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The autophagy–lysosome system is a master regulator of cellular homeostasis, integrating quality control, metabolism, and cell fate through the selective degradation of cytoplasmic components. Disruption of either autophagic flux or lysosomal function compromises this degradative pathway and leads to diverse pathological conditions. Emerging evidence identifies the autophagy–lysosome network as a central signaling hub that connects metabolic balance to disease progression, particularly in neurodegenerative disorders and cancer. Although cancer and neurodegenerative diseases exhibit seemingly opposite outcomes—uncontrolled proliferation versus progressive neuronal loss—both share common mechanistic foundations within the autophagy–lysosome axis. Here, we synthesize recent advances on the roles of autophagy and lysosomal mechanisms in neurodegenerative diseases and cancer, especially on how defects in lysosomal acidification, membrane integrity, and autophagosome–lysosome fusion contribute to toxic protein accumulation and organelle damage in Alzheimer’s and Parkinson’s diseases, while the same machinery is repurposed by tumor cells to sustain anabolic growth, stress tolerance, and therapy resistance. We also highlight emerging lysosome-centered therapeutic approaches, including small molecules that induce lysosomal membrane permeabilization, nanomedicine-based pH correction, and next-generation protein degradation technologies. Finally, we discuss the major challenges and future opportunities for translating these mechanistic insights into clinical interventions.

Surface topography, as one of the significant roles in surface integrity, has a great impact on the performances and service life of the machined parts. This research focuses on the surface topography model for the ball-nose end miller in machining of AISI P20 steel. First, the model is developed to predict the surface topography and surface roughness in ball-nose end milling process. Secondly, the accuracy of the developed surface topography model was verified by a series of milling experiments. Thirdly, the effects of corner radius and diameter of ball-nose end miller on surface roughness is analyzed, it is observed that the ratio of feed per tooth ( f z ) to radial depth of cutting ( a e ) for obtaining minimum surface roughness is related to the ratio of diameter ( D ) to corner radius ( r ) of ball-nose end miller. Finally, in terms of the minimum surface roughness, a mathematical model is established with consideration of corner radius and diameter of ball-nose end miller. This research indicates that proper selection of cutting parameters ( f z and a e ) with consideration of diameter and radius corner of ball-nose end miller is a novel avenue for acquiring desired surface roughness.

The machining errors are affected by positioning errors. However, the relationship between positioning error and machining error has not been fully comprehended due to the complexity of the positioning error distribution within the machining range of machine tool. The purpose of this paper is to elucidate the effects of positioning error of different distributions on machining error, and thereby improve the machining accuracy of machine tools. First, a test piece with multiple features is designed, positioning error measurement and test piece machining are conducted on four machine tools with different structures. Then, uncertainty assessment is introduced to examine the factors that affect measurement accuracy in the positioning error measurement process of laser interferometers, ensuring the validity of measurement results and the rationality of subsequent data analysis. Finally, on the basis of meeting the uncertainty assessment requirements in the measurement results, the effects of positioning error on machining error are investigated combining with actual machining, and the manifestation of the tool tip motion trajectory deviation caused by positioning errors on machining errors is clarified. The uncertainty assessment results suggest that incorrect material thermal compensation has the most significant effect on the measurement results, and actual machining results reveal that the distribution of positioning error, as well as the relative position between the test piece and tool coordinate system, will affect the machining error. This paper provides a practical reference for engineers to predict machining errors based on positioning errors.

Blue‐green roofs (BGRs), compared to traditional green roofs, can provide enhanced water storage capacity for stormwater management and drought mitigation. However, the potential of utilizing stored water for external purposes, such as toilet flushing and lawn irrigation, remains underexplored. This study developed a daily time‐step hydrological model based on the water balance equation to simulate the runoff reduction ratio, water supply reliability, and irrigation time fraction of BGRs with water supply functions. Using summer rainfall and evapotranspiration (ET) data (1980–2013) from six climatically different cities based on Köppen climate zone classification in the United States (Atlanta, Flagstaff, Billings) and China (Guangzhou, Jinan, Lanzhou), the study revealed that: (a) BGRs with external water supply significantly improve runoff control and water supply benefits but increase irrigation demands, particularly in arid regions; (b) increasing storage layer capacity enhances BGRs' performance, but benefits diminish beyond 50 mm; (c) adaptive water supply strategies based on climate variations can improve both flood control benefit and irrigation reliability for BGRs; and (d) water deficits can be avoided in high‐rainfall or low‐ET regions. These findings provide valuable insights for optimizing BGR design and management in diverse climatic conditions. Plain Language Summary Blue‐green roofs (BGRs) offer enhanced water storage capacity compared to traditional green roofs, aiding stormwater management and drought mitigation. This study developed a daily time‐step hydrological model to simulate runoff reduction performance, water supply reliability, and irrigation demand of BGRs with water supply functions. Using summer rainfall and evapotranspiration (ET) data (1980–2013) from six cities in the U.S. and China, the study found that: (a) BGRs with external water supply greatly increase runoff reduction and water supply benefits, but need higher irrigation demands; (b) increase in storage layer capacity enhances system's performance, but benefits decrease beyond 50 mm; (c) climate‐adaptive water supply strategies can help increase runoff control benefit and satisfy irrigation demand for BGRs; and (d) water supply and demand balance is achievable in areas with high rainfall or low ET. These findings can help proper BGR design and management in areas with different climate conditions. Key Points A daily hydrological model was developed for water‐supply enabled blue‐green roofs Blue‐green roofs' external water supply potential was explored for non‐potable applications Hydrologic performance for blue‐green roofs were assessed across six cities

