Cumulative Dis/Advantage and Health Pattern in Late Life: A Comparison between Genders and Welfare State Regimes

This study provides a cross-national perspective to apply Cumulative Dis/Advantage (CDA) in explaining health inequality between developing and developed countries in the context of Welfare State Theory. Cross-sectional data from the international Health Retirement Study (United States, China, Mexico, and England) in 2013–2014 were used (n = 97,978). Four health indicators were included: self-reported health, depressive symptoms, functional ability, and memory. Regression models were fitted to examine the moderation roles of country and gender. Results indicated older Chinese and Mexican had poorer health status than their British and American counterparts consistently except for Mexicans’ memory. Cumulative health gaps between developing and developed countries existed only for functional ability. There is no evidence of a widening gap in health status between genders in late life. CDA explains the increasing gaps of functional ability across age groups between countries. General health and mental health, may however, depend more on individuals’ intrinsic capacity and human agency

Efficient Scene Text Detection with Textual Attention Tower

Scene text detection has received attention for years and achieved an impressive performance across various benchmarks. In this work, we propose an efficient and accurate approach to detect multioriented text in scene images. The proposed feature fusion mechanism allows us to use a shallower network to reduce the computational complexity. A self-attention mechanism is adopted to suppress false positive detections. Experiments on public benchmarks including ICDAR 2013, ICDAR 2015 and MSRA-TD500 show that our proposed approach can achieve better or comparable performances with fewer parameters and less computational cost.Comment: Accepted by ICASSP 202

Structure-Feature based Graph Self-adaptive Pooling

Various methods to deal with graph data have been proposed in recent years. However, most of these methods focus on graph feature aggregation rather than graph pooling. Besides, the existing top-k selection graph pooling methods have a few problems. First, to construct the pooled graph topology, current top-k selection methods evaluate the importance of the node from a single perspective only, which is simplistic and unobjective. Second, the feature information of unselected nodes is directly lost during the pooling process, which inevitably leads to a massive loss of graph feature information. To solve these problems mentioned above, we propose a novel graph self-adaptive pooling method with the following objectives: (1) to construct a reasonable pooled graph topology, structure and feature information of the graph are considered simultaneously, which provide additional veracity and objectivity in node selection; and (2) to make the pooled nodes contain sufficiently effective graph information, node feature information is aggregated before discarding the unimportant nodes; thus, the selected nodes contain information from neighbor nodes, which can enhance the use of features of the unselected nodes. Experimental results on four different datasets demonstrate that our method is effective in graph classification and outperforms state-of-the-art graph pooling methods.Comment: 7 pages, 4 figures, The Web Conference 202

A systematic collection of medical image datasets for deep learning

The astounding success made by artificial intelligence in healthcare and other fields proves that it can achieve human-like performance. However, success always comes with challenges. Deep learning algorithms are data dependent and require large datasets for training. Many junior researchers face a lack of data for a variety of reasons. Medical image acquisition, annotation, and analysis are costly, and their usage is constrained by ethical restrictions. They also require several other resources, such as professional equipment and expertise. That makes it difficult for novice and non-medical researchers to have access to medical data. Thus, as comprehensively as possible, this article provides a collection of medical image datasets with their associated challenges for deep learning research. We have collected the information of approximately 300 datasets and challenges mainly reported between 2007 and 2020 and categorized them into four categories: head and neck, chest and abdomen, pathology and blood, and others. The purpose of our work is to provide a list, as up-to-date and complete as possible, that can be used as a reference to easily find the datasets for medical image analysis and the information related to these datasets

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5

Ultrastructural insights into cellular organization, energy storage and ribosomal dynamics of an ammonia-oxidizing archaeon from oligotrophic oceans

IntroductionNitrososphaeria, formerly known as Thaumarchaeota, constitute a diverse and widespread group of ammonia-oxidizing archaea (AOA) inhabiting ubiquitously in marine and terrestrial environments, playing a pivotal role in global nitrogen cycling. Despite their importance in Earth’s ecosystems, the cellular organization of AOA remains largely unexplored, leading to a significant unanswered question of how the machinery of these organisms underpins metabolic functions.MethodsIn this study, we combined spherical-chromatic-aberration-corrected cryo-electron tomography (cryo-ET), scanning transmission electron microscopy (STEM), and energy dispersive X-ray spectroscopy (EDS) to unveil the cellular organization and elemental composition of Nitrosopumilus maritimus SCM1, a representative member of marine Nitrososphaeria.Results and DiscussionOur tomograms show the native ultrastructural morphology of SCM1 and one to several dense storage granules in the cytoplasm. STEM-EDS analysis identifies two types of storage granules: one type is possibly composed of polyphosphate and the other polyhydroxyalkanoate. With precise measurements using cryo-ET, we observed low quantity and density of ribosomes in SCM1 cells, which are in alignment with the documented slow growth of AOA in laboratory cultures. Collectively, these findings provide visual evidence supporting the resilience of AOA in the vast oligotrophic marine environment

Social resources, pension policy, and older adults’ mental, physical, and cognitive health: A cross-national comparison between China, England, Mexico, and the United States

Population aging is accelerating across the globe. A cross-national comparison perspective is imperative and important because such comparison provides an opportunity to contrast experiences of different countries and learn from each other. Promoting healthy aging is one of the ultimate goals of social policies related to older adults. Guided by the integrative theoretical framework based on the social ecological model and life course perspective, this dissertation investigates the relationship of social resources with older adults’ physical, mental, and cognitive health in China, the United States, England, and Mexico using the Harmonized Health and Retirement Study (HRS) dataset and its international sister studies. Four countries were chosen primarily based on their geographic location, different level of economic development, and availability in the Harmonized HRS dataset. The dissertation comprises three projects. The first project explored the relationship between retirement/pension and depressive symptoms of older adults across the life course. Regression models were estimated using Structural Equation Modelling. Results indicated that retirement was associated with higher levels of depressive symptoms for the US and with lower levels of depressive symptoms for Mexico and England. Having a public pension was associated with lower levels of depressive symptoms for Mexico and with higher levels of depressive symptoms for the US and China. Having a private pension was associated with lower levels of depressive symptoms for the US, China, and England. The study showed that continuity theory demonstrates cross-national variation in explaining the association between retirement and depressive symptoms. The second project tested the cross-cultural applicability of the shared resource hypothesis in explaining mental health concordance among older couples. Dyadic data were analyzed to examine the actor and partner effects of demographic, health, and household variables on depressive symptoms using both multilevel model and Structural Equation Model. Results indicated both husbands’ and wives’ depressive symptoms were associated with their own and the spouses’ social and health status. Most couple-level resources were nonsignificant predictors for Chinese and Mexican couples’ concordance, but having more social and financial resources was associated with higher concordance among British and American couples. It is concluded that the shared resource hypothesis was more applicable to depressive symptom concordance within couples in the US and England, but not in China and Mexico. The third project examined health inequalities between genders and countries in the context of Cumulative Dis/Advantage (CDA) and Welfare State theories. Regression models were fitted to examine the moderation roles of country and gender. Health patterns across age groups were cross-examined by linear regression models and negative binomial models. Results indicated older Chinese and Mexican respondents had poorer health status than their British and American counterparts consistently except for Mexicans’ memory. Cumulative health gaps between developing and developed countries existed only for functional ability. However, there is no evidence of gender gaps in health status across age groups. CDA explains the increasing gaps of functional ability across age groups between countries. General health and mental health, however, may depend more on individuals’ intrinsic capacity and human agency. Findings from these interconnected projects corroborate the person-in-environment perspective and suggest older adults’ health is influenced by multilevel factors including micro demographic characteristics, meso household resources, and macro culture/policy contexts across countries. The cross-national comparisons provide a unique perspective on variables associated with older adults’ health in different societal contexts. Suggestions were recommended for clinical practice to work with diverse aging population and for decision makers to improve policy design, with the ultimate goal to promote healthy aging and reduce health disparity in later life

Testing the Missingness Mechanism in Longitudinal Health Survey among Older Adults: A Case Study on Health and Retirement Study

Background: Studies using data from longitudinal health survey of older adults usually assumed the data were missing completely at random (MCAR) or missing at random (MAR). Thus subsequent analyses used multiple imputation or likelihood-based methods to handle missing data. However, little existing research actually examines whether the data met the MCAR/MAR assumptions before performing data analyses. Methods: This study first summarized seven commonly used statistical methods to test the missing mechanism and discussed their application conditions. Then using two-wave longitudinal data from the Health and Retirement Study (HRS; wave 2014-2015 and wave 2016-2017; n=18,747), this study applied different approaches to test the missingness mechanism of several demographic and health variables. Results: Results indicated the data did not meet the MCAR assumption even though they had a very low nonresponse rate. Health measures met the MAR assumptions. Demographic variables provided good auxiliary information for health variables. Ridout’s logistic regression model demonstrated applicability to a wide range of scenarios. Conclusion: Our findings supported the MAR assumptions for the demographic and health variables in HRS, and therefore provided statistical justification to HRS researchers about using imputation or likelihood-based methods to deal with missing data. However, researchers are encouraged to test the missingness mechanism of the specific variables/data when using a new dataset, and choose the appropriate methods depending on the research goal and nature of the data. Development of related statistical packages is urgently needed to facilitate the application of methods testing missingness mechanism to social and behavioral researc

Social resources, pension policy, and older adults’ mental, physical, and cognitive health: A cross-national comparison between China, England, Mexico, and the United States

Population aging is accelerating across the globe. A cross-national comparison perspective is imperative and important because such comparison provides an opportunity to contrast experiences of different countries and learn from each other. Promoting healthy aging is one of the ultimate goals of social policies related to older adults. Guided by the integrative theoretical framework based on the social ecological model and life course perspective, this dissertation investigates the relationship of social resources with older adults’ physical, mental, and cognitive health in China, the United States, England, and Mexico using the Harmonized Health and Retirement Study (HRS) dataset and its international sister studies. Four countries were chosen primarily based on their geographic location, different level of economic development, and availability in the Harmonized HRS dataset. The dissertation comprises three projects.The first project explored the relationship between retirement/pension and depressive symptoms of older adults across the life course. Regression models were estimated using Structural Equation Modelling. Results indicated that retirement was associated with higher levels of depressive symptoms for the US and with lower levels of depressive symptoms for Mexico and England. Having a public pension was associated with lower levels of depressive symptoms for Mexico and with higher levels of depressive symptoms for the US and China. Having a private pension was associated with lower levels of depressive symptoms for the US, China, and England. The study showed that continuity theory demonstrates cross-national variation in explaining the association between retirement and depressive symptoms. The second project tested the cross-cultural applicability of the shared resource hypothesis in explaining mental health concordance among older couples. Dyadic data were analyzed to examine the actor and partner effects of demographic, health, and household variables on depressive symptoms using both multilevel model and Structural Equation Model. Results indicated both husbands’ and wives’ depressive symptoms were associated with their own and the spouses’ social and health status. Most couple-level resources were nonsignificant predictors for Chinese and Mexican couples’ concordance, but having more social and financial resources was associated with higher concordance among British and American couples. It is concluded that the shared resource hypothesis was more applicable to depressive symptom concordance within couples in the US and England, but not in China and Mexico. The third project examined health inequalities between genders and countries in the context of Cumulative Dis/Advantage (CDA) and Welfare State theories. Regression models were fitted to examine the moderation roles of country and gender. Health patterns across age groups were cross-examined by linear regression models and negative binomial models. Results indicated older Chinese and Mexican respondents had poorer health status than their British and American counterparts consistently except for Mexicans’ memory. Cumulative health gaps between developing and developed countries existed only for functional ability. However, there is no evidence of gender gaps in health status across age groups. CDA explains the increasing gaps of functional ability across age groups between countries. General health and mental health, however, may depend more on individuals’ intrinsic capacity and human agency. Findings from these interconnected projects corroborate the person-in-environment perspective and suggest older adults’ health is influenced by multilevel factors including micro demographic characteristics, meso household resources, and macro culture/policy contexts across countries. The cross-national comparisons provide a unique perspective on variables associated with older adults’ health in different societal contexts. Suggestions were recommended for clinical practice to work with diverse aging population and for decision makers to improve policy design, with the ultimate goal to promote healthy aging and reduce health disparity in later life.</p