StudyPortal - A Novel Method to Visualize Study Research Networks.

Stud Health Technol Inform. 2019;258:163

Authors: Varghese J, Fujarski M, Dugas M

Abstract
International trial databases as ClinicalTrials.gov or the EU Clinical Trials Register lack geographic visualization of clinical trials. Utilizing key requirements from patient support groups and clinical researchers, an interactive online platform called StudyPortal was designed. It enables patients, health providers and clinical researchers to find and localize suitable studies or whole research networks for selected diseases in a geographic proximity. A semantic layer enables multilingual disease input and autosuggestion. Trial information is pulled and processed from ClinicalTrials.gov. In addition, author affiliations of disease-related PubMed articles are retrievable in order to boost sensitivity of visualized research networks. The integration of a geodatabase and maps enables access to geospatial study search and visualization. A preliminary implementation of the platform is already accessible on the web: https://studyportal.uni-muenster.de. It showed that over 70% of trials and over 90% of scientific articles are visualized correctly by applying expert review and using Web of Science and the WHO trial database as external sources. Publication and trial-registration bias are significant issues that limit completeness of visualization. ClinicalTrials.gov, MEDLINE and geomaps are well-maintained but disconnected sources. StudyPortal integrates these sources to render a novel geospatial view of regional or global clinical research landscapes of US and European trials in real-time. Future work will focus on extensive search filters for recruitment status and intervention characteristics.

PMID: 30942737 [PubMed - in process]

Interoperability Improvement of Mobile Patient Survey (MoPat) Implementing Fast Health Interoperability Resources (FHIR).

Stud Health Technol Inform. 2019;258:141-145

Authors: Storck M, Hollenberg L, Dugas M, Soto-Rey I

Abstract
Despite the advances in health information technology and the increasing usage of electronic systems, syntactic and semantic interoperability between different health information systems remains challenging. An emerging standard to tackle interoperability issues is HL7 FHIR, which uses modern web technologies for communication like Representational State Transfer. The electronic patient reported outcome system Mobile Patient Survey (MoPat) was adapted to support metadata import and clinical data export using HL7 FHIR. Thereby, the data models of HL7 FHIR and MoPat were compared and the existing import and export functions of MoPat were extended to support HL7 FHIR. A test protocol including eight test datasets to proof functioning of the new features was successfully conducted. In the near future, a real time searching toolbar of FHIR metadata resources will be integrated within MoPat. MoPat FHIR import and export functions are ready to be used in a clinical setting in combination with a FHIR compliant clinical data server.

PMID: 30942732 [PubMed - in process]

Drivers for the development of an Animal Health Surveillance Ontology (AHSO).

Prev Vet Med. 2019 May 01;166:39-48

Authors: Dórea FC, Vial F, Hammar K, Lindberg A, Lambrix P, Blomqvist E, Revie CW

Abstract
Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.

PMID: 30935504 [PubMed - in process]

Talk2Me: Automated linguistic data collection for personal assessment.

PLoS One. 2019;14(3):e0212342

Authors: Komeili M, Pou-Prom C, Liaqat D, Fraser KC, Yancheva M, Rudzicz F

Abstract
Language is one the earliest capacities affected by cognitive change. To monitor that change longitudinally, we have developed a web portal for remote linguistic data acquisition, called Talk2Me, consisting of a variety of tasks. In order to facilitate research in different aspects of language, we provide baselines including the relations between different scoring functions within and across tasks. These data can be used to augment studies that require a normative model; for example, we provide baseline classification results in identifying dementia. These data are released publicly along with a comprehensive open-source package for extracting approximately two thousand lexico-syntactic, acoustic, and semantic features. This package can be applied arbitrarily to studies that include linguistic data. To our knowledge, this is the most comprehensive publicly available software for extracting linguistic features. The software includes scoring functions for different tasks.

PMID: 30917120 [PubMed - in process]

Cognitive training for people with mild to moderate dementia.

