Healthcare (Basel). 2022 May 10;10(5):881. doi: 10.3390/healthcare10050881.

ABSTRACT

The evolution of the coronavirus (COVID-19) disease took a toll on the social, healthcare, economic, and psychological prosperity of human beings. In the past couple of months, many organizations, individuals, and governments have adopted Twitter to convey their sentiments on COVID-19, the lockdown, the pandemic, and hashtags. This paper aims to analyze the psychological reactions and discourse of Twitter users related to COVID-19. In this experiment, Latent Dirichlet Allocation (LDA) has been used for topic modeling. In addition, a Bidirectional Long Short-Term Memory (BiLSTM) model and various classification techniques such as random forest, support vector machine, logistic regression, naive Bayes, decision tree, logistic regression with stochastic gradient descent optimizer, and majority voting classifier have been adapted for analyzing the polarity of sentiment. The effectiveness of the aforesaid approaches along with LDA modeling has been tested, validated, and compared with several benchmark datasets and on a newly generated dataset for analysis. To achieve better results, a dual dataset approach has been incorporated to determine the frequency of positive and negative tweets and word clouds, which helps to identify the most effective model for analyzing the corpora. The experimental result shows that the BiLSTM approach outperforms the other approaches with an accuracy of 96.7%.

PMID:35628018 | DOI:10.3390/healthcare10050881

Sci Data. 2022 May 27;9(1):239. doi: 10.1038/s41597-022-01352-z.

ABSTRACT

Graph databases capture richly linked domain knowledge by integrating heterogeneous data and metadata into a unified representation. Here, we present the use of bespoke, interactive data graphics (bar charts, scatter plots, etc.) for visual exploration of a knowledge graph. By modeling a chart as a set of metadata that describes semantic context (SPARQL query) separately from visual context (Vega-Lite specification), we leverage the high-level, declarative nature of the SPARQL and Vega-Lite grammars to concisely specify web-based, interactive data graphics synchronized to a knowledge graph. Resources with dereferenceable URIs (uniform resource identifiers) can employ the hyperlink encoding channel or image marks in Vega-Lite to amplify the information content of a given data graphic, and published charts populate a browsable gallery of the database. We discuss design considerations that arise in relation to portability, persistence, and performance. Altogether, this pairing of SPARQL and Vega-Lite-demonstrated here in the domain of polymer nanocomposite materials science-offers an extensible approach to FAIR (findable, accessible, interoperable, reusable) scientific data visualization within a knowledge graph framework.

PMID:35624233 | DOI:10.1038/s41597-022-01352-z

Database (Oxford). 2022 May 25;2022:baac035. doi: 10.1093/database/baac035.

ABSTRACT

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec.

PMID:35616100 | DOI:10.1093/database/baac035

Stud Health Technol Inform. 2022 May 25;294:790-795. doi: 10.3233/SHTI220585.

ABSTRACT

SNIK is a knowledge base about the management of health information systems generated by extracting Linked Data from textbooks and other sources. SNIK describes functions, roles executing these functions, and entity types, the information used or updated by these functions. We present SNIK Quiz, a browser game in which students answer multiple-choice questions about information management in hospitals based on SNIK. The questions are semi-automatically generated using templates in order to train basic facts, more complex patterns, and connections between textbooks encoded in SNIK.

PMID:35612205 | DOI:10.3233/SHTI220585

Crit Rev Food Sci Nutr. 2022 May 25:1-19. doi: 10.1080/10408398.2022.2079112. Online ahead of print.

