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The migration behavior of nitrate ester-plasticized polyether (NEPE) propellant components significantly impacts safety. Experimentally observing the migration process is challenging and time-consuming. This study employed molecular dynamics (MD) methods to simulate a molecular model of the NEPE propellant/liner interface layer, enabling the prediction of component migration behavior. We visualized the migration process of all propellant components and studied the interface layer components' migration patterns, diffusion coefficients, and concentration gradient distributions. The contents of migrated components of the propellant and liner at different accelerated aging times at 70 °C were determined using high-performance liquid chromatography (HPLC), and the simulation patterns were compared with experimental observations. The results indicate that the migrated components in the propellant mainly consist of nitrate esters (Nitroglycerin, abbreviated as NG, and Butanetriol Trinitrate, abbreviated as BTTN), stabilizers (NMethylnitroaniline, abbreviated as MNA, and 2-Nitrodiphenylamine, abbreviated as 2-NDPA), and solid component (Hexahydro-1,3,5-trinitro-1,3,5-triazine, abbreviated as RDX). The migration process is primarily dominated by the substantial migration of nitrate esters, which is a key factor contributing to the degradation of propellant performance during storage. The migration process can be divided into three stages: swift migration, steady slow migration, and migration equilibrium. The diffusion coefficients are ranked from most significant to most minor as NG > BTTN > RDX > MNA > 2-NDPA. Three migration stages consistent with the simulation process were observed using HPLC, and the migration behavior of nitrate esters aligned with simulation patterns. Our designed full-component model can qualitatively predict component migration behavior. Additionally, a faster diffusion coefficient does not necessarily lead to a greater amount of migration. We employed MD simulations combined with density functional theory (DFT) calculations to explain this phenomenon and found that intermolecular interactions may influence the diffusion coefficient. At the same time, the migration amount is highly correlated with molecular polarity. Therefore, increasing the molecular polarity difference between easily migrating components and liner materials is a beneficial strategy for slowing the migration process and enhancing the storage safety of propellants.

Alkaline metal oxides have received significant attention recently due to their abundance, inherent conductivity, optical absorption, and thermal stability. Here, a straightforward co-precipitation method was employed to obtain both undoped BaO and Mo-doped BaO nanoparticles. Various techniques were used to characterize the synthesized nanoparticles’ structural, Raman spectral, optical, thermal, and electrical properties. X-ray diffraction (XRD) results revealed that Mo was successfully doped into tetragonal nanocrystalline BaO. The W-H plot showed that as Mo doping increases from 2 % to 6 %, the crystallite size grows while the lattice structure remains well-ordered with even strain distribution. Scanning electron microscopy (SEM) was used to examine its surface features. The purity and crystalline character of the samples were further confirmed via Raman spectroscopy, which shows that the peak intensity of the spectra increases with the increase of particle size owing to the rise in the force constant. UV spectroscopy was used to observe the energy band gap, which is found to decrease from 4.2 eV to 3.8 eV, and then it drops to 3.4 eV as the Mo content increases. This is reasonable because of the size-dependent attraction between metallic ions and conduction electrons. PL spectra concluded that Mo doping leads to the enhancement of the optical characteristics of BaO. Adding Mo to BaO also modifies the material’s thermal properties, potentially affecting its suitability for applications that require thermal durability. This finding exhibits that even slight doping of Mo4+ into BaO can significantly impact their structural, thermal, optical, and electrical characteristics. It enriches the existing body of knowledge of BaO nanoparticles and lays the foundation for its future research.

BackgroundImmune checkpoint inhibitors (ICIs) have become a prevalent tool in anti-tumor therapy in recent years. They may cause immune-related adverse events (irAEs) including potentially life-threatening cardiovascular toxicities such as myocarditis.Case presentationIn this report, we describe a 69-year-old man with recurrent esophageal cancer who developed myocarditis after receiving three cycles of sintilimab combined with nab-paclitaxel. Despite a rising cardiac troponin I (cTnI), he initially reported no discomfort. He was later suspected of having with sintilimab-induced myocarditis. Although treatment with methylprednisolone reduced his cTnI levels, he still experienced significant discomfort. Moreover, he developed pneumonia and septic shock.ConclusionIn our literature search to identify all reported cases of sintilimab-associated adverse events involving myocarditis, we found 14 patients, including those with esophageal cancer, thymoma, lung cancer, gastric cancer, hepatobiliary carcinoma, and chordoma. The primary treatment for ICI-induced cardiotoxicity is methylprednisolone. However, the long-term or high-dose use of steroids can also induce side effects, which have not been the focus of these case reports. This is the first reported case of asymptomatic immune-mediated myocarditis occurring during the treatment of esophageal cancer with sintilimab. It is also the first to address the side effects of methylprednisolone used in the treatment of sintilimab-related myocarditis. To facilitate an early diagnosis, regular monitoring is required during sintilimab treatment. We should also focus on the prevention and management of adverse effects related to steroid use.

Ship contour extraction is vital for extracting the geometric features of ships, providing comprehensive information essential for ship recognition. The main factors affecting the contour extraction performance are speckle noise and amplitude inhomogeneity, which can lead to over-segmentation and missed detection of ship edges. Polarimetric synthetic aperture radar (PolSAR) images contain rich target scattering information. Under different transmitting and receiving polarization, the amplitude and phase of pixels can be different, which provides the potential to meet the uniform requirement. This paper proposes a novel ship contour extraction framework from PolSAR images based on polarization modulation. Firstly, the image is partitioned into the foreground and background using a super-pixel unsupervised clustering approach. Subsequently, an optimization criterion for target amplitude modulation to achieve uniformity is designed. Finally, the ship’s contour is extracted from the optimized image using an edge-detection operator and an adaptive edge extraction algorithm. Based on the contour, the geometric features of ships are extracted. Moreover, a PolSAR ship contour extraction dataset is established using Gaofen-3 PolSAR images, combined with expert knowledge and automatic identification system (AIS) data. With this dataset, we compare the accuracy of contour extraction and geometric features with state-of-the-art methods. The average errors of extracted length and width are reduced to 20.09 m and 8.96 m. The results demonstrate that the proposed method performs well in both accuracy and precision.

ObjectiveSWI image signal is related to venous reflux disorder and perfusion defect. Computed tomography perfusion (CTP) contains perfusion information in space and time. There is a complementary basis between them to affect the prognosis of cerebral infarction.MethodsSixty-six patients included in the retrospective study were designated as the training set. Effective perfusion indicator features and imaging radiomic features of the peri-infarction area on Susceptibility weighted imaging (SWI) and CTP modality images were extracted from each case. Thirty-three patients from the prospectively included group were designated as the test set of the machine learning model based on a sparse representation method. The predicted results were compared with the DWI results of the patients’ 7–10 days review to assess the validity and accuracy of the prediction.ResultsThe AUC of the SWI + CTP integrated model was 0.952, the ACC was 0.909, the SEN was 0.889, and the SPE was 0.933. The prediction performance is the highest. Compared with the value of AUC: the SWI model is 0.874, inferior to the performance of the SWI + CTP model, and the CTP model is 0.715.ConclusionThe prediction efficiency of the changing trend of infarction volume is further improved by the correlation between the combination of the two image features.

Chronic inflammation is a hallmark of obesity and is recognized as a cancer risk. While EPA and DHA combat inflammation associated with obesity, their effects on obesity-linked intestinal tumors requires further study. This study used the azoxymethane/dextran sulfate sodium (AOM/DSS) model in mice made obese by a high-fat diet to assess the effects of EPA and DHA on tumorigenesis. The results showed that obesity aggravates colon tumor development in mice, yet both EPA and DHA significantly suppressed tumor formation in the obese murine colon. EPA and DHA altered the intestinal epithelium's fatty acid profile, enhancing its anti-inflammatory properties. Both treatments led to a decrease in the pro-inflammatory Desulfovibrio, while EPA specifically increased probiotic Parasutterella and decreased mucus- destroying Bacteroides. In conclusion, EPA and DHA have inhibitory effects on colon tumor formation in obese mice, primarily by modulating the gut flora and inhibiting inflammation in the colon.