Cochrane Database Syst Rev. 2019 Mar 25;3:CD013069

Authors: Bahar-Fuchs A, Martyr A, Goh AM, Sabates J, Clare L

Abstract
BACKGROUND: Cognitive impairment, a defining feature of dementia, plays an important role in the compromised functional independence that characterises the condition. Cognitive training (CT) is an approach that uses guided practice on structured tasks with the direct aim of improving or maintaining cognitive abilities.
OBJECTIVES: • To assess effects of CT on cognitive and non-cognitive outcomes for people with mild to moderate dementia and their caregivers.• To compare effects of CT with those of other non-pharmacological interventions, including cognitive stimulation or rehabilitation, for people with mild to moderate dementia and their caregivers.• To identify and explore factors related to intervention and trial design that may be associated with the efficacy of CT for people with mild to moderate dementia and their caregivers.
SEARCH METHODS: We searched ALOIS, the Cochrane Dementia and Cognitive Improvement Group Specialised Register, on 5 July 2018. ALOIS contains records of clinical trials identified through monthly searches of several major healthcare databases and numerous trial registries and grey literature sources. In addition to this, we searched MEDLINE, Embase, PsycINFO, CINAHL, LILACS, Web of Science Core Collection, ClinicalTrials.gov, and the World Health Organization's trials portal, ICTRP, to ensure that searches were comprehensive and up-to-date.
SELECTION CRITERIA: We included randomised controlled trials (RCTs) that described interventions for people with mild to moderate dementia and compared CT versus a control or alternative intervention.
DATA COLLECTION AND ANALYSIS: We extracted relevant data from published manuscripts and through contact with trial authors if required. We assessed risk of bias using the Cochrane 'Risk of bias' tool. We divided comparison conditions into active or passive control conditions and alternative treatments. We used a large number of measures and data to evaluate 19 outcomes at end of treatment, as well as 16 outcomes at follow-up in the medium term; we pooled this information in meta-analyses. We calculated pooled estimates of treatment effect using a random-effects model, and we estimated statistical heterogeneity using a standard Chi² statistic. We graded the evidence using GradePro.
MAIN RESULTS: The 33 included trials were published between 1988 and 2018 and were conducted in 12 countries; most were unregistered, parallel-group, single-site RCTs, with samples ranging from 12 to 653 participants. Interventions were between two and 104 weeks long. We classified most experimental interventions as 'straight CT', but we classified some as 'augmented CT', and about two-thirds as multi-domain interventions. Researchers investigated 18 passive and 13 active control conditions, along with 15 alternative treatment conditions, including occupational therapy, mindfulness, reminiscence therapy, and others.The methodological quality of studies varied, but we rated nearly all studies as having high or unclear risk of selection bias due to lack of allocation concealment, and high or unclear risk of performance bias due to lack of blinding of participants and personnel.We used data from 32 studies in the meta-analysis of at least one outcome. Relative to a control condition, we found moderate-quality evidence showing a small to moderate effect of CT on our first primary outcome, composite measure of global cognition at end of treatment (standardised mean difference (SMD) 0.42, 95% confidence interval (CI) 0.23 to 0.62), and high-quality evidence showing a moderate effect on the secondary outcome of verbal semantic fluency (SMD 0.52, 95% CI 0.23 to 0.81) at end of treatment, with these gains retained in the medium term (3 to 12 months post treatment). In relation to many other outcomes, including our second primary outcome of clinical disease severity in the medium term, the quality of evidence was very low, so we were unable to determine whether CT was associated with any meaningful gains.When compared with an alternative treatment, we found that CT may have little to no effect on our first primary outcome of global cognition at end of treatment (SMD 0.21, 95% CI -0.23 to 0.64), but the quality of evidence was low. No evidence was available to assess our second primary outcome of clinical disease severity in the medium term. We found moderate-quality evidence showing that CT was associated with improved mood of the caregiver at end of treatment, but this was based on a single trial. The quality of evidence in relation to many other outcomes at end of treatment and in the medium term was too low for us to determine whether CT was associated with any gains, but we are moderately confident that CT did not lead to any gains in mood, behavioural and psychological symptoms, or capacity to perform activities of daily living.
AUTHORS' CONCLUSIONS: Relative to a control intervention, but not to a variety of alternative treatments, CT is probably associated with small to moderate positive effects on global cognition and verbal semantic fluency at end of treatment, and these benefits appear to be maintained in the medium term. Our certainty in relation to many of these findings is low or very low. Future studies should take stronger measures to mitigate well-established risks of bias, and should provide long-term follow-up to improve our understanding of the extent to which observed gains are retained. Future trials should also focus on direct comparison of CT versus alternative treatments rather than passive or active control conditions.

PMID: 30909318 [PubMed - as supplied by publisher]

Too many tags spoil the metadata: investigating the knowledge management of scientific research with semantic web technologies.

