Front Psychol. 2022 Jan 27;12:819237. doi: 10.3389/fpsyg.2021.819237. eCollection 2021.

ABSTRACT

We propose an AI-assisted design concept exploration tool, the "Character Space Construction" ("CSC"). Concept designers explore and articulate the target product aesthetics and semantics in language, which is expressed using "Design Concept Phrases" ("DCPs"), that is, compound adjective phrases, and contrasting terms that convey what are not their target design concepts. Designers often utilize this dichotomy technique to communicate the nature of their aesthetic and semantic design concepts with stakeholders, especially in an early design development phase. The CSC assists this designers' cognitive activity by constructing a "Character Space" ("CS"), which is a semantic quadrant system, in a structured manner. A CS created by designers with the assistance of the CSC enables them to discern and explain their design concepts in contrast with opposing terms. These terms in a CS are retrieved and combined in the CSC by using a knowledge graph. The CSC presents terms and phrases as lists of candidates to users from which users will choose in order to define the target design concept, which is then visualized in a CS. The participants in our experiment, who were in the "arts and design" profession, were given two conditions under which to create DCPs and explain them. One group created and explained the DCPs with the assistance of the proposed CSC, and the other did the same task without this assistance, given the freedom to use any publicly available web search tools instead. The result showed that the group assisted by the CSC indicated their tasks were supported significantly better, especially in exploration, as measured by the Creativity Support Index (CSI).

PMID:35153935 | PMC:PMC8828642 | DOI:10.3389/fpsyg.2021.819237

J Pain Symptom Manage. 2022 Feb 10:S0885-3924(22)00063-X. doi: 10.1016/j.jpainsymman.2022.01.023. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: Proper breakthrough cancer pain (BTcP) management is of pivotal importance. Although rapid-acting, oral and nasal transmucosal, fentanyl formulations (rapid-onset opioids, ROOs) are licensed for BTcP treatment, not all guidelines recommend their use. Presumably, some research gaps need to be bridged to produce solid evidence. We present a bibliometric network analysis on ROOs for BTcP treatment.

METHODS: Documents were retrieved from the Web of Science (WOS) online database. The string was "rapid onset opioids" or "transmucosal fentanyl" and "breakthrough cancer pain". Year of publication, journal metrics (impact factor and quartile), title, document type, topic, and clinical setting (in-patients, outpatients, and palliative care) were extracted. The software tool VOSviewer (version 1.6.17) was used to analyze the semantic network analyzes, bibliographic coupling, journals analysis, and research networks.

RESULTS: 502 articles were found in WOS. A declining trend in published articles from 2014 to 2021 was observed. Approximately 50% of documents regard top quartile (Q1) journals. Most articles focused on ROOs efficacy, but abuse and misuse issues are poorly addressed. With respect to article type, we calculated 132 clinical investigations. The semantic network analysis found interconnections between the terms "breakthrough cancer pain", "opioids", and "cancers". The top co-cited article was published in 2000 and addressed pain assessment. The largest number of partnerships regarded the United States, Italy, and England.

CONCLUSION: In this research area, most articles are published in top-ranked journals. Nevertheless, paramount topics should be better addressed, and the implementation of research networks is needed.

PMID:35151801 | DOI:10.1016/j.jpainsymman.2022.01.023

Brain Inform. 2022 Feb 12;9(1):5. doi: 10.1186/s40708-022-00153-9.

ABSTRACT

Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.

PMID:35150379 | DOI:10.1186/s40708-022-00153-9

Neural Netw. 2022 Apr;148:176-182. doi: 10.1016/j.neunet.2022.01.007. Epub 2022 Jan 29.

ABSTRACT

Many approaches in generalized zero-shot learning (GZSL) rely on cross-modal mapping between the image feature space and the class embedding space, which achieves knowledge transfer from seen to unseen classes. However, these two spaces are completely different space and their manifolds are inconsistent, the existing methods suffer from highly overlapped semantic description of different classes, as in GZSL tasks unseen classes can be easily misclassified into seen classes. To handle these problems, we adopt a novel semantic embedding network which helps to encode more discriminative information from initial semantic attributes to semantic embeddings in visual space. Meanwhile, a distribution alignment constraint is adopted to help keep the distribution of the learned semantic embeddings consistent with the distribution of real image features. Moreover, an auxiliary classifier is adopted to strengthen the quality of the learned semantic embeddings. Finally, a relation network is used to classify the unseen images by computing the relation scores between the semantic embeddings and image features, which is much more flexible than the fixed distance metric functions. Experimental results demonstrate that our proposed method is superior to other state-of-the-arts.

PMID:35144151 | DOI:10.1016/j.neunet.2022.01.007

Artif Intell Law (Dordr). 2022 Feb 3:1-15. doi: 10.1007/s10506-022-09309-8. Online ahead of print.

ABSTRACT

This paper reflects my address as IAAIL president at ICAIL 2021. It is aimed to give my vision of the status of the AI and Law discipline, and possible future perspectives. In this respect, I go through different seasons of AI research (of AI and Law in particular): from the Winter of AI, namely a period of mistrust in AI (throughout the eighties until early nineties), to the Summer of AI, namely the current period of great interest in the discipline with lots of expectations. One of the results of the first decades of AI research is that "intelligence requires knowledge". Since its inception the Web proved to be an extraordinary vehicle for knowledge creation and sharing, therefore it's not a surprise if the evolution of AI has followed the evolution of the Web. I argue that a bottom-up approach, in terms of machine/deep learning and NLP to extract knowledge from raw data, combined with a top-down approach, in terms of legal knowledge representation and models for legal reasoning and argumentation, may represent a promotion for the development of the Semantic Web, as well as of AI systems. Finally, I provide my insight in the potential of AI development, which takes into account technological opportunities and theoretical limits.

