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The vast majority of in vitro studies have demonstrated that PINK1 phosphorylates Parkin to work together in mitophagy to protect against neuronal degeneration. However, it remains largely unclear how PINK1 and Parkin are expressed in mammalian brains. This has been difficult to address because of the intrinsically low levels of PINK1 and undetectable levels of phosphorylated Parkin in small animals. Understanding this issue is critical for elucidating the in vivo roles of PINK1 and Parkin. Recently, we showed that the PINK1 kinase is selectively expressed as a truncated form (PINK1–55) in the primate brain. In the present study, we used multiple antibodies, including our recently developed monoclonal anti-PINK1, to validate the selective expression of PINK1 in the primate brain. We found that PINK1 was stably expressed in the monkey brain at postnatal and adulthood stages, which is consistent with the findings that depleting PINK1 can cause neuronal loss in developing and adult monkey brains. PINK1 was enriched in the membrane-bound fractionations, whereas Parkin was soluble with a distinguishable distribution. Immunofluorescent double staining experiments showed that PINK1 and Parkin did not colocalize under physiological conditions in cultured monkey astrocytes, though they did colocalize on mitochondria when the cells were exposed to mitochondrial stress. These findings suggest that PINK1 and Parkin may have distinct roles beyond their well-known function in mitophagy during mitochondrial damage.

Abstract Electronic Health Record (EHR) systems are particularly valuable in pediatrics due to high barriers in clinical studies, but pediatric EHR data often suffer from low content density. Existing EHR code embeddings tailored for the general patient population fail to address the unique needs of pediatric patients. To bridge this gap, we introduce a transfer learning approach, MUltisource Graph Synthesis (MUGS), aimed at accurate knowledge extraction and relation detection in pediatric contexts. MUGS integrates graphical data from both pediatric and general EHR systems, along with hierarchical medical ontologies, to create embeddings that adaptively capture both the homogeneity and heterogeneity between hospital systems. These embeddings enable refined EHR feature engineering and nuanced patient profiling, proving particularly effective in identifying pediatric patients similar to specific profiles, with a focus on pulmonary hypertension (PH). MUGS embeddings, resistant to negative transfer, outperform other benchmark methods in multiple applications, advancing evidence-based pediatric research.

Abstract The application of medical image segmentation technology enables accurate localization of human tissues, providing doctors with a reliable foundation for diagnosis. While deep learning methods have proven effective in this task, most current approaches rely on a single prediction framework, which overlooks Edge semantic features and results in flawed texture features. Moreover, existing supervised methods face challenges due to limited availability of high‐quality annotations in the field of medical imaging. In this article, a Semi‐supervised Contextual Cognitive Augmentation‐based Cross‐teaching Network is proposed. A Contextual Cognitive Enhancement Module is introduced consisting of two components: data augmentation and information extraction. The data augmentation component provides multi‐level data distribution by incorporating diverse perturbation strategies such as Discrete Cosine Transform and Gaussian noise. The information extraction component employs the Comprehensive Information Extraction module, which consists of Global Perception Information Extraction module and Multi‐channel Information Extraction module to extract perceptual information from images and enhance interaction between image channels, respectively. Additionally, a cross‐teaching strategy is adopted and a hybrid loss function is utilized to encourage knowledge sharing among the networks, leveraging the advantages of dual networks for improved performance. Experimental results demonstrate significant enhancements in multiclass medical image segmentation compared to several state‐of‐the‐art single‐framework networks.

Abstract Background Breast cancer (BC) is the most common cancer in women and poses a significant health burden, especially in China. Despite advances in diagnosis and treatment, patient variability and limited early detection contribute to poor outcomes. This study examines the role of CD8 + T cells in the tumor microenvironment to identify new biomarkers that improve prognosis and guide treatment strategies. Methods CD8 + T-cell marker genes were identified using single-cell RNA sequencing (scRNA-seq), and a CD8 + T cell-related gene prognostic signature (CTRGPS) was developed using 10 machine-learning algorithms. The model was validated across seven independent public datasets from the GEO database. Clinical features and previously published signatures were also analyzed for comparison. The clinical applications of CTRGPS in biological function, immune microenvironment, and drug selection were explored, and the role of hub genes in BC progression was further investigated. Results We identified 71 CD8 + T cell-related genes and developed the CTRGPS, which demonstrated significant prognostic value, with higher risk scores linked to poorer overall survival (OS). The model’s accuracy and robustness were confirmed through Kaplan-Meier and ROC curve analyses across multiple datasets. CTRGPS outperformed existing prognostic signatures and served as an independent prognostic factor. The role of the hub gene TTK in promoting malignant proliferation and migration of BC cells was validated. Conclusion The CTRGPS enhances early diagnosis and treatment precision in BC, improving clinical outcomes. TTK, a key gene in the signature, shows promise as a therapeutic target, supporting the CTRGPS’s potential clinical utility.

Abstract Recent years have witnessed notable advancements in medical image segmentation through deep convolutional neural networks. However, a notable limitation lies in the local operation of convolution, which hinders the ability to fully exploit global semantic information. To overcome the challenges prevalent in medical image segmentation, the feature ensemble network with multi‐scale atrous transformer is proposed. At the core of the approach lies the multi‐scale contextual integration module, which is based on the multi‐scale atrous transformer and facilitates contextual integration of multi‐level features. To extract discriminative fine‐grained features of the target region, a hybrid attention mechanism that synergistically combines spatial and channel attention, thereby sharpening the model's focus on crucial target information within high‐level features, is incorporated. Additionally, the channel‐aware feature reconstruction module is introduced as an innovative component engineered to tackle feature similarity issues across different categories. This module performs feature reconstruction based on channel perception, effectively widening the feature gap between categories and enhancing the segmentation capability. It is worth mentioning that our approach surpasses the state‐of‐the‐art method using three benchmark datasets in medical image segmentation.

Abstract As one of the most common tumors in women, the pathogenesis and tumor heterogeneity of breast cancer have long been the focal point of research, with the emergence of tumor metastasis and drug resistance posing persistent clinical challenges. The emergence of single-cell sequencing (SCS) technology has introduced novel approaches for gaining comprehensive insights into the biological behavior of malignant tumors. SCS is a high-throughput technology that has rapidly developed in the past decade, providing high-throughput molecular insights at the individual cell level. Furthermore, the advent of multitemporal point sampling and spatial omics also greatly enhances our understanding of cellular dynamics at both temporal and spatial levels. The paper provides a comprehensive overview of the historical development of SCS, and highlights the most recent advancements in utilizing SCS and spatial omics for breast cancer research. The findings from these studies will serve as valuable references for future advancements in basic research, clinical diagnosis, and treatment of breast cancer.

BackgroundMumps is a viral respiratory disease characterized by facial swelling and transmitted through respiratory secretions. Despite the availability of an effective vaccine, mumps outbreaks have reemerged globally, including in China, where it remains a significant public health issue. In Yunnan province, China, the incidence of mumps has fluctuated markedly and is higher than that in mainland China, underscoring the need for improved outbreak prediction methods. Traditional surveillance methods, however, may not be sufficient for timely and accurate outbreak prediction. ObjectiveOur study aims to leverage the Baidu search index, representing search volumes from China’s most popular search engine, along with environmental data to develop a predictive model for mumps incidence in Yunnan province. MethodsWe analyzed mumps incidence in Yunnan Province from 2014 to 2023, and used time series data, including mumps incidence, Baidu search index, and environmental factors, from 2016 to 2023, to develop predictive models based on long short-term memory networks. Feature selection was conducted using Pearson correlation analysis, and lag correlations were explored through a distributed nonlinear lag model (DNLM). We constructed four models with different combinations of predictors: (1) model BE, combining the Baidu index and environmental factors data; (2) model IB, combining mumps incidence and Baidu index data; (3) model IE, combining mumps incidence and environmental factors; and (4) model IBE, integrating all 3 data sources. ResultsThe incidence of mumps in Yunnan showed significant variability, peaking at 37.5 per 100,000 population in 2019. From 2014 to 2023, the proportion of female patients ranged from 41.3% in 2015 to 45.7% in 2020, consistently lower than that of male patients. After excluding variables with a Pearson correlation coefficient of