This study analyzes the performance degradation of traditional hybrid wind turbines under high blade-tip-speed ratio conditions and proposes solutions through two-dimensional Computational Fluid Dynamics (CFD) simulations. It also introduces the design of two innovative asynchronous-hybrid dual-rotor wind turbines. The results indicate a remarkable 98.5% enhancement in torque performance at low blade-tip-speed ratios with the hybrid wind turbine model. However, as the blade-tip-speed ratio increases, it leads to negative torque generation within the inner rotor of the conventional design, resulting in a reduction of the power coefficient by up to 13.1%. The introduction of the new wind turbine design addresses this challenge by eliminating negative torque at high blade-tip-speed ratios through adjustments in the inner rotor’s operating range. This modification not only rectifies the negative torque issue but also enhances the performance of the outer rotor in the leeward region, consequently boosting the overall power coefficient. Moreover, the optimized inner rotor configuration effectively disrupts and shortens the wake length by 16.7%, with this effect intensifying as the rotational speed increases. This optimization is pivotal for enhancing the efficiency of multi-machine operations within wind farms.

Examining dual vertical-axis wind turbines (VAWTs) across various turbulence scenarios is crucial for advancing the efficiency of urban energy generation and promoting sustainable development. This study introduces a novel approach by employing two-dimensional numerical analysis through computational fluid dynamics (CFD) software to investigate the performance of VAWTs under varying turbulence intensity conditions, a topic that has been relatively unexplored in existing research. The analysis focuses on the self-starting capabilities and the effective utilization of wind energy, which are key factors in urban wind turbine deployment. The results reveal that while the impact of increased turbulence intensity on the self-starting performance of VAWTs is modest, there is a significant improvement in wind energy utilization within a specific turbulence range, leading to an average power increase of 1.41%. This phenomenon is attributed to the more complex flow field induced by heightened turbulence intensity, which delays the onset of dynamic stall through non-uniform aerodynamic excitation of the blade boundary layer. Additionally, the inherent interaction among VAWTs contributes to enhanced turbine output power. However, this study also highlights the trade-off between increased power and the potential for significant fatigue issues in the turbine rotor. These findings provide new insights into the optimal deployment of VAWTs in urban environments, offering practical recommendations for maximizing energy efficiency while mitigating fatigue-related risks.

The transparency of mine geology is a new requirement for safe and efficient mining of coal mines. For working faces with complex geological conditions, channel wave detection technology is one of the commonly used geophysical prospecting methods. If there are multiple geological anomalies on the working face, its attributes cannot be accurately determined. Distinguish and explain. Taking a working face in Yangquan, Shanxi as an example, a three-dimensional wave field simulation was carried out on a working face including faults, collapsed columns, thin coal belts, and deflection. Analysis of dispersion characteristics; combined with the numerical model of the longitudinal wave amplitude and velocity, shear wave amplitude and velocity, high-frequency groove wave amplitude and velocity, and low-frequency groove wave amplitude and velocity 8 types of CT inversion imaging, the fault, collapse column, thin different response characteristics of four geological anomalies, coal belt and flexure, on channel wave attributes. The results show that: ①The main frequency and velocity of the channe wave anomaly caused by the fault is basically the same as that of the coal seam without geological anomaly. The amplitude of shear wave, high frequency and low frequency channe wave can significantly identify the fault, and the speed of shear wave, high frequency channe wave and low frequency channe wave can be compared. Significantly identify faults, the longitudinal wave amplitude and longitudinal wave velocity cannot effectively identify the fault; ②The single shot characteristics of the collapsed column are characterized by low-order weakening and high-order enhancement. The amplitude of shear wave, high frequency, and low frequency channe wave can significantly identify the collapsed column, shear wave, and high frequency. The frequency slot wave velocity can identify the collapse column more significantly, while the longitudinal wave amplitude, longitudinal wave velocity and low frequency slot wave velocity cannot effectively identify the collapse column; ③The main frequency of the thin coal belt is basically unchanged, the speed is reduced, the high frequency slot wave amplitude and velocity, low frequency The amplitude and velocity of the channe wave can distinguish thin coal belts, the amplitude of longitudinal wave and transverse wave can distinguish thin coal belts significantly, and the amplitude of longitudinal wave and transverse wave cannot effectively identify thin coal belts; ④The amplitude of the deflection spectrum is enhanced, and the high frequency channe The wave amplitude and the low-frequency groove wave amplitude can distinguish the deflection more significantly, and the other properties of the groove wave are not obvious in the recognition of the deflection. According to the response characteristics of different geological anomalies on single shot, frequency dispersion, frequency spectrum and wavefield CT imaging, the geological recognition of channe wave anomalies is realized, and the shape, location and size of various geological anomalies are accurately explained, which is geologically transparent. The establishment of the chemical working face laid the “material” foundation.

This paper proposes a multi-perspective adaptive examination cheating behavior detection method to meet the demand for automated monitoring throughout the entire process in paperless online exams. Unlike current dual-perspective cheating behavior detection methods, we expand the monitoring field of view by using three cameras with different perspectives: the overhead perspective, the horizontal perspective, and the face perspective. This effectively covers areas where cheating may occur. An adaptive cheating behavior detection system based on three perspectives is proposed, including a gaze direction recognition model based on Swin Transformer, a cheating tool detection model based on Lightweight-YOLOv5-Coordinate Attention, and a cheating behavior determination model based on Multilayer Perceptron. To reduce computational complexity and ensure efficient processing while expanding the monitoring field of view, the system uses the results of the gaze direction recognition model to adaptively select the cheating behavior detection model from different perspectives, reducing the three-perspective system to dual-perspective. In online simulation tests, our method achieves cheating behavior determination at 35 frames per second, with an average recognition rate of 95%. It has good real-time performance, accuracy, and a large monitoring range.

