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This work presents an exploratory study that retrieves, processes, and analyses Twitter data to gain insights about the relevance and perceptions of the wine industry in the Douro Portuguese region (including Porto and Douro wines), as well as other regions in the country. The main techniques and algorithms used in our work belong to the families of natural language processing and machine learning, and the practical relevance of the proposed methodology has been proven in the analysis of 1.2 million unique messages from more than 764,000 distinct users retrieved from the Twitter platform. Derived results from this study are valuable to provide insights that can be further used in the context of Business Informatics to promote better and more efficient marketing campaigns, for example, centering the topic on the most interested people or communicating with the most appropriate words., is work was supported by the Associate Laboratory for Green Chemistry—LAQV, financed by the Portuguese Foundation for Science and Technology (FCT/MCTES) Ref. UID/QUI/50006/2020; the Portuguese Foundation for Sci ence and Technology (FCT/MCTES) under the scope of the strategic funding of UIDB/04469/2020 unit and Bio TecNorte operation funded by the European Regional De velopment Fund (ERDF) under the scope of Norte2020—Programa Operacional Regional do Norte. Ref. NORTE-01-0145-FEDER-000004; the Conseller´ıa de Edu cacion, Universidades e Formaci ´ on Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group, the “Centro singular de investigacion de Galicia” (accreditation 2019-2022) funded by the European Regional Development Fund (ERDF)-Ref. ED431G2019/06; and Portuguese Foundation for Science and Technology for a PhD Grant (SFRH/BD/145497/2019)., info:eu-repo/semantics/publishedVersion

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUG Convolutional neural networks have pushed forward image analysis research and computer vision over the last decade, constituting a state-of-the-art approach in object detection today. The design of increasingly deeper and wider architectures has made it possible to achieve unprecedented levels of detection accuracy, albeit at the cost of both a dramatic computational burden and a large memory footprint. In such a context, cloud systems have become a mainstream technological solution due to their tremendous scalability, providing researchers and practitioners with virtually unlimited resources. However, these resources are typically made available as remote services, requiring communication over the network to be accessed, thus compromising the speed of response, availability, and security of the implemented solution. In view of these limitations, the on-device paradigm has emerged as a recent yet widely explored alternative, pursuing more compact and efficient networks to ultimately enable the execution of the derived models directly on resource-constrained client devices. This study provides an up-to-date review of the more relevant scientific research carried out in this vein, circumscribed to the object detection problem. In particular, the paper contributes to the field with a comprehensive architectural overview of both the existing lightweight object detection frameworks targeted to mobile and embedded devices, and the underlying convolutional neural networks that make up their internal structure. More specifically, it addresses the main structural-level strategies used for conceiving the various components of a detection pipeline (i.e., backbone, neck, and head), as well as the most salient techniques proposed for adapting such structures and the resulting architectures to more austere deployment environments. Finally, the study concludes with a discussion of the specific challenges and next steps to be taken to move toward a more convenient accuracy–speed trade-off. Xunta de Galicia | Ref. ED431C2018/55 Xunta de Galicia | Ref. ED431G2019/06

Glycoside hydrolases are responsible for the enzymatic deconstruction of complex carbohydrates. Most of the families are known to conserve the catalytic machinery and molecular mechanisms. This work introduces a new method to predict glycolytic abilities in sequenced genomes and thus, gain a better understanding of how to target specific carbohydrates and identify potentially interesting sources of specialised enzymes. Genome sequences are aligned to those of organisms with expertly curated glycolytic abilities. Clustering of homology scores helps identify organisms that share common abilities and the most promising organisms regarding specific glycolytic abilities. The method has been applied to members of the bacterial families Ruminococcaceae (39 genera), Eubacteriaceae (11 genera) and Lachnospiraceae (59 genera), which hold major representatives of the human gut microbiota. The method predicted the potential presence of glycoside hydrolases in 1701 species of these genera. Here, the validity and practical usefulness of the method is discussed based on the predictions obtained for members of the genus Ruminococcus. Results were consistent with existing literature and offer useful, complementary insights to comparative genomics and physiological testing. The implementation of the Gleukos web portal (http://sing-group.org/gleukos) offers a public service to those interested in targeting microbial carbohydrate metabolism for biotechnological and health applications., SING group thanks CITI (Centro de Investigacion, Transferencia e Innovacion) from the University of Vigo for hosting its IT infrastructure. Funding: This work was funded by the Spanish “Ministerio de Economıa y Competitividad—Agencia Estatal de Investigacion” (grant AGL2016-78311-R; AEI/ FEDER, UE) and by the grant IDI/2018/000236 from the Government of the Principality of Asturias. This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2019 unit and the Consellerıa de Educacion, Universidades e Formacion Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group. Conflict of interest: Abelardo Margolles and Borja Sanchez are scientific founders of Microviable Therapeutics SL. The rest of the authors have no conflicts of interest to declare, info:eu-repo/semantics/publishedVersion

Hospital-acquired Infections (HAIs) surveillance, defined as the systematic collection of data related to a certain health event, is considered an essential dimension for a prevention HAI program to be effective. In recent years, new automated HAI surveillance methods have emerged with the wide adoption of electronic health records (EHR). Here we present the validation results against the gold standard of HAIs diagnosis of the InNoCBR system deployed in the Ourense University Hospital Complex (Spain). Acting as a totally autonomous system, InNoCBR achieves a HAI sensitivity of 70.83% and a specificity of 97.76%, with a positive predictive value of 77.24%. The kappa index for infection type classification is 0.67. Sensitivity varies depending on infection type, where bloodstream infection attains the best value (93.33%), whereas the respiratory infection could be improved the most (53.33%). Working as a semi-automatic system, InNoCBR reaches a high level of sensitivity (81.73%), specificity (99.47%), and a meritorious positive predictive value (94.33%). Xunta de Galicia | Ref. ED431C2018/55-GRC

Nowadays, bioinformatics is one of the most important areas in modern biology and the creation of high-quality scientific software supporting this recent research area is one of the core activities of many researchers. In this context, high-quality sequence datasets are needed to perform inferences on the evolution of species, genes, and gene families, or to get evidence for adaptive amino acid evolution, among others. Nevertheless, sequence data are very often spread over several databases, many useful genomes and transcriptomes are non-annotated, the available annotation is not for the desired coding sequence isoform, and/or is unlikely to be accurate. Moreover, although the FASTA text-based format is quite simple and usable by most software applications, there are a number of issues that may be critical depending on the software used to analyse such files. Therefore, researchers without training in informatics often use a fraction of all available data. The above issues can be addressed using already available software applications, but there is no easy-to-use single piece of software that allows performing all these tasks within the same graphical interface, such as the one here presented, named BDBM (Blast DataBase Manager). BDBM can be used to efficiently get gene sequences from annotated and non-annotated genomes and transcriptomes. Moreover, it can be used to look for alternatives to existing annotations and to easily create reliable custom databases. Such databases are essential to prepare high-quality datasets. The analyses that we have performed on the Coffea canephora genome using BDBM aimed at the identification of the S-locus region (that harbours the genes involved in gametophytic self-incompatibility) led to the conclusion that there are two likely regions, one on chromosome 2 (around region 6600000-6650000), and another on chromosome 5 (around 15830000-15930000). Such findings are discussed in the context of the Rubiaceae gametophytic self-incompatibility evolution.

