J Biomed Inform. 2023 Apr 25:104372. doi: 10.1016/j.jbi.2023.104372. Online ahead of print.

ABSTRACT

Phenotype-based prioritization of candidate genes and diseases has become a well-established approach for multi-omics diagnostics of rare diseases. Most current algorithms exploit semantic analysis and probabilistic statistics based on Human Phenotype Ontology and are commonly superior to naive search methods. However, these algorithms are mostly less interpretable and do not perform well in real clinical scenarios due to noise and imprecision of query terms, and the fact that individuals may not display all phenotypes of the disease they belong to. We present a Phenotype-driven Likelihood Ratio analysis approach (PheLR) assisting interpretable clinical diagnosis of rare diseases. With a likelihood ratio paradigm, PheLR estimates the posterior probability of candidate diseases and how much a phenotypic feature contributes to the prioritization result. Benchmarked using simulated and realistic patients, PheLR shows significant advantages over current approaches and is robust to noise and inaccuracy. To facilitate clinical practice and visualized differential diagnosis, PheLR is implemented as an online web tool (http://phelr.nbscn.org).

PMID:37105510 | DOI:10.1016/j.jbi.2023.104372

Orphanet J Rare Dis. 2023 Apr 26;18(1):95. doi: 10.1186/s13023-023-02683-9.

ABSTRACT

BACKGROUND: Inherited Metabolic Disorders (IMDs) are rare diseases where one impaired protein leads to a cascade of changes in the adjacent chemical conversions. IMDs often present with non-specific symptoms, a lack of a clear genotype-phenotype correlation, and de novo mutations, complicating diagnosis. Furthermore, products of one metabolic conversion can be the substrate of another pathway obscuring biomarker identification and causing overlapping biomarkers for different disorders. Visualization of the connections between metabolic biomarkers and the enzymes involved might aid in the diagnostic process. The goal of this study was to provide a proof-of-concept framework for integrating knowledge of metabolic interactions with real-life patient data before scaling up this approach. This framework was tested on two groups of well-studied and related metabolic pathways (the urea cycle and pyrimidine de-novo synthesis). The lessons learned from our approach will help to scale up the framework and support the diagnosis of other less-understood IMDs.

METHODS: Our framework integrates literature and expert knowledge into machine-readable pathway models, including relevant urine biomarkers and their interactions. The clinical data of 16 previously diagnosed patients with various pyrimidine and urea cycle disorders were visualized on the top 3 relevant pathways. Two expert laboratory scientists evaluated the resulting visualizations to derive a diagnosis.

RESULTS: The proof-of-concept platform resulted in varying numbers of relevant biomarkers (five to 48), pathways, and pathway interactions for each patient. The two experts reached the same conclusions for all samples with our proposed framework as with the current metabolic diagnostic pipeline. For nine patient samples, the diagnosis was made without knowledge about clinical symptoms or sex. For the remaining seven cases, four interpretations pointed in the direction of a subset of disorders, while three cases were found to be undiagnosable with the available data. Diagnosing these patients would require additional testing besides biochemical analysis.

CONCLUSION: The presented framework shows how metabolic interaction knowledge can be integrated with clinical data in one visualization, which can be relevant for future analysis of difficult patient cases and untargeted metabolomics data. Several challenges were identified during the development of this framework, which should be resolved before this approach can be scaled up and implemented to support the diagnosis of other (less understood) IMDs. The framework could be extended with other OMICS data (e.g. genomics, transcriptomics), and phenotypic data, as well as linked to other knowledge captured as Linked Open Data.

PMID:37101200 | DOI:10.1186/s13023-023-02683-9

Bioinformatics. 2023 Apr 25:btad257. doi: 10.1093/bioinformatics/btad257. Online ahead of print.

ABSTRACT

MOTIVATION: Molecular complexes play a major role in the regulation of biological pathways. The Biological Pathway Exchange format (BioPAX) facilitates the integration of data sources describing interactions some of which involving complexes. The BioPAX specification explicitly prevents complexes to have any component that is another complex (unless this component is a black-box complex whose composition is unknown). However, we observed that the well-curated Reactome pathway database contains such recursive complexes of complexes. We propose reproductible and semantically-rich SPARQL queries for identifying and fixing invalid complexes in BioPAX databases, and evaluate the consequences of fixing these non-conformities in the Reactome database.