Summary: Background: Molecular diagnosis is crucial for biomarker-assisted glioma resection and management. However, some limitations of current molecular diagnostic techniques prevent their widespread use intraoperatively. With the unique advantages of ultrasound, this study developed a rapid intraoperative molecular diagnostic method based on ultrasound radio-frequency signals. Methods: We built a brain tumor ultrasound bank with 169 cases enrolled since July 2020, of which 43483 RF signal patches from 67 cases with a pathological diagnosis of glioma were a retrospective cohort for model training and validation. IDH1 and TERT promoter (TERTp) mutations and 1p/19q co-deletion were detected by next-generation sequencing. We designed a spatial–temporal integration model (STIM) to diagnose the three molecular biomarkers, thus establishing a rapid intraoperative molecular diagnostic system for glioma, and further analysed its consistency with the fifth edition of the WHO Classification of Tumors of the Central Nervous System (WHO CNS5). We tested STIM in 16-case prospective cohorts, which contained a total of 10384 RF signal patches. Two other RF-based classical models were used for comparison. Further, we included 20 cases additional prospective data for robustness test (ClinicalTrials.gov NCT05656053). Findings: In the retrospective cohort, STIM achieved a mean accuracy and AUC of 0.9190 and 0.9650 (95% CI, 0.94–0.99) respectively for the three molecular biomarkers, with a total time of 3 s and a 96% match to WHO CNS5. In the prospective cohort, the diagnostic accuracy of STIM is 0.85 ± 0.04 (mean ± SD) for IDH1, 0.84 ± 0.05 for TERTp, and 0.88 ± 0.04 for 1p/19q. The AUC is 0.89 ± 0.02 (95% CI, 0.84–0.94) for IDH1, 0.80 ± 0.04 (95% CI, 0.71–0.89) for TERTp, and 0.85 ± 0.06 (95% CI, 0.73–0.98) for 1p/19q. Compared to the second best available method based on RF signal, the diagnostic accuracy of STIM is improved by 16.70% and the AUC is improved by 19.23% on average. Interpretation: STIM is a rapid, cost-effective, and easy-to-manipulate AI method to perform real-time intraoperative molecular diagnosis. In the future, it may help neurosurgeons designate personalized surgical plans and predict survival outcomes. Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Abstract The hippocampus has been implicated in the pathogenesis of insomnia disorder (ID) and the purpose of this study was to investigate the neuroprotective mechanism of the natural flavone Kurarinone (Kur) on hippocampal neurotoxicity as a potential treatment of ID. The effect of Kur on hippocampal neuronal cell (HNC) viability and apoptosis were assessed by Cell counting kit‐8 (CCK‐8) assay and flow cytometry, respectively. Then, the effect of Kur on β‐site amyloid precursor protein‐cleaving enzyme 1 (BACE1), brain‐derived neurotrophic factor (BDNF), and phosphatidylinositol‐3‐kinase (PI3K)/protein kinase B (AKT) phosphorylation level were measured by Western blot. Further, SwissTargetPrediction analysis and molecular docking experiments were used to detect a potential target of Kur. Then, the p‐chlorophenylalanine (PCPA) model was established in vivo to further study the effect of BACE1 expression on Kur and HNC. As a result, HNC viability was only significantly decreased by 2 μM of Kur. Kur reversed the impacts of corticosterone upon inhibiting viability (0.25–1 μM), PI3K (0.5–1 μM)/AKT phosphorylation, and BDNF (1 μM) level, and enhancing the apoptosis (0.25–1 μM) and BACE1 expression (1 μM) in HNCs. BACE1 was a potential target of Kur. Notably, Kur (150 mg/kg) attenuated PCPA‐induced upregulation of BACE1 expression in rat hippocampal tissues as ZRAS (0.8 g/kg). The effects of Kur (1 μM) on corticosterone‐treated HNCs were reversed by BACE1 overexpression. Collectively, Kur downregulates BACE1 level to activate PI3K/AKT, thereby attenuating corticosterone‐induced toxicity in HNCs, indicating that Kur possibly exerted a neuroprotective effect, which providing a new perspective for the treatment of insomnia disorders.

Abstract Accurate delineation of glioma infiltrative margins remains a challenge due to the low density of cancer cells in these regions. Here, a hierarchical imaging strategy to define glioma margins by locating the immunosuppressive tumor‐associated macrophages (TAMs) is proposed. A pH ratiometric fluorescent probe CP2‐M that targets immunosuppressive TAMs by binding to mannose receptor (CD206) is developed, and it subsequently senses the acidic phagosomal lumen, resulting in a remarkable fluorescence enhancement. With assistance of CP2‐M, glioma xenografts in mouse models with a tumor‐to‐background ratio exceeding 3.0 for up to 6 h are successfully visualized. Furthermore, by intra‐operatively mapping the pH distribution of exposed tissue after craniotomy, the glioma allograft in rat models is precisely excised. The overall survival of rat models significantly surpasses that achieved using clinically employed fluorescent probes. This work presents a novel strategy for locating glioma margins, thereby improving surgical outcomes for tumors with infiltrative characteristics.

Abstract Aiming at the problem of poor effect of object detection with visible images under low light conditions, the decision-level fusion detection method of visible and infrared images is studied. Taking YOLOX as the object detection network based on deep learning, a decision-level fusion detection algorithm of visible and infrared images based on light sensing is proposed. Experiments are carried out on LLVIP dataset, which is a visible-infrared paired dataset for low light vision. Through comparative analysis, it is found that the decision-level fusion algorithm based on Soft-NMS and light sensing obtained the optimal AP value of 69.0%, which is 11.4% higher than the object detection with visible images and 1.1% higher than the object detection with infrared images. The experimental results show that the decision-level fusion algorithm based on Soft-NMS and light sensing can effectively fuse the complementary information of visible and infrared images, and improve the object detection effect under low light conditions.

The current use of elastography ultrasound faces challenges, including vulnerability to subjective manipulation, echo signal attenuation, unknown risks of elastic pressure and high imaging hardware cost. Here, the author shows a virtual elastography to empower low-end ultrasound devices with state-of-art elastography function.

Deep convolutional neural networks (CNNs) have significantly enhanced image classification in the past decade. This paper proposes Multi-branch Cascade Receptive Field Residual Networks (MCRF-ResNets) based on the original Residual Network (ResNet) architecture for classification and object detection. MCRF-ResNets incorporate multiple branches with different receptive field (RF) sizes to improve image classification performance. Each MCRF residual block contains a $5\times5$ and a $3\times3$ RF-sized convolution block. The $5\times5$ and $3\times3$ RF-sized blocks recognize larger and smaller objects, respectively. Group convolutions are used to reduce redundancy and balance feature extraction and parameter usage in the multiple branches. The proposed model approach demonstrates significant improvement in model performance on CIFAR-10 and CIFAR-100 datasets and a subset of the ImageNet 2012 dataset. The model achieves a 2.8% increase in top-1 accuracy compared with the baseline ResNet-50 model with a similar number of parameters on the subset of the ImageNet 2012 dataset. In addition, the proposed method shows good object detection on Pascal VOC and MS COCO datasets.

BackgroundA model to predict preoperative outcomes after percutaneous nephrolithotomy (PCNL) with renal staghorn stones is developed to be an essential preoperative consultation tool.ObjectiveIn this study, we constructed a predictive model for one-time stone clearance after PCNL for renal staghorn calculi, so as to predict the stone clearance rate of patients in one operation, and provide a reference direction for patients and clinicians.MethodsAccording to the 175 patients with renal staghorn stones undergoing PCNL at two centers, preoperative/postoperative variables were collected. After identifying characteristic variables using PCA analysis to avoid overfitting. A predictive model was developed for preoperative outcomes after PCNL in patients with renal staghorn stones. In addition, we repeatedly cross-validated their model’s predictive efficacy and clinical application using data from two different centers.ResultsThe study included 175 patients from two centers treated with PCNL. We used a training set and an external validation set. Radionics characteristics, deep migration learning, clinical characteristics, and DTL+Rad-signature were successfully constructed using machine learning based on patients’ pre/postoperative imaging characteristics and clinical variables using minimum absolute shrinkage and selection operator algorithms. In this study, DTL-Rad signal was found to be the outstanding predictor of stone clearance in patients with renal deer antler-like stones treated by PCNL. The DTL+Rad signature showed good discriminatory ability in both the training and external validation groups with AUC values of 0.871 (95% CI, 0.800-0.942) and 0.744 (95% CI, 0.617-0.871). The decision curve demonstrated the radiographic model’s clinical utility and illustrated specificities of 0.935 and 0.806, respectively.ConclusionWe found a prediction model combining imaging characteristics, neural networks, and clinical characteristics can be used as an effective preoperative prediction method.

Abstract Background Ferroptosis is an iron-dependent programmed cell death modality that may have a tumor-suppressive function. Therefore, regulating ferroptosis in tumor cells could serve as a novel therapeutic approach. This article focuses on ferroptosis-associated long non-coding RNAs (lncRNAs) and their potential application as a prognostic predictor for bladder cancer (BCa). Methods We retrieved BCa-related transcriptome information and clinical information from the TCGA database and ferroptosis-related gene sets from the FerrDb database. Least absolute shrinkage and selection operator regression (LASSO) and Cox regression models were used to identify and develop predictive models and validate the model accuracy. Finally, we explored the inter-regulatory relationships between ferroptosis-related genes and immune cell infiltration, immune checkpoints, and m6A methylation genes. Results Kaplan–Meier analyses screened 11 differentially expressed lncRNAs associated with poor BCa prognosis. The signature (AUC = 0.720) could be utilized to predict BCa prognosis. Additionally, GSEA revealed immune and tumor-related pathways in the low-risk group. TCGA showed that the p53 signaling pathway, ferroptosis, Kaposi sarcoma − associated herpesvirus infection, IL − 17 signaling pathway, MicroRNAs in cancer, TNF signaling pathway, PI3K − Akt signaling pathway and HIF − 1 signaling pathway were significantly different from those in the high-risk group. Immune checkpoints, such as PDCD-1 (PD-1), CTLA4, and LAG3, were differentially expressed between the two risk groups. m6A methylation-related genes were significantly differentially expressed between the two risk groups. Conclusion A new ferroptosis-associated lncRNAs signature developed for predicting the prognosis of BCa patients will improve the treatment and management of BCa patients.

