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ObjectiveTo investigate the prevalence and influencing factors of overweight， obesity and central obesity among elderly residents in Xuhui District， and to analyze the epidemiological status of central obesity in elderly people with different body mass indexes.MethodsThe third round of health status and health service utilization monitoring data in Xuhui District was used. The information collected from questionnaire survey and physical examination were analyzed. SPSS 20.0 software was used for χ2 test， trend χ2 test and multinominal logistic regression analysis.Results5 096 survey subjects were included. The prevalence of overweight， general obesity， and central obesity in the residents aged 60 and above in Xuhui District were 34.3%， 6.5%， and 29.2%， respectively. There was gender difference in the rates of overweight， obesity， and central obesity among the residents. The overweight and central obesity rates in males were higher than those in females， while the obesity rate was lower than that in females （P

ObjectiveTo investigate the high-risk detection rate and aggregation of cardiovascular diseases（CVD） in 8 districts of Shanghai and influencing factors， and to provide scientific references for prevention and control of CVD.MethodsBased on the Cardiovascular Disease Screening and Management Program in Shanghai from 2016 to 2021， 104 685 participants aged 35 to 75 in 8 districts of Shanghai were selected for analysis. χ2 test and multivariate logistic regression were used for statistical analysis of the influencing factors of CVD and aggregation of CVD.ResultsThe proportion of high-risk CVD individuals in the population was 19.17%， including the high-risk individuals with hypertension （8.65%）， dyslipidemia （6.33%）， CVD history （5.58%）， and WHO assessed risk ≥20% types （2.69%）， respectively. Old age， overweight and obesity， central obesity， smoking， drinking， farmers， unmarried， and low family income were the risk factors of CVD， while high education level was the protective factor. In the participants， 16 323 people （81.34%） were classified as CVD high-risk groups； The number of aggregation of 1， 2 and ≥3 high risk types of CVD were 16 323（81.34%）， 3 236（16.13%）， 509（2.54%）， respectively. Old age， low education level， low annual family income， farmers， unmarried， smoking， drinking， overweight， obesity and central obesity were associated with the risk of aggregation of high risk types of CVD， and the correlation strength increased with the increase of aggregation types.ConclusionThe prevention and control of CVD in Shanghai should focus on the hypertension， elderly， overweight， obesity， central obesity， smoking， drinking， low educated， low family income， farmers and unmarried people， and targeted intervention measures should be taken to reduce the risk of CVD among residents.

Exploring the use of red cultural design in tourism creative design is to innovate creative tourism products. In this paper, the mathematical principle of support vector machine is introduced in detail in big data, and the optimal classification discriminant function is sought using the Lagrange multiplier method. The principle of the particle swarm optimization algorithm is also explained, and the steps and flow chart of the particle swarm optimization algorithm is given. The SVM algorithm is optimized according to the particle swarm optimization algorithm, and the PSO-SVM model is constructed. Finally, the PSO-SVM model is used for index mining of the application of red tourism cultural and creative design, which is analyzed and explained from two aspects of design elements and design performance, respectively.

Change detection (CD) is a challenging task considering land surface cover changes. Recently, deep learning has been introduced into CD. However, these methods suffer from shortcomings such as insufficient utilization of shallow features and inadequate aggregation of semantic context, which result in pseudo changes and poor integrity of change objects. To address this, an enhanced shallow feature difference and semantic context network (ESDSCNet) is proposed in this study for remote sensing CD. It uses HRNet to extract multi-scale features from bi-temporal images to obtain shallow and deep features. To fully exploit the shallow features, they are input into the difference statistical texture learning (DSTL) module to extract more discriminative features. Subsequently, the features enhanced by DSTL are fed into the change object contextual representation (COCR) module along with the deep difference features extracted by HRNet to characterize the contextual information of the change object. To verify the performance of ESDSCNet in different scenes, this paper takes building change detection as a case and conducts experiments based on three datasets. The experimental results reveal that ESDSCNet outperforms six other advanced methods, regardless of the intersection over union or the harmonic mean of precision rate and recall rate, which further confirms the effectiveness of the proposed network. In addition, another speciality of this paper is that ESDSCNet can identify not only 'Where has changed' but also 'What has changed what', so it has potential application in semantic change detection besides building change detection. [ABSTRACT FROM AUTHOR]

Background: Chondroblastoma is a primary bone tumor typically arising from the intramedullary space of the epiphysis or epimetaphysis. A non-epiphyseal chondroblastoma is uncommon. Case report: An 11-year-old girl presented with an eccentric cortical osteolytic lesion in the distal femur metaphysis. The typical morphology, diffuse H3.3 K36M immunohistochemical expression and H3F3B point mutation (c. 110A > T) unequivocally supported the diagnosis of chondroblastoma. Discussion: We described a non-epiphyseal cortical-based chondroblastoma involving the distal femur harboring the typical H3F3B mutation. Non-epiphyseal chondroblastoma may harbor the H3F3B mutation. [ABSTRACT FROM AUTHOR]