Structural variant analysis for linked-read sequencing data with gemtools.

Bioinformatics. 2019 11 01;35(21):4397-4399

Authors: Greer SU, Ji HP

Abstract
SUMMARY: Linked-read sequencing generates synthetic long reads which are useful for the detection and analysis of structural variants (SVs). The software associated with 10× Genomics linked-read sequencing, Long Ranger, generates the essential output files (BAM, VCF, SV BEDPE) necessary for downstream analyses. However, to perform downstream analyses requires the user to customize their own tools to handle the unique features of linked-read sequencing data. Here, we describe gemtools, a collection of tools for the downstream and in-depth analysis of SVs from linked-read data. Gemtools uses the barcoded aligned reads and the Megabase-scale phase blocks to determine haplotypes of SV breakpoints and delineate complex breakpoint configurations at the resolution of single DNA molecules. The gemtools package is a suite of tools that provides the user with the flexibility to perform basic functions on their linked-read sequencing output in order to address even more questions.
AVAILABILITY AND IMPLEMENTATION: The gemtools package is freely available for download at: https://github.com/sgreer77/gemtools.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID: 30938757 [PubMed - indexed for MEDLINE]

Automatic Construction of a Depression-Domain Lexicon Based on Microblogs: Text Mining Study.

JMIR Med Inform. 2020 Jun 23;8(6):e17650

Authors: Li G, Li B, Huang L, Hou S

Abstract
BACKGROUND: According to a World Health Organization report in 2017, there was almost one patient with depression among every 20 people in China. However, the diagnosis of depression is usually difficult in terms of clinical detection owing to slow observation, high cost, and patient resistance. Meanwhile, with the rapid emergence of social networking sites, people tend to share their daily life and disclose inner feelings online frequently, making it possible to effectively identify mental conditions using the rich text information. There are many achievements regarding an English web-based corpus, but for research in China so far, the extraction of language features from web-related depression signals is still in a relatively primary stage.
OBJECTIVE: The purpose of this study was to propose an effective approach for constructing a depression-domain lexicon. This lexicon will contain language features that could help identify social media users who potentially have depression. Our study also compared the performance of detection with and without our lexicon.
METHODS: We autoconstructed a depression-domain lexicon using Word2Vec, a semantic relationship graph, and the label propagation algorithm. These two methods combined performed well in a specific corpus during construction. The lexicon was obtained based on 111,052 Weibo microblogs from 1868 users who were depressed or nondepressed. During depression detection, we considered six features, and we used five classification methods to test the detection performance.
RESULTS: The experiment results showed that in terms of the F1 value, our autoconstruction method performed 1% to 6% better than baseline approaches and was more effective and steadier. When applied to detection models like logistic regression and support vector machine, our lexicon helped the models outperform by 2% to 9% and was able to improve the final accuracy of potential depression detection.
CONCLUSIONS: Our depression-domain lexicon was proven to be a meaningful input for classification algorithms, providing linguistic insights on the depressive status of test subjects. We believe that this lexicon will enhance early depression detection in people on social media. Future work will need to be carried out on a larger corpus and with more complex methods.

PMID: 32574151 [PubMed - as supplied by publisher]

Movie Review Summarization Using Supervised Learning and Graph-Based Ranking Algorithm.

Comput Intell Neurosci. 2020;2020:7526580

Authors: Khan A, Gul MA, Zareei M, Biswal RR, Zeb A, Naeem M, Saeed Y, Salim N

Abstract
With the growing information on web, online movie review is becoming a significant information resource for Internet users. However, online users post thousands of movie reviews on daily basis and it is hard for them to manually summarize the reviews. Movie review mining and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is desirable to summarize the lengthy movie reviews, and it will allow users to quickly recognize the positive and negative aspects of a movie. This study employs a feature extraction technique called bag of words (BoW) to extract features from movie reviews and represent the reviews as a vector space model or feature vector. The next phase uses Naïve Bayes machine learning algorithm to classify the movie reviews (represented as feature vector) into positive and negative. Next, an undirected weighted graph is constructed from the pairwise semantic similarities between classified review sentences in such a way that the graph nodes represent review sentences, while the edges of graph indicate semantic similarity weight. The weighted graph-based ranking algorithm (WGRA) is applied to compute the rank score for each review sentence in the graph. Finally, the top ranked sentences (graph nodes) are chosen based on highest rank scores to produce the extractive summary. Experimental results reveal that the proposed approach is superior to other state-of-the-art approaches.