Abstract Nuclear transcription factor Mesenchyme Homeobox 2 (MEOX2) is a homeobox gene that is originally discovered to suppress the growth of vascular smooth muscle and endothelial cells. However, whether or not it is connected to cancer is yet unknown. Here, we report that MEOX2 functions as a tumor-initiating element in glioma. Bioinformatic analyses of public databases and investigation of MEOX2 expression in patients with glioma demonstrated that MEOX2 was abundant at both mRNA and protein levels in glioma. MEOX2 expression was shown to be inversely linked with the prognosis of glioma patients. MEOX2 inhibition changed the morphology of glioma cells, inhibited cell proliferation and motility, whereas had no effect on cell apoptosis. Besides, silencing MEOX2 also hampered the epithelial-mesenchymal transition (EMT), focal adhesion formation, and F-actin assembly. Overexpression of MEOX2 exhibited opposite effects. Importantly, RNA-sequencing, ChIP-qPCR assay, and luciferase reporter assay revealed Cathepsin S (CTSS) as a novel transcriptional target of MEOX2 in glioma cells. Consistently, MEOX2 causes glioma tumor development in mice and greatly lowers the survival period of tumor-bearing mice. Our findings indicate that MEOX2 promotes tumorigenesis and progression of glioma partially through the regulation of CTSS. Targeting MEOX2-CTSS axis might be a promising alternative for the treatment of glioma.

Accurately reconstructing the strain field within stiffened ship panels is crucial for effective structural health monitoring. This study presents a groundbreaking approach to strain field reconstruction in such panels, utilizing optical fiber sensors in conjunction with the strain function-inverse finite element method (SF-iFEM). A novel technique for solving nodal strain vectors, based on the element strain function, has been devised to improve the accuracy of strain reconstruction using the inverse finite element method (iFEM), addressing the limitations associated with traditional nodal displacement vector solutions. Moreover, the proposed method for determining the equivalent neutral layer of stiffened ship panels not only reduces the number of elements effectively but also establishes a strain function between the inner and outer surfaces of the structure. Using this function, a layout scheme for optical fiber sensors on the inner side of ship stiffened panels is provided, overcoming the symmetrical arrangement constraints of iFEM for sensor placement on both the inner and outer sides of the structure. The results demonstrate a significant improvement in strain reconstruction accuracy under bending and bending–torsion deformations compared to conventional iFEM. Consequently, the findings of this research will contribute to enhancing the engineering applicability of iFEM in ship structure health monitoring.

With the growing utilization of disc motors, the enhancement of their operational stability has become a critical research area. The existing studies usually focus on improving the pole structure of the rotor or the stator structure to optimize one performance of the motor and less on optimizing multiple performances. This paper simultaneously improves the rotor pole structure and stator tooth structure of the motor in order to optimize the sinusoidal waveform of the no-load back electromotive force and the cogging torque at the same time to achieve the goal of reducing the vibration and noise of the permanent-magnet synchronous dual-rotor statorless magnetically coupled disc motor and improve its operational stability. A finite element simulation model of a 20-pole, 24-slot permanent-magnet synchronous dual-rotor statorless magnetically coupled disc motor is established to analyze the influence of various factors, including the number of magnetic pole steps, the opening position, depth, and width of the stator auxiliary slot, on the motor performance. The results show that this stator–rotor combination improvement method effectively reduces the total harmonic distortion (THD) and attenuates multiple harmonics, and the peak cogging torque pulsation is significantly improved while other properties of the motor meet the technical requirements, and the motor performance is improved.

The double vertical-axis wind turbine (VAWT) system serves as a high-performance design solution. The Nautilus wind turbine investigated in this research imitated the structure of a Nautilus shell and is a vertical-axis drag-type wind turbine that exhibits relatively low efficiency. Therefore, the improvement of its wind energy efficiency is of paramount importance. This paper utilizes Computational Fluid Dynamics (CFD) software, based on the Reynolds-averaged Navier–Stokes equations and dynamic meshing techniques, to conduct numerical investigations on the aerodynamic performance of the Nautilus wind turbine array layout. The effects of wind direction, spacing ratio, and rotation direction are individually studied, and the interpretations and explanations are provided based on flow field characteristics. The results show that when the wind direction is 90°, i.e., a transverse layout, the closer the spacing between the transverse turbines, the higher the average power coefficient of the entire wind turbine system, with little effect from the three rotation directions. The maximum average power coefficient reached 28.9% and the power gain factor (TPGF) reached 11.1%. The enhancement effect primarily originates from the wake interaction among neighboring turbines. The experimental results showed a deviation of 8.1% compared to the CFD simulation results, thus validating the accuracy of the numerical CFD modeling. Ultimately, several array layouts are proposed, based on the prevalent wind direction and spacing ratio research. The enhancement of the wind turbine array’s situation could significantly increase the average efficiency of the entire wind turbine cluster. Consequently, this study provides a reference for the practical application of biomimetic vertical-axis drag-type wind turbine systems in actual engineering.

Temperature control is a critical aspect for the semiconductor material growth of the Molecular Beam Epitaxy system. The growth rate depends on the temperature of the beam source, and the quality of epi-layers heavily depends on the temperature of the substrate. In this paper, we reported an intelligent temperature control algorithm based on the BP neural network, which is specially optimized for the Molecular Beam Epitaxy system. After training the BP neural network by the collected temperature data of the Molecular Beam Epitaxy, the intelligent temperature control algorithm keeps the temperature error within 0.1°C. Moreover, this intelligent algorithm in the field of Molecular Beam Epitaxy achieves a better control effect, reduces the regulation time of the system and makes the system overshoot to zero.

The control system of offshore aquaculture platform was analyzed and the collaborative control technology with edge intelligence was introduced under the open architecture of cloud computing.A distributed intelligent control mode was established.A general framework architecture of cloud-edge collaborative control system for offshore aquaculture platform and an open architecture of intelligent aquaculture cloud platform were proposed exploratively, which could be deployed flexibly with different patterns of cloud-network-edge-terminal-intelligence.A development idea of control system and an implementation scheme of could central control and edge intelligence for offshore aquaculture platform were put forward.An attempt was made to build the theoretical framework and ideology system of cloud-edge collaborative control, and it was to construct the manner with the whole process of intelligent aquaculture and land-sea linkage operation and management mode for offshore fish-farm, with a view to finally realizing the intelligent fishery industrial ecology and green sustainable development goals.

ObjectiveThe diagnosis of prostate cancer has been a challenging task. Compared with traditional diagnosis methods, the radiofrequency (RF) signal is not only non-invasive but also rich in microscopic lesion information. This paper proposes a novel and accurate method for detecting prostate cancer based on the ultrasound RF signal.MethodOur approach is based on low-dimensional features in the frequency domain and high-throughput features in the spatial domain. The whole process could be divided into two parts: first, we calculate three feature maps from the ultrasound original RF signal, and 1,050 radiomics features are extracted from the three feature maps; second, we extracted 37 spectral features from the normalized frequency spectrum after Fourier transform.ResultsWe use LASSO regression as the method for feature selection; moreover, we use support vector machine (SVM) for classification 10-fold cross-validation for examining the classification performance of the SVM. An AUC (area under the receiver operating characteristic curve) of 0.84 was obtained on 71 subjects.ConclusionsOur method is feasible to detect prostate cancer based on the ultrasound RF signal with superior classification performance.