RESULTS: For the Homo sapiens version of Reactome, we identify 5,833 recursively defined complexes out of the 14,987 complexes (39%). This situation is not specific to the human dataset, as all tested species of Reactome exhibit between 30% (Plasmodium falciparum) and 40% (Sus scrofa, Bos taurus, Canis familiaris, Gallus gallus) of recursive complexes. As an additional consequence, the procedure also allows the detection of complex redundancies. Overall, this method improves the conformity and the automated analysis of the graph by repairing the topology of the complexes in the graph. This will allow to apply further reasoning methods on better consistent data.

AVAILABILITY: We provide a jupyter notebook detailing the analysis https://github.com/cjuigne/non_conformities_detection_biopax.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37097895 | DOI:10.1093/bioinformatics/btad257

Syst Biol. 2023 Apr 24:syad025. doi: 10.1093/sysbio/syad025. Online ahead of print.

ABSTRACT

The spectacular radiation of insects has produced a stunning diversity of phenotypes. During the past 250 years, research on insect systematics has generated hundreds of terms for naming and comparing them. In its current form, this terminological diversity is presented in natural language and lacks formalization, which prohibits computer-assisted comparison using semantic web technologies. Here we propose a Model for Describing Cuticular Anatomical Structures (MoDCAS) which incorporates structural properties and positional relationships for standardized, consistent, and reproducible descriptions of arthropod phenotypes. We applied the MoDCAS framework in creating the ontology for the Anatomy of the Insect Skeleto-Muscular system (AISM). The AISM is the first general insect ontology that aims to cover all taxa by providing generalized, fully logical, and queryable, definitions for each term. It was built using the Ontology Development Kit (ODK), which maximizes interoperability with Uberon (Uberon multi-species anatomy ontology) and other basic ontologies, enhancing the integration of insect anatomy into the broader biological sciences. A template system for adding new terms, extending, and linking the AISM to additional anatomical, phenotypic, genetic, and chemical ontologies is also introduced. The AISM is proposed as the backbone for taxon-specific insect ontologies and has potential applications spanning systematic biology and biodiversity informatics, allowing users to (1) use controlled vocabularies and create semi-automated computer-parsable insect morphological descriptions; (2) integrate insect morphology into broader fields of research, including ontology-informed phylogenetic methods, logical homology hypothesis testing, evo-devo studies, and genotype to phenotype mapping; and (3) automate the extraction of morphological data from the literature, enabling the generation of large-scale phenomic data, by facilitating the production and testing of informatic tools able to extract, link, annotate, and process morphological data. This descriptive model and its ontological applications will allow for clear and semantically interoperable integration of arthropod phenotypes in biodiversity studies.

PMID:37094905 | DOI:10.1093/sysbio/syad025

J Biomed Semantics. 2023 Apr 18;14(1):4. doi: 10.1186/s13326-023-00284-w.

ABSTRACT

The majority of available datasets in open government data are statistical. They are widely published by various governments to be used by the public and data consumers. However, most open government data portals do not provide the five-star Linked Data standard datasets. The published datasets are isolated from one another while conceptually connected. This paper constructs a knowledge graph for the disease-related datasets of a Canadian government data portal, Nova Scotia Open Data. We leveraged the Semantic Web technologies to transform the disease-related datasets into Resource Description Framework (RDF) and enriched them with semantic rules. An RDF data model using the RDF Cube vocabulary was designed in this work to develop a graph that adheres to best practices and standards, allowing for expansion, modification and flexible re-use. The study also discusses the lessons learned during the cross-dimensional knowledge graph construction and integration of open statistical datasets from multiple sources.

PMID:37072859 | DOI:10.1186/s13326-023-00284-w

IEEE Trans Pattern Anal Mach Intell. 2023 Aug;45(8):9654-9668. doi: 10.1109/TPAMI.2023.3262140. Epub 2023 Jun 30.