PMID: 32565772 [PubMed - in process]

I know what you're probably going to say: Listener adaptation to variable use of uncertainty expressions.

Cognition. 2020 Jun 10;203:104285

Authors: Schuster S, Degen J

Abstract
Pragmatic theories of utterance interpretation share the assumption that listeners reason about alternative utterances that a speaker could have produced, but didn't. For such reasoning to be successful, listeners must have precise expectations about a speaker's production choices. This is at odds with the considerable variability across speakers that exists at all levels of linguistic representation. This tension can be reconciled by listeners adapting to the statistics of individual speakers. While linguistic adaptation is increasingly widely attested, semantic/pragmatic adaptation is underexplored. Moreover, what kind of representations listeners update during semantic/pragmatic adaptation - estimates of the speaker's lexicon, or estimates of the speaker's utterance preferences - remains poorly understood. In this work, we investigate semantic/pragmatic adaptation in the domain of uncertainty expressions like might and probably. In a series of web-based experiments, we find 1) that listeners vary in their expectations about a generic speaker's use of uncertainty expressions; 2) that listeners rapidly update their expectations about the use of uncertainty expressions after brief exposure to a speaker with a specific usage of uncertainty expressions; and 3) that listeners' interpretations of uncertainty expressions change after being exposed to a specific speaker. We present a novel computational model of semantic/pragmatic adaptation based on Bayesian belief updating and show, through a series of model comparisons, that semantic/pragmatic adaptation is best captured by listeners updating their beliefs both about the speaker's lexicon and their utterance preferences. This work has implications for both semantic theories of uncertainty expressions and psycholinguistic theories of adaptation: it highlights the need for dynamic semantic representations and suggests that listeners integrate their general linguistic knowledge with speaker-specific experiences to arrive at more precise interpretations.

PMID: 32535344 [PubMed - as supplied by publisher]

Ontological framework for standardizing and digitizing clinical pathways in healthcare information systems.

Comput Methods Programs Biomed. 2020 Jun 01;196:105559

Authors: Alahmar A, Crupi ME, Benlamri R

Abstract
BACKGROUND AND OBJECTIVE: Most healthcare institutions are reorganizing their healthcare delivery systems based on Clinical Pathways (CPs). CPs are novel medical management plans to standardize medical activities, reduce cost, optimize resource usage, and improve the quality of service. However, most CPs are still paper-based and not fully integrated with Health Information Systems (HIS). More CP computerization research is therefore needed to fully benefit from CP's practical potentials. A major contribution of this research is the vision that CP systems deserve to be placed at the centre of HIS, because within CPs lies the very heart of medical planning, treatment and impressions, including healthcare quality and cost factors.
METHODS: An important contribution to the realization of this vision is to fully standardize and digitize CPs so that they become machine-readable and smoothly linkable across various HIS. To achieve this goal, this research proposes a framework for (i) CP knowledge representation and sharing using ontologies, (ii) CP standardization based on SNOMED CT and HL7, and (iii) CP digitization based on a novel coding system to encode CP data. To show the feasibility of the proposed framework we developed a prototype clinical pathway management system (CPMS) based on CPs currently in use at hospitals.
RESULTS: The results show that CPs can be fully standardized and digitized using SNOMED CT terms and codes, and the CPMS can work as an independent system, performing novel CP-related functions, including useful data analytics. CPs can be compared easily for auditing and quality management. Furthermore, the CPMS was smoothly linked to a hospital EMR and CP data were captured in EMR without any loss.
CONCLUSION: The proposed framework is promising and contributes toward solving major challenges related to CP standardization, digitization, and inclusion in today's modern computerized hospitals.

PMID: 32531654 [PubMed - as supplied by publisher]

The molecular entities in linked data dataset.

