graphs. The SBML Qualitative Models package defines the structure and syntax necessary to describe qualitative models that associate discrete levels of activities with entity pools and the transitions between states that describe the processes involved. This is particularly suited to logical models (Boolean or multi-valued) and some classes of Petri net models can be encoded with the approach.
Synthetic Biology Open Language (SBOL) Version 2.0.0
Authors: Bartley Bryan , Beal Jacob , Clancy Kevin , Misirli Goksel , Roehner Nicholas , Oberortner Ernst , Pocock Matthew , Bissell Michael , Madsen Curtis , Nguyen Tramy , Zhang Zhen , Gennari John H. , Myers Chris , Wipat Anil , Sauro Herbert
Published: December 2017

Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems would be to improve the exchange of information about designed systems between laboratories. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.0 of SBOL, introducing a standardized format for the electronic exchange of information on the structural and functional aspects of biological designs. The standard has been designed to support the explicit and unambiguous description of biological designs by means of a well defined data model. The standard also includes rules and best practices on how to use this data model and populate it with relevant design details. The publication of this specification is intended to make these capabilities more widely accessible to potential developers and users in the synthetic biology community and beyond.
Taxonomic and functional metagenomic analysis of anodic communities in two pilot-scale microbial fuel cells treating different industrial wastewaters
Authors: Kiseleva Larisa , Garushyants Sofya K. , Ma Hongwu , Simpson David J.W. , Fedorovich Viatcheslav , Cohen Michael F. , Goryanin Igor
Published: December 2017

The combined processes of microbial biodegradation accompanied by extracellular electron transfer make microbial fuel cells (MFCs) a promising new technology for cost-effective and sustainable wastewater treatment. Although a number of microbial species that build biofilms on the anode surfaces of operating MFCs have been identified, studies on the metagenomics of entire electrogenic communities are limited. Here we present the results of wholegenome metagenomic analysis of electrochemically active robust anodic microbial communities, and their anaerobic digester (AD) sludge inocula, from two pilot-scale MFC bioreactors fed with different distillery wastewaters operated under ambient conditions in distinct climatic zones. Taxonomic analysis showed that Proteobacteria, Bacteroidetes and Firmicutes were abundant in AD sludge from distinct climatic zones, and constituted the dominant core of the MFC microbiomes. Functional analysis revealed species involved in degradation of organic compounds commonly present in food industry wastewaters. Also, accumulation of methanogenic Archaea was observed in the electrogenic biofilms, suggesting a possibility for simultaneous electricity and biogas recovery from one integrated wastewater treatment system. Finally, we found a range of species within the anode communities possessing the capacity for extracellular electron transfer, both via direct contact and electron shuttles, and show differential distribution of bacterial groups on the carbon cloth and activated carbon granules of the anode surface. Overall, this study provides insights into structural shifts that occur in the transition from an AD sludge to an MFC microbial community and the metabolic potential of electrochemically active microbial populations with wastewater-treating MFCs.
OpenLabNotes – An Electronic Laboratory Notebook Extension for OpenLabFramework
Authors: List Markus , Franz Michael , Tan Qihua , Mollenhauer Jan , Baumbach Jan
Published: December 2017

Electronic laboratory notebooks (ELNs) are more accessible and reliable than their paper based alternatives and thus find widespread adoption. While a large number of commercial products is available, small- to mid-sized laboratories can often not afford the costs or are concerned about the longevity of the providers. Turning towards free alternatives, however, raises questions about data protection, which are not sufficiently addressed by available solutions. To serve as legal documents, ELNs must prevent scientific fraud through technical means such as digital signatures. It would also be advantageous if an ELN was integrated with a laboratory information management system to allow for a comprehensive documentation of experimental work including the location of samples that were used in a particular experiment. Here, we present OpenLabNotes, which adds state-of-the-art ELN capabilities to OpenLabFramework, a powerful and flexible laboratory information management system. In contrast to comparable solutions, it allows to protect the intellectual property of its users by offering data protection with digital signatures. OpenLabNotes effectively closes the gap between research documentation and sample management, thus making Open- LabFramework more attractive for laboratories that seek to increase productivity through electronic data management.
RetroMine, or how to provide in-depth retrospective studies from Medline in a glance: the hepcidin use-case
Authors: Cadeville Bertrand Ameline de , Loréal Olivier , Moussouni-Marzolf Fouzia
Published: December 2017