Rainfall event separation is mainly based on the selection of the minimum inter-event time (MIET). The traditional approach to determining a suitable MIET for estimating the probability density functions is often using the frequency histograms. However, this approach cannot avoid arbitrariness and subjectivity in selecting the histogram parameters. To overcome the above limitations, this study proposes a kernel density estimation (KDE) approach for rainfall event separation and characterization at any specific site where the exponential distributions are suitable for characterizing the rainfall event statistics. Using the standardized procedure provided taking into account the Poisson and Kolmogorov–Smirnov (K-S) statistical tests, the optimal pair of the MIET and rainfall event volume threshold can be determined. Two climatically different cities, Hangzhou and Jinan of China, applying the proposed approach are selected for demonstration purposes. The results show that the optimal MIETs determined are 12 h for Hangzhou and 10 h for Jinan while the optimal event volume threshold values are 3 mm for both Hangzhou and Jinan. The KDE-based approach can facilitate the rainfall statistical representation of the analytical probabilistic models of urban drainage/stormwater control facilities.

For efficient plant reproduction, seed dormancy delays seed germination until the environment is suitable for the next generation growth and development. The phytohormone abscisic acid (ABA) plays important role in the induction and maintenance of seed dormancy. Previous studies have identified that WRKY transcription factors can regulate ABA signaling pathway. Here, we identified an Oswrky29 mutant with enhanced dormancy in a screen of T-DNA insertion population. OsWRKY29 is a member of WRKY transcription factor family which located in the nuclear. The genetic analyses showed that both knockout and RNAi lines of OsWRKY29 had enhanced seed dormancy whereas its overexpression lines displayed reduced seed dormancy. When treated with ABA, OsWRKY29 knockout and RNAi lines showed greater sensitivity than its overexpression lines. In addition, the expression levels of ABA positive response factors OsVP1 and OsABF1 were higher in the OsWRKY29 mutants but were lower in its overexpression lines. Further assays showed that OsWRKY29 could bind to the promoters of OsABF1 and OsVP1 to inhibit their expression. In summary, we identified a new ABA signaling repressor OsWRKY29 that represses seed dormancy by directly downregulating the expression of OsABF1 and OsVP1 .

Plants rapidly induce strong abscisic acid (ABA) signaling in response to stress, but how they weaken ABA signaling to resume normal growth after stress is unclear. Here, we find that arginine methyltransferase 6b (OsPRMT6b) methylates three arginine residues (R48, R79, R113) in ABA receptor OsPYL/RCAR10 (OsPYR1-LIKE/REGULATORY COMPONENT OF ABA RECEPTOR, R10), thereby enhancing its interaction with Tiller Enhancer (TE) and promoting its ubiquitination and degradation through the 26S-proteasome pathway. OsPRMT6b is induced by ABA at both transcriptional and translational levels. Further, we find that R10 protein accumulates under high temperature stress but declines as temperature drops, whereas OsPRMT6b protein shows an opposite trend. And WT plants display a better growth recovery than osprmt6b mutants after high temperature. These findings suggest that OsPRMT6b acts as a switch to downregulate ABA signaling for growth recovery after high temperature stress. The methyltransferase OsPRMT6b is found to act as an ABA responsive factor to finely turn down ABA signaling via methylation of ABA receptor, thus facilitating growth recovery as high-temperature stress subsides.