Data Brief. 2020 Aug;31:105757

Authors: Tomaszuk D, Szeremeta Ł

Abstract
The Molecular Entities in Linked Data (MEiLD) dataset comprises data of distinct atoms, molecules, ions, ion pairs, radicals, radical ions, and others that can be identifiable as separately distinguishable chemical entities. The dataset is provided in a JSON-LD format and was generated by the SDFEater, a tool that allows parsing atoms, bonds, and other molecule data. MEiLD contains 349,960 of 'small' chemical entities. Our dataset is based on the SDF files and is enriched with additional ontologies and line notation data. As a basis, the Molecular Entities in Linked Data dataset uses the Resource Description Framework (RDF) data model. Saving the data in such a model allows preserving the semantic relations, like hierarchical and associative, between them. To describe chemical molecules, vocabularies such as Chemical Vocabulary for Molecular Entities (CVME) and Simple Knowledge Organization System (SKOS) are used. The dataset can be beneficial, among others, for people concerned with research and development tools for cheminformatics and bioinformatics. In this paper, we describe various methods of access to our dataset. In addition to the MEiLD dataset, we publish the Shapes Constraint Language (SHACL) schema of our dataset and the CVME ontology. The data is available in Mendeley Data.

PMID: 32529012 [PubMed]

FAIR-compliant clinical, radiomics and DICOM metadata of RIDER, Interobserver, Lung1 and Head-Neck1 TCIA collections.

Med Phys. 2020 Jun 10;:

Authors: Kalendralis P, Shi Z, Traverso A, Choudhury A, Sloep M, Zhovannik I, Starmans MPA, Grittner D, Feltens P, Monshouwer R, Klein S, Fijten R, Aerts H, Dekker A, van Soest J, Wee L

Abstract
PURPOSE: One of the most frequently cited radiomics investigations showed that features automatically extracted from routine clinical images could be used in prognostic modelling. These images have been made publicly accessible via The Cancer Imaging Archive (TCIA). There have been numerous requests for additional explanatory metadata on the following datasets - RIDER, Interobserver, Lung1 and Head-Neck1. To support repeatability, reproducibility, generalizability and transparency in radiomics research, we publish the subjects' clinical data, extracted radiomics features and Digital Imaging and Communications in Medicine (DICOM) headers of these four datasets with descriptive metadata, in order to be more compliant with findable, accessible, interoperable and re-usable (FAIR) data management principles.
ACQUISITION AND VALIDATION METHODS: Overall survival time intervals were updated using a national citizens registry after internal ethics board approval. Spatial offsets of the Primary Gross Tumor Volume (GTV) regions of interest (ROIs) associated with the Lung1 CT series were improved on the TCIA. GTV radiomics features were extracted using the open-source ontology-guided radiomics workflow (O-RAW). We reshaped the output of O-RAW to map features and extraction settings to the latest version of Radiomics Ontology, so as to be consistent with the Image Biomarker Standardization Initiative (IBSI). DICOM metadata was extracted using a research version of Semantic DICOM (SOHARD, GmbH, Fuerth; Germany). Subjects' clinical data was described with metadata using the Radiation Oncology Ontology. All of the above were published in Resource Descriptor Format (RDF), i.e. triples. Example SPARQL queries are shared with the reader to use on the online triples archive, which are intended to illustrate how to exploit this data submission.
DATA FORMAT: The accumulated RDF data is publicly accessible through a SPARQL endpoint where the triples are archived. The endpoint is remotely queried through a graph database web application at http://sparql.cancerdata.org. SPARQL queries are intrinsically federated, such that we can efficiently cross-reference clinical, DICOM and radiomics data within a single query, while being agnostic to the original data format and coding system. The federated queries work in the same way even if the RDF data were partitioned across multiple servers and dispersed physical locations.
POTENTIAL APPLICATIONS: The public availability of these data resources is intended to support radiomics features replication, repeatability and reproducibility studies by the academic community. The example SPARQL queries may be freely used and modified by readers depending on their research question. Data interoperability and reusability is supported by referencing existing public ontologies. The RDF data is readily findable and accessible through the aforementioned link. Scripts used to create the RDF are made available at a code repository linked to this submission : https://gitlab.com/UM-CDS/FAIR-compliant_clinical_radiomics_and_DICOM_metadata.

