Social support and health behaviors of older adults during the COVID-19 pandemic in China: a moderated mediation model of loneliness and economic income

Background: The literature shows that social support is an important factor influencing health behaviors. This study aimed to explore the relationships and intrinsic pathways of social support, loneliness, economic income, and health behaviors among older adults during the Corona Virus Disease 2019 (COVID-19) pandemic, and to provide a theoretical basis for the implementation of health behaviors interventions for older adults. Methods: A cluster-random-sampling survey was adopted within two towns in Dongguan, China. Demographic characteristics, social support, loneliness, economic income and health behaviors were measured. The Social Support Appraisals scale (SS-A), the ULS-8 Loneliness Scale, and the Self-rated abilities for health practice scale (SRAHPS) were used to measure social support, loneliness, and health behaviors in older adults, respectively. A moderated mediation model was built to examine the relationships among social support, loneliness, economic income, and health behaviors using the SPSS PROCESS 4.0 macro. We conducted bootstrapping of regression estimates with 5000 samples and a 95% confidence interval. Results: 621 older adults completed the questionnaire. Most of the participants were female, accounting for 75.0%, and the average age was 81.11 years (SD = 8.11). The median (interquartile range) of the participants’ average monthly economic income was 800 (500–1000)RMB. The results of the mediation analysis showed that loneliness partly mediated the relationship between social support and health behaviors (B = 0.024, 95%CI: 0.007, 0.042), with the mediating effect accounting for 4.56% of the total effect. The moderation mediation analysis revealed a positive moderating role of economic income in the relationship between social support and loneliness (B = 0.114, 95%CI: 0.054, 0.174). Specifically, the relationship between social support and loneliness was found to be weaker for older adults with a high economic income compared to those with a lower economic income. Conclusion: The provision of enhanced social support and the alleviation of loneliness among older adults during an epidemic can facilitate the development of healthy behaviours, particularly among those who are economically disadvantaged

Novel image markers for non-small cell lung cancer classification and survival prediction

BACKGROUND: Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients. RESULTS: In this paper we have proposed an integrated framework for NSCLC computer-aided diagnosis and survival analysis using novel image markers. The entire biomedical imaging informatics framework consists of cell detection, segmentation, classification, discovery of image markers, and survival analysis. A robust seed detection-guided cell segmentation algorithm is proposed to accurately segment each individual cell in digital images. Based on cell segmentation results, a set of extensive cellular morphological features are extracted using efficient feature descriptors. Next, eight different classification techniques that can handle high-dimensional data have been evaluated and then compared for computer-aided diagnosis. The results show that the random forest and adaboost offer the best classification performance for NSCLC. Finally, a Cox proportional hazards model is fitted by component-wise likelihood based boosting. Significant image markers have been discovered using the bootstrap analysis and the survival prediction performance of the model is also evaluated. CONCLUSIONS: The proposed model have been applied to a lung cancer dataset that contains 122 cases with complete clinical information. The classification performance exhibits high correlations between the discovered image markers and the subtypes of NSCLC. The survival analysis demonstrates strong prediction power of the statistical model built from the discovered image markers

Novel Image Markers for Non-Small Cell Lung Cancer Classification and Survival Prediction

BACKGROUND: Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients. RESULTS: In this paper we have proposed an integrated framework for NSCLC computer-aided diagnosis and survival analysis using novel image markers. The entire biomedical imaging informatics framework consists of cell detection, segmentation, classification, discovery of image markers, and survival analysis. A robust seed detection-guided cell segmentation algorithm is proposed to accurately segment each individual cell in digital images. Based on cell segmentation results, a set of extensive cellular morphological features are extracted using efficient feature descriptors. Next, eight different classification techniques that can handle high-dimensional data have been evaluated and then compared for computer-aided diagnosis. The results show that the random forest and adaboost offer the best classification performance for NSCLC. Finally, a Cox proportional hazards model is fitted by component-wise likelihood based boosting. Significant image markers have been discovered using the bootstrap analysis and the survival prediction performance of the model is also evaluated. CONCLUSIONS: The proposed model have been applied to a lung cancer dataset that contains 122 cases with complete clinical information. The classification performance exhibits high correlations between the discovered image markers and the subtypes of NSCLC. The survival analysis demonstrates strong prediction power of the statistical model built from the discovered image markers

