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To address the shortcomings of light detection and ranging (LiDAR) sensors in extracting road surface elevation information in front of a vehicle, a scheme for digital terrain construction based on the fusion of an Inertial Measurement Unit (IMU) and LiDAR perception is proposed. First, two sets of sensor coordinate systems were configured, and the parameters of LiDAR and IMU were calibrated. Then, a terrain construction system based on the fusion perception of IMU and LiDAR was established, and improvements were made to the state estimation and mapping architecture. Terrain construction experiments were conducted in an academic setting. Finally, based on the output information from the terrain construction system, a moving average-like algorithm was designed to process point cloud data and extract the road surface elevation information at the vehicle’s trajectory position. By comparing the extraction effects of four different sliding window widths, the 4 cm width sliding window, which yielded the best results, was ultimately selected, making the extracted road surface elevation information more accurate and effective.

Patients with knee osteoarthritis walk with reduced speed and knee flexion excursion in the early stance phase. A slow walking speed is also associated with falls in older adults. A novel vision-based smartphone application could potentially facilitate the early detection of knee osteoarthritis and fall prevention. This study aimed to test the validity and reliability of the app-captured gait speed and peak knee flexion during the initial stance phase of gait. Twenty adults (aged 23–68 years) walked at self-selected comfortable walking speeds while the gait speed and knee flexion were simultaneously measured using retroreflective sensors and Xsens motion trackers and the app in two separate sessions for validity and reliability tests. Pearson’s r correlation and Bland–Altman plots were used to examine the correlations and agreements between the sensor- and app-measured outcomes. One-sample t-tests were performed to examine whether systematic bias existed. The intraclass correlation coefficient (ICC) was calculated to assess the test–retest reliability of the app. Very high correlations were found between the sensor and app measurements for gait speed (r = 0.98, p < 0.001) and knee flexion (r = 0.91–0.92, all p < 0.001). No significant bias was detected for the final app version. The app also showed a good to excellent test–retest reliability for measuring the gait speed and peak knee flexion (ICC = 0.86–0.94). This vision-based smartphone application is valid and reliable for capturing the walking speed and knee flexion during the initial stance of gait, potentially aiding in the early detection of knee osteoarthritis and fall prevention in community living locations.

Aging is an important risk factor for tumorigenesis. Metabolic reprogramming is a hallmark of both aging and tumor initiation. However, the manner in which the crosstalk between aging and metabolic reprogramming affects the tumor microenvironment (TME) to promote tumorigenesis was poorly explored. We utilized a computational approach proposed by our previous work, MMP3C (Modeling Metabolic Plasticity by Pathway Pairwise Comparison), to characterize aging-related metabolic plasticity events using pan-cancer bulk RNA-seq data. Our analysis revealed a high degree of metabolically organized heterogeneity across 17 aging-related cancer types. In particular, a higher degree of several energy generation pathways, i.e., glycolysis and impaired oxidative phosphorylation, was observed in older patients. Similar phenomena were also found via single-cell RNA-seq analysis. Furthermore, those energy generation pathways were found to be weakened in activated T cells and macrophages, whereas they increased in exhausted T cells, immunosuppressive macrophages, and Tregs in older patients. It was suggested that aging-induced metabolic switches alter glucose utilization, thereby influencing immune function and resulting in the remodeling of the TME. This work offers new insights into the associations between tumor metabolism and the TME mediated by aging, linking with novel strategies for cancer therapy.

In this study, a series of novel nanofibers based on gelatin (GA) loading with purple potato anthocyanin (PPA) and syringic acid (SA) were obtained by electrospinning technology. The effects of SA on mechanical properties, thermal stability, antioxidant capacity, and antimicrobial activity of the GA/PPA nanofibers were systematically characterized. The scanning electron microscopy observation results revealed a smooth surface on the nanofibers. The incorporation of SA enhanced the viscosity of the electrospun solutions, and it increased the average diameter of nanofibers from 0.17 μm to 0.28 μm. The tensile strength and thermal stability of the obtained nanofibers were enhanced with the addition of a suitable level of SA (1.5%, w/v), which strengthened the intermolecular interaction. The GA/PPA/SA nanofibers presented over 80% antioxidant capacity and strong antibacterial activity against E. coli and S. aureus. Meanwhile, the sensitivity responses of nanofibers to NH3 revealed that GA/PPA/SA II nanofibers (1.5% w/v SA) presented good sensitivity of colorimetric behavior to ammonia. A pork spoilage test was performed to evaluate practical application of the nanofibers, and an obvious color change (dark purple to green) was observed. These results indicated GA/PPA/SA II nanofibers can be utilized as an active and intelligent multipurpose packaging material to preserve and track the freshness of pork.

