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Abstract Objective To evaluate the performance of a semi-automated artificial intelligence (AI) software program (CerebralDoc® system) in aneurysm detection and morphological measurement. Methods In this study, 354 cases of computed tomographic angiography (CTA) were retrospectively collected in our hospital. Among them, 280 cases were diagnosed with aneurysms by either digital subtraction angiography (DSA) and CTA (DSA group, n = 102), or CTA-only (non-DSA group, n = 178). The presence or absence of aneurysms, as well as their location and related morphological features determined by AI were evaluated using DSA and radiologist findings. Besides, post-processing image quality from AI and radiologists were also rated and compared. Results In the DSA group, AI achieved a sensitivity of 88.24% and an accuracy of 81.97%, whereas radiologists achieved a sensitivity of 95.10% and an accuracy of 84.43%, using DSA results as the gold standard. The AI in the non-DSA group achieved 81.46% sensitivity and 76.29% accuracy, as per the radiologists’ findings. The comparison of position consistency results showed better performance under loose criteria than strict criteria. In terms of morphological characteristics, both the DSA and the non-DSA groups agreed well with the diagnostic results for neck width and maximum diameter, demonstrating excellent ICC reliability exceeding 0.80. The AI-generated images exhibited superior quality compared to the standard software for post-processing, while also demonstrating a significantly reduced processing time. Conclusions The AI-based aneurysm detection rate demonstrates a commendable performance, while the extracted morphological parameters exhibit a remarkable consistency with those assessed by radiologists, thereby showcasing significant potential for clinical application.

Abstract Neurons rely heavily on high mitochondrial metabolism to provide sufficient energy for proper development. However, it remains unclear how neurons maintain high oxidative phosphorylation (OXPHOS) during development. Mitophagy plays a pivotal role in maintaining mitochondrial quality and quantity. We herein describe that G protein-coupled receptor 50 (GPR50) is a novel mitophagy receptor, which harbors the LC3-interacting region (LIR) and is required in mitophagy under stress conditions. Although it does not localize in mitochondria under normal culturing conditions, GPR50 is recruited to the depolarized mitochondrial membrane upon mitophagy stress, which marks the mitochondrial portion and recruits the assembling autophagosomes, eventually facilitating the mitochondrial fragments to be engulfed by the autophagosomes. Mutations Δ502-505 and T532A attenuate GPR50-mediated mitophagy by disrupting the binding of GPR50 to LC3 and the mitochondrial recruitment of GPR50. Deficiency of GPR50 causes the accumulation of damaged mitochondria and disrupts OXPHOS, resulting in insufficient ATP production and excessive ROS generation, eventually impairing neuronal development. GPR50-deficient mice exhibit impaired social recognition, which is rescued by prenatal treatment with mitoQ, a mitochondrially antioxidant. The present study identifies GPR50 as a novel mitophagy receptor that is required to maintain mitochondrial OXPHOS in developing neurons.

Abstract Postpancreatectomy hemorrhage (PPH) is an important risk factor for postoperative complications after laparoscopic pancreaticoduodenectomy (LPD). Recent studies have reported that the use of ligamentum teres hepatis (LTH) in LPD may reduce the risk of PPH. Therefore, this study aims to investigate whether wrapping the hepatic hilar artery with the LTH can reduce PPH after LPD. We reviewed the data of 131 patients who underwent LPD in our team from April 2018 to December 2023. The patients were divided into Groups A (60 patients) and B (71 patients) according to whether the hepatic portal artery was wrapped or not. The perioperative data of the two groups were compared to evaluate the effect of LTH wrapping the hepatic hilar artery on LPD. The platelet count of Group A was (225.25 ± 87.61) × 10^9/L, and that of Group B was (289.38 ± 127.35) × 10^9/L, with a statistically significant difference (p

Abstract In order to investigate the acoustic oscillation characteristics of gas–liquid pintle rocket engines and elucidate the path by which spray combustion process provides energy to the combustor pressure oscillation, a LOX/GCH4 pintle engine with rectangular combustor was designed. By adding transverse velocity disturbance for the first time, the acoustic response of spray combustion process was simulated, and the effect of excitation amplitude on acoustic response was researched. Numerical results show that the adopted transverse velocity disturbance can excite the first-order transverse acoustic oscillation with same excitation frequency in the engine combustor. The acoustic response maintenance mechanism under extrinsic excitation is summarized for pintle engines. Besides, the temperature distribution inside the engine combustor tends to be uniform, and the low-frequency oscillation caused by the flame transverse swing gradually disappears. The amplitude of combustor pressure oscillation is dominated by excitement amplitude and phase difference between the pressure and heat release in combustion reaction region. In addition, the time-averaged combustor pressure can be amplified mainly by transverse velocity disturbance. The research work can provide a reference for related fire tests on the acoustic response of a subscale gas–liquid pintle engine.

Objective Focal cervical kyphotic deformity (FCK) without neurologic compression is not uncommon in patients with cervical spondylotic myelopathy (CSM) who underwent anterior cervical decompression and fusion (ACDF) surgery. It remains unclear whether FCK at non‐responsible levels needs to be treated simultaneously. This study aims to investigate whether FCK at non‐responsible levels is the prognostic factor for CSM and elucidate the surgical indication for FCK. Methods Patients with CSM who underwent ACDF between January 2016 and April 2021 were included. Patients were divided into two groups according to the presence of FCK and two classifications according to global cervical sagittal alignment. Clinical outcomes were compared using Japanese Orthopaedic Association (JOA) scores and recovery rate (RR) of neurologic function. Univariate and multivariate analysis based on RR assessed the relationship between various possible prognostic factors and clinical outcomes. The receiver operating characteristic curve (ROC) was used to determine the optimal cutoff value of the focal Cobb angle to predict poor clinical outcomes. Results A total of 94 patients were included, 41 with FCK and 53 without. Overall, the RR of neurologic function was significantly lower in the FCK than in the non‐FCK group. Further analysis showed that the RR difference between the two groups was only observed in hypo‐lordosis classification (kyphotic and sigmoid alignment), but not in the lordosis classification. Multivariate analysis showed that the preoperative focal Cobb angle in the FCK level (OR = 0.42; 95% CI = 0.18–0.97) was independently associated with clinical outcomes in the hypo‐lordosis classification. The optimal cutoff point of the preoperative focal kyphotic Cobb angle was calculated at 4.05°. Conclusion For CSM with hypo‐lordosis, FCK was a risk factor for poor postoperative outcomes. Surgeons may consider treating the FCK simultaneously if the focal kyphotic Cobb angle of FCK is greater than 4.05° and is accompanied by cervical global kyphotic or sigmoid deformity.

Background and purpose: Diabetic retinopathy (DR) is a prevalent complication of diabetes, with a rising global incidence, and can result in significant vision impairment and potential blindness in adults. Corilagin (COR) has been shown to regulate several pathological processes. However, the specific protective role and mechanism of action of COR in DR remain unknown. Experimental approach: The protective effects and mechanisms of COR in DR were examined using the ARPE-19 cell line and C57BL/6 mice. Intraretinal tissue damage and molecular markers were evaluated to investigate the impact of COR on oxidative stress and cell death pathways. Key results: In vitro, COR significantly reduced the cytotoxic effects of high glucose (HG) on ARPE-19 cells. Furthermore, COR also effectively decreased HG-induced lipid peroxidation, iron deposition, and ferroptosis and reduced damage to retinal tight junction proteins. Similarly, an in vivo study of streptozotocin (STZ)-induced DM mice showed that the daily gavage of COR for eight weeks notably alleviated DR. Mechanistically, COR activated the Nrf2 antioxidant signaling pathway both in vivo and in vitro, preventing HG-induced alterations in morphological and biochemical parameters. Notably, our study demonstrated that compared with controls, Nrf2 knockout mice and siNrf2-treated cells were more vulnerable to ferroptosis under HG conditions, and the protective effect of COR on DR was substantially diminished in these models. Conclusion and implications: These data indicate that COR has a protective effect against HG-induced retinal injury via a mechanism associated with the Nrf2-dependent antioxidant pathway and ferroptosis regulation.