ABSTRACT

Fruits contain enormous source of vitamins that provides energy to the human body. These are also affluent in essential and vital vitamins, minerals, fiber, and health-promoting components, which has led to an increase in fruit consumption in recent years. Though fruit consumption has expanded considerably in recent years, the use of synthetic chemicals to ripen or store fruits has been steadily increasing, resulting in postharvest deterioration. Alternatives to synthetic chemicals should be considered to control this problem. Instead of utilizing synthetic chemicals, this study suggests using natural plant products to control postharvest decay. The aim of this study indicates how natural plant products can be useful and effective to eliminate postharvest diseases rather than using synthetic chemicals. Several electronic databases were investigated as information sources, including Google Scholar, PubMed, Web of Science, Scopus, ScienceDirect, SpringerLink, Semantic Scholar, MEDLINE, and CNKI Scholar. The current review focused on the postharvest of fruits has become more and more necessary because of these vast demands of fruits. Pathogen-induced diseases are the main component and so the vast portion of fruits get wasted after harvest. Besides, it may occur harmful during harvesting and subsequent handling, storage, and marketing and after consumer purchasing and also causes for numerous endogenous and exogenous diseases via activating ROS, oxidative stress, lipid peroxidation, etc. However, pathogenicity can be halted by using postharvest originating natural fruits containing bioactive elements that may be responsible for the management of nutritional deficiency, inflammation, cancer, and so on. However, issues arising during the postharvest diseases must be controlled and resolved before releasing the horticultural commodities for commercialization. Therefore, the control of postharvest pathogens still depends on the use of synthetic fungicides; however, due to the problem of the development of the fungicide-resistant strains there is a good demand of public to eradicate the use of pesticides with the arrival of numerous diseases that are expanded in their intensity by the specific chemical product. By using of the organic or natural products for controlling postharvest diseases of fruits has become a mandatory step to take. In addition, antimicrobial packaging may have a greater impact on long-term food security by lowering the risk of pathogenicity and increasing the longevity of fruit shelf life. Taken together, natural chemicals as acetaldehyde, hexanal, eugenol, linalool, jasmonates, glucosinolates, essential oils, and many plant bioactive are reported for combating of the postharvest illnesses and guide to way of storage of fruits in this review.

PMID:35612470 | DOI:10.1080/10408398.2022.2079112

Stud Health Technol Inform. 2022 May 25;294:419-420. doi: 10.3233/SHTI220491.

ABSTRACT

To enhance their practice, healthcare professionals need to cross-link various usage recommendations provided by heterogeneous vocabularies that must be retrieved and integrated conjointly. This is the aim of the Knowledge Warehouse / K-Ware platform. It enables establishing relevant bridges between different knowledge sources (structured vocabularies, thesaurus, ontologies) expressed in the semantic web standard languages (i.e. SKOS, OWL, RDF). This poster presents the strategy applied in K-Ware to hide the different aspects of linking literals with medical entities encoded in these knowledge sources to fetch some publications abstracts from Pubmed.

PMID:35612113 | DOI:10.3233/SHTI220491

Stud Health Technol Inform. 2022 May 25;294:372-376. doi: 10.3233/SHTI220477.

ABSTRACT

In a national effort aiming at cross-hospitals data interoperability, the Swiss Personalized Health Network elected RDF as preferred data and meta-data representation format. Yet, most clinical research software solutions are not designed to interact with RDF databases. We present a modular Python toolkit allowing easy conversion from RDF graphs to i2b2, adaptable to other common data models (CDM) with reasonable efforts. The tool was designed with feedback from clinicians in both oncology and laboratory research.

PMID:35612099 | DOI:10.3233/SHTI220477

Stud Health Technol Inform. 2022 May 25;294:302-306. doi: 10.3233/SHTI220463.

ABSTRACT

Suitable causal inference in biostatistics can be best achieved by knowledge representation thanks to causal diagrams or directed acyclic graphs. However, necessary and sufficient causes are not easily represented. Since existing ontologies do not fill this gap, we designed OntoBioStat in order to enable covariate selection support based on causal relation representations. OntoBioStat automatic ontological causal diagram construction and inferences are detailed in this study. OntoBioStat inferences are allowed by Semantic Web Rule Language rules and axioms. First, statements made by the users include outcome, exposure, covariate, and causal relation specification. Then, reasoning enable automatic construction using generic instances of Meta_Variable and Necessary_Variable classes. Finally, inferred classes highlighted potential bias such as confounder-like. Ontological causal diagram built with OntoBioStat was compared to a standard causal diagram (without OntoBioStat) in a theoretical study. It was found that confounding and bias were not completely identified by the standard causal diagram, and erroneous covariate sets were provided. Further research is needed in order to make OntoBioStat more usable.

PMID:35612081 | DOI:10.3233/SHTI220463

Bull Cancer. 2022 May 19:S0007-4551(22)00143-6. doi: 10.1016/j.bulcan.2022.03.006. Online ahead of print.

ABSTRACT

INTRODUCTION: Today, patients with lung cancer and their relatives can easily search information on the Internet and express themselves online.

METHODS: Within this web-ethnographic research, we found, based on 246 search terms related to lung cancer, and collected, a sample of 136 online conversations that were published between January 2004 and September 2018, including 1220 messages by 762 authors.

