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In order to expand the application of silver carp protein hydrolysate in frozen dough, this paper investigated the effects of silver carp protein hydrolysates on the freezable water content, water distribution and migration, microstructure, rheological properties, fermentation properties of frozen dough during frozen storage, and analyzed specific volume, textural properties and sensory score of bread. The DSC and LF-NMR results showed that the addition of silver carp protein hydrolysate inhibited the increase of freezable water content and water fluidity of dough during the frozen storage. The SEM, rheological and fermentation properties results showed that the addition of silver carp protein hydrolysate reduced the mechanical damage of gluten network structure, and increased the proportion of elastic properties and fermentation volume of dough during frozen storage. The results of specific volume and texture showed that the addition of silver carp protein hydrolysate improved the specific volume and elasticity of bread, and reduced its hardness and chewability. Silver carp protein hydrolysate also improved sensory score of bread. In conclusion, it can be seen that silver carp protein hydrolysate can effectively improve the quality of frozen dough and bread.

The Fe–Ni–Mo–C–Cu (Q61) composites reinforced by Al2O3 particles with different contents were prepared by powder forging. The microstructure and properties of the tempered and quenched Q61 composites were studied. The results show that, when the mass fraction of Al2O3 is 0.15%, the Al2O3 particles are distributed homogeneously in the matrix. Compared with the single Q61 without reinforced particles under the same state, the hardness and yield strength of the tempered composites are increased from HRC 38 and 1106 MPa to HRC 39.8 and 1121 MPa, respectively, while the elongation is decreased from 12% to 6.5%; the hardness of the quenched Q61 composites is enhanced from HRC 61.5 to HRC 63.2, and the wear rate is reduced from 5.27×10−6 mm3·m−1·N−1 to 3.08×10−6 mm3·m−1·N−1, lower than that of the typical 40Cr gear materials (3.34×10−6 mm3·m−1·N−1). However, the Al2O3 particles are aggregated gradually when the Al2O3 addition content is more than 0.15%. Though the yield strength is still improved, the plasticity is reduced significantly for the tempered composites, and the wear rate is greatly increased for the quenched composites, showing the degraded wear resistance. In summary, the Q61 composites with 0.15% Al2O3 particles show the higher comprehensive mechanical properties in the tempered state and the good wear resistance in the quenched states, respectively.

Abstract Background Clinical practice guidelines on limitation of life-sustaining treatments (LST) in the intensive care unit (ICU), in the form of withholding or withdrawal of LST, state that there is no ethical difference between the two. Such statements are not uniformly accepted worldwide, and there are few studies on LST limitation in Asia. This study aimed to evaluate the predictors and outcomes of withholding and withdrawal of LST in Singapore, focusing on the similarities and differences between the two approaches. Methods This was a multicentre observational study of patients admitted to 21 adult ICUs across 9 public hospitals in Singapore over an average of three months per year from 2014 to 2019. The primary outcome measures were withholding and withdrawal of LST (cardiopulmonary resuscitation, invasive mechanical ventilation, and vasopressors/inotropes). The secondary outcome measure was hospital mortality. Multivariable generalised mixed model analysis was used to identify independent predictors for withdrawal and withholding of LST and if LST limitation predicts hospital mortality. Results There were 8907 patients and 9723 admissions. Of the former, 80.8% had no limitation of LST, 13.0% had LST withheld, and 6.2% had LST withdrawn. Common independent predictors for withholding and withdrawal were increasing age, absence of chronic kidney dialysis, greater dependence in activities of daily living, cardiopulmonary resuscitation before ICU admission, higher Acute Physiology and Chronic Health Evaluation (APACHE) II score, and higher level of care in the first 24 h of ICU admission. Additional predictors for withholding included being of Chinese race, the religions of Hinduism and Islam, malignancy, and chronic liver failure. The additional predictor for withdrawal was lower hospital paying class (with greater government subsidy for hospital bills). Hospital mortality in patients without LST limitation, with LST withholding, and with LST withdrawal was 10.6%, 82.1%, and 91.8%, respectively (p

Abstract The association between Hepatitis B virus (HBV) infection and colorectal liver metastases (CRLM) remains ambiguous in current population‐based evidence. To clarify this, we present a retrospective analysis of 5871 colorectal cancer (CRC) patients. Propensity score matching (PSM) was applied to harmonize age and sex disparities within HBV+ (n = 1696) and HBV‐ (n = 4175) groups and further within HBV+ subgroups of chronic (CHB, n = 474) and occult (OHB, n = 1222) infections. Our initial results indicated a significant association between HBV infection and synchronous colorectal liver metastasis (SYN‐CRLM); however, this association dissipated after PSM was employed to adjust for confounding variables. No significant association was observed between HBV infection and metachronous colorectal liver metastases (MET‐CRLM) both before and after PSM. Further analysis revealed that HBV replication status did not influence the incidence of CRLM. However, HBV+ participants demonstrated an increased incidence of metachronous extrahepatic metastases, particularly to the lungs. Our findings imply that neither past nor present HBV infection is significantly correlated with the occurrence of SYN‐CRLM or MET‐CRLM. The absence of an association between HBV replication status and CRLM incidence highlights the importance of incorporating a broader range of factors in the clinical management of CRLM beyond the status of HBV infection.

