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Altered cholesterol metabolism has been linked to a poor prognosis in various types of cancer. Cholesterol oxidation can lead to lipid peroxidation, membrane damage, and cell death. Ferroptosis is a regulated form of cell death characterized by the accumulation of lipid peroxides, which significantly inhibits the growth of ovarian cancer cells. SQLE is the primary enzyme responsible for catalyzing cholesterol lipid synthesis and is notably expressed in ovarian cancer tissues and cells. This study aims to investigate the role of squalene monooxygenase (SQLE) in ferroptosis in ovarian cancer. The protein and mRNA expression of SQLE was assessed using qRT-PCR, Western Blot, and immunohistochemistry. The association between SQLE and ferroptosis was demonstrated through analysis of TCGA and GTEx databases, TMT protein sequencing, as well as validation by qRT-PCR, Western Blot, immunofluorescence, ROS detection, and lipid peroxide detection. Animal experiments further confirmed the relationship between SQLE and ferroptosis in ovarian cancer. The protein and mRNA expression of SQLE was found to be upregulated in both ovarian cancer tissues and cell lines. Decreased SQLE expression led to ferroptosis in ovarian cancer cells, thereby increasing their sensitivity to ferroptosis inducers. Our research demonstrates that SQLE is significantly upregulated in both ovarian cancer tissues and cells. The overexpression of SQLE in ovarian cancer may facilitate tumorigenesis by conferring resistance to ferroptosis, thus shedding light on potential novel therapeutic strategies for ovarian cancer.

Background Ovarian cancer is the eighth most common cancer globally, with environmental and occupational exposures emerging as critical determinants of ovarian carcinogenesis. Despite accumulating evidence, comprehensive global assessments of the burden of ovarian cancer attributable to these risks remain limited, especially among women aged 20–49 years. Methods We conducted a cross-sectional analysis using data from the Global Burden of Disease (GBD) Study 2021 to evaluate the global burden of ovarian cancer associated with environmental risks and occupational risks among females aged 20–49 years from 1990 to 2021. Outcomes included deaths, disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs). Temporal trends were analyzed using linear regression models, and future projections to 2050 were generated using autoregressive integrated moving average (ARIMA) and exponential smoothing (ES) models. Results In 2021, ovarian cancer linked to environmental risks among females aged 20–49 years resulted in 38 deaths (95% uncertainty interval [UI]: 17–69) and 1786 DALYs (95% UI: 781–3233). The age-standardized DALYs rate (ASDAR) was 0.09 per 100,000 population (95% UI: 0.04–0.16). Similar results were observed for occupational risks. From 1990 to 2021, the number of cases and age-standardized rates (ASRs) for ovarian cancer linked to both environmental and occupational risks initially increased and then declined. Regionally, high-middle Sociodemographic Index (SDI) regions exhibited peak ASRs, while middle and low-middle SDI regions showed increasing trends. Projections from 2022 to 2050 indicated an upward trend in the number of cases using the ARIMA model, with decreasing trends for ASDR and ASYLLR. Conclusion Our study highlights the significant burden of ovarian cancer associated with environmental and occupational risks among women aged 20–49 years. The observed trends underscore the need for continued investment in prevention and control strategies, particularly in regions with high ASRs.

Fringe projection profilometry (FPP) has been widely applied to non-contact three-dimensional measurement in industries owing to its high accuracy and speed. The point cloud, which is a measurement result of the FPP system, typically contains a large number of invalid points caused by the background, ambient light, shadows, and object edge regions. Research on noisy point detection and elimination has been conducted over the past two decades. However, existing invalid point removal methods are based on image intensity analysis and are only applicable to simple measurement backgrounds that are purely dark. In this paper, we propose a novel invalid point removal framework that consists of two aspects: (1) A convolutional neural network (CNN) is designed to segment the foreground from the background of different intensity conditions in FPP measurement circumstances to remove background points and the most discrete points in background regions. (2) A two-step method based on the fringe image intensity threshold and a bilateral filter is proposed to eliminate the small number of discrete points remaining after background segmentation caused by shadows and edge areas on objects. Experimental results verify that the proposed framework (1) can remove background points intelligently and accurately in different types of complex circumstances, and (2) performs excellently in discrete point detection from object regions.

Dynamic functional connectivity, derived from resting-state functional magnetic resonance imaging (rs-fMRI), has emerged as a crucial instrument for investigating and supporting the diagnosis of neurological disorders. However, prevalent features of dynamic functional connectivity predominantly capture either temporal or spatial properties, such as mean and global efficiency, neglecting the significant information embedded in the fusion of spatial and temporal attributes. In addition, dynamic functional connectivity suffers from the problem of temporal mismatch, i.e., the functional connectivity of different subjects at the same time point cannot be matched. To address these problems, this article introduces a novel feature extraction framework grounded in two-directional two-dimensional principal component analysis. This framework is designed to extract features that integrate both spatial and temporal properties of dynamic functional connectivity. Additionally, we propose to use Fourier transform to extract temporal-invariance properties contained in dynamic functional connectivity. Experimental findings underscore the superior performance of features extracted by this framework in classification experiments compared to features capturing individual properties.

