J Biomed Semantics. 2022 Jan 31;13(1):4. doi: 10.1186/s13326-021-00257-x.

ABSTRACT

BACKGROUND: Electronic Laboratory Notebooks (ELNs) are used to document experiments and investigations in the wet-lab. Protocols in ELNs contain a detailed description of the conducted steps including the necessary information to understand the procedure and the raised research data as well as to reproduce the research investigation. The purpose of this study is to investigate whether such ELN protocols can be used to create semantic documentation of the provenance of research data by the use of ontologies and linked data methodologies.

METHODS: Based on an ELN protocol of a biomedical wet-lab experiment, a retrospective provenance model of the raised research data describing the details of the experiment in a machine-interpretable way is manually engineered. Furthermore, an automated approach for knowledge acquisition from ELN protocols is derived from these results. This structure-based approach exploits the structure in the experiment's description such as headings, tables, and links, to translate the ELN protocol into a semantic knowledge representation. To satisfy the Findable, Accessible, Interoperable, and Reuseable (FAIR) guiding principles, a ready-to-publish bundle is created that contains the research data together with their semantic documentation.

RESULTS: While the manual modelling efforts serve as proof of concept by employing one protocol, the automated structure-based approach demonstrates the potential generalisation with seven ELN protocols. For each of those protocols, a ready-to-publish bundle is created and, by employing the SPARQL query language, it is illustrated that questions about the processes and the obtained research data can be answered.

CONCLUSIONS: The semantic documentation of research data obtained from the ELN protocols allows for the representation of the retrospective provenance of research data in a machine-interpretable way. Research Object Crate (RO-Crate) bundles including these models enable researchers to easily share the research data including the corresponding documentation, but also to search and relate the experiment to each other.

PMID:35101121 | PMC:PMC8802522 | DOI:10.1186/s13326-021-00257-x

J Med Internet Res. 2022 Feb 1;24(2):e32309. doi: 10.2196/32309.

ABSTRACT

BACKGROUND: Being diagnosed with cancer during childhood or adolescence can disrupt important periods in an individual's physical, psychosocial, and spiritual development and potentially reduce the quality of life (QOL) after treatment. Research is urgently required to improve the QOL for child and adolescent cancer survivors, and it is necessary to analyze the trends in prior research reported in international academic journals to identify knowledge structures.

OBJECTIVE: This study aims to identify the main keywords based on network centrality, subgroups (clusters) of keyword networks by using a cohesion analysis method, and the main theme of child and adolescent cancer survivor-related research abstracts through topic modeling. This study also aims to label the subgroups by comparing the results of the cohesion and topic modeling.

METHODS: A text network analysis method and topic modeling were used to explore the main trends in child and adolescent cancer survivor research by structuring a network of keyword (semantic morphemes) co-occurrence in the abstracts of articles published in 5 major web-based databases from 2016 to 2020. A total of 1677 child and adolescent cancer survivor-related studies were used for data analyses. Data selection, processing, and analyses were also conducted.

RESULTS: The top 5 keywords in terms of degree and eigenvector centrality were risk, control interval, radiation, childhood cancer treatment, and diagnosis. Of the 1677 studies used for data analyses, cluster 1 included 780 (46.51%) documents under risk management, cluster 2 contained 557 (33.21%) articles under health-related QOL and supportive care, and cluster 3 consisted of 340 (20.27%) studies under cancer treatment and complications.

CONCLUSIONS: This study is significant in that it confirms the knowledge structure based on the main keywords and cross-disciplinary trends in child and adolescent cancer survivor research published in the last 5 years worldwide. The primary goal of child and adolescent cancer survivor research is to prevent and manage the various aspects of the problems encountered during the transition to a normal life and to improve the overall QOL. To this end, it is necessary to further revitalize the study of the multidisciplinary team approach for the promotion of age-specific health behaviors and the development of intervention strategies with increased feasibility for child and adolescent cancer survivors.

PMID:35103615 | DOI:10.2196/32309

Bioinspir Biomim. 2022 Jan 26. doi: 10.1088/1748-3190/ac4f2e. Online ahead of print.