J Cheminform. 2019 Mar 21;11(1):23

Authors: Kanza S, Gibbins N, Frey JG

Abstract
Scientific research is increasingly characterised by the volume of documents and data that it produces, from experimental plans and raw data to reports and papers. Researchers frequently struggle to manage and curate these materials, both individually and collectively. Previous studies of Electronic Lab Notebooks (ELNs) in academia and industry have identified semantic web technologies as a means for organising scientific documents to improve current workflows and knowledge management practices. In this paper, we present a qualitative, user-centred study of researcher requirements and practices, based on a series of discipline-specific focus groups. We developed a prototype semantic ELN to serve as a discussion aid for these focus groups, and to help us explore the technical readiness of a range of semantic web technologies. While these technologies showed potential, existing tools for semantic annotation were not well-received by our focus groups, and need to be refined before they can be used to enhance current researcher practices. In addition, the seemingly simple notion of "tagging and searching" documents appears anything but; the researchers in our focus groups had extremely personal requirements for how they organise their work, so the successful incorporation of semantic web technologies into their practices must permit a significant degree of customisation and personalisation.

PMID: 30900066 [PubMed]

A new wave of innovation in Semantic web tools for drug discovery.

Expert Opin Drug Discov. 2019 Mar 19;:1-12

Authors: Kanza S, Frey JG

Abstract
INTRODUCTION: The use of semantic web technologies to aid drug discovery has gained momentum over recent years. Researchers in this domain have realized that semantic web technologies are key to dealing with the high levels of data for drug discovery. These technologies enable us to represent the data in a formal, structured, interoperable and comparable way, and to tease out undiscovered links between drug data (be it identifying new drug-targets or relevant compounds, or links between specific drugs and diseases). Areas covered: This review focuses on explaining how semantic web technologies are being used to aid advances in drug discovery. The main types of semantic web technologies are explained, outlining how they work and how they can be used in the drug discovery process, with a consideration of how the use of these technologies has progressed from their initial usage. Expert opinion: The increased availability of shared semantic resources (tools, data and importantly the communities) have enabled the application of semantic web technologies to facilitate semantic (context dependent) search across multiple data sources, which can be used by machine learning to produce better predictions by exploiting the semantic links in knowledge graphs and linked datasets.

PMID: 30884989 [PubMed - as supplied by publisher]

Ontology-Defined Middleware for Internet of Things Architectures.

Sensors (Basel). 2019 Mar 07;19(5):

Authors: Caballero V, Valbuena S, Vernet D, Zaballos A

Abstract
The Internet of Things scenario is composed of an amalgamation of physical devices. Those physical devices are heterogeneous in their nature both in terms of communication protocols and in data exchange formats. The Web of Things emerged as a homogenization layer that uses well-established web technologies and semantic web technologies to exchange data. Therefore, the Web of Things enables such physical devices to the web, they become Web Things. Given such a massive number of services and processes that the Internet of Things/Web of Things enables, it has become almost mandatory to describe their properties and characteristics. Several web ontologies and description frameworks are devoted to that purpose. Ontologies such as SOSA/SSN or OWL-S describe the Web Things and their procedures to sense or actuate. For example, OWL-S complements SOSA/SSN in describing the procedures used for sensing/actuating. It is, however, not its scope to be specific enough to enable a computer program to interpret and execute the defined flow of control. In this work, it is our goal to investigate how we can model those procedures using web ontologies in a manner that allows us to directly deploy the procedure implementation. A prototype implementation of the results of our research is implemented along with an analysis of several use cases to show the generality of our proposal.

PMID: 30866533 [PubMed - in process]

"Similar query was answered earlier": processing of patient authored text for retrieving relevant contents from health discussion forum.

Health Inf Sci Syst. 2019 Dec;7(1):4

Authors: Saha SK, Prakash A, Majumder M

Abstract
Online remedy finders and health-related discussion forums have become increasingly popular in recent years. Common web users write their health problems there and request suggestion from experts or other users. As a result, these forums became a huge repository of information and discussions on various health issues. An intelligent information retrieval system can help to utilize this repository in various applications. In this paper, we propose a system for the automatic identification of existing similar forum posts given a new post. The system is based on computing similarity between two patient authored texts. For computing the similarity between the current post and existing posts, the system uses a hybrid strategy based on template information, topic modelling, and latent semantic indexing. The system is tested using a set of real questions collected from a homeopathy forum namely abchomeopathy.com. The relevance of the posts retrieved by the system is evaluated by human experts. The evaluation results demonstrate that the precision of the system is 88.87%.