Abstract Background Brain tumor segmentation is a challenging problem in medical image processing and analysis. It is a very time-consuming and error-prone task. In order to reduce the burden on physicians and improve the segmentation accuracy, the computer-aided detection (CAD) systems need to be developed. Due to the powerful feature learning ability of the deep learning technology, many deep learning-based methods have been applied to the brain tumor segmentation CAD systems and achieved satisfactory accuracy. However, deep learning neural networks have high computational complexity, and the brain tumor segmentation process consumes significant time. Therefore, in order to achieve the high segmentation accuracy of brain tumors and obtain the segmentation results efficiently, it is very demanding to speed up the segmentation process of brain tumors. Results Compared with traditional computing platforms, the proposed FPGA accelerator has greatly improved the speed and the power consumption. Based on the BraTS19 and BraTS20 dataset, our FPGA-based brain tumor segmentation accelerator is 5.21 and 44.47 times faster than the TITAN V GPU and the Xeon CPU. In addition, by comparing energy efficiency, our design can achieve 11.22 and 82.33 times energy efficiency than GPU and CPU, respectively. Conclusion We quantize and retrain the neural network for brain tumor segmentation and merge batch normalization layers to reduce the parameter size and computational complexity. The FPGA-based brain tumor segmentation accelerator is designed to map the quantized neural network model. The accelerator can increase the segmentation speed and reduce the power consumption on the basis of ensuring high accuracy which provides a new direction for the automatic segmentation and remote diagnosis of brain tumors.

Background: The incidence of clear cell renal cell carcinoma (ccRCC) is high and has increased gradually in recent years. At present, due to the lack of effective prognostic indicators, the prognosis of ccRCC patients is greatly affected.Necroptosis is a type of cell death, and along with cell necrosis is considered a new cancer treatment strategy. The aim of this study was to construct a new marker for predicting the prognosis of ccRCC patients based on long non-coding RNA (nrlncRNAs) associated with necroptosis.Methods: RNA sequence data and clinical information of ccRCC patients from the Cancer Genome Atlas database (TCGA) were downloaded. NrlncRNA was identified by Pearson correlation study. The differentially expressed nrlncRNA and nrlncRNA pairs were identified by univariate Cox regression and Lasso-Cox regression. Finally, a Kaplan-Meier survival study, Cox regression, clinicopathological features correlation study, and receiver operating characteristic (ROC) spectrum were used to evaluate the prediction ability of 25-nrlncrnas for markers. In addition, correlations between the risk values and sensitivity to tumor-infiltrating immune cells, immune checkpoint inhibitors, and targeted drugs were also investigated.Results: In the current research, a novel marker of 25-nrlncRNAs pairs was developed to improve prognostic prediction in patients with ccRCC. Compared with clinicopathological features, nrlncRNAs had a higher diagnostic validity for markers, with the 1-year, 3-years, and 5-years operating characteristic regions being 0.902, 0.835, and 0.856, respectively, and compared with the stage of 0.868, an increase of 0.034. Cox regression and stratified survival studies showed that this marker could be an independent predictor of ccRCC patients. In addition, patients with different risk scores had significant differences in tumor-infiltrating immune cells, immune checkpoint, and semi-inhibitory concentration of targeted drugs. The feature could be used to evaluate the clinical efficacy of immunotherapy and targeted drug therapy.Conclusion: 25-nrlncRNAs pair markers may help to evaluate the prognosis and molecular characteristics of ccRCC patients, which improve treatment methods and can be more used in clinical practice.

BackgroundBladder cancer (BCa) is a remarkably malignant and heterogeneous neoplastic disease, and its prognosis prediction is still challenging. Even with the mounting researches on the mechanisms of tumor immunotherapy, the prognostic value of T-cell proliferation regulators in bladder cancer remains elusive.MethodsHerein, we collected mRNA expression profiles and relevant clinical information of bladder cancer sufferers from a publicly available data base. Then, the LASSO Cox regression model was utilized to establish a multi-gene signature for the TCGA cohort to predict the prognosis and staging of bladder cancer. Eventually, the predictive power of the model was validated by randomized grouping.ResultsThe outcomes revealed that most genes related to T-cell proliferation in the TCGA cohort exhibited different expressions between BCa cells and neighboring healthy tissues. Univariable Cox regressive analyses showed that four DEGs were related to OS in bladder cancer patients (p

Wheat–maize rotation is one of the most popular systems and successful intensification cropping systems in Northern China, while soils in some of this area are contaminated by cadmium (Cd). However, few studies have performed experiments on the reduction of Cd accumulation in the wheat–maize rotation system. In this study, wheat- and maize-derived biochars are applied to the Cd-contaminated soil to reduce the Cd accumulation in the wheat and maize plants. The results showed that soil biochar applications can significantly decrease DTPA-extracted Cd concentrations by 12.7–26.0% and 13.1–20.5% by wheat- and maize-derived biochars, respectively. Sequential extractions showed that biochar applications significantly reduced the Cd mobility and bioavailability in soils and changed the exchangeable and carbonate-bound fractions of Cd to organic material-bound and residual fractions. The biochar applications increased the plant growth, yield, and quality of both wheat and maize, especially a significant increase in high dosages. The biochar applications also improved the antioxidant enzyme activities and reduced the reduction of MDA in both wheat and maize roots. The Cd concentrations in wheat grains were reduced by 38.1–91.5% and 65.9–80%, and maize grain Cd concentrations were reduced by 20.9–54.2% and 30.8–44% by wheat- and maize-derived biochar applications, respectively, and the Cd concentrations in the root, stem, and leaf were also significantly reduced. The wheat-derived biochar was more effective on the Cd reduction in soil bioavailable fractions and Cd accumulation in crop plants.

A representative general polarimetric model-based decomposition framework was proposed by Chen et al., which implements a simultaneous full-parameter inversion by using complete polarimetric information and solves several limitations in previous decomposition methods. However, there are still shortcomings in Chen’s work. Firstly, only the real part of the parameter β in the generalized surface scattering model is considered. Secondly, inappropriate initial input values may lead to local optima in the nonlinear least squares optimization algorithm. Thirdly, the volume scattering component is underestimated in the volume scattering-dominated scene, but overestimated in buildings with large orientation (LOB) areas. Finally, nonlinear optimization is time-consuming computationally. To overcome those issues, an improved generalized polarimetric model-based decomposition method is proposed in this paper. The imaginary part of the parameter β is incorporated into the decomposition framework of the proposed method. Ingeniously utilizing the internal relationship in the generic equations composed of coherent matrix elements, the model parameters can be inversed by simplifying the nonlinear equations to linear equations. Therefore, compared with Chen’s method, the proposed method does not rely on the initial input values, and improves the computational efficiency. In addition, a hierarchical decomposition scheme is presented to solve the problem of underestimation or overestimation of volume scattering component mentioned above. The performance and advantages of this method are evaluated with L-band and C-band polarimetric synthetic aperture radar (PolSAR) data sets. Comparison studies are carried out with other model-based decomposition methods, demonstrating that the proposed method can further improve decomposition performance, especially in LOB areas.

This paper presents a novel unsupervised learning framework for estimating scene depth and camera pose from video sequences, fundamental to many high-level tasks such as 3D reconstruction, visual navigation, and augmented reality. Although existing unsupervised methods have achieved promising results, their performance suffers in challenging scenes such as those with dynamic objects and occluded regions. As a result, multiple mask technologies and geometric consistency constraints are adopted in this research to mitigate their negative impacts. Firstly, multiple mask technologies are used to identify numerous outliers in the scene, which are excluded from the loss computation. In addition, the identified outliers are employed as a supervised signal to train a mask estimation network. The estimated mask is then utilized to preprocess the input to the pose estimation network, mitigating the potential adverse effects of challenging scenes on pose estimation. Furthermore, we propose geometric consistency constraints to reduce the sensitivity of illumination changes, which act as additional supervised signals to train the network. Experimental results on the KITTI dataset demonstrate that our proposed strategies can effectively enhance the model’s performance, outperforming other unsupervised methods.