Soil respiration (Rs), the second-largest flux in the global carbon cycle, is a crucial but uncertain component. To improve the understanding of global Rs, we constructed single global models, and specific models classified by climate type, land cover type, year of the data record, and elevation range using the random forest algorithm to predict global Rs values and explore the associated uncertainty in the models. The results showed a similar overall predictive performance for the models, with an R-squared value greater than 0.63; however, significant differences were observed compared to the global Rs estimate (23 Pg C). All the models estimated larger values of Rs than the single global model, mainly owing to imbalances in the sample data on which the prediction models were based. One exception to this result is the land cover model, which estimates a smaller global Rs for 2020 (95.1 Pg C). Overall, the single global model estimates were closer to those obtained for temperate zones owing to differences in the training data distribution, which resulted in smaller global estimates than those of other classification-specific models. Prediction models using observations before 2000 tend to underestimate the global Rs. However, the use of classification-specific Rs models proved helpful in addressing the persistent temporal and spatial imbalances in Rs sampling. Expanding the coverage of Rs records both temporally and spatially and updating the global Rs database promptly would improve the estimation accuracy of global Rs prediction models while enhancing the understanding of the overall global carbon budget and the feedback of soil carbon with regard to climate warming.

Abstract Conventional liquid-phase methods lack precise control in synthesizing and processing materials with macroscopic sizes and atomic thicknesses. Water interfaces are ubiquitous and unique in catalyzing many chemical reactions. However, investigations on two-dimensional (2D) materials related to water interfaces remain limited. Here we report the growth of millimeter-sized 2D PbI2 single crystals at the water-air interface. The growth mechanism is based on an inherent ion-specific preference, i.e. iodine and lead ions tend to remain at the water-air interface and in bulk water, respectively. The spontaneous accumulation and in-plane arrangement within the 2D crystal of iodide ions at the water-air interface leads to the unique crystallization of PbI2 as well as other metal iodides. In particular, PbI2 crystals can be customized to specific thicknesses and further transformed into millimeter-sized mono- to few-layer perovskites. Additionally, we have developed water-based techniques, including water-soaking, spin-coating, water-etching, and water-flow-assisted transfer to recycle, thin, pattern, and position PbI2, and subsequently, perovskites. Our water-interface mediated synthesis and processing methods represents a significant advancement in achieving simple, cost-effective, and energy-efficient production of functional materials and their integrated devices.

Abstract Background Malignant esophageal fistula (MEF), which occurs in 5% to 15% of esophageal cancer (EC) patients, has a poor prognosis. Accurate identification of esophageal cancer patients at high risk of MEF is challenging. The goal of this study was to build and validate a model to predict the occurrence of esophageal fistula in EC patients. Methods This study retrospectively enrolled 122 esophageal cancer patients treated by chemotherapy or chemoradiotherapy (53 with fistula, 69 without), and all patients were randomly assigned to a training (n = 86) and a validation (n = 36) cohort. Radiomic features were extracted from pre-treatment CTs, clinically predictors were identified by logistic regression analysis. Lasso regression model was used for feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the clinical nomogram, radiomics-clinical nomogram and radiomics prediction model. The models were validated and compared by discrimination, calibration, reclassification, and clinical benefit. Results The radiomic signature consisting of ten selected features, was significantly associated with esophageal fistula (P = 0.001). Radiomics-clinical nomogram was created by two predictors including radiomics signature and stenosis, which was identified by logistic regression analysis. The model showed good discrimination with an AUC = 0.782 (95% CI 0.684–0.8796) in the training set and 0.867 (95% CI 0.7461–0.987) in the validation set, with an AIC = 101.1, and good calibration. When compared to the clinical prediction model, the radiomics-clinical nomogram improved NRI by 0.236 (95% CI 0.153, 0.614) and IDI by 0.125 (95% CI 0.040, 0.210), P = 0.004. Conclusion We developed and validated the first radiomics-clinical nomogram for malignant esophageal fistula, which could assist clinicians in identifying patients at high risk of MEF.

The production of selenium-enriched fish can contribute to alleviating selenium deficiency in human diets. However, it is still unclear which selenium source, as an additive, can efficiently and cost-effectively produce high-quality selenium-enriched fish. This study evaluated the effects of selenium nanoparticles (SeNP), selenite, and selenomethionine (SeMet) on the growth, antioxidant capacity, selenium content, selenium speciation, and meat quality of grass carp. Ten diets were prepared, including a basal diet (BD) and three concentrations (0.1, 0.3, and 0.9 mg/kg) of SeNP, selenite, and SeMet. A total of 600 fish (250.79 ± 1.57 g) were randomly assigned to 30 tanks (3 tanks/group). Fish were fed the experimental diet three times daily for 60 d. In this study, SeNP most significantly promoted the growth and antioxidant capacity of grass carp, with 0.3 mg/kg SeNP identified as the optimal additive concentration. Additionally, SeNP demonstrated equally excellent bioavailability as SeMet and significantly increased the content of SeMet in grass carp (Ctenopharyngodon idella) muscle. Furthermore, compared to SeMet and selenite, dietary SeNP could more significantly enhance the content of selenocysteine (SeCys2) and methylselenocysteine (MeSeCys) in grass carp muscle tissue. In addition, we have demonstrated that SeCys2 and MeSeCys promote apoptosis of cancer cells (HeLa) through the mitochondrial apoptotic pathway (involving Bax and Bcl-2). Furthermore, as an additive, 0.3 mg/kg SeNP significantly improved the flesh quality of grass carp by reducing crude fat and heavy metal content, as well as increasing the levels of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) and the ratio of n-3/n-6 polyunsaturated fatty acid (PUFA). In summary, SeNP is the most suitable additive for producing selenium-enriched fish.

Abstract Excessively high temperatures inside a power transformer can accelerate the ageing rate and even cause unplanned outages. Accurately estimating the hot‐spot temperature (HST) of a power transformer is important for regulating the load and ensuring long‐term operation. In this study, a closed‐loop model of a 110‐kV product transformer with an oil natural/air forced (ONAF) cooling mode is built to predict HST, in which the compression ratio of windings, equivalent heat source and radiator geometry are considered. According to the flow field of the winding, the ‘dead oil zone’ and reverse oil flow are observed in the horizontal oil channels. The accuracy of the proposed model is experimentally validated by temperature‐rise tests. The results show that the absolute errors between the measured results and simulation are below 5°C, and the average error is ∼2°C. Furthermore, the arrangements of windings and radiators are studied to enhance the oil circulation. By adding 5 washers to the high‐voltage winding, the average temperature can be decreased by 6°C compared with the case without a washer. Elevating the radiator can also mitigate the characteristic temperatures, and the optimal height difference between the radiator and winding centre is suggested to be 0.66 m.