The rapid expansion of biomedical literature has provoked an increased development of advanced text mining tools to rapidly extract relevant events from the continuously increasing amount of knowledge published periodically in PubMed. However, bioinvestigators are still reluctant to use these tools for two reasons: i) a large volume of events is often extracted upon a query, and this volume is hard to manage, and ii) background events dominate search results and overshadow more pertinent published information, especially for domain experts. In this paper, we propose an approach that incorporates the temporal dimension of published events to the process of information extraction to improve data selection and prioritize more pertinent periodically published knowledge for scientists. Indeed, instead of providing the total knowledge associated with a PubMed query, which is usually a mix of trivial background information and nonbackground information, we propose a method that incorporates time and selects non background and highly relevant biological entities and events published over time for bioinvestigators. Before excluding background events from the total knowledge extracted, a quantification of their amount is also provided. This work is illustrated by a case study regarding Hepcidin gene publications over a decade, a duration that is sufficiently long enough to generate alternative views on the overall data extracted.
Machine Learning approach to discriminate Saccharomyces cerevisiae yeast cells using sophisticated image features
Authors: Tleis Mohamed , Verbeek Fons J.
Published: December 2017

In biological research, Saccharomyces cerevisiae yeast cells are used to study the behaviour of proteins. This is a time consuming and not completely objective process. Hence, Image analysis platforms are developed to address these problems and to offer analysis per cell as well. The robust segmentation algorithms implemented in such platforms enables us to apply a machine learning approach on the measured cells. Such approach is based on a set of relevant individual cell features extracted from the microscope images of the yeast cells. In this paper, we composed a set of features to represent the intensity and morphology characteristics in a more sophisticated way. These features are based on first and second order histograms and wavelet-based texture measurement. To show the discrimination power of these features, we built a classification model to discriminate between different groups. The building process involved evaluation of a set of classification systems, data sampling techniques, data normalization schemes and attribute selection algorithms. The results show a significant ability to discriminate different cell strains and conditions; subsequently it reveals the benefits of the classification model based on the introduced features. This model is promising in revealing subtle patterns in future high-throughput yeast studies.
Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data
Authors: Tomazzoli Maíra M. , Pai Neto Remi D. , Moresco Rodolfo , Westphal Larissa , Zeggio Amelia R. S. , Specht Leandro , Costa Christopher , Rocha Miguel , Maraschin Marcelo
Published: December 2017

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plant’s resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( λ= 280-400 ηm), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.
Online High-throughput Mutagenesis Designer Using Scoring Matrix of Sequence-specific Endonucleases
Authors: Guo Dayong , Li Xiaojing , Zhu Pan , Feng Yanrong , Yang Juan , Zheng Zhihong , Yang Wei , Zhang Enuo , Zhou Shenglai , Wang Hongyu
Published: December 2017

CRISPR Cas9 and other sequence-specific endonucleases are fundamental genome editors supporting gene knockout and gene therapy. A speedy and accurate computational allele designer is required for a high through-put gene mutagenesis pipeline using these new techniques.

An automatic system, Cas9 online designer (COD), was created to screen Cas9 targets and off-targets, as well as to provide gene knockout and genotyping strategies. A gene knockout rat model was successfully created and genotyped under the direction of this online system confirming its ability to predict real targets and off-targets. Gene knockout strategies to mutate 72 rat cytochrome P450 genes were designed instantly by the system to demonstrate its high-throughput efficiency. Also, the system used an off-target scoring matrix which can be applied to any sequence-specific genome editing tools besides Cas9.

The COD system (http://cas9.wicp.net) has established a speedy, accurate, flexible and high through-put computational gene knockout pipeline supporting the sequence-specific endonuclease induced mutagenesis.
Feature Learning applied to the Estimation of Tensile Strength at Break in Polymeric Material Design
Authors: Fiorella Cravero , Jimena Martínez M. , Gustavo Vazquez , Díaz Mónica F. , Ignacio Ponzoni
Published: December 2017