Background: Major depressive disorder (MDD) plays a crucial role in the occurrence of heart failure (HF). This investigation was undertaken to explore the possible mechanism of MDD’s involvement in HF pathogenesis and identify candidate biomarkers for the diagnosis of MDD with HF. Methods: GWAS data for MDD and HF were collected, and Mendelian randomization (MR) analysis was performed to investigate the causal relationship between MDD and HF. Differential expression analysis (DEA) and WGCNA were used to detect HF key genes and MDD-associated secretory proteins. Protein–protein interaction (PPI), functional enrichment, and cMAP analysis were used to reveal potential mechanisms and drugs for MDD-related HF. Then, four machine learning (ML) algorithms (including GLM, RF, SVM, and XGB) were used to screen candidate biomarkers, construct diagnostic nomograms, and predict MDD-related HF. Furthermore, the MCPcounter algorithm was used to explore immune cell infiltration in HF, and MR analysis was performed to explore the causal effect of immunophenotypes on HF. Finally, the validation of the association of MDD with reduced left ventricular ejection fraction (LVEF) and the performance assessment of diagnostic biomarkers was accomplished based on animal models mimicking MDD. Results: The MR analysis showed that the MDD was linked to an increased risk of HF (OR = 1.129, p < 0.001). DEA combined with WGCNA and secretory protein gene set identified 315 HF key genes and 332 MDD-associated secretory proteins, respectively. Through PPI and MCODE analysis, 78 genes were pinpointed as MDD-related pathogenic genes for HF. The enrichment analysis revealed that these genes were predominantly enriched in immune and inflammatory regulation. Through four ML algorithms, two hub genes (ISLR/SFRP4) were identified as candidate HF biomarkers, and a nomogram was developed. ROC analysis showed that the AUC of the nomogram was higher than 0.90 in both the HF combined dataset and two external cohorts. In addition, an immune cell infiltration analysis revealed the immune dysregulation in HF, with ISLR/SFRP4 displaying notable associations with the infiltration of B cells, CD8 T cells, and fibroblasts. More importantly, animal experiments showed that the expression levels of ISLR (r = −0.653, p < 0.001) and SFRP4 (r = −0.476, p = 0.008) were significantly negatively correlated with LVEF. Conclusions: The MR analysis indicated that MDD is a risk factor for HF at the genetic level. Bioinformatics analysis and the ML results suggest that ISLR and SFRP4 have the potential to serve as diagnostic biomarkers for HF. Animal experiments showed a negative correlation between the serum levels of ISLR/SFRP4 and LVEF, emphasizing the need for additional clinical studies to elucidate their diagnostic value.

Pulsed lasers alter the optical properties of semiconductors and affect the photoelectric function of the photodetectors significantly, resulting in transient changes known as bleaching. Bleaching has a profound impact on the control and interference of photodetector applications. Experiments using pump–probe techniques have made significant contributions to understanding ultrafast carrier dynamics. However, there are few theoretical studies to the best of our knowledge. Here, carrier dynamic models for semiconductors and photodetectors are established, respectively, employing the rectified carrier drift-diffusion model. The pulsed laser bleaching effect on seven types of semiconductors and photodetectors from visible to long-wave infrared is demonstrated. Additionally, a continuous bleaching method is provided, and the finite-difference time-domain (FDTD) method is used to solve carrier dynamic theory models. Laser parameters for continuous bleaching of semiconductors and photodetectors are calculated. The proposed bleaching model and achieved laser parameters for continuous bleaching are essential for several applications using semiconductor devices, such as infrared detection, biological imaging, and sensing.

