CloseTalker: secure, short-range ad hoc wireless communication

Secure communication is difficult to arrange between devices that have not previously shared a secret. Previous solutions to the problem are susceptible to man-in-the-middle attacks, require additional hardware for out-of-band communication, or require an extensive public-key infrastructure. Furthermore, as the number of wireless devices explodes with the advent of the Internet of Things, it will be impractical to manually configure each device to communicate with its neighbors. Our system, CloseTalker, allows simple, secure, ad hoc communication between devices in close physical proximity, while jamming the signal so it is unintelligible to any receivers more than a few centimeters away. CloseTalker does not require any specialized hardware or sensors in the devices, does not require complex algorithms or cryptography libraries, occurs only when intended by the user, and can transmit a short burst of data or an address and key that can be used to establish long-term or long-range communications at full bandwidth. In this paper we present a theoretical and practical evaluation of CloseTalker, which exploits Wi-Fi MIMO antennas and the fundamental physics of radio to establish secure communication between devices that have never previously met. We demonstrate that CloseTalker is able to facilitate secure in-band communication between devices in close physical proximity (about 5 cm), even though they have never met nor shared a key

Multimorbidity Resilience and COVID-19 Pandemic Self-reported Impact and Worry among Older Adults: A Study Based on the Canadian Longitudinal Study on Aging (CLSA)

Background The Coronavirus Disease-2019 (COVID-19) pandemic has created a spectrum of adversities that have affected older adults disproportionately. This paper examines older adults with multimorbidity using longitudinal data to ascertain why some of these vulnerable individuals coped with pandemic-induced risk and stressors better than others – termed multimorbidity resilience. We investigate pre-pandemic levels of functional, social and psychological forms of resilience among this sub-population of at-risk individuals on two outcomes – self-reported comprehensive pandemic impact and personal worry. Methods This study was conducted using Follow-up 1 data from the Canadian Longitudinal Study on Aging (CLSA), and the Baseline and Exit COVID-19 study, conducted between April and December in 2020. A final sub-group of 9211 older adults with two or more chronic health conditions were selected for analyses. Logistic regression and Generalized Linear Mixed Models were employed to test hypotheses between a multimorbidity resilience index and its three sub-indices measured using pre-pandemic Follow-up 1 data and the outcomes, including covariates. Results The multimorbidity resilience index was inversely associated with pandemic comprehensive impact at both COVID-19 Baseline wave (OR = 0.83, p < 0.001, 95% CI: [0.80,0.86]), and Exit wave (OR = 0.84, p < 0.001, 95% CI: [0.81,0.87]); and for personal worry at Exit (OR = 0.89, p < 0.001, 95% CI: [0.86,0.93]), in the final models with all covariates. The full index was also associated with comprehensive impact between the COVID waves (estimate = − 0.19, p < 0.001, 95% CI: [− 0.22, − 0.16]). Only the psychological resilience sub-index was inversely associated with comprehensive impact at both Baseline (OR = 0.89, p < 0.001, 95% CI: [0.87,0.91]) and Exit waves (OR = 0.89, p < 0.001, 95% CI: [0.87,0.91]), in the final model; and between these COVID waves (estimate = − 0.11, p < 0.001, 95% CI: [− 0.13, − 0.10]). The social resilience sub-index exhibited a weak positive association (OR = 1.04, p < 0.05, 95% CI: [1.01,1.07]) with personal worry, and the functional resilience measure was not associated with either outcome. Conclusions The findings show that psychological resilience is most pronounced in protecting against pandemic comprehensive impact and personal worry. In addition, several covariates were also associated with the outcomes. The findings are discussed in terms of developing or retrofitting innovative approaches to proactive coping among multimorbid older adults during both pre-pandemic and peri-pandemic periods

Eating disorders in weight-related therapy (EDIT): protocol for a systematic review with individual participant data meta-analysis of eating disorder risk in behavioural weight management

The Eating Disorders In weight-related Therapy (EDIT) Collaboration brings together data from randomised controlled trials of behavioural weight management interventions to identify individual participant risk factors and intervention strategies that contribute to eating disorder risk. We present a protocol for a systematic review and individual participant data (IPD) meta-analysis which aims to identify participants at risk of developing eating disorders, or related symptoms, during or after weight management interventions conducted in adolescents or adults with overweight or obesity. We systematically searched four databases up to March 2022 and clinical trials registries to May 2022 to identify randomised controlled trials of weight management interventions conducted in adolescents or adults with overweight or obesity that measured eating disorder risk at pre- and post-intervention or follow-up. Authors from eligible trials have been invited to share their deidentified IPD. Two IPD meta-analyses will be conducted. The first IPD meta-analysis aims to examine participant level factors associated with a change in eating disorder scores during and following a weight management intervention. To do this we will examine baseline variables that predict change in eating disorder risk within intervention arms. The second IPD meta-analysis aims to assess whether there are participant level factors that predict whether participation in an intervention is more or less likely than no intervention to lead to a change in eating disorder risk. To do this, we will examine if there are differences in predictors of eating disorder risk between intervention and no-treatment control arms. The primary outcome will be a standardised mean difference in global eating disorder score from baseline to immediately post-intervention and at 6- and 12- months follow-up. Identifying participant level risk factors predicting eating disorder risk will inform screening and monitoring protocols to allow early identification and intervention for those at risk

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

XA4C: eXplainable representation learning via Autoencoders revealing Critical genes.

Machine Learning models have been frequently used in transcriptome analyses. Particularly, Representation Learning (RL), e.g., autoencoders, are effective in learning critical representations in noisy data. However, learned representations, e.g., the "latent variables" in an autoencoder, are difficult to interpret, not to mention prioritizing essential genes for functional follow-up. In contrast, in traditional analyses, one may identify important genes such as Differentially Expressed (DiffEx), Differentially Co-Expressed (DiffCoEx), and Hub genes. Intuitively, the complex gene-gene interactions may be beyond the capture of marginal effects (DiffEx) or correlations (DiffCoEx and Hub), indicating the need of powerful RL models. However, the lack of interpretability and individual target genes is an obstacle for RL's broad use in practice. To facilitate interpretable analysis and gene-identification using RL, we propose "Critical genes", defined as genes that contribute highly to learned representations (e.g., latent variables in an autoencoder). As a proof-of-concept, supported by eXplainable Artificial Intelligence (XAI), we implemented eXplainable Autoencoder for Critical genes (XA4C) that quantifies each gene's contribution to latent variables, based on which Critical genes are prioritized. Applying XA4C to gene expression data in six cancers showed that Critical genes capture essential pathways underlying cancers. Remarkably, Critical genes has little overlap with Hub or DiffEx genes, however, has a higher enrichment in a comprehensive disease gene database (DisGeNET) and a cancer-specific database (COSMIC), evidencing its potential to disclose massive unknown biology. As an example, we discovered five Critical genes sitting in the center of Lysine degradation (hsa00310) pathway, displaying distinct interaction patterns in tumor and normal tissues. In conclusion, XA4C facilitates explainable analysis using RL and Critical genes discovered by explainable RL empowers the study of complex interactions

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