Objective This study compared the clinical efficacy of off-pump coronary artery bypass grafting (OPCAB) with percutaneous coronary intervention (PCI) in the treatment of left main coronary artery and/or triple-vessel disease (LM and/or TVD). Methods We retrospectively enrolled 1484 consecutive patients with LM and/or TVD in Shanxi Cardiovascular Hospital from January 2015 to December 2022 and divided them into the OPCAB group (n = 583) and the PCI group (with second-generation drug-eluting stents) (n = 901). Propensity score matching was used for 316 equally matched pairs of patients in the groups. The outcomes were all-cause and cardiac mortality, angina recurrence, and major adverse cardiac and cerebrovascular events, which comprised non-lethal myocardial infarction, stroke, and repeat revascularization. Results The mean follow-up was 3.93 years in the OPCAB group and 4.19 years in the PCI group. There were no significant differences in all-cause mortality, cardiac mortality, major adverse cardiac and cerebrovascular events, or non-lethal myocardial infarction between the groups. The incidence of repeat revascularization and angina recurrence was higher in the PCI group than in the OPCAB group. However, the incidence of stroke was higher in the OPCAB group than in the PCI group. Conclusions OPCAB may be a favorable coronary revascularization strategy for LM and/or TVD.

AimThis study aimed to evaluate the factor structure of the Oral Behaviors Checklist (OBC) in Chinese temporomandibular disorder (TMDs) patients and compare the outcomes with those of Western patients. Additionally, it examined the correlations between different OBC subscale scoring methods.MethodsA total of 869 patients completed a survey that included demographic information, the Symptom Questionnaire, and OBC. This was followed by a clinical examination and diagnosis based on the Diagnostic Criteria for TMDs (DC/TMDs). Exploratory factor analysis, along with confirmatory factor analysis, was applied to waking-state oral behaviors, revealing two key factors: Chinese non-functional (C-NFA) and functional (C-FA) oral activities. Items were contrasted with those of Italian TMDs patients (I-NFA and I-FA), and subscale scores were computed, compared, and correlated using Kruskal Wallis and Post-hoc and Spearman’s rank-order correlation (α = 0.05).ResultsVariations in NFA and FA items were observed between Chinese and Italian TMDs patients. For both NFA scoring methods, significant differences were noted between pain-related and intra-articular TMDs. The C-NFA and I-NFA, as well as C-FA and I-FA, scoring methods yielded scores with strong correlations (r > 0.8).ConclusionNFA and FA subscale items were determined for Chinese TMDs patients. Despite item discrepancies, C-NFA and C-FA scores were strongly correlated with I-NFA and I-FA scores, respectively. The OBC can be effectively simplified for use with Chinese TMDs patients. Developing and validating an East–West short-form version of the OBC should be prioritized, given the variations in oral behaviors across countries and cultures.

Feature extraction is a common method for obtaining the discontinuities of rock masses from point cloud. In this study, a feature pre-segmentation method for identifying the discontinuities from point cloud obtained from underground drift is proposed. Specifically, the proposed method involves: (1) acquiring underground drift point cloud using 3D laser scanning; (2) obtaining a more complete, regular, and simplified drift point cloud by filling void holes and voxel filtering down-sample; (3) proposing a P-RG algorithm to pre-segment linear and planar features of drift point cloud, which enables better identification of discontinuities and ultimately obtaining the discontinuities and the orientation from the point cloud; and (4) using the DBSCAN clustering algorithm to cluster the dominant group. The proposed method was tested using the underground drift of a gold mine in Jilin Province, China, and compared with traditional feature extraction methods, demonstrating its accuracy and efficiency in identifying discontinuities in underground drift.

Sliding bearings are crucial rotating mechanical components in nuclear power plants, and their failures can result in severe economic losses and human casualties. Deep learning provides a new approach to bearing fault diagnosis, but there is currently a lack of a universal fault diagnosis model for studying bearing-rotor systems under various operating conditions, speeds and faults. Research on bearing-rotor systems supported by sliding bearings is limited, leading to insufficient fault data. To address these issues, this paper proposes a fault diagnosis model framework for bearing-rotor systems based on a deep convolutional generative adversarial network (TF-DLGAN). This model not only exhibits outstanding fault diagnosis performance but also addresses the issue of insufficient fault data. An experimental platform is constructed to conduct fault experiments under various operating conditions, speeds and faults, establishing a dataset for sliding bearing-rotor system faults. Finally, the model's effectiveness is validated using this dataset.

Abstract Background Sepsis is a life-threatening organ dysfunction caused by an excessive host response to infection, manifested by elevated levels of inflammatory cytokines. At present, the use of hemoperfusion to remove inflammatory cytokines from the bloodstream has been expanding. Meanwhile, the pharmacokinetics and pharmacodynamics characteristics of antibiotics in critically ill patients may be impacted by hemoperfusion. Case presentation The patient was a 69-year-old male with poorly controlled type 2 diabetes. When admitted to the ICU, Multiple Organ Dysfunction Syndrome (MODS) appeared within 48 h, and he was suspected of septic shock due to acute granulocytopenia and significantly increased procalcitonin. Broad-spectrum antibiotics imipenem was administered according to Sepsis 3.0 bundle and hemoperfusion lasting 4 h with a neutron-macroporous resin device (HA-380, Jafron, China) five times was conducted to lower the extremely high value of serum inflammatory factors. Blood samples were collected to measure imipenem plasma concentration to investigate the effect of hemoperfusion quantitatively. This study showed that 4 h of hemoperfusion had a good adsorption ability on inflammatory factors and could remove about 75.2% of imipenem. Conclusions This case demonstrated the high adsorption capacity of hemoperfusion on imipenem in critically ill patients. It implies a timely imipenem supplement is required, especially before hemoperfusion.

Climate warming is causing rapid permafrost degradation, including thaw-induced subsidence, potentially resulting in heightened carbon release. Nevertheless, our understanding of the levels and variations of carbon components in permafrost, particularly during the degradation process, remains limited. The uncertainties arising from this process lead to inaccurate assessments of the climate effects during permafrost degradation. With vast expanses of permafrost in the Tibetan Plateau, there is limited research available on SOC components, particularly in the central Tibetan Plateau. Given remarkable variations in hydrothermal conditions across different areas of the Tibetan Plateau, the existing limited studies make it challenging to assess the overall SOC components in the permafrost across the Tibetan Plateau and simulate their future changes. In this study, we examined the properties of soil organic carbon (SOC) and microbial necromass carbon (MicrobialNC) in a representative permafrost thaw-subsidence area at the southern edge of continuous permafrost in the central Tibetan Plateau. The results indicate that prior to the thaw-subsidence, the permafrost had a SOC content of 72.68 ± 18.53 mg g−1, with MicrobialNC accounting for 49.6%. The thaw-subsidence of permafrost led to a 56.4% reduction in SOC, with MicrobialNC accounting for 70.0% of the lost SOC. MicrobialNC constitutes the primary component of permafrost SOC, and it is the main component that is lost during thaw-subsidence formation. Changes in MicrobialNC are primarily correlated with factors pH, plant input, and microbial properties. The present study holds crucial implications for both the ecological and biogeochemical processes associated with carbon release from permafrost, and it furnishes essential data necessary for modeling the global response of permafrost to climate warming. Based on this study and previous research, permafrost thawing in the Tibetan Plateau causes substantial loss of SOC. However, there's remarkable heterogeneity in SOC component changes across different regions, warranting further in-depth investigation.

