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Infrared image processing is an effective method for diagnosing faults in electrical equipment, in which target device segmentation and temperature feature extraction are key steps. Target device segmentation separates the device to be diagnosed from the image, while temperature feature extraction analyzes whether the device is overheating and has potential faults. However, the segmentation of infrared images of electrical equipment is slow due to issues such as high computational complexity, and the temperature information extracted lacks accuracy due to the insufficient consideration of the non-linear relationship between the image grayscale and temperature. Therefore, in this study, we propose an optimized maximum between-class variance thresholding method (OTSU) segmentation algorithm based on the Gray Wolf Optimization (GWO) algorithm, which accelerates the segmentation speed by optimizing the threshold determination process using OTSU. The experimental results show that compared to the non-optimized method, the optimized segmentation method increases the threshold calculation time by more than 83.99% while maintaining similar segmentation results. Based on this, to address the issue of insufficient accuracy in temperature feature extraction, we propose a temperature value extraction method for infrared images based on the K-nearest neighbor (KNN) algorithm. The experimental results demonstrate that compared to traditional linear methods, this method achieves a 73.68% improvement in the maximum residual absolute value of the extracted temperature values and a 78.95% improvement in the average residual absolute value.

The axial vibration of transformer windings under short-circuit impact is a critical challenge affecting their mechanical stability and operational reliability. Traditional dynamic analysis methods often overlook the prestressed equilibrium state, resulting in significant prediction deviations. To address this issue, this study proposes a coupled static–dynamic framework that integrates nonlinear pad modeling with experimental validation. Static analysis of the axial pressing process revealed that the actual pad stress (1.92 MPa) was significantly lower than the design target (3 MPa) due to the self-weight of the winding, resulting in a non-uniform preload distribution. Dynamic simulations incorporating time-varying electromagnetic forces revealed that the axial displacement amplitude exhibited end-constrained and upper-middle and lower-middle region-enhanced characteristics: the bottom showed the most constraint, while the upper-middle and lower-middle regions exhibited the largest amplitudes. A comparative analysis further demonstrated that insufficient preload caused momentary pad decompression, whereas adequate preload ensured continuous contact and maintained structural stability. The experimental monitoring using fiber Bragg grating sensors qualitatively confirmed the simulated vibration trends, capturing a rapid increase during the initial short-circuit and a secondary surge upon reclosing, despite sensor calibration limitations. Moreover, successive short-circuit events with short intervals led to stress accumulation, highlighting the importance of protection coordination. Overall, the proposed framework enhances vibration prediction accuracy and offers practical guidance for optimizing preload levels, pad layout, and protection strategies to improve transformer short-circuit withstand capability.

Rheumatoid arthritis (RA) is a chronic inflammatory disease that eventually leads to disability. Inflammatory cell infiltration, severe joint breaking and systemic bone loss are the main clinical symptoms. In this study, we established a collagen-induced arthritis (CIA) model and found a large number of M1 macrophages and pyroptosis, which are important sources of proinflammatory cytokines. Punicalagin (PUN) is an active substance extracted from pomegranate peel. We found that it inhibited joint inflammation, cartilage damage and systemic bone destruction in CIA mice. PUN effectively alleviated the high expression of inflammatory cytokines in synovial tissue in vivo . PUN treatment shifted macrophages from the M1 phenotype to the M2 phenotype after stimulation with lipopolysaccharide (LPS) and interferon (IFN)-γ. The expression of inducible nitric oxide synthase (iNOS) and other proinflammatory cytokines released by M1 macrophages was decreased in the PUN treatment group. However, simultaneously, the expression of markers of anti-inflammatory M2 macrophages, such as arginase (Arg)-1 and interleukin (IL)-10, was increased. In addition, PUN treatment attenuated pyroptosis by downregulating the expression of NLRP3 and caspase-1, thereby preventing inflammatory cell death resulting from the release of IL-1β and IL-18. Mechanistically, PUN inhibited the activation of receptor activators of the nuclear factor-κB (NF-κB) signaling pathway, which contributes to M1 polarization and pyroptosis of macrophages. We concluded that PUN ameliorated pathological inflammation by inhibiting M1 phenotype polarization and pyroptosis and has great potential as a therapeutic treatment for human RA.

