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In the detection of small targets such as insulator defects and flashovers, the existing YOLOv11 has problems such as insufficient feature extraction and difficulty in balancing model lightweight and detection accuracy. We propose a lightweight architecture called FocusNet based on YOLOv11n. To improve the feature expression ability of small targets, Aggregation Diffusion Neck is designed to achieve deep integration and optimization of features at different levels through multiple rounds of multi-scale feature fusion and scale adaptation, and Focus module is introduced to focus on and strengthen the key features of small targets. On this basis, to achieve efficient deployment, the Group-Level First-Order Taylor Expansion Importance Assessment Method is proposed to eliminate channels that have little impact on detection accuracy to streamline the model structure. Then, Channel Distribution Distillation compensates for the slight accuracy loss caused by pruning, and finally achieves the dual optimization of high accuracy and high efficiency. Furthermore, we analyze the interpretability of FocusNet via heatmaps generated by KPCA-CAM. Experiments show that FocusNet achieves 98.50% precision and 99.20% mAP@0.5 on a proprietary insulator defect detection database created for this project using only 3.80 GFLOPs. This research provides reliable technical support for insulator monitoring in power systems.

Apricot trees, serving as critical agricultural resources, hold a significant role within the agricultural domain. Conventional methods for detecting pests and diseases in these trees are notably labor-intensive. Many conditions affecting apricot trees manifest distinct visual symptoms that are ideally suited for precise identification and classification via deep learning techniques. Despite this, the academic realm currently lacks extensive, realistic datasets and deep learning strategies specifically crafted for apricot trees. This study introduces ATZD01, a publicly accessible dataset encompassing 11 categories of apricot tree pests and diseases, meticulously compiled under genuine field conditions. Furthermore, we introduce an innovative detection algorithm founded on convolutional neural networks, specifically devised for the management of apricot tree pests and diseases. To enhance the accuracy of detection, we have developed a novel object detection framework, APNet, alongside a dedicated module, the Adaptive Thresholding Algorithm (ATA), tailored for the detection of apricot tree afflictions. Experimental evaluations reveal that our proposed algorithm attains an accuracy rate of 87.1% on ATZD01, surpassing the performance of all other leading algorithms tested, thereby affirming the effectiveness of our dataset and model. The code and dataset will be made available at https://github.com/meanlang/ATZD01 .

Previous studies have reported associations between subjective cognitive decline (SCD) and both cardiometabolic multimorbidity (CMM, the co-occurrence of ≥2 cardiometabolic diseases (CMDs), including coronary heart disease, stroke, and diabetes) and lifestyle factors (LFs). While urban-rural disparities in health care access and risk factor distribution are well known, variations in these associations and the interaction between LFs and CMM among individuals with SCD in non-high-income countries remain unclear. This study aimed to investigate the association of CMM and LFs with SCD in older adults living in rural or urban areas in China. This population-based study included 41 859 older adults (median age 72.0 years; 52.48% female; 38.95% rural) from 31 provincial regions in China. Subjective cognitive decline was assessed using the Eight-Item Informant Interview to Differentiate Aging and Dementia. High-risk LFs included tobacco smoking, alcohol drinking, unhealthy diet, low physical activity, and unhealthy body shape. Cardiometabolic diseases were assessed by self-reported physician diagnoses. Lifestyle factors were collected via interviewer-administered questionnaires. Logistic regression, relative excess risk due to interaction and attributable proportion were used to assess associations and additive interactions. Cardiometabolic multimorbidity (odds ratio (OR) = 2.36; 95% confidence intervals (CI) = 2.10, 2.66) and the number of CMDs (OR = 1.49; 95% CI = 1.43, 1.56) were significantly associated with an increased likelihood of SCD. Gradients in the associations between the number of high-risk LFs and SCD were observed (P < 0.05), except for five high-risk LFs. These associations were stronger in rural than in urban residents (P for interaction <0.05). Significant additive interaction was found between high-risk LFs and CMM (relative excess risk due to interaction = 1.63, 95% CI = 0.67, 2.59; attributable proportion = 0.54, 95% CI = 0.22, 0.86) for SCD. The coexistence of CMM and high-risk LFs exhibited an additive association with SCD. These findings highlight the need for integrated management of modifiable CMDs and lifestyle risk factors, and may inform prioritisation of rural populations.