In practical asphalt pavement engineering, asphalt mixture experiences both tensile and compressive stresses, and the stress state plays a pivotal role in determining fatigue performance. This study thoroughly explores the fatigue damage characteristics of asphalt mixture, specifically emphasizing a meticulous analysis of the differences between tensile and compressive moduli. Initially, the compressive and tensile moduli of the asphalt mixture were calculated based on the dual modulus theory. Subsequently, differences in tensile and compressive moduli, along with fatigue performance characteristics, were evaluated through indirect tensile tests. Ultimately, a comprehensive fatigue damage model was proposed to ascertain the critical damage degree of the asphalt mixture, integrating both modulus decay and fatigue damage. The findings reveal that decay rates for the tensile modulus were higher than those observed for the compressive modulus. The tensile damage should be considered more in the design of pavement structures. Upon comparing the critical damage degree determined using the traditional modulus decay model with the comprehensive fatigue damage model, it can be deduced that the latter shows favorable applicability. The compressive fatigue modulus can better characterize the fatigue damage behavior. The model proposed in this study integrates modulus decay and fatigue damage information, offering a more comprehensive depiction of asphalt mixture damage behavior during fatigue processes.

Active fire (AF) detection is essential for early warning of wildfires to help suppress and mitigate damage. This study presents an AF neural network (AF-Net) model based on object-contextual representations (OCR) for AF segmentation from very high-resolution (VHR) unmanned aerial vehicles (UAVs) remote sensing images. To efficiently detect heat anomalies in forests from large UAV scenes, we have to handle the class imbalance between small AF pixels and large-area complex background information. Class imbalance affects the model optimization and makes the training process stuck at a local minimum. Our work aims to address this issue by improving the object-contextual feature representations associated with fire in three ways. First, we employ a grid-based sampling strategy by constraining sampling ranges and reducing background samples. It improves the proportion of foreground pixels from 5.6% to 7.9% and maintains at least one AF pixel in each sample. Then, we simplify the OCR module to strengthen small object representations related to the AF using a self-attention unit. The OCR module receives multiscale pixel representations as input from the HRNet-W48 backbone. Lastly, the weighted binary cross-entropy loss and the Lovász hinge loss are combined to improve the detection accuracy by optimizing the foreground IoU. We evaluate the performance of the proposed AF-Net on one aerial AF benchmark (FLAME dataset). The proposed framework improves the mIoU score from 78.17% (baseline U-Net) to 91.14%.

This essay aims to comparatively study the microscopic morphology and grinding properties of brazed diamonds with amorphous and crystalline filler alloy in the case of rare-earth Nd-doped modified Ni-based filler alloy. The results show that at the same content of Nd, Cr7C3 is mainly generated at the brazing interface of crystalline samples. In contrast, Cr27C6 is mainly generated at the brazing interface of amorphous samples. Amorphous solder material achieves a robust chemical metallurgical union, producing a dense and homogeneous carbide layer. The bonding strength between amorphous solder material and diamond is further enhanced. The addition of rare earth element Nd contributes greatly to the forming ability of amorphous filler alloy. Nd atoms in the amorphous solder material readily bond with Ni atoms, reducing the chemical corrosion of Ni atoms on diamonds and resulting in a lower degree of graphitization. When the amount of Nd added is 1.0 wt%, the morphology of amorphous filler alloy brazing diamond is more complete than the effect of the crystalline state, the degree of thermal damage is lower, and the surface of amorphous filler alloy brazing is smoother. Frictional wear experiments show that the amorphous filler alloy holds diamond better than the crystalline filler alloy, and the diamond specimens prepared with the amorphous filler alloy demonstrate a significantly superior grinding performance in comparison to the diamond specimens prepared with the crystalline filler alloy.

Distinguishing multiple targets in the same resolution cell is an important and challenging task in the forward-looking imaging process of monopulse radar. Although Doppler processing can improve the recognition performance for multiple targets at high squint angles, the precise estimation of Doppler frequency remains challenging under conditions with unknown target numbers and energy leakage from strong point targets. To address these issues, this paper proposes a Fast Iterative Interpolated Beamforming (FIIB) algorithm with model order estimation and single snapshot processing for monopulse forward-looking imaging, which combines information theory to unbiasedly estimate the number of targets and Doppler frequencies. The simulation results show the superiority of the proposed FIIB algorithm over the Chirp-Z Transform (CZT) algorithm for estimating target numbers and Doppler frequencies within the same resolution cell in the presence of multiple point targets. In addition, the proposed FIIB algorithm can accurately estimate point targets beyond a ±5° azimuth angle in monopulse angle measurement tasks. Real-data experiments also reveal that FIIB-based monopulse forward-looking imaging has high focusing capability and imaging contrast and can effectively suppress background clutter.

Radar forward-looking imaging is critical in many civil and military fields, such as aircraft landing, autonomous driving, and geological exploration. Although the super-resolution forward-looking imaging algorithm based on spectral estimation has the potential to discriminate multiple targets within the same beam, the estimation of the angle and magnitude of the targets are not accurate due to the influence of sidelobe leakage. This paper proposes a multi-channel super-resolution forward-looking imaging algorithm based on the improved Fast Iterative Interpolated Beamforming (FIIB) algorithm to solve the problem. First, the number of targets and the coarse estimates of angle and magnitude are obtained from the iterative adaptive approach (IAA). Then, the accurate estimates of angle and magnitude are achieved by the strategy of iterative interpolation and leakage subtraction in FIIB. Finally, a high-resolution forward-looking image is obtained through non-coherent accumulation. The simulation results of point targets and scenes show that the proposed algorithm can distinguish multiple targets in the same beam, effectively improve the azimuthal resolution of forward-looking imaging, and attain the accurate reconstruction of point targets and the contour reconstruction of extended targets.