The hot spot temperature of transformer windings is an important indicator for measuring insulation performance, and its accurate inversion is crucial to ensure the timely and accurate fault prediction of transformers. However, existing studies mostly directly input obtained experimental or operational data into networks to construct data-driven models, without considering the lag between temperatures, which may lead to the insufficient accuracy of the inversion model. In this paper, a method for inverting the hot spot temperature of transformer windings based on the SA-GRU model is proposed. Firstly, temperature rise experiments are designed to collect the temperatures of the entire side and top of the transformer tank, top oil temperature, ambient temperature, the cooling inlet and outlet temperatures, and winding hot spot temperature. Secondly, experimental data are integrated, considering the lag of the data, to obtain candidate input feature parameters. Then, a feature selection algorithm based on mutual information (MI) is used to analyze the correlation of the data and construct the optimal feature subset to ensure the maximum information gain. Finally, Self-Attention (SA) is applied to optimize the Gate Recurrent Unit (GRU) network, establishing the GRU-SA model to perceive the potential patterns between output feature parameters and input feature parameters, achieving the precise inversion of the hot spot temperature of the transformer windings. The experimental results show that considering the lag of the data can more accurately invert the hot spot temperature of the windings. The inversion method proposed in this paper can reduce redundant input features, lower the complexity of the model, accurately invert the changing trend of the hot spot temperature, and achieve higher inversion accuracy than other classical models, thereby obtaining better inversion results.

This literature review synthesizes the role of soil moisture in regulating carbon sequestration and greenhouse gas emissions (CS-GHG). Soil moisture directly affects photosynthesis, respiration, microbial activity, and soil organic matter dynamics, with optimal levels enhancing carbon storage while extremes, such as drought and flooding, disrupt these processes. A quantitative analysis is provided on the effects of soil moisture on CS-GHG across various ecosystems and climatic conditions, highlighting a “Peak and Decline” pattern for CO₂ emissions at 40% water-filled pore space (WFPS), while CH₄ and N₂O emissions peak at higher levels (60–80% and around 80% WFPS, respectively). The review also examines ecosystem models, discussing how soil moisture dynamics are incorporated to simulate photosynthesis, microbial activity, and nutrient cycling. Sustainable soil moisture management practices, including conservation agriculture, agroforestry, and optimized water management, prove effective in enhancing carbon sequestration and mitigating GHG emissions by maintaining ideal soil moisture levels. The review further emphasizes the importance of advancing multiscale observations and feedback modeling through high-resolution remote sensing and ground-based data integration, as well as hybrid modeling frameworks. The interactive model-experiment framework emerges as a promising approach for linking experimental data with model refinement, enabling continuous improvement of CS-GHG predictions. From a policy perspective, shifting focus from short-term agricultural productivity to long-term carbon sequestration is crucial. Achieving this shift will require financial incentives, robust monitoring systems, and collaboration among stakeholders to ensure sustainable practices effectively contribute to climate mitigation goals.

Background Ferroptosis is an iron-related form of programmed cell death. Accumulating evidence has identified the pathogenic role of ferroptosis in multiple orthopedic disorders. However, the relationship between ferroptosis and SONFH is still unclear. In addition, despite being a common disease in orthopedics, there is still no effective treatment for SONFH. Therefore, clarifying the pathogenic mechanism of SONFH and investigating pharmacologic inhibitors from approved clinical drugs for SONFH is an effective strategy for clinical translation. Melatonin (MT), an endocrine hormone that has become a popular dietary supplement because of its excellent antioxidation, was supplemented from an external source to treat glucocorticoid-induced damage in this study. Methods Methylprednisolone, a commonly used glucocorticoid in the clinic, was selected to simulate glucocorticoid-induced injury in the current study. Ferroptosis was observed through the detection of ferroptosis-associated genes, lipid peroxidation and mitochondrial function. Bioinformatics analysis was performed to explore the mechanism of SONFH. In addition, a melatonin receptor antagonist and shGDF15 were applied to block the therapeutic effect of MT to further confirm the mechanism. Finally, cell experiments and the SONFH rat model were used to detect the therapeutic effects of MT. Results MT alleviated bone loss in SONFH rats by maintaining BMSC activity through suppression of ferroptosis. The results are further verified by the melatonin MT2 receptor antagonist that can block the therapeutic effects of MT. In addition, bioinformatic analysis and subsequent experiments confirmed that growth differentiation factor 15 (GDF15), a stress response cytokine, was downregulated in the process of SONFH. On the contrary, MT treatment increased the expression of GDF15 in bone marrow mesenchymal stem cells. Lastly, rescue experiments performed with shGDF15 confirmed that GDF15 plays a key role in the therapeutic effects of melatonin. Conclusions We proposed that MT attenuated SONFH by inhibiting ferroptosis through the regulation of GDF15, and supplementation with exogenous MT might be a promising method for the treatment of SONFH. Graphical Abstract

Osteoarthritis (OA) is a chronic disease due to the deterioration of cartilage structure and function, involving the progressive degradation of the cartilage extracellular matrix. Cathepsins, lysosomal cysteine proteases, play pivotal roles in various biological and pathological processes, particularly in protein degradation. Excess cathepsins levels are reported to contribute to the development of OA. However, the causal relationship between the cathepsin family and knee and hip OA remains uncertain. Therefore, this study utilized bidirectional Mendelian Randomization (MR) analyses to explore this causal association. Our results indicated that elevated serum levels of cathepsin O increase the overall risk of knee OA, while increased serum levels of cathepsin H enhance the risk of hip OA. Conversely, the reverse MR analyses did not reveal a reverse causal relationship between them. In summary, OA in different anatomical locations may genetically result from pathological elevations in different serum cathepsin isoforms, which could be utilized as diagnostic and therapeutic targets in clinical practice.