PMID: 30863540 [PubMed]

How to Develop a Drug Target Ontology: KNowledge Acquisition and Representation Methodology (KNARM).

Methods Mol Biol. 2019;1939:49-69

Authors: Küçük McGinty H, Visser U, Schürer S

Abstract
Technological advancements in many fields have led to huge increases in data production, including data volume, diversity, and the speed at which new data is becoming available. In accordance with this, there is a lack of conformity in the ways data is interpreted. This era of "big data" provides unprecedented opportunities for data-driven research and "big picture" models. However, in-depth analyses-making use of various data types and data sources and extracting knowledge-have become a more daunting task. This is especially the case in life sciences where simplification and flattening of diverse data types often lead to incorrect predictions. Effective applications of big data approaches in life sciences require better, knowledge-based, semantic models that are suitable as a framework for big data integration, while avoiding oversimplifications, such as reducing various biological data types to the gene level. A huge hurdle in developing such semantic knowledge models, or ontologies, is the knowledge acquisition bottleneck. Automated methods are still very limited, and significant human expertise is required. In this chapter, we describe a methodology to systematize this knowledge acquisition and representation challenge, termed KNowledge Acquisition and Representation Methodology (KNARM). We then describe application of the methodology while implementing the Drug Target Ontology (DTO). We aimed to create an approach, involving domain experts and knowledge engineers, to build useful, comprehensive, consistent ontologies that will enable big data approaches in the domain of drug discovery, without the currently common simplifications.

PMID: 30848456 [PubMed - in process]

Databases for evaluating interferences between affective content and image quality.

Data Brief. 2019 Apr;23:103700

Authors: Gasparini F, Ciocca G, Corchs S

Abstract
The two databases here described were generated to evaluate the role of affective content while assessing image quality (Corchs et al., 2018) [1]. The databases are composed of images JPEG-compressed together with the subjective quality scores collected during psychophysical experiments. To reduce interferences in quality perception due to image semantic, we have restricted the semantic content, choosing only close-ups of face images, and we have considered only two emotion categories (happy and sad). We have selected 23 images with happy faces and 23 images with sad faces of high quality. For what concerns image quality we have considered JPEG-distortion with 4 levels of compression, corresponding to q-factors 10, 15, 20, 30. The first image database, hereafter called MMSP-FaceA, is thus composed of 230 images (23+23) × 5 quality levels (including the original high quality pristine images). To better consider only interferences in quality perception due to affective content, we have generated a second image database where the background of images belonging to MMSP-FaceA has been cut off. This second image database is labelled as MMSP-FaceB. Psychophysical experiments were conducted, on a controlled web-based interface, where participants rated the image quality of the two databases in a five point scale. The two final databases MMSP-FaceA and MMSP-FaceB are thus composed of 230 images each, together with the raw quality scores assigned by the observers, and are available at our laboratory web site: www.mmsp.unimib.it/download.

PMID: 30828597 [PubMed]

Towards the semantic enrichment of Computer Interpretable Guidelines: a method for the identification of relevant ontological terms.

AMIA Annu Symp Proc. 2018;2018:922-931

Authors: Quesada-Martínez M, Marcos M, Abad-Navarro F, Martínez-Salvador B, Fernández-Breis JT

Abstract
Clinical Practice Guidelines (CPGs) contain recommendations intended to optimize patient care, produced based on a systematic review of evidence. In turn, Computer-Interpretable Guidelines (CIGs) are formalized versions of CPGs for use as decision-support systems. We consider the enrichment of the CIG by means of an OWL ontology that describes the clinical domain of the CIG, which could be exploited e.g. for the interoperability with the Electronic Health Record (EHR). As a first step, in this paper we describe a method to support the development of such an ontology starting from a CIG. The method uses an alignment algorithm for the automated identification of ontological terms relevant to the clinical domain of the CIG, as well as a web platform to manually review the alignments and select the appropriate ones. Finally, we present the results of the application of the method to a small corpus of CIGs.

PMID: 30815135 [PubMed - in process]

Italian Age of Acquisition Norms for a Large Set of Words (ItAoA).