The Loess Plateau in China is renowned for its dense gullies and complex terrain, with drastic changes primarily due to soil erosion and human activities, significantly affecting the evolution of the ecological environment. The complex terrains and dense vegetation make precise terrain measurement and modeling challenging. Although the development of Unmanned Aerial Vehicle (UAV) light detection and ranging (LiDAR) scanning and photogrammetry technologies has improved data acquisition precision, relying solely on one remote sensing technology struggles with accurately extracting bare earth information. This study adopted a method that fuses UAV lidar scanning with aerial photogrammetric imagery, generating detailed lidar point cloud data that includes coordinate, reflectance, true color, and texture information to enhance data classifiability and interpretability. Subsequently, a point cloud classification model based on the Transformer architecture (Stratified Transformer) is introduced to intelligently complete the initial ground point cloud extraction in complex gully terrains. Further, to address residual non-ground noise in the initial ground point clouds, a new point cloud classification optimization algorithm (MDD, Multi-scale C2M Distance Difference) is proposed. This algorithm, based on the characteristics of discrete and non-continuous with the ground surface of the noisy point clouds, effectively eliminates the discrete noisy point clouds by analyzing the distances between the point clouds and TINs (Triangular Irregular Networks) of different scales and their differences. This study effectively addresses the technical challenges of ground point cloud extraction in the mixed environment of complex terrain and vegetation, solving the problem of precise terrain measurement and intelligent data processing in complex gully terrains, and offering new technical pathways for detecting geomorphological changes.

BackgroundA substantial portion of non-obese population is afflicted with Non-alcoholic Fatty Liver Disease (NAFLD). The Triglyceride Glucose (TyG) index, a quantifier of insulin resistance magnitude, is determined by the product of fasting plasma glucose and triglyceride concentrations. The relationship between the TyG index and NAFLD within this cohort remains ambiguous.MethodsWe conducted a retrospective analysis utilizing datasets acquired from the Dryad digital repository. Non-obese participants (BMI

This paper investigates the ventilation and heat dissipation performance of a 110 kV indoor substation under natural ventilation conditions using computational fluid dynamics (CFD). The objectives are to evaluate the influences of air inlet design parameters including location and size, and transformer load, on the airflow distribution, temperature field, and cooling efficiency. The study finds that staggered opposite inlets optimize cooling uniformity without airflow attenuation. Compared to a single inlet, the maximum transformer temperature is reduced by 1.3 °C and energy utilization increases by 9.1 % with staggered inlets. Increasing the inlet length ratio initially improves cooling until an optimal point (The length ratio is 1.10), while reducing the inlet height ratio decreases airflow and efficiency. With load increasing, the intake airflow rises but at a reduced rate, and the temperature difference can exceed 15 °C under high loads. In summary, optimizing inlet design enhances natural ventilation performance in indoor substations, but limitations exist at high loads, indicating supplemental mechanical ventilation may be required.

IntroductionSea jellyfish stings pose a threat to human health, and traditional detection methods face challenges in terms of accuracy and real-time capabilities.MethodsTo address this, we propose a novel algorithm that integrates YOLOv4 object detection, an attention mechanism, and PID control. We enhance YOLOv4 to improve the accuracy and real-time performance of detection. Additionally, we introduce an attention mechanism to automatically focus on critical areas of sea jellyfish stings, enhancing detection precision. Ultimately, utilizing the PID control algorithm, we achieve adaptive adjustments in the robot's movements and posture based on the detection results. Extensive experimental evaluations using a real sea jellyfish sting image dataset demonstrate significant improvements in accuracy and real-time performance using our proposed algorithm. Compared to traditional methods, our algorithm more accurately detects sea jellyfish stings and dynamically adjusts the robot's actions in real-time, maximizing protection for human health.Results and discussionThe significance of this research lies in providing an efficient and accurate sea jellyfish sting detection algorithm for intelligent robot systems. The algorithm exhibits notable improvements in real-time capabilities and precision, aiding robot systems in better identifying and addressing sea jellyfish stings, thereby safeguarding human health. Moreover, the algorithm possesses a certain level of generality and can be applied to other applications in target detection and adaptive control, offering broad prospects for diverse applications.

Blind super-resolution (Blind SR) has become a popular research topic in computer vision in super-resolution, which aims to enhance low-resolution (LR) images with unknown or partially known degradation blur kernels. At present, most existing methods need to estimate the degradation kernel first and then use the estimated degradation kernel to get the final super-resolution result. However, degradation kernels are expected to burden the network. Moreover, the estimated degradation kernels are often inaccurate. Most existing methods fail to estimate blur kernels to produce satisfactory restoration results. In this paper, we propose a blind SR model based on deep learning, namely A Progressive Decoupled Network for Blind Super-Resolution (PDNet). The method differs from other methods since it decouples the degradation information from the super-resolution content and does not estimate the degradation kernel. The proposed method separates the degradation blur kernel while preserving super-resolution components progressively from degradation low-resolution images. To this end, a dual-path architecture is constructed, learning the combined features and the mutual relationships. Specifically, we design a decoupled representation module to exploit the coarse feature and a decoupled cross-attention module to separate the super-resolution and degradation content from the combined features. Our model can effectively recover super-resolution images with high fidelity and preserve edge details. The comprehensive experiments are conducted on various datasets and evaluate the performance of the proposed method against state-of-the-art approaches. The results demonstrate that our model outperforms existing methods in terms of both quantitative and qualitative evaluations.

Intelligent unmanned vending machines (UVMs) based on machine vision have attracted great attention in the unmanned retail industry. However, due to the complexity of practical application scenarios and environments, the existing vision-based intelligent UVMs face challenges related to missed-detection and mis-detection of product, and require costly physical components such as the infrared radio frequency sensors to capture shopping behaviors. In this study, we propose a BP-YOLO, the real-time model that integrates optimized YOLOv7 and BlazePose for product detection and shopping behaviors recognition. BP-YOLO can accurately detect the products purchased by consumers and their shopping behaviors in complex scenarios. To address the problems of missed-detection and mis-detection, we introduce the 3D attention mechanism SimAM and the deformable ConvNets v2 (DCNv2) to recombine and optimize the one-stage object detection model YOLOv7. This method reduces the interference of the invalid information in complex scenarios by adaptively weighting each channel and 3D spatial features, focuses on feature information in a sparse space, and minimizes the loss of feature information during the transmission process based on multi-scale feature extraction and fusion. To recognize and judge the shopping behaviors of consumers, we track the hand and arm key points of consumers using the pose estimation model BlazePose. Using the mAP@[0.5:0.95] as the evaluation metric for product detection, the experimental results on a customized product dataset show that BP-YOLO achieves an average accuracy of 96.17% for all product categories detection; the average success rate of consumer shopping recognition reaches 92%, 98%, and 94.7% under three light and noise intensity, respectively. Therefore, our BP-YOLO model for intelligent UVMs has effectiveness in commercial deployment.