Useful insight into the evolution of genes and gene families can be provided by the analysis of all available genome datasets rather than just a few, which are usually those of model species. Handling and transforming such datasets into the desired format for downstream analyses is, however, often a difficult and time-consuming task for researchers without a background in informatics. Therefore, we present two simple and fast protocols for data preparation, using an easy-to-install, open-source, cross-platform software application with user-friendly, rich graphical user interface (SEDA; http://www.sing-group.org/seda/index.html ). The first protocol is a substantial improvement over one recently published (Lopez-Fernandez et al. Practical applications of computational biology and bioinformatics, 12th International conference. Springer, Cham, pp 88–96 (2019)[1]), which was used to study the evolution of GULO, a gene that encodes the enzyme responsible for the last step of vitamin C synthesis. In this paper, we show how the sequence data file used for the phylogenetic analyses can now be obtained much faster by changing the way coding sequence isoforms are removed, using the newly implemented SEDA operation “Remove isoforms”. This protocol can be used to easily show that putative functional GULO genes are present in several Prostotomian groups such as Molluscs, Priapulida and Arachnida. Such findings could have been easily missed if only a few Protostomian model species had been used. The second protocol allowed us to identify positively selected amino acid sites in a set of 19 primate HLA immunity genes. Interestingly, the proteins encoded by MHC class II genes can show just as many positively selected amino acid sites as those encoded by classical MHC class I genes. Although a significant percentage of codons, which can be as high as 14.8%, are evolving under positive selection, the main mode of evolution of HLA immunity genes is purifying selection. Using a large number of primate species, the probability of missing the identification of positively selected amino acid sites is lower. Both projects were performed in less than one week, and most of the time was spent running the analyses rather than preparing the files. Such protocols can be easily adapted to answer many other questions using a phylogenetic approach.

Social Media have changed the conditions and rules of Social Networking (SNet) where it comes from people intermingling with each other, i.e., SNet is to be understood as a process that works on the principle of many-to-many; any individual can create and share content. It is intended to explore explore the complex dynamics between SNet, Logic Programming (LP), and the Laws of Thermodynamic (LoT) in terms of entropy by drawing attention to how Multi-Value Logic (MVL) intertwines with SNet, LP and LoT, i.e., its norms, strategies, mechanisms, and methods for problem solving that underpin its dynamics when looks to programmability, connectivity, and organizational performance. Indeed, one’s focus is on the tactics and strategies of MVL to evaluate the issues under which social practices unfold and to assess their impact on organizational performance.

Concerning the field of Health Sciences, one of the trends with the greatest impact today is related to the feeding and consumption of foods intended for chronic patients with a nutritional association. According to this, the gluten-free diet is one of the diets with a today's fastest-growing due to their health therapy associations for different diseases and because of more and more people are freely choosing to follow a gluten-free diet. On this subject, e-commerce platforms are destined to be a key point for the sale of this kind of products due to: (i) the change in the business model caused by the electronic platforms and (ii) the expected increase consumption of disease designed special foods. For this reason, the experiences written by chronic disease patients and consumers in these platforms have become increasingly relevant. In this sense, the present work applies biomedical text mining methods and demographic knowledge inference techniques to a large number of gluten-free foods consumer's experiences to discover a wide range of health-related topics associated to gluten-free products, alimentary social trends and demographics differences. The results of these analyses show significant trends over time; differences between males and females consumption and a wide range of health-related conclusions, such as: why consumers start a free gluten diet or what are the most discussed diseases and adverse reactions related to these products., This work was supported by: the Associate Laboratory for Green Chemistry - LAQV financed by the Portuguese Foundation for Science and Technology (FCT/MCTES) Ref. UID/QUI/50006/2020; the Portuguese Foundation for Science and Technology (FCT/MCTES) under the scope of the strategic funding of UIDB/04469/2020 unit and BioTecNorte operation funded by the European Regional Development Fund (ERDF) under the scope of Norte2020— Programa Operacional Regional do Norte. Ref. NORTE-01-0145-FEDER-000004; the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group, the “Centro singular de investigación de Galicia” (accreditation 2019-2022) funded by the European Regional Development Fund (ERDF)-Ref. ED431G2019/06. The authors also acknowledge the postdoctoral fellowship of Martín Pérez-Pérez, funded by the Xunta de Galicia. Ref. ED481B-2019-032., info:eu-repo/semantics/publishedVersion

Ambient Intelligence (AmI) encompasses technological infrastructures capable of sensing data from environments and extracting high-level knowledge to detect or recognize users’ features and actions, as well as entities or events in their surroundings. Visual perception, particularly object detection, has become one of the most relevant enabling factors for this context-aware user-centered intelligence, being the cornerstone of relevant but complex tasks, such as object tracking or human action recognition. In this context, convolutional neural networks have proven to achieve state-of-the-art accuracy levels. However, they typically result in large and highly complex models that typically demand computation offloading onto remote cloud platforms. Such an approach has security- and latency-related limitations and may not be appropriate for some AmI use cases where the system response time must be as short as possible, and data privacy must be guaranteed. In the last few years, the on-device paradigm has emerged in response to those limitations, yielding more compact and efficient neural networks able to address inference directly on client machines, thus providing users with a smoother and better-tailored experience, with no need of sharing their data with an outsourced service. Framed in that novel paradigm, this work presents a review of the recent advances made along those lines in object detection, providing a comprehensive study of the most relevant lightweight CNN-based detection frameworks, discussing the most paradigmatic AmI domains where such an approach has been successfully applied, the different challenges arisen, the key strategies and techniques adopted to create visual solutions for image-based object classification and localization, as well as the most relevant factors to bear in mind when assessing or comparing those techniques, such as the evaluation metrics or the hardware setups used. Xunta de Galicia | Ref. ED431G2019/06 Xunta de Galicia | Ref. ED431C2018/55-GRC

Colorectal cancer is a major health problem, where advances towards computer-aided diagnosis (CAD) systems to assist the endoscopist can be a promising path to improvement. Here, a deep learning model for real-time polyp detection based on a pre-trained YOLOv3 (You Only Look Once) architecture and complemented with a post-processing step based on an object-tracking algorithm to reduce false positives is reported. The base YOLOv3 network was fine-tuned using a dataset composed of 28,576 images labelled with locations of 941 polyps that will be made public soon. In a frame-based evaluation using isolated images containing polyps, a general F1 score of 0.88 was achieved (recall = 0.87, precision = 0.89), with lower predictive performance in flat polyps, but higher for sessile, and pedunculated morphologies, as well as with the usage of narrow band imaging, whereas polyp size

In this work, different social data mining approaches are used to characterize the user churning and social traits of the Bioinformatics community over the first ten years of Stack Overflow. The proposed workflow consists of a four-step procedure that allows the characterization of users based on the social exchange exhibited by the Bioinformatics community and the Developers communities, notably the Python, Matlab and R programming communities. The motivation is to improve user churning and the quality of social interactions by categorizing user traits and being able to understand how Stack Overflow, and other Stack Exchange databases, may better respond to current user concerns and interests. Therefore, initial user identification was complemented by a second categorization focused on user "genealogy". The goal was to explore the evolving of user interests and, in particular, the migration and swapping of users across different communities throughout the years. Noticeably, a considerable number of Bioinformatics users has moved to the Developers communities. An in-depth exploration of the most popular topics of conversation in Bioinformatics enabled a better understanding of the triggers and contents of these conversations., Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group, and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04469/2020 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte. SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from the University of Vigo for hosting its IT infrastructure, info:eu-repo/semantics/publishedVersion