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Incorporating two organic ligands with different functionalities into a titanium-oxo cluster entity simultaneously can endow the material with their respective properties and provide synergistic performance enhancement, which is of great significance for enriching the structure and properties of titanium-oxo clusters (TOCs). However, the synthesis of such TOCs is highly challenging. In this work, we successfully synthesized a TBC4A-functionalized TOC, [Ti2(TBC4A)2(MeO)2] (Ti2; MeOH = methanol, TBC4A = tert-butylcalix[4]arene). By adjusting the solvent system, we successfully introduced 1,10-phenanthroline (Phen) and prepared TBC4A and Phen co-protected [Ti2(TBC4A)2(Phen)2] (Ti2-Phen). Moreover, when Phen was replaced with bulky 4,7-diphenyl-1,10-phenanthroline (Bphen), [Ti2(TBC4A)2(Bphen)2] (Ti2-Bphen), which is isostructural with Ti2-Phen, was obtained, demonstrating the generality of the synthetic method. Remarkably, Ti2-Phen demonstrates good stability and stronger light absorption, as well as superior photoelectric performance compared to Ti2. Density functional theory (DFT) calculations reveal that there exists ligand-to-core charge transfer (LCCT) in Ti2, while an unusual ligand-to-ligand charge transfer (LLCT) is present in Ti2-Phen, accompanied by partial LCCT. Therefore, the superior light absorption and photoelectric properties of Ti2-Phen are attributed to the existence of the unusual LLCT phenomenon. This study not only deeply explores the influence of Phen on the performance of the material but also provides a reference for the preparation of materials with excellent photoelectric performance.

The Asian longhorned beetle (ALB) causes substantial economic and ecological losses, thus, an environmentally friendly management strategy is needed. Here, we propose high trunk truncation (HTT), the removal of the above 200 cm portion of trees, as a sustainable management strategy to control ALB. To examine the hypothesis, an initial step involved the assessment of various biological characteristics of ALB. Subsequently, a controlled field experiment was carried out utilizing HTT. Finally, HTT was applied in two additional ALB infestation regions. The results of the study of the biological characteristics of ALB showed that 76.31–78.88% of frass holes and 85.08–87.93% of emergence holes were located on branches above 200 cm. Adults preferred to feed on branches 2–3 cm in diameter, ALB eggs were predominantly laid on 5 cm branches, and both were primarily located above 200 cm. These results revealed a correlation between the number of ALBs and the tree crown height. The controlled field experiment showed that the number of ALBs was significantly decreased when the HTT strategy was implemented: approximately 90% of frass holes and 95% of adults were eradicated by HTT compared with the control. Different field surveys involving HTT have shown similar results. These findings provide valuable insights into a sustainable and efficient management strategy for reducing the number of ALBs.

As a recently developed high-strength aluminium alloy used specifically for laser additive manufacturing, AlMgMnSc alloy possesses superior mechanical properties and excellent processability. Extreme high-speed laser deposition (EHLD) is a novel surface-modification technique, which is characterised by high depositing speed, rapid cooling, rate and minimal dilution rate. To offer a new method for surface repairing high-strength aluminium alloys, an AlMgMnSc alloy coating, containing two deposition layers, is prepared on a 6061 aluminium-alloy axle using the EHLD technique. Meanwhile, the microstructure, composition distribution, and microhardness variation of the fabricated coating are studied. The results reveal that the coating is dense and crack-free, which is well-bonded with the substrate. Additionally, layer 1 is mainly composed of large columnar and equiaxed grains, while layer 2 consists of a fully equiaxed grain structure with an average grain size of about 4.5 μm. Moreover, the microhardness of the coating (about 104~118 HV) is similar to the substrate (about 105 HV), proving the feasibility of repairing high-strength aluminium alloys using AlMgMnSc alloy powders through the EHLD technique.

The negative stiffness bistable damper (NSBD) was proposed to suppress structural dynamic responses in our previous study. The vibration mitigation performance of the NSBD is influenced by its design parameters, including negative stiffness, cubic stiffness, and damping coefficients. However, it is extremely challenging to directly acquire the ideal design parameters of the NSBD owing to its inherent nonlinearity. To address this disadvantage, the optimal design approach for the NSBD, based on the equivalent linearization method (ELM) and genetic algorithm (GA), is presented in this paper. The nonlinear NSBD system can be transformed to a linear system utilizing the ELM based on the pseudo-excitation method (PEM). The linearization model that corresponds to the nonlinear NSBD is fairly accurate in its approximation and can be indicated from the numerical results. Then, the main structure’s peak response is minimized through the optimization of the design parameters of the NSBD using the H∞ norm and GA. Moreover, the proposed approach’s effectiveness is assessed using the optimal parameters to calculate the displacement responses of a tall building equipped with the NSBD during various seismic excitations. As revealed by the numerical results, the displacement of the tall building can be effectively restrained by the optimized NSBD.