Background: CLEC4E has been reported to promote lung cancer progression. Tumor-associated macrophages (TAMs) play an important role in tumorigenesis. Whether the expression of CLEC4E in TAMs is associated with gastric carcinogenesis remains unclear. Methods: The TIMER, UALCAN, UCSC Xena, and KM plotter databases are used to examine the expression of CLEC4E and its prognostic significance in gastric cancer (GC). Additionally, GO, KEGG, and GSEA analysis were conducted, and single-cell RNA-seq (scRNA-seq) datasets were utilized. The Coremine medical database was used to predict therapeutic drugs, and molecular docking was performed. Human GC samples were obtained, and co-culture models were constructed to evaluate the effects of CLEC4E in TAMs on tumor growth, migration, and invasion in vitro. Results: CLEC4E was significantly upregulated in GC, and high CLEC4E expression was associated with poor prognosis. Western blotting and immunostaining showed increased protein levels of CLEC4E in GC. GO, KEGG, and GSEA results indicated that CLEC4E is involved in immune response. Immune infiltration analysis demonstrated that CLEC4E expression positively correlated with multiple immune cell types. scRNA-seq analyses revealed that CLEC4E was predominantly expressed in myeloid cells specifically TAMs, in GC. In vitro experiments confirmed that MFC induced CLEC4E expression in TAMs to mediate tumor progression. Specifically targeting CLEC4E by si-CLEC4E or stigmasterol inhibited cancer cell migration and invasion. Conclusion: CLEC4E is a potential prognostic biomarker and new therapeutic target for GC that can be specifically targeted by stigmasterol.

In this study, the properties of Ni-based filler alloy are improved by adding rare earth Sm to them and brazing them at a temperature of 1080 °C. Relevant tests are carried out on filler alloy and brazed joints by means of electron microscopy (SEM), energy spectroscopy (EDS) and X-ray diffractometry (XRD). The results show that the addition of rare earth Sm can refine the grain of the filler alloy, improve the hardness of the filler alloy and reduce the thermal damage of the diamond after welding. The addition of rare earth Sm affects the diffusion of Cr, which reacts with the diamond at the interface to form Cr3C2 and Cr7C3, and the formation of these two carbides improves the bonding strength between the filler alloy and the diamond. When the addition of rare earth Sm is 1.5 wt%, the melting point of the filler alloy is the lowest, the grain refinement is the best, the hardness is the highest, and the thermal damage of the brazed sample is the lowest. In addition, the filler alloy of 1.5 wt%Sm has the greatest effect on the diffusion of the Cr element, the bonding strength between the filler alloy and diamond is the highest, and the mechanical properties of the brazed specimens are the best.

Increasing soil contamination with nickel (Ni) and copper (Cu) is a growing environmental concern, adversely affecting ecosystems and the survival of both plants and animals. This study investigated the morphological and physiological responses of Euphorbia marginata Pursh seedlings to varying concentrations of Ni and Cu over a 45-day period. The findings revealed that low concentrations of Ni and Cu enhanced morphological indexes, root indexes, biomass, and photosynthetic pigment content of E. marginata, while high concentrations inhibited these parameters. Compared to the control, Ni and Cu stresses induced membrane peroxidation, increased cell membrane permeability, and inhibited the synthesis of soluble proteins and proline in the leaves. The seedlings demonstrated an ability to mitigate Ni and Cu toxicity by increasing soluble sugar content and enhancing the activities of peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT). Notably, E. marginata exhibited a higher capacity for Cu2+ enrichment and translocation compared to Ni2+. Combined Ni and Cu treatments reduced the maximum enrichment and translocation levels of both metals in E. marginata. This study highlights the superior tolerance of E. marginata to Ni and Cu stresses and elucidates the mechanisms underlying its response, providing a theoretical basis for the use of landscape plants in the remediation of heavy-metal-contaminated soils.

Abstract Reducing greenhouse gas emissions has turned into a pillar of climate change mitigation. Truck platooning is proposed as a strategy to lower emissions from vehicles on roads. However, the potential interactive impacts of this technology on road infrastructure emissions remain unclear. Here, we evaluate the decarbonization effects of truck platooning on the integrated vehicle-road system at a large-scale road network level, spanning 1457 road sections across North America. We show that truck platooning decreases emissions induced by truck operations, but it degrades faster the durability of road infrastructure and leads to a 27.9% rise in road emissions due to more frequent maintenance work. Overall, truck platooning results in a 5.1% emission reduction of the integrated vehicle-road system. In contrast to the benefits of emission reduction, truck platooning leads to additional financial burdens on car users and transportation agencies, calling for the consideration of tradeoffs between emissions and costs and between agencies and users. Our research provides insights into the potential applications of truck platooning to mitigate climate change.

Human pose estimation (HPE) is an integral component of numerous applications ranging from healthcare monitoring to human-computer interaction, traditionally relying on vision-based systems. These systems, however, face challenges such as privacy concerns and dependency on lighting conditions. As an alternative, short-range radar technology offers a non-invasive, lighting-insensitive solution that preserves user privacy. This paper presents a novel radar-based framework for HPE, SCRP-Radar (space-aware coordinate representation for human pose estimation using single-input single-output (SISO) ultra-wideband (UWB) radar). The methodology begins with clutter suppression and denoising techniques to enhance the quality of radar echo signals, followed by the construction of a micro-Doppler (MD) matrix from these refined signals. This matrix is segmented into bins to extract distinctive features that are critical for pose estimation. The SCRP-Radar leverages the Hrnet and LiteHrnet networks, incorporating space-aware coordinate representation to reconstruct 2D human poses with high precision. Our method redefines HPE as dual classification tasks for vertical and horizontal coordinates, which is a significant departure from existing methods such as RF-Pose, RF-Pose 3D, UWB-Pose, and RadarFormer. Extensive experimental evaluations demonstrate that SCRP-Radar significantly surpasses these methods in accuracy and robustness, consistently exhibiting lower average error rates, achieving less than 40 mm across 17 skeletal key-points. This innovative approach not only enhances the precision of radar-based HPE but also sets a new benchmark for future research and application, particularly in sectors that benefit from accurate and privacy-preserving monitoring technologies.