With the acceleration of urbanization, the density of people in public spaces such as airports and shopping malls continues to rise. Sudden health incidents, such as fainting and acute cardiovascular events, pose a serious threat to public safety due to their sudden nature and short rescue window. Traditional manual monitoring is limited by labor costs and attention decay, making real-time and accurate early warning of such incidents difficult. The growing demand for intelligent public safety governance, coupled with advancements in artificial intelligence technologies, provides an opportunity for technological innovation in this field. Although existing video surveillance and behavior recognition technologies have been applied in public safety, they still face significant challenges in complex scenarios: crowd density and occlusion reduce the robustness of object detection and tracking, there is a lack of synchronization and fusion capabilities for multi-source video streams, the early warning system and visual analysis are disconnected, and the quality of specialized datasets limits algorithm optimization. To address these issues, this paper proposes an automatic early warning and visual analysis framework for sudden health incidents in public spaces, based on multi-source video streams and behavior recognition algorithms, and builds an end-to-end intelligent analysis pipeline. The framework achieves multi-source video stream synchronization through a distributed access mechanism, and extracts multi-scale features using the DarkNet53 backbone network. An innovative cross-frame attention module is introduced to strengthen the target area expression by associating temporal features, improving detection stability in occlusion and deformation scenarios. A twin network is used to achieve precise target displacement prediction, and a data association strategy based on appearance similarity and motion consistency is employed to ensure the continuity and identity consistency of individual spatiotemporal trajectories. Finally, relying on multi-source information fusion, the framework triggers multi-level automatic early warnings, and constructs a visual analysis platform with a Web GIS map, data dashboard, and interpretable video summaries to support emergency decision-making. This research provides a technical paradigm and practical solution for the intelligent management of sudden health incidents in public spaces, while enriching the theoretical and methodological applications of computer vision in the field of public safety.

Specialized households serve as the primary units within specialized villages in China, and their capacity to withstand risks and external influences significantly shapes the future trajectory of specialized villages and the overall vitality of the rural economy. In this study, we established a measurement indicator system based on the definition of specialized households’ resilience, elucidating the logical connection between specialized households’ resilience and rural industrial development in China. The musical instrument industry in Lankao County, Henan Province of China, was employed as a case; survey data, the entropy method, and an obstacle diagnosis model were used to examine how instrument production specialized households responded to the challenges posed by Corona Virus Disease 2019 (COVID-19) and the tightening of national environmental protection policies, yielding the following key findings: 1) there exists substantial variation in the comprehensive resilience levels among different specialized households; 2) the ability to learn and adapt is the most significant contributor to the overall resilience level of specialized households; 3) technological proficiency and access to skilled talent emerge as pivotal factors influencing specialized households’ resilience; 4) the positioning of specialized households within the industrial supply chain and the stability of their income have a direct bearing on their resilience level. The influence of specialized households’ resilience on industrial development primarily manifests in the following ways: stronger resilience correlates with increased stability in production and sales, fostering a more proactive approach to future actions. However, heightened exposure to the external macroeconomic environment can lead to a higher rate of export reduction. To enhance the development resilience of entities like specialized households and family farms, and to invigorate rural economic development, escalating investments in rural science and technology and prioritizing the training of technical talent become imperative.

Specialized households serve as the primary units within specialized villages in China,and their capacity to withstand risks and external influences significantly shapes the future trajectory of specialized villages and the overall vitality of the rural economy.In this study,we established a measurement indicator system based on the definition of specialized households'resilience,elucidating the logical connection between specialized households'resilience and rural industrial development in China.The musical instrument in-dustry in Lankao County,Henan Province of China,was employed as a case;survey data,the entropy method,and an obstacle diagnos-is model were used to examine how instrument production specialized households responded to the challenges posed by Corona Virus Disease 2019(COVID-19)and the tightening of national environmental protection policies,yielding the following key findings:1)there exists substantial variation in the comprehensive resilience levels among different specialized households;2)the ability to learn and ad-apt is the most significant contributor to the overall resilience level of specialized households;3)technological proficiency and access to skilled talent emerge as pivotal factors influencing specialized households'resilience;4)the positioning of specialized households with-in the industrial supply chain and the stability of their income have a direct bearing on their resilience level.The influence of special-ized households'resilience on industrial development primarily manifests in the following ways:stronger resilience correlates with in-creased stability in production and sales,fostering a more proactive approach to future actions.However,heightened exposure to the ex-ternal macroeconomic environment can lead to a higher rate of export reduction.To enhance the development resilience of entities like specialized households and family farms,and to invigorate rural economic development,escalating investments in rural science and technology and prioritizing the training of technical talent become imperative.