MYB98 is master regulator of the molecular network involved in pollen tube attraction. Until recently, it was unclear how this gene exhibits exclusively synergid cell-specific expression in ovule. Our recent study has established that a 16-bp-long SaeM element is crucial for its synergid cell-specific expression in ovule, and an 84-bp-long fragment harboring SaeM is sufficient to drive the process. In this study, we have developed a workflow to predict functional roles of potential transcription factors (TFs) putatively binding to the promoter region, taking MYB98 promoter as a test subject. After sequential assessment of co-expression pattern, network analysis, and potential master regulator identification, we have proposed a multi-TF model for MYB98 regulation. Our study suggests that ANL2, GT-1, and their respective homologs could be direct regulators of MYB98 and indicates that TCP15, TCP16, FRS9, and HB34 are likely master regulators of the majority of the TFs involved in its regulation. Comprehensive studies in the future are expected to offer more insights into such propositions. Developed workflow can be used while designing similar regulome-related studies for any other species and genes.

In order to more accurately analyze the problem of time delay simulation and calculation in the time-sensitive network (TSN) of vehicular Ethernet, a TSN reservation class data delay analysis model improved based on the State–Action–Reward–State–Action (SARSA) reinforcement learning algorithm is proposed. Firstly, the TSN data queue forwarding delay model and reservation class data delay analysis intelligent body model are established, then the TSN traffic scheduling mechanism is improved by the SARSA reinforcement learning algorithm, and the improved TSN network reservation class data analysis model is established for the uncertainty of traffic scheduling in the network; finally, the fitting performance of the proposed method is verified by simulation and experimental validation. The results show that the deviation between the two is less than 5% under different BE loads, i.e., the established reservation class data delay analysis model is able to correctly fit the scheduling mechanism of the vehicle-mounted TSN network, which proves the reasonableness of the model simulation.

As the upgrade of people’s requirements for automotive driving comfort, conventional passive suspensions for cars have fallen short of existing demands due to their nonadjustable damping and stiffness, so semiactive suspensions and active suspensions have gained growing acceptance. Compared with active suspensions, semiactive suspensions offer the advantages of a low manufacturing cost and reliable structure, and thus have become the preferred choice for most vehicles. To optimize the control effect of semiactive suspensions under different working conditions, this paper completed the modeling of magnetorheological semiactive suspension system dynamics and road inputs; then, the design of binocular camera sensing algorithms was performed to obtain the real-time distance of the target using the point cloud ranging function, and the parameters required for suspension control were also obtained. This was followed by the completion of the control-mode-switching rules and the design of the suspension controller. According to the different control objectives, the mode could be divided into the obstacle-road mode, straight-road mode, and curved-road mode. The suspension controller included the BP-PID (neural network PID controller) controller and the force distributor. Finally, the effectiveness of the mode-switching rules and the control method was verified through system simulation and the hardware-in-the-loop test.

In this study, the effects of preloaded uniaxial compressive stress and defect (prefabricated by laser cutting) curvature on blast-induced cracks and stress wave propagation were investigated in polymethyl methacrylate (PMMA) specimens using caustics theory. Based on the mathematical relationship between stress and optics, the fracture behavior (the propagation path, dynamic stress intensity factors (DSIFs), propagation velocity, initiation angle of the main crack, and damage degree between the defect and the blasthole) was calculated and analyzed quantitatively. The results show that the preloaded stress could mainly restrain the main crack propagation in the horizontal direction and reduce the initiation angle and damage degree at the defects. Meanwhile, the crack initiation position of the positive curvature defect was not at the end of the defect. In addition, the curvature of the defect significantly affected the blast-induced fracture compared to preloaded stresses. Comparing the main crack with other curvature defects, the DSIFs, velocity, crack arrest time, crack length, and horizontal offset distance of the main crack with negative curvature defect were the largest. The variation in the full stress field in the specimen under blasting and preloaded stress was simulated by a numerical model. The results show that the curvature of the defect has a significant effect on tensile waves at the end of defects, and the tensile stress from high to low was C-25, C0, and C25, in that order.