In this paper, we propose an innovative approach for transforming 2D human pose estimation into 3D models using Single Input–Single Output (SISO) Ultra-Wideband (UWB) radar technology. This method addresses the significant challenge of reconstructing 3D human poses from 1D radar signals, a task traditionally hindered by low spatial resolution and complex inverse problems. The difficulty is further exacerbated by the ambiguity in 3D pose reconstruction, as multiple 3D poses may correspond to similar 2D projections. Our solution, termed the Radar PoseLifter network, leverages the micro-Doppler signatures inherent in 1D radar echoes to effectively convert 2D pose information into 3D structures. The network is specifically designed to handle the long-range dependencies present in sequences of 2D poses. It employs a fully convolutional architecture, enhanced with a dilated temporal convolutions network, for efficient data processing. We rigorously evaluated the Radar PoseLifter network using the HPSUR dataset, which includes a diverse range of human movements. This dataset comprises data from five individuals with varying physical characteristics, performing a variety of actions. Our experimental results demonstrate the method’s robustness and accuracy in estimating complex human poses, highlighting its effectiveness. This research contributes significantly to the advancement of human motion capture using radar technology. It presents a viable solution for applications where precision and reliability in motion capture are paramount. The study not only enhances the understanding of 3D pose estimation from radar data but also opens new avenues for practical applications in various fields.

With the demand response and the massive access of distributed energy to the distribution network, it is possible to solve the transmission congestion problem by coordinating the controllable resources in a transmission network and distribution network. Aiming at resolving the problems of scattered side response resources and difficult-to-negotiate compensation prices, a bi-level optimal congestion scheduling strategy based on flexible load dynamic compensation prices is proposed. Under this strategy, the transmission network layer aims at minimizing the congestion cost and optimizes the adjustment scheme of the generator set and the node price. The active distribution network layer obtains the dynamic compensation price of the flexible load of the distribution network through the load characteristics and the node price. Through the interaction and coordination between the two layers, an optimal congestion scheduling scheme is obtained, and the transmission and distribution jointly solve the congestion problem. Based on the modified IEEE-39 experimental system, the effectiveness of the proposed strategy is verified via a simulation.

This study examines the influencing factors of the local government’s Internet service capability construction in China. The research framework for this study was the “technology-organization-environment” (TOE) framework. A total of 277 local governments were collected from Chinese cities and used for the data analysis. The data analysis was conducted with Stata. The results of this research demonstrated that there is a significant positive correlation between information technology talents, peer pressure, leader attachment, sustainable competitiveness, data open platform, and the government’s Internet service capability. Surprisingly, there is a negative correlation between per capita budget expenditure and the government’s Internet service capability. The aforementioned conclusions hold even after the robustness test. Discussion is held regarding how these conclusions may affect the development of the government’s Internet service capability.

Abstract Dual‐function radar and communication (DFRC) have recently received significant attention as an effective way to alleviate spectrum congestion and reduce system costs. Joint transmit beamforming plays a critical role in DFRC systems, as it can effectively reduce mutual interference and improve the performance of both the radar and communication systems. This paper investigates the problem of robust beamforming design with imperfect channel state information (CSI) to ensure communication performance while optimizing radar performance. Considering elliptically bounded CSI errors, the objective function is designed to minimize the radar loss function, which is related to the radar transmit beam pattern, subject to worst‐case signal‐to‐interference‐plus‐noise ratio constraints at each user, which ensure fairness between users. An efficient algorithm based on semidefinite relaxation and the S‐procedure is proposed to solve the non‐convex problem. Simulation results are presented to illustrate the effectiveness of the proposed robust beamforming scheme, which can reduce the impact of channel errors.

Abstract Purpose To investigate the effects of oral administration of magnesium‐L‐threonate, a novel magnesium compound, on the analgesic effect of opioids in patients with advanced cancer. Methods We performed a prospective, randomized, double‐blind trial at a tertiary hospital in Shanghai, China. Eligible cancer patients who took opioids orally were assigned randomly to receive L‐TAMS capsules (1.5 g or 2.0 g according to weight) or a placebo (starch capsules). The primary outcome was the increase in the daily oral dose of morphine in each of the two groups, measured at 7, 14, 21, 30, 60, and 90 days during this trial. Results A total of 116 patients from the oncology and pain departments, including inpatients and outpatients, were screened; 83 were enrolled. The increases in daily morphine doses began to differ from day 30 (L‐TAMS group 9.85 mg/d vs. Placebo group 20.49 mg/d, p

Owing to the advantages of simple structure, low power consumption and high-density integration, memristors or memristive devices are attracting increasing attention in the fields such as next generation non-volatile memories, neuromorphic computation and data encryption. However, the deposition of memristive films often requires expensive equipment, strict vacuum conditions, high energy consumption, and extended processing times. In contrast, electrochemical anodizing can produce metal oxide films quickly (e.g. 10 s) under ambient conditions. By means of the anodizing technique, oxide films, oxide nanotubes, nanowires and nanodots can be fabricated to prepare memristors. Oxide film thickness, nanostructures, defect concentrations, etc, can be varied to regulate device performances by adjusting oxidation parameters such as voltage, current and time. Thus memristors fabricated by the anodic oxidation technique can achieve high device consistency, low variation, and ultrahigh yield rate. This article provides a comprehensive review of the research progress in the field of anodic oxidation assisted fabrication of memristors. Firstly, the principle of anodic oxidation is introduced; then, different types of memristors produced by anodic oxidation and their applications are presented; finally, features and challenges of anodic oxidation for memristor production are elaborated.