Several feature extraction approaches for QSPR modelling in Cheminformatics are discussed in this paper. In particular, this work is focused on the use of these strategies for predicting mechanical properties, which are relevant for the design of polymeric materials. The methodology analysed in this study employs a feature learning method that uses a quantification process of 2D structural characterization of materials with the autoencoder method. Alternative QSPR models inferred for tensile strength at break (a well-known mechanical property of polymers) are presented. These alternative models are contrasted to QSPR models obtained by feature selection technique by using accuracy measures and a visual analytic tool. The results show evidence about the benefits of combining feature learning approaches with feature selection methods for the design of QSPR models.
Synthetic Biology Open Language (SBOL) Version 2.1.0
Authors: Beal Jacob , Cox Robert Sidney , Grünberg Raik , McLaughlin James , Nguyen Tramy , Bartley Bryan , Bissell Michael , Choi Kiri , Clancy Kevin , Macklin Chris , Madsen Curtis , Misirli Goksel , Oberortner Ernst , Pocock Matthew , Roehner Nicholas , Samineni Meher , Zhang Michael , Zhang Zhen , Zundel Zach , Gennari John H. , Myers Chris , Sauro Herbert , Wipat Anil
Published: December 2017

Synthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems would be to improve the exchange of information about designed systems between laboratories. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.1 of SBOL that builds upon version 2.0 published in last year’s JIB special issue. In particular, SBOL 2.1 includes improved rules for what constitutes a valid SBOL document, new role fields to simplify the expression of sequence features and how components are used in context, and new best practices descriptions to improve the exchange of basic sequence topology information and the description of genetic design provenance, as well as miscellaneous other minor improvements.
PGMiner reloaded, fully automated proteogenomic annotation tool linking genomes to proteomes
Authors: Has Canan , Lashin Sergey A. , Kochetov Alexey , Allmer Jens
Published: December 2017

Improvements in genome sequencing technology increased the availability of full genomes and transcriptomes of many organisms. However, the major benefit of massive parallel sequencing is to better understand the organization and function of genes which then lead to understanding of phenotypes. In order to interpret genomic data with automated gene annotation studies, several tools are currently available. Even though the accuracy of computational gene annotation is increasing, a combination of multiple lines of experimental evidences should be gathered. Mass spectrometry allows the identification and sequencing of proteins as major gene products; and it is only these proteins that conclusively show whether a part of a genome is a coding region or not to result in phenotypes. Therefore, in the field of proteogenomics, the validation of computational methods is done by exploiting mass spectrometric data. As a result, identification of novel protein coding regions, validation of current gene models, and determination of upstream and downstream regions of genes can be achieved. In this paper, we present new functionality for our proteogenomic tool, PGMiner which performs all proteogenomic steps like acquisition of mass spectrometric data, peptide identification against preprocessed sequence databases, assignment of statistical confidence to identified peptides, mapping confident peptides to gene models, and result visualization. The extensions cover determining proteotypic peptides and thus unambiguous protein identification. Furthermore, peptides conflicting with gene models can now automatically assessed within the context of predicted alternative open reading frames.
DaTo: an atlas of biological databases and tools
Authors: Li Qilin , Zhou Yincong , Jiao Yingmin , Zhang Zhao , Bai Lin , Tong Li , Yang Xiong , Sommer Björn , Hofestädt Ralf , Chen Ming
Published: December 2017

This work presents DaTo, a semi-automatically generated world atlas of biological databases and tools. It extracts raw information from all PubMed articles which contain exact URLs in their abstract section, followed by a manual curation of the abstract and the URL accessibility. DaTo features a user-friendly query interface, providing extensible URL-related annotations, such as the status, the location and the country of the URL. A graphical interaction network browser has also been integrated into the DaTo web interface to facilitate exploration of the relationship between different tools and databases with respect to their ontology-based semantic similarity. Using DaTo, the geographical locations, the health statuses, as well as the journal associations were evaluated with respect to the historical development of bioinformatics tools and databases over the last 20 years. We hope it will inspire the biological community to gain a systematic insight into bioinformatics resources. DaTo is accessible via http://bis.zju.edu.cn/DaTo/.
Computational Lipidomics and Lipid Bioinformatics: Filling In the Blanks
Authors: Pauling Josch K. , Klipp Edda
Published: December 2017

Lipids are highly diverse metabolites of pronounced importance in health and disease. While metabolomics is a broad field under the omics umbrella that may also relate to lipids, lipidomics is an emerging field which specializes in the identification, quantification and functional interpretation of complex lipidomes. Today, it is possible to identify and distinguish lipids in a high-resolution, high-throughput manner and simultaneously with a lot of structural detail. However, doing so may produce thousands of mass spectra in a single experiment which has created a high demand for specialized computational support to analyze these spectral libraries. The computational biology and bioinformatics community has so far established methodology in genomics, transcriptomics and proteomics but there are many (combinatorial) challenges when it comes to structural diversity of lipids and their identification, quantification and interpretation. This review gives an overview and outlook on lipidomics research and illustrates ongoing computational and bioinformatics efforts. These efforts are important and necessary steps to advance the lipidomics field alongside analytic, biochemistry, biomedical and biology communities and to close the gap in available computational methodology between lipidomics and other omics sub-branches.
On Entropy Generation and the Effect of Heat and Mass Transfer Coupling in a Distillation Process
Authors: Burgos-Madrigal Paulina , Mendoza Diego F. , López de Haro Mariano
Published: December 2017