Background Adolescent depression is becoming one of the major public health concerns, because of its increased prevalence and risk of significant functional impairment and suicidality. Clinical depression commonly emerges in adolescence; therefore, the prevention and intervention of depression at this stage is crucial. Recent evidence supports the importance of the gut microbiota (GM) in the modulation of multiple functions associated with depression through the gut-brain axis (GBA). However, the underlying mechanisms remain poorly understood. Therefore, in the current study, we aimed to screen the microbiota out from healthy and depressive adolescents, delineate the association of the targeted microbiota and the adolescent depression, address the salutary effects of the targeted microbiota on anti-depressive behaviors in mice involving the metabolism of the tryptophan (Trp)-derived neurotransmitters along the GBA. Results Here, we found the gut microbiota from healthy adolescent volunteers, first diagnosis patients of adolescent depression, and sertraline interveners after first diagnosis displayed significant difference, the relative abundance of Faecalibacterium , Roseburia , Collinsella , Blautia , Phascolarctobacterium , Lachnospiraceae-unclassified decreased in adolescent depressive patients, while restored after sertraline treatment. Of note, the Roseburia abundance exhibited a high efficiency in predicting adolescent depression. Intriguingly, transplantation of the fecal microbiota from healthy adolescent volunteers to the chronic restraint stress (CRS)-induced adolescent depressed mice significantly ameliorated mouse depressive behaviors, in which the Roseburia exerted critical roles, since its effective colonization in the mouse colon resulted in remarkably increased 5-HT level and reciprocally decreased kynurenine (Kyn) toxic metabolites quinolinic acid (Quin) and 3-hydroxykynurenine (3-HK) levels in both the mouse brain and colon. The specific roles of the Roseburia were further validated by the target bacteria transplantation mouse model, Roseburia intestinalis ( Ri .) was gavaged to mice and importantly, it dramatically ameliorated CRS-induced mouse depressive behaviors, increased 5-HT levels in the brain and colon via promoting tryptophan hydroxylase-2 (TPH2) or -1 (TPH1) expression. Reciprocally, Ri. markedly restrained the limit-step enzyme responsible for kynurenine (indoleamine2,3-dioxygenase 1, IDO1) and quinolinic acid (3-hydroxyanthranilic acid 3,4-dioxygenase, 3HAO) generation, thereby decreased Kyn and Quin levels. Additionally, Ri . administration exerted a pivotal role in the protection of CRS-induced synaptic loss, microglial activation, and astrocyte maintenance. Conclusions This study is the first to delineate the beneficial effects of Ri . on adolescent depression by balancing Trp-derived neurotransmitter metabolism and improving synaptogenesis and glial maintenance, which may yield novel insights into the microbial markers and therapeutic strategies of GBA in adolescent depression. 5cVo2w5j9eyh76CmRyRzSQ Video Abstract

MET activation includes gene mutation, amplification, and protein overexpression. Clinical evidence suggests that MET activation is both a primary oncogenic driver in lung cancer, and a secondary driver after acquired resistance to EGFR tyrosine kinase inhibitors (TKIs). Several small molecule TKIs have already shown to be effective in the MET pathway. However, the activation form and the diagnostic criteria of MET oncogene are still controversial, especially in patients resistant to EGFR TKIs or ALK TKIs. With the development of new MET inhibitors, a quantity of emerging trials has focused on the mechanism of acquired resistance to MET TKIs and therapeutic strategies after resistance.

Currently, the global energy revolution in the direction of green and low-carbon technologies is flourishing. The large-scale integration of renewable energy into the grid has led to significant fluctuations in the net load of the power system. To meet the energy balance requirements of the power system, the pressure on conventional power generation units to adjust and regulate has increased. The efficient utilization of the regulation capability of controllable industrial loads and energy storage can achieve the similarity between renewable energy curves and load curves, thereby reducing the peak-to-valley difference and volatility of the net load. This approach also decreases the adjustment pressure on conventional generating units. Therefore, this paper proposes a two-stage optimization scheduling strategy considering the similarity between renewable energy and load, including energy storage and industrial load participation. The combination of the Euclidean distance, which measures the similarity between the magnitude of renewable energy–load curves, and the load tracking coefficient, which measures the similarity in curve shape, is used to measure the similarity between renewable energy and load profiles. This measurement method is introduced into the source-load-storage optimal scheduling to establish a two-stage optimization model. In the first stage, the model is set up to maximize the similarity between renewable energy and the load profile and minimize the cost of energy storage and industrial load regulation to obtain the desired load curve and new energy output curve. In the second stage, the model is set up to minimize the overall operation cost by considering the costs associated with abandoning the new energy sources and shedding loads to optimize the output of conventional generator sets. Through a case analysis, it is verified that the proposed scheduling strategy can achieve the tracking of the load curve to the new energy curve, reducing the peak-to-valley difference of the net load curve by 48.52% and the fluctuation by 67.54% compared to the original curve. These improvements effectively enhance the net load curve and reduce the difficulty in regulating conventional power generation units. Furthermore, the strategy achieves the full discard of renewable energy and reduces the system operating costs by 4.19%, effectively promoting the discard of renewable energy and reducing the system operating costs.