High-rise building fires pose a serious threat to the lives and property safety of people. The lack of reliable and accurate positioning means is one of the main difficulties faced by rescuers. In the absence of prior knowledge of the high-rise building fire environment, the coverage deployment of mobile base stations is a challenging problem that has not received much attention in the literature. This paper studies the problem of the autonomous optimal deployment of base stations in high-rise building fire environments based on a UAV group. A novel problem formulation is proposed that solves the non-line-of-sight (NLOS) positioning problem in complex and unknown environments. The purpose of this paper is to realize the coverage and deployment of mobile base stations in complex and unknown fire environments. The NLOS positioning problem in the fire field environment is turned into the line-of-sight (LOS) positioning problem through the optimization algorithm. And there are more than three LOS base stations nearby at any point in the fire field. A control law which is formulated in a mathematically precise problem statement is developed that guarantees to meet mobile base stations’ deployment goals and to avoid collision. Finally, the positioning accuracy of our method and that of the common method were compared under many different cases. The simulation result showed that the positioning error of a simulated firefighter in the fire field environment was improved from more than 10 m (the positioning error of the traditional method) to less than 1 m.

Pre-sowing treatment of seeds by plasma can improve seed vigor and promote seed germination and growth. To solve the problems of low processing volume and uneven treatment in plasma seed treatment devices, according to the process scheme of medium-blocking discharge plasma seed treatment, a medium-blocking discharge vibration-uniform material plasma seed treatment device was designed, the structure and working principle of the vibration-uniform material device were systematically analyzed, and the mathematical model of seed force was established. According to electromagnetic vibration theory, the seed sorting and conveying principles were analyzed in the lower trough, and the relevant parameters were selected and calculated. Using EDEM discrete element simulation software, a numerical simulation of alfalfa seed feeding and vibration-uniform material process was carried out. A three-factor, three-level orthogonal test was established. The results showed that the vibration amplitude and groove shape significantly affected the coefficient of variation of seed uniformity on the groove during the seed feeding and vibration-uniform material processes, and the groove wheel speed had a certain effect on the coefficient of variation of uniformity. The main order of factors affecting the uniformity of seed spreading was vibration amplitude B > notch shape C > speed A. The optimal speed was 35 r/min, the optimal notch shape was circular, and the optimal vibration amplitude was 0.55 mm.

The Hanjiang River is the second-largest river in Guangdong Province, China. The modern sedimentation pattern of the Hanjiang subaqueous delta has been significantly affected by changes in the sediment delivered by the Hanjiang River. Based on multiperiod charts and columnar samples from the Hanjiang subaqueous delta, the influence of a sharp decrease in the sediment flux from the Hanjiang River on the deposition of the Hanjiang subaqueous delta in the past 60 years was studied through a combination of chart-based analysis of the evolution of erosion and deposition and the analysis of sediment samples. The results showed that the significant reductions in sediment fluxes from the Hanjiang River and Rongjiang River had obviously spatially differentiated effects on the deposition pattern of the Hanjiang subaqueous delta. The significant decreases in sediment fluxes from the Hanjiang River and the Rongjiang River caused the sediment grain size of the columnar samples in the near-estuary area of the Hanjiang River subaqueous delta to coarsen upward, the deposition rate to decrease, and the depositional state to change to an erosional state. Since the sediments discharged into the ocean after reservoir impoundment are mainly fine-grained suspended sediments, the supply of coarse-grained sediments in the Hanjiang subaqueous delta decreased, resulting in the upward fining of the columnar samples. The spatial response pattern of the Hanjiang subaqueous delta to the sharp decrease in sediment flux into the sea differed significantly from those of the Yangtze and Yellow subaqueous deltas.