The entropy production rates as obtained from the exergy analysis, entropy balance and the nonequilibrium thermodynamics approach are compared for two distillation columns. The first case is a depropanizer column involving a mixture of ethane, propane, n-butane and n-pentane. The other is a weighed sample of Mexican crude oil distilled with a pilot scale fractionating column. The composition, temperature and flow profiles, for a given duty and operating conditions in each column, are obtained with the Aspen Plus V8.4 software by using the RateFrac model with a rate-based nonequilibrium column. For the depropanizer column the highest entropy production rate is found in the central trays where most of the mass transfer occurs, while in the second column the highest values correspond to the first three stages (where the vapor mixture is in contact with the cold liquid reflux), and to the last three stages (where the highest temperatures take place). The importance of the explicit inclusion of thermal diffusion in these processes is evaluated. In the depropanizer column, the effect of the coupling between heat and mass transfer is found to be negligible, while for the fractionating column it becomes appreciable.
International Student Satisfaction and Loyalty: A Comparative Study of Malaysian and Australian Higher Learning Institutions
Authors: Sultana Seyama , Momen Abdul
Published: December 2017

Purpose – Factors affecting international student satisfaction and loyalty are crucial for higher learning institutions of Malaysia as the country wants to become a global education hub. The purpose of this paper is to determine the most contributing factors affecting the level of international student satisfaction and how these factors affect loyalty of the students. These factors are academic issues, economic considerations, image and prestige and infrastructure of the university. The study involved the same research in Australia in order to compare situations in these two nations.

Methodology – A structured questionnaire was used to collect data to determine the significant factors affecting the level of satisfaction and loyalty of international students of Malaysian and Australian public universities.

Findings – The primary results demonstrated that towards international students, academic issues and economic considerations are more important than rest of two other factors in Malaysia where in Australia image and prestige are quite significant.

Value – This study contributes to the research of Malaysian educational field and research of Malaysia as a global educational hub as it involves comparison with a developed country like Australia.
Storia e fortuna di una similitudine nazianzenica: οἷόν τι πέλαγος οὐσίας ἄπειρον καὶ ἀόριστον
Authors: Settecase Marco
Published: December 2017

After clarifying the theological implications of the phrase τὸ πολὺ πέλαγος τοῦ καλοῦ in Plat.Symp. 210d4 and demonstrating that it cannot be considered the archetype, the article aims to examine theNachlebenof Gregory of Nazianzus’ expression πέλαγος οὐσίας ἄπειρον καὶ ἀόριστον (Or. 38.7 and 45.3): not only is it spread in late antique and mediaeval literature both Greek and Latin, but it is also present in Dante’sCommedia. The huge diffusion of this image across time and space, supported by a careful examination of the use that is made of it by authors who pick it up, makes it possible to demonstrate that πέλαγος οὐσίας ἄπειρον καὶ ἀόριστον became a very well known proverbialtoposin the Christian world.
Calcitonin gene-related peptide (CGRP): role in peripheral nerve regeneration
Authors: Jung Albert M.
Published: December 2017

Calcitonin gene-related peptide (CGRP) is a neuropeptide that has an important anti-inflammatory role in the immune system. Research has shown that CGRP is an integral part in peripheral nerve regeneration by (1) suppressing tumor necrosis factor-α, (2) forming an initial nerve bridge by increasing fibroblast motility and extracellular matrix synthesis, (3) vascularizing the spinal cord injury site, and (4) inducing Schwann cell (SC) proliferation. In this treatise, the following hypotheses will be explored: (1) CGRP is induced by c-Jun to regulate SC dedifferentiation, (2) CGRP promotes the chemotaxic migration of SCs along the nerve bridge, and (3) CGRP induces myelinophagy by activating various signaling pathways, such as p38 mitogen-activated protein kinase and Raf/extracellular signal-regulated kinase. These processes provide a framework for understanding the role of CGRP in peripheral nerve regeneration, which may be important in developing better strategies for nerve repair and gaining further insight into demyelinating diseases.
Expected genetic gain and genotype by environment interaction in almond (Dipteryx panamensis (Pittier) Rec. and Mell) in Costa Rica
Authors: León N. , Murillo O. , Badilla Y. , Ávila C. , Murillo R.
Published: December 2017