Real driving emissions (RDE) tests were conducted on a China VI B compliant light-duty diesel vehicle in four cities with different altitudes. The emission factors of particle number (PN), nitrogen oxides (NOx), and carbon monoxide (CO) were calculated using the carbon dioxide (CO2) moving average window (MAW) method and compared across different altitudes and driving stages. The tests covered urban, rural, and motorway sections in these four cities. The results showed that NOX and CO emissions were highest in urban sections. The study also analyzed the impact of altitude, traffic conditions, and exhaust temperature on emission characteristics. The results indicated that CO and NOX emissions increased with altitude, while PN emissions showed different trends due to aftertreatment effects. In high-altitude areas, CO and PN emissions also increased with higher cylinder and exhaust temperatures. The emissions of all pollutants were significantly lower than the China VI b emission standards. We suggest further research to optimize aftertreatment systems to better control emissions and improve efficiency under varying altitudes and route conditions.

Abstract Non‐healing diabetic wounds and ulcer complications, with persistent cell dysfunction and obstructed cellular processes, are leading causes of disability and death in patients with diabetes. Currently, there is a lack of guideline‐recommended hypoglycemic drugs in clinical practice, likely due to limited research and unclear mechanisms. In this study, it is demonstrated that liraglutide significantly accelerates wound closure in diabetic mouse models (db/db mice and streptozotocin‐induced mice) by improving re‐epithelialization, collagen deposition, and extracellular matrix remodeling, and enhancing the proliferation, migration, and adhesion functions of keratinocytes. However, these effects of improved healing by liraglutide are abrogated in dedicator of cytokinesis 5 (Dock5) keratinocyte‐specific knockout mice. Mechanistically, liraglutide induces cellular function through stabilization of unconventional myosin 1c (Myo1c). Liraglutide directly binds to Myo1c at arginine 93, enhancing the Myo1c/Dock5 interaction by targeting Dock5 promoter and thus promoting the proliferation, migration, and adhesion of keratinocytes. Therefore, this study provides insights into liraglutide biology and suggests it may be an effective treatment for diabetic patients with wound‐healing pathologies.

Abstract BackgroundType 2 diabetes mellitus (T2DM) is a common health issue, with heart failure (HF) being a common and lethal long-term complication. Although insulin is widely used for the treatment of T2DM, evidence regarding the efficacy of insulin compared to noninsulin therapies on incident HF risk is missing among randomized controlled trials. Real-world evidence on insulin’s effect on long-term HF risk may supplement existing guidelines on the management of T2DM. ObjectiveThis study aimed to compare insulin therapy against other medications on HF risk among patients with T2DM using real-world data extracted from insurance claims. MethodsA retrospective, observational study was conducted based on insurance claims data from a single health care network. The study period was from January 1, 2016, to August 11, 2021. The cohort was defined as patients having a T2DM diagnosis code. The inclusion criteria were patients who had at least 1 record of a glycated hemoglobin laboratory test result; full insurance for at least 1 year (either commercial or Medicare Part D); and received glucose-lowering therapy belonging to 1 of the following groups: insulin, glucagon-like peptide 1 receptor agonists (GLP-1 RAs), dipeptidyl peptidase-4 inhibitors (DPP-4Is), or sodium-glucose cotransporter-2 inhibitors (SGLT2Is). The main outcome was the 5-year incident HF rate. Baseline covariates, including demographic characteristics, comorbidities, and laboratory test results, were adjusted to correct for confounding. ResultsAfter adjusting for a broad list of confounders, patients receiving insulin were found to be associated with an 11.8% (95% CI 11.0%‐12.7%), 12.0% (95% CI 11.5%‐12.4%), and 15.1% (95% CI 14.3%‐16.0%) higher 5-year HF rate compared to those using GLP-1 RAs, DPP-4Is, and SGLT2Is, respectively. Subgroup analysis showed that insulin’s effect of a higher HF rate was significant in the subgroup with high HF risk but not significant in the subgroup with low HF risk. ConclusionsThis study generated real-world evidence on the association of insulin therapy with a higher 5-year HF rate compared to GLP-1 RAs, DPP-4Is, and SGLT2Is based on insurance claims data. These findings also demonstrated the value of real-world data for comparative effectiveness studies to complement established guidelines. On the other hand, the study shares the common limitations of observational studies. Even though high-dimensional confounders are adjusted, remaining confounding may exist and induce bias in the analysis.

Abstract The Phenome-Wide Association Study (PheWAS) is increasingly used to broadly screen for potential treatment effects, e.g., IL6R variant as a proxy for IL6R antagonists. This approach offers an opportunity to address the limited power in clinical trials to study differential treatment effects across patient subgroups. However, limited methods exist to efficiently test for differences across subgroups in the thousands of multiple comparisons generated as part of a PheWAS. In this study, we developed an approach that maximizes the power to test for heterogeneous genotype–phenotype associations and applied this approach to an IL6R PheWAS among individuals of African (AFR) and European (EUR) ancestries. We identified 29 traits with differences in IL6R variant-phenotype associations, including a lower risk of type 2 diabetes in AFR (OR 0.96) vs EUR (OR 1.0, p-value for heterogeneity = 8.5 × 10–3), and higher white blood cell count (p-value for heterogeneity = 8.5 × 10–131). These data suggest a more salutary effect of IL6R blockade for T2D among individuals of AFR vs EUR ancestry and provide data to inform ongoing clinical trials targeting IL6 for an expanding number of conditions. Moreover, the method to test for heterogeneity of associations can be applied broadly to other large-scale genotype–phenotype screens in diverse populations.

Abstract Firstly, the Canny operator combined with circle fitting is used to position the working surface. Secondly, the median filter and the Laplacian operator are selected for preprocessing. Due to uneven illumination, surface texture, or environmental dust, the extracted scratches exhibit fragmentation, and traditional morphological operations cannot handle scratches well, so an improved Canny operator combined with probabilistic Hough transform is proposed for scratch connection. A bidirectional connection is introduced to optimize the edge detection process of the Canny operator, and a parallel algorithm is introduced to shorten the detection time. Finally, the standard Hough transform needs to traverse the entire parameter space, and the calculation is complicated, and the probability Hough transform is selected. The experimental results show that compared with the traditional Canny algorithm, the precision, recall, and F1‐score of the improved Canny algorithm are increased by 3.2%, 13.2%, and 6.0%, respectively. After adding parallel processing, the average detection time was reduced by 45 ms. Finally, the accuracy of scratches extracted using this algorithm reached 97.96%, the leak rate, and mistake rate of this study are only 3.33% and 2.00%, which provides a more accurate and efficient theoretical support for the research on brake disc scratch detection.

Rock mass in tunnel, underground chamber and other underground engineering is inevitably affected by multiple blasting impact, rock mass cracking and failure are often affected by these damaged rock mass, so it is meaningful to study the rock mass affected by dynamic damage. To explore the influence of cyclic impact on the internal structure of marble, NMR (Nuclear Magnetic Resonance) technique was adopted to test the marble. It was found that the impact would rise the porosity of marble, and the pore size distribution would also change greatly. Then uniaxial compression experiments were conducted on core samples. The results show that the impact will cause significant damage to the marble, and the mechanical properties of the marble will be obviously degraded. With the increase of impact times, the peak strength and tangential deformation modulus tend to decrease. The impact effect promotes the initiation and development of rock microcracks, pushes forward the development of the compaction stage, and increases the strain at the full compaction point. In addition, the damage constitutive model with the combination of different disturbances and loads was constructed in stages according to the principle of statistics, and the experimental results were used to verify the damage constitutive model. The crack closure and failure of the samples subjected to different impact times were well reflected in the model, and the practical significance of the parameters in the model was analyzed. The failure mode of the sample subjected to different impact times is discussed and verified by acoustic emission technology.