Inverse synthetic aperture radar (ISAR) imaging can be improved by processing Range-Instantaneous Doppler (RID) images, according to a method proposed in this paper that uses neural networks. ISAR is a significant imaging technique for moving targets. However, scatterers span across several range bins and Doppler bins while imaging a moving target over a large accumulated angle. Defocusing consequently occurs in the results produced by the conventional Range Doppler Algorithm (RDA). Defocusing can be solved with the time-frequency analysis (TFA) method, but the resolution performance is reduced. The proposed method provides the neural network with more details by using a string of RID frames of images as input. As a consequence, it produces better resolution and avoids defocusing. Furthermore, we have developed a positional encoding method that precisely represents pixel positions while taking into account the features of ISAR images. To address the issue of an imbalance in the ratio of pixel count between target and non-target areas in ISAR images, we additionally use the idea of Focal Loss to improve the Mean Squared Error (MSE). We conduct experiments with simulated data of point targets and full-wave simulated data produced by FEKO to assess the efficacy of the proposed approach. The experimental results demonstrate that our approach can improve resolution while preventing defocusing in ISAR images.

The high-resolution image obtained by ultrawideband synthetic aperture radar (UWB SAR) includes rich features such as shape and scattering features, which can be utilized for landmine discrimination and detection. Due to the high performance and automatic feature learning ability, deep network-based detection methods have been widely employed in SAR target detection. However, existing deep networks do not consider the target characteristics in SAR images, and their structures are too complicated. Therefore, lightweight deep networks with efficient and interpretable blocks are essential. This work investigates how to utilize the SAR characteristics to design a lightweight deep network. The widely employed constant false alarm rates (CFAR) detector is used as a prototype and transformed into trainable multiple-feature network filters. Based on CFAR filters, we propose a new class of networks called CFARNets which can serve as an alternative to convolutional neural networks (CNNs). Furthermore, a two-stage detection method based on CFARNets is proposed. Compared to prevailing CNNs, the complexity and number of parameters of CFARNets are significantly reduced. The features extracted by CFARNets are interpretable as CFAR filters have definite physical significance. Experimental results show that the proposed CFARNets have comparable detection performance compared to other real-time state-of-the-art detectors but with faster inference speed.

A radar human behavior perception system has penetration detection ability, which gives it a wide application prospect in the fields of security, rescue, medical treatment, and so on. Although the development of deep learning technology has promoted radar sensor research in human behavior perception, it requires more prompted dataset availability. This paper provides a four-dimensional imaging dataset of human activity using ultra-wideband radar, UWB-HA4D, which uses three-dimensional ultra-wideband multiple-input multiple-output radar as the detection sensor to capture the range-azimuth-height-time four-dimensional activity data of a human target. The dataset contains the activity data of 2757 groups for 11 human targets, including 10 common activities, such as walking, waving, and boxing. It also contains penetration and nonpenetration detection experimental scenarios. The radar system parameters, data generation process, data distribution, and other information of the dataset are introduced in detail herein. Meanwhile, several deep learning algorithms that are based on the PaddlePaddle framework and are widely used in the computer version field are applied to this dataset for human activity recognition. The experimental comparison results can be used to provide references for scholars and facilitate further investigation and research on this basis.

Multivariate flood frequency analysis has been widely used in the design and risk assessment of hydraulic structures. However, analytical solutions are often obtained based on an idealized linear reservoir model in which a linear routing process is assumed, and consequently, the flood risk is likely to be over- or underestimated. The present study proposes a nonlinear reservoir model in which the relationships of reservoir water level with reservoir volume and discharge are assumed to be nonlinear in order to more accurately describe the routing process as it takes into consideration the interactions between hydrological loading and different discharge structures. The structure return period is calculated based on the copula function and compared with that based on the linear reservoir model and the bivariate return period based on the Kendall distribution function. The results show that the structure return period based on the linear model leads to an underestimation of the flood risk under the conditions of high reservoir water level. For the same reservoir, linear and nonlinear reservoir models give quite different reservoir volume-water level and discharge-water level curves; therefore, they differ substantially in the sensitivity to flood events with different combinations of flood peak and volume. We also analyze the effects of the parameters involved in the reservoir volume-water level and discharge-water level relationships on the maximum water level at different return periods in order to better understand the applicability and effectiveness of the proposed method for different hydraulic projects. HIGHLIGHTS The structure return period is calculated based on the nonlinear reservoir model.; Nonlinear relationships of water level with volume and discharge are assumed.; Interactions between hydrological loading and different structures are analyzed.; Effects of different storage and discharge capacity parameters are discussed.;

Phagocytosis, macropinocytosis, and G protein coupled receptor-mediated chemotaxis are Ras-regulated and actin-driven processes. The common regulator for Ras activity in these three processes remains unknown. Here, we show that C2GAP2, a Ras GTPase activating protein, highly expressed in the vegetative growth state in model organism Dictyostelium. C2GAP2 localizes at the leading edge of chemotaxing cells, phagosomes during phagocytosis, and macropinosomes during micropinocytosis. c2gapB− cells lacking C2GAP2 displayed increased Ras activation upon folic acid stimulation and subsequent impaired chemotaxis in the folic acid gradient. In addition, c2gaB- cells have elevated phagocytosis and macropinocytosis, which subsequently results in faster cell growth. C2GAP2 binds multiple phospholipids on the plasma membrane and the membrane recruitment of C2GAP2 requires calcium. Taken together, we show a shared negative regulator of Ras signaling that mediates Ras signaling for chemotaxis, phagocytosis, and macropinocytosis.