PMID:35132296 | PMC:PMC8811736 | DOI:10.1007/s10506-022-09309-8

BMC Med Inform Decis Mak. 2022 Feb 5;22(1):33. doi: 10.1186/s12911-022-01770-4.

ABSTRACT

BACKGROUND: Semantic similarity is a valuable tool for analysis in biomedicine. When applied to phenotype profiles derived from clinical text, they have the capacity to enable and enhance 'patient-like me' analyses, automated coding, differential diagnosis, and outcome prediction. While a large body of work exists exploring the use of semantic similarity for multiple tasks, including protein interaction prediction, and rare disease differential diagnosis, there is less work exploring comparison of patient phenotype profiles for clinical tasks. Moreover, there are no experimental explorations of optimal parameters or better methods in the area.

METHODS: We develop a platform for reproducible benchmarking and comparison of experimental conditions for patient phentoype similarity. Using the platform, we evaluate the task of ranking shared primary diagnosis from uncurated phenotype profiles derived from all text narrative associated with admissions in the medical information mart for intensive care (MIMIC-III).

RESULTS: 300 semantic similarity configurations were evaluated, as well as one embedding-based approach. On average, measures that did not make use of an external information content measure performed slightly better, however the best-performing configurations when measured by area under receiver operating characteristic curve and Top Ten Accuracy used term-specificity and annotation-frequency measures.

CONCLUSION: We identified and interpreted the performance of a large number of semantic similarity configurations for the task of classifying diagnosis from text-derived phenotype profiles in one setting. We also provided a basis for further research on other settings and related tasks in the area.

PMID:35123470 | DOI:10.1186/s12911-022-01770-4

BMC Bioinformatics. 2022 Feb 1;23(1):56. doi: 10.1186/s12859-022-04578-1.

ABSTRACT

BACKGROUND: Besides Boolean retrieval with medical subject headings (MeSH), PubMed provides users with an alternative way called "Related Articles" to access and collect relevant documents based on semantic similarity. To explore the functionality more efficiently and more accurately, we proposed an improved algorithm by measuring the semantic similarity of PubMed citations based on the MeSH-concept network model.

RESULTS: Three article similarity networks are obtained using MeSH-concept random walk with restart (MCRWR), MeSH random walk with restart (MRWR) and PubMed related article (PMRA) respectively. The area under receiver operating characteristic (ROC) curve of MCRWR, MRWR and PMRA is 0.93, 0.90, and 0.67 respectively. Precisions of MCRWR and MRWR under various similarity thresholds are higher than that of PMRA. Mean value of P5 of MCRWR is 0.742, which is much higher than those of MRWR (0.692) and PMRA (0.223). In the article semantic similarity network of "Genes & Function of organ & Disease" based on MCRWR algorithm, four topics are identified according to golden standards.

CONCLUSION: MeSH-concept random walk with restart algorithm has better performance in constructing article semantic similarity network, which can reveal the implicitly semantic association between documents. The efficiency and accuracy of retrieving semantic-related documents have been improved a lot.

PMID:35105306 | DOI:10.1186/s12859-022-04578-1

Interdiscip Sci. 2022 Feb 1. doi: 10.1007/s12539-021-00500-0. Online ahead of print.

ABSTRACT

N4-Acetylcytidine (ac4C) is a highly conserved post-transcriptional and an extensively existing RNA modification, playing versatile roles in the cellular processes. Due to the limitation of techniques and knowledge, large-scale identification of ac4C is still a challenging task. RNA sequences are like sentences containing semantics in the natural language. Inspired by the semantics of language, we proposed a hybrid model for ac4C prediction. The model used long short-term memory and convolution neural network to extract the semantic features hidden in the sequences. The semantic and the two traditional features (k-nucleotide frequencies and pseudo tri-tuple nucleotide composition) were combined to represent ac4C or non-ac4C sequences. The eXtreme Gradient Boosting was used as the learning algorithm. Five-fold cross-validation over the training set consisting of 1160 ac4C and 10,855 non-ac4C sequences obtained the area under the receiver operating characteristic curve (AUROC) of 0.9004, and the independent test over 469 ac4C and 4343 non-ac4C sequences reached an AUROC of 0.8825. The model obtained a sensitivity of 0.6474 in the five-fold cross-validation and 0.6290 in the independent test, outperforming two state-of-the-art methods. The performance of semantic features alone was better than those of k-nucleotide frequencies and pseudo tri-tuple nucleotide composition, implying that ac4C sequences are of semantics. The proposed hybrid model was implemented into a user-friendly web-server which is freely available to scientific communities: http://47.113.117.61/ac4c/ . The presented model and tool are beneficial to identify ac4C on large scale.

PMID:35106702 | DOI:10.1007/s12539-021-00500-0

World Wide Web. 2022 Jan 28:1-24. doi: 10.1007/s11280-021-00992-2. Online ahead of print.

ABSTRACT

The ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of automatic depression prediction, most machine learning models lead to predictions that are obscure to humans. In this work, we propose explainable Multi-Aspect Depression Detection with Hierarchical Attention Network MDHAN, for automatic detection of depressed users on social media and explain the model prediction. We have considered user posts augmented with additional features from Twitter. Specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words' importance, and capture semantic sequence features from the user timelines (posts). Our hierarchical attention model is developed in such a way that it can capture patterns that leads to explainable results. Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-aspect features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction.

PMID:35106059 | PMC:PMC8795347 | DOI:10.1007/s11280-021-00992-2

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