ABSTRACT

Recently, unsupervised learning has made impressive progress on various tasks. Despite the dominance of discriminative models, increasing attention is drawn to representations learned by generative models and in particular, Generative Adversarial Networks (GANs). Previous works on the interpretation of GANs reveal that GANs encode semantics in feature maps in a linearly separable form. In this work, we further find that GAN's features can be well clustered with the linear separability assumption. We propose a novel clustering algorithm, named KLiSH, which leverages the linear separability to cluster GAN's features. KLiSH succeeds in extracting fine-grained semantics of GANs trained on datasets of various objects, e.g., car, portrait, animals, and so on. With KLiSH, we can sample images from GANs along with their segmentation masks and synthesize paired image-segmentation datasets. Using the synthesized datasets, we enable two downstream applications. First, we train semantic segmentation networks on these datasets and test them on real images, realizing unsupervised semantic segmentation. Second, we train image-to-image translation networks on the synthesized datasets, enabling semantic-conditional image synthesis without human annotations.

PMID:37030793 | DOI:10.1109/TPAMI.2023.3262140

IEEE Trans Pattern Anal Mach Intell. 2023 Aug;45(8):9583-9594. doi: 10.1109/TPAMI.2023.3257026. Epub 2023 Jun 30.

ABSTRACT

Point cloud completion aims to predict complete shape from its partial observation. Current approaches mainly consist of generation and refinement stages in a coarse-to-fine style. However, the generation stage often lacks robustness to tackle different incomplete variations, while the refinement stage blindly recovers point clouds without the semantic awareness. To tackle these challenges, we unify point cloud Completion by a generic Pretrain-Prompt-Predict paradigm, namely CP3. Inspired by prompting approaches from NLP, we creatively reinterpret point cloud generation and refinement as the prompting and predicting stages, respectively. Then, we introduce a concise self-supervised pretraining stage before prompting. It can effectively increase robustness of point cloud generation, by an Incompletion-Of-Incompletion (IOI) pretext task. Moreover, we develop a novel Semantic Conditional Refinement (SCR) network at the predicting stage. It can discriminatively modulate multi-scale refinement with the guidance of semantics. Finally, extensive experiments demonstrate that our CP3 outperforms the state-of-the-art methods with a large margin. code will be available at https://github.com/MingyeXu/cp3.

PMID:37027257 | DOI:10.1109/TPAMI.2023.3257026

Cognition. 2023 Apr 5;236:105445. doi: 10.1016/j.cognition.2023.105445. Online ahead of print.

ABSTRACT

Human olfaction can be extraordinarily sensitive, and its most common assessment method is odor identification (OID), where everyday odors are matched to word labels in a multiple-choice format. However, many older persons are unable to identify familiar odors, a deficit that is associated with the risk of future dementia and mortality. The underlying processes subserving OID in older adults are poorly understood. Here, we analyzed error patterns in OID to test whether errors could be explained by perceptual and/or semantic similarities among the response alternatives. We investigated the OID response patterns in a large, population-based sample of older adults in Sweden (n = 2479; age 60-100 years). Olfaction was assessed by a 'Sniffin ́ TOM OID test with 16 odors; each trial involved matching a target odor to a correct label among three distractors. We analyzed the pattern of misidentifications, and the results showed that some distractors were more frequently selected than others, suggesting cognitive or perceptual factors may be present. Relatedly, we conducted a large online survey of older adults (n = 959, age 60-90 years) who were asked to imagine and rate the perceptual similarity of the target odors and the three corresponding distractors (e.g. "How similar are these smells: apple and mint?"). We then used data from the Swedish web corpus and the Word2Vec neural network algorithm to quantify the semantic association strength between the labels of each target odor and its three distractors. These data sources were used to predict odor identification errors. We found that the error patterns were partly explained by both the semantic similarity between target-distractor pairs, and the imagined perceptual similarity of the target-distractor pair. Both factors had, however, a diminished prediction in older ages, as responses became gradually less systematic. In sum, our results suggest that OID tests not only reflect olfactory perception, but also likely involve the mental processing of odor-semantic associations. This may be the reason why these tests are useful in predicting dementia onset. Our insights into olfactory-language interactions could be harnessed to develop new olfactory tests that are tailored for specific clinical purposes.