Cutting force prediction is very important for optimizing machining parameters ahead of the costly physical test. Ball-end milling operation is widely used for machining sculptured surface. Mechanistic approach can precisely predict elemental cutting force at each cutting element and integrate them into the cutter tooth with high fidelity to predict the cutting force for ball-end milling operation. However, the intersection between the cutting tool and workpiece could be complicated due to the trochoid motion trajectory of cutting edge and constantly changing workpiece geometry, making it difficult to determine the cutter-workpiece engagement (CWE) and undeformed chip thickness (UCT). In this present research, a modified cutting force prediction model was developed with considering the true trajectory of cutting edge and in-process workpiece geometry in ball-end milling operation. First, a triangular mesh model of the in-process workpiece surface was developed, and its mesh points were continuously updated by the intersection between the vertical reference line of the selected mesh point and the motion trajectory of cutting edge. Secondly, the UCT was calculated directly using the linear distance between a selected point on the cutting edge and the intersection between the radial reference line of the selected point and the triangular mesh of the in-process workpiece surface. Meanwhile, the CWE was expressed as a step function of UCT. Thirdly, a modified mechanistic approach was established by incorporation into the developed UCT and CWE models. The cutting force of ball-end milling operation was predicted with mechanistic approaches. Finally, ball-end milling experiments of AISI P20 steel were carried out for calibrating cutting force coefficients and validating cutting force model. The relative error between the predicted and measured cutting force is less than 15%, which indicates the predicted cutting force is in good agreement with measured cutting force. The works presented in this paper are one important step for optimizing machining parameters and compensating cutting force induced form error, which could improve the surface accuracy and machining efficiency.

Machined surface topography prediction is an important and useful tool for optimizing cutting parameters. However, accurate prediction of machined surface topography in ball-end milling operation has been extremely challenging, due to the complexity in tool-workpiece interaction induced by the trochoidal motion of cutting edge and computing burden. In this present research, a modified iterative approach was proposed to solve the intersections between the cutting-edge sweeping surface and the discrete Z-vector model of workpiece, which were used to predict the machined surface topography in ball-end milling operation. Firstly, the accurate model of cutting-edge sweeping surface was established utilizing homogeneous coordinate transformation, in which the tool runout was considered. Secondly, the cutting-edge sweeping surface was dispersed into a series of patches in accordance with equal parameter interval, and the in-cut patch was extracted by using the minimum and maximum axial immersion angle of the cutting edge. Thirdly, the intersection between each in-cut patch and discrete Z-vector was solved using the Newton’s method, which was used to update the endpoint of the corresponding discrete Z-vector. Finally, ball-end milling experiments of AISI P20 steel were carried out to validate the proposed approach as well as investigate the effect of cutting parameters on the machined surface topography and roughness. The predicted machined surface topography and roughness were in good agreement with the measured results. Moreover, the proposed approach needs less computing time than the traditional iterative approaches at the same predicting accuracy. This research also provides guidance for optimizing cutting parameters to control surface quality in ball-end milling operation.

Objective Assessing the types of physical activity (PA) in young children is crucial for exploring their relationship with health. Although existing machine learning methods have made certain progress, there are still deficiencies in non-contact, real-time dynamic monitoring and recognition accuracy. This study aims to utilize computer vision technology to construct non-contact, real-time dynamic, and high-precision recognition models for the types of physical activities of children aged 3–6 years in different age groups and overall, to provide scientific and practical tools for monitoring children’s daily physical activities and preventing health problems such as obesity. Methods The portable Drift Ghost XL camera collected video data of 11 types of physical activities from 72 children aged 3 to 6 (average age = 4.7 ± 0.9). These activities included: sitting still, sitting activity, standing still, standing activity, walking, running, crawling, jumping, cycling, climbing, and stair walking. Each type was recorded for 8 to 10 min. A total of 18,870 valid samples were obtained using the time-slice method. The PA type dataset was labeled and constructed using Labelimg software and expanded fivefold through data augmentation. Based on Yolov11 object detection technology, computer vision recognition models for PA types of children aged 3–4, 4–5, 5–6, and 3–6 were established, respectively. Their performance was evaluated using metrics such as F1 score and mAP. Results The model of children aged 3–6 achieved an F1 score of 96.0%. Among the age-specific models, the model of children aged 3–4 and 4–5 achieved the highest recognition rate of 95.0%, while the model of children aged 5–6 reached 94.0%. Regarding recognition accuracy, the model of children aged 3–6 achieved 98.3%. Among the age-specific models, the model of children aged 3–4 had the highest recognition rate of 98.2%, followed by 98.0% for the model of children aged 4–5 and 97.6% for the model of children aged 5–6. Yolov11 achieved recognition rates ranging from 96.9% to 99.4% for 11 PA types in the model of children aged 3–6, as follows: 98.9% (sitting still), 99.1% (sitting activity), 98.2% (standing still), 97.9% (standing activity), 97.7% (walking), 98.4% (running), 98.3% (crawling), 97.7% (jumping), 98.7% (cycling), 99.4% (climbing), and 96.9% (stair walking). Conclusions Computer vision technology could effectively recognize PA types in children aged 3–6 under non-wearable, real-time dynamic conditions, with performance surpassing existing wearable device-dependent machine learning techniques. Age-specific models (children aged 3–4, 4–5, 5–6) also demonstrated excellent performance, confirming the method’s effectiveness and applicability across different developmental stages of early childhood.