The identification of clinically relevant bacterial amino acid changes can be performed using different methods aimed at the identification of genes showing positively selected amino acid sites (PSS). Nevertheless, such analyses are time consuming, and the frequency of genes showing evidence for PSS can be low. Therefore, the development of a pipeline that allows the quick and efficient identification of the set of genes that show PSS is of interest. Here, we present Auto-PSS-Genome, a Compi-based pipeline distributed as a Docker image, that automates the process of identifying genes that show PSS using three different methods, namely codeML, FUBAR, and omegaMap. Auto-PSS-Genome accepts as input a set of FASTA files, one per genome, containing all coding sequences, thus minimizing the work needed to conduct positively selected sites analyses. The Auto-PSS-Genome pipeline identifies orthologous gene sets and corrects for multiple possible problems in input FASTA files that may prevent the automated identification of genes showing PSS. A FASTA file containing all coding sequences can also be given as an external global reference, thus easing the comparison of results across species, when gene names are different. In this work, we use Auto-PSS-Genome to analyse Mycobacterium leprae (that causes leprosy), and the closely related species M. haemophilum, that mainly causes ulcerating skin infections and arthritis in persons who are severely immunocompromised, and in children causes cervical and perihilar lymphadenitis. The genes identified in these two species as showing PSS may be those that are partially responsible for virulence and resistance to drugs.

Journal pre proof, Big data importance and potential are becoming more and more relevant nowadays, enhanced by the explosive growth of information volume that is being generated on the Internet in the last years. In this sense, many experts agree that social media networks are one of the internet areas with higher growth in recent years and one of the fields that are expected to have a more significant increment in the coming years. Similarly, social media sites are quickly becoming one of the most popular platforms to discuss health issues and exchange social support with others. In this context, this work presents a new methodology to process, classify, visualise and analyse the big data knowledge produced by the sociome on social media platforms. This work proposes a methodology that combines natural language processing techniques, ontology-based named entity recognition methods, machine learning algorithms and graph mining techniques to: (i) reduce the irrelevant messages by identifying and focusing the analysis only on individuals and patient experiences from the public discussion; (ii) reduce the lexical noise produced by the different ways in how users express themselves through the use of domain ontologies; (iii) infer the demographic data of the individuals through the combined analysis of textual, geographical and visual profile information; (iv) perform a community detection and evaluate the health topic study combining the semantic processing of the public discourse with knowledge graph representation techniques; and (v) gain information about the shared resources combining the social media statistics with the semantical analysis of the web contents. The practical relevance of the proposed methodology has been proven in the study of 1.1 million unique messages from more than 400,000 distinct users related to one of the most popular dietary fads that evolve into a multibillion-dollar industry, i.e., gluten-free food. Besides, this work analysed one of the least research fields studied on Twitter concerning public health (i.e., the allergies or immunology diseases as celiac disease), discovering a wide range of health-related conclusions., SING group thanks CITI (Centro de Investigacion, Transferencia e Innovacion) from the University of Vigo for hosting its IT infrastructure. This work was supported by: the Associate Laboratory for Green Chemistry-LAQV, which is financed by national funds from and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of [UIDB/50006/2020] and [UIDB/04469/2020] units, and BioTecNorte operation [NORTE010145FEDER000004] funded by the European Regional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte, the Xunta de Galicia (Centro singular de investigacion de Galicia accreditation 2019-2022) and the European Union (European Regional Development Fund - ERDF)- Ref. [ED431G2019/06] , and Conselleria de Educacion, Universidades e Formacion Profesional (Xunta de Galicia) under the scope of the strategic funding of [ED431C2018/55GRC] Competitive Reference Group. The authors also acknowledge the post-doctoral fellowship [ED481B2019032] of Martin PerezPerez, funded by the Xunta de Galicia. Funding for open access charge: Universidade de Vigo/CISUG, info:eu-repo/semantics/publishedVersion

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.future.2020.04.025.Supplementary material 1: this file contains the 23 user communities detected using the GLay algorithm., In recent years, the number of active users in social media has grown exponentially. Despite the thematic diversity of the messages, social media have become an important vehicle to disseminate health information as well as to gather insights about patients experiences and emotional intelligence. Therefore, the present work proposes a new methodology of analysis to identify and interpret the behaviour, perceptions and appreciations of patients and close relatives towards a health condition through their social interactions. At the core of this methodology are techniques of natural language processing and machine learning as well as the reconstruction of knowledge graphs, and further graph mining. The case study is the diabetes community, and more specifically, the patients communicating about type 1 diabetes (T1D) and type 2 diabetes (T2D). The results produced in this study show the effectiveness of the proposed method to discover useful and non-trivial knowledge about patient perceptions of disease. Such knowledge may be used in the context of Health Informatics to promote healthy lifestyles in more efficient ways as well as to improve communication with the patients., This work was partially supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, COMPETE 2020 (POCI-01-0145-FEDER-006684), the Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2019–2022) and the European Union (European Regional Development Fund - ERDF)- Ref. ED431G2019/06, and Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group. The authors also acknowledge the Postdoc contract of Martín Pérez-Pérez, funded by the Xunta de Galicia., info:eu-repo/semantics/publishedVersion

Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jff.2020.103969., This work supports the massive presence of potential immunomodulatory peptides in the human gut metaproteome. These peptides were identified through the MAHMI database as potentially anti-inflammatory, and sixteen of them synthesized for characterize their mechanism of action. From them, peptide HM14 was encrypted in an extracellular protein produced by Bifidobacterium longum, a common member of the human microbiota, and displayed the highest anti-inflammatory capability. Molecular mechanism of action of HM14 pointed to a specific interaction between this immunomodulatory peptide and antigen presenting cells, which resulted in a higher formation of iTreg cells. Moreover, HM14 was effective in decreasing pro-inflammatory parameters in PBMCs isolated from a cohort of Crohns patients. Finally, non-targeted metabolomics confirmed the ability of HM14 to modulate the metabolic activity of PBMCs to fulfil its energy and biosynthetic requirements. Overall, our combined in silico/multiomics approach supports the human gut metaproteome as a source for immunomodulatory peptides., Our work is supported by the Spanish “Programa Estatal de Investigación. Desarrollo e Innovación Orientada a los Retos de la Sociedad” (grants AGL2013-44761-P and AGL2016-78311-R); the Asociación Española Contra el Cancer (“Obtención de péptidos bioactivos contra el Cáncer Colo-Rectal a partir de secuencias genéticas de microbiomas intestinales”, Grant PS-2016), by the Asturias Regional Plan I + D + i for research groups (FYCYT-IDI/2018/000236) and by the Autonomic “Investigadores Emerxentes do Sistema Universitario de Galicia” (Grant EM2014/046). This work was partially supported by the Consellería de Educación. Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group. Finally, the authors wish to thank Jaume Morales and Rubén García form Agilent Technologies for technical support., info:eu-repo/semantics/publishedVersion