Three drought indices (the Standardized Precipitation Index [SPI], Evaporative Stress Index [ESI], and Soil Moisture Anomaly Index [SMAI]) were integrated using triple collocation (TC) to produce the merged drought index (MDI). The new index was then compared with the Gravity recovery and climate experiment (GRACE)–Drought severity index (DSI), a comprehensive drought index reflecting storage variation in surface, sub-surface, and groundwater levels across East Asia and Australia, from 2003 to 2014. Before merging the three drought indices, their performance was analyzed. The mean correlation between the three drought indices and the GRACE–DSI indicated that the performance of the ESI was superior to the SMAI and SPI over the study areas. In terms of average weight results using the merging approach, the ESI was associated with larger weights (0.372 and 0.359) and contributions (43% and 38%), followed by the SMAI and SPI for East Asia and Australia, respectively. The SMAI achieved a similar weight (0.360) and contribution (39%) as the ESI across Australia. To determine the robustness of the MDI as estimated by TC weights, we evaluated the MDI and the reference GRACE-DSI with respect to documented drought records in the study areas. The MDI produced trends similar to those of the GRACE-DSI in Australia, while MDI and GRACE-DSI trends were not similar in East Asia. The correlation between the MDI and GRACE-DSI in Australia (0.41–0.62) was also higher than in East Asia (0.24–0.32) during the study periods. This discrepancy was due to the conceptual difference in that MDI reflects the near-surface water storage variation while GRACE-DSI reflects the variation of deeper water. Nevertheless, our results showed that the MDI out-performed single drought indices and was able to capture documented drought events across the study regions. This suggests that merging different drought indices into a single tool can better represent droughts, and may be a valuable approach for water resource management.

Osteoarthritis (OA) is distinguished by pathological alterations in the synovial membrane, articular cartilage, and subchondral bone, resulting in physical symptoms such as pain, deformity, and impaired mobility. Numerous research studies have validated the effectiveness of low-intensity pulsed ultrasound (LIPUS) in OA treatment. The periodic mechanical waves generated by LIPUS can mitigate cellular ischemia and hypoxia, induce vibration and collision, produce notable thermal and non-thermal effects, alter cellular metabolism, expedite tissue repair, improve nutrient delivery, and accelerate the healing process of damaged tissues. The efficacy and specific mechanism of LIPUS is currently under investigation. This review provides an overview of LIPUS’s potential role in the treatment of OA, considering various perspectives such as the synovial membrane, cartilage, subchondral bone, and tissue engineering. It aims to facilitate interdisciplinary scientific research and further exploration of LIPUS as a complementary technique to existing methods or surgery. Ongoing research is focused on determining the optimal dosage, frequency, timing, and treatment strategy of LIPUS for OA. Additional research is required to clarify the precise mechanism of action and potential impacts on cellular, animal, and human systems prior to its integration into therapeutic applications.

The peripheral nervous system (PNS) is essential for performing and maintaining various motor and sensory functions. Abnormalities can lead to a series of peripheral neurological conditions, such as paraesthesia, pain, or spasms, which are debilitating and lowering the quality of life. The current guidelines for diagnosis rely predominantly on clinical symptoms resulting from PNS dysfunction, which occur already at an advanced stage. There are currently no effective methods that visually reflect the extent of peripheral neuropathy. In our study, we present a novel in vivo and in situ real-time imaging of peripheral nerves based on the second near-infrared window (NIR-II) fluorescence. In NIR-II system, PbS Qds with NIR-II fluorescence specifically bound to motor neuron-specific protein agrin, acting as image contrast. In mice model, peripheral nerves were visible as soon as after 2 h post injection. We provide evidence for the efficacy of this approach, which allows to directly demonstrate peripheral nerves, their structure, and potential damage sites and degree. Furthermore, our products were of good biocompatibility, while the neural fluorescence signal was solid, bright and stable for 4 h in vivo. Thus, overall, our results suggest that NIR-II is an effective new method for direct imaging of peripheral nerves in vivo , opening new horizons on early, improved and more precise, targeted diagnosis. A resulting more rapid installation of personalized therapy facilitates a better prognosis of clinical peripheral neuropathy.