RESULTS: The authors of messages, many of them close relatives of patients (35%), share their experience (34%). Seven areas of worrying concern, each of them prominent in 10 to 24% of the corpus, can be grouped under three headings: accepting the disease in order for the patient or their caregiver to fight it ("decide on the prognosis", "managing the treatments", "stopping the progression"), conjuring fate ("naming the guilty ones", "conjuring powerlessness"), asserting resilience ("adopt the right attitude" and "telling one's story in order to survive"). The question of time - disrupted, lost, to be caught up with or controlled - runs through all the issues.

DISCUSSION: The patients' and caregivers' concerns go beyond the pace of medical treatment and beyond death. Their mental representations of the disease influence their adherence to the care pathway. Welcoming them in our care and dialogue goes hand in hand with personalized treatment.

PMID:35599172 | DOI:10.1016/j.bulcan.2022.03.006

Stud Health Technol Inform. 2022 May 16;293:169-170. doi: 10.3233/SHTI220364.

ABSTRACT

Just-in-time adaptive interventions (JITAIs) can promote behavior change in patients. It was the aim of our study to make JITAIs adaptable, i.e., to configure JITAIs for different purposes and to personalize them for different participants, whilst enabling central maintenance and integrated data analysis across deployments and individuals. We present a concept for adaptable JITAIs that was created following a design science approach. It builds on multi-level conceptual modeling and knowledge graphs and will be evaluated in user studies.

PMID:35592977 | DOI:10.3233/SHTI220364

Stud Health Technol Inform. 2022 May 16;293:59-66. doi: 10.3233/SHTI220348.

ABSTRACT

Decision-making based on so-called medical guidelines supported by semantic AI solutions is an essential and significant task for medical personnel in both a pre-clinical setting and an inner-clinical environment. Semantic representations of medical guidelines and Fast Healthcare Interoperability Resources (FHIR) using Semantic Web technologies, i.e., Resource Description Framework (RDF), rules (RuleML and Prova), and Shape Constraint Language (SHACL), provide a semantic knowledge base for the decision-making process and ease technical implementation and automation tasks. Current medical decision support systems lack Semantic Web integration using FHIR-RDF representations as a data source. In this paper, we implement a particular medical guideline using two different approaches: Prova [8] and SHACL [13]. We generate a series of raw FHIR-data for a selected guideline, the ABCDE approach, and compare the implemented two programs' (Prova and SHACL) results. Both approaches deliver the same results in terms of content. Both may be used within a distributed medical environment depending on the need of organizations.

PMID:35592961 | DOI:10.3233/SHTI220348

FASEB J. 2022 May;36 Suppl 1. doi: 10.1096/fasebj.2022.36.S1.R5732.

ABSTRACT

OBJECTIVE: Our long-term goal is the development of a clinically-relevant, validated enterprise data warehouse for research. The Intergenerational Health Knowledgebase (IHK) will advance and accelerate the study of the developmental origins of adult disease. We sought to harmonize data from the electronic health record (EHR) and external data sources and efficiently synthesize key variables from pregnancy and early life in the IHK. The objective was to make the IHK be efficient, effective, flexible and able to integrate with participation information for on-campus biobanks.

METHODS: The development of the IHK is an iterative process involving multiple steps: 1) clinical data team (CDT) members identify and locate clinically significant distinct clinical variables in the front-facing EHR pertaining to obstetrics and pediatrics. 2) The Biomedical Informatics group in the Institute for Clinical and Translational Science (ICTS-BMI) group locates these variables in the transactional database of the EHR. 3) The CDT members validate the extracted variables. Extracted data is compared with expected outcomes from the EHR. Inconsistencies are reviewed by the CDT and ICTS-BMI group to pinpoint potential causes. 4) With confirmed data variables, ICTS-BMI develops novel data tables and user-friendly SQL queries. The resultant OBstetrics Data Integration Architecture structure integrates multiple data sources within the institution which includes, but is not limited to, imaging data, diagnoses, vital sign information, medications, medication administration, and procedures for the maternal-fetal dyad. Powering this architecture is a common semantic layer that uses established standardized vocabularies and value sets. This includes storing patient demographic data, patient diagnoses using ICD9-10 codes, storing laboratory results using LOINC codes, and problems using ICD9-10/SNOMED-CT codes. In our EHR, all data is joined using pregnancy episode identification numbers linking maternal and neonatal data under a singular pregnancy instance. ICTS-BMI developed a secure web-based Knowledgebase viewer for performing queries of the IHK without expert SQL knowledge.