The microwave-derived vegetation optical depth (VOD) products were used to monitor aboveground biomass (AGB) at regional to global scales, but the ability of VOD to monitor AGB in China is uncertain. This study evaluated the sensitivity of four VOD products (e.g. L-VOD, IB-VOD, LPDR-VOD, and Liu-VOD) and optical vegetation indices (VI) (e.g. NDVI, EVI, LAI, and tree cover from MODIS) to the AGB across China. Our results showed tree cover product has the highest spatial agreement with reference AGBs (indicated by the median correlation value of 0.85), followed by L-VOD (with a median correlation value of 0.80), which performs better than other VIs and VODs. Further comparisons between reference and estimated AGB computed using the fitted logistic regression showed that AGB estimations from tree cover and L-VOD outperformed the estimations from other VIs and VODs over most vegetation types (except forest), indicated by the higher median correlation value of 0.86 and 0.83 and lower RMSD of 23.9 and 27.3 Mg/ha, respectively. The good performance of tree cover could be partly due to that tree cover product is not independent from the reference AGBs. The good performance of L-VOD can be explained by its higher sensitivity to the vegetation characteristics of the entire canopy (including woody component), relative to other VODs and VIs. Among the six reference AGB products, Saatchi-WT and Saatchi-RF products were found to have the best correlations with VIs and VODs. This study demonstrates that microwave VODs, particularly L-VOD, are effective proxies for large-scale monitoring of vegetation AGB in China.

This study explores the control framework for the trajectory tracking problem concerning unmanned surface vessels (USVs) in the presence of time-varying communication delays. To address the aforementioned problem, a novel networked predictive sliding mode control architecture is proposed by integrating a discrete sliding mode control technique and predictive control scheme. By leveraging a first-order forward Euler discretization approach, a discrete-time model of USVs was initially formulated. Then, a virtual velocity controller was developed to convert the position tracking into expected velocity tracking, which was achieved by utilizing a sliding mode control. Subsequently, a networked predictive control technique was performed to compensate for the time-varying delays. Finally, theoretical analysis and extensive comparative simulation tests demonstrated that the proposed control scheme guaranteed complete compensation for time-varying delays while ensuring the stability of the closed-loop system.

In the realm of advanced optical fiber sensing (OFS) technologies, Fiber Bragg Grating (FBG) has garnered widespread application in the monitoring of temperature, strain, and external refractive indices, particularly within high-radiation environments such as high-energy physics laboratories, nuclear facilities, and space satellites. Notably, FBGs inscribed using femtosecond lasers are favored for their superior radiation resistance. Among various inscription techniques, the point-by-point (PbP) and line-by-line (LbL) methods are predominant; however, their comparative impacts on radiation durability have not been adequately explored. In this research, FBGs were inscribed on a single-mode fiber using both the PbP and LbL methods, and subsequently subjected to a total irradiation dose of 5.04 kGy (radiation flux of 2 rad/s) over 70 h in a 60Co-γ radiation environment. By evaluating the changes in temperature- and strain-sensing performance of the FBG pre-irradiation and post-irradiation, this study identifies a more favorable technique for writing anti-irradiation FBG sensors. Moreover, an analysis into the radiation damage mechanisms in optical fibers, alongside the principles of femtosecond laser inscription, provides insights into the enhanced radiation resistance observed in femtosecond laser-written FBGs. This study thus furnishes significant guidance for the development of highly radiation-resistant FBG sensors, serving as a critical reference in the field of high-performance optical fiber sensing technologies.

The ScanSAR mode image obtained by the Gaofen-3 (GF-3) satellite has an imaging width of up to 130–500 km, which is of great significance in monitoring oceanography, meteorology, water conservancy, and transportation. To address the issues of subswath misalignment and the inability to correct in the processing of GF-3 ScanSAR images in coastal areas using software such as PIE, ENVI, and SNAP, a method for mosaicking and correcting GF-3 ScanSAR images with subswaths that overlap within specified range constraints is proposed. This method involves correlating the coefficients of each subswath thumbnail image in order to determine the extent of the overlap range. Given that the matching points are constrained to the overlap between subswaths, the normalized cross-correlation (NCC) matching algorithm is utilized to calculate the matching points between subswaths. Subsequently, the random sampling consistency (RANSAC) algorithm is employed to eliminate the mismatching points. Subsequently, the subswaths should be mosaicked together with the stitching translation of subswaths, based on the coordinates of the matching points. The image brightness correction coefficient is calculated based on the average grayscale value of pixels in the overlapping region. This is performed in order to correct the grayscale values of adjacent subswaths and thereby reducing the brightness difference at the junction of subswaths. The entire ScanSAR slant range image is produced. By employing the Range–Doppler model for indirect orthorectification, corrected images with geographic information are generated. The experiment utilized three coastal GF-3 ScanSAR images for mosaicking and correction, and the results were contrasted with those attained through PIE software V7.0 processing. This was conducted to substantiate the efficacy and precision of the methodology for mosaicking and correcting coastal GF-3 ScanSAR images.

The semi-autogenous (SAG) mill is crucial equipment in the beneficiation process, and power consumption is a key indicator of its operational status. Due to the complex and variable operating environment, the power consumption of the SAG mill has the characteristics of strong coupling of multiple factors, nonlinearity and uncertainty. In order to effectively extract the features that affect the mill power consumption prediction performance and dynamically adjust the weights of each feature, we propose a hybrid prediction model based on channel attention convolutional network (CACN) and long short-term memory (LSTM). The CACN-based network extracts high-dimensional features of input parameters and dynamically assigns weights to them to better capture the key features that characterize the power consumption of the SAG mill, and the LSTM captures long-term dependencies to enable accurate prediction of SAG mill power consumption. To validate the superiority of the proposed method, actual hourly power consumption data from a SAG mill in the beneficiation plant in Yunnan Province is utilized, and experiments are conducted comparing it with models such as GRU, ARIMA, SVM, LSTM, TCN, CNN-GRU, and CNN-LSTM. Experimental results confirm that the proposed model has better prediction performance than other models, and indicators such as R2 have increased by at least 5%.

Abstract Glucagon receptor (GCGR) agonism offers potentially greater effects on the mitigation of hepatic steatosis. However, its underlying mechanism is not fully understood. Here, it screened tetraspanin CD9 might medicate hepatic effects of GCGR agonist. CD9 is decreased in the fatty livers of patients and upregulated upon GCGR activation. Deficiency of CD9 in the liver exacerbated diet‐induced hepatic steatosis via complement factor D (CFD) regulated fatty acid metabolism. Specifically, CD9 modulated hepatic fatty acid synthesis and oxidation genes through regulating CFD expression via the ubiquitination‐proteasomal degradation of FLI1. In addition, CD9 influenced body weight by modulating lipogenesis and thermogenesis of adipose tissue through CFD. Moreover, CD9 reinforcement in the liver alleviated hepatic steatosis, and blockage of CD9 abolished the remission of hepatic steatosis induced by cotadutide treatment. Thus, CD9 medicates the hepatic beneficial effects of GCGR signaling, and may server as a promising therapeutic target for hepatic steatosis.