PMID: 32521049 [PubMed - as supplied by publisher]

Spelling Errors and Shouting Capitalization Lead to Additive Penalties to Trustworthiness of Online Health Information: Randomized Experiment With Laypersons.

J Med Internet Res. 2020 Jun 10;22(6):e15171

Authors: Witchel HJ, Thompson GA, Jones CI, Westling CEI, Romero J, Nicotra A, Maag B, Critchley HD

Abstract
BACKGROUND: The written format and literacy competence of screen-based texts can interfere with the perceived trustworthiness of health information in online forums, independent of the semantic content. Unlike in professional content, the format in unmoderated forums can regularly hint at incivility, perceived as deliberate rudeness or casual disregard toward the reader, for example, through spelling errors and unnecessary emphatic capitalization of whole words (online shouting).
OBJECTIVE: This study aimed to quantify the comparative effects of spelling errors and inappropriate capitalization on ratings of trustworthiness independently of lay insight and to determine whether these changes act synergistically or additively on the ratings.
METHODS: In web-based experiments, 301 UK-recruited participants rated 36 randomized short stimulus excerpts (in the format of information from an unmoderated health forum about multiple sclerosis) for trustworthiness using a semantic differential slider. A total of 9 control excerpts were compared with matching error-containing excerpts. Each matching error-containing excerpt included 5 instances of misspelling, or 5 instances of inappropriate capitalization (shouting), or a combination of 5 misspelling plus 5 inappropriate capitalization errors. Data were analyzed in a linear mixed effects model.
RESULTS: The mean trustworthiness ratings of the control excerpts ranged from 32.59 to 62.31 (rating scale 0-100). Compared with the control excerpts, excerpts containing only misspellings were rated as being 8.86 points less trustworthy, those containing inappropriate capitalization were rated as 6.41 points less trustworthy, and those containing the combination of misspelling and capitalization were rated as 14.33 points less trustworthy (P<.001 for all). Misspelling and inappropriate capitalization show an additive effect.
CONCLUSIONS: Distinct indicators of incivility independently and additively penalize the perceived trustworthiness of online text independently of lay insight, eliciting a medium effect size.

PMID: 32519676 [PubMed - in process]

Connected Traffic Data Ontology (CTDO) for Intelligent Urban Traffic Systems Focused on Connected (Semi) Autonomous Vehicles.

Sensors (Basel). 2020 May 23;20(10):

Authors: Viktorović M, Yang D, Vries B

Abstract
For autonomous vehicles (AV), the ability to share information about their surroundings is crucial. With Level 4 and 5 autonomy in sight, solving the challenge of organization and efficient storing of data, coming from these connected platforms, becomes paramount. Research done up to now has been mostly focused on communication and network layers of V2X (Vehicle-to-Everything) data sharing. However, there is a gap when it comes to the data layer. Limited attention has been paid to the ontology development in the automotive domain. More specifically, the way to integrate sensor data and geospatial data efficiently is missing. Therefore, we proposed to develop a new Connected Traffic Data Ontology (CTDO) on the foundations of Sensor, Observation, Sample, and Actuator (SOSA) ontology, to provide a more suitable ontology for large volumes of time-sensitive data coming from multi-sensory platforms, like connected vehicles, as the first step in closing the existing research gap. Additionally, as this research aims to further extend the CTDO in the future, a possible way to map to the CTDO with ontologies that represent road infrastructure has been presented. Finally, new CTDO ontology was benchmarked against SOSA, and better memory performance and query execution speeds have been confirmed.

PMID: 32456152 [PubMed - in process]

Pushing the Scalability of RDF Engines on IoT Edge Devices.

Sensors (Basel). 2020 May 14;20(10):