Accurate vessel travel time estimation is crucial for optimizing port operations and ensuring port safety. Existing vessel travel time prediction models primarily rely on path-finding algorithms and corresponding distance/speed relationships to calculate travel time. However, these models overlook the complex nature of vessel travel time, which is influenced by multiple traffic-related factors such as collision avoidance, shortest path selection, and vessel personnel performance. The lack of consideration for these specific aspects limits the accuracy and applicability of current models. We propose a novel context-aware deep learning approach for inland vessel travel time prediction. Firstly, we introduce a complex network that captures vessel–vessel interaction contexts, providing valuable traffic environment information as an input for the deep learning model. Additionally, we employ a convolutional neural network to extract spatial trajectory information, which is then integrated with interaction contexts and indirect context information. In the vessel travel time prediction procedure, we utilize a long short-term memory network to capture the temporal dependence within consecutive channel sections’ fused multiple context feature sets. Extensive experiments incorporating historical data from the Wuhan section of the Yangtze River in China demonstrate the superiority of our proposed model over classical models in predicting vessel travel time. Importantly, our model accounts for the specific traffic contexts that had previously been overlooked, leading to improved accuracy and applicability in inland vessel travel time prediction.

The current vehicle stability judgment methods in the stability control of distributed drive electric vehicles focuses on using the phase plane method, but the existing phase plane stability boundary delineation methods ignore the influence of the front wheel rotation angle. Therefore, a new phase plane partitioning method (NPPPM) is proposed in this paper, and a new phase plane stability domain library is established. Based on this, the direct yaw moment control of distributed drive electric vehicle through phase plane analysis is established. Firstly, the influence of the front wheel turning angle on the phase plane is considered in the design of the phase plane stability boundary, and a new stability boundary function is fitted by the least squares method. This approach avoids the lateral instability caused by too large a front-wheel turning angle and improves the accuracy of vehicle stability judgment. Next, a hierarchical direct transverse moment controller is constructed using Matlab/Simulink. Among them, the upper layer controller adopts sliding mode control with the sideslip angle error of the center of mass as the tracking target. The lower controller adopts the optimal configuration algorithm with the optimal road adhesion utilization as the objective function. Finally, the effectiveness and superiority of the proposed phase plane partitioning method (NPPPM) for distributed drive electric vehicles with sinusoidal and angular order inputs for the front wheel rotation angle are verified by simulation.

The optimal regimens of piperacillin/sulbactam (PIS 2:1), piperacillin/tazobactam (PTZ 8:1), and cefoperazone/sulbactam (CSL 2:1) are not well defined in patients based on renal function. This study was conducted to identify optimal regimens of BLBLIs in these patients. The antimicrobial sensitivity test was performed by a two-fold agar dilution method. Monte Carlo simulation (MCS) was used to simulate the probability of target attainment (PTA) and the cumulative fraction of response (CFR) for various dosing regimens in patients with different renal functions. For strains with an MIC ≤ 8/4 mg/L, PIS 4.5 g q6h achieved 99.03%PTA in the subset of patients with creatinine clearance (CrCL) > 90 mL/min. For patients with CrCL 60–90 mL/min, PIS 4.5 g q6h achieved 81.2% CFR; for those with CrCL 40–59 mL/min, PIS 4.5 g q8h achieved 80.25% CFR. However, for patients infected by ESBL-producing Enterobacteriaceae, PIS 4.5 g q6h achieved a CFR lower than 80%. For patients infected by A. baumannii with a CrCL of 31–60 mL/min, PIS 6.0 g q8h and 4.5 g q6h achieved 81.24% and 82.42% CFR, respectively. For those infected by P. aeruginosa, PIS 4.5 g q6h reached 90% CFR. PIS and PTZ achieved a similar CFR when piperacillin was at the same dose. The CFRs of CSL were much lower than those of the other two agents in Enterobacteriaceae and P. aeruginosa infections. The antibacterial spectrum of PIS is superior to that of PTZ and CSL. Higher dosages and dosing adjustment according to renal function should be considered to treat Gram-negative bacterial BSIs.

