The ubiquitous nature of smartphone ownership, its broad application and usage, along with its interactive delivery of timely feedback are appealing for health-related behavior change interventions
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mobile apps. However, users' perspectives about such apps are vital in better bridging the gap between their design intention and effective practical usage. In this vein, a modified technology acceptance model (mTAM) is proposed here, to explain the relationship between users' perspectives when using an AI-based smartphone app for personalized nutrition and healthy living, namely, PROTEIN, and the mTAM constructs toward behavior change in their nutrition and physical activity habits. In particular, online survey data from 85 users of the PROTEIN app within a period of 2 months were subjected to confirmatory factor analysis (CFA) and regression analysis (RA) to reveal the relationship of the mTAM constructs, i.e., perceived usefulness (PU), perceived ease of use (PEoU), perceived novelty (PN), perceived personalization (PP), usage attitude (UA), and usage intention (UI) with the users' behavior change (BC), as expressed
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the acceptance/rejection of six related hypotheses (H1–H6), respectively. The resulted CFA-related parameters, i.e., factor loading (FL) with the related
p
-value, average variance extracted (AVE), and composite reliability (CR), along with the RA results, have shown that all hypotheses H1–H6 can be accepted (
p
< 0.001). In particular, it was found that, in all cases, FL > 0.5, CR > 0.7, AVE > 0.5, indicating that the items/constructs within the mTAM framework have good convergent validity. Moreover, the adjusted coefficient of determination (
R
2
) was found within the range of 0.224–0.732, justifying the positive effect of PU, PEoU, PN, and PP on the UA, that in turn positively affects the UI, leading to the BC. Additionally, using a hierarchical RA, a significant change in the prediction of BC from UA when the UI is used as a mediating variable was identified. The explored mTAM framework provides the means for explaining the role of each construct in the functionality of the PROTEIN app as a supportive tool for the users to improve their healthy living by adopting behavior change in their dietary and physical activity habits. The findings herein offer insights and references for formulating new strategies and policies to improve the collaboration among app designers, developers, behavior scientists, nutritionists, physical activity/exercise physiology experts, and marketing experts for app design/development toward behavior change.
Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome is characterized by aplasia of the female reproductive tract; the syndrome can include renal anomalies, absence or dysgenesis, and skeletal anomalies. ...While functional models have elucidated several candidate genes, only WNT4 (MIM: 603490) variants have been definitively associated with a subtype of MRKH with hyperandrogenism (MIM: 158330). DNA from 148 clinically diagnosed MRKH probands across 144 unrelated families and available family members from North America, Europe, and South America were exome sequenced (ES) and by family-based genomics analyzed for rare likely deleterious variants. A replication cohort consisting of 442 Han Chinese individuals with MRKH was used to further reproduce GREB1L findings in diverse genetic backgrounds. Proband and OMIM phenotypes annotated using the Human Phenotype Ontology were analyzed to quantitatively delineate the phenotypic spectrum associated with GREB1L variant alleles found in our MRKH cohort and those previously published. This study reports 18 novel GREB1L variant alleles, 16 within a multiethnic MRKH cohort and two within a congenital scoliosis cohort. Cohort-wide analyses for a burden of rare variants within a single gene identified likely damaging variants in GREB1L (MIM: 617782), a known disease gene for renal hypoplasia and uterine abnormalities (MIM: 617805), in 16 of 590 MRKH probands. GREB1L variant alleles, including a CNV null allele, were found in 8 MRKH type 1 probands and 8 MRKH type II probands. This study used quantitative phenotypic analyses in a worldwide multiethnic cohort to identify and strengthen the association of GREB1L to isolated uterine agenesis (MRKH type I) and syndromic MRKH type II.
Rare variant enrichment analysis was performed on a multiethnic cohort of 148 MRKH individuals, identifying GREB1L as the only gene approaching exome-wide significance. Screening for rare, predicted deleterious variants across this cohort and a cohort of 442 Han Chinese MRKH probands identified 16 of 590 (2.7%) cases with GREB1L variants.
Free fatty acids provide an important energy source as nutrients, and act as signalling molecules in various cellular processes. Several G-protein-coupled receptors have been identified as ...free-fatty-acid receptors important in physiology as well as in several diseases. GPR120 (also known as O3FAR1) functions as a receptor for unsaturated long-chain free fatty acids and has a critical role in various physiological homeostasis mechanisms such as adipogenesis, regulation of appetite and food preference. Here we show that GPR120-deficient mice fed a high-fat diet develop obesity, glucose intolerance and fatty liver with decreased adipocyte differentiation and lipogenesis and enhanced hepatic lipogenesis. Insulin resistance in such mice is associated with reduced insulin signalling and enhanced inflammation in adipose tissue. In human, we show that GPR120 expression in adipose tissue is significantly higher in obese individuals than in lean controls. GPR120 exon sequencing in obese subjects reveals a deleterious non-synonymous mutation (p.R270H) that inhibits GPR120 signalling activity. Furthermore, the p.R270H variant increases the risk of obesity in European populations. Overall, this study demonstrates that the lipid sensor GPR120 has a key role in sensing dietary fat and, therefore, in the control of energy balance in both humans and rodents. PUBLICATION ABSTRACT
The comprehensive assessment of patients with hypertrophic cardiomyopathy is a complex process, with each step concurrently focusing on confirmation of the diagnosis, differentiation between ...sarcomeric and non-sarcomeric disease (phenocopy), and prognostication. Novel modalities such as genetic testing and advanced imaging have allowed for substantial advancements in the understanding of this condition and facilitate patient management. However, their availability is at present not universal, and interpretation requires a high level of expertise. In this setting, electrocardiography, a fast and widely available method, still retains a significant role in everyday clinical assessment of this population. In our review, we follow a stepwise approach for the interpretation of each electrocardiographic segment, discussing clinical implications of electrocardiographic patterns in sarcomeric disease, their value in the differential diagnosis from phenocopies, and impact on patient management. Outlining the substantial amount of information to be obtained from a simple tracing, we exhibit how electrocardiography is likely to remain an integral diagnostic tool in the future as well.
The development of smart cities has been the epicentre of many researchers' efforts during the past decade. One of the key requirements for smart city networks is mobility and this is the reason ...stable, reliable and high-quality wireless communications are needed in order to connect people and devices. Most research efforts so far, have used different kinds of wireless and sensor networks, making interoperability rather difficult to accomplish in smart cities. One common solution proposed in the recent literature is the use of software defined networks (SDNs), in order to enhance interoperability among the various heterogeneous wireless networks. In addition, SDNs can take advantage of the data retrieved from available sensors and use them as part of the intelligent decision making process contacted during the resource allocation procedure. In this paper, we propose an architecture combining heterogeneous wireless networks with social networks using SDNs. Specifically, we exploit the information retrieved from location based social networks regarding users' locations and we attempt to predict areas that will be crowded by using specially-designed machine learning techniques. By recognizing possible crowded areas, we can provide mobile operators with recommendations about areas requiring datacell activation or deactivation.
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