In the quest for sustainable, high-yield crop production, achieving the optimal balance between nitrogen (N) and potassium (K) stands as a pivotal goal. We carried out a 3-year field experiment to evaluate the impacts of the combined application of three N rates (90, 120,180 kg N ha−1) and three K rates (120, 160, 210 kg K2O ha−1) on rice yield, fertilizer use efficiency, greenhouse gas (GHG) emissions, global warming potential (GWP), GHG intensity (GHGI), and net ecosystem economic benefits (NEEB) in rice paddy. Our results showed that increasing K under the same N treatment proves beneficial for yield gains (average increase of 4.8 %). Such beneficial effect is also reflected in different N treatments, evidenced by the comparable grain yields between N120 × K160 (9.1 t ha−1) and N180×K120 (9.2 t ha−1). Increasing K significantly enhanced N use efficiency (NUE) through the stimulation of NR, GS/GOGAT, and GDH enzyme activities. This enhancement, along with the strengthened radiation use efficiency (4.7 %), contributed to an overall improvement in grain yield. Compared with N180×K120, both rice varieties, under N120×K160 treatment, showed higher NUE with a 47.6 % increase in PFPN, 12.1 % in NHI, 7.6 % in NUEg, and 9.5 % in NUEb, respectively. Higher NUE also reduced GHGI and improved NEEB. Compared with N180 ×K120 treatment, the GWP and GHGI under N120×K160 treatment decreased by 32.7 % and 31.0 %, while the NEEB increased by $31 ha−1. The composite sustainability scores suggested that the N120×K160 treatment was generally more sustainable compared with other treatments. These findings underscore the significance of optimizing N and K application ratios in rice paddy cultivation, not only for maximizing yields and nutrient use efficiency but also for reducing greenhouse gas emissions and enhancing economic sustainability, ultimately promoting a more environmentally and economically responsible approach to rice farming.

BackgroundPrevious studies based on resting-state functional magnetic resonance imaging(rs-fMRI) and voxel-based morphometry (VBM) have demonstrated significant abnormalities in brain structure and resting-state functional brain activity in patients with early-onset schizophrenia (EOS), compared with healthy controls (HCs), and these alterations were closely related to the pathogenesis of EOS. However, previous studies suffer from the limitations of small sample sizes and high heterogeneity of results. Therefore, the present study aimed to effectively integrate previous studies to identify common and specific brain functional and structural abnormalities in patients with EOS.MethodsThe PubMed, Web of Science, Embase, Chinese National Knowledge Infrastructure (CNKI), and WanFang databases were systematically searched to identify publications on abnormalities in resting-state regional functional brain activity and gray matter volume (GMV) in patients with EOS. Then, we utilized the Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) software to conduct a whole-brain voxel meta-analysis of VBM and rs-fMRI studies, respectively, and followed by multimodal overlapping on this basis to comprehensively identify brain structural and functional abnormalities in patients with EOS.ResultsA total of 27 original studies (28 datasets) were included in the present meta-analysis, including 12 studies (13 datasets) related to resting-state functional brain activity (496 EOS patients, 395 HCs) and 15 studies (15 datasets) related to GMV (458 EOS patients, 531 HCs). Overall, in the functional meta-analysis, patients with EOS showed significantly increased resting-state functional brain activity in the left middle frontal gyrus (extending to the triangular part of the left inferior frontal gyrus) and the right caudate nucleus. On the other hand, in the structural meta-analysis, patients with EOS showed significantly decreased GMV in the right superior temporal gyrus (extending to the right rolandic operculum), the right middle temporal gyrus, and the temporal pole (superior temporal gyrus).ConclusionThis meta-analysis revealed that some regions in the EOS exhibited significant structural or functional abnormalities, such as the temporal gyri, prefrontal cortex, and striatum. These findings may help deepen our understanding of the underlying pathophysiological mechanisms of EOS and provide potential biomarkers for the diagnosis or treatment of EOS.

Abstract The abnormal deformation of urban surfaces threatens the human living environment, and extreme regional weather can affect the response law of surface deformation. To explore the changes in surface time series response after extreme weather and the causes of deformation in Zhengzhou, the MTInSAR was used to obtain the surface deformation from 2020 to 2022, and the time series changes of groundwater equivalent water height were retrieved by GRACE. The results show that: (a) There are three large subsidence bowls in Zhengzhou, and the maximum subsidence rate is −40.2 mm/yr. (b) The extreme rainstorm in Zhengzhou alleviated the surface deformation quickly, lasting approximately 6 months. However, surface subsidence still occurred after the extreme rainstorm. The water storage coefficient of the elastic skeleton in the Zhengzhou area showed an increasing trend. (c) Precipitation can lead to surface uplift by influencing the change of groundwater level. There is a delay time of 0.75–1 month between groundwater level change and surface uplift response in the characteristic region. These results provide scientific data support and causal analysis for disaster prevention and reduction of abnormal deformation in Zhengzhou.

ObjectivesPatients with type 1 diabetes (T1D) face unique challenges in glycaemic control due to the complexity and uniqueness of the dietary structure in China, especially in terms of postprandial glycaemic response (PPGR). This study aimed to establish a personalized model for predicting PPGR in patients with T1D.Materials and methodsData provided by the First People’s Hospital of Yunnan Province, 13 patients with T1D, were recruited and provided with an intervention for at least two weeks. All patients were asked to wear a continuous glucose monitoring (CGM) device under free-living conditions during the study period. To tackle the challenge of incomplete data from wearable devices for CGM measurements, the GAIN method was used in this paper to achieve a more rational interpolation process. In this study, patients’ PPGRs were calculated, and a LightGBM prediction model was constructed based on a Bayesian hyperparameter optimisation algorithm and a random search algorithm, which integrated glucose measurement, insulin dose, dietary nutrient content, blood measurement and anthropometry as inputs.ResultsThe experimental outcomes revealed that the PPGR prediction model presented in this paper demonstrated superior accuracy (R=0.63) compared to both the carbohydrate content only model (R=0.14) and the baseline model emulating the standard of care for insulin administration (R=0.43). In addition, the interpretation of the model using the SHAP method showed that blood glucose levels at meals and blood glucose trends 30 minutes before meals were the most important features of the model.ConclusionThe proposed model offers a heightened precision in predicting PPGR in patients with T1D, so it can better guide the diet plan and insulin intake dose of patients with T1D.

The geometric statistical characteristics of rock mass discontinuities, such as mean trace length and intensity, cannot be measured directly and require the use of statistical tools (such as sampling lines, sampling windows, and drilling) for analysis. In this paper, we propose a method for extracting discontinuity characteristics using the 3DEC software in three-dimensional (3D) drift models (3DM). Specifically, we introduce the Surface Disk Discontinuity Model (SDDM) as a representation model for planar and linear discontinuities identified in point clouds. By importing the 3DM and SDDM into 3DEC and utilizing a multi-circular window, we calculate the mean trace lengths and two-dimensional (2D) intensity (P21). Additionally, we propose a method to estimate the three-dimensional (3D) intensity (P32), which provides a more comprehensive reflection of the discontinuous characteristics in different spatial orientations. To validate the proposed method, we apply it to the underground drift of the Jiapigou gold mine in Jilin Province to extract the geometric statistical characteristics of discontinuities. The results demonstrate that our method successfully captures the orientation, trace length, and P32 characteristics of four dominant discontinuity groups in the study area. This approach proves to be an effective technique for utilizing 3D laser scanning to obtain accurate discontinuity characteristics of rock mass.

Summary: Metabolic diseases such as obesity and diabetes induce lipotoxic cardiomyopathy, which is characterized by myocardial lipid accumulation, dysfunction, hypertrophy, fibrosis and mitochondrial dysfunction. Here, we identify that mitochondrial glycerol 3-phosphate dehydrogenase (mGPDH) is a pivotal regulator of cardiac fatty acid metabolism and function in the setting of lipotoxic cardiomyopathy. Cardiomyocyte-specific deletion of mGPDH promotes high-fat diet induced cardiac dysfunction, pathological hypertrophy, myocardial fibrosis, and lipid accumulation. Mechanically, mGPDH deficiency inhibits the expression of desuccinylase SIRT5, and in turn, the hypersuccinylates majority of enzymes in the fatty acid oxidation (FAO) cycle and promotes the degradation of these enzymes. Moreover, manipulating SIRT5 abolishes the effects of mGPDH ablation or overexpression on cardiac function. Finally, restoration of mGPDH improves lipid accumulation and cardiomyopathy in both diet-induced and genetic obese mouse models. Thus, our study indicates that targeting mGPDH could be a promising strategy for lipotoxic cardiomyopathy in the context of obesity and diabetes.