Abstract Objectives This study aims to evaluate the efficacy of multi-model incorporated by radiomics, deep learning, and transcriptomics features for predicting pathological grade and survival in patients with clear cell renal cell carcinoma (ccRCC). Methods In this study, data were collected from 177 ccRCC patients, including radiomics features, deep learning (DL) features, and RNA sequencing data. Diagnostic models were then created using these data through least absolute shrinkage and selection operator (LASSO) analysis. Additionally, a multi-model was developed by combining radiomics, DL, and transcriptomics features. The prognostic performance of the multi-model was evaluated based on progression-free survival (PFS) and overall survival (OS) outcomes, assessed using Harrell’s concordance index (C-index). Furthermore, we conducted an analysis to investigate the relationship between the multi-model and immune cell infiltration. Results The multi-model demonstrated favorable performance in discriminating pathological grade, with area under the ROC curve (AUC) values of 0.946 (95% CI: 0.912–0.980) and 0.864 (95% CI: 0.734–0.994) in the training and testing cohorts, respectively. Additionally, it exhibited statistically significant prognostic performance for predicting PFS and OS. Furthermore, the high-grade group displayed a higher abundance of immune cells compared to the low-grade group. Conclusions The multi-model incorporated radiomics, DL, and transcriptomics features demonstrated promising performance in predicting pathological grade and prognosis in patients with ccRCC. Critical relevance statement We developed a multi-model to predict the grade and survival in clear cell renal cell carcinoma and explored the molecular biological significance of the multi-model of different histological grades. Key points 1. The multi-model achieved an AUC of 0.864 for assessing pathological grade. 2. The multi-model exhibited an association with survival in ccRCC patients. 3. The high-grade group demonstrated a greater abundance of immune cells. Graphical Abstract

BackgroundBoth the mother and the infant are negatively impacted by macrosomia. Macrosomia is three times as common in hyperglycemic mothers as in normal mothers. This study sought to determine why hyperglycemic mothers experienced higher macrosomia. Methods: Hematoxylin and Eosin staining was used to detect the placental structure of normal mother(NN), mothers who gave birth to macrosomia(NM), and mothers who gave birth to macrosomia and had hyperglycemia (DM). The gene expressions of different groups were detected by RNA-seq. The differentially expressed genes (DEGs) were screened with DESeq2 R software and verified by qRT-PCR. The STRING database was used to build protein-protein interaction networks of DEGs. The Cytoscape was used to screen the Hub genes of the different group.ResultsThe NN group’s placental weight differed significantly from that of the other groups. The structure of NN group’s placenta is different from that of the other group, too. 614 and 3207 DEGs of NM and DM, respectively, were examined in comparison to the NN group. Additionally, 394 DEGs of DM were examined in comparison to NM. qRT-PCR verified the results of RNA-seq. Nucleolar stress appears to be an important factor in macrosomia, according on the results of KEGG and GO analyses. The results revealed 74 overlapped DEGs that acted as links between hyperglycemia and macrosomia, and 10 of these, known as Hub genes, were key players in this process. Additionally, this analysis believes that due of their close connections, non-overlapping Hubs shouldn’t be discounted.ConclusionIn diabetic mother, ten Hub genes (RPL36, RPS29, RPL8 and so on) are key factors in the increased macrosomia in hyperglycemia. Hyperglycemia and macrosomia are linked by 74 overlapping DEGs. Additionally, this approach contends that non-overlapping Hubs shouldn’t be ignored because of their tight relationships.

Abstract In scenarios with constrained physical aperture sizes, aiming to enhance the resolution and accuracy of Direction of Arrival (DOA) estimation, this paper proposes a novel approach that integrates a moving synthetic virtual array with Sparse Bayesian Learning (SBL) for DOA estimation. Initially, a virtual array is constructed based on the motion characteristics of the target. Subsequently, the SBL method is employed to estimate the DOA of the target. Simulation experiments validate the effectiveness of this approach, demonstrating comparable DOA estimation performance to synthetic aperture methods with larger aperture sizes, even in situations with limited aperture expansion. Furthermore, under constant virtual aperture expansion, this method surpasses non‐SBL methods regarding DOA resolution.

Corticotropin-releasing factor family peptides (CRF peptides) comprise corticotropin releasing hormone (CRH), urocortin (UCN1), UCN2 and UCN3. CRH is first isolated in the brain and later with UCNs found in many peripheral cells/tissues including the colon. CRH and UCNs function via the two types of receptors, CRF1 and CRF2, with CRH mainly acting on CRF1, UCN1 on both CRF1 &CRF2 and UCN2-3 on CRF2. Compiling evidence shows that CRH participates in inflammation and cancers via both indirect central effects related to stress response and direct peripheral influence. CRH, as a stress-response mediator, plays a significant central role in promoting the development of colitis involving colon motility, immunity and gut flora, while a few anti-colitis results of central CRH are also reported. Moreover, CRH is found to directly influence the motility and immune/inflammatory cells in the colon. Likewise, CRH is believed to be greatly related to tumorigenesis of many kinds of cancers including colon cancer via the central action during chronic stress while the peripheral effects on colitis-associated-colon cancer (CAC) are also proved. We and others observe that CRH/CRF1 plays a significant peripheral role in the development of colitis and CAC in that CRF1 deficiency dramatically suppresses the colon inflammation and CAC. However, up to date, there still exist not many relevant experimental data on this topic, and there seems to be no absolute clearcut between the central and direct peripheral effects of CRH in colitis and colon cancer. Taken together, CRH, as a critical factor in stress and immunity, may participate in colitis and CAC as a centrally active molecule; meanwhile, CRH has direct peripheral effects regulating the development of colitis and CAC, both of which will be summarized in this review.

The production of selenium-enriched fish contributes to alleviating selenium deficiency for humans. In this study, selenium nanoparticles (SeNPs) comparable in bioavailability to selenomethionine (SeMet), increased SeMet content in O. macrolepis (Onychostoma macrolepis) muscle. Additionally, dietary SeNPs significantly enhanced selenocysteine (SeCys2) and methylselenocysteine (MeSeCys) levels in O. macrolepis muscle. The effect of SeNPs on selenium speciation in grass carp muscle was consistent with O. macrolepis results. SeCys2 and MeSeCys showed antioxidant capacity in HEK293T cells, indicating enhanced health benefits of Se-enriched fish produced using SeNPs. Furthermore, the addition of 0.3 mg/kg SeNPs significantly improved the flesh quality of O. macrolepis by reducing the content of crude fat and heavy metals, as well as increasing the levels of crude protein, eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and the ratio of n-3/n-6 polyunsaturated fatty acids (PUFAs). Therefore, selenium-enriched fish produced from SeNPs is a good source for improving human dietary selenium intake.

Flavor profiles of various Pyrus spp. cultivars exhibit significant variations, yet the underlying flavor-contributing factors remain elusive. In this investigation, a comprehensive approach encompassing metabolomics analysis, volatile fingerprint analysis, and descriptive sensory analysis was employed to elucidate the flavor disparities among Nanguoli, Korla fragrant pear, and Qiuyueli cultivars and uncover potential flavor contributor. The study comprehensively characterized the categories and concentrations of nonvolatile and volatile metabolites, and 925 metabolites were identified. Flavonoids and esters dominated the highest cumulative response, respectively. Utilizing weighted correlation network analysis (WGCNA), seven highly correlated modules were identified, yielding 407 pivotal metabolites. Further correlation analysis of the differential substances provided potential flavor constituents strongly associated with various sensory attributes; taste factors had a certain association with olfactory characteristics. Our findings demonstrated the manifestation of flavor was a result of the synergistic effect of various compounds; evaluation olfactory flavor necessitated a comprehensive consideration of taste substances.

As one of the most innovative cement-based materials, ultra-high performance concrete (UHPC), with excellent durability and mechanical properties, has been widely used in strengthening existing bridges. In this study, in-situ four-point bending tests were carried out to investigate the flexural behavior of precast reinforced concrete (RC) hollow slab beams in service for 15 years strengthened with UHPC. Among them, three hollow slab beams were strengthened with UHPC, and the interface treatment was chiseling, planting rebars, and a combination of chiseling and planting rebars, respectively. The remaining one without any strengthening treatment was used as the control specimen. To evaluate the enhancement effect of different interface treatments on UHPC-strengthened beams, the cracking load, ultimate load, crack development and failure modes of UHPC-strengthened beams were analyzed. Results indicated that the stiffness, deflection capacity and flexural capacity of UHPC-strengthened beams was significantly improved. Meanwhile, the stiffness of UHPC-strengthened beams in the pre-damage stage was increased by 49%–94%, when compared with the unstrengthened beam. Correspondingly, the ultimate flexural capacity was increased by 29%–38%. The interface chiseling treatment was more favorable to enhance the deformation capacity of UHPC-strengthened beams. The interface planting rebar treatment was more favorable to enhancing the ductility of UHPC-strengthened beams. The crack development was effectively suppressed by the interface chiseling and planting rebars together. This contributes to a higher load capacity reserve for UHPC-strengthened beams. The bearing capacity under serviceability limit state of the UHPC-strengthened beams was increased by 1.25, 2, and 2.5 times through the interface treatments of chiseling, planting rebars, and a combination of both, respectively.