RESULTS: The IHK contains indexed maternal and child data. The IHK is frequently updated to contain every pregnancy cared for at the University of Iowa Hospitals & Clinics (UIHC) (currently 62,000+ pregnancies). Because of our team's complementary expertise, they were able to anticipate needed information, identify and validate key variables in the EMR, and plan pre-defined queries. As a result, we have quickly provided data for studies ranging from the effects of maternal environmental exposures, the role of patient distance to the hospital in pregnancy outcomes, medication use during pregnancy, and the association of depression with adverse outcomes.

CONCLUSION: The development of the novel IHK has facilitated pregnancy-related research and identified new targets for improving maternal health and following long-term health outcomes of parents and children. Further, integration of the IHK with the UIHC Women's Health Tissue Repository facilitates using biological specimens for research.

PMID:35553641 | DOI:10.1096/fasebj.2022.36.S1.R5732

J Med Internet Res. 2022 May 11;24(5):e36086. doi: 10.2196/36086.

ABSTRACT

BACKGROUND: Digital technology uses in cardiology have become a popular research focus in recent years. However, there has been no published bibliometric report that analyzed the corresponding academic literature in order to derive key publishing trends and characteristics of this scientific area.

OBJECTIVE: We used a bibliometric approach to identify and analyze the academic literature on digital technology uses in cardiology, and to unveil popular research topics, key authors, institutions, countries, and journals. We further captured the cardiovascular conditions and diagnostic tools most commonly investigated within this field.

METHODS: The Web of Science electronic database was queried to identify relevant papers on digital technology uses in cardiology. Publication and citation data were acquired directly from the database. Complete bibliographic data were exported to VOSviewer, a dedicated bibliometric software package, and related to the semantic content of titles, abstracts, and keywords. A term map was constructed for findings visualization.

RESULTS: The analysis was based on data from 12,529 papers. Of the top 5 most productive institutions, 4 were based in the United States. The United States was the most productive country (4224/12,529, 33.7%), followed by United Kingdom (1136/12,529, 9.1%), Germany (1067/12,529, 8.5%), China (682/12,529, 5.4%), and Italy (622/12,529, 5.0%). Cardiovascular diseases that had been frequently investigated included hypertension (152/12,529, 1.2%), atrial fibrillation (122/12,529, 1.0%), atherosclerosis (116/12,529, 0.9%), heart failure (106/12,529, 0.8%), and arterial stiffness (80/12,529, 0.6%). Recurring modalities were electrocardiography (170/12,529, 1.4%), angiography (127/12,529, 1.0%), echocardiography (127/12,529, 1.0%), digital subtraction angiography (111/12,529, 0.9%), and photoplethysmography (80/12,529, 0.6%). For a literature subset on smartphone apps and wearable devices, the Journal of Medical Internet Research (20/632, 3.2%) and other JMIR portfolio journals (51/632, 8.0%) were the major publishing venues.

CONCLUSIONS: Digital technology uses in cardiology target physicians, patients, and the general public. Their functions range from assisting diagnosis, recording cardiovascular parameters, and patient education, to teaching laypersons about cardiopulmonary resuscitation. This field already has had a great impact in health care, and we anticipate continued growth.

PMID:35544307 | DOI:10.2196/36086

Nucleic Acids Res. 2022 May 10:gkac310. doi: 10.1093/nar/gkac310. Online ahead of print.

ABSTRACT

With the proliferation of genomic sequence data for biomedical research, the exploration of human genetic information by domain experts requires a comprehensive interrogation of large numbers of scientific publications in PubMed. However, a query in PubMed essentially provides search results sorted only by the date of publication. A search engine for retrieving and interpreting complex relations between biomedical concepts in scientific publications remains lacking. Here, we present pubmedKB, a web server designed to extract and visualize semantic relationships between four biomedical entity types: variants, genes, diseases, and chemicals. pubmedKB uses state-of-the-art natural language processing techniques to extract semantic relations from the large number of PubMed abstracts. Currently, over 2 million semantic relations between biomedical entity pairs are extracted from over 33 million PubMed abstracts in pubmedKB. pubmedKB has a user-friendly interface with an interactive semantic graph, enabling the user to easily query entities and explore entity relations. Supporting sentences with the highlighted snippets allow to easily navigate the publications. Combined with a new explorative approach to literature mining and an interactive interface for researchers, pubmedKB thus enables rapid, intelligent searching of the large biomedical literature to provide useful knowledge and insights. pubmedKB is available at https://www.pubmedkb.cc/.

