bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Summary 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

Summary 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

Summary 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

Summary 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

Summary 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

Summary 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

Summary 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

Summary 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

Summary 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

SummarySynthetic 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

Abstract
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

Summary
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

SummaryLipids 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

AbstractThe 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.