Front Psychol. 2019;10:278

Authors: Montefinese M, Vinson D, Vigliocco G, Ambrosini E

Abstract
Age of acquisition (AoA) is an important psycholinguistic variable that affects the performance of healthy individuals and patients in a large variety of cognitive tasks. For this reason, it becomes more and more compelling to collect new AoA norms for a large set of stimuli in order to allow better control and manipulation of AoA in future research. An important motivation of the present study is to extend previous Italian norms by collecting AoA ratings for a much larger range of Italian words for which concreteness and semantic-affective norms are now available thus ensuring greater coverage of words varying along these dimensions. In the present study, we collected AoA ratings for 1,957 Italian content words (adjectives, nouns, and verbs), by asking healthy adult participants to estimate the age at which they thought they had learned the word in a Web survey procedure. First, we found high split-half correlation within our sample, suggesting strong internal reliability. Second, our data indicate that the ratings collected in this study are as valid and reliable as those collected in previous studies for Italian across different age populations (adult and children) and other languages. Finally, we analyzed the relation between AoA ratings and other lexical-semantic variables (e.g., word frequency, imageability, valence, arousal) and showed that these correlations were generally consistent with the correlations reported in other normative studies for Italian and other languages. Therefore, our new AoA norms are a valuable source of information for future research in the Italian language. The full database is available at the Open Science Framework (osf.io/3trg2).

PMID: 30814969 [PubMed]

Fundamental Visual Concept Learning from Correlated Images and Text.

IEEE Trans Image Process. 2019 Feb 18;:

Authors: Du Y, Wang H, Cui Y, Huang X

Abstract
Heterogeneous web media consists of many visual concepts, such as objects, scenes and activities, that cannot be semantically decomposed. The task of learning fundamental visual concepts (FVCs) plays an important role in automatically understanding the elements that compose all visual media, as well as in applications of retrieval, annotation, etc. In this paper, we formulate the problem of FVC learning and propose an approach to this problem called neighboring concept distributing (NCD). Our approach models all data using a concept graph, which considers the visual patches in images as nodes and generates the inter-image edges between visual patches in different images and the intra-image edges between visual patches in the same image. The NCD approach distributes semantic information from images to visual patches based on measurements over the concept graph, including fitness, distinctiveness, smoothness and sparseness, without any pre-trained concept detectors or classifiers. We analyze the learnability of the proposed approach and find that, under some conditions, all concepts can be correctly learned with an arbitrarily high probability as the size of the data increases.We demonstrate the performance of the NCD approach using three public datasets. Experimental results show that our approach outperforms state-of-the-art approaches when learning visual concepts from correlated media.

PMID: 30794173 [PubMed - as supplied by publisher]

Semantic Queries Expedite MedDRA Terms Selection Thanks to a Dedicated User Interface: A Pilot Study on Five Medical Conditions.

Front Pharmacol. 2019;10:50

Authors: Souvignet J, Declerck G, Trombert-Paviot B, Asfari H, Jaulent MC, Bousquet C

Abstract
Background: Searching into the MedDRA terminology is usually limited to a hierarchical search, and/or a string search. Our objective was to compare user performances when using a new kind of user interface enabling semantic queries versus classical methods, and evaluating term selection improvement in MedDRA. Methods: We implemented a forms-based web interface: OntoADR Query Tools (OQT). It relies on OntoADR, a formal resource describing MedDRA terms using SNOMED CT concepts and corresponding semantic relations, enabling terminological reasoning. We then compared time spent on five examples of medical conditions using OQT or the MedDRA web-based browser (MWB), and precision and recall of the term selection. Results: OntoADR Query Tools allows the user to search in MedDRA: One may enter search criteria by selecting one semantic property from a dropdown list and one or more SNOMED CT concepts related to the range of the chosen property. The user is assisted in building his query: he can add criteria and combine them. Then, the interface displays the set of MedDRA terms matching the query. Meanwhile, on average, the time spent on OQT (about 4 min 30 s) is significantly lower (-35%; p < 0.001) than time spent on MWB (about 7 min). The results of the System Usability Scale (SUS) gave a score of 62.19 for OQT (rated as good). We also demonstrated increased precision (+27%; p = 0.01) and recall (+34%; p = 0.02). Computed "performance" (correct terms found per minute) is more than three times better with OQT than with MWB. Discussion: This pilot study establishes the feasibility of our approach based on our initial assumption: performing MedDRA queries on the five selected medical conditions, using terminological reasoning, expedites term selection, and improves search capabilities for pharmacovigilance end users. Evaluation with a larger number of users and medical conditions are required in order to establish if OQT is appropriate for the needs of different user profiles, and to check if conclusions can be extended to other kinds of medical conditions. The application is currently limited by the non-exhaustive coverage of MedDRA by OntoADR, but nevertheless shows good performance which encourages continuing in the same direction.