ABSTRACT

The field encompassing biomimetics, bioinspiration and nature inspiration in engineering science is growing steadily, pushed by exogenous factors like the search for potentially sustainable engineering solutions that might exist already in nature. With help of information provided by bibliometric database and further processed with dynamic network and semantic analysis tool, we provide insight at two scales on the corpus of nature inspired engineering field and its dynamics. At macro scale, the Web of Science® (WoS) categories, countries and institutions are ranked and ordered by thematic clusters and country networks, highlighting leading countries and institutions and how they focus on specific topics. Such an insight provides an overview at a macro scale that can be valuable to orient scientific strategy at the country level. At meso scale where science is incarnated by collaborative networks of authors and institutions that run across countries, we identify six semantic clusters and subclusters within them, and their dynamics. We also pinpoint leading academic collaborative networks and their activity in relation with the six semantic clusters. Trends and prospective are also discussed. Typically one observe that the field is becoming mature since, starting by imitating nature, it proceeded with mimicking more complex natural structures and functions and now it investigates ways used in nature in response to changes in the environment and implements them in innovative and adaptive artefacts. The sophistication of devices, methods and tools has been increasing over the years as well as their functionalities and adaptability whereas the size of devices has decreased at the same time.

PMID:35081515 | DOI:10.1088/1748-3190/ac4f2e

Neural Comput Appl. 2022 Jan 15:1-21. doi: 10.1007/s00521-022-06887-1. Online ahead of print.

ABSTRACT

Increasing demand in distance education, e-learning, web-based learning, and other digital sectors (e.g., entertainment) has led to excessive amounts of e-content. Learning objects (LOs) are among the most important components of electronic content (e-content) and are preserved in learning object repositories (LORs). LORs produce different types of electronic content. In producing e-content, several visualization techniques are employed to attract users and ensure a better understanding of the provided information. Many of these visualization systems match images with corresponding text using methods such as semantic web, ontologies, natural language processing, statistical techniques, neural networks, and deep neural networks. Unlike these methods, in this study, an automatic and intelligent content visualization system is developed using deep learning and popular artificial intelligence techniques. The proposed system includes subsystems that segment images to panoptic image instances and use these image instances to generate new images using a genetic algorithm, an evolution-based technique that is one of the best-known artificial intelligence methods. This large-scale proposed system was used to test different amounts of LOs for various science fields. The results show that the developed system can be efficiently used to create visually enhanced content for digital use.

PMID:35068702 | PMC:PMC8760887 | DOI:10.1007/s00521-022-06887-1

J Ginseng Res. 2022 Jan;46(1):39-53. doi: 10.1016/j.jgr.2021.07.008. Epub 2021 Jul 30.

ABSTRACT

Ginsenoside Rb1 (Rb1), one of the most important ingredients in Panax ginseng Meyer, has been confirmed to have favorable activities, including reducing antioxidative stress, inhibiting inflammation, regulating cell autophagy and apoptosis, affecting sugar and lipid metabolism, and regulating various cytokines. This study reviewed the recent progress on the pharmacological effects and mechanisms of Rb1 against cardiovascular and nervous system diseases, diabetes, and their complications, especially those related to neurodegenerative diseases, myocardial ischemia, hypoxia injury, and traumatic brain injury. This review retrieved articles from PubMed and Web of Science that were published from 2015 to 2020. The molecular targets or pathways of the effects of Rb1 on these diseases are referring to HMGB1, GLUT4, 11β-HSD1, ERK, Akt, Notch, NF-κB, MAPK, PPAR-γ, TGF-β1/Smad pathway, PI3K/mTOR pathway, Nrf2/HO-1 pathway, Nrf2/ARE pathway, and MAPK/NF-κB pathway. The potential effects of Rb1 and its possible mechanisms against diseases were further predicted via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and disease ontology semantic and enrichment (DOSE) analyses with the reported targets. This study provides insights into the therapeutic effects of Rb1 and its mechanisms against diseases, which is expected to help in promoting the drug development of Rb1 and its clinical applications.

PMID:35058726 | PMC:PMC8753521 | DOI:10.1016/j.jgr.2021.07.008

Int J Environ Res Public Health. 2022 Jan 11;19(2):794. doi: 10.3390/ijerph19020794.