With the wide application of power equipment consisting of high switching frequencies and large current switching devices in the integrated power system on ships, the low-frequency radiation interference caused by the powerful electromagnetic equipment becomes more and more serious. Establishing an equivalent model of radiation interference based on the near-field measurement data is the key to subsequent electromagnetic compatibility design. Due to the large size of the equipment, there will be many measurement points in the near-field measurement area, which leads to long testing times and low modeling efficiency. To solve the above problems, this article first proposes a method of near-field measurement and a modeling method of powerful electromagnetic equipment radiation based on field distribution characteristics. Firstly, the near-field measurement data are obtained by the sparse and uniform sampling of the near-field measurement plane at large sampling intervals. Then, the near-field measuring plane is separated into several regions by the magnetic field’s distribution characteristics. The near-field measurement data required for modeling are obtained by further sampling in the region with a large magnetic field amplitude. Finally, the equivalent radiation model is obtained by the equivalent dipole method. Simulations and experiments show that the method can significantly reduce the amount of measurement data and testing time while improving the efficiency of equivalent radiation modeling and maintaining the accuracy of the modeling.

As an effective solution for data visualization and analysis, the large-scale high-resolution display wall system has been widely used in various scientific research fields. On the basis of investigating existing system cases and research results, this paper introduces the SHU-VAS system (60 screens, 120 megapixels) for data visualization and analysis. In order to improve the efficiency of human-computer interaction in large-scale high-definition display wall systems, we propose an interaction framework based on gesture and double-precision pointing technology. During the interaction process, an adaptive mode switching method based on user action features is used to switch between rough and precise control modes. In the evaluation experiments, we analyzed the characteristics of different interaction methods in movement and navigation interaction tasks, and verified the effectiveness of the gesture-based interaction framework proposed in this paper.

To solve the problem of feature distribution discrepancy in cross-corpus speech emotion recognition tasks, this paper proposed an emotion recognition model based on multi-task learning and subdomain adaptation, which alleviates the impact on emotion recognition. Existing methods have shortcomings in speech feature representation and cross-corpus feature distribution alignment. The proposed model uses a deep denoising auto-encoder as a shared feature extraction network for multi-task learning, and the fully connected layer and softmax layer are added before each recognition task as task-specific layers. Subsequently, the subdomain adaptation algorithm of emotion and gender features is added to the shared network to obtain the shared emotion features and gender features of the source domain and target domain, respectively. Multi-task learning effectively enhances the representation ability of features, a subdomain adaptive algorithm promotes the migrating ability of features and effectively alleviates the impact of feature distribution differences in emotional features. The average results of six cross-corpus speech emotion recognition experiments show that, compared with other models, the weighted average recall rate is increased by 1.89~10.07%, the experimental results verify the validity of the proposed model.

Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have reduced susceptibility to neutralization by vaccines. In response to the constantly updated variants, a global vaccine booster vaccination program has been launched. In this study, we detected neutralizing antibody levels against wild-type (WT), Delta (B1.617.2), and Omicron BA.1 viruses in serum after each dose of CoronaVac vaccination. We found that booster vaccination significantly increased the levels of neutralizing antibodies against WT, Delta, and Omicron BA.1. Compared with only one vaccination, neutralizing antibody levels increased by 19.2–21.6-fold after a booster vaccination, whilst two vaccinations only produced a 1.5–3.4-fold increase. Our results support the conclusion that the CoronaVac vaccine booster can increase neutralizing antibody levels and cross-reactivity and enhance the body’s ability to effectively resist the infection of new coronavirus variants, emphasizing the need for booster vaccination.

Three kinds of phenolic acid-grafted soluble soybean polysaccharide (SSPS) with similar grafting ratios were prepared, and their structure was characterized by FT-IR, UV-vis and 1 H NMR. The impact of phenolic acid on the antioxidant activity of SSPS was evaluated. Then, films were prepared by using phenolic acid-grafted SSPS. The physical, mechanical and biological performances of phenolic acid-grafted SSPS films were further investigated. The results indicated that an ester linkage was formed between the SSPS and phenolic acid. The grafting ratio of para-hydroxybenzoic acid, protocatechuic acid and gallic acid-grafted SSPS was 29.45, 31.76 and 30.74 mg/g, respectively. Phenolic acid endowed SSPS with improved antioxidant properties. Gallic acid (GA)-grafted SSPS possessed the best DPPH radical scavenging ability and reducing power, which may be related to the three phenolic hydroxyl groups in GA. Phenolic acid-grafted SSPS films showed increased moisture content and decreased water solubility compared to SSPS film. The phenolic acid-g-SSPS decreased the mechanical properties but enhanced the water vapor barrier property, and antioxidant and antibacterial properties of SSPS film. Meanwhile, the para-hydroxybenzoic acid-grafted SSPS film showed the lowest water vapor permeability (3.70 × 10−7 g mm/h cm2 Pa), and the GA-grafted SSPS film exhibited the best antioxidant and antibacterial activities.