PMID: 30792654 [PubMed]

A Semantic-Enabled Platform for Realizing an Interoperable Web of Things.

Sensors (Basel). 2019 Feb 19;19(4):

Authors: Lanza J, Sánchez L, Gómez D, Santana JR, Sotres P

Abstract
Nowadays, the Internet of Things (IoT) ecosystem is experiencing a lack of interoperability across the multiple competing platforms that are available. Consequently, service providers can only access vertical data silos that imply high costs and jeopardize their solutions market potential. It is necessary to transform the current situation with competing non-interoperable IoT platforms into a common ecosystem enabling the emergence of cross-platform, cross-standard, and cross-domain IoT services and applications. This paper presents a platform that has been implemented for realizing this vision. It leverages semantic web technologies to address the two key challenges in expanding the IoT beyond product silos into web-scale open ecosystems: data interoperability and resources identification and discovery. The paper provides extensive description of the proposed solution and its implementation details. Regarding the implementation details, it is important to highlight that the platform described in this paper is currently supporting the federation of eleven IoT deployments (from heterogeneous application domains) with over 10,000 IoT devices overall which produce hundreds of thousands of observations per day.

PMID: 30791498 [PubMed - in process]

Cultural influences on perception of disability and disabled people: a comparison of opinions from students in the United Kingdom (UK) Pakistan (PAK) about a generic wheelchair using a semantic differential scale.

Disabil Rehabil Assist Technol. 2019 Feb 19;:1-13

Authors: Asghar S, Edward Torrens G, Harland R

Abstract
Assistive Technology (AT) product use occurs within a socio-cultural setting. The growth internationally in the AT product market suggests that designers need to be aware of the influences that diverse cultures may have on the societal perception of an AT product through its semantic attributes. The study aimed to evaluate the visual interaction with an AT product by young adults from Pakistan, a collectivist society, and the United Kingdom (UK), an individualist society. A paper-based questionnaire survey was carried out with 281 first-year undergraduate students from the UK and Pakistan to evaluate their perception towards the visual representation of a generic conventional wheelchair image. A semantics differential (SD) scale method was used involving a seven-point bipolar SD scale incorporating sixteen pairs of adjectives defining functional, meaning, and usability attributes of the product. The mean (M) and standard deviation (sd) values were obtained for each pair of adjectives and compared between both groups by employing appropriate parametric tests. The results show that having a diverse cultural background did not appear to have overtly influenced the meanings ascribed to the generic manual wheelchair, which was unexpected. The University 'Internationalist' environment may have influenced the results. Some minor but critical differences were found for some pairs of adjectives (bulky-compact, heavy-light), having p-value less than .05 (p < .05) that related to previous experience of wheelchairs and/or their use. Further studies are planned to investigate and validate outcomes with other student and non-student groups. Implications for Rehabilitation The semantic attributes of assistive technologies highlight a number of aspects that have implications for those involved in Assistive Technology (AT) product development, manufacturing and marketing. • For online sales, the AT products rely on the web page image to communicate the purpose and attributes of the product. There are limited explorations related to the semantic/communicative attributes of AT product presented in images, as perceived by individuals from diverse cultural backgrounds. • The knowledge towards semantic meaning ascribed to the AT product is important to investigate to provide a perspective that goes beyond practical functions of the AT product towards the communicative function. • Information of comprehending semantics and significance of the AT product from a social (non-users) viewpoint may benefits manufacturers in the development of AT products that best meet the societal needs, preferences and expectations. • A model of best practice, with a focus on semantic manipulation will offer Industrial Designers (ID) internationally with the suitable process and tools to reframe perceptions of disability and enhance acceptance of AT products not only for users, but also for the society around them.

PMID: 30776927 [PubMed - as supplied by publisher]

Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy.