PMID:37027897 | DOI:10.1016/j.cognition.2023.105445

IEEE Trans Neural Netw Learn Syst. 2023 Jan 23;PP. doi: 10.1109/TNNLS.2023.3236776. Online ahead of print.

ABSTRACT

Visual reasoning between visual images and natural language remains a long-standing challenge in computer vision. Conventional deep supervision methods target at finding answers to the questions relying on the datasets containing only a limited amount of images with textual ground-truth descriptions. Facing learning with limited labels, it is natural to expect to constitute a larger scale dataset consisting of several million visual data annotated with texts, but this approach is extremely time-intensive and laborious. Knowledge-based works usually treat knowledge graphs (KGs) as static flattened tables for searching the answer, but fail to take advantage of the dynamic update of KGs. To overcome these deficiencies, we propose a Webly supervised knowledge-embedded model for the task of visual reasoning. On the one hand, vitalized by the overwhelming successful Webly supervised learning, we make much use readily available images from the Web with their weakly annotated texts for an effective representation. On the other hand, we design a knowledge-embedded model, including the dynamically updated interaction mechanism between semantic representation models and KGs. Experimental results on two benchmark datasets demonstrate that our proposed model significantly achieves the most outstanding performance compared with other state-of-the-art approaches for the task of visual reasoning.

PMID:37022067 | DOI:10.1109/TNNLS.2023.3236776

Front Med (Lausanne). 2023 Mar 15;10:1073313. doi: 10.3389/fmed.2023.1073313. eCollection 2023.

ABSTRACT

This paper provides an overview of current linguistic and ontological challenges which have to be met in order to provide full support to the transformation of health ecosystems in order to meet precision medicine (5 PM) standards. It highlights both standardization and interoperability aspects regarding formal, controlled representations of clinical and research data, requirements for smart support to produce and encode content in a way that humans and machines can understand and process it. Starting from the current text-centered communication practices in healthcare and biomedical research, it addresses the state of the art in information extraction using natural language processing (NLP). An important aspect of the language-centered perspective of managing health data is the integration of heterogeneous data sources, employing different natural languages and different terminologies. This is where biomedical ontologies, in the sense of formal, interchangeable representations of types of domain entities come into play. The paper discusses the state of the art of biomedical ontologies, addresses their importance for standardization and interoperability and sheds light to current misconceptions and shortcomings. Finally, the paper points out next steps and possible synergies of both the field of NLP and the area of Applied Ontology and Semantic Web to foster data interoperability for 5 PM.

PMID:37007792 | PMC:PMC10050682 | DOI:10.3389/fmed.2023.1073313

BMC Health Serv Res. 2023 Apr 1;23(1):328. doi: 10.1186/s12913-023-09325-7.

ABSTRACT

BACKGROUND: Low and lower middle-income countries suffer lack of healthcare providers and proper workforce education programs, a greater spread of illnesses, poor surveillance, efficient management, etc., which are addressable by a central policy framework implementation. Accordingly, an eHealth policy framework is required specifically for these countries to successfully implement eHealth solutions. This study explores existing frameworks and fills the gap by proposing an eHealth policy framework in the context of developing countries.

METHODS: This PRISMA-based (PRISMA Preferred Reporting Items For Systematic Reviews and Meta-Analyses) systematic review used Google Scholar, IEEE, Web of Science, and PubMed latest on 23rd May 2022, explored 83 publications regarding eHealth policy frameworks, and extracted 11 publications scrutinizing eHealth policy frameworks in their title, abstract, or keywords. These publications were analyzed by using both expert opinion and Rstudio programming tools. They were explored based on their developing/developed countries' context, research approach, main contribution, constructs/dimensions of the framework, and related categories. In addition, by using cloudword and latent semantic space techniques, the most discussed concepts and targeted keywords were explored and a correlation test was conducted to depict the important concepts mentioned in the related literature and extract their relation with the targeted keywords in the interest of this study.

RESULTS: Most of these publications do not develop or synthesize new frameworks for eHealth policy implementation, but rather introduce eHealth implementation frameworks, explain policy dimensions, identify and extract relevant components of existing frameworks or point out legal or other relevant eHealth implementation issues.