ABSTRACT

Accident, injury, and fatality rates remain disproportionately high in the construction industry. Information from past mishaps provides an opportunity to acquire insights, gather lessons learned, and systematically improve safety outcomes. Advances in data science and industry 4.0 present new unprecedented opportunities for the industry to leverage, share, and reuse safety information more efficiently. However, potential benefits of information sharing are missed due to accident data being inconsistently formatted, non-machine-readable, and inaccessible. Hence, learning opportunities and insights cannot be captured and disseminated to proactively prevent accidents. To address these issues, a novel information sharing system is proposed utilizing linked data, ontologies, and knowledge graph technologies. An ontological approach is developed to semantically model safety information and formalize knowledge pertaining to accident cases. A multi-algorithmic approach is developed for automatically processing and converting accident case data to a resource description framework (RDF), and the SPARQL protocol is deployed to enable query functionalities. Trials and test scenarios utilizing a dataset of 200 real accident cases confirm the effectiveness and efficiency of the system in improving information access, retrieval, and reusability. The proposed development facilitates a new "open" information sharing paradigm with major implications for industry 4.0 and data-driven applications in construction safety management.

PMID:35055616 | DOI:10.3390/ijerph19020794

Front Artif Intell. 2022 Jan 3;4:769455. doi: 10.3389/frai.2021.769455. eCollection 2021.

ABSTRACT

Computer-based learning environments serve as a valuable asset to help strengthen teacher preparation and preservice teacher self-regulated learning. One of the most important advantages is the opportunity to collect ambient data unobtrusively as observable indicators of cognitive, affective, metacognitive, and motivational processes that mediate learning and performance. Ambient data refers to teacher interactions with the user interface that include but are not limited to timestamped clickstream data, keystroke and navigation events, as well as document views. We review the claim that computers designed as metacognitive tools can leverage the data to serve not only teachers in attaining the aims of instruction, but also researchers in gaining insights into teacher professional development. In our presentation of this claim, we review the current state of research and development of a network-based tutoring system called nBrowser, designed to support teacher instructional planning and technology integration. Network-based tutors are self-improving systems that continually adjust instructional decision-making based on the collective behaviors of communities of learners. A large part of the artificial intelligence resides in semantic web mining, natural language processing, and network algorithms. We discuss the implications of our findings to advance research into preservice teacher self-regulated learning.

PMID:35047767 | PMC:PMC8762201 | DOI:10.3389/frai.2021.769455

Top Cogn Sci. 2022 Jan;14(1):93-110. doi: 10.1111/tops.12586. Epub 2022 Jan 18.

ABSTRACT

People undergo many idiosyncratic experiences throughout their lives that may contribute to individual differences in the size and structure of their knowledge representations. Ultimately, these can have important implications for individuals' cognitive performance. We review evidence that suggests a relationship between individual experiences, the size and structure of semantic representations, as well as individual and age differences in cognitive performance. We conclude that the extent to which experience-dependent changes in semantic representations contribute to individual differences in cognitive aging remains unclear. To help fill this gap, we outline an empirical agenda that utilizes network analysis and involves the concurrent assessment of large-scale semantic networks and cognitive performance in younger and older adults. We present preliminary data to establish the feasibility and limitations of such empirical, network-analytical approaches.

PMID:35040557 | DOI:10.1111/tops.12586

BMJ Open. 2022 Jan 17;12(1):e053327. doi: 10.1136/bmjopen-2021-053327.

ABSTRACT

OBJECTIVE: To identify the key individual-level (demographics, attitudes, mobility) and contextual (COVID-19 case numbers, tiers of mobility restrictions, urban districts) determinants of adopting the NHS COVID-19 contact tracing app and continued use overtime.

DESIGN AND SETTING: A three-wave panel survey conducted in England in July 2020 (background survey), November 2020 (first measure of app adoption) and March 2021 (continued use of app and new adopters) linked with official data.

PARTICIPANTS: N=2500 adults living in England, representative of England's population in terms of regional distribution, age and gender (2011 census).

PRIMARY OUTCOME: Repeated measures of self-reported app usage.

ANALYTICAL APPROACH: Multilevel logistic regression linking a range of individual level (from survey) and contextual (from linked data) determinants to app usage.