Authors: Le-Tuan A, Hayes C, Hauswirth M, Le-Phuoc D

Abstract
Semantic interoperability for the Internet of Things (IoT) is enabled by standards and technologies from the Semantic Web. As recent research suggests a move towards decentralised IoT architectures, we have investigated the scalability and robustness of RDF (Resource Description Framework)engines that can be embedded throughout the architecture, in particular at edge nodes. RDF processing at the edge facilitates the deployment of semantic integration gateways closer to low-level devices. Our focus is on how to enable scalable and robust RDF engines that can operate on lightweight devices. In this paper, we have first carried out an empirical study of the scalability and behaviour of solutions for RDF data management on standard computing hardware that have been ported to run on lightweight devices at the network edge. The findings of our study shows that these RDF store solutions have several shortcomings on commodity ARM (Advanced RISC Machine) boards that are representative of IoT edge node hardware. Consequently, this has inspired us to introduce a lightweight RDF engine, which comprises an RDF storage and a SPARQL processor for lightweight edge devices, called RDF4Led. RDF4Led follows the RISC-style (Reduce Instruction Set Computer) design philosophy. The design constitutes a flash-aware storage structure, an indexing scheme, an alternative buffer management technique and a low-memory-footprint join algorithm that demonstrates improved scalability and robustness over competing solutions. With a significantly smaller memory footprint, we show that RDF4Led can handle 2 to 5 times more data than popular RDF engines such as Jena TDB (Tuple Database) and RDF4J, while consuming the same amount of memory. In particular, RDF4Led requires 10%-30% memory of its competitors to operate on datasets of up to 50 million triples. On memory-constrained ARM boards, it can perform faster updates and can scale better than Jena TDB and Virtuoso. Furthermore, we demonstrate considerably faster query operations than Jena TDB and RDF4J.

PMID: 32422961 [PubMed]

GWAS Central: a comprehensive resource for the discovery and comparison of genotype and phenotype data from genome-wide association studies.

Nucleic Acids Res. 2020 01 08;48(D1):D933-D940

Authors: Beck T, Shorter T, Brookes AJ

Abstract
The GWAS Central resource provides a toolkit for integrative access and visualization of a uniquely extensive collection of genome-wide association study data, while ensuring safe open access to prevent research participant identification. GWAS Central is the world's most comprehensive openly accessible repository of summary-level GWAS association information, providing over 70 million P-values for over 3800 studies investigating over 1400 unique phenotypes. The database content comprises direct submissions received from GWAS authors and consortia, in addition to actively gathered data sets from various public sources. GWAS data are discoverable from the perspective of genetic markers, genes, genome regions or phenotypes, via graphical visualizations and detailed downloadable data reports. Tested genetic markers and relevant genomic features can be visually interrogated across up to sixteen multiple association data sets in a single view using the integrated genome browser. The semantic standardization of phenotype descriptions with Medical Subject Headings and the Human Phenotype Ontology allows the precise identification of genetic variants associated with diseases, phenotypes and traits of interest. Harmonization of the phenotype descriptions used across several GWAS-related resources has extended the phenotype search capabilities to enable cross-database study discovery using a range of ontologies. GWAS Central is updated regularly and available at https://www.gwascentral.org.

PMID: 31612961 [PubMed - indexed for MEDLINE]

Zostavax vaccine effectiveness among US elderly using real-world evidence: Addressing unmeasured confounders by using multiple imputation after linking beneficiary surveys with Medicare claims.

Pharmacoepidemiol Drug Saf. 2019 07;28(7):993-1001

Authors: Izurieta HS, Wu X, Lu Y, Chillarige Y, Wernecke M, Lindaas A, Pratt D, MaCurdy TE, Chu S, Kelman J, Forshee R

Abstract
PURPOSE: Medicare claims can provide real-world evidence (RWE) to support the Food and Drug Administration's ability to conduct postapproval studies to validate products' safety and effectiveness. However, Medicare claims do not contain comprehensive information on some important sources of bias. Thus, we piloted an approach using the Medicare Current Beneficiary Survey (MCBS), a nationally representative survey of the Medicare population, to (a) assess cohort balance with respect to unmeasured confounders in a herpes zoster vaccine (HZV) effectiveness claims-based study and (b) augment Medicare claims with MCBS data to include unmeasured covariates.
METHODS: We reanalyzed data from our published HZV effectiveness Medicare analysis, using linkages to MCBS to obtain information on impaired mobility, education, and health-seeking behavior. We assessed survey variable balance between the matched cohorts and selected imbalanced variables for model adjustment, applying multiple imputation by chained equations (MICE) to impute these potential unmeasured confounders.
RESULTS: The original HZV effectiveness study cohorts appeared well balanced with respect to variables we selected from the MCBS. Our imputed results showed slight shifts in HZV effectiveness point estimates with wider confidence intervals, but indicated no statistically significant differences from the original study estimates.
CONCLUSIONS: Our innovative use of linked survey data to assess cohort balance and our imputation approach to augment Medicare claims with MCBS data to include unmeasured covariates provide potential solutions for addressing bias related to unmeasured confounding in large database studies, thus adding new tools for RWE studies.