Almendro (Dipteryx panamensis (Pittier) Record & Mell.) is one of the native tree species with higher wood density planted in Latin American tropical region, which has given it a reputation and international acceptance in the wood-floors markets. The tree has also a remarkable ecological value, as an important food source and habitat for several endangered fauna. This research studied the potential for genetic improvement and stability of a collection of 29 open-pollinated families of Dipteryx panamensis at 48 months of age. The materials came from three different local native populations northern from Costa Rica. Individual heritabilities for commercial volume registered moderate values, between 9 % and 22 %, while mean family heritability, ranged between 47 % and 74 % for the traits evaluated. No significant genetic differences among provenances for none of the traits evaluated were observed. The genetic correlation for commercial volume between both sites showed a value of r = 0.96, which suggests low G × E interaction. If selected the top 20 individuals from the genetic ranking in commercial volume, an expected genetic gain above 50 % in year 4 can be obtained. The use of improved almendro seeds could have great impact and motivate plantations with this tree species in tropical regions. It is concluded that a unique breeding program for the whole country is feasible.
Spiritualität und Seelsorge in der Gesundheitsversorgung von Muslimen
Authors: Ilkilic Ilhan , Göksu Ahmet
Published: December 2017

Der Beitrag untersucht das Konzept von Spiritualität und Seelsorge bei der Versorgung muslimischer Patienten. Hierzu werden eingangs das muslimische Menschenbild, Krankheitsverständnis und Krankheitsdeutungen behandelt. Durch die Erörterung des Seelenbegriffs in der islamischen Geistestradition werden Anknüpfungspunkte an den wissenschaftlichen Diskurs von Spiritual Care und Seelsorge dargestellt und anhand des gegenwärtigen Bedarfs deren Aktualität aufgezeigt. Im nächsten Schritt wird die Praxis der muslimischen Krankenseelsorge ausgearbeitet und anhand von Beispielen der seelsorgerischen und spirituellen Betreuung durch Imame in Deutschland konkretisiert. Abschließend werden darüberhinaus islamische Rituale bei Sterbenden und Verstorbenen nachgezeichnet, um so einen Überblick über die professionelle Arbeit der Imame zu geben. Es wird die Position vertreten, dass eine professionelle spirituelle und seelsorgerische Betreuung und Begleitung muslimischer Patienten mit der Verwirklichung des hier aufgezeigten Programms in deutschen Krankenhäusern sehr wohl möglich und erforderlich ist.
Zum Imam oder zum Psychotherapeuten?
Authors: Balci-Sentürk Emine , Freund Henning
Published: December 2017

Zahlreiche Studien deuten auf einen hohen Bedarf an psychosozialer Versorgung bei Migranten hin. Häufig wird jedoch in diesen Studien die Religiosität außer Acht gelassen, obwohl eine besondere Bedeutung der Religiosität hinsichtlich der psychischen Gesundheit bekannt ist. Empirische Untersuchungen zur psychosozialen Versorgung von Muslimen sind nur eingeschränkt zu finden. Die vorliegende explorative Studie untersucht das Hilfesuchverhalten von Muslimen in Deutschland. Einen Schwerpunkt stellt hier die Inanspruchnahme von Beratung bei Imamen und weiblichen Religionsbediensteten (BI) sowie die Inanspruchnahme von Psychotherapie bei nichtmuslimischen Psychotherapeuten (PNMP) dar. Es wurde vermutet, dass vor allem die Zentralität der Religiosität einen Einfluss auf das Hilfesuchverhalten hat. An der Onlinebefragung nahmen insgesamt 579 Personen muslimischen Glaubens teil. Die Vermutungen zum Einfluss der Zentralität der Religiosität konnten bestätigt werden. Hochreligiöse Muslime suchen bei belastenden Problemen häufiger Imame und weibliche Religionsbedienstete auf als Psychotherapeuten. Die logistischen Regressionsanalysen deuten darauf hin, dass die Wahrscheinlichkeit, BI in Anspruch zu nehmen, mit einer Zunahme der Zentralität der Religiosität steigt, wobei die Wahrscheinlichkeit PNMP in Anspruch zu nehmen hierbei sinkt. Praktische Implikationen der Ergebnisse werden im Kapitel Diskussion ausgeführt.