Factors related to dietary quality among older stroke high-risk population in Tianjin community, China: a multicenter study.

BACKGROUND: Stroke is a common and frequently-occurring disease in older people. It has the characteristics of high morbidity, high mortality, high recurrence rate and high disability rate. Most stroke risk studies are based on pathophysiology, however psychosocial factors such as diet quality are often understudied. The aim of this study was to assess stroke risk in urban community residents in Tianjin and investigate the factors that affect the dietary quality of older stroke high-risk populations. METHODS: Using a cross-sectional, multicenter study, recruit people aged 60 to 80 in Tianjin. Dietary intake data were obtained through a validated food frequency questionnaire, which were used to calculate Alternate Healthy Eating Index-2010 (AHEI-2010) and to analyze its association with sociodemographic characteristics, stroke risk factors and health marker variables. RESULTS: A total of 1068 participants from 4 community health service centers in Tianjin were recruited, including 300 low-risk individuals and 768 high-risk individuals. Compared with the low-risk group (62.75 ± 3.59), the AHEI-2010 mean score of the high-risk group (56.83 ± 6.54) was significantly lower. The top three most common risk factors among participants were dyslipidemia (80.3%), hypertension (60.6%), and physical inactivity (58.2%). Multiple logistic regression showed that diet quality was independently and significantly associated with stroke risk (OR = 0.765; 95%CI: 0.690-0.848, p < 0.001). CONCLUSION: The diet quality of high-risk stroke population in Tianjin is far from ideal. At the same time, public health knowledge needs to be disseminated and educated, especially among those at high risk of cerebrovascular disease, with a focus on improving psychosocial factors such as diet quality.

Structural brain network measures in elderly patients with cerebral small vessel disease and depressive symptoms.

OBJECTIVES: To investigate the relationship between diffusion tensor imaging (DTI) indicators and cerebral small vessel disease (CSVD) with depressive states, and to explore the underlying mechanisms of white matter damage in CSVD with depression. METHOD: A total of 115 elderly subjects were consecutively recruited from the neurology clinic, including 36 CSVD patients with depressive state (CSVD+D), 34 CSVD patients without depressive state (CSVD-D), and 45 controls. A detailed neuropsychological assessment and multimodal magnetic resonance imaging (MRI) were performed. Based on tract-based spatial statistics (TBSS) analysis and structural network analysis, differences between groups were compared, including white matter fiber indicators (fractional anisotropy and mean diffusivity) and structural brain network indicators (global efficiency, local efficiency and network strength), in order to explore the differences and correlations of DTI parameters among the three groups. RESULTS: There were no significant differences in terms of CSVD burden scores and conventional imaging findings between the CSVD-D and CSVD+D groups. Group differences were found in DTI indicators (p <  0.05), after adjusting for age, gender, education level, and vascular risk factors (VRF), there were significant correlations between TBSS analysis indicators and depression, including: fractional anisotropy (FA) (r = - 0.291, p <  0.05), mean diffusivity (MD) (r = 0.297, p < 0.05), at the same time, between structural network indicators and depression also show significant correlations, including: local efficiency (ELocal) (r = - 0.278, p < 0.01) and network strength (r = - 0.403, p < 0.001). CONCLUSIONS: Changes in FA, MD values and structural network indicators in DTI parameters can predict the depressive state of CSVD to a certain extent, providing a more direct structural basis for the hypothesis of abnormal neural circuits in the pathogenesis of vascular-related depression. In addition, abnormal white matter alterations in subcortical neural circuits probably affect the microstructural function of brain connections, which may be a mechanism for the concomitant depressive symptoms in CSVD patients.

Functionalization of cellulosic hydrogels with Cu2O@CuO nanospheres: Toward antifouling applications.

Immobilizing metal oxide nanoparticles onto polymer substrate could endow antibacterial performance and enhance mechanical property. In-situ strategy is extensively used to better control loading percentage, uniform distribution and particle size of nanoparticles. However, it still remains challenge in depositing stable bicomponents copper oxide nanoparticles on non-adhesive surface of cellulose hydrogel in high density. In this study, Cu2O@CuO nanospheres were in-situ deposited onto cellulose hydrogels via liquid phase reduction. Particularly, sodium hydroxide in the cellulose hydrogel severed as the precipitant, which not only save the usage of chemicals, but also enhanced binding between nanoparticles and the hydrogel. Furthermore, Cu2O@CuO nanospheres demonstrate biocidal antifouling performance against Escherichia coli by releasing biocide. After hydrolysis of precipitation layer, the exposed cellulose hydrogel exhibits fouling-resistant property for Chlorella Vulgaris due to the hydration layer on its surface. Such composites hold great promise in antifouling coatings and other applications.

High-Quality Proposals for Weakly Supervised Object Detection.

Despite significant efforts made so far for Weakly Supervised Object Detection (WSOD), proposal generation and proposal selection are still two major challenges. In this paper, we focus on addressing the two challenges by generating and selecting high-quality proposals. To be specific, for proposal generation, we combine selective search and a Gradient-weighted Class Activation Mapping (Grad-CAM) based technique to generate more proposals having higher Intersection-Over-Union (IOU) with ground truth boxes than those obtained by greedy search approaches, which can better envelop the entire objects. As regards proposal selection, for each object class, we choose as many confident positive proposals as possible and meanwhile only select class-specific hard negatives to focus training on more discriminative negative proposals by up-weighting their losses, which can make training more effective. The proposed proposal generation and proposal selection approaches are generic and thus can be broadly applied to many WSOD methods. In this work, we unify them into the framework of Online Instance Classifier Refinement (OICR). Experimental results on the PASCAL VOC 2007 and 2012 datasets and MS COCO dataset demonstrate that our method significantly improves the baseline method OICR by large margins (13.4% mAP and 11.6% CorLoc gains on the VOC 2007 dataset, 15.0% mAP and 8.9% CorLoc gains on the VOC 2012 dataset, and 6.4% mAP and 5.0% CorLoc gains on the COCO dataset) and achieves the state-of-the-art results compared with existing methods.