RESULTS: We observe initial app uptake at 41%, 95% CI (0.39% to 0.43%), and a 12% drop-out rate by March 2021, 95% CI (0.10% to 0.14%). We also found that 7% of nonusers as of wave 2 became new adopters by wave 3, 95% CI (0.05% to 0.08%). Initial uptake (or failure to use) of the app associated with social norms, privacy concerns and misinformation about third-party data access, with those living in postal districts with restrictions on mobility less likely to use the app. Perceived lack of transparent evidence of effectiveness was associated with drop-out of use. In addition, those who trusted the government were more likely to adopt in wave 3 as new adopters.

CONCLUSIONS: Successful uptake of the contact tracing app should be evaluated within the wider context of the UK Government's response to the crisis. Trust in government is key to adoption of the app in wave 3 while continued use is linked to perceptions of transparent evidence. Providing clear information to address privacy concerns could increase uptake, however, the disparities in continued use among ethnic minority participants needs further investigation.

PMID:35039293 | PMC:PMC8764714 | DOI:10.1136/bmjopen-2021-053327

Methods Mol Biol. 2022;2443:527-540. doi: 10.1007/978-1-0716-2067-0_28.

ABSTRACT

Recent advances in high-throughput technologies have resulted in tremendous increase in the amount of data in the agronomic domain. There is an urgent need to effectively integrate complementary information to understand the biological system in its entirety. We have developed AgroLD, a knowledge graph that exploits the Semantic Web technology and some of the relevant standard domain ontologies, to integrate information on plant species and in this way facilitating the formulation of new scientific hypotheses. This chapter outlines some integration results of the project, which initially focused on genomics, proteomics and phenomics.

PMID:35037225 | DOI:10.1007/978-1-0716-2067-0_28

AI Soc. 2022 Jan 9:1-13. doi: 10.1007/s00146-021-01371-1. Online ahead of print.

ABSTRACT

This paper is a survey of standards being used in the domain of digital cultural heritage with focus on the Metadata Encoding and Transmission Standard (METS) created by the Library of Congress in the United States of America. The process of digitization of cultural heritage requires silo breaking in a number of areas-one area is that of academic disciplines to enable the performance of rich interdisciplinary work. This lays the foundation for the emancipation of the second form of silo which are the silos of knowledge, both traditional and born digital, held in individual institutions, such as galleries, libraries, archives and museums. Disciplinary silo breaking is the key to unlocking these institutional knowledge silos. Interdisciplinary teams, such as developers and librarians, work together to make the data accessible as open data on the "semantic web". Description logic is the area of mathematics which underpins many ontology building applications today. Creating these ontologies requires a human-machine symbiosis. Currently in the cultural heritage domain, the institutions' role is that of provider of this open data to the national aggregator which in turn can make the data available to the trans-European aggregator known as Europeana. Current ingests to the aggregators are in the form of machine readable cataloguing metadata which is limited in the richness it provides to disparate object descriptions. METS can provide this richness.

PMID:35035111 | PMC:PMC8743025 | DOI:10.1007/s00146-021-01371-1

Bioinformatics. 2022 Jan 7:btac013. doi: 10.1093/bioinformatics/btac013. Online ahead of print.

ABSTRACT

MOTIVATION: Information on protein-protein interactions is collected in numerous primary databases with their own curation process. Several meta-databases aggregate primary databases to provide more exhaustive datasets. In addition to exhaustivity, aggregation contributes to reliability by providing an overview of the various studies and detection methods supporting an interaction. However, interactions listed in different primary databases are partly redundant because some publications reporting protein-protein interactions have been curated by multiple primary databases. Mere aggregation can thus introduce a bias if these redundancies are not identified and eliminated. To overcome this bias, meta-databases rely on the Molecular Interaction ontology that describes interaction detection methods, but they do not fully take advantage of the ontology's rich semantics, which leads to systematically overestimating interaction reproducibility.