ObjectiveMany studies have drawn their attention to the immunotherapy of bladder urothelial carcinoma in terms of immunologic mechanisms of human body. These include immunogenicity of the tumor cells and involvement of long non-coding RNA (lncRNA). We constructed a necroptosis-related long noncoding RNA (nrlncRNA) risk factor model to predict BLCA outcomes and calculate correlations with chemosensitivity and immune infiltration.MethodsTranscriptomic data from BLCA specimens were accessed from The Cancer Genome Atlas, and nrlncRNAs were identified by performing co-expression analysis. Univariate analysis was performed to identify differentially expressed nrlncRNA pairs. We constructed least absolute contraction and selector operation regression models and drew receiver operating characteristic curves for 1-, 3-, and 5-year survival rates. Akaike information criterion (AIC) values for survival over 1 year were determined as cutoff values in high- and low-risk subgroups. We reassessed the differences between subgroups in terms of survival, clinicopathological characteristics, chemotherapy efficacy, tumor-infiltrating immune cells, and markers of immunosuppression.ResultsWe identified a total of 260 necroptosis-related lncRNA pairs, of which we incorporated 13 into the prognostic model. Areas under the curve of 1-, 3-, and 5- year survival time were 0.763, 0.836, and 0.842, respectively. We confirmed the excellent predictive performance of the risk model. Based on AIC values, we confirmed that the high-risk group was susceptible to unfavorable outcomes. The risk scores correlated with survival were age, clinical stage, grade, and tumor node metastases. The risk model was an independent predictor and demonstrated higher predictive power. The risk model can also be utilized to determine immune cell infiltration status, expression levels of immune checkpoint genes, and the sensitivity to cisplatin, doxorubicin, and methotrexate.ConclusionWe constructed a novel necroptosis-related signature that predicts BLCA outcomes and performs satisfactorily in the immune landscape and chemotherapeutic responses.

Insect pest control is considered as a significant factor in the yield of commercial crops. Thus, to avoid economic losses, we need a valid method for insect pest recognition. In this paper, we proposed a feature fusion residual block to perform the insect pest recognition task. Based on the original residual block, we fused the feature from a previous layer between two 1×1 convolution layers in a residual signal branch to improve the capacity of the block. Furthermore, we explored the contribution of each residual group to the model performance. We found that adding the residual blocks of earlier residual groups promotes the model performance significantly, which improves the capacity of generalization of the model. By stacking the feature fusion residual block, we constructed the Deep Feature Fusion Residual Network (DFF-ResNet). To prove the validity and adaptivity of our approach, we constructed it with two common residual networks (Pre-ResNet and Wide Residual Network (WRN)) and validated these models on the Canadian Institute For Advanced Research (CIFAR) and Street View House Number (SVHN) benchmark datasets. The experimental results indicate that our models have a lower test error than those of baseline models. Then, we applied our models to recognize insect pests and obtained validity on the IP102 benchmark dataset. The experimental results show that our models outperform the original ResNet and other state-of-the-art methods.

SLA (stereolithography), as a rapid and accurate additive manufacturing method, can be used to mold the microchannel. The stair effect is inevitable when the part is printed layer by layer, which has an important influence on the printing performance. In the current work, the power-law flow in the microchannel with nano-scale stairs manufactured by SLA is simulated and investigated. To improve the stability caused by the non-Newtonian behavior, a modified lattice Boltzmann method (LBM) is proposed and validated. Then, a series of simulations are conducted and analyzed, the results show that both the stair effect and power-law index are important factors. The stairs on the surface force the streamlines to be curved and increase the outlet velocity. In addition, different power-law indexes result in completely different flows. The small power-law index leads to a much larger velocity than other cases, while the large power-law index makes the outlet velocity unstable at the middle position.

Fulvic acids are organic compounds widely distributed in soils, and the application of fulvic acids is thought to increase crop yield and quality. However, the effects vary among various sources and doses of fulvic acids and environmental and growth conditions of crops. Here, we investigated the effects of bioresource-derived (corn straw) fulvic acids on plant production and quality of tomato plants and soil chemical properties in soil cultures while experiments on seed germination and hydroponics were conducted to explore the underlying mechanism. Base dressing with 2.7 g kg–1 increased the yield of tomato by 35.0% at most as increased fruit number. Fulvic acids increased the concentrations of minerals, such as Ca, Fe, and Zn and the concentrations of citric, malic, and some amino acids in berries of tomato but did not affect the concentrations of soluble sugars and aromatic substances in tomato fruits. Similarly, fulvic acids at 80–160 mg L–1 increased germination rate, growth vigor, and radicle elongation of tomato seeds while it increased plant biomass, concentrations of nutrients, and root length of tomato plants in hydroponics to the greatest extent in general. The increases in yield and quality can be attributed to the improvement in root growth and, thus, increased nutrient uptake. In addition, the base application of fulvic acids improved soil cation exchange capacity and soil organic matter to an extent. In conclusion, base dressing and the addition into solution of fulvic acids at moderate doses facilitate root growth and nutrient uptake and, thus, vegetable production and quality; therefore, fulvic acids can be an effective component for designing new biofertilizers for sustainable agricultural production.

In this paper, nano SiO2 particles modified organic silane coatings were successfully prepared to aim at the application of the self-cleaning coating on PMMA substrate for deep-sea optical windows. The chemicals, surface microstructure, wettability, hardness, adhesion, transparency, water scouring resistance as well as microorganism attachment rate of the coatings were investigated. The results showed that adding SiO2 nanoparticles into the organic silicon coating can effectively improve the hydrophobicity due to generating a micro-nano structure surface. However, excessive addition would result in a decrease in hydrophobicity, adhesion, as well as transparency, due to the inorganic SiO2 particle destroying the integrity of the organic coating. The optimal coating was obtained by adding 0.5 wt% nano SiO2 particles, which possessed a water contact angle of 114.2°, hardness of 4H, adhesion level of 0, and visible light transmittance of 0.886. After 40-h water scouring, the water contact angle decreased to 108.3° and the visible light transmittance decreased to 0.839, suggesting good water scouring resistance. The microorganism attachment rate of the S05 coating was 0.17% after a 6 h immersion test, which was about half that of the PMMA substrate.

Objectives: Delayed neurocognitive recovery (DNR) seriously affects the post-operative recovery of elderly surgical patients, but there is still a lack of effective methods to recognize high-risk patients with DNR. This study proposed a machine learning method based on a multi-order brain functional connectivity (FC) network to recognize DNR.Method: Seventy-four patients who completed assessments were included in this study, in which 16/74 (21.6%) had DNR following surgery. Based on resting-state functional magnetic resonance imaging (rs-fMRI), we first constructed low-order FC networks of 90 brain regions by calculating the correlation of brain region signal changing in the time dimension. Then, we established high-order FC networks by calculating correlations among each pair of brain regions. Afterward, we built sparse representation-based machine learning model to recognize DNR on the extracted multi-order FC network features. Finally, an independent testing was conducted to validate the established recognition model.Results: Three hundred ninety features of FC networks were finally extracted to identify DNR. After performing the independent-sample T test between these features and the categories, 15 features showed statistical differences (P < 0.05) and 3 features had significant statistical differences (P < 0.01). By comparing DNR and non-DNR patients’ brain region connection matrices, it is found that there are more connections among brain regions in DNR patients than in non-DNR patients. For the machine learning recognition model based on multi-feature combination, the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the classifier reached 95.61, 92.00, 66.67, and 100.00%, respectively.Conclusion: This study not only reveals the significance of preoperative rs-fMRI in recognizing post-operative DNR in elderly patients but also establishes a promising machine learning method to recognize DNR.