Polyetheretherketone (PEEK) is a frequently utilized orthopedic implant material in clinical settings. However, the adverse inflammatory and immunological reactions due to the PEEK surface after implantation often cause poor osseointegration and thereby hinder its clinical utility. To improve osseointegration and enhance the success rate of PEEK implants, the surface can be biofunctionalized to be immunomodulatory. Consequently, in this study, lithium-doped bioglass nanospheres (Li/BGs) was coated on the sulfonated PEEK surface. Furthermore, we assessed the anti-inflammatory potential of the biofunctionalized PEEK implants and their impact on osteogenesis. In our study, it was observed that the in vitro biofunctionalized PEEK surface exhibited enhanced osteogenesis and immunomodulatory osteogenesis properties, while also effectively suppressing the acute inflammatory response initiated by macrophages. Furthermore, our in vivo experiments demonstrated that the biofunctionalized PEEK surface contributed to improved osteogenesis properties and mitigated the formation of fibrous capsules. Therefore, the in vitro and in vivo results demonstrated that PEEK surface modification with Li/BGs not only changed the disadvantage of being bioinert on the surface but also endowed it with osteoimmunomodulatory regulation and bone-promoting properties. Thus, the biofunctionalized PEEK may be a promising candidate for use as an orthopedic implant in clinical settings.

Abstract Background Small (

Abstract The carbon capture and storage (CCS) technique gains much attention due to its role in reducing CO2 emissions. By introducing flexible CCS devices into conventional power plants, the low‐carbon economic operation of the power system can be achieved. However, the uncertainty of renewable energy makes it hard to obtain an optimal operation policy. First, the detailed model of CCS consisting of bypass venting stacks, solvent storage tanks, absorber, and stripper is described. Then, a low‐carbon economic dispatch model of the power system with CCS is proposed to minimize the total operation and renewable energy abandonment penalty costs. Next, the problem is reformulated as multi‐stage stochastic programming, and the stochastic dual dynamic programming (SDDP) method is applied to relieve the computation burden. To control the probability of stopping the algorithm prematurely, a new stopping criterion based on a two‐sided hypothesis test is proposed. Finally, the effectiveness of the proposed model and the new stopping criterion is demonstrated by case studies on a practical power system.

Based on the data of field outcrops, drilling cores, casting thin sections, well logging interpretation, oil/gas shows during drilling, and oil/gas testing results, and combined with modern salt-lake sediments in the Qinghai Lake, the Neogene saline lake beach-bars in southwestern Qaidam Basin are studied from the perspective of sedimentary characteristics, development patterns, sand control factors, and hydrocarbon accumulation characteristics. Beach-bar sand bodies are widely developed in the Neogene saline lake basin, and they are lithologically fine sandstone and siltstone, with wavy bedding, low-angle cross bedding, and lenticular-vein bedding. In view of spatial-temporal distribution, the beach-bar sand bodies are stacked in multiple stages vertically, migratory laterally, and extensive and continuous in NW−SE trending pattern in the plane. The stacking area of the Neogene beach-bar sandstone is predicted to be 3 000 km2. The water salinity affects the sedimentation rate and offshore distance of beach-bar sandstone, and the debris input from the source area affects the scale and enrichment of beach-bar sandstone. The ancient landform controls the morphology and stacking style of beach-bar sandstone, and the northwest monsoon driving effect controls the long-axis extension direction of beach-bar sandstone. The beach-bars have a reservoir-forming feature of “one reservoir in one sand body”, with thick beach-bar sand bodies controlling the effective reservoir distribution and oil-source faults controlling the oil/gas migration and accumulation direction. Three favorable exploration target zones in Zhahaquan, Yingdong−eastern Wunan and Huatugou areas are proposed based on the analysis of reservoir-forming elements.

Abstract Background TUBA1C is an α-tubulin isoform involved in mitosis, and its dysregulation has been implicated in tumor progression. There is still no clear understanding of its role in bladder urothelial carcinoma (BLCA). Methods This study examined the differential expression of TUBA1C and its prognostic significance in bladder cancer based on data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) and also assessed the correlation of TUBA1C expression level with immune cell infiltration and immune checkpoint gene expression levels and the half-inhibitory concentration (IC50) of different chemotherapeutic agents. Immunotherapy response was estimated using the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. We detected TUBA1C expression in BLCA cells using PCR and Western blotting. Functional assays, including CCK-8, colony formation, transwell, apoptosis and cell cycle assays, were also performed to assess the oncogenic role of TUBA1C in BLCA. Result In three independent public cohorts, TUBA1C was significantly upregulated in bladder tumor tissues, and high TUBA1C expression in bladder cancer was associated with a poorer outcome than low expression. TUBA1C was an independent prognostic risk factor for bladder cancer, and numerous immune checkpoint genes and infiltrating immune cells were associated with TUBA1C. TIDE analysis revealed that TUBA1C showed great potential for predicting the immunotherapy response in bladder cancer patients. In addition, drug sensitivity analysis revealed that high TUBA1C expression indicated sensitivity to multiple chemotherapeutic agents. Functional assays revealed that silencing TUBA1C significantly inhibited the proliferation, migration and invasion of BLCA cells and induced apoptosis and cell cycle arrest. Conclusion The overexpression of TUBA1C in bladder cancer predicts a poor prognosis and may also be a potential immunotherapeutic target. As a prognostic marker, TUBA1C influences tumor progression by regulating the cell cycle.

In this study, a self-responsive fluorescence aptasensor was established for the determination of lactoferrin (Lf) in dairy products. Herein, the aptamer itself functions as both a recognition element that specifically binds to Lf and a fluorescent signal reporter in conjunction with fluorescent moiety. In the presence of Lf, the aptamer preferentially binds to Lf due to its specific and high-affinity recognition by folding into a self-assembled and three-dimensional spatial structure. Meanwhile, its reduced spatial distance in the aptamer–Lf complex induces a FRET phenomenon based on the quenching of 6-FAM by amino acids in the Lf protein, resulting in a turn-off of the fluorescence of the system. As a result, the Lf concentration can be determined straightforwardly corresponding to the change in the self-responsive fluorescence signal. Under the optimized conditions, good linearities (R2 > 0.99) were achieved in an Lf concentration range of 2~10 μg/mL for both standard solutions and the spiked matrix, as well as with the desirable detection limits of 0.68 μg/mL and 0.46 μg/mL, respectively. Moreover, the fluorescence aptasensor exhibited reliable recoveries (89.5–104.3%) in terms of detecting Lf in three commercial samples, which is comparable to the accuracy of the HPCE method. The fluorescence aptasensor offers a user-friendly, cost-efficient, and promising sensor platform for point-of-need detection.