PMID: 31168897 [PubMed - indexed for MEDLINE]

CSNet: A new DeepNet framework for ischemic stroke lesion segmentation.

Comput Methods Programs Biomed. 2020 May 01;193:105524

Authors: Kumar A, Upadhyay N, Ghosal P, Chowdhury T, Das D, Mukherjee A, Nandi D

Abstract
BACKGROUND AND OBJECTIVES: Acute stroke lesion segmentation is of paramount importance as it can aid medical personnel to render a quicker diagnosis and administer consequent treatment. Automation of this task is technically exacting due to the variegated appearance of lesions and their dynamic development, medical discrepancies, unavailability of datasets, and the requirement of several MRI modalities for imaging. In this paper, we propose a composite deep learning model primarily based on the self-similar fractal networks and the U-Net model for performing acute stroke diagnosis tasks automatically to assist as well as expedite the decision-making process of medical practitioners.
METHODS: We put forth a new deep learning architecture, the Classifier-Segmenter network (CSNet), involving a hybrid training strategy with a self-similar (fractal) U-Net model, explicitly designed to perform the task of segmentation. In fractal networks, the underlying design strategy is based on the repetitive generation of self-similar fractals in place of residual connections. The U-Net model exploits both spatial as well as semantic information along with parameter sharing for a faster and efficient training process. In this new architecture, we exploit the benefits of both by combining them into one hybrid training scheme and developing the concept of a cascaded architecture, which further enhances the model's accuracy by removing redundant parts from the Segmenter's input. Lastly, a voting mechanism has been employed to further enhance the overall segmentation accuracy.
RESULTS: The performance of the proposed architecture has been scrutinized against the existing state-of-the-art deep learning architectures applied to various biomedical image processing tasks by submission on the publicly accessible web platform provided by the MICCAI Ischemic Stroke Lesion Segmentation (ISLES) challenge. The experimental results demonstrate the superiority of the proposed method when compared to similar submitted strategies, both qualitatively and quantitatively in terms of some of the well known evaluation metrics, such as Accuracy, Dice-Coefficient, Recall, and Precision.
CONCLUSIONS: We believe that our method may find use as a handy tool for doctors to identify the location and extent of irreversibly damaged brain tissue, which is said to be a critical part of the decision-making process in case of an acute stroke.

PMID: 32417618 [PubMed - as supplied by publisher]

Risk Response for Municipal Solid Waste Crisis Using Ontology-Based Reasoning.

Int J Environ Res Public Health. 2020 May 09;17(9):

Authors: Yang Q, Zuo C, Liu X, Yang Z, Zhou H

Abstract
Many cities in the world are besieged by municipal solid waste (MSW). MSW not only pollutes the ecological environment but can even induce a series of public safety crises. Risk response for MSW needs novel changes. This paper innovatively adopts the ideas and methods of semantic web ontology to build an ontology-based reasoning system for MSW risk response. Through the integration of crisis information and case resources in the field of MSW, combined with the reasoning ability of Semantic Web Rule Language (SWRL), a system of rule reasoning for risk transformation is constructed. Knowledge extraction and integration of MSW risk response can effectively excavate semantic correlation of crisis information along with key transformation points in the process of crisis evolution through rule reasoning. The results show that rule reasoning of transformation can effectively improve intelligent decision-making regarding MSW risk response.

PMID: 32397529 [PubMed - in process]

iPiDi-PUL: identifying Piwi-interacting RNA-disease associations based on positive unlabeled learning.