Insect pests are one of the main threats to the commercially important crops. An effective insect pest recognition method can avoid economic losses. In this paper, we proposed a new and simple structure based on the original residual block and named as feature reuse residual block which combines feature from the input signal of a residual block with the residual signal. In each feature reuse residual block, it enhances the capacity of representation by learning half and reuse half feature. By stacking the feature reuse residual block, we obtained the feature reuse residual network (FR-ResNet) and evaluated the performance on IP102 benchmark dataset. The experimental results showed that FR-ResNet can achieve significant performance improvement in terms of insect pest classification. Moreover, to demonstrate the adaptive of our approach, we applied it to various kinds of residual networks, including ResNet, Pre-ResNet, and WRN, and we tested the performance on a series of benchmark datasets: CIFAR-10, CIFAR-100, and SVHN. The experimental results showed that the performance can be improved obviously than original networks. Based on these experiments on CIFAR-10, CIFAR-100, SVHN, and IP102 benchmark datasets, it demonstrates the effectiveness of our approach.

The wave parameter is an important environmental input condition. Traditional contact wave measurement methods are unable to meet the requirements of high precision, non-contact, and ship wave field assessment. Alternatively, stereo vision technology can realize a non-contact and mobile form of measurement. However, this technology suffers from poor timeliness and adaptability. This paper proposes a comprehensive wave measurement method that is based on stereo vision, wherein the gridding of siftGPU is used to achieve the fast matching of large images. The whole algorithm can be run within 6 s and it guarantees more than 20,000 feature-matching logarithms. Furthermore, by utilizing the least squares method and sea surface wave surface theory, the sea surface base level can be calculated without control points, along with the inversion of the sea wave parameters (wave height, period, and wave direction) and error point fitting. The rationality and superiority of the algorithm were verified through multiple comparison experiments. Compared with the Richard Brancker Research (RBR) wave height meter, the measurement error of the wave height is less than 10%, the period error is less than 0.5 s, and the wave direction error is less than 10° with the proposed method.

Objectives: Brain arteriovenous malformation (AVM) is one of the most common causes of intracranial hemorrhage in young adults, and its expeditious diagnosis on digital subtraction angiography (DSA) is essential for clinical decision-making. This paper firstly proposed a deep learning network to extract vascular time-domain features from DSA videos. Then, the temporal features were combined with spatial radiomics features to build an AVM-assisted diagnosis model.Materials and method: Anteroposterior position (AP) DSA videos from 305 patients, 153 normal and 152 with AVM, were analyzed. A deep learning network based on Faster-RCNN was proposed to track important vascular features in DSA. Then the appearance order of important vascular structures was quantified as the temporal features. The structure distribution and morphological features of vessels were quantified as 1,750 radiomics features. Temporal features and radiomics features were fused in a classifier based on sparse representation and support vector machine. An AVM diagnosis and grading system that combined the temporal and spatial radiomics features of DSA was finally proposed. Accuracy (ACC), sensitivity (SENS), specificity (SPEC), and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of the radiomics model.Results: For cerebrovascular structure detection, the average precision (AP) was 0.922, 0.991, 0.769, 0.899, and 0.929 for internal carotid artery, Willis circle, vessels, large veins, and venous sinuses, respectively. The mean average precision (mAP) of five time phases was 0.902. For AVM diagnosis, the models based on temporal features, radiomics features, and combined features achieved AUC of 0.916, 0.918, and 0.942, respectively. In the AVM grading task, the proposed combined model also achieved AUC of 0.871 in the independent testing set.Conclusion: DSA videos provide rich temporal and spatial distribution characteristics of cerebral blood vessels. Clinicians often interpret these features based on subjective experience. This paper proposes a scheme based on deep learning and traditional machine learning, which effectively integrates the complex spatiotemporal features in DSA, and verifies the value of this scheme in the diagnosis of AVM.

To improve the forming quality of V-shaped clamps, a basis for optimal V-shaped clamp mold surface design is provided. In this paper, the V-shaped clamp stamping process is simulated using Abaqus and the influence of the mold’s friction coefficient on the V-shaped clamp stamping parameters is revealed. The regional friction coefficient of the V-shaped clamp mold surface is optimized using an equal horizontal design scheme and the optimal friction coefficient combination for the mold surface is obtained, along with comparison and analysis of the molding parameters before and after optimization. Results show that the mold’s surface friction properties have significant regional characteristics. Increasing the friction coefficient of the bottom, the rounded section of the punch’s V-shaped groove and the bulge filleted corner of the punch while reducing the friction coefficient of the bulge filleted corner and crimping area of the die head contributes to the material flow. Mold surface optimization makes the material flow velocity more uniform, disperses stress on the part’s surface, reduces strain and thickness reduction of the key parts, and improves overall quality. Measurements show that the V-shaped clamp mold’s surface friction characteristics affect the molding parameters significantly and mold optimization improves the V-shaped clamp’s molding quality.

Tooth flank twist is a common phenomenon in the helical crowning modification of helical cylindrical gear, which reduces the performance of the gear. At present, the measurement of tooth flank twist is still in the stage of replacing surface with line. The development of 3-D measurement technology makes the acquisition of gear information more comprehensive and complete. By processing the 3-D point cloud data, the twist deviation on the tooth flank can be found, which is particularly important for improving the quality of the tooth flank. In this paper, a characterization method of tooth flank twist based on orthogonal polynomials was proposed, which can effectively separate the twist deviations existing on the tooth flank. The correctness of the method was verified by measurement experiments. Taking worm grinding wheel grinding to process helical crowning modification as an example, the model of various parameters of the gear and the amount of twist was established and the twist deviation in processing was predicted. This provides a new method for eliminating the twist deviation of the tooth flank.

Abstract Background The relationship between acute kidney injury (AKI) and stroke needs quantitative summary. Therefore, we analyzed the associations between AKI and stroke including the incidence, risk factors of AKI after stroke, and the influence of AKI after a stroke on prognosis of stroke. Methods Articles published until November 2019 were searched based on the following databases: PubMed, Web of Science, EMBASE, Medline, and Google Scholar. We computed the following results [rates of AKI incidence after a stroke, odds ratios (ORs) or relative risks (RRs) estimates and the 95% confidence intervals (CIs) for the association between risk factors and AKI, ORs or RRs and the CIs for the association between AKI and outcomes after a stroke] by using STATA 13.0 software. Results The study reported an overall incidence of AKI of 12% with a random‐effects model. Additionally, the present study showed that higher National Institutes of Health Stroke Scale (NIHSS) score on admission and history of hypertension were associated with higher risk of AKI after stroke. Moreover, the study showed that AKI after stroke was associated with higher in‐hospital mortality, higher 1‐month mortality, higher long‐term mortality, and poorer functional outcome. Conclusions Acute kidney injury appears to be a common complication after stroke and is related to increased mortality and disability in stroke. Additionally, high NIHSS score on admission and history of hypertension were the critical risk factors for the AKI after stroke. More large‐scale studies should be made to explore AKI after stroke.

Dietary methionine restriction (MR) could prevent age-related diseases and extend lifespan. The present study compared the effects of different levels of MR on oxidative stress and behavioral deficits in middle-aged mice fed a diet with various contents of fat. C57BL/6J middle-aged male mice were divided into nine groups with various combinations of dietary fat (low, medium, and high) and MR (0%, 60%, and 80%). MR attenuated dietary fat-induced oxidative stress and anxiety; improved athletic ability, learning, memory, and cognitive ability in conjunction with an improved thyroid function in a dose-dependent manner. Moreover, the beneficial effect of MR60 was prominent in middle-aged mice fed a HF diet relative to the corresponding MR0 group. These results provide more information on the appropriate methionine content to design personalized nutritional diets.