Effects of Outlets on Cracking Risk and Integral Stability of Super-High Arch Dams

In this paper, case study on outlet cracking is first conducted for the Goupitan and Xiaowan arch dams. A nonlinear FEM method is then implemented to study effects of the outlets on integral stability of the Xiluodu arch dam under two loading conditions, i.e., normal loading and overloading conditions. On the basis of the case study and the numerical modelling, the outlet cracking mechanism, risk, and corresponding reinforcement measures are discussed. Furthermore, the numerical simulation reveals that (1) under the normal loading conditions, the optimal distribution of the outlets will contribute to the tensile stress release in the local zone of the dam stream surface and decrease the outlet cracking risk during the operation period. (2) Under the overloading conditions, the cracks initiate around the outlets, then propagate along the horizontal direction, and finally coalesce with those in adjacent outlets, where the yield zone of the dam has a shape of butterfly. Throughout this study, a dam outlet cracking risk control and reinforcement principle is proposed to optimize the outlet design, select the appropriate concrete material, strengthen the temperature control during construction period, design reasonable impounding scheme, and repair the cracks according to their classification.</jats:p

Transcrib3D: 3D Referring Expression Resolution through Large Language Models

If robots are to work effectively alongside people, they must be able to interpret natural language references to objects in their 3D environment. Understanding 3D referring expressions is challenging -- it requires the ability to both parse the 3D structure of the scene and correctly ground free-form language in the presence of distraction and clutter. We introduce Transcrib3D, an approach that brings together 3D detection methods and the emergent reasoning capabilities of large language models (LLMs). Transcrib3D uses text as the unifying medium, which allows us to sidestep the need to learn shared representations connecting multi-modal inputs, which would require massive amounts of annotated 3D data. As a demonstration of its effectiveness, Transcrib3D achieves state-of-the-art results on 3D reference resolution benchmarks, with a great leap in performance from previous multi-modality baselines. To improve upon zero-shot performance and facilitate local deployment on edge computers and robots, we propose self-correction for fine-tuning that trains smaller models, resulting in performance close to that of large models. We show that our method enables a real robot to perform pick-and-place tasks given queries that contain challenging referring expressions. Project site is at https://ripl.github.io/Transcrib3D.Comment: CORLW 202

Case Report: Two cases of staged combined treatment for complex skull-exposing wounds: synergistic effects of mechanical tension and moist wound healing

Complex skull-exposing wounds complicated by repeated surgical failures, implant-associated infections, and a history of targeted drug therapy present substantial challenges for reconstruction. This study retrospectively analyzed two such cases to evaluate the clinical outcomes of a staged treatment strategy integrating a sustained skin-stretching device (SSD) with moist wound therapy. Case 1, with four previous failed surgeries, achieved complete closure within 40 days, with no recurrence during 6 months of follow-up. Case 2, after three debridement procedures, achieved closure in 45 days with stable scar formation and no dehiscence at 6 months. The treatment protocol incorporated SSD with silver ion dressings, recombinant human basic fibroblast growth factor (rh-bFGF), mussel adhesive protein-based dressings, and Moist Exposed Burn Ointment (MEBO), supplemented by surgical debridement as required. Controlled mechanical tension and an optimized moist microenvironment promoted progressive wound edge advancement, effective infection control, and tissue regeneration. The findings indicate that staged mechanical traction combined with moist wound dressings may represent a minimally invasive and effective approach for managing complex cranial wounds

Statler: State-Maintaining Language Models for Embodied Reasoning

There has been a significant research interest in employing large language models to empower intelligent robots with complex reasoning. Existing work focuses on harnessing their abilities to reason about the histories of their actions and observations. In this paper, we explore a new dimension in which large language models may benefit robotics planning. In particular, we propose Statler, a framework in which large language models are prompted to maintain an estimate of the world state, which are often unobservable, and track its transition as new actions are taken. Our framework then conditions each action on the estimate of the current world state. Despite being conceptually simple, our Statler framework significantly outperforms strong competing methods (e.g., Code-as-Policies) on several robot planning tasks. Additionally, it has the potential advantage of scaling up to more challenging long-horizon planning tasks.Comment: Accepted at ICRA 2024; Project website: https://statler-lm.github.io