Brief Bioinform. 2020 May 11;:

Authors: Wei H, Xu Y, Liu B

Abstract
Accumulated researches have revealed that Piwi-interacting RNAs (piRNAs) are regulating the development of germ and stem cells, and they are closely associated with the progression of many diseases. As the number of the detected piRNAs is increasing rapidly, it is important to computationally identify new piRNA-disease associations with low cost and provide candidate piRNA targets for disease treatment. However, it is a challenging problem to learn effective association patterns from the positive piRNA-disease associations and the large amount of unknown piRNA-disease pairs. In this study, we proposed a computational predictor called iPiDi-PUL to identify the piRNA-disease associations. iPiDi-PUL extracted the features of piRNA-disease associations from three biological data sources, including piRNA sequence information, disease semantic terms and the available piRNA-disease association network. Principal component analysis (PCA) was then performed on these features to extract the key features. The training datasets were constructed based on known positive associations and the negative associations selected from the unknown pairs. Various random forest classifiers trained with these different training sets were merged to give the predictive results via an ensemble learning approach. Finally, the web server of iPiDi-PUL was established at http://bliulab.net/iPiDi-PUL to help the researchers to explore the associated diseases for newly discovered piRNAs.

PMID: 32393982 [PubMed - as supplied by publisher]

SIB Literature Services: RESTful customizable search engines in biomedical literature, enriched with automatically mapped biomedical concepts.

Nucleic Acids Res. 2020 May 07;:

Authors: Julien G, Déborah C, Pierre-André M, Luc M, Emilie P, Patrick R

Abstract
Thanks to recent efforts by the text mining community, biocurators have now access to plenty of good tools and Web interfaces for identifying and visualizing biomedical entities in literature. Yet, many of these systems start with a PubMed query, which is limited by strong Boolean constraints. Some semantic search engines exploit entities for Information Retrieval, and/or deliver relevance-based ranked results. Yet, they are not designed for supporting a specific curation workflow, and allow very limited control on the search process. The Swiss Institute of Bioinformatics Literature Services (SIBiLS) provide personalized Information Retrieval in the biological literature. Indeed, SIBiLS allow fully customizable search in semantically enriched contents, based on keywords and/or mapped biomedical entities from a growing set of standardized and legacy vocabularies. The services have been used and favourably evaluated to assist the curation of genes and gene products, by delivering customized literature triage engines to different curation teams. SIBiLS (https://candy.hesge.ch/SIBiLS) are freely accessible via REST APIs and are ready to empower any curation workflow, built on modern technologies scalable with big data: MongoDB and Elasticsearch. They cover MEDLINE and PubMed Central Open Access enriched by nearly 2 billion of mapped biomedical entities, and are daily updated.

PMID: 32379317 [PubMed - as supplied by publisher]

A multicentric IT platform for storage and sharing of imaging-based radiation dosimetric data.

Int J Comput Assist Radiol Surg. 2020 May 02;:

Authors: Spaltenstein J, van Dooren N, Pasquier G, Roduit N, Brenet M, Pasquier G, Gibaud B, Mildenberger P, Ratib O

Abstract
PURPOSE: The MEDIRAD project is about the effects of low radiation dose in the context of medical procedures. The goal of the work is to develop an informatics service that will provide the researchers of the MEDIRAD project with a platform to share acquired images, along with the associated dosimetric data pertaining to the radiation resulting from the procedure.
METHODS: The authors designed a system architecture to manage image data and dosimetric data in an integrated way. DICOM and non-DICOM data are stored in separated repositories, and the link between the two is provided through a semantic database, i.e., a database whose information schema in aligned with an ontology.
RESULTS: The system currently supports CT, PET, SPECT, and NM images as well as dose reports. Currently, two workflows for non-DICOM data generated from dosimetric calculations have been taken into account, one concerning Monte Carlo-based calculation of organ doses in Chest CT, and the other estimation of doses in nontarget organs in 131I targeted radionuclide therapy of the thyroid.
CONCLUSION: The system is currently deployed, thus providing access to image and related dosimetric data to all MEDIRAD users. The software was designed in such a way that it can be reused to support similar needs in other projects.

PMID: 32361856 [PubMed - as supplied by publisher]

Blended Face-to-Face and Web-Based Smoking Cessation Treatment: Qualitative Study of Patients' User Experience.