CONCLUSION: After a thorough exploration of related literature, this study identified the main factors affecting an effective eHealth policy framework, found a gap in the context of developing countries, and proposed a four-step eHealth policy implementation guideline for successful implementation of eHealth in the context of developing. The limitation of this study is the lack of a proper amount of practically implemented eHealth policy framework cases in developing countries published in the literature for the review. Ultimately, this study is part of the BETTEReHEALTH (More information about the BETTEReHEALTH project at https://betterehealth.eu ) project funded by the European Union Horizon's 2020 under agreement number 101017450.

PMID:37005588 | DOI:10.1186/s12913-023-09325-7

Int Urogynecol J. 2023 Apr 1. doi: 10.1007/s00192-023-05511-7. Online ahead of print.

ABSTRACT

INTRODUCTION AND HYPOTHESIS: Toileting behaviors are related to lower urinary tract symptoms and bladder dysfunction and are an important factor affecting bladder health. The aim of this study was to translate the Toileting Behaviors-Women's Elimination Behaviors (TB-WEB) Scale into Turkish and to validate its internal consistency, test-retest reliability, and construct and criterion validity for use in Turkish pregnant women.

METHOD: The research was conducted with 226 pregnant women who presented to the antenatal outpatient clinics of a university hospital in Türkiye for antenatal follow-up. Data were collected using a sociodemographic questionnaire prepared by the researchers and the TB-WEB Scale. Descriptive data were analyzed using numbers, percentage and mean values, whereas psychometric analysis of the scale was performed using semantic equivalence, content validity, explanatory and confirmatory factor analysis, Cronbach's α, item-total correlation, and test-retest analysis.

RESULTS: The scale consisted of 20 items and five subscales. The content validity index of the items was found to be 93%. Cronbach's α coefficient was found to be 0.77 for the whole scale; 0.60 for the place preference for voiding subscale; 0.73 for the premature voiding subscale; 0.84 for the delayed voiding subscale; 0.83 for the straining voiding subscale; and 0.88 for the position preference for voiding subscale. The scale mediates 62% of the total variance. Confirmatory factor analysis found that item factor loadings varied between 0.31 and 0.99 and root mean square error of approximation (RMSEA) value was found 0.078.

CONCLUSION: The Turkish version of the TB-WEB Scale is a valid and reliable instrument in evaluating women's toileting behaviors during pregnancy.

PMID:37004519 | DOI:10.1007/s00192-023-05511-7

PeerJ. 2023 Mar 24;11:e15076. doi: 10.7717/peerj.15076. eCollection 2023.

ABSTRACT

BACKGROUND: Considering that adolescents spend considerable time on the Internet and social media and experience high levels of stress, it is difficult to find a study that investigates adolescent stress through a big data-based network analysis of social media. Hence, this study was designed to provide basic data to establish desirable stress coping strategies for adolescents based on a big data-based network analysis of social media for Korean adolescent stress. The purpose of this study was to (1) identify social media words that express stress in adolescents and (2) investigate the associations between those words and their types.

METHODS: To analyse adolescent stress, we used social media data collected from online news and blog websites and performed semantic network analysis to understand the relationships among keywords extracted in the collected data.

RESULTS: The top five words used by Korean adolescents were counselling, school, suicide, depression, and activity in online news, and diet, exercise, eat, health, and obesity in blogs. As the top keywords of the blog are mainly related to diet and obesity, it reflects adolescents' high degree of interest in their bodies; the body is also a primary source of adolescent stress. In addition, blogs contained more content about the causes and symptoms of stress than online news, which focused more on stress resolution and coping. This highlights the trend that social blogging is a new channel for sharing personal information.

CONCLUSIONS: The results of this study are valuable as they were derived through a social big data analysis of data obtained from online news and blogs, providing a wide range of implications related to adolescent stress. Hence this study can contribute basic data for the stress management of adolescents and their mental health management in the future.

PMID:36992939 | PMC:PMC10042152 | DOI:10.7717/peerj.15076

Sci Data. 2023 Mar 27;10(1):171. doi: 10.1038/s41597-023-01993-8.