© 2020 by the authors., Bifidobacteria are among the most abundant microorganisms inhabiting the intestine of humans and many animals. Within the genus Bifidobacterium, several beneficial effects have been attributed to strains belonging to the subspecies Bifidobacterium longum subsp. longum and Bifidobacterium longum subsp. infantis, which are often found in infants and adults. The increasing numbers of sequenced genomes belonging to these two subspecies, and the availability of novel computational tools focused on predicting glycolytic abilities, with the aim of understanding the capabilities of degrading specific carbohydrates, allowed us to depict the potential glycoside hydrolases (GH) of these bacteria, with a focus on those GH profiles that differ in the two subspecies. We performed an in silico examination of 188 sequenced B. longum genomes and depicted the commonly present and strain-specific GHs and GH families among representatives of this species. Additionally, GH profiling, genome-based and 16S rRNA-based clustering analyses showed that the subspecies assignment of some strains does not properly match with their genetic background. Furthermore, the analysis of the potential GH component allowed the distinction of clear GH patterns. Some of the GH activities, and their link with the two subspecies under study, are further discussed. Overall, our in silico analysis poses some questions about the suitability of considering the GH activities of B. longum subsp. longum and B. longum subsp. infantis to gain insight into the characterization and classification of these two subspecies with probiotic interest., The research included in the present work was funded by the grants AGL2016-78311-R (funded by AEI/FEDER, UE), RTI2018-095021-J-I00 (funded by MCIU/AEI/FEDER, UE), and IDI/2018/000236 (funded by PCTI Gobierno del Principado de Asturias/FEDER, UE). This study was supported by the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group, the “Centro singular de investigación de Galicia” (accreditation 2019-2022) and the European Union (European Regional Development Fund—ERDF)-Ref. ED431G2019/06, and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UIDB/04469/2020 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020—Programa Operacional Regional do Norte.

Supplementary material related to this article can be foundonline at https://doi.org/10.1016/j.future.2019.12.021 ; "Available online 17 December 2019", Today, Social Media is a key information source for a wide range of domains as a means to gain a better understanding of information flows and user communities. This work introduces a methodology combining machine learning and graph mining approaches to address relevant aspects of quality of service and user intrinsic motivation from the users perspective. The focus of the present analysis is set on the social interactions among software developers via Stack Overflow. Over the last 10 years, software developers have become intensively involved in knowledge sharing and platforms such as Stack Overflow have accumulated a lot of development data and knowledge. The proposed methodology is applied to explore the social dynamics of the Java programming language community and bring forward relevant, non-trivial knowledge about developer interests, information flows and user engagement and reputation. The ultimate aim is to improve question preparation towards better question routing and voting outcome., This study was supported by the Consellería de Educación,Universidades e Formación Profesional (Xunta de Galicia), Spainunder the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group, and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2019 unit. SING group thanks CITI(Centro de Investigación, Transferencia e Innovación) from theUniversity of Vigo for hosting its IT infrastructure., info:eu-repo/semantics/publishedVersion

Lactobacillus genus includes both probiotic and representative strains of the human gut microbiota. Independent studies have reported on the anti-inflammatory properties of different Lactobacillus strains, although we are far from understanding the underlying molecular interplay. In this work we show that a daily administration of Lactobacillus acidophilus DSM20079T (DSM20079) to healthy piglets resulted in plasmatic increases of the anti-inflammatory IL10, whilst IL12 and the pro-inflammatory ratio IL12+TNF/IL10 decreased. The extracellular protein fraction of DSM20079 was identified as the responsible for the crosstalk interaction that elicited these tolerogenic effects. This strain was able to activate innate immune pathways in dendritic cells and to decrease the production of pro-inflammatory cytokines in both CD4+/CD8+ T cell subsets in healthy donors and in Crohns Disease patients. The tolerogenic effect exerted by the extracellular proteins of this strain suggests their potential use as coadjutant for therapeutic applications targeting chronic inflammatory illnesses., Our work is supported by the Spanish “Programa Estatal de Investigación, Desarrollo e Inovación Orientada a los Retos de la Sociedad” (grant AGL2016-78311-R); the Asociación Española Contra el Cancer (“Obtención de péptidos bioactivos contra el Cáncer ColoRectal a partir de secuencias genéticas de microbiomas intestinales”, Grant PS-2016) and by the Asturias Regional Plan I+D+i for research groups (FYCYT-IDI/2018/000236). This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER006684). SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from University of Vigo for hosting its IT infrastructure. LJR was supported by the Principado de Asturias, PCTI 2018–2020 (GRUPIN: IDI2018-000237) and FEDER. This work was partially supported by the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group., info:eu-repo/semantics/publishedVersion

Background and objective 2D-gel electrophoresis is widely used in combination with MALDI-TOF mass spectrometry in order to analyze the proteome of biological samples. For instance, it can be used to discover proteins that are differentially expressed between two groups (e.g. two disease conditions, case vs. control, etc.) thus obtaining a set of potential biomarkers. This procedure requires a great deal of data processing in order to prepare data for analysis or to merge and integrate data from different sources. This kind of work is usually done manually (e.g. copying and pasting data into spreadsheet files), which is highly time consuming and distracts the researcher from other important, core tasks. Moreover, engaging in a repetitive process in a non-automated, handling-based manner is prone to error, thus threatening reliability and reproducibility. The objective of this paper is to present S2P, an open source software to overcome these drawbacks. Methods S2P is implemented in Java on top of the AIBench framework, and relies on well-established open source libraries to accomplish different tasks. Results S2P is an AIBench based desktop multiplatform application, specifically aimed to process 2D-gel and MALDI-mass spectrometry protein identification-based data in a computer-aided, reproducible manner. Different case studies are presented in order to show the usefulness of S2P. Conclusions S2P is open source and free to all users at http://www.sing-group.org/s2p . Through its user-friendly GUI interface, S2P dramatically reduces the time that researchers need to invest in order to prepare data for analysis.

One of the most relevant problems affecting the efficient use of e-mail to communicate worldwide is the spam phenomenon. Spamming involves flooding Internet with undesired messages aimed to promote illegal or low value products and services. Beyond the existence of different well-known machine learning techniques, collaborative schemes and other complementary approaches, some popular anti-spam frameworks such as SpamAssassin or Wirebrush4SPAM enabled the possibility of using regular expressions to effectively improve filter performance. In this work, we provide a review of existing proposals to automatically generate fully functional regular expressions from any input dataset combining spam and ham messages. Due to configuration difficulties and the low performance achieved by analysed schemes, in this work we introduce DiscoverRegex, a novel automatic spam pattern-finding tool. Patterns generated DiscoverRegex outperform those created by existing approaches (able to avoid FP errors) whilst minimising the computational resources required for its proper operation. DiscoverRegex source code is publicly available at https://github.com/sing-group/DiscoverRegex .

Internet e-mail service emerged in the late seventies to implement fast message exchanging through computer networks. Network users immediately discovered the value of this service (sometimes for improper purposes such as spamming). As e-mail became indispensable to increase personal productivity, the volume of spam deliveries was constantly growing. With the passage of time, a great number of proposals and tools have emerged to fight against spam. However, the vast majority of them do not properly take into consideration the inner attributes of spam and ham messages such as the noise or the presence of concept drift. In this work, we provide a detailed empirical study of concept drift in the e-mail domain taking into consideration two key aspects: existing types of concept drift and the real class of messages (spam and ham). As a result, our study reveals different weaknesses of multiple e-mail filtering alternatives and other relevant works in this domain and identifies new strategies to develop more accurate filters. Finally, the experimentation carried out in this work has motivated the development of a concept drift analyser tool for the e-mail domain that can be freely downloaded from https://github.com/sing-group/conceptDriftAnalyser.git .