RESULTS: We propose a precise definition of explicit and implicit redundancy, and show that both can be easily detected using Semantic Web technologies. We apply this process to a dataset from the APID meta-database and show that while explicit redundancies were detected by the APID aggregation process, about 15% of APID entries are implicitly redundant and should not be taken into account when presenting confidence-related metrics. More than 90% of implicit redundancies result from the aggregation of distinct primary databases, while the remaining occurs between entries of a single database. Finally, we build a" reproducible interactome" with interactions that have been reproduced by multiple methods or publications. The size of the reproducible interactome is drastically impacted by removing redundancies for both yeast (-59%) and human (-56%), and we show that this is largely due to implicit redundancies.

AVAILABILITY: Software, data and results are available at https://gitlab.com/nnet56/reproducible-interactome, https://reproducible-interactome.genouest.org/, Zenodo (doi : 10.5281/zenodo.5595037) and NDEx (doi : 10.18119/N94302, doi : 10.18119/N97S4D.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:35015827 | DOI:10.1093/bioinformatics/btac013

Molecules. 2021 Dec 23;27(1):65. doi: 10.3390/molecules27010065.

ABSTRACT

The level of ambiguity in describing glycan structure has significantly increased with the upsurge of large-scale glycomics and glycoproteomics experiments. Consequently, an ontology-based model appears as an appropriate solution for navigating these data. However, navigation is not sufficient and the model should also enable advanced search and comparison. A new ontology with a tree logical structure is introduced to represent glycan structures irrespective of the precision of molecular details. The model heavily relies on the GlycoCT encoding of glycan structures. Its implementation in the GlySTreeM knowledge base was validated with GlyConnect data and benchmarked with the Glycowork library. GlySTreeM is shown to be fast, consistent, reliable and more flexible than existing solutions for matching parts of or whole glycan structures. The model is also well suited for painless future expansion.

PMID:35011294 | DOI:10.3390/molecules27010065

Behav Brain Funct. 2022 Jan 7;18(1):1. doi: 10.1186/s12993-022-00186-4.

ABSTRACT

BACKGROUND: Mathematical expressions mainly include arithmetic (such as 8 - (1 + 3)) and algebra (such as a - (b + c)). Previous studies have shown that both algebraic processing and arithmetic involved the bilateral parietal brain regions. Although previous studies have revealed that algebra was dissociated from arithmetic, the neural bases of the dissociation between algebraic processing and arithmetic is still unclear. The present study uses functional magnetic resonance imaging (fMRI) to identify the specific brain networks for algebraic and arithmetic processing.

METHODS: Using fMRI, this study scanned 30 undergraduates and directly compared the brain activation during algebra and arithmetic. Brain activations, single-trial (item-wise) interindividual correlation and mean-trial interindividual correlation related to algebra processing were compared with those related to arithmetic. The functional connectivity was analyzed by a seed-based region of interest (ROI)-to-ROI analysis.

RESULTS: Brain activation analyses showed that algebra elicited greater activation in the angular gyrus and arithmetic elicited greater activation in the bilateral supplementary motor area, left insula, and left inferior parietal lobule. Interindividual single-trial brain-behavior correlation revealed significant brain-behavior correlations in the semantic network, including the middle temporal gyri, inferior frontal gyri, dorsomedial prefrontal cortices, and left angular gyrus, for algebra. For arithmetic, the significant brain-behavior correlations were located in the phonological network, including the precentral gyrus and supplementary motor area, and in the visuospatial network, including the bilateral superior parietal lobules. For algebra, significant positive functional connectivity was observed between the visuospatial network and semantic network, whereas for arithmetic, significant positive functional connectivity was observed only between the visuospatial network and phonological network.

CONCLUSION: These findings suggest that algebra relies on the semantic network and conversely, arithmetic relies on the phonological and visuospatial networks.

PMID:34996499 | PMC:PMC8740448 | DOI:10.1186/s12993-022-00186-4

JMIR Mhealth Uhealth. 2022 Jan 7;10(1):e30557. doi: 10.2196/30557.

ABSTRACT

BACKGROUND: There is a growing need for the integration of patient-generated health data (PGHD) into research and clinical care to enable personalized, preventive, and interactive care, but technical and organizational challenges, such as the lack of standards and easy-to-use tools, preclude the effective use of PGHD generated from consumer devices, such as smartphones and wearables.