With the sustained growth of the economy and significant changes in social demographics, the issue of elderly-related diseases has increasingly drawn attention, particularly. Alzheimer’s disease (AD), as a representative disease of neurodegenerative diseases, has become a major challenge, affecting the health and quality of life of the elderly population severely. In recent years, the incidence, prevalence and mortality rates of AD have increased in China, imposing substantial economic burdens on families, society and the entire healthcare system. To proactively address this challenge and respond to the national ‘Healthy China Action’ initiative, leading experts from authoritative institutions jointly authored the China Alzheimer Report 2025. Building on previous editions, this report updates epidemiological data on AD in China, thoroughly analyses the latest economic burdens of the disease and comprehensively evaluates the current status of AD diagnosis and treatment services, as well as the allocation of public health resources in our country. Its release reflects China’s progress in AD research and prevention, underscores societal concern for elderly health and aims to provide scientific guidance and data support for AD prevention, diagnosis and treatment. It also facilitates academic exchanges and cooperation, enhancing public awareness and promoting active participation in elderly healthcare, towards achieving ‘healthy ageing’ in China.

This study examined the moderating effect of overall social support and the different types of social support on cognitive functioning in depressed older adults. We also investigated whether the moderating effect varied according to age. A total of 2,500 older adults (≥60 years old) from Shanghai, China were enrolled using a multistage cluster sampling method. Weighted linear regression and multiple linear regression was utilized to analyze the moderating effect of social support on the relationship between depressive symptoms and cognitive function and to explore its differences in those aged 60-69, 70-79, and 80 years and above. After adjusting for covariates, the results indicated that overall social support (β = 0.091,  = 0.043) and support utilization (β = 0.213,  < 0.001) moderated the relationship between depressive symptoms and cognitive function. Support utilization reduced the possibility of the cognitive decline in depressed older adults aged 60-69 years (β = 0.310,   < 0.001) and 80 years and above (β = 0.199,   < 0.001), while objective support increased the possibility of cognitive decline in depressed older people aged 70-79 years (β = -0.189,   < 0.001). Our findings highlight the buffering effects of support utilization on cognitive decline in depressed older adults. We suggest that age-specific measures should be taken when providing social support to depressed older adults in order to reduce the deterioration of cognitive function.

Studies have suggested that public health emergencies can have many psychological effects on college students, therefore, the aim of this study is to investigate current situation of college students' anxiety and its determinants in the time of an unexpected pandemic. We conducted convenience sampling to collect the data through network-based online questionnaires in February 2020, a total of 17,876 college students were included in the analysis. Chi-square test and multivariate logistic were used to identify the associations between the outbreak experiences and anxiety detection. This study found that detection rate of anxiety among college students was 18.2%. The differences in male students, students whose self-perceived risk of infection were high, who were greatly affected by the outbreak, eager to go back to school, reluctant to leave home and stay at home enough were of statistical significance among different anxiety level (OR>1, P<0.05). And the severe anxiety rate of students who living in cities was significantly higher (2.337[1.468, 3.721]). Although our results show that anxiety among college students was at a low level, various universities should focus on the online activities and develop appropriate epidemic management plans to prevent their feelings of worry, tension and panic.

This paper mainly introduces the longitudinal transmission mechanism of urban vegetable prices in Chongqing area. On the basis of the supply chain theory,the " farm price- wholesale price- retail price" of vegetables in Chongqing has been identified as the research object. Through statistical analysis,it is found that the above three types of price show a modest annual fluctuation of 7- 12% from 2008 to2013. Later,the paper focuses on the empirical research on the weekly price data in 2013 applying the co-integration theory,which reveals that the farm price of vegetables is largely influenced by themselves; the wholesale price of vegetables is mainly affected by their own price,the production price and the retail price of the earlier stage without the current influence of the production price; and the retail price of vegetables is also affected by their own price,but is not affected by the their current local price and the wholesale price. Vegetable prices have shown an obvious positive transmission mechanism in the links from production to retail,but the feedback effect of retail on production is not brought into play.

Objective Myocardial extracellular volume (ECV) fraction is an important imaging biomarker in clinical decision-making. CT-ECV is a potential alternative to MRI for ECV quantification. We conducted a meta-analysis to comprehensively assess the reliability of CT for ECV quantification with MRI as a reference. Methods We systematically searched PubMed, EMBASE, and the Cochrane Library for relevant articles published since the establishment of the database in July 2022. The articles comparing CT-ECV with MRI as a reference were included. Meta-analytic methods were applied to determine the pooled weighted bias, limits of agreement (LOA), and correlation coefficient ( r ) between CT-ECV and MRI-ECV. Results Seventeen studies with a total of 459 patients and 2231 myocardial segments were included. The pooled mean difference (MD), LOA, and r for ECV quantification at the per-patient level was (0.07%; 95% LOA: − 0.42 to 0.55%) and 0.89 (95% CI: 0.86–0.91), respectively, while on the per-segment level was (0.44%; 95% LOA: 0.16–0.72%) and 0.84 (95% CI: 0.82–0.85), respectively. The pooled r from studies with the ECV iodine method for ECV quantification was significantly higher compared to those with the ECV sub method (0.94 (95% CI: 0.91–0.96) vs. 0.84 (95% CI: 0.80–0.88), respectively, p  = 0.03). The pooled r from septal segments was significantly higher than those from non-septal segments (0.88 (95% CI: 0.86–0.90) vs. 0.76 (95% CI: 0.71–0.90), respectively, p  = 0.009). Conclusion CT showed a good agreement and excellent correlation with MRI for ECV quantification and is a potentially attractive alternative to MRI. Clinical relevance statement The myocardial extracellular volume fraction can be acquired using a CT scan, which is not only a viable alternative to myocardial extracellular volume fraction derived from MRI but is also less time-consuming and costly for patients. Key Points • Noninvasive CT-ECV is a viable alternative to MRI-ECV for ECV quantification. • CT-ECV using the ECV iodine method showed more accurate myocardial ECV quantification than ECV sub . • Septal myocardial segments showed lower measurement variability than non-septal segments for the ECV quantification.