Chemotaxis plays an essential role in recruitment of leukocytes to sites of inflammation. Eukaryotic cells sense chemoattractant with G protein-coupled receptors (GPCRs) and chemotax toward gradients with an enormous concentration range through adaptation. Cells in adaptation no longer respond to the present stimulus but remain sensitive to stronger stimuli. Thus, adaptation provides a fundamental strategy for eukaryotic cells to chemotax through a gradient. Ras activation is the first step in the chemosensing GPCR signaling pathways that displays a transient activation behavior in both model organism Dictyostelium discoideum and mammalian neutrophils. Recently, it has been revealed that C2GAP1 and CAPRI control the GPCR-mediated adaptation in D. discoideum and human neutrophils, respectively. More importantly, both Ras inhibitors regulate the sensitivity of the cells. These findings suggest an evolutionarily conserved molecular mechanism by which eukaryotic cells gate concentration range of chemoattractants for chemotaxis.

Stable and reliable autonomous localization technology is fundamental for realizing autonomous driving. Localization systems based on global positioning system (GPS), cameras, LIDAR, etc., can be affected by building occlusion or drastic changes in the environment. These effects can degrade the localization accuracy and even cause the problem of localization failure. Localizing ground-penetrating radar (LGPR) as a new type of localization can rely only on robust subsurface information for autonomous localization. LGPR is mostly a 2D-2D registration process. This paper describes the LGPR as a slice-to-volume registration (SVR) problem and proposes an end-to-end TSVR-Net-based regression localization method. Firstly, the information of different dimensions in 3D data is used to ensure the high discriminative power of the data. Then the attention module is added to the design to make the network pay attention to important information and high discriminative regions while balancing the information weights of different dimensions. Eventually, it can directly regress to predict the current data location on the map. We designed several sets of experiments to verify the method’s effectiveness by a step-by-step analysis. The superiority of the proposed method over the current state-of-the-art LGPR method is also verified on five datasets. The experimental results show that both the deep learning method and the increase in dimensional information can improve the stability of the localization system. The proposed method exhibits excellent localization accuracy and better stability, providing a new concept to realize the stable and reliable real-time localization of ground-penetrating radar images.

Abstract Purpose The aim of this study was to analyze prognostic factors for ovarian metastases (OM) in colorectal cancer (CRC) using data from a Chinese center. In addition, the study aimed at developing a new clinical scoring system for prognosis of OM of CRC patients after surgery. Patients and methods Data of CRC patients with OM were collected from a single Chinese institution (n = 67). Kaplan-Meier analysis was used to evaluate cumulative survival of patients. Factors associated with prognosis of overall survival (OS) were explored using Cox’s proportional hazard regression models. A scoring system to determine effectiveness of prognosis was developed. Results Median OS values for patients with or without surgery were 22 and 7 months, respectively. Size of OM, number of OM, peritoneal metastasis (PM), Peritoneal cancer index (PCI), and completeness of cytoreduction (CC) were associated with OS of patients through univariate analysis. Multivariate analysis using a Cox regression model showed that only CC was an independent predictor for OS. Three variables (the size of OM >15cm, PCI ≥ 10, and carcinoembryonic antigen (CEA) >30 ng/mL) assigned one point each were used to develop a risk score. The resulting score was used for prognosis of OS. Conclusion Surgical treatment of metastatic sites is effective and safe for CRC patients with OM. CC-0 is recommended for improved prognosis. The scoring system developed in this study is effective for prediction of OS of patients after surgery.

Obtaining the internal layout of an unfamiliar building before entering the building has important practical significance and research value, as it can be applied for various services, such as anti-terrorism operations and disaster relief. Low-frequency electromagnetic waves can propagate through common building materials, and then the target information behind the wall is obtained safely and stably. Therefore, using low frequency radio waves to obtain the information behind the wall has become the research focus in the field of building layout reconstruction. To reveal the development context of this field and predict the possible future development trends, this paper summarizes the domestic and foreign public literature in this field since the onset of the 21st century. The results of the relevant literature indicate that the techniques of using low-frequency electromagnetic waves to reconstruct building layout currently include three types: through-the-wall radar imaging technology based on reflected wave measurement, radio-frequency tomography technology based on transmitted wave measurement, and wall position estimation technology based on multipath signals. These three technologies have achieved several practical research results. This article clarifies the development history of the main content covered by these technologies, which mainly includes the principle of through-the-wall radar imaging of stationary targets behind the wall, the observation mode of building internal structure based on through-the-wall radar, the reconstruction technology of building internal structure on the basis of through-the-wall radar imaging, the inversion technology of building internal structure on the basis of radio-frequency tomography, and the wall position estimation technology based on multipath signals. We also discuss the development trend of this field. In the past two decades, the development history of building layout penetrating imaging using low-frequency radio waves shows a change from the traditional airborne and vehicle-mounted building-layout-reconstruction platforms to new platforms such as microrobots and unmanned aerial vehicles. The corresponding reconstruction method has been developed from the traditional radar imaging technology to a variety of new methods, including image enhancement and sparse reconstruction. The results indicate that the building-layout-reconstruction technology is developing in the direction of systematization, diversification, and intelligence.

