Dynamic interfacial electrostatic energy harvesting via a single wire.

Spontaneously occurred electrostatic breakdown releases enormous energy, but harnessing the energy remains a notable challenge due to its irregularity and instantaneity. Here, we propose a revolutionary method that effectively harvests the energy of dynamic interfacial electrostatic breakdown by simply imbedding a conductive wire (diameter, 25 micrometers) beneath dielectric materials to regulate the originally chaotic and distributed electrostatic energy resulted from contact electrification into aggregation, effectively transforming mechanical energy into electricity. A point-charge physical model is proposed to explain the power generation process and output characteristics, guide structural design, and enhance output performance. Furthermore, a quantified triboelectric series including 72 dielectric material pairs is established for materials choice and optimization. In addition, a high voltage of over 10 kilovolts is achieved using polytetrafluoroethylene and polyethylene terephthalate. This work opens a door for effectively using electrostatic energy, offering promising applications ranging from novel high-voltage power sources, smart clothing, and internet of things.

A High-Voltage-Specialized Direct-Current Triboelectric Nanogenerator for Air Purification.

Human health and the environment face significant challenges of air pollution, which is predominantly caused by PM2.5 or PM10 particles. Existing control methods often require elevated energy consumption or bulky high-voltage electrical equipment. To overcome these limitations, a self-powered, convenient, and compact direct current high-voltage triboelectric nanogenerator based on triboelectrification and electrostatic breakdown effects is proposed. By optimizing the structure-design of the direct current triboelectric nanogenerator and corresponding output voltage, it can easily achieve an output voltage of over 3 kV with a high charge density of 320 µC m-2 . A power management circuit is designed to overcome the influence of third domain self-breakdown, optimize 92.5% amplitude of voltage shake, and raise 5% charge utilization ratio. With a device size as tiny as 2.25 cm3 , it can continuously drive carbon nanowires to generate negative ions that settle dust within 300 s. This compact, simple, efficient, and safe high-voltage direct current triboelectric nanogenerator represents a promising sustainable solution. It offers efficient dust mitigation, fostering cleaner environments, and enhancing overall health.

Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs.

PURPOSE: To develop a deep learning approach based on deep residual neural network (ResNet101) for the automated detection of glaucomatous optic neuropathy (GON) using color fundus images, understand the process by which the model makes predictions, and explore the effect of the integration of fundus images and the medical history data from patients. METHODS: A total of 34,279 fundus images and the corresponding medical history data were retrospectively collected from cohorts of 2371 adult patients, and these images were labeled by 8 glaucoma experts, in which 26,585 fundus images (12,618 images with GON-confirmed eyes, 1114 images with GON-suspected eyes, and 12,853 NORMAL eye images) were included. We adopted 10-fold cross-validation strategy to train and optimize our model. This model was tested in an independent testing dataset consisting of 3481 images (1524 images from NORMAL eyes, 1442 images from GON-confirmed eyes, and 515 images from GON-suspected eyes) from 249 patients. Moreover, the performance of the best model was compared with results obtained by two experts. Accuracy, sensitivity, specificity, kappa value, and area under receiver operating characteristic (AUC) were calculated. Further, we performed qualitative evaluation of model predictions and occlusion testing. Finally, we assessed the effect of integrating medical history data in the final classification. RESULTS: In a multiclass comparison between GON-confirmed eyes, GON-suspected eyes and NORMAL eyes, our model achieved 0.941 (95% confidence interval [CI], 0.936-0.946) accuracy, 0.957 (95% CI, 0.953-0.961) sensitivity, and 0.929 (95% CI, 0.923-0.935) specificity. The AUC distinguishing referrals (GON-confirmed and GON-suspected eyes) from observation was 0.992 (95% CI, 0.991-0.993). Our best model had a kappa value of 0.927, while the two experts' kappa values were 0.928 and 0.925 independently. The best 2 binary classifiers distinguishing GON-confirmed/GON-suspected eyes from NORMAL eyes obtained 0.955, 0.965 accuracy, 0.977, 0.998 sensitivity, and 0.929, 0.954 specificity, while the AUC was 0.992, 0.999 respectively. Additionally, the occlusion testing showed that our model identified the neuroretinal rim region, retinal nerve fiber layer (RNFL) defect areas (superior or inferior) as the most important parts for the discrimination of GON, which evaluated fundus images in a way similar to clinicians. Finally, the results of integration of fundus images with medical history data showed a slight improvement in sensitivity and specificity with similar AUCs. CONCLUSIONS: This approach could discriminate GON with high accuracy, sensitivity, specificity, and AUC using color fundus photographs. It may provide a second opinion on the diagnosis of glaucoma to the specialist quickly, efficiently and at low cost, and assist doctors and the public in large-scale screening for glaucoma.

[Role of GDNF in the behavior and cognitive impairment of mice induced by chronic stress and aging].

OBJECTIVE: To investigate the effects of chronic stress on the spatial learning-memory and the role of glial cell line-derived neurotrophic factor (GDNF) of prefrontal cortex (PFC) and hippocampus (HP) in different age mice. METHODS: The chronic stress model mice in 21 days with multiple chronic unpredictable stressors were applied. The spontaneous behavior and spatial learning-memory ability of mice were tested, using Open field and Morris water maze task, and the expression of GDNF in HP and PFC were detected by immunohistochemical method. RESULTS: Compared with young mice, the spontaneous behaviors were significantly decreased and the spatial learning-memory function were significantly decreased (P < 0.05, P < 0.01) in aged mice. The GDNF expression in the CA3, DG of HP and PFC were significantly reduced in aged mice (P < 0.05, P < 0.01). After chronic stress, the spontaneous behaviors were remarkably decreased and the ability of spatial learning-memory of the stress group mice were significantly decreased (P < 0.05, P < 0.01) compared with those of the control group mice. The expression of GDNF in HP and PFC were remarkably reduced (P < 0.05, P < 0.01) in stress group mice. The aged stress mice had more serious changes after chronic stress. CONCLUSION: The brain aging and chronic stress in mice causes behavioral changes and the damage of spatial learning-memory function, and which may be nearly related to the expression of GDNF in HP and PFC.