OBJECTIVE: This study outlines how we used mobile apps and semantic web standards such as HTTP 2.0, Representational State Transfer, JSON (JavaScript Object Notation), JSON Schema, Transport Layer Security (version 1.3), Advanced Encryption Standard-256, OpenAPI, HTML5, and Vega, in conjunction with patient and provider feedback to completely update a previous version of mindLAMP.

METHODS: The Learn, Assess, Manage, and Prevent (LAMP) platform addresses the abovementioned challenges in enhancing clinical insight by supporting research, data analysis, and implementation efforts around PGHD as an open-source solution with freely accessible and shared code.

RESULTS: With a simplified programming interface and novel data representation that captures additional metadata, the LAMP platform enables interoperability with existing Fast Healthcare Interoperability Resources-based health care systems as well as consumer wearables and services such as Apple HealthKit and Google Fit. The companion Cortex data analysis and machine learning toolkit offer robust support for artificial intelligence, behavioral feature extraction, interactive visualizations, and high-performance data processing through parallelization and vectorization techniques.

CONCLUSIONS: The LAMP platform incorporates feedback from patients and clinicians alongside a standards-based approach to address these needs and functions across a wide range of use cases through its customizable and flexible components. These range from simple survey-based research to international consortiums capturing multimodal data to simple delivery of mindfulness exercises through personalized, just-in-time adaptive interventions.

PMID:34994710 | DOI:10.2196/30557

J Biomed Semantics. 2022 Jan 6;13(1):1. doi: 10.1186/s13326-021-00253-1.

ABSTRACT

BACKGROUND: The advancement of science and technologies play an immense role in the way scientific experiments are being conducted. Understanding how experiments are performed and how results are derived has become significantly more complex with the recent explosive growth of heterogeneous research data and methods. Therefore, it is important that the provenance of results is tracked, described, and managed throughout the research lifecycle starting from the beginning of an experiment to its end to ensure reproducibility of results described in publications. However, there is a lack of interoperable representation of end-to-end provenance of scientific experiments that interlinks data, processing steps, and results from an experiment's computational and non-computational processes.

RESULTS: We present the "REPRODUCE-ME" data model and ontology to describe the end-to-end provenance of scientific experiments by extending existing standards in the semantic web. The ontology brings together different aspects of the provenance of scientific studies by interlinking non-computational data and steps with computational data and steps to achieve understandability and reproducibility. We explain the important classes and properties of the ontology and how they are mapped to existing ontologies like PROV-O and P-Plan. The ontology is evaluated by answering competency questions over the knowledge base of scientific experiments consisting of computational and non-computational data and steps.

CONCLUSION: We have designed and developed an interoperable way to represent the complete path of a scientific experiment consisting of computational and non-computational steps. We have applied and evaluated our approach to a set of scientific experiments in different subject domains like computational science, biological imaging, and microscopy.

PMID:34991705 | DOI:10.1186/s13326-021-00253-1

Int J Med Inform. 2021 Dec 25;158:104674. doi: 10.1016/j.ijmedinf.2021.104674. Online ahead of print.

ABSTRACT

BACKGROUND: Information asymmetry causes barriers for the patient's decision-making in the online health community. Patients can rely on the physician's self-disclosed information to alleviate it. However, the impact of physician's self-disclosed information on the patient's decision has rarely been discussed.

OBJECTIVES: To investigate the impact of the physician's self-disclosed information on the patient's decision in the online health community and to examine the moderating effect of the physician's online reputation.

METHODS: Drawing on the limited-capacity model of attention, we develop a theoretical model to estimate the impact of physician's self-disclosure information on patient's decision and the contingent roles of physician's online reputation in online healthcare community by econometric methods. We designed a web crawler based on R language program to collect more than 20,000 physicians' data from their homepage in Haodf-a leading online healthcare community platform in China. The attributes of the physician's information disclosure are measured by the following variables: emotion orientation, the quantity of information and the semantic topics diversity.

RESULTS: The empirical analysis derives the following findings: (1) The emotion orientation in physician's self-disclosure information is positively associated with patient's decision; (2) Both excessive quantity of information and semantic topics diversity can raise barriers for patient's decision; (3) When the level of physician's online reputation is high, the negative effect of the quantity of information and semantic topics diversity are all strengthened while the positive effect of the emotion orientation is not strengthened.