When changes at few amino acid sites are the target of selection, adaptive amino acid changes in protein sequences can be identified using maximum-likelihood methods based on models of codon substitution (such as codeml). Although such methods have been employed numerous times using a variety of different organisms, the time needed to collect the data and prepare the input files means that tens or hundreds of coding regions are usually analyzed. Nevertheless, the recent availability of flexible and easy to use computer applications that collect relevant data (such as BDBM) and infer positively selected amino acid sites (such as ADOPS), means that the entire process is easier and quicker than before. However, the lack of a batch option in ADOPS, here reported, still precludes the analysis of hundreds or thousands of sequence files. Given the interest and possibility of running such large-scale projects, we have also developed a database where ADOPS projects can be stored. Therefore, this study also presents the B+ database, which is both a data repository and a convenient interface that looks at the information contained in ADOPS projects without the need to download and unzip the corresponding ADOPS project file. The ADOPS projects available at B+ can also be downloaded, unzipped, and opened using the ADOPS graphical interface. The availability of such a database ensures results repeatability, promotes data reuse with significant savings on the time needed for preparing datasets, and effortlessly allows further exploration of the data contained in ADOPS projects. Financiado para publicación en acceso aberto: Universidade de Vigo/CISUG Ministerio de Economía y Competitividad | Ref. TIN2013-47153-C3-3-R

BACKGROUND: Wild-type (wt) polyglutamine (polyQ) regions are implicated in stabilization of protein-protein interactions (PPI). Pathological polyQ expansion, such as that in human Ataxin-1 (ATXN1), that causes spinocerebellar ataxia type 1 (SCA1), results in abnormal PPI. For ATXN1 a larger number of interactors has been reported for the expanded (82Q) than the wt (29Q) protein. METHODS: To understand how the expanded polyQ affects PPI, protein structures were predicted for wt and expanded ATXN1, as well as, for 71 ATXN1 interactors. Then, the binding surfaces of wt and expanded ATXN1 with the reported interactors were inferred. RESULTS: Our data supports that the polyQ expansion alters the ATXN1 conformation and that it enhances the strength of interaction with ATXN1 partners. For both ATXN1 variants, the number of residues at the predicted binding interface are greater after the polyQ, mainly due to the AXH domain. Moreover, the difference in the interaction strength of the ATXN1 variants was due to an increase in the number of interactions at the N-terminal region, before the polyQ, for the expanded form. CONCLUSIONS: There are three regions at the AXH domain that are essential for ATXN1 PPI. The N-terminal region is responsible for the strength of the PPI with the ATXN1 variants. How the predicted motifs in this region affect PPI is discussed, in the context of ATXN1 post-transcriptional modifications. This work was financed by the project Norte-01-0145-FEDER-000008 -Porto Neurosciences and Neurologic Disease Research Initiative at I3S, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). Sara Rocha is supported by a post-doctoral fellowship under this project. Hugo López-Fernández is supported by a postdoctoral fellowship from Xunta de Galicia (ED481B 2016/068–0). SING group thanks Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) for the ED431C2018/55-GRC grant and CITI (Centro de Investigación, Transferencia e Innovación) from University of Vigo for hosting its IT infrastructure. The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

The human body immune system, metabolism and homeostasis are affected by microbes. Dysbiosis occurs when the homeostatic equilibrium is disrupted due to an alteration in the normal microbiota of the intestine. Dysbiosis can cause cancer, and also affect a patient’s ability to respond to treatment. Metataxonomics seeks to identify the bacteria present in a biological sample, based on the sequencing of the 16S rRNA genetic marker. Precision medicine attempts to find relationships between the microbiota and the risk of acquiring cancer, and design new therapies targeting bacteria. Flexible and portable bioinformatic pipelines are necessary to be able to bring metataxonomics to the clinical field, which allow groups of biological samples to be classified according to their diversity in the microbiota. With this aim we implemented Metatax, a new pipeline to analyze biological samples based on 16S rRNA gene sequencing. The results obtained with our pipeline should complement those obtained by sequencing a patient's DNA and RNA, in addition to clinical data, to improve knowledge of the possible reasons for a disease or a worse response to treatment.

Background: L-ascorbate (Vitamin C) is an important antioxidant and co-factor in eukaryotic cells, and in mammals it is indispensable for brain development and cognitive function. Vertebrates usually become L-ascorbate auxothrophs when the last enzyme of the synthetic pathway, an L-gulonolactone oxidase (GULO), is lost. Since Protostomes were until recently thought not to have a GULO gene, they were considered to be auxothrophs for Vitamin C. Results: By performing phylogenetic analyses with tens of non-Bilateria and Protostomian genomes, it is shown, that a GULO gene is present in the non-Bilateria Placozoa, Myxozoa (here reported for the first time) and Anthozoa groups, and in Protostomians, in the Araneae family, the Gastropoda class, the Acari subclass (here reported for the first time), and the Priapulida, Annelida (here reported for the first time) and Brachiopoda phyla lineages. GULO is an old gene that predates the separation of Animals and Fungi, although it could be much older. We also show that within Protostomes, GULO has been lost multiple times in large taxonomic groups, namely the Pancrustacea, Nematoda, Platyhelminthes and Bivalvia groups, a pattern similar to that reported for Vertebrate species. Nevertheless, we show that Drosophila melanogaster seems to be capable of synthesizing L-ascorbate, likely through an alternative pathway, as recently reported for Caenorhabditis elegans. Conclusions: Non-Bilaterian and Protostomians seem to be able to synthesize Vitamin C either through the conventional animal pathway or an alternative pathway, but in this animal group, not being able to synthesize L-ascorbate seems to be the exception rather than the rule. This work is financed by the project Norte-01-0145-FEDER-000008 - Porto Neurosciences and Neurologic Disease Research Initiative at I3S, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). S. F. Henriques is supported by a post-doctoral fellowship also funded by the above mentioned project. SING group is supported by the Consellería de Educación, Universidades e Formación Pro-fesional (Xunta de Galicia) by the ED431C2018/55-GRC grant. H. López-Fer-nández is supported by a post-doctoral fellowship from Xunta de Galicia (ED481B 2016/068–0). The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Motivation Single cells often show stochastic behaviour and variations in the physiological state of individual cells affect the behaviour observed in cell populations. This may be partially explained by variations in the concentration and spatial location of molecules within and in the vicinity of each cell. Methods This paper introduces an agent-based model that represents single-molecule transport through the cellular envelope of Escherichia coli at the micrometre scale. This model enables broader observation of molecular transport throughout the different membrane layers and the study of the effect of molecular concentration in cellular noise. Simulations considered various low molecular weight molecules, i.e. ampicillin, bosentan, coumarin, saquinavir, and terbutaline, and a gradient of molecular concentrations. The model ensured stochasticity in the location of the agents, using diffusing spherical particles with physical dimensions. Results Simulation results were validated against theoretical and experimental data. For example, theoretically, ampicillin molecules take 0.6s to cross the entire cell envelope, and computational simulations took 0.68s, 0.68s, 0.70s, and 0.69s, for concentrations of 1.44M, 13.21M, 26.4M and 105.61M, respectively. Replicate standard deviation decreased with growing initial concentrations of the molecules. In turn, no clear relationship could be observed between molecular size and variability. Conclusions This work presented a novel agent-based model to study the effect of the initial concentration of low molecular weight molecules on cellular noise. Cellular noise during molecule diffusion was found to be concentration-dependent and size-independent. The new model holds considerable potential for future, more complex analyses, when emerging experimental data may enable modelling of membrane transport mechanisms., Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group, the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2019 unit and UID/EQU/00511/2019 units, COMPETE 2020 (POCI-01-0145-FEDER-006939), and NORTE‐01‐0145‐FEDER‐000005 – LEPABE-2-ECO-INNOVATION, supported by North Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund. SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from the University of Vigo for hosting its IT infrastructure. Also, the authors acknowledge the PhD grants of Martín Pérez-Pérez and Gael Pérez-Rodríguez, funded by the Xunta de Galicia, info:eu-repo/semantics/publishedVersion