Abstract Background This study aims to establish a radiomics analysis system for the diagnosis and clinical behaviour prediction of hepatocellular carcinoma (HCC) based on multi-parametric ultrasound imaging. Methods A total of 177 patients with focal liver lesions (FLLs) were included in the study. Every patient underwent multi-modal ultrasound examination, including B-mode ultrasound (BMUS), shear wave elastography (SWE), and shear wave viscosity (SWV) imaging. The radiomics analysis system was built on sparse representation theory (SRT) and support vector machine (SVM) for asymmetric data. Through the sparse regulation from the SRT, the proposed radiomics system can effectively avoid over-fitting issues that occur in regular radiomics analysis. The purpose of the proposed system includes differential diagnosis between benign and malignant FLLs, pathologic diagnosis of HCC, and clinical prognostic prediction. Three biomarkers, including programmed cell death protein 1 (PD-1), antigen Ki-67 (Ki-67) and microvascular invasion (MVI), were included and analysed. We calculated the accuracy (ACC), sensitivity (SENS), specificity (SPEC) and area under the receiver operating characteristic curve (AUC) to evaluate the performance of the radiomics models. Results A total of 2560 features were extracted from the multi-modal ultrasound images for each patient. Five radiomics models were built, and leave-one-out cross-validation (LOOCV) was used to evaluate the models. In LOOCV, the AUC was 0.94 for benign and malignant classification (95% confidence interval [CI]: 0.88 to 0.98), 0.97 for malignant subtyping (95% CI: 0.93 to 0.99), 0.97 for PD-1 prediction (95% CI: 0.89 to 0.98), 0.94 for Ki-67 prediction (95% CI: 0.87 to 0.97), and 0.98 for MVI prediction (95% CI: 0.93 to 0.99). The performance of each model improved when the viscosity modality was included. Conclusions Radiomics analysis based on multi-modal ultrasound images could aid in comprehensive liver tumor evaluations, including diagnosis, differential diagnosis, and clinical prognosis.

Artesunate (ART) is a prominent anti-malarial with significant anti-cancer properties. Our previous studies showed that ART enhances lysosomal function and ferritin degradation, which was necessary for its anti-cancer properties. ART targeting to mitochondria also significantly improved its efficacy, but the effect of ART on mitophagy, an important cellular pathway that facilitates the removal of damaged mitochondria, remains unknown. Here, we first observed that ART mainly localizes in the mitochondria and its probe labeling revealed that it binds to a large number of mitochondrial proteins and causes mitochondrial fission. Second, we found that ART treatment leads to autophagy induction and the decrease of mitochondrial proteins. When autophagy is inhibited, the decrease of mitochondrial proteins could be reversed, indicating that the degradation of mitochondrial proteins is through mitophagy. Third, our results showed that ART treatment stabilizes the full-length form of PTEN induced putative kinase 1 (PINK1) on the mitochondria and activates the PINK1-dependent pathway. This in turn leads to the recruitment of Parkin, sequestosome 1 (SQSTM1), ubiquitin and microtubule-associated proteins 1A/1B light chain 3 (LC3) to the mitochondria and culminates in mitophagy. When PINK1 is knocked down, ART-induced mitophagy is markedly suppressed. Finally, we investigated the effect of mitophagy by ART on mitochondrial functions and found that knockdown of PINK1 alters the cellular redox status in ART-treated cells, which is accompanied with a significant decrease in glutathione (GSH) and increase in mitochondrial reactive oxidative species (mROS) and cellular lactate levels. Additionally, knockdown of PINK1 leads to a significant increase of mitochondrial depolarization and more cell apoptosis by ART, suggesting that mitophagy protects from ART-induced cell death. Taken together, our findings reveal the molecular mechanism that ART induces cytoprotective mitophagy through the PINK1-dependent pathway, suggesting that mitophagy inhibition could enhance the anti-cancer activity of ART. Keywords: Artesunate, Artemisinin, Mitophagy, PINK1, Parkin, ROS

Abstract Background In ultrasound plane wave imaging (PWI), image details are often blurred by the off-axis artefacts resulting from high sidelobe. Recently plane wave compounding (PWC) is proposed as a promising technique for the sidelobe suppression in the PWI. However, its high demand for the frame number results in an obvious frame rate loss, which is intolerable in the ultrafast imaging modality. To reduce the number of frames required for compounding, coherence in the compounding frames should be exploited. Methods In this paper, we propose a global effective distance-based sidelobe suppressing method for the PWC with a limited frame number, where the global effective distance is introduced to measure the inter-frame coherence. Specifically, the effective distance is firstly computed by using a sparse representation-based algorithm. Then, the sidelobe localization is carried out on the basis of the effective distance. Finally, the target-dependent weighting factor is adopted to suppress the sidelobe. Results To assert the superiority of our proposed method, we compare the performances of different sidelobe reduction methods on both simulated and experimental PWC data. In case of 5 steering angles, our method shows a 19 dB reduction in the peak sidelobe level compared to the normal PWC in the point spread function test, and the contrast ratio is enhanced by more than 10% in both the simulation and phantom studies. Conclusions Consequently, the proposed method is convinced to be a promising approach in enhancing the PWC image quality.

Oriented manmade targets can produce significant cross-polarization power. The scattering mechanism interpretation of them is still challenging. Within the framework of traditional scattering models, the scattering mechanism of oriented manmade targets will be interpreted as volume scattering. Recently, many advanced approaches have been proposed to mitigate the cross-polarization terms of the coherency matrix or distribute the power of cross-polarization to new scattering models, such as orientation angle compensation and multiple scattering components decomposition. Among these methods, the general model-based decomposition with physically meaningful double-bounce and odd-bounce scattering models has been proposed by modeling their independent orientation angles and becomes a widely accepted method. However, the two vital parameters of generalized scattering models: double- and odd-bounce orientation angles are derived through nonlinear optimization procedure. These generalized models lead to a heavy computation burden for parameters inversion. In this paper, we disclose the latent relationship between the double-bounce orientation angle and polarization orientation angle by data fitting experiments. With this simplified relationship, a refined double-bounce scattering model is established. Then, the odd-bounce orientation angle can be derived through equations. In this way, the nonlinear optimization procedure can be converted to a linear solution. A fast generalized model-based decomposition is developed thereafter. The main contribution of this work is to inherit the generalized models while speeding up the parameter calculation procedure. The comparison studies are carried out with X-band airborne PiSAR, L-band spaceborne ALOS-2, and C-band spaceborne Radarsat-2 PolSAR datasets. Compared with the state-of-the-art approaches, the proposed decomposition achieves improved interpretation performance from both visual and quantitative investigations especially for oriented built-up areas.

Utilizing a novel method with the resonance frequency of a LC circuit, we measured the superconducting anisotropy of single crystals of an Fe-based superconductor FeSe with applied magnetic field up to 16 T. We found that the temperature dependence of the upper critical field Hc2(T) of FeSe coincides with the Werthamer-Helfand-Hohenberg (WHH) model when taking the Maki parameter α into consideration, suggesting an important role played by spin-paramagnetic effect in suppressing the superconductivity. When temperature T → 0, the values of Hc2,∥c(0) and Hc2,∥ab(0) derived from the WHH fitting are close to and fall within the range of the Pauli limit, for field H0 applied parallel to the c-axis and to the ab-plane, respectively. As compared with other typical iron-based high-Tc superconductors, lower values of Hc2(0) and higher superconducting anisotropy Γ(0) were observed in FeSe.

Because of the appealing properties, ionic liquids (ILs) are believed to be promising alternatives for the CO2 absorption in the flue gas. Several ILs, such as [NH2emim][BF4], [C4mim][OAc], and [NH2emim[OAc], have been used to capture CO2 of the simulated flue gas in this work. The structural changes of the ILs before and after absorption were also investigated by quantum chemical methods, FTIR, and NMR technologies. However, the experimental results and theoretical calculation showed that the flue gas component SO2 would significantly weaken the CO2 absorption performance of the ILs. SO2 was more likely to react with the active sites of the ILs than CO2. To improve the absorption capacity, the ionic liquid (IL) mixture [C4mim][OAc]/ [NH2emim][BF4] were employed for the CO2 absorption of the flue gas. It is found that the CO2 absorption capacity would be increased by about 25%, even in the presence of SO2. The calculation results suggested that CO2 could not compete with SO2 for reacting with the IL during the absorption process. Nevertheless, SO2 might be first captured by the [NH2emim][BF4] of the IL mixture, and then the [C4mim][OAc] ionic liquid could absorb more CO2 without the interference of SO2.