Reconstructing Solvation Structure by Steric Hindrance-Coordination Push-Pull of Dipolymer-H2O-Zn2+ toward Long-life Aqueous Zinc-Metal Batteries.

Aqueous zinc-metal batteries are prospective energy storge devices due to their intrinsically high safety and cost effectiveness. Yet, uneven deposition of zinc ions in electrochemical reduction and side reactions at the anode interface significantly hinder their development and application. Here, we propose a solvation-interface attenuation strategy enabled by a frustrated tertiary amine amphiphilic dipolymer electrolyte additive. The configuration of superhydrophilic segments with covalently bonded lipophilic spacers enables coupled steric hindrance/coordination, which establishes a balanced push-pull dynamic of dipolymer-H2O-Zn2+. Such interplay reconstructs the solvation structure of Zn2+ and allows the formation of a stable dipolymer-inorganic hybrid solid electrolyte interface (SEI) layer. This SEI layer effectively shields the zinc-metal anode from water and anions, significantly reducing side reactions. In addition, the dipolymer adsorbed at the zinc-metal anode interface regulates the interfacial electrochemical reduction kinetics and ensures uniform zinc deposition. As a result, the Zn-Zn symmetric cells with dipolymer-containing electrolyte exhibit remarkable cycling stability exceeding 5800 h (242 days). The Zn-NVO batteries and Zn-AC hybrid ion supercapacitors also deliver stable cycling for up to 1440 h (60 days) with high-capacity retention over 80 %. This research demonstrates the potential to facilitate the development and commercialization of zinc-based energy storage devices.

Dual supervised sampling networks for real-time segmentation of cervical cell nucleus.

The morphology of the cervical cell nucleus is the most important consideration for pathological cell identification. And a precise segmentation of the cervical cell nucleus determines the performance of the final classification for most traditional algorithms and even some deep learning-based algorithms. Many deep learning-based methods can accurately segment cervical cell nuclei but will cost lots of time, especially when dealing with the whole-slide image (WSI) of tens of thousands of cells. To address this challenge, we propose a dual-supervised sampling network structure, in which a supervised-down sampling module uses compressed images instead of original images for cell nucleus segmentation, and a boundary detection network is introduced to supervise the up-sampling process of the decoding layer for accurate segmentation. This strategy dramatically reduces the convolution calculation in image feature extraction and ensures segmentation accuracy. Experimental results on various cervical cell datasets demonstrate that compared with UNet, the inference speed of the proposed network is increased by 5 times without losing segmentation accuracy. The codes and datasets are available at https://github.com/ldrunning/DSSNet.

A Model for Risk Prediction of Cerebrovascular Disease Prevalence-Based on Community Residents Aged 40 and above in a City in China.

Cerebrovascular disease (CVD) is the leading cause of death in many countries including China. Early diagnosis and risk assessment represent one of effective approaches to reduce the CVD-related mortality. The purpose of this study was to understand the prevalence and influencing factors of cerebrovascular disease among community residents in Qingyunpu District, Nanchang City, Jiangxi Province, and to construct a model of cerebrovascular disease risk index suitable for local community residents. A stratified cluster sampling method was used to sample 2147 community residents aged 40 and above, and the prevalence of cerebrovascular diseases and possible risk factors were investigated. It was found that the prevalence of cerebrovascular disease among local residents was 4.5%. Poisson regression analysis found that old age, lack of exercise, hypertension, diabetes, smoking, and family history of cerebrovascular disease are the main risk factors for local cerebrovascular disease. The relative risk ORs were 3.284, 2.306, 2.510, 3.194, 1.949, 2.315, respectively. For these six selected risk factors, a cerebrovascular disease risk prediction model was established using the Harvard Cancer Index method. The R value of the risk prediction model was 1.80 (sensitivity 81.8%, specificity 47.0%), which was able to well predict the risk of cerebrovascular disease among local residents. This provides a scientific basis for the further development of local cerebrovascular disease prevention and control work.

StainNet: A Fast and Robust Stain Normalization Network.

Stain normalization often refers to transferring the color distribution to the target image and has been widely used in biomedical image analysis. The conventional stain normalization usually achieves through a pixel-by-pixel color mapping model, which depends on one reference image, and it is hard to achieve accurately the style transformation between image datasets. In principle, this difficulty can be well-solved by deep learning-based methods, whereas, its complicated structure results in low computational efficiency and artifacts in the style transformation, which has restricted the practical application. Here, we use distillation learning to reduce the complexity of deep learning methods and a fast and robust network called StainNet to learn the color mapping between the source image and the target image. StainNet can learn the color mapping relationship from a whole dataset and adjust the color value in a pixel-to-pixel manner. The pixel-to-pixel manner restricts the network size and avoids artifacts in the style transformation. The results on the cytopathology and histopathology datasets show that StainNet can achieve comparable performance to the deep learning-based methods. Computation results demonstrate StainNet is more than 40 times faster than StainGAN and can normalize a 100,000 × 100,000 whole slide image in 40 s.

Analysis of gum bleeding among children aged 12-15 years in Jiangxi Province and related influencing factors.

OBJECTIVE: To study the prevalence of gum bleeding in children aged 12-15 years in Jiangxi Province and related influencing factors for the development of effective prevention and treatment strategies. METHODS: A multistage cluster stratified sampling method was used to select 8,160 children aged 12-15 years for this study. Enrolled children completed a set of survey questionnaires covering children's gender, age, parents' educational level, oral health knowledge scores, attitude scores, and brushing habits in addition of dental examination. All the data were analyzed using the chi-square test and logistic regression. RESULTS: Among 8,160 children, the gum bleeding rate was 66.5 percent (95% CI: 65.8%-68.1%). The gum bleeding rate in urban children (68.0 percent) was higher than that in rural areas (65.0 percent) (P < 0.01); the gum bleeding rate in boys (67.6 percent) was higher than that in girls (65.4 percent) (P < 0.05). The results of binary logistic regression analysis showed that age, urban and rural areas, mother education, knowledge score, attitude score, and brushing frequency were all important factors affecting gum bleeding. CONCLUSION: This study showed that incidence of gum bleeding in Jiangxi children is high which is affected by their age, mother's education, and several other factors. These new findings form the baseline information essential for the development of more effective approaches to prevent and control children gum bleeding in Jiangxi and other regions in the future.