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Traditional cancer rate estimations are often limited in spatial resolutions and lack considerations of environmental factors. Satellite imagery has become a vital data source for monitoring diverse urban environments, supporting applications across environmental, socio-demographic, and public health domains. However, while deep learning (DL) tools, particularly convolutional neural networks, have demonstrated strong performance in extracting features from high-resolution imagery, their reliance on local spatial cues often limits their ability to capture complex, non-local, and higher-order structural information. To overcome this limitation, we propose a novel LLM-based multi-agent coordination system for satellite image analysis, which integrates visual and contextual reasoning through a simplicial contrastive learning framework (Agent- SNN). Our Agent-SNN contains two augmented superpixel-based graphs and maximizes mutual information between their latent simplicial complex representations, thereby enabling the system to learn both local and global topological features. The LLM-based agents generate structured prompts that guide the alignment of these representations across modalities. Experiments with satellite imagery of Los Angeles and San Diego demonstrate that Agent-SNN achieves signi cant improvements over state-of-the-art baselines in regional cancer prevalence estimation tasks. 

The ocean plays a major role in controlling atmospheric carbon at decadal to millennial timescales, with benthic carbon representing the only geologic‐scale storage of oceanic carbon. Despite its importance, detailed benthic ocean observations are limited and representation of the benthic carbon cycle in ocean and Earth system models (ESMs) is mostly empirical with little prognostic capacity, which hinders our ability to properly understand the long‐term evolution of the carbon cycle and climate change‐related feedbacks. The Benthic Ecosystem and Carbon Synthesis (BECS) working group, with the support of the US Ocean Carbon & Biogeochemistry Program (OCB), identified key challenges limiting our understanding of benthic systems, opportunities to act on these challenges, and pathways to increase the representation of these systems in global modeling and observational efforts. We propose a set of priorities to advance mechanistic understanding and better quantify the importance of the benthos: (a) implementing a model intercomparison exercise with existing benthic models to support future model development, (b) data synthesis to inform both model parameterizations and future observations, (c) increased deployment of platforms and technologies in support of in situ benthic monitoring (e.g., from benchtop to field mesocosm), and (d) global coordination of a benthic observing program (“GEOSed”) to fill large regional data gaps and evaluate the mechanistic understanding of benthic processes acquired throughout the previous steps. Addressing these priorities will help inform solutions to both global and regional resource management and climate adaptation strategies. 

Abstract Background Hip-worn accelerometer cut-points have poor validity for assessing children’s sedentary time, which may partly explain the equivocal health associations shown in prior research. Improved processing/classification methods for these monitors would enrich the evidence base and inform the development of more effective public health guidelines. The present study aimed to develop and evaluate a novel computational method (CHAP-child) for classifying sedentary time from hip-worn accelerometer data. Methods Participants were 278, 8–11-year-olds recruited from nine primary schools in Melbourne, Australia with differing socioeconomic status. Participants concurrently wore a thigh-worn activPAL (ground truth) and hip-worn ActiGraph (test measure) during up to 4 seasonal assessment periods, each lasting up to 8 days. activPAL data were used to train and evaluate the CHAP-child deep learning model to classify each 10-s epoch of raw ActiGraph acceleration data as sitting or non-sitting, creating comparable information from the two monitors. CHAP-child was evaluated alongside the current practice 100 counts per minute (cpm) method for hip-worn ActiGraph monitors. Performance was tested for each 10-s epoch and for participant-season level sedentary time and bout variables (e.g., mean bout duration). Results Across participant-seasons, CHAP-child correctly classified each epoch as sitting or non-sitting relative to activPAL, with mean balanced accuracy of 87.6% (SD = 5.3%). Sit-to-stand transitions were correctly classified with mean sensitivity of 76.3% (SD = 8.3). For most participant-season level variables, CHAP-child estimates were within ± 11% (mean absolute percent error [MAPE]) of activPAL, and correlations between CHAP-child and activPAL were generally very large (> 0.80). For the current practice 100 cpm method, most MAPEs were greater than ± 30% and most correlations were small or moderate (≤ 0.60) relative to activPAL. Conclusions There was strong support for the concurrent validity of the CHAP-child classification method, which allows researchers to derive activPAL-equivalent measures of sedentary time, sit-to-stand transitions, and sedentary bout patterns from hip-worn triaxial ActiGraph data. Applying CHAP-child to existing datasets may provide greater insights into the potential impacts and influences of sedentary time in children. 

Background : Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults. Methods : Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth. Data from 981 adults aged 35–99 years from cohorts in two continents were used to train the model, which we call CHAP-Adult (Convolutional Neural Network Hip Accelerometer Posture-Adult). Validation was conducted among 419 randomly selected adults not included in model training. Results : Mean errors (activPAL − CHAP-Adult) and 95% limits of agreement were: sedentary time −10.5 (−63.0, 42.0) min/day, breaks in sedentary time 1.9 (−9.2, 12.9) breaks/day, mean bout duration −0.6 (−4.0, 2.7) min, usual bout duration −1.4 (−8.3, 5.4) min, alpha .00 (−.04, .04), and time in ≥30-min bouts −15.1 (−84.3, 54.1) min/day. Respective mean (and absolute) percent errors were: −2.0% (4.0%), −4.7% (12.2%), 4.1% (11.6%), −4.4% (9.6%), 0.0% (1.4%), and 5.4% (9.6%). Pearson’s correlations were: .96, .92, .86, .92, .78, and .96. Error was generally consistent across age, gender, and body mass index groups with the largest deviations observed for those with body mass index ≥30 kg/m 2 . Conclusions : Overall, these strong validation results indicate CHAP-Adult represents a significant advancement in the ambulatory measurement of sitting and sitting patterns using hip-worn accelerometers. Pending external validation, it could be widely applied to data from around the world to extend understanding of the epidemiology and health consequences of sitting. 

Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults. Methods: Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth. Data from 981 adults aged 35–99 years from cohorts in two continents were used to train the model, which we call CHAP-Adult (Convolutional Neural Network Hip Accelerometer Posture-Adult). Validation was conducted among 419 randomly selected adults not included in model training. Results: Mean errors (activPAL − CHAP-Adult) and 95% limits of agreement were: sedentary time −10.5 (−63.0, 42.0) min/day, breaks in sedentary time 1.9 (−9.2, 12.9) breaks/day, mean bout duration −0.6 (−4.0, 2.7) min, usual bout duration −1.4 (−8.3, 5.4) min, alpha .00 (−.04, .04), and time in ≥30-min bouts −15.1 (−84.3, 54.1) min/day. Respective mean (and absolute) percent errors were: −2.0% (4.0%), −4.7% (12.2%), 4.1% (11.6%), −4.4% (9.6%), 0.0% (1.4%), and 5.4% (9.6%). Pearson’s correlations were: .96, .92, .86, .92, .78, and .96. Error was generally consistent across age, gender, and body mass index groups with the largest deviations observed for those with body mass index ≥30 kg/m2. Conclusions: Overall, these strong validation results indicate CHAP-Adult represents a significant advancement in the ambulatory measurement of sitting and sitting patterns using hip-worn accelerometers. Pending external validation, it could be widely applied to data from around the world to extend understanding of the epidemiology and health consequences of sitting.