Abstract Rockbursts are a complex phenomenon characterized by a violent ejection of rock fragments from the free face of deep underground mines. Due to the sudden, disruptive, and complex nature of these events, accurate predictions of rockbursts are difficult. Therefore, geotechnical intervention is imperative to prevent the occurrence of a rockburst. The scenarios of microwave destressing is introduced here as an application method to potentially prevent their occurrence. To understand the influences of microwave destressing, such as microwave exposure duration, spatial layout, and geometric parameters of the microwave preconditioned zone (MPZ), on the occurrence of a rockburst, numerical simulation was conducted based on an impact-induced rockburst model. The feasibility of microwave destressing technology was validated numerically. This paper also provides guidance for the utilization of microwave destressing in practice. Discontinuous MPZ is suitable for a known blasting source with a fixed position, and continuous MPZ can be applied for a working condition with multiple blasting sources with variable positions. A closer distance of MPZ from the free face results in a better microwave destressing effect. However, the closer distance of MPZ from the free face also introduces more negative influences on the static stability of surrounding rock.

Abstract Background In the classification of bruxism patients based on electroencephalogram (EEG), feature extraction is essential. The method of using multi-channel EEG fusing electrocardiogram (ECG) and Electromyography (EMG) signal features has been proved to have good performance in bruxism classification, but the classification performance based on single channel EEG signal is still understudied. We investigate the efficacy of single EEG channel in bruxism classification. Methods We have extracted time-domain, frequency-domain, and nonlinear features from single EEG channel to classify bruxism. Five common bipolar EEG recordings from 2 bruxism patients and 4 healthy controls during REM sleep were analyzed. The time domain (mean, standard deviation, root mean squared value), frequency domain (absolute, relative and ratios power spectral density (PSD)), and non-linear features (sample entropy) of different EEG frequency bands were analyzed from five EEG channels of each participant. Fine tree algorithm was trained and tested for classifying sleep bruxism with healthy controls using five-fold cross-validation. Results Our results demonstrate that the C4P4 EEG channel was most effective for classification of sleep bruxism that yielded 95.59% sensitivity, 98.44% specificity, 97.84% accuracy, and 94.20% positive predictive value (PPV). Conclusions Our results illustrate the feasibility of sleep bruxism classification using single EEG channel and provides an experimental foundation for the development of a future portable automatic sleep bruxism detection system.

Abstract Background The main challenge in personalized treatment of breast cancer (BC) is how to integrate massive amounts of computing resources and data. This study aimed to identify a novel molecular target that might be effective for BC prognosis and for targeted therapy by using network-based multidisciplinary approaches. Methods Differentially expressed genes (DEGs) were first identified based on ESTIMATE analysis. A risk model in the TCGA-BRCA cohort was constructed using the risk score of six DEGs and validated in external and clinical in-house cohorts. Subsequently, independent prognostic factors in the internal and external cohorts were evaluated. Cell viability CCK-8 and wound healing assays were performed after PTGES3 siRNA was transiently transfected into the BC cell lines. Drug prediction and molecular docking between PTGES3 and drugs were further analyzed. Cell viability and PTGES3 expression in two BC cell lines after drug treatment were also investigated. Results A novel six-gene signature (including APOOL, BNIP3, F2RL2, HINT3, PTGES3 and RTN3) was used to establish a prognostic risk stratification model. The risk score was an independent prognostic factor that was more accurate than clinicopathological risk factors alone in predicting overall survival (OS) in BC patients. A high risk score favored tumor stage/grade but not OS. PTGES3 had the highest hazard ratio among the six genes in the signature, and its mRNA and protein levels significantly increased in BC cell lines. PTGES3 knockdown significantly inhibited BC cell proliferation and migration. Three drugs (gedunin, genistein and diethylstilbestrol) were confirmed to target PTGES3, and genistein and diethylstilbestrol demonstrated stronger binding affinities than did gedunin. Genistein and diethylstilbestrol significantly inhibited BC cell proliferation and reduced the protein and mRNA levels of PTGES3. Conclusions PTGES3 was found to be a novel drug target in a robust six-gene prognostic signature that may serve as a potential therapeutic strategy for BC.

Abstract Background The enrichment of peri-cancerous adipose tissue is a distinctive feature of colorectal cancer (CRC), accelerating disease progression and worsening prognosis. The communication between tumor cells and adjacent adipocytes plays a crucial role in CRC advancement. However, the precise regulatory mechanisms are largely unknown. This study aims to explore the mechanism of migration and invasion inhibitory protein (MIIP) downregulation in the remodeling of tumor cell-adipocyte communication and its role in promoting CRC. Results MIIP expression was found to be decreased in CRC tissues and closely associated with adjacent adipocyte browning. In an in vitro co-culture model, adipocytes treated with MIIP-downregulated tumor supernatant exhibited aggravated browning and lipolysis. This finding was further confirmed in subcutaneously allografted mice co-injected with adipocytes and MIIP-downregulated murine CRC cells. Mechanistically, MIIP interacted with the critical lipid mobilization factor AZGP1 and regulated AZGP1’s glycosylation status by interfering with its association with STT3A. MIIP downregulation promoted N-glycosylation and over-secretion of AZGP1 in tumor cells. Subsequently, AZGP1 induced adipocyte browning and lipolysis through the cAMP-PKA pathway, releasing free fatty acids (FFAs) into the microenvironment. These FFAs served as the primary energy source, promoting CRC cell proliferation, invasion, and apoptosis resistance, accompanied by metabolic reprogramming. In a tumor-bearing mouse model, inhibition of β-adrenergic receptor or FFA uptake, combined with oxaliplatin, significantly improved therapeutic efficacy in CRC with abnormal MIIP expression. Conclusions Our data demonstrate that MIIP plays a regulatory role in the communication between CRC and neighboring adipose tissue by regulating AZGP1 N-glycosylation and secretion. MIIP reduction leads to AZGP1 oversecretion, resulting in adipose browning-induced CRC rapid progression and poor prognosis. Inhibition of β-adrenergic receptor or FFA uptake, combined with oxaliplatin, may represent a promising therapeutic strategy for CRC with aberrant MIIP expression.

Abstract Organic light-emitting diodes (OLEDs) exploiting simple binary emissive layers (EMLs) blending only emitters and hosts have natural advantages in low-cost commercialization. However, previously reported OLEDs based on binary EMLs hardly simultaneously achieved desired comprehensive performances, e.g., high efficiency, low efficiency roll-off, narrow emission bands, and high operation stability. Here, we report a molecular-design strategy. Such a strategy leads to a fast reverse intersystem crossing rate in our designed emitter h-BNCO-1 of 1.79×105 s−1. An OLED exploiting a binary EML with h-BNCO-1 achieves ultrapure emission, a maximum external quantum efficiency of over 40% and a mild roll-off of 14% at 1000 cd·m−2. Moreover, h-BNCO-1 also exhibits promising operational stability in an alternative OLED exploiting a compact binary EML (the lifetime reaching 95% of the initial luminance at 1000 cd m−2 is ~ 137 h). Here, our work has thus provided a molecular-design strategy for OLEDs with promising comprehensive performance.