ABSTRACT

The Human Reference Atlas (HRA) is defined as a comprehensive, three-dimensional (3D) atlas of all the cells in the healthy human body. It is compiled by an international team of experts who develop standard terminologies that they link to 3D reference objects, describing anatomical structures. The third HRA release (v1.2) covers spatial reference data and ontology annotations for 26 organs. Experts access the HRA annotations via spreadsheets and view reference object models in 3D editing tools. This paper introduces the Common Coordinate Framework (CCF) Ontology v2.0.1 that interlinks specimen, biological structure, and spatial data, together with the CCF API that makes the HRA programmatically accessible and interoperable with Linked Open Data (LOD). We detail how real-world user needs and experimental data guide CCF Ontology design and implementation, present CCF Ontology classes and properties together with exemplary usage, and report on validation methods. The CCF Ontology graph database and API are used in the HuBMAP portal, HRA Organ Gallery, and other applications that support data queries across multiple, heterogeneous sources.

PMID:36973309 | PMC:PMC10043028 | DOI:10.1038/s41597-023-01993-8

Prim Care Companion CNS Disord. 2023 Mar 14;25(2):22m03348. doi: 10.4088/PCC.22m03348.

ABSTRACT

Objective: To assess the humanistic and economic burden of attention-deficit/hyperactivity disorder (ADHD) among adult patients treated with immediate-release (IR) only or extended-release (ER) only stimulants and those unmedicated versus treated with ER + IR stimulants.

Methods: This study analyzed linked data from National Health and Wellness Survey and claims to assess the differences in patient characteristics and outcomes, including health-related quality of life (HRQoL), work productivity and activity impairment, and health care resource utilization (HRU) and associated costs by comparing ADHD patients treated with either IR or ER and those unmedicated for ADHD versus ER + IR.

Results: The burden of ADHD was compared among adults on stimulant medications with different duration of effect (DoE) (ER + IR: n = 34, ER: n = 184, IR: n = 149) and the unmedicated group (n = 114). Bivariate analysis showed the IR (P = .047) and unmedicated groups (P = .01) had significantly lower Medical Outcomes Study 36-item Short Form physical component summary scores versus ER + IR. The unmedicated group had higher HRU and associated costs versus other groups. Multivariable analysis revealed that the unmedicated group had twice as many outpatient visits (P = .001) and higher total annual direct costs than those on ER + IR (risk ratio = 2.20, P = .016). Patients with mental health comorbidities had significantly poorer HRQoL mental component summary scores and higher activity impairment versus those without mental health comorbidities (P = .001 and P < .001, respectively).

Conclusions: Patients with ADHD treated with longer DoE formulations had substantially better economic outcomes versus shorter DoE formulation or unmedicated groups, offering potential cost savings to the health care system and the patient. Furthermore, it is important to consider the effect of mental health comorbidities in the overall management of ADHD.

PMID:36946563 | DOI:10.4088/PCC.22m03348

Schizophr Bull. 2023 Mar 22;49(Supplement_2):S142-S152. doi: 10.1093/schbul/sbac056.

ABSTRACT

BACKGROUND AND HYPOTHESIS: Mapping a patient's speech as a network has proved to be a useful way of understanding formal thought disorder in psychosis. However, to date, graph theory tools have not explicitly modelled the semantic content of speech, which is altered in psychosis.

STUDY DESIGN: We developed an algorithm, "netts," to map the semantic content of speech as a network, then applied netts to construct semantic speech networks for a general population sample (N = 436), and a clinical sample comprising patients with first episode psychosis (FEP), people at clinical high risk of psychosis (CHR-P), and healthy controls (total N = 53).

STUDY RESULTS: Semantic speech networks from the general population were more connected than size-matched randomized networks, with fewer and larger connected components, reflecting the nonrandom nature of speech. Networks from FEP patients were smaller than from healthy participants, for a picture description task but not a story recall task. For the former task, FEP networks were also more fragmented than those from controls; showing more connected components, which tended to include fewer nodes on average. CHR-P networks showed fragmentation values in-between FEP patients and controls. A clustering analysis suggested that semantic speech networks captured novel signals not already described by existing NLP measures. Network features were also related to negative symptom scores and scores on the Thought and Language Index, although these relationships did not survive correcting for multiple comparisons.