Abstract Background Novel molecular biomarkers for the risk assessment and early detection of coronary heart disease (CHD) are urgently needed for disease prevention. Altered methylation of ATP-binding cassette subfamily G member 1 (ABCG1) has been implicated in CHD but was mostly studied in Caucasians. Exploring the potential relationship between ABCG1 methylation in blood and CHD among the Chinese population would yield valuable insights. Methods Peripheral blood samples were obtained from a case–control study (287 CHD patients vs. 277 controls) and a prospective nested case–control study (171 CHD patients and 197 matched controls). DNA extraction and bisulfite-specific PCR amplification techniques were employed for sample processing. Quantitative assessment of methylation levels was conducted using mass spectrometry. Statistical analyses involved the utilization of logistic regression and nonparametric tests. Results We found hypomethylation of ABCG1 in whole blood was associated with the risk of CHD in both studies, which was enhanced in heart failure (HF) patients, female and younger subjects. When combined with baseline characteristics, altered ABCG1 methylation showed improved predictive effect for differentiating CHD cases, ischemic cardiomyopathy (ICM) cases, younger than 60 years CHD cases, and female CHD cases from healthy controls (area under the curve (AUC) = 0.68, 0.71, 0.74, and 0.73, respectively). Conclusions We demonstrated a robust link between ABCG1 hypomethylation in whole blood and CHD risk in the Chinese population and provided novel evidence indicating that aberrant ABCG1 methylation in peripheral blood can serve as an early detection biomarker for CHD patients.

Abstract Introduction Acute exacerbation (AE) is a devastating complication of rheumatoid arthritis-associated interstitial lung disease (RA-ILD) and leads to high mortality. This study aimed to investigate the incidence, risk factors, and prognosis of acute exacerbation of rheumatoid arthritis-associated interstitial lung disease (AE-RA-ILD). Methods PubMed, EMBASE, Web of Science, and Medline were searched through 8 February 2023. Two independent researchers selected eligible articles and extracted available data. The Newcastle Ottawa Scale was used to assess the methodological quality of studies used for meta-analysis. The incidence and prognosis of AE-RA-ILD were investigated. Weighted mean differences (WMDs) with corresponding 95% confidence intervals (CIs) and pooled odds ratios (ORs) with 95% CIs were calculated to explore the risk factors of AE in RA-ILD. Results Twenty-one of 1,589 articles were eligible. A total of 385 patients with AE-RA-ILD, of whom 53.5% were male, were included. The frequency of AE in patients with RA-ILD ranged from 6.3 to 55.6%. The 1-year and 5-year AE incidences were 2.6–11.1% and 11–29.4%, respectively. The all-cause mortality rate of AE-RA-ILD was 12.6–27.9% at 30 days and 16.7–48.3% at 90 days. Age at RA diagnosis (WMD: 3.61, 95% CI: 0.22–7.01), male sex (OR: 1.60, 95% CI:1.16–2.21), smoking (OR: 1.50, 95% CI: 1.08–2.08), lower forced vital capacity predicted (FVC%; WMD: −8.63, 95% CI: −14.68 to − 2.58), and definite usual interstitial pneumonia (UIP) pattern (OR: 1.92, 95% CI: 1.15–3.22) were the risk factors of AE-RA-ILD. Moreover, the use of corticosteroids, methotrexate, and biological disease-modifying anti-rheumatic drugs, was not associated with AE-RA-ILD. Conclusion AE-RA-ILD was not rare and had a poor prognosis. Age at RA diagnosis, male sex, smoking, lower FVC%, and definite UIP pattern increased the risk of AE-RA-ILD. The use of medications, especially methotrexate and biological disease-modifying anti-rheumatic drugs, may not be related to AE-RA-ILD. Registration CRD42023396772.

Abstract Bulk photovoltaic effect (BPVE), a second-order nonlinear optical effect governed by the quantum geometric properties of materials, offers a promising approach to overcome the Shockley-Quiesser limit of traditional photovoltaic effect and further improve the efficiency of energy harvesting. Here, we propose an effective platform, the nano edges embedded in assembled van der Waals (vdW) homo- or hetero-structures with strong symmetry breaking, low dimensionality and abundant species, for BPVE investigations. The BPVE-induced photocurrents strongly depend on the orientation of edge-embedded structures and polarization of incident light. Reversed photocurrent polarity can be observed at left and right edge-embedded structures. Our work not only visualizes the unique optoelectronic effect in vdW nano edges, but also provides an effective strategy for achieving BPVE in engineered vdW structures.

With the requirements of carbon neutrality and scientific and technological progress, massive distributed renewable energy resources have been integrated in distribution networks. The flexible interconnected distribution networks (FDNs) provide an effective way to avoid the negative effects of power quality, network loss and relay protection. This paper proposes a feasibility analysis model of distributed photovoltaic power generations (PVs) integration for FDNs based on distributionally robust. It uses moment-based ambiguity sets to model the uncertainty of PV outputs. Based on the circular constraint linearization method and duality theory, the solution method of the above model is designed. In this paper, two typical FDN structures are considered, namely, the closed-loop flexible interconnected structure between feeders and the flexible interconnected structure between feeder loops. The hosting capacity of the PV and energy storage system of the two structures is analyzed in case study, and the effectiveness of the proposed model and method is verified.

The China-Europe Railway Express (C-ER Express) provides a transcontinental rail container service between China and Europe. As most C-ER Expresses are affected by frequent natural disasters and public health incidents, it faces the increasing risk of network vulnerability. When previous studies investigated the evolution of network vulnerability through local information, they often overlook the complexity of the network’s multidimensional characteristics. The nonlinear load-capacity (NLC) model proposed in this paper integrates local and global information of the network. This approach enables a detailed investigation into how condition thresholds and different types of nodes influence network vulnerability. Firstly, a feature matrix is constructed for C-ER Express based on the topological measures, freight information, and external environment scores. Then, the autoencoder is used to extract the low-dimensional dense information, and the DBSCAN is used to classify C-ER Express into distinct clusters. Secondly, The NLC model integrates feature coefficient to describe the initial capacity of nodes. Subsequently, the failure load is redistributed proportionally to neighboring nodes and remaining normal nodes based on time-varying load and initial capacity of nodes. Finally, the improved NLC model is applied to the C-ER Express under different simulation scenarios. Simulation results show that a reasonable condition threshold can mitigate the impact of small-scale node failures on the network. The DBSCAN attack strategy can effectively identify the node types and prevent the network from chain reactions brought by different types of node failures. This research study is expected to provide some reference value for relevant research about vulnerability analysis of the C-ER Express network.

The spatial–temporal variability of soil moisture plays a pivotal role in the reconstruction and management of ecosystems in mining areas. However, the actual driving mechanisms behind long-term, continuous mining disturbances remain unclear due to a lack of continuous data. To address this gap, a network of instrumented sites, known as Disturbed Critical Zone Observatories (DCZO), has been established for ecological monitoring in mining areas. In this study, we used Sentinel-1A images of the Dafosi Mine from 2014 to 2022 and one year of continuous DCZO monitoring data. We employed Theil-Sen median trend analysis, empirical orthogonal function (EOF), and structural equation modeling (SEM) to quantitatively explore the factors influencing the variability of soil moisture and soil nutrients under continuous mining disturbances. The results showed: (1) Inter-annual variations in soil moisture differed among coal seams mined in various years, with the steepest decline observed in seams mined in 2021–2022 > 2018–2020 > 2015–2017 > 2013–2014. (2) Mining areas exhibited a significant reduction in Available Nitrogen (AN), Available Phosphorus (AP), and Available Potassium (AK), with decreases of 25.12 %, 25.38 %, and 22.59 %, respectively, compared to non-mining areas. (3) The first two EOFs explained 80.42 % of the observed spatiotemporal soil moisture pattern, 77.69 % of AN, 70.21 % of AP, and 88.32 % of AK, accounting for the total variability in soil moisture and soil nutrients. (4) The contribution rates of mining to soil moisture and nutrient variation were 27.31 % and 13.91 %, with subsidence having the most pronounced negative impact. Additionally, our study revealed that while soil moisture can partially recover through natural processes, soil nutrients require artificial reclamation during mining. This study offers valuable insights for sustainable mining management.