High-k metal oxides are gradually replacing the traditional SiO2 dielectric layer in the new generation of electronic devices. In this paper, we report the production of five-element high entropy metal oxides (HEMOs) dielectric films by solution method and analyzed the role of each metal oxide in the system by characterizing the film properties. On this basis, we found optimal combination of (AlGaTiYZr)Ox with the best dielectric properties, exhibiting a low leakage current of 1.2 × 10−8 A/cm2 @1 MV/cm and a high dielectric constant, while the film’s visible transmittance is more than 90%. Based on the results of factor analysis, we increased the dielectric constant up to 52.74 by increasing the proportion of TiO2 in the HEMOs and maintained a large optical bandgap (>5 eV). We prepared thin film transistors (TFTs) based on an (AlGaTiYZr)Ox dielectric layer and an InGaZnOx (IGZO) active layer, and the devices exhibit a mobility of 18.2 cm2/Vs, a threshold voltage (Vth) of −0.203 V, and an subthreshold swing (SS) of 0.288 V/dec, along with a minimal hysteresis, which suggests a good prospect of applying HEMOs to TFTs. It can be seen that the HEMOs dielectric films prepared based on the solution method can combine the advantages of various high-k dielectrics to obtain better film properties. Moreover, HEMOs dielectric films have the advantages of simple processing, low-temperature preparation, and low cost, which are expected to be widely used as dielectric layers in new flexible, transparent, and high-performance electronic devices in the future.

In this paper, a novel technique for connecting residential shear walls to floor slabs is investigated. Shear wall structures with two different connection methods were established and numerically analyzed using ABAQUS (2024) finite element software. The two structures were connected by sleeve grouted connections (hereinafter referred to as the original structure) and profile + bolted connections (hereinafter referred to as the new structure). Numerical analyses yielded a positive maximum load for the new structure 1.41 times that of the original structure and a negative maximum load 1.12 times that of the original structure. The ratio of the ultimate tensile strength (load value corresponding to the peak point) to the yield strength (load value corresponding to the yield point) of the two structures (strength-to-yield ratio) was in the range of 1.15–1.27. The original structure was 2.62 times more ductile in the negative direction and 2.24 times more ductile in the positive direction than the new structure. The stiffness degradation of the new structure was greater in the later stages of loading, and that of the original structure was greater in the early stages of loading. The original structure had 1.08 times the energy-consuming capacity of the new structure. The cost of labor and materials for the original structure was approximately 1.50 times the cost of the new structure. The results of the data analysis showed that, compared to the original structure, the new structure had comparable performance in terms of strength-to-flexure ratio, ductility, stiffness degradation, and energy dissipation capacity. However, the new structure was more advantageous in terms of load-bearing capacity and required lower construction costs than the original structure. Therefore, the connection nodes designed in this paper are of great significance for engineering practice.

Microwave-assisted mechanical rock breaking represents an innovative technology in the realm of mining excavation. The intricate and variable characteristics of geological formations necessitate a comprehensive understanding of the interplay between microwave-induced rock damage and the subsequent deterioration in rock strength. This study conducted microwave irradiation damage assessments on 78 distinct rock samples, encompassing granite, sandstone, and marble. A total of ten critical parameters were identified: Microwave Irradiation Time (MIT), Microwave Irradiation Power (MIP), Longitudinal Wave Velocity prior to Microwave Treatment (LWVB), Longitudinal Wave Velocity post-Microwave Treatment (LWVA), Percentage Decrease in Longitudinal Wave Velocity (LWVP), Porosity before Microwave Treatment (PB), Porosity after Microwave Treatment (PA), Percentage Increase in Porosity (PP), and Uniaxial Compressive Strength following Microwave Treatment (UCSA). Utilizing the Pied Kingfisher Optimizer (PKO) alongside Extreme Gradient Boosting (XGBoost), we developed a PKO-XGBoost machine learning model to elucidate the relationship between UCSA and the nine additional parameters. This model was benchmarked against other prevalent machine learning frameworks, with Shapley additive explanatory methods employed to assess each parameter’s influence on UCSA. The findings reveal that the PKO-XGBoost model provides superior accuracy in delineating relationships among rock physical properties, microwave irradiation variables, microscopic attributes of rocks, and UCSA. Notably, PA emerged as having the most significant effect on UCSA, indicating that microwave-induced microscopic damage is a primary contributor to reductions in rock strength. Additionally, MR exhibited substantial influence; under identical microwave irradiation conditions, rocks with lower density demonstrated greater susceptibility to strength degradation. Furthermore, during microwave-assisted rock breaking operations, it is imperative to establish optimal MIT and MIP values to effectively diminish UCSA while facilitating mechanical cutting processes. The insights derived from this research offer a more rapid, cost-efficient approach for accurately assessing correlations between microwave irradiation parameters and resultant rock damage—providing essential data support for enhancing mechanical rock-breaking efficiency.

This is an eassay in the field of process mineralogy. Using optical microscope, chemical multi-element analysis, chemical phase analysis, X-ray diffraction, scanning electron microscopy-energy spectrometer and other comprehensive methods, the process mineralogical characteristics of a pegmatite lithium beryllium ore were studied. The mineral composition, chemical properties, symbiosis relationship of minerals, particle size distribution of main minerals and connection growth characteristics in the ore were ascertained. On this basis, the occurrence state of valuable elements such as lithium and beryllium in the ore and the mineralogical factors affecting the recovery of valuable elements such as lithium and beryllium in the ore are described, which provides a basis for the beneficiation work of the mine.

This is an essay in the field of mining engineering, which introduces the research results of process mineralogy and mineral processing technology of a low-grade rubidium ore from Qinghai Province. By means of chemical analysis, microscopic identification, MLA and electron probe microanalysis, the occurrence of rubidium in the ore is found out. The main useful element of the ore is Rb, and the main rubidium bearing minerals are mica and potash feldspar. On this basis, the combined process of crushing, coarse-grained mica screening, magnetic gravity gravity flotation was used to separate the ore, and Nb, Ta and Rb were recovered from the ore. The test results show that the lithium, rubidium and cesium in the flotation mica concentrate has good leachability, and the operation leaching rate can reach more than 82%.

Abstract The present study endeavors to unveil the progression of research subjects within the realm of China’s ecotranslatology studies through the examination of journal papers in the databases of China National Knowledge Infrastructure (CNKI) and Web of Science (WOS) from 2001 to 2022, utilizing CiteSpace software for mapping and analyzing cooccurrences among authors, institutions, and keywords. The findings indicate that within ecotranslatology studies, a prominent theory-founding research group represented by Hu Gengshen has been established, yet no comparable clusters have been identified among scholars engaged in debate or applied studies. Additionally, a lack of effective cooperation and diversity among research entities as well as inadequate responses to questions and concerns have been observed. Furthermore, research focuses have undergone a shift, progressing from theory founding studies (2001–2008) to confirmation and verification studies (2009–2014) and finally to revision and applied studies (since 2015). Additionally, research topics have transitioned from grand, abstract theoretical foundations to subtle, specific theory revisions and applied translation studies, yet a dearth of quantitative research, comparative studies, or field studies have been conducted. In conclusion, it is suggested that future ecotranslatology studies should be more responsive to doubts and questions, cultivate diverse research entities, and place greater emphasis on field investigation within qualitative research.

Geothermal energy is a kind of clean energy with a large amount of energy. Some energy problems can be solved by collecting its heat and using thermoelectric generators. This paper studies a thermoelectric generator that utilizes shallow geothermal energy to generate electricity. Its physical model is built including heat exchanger, generator and radiator. The heat exchanger adopts gravity heat pipe, the generator adopts thermoelectric module, and the radiator adopts fin. To improve its efficiency, the effects of different working fluids and heat pipe diameters on heat transfer and the effects of different numbers of thermoelectric modules on power generation are considered. This paper considers the effects of different working fluids, heat pipe diameters and different numbers of thermoelectric modules on heat transfer and power generation. The study found that the heat transfer effect of the heat pipe can be better when water is used as the working fluid and the large pipe diameter. Properly increasing the number of TEMs can improve the power generation. This generator does not require energy consumption and moving equipment, thus reducing maintenance requirements.

