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This study explicates a concept of transactive resilience for cross-sector disaster communication via integrating the transactive memory system and the network theory of social capital. The concept was validated through n=34 in-depth interviews with key disaster-responding organizations (public, private, and nonprofits sectors) of a U.S. city. Findings indicate that transactive resilience network is built on strong relational trust (interpersonal and informal relationships), perceived expertise (consensus/accuracy), and coordination and communication (two-way, flexibility, and optimal network characteristics). 

We present the development, architecture, and features of a new multi-device mHealth software platform to support near real-time remote monitoring of metabolic health and timely intervention in the treatment and survivorship of cancer patients. Our platform, mEnergy, leverages a human- centered design process, and integrates in a unified, web-based framework consumer-grade hardware—Fitbit wearable sensor devices, smartphones, and Withings smart scales. mEnergy can aid oncologists in identifying early indicators of muscle-wasting (sarcopenia) due to sleep disturbance, insufficient weight recov- ery, or reduced/limited activity. The platform aims for a smooth transition into clinical practice and increased adherence to evidence-based recommendations, in particular in underserved geographical areas. This toxicity-surveillance approach based on mHealth technologies can improve treatment outcomes, quality of life, and survivorship 

Energy-efficient image acquisition on the edge is crucial for enabling remote sensing applications where the sensor node has weak compute capabilities and must transmit data to a remote server/cloud for processing. To reduce the edge energy consumption, this paper proposes a sensor-algorithm co-designed system called SNAPPIX, which compresses raw pixels in the analog domain inside the sensor. We use coded exposure (CE) as the in-sensor compression strategy as it offers the flexibility to sample, i.e., selectively expose pixels, both spatially and temporally. SNAPPIX has three contributions. First, we propose a task-agnostic strategy to learn the sampling/exposure pattern based on the classic theory of efficient coding. Second, we co- design the downstream vision model with the exposure pattern to address the pixel-level non-uniformity unique to CE-compressed images. Finally, we propose lightweight augmentations to the image sensor hardware to support our in-sensor CE compres- sion. Evaluating on action recognition and video reconstruction, SNAPPIX outperforms state-of-the-art video-based methods at the same speed while reducing the energy by up to 15.4×. We have open-sourced the code at: https://github.com/horizon- research/SnapPix. 

INTRODUCTION: It is unclear whether aggregated plasma protein risk scores (PPRS) could be useful to predict the risks of mild cognitive impairment (MCI) and Alzheimer’s disease (AD). METHODS: The Cox proportional hazard model with the LASSO penalty was used to build the PPRS for MCI and AD in 1,515 Framingham Heart Study Generation2 with 1,128 proteins measured in plasma at exam 5 [cognitive normal (CN)=1,258, MCI=129, AD=128]. RESULTS: MCI PPRS had a hazard ratio (HR) of 6.97[5.34,9.12], with a discriminating power (C-index=82.52%). AD PPRS had an HR of 5.74[4.67,7.05] (C-index=88.15%). Both PPRSs were also significantly associated with cognitive changes, brain-atrophy, and plasma AD biomarkers. Proteins in the MCI and AD PPRSs were enriched in several pathways related to leukocyte, chemotaxis, immunity, inflammation, and cellular migration. DISCUSSION: This study suggests that PPRS serve well to predict the risk of developing MCI and AD as well as cognitive changes and AD related pathogenesis in the brain.