Evaluation of Preferential Flow Processes in Reclamation Soil Covers

To predict the effectiveness of land reclamation, it is important to understand how water and solutes are transported within reconstructed landscapes. The objective of this study was to examine the influence of preferential flow on salt leaching in reclamation soil covers. The study site was a reconstructed landscape where saline-sodic minespoil from oil sands mining was capped with layers of glacial and peat mix soil. Preferential flow was investigated using laboratory column experiments and in situ adsorptive dye and conservative tracer experiments. Results from column experiments and dye tracer experiments indicate that preferential flow is an important and prevalent mechanism of solute transport. Column experiments, which used time-domain reflectometry to monitor the transport of a chloride tracer through an undisturbed core of peat mix soil, determined immobile water fractions (èim/è) ranging from 80-99% and diffusive mass transfer rates (á) between 0.15 - 2.0 h-1. Breakthrough curves showed the early arrival of chloride and extended tailing. Dye tracer experiments, in which Brilliant Blue dye was applied in solution to the soil surface, were carried out at 6 hillslopes plots. Approximately 24 hours after dye application, a vertical soil face was excavated to reveal stained flow patterns. Preferential flow as macropore flow, fingering, and / or funneling was observed at each plot. Results from the conservative tracer field study indicated soil solutes were flushed by a combination of vertical and lateral flow processes. A large pulse of bromide and chloride was applied across the lower slope of the 0.35-m cover. Soil sampling at approximately 1 and 2 years later determined vertical leaching, lateral translocation downslope, and upwards movement of soil solutes. Matrix flow during the spring melt, combined with matrix flow and / or preferential flow during summer and fall periods, was responsible for the vertical leaching of solutes. Subsurface flow generated in response to the spring melt or due to differences in soil hydraulic conductivity was responsible for the lateral transport of solutes. As a result of advective or diffusive processes, solutes were transported upwards into the overlying soil. These results suggested that despite the existence of preferential flow, there were other mechanisms of solute transport which served to leach and flush salts from the soil

Investigating “Gene Ontology”- based semantic similarity in the context of functional genomics

Gene functional annotations are an essential part of knowledge discovery in the analysis of large datasets, with the Gene Ontology [Ashburner et al., 2000] as the de facto standard for such annotations. A considerable number of approaches for quantifying functional similarity between gene products based on the semantic similarity between their annotations have been developed, but little guidance exists as to which of these measures are the most appropriate for different purposes. This was addressed here by comparing the performances of a number of similarity measures and associated parameters. This comparison provided some interesting new insights as well as confirming emerging trends from the literature. There is also a pressing need for novel ways of applying these measures to facilitate the functional analysis of lists of gene products. We developed a novel algorithm, FuSiGroups, to group GO terms based on their semantic similarity and genes based on their functional similarity. This two-fold grouping results in groups of not only functionally similar genes but also an associated set of related GO terms that characterise a single functional aspect relating the genes in the group, which facilitates analysis by creating more coherent groups. Each gene can belong to multiple groups, so the groups more accurately reflect the complexity of biological reality than clusters generated using traditional approaches. FuSiGroups was tested on a number of scenarios and in each case, successfully generated biologically relevant groups, identifying the key functional aspects of the dataset. The algorithm also managed to eliminate genes that were functionally unrelated to the bulk of the dataset and distinguish between different biological pathways. Although dataset size is currently a limiting factor, with smaller datasets performing the best, FuSiGroups has been demonstrated as a promising approach for the functional analysis of gene products.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Investigating “Gene Ontology”- based semantic similarity in the context of functional genomics

Gene functional annotations are an essential part of knowledge discovery in the analysis of large datasets, with the Gene Ontology [Ashburner et al., 2000] as the de facto standard for such annotations. A considerable number of approaches for quantifying functional similarity between gene products based on the semantic similarity between their annotations have been developed, but little guidance exists as to which of these measures are the most appropriate for different purposes. This was addressed here by comparing the performances of a number of similarity measures and associated parameters. This comparison provided some interesting new insights as well as confirming emerging trends from the literature. There is also a pressing need for novel ways of applying these measures to facilitate the functional analysis of lists of gene products. We developed a novel algorithm, FuSiGroups, to group GO terms based on their semantic similarity and genes based on their functional similarity. This two-fold grouping results in groups of not only functionally similar genes but also an associated set of related GO terms that characterise a single functional aspect relating the genes in the group, which facilitates analysis by creating more coherent groups. Each gene can belong to multiple groups, so the groups more accurately reflect the complexity of biological reality than clusters generated using traditional approaches. FuSiGroups was tested on a number of scenarios and in each case, successfully generated biologically relevant groups, identifying the key functional aspects of the dataset. The algorithm also managed to eliminate genes that were functionally unrelated to the bulk of the dataset and distinguish between different biological pathways. Although dataset size is currently a limiting factor, with smaller datasets performing the best, FuSiGroups has been demonstrated as a promising approach for the functional analysis of gene products