CONCLUSIONS: Overall, these data suggest that semantic networks can enable deeper phenotyping of formal thought disorder in psychosis. Whilst here we focus on network fragmentation, the semantic speech networks created by Netts also contain other, rich information which could be extracted to shed further light on formal thought disorder. We are releasing Netts as an open Python package alongside this manuscript.

PMID:36946531 | DOI:10.1093/schbul/sbac056

Cogn Process. 2023 Mar 21. doi: 10.1007/s10339-023-01131-7. Online ahead of print.

ABSTRACT

During the epidemic, online advertising became more important, and several studies have suggested that internet users tend to avoid viewing online ads, such as banner ads. Previous studies have shown that product items that use animation lead to increased visual attention to all items on a webpage at the expense of attention to nonanimated items on the same webpage. However, few studies have compared the impact of the picture and text forms taken by static banners on the effectiveness of banner ads. At the same time, whether semantic factors (theme consistency) moderate the influence of structural factors (picture and text forms) on banner advertising remains unknown. The aim of this paper is to examine the influence of structural factors and semantic factors of ads on participants' visual attention to and memory of banner ads. The participants (twenty-seven males and forty females aged 18-26 years) were divided into two groups, one for consistent ad-web content themes and the other for inconsistent ad-web content themes. Then, the participants were asked to browse 16 complete pages (4 pages each of text-type web content and text-type banner ads, picture-type web content and picture-type banner ads, text-type web content and picture-type banner ads, and picture-type web content and text-type banner ads), and their eye movements were recorded to measure the participants' level of attention to the banner ads. A recognition task was used to measure the participants' memories of the banner ads. The results showed that the text-type banner ad had a lower blindness rate and exerted better attention and memory effects than the picture-type banner ad, and the text-type banner ad had a lower blindness rate and better attention and memory effects when positioned in the background of picture-type web content than when positioned in the background of text-type web content. A significant interaction effect among banner ad type, web content type and theme consistency showed that ad-web content theme consistency moderated the effect of web content type and banner ad type on ad effectiveness. Taken together, the results of these tasks demonstrate that theme consistency moderates the effect of web content type and banner ad type on ad effectiveness in a top-down manner. To reduce the negative effect of banner blindness, placing text-type banner ads in picture-type web content and setting a consistent theme between the banner ad and the web content is the more effective choice. The findings from this study can be used to assist advertising agencies in designing more effective and efficient banner ads from the perspective of basic psychology.

PMID:36943584 | DOI:10.1007/s10339-023-01131-7

Public Health. 2023 May;218:12-20. doi: 10.1016/j.puhe.2023.02.008. Epub 2023 Feb 15.

ABSTRACT

INTRODUCTION: The UK shielding policy intended to protect people at the highest risk of harm from COVID-19 infection. We aimed to describe intervention effects in Wales at 1 year.

METHODS: Retrospective comparison of linked demographic and clinical data for cohorts comprising people identified for shielding from 23 March to 21 May 2020; and the rest of the population. Health records were extracted with event dates between 23 March 2020 and 22 March 2021 for the comparator cohort and from the date of inclusion until 1 year later for the shielded cohort.

RESULTS: The shielded cohort included 117,415 people, with 3,086,385 in the comparator cohort. The largest clinical categories in the shielded cohort were severe respiratory condition (35.5%), immunosuppressive therapy (25.9%) and cancer (18.6%). People in the shielded cohort were more likely to be female, aged ≥50 years, living in relatively deprived areas, care home residents and frail. The proportion of people tested for COVID-19 was higher in the shielded cohort (odds ratio [OR] 1.616; 95% confidence interval [CI] 1.597-1.637), with lower positivity rate incident rate ratios 0.716 (95% CI 0.697-0.736). The known infection rate was higher in the shielded cohort (5.9% vs 5.7%). People in the shielded cohort were more likely to die (OR 3.683; 95% CI: 3.583-3.786), have a critical care admission (OR 3.339; 95% CI: 3.111-3.583), hospital emergency admission (OR 2.883; 95% CI: 2.837-2.930), emergency department attendance (OR 1.893; 95% CI: 1.867-1.919) and common mental disorder (OR 1.762; 95% CI: 1.735-1.789).