PMID:35536289 | DOI:10.1093/nar/gkac310

J Biomed Semantics. 2022 May 8;13(1):13. doi: 10.1186/s13326-022-00269-1.

ABSTRACT

BACKGROUND: The high volume of research focusing on extracting patient information from electronic health records (EHRs) has led to an increase in the demand for annotated corpora, which are a precious resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multipurpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field.

METHODS: In this study, a semantically annotated corpus was developed using clinical text from multiple medical specialties, document types, and institutions. In addition, we present, (1) a survey listing common aspects, differences, and lessons learned from previous research, (2) a fine-grained annotation schema that can be replicated to guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations.

RESULTS: This study resulted in SemClinBr, a corpus that has 1000 clinical notes, labeled with 65,117 entities and 11,263 relations. In addition, both negation cues and medical abbreviation dictionaries were generated from the annotations. The average annotator agreement score varied from 0.71 (applying strict match) to 0.92 (considering a relaxed match) while accepting partial overlaps and hierarchically related semantic types. The extrinsic evaluation, when applying the corpus to two downstream NLP tasks, demonstrated the reliability and usefulness of annotations, with the systems achieving results that were consistent with the agreement scores.

CONCLUSION: The SemClinBr corpus and other resources produced in this work can support clinical NLP studies, providing a common development and evaluation resource for the research community, boosting the utilization of EHRs in both clinical practice and biomedical research. To the best of our knowledge, SemClinBr is the first available Portuguese clinical corpus.

PMID:35527259 | DOI:10.1186/s13326-022-00269-1

Int J Popul Data Sci. 2022 Apr 27;7(1):1732. doi: 10.23889/ijpds.v6i1.1732. eCollection 2022.

ABSTRACT

The Population Health Research Network (PHRN) is an Australian national data linkage infrastructure that links a wide range of health and human services data in privacy-preserving ways. The data linkage infrastructure enables researchers to apply for access to routinely collected, linked, administrative data from the six states and two territories which make up the Commonwealth of Australia, as well as data collected by the Australian Government. The PHRN is a distributed network where data is collected and managed at the respective jurisdictional and/or cross-jurisdictional levels. As a result, access to linked data from multiple jurisdictions requires complex approval processes. This paper describes Australia's approach to enabling access to linked data from multiple jurisdictions. It covers the identification of, and agreement to, a minimum set of data items to be included in a unified national application form, the development and implementation of a national online application system and the harmonisation of business processes for cross-jurisdictional research projects. Utilisation of the online application system and the ongoing challenges of data linkage across jurisdictions are discussed. Changes to the data custodian and ethics committee approval criteria were out of scope for this project.

PMID:35520098 | PMC:PMC9052959 | DOI:10.23889/ijpds.v6i1.1732

Genes Genomics. 2022 May 5. doi: 10.1007/s13258-022-01245-w. Online ahead of print.

ABSTRACT

BACKGROUND: Cross-platform or cross-experiment transcriptome data is hard to compare as the original gene expression values from different platforms cannot be compared directly. The inherent gene expression ranking information is rarely utilized.

OBJECTIVE: Use of reduced vector to represent transcriptome data independent of platforms.

METHODS: Thus, we turned the expression profile into a rank vector, where a higher expression has a higher rank value, then applied Latent semantic analysis (LSA) to get compact and continuous 100-dimensional vector representations for samples.

RESULTS: Results showed that the reconstructed vector has a precision of 96.7% in recovering tissue labels from an independent dataset. A user-friendly tool TissueSpace was developed, which provides users the following functionalities: (1) convert different gene ID types to Ensembl gene IDs; (2) project any human transcriptome profile to get vector representation for downstream analysis; (3) functional enrichment for each of the 100-dimensional vector features. Case studies for its applications in human common diseases indicate its usefulness.

CONCLUSIONS: TissueSpace could be used to generate testable hypotheses for translational medicine. The TissueSpace tool is available at http://bioinformatics.fafu.edu.cn/tissuespace/ .

PMID:35511320 | DOI:10.1007/s13258-022-01245-w

Subst Abus. 2022;43(1):1100-1109. doi: 10.1080/08897077.2022.2060445.