Professor articulador: uma proposta de trabalho na escola Sesi-RS / Articulator teacher: a work proposal at Sesi-RS school

O estudo tem como objetivo analisar as contribuições de uma escola que contextualiza em sua prática o mundo do trabalho e a excelência acadêmica para a formação integral do aluno, impulsionando-o à construção de seus projetos de vida. Neste relato, discutiremos possibilidades de articulações de espaço e tempo escolares em uma instituição que respeita os diferentes estilos e formas de aprender, as diversas realidades, culturas e linguagens dos jovens a partir da experiência de professores (chamados de Professores Articuladores) com seus alunos da Escola de Ensino Médio do Serviço Social da Indústria-SESI/RS. É uma experiência que ocorre na escola desde 2014  e  seus resultados repercutem em todos os âmbitos escolares, contribuindo com ações e projetos, e sinalizam novas possibilidades de trabalhar com alunos que estão vivenciando a adolescência e a juventude, favorecendo os efeitos da escola para os jovens e potencializando os interesses e os modos de ser e de aprender de cada jovem-aluno

The Translational Data Catalog - discoverable biomedical datasets.

peer reviewedThe discoverability of datasets resulting from the diverse range of translational and biomedical projects remains sporadic. It is especially difficult for datasets emerging from pre-competitive projects, often due to the legal constraints of data-sharing agreements, and the different priorities of the private and public sectors. The Translational Data Catalog is a single discovery point for the projects and datasets produced by a number of major research programmes funded by the European Commission. Funded by and rooted in a number of these European private-public partnership projects, the Data Catalog is built on FAIR-enabling community standards, and its mission is to ensure that datasets are findable and accessible by machines. Here we present its creation, content, value and adoption, as well as the next steps for sustainability within the ELIXIR ecosystem

Infrastructure for synthetic health data

editorial reviewedMachine learning (ML) methods are becoming ever more prevalent across all domains of lifesciences. However, a key component of effective ML is the availability of large datasets thatare diverse and representative. In the context of health systems, with significant heterogeneityof clinical phenotypes and diversity of healthcare systems, there exists a necessity to developand refine unbiased and fair ML models. Synthetic data are increasingly being used to protectthe patient’s right to privacy and overcome the paucity of annotated open-access medical data. Here, we present our proof of concept for the generation of synthetic health data and our proposed FAIR implementation of the generated synthetic datasets. The work was developed during and after the one-week-long BioHackathon Europe, by together 20 participants (10 new to the project), from different countries (NL, ES, LU, UK, GR, FL, DE, . . . ).</p

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Webulous and the Webulous Google Add-On - a web service and application for ontology building from templates

Background: Authoring bio-ontologies is a task that has traditionally been undertaken by skilled experts trained in understanding complex languages such as the Web Ontology Language (OWL), in tools designed for such experts. As requests for new terms are made, the need for expert ontologists represents a bottleneck in the development process. Furthermore, the ability to rigorously enforce ontology design patterns in large, collaboratively developed ontologies is difficult with existing ontology authoring software.Description: We present Webulous, an application suite for supporting ontology creation by design patterns. Webulous provides infrastructure to specify templates for populating ontology design patterns that get transformed into OWL assertions in a target ontology. Webulous provides programmatic access to the template server and a client application has been developed for Google Sheets that allows templates to be loaded, populated and resubmitted to the Webulous server for processing.Conclusions: The development and delivery of ontologies to the community requires software support that goes beyond the ontology editor. Building ontologies by design patterns and providing simple mechanisms for the addition of new content helps reduce the overall cost and effort required to develop an ontology. The Webulous system provides support for this process and is used as part of the development of several ontologies at the European Bioinformatics Institute

Creating a metadata profile for clinical trial protocols

&lt;p&gt;The purpose of this recipe is to describe the process to define and standardize study and protocol-level (meta)data commonly collected in paediatric clinical trials, with the aim of making trial data more Findable through a common Interoperable metadata profile. The recipe details how to:&nbsp;&lt;/p&gt;&lt;p&gt;- Collect &amp; refine a list of representative variables&nbsp;&lt;/p&gt;&lt;p&gt;- Represent protocol-level additional (meta)data in a complementary data model&nbsp;&lt;/p&gt;&lt;p&gt;- Define extraction processes for populating variables of interest&lt;/p&gt