This special issue includes extended versions of a number of papers selected from the International Conference on Practical Applications of Computational Biology and Bioinformatics (PACBB 2018) that was held in Toledo (Spain) in June 2018. This forum, already in its twelfth edition, aims to reunite and promote the collaboration among researchers developing applied Bioinformatics or Chemoinformatics research., info:eu-repo/semantics/publishedVersion

[Background] Transcriptomics profiling aims to identify and quantify all transcripts present within a cell type or tissue at a particular state, and thus provide information on the genes expressed in specific experimental settings, differentiation or disease conditions. RNA-Seq technology is becoming the standard approach for such studies, but available analysis tools are often hard to install, configure and use by users without advanced bioinformatics skills. [Methods] Within reason, DEWE aims to make RNA-Seq analysis as easy for non-proficient users as for experienced bioinformaticians. DEWE supports two well-established and widely used differential expression analysis workflows: using Bowtie2 or HISAT2 for sequence alignment; and, both applying StringTie for quantification, and Ballgown and edgeR for differential expression analysis. Also, it enables the tailored execution of individual tools as well as helps with the management and visualisation of differential expression results. [Results] DEWE provides a user-friendly interface designed to reduce the learning curve of less knowledgeable users while enabling analysis customisation and software extension by advanced users. Docker technology helps overcome installation and configuration hurdles. In addition, DEWE produces high quality and publication-ready outputs in the form of tab-delimited files and figures, as well as helps researchers with further analyses, such as pathway enrichment analysis. [Conclusions] The abilities of DEWE are exemplified here by practical application to a comparative analysis of monocytes and monocyte-derived dendritic cells, a study of clinical relevance. DEWE installers and documentation are freely available at https://www.sing-group.org/dewe., This work was supported by the Spanish “Programa Estatal de Investigación, Desarrollo e Inovación Orientada a los Retos de la Sociedad” (grant AGL2013-44039R); the Asociación Española Contra el Cancer (“Obtención de péptidos bioactivos contra el Cáncer Colo-Rectal a partir de secuencias genéticas de microbiomas intestinales”, grant PS-2016); the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group; the Portuguese Foundation for Science and Technology under the scope of the strategic funding of UID/BIO/04469/2019 unit; and the Asturias Regional Plan I + D + i for research groups (FYCYT-IDI/2018/000236). H. López-Fernández is supported by a post-doctoral fellowship from Xunta de Galicia (ED481B 2016/068-0).

Advances in the field of genome sequencing have enabled a comprehensive analysis and annotation of the dynamics of the protein inventory of cells. This has been proven particularly rewarding for microbial cells, for which the majority of proteins are already accessible to analysis through automatic metagenome annotation. The large-scale in silico screening of proteomes and metaproteomes is key to uncover bioactivities of translational, clinical and biotechnological interest, and to help assign functions to certain proteins, such as those predicted as hypothetical. This work introduces a new method for the prediction of the bioactivity potential of proteomes/metaproteomes, supporting the discovery of functionally relevant proteins based on prior knowledge. This methodology complements functional annotation enrichment methods by allowing the assignment of functions to proteins annotated as hypothetical/putative/uncharacterised, as well as and enabling the detection of specific bioactivities and the recovery of proteins from defined taxa. This work shows how the new method can be applied to screen proteome and metaproteome sets to obtain predictions of clinical or biotechnological interest based on reference datasets. Notably, with this methodology, the large information files obtained after DNA sequencing or protein identification experiments can be associated for translational purposes that, in cases such as antibiotic-resistance pathogens or foodborne diseases, may represent changes in how these important and global health burdens are approached in the clinical practice. Finally, the Sequence-based Expert-driven pRoteome bioactivity Prediction EnvironmENT, a public Web service implemented in Scala functional programming style, is introduced as means to ensure broad access to the method as well as to discuss main implementation issues, such as modularity, extensibility and interoperability., This work was supported by the Spanish “Programa Estatal de Investigación, Desarrollo e Inovación Orientada a los Retos de la Sociedad” (grant AGL2013-44039R); the Asociación Española Contra el Cancer (“Obtención de péptidos bioactivos contra el Cáncer Colo-Rectal a partir de secuencias genéticas de microbiomas intestinales”, grant PS-2016). This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER006684). SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from University of Vigo for hosting its IT infrastructure.

The combination of antimicrobial products is a promising biomedical strategy against the ever growing number of resistant strains emerging in healthcare and community settings. Agents with alternative modes of action, such as antimicrobial peptides (AMPs), and the efficacy of combined actions are being evaluated. Despite the availability of various antimicrobial data repositories, a wealth of information remains scattered through the scientific literature. This work introduces the automated curation pipeline implemented to produce the first ever network reconstruction of AMP-drug combinations. This network relates to antimicrobial combinations experimentally tested against Pseudomonas aeruginosa infections and includes 239 combinations among AMPs and other antimicrobials. The aim is to provide a global view of available interaction data and help design new antimicrobial studies. Reconstruction is on-going, coping with new experimental results for P. aeruginosa, and will be soon extended to other meaningful microbial pathogens. The network is publicly accessible at http://sing.ei.uvigo.es/antimicrobialCombination/., The research leading to these results has received funding from: the European Union's Seventh Framework Programme FP7/REGPOT-2012-2013.1 under grant agreement n° 316265, BIOCAPS; FCT through the project AntiPep PTDC/SAU-SAP/113196/2009/FCOMP-01-0124-FEDER016012; the Strategic Project PEst-OE/EQB/LA0023/2013; the Project “BioHealth - Biotechnology and Bioengineering approaches to improve health quality", NORTE-07-0124FEDER-000027, co-funded by the Programa Operacional Regional do Norte (ON.2 – O Novo Norte), QREN, FEDER; the project “RECI/BBB-EBI/0179/2012 - Consolidating Research Expertise and Resources on Cellular and Molecular Biotechnology at CEB/IBB”, FCOMP-01-0124-FEDER027462; and the Agrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa (2012/273). This document reflects only the author’s views and the European Union is not liable for any use that may be made of the information contained herein. The authors also acknowledge the Foundation for Science and Technology (FCT) for the PhD Grant of Paula Jorge, Ref. SFRH/BD/88192/2012. The authors do not have any conflicts of interest., info:eu-repo/semantics/publishedVersion