The urgent demand for terawatt-scale clean energy necessitates the rational design of noble metal catalysts with minimal noble metal loading while maintaining high catalytic activity. However, the durability of low-loading catalysts is a critical concern for their successful industrial implementation. Here, we present a capping strategy using an amorphous HfO2 (m-HfO2) to address this issue. Take Pt/C catalysts with Pt loading as low as 81.39 ng cm−2 as an example, we demonstrate that the m-HfO2 layer (10 nm) serves as an efficient mass transport channel for underneath Pt active sites, and effectively mitigates bubble-induced blockage of active sites by separating bubble formation sites with Pt active sites. Thus, the resulting catalyst exhibits a remarkable mass activity of 122.87 A mg−1 and an overpotential of 11 mV at 10 mA cm−2. Furthermore, the m-HfO2 plays a crucial role in eliminating the structural transformation and extending the lifetime of Pt-based catalysts, as evidenced by no loss of specific activity after consecutively cycling the catalyst for over 100 h. Such a capping strategy is potentially applied to other types of reactions and catalyst systems.

Abstract Objectives Mucosal healing (MH) is an important goal in the treatment of patients with Crohn’s disease (CD). Noninvasive assessment of MH with normalized iodine concentration (NIC) is unknown. Methods In this retrospective study, 94 patients with diagnosed CD underwent endoscopy and dual-energy CT enterography (DECTE) at the post-infliximab treatment review. Two radiologists reviewed DECTE images by consensus for assessing diseased bowel segments of the colon or terminal ileum, and the NIC was measured. Patients were divided into transmural healing (TH), MH and non-MH groups. The diagnostic performance of the MH and non-MH groups with clinical factors and NIC was assessed utilizing receiver operating characteristic (ROC) curve analysis. Results Of the 94 patients included in our study, 8 patients achieved TH, 34 patients achieved MH, and 52 patients did not achieve MH at the post-IFX treatment review. The area under the ROC curve (AUC), sensitivity, specificity, and accuracy values were 0.929 (95% confidence interval [CI] 0.883–0.967), 0.853, 0.827, and 0.837, respectively, for differentiating MHs from non-MHs, and the optimal NIC threshold was 0.448. The AUC of the combined model for distinguishing MHs from non-MHs in CD patients, which was based on the NIC and calprotectin, was 0.964 (95% CI 0.935–0.987). Conclusions The normalized iodine concentration measurement in DECTE has good performance in assessment MH in patients with CD. Iodine concentration from DECTE can be used as a radiologic marker for MH.

High myopia is a state of refractive error with myopia over -6.00D. High myopia is typically accompanied by multiple fundus lesions, thus making patients with high myopia suffer from varying degrees of impairment in visual function. As an emerging auxiliary way in ophthalmology, optical coherence tomography angiography(OCTA)can efficiently and non-invasively obtain microvascular stratified images of the retina and choroid and quantitatively analyze blood flow signals. Since the advent of OCTA, there have been numerous studies observing fundus changes in those with high myopia through OCTA. In this paper, some studies in which OCTA is applied to obtain retinal and choroidal thickness from patients with high myopia are reviewed, with a view to revealing the correlation between high myopia and the parameters such as retinal thickness, choroidal thickness, vessel density and the area of the foveal avascular zone and providing novel ideas to deeply investigate the mechanism of high myopia and delay the occurrence and development of high myopia.

Lithium metal penetration into solid-state electrolytes is a major drawback for developing all-solid-state batteries. Here, authors, via operando force microscopy, demonstrate that the trapping of electrons in garnet-type solid-state electrolyte grain boundaries is the origin of cell failure.

Recently, aiming for the enhanced dispersibility of graphene-based nanomaterials in lubricating oil matrices to serve as highly efficient lubricant additives, numerous modification approaches have been extensively studied. However, these previous modification routes usually involve a tedious multistep modification process or multitudinous toxic reagents, restricting their extensive practical application. In this work, novel graphene oxide (GO) nanoadditives (RGO-g-BO) featuring excellent durable dispersion capability and remarkable tribological performance were successfully prepared via an environmentally friendly one-step approach consisting of surface grafting of long-chain bromooctadecane (BO) and in situ chemical reduction. Benefiting from the greatly improved lipophilicity (resulting from the introduction of hydrophobic long-chain alkane groups and chemical reduction), along with the miniaturization effect, RGO-g-BO exhibits superior long-term dispersion stability in the finished oil. Moreover, the tribological properties results demonstrated that the finished oil filled with RGO-g-BO nanolubricants achieved an outstanding friction-reducing and antiwear performance. Particularly, under the optimum content of RGO-g-BO (as low as 0.005 wt%), the friction coefficient as well as the wear volume of the composite finished oil were greatly reduced by 13% and 53%, respectively, as compared with nascent finished oil. Therefore, in view of the advantages of low-cost, one-step facile synthesis, desirable dispersion capability, and remarkable tribological performance, RGO-g-BO holds great prospects as a highly efficient lubrication additive in the tribology field.

In multi-energy complementary power generation systems, the complete consumption of wind and photovoltaic resources often requires more costs, and tolerable energy abandonment can bring about the more reasonable optimization of operation schemes. This paper presents a scheduling model for a combined power generation system that incorporates pumped storage, wind, solar, and fire energy sources. Through a comparison of schemes, the energy regulation function of the pumped storage power station was verified and analyzed. The CPLEX solver and MOPSO algorithm were employed to solve the daily output of a pumped storage power station in the Gansu region under various scenarios. The incorporation of pumped storage power plants has the potential to provide many benefits, including a reduction in operating expenses by about CNY 1.1163 million, a decrease in carbon emissions by 491.24 t, an enhancement in the stability of thermal power by 2.39%, and an improvement in the combined system capability to absorb additional energy. The correlation between the indicators of the combined system and the penetration rate of renewable energy is non-linearly influenced by changes in the power capacity configuration. Ultimately, the multi-objective optimization computation yields the ideal operational scheme for each power source, taking into account a tolerable energy abandonment mode.

This paper addresses the performance and environmental impact evaluation of a developed UHPC with recycled mixed powder (RMP) and aeolian sand (AS). The RMP is obtained from the crushing and grinding of waste concrete blocks and clay bricks. The RMP and AS are used instead of cement and river sand during the UHPC's production. The macroperformance, hydration kinetics, microstructure development, and environmental impact of the newly produced UHPC are evaluated. The experimental results reveal that the effect and mechanism of RMP on the fluidity of UHPC are different from that of AS. The RMP and AS show a specific synergistic effect. The RMP can promote the initial hydration of the mixture but restricts its cumulative heat. The AS acts as a nucleation site to promote the secondary hydration reaction involving RMP. The ecological assessment demonstrates that the developed UHPC incorporating RMP and AS can reduce the burden on the environment.