Abstract The utilization of scale invariant feature transform algorithm in synthetic-aperture radar images (SAR–SIFT) to match image features may lead to principal orientation assignments of descriptors being affected by speckle noise, thereby diminishing accuracy. In this study, we propose using the Fourier histogram of oriented ratio gradient (Fourier HORG) descriptor for robust matching of SAR images. This method is based on the SAR–SIFT algorithm framework. During feature description, the rotation-invariant Fourier HORG descriptor is established by performing Fourier analysis on the ratio gradient in the polar coordinate system, whereby the principal orientation assignment process is avoided and the robustness of SAR image registration improved. A matching experiment was conducted involving four sets of SAR image pairs, and the results demonstrated that our method exhibited higher accuracy and robustness compared to image matching based on the Fourier histogram of oriented gradient (Fourier HOG) descriptor and the SAR–SIFT algorithm, thus confirming the effectiveness of our proposed method.

ObjectiveThis study aimed to investigate the association between the risk of suicidal behaviors and student-supervisor relationships and subjective family socioeconomic status (SFSS) in medical graduate students, and to propose preventive strategies to reduce the suicidal risk among medical graduate students.Materials and methodsA total of 1,310 validated questionnaires were collected from medical graduate students, which included demographic information, study programs, the Suicidal Behaviors Questionnaire-Revised (SBQ-R) questionnaire, the Leader-Member Exchange 7 (LMX-7) questionnaire, and SFSS by MacArthur Scale. Multiple regression analysis was employed to examine the associations between variables and adjust for confounders. A moderation analysis, containing simple slope analysis and Johnson-Neyman interval plots were used to analyze the moderating effect of the SFSS in the association of SBQ-R and LMX-7 scores.ResultsA total of 88 participants (6.7%) were at risk of suicidal behaviors. In the high-quality student-supervisor relationship group (LMX-7 score ≥ 25), SFSS was significantly higher than in the low- and moderate-quality relationship group (p=0.002). The median SBQ-R score and proportion of suicide risk was significantly lower (p

AimThe objective of this study is to explore the relationship between sex and jaw function and to test whether anxiety mediates the causal relationship between sex and jaw function in temporomandibular disorders (TMDs) patients.MethodsA total of 488 participants with TMD were included in the analysis. Demographic data were collected. Generalized anxiety symptoms and anxiety severity were initially assessed using the GAD-7 questionnaire. And jaw function limitation was measured using the JFLS-8 scale. A directed acyclic graph (DAG) was used in this study to evaluate the hypotheses. Mediation analysis was conducted to explore causality and to calculate the total effect, natural direct effect (NDE) and natural indirect effect (NIE).ResultsIn TMD patients, there was a significant association between female and jaw function (r = 0.17, p

Chrysanthemi indic Flos (CIF) has been commonly consumed for the treatment of inflammation and related skin diseases. However, the potential bioactive components responsible for its anti-inflammatory and sensitive skin (SS) improvement activities, and the correlated mechanisms of action still remain unknown. In this work, it was firstly found that the CIF extract (CIFE) displayed arrestive free radical scavenging activity on DPPH and ABTS radicals, with no significant difference with positive control Trolox (p > 0.05). Then, compared to the negative group, CIFE markedly decreased the productions of the pro-inflammatory cytokines (IL-1β, IL-6, PEG2, TNF-α, IFN-γ, NO) in LPS induced RAW264.7 cells in a dose-dependent manner (p < 0.01). Besides, CIFE strongly inhibited the COX-2 and hyaluronidase (HAase) with the IC50 values of 1.06 ± 0.01 μg/mL and 12.22 ± 0.39 μg/mL, indicating higher inhibitory effect than positive control of aspirin of 6.33 ± 0.05 μg/mL (p < 0.01), and comparable inhibitory effect with indometacin of 0.60 ± 0.03 μg/mL, and ascorbic acid of 11.03 ± 0.41 μg/mL (p > 0.05), respectively. Furthermore, kinetic assays with Lineweaver-Burk plot (Michaelis Menten equation) suggested that CIFE reversibly inhibited the COX-2 and HAase, with a mixed characteristics of competitive and non-competitive inhibition. Thereafter, multi-target affinity ultrafiltration liquid chromatography-mass spectrometry (UF-LC/MS) method was employed to fast fish out the potential COX-2 and HAase in CIFE. Herein, 13 components showed various affinity binding degrees to the COX-2 and HAase, while those components with relative binding affinity (RBA) value higher than 3.0, such as linarin and chlorogenic acid isomers, were deemed to be the most bioactive components for the anti-inflammatory and SS improvement activities of CIFE. Finally, the interaction mechanism, including binding energy, inhibition constant, docking sites, and the key amino acids involved in hydrogen bonds between the potential ligands and COX-2/HAase were simulated and confirmed with the molecule docking analysis. In summary, this study showcased the prominent anti-inflammatory and SS improvement activities of CIF, which would provide further insights on this functional medicinal plant to be a natural anti-SS remedy.

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) image characteristics, such as large geometric distortions, striped inhomogeneous radiation, severe speckle noise and ultra-high resolution, pose significant challenges to multi-view UAV SAR image registration. To address these issues, we propose a robust multi-view UAV SAR image registration algorithm based on the selective correlation of the log gradient descriptor. First, slant range UAV SAR images are geocoded using a publicly available digital elevation model to attenuate the topographic distortions. Second, we follow a template matching framework to obtain gridded correspondences. A log gradient that effectively handles noise in dark areas while resisting speckle noise is developed. A histogram of oriented log gradient (HOLG) is designed based on a simplified histogram of oriented gradient structure to weaken the effects of striped radiation noise. Moreover, a selective correlation strategy is designed, and a similarity metric named HOLGSC is proposed based on the selective correlation of the HOLG descriptors to overcome the effects of the unstable geometric structures of shadows and trees. Finally, after removing outliers from the matching results, we introduce the as-projective-as possible transformation model to establish geometric correspondences. We obtain several UAV SAR images and compose nine multi-view UAV SAR image pairs for experiments. The experimental results confirm that the proposed algorithm is effective and outperforms the existing state-of-the-art algorithms. In addition, a preliminary stitching result of these UAV SAR images is also presented.

Abstract Podocytes are particularly sensitive to lipid accumulation, which has recently emerged as a crucial pathological process in the progression of proteinuric kidney diseases like diabetic kidney disease and focal segmental glomerulosclerosis. However, the underlying mechanism remains unclear. Here, podocytes predominantly expressed protein dedicator of cytokinesis 5 (Dock5) is screened to be critically related to podocyte lipid lipotoxicity. Its expression is reduced in both proteinuric kidney disease patients and mouse models. Podocyte‐specific deficiency of Dock5 exacerbated podocyte injury and glomeruli pathology in proteinuric kidney disease, which is mainly through modulating fatty acid uptake by the liver X receptor α (LXRα)/scavenger receptor class B (CD36) signaling pathway. Specifically, Dock5 deficiency enhanced CD36‐mediated fatty acid uptake of podocytes via upregulating LXRα in an m6A‐dependent way. Moreover, the rescue of Dock5 expression ameliorated podocyte injury and proteinuric kidney disease. Thus, the findings suggest that Dock5 deficiency is a critical contributor to podocyte lipotoxicity and may serve as a promising therapeutic target in proteinuric kidney diseases.

Abstract Transport infrastructure allows society to function. Such systems continuously improve through manufacturing transformation and technology upgrading. However, its metabolism mechanism of material degradation and quantities from consumption to obsolescence remains unclear. Here we estimate the recycling potential of typical large-scale public transport vehicles (LPTV) in China, in particular, predicting the end-of-life quantity of railway and aviation equipment between 2000 and 2050. Their total recycling potential has been experiencing rapid growth. The total obsolescence mass in 2020 exceeded 33 million tons, and it is expected to reach another 74 million tons by 2050, roughly twice the amount in 2020. By 2050, waste LPTV in China will contain at least 72 million tons of steel, 838 kilotons of aluminum, 2539 tons of titanium, and 223 tons of neodymium. We also compare waste LPTV to e-waste and end-of-life private vehicles. Interestingly, their growth of generation quantity indicates a distinct industry succession from an industrial ecological perspective.