The Smart Drug Search is publicly accessible at http://sing.ei.uvigo.es/sds/. The BIOMedical Search Engine Framework is freely available for non-commercial use at https://github.com/agjacome/biomsef, Background and Objectives: Text mining and semantic analysis approaches can be applied to the construction of biomedical domain-specific search engines and provide an attractive alternative to create personalized and enhanced search experiences. Therefore, this work introduces the new open-source BIOMedical Search Engine Framework for the fast and lightweight development of domain-specific search engines. The rationale behind this framework is to incorporate core features typically available in search engine frameworks with flexible and extensible technologies to retrieve biomedical documents, annotate meaningful domain concepts, and develop highly customized Web search interfaces. Methods: The BIOMedical Search Engine Framework integrates taggers for major biomedical concepts, such as diseases, drugs, genes, proteins, compounds and organisms, and enables the use of domain-specific controlled vocabulary. Technologies from the Typesafe Reactive Platform, the AngularJS JavaScript framework and the Bootstrap HTML/CSS framework support the customization of the domain-oriented search application. Moreover, the RESTful API of the BIOMedical Search Engine Framework allows the integration of the search engine into existing systems or a complete web interface personalization. Results The construction of the Smart Drug Search is described as proof-of-concept of the BIOMedical Search Engine Framework. This public search engine catalogs scientific literature about antimicrobial resistance, microbial virulence and topics alike. The keyword-based queries of the users are transformed into concepts and search results are presented and ranked accordingly. The semantic graph view portraits all the concepts found in the results and the researcher may look into the relevance of different concepts, the strength of direct relations, and non-trivial, indirect relations. The number of occurrences of the concept shows its importance to the query, and the frequency of concept co-occurrence is indicative of biological relations meaningful to that particular scope of research. Conversely, indirect concept associations, i.e. concepts related by other intermediary concepts, can be useful to integrate information from different studies and look into non-trivial relations. Conclusions The BIOMedical Search Engine Framework supports the development of domain-specific search engines. The key strengths of the framework are modularity and extensibility in terms of software design, the use of open-source consolidated Web technologies, and the ability to integrate any number of biomedical text mining tools and information resources. Currently, the Smart Drug Search keeps over 1,186,000 documents, containing more than 11,854,000 annotations for 77,200 different concepts., This work was partially funded by the [14VI05] ContractProgramme from the University ofVigo and theAgrupamento INBIOMED from DXPCTSUG-FEDER unha maneira de facer Europa(2012/273).The research leading to these results has also received funding from the European Union Seventh Framework Programme FP7/REGPOT-2012-2013.1 under grant agreement n° 316265,BIOCAPS.This document reflects only the author’s views, and the European Union is not liable for any use that may be made of the information contained herein

Chemoprevention is the use of natural and/or synthetic substances to block, reverse, or retard the process of carcinogenesis. In this field, the use of antitumor peptides is of interest as, (i) these molecules are small in size, (ii) they show good cell diffusion and permeability, (iii) they affect one or more specific molecular pathways involved in carcinogenesis, and (iv) they are not usually genotoxic. We have checked the Web of Science Database (23/11/2015) in order to collect papers reporting on bioactive peptide (1691 registers), which was further filtered searching terms such as "antiproliferative," "antitumoral," or "apoptosis" among others. Works reporting the amino acid sequence of an antiproliferative peptide were kept (60 registers), and this was complemented with the peptides included in CancerPPD, an extensive resource for antiproliferative peptides and proteins. Peptides were grouped according to one of the following mechanism of action: inhibition of cell migration, inhibition of tumor angiogenesis, antioxidative mechanisms, inhibition of gene transcription/cell proliferation, induction of apoptosis, disorganization of tubulin structure, cytotoxicity, or unknown mechanisms. The main mechanisms of action of those antiproliferative peptides with known amino acid sequences are presented and finally, their potential clinical usefulness and future challenges on their application is discussed.

Nowadays, it is recognized worldwide that healthcare-associated infections are responsible for an increase in patient morbidity, mortality, and higher costs related to prolonged hospital stays. As electronic health data are increasingly available today, there is a unique opportunity to implement real-time decision support systems for automating the surveillance of healthcare-associated infections. As a consequence, different electronic surveillance systems have been implemented to date with varying degrees of success. However, there have been few instances in which clinical data and physician narratives with the potential to significantly improve electronic surveillance alternatives have been adopted. In this context, the present work introduces a case-based reasoning system for the automatic surveillance and diagnosis of healthcare-associated infections. The developed system makes use of different machine learning techniques in order to (i) automatically extract evidence from different types of data including clinical unstructured documents, (ii) incorporate static a priori knowledge handled by infection preventionists, and (iii) dynamically generate new knowledge as well as understandable explanations about the system's decisions. Results obtained from a real deployment in a public hospital belonging to the Spanish National Health System trained with 2569 samples belonging to 1800 patients during more than 10 consecutive months recognize the usefulness of the system. Display Omitted Automatic surveillance of healthcare-associated infections.Diagnostic decision support system aiding monitoring and control.Case-based reasoning system for classifying nosocomial infections.Static rule-based knowledge representation and dynamic induction process.Natural language processing for physician narratives and nurses' comments.

Over the last years, research on web spam filtering has gained interest from both academia and industry. In this context, although there are a good number of successful antispam techniques available (i.e., content-based, link-based, and hiding), an adequate combination of different algorithms supported by an advanced web spam filtering platform would offer more promising results. To this end, we propose the WSF2 framework, a new platform particularly suitable for filtering spam content on web pages. Currently, our framework allows the easy combination of different filtering techniques including, but not limited to, regular expressions and well-known classifiers (i.e., Naïve Bayes, Support Vector Machines, and C5.0). Applying our WSF2 framework over the publicly available WEBSPAM-UK2007 corpus, we have been able to demonstrate that a simple combination of different techniques is able to improve the accuracy of single classifiers on web spam detection. As a result, we conclude that the proposed filtering platform is a powerful tool for boosting applied research in this area.

SEDA (SEquence DAtaset builder) is a multiplatform desktop application for the manipulation of FASTA files containing DNA or protein sequences. The convenient graphical user interface gives access to a collection of simple (filtering, sorting, or file reformatting, among others) and advanced (BLAST searching, protein domain annotation, gene annotation, and sequence alignment) utilities not present in similar applications, which eases the work of life science researchers working with DNA and/or protein sequences, especially those who have no programming skills. This paper presents general guidelines on how to build efficient data handling protocols using SEDA, as well as practical examples on how to prepare high-quality datasets for single gene phylogenetic studies, the characterization of protein families, or phylogenomic studies. The user-friendliness of SEDA also relies on two important features: (i) the availability of easy-to-install distributable versions and installers of SEDA, including a Docker image for Linux, and (ii) the facility with which users can manage large datasets. SEDA is open-source, with GNU General Public License v3.0 license, and publicly available at GitHub (https://github.com/sing-group/seda). SEDA installers and documentation are available at https://www.sing-group.org/seda/.