JMIR Form Res. 2020 Jan 27;:

Authors: Siemer L, Ben Allouch S, Pieterse ME, Brusse-Keizer M, Sanderman R, Postel MG

Abstract
BACKGROUND: Blended Web-based and face-to-face treatment is a promising electronic health service because it is expected that in a blended treatment the strengths of one mode of delivery will compensate for the weaknesses of the other.
OBJECTIVE: The aim of this study was to explore this expectation by examining the patients' user experience (UX) in a blended smoking cessation treatment (BSCT) in routine care.
METHODS: Patients' UX was collected by in-depth interviews (n=10) at an outpatient smoking cessation clinic in the Netherlands. Content analysis of the semantic domains was used to analyze the patients' UX. For the description of the UX, Hassenzahl's UX model was applied, examining the 4 of the 5 key elements of UX that form the UX from a user's perspective: (1) standards and expectations, (2) apparent character (pragmatic and hedonic attributes), (3) usage situation, and (4) consequences (appeal, emotions, and behavior).
RESULTS: BSCT appeared to be a mostly positively experienced service. Patients had a positive-pragmatic standard and neutral-open expectation toward BSCT at the treatment start. The pragmatic attributes of the F2F session were mostly perceived as positive, whereas the pragmatic attributes of the Web sessions were perceived as both positive and negative. For the hedonic attributes, there seems to be a difference between the F2F sessions and the Web sessions. Specifically, the hedonic attributes of the Web sessions were experienced as mostly negative, whereas those of the F2F sessions were mostly positive. For the usage situation, the physical and social contexts were experienced positively, whereas the task and technical contexts were experienced negatively. Nevertheless, the consequential appeal of BSCT was positive. However, the consequential emotions and behavior varied, ultimately resulting in diverse combinations of consequential appeal, emotions, and behavior (positive, negative, and mixed).
CONCLUSIONS: This study provided insight into the UX of a blended treatment, and the results support the expectation that in a blended treatment, one mode of delivery may compensate for the weaknesses of the other. However, in this certain setting, this is mainly achieved in only one way: F2F sessions compensated for the weaknesses of the Web sessions. As a practical conclusion, this may mean that the Web sessions, supported by the strength of the F2F sessions, offer an interesting approach for further improving the blended treatment. Our theoretical findings reflect the relevance of the aspects of hedonism, such as fun, joy, or happiness in the UX, which were not mentioned in relation to the Web sessions and were only scarcely mentioned in relation to the F2F sessions. Future research should further investigate the role of hedonistic aspects in a blended treatment, and if increased enjoyment of a blended treatment could increase treatment adherence and, ultimately, effectiveness.

PMID: 32343245 [PubMed - as supplied by publisher]

Epistemic vigilance online: Textual inaccuracy and children's selective trust in webpages.

Br J Dev Psychol. 2020 Apr 28;:

Authors: Einav S, Levey A, Patel P, Westwood A

Abstract
In this age of 'fake news', it is crucial that children are equipped with the skills to identify unreliable information online. Our study is the first to examine whether children are influenced by the presence of inaccuracies contained in webpages when deciding which sources to trust. Forty-eight 8- to 10-year-olds viewed three pairs of webpages, relating to the same topics, where one webpage per pair contained three obvious inaccuracies (factual, typographical, or exaggerations, according to condition). The paired webpages offered conflicting claims about two novel facts. We asked participants questions pertaining to the novel facts to assess whether they systematically selected answers from the accurate sources. Selective trust in the accurate webpage was found in the typos condition only. This study highlights the limitations of 8- to 10-year-olds in critically evaluating the accuracy of webpage content and indicates a potential focus for educational intervention. Statement of contribution What is already known on this subject? Children display early epistemic vigilance towards spoken testimony. They use speakers' past accuracy when deciding whom to trust regarding novel information. Little is known about children's selective trust towards web-based sources. What does this study add? This study is the first to examine whether textual inaccuracy affects children's trust in webpages. Typos but not semantic errors led to reduced trust in a webpage compared to an accurate source. Children aged 8-10 years show limited evaluation of the accuracy of online content.

PMID: 32342990 [PubMed - as supplied by publisher]

Semantic Networks and Mechanisms of Exposure Therapy: Implications for the Treatment of Panic Disorder.

Am J Psychiatry. 2020 03 01;177(3):197-199

Authors: Cisler JM

PMID: 32114776 [PubMed - indexed for MEDLINE]