2-methyltetrahydrofuran (MTHF2) has been recently regarded as a promising alternative engine fuel. However, the chemical reaction mechanism for MTHF2 combustion in the engine has not been reported to date. In this study, a reduced diesel/MTHF2 reaction mechanism with only 78 species among 233 reactions was constructed for diesel/MTHF2 dual-fuel engine simulations. Firstly, a diesel surrogate mechanism involving the sub-mechanisms of n-decane, iso-octane, methylcyclohexane (MCH), toluene, a reduced mechanism of C2-C3 species and a detailed mechanism of H2/CO/C1 was selected. Secondly, a skeletal MTHF2 mechanism containing 54 species and 294 reactions was formulated under engine-relevant conditions using combined mechanism reduction methods. Thirdly, a reduced sub-mechanism of MTHF2 oxidation with 11 species and 13 reactions was extracted and combined with the four-component diesel surrogate fuel mechanism. Subsequently, the reduced diesel/MTHF2 mechanism was obtained by improving the combined five-component mechanism based on sensitivity analysis. Finally, the proposed mechanism was validated with selected experimental data of ignition delay times, flame species concentrations and laminar flame speeds. In addition, the new measurements from diesel/MTHF2 dual-fuel engine combustion were obtained and then utilized to further assess the developed mechanism. Overall, the developed diesel/MTHF2 mechanism can be used for diesel and MTHF2 dual-fuel engine combustion simulation.

Correct prediction of potential miRNA–disease pairs can considerably accelerate the experimental process in biomedical research. However, many methods cannot effectively learn the complex information contained in multisource data, limiting the performance of the prediction model. A heterogeneous network prediction model (MEAHNE) is proposed to make full use of the complex information contained in multisource data. To fully mine the potential relationship between miRNA and disease, we collected multisource data and constructed a heterogeneous network. After constructing the network, we mined potential associations in the network through a designed heterogeneous network framework (MEAHNE). MEAHNE first learned the semantic information of the metapath instances, then used the attention mechanism to encode the semantic information as attention weights and aggregated nodes of the same type using the attention weights. The semantic information was also integrated into the node. MEAHNE optimized parameters through end-to-end training. MEAHNE was compared with other state-of-the-art heterogeneous graph neural network methods. The values of the area under the precision–recall curve and the receiver operating characteristic curve demonstrated the superiority of MEAHNE. In addition, MEAHNE predicted 20 miRNAs each for breast cancer and nasopharyngeal cancer and verified 18 miRNAs related to breast cancer and 14 miRNAs related to nasopharyngeal cancer by consulting related databases.

The automatic parking system based on vision is greatly affected by uneven lighting, which is difficult to make an accurate judgment on parking spaces in the case of complex image information. To solve this problem, this paper proposes a parking space visual detection and image processing method based on deep learning. Firstly, a 360-degree panoramic system was designed to photograph the vehicle environment. The image has been processed to obtain a panoramic aerial view, which was input as the original image of the parking space detection system. Secondly, the Faster R-CNN (Region-Convolutional Neural Network) parking detection model was established based on deep learning. It was aimed to detect and extract the parking space from the input image. Thirdly, the problems of uneven illumination and complex background were solved effectively by removing the background light from the image. Finally, a parking space extraction method based on the connected region has been designed, which further simplified the parking space extraction and image processing. The experiment results show that the mAP (mean Average Precision) value of the Faster R-CNN model using 101-Floor ResNet as the feature extraction network is 89.30%, which is 2.28% higher than that of the Faster R-CNN model using 50-Floor ResNet as the feature extraction network. The model built in this paper can detect most parking spaces well. The position of the output target box is accurate. In some test scenarios, the confidence of parking space recognition can even reach 100%. In summary, the proposed method can realize the effective identification and accurate positioning of parking spaces.

Electrochemical multi-wire sawing (EMWS) is a hybrid machining method based on a traditional multi-wire sawing (MWS) system. