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In the AI-for-science era, scientific computing scenarios such as concurrent learning and high-throughput computing demand a new generation of infrastructure that supports scalable computing resources and automated workflow management on both cloud and high-performance supercomputers. Here we introduce Dflow, an open-source Python toolkit designed for scientists to construct workflows with simple programming interfaces. It enables complex process control and task scheduling across a distributed, heterogeneous infrastructure, leveraging containers and Kubernetes for flexibility. Dflow is highly observable and can scale to thousands of concurrent nodes per workflow, enhancing the efficiency of complex scientific computing tasks. The basic unit in Dflow, known as an Operation (OP), is reusable and independent of the underlying infrastructure or context. Dozens of workflow projects have been developed based on Dflow, spanning a wide range of projects. We anticipate that the reusability of Dflow and its components will encourage more scientists to publish their workflows and OP components. These components, in turn, can be adapted and reused in various contexts, fostering greater collaboration and innovation in the scientific community.

Objective: We aimed to explore the association between changes in social activities and the occurrence/persis-tence of depressive symptoms and investigate the difference in effect sizes among the types and combinations of social activities.Methods: The study adopted a valid 15-item Geriatric Depression Scale to assess depressive symptoms in 2480 community-dwelling adults aged 64/65 years. Changes in social-related, learning, and personal activities were classified into four categories: continued low frequency (CLF), increased frequency (IF), decreased frequency (DF), and continued regular frequency (CRF)1. Relative ratios (RRs) and 95% confidence intervals (95% CI) were calculated using a modified Poisson regression model.Results: Those without depressive symptoms at baseline and who engaged in social-related (RRIF = 0.56 (0.39, 0.81), RRCRF = 0.55 (0.41, 0.74)), learning (RRIF = 0.63 (0.44, 0.89), RRCRF = 0.62 (0.46, 0.85)), and personal activities (RRIF = 0.37 (0.24, 0.57), RRCRF = 0.41 (0.30, 0.56)) at IF or CRF were less likely to develop depressive symptoms. Those with depressive symptoms at baseline, engaging in personal activities at IF (RR=0.67 (0.51, 0.87)) and CRF (RR= 0.80 (0.65-1.00)) were less likely to have persistent depressive symptoms. Participation in all three activities consistently at a regular frequency was inversely associated with the occurrence/persistence of depressive symptoms.Conclusions: The effect of personal activities was more manifest in preventing depressive symptoms than the other two kinds, regardless of depressive symptoms at baseline. Regularly engaging in a combination of all three activities at baseline and follow-up was associated with the occurrence and persistence of depressive symptoms.

Background: The prevalence of allergy and other common chronic diseases is higher in developed than developing countries, and higher in urban than rural regions. Urbanization through its modification of environmental microbiomes may play a predominant role in the development of these conditions. However, no studies have been conducted to compare the microbiome in house dust among areas with different urbanization levels. Methods: House dust from Xinxiang rural area (XR, n = 74), Xinxiang urban area (XU, n = 33), and Zhengzhou urban area (ZU, n = 32) in central China, and from Australia (AU, n = 58 [with pets AUP, n = 15, without pets AUNP, n = 43]) were collected during a summer season in China and Australia. High-throughput sequencing of 16S rDNA was employed to profile house dust bacterial communities. Results: Settled dust collected in China was dominant with 2 bacterial phyla: Proteobacteria and Actinobacteria, while floor dust collected in Australia had a higher proportion of phylum Proteobacteria, Firmicutes, and Actinobacteria. XR dust samples presented higher bacterial richness and diversity compared with XU or ZU samples. Urbanization level (r2 = 0.741 P < 0.001) had a significant correlation with the distribution of house dust bacterial community. At the genus level, there was a positive correlation (r coefficient > 0.5) between urbanization level and bacterial genera Streptococcus, Bartonella, Staphylococcus, Pseudomonas, Acinetobacter, Bacteroides, Corynebacterium_1, and Enhydrobacter and a negative correlation (r coefficient < −0.5) with Rhodanobacter. Conclusion: There was a significant difference in house dust microbiota among different urbanization areas. The areas with a lower urbanization level presented higher dust-borne bacterial richness and diversity. Modern urbanization has a significant influence on the bacterial microbiome profiles of indoor dust.