Lab Invest. 2019 Feb 15;:

Authors: Signaevsky M, Prastawa M, Farrell K, Tabish N, Baldwin E, Han N, Iida MA, Koll J, Bryce C, Purohit D, Haroutunian V, McKee AC, Stein TD, White CL, Walker J, Richardson TE, Hanson R, Donovan MJ, Cordon-Cardo C, Zeineh J, Fernandez G, Crary JF

Abstract
Accumulation of abnormal tau in neurofibrillary tangles (NFT) occurs in Alzheimer disease (AD) and a spectrum of tauopathies. These tauopathies have diverse and overlapping morphological phenotypes that obscure classification and quantitative assessments. Recently, powerful machine learning-based approaches have emerged, allowing the recognition and quantification of pathological changes from digital images. Here, we applied deep learning to the neuropathological assessment of NFT in postmortem human brain tissue to develop a classifier capable of recognizing and quantifying tau burden. The histopathological material was derived from 22 autopsy brains from patients with tauopathies. We used a custom web-based informatics platform integrated with an in-house information management system to manage whole slide images (WSI) and human expert annotations as ground truth. We utilized fully annotated regions to train a deep learning fully convolutional neural network (FCN) implemented in PyTorch against the human expert annotations. We found that the deep learning framework is capable of identifying and quantifying NFT with a range of staining intensities and diverse morphologies. With our FCN model, we achieved high precision and recall in naive WSI semantic segmentation, correctly identifying tangle objects using a SegNet model trained for 200 epochs. Our FCN is efficient and well suited for the practical application of WSIs with average processing times of 45 min per WSI per GPU, enabling reliable and reproducible large-scale detection of tangles. We measured performance on test data of 50 pre-annotated regions on eight naive WSI across various tauopathies, resulting in the recall, precision, and an F1 score of 0.92, 0.72, and 0.81, respectively. Machine learning is a useful tool for complex pathological assessment of AD and other tauopathies. Using deep learning classifiers, we have the potential to integrate cell- and region-specific annotations with clinical, genetic, and molecular data, providing unbiased data for clinicopathological correlations that will enhance our knowledge of the neurodegeneration.

PMID: 30770886 [PubMed - as supplied by publisher]

An Ontology Approach for Knowledge Representation of ECG Data.

Stud Health Technol Inform. 2019;257:520-525

Authors: Zouri M, Zouri N, Ferworn A

Abstract
The number of features that can be extracted from ECG signals has increased with the advancement in signal processing techniques. At the same time, there is an increase in research efforts to support efficient and effective analysis and interpretation of these signals. In this paper, we propose the use of ontology for knowledge representation and discovery of ECG data. Given the lack of a widely acceptable standards, the use of ontology can support the establishment of common understanding of the kind of knowledge that can be extracted from the ECG data and shared among various heterogeneous systems. The proposed ontology is both platform and application independent. Furthermore, it is possible to enrich the proposed ontology with new knowledge that may not explicitly be expressed in the data.

PMID: 30741250 [PubMed - in process]

The role of stress in drug addiction. An integrative review.

Physiol Behav. 2019 Jan 31;:

Authors: Ruisoto P, Contador I

Abstract
BACKGROUND: The high prevalence and burden to society of drug abuse and addiction is undisputed. However, its conceptualisation as a brain disease is controversial, and available interventions insufficient. Research on the role of stress in drug addiction may bridge positions and develop more effective interventions.
AIM: The aim of this paper is to integrate the most influential literature to date on the role of stress in drug addiction.
METHODS: A literature search was conducted of the core collections of Web of Science and Semantic Scholar on the topic of stress and addiction from a neurobiological perspective in humans. The most frequently cited articles and related references published in the last decade were finally redrafted into a narrative review based on 130 full-text articles.
RESULTS AND DISCUSSION: First, a brief overview of the neurobiology of stress and drug addiction is provided. Then, the role of stress in drug addiction is described. Stress is conceptualised as a major source of allostatic load, which result in progressive long-term changes in the brain, leading to a drug-prone state characterized by craving and increased risk of relapse. The effects of stress on drug addiction are mainly mediated by the action of corticotropin-releasing factor and other stress hormones, which weaken the hippocampus and prefrontal cortex and strengthen the amygdala, leading to a negative emotional state, craving and lack of executive control, increasing the risk of relapse. Both, drugs and stress result in an allostatic overload responsible for neuroadaptations involved in most of the key features of addiction: reward anticipation/craving, negative affect, and impaired executive functions, involved in three stages of addiction and relapse.
CONCLUSION: This review elucidates the crucial role of stress in drug addiction and highlights the need to incorporate the social context where brain-behaviour relationships unfold into the current model of addition.

PMID: 30711532 [PubMed - as supplied by publisher]