Background There are many studies indicating that alterations in the abundance of certain gut microbiota are associated with colorectal cancer (CRC). However, a causal relationship has not been identified due to confounding factors such as lifestyle, environmental, and possible reverse causal associations between the two. Furthermore, certain host gene mutations can also contribute to the development of CRC. However, the association between genes and gut microbes in patients with CRC has not been extensively studied. Methods We conducted a two-sample Mendelian randomization (MR) study to reveal the causal relationship between gut microbiota and CRC. We obtained SNPs associated with gut microbiome abundance as instrumental variables (IVs) from a large-scale, multi-ethnic GWAS study, and extracted CRC-related datasets from an East Asian Population genetic consortia GWAS (AGWAS) study and FinnGen consortium, respectively. We analyzed a total of 166 bacterial features at four taxonomic levels, including order, family, genus, and species. The inverse-variance-weighted (IVW), weighted median, MR-Egger, and simple median methods were applied to the MR analysis, and the robustness of the results were tested using a series of sensitivity analyses. We extracted IVs of gut microbiota with direct causal association with CRC for SNP annotation to identify the genes in which these genetic variants were located to reveal the possible host gene-microbiome associations in CRC patients. Results The findings from our MR analysis based on CRC-associated GWAS datasets from AGWAS revealed causal relationships between 6 bacterial taxa and CRC at a locus-wide significance level (P < 1 × 10 –5 ). The IVW method found that family Porphyromonadaceae , genera Anaerotruncus , Intestinibacter , Slackia , and Ruminococcaceae UCG004, and species Eubacterium coprostanoligenes group were positively associated with CRC risk, which was generally consistent with the results of other complementary analyses. The results of a meta-analysis of the MR estimates from the AGWAS and the FinnGen datasets showed that family Porphyromonadaceae and genera Slackia , Anaerotruncus , and Intestinibacter replicated the same causal association. Sensitivity analysis of all causal associations did not indicate significant heterogeneity, horizontal pleiotropy, or reverse causal associations. We annotated the SNPs at a locus-wide significance level of the above intestinal flora and identified 24 host genes that may be related to pathogenic intestinal microflora in CRC patients. Conclusion This study supported the causal relationship of gut microbiota on CRC and revealed a possible correlation between genes and pathogenic microbiota in CRC. These findings suggested that the study of the gut microbiome and its further multi-omics analysis was important for the prevention and treatment of CRC.

Background Previous research suggested an association between maternal exposure to ambient air pollutants and the risk of congenital heart disease (CHD). However, the effect of individual prenatal exposure to indoor air pollutants on CHD occurrence was not reported. Methods We performed a hospital-based case–control study to investigate the association between personal air pollution exposure during pregnancy and the risk of CHD in offspring. A total of 44 cases and 75 controls were included from two hospitals in East China. We investigated maternal and residential environmental characteristics using a questionnaire and obtained personal indoor air samples to assess particulate matter (PM) and volatile organic compounds (VOCs) from 22–30 gestational weeks. Formaldehyde, benzene, toluene, xylene, total volatile organic compounds (TVOCs), PM 2.5 , and PM 10 were assessed. Logistic regression was performed to assess associations and interactions between individual indoor air pollutants and CHD after adjusting for confounders. The potential residential environmental factors affecting the risks of indoor air pollutants on CHD were also assessed. Results Median TVOC (0.400 vs. 0.005 mg/m 3 , P  < 0.001) exposure levels in cases were significantly higher than controls. A logistic regression model adjusted for confounders revealed that exposure to high levels of indoor TVOCs (AOR 7.09, 95% CI 2.10–23.88) during pregnancy was associated with risks for CHD and the occurrence of some major CHD subtype in offspring. These risk effects were enhanced in pregnant women living in a newly renovated house but were mitigated by household use of smoke ventilators when cooking. We observed a positive interaction of maternal exposure to TVOCs and PM 2.5 and the risk for CHD. Conclusions Maternal exposure to indoor VOCs and PMs may increase the risk of giving birth to foetuses with CHD.