BACKGROUND Nowadays, the use of social media is part of daily life, with more and more people, including governments and health organizations, using at least one platform regularly. Social media enables users to interact among large groups of people that share the same interests and suffer the same afflictions. Notably, these channels promote the ability to find and share information about health and medical conditions. OBJECTIVE This study aimed to characterize the bowel disease (BD) community on Twitter, in particular how patients understand, discuss, feel, and react to the condition. The main questions were as follows: Which are the main communities and most influential users?; Where are the main content providers from?; What are the key biomedical and scientific topics under discussion? How are topics interrelated in patient communications?; How do external events influence user activity?; What kind of external sources of information are being promoted? METHODS To answer these questions, a dataset of tweets containing terms related to BD conditions was collected from February to August 2018, accounting for a total of 24,634 tweets from 13,295 different users. Tweet preprocessing entailed the extraction of textual contents, hyperlinks, hashtags, time, location, and user information. Missing and incomplete information about the user profiles was completed using different analysis techniques. Semantic tweet topic analysis was supported by a lexicon-based entity recognizer. Furthermore, sentiment analysis enabled a closer look into the opinions expressed in the tweets, namely, gaining a deeper understanding of patients’ feelings and experiences. RESULTS Health organizations received most of the communication, whereas BD patients and experts in bowel conditions and nutrition were among those tweeting the most. In general, the BD community was mainly discussing symptoms, BD-related diseases, and diet-based treatments. Diarrhea and constipation were the most commonly mentioned symptoms, and cancer, anxiety disorder, depression, and chronic inflammations were frequently part of BD-related tweets. Most patient tweets discussed the bad side of BD conditions and other related conditions, namely, depression, diarrhea, and fibromyalgia. In turn, gluten-free diets and probiotic supplements were often mentioned in patient tweets expressing positive emotions. However, for the most part, tweets containing mentions to foods and diets showed a similar distribution of negative and positive sentiments because the effects of certain food components (eg, fiber, iron, and magnesium) were perceived differently, depending on the state of the disease and other personal conditions of the patients. The benefits of medical cannabis for the treatment of different chronic diseases were also highlighted. CONCLUSIONS This study evidences that Twitter is becoming an influential space for conversation about bowel conditions, namely, patient opinions about associated symptoms and treatments. So, further qualitative and quantitative content analyses hold the potential to support decision making among health-related stakeholders, including the planning of awareness campaigns.

This work reports on a large-scale potential neuropeptide activity screening in human gut microbiomes deposited in public databases. In our experimental approach, the sequences of the bioactive peptides collected in the MAHMI database, mainly predicted as immunomodulatory or antitumoral, were crossed with those of the neuroactive/digestive peptides. From 91,325,790 potential bioactive peptides, only 581 returned a match when crossed against the 5949 neuroactive peptides from the NeuroPep database and the 15 digestive hormones. Relevant bacterial taxa, such as Ruminococcus sp., Clostridium sp. were found among the main producers of the matching sequences, and many of the matches corresponded to adiponectin and the hormone produced by adipocites, which is involved in glucose homeostasis. These results show, for the first time, the presence of potentially bioactive peptides produced by gut microbiota members over the nervous cells, most notably, peptides with already predicted immunomodulatory or anti-inflammatory activity. Classical (Lactobacillus sp.) and next-generation (Faecalibacterium sp.) probiotics are shown to produce these peptides, which are proposed as a potential mechanism of action of psychobiotics. Our previous experimental results showed that many of these peptides were active when incubated with immune cells, such as dendritic cells, so their effect over the nervous system innervating the gut mucosa holds significant potential and should be explored., This work was supported by the Spanish “Programa Estatal de Investigación, Desarrollo e Inovación Orientada a los Retos de la Sociedad” (Grant AGL2016-78311-R); the Asociación Española Contra el Cancer (“Obtención de péptidos bioactivos contra el Cáncer Colo-Rectal a partir de secuencias genéticas de microbiomas intestinales”, grant PS-2016). This study was also supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER006684). SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from University of Vigo for hosting its IT infrastructure. This work was partially supported by the Asturias Regional Plan I+D+i for research groups (FYCYT-IDI/2018/000236) and by the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group.

Protein-protein interaction (PPI) data is essential to elucidate the complex molecular relationships in living systems, and thus understand the biological functions at cellular and systems levels. The complete map of PPIs that can occur in a living organism is called the interactome. For animals, PPI data is stored in multiple databases (e.g., BioGRID, CCSB, DroID, FlyBase, HIPPIE, HitPredict, HomoMINT, INstruct, Interactome3D, mentha, MINT, and PINA2) with different formats. This makes PPI comparisons difficult to perform, especially between species, since orthologous proteins may have different names. Moreover, there is only a partial overlap between databases, even when considering a single species. The EvoPPI ( http://evoppi.i3s.up.pt ) web application presented in this paper allows comparison of data from the different databases at the species level, or between species using a BLAST approach. We show its usefulness by performing a comparative study of the interactome of the nine polyglutamine (polyQ) disease proteins, namely androgen receptor (AR), atrophin-1 (ATN1), ataxin 1 (ATXN1), ataxin 2 (ATXN2), ataxin 3 (ATXN3), ataxin 7 (ATXN7), calcium voltage-gated channel subunit alpha1 A (CACNA1A), Huntingtin (HTT), and TATA-binding protein (TBP). Here we show that none of the human interactors of these proteins is common to all nine interactomes. Only 15 proteins are common to at least 4 of these polyQ disease proteins, and 40% of these are involved in ubiquitin protein ligase-binding function. The results obtained in this study suggest that polyQ disease proteins are involved in different functional networks. Comparisons with Mus musculus PPIs are also made for AR and TBP, using EvoPPI BLAST search approach (a unique feature of EvoPPI), with the goal of understanding why there is a significant excess of common interactors for these proteins in humans.

The large scale genome datasets that are now available can provide unprecedented insight into the evolution of genes and gene families. Nevertheless, handling and transforming such datasets into the desired format for downstream analyses is often a difficult and time-consuming task for researchers without a background in informatics. Here, we present a simple and fast protocol for data preparation and high quality phylogenetic tree inferences using simple to install cross-platform software applications with rich graphical interfaces. To illustrate its potential, this protocol was used to provide insight into the evolution of GULO gene in animals, a gene that encodes the enzyme responsible for the last step of vitamin C synthesis in this group of organisms. We find that GULO is always a single copy gene in all animal groups with the exception of Echinodermata. Surprisingly, we find potentially functional GULO genes in several Prostotomian groups such as Molluscs, Priapulida and Arachnida. To our knowledge, this is the first time a putative functional GULO gene is reported in Protostomians. All previously reported GULO gene losses were easily identified using the presented protocol.

A novel ecosystem to promote the physical, emotional and psychic health and well-being of the elderly is presented. Our proposal was designed to add several services developed to meet the needs of the senior population, namely services to improve social inclusion and increase contribution to society. Moreover, the solution monitors the vital signs of elderly individuals, as well as environmental parameters and behavior patterns, in order to seek eminent danger situations and predict potential hazardous issues, acting in accordance with the various alert levels specified for each individual. The platform was tested by seniors in a real scenario. The experimental results demonstrated that the proposed ecosystem was well accepted and is easy to use by seniors. info:eu-repo/semantics/publishedVersion