The complexity of central nervous system diseases together with their intricate pathogenesis complicate the establishment of effective treatment strategies. Presently, the superiority of adipose-derived mesenchymal stem cells (ADSCs) on neuronal injuries has attracted significant attention. Similarly, extracellular vesicles (EVs) are potential interventional agents that could identify and treat nerve injuries. Herein, we reviewed the potential effects of ADSCs and EVs on amyotrophic lateral sclerosis (ALS) injured nerves, and expound on their practical application in the clinic setting. This article predominantly focused on the therapeutic role of ADSCs concerning the pathogenesis of ALS, the protective and reparative effects of EVs on nerve injury, as well as the impact following the combined usage of ADSCs and EVs in ALS.

The pathogenesis of spinal cord injury (SCI) is complex. At present, there is no effective treatment for SCI, with most current interventions focused on improving the symptoms. Inflammation, apoptosis, autophagy, and oxidative stress caused by secondary SCI may instigate serious consequences in the event of SCI. The mammalian target of rapamycin (mTOR), as a key signaling molecule, participates in the regulation of inflammation, apoptosis, and autophagy in several processes associated with SCI. Quercetin can reduce the loss of myelin sheath, enhance the ability of antioxidant stress, and promote axonal regeneration. Moreover, quercetin is also a significant player in regulating the mTOR signaling pathway that improves pathological alterations following neuronal injury. Herein, we review the therapeutic effects of quercetin in SCI through its modulation of the mTOR signaling pathway and elaborate on how it can be a potential interventional agent for SCI.

Summary: The social ameba Dictyostelium discoideum has emerged as a powerful model to study mitochondrial genetics and bioenergetics. However, a comprehensive inventory of mitochondrial proteins that is critical to understanding mitochondrial processes has yet to be curated. Here, we utilized high-throughput multiplexed protein quantitation and homology analyses to generate a high-confidence mitochondrial protein compendium consisting of 936 proteins. Our proteomic approach, which utilizes mass spectrometry in combination with mathematical modeling, was validated through mitochondrial targeting sequence prediction and live-cell imaging. Our final compendium consists of 936 proteins. Nearly, a third of D. discoideum mitochondrial proteins do not have homologs in humans, budding yeasts, or an ancestral alphaproteobacteria. Additionally, we leverage our compendium to highlight the complexity of metabolic reprogramming during starvation-induced development. Our compendium lays a foundation to investigate mitochondrial processes that are unique in ameba and to understand the functions of conserved mitochondrial proteins in D. discoideum.

A model for predicting the prognosis of patients with heart failure with reduced left ventricular ejection fraction (HFrEF) is currently not available. This study aimed to develop an age-biomarker-clinical history prognostic index (ABC-PI) and validate it for the assessment of individual prognosis.

Abstract Background Celastrol has been proven effective in anti-inflammatory but was limited in the clinic due to the poor solubility and side effects induced by low bioavailability. Osteoarthritis has acidic and inflammatory environment. Our aim was to load celastrol into HMSNs and capped with chitosan to construct a pH-responsive nanoparticle medicine (CSL@HMSNs-Cs), which is of high solubility for osteoarthritis intra-articular injection treatment. Methods The CSL@HMSNs-Cs were assembled and the characteristics were measured. The CSL@HMSNs-Cs was applied in vitro in the chondrocytes collected from rats cartilage tissue and in vivo in the MIA induced knee osteoarthritis rats via intra-articular injection. Cytotoxicity assay, pH-responsive release, pain behavior, MRI, safranin o fast green staining, ELISA and western blot analysis were applied to evaluate the bioavailability and therapeutic effect of CSL@HMSNs-Cs. Results CSL@HMSNs-Cs was stable due to the protection of the chitosan layers in alkaline environment (pH = 7.7) but revealed good solubility and therapeutic effect in acidic environment (pH = 6.0). The cytotoxicity assay showed no cytotoxicity at relatively low concentration (200 μg/mL) and the cell viability of chondrocytes stimulated by IL-1β was increased in CSL@HMSNs-Cs group. Paw withdrawal threshold in CSL@HMSNs-Cs group is increased, and MRI and Safranin O Fast Green staining showed improvements in articular surface erosion and joint effusion. The upregulated expression levels of IL-1β, TNF-α, IL-6, MMP-3 and MMP-13 and NF-κB signaling pathway of chondrocytes were inhibited in CSL@HMSNs-Cs group. Conclusion Hollow mesoporous silica nanoparticles were an ideal carrier for natural drugs with poor solubility and were of high biocompatibility for intra-articular injection. These intra-articular injectable CSL@HMSNs-Cs with improved solubility, present a pH-responsive therapeutic strategy against osteoarthritis.

Colorectal cancer (CRC) is highly prevalent worldwide, but there has been limited development of efficient and affordable treatment. Induced autophagy has recently been recognized as a novel therapeutic strategy in cancer treatment, and disulfiram (DSF), a well-known antialcohol drug, is also found to inhibit tumor growth in various malignancies. Recently, DSF has been reported to induce excessive autophagy in oral squamous cells; however, little is known about whether it can induce autophagy and suppress proliferation in CRC. In this study, we investigate the effect of DSF with copper (DSF/Cu) on CRC both in vitro and in vivo and find that the combination significantly inhibits CRC cell viability and mainly induces autophagy instead of apoptosis. Furthermore, we use whole genome CRISPR library screening and identify a new mechanism by which DSF triggers autophagy by ULK1. Overall, these findings provide a potential CRC treatment.

