Infectious Disease Modeling of Social Contagion in Networks

Many behavioral phenomena have been found to spread interpersonally through social networks, in a manner similar to infectious diseases. An important difference between social contagion and traditional infectious diseases, however, is that behavioral phenomena can be acquired by non-social mechanisms as well as through social transmission. We introduce a novel theoretical framework for studying these phenomena (the SISa model) by adapting a classic disease model to include the possibility for ‘automatic’ (or ‘spontaneous’) non-social infection. We provide an example of the use of this framework by examining the spread of obesity in the Framingham Heart Study Network. The interaction assumptions of the model are validated using longitudinal network transmission data. We find that the current rate of becoming obese is 2 per year and increases by 0.5 percentage points for each obese social contact. The rate of recovering from obesity is 4 per year, and does not depend on the number of non-obese contacts. The model predicts a long-term obesity prevalence of approximately 42, and can be used to evaluate the effect of different interventions on steady-state obesity. Model predictions quantitatively reproduce the actual historical time course for the prevalence of obesity. We find that since the 1970s, the rate of recovery from obesity has remained relatively constant, while the rates of both spontaneous infection and transmission have steadily increased over time. This suggests that the obesity epidemic may be driven by increasing rates of becoming obese, both spontaneously and transmissively, rather than by decreasing rates of losing weight. A key feature of the SISa model is its ability to characterize the relative importance of social transmission by quantitatively comparing rates of spontaneous versus contagious infection. It provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviors, health states, ideas or diseases with reservoirs.National Institutes of Health (U.S.) (grant R01GM078986)National Science Foundation (U.S.)Bill & Melinda Gates FoundationTempleton FoundationNational Institute on Aging (grant P01 AG031093)Framingham Heart Study (contract number N01-HC-25195

Microsatellite instability, Epstein–Barr virus, mutation of type II transforming growth factor β receptor and BAX in gastric carcinomas in Hong Kong Chinese

Microsatellite instability (MI), the phenotypic manifestation of mismatch repair failure, is found in a proportion of gastric carcinomas. Little is known of the links between MI and Epstein–Barr virus (EBV) status and clinicopathological elements. Examination of genes mutated through the MI mechanism could also be expected to reveal important information on the carcinogenic pathway. Seventy-nine gastric carcinomas (61 EBV negative, 18 EBV positive) from local Hong Kong Chinese population, an intermediate-incidence area, were examined. Eight microsatellite loci, inclusive of the A10 tract of type II transforming growth factor β receptor (TβR-II), were used to evaluate the MI status. MI in the BAX and insulin-like growth factor II receptor (IGF-IIR) genes were also examined. High-level MI (>40% unstable loci) was detected in ten cases (12.7%) and low-level MI (1–40% unstable loci) in three (3.8%). High-level MI was detected in two EBV-associated cases (11%) and the incidence was similar for the EBV-negative cases (13%). The high-level MIs were significantly associated with intestinal-type tumours (P = 0.03) and a more prominent lymphoid infiltrate (P = 0.04). Similar associations were noted in the EBV-positive carcinomas. The high-level MIs were more commonly located in the antrum, whereas the EBV-associated carcinomas were mostly located in body. Thirteen cardia cases were negative for both high-level MI and EBV. All patients aged below 55 were MI negative (P = 0.049). Of the high-level MIs, 80% had mutation in TβR-II, 40% in BAX and 0% in IGF-IIR. Of low-level MIs, 33% also had TβR-II mutation. These mutations were absent in the MI-negative cases. Of three lymphoepithelioma-like carcinomas, two cases were EBV positive and MI negative, one case was EBV negative but with high-level MI. In conclusion, high-level MIs were present regardless of the EBV status, and were found in a particular clinicopathological subset of gastric carcinoma patient. Inactivation of important growth regulatory genes observed in these carcinomas confirms the importance of MI in carcinogenesis. © 1999 Cancer Research Campaig

The Human Phenotype Ontology in 2024: phenotypes around the world

\ua9 The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

Extraction, segmentation and recognition of vehicle’s license plate numbers

In this paper, an automatic vehicle license plate recognition method for Western Australia license plates is proposed. The method consists of three stages, namely, (1) plate extraction; (2) character segmentation; and (3) character recognition. The primary techniques employed in each stage are edge detection, connected component analysis and template matching. An image set of 100 vehicles is generated and used to evaluate the algorithm. The experimental test shows the algorithm’s success rate of 97%, 97% and 98% in Stages 1, 2 and 3, respectively. The respective average time taken in each stage was 234 ms, 37 ms and 29 ms