CONCLUSIONS: This study has a profound importance for a deep understanding of the impact of physician's self-disclosure information and contributes to the literature on information disclosure, the limited capacity model of attention, patient's decision. Also, this study provides implications for practice.

PMID:34968960 | DOI:10.1016/j.ijmedinf.2021.104674

One Health. 2021 Dec 3;13:100357. doi: 10.1016/j.onehlt.2021.100357. eCollection 2021 Dec.

ABSTRACT

PADI-web (Platform for Automated extraction of animal Disease Information from the web) is a biosurveillance system dedicated to monitoring online news sources for the detection of emerging animal infectious diseases. PADI-web has collected more than 380,000 news articles since 2016. Compared to other existing biosurveillance tools, PADI-web focuses specifically on animal health and has a fully automated pipeline based on machine-learning methods. This paper presents the new functionalities of PADI-web based on the integration of: (i) a new fine-grained classification system, (ii) automatic methods to extract terms and named entities with text-mining approaches, (iii) semantic resources for indexing keywords and (iv) a notification system for end-users. Compared to other biosurveillance tools, PADI-web, which is integrated in the French Platform for Animal Health Surveillance (ESA Platform), offers strong coverage of the animal sector, a multilingual approach, an automated information extraction module and a notification tool configurable according to end-user needs.

PMID:34950760 | PMC:PMC8671119 | DOI:10.1016/j.onehlt.2021.100357

Front Public Health. 2021 Dec 6;9:798905. doi: 10.3389/fpubh.2021.798905. eCollection 2021.

ABSTRACT

The exponential growth of social media users has changed the dynamics of retrieving the potential information from user-generated content and transformed the paradigm of information-retrieval mechanism with the novel developments on the concept of "web of data". In this regard, our proposed Ontology-Based Sentiment Analysis provides two novel approaches: First, the emotion extraction on tweets related to COVID-19 is carried out by a well-formed taxonomy that comprises possible emotional concepts with fine-grained properties and polarized values. Second, the potential entities present in the tweet can be analyzed for semantic associativity. The extraction of emotions can be performed in two cases: (i) words directly associated with the emotional concepts present in the taxonomy and (ii) words indirectly present in the emotional concepts. Though the latter case is very challenging in processing the tweets to find the hidden patterns and extract the meaningful facts associated with it, our proposed work is able to extract and detect almost 81% of true positives and considerably able to detect the false negatives. Finally, the proposed approach's superior performance is witnessed from its comparison with other peer-level approaches.

PMID:34938715 | PMC:PMC8685242 | DOI:10.3389/fpubh.2021.798905

Plant Cell Physiol. 2021 Dec 17:pcab173. doi: 10.1093/pcp/pcab173. Online ahead of print.

ABSTRACT

The advancement of metabolomics in terms of techniques for measuring small molecules has enabled the rapid detection and quantification of numerous cellular metabolites. Metabolomic data provide new opportunities to gain a deeper understanding of plant metabolism that can improve the health of both plants and humans that consume them. Although major public repositories for general metabolomic data have been established, the community still has shortcomings related to data sharing, especially in terms of data reanalysis, reusability, and reproducibility. To address these issues, we developed the RIKEN Plant Metabolome MetaDatabase (RIKEN PMM, http://metabobank.riken.jp/pmm/db/plantMetabolomics), which stores mass spectrometry-based (e.g. GC-MS-based) metabolite profiling data of plants together with their detailed, structured experimental metadata, including sampling and experimental procedures. Our metadata are described as Linked Open Data (LOD) based on the Resource Description Framework (RDF) using standardized and controlled vocabularies, such as the Metabolomics Standards Initiative Ontology (MSIO), which are to be integrated with various life and biomedical science data using the World Wide Web. RIKEN PMM implements intuitive and interactive operations for plant metabolome data, including raw data (netCDF format), mass spectra (NIST MSP format), and metabolite annotations. The feature is suitable not only for biologists who are interested in metabolomic phenotypes, but also for researchers who would like to investigate life science in general through plant metabolomic approaches.

PMID:34918130 | DOI:10.1093/pcp/pcab173