How phagocytes find invading microorganisms and eliminate pathogenic ones from human bodies is a fundamental question in the study of infectious diseases. About 2.5 billion years ago, eukaryotic unicellular organisms–protozoans–appeared and started to interact with various bacteria. Less than 1 billion years ago, multicellular animals–metazoans–appeared and acquired the ability to distinguish self from non-self and to remove harmful organisms from their bodies. Since then, animals have developed innate immunity in which specialized white-blood cells phagocytes- patrol the body to kill pathogenic bacteria. The social amoebae Dictyostelium discoideum are prototypical phagocytes that chase various bacteria via chemotaxis and consume them as food via phagocytosis. Studies of this genetically amendable organism have revealed evolutionarily conserved mechanisms underlying chemotaxis and phagocytosis and shed light on studies of phagocytes in mammals. In this review, we briefly summarize important studies that contribute to our current understanding of how phagocytes effectively find and kill pathogens via chemotaxis and phagocytosis.

Chemotaxis, which is G protein-coupled receptor (GPCR)-mediated directional cell migration, plays pivotal roles in diverse human diseases, including recruitment of leukocytes to inflammation sites and metastasis of cancer. It is still not fully understood how eukaryotes sense and chemotax in response to chemoattractants with an enormous concentration range. A genetically traceable model organism, Dictyostelium discoideum, is the best-studied organism for GPCR-mediated chemotaxis. Recently, we have shown that C2GAP1 controls G protein coupled receptor-mediated Ras adaptation and chemotaxis. Here, we investigated the molecular mechanism and the biological function of C2GAP1 membrane targeting for chemotaxis. We show that calcium and phospholipids on the plasma membrane play critical roles in membrane targeting of C2GAP1. Cells lacking C2GAP1 (c2gapA–) displayed an improved chemotaxis in response to chemoattractant gradients at subsensitive or low concentrations (1 μM). Taken together, our results demonstrate that the membrane targeting of C2GAP1 enables Dictyostelium to sense chemoattractant gradients at a higher concentration range. This mechanism is likely an evolutionarily conserved molecular mechanism of Ras regulation in the adaptation and chemotaxis of eukaryotes.

Nano-SiO2 surface grafted APTES (T-SiO2) was realized by covalent functionalization technology, and functionalized nano-SiO2 modified epoxy resin composite (T-SiO2/EP) was prepared. The surface functional groups and chemical elements of the functionalized nano-SiO2were analyzed and the mechanical and tribological properties of the T-SiO2/EP were tested. The results show that the mechanical and tribological properties of the epoxy resin are effectively improved due to the introduction of functionalized nano-SiO2. When the content of functionalized nano-SiO2 is 2%(mass fraction, same as below), the microhardness and fracture toughness of the composites (2%T-SiO2/EP) can reach the maximum, which are 70.2HD and 1.02MPa·m1/2 respectively, moreover, in dry friction condition, the friction coefficient and the wear loss reach the minimum, which are 0.49mg and 1.7mg respectively. Compared with pure epoxy resin, they are reduced by 31.9% and 34.6%, and compared with 2% unmodified nano-SiO2 reinforced epoxy resin composite, they are reduced by 14% and 10.5%,and the corresponding wear mechanism is analyzed.

Compressed sensing (CS) is a sparsity-driven and regularization-based approach. The CS theory has led to superior imaging methods, offering a number of benefits, including increased image quality and robustness to the limitations in data quantity. In such imaging methods, if the targets are not originally sparse (not sparse in the canonical basis), sparse representation is required. However, in synthetic aperture radar (SAR) imaging scenarios, owing to the potentially random phase of the scene, it is very difficult to find bases to sparsify a complex-valued and extended scene. In this paper, an improved framework to handle the magnitude and phase of the scene separately is proposed. Although the phase of the scene is potentially random, the magnitude can usually be sparsely represented. In the proposed framework, besides the sparsity, the real-valued information of the magnitude and the coefficient distribution of the sparse representation are also utilized. In the reconstruction process, the magnitude of the scene is constrained to be real-valued, and the coefficients of the sparse representation are limited in the low-frequency area. A novel gradient-based optimization algorithm is developed to solve the proposed framework effectively. Since more a priori knowledge is utilized in the proposed algorithm, it can reduce the computational complexity and improve the reconstruction precision. The simulation results and real data results from an airborne SAR system are presented to show the superiority of the proposed algorithm.

Radar plays an increasingly important role in target detection because of its all-weather detection capability. Furthermore, to detect weak and flexible target, effectively coherent integration method is required in radar signal processing to enhance the target signal intensity. Based on the Radon-Fourier transform (RFT) algorithm, which is widely used to solve the problem of weak target detection, range migration and Doppler ambiguity in low signal-to-noise ratio (SNR) environment, an improved multiple pulse repetition intervals (PRIs) RFT (MRFT) coherent integration method is presented in this paper. The method adopts the Linear frequency modulated (LFM) signal with three different PRIs as the transmitted signal, then combines with the RFT outputs of three RPIs to enhance the target energy and suppress the blind speed side lobe (BSSL) caused by RFT. Meanwhile, the design rules and constraints of this method are also discussed in detail. The experiments with simulation data and real measured data are provided to demonstrate that the designed method can get better BSSL suppression capability and higher detection rate than conventional methods.