ABSTRACT

Background: Homelessness and substance use are intricately related, and both are prevalent among emergency department (ED) patients. This study examined the longitudinal association of substance use characteristics with future homeless shelter entry among ED patients with any drug use or unhealthy alcohol use. Methods: We present results from a longitudinal cohort study of public hospital ED patients who screened positive for drug use or unhealthy alcohol use and who were not homeless at their baseline (index) ED visit. The primary outcome was homeless shelter entry within 12 months of baseline, ascertained in city homeless shelter administrative data. Primary independent variables of interest were alcohol use severity (AUDIT), drug use severity (DAST-10), and types of drugs used, as reported on baseline survey questionnaires. Results: Analyses included 1,210 ED patients. By 12 months following the baseline ED visit, 114 (9.4%) had entered a homeless shelter. Among patients with the most severe problems related to drug use (DAST-10 score 9-10), 40.9% entered a shelter within 12 months. Past shelter use was the strongest predictor of future shelter entry; once adjusting for historic shelter use the relationship of AUDIT and DAST-10 scores with future shelter entry was no longer statistically significant in multivariable models. Conclusions: ED patients with past year drug use or unhealthy alcohol use had relatively high likelihood of future shelter entry. Risk for homelessness should be addressed in future interventions with this population. Findings illustrate the complexity of relationships between substance use and homelessness.

PMID:35499455 | DOI:10.1080/08897077.2022.2060445

Front Psychol. 2022 Apr 14;13:805792. doi: 10.3389/fpsyg.2022.805792. eCollection 2022.

ABSTRACT

The objective of this article was to review existing research to assess the evidence for predictive processing (PP) in sign language, the conditions under which it occurs, and the effects of language mastery (sign language as a first language, sign language as a second language, bimodal bilingualism) on the neural bases of PP. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. We searched peer-reviewed electronic databases (SCOPUS, Web of Science, PubMed, ScienceDirect, and EBSCO host) and gray literature (dissertations in ProQuest). We also searched the reference lists of records selected for the review and forward citations to identify all relevant publications. We searched for records based on five criteria (original work, peer-reviewed, published in English, research topic related to PP or neural entrainment, and human sign language processing). To reduce the risk of bias, the remaining two authors with expertise in sign language processing and a variety of research methods reviewed the results. Disagreements were resolved through extensive discussion. In the final review, 7 records were included, of which 5 were published articles and 2 were dissertations. The reviewed records provide evidence for PP in signing populations, although the underlying mechanism in the visual modality is not clear. The reviewed studies addressed the motor simulation proposals, neural basis of PP, as well as the development of PP. All studies used dynamic sign stimuli. Most of the studies focused on semantic prediction. The question of the mechanism for the interaction between one's sign language competence (L1 vs. L2 vs. bimodal bilingual) and PP in the manual-visual modality remains unclear, primarily due to the scarcity of participants with varying degrees of language dominance. There is a paucity of evidence for PP in sign languages, especially for frequency-based, phonetic (articulatory), and syntactic prediction. However, studies published to date indicate that Deaf native/native-like L1 signers predict linguistic information during sign language processing, suggesting that PP is an amodal property of language processing.

SYSTEMATIC REVIEW REGISTRATION: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021238911], identifier [CRD42021238911].

PMID:35496220 | PMC:PMC9047358 | DOI:10.3389/fpsyg.2022.805792

PeerJ Comput Sci. 2022 Mar 10;8:e921. doi: 10.7717/peerj-cs.921. eCollection 2022.

ABSTRACT

Scientific data management plays a key role in the reproducibility of scientific results. To reproduce results, not only the results but also the data and steps of scientific experiments must be made findable, accessible, interoperable, and reusable. Tracking, managing, describing, and visualizing provenance helps in the understandability, reproducibility, and reuse of experiments for the scientific community. Current systems lack a link between the data, steps, and results from the computational and non-computational processes of an experiment. Such a link, however, is vital for the reproducibility of results. We present a novel solution for the end-to-end provenance management of scientific experiments. We provide a framework, CAESAR (CollAborative Environment for Scientific Analysis with Reproducibility), which allows scientists to capture, manage, query and visualize the complete path of a scientific experiment consisting of computational and non-computational data and steps in an interoperable way. CAESAR integrates the REPRODUCE-ME provenance model, extended from existing semantic web standards, to represent the whole picture of an experiment describing the path it took from its design to its result. ProvBook, an extension for Jupyter Notebooks, is developed and integrated into CAESAR to support computational reproducibility. We have applied and evaluated our contributions to a set of scientific experiments in microscopy research projects.

PMID:35494870 | PMC:PMC9044346 | DOI:10.7717/peerj-cs.921