CONCLUSION: Deaths and healthcare utilisation were higher amongst shielded people than the general population, as would be expected in the sicker population. Differences in testing rates, deprivation and pre-existing health are potential confounders; however, lack of clear impact on infection rates raises questions about the success of shielding and indicates that further research is required to fully evaluate this national policy intervention.

PMID:36933354 | PMC:PMC9928733 | DOI:10.1016/j.puhe.2023.02.008

Lancet. 2022 Nov;400 Suppl 1:S29. doi: 10.1016/S0140-6736(22)02239-5. Epub 2022 Nov 24.

ABSTRACT

BACKGROUND: The Family Nurse Partnership (FNP) is an early home visiting service supporting young mothers. A randomised controlled trial of FNP in England found no effect on short-term primary outcomes or maltreatment in children up to age 7 years, but positive impacts on some educational outcomes. We report preliminary results of a national evaluation of FNP using linked administrative data.

METHODS: We constructed a cohort of all mothers in England aged 13-19 years who gave birth between April 1, 2010, and March 31, 2019, to their firstborn child or children, using linked administrative data from hospital admissions (Hospital Episode Statistics) and education and social care (National Pupil Database). We evaluated differences in a range of policy relevant child and maternal outcomes, comparing mothers who were enrolled in FNP with those who were not, using propensity score matching.

FINDINGS: Of 110 960 mothers in our linked cohort, 26 290 (24%) were enrolled in FNP. FNP mothers were younger, more deprived, and more likely to have adversity or social care histories than mothers not enrolled. Compared with mothers not enrolled in FNP, those in FNP did not have fewer unplanned hospital admissions for injury or maltreatment in children by age 2 years, lower rates of children looked after in out-of-home care by age 7 years, or improved maternal outcomes, but were more likely to achieve a good level of development at school entry. We present findings among subgroups of younger maternal age (13-15 years), increased deprivation according to quintile of Index of Multiple Deprivation, and adversity and social care history. We also present sensitivity analyses that aim to minimise confounding.

INTERPRETATION: Our study supports findings from previous trials of FNP showing little benefit for measured child maltreatment and maternal outcomes, but some evidence of benefit for school readiness. Interpretation of results needs careful consideration of the impact of residual confounding due to unmeasured or undisclosed factors (eg, family violence) linked to targeting of FNP to higher risk mothers, and surveillance bias.

FUNDING: National Institute for Health and Care Research.

PMID:36929972 | DOI:10.1016/S0140-6736(22)02239-5

Brain. 2023 Mar 16:awad013. doi: 10.1093/brain/awad013. Online ahead of print.

ABSTRACT

Stroke significantly impacts the quality of life. However, the long-term cognitive evolution in stroke is poorly predictable at the individual level. There is an urgent need to better predict long-term symptoms based on acute clinical neuroimaging data. Previous works have demonstrated a strong relationship between the location of white matter disconnections and clinical symptoms. However, rendering the entire space of possible disconnection-deficit associations optimally surveyable will allow for a systematic association between brain disconnections and cognitive-behavioural measures at the individual level. Here we present the most comprehensive framework, a composite morphospace of white matter disconnections (disconnectome) to predict neuropsychological scores 1 year after stroke. Linking the latent disconnectome morphospace to neuropsychological outcomes yields biological insights that are available as the first comprehensive atlas of disconnectome-deficit relations across 86 scores-a Neuropsychological White Matter Atlas. Our novel predictive framework, the Disconnectome Symptoms Discoverer, achieved better predictivity performances than six other models, including functional disconnection, lesion topology and volume modelling. Out-of-sample prediction derived from this atlas presented a mean absolute error below 20% and allowed personalize neuropsychological predictions. Prediction on an external cohort achieved an R2 = 0.201 for semantic fluency. In addition, training and testing were replicated on two external cohorts achieving an R2 = 0.18 for visuospatial performance. This framework is available as an interactive web application (http://disconnectomestudio.bcblab.com) to provide the foundations for a new and practical approach to modelling cognition in stroke. We hope our atlas and web application will help to reduce the burden of cognitive deficits on patients, their families and wider society while also helping to tailor future personalized treatment programmes and discover new targets for treatments. We expect our framework's range of assessments and predictive power to increase even further through future crowdsourcing.

PMID:36928757 | DOI:10.1093/brain/awad013