Abstract Background Osteoarthritis is a chronic degenerative joint disease, and increasing evidences suggest that the pathogenic mechanism involves immune system and inflammation. Aims The aim of current study was to uncover hub genes linked to immune infiltration in osteoarthritis synovial tissue using comprehensive bioinformatics analysis and experimental confirmation. Methods Multiple microarray datasets (GSE55457, GSE55235, GSE12021 and GSE1919) for osteoarthritis in Gene Expression Omnibus database were downloaded for analysis. Differentially expressed genes (DEGs) were identified using Limma package in R software, and immune infiltration was evaluated by CIBERSORT algorithm. Then weighted gene co-expression network analysis (WGCNA) was performed to uncover immune infiltration-associated gene modules. Protein–protein interaction (PPI) network was constructed to select the hub genes, and the tissue distribution of these genes was analyzed using BioGPS database. Finally, the expression pattern of these genes was confirmed by RT-qPCR using clinical samples. Results Totally 181 DEGs between osteoarthritis and normal control were screened. Macrophages, mast cells, memory CD4 T cells and B cells accounted for the majority of immune cell composition in synovial tissue. Osteoarthritis synovial showed high abundance of infiltrating resting mast cells, B cells memory and plasma cells. WGCNA screened 93 DEGs related to osteoarthritis immune infiltration. These genes were involved in TNF signaling pathway, IL-17 signaling pathway, response to steroid hormone, glucocorticoid and corticosteroid. Ten hub genes including MYC, JUN, DUSP1, NFKBIA, VEGFA, ATF3, IL-6, PTGS2, IL1B and SOCS3 were selected by using PPI network. Among them, four genes (MYC, JUN, DUSP1 and NFKBIA) specifically expressed in immune system were identified and clinical samples revealed consistent change of these four genes in synovial tissue retrieved from patients with osteoarthritis. Conclusion A 4-gene-based diagnostic model was developed, which had well predictive performance in osteoarthritis. MYC, JUN, DUSP1 and NFKBIA might be biomarkers and potential therapeutic targets in osteoarthritis.

Abstract Background Oral squamous cell carcinoma (OSCC) accounts for a frequently-occurring head and neck cancer, which is characterized by high rates of morbidity and mortality. Metabolism-related genes (MRGs) show close association with OSCC development, metastasis and progression, so we constructed an MRGs-based OSCC prognosis model for evaluating OSCC prognostic outcome. Methods This work obtained gene expression profile as well as the relevant clinical information from the The Cancer Genome Atlas (TCGA) database, determined the MRGs related to OSCC by difference analysis, screened the prognosis-related MRGs by performing univariate Cox analysis, and used such identified MRGs for constructing the OSCC prognosis prediction model through Lasso-Cox regression. Besides, we validated the model with the GSE41613 dataset based on Gene Expression Omnibus (GEO) database. Results The present work screened 317 differentially expressed MRGs from the database, identified 12 OSCC prognostic MRGs through univariate Cox regression, and then established a clinical prognostic model composed of 11 MRGs by Lasso-Cox analysis. Based on the optimal risk score threshold, cases were classified as low- or high-risk group. As suggested by Kaplan–Meier (KM) analysis, survival rate was obviously different between the two groups in the TCGA training set (P

Abstract Background Distant metastasis, which occurs at a rate of 25% in patients with esophageal cancer (EC), has a poor prognosis, with previous studies reporting an overall survival of only 3–10 months. However, few studies have been conducted to predict distant metastasis in EC, owing to a dearth of reliable biomarkers. The purpose of this study was to develop and validate an accurate model for predicting distant metastasis in patients with EC. Methods A total of 299 EC patients were enrolled and randomly assigned to a training cohort (n = 207) and a validation cohort (n = 92). Logistic univariate and multivariate regression analyses were used to identify clinical independent predictors and create a clinical nomogram. Radiomic features were extracted from contrast-enhanced computed tomography (CT) images taken prior to treatment, and least absolute shrinkage and selection operator (Lasso) regression was used to screen the associated features, which were then used to develop a radiomic signature. Based on the screened features, four machine learning algorithms were used to build radiomics models. The joint nomogram with radiomic signature and clinically independent risk factors was developed using the logical regression algorithm. All models were validated and compared by discrimination, calibration, reclassification, and clinical benefit. Results Multivariable analyses revealed that age, N stage, and degree of pathological differentiation were independent predictors of distant metastasis, and a clinical nomogram incorporating these factors was established. A radiomic signature was developed by a set of sixteen features chosen from 851 radiomic features. The joint nomogram incorporating clinical factors and radiomic signature performed better [AUC(95% CI) 0.827(0.742–0.912)] than the clinical nomogram [AUC(95% CI) 0.731(0.626–0.836)] and radiomics predictive models [AUC(95% CI) 0.754(0.652–0.855), LR algorithms]. Calibration and decision curve analyses revealed that the radiomics–clinical nomogram outperformed the other models. In comparison with the clinical nomogram, the joint nomogram's NRI was 0.114 (95% CI 0.075–0.345), and its IDI was 0.071 (95% CI 0.030–0.112), P = 0.001. Conclusions We developed and validated the first radiomics–clinical nomogram for distant metastasis in EC which may aid clinicians in identifying patients at high risk of distant metastasis.

Abstract Background Clear cell renal cell cancer (ccRCC) is accompanied by T-cell infiltration. In this study, we sought to determine the difference in T-cell infiltration and the T-cell receptor (TCR) immune repertoire between ccRCC and peritumour tissue. Methods T-cell infiltration was examined using immunohistochemistry (IHC) and haematoxylin and eosin (HE) staining. The chi-squared test and Pearson correlation analysis were applied to evaluate the relationship between clinical traits and CD3, CD4, and CD8 expression. Immune repertoire sequencing (IR-Seq) was used to describe the profile of the TCR repertoire. Results The adjacent tissue showed increased expression of CD3, CD4 and CD8 compared with ccRCC tissue (P CD3 = 0.033; P CD4 = 0.014; P CD8 = 0.004). Indicated CD3+ T-cell density in ccRCC tissue was positively correlated with that in peritumour tissue (P = 0.010, r = 0.514), which implied the T cells in peritumour tissue directly infect the number of cells infiltrating in ccRCC tissue. Moreover, there was a positive correlation between Vimentin expression and indicated positive T-cell marker in ccRCC tissue (P CD3 = 0.035; P CD4 = 0.020; P CD8 = 0.027). Advanced stage revealed less CD4+ T-cell infiltration in ccRCC tissue (P CD4 = 0.023). The results from IR-Seq revealed an obvious increase in VJ and VDJ segment usage, as well as higher complementarity-determining region 3 (CDR3) amino acid (aa) clonotypes in ccRCC. The matched antigen recognized by the TCR of ccRCC may be potential targets. Conclusions The current study collectively demonstrates diminished T-cell infiltration and increased CDR3 aa diversity in ccRCC, which may be associated with immunotherapeutic targets for ccRCC patients.

Ultrasound-promoted transient liquid phase bonding (U-TLP) is a high quality, high efficiency, and low-cost method for fast bonding of difficult-wetting materials in the atmospheric environment. In this paper, U-TLP was used to bond SiC particles reinforced aluminium-based metal matrix composite which particle volume fraction was 70%. The pure zinc foil was used as the intermediate layer. The effects of ultrasonic on microstructure evolution and mechanical properties of joints during the transient liquefaction stage were investigated. The mechanism of ultrasonic effects in the transient liquefaction stage of U-TLP was also inducted. The results showed that high volume fraction SiCp/Al MMCs were bonded well at low temperature in the air environment. Ultrasonic vibration can remove the oxide film on the surface of aluminum matrix composites, enhance the wettability of SiC particles with weld metal, promote atomic diffusion and homogenization of SiC particles, and improve the welding quality and efficiency. Reasonable increase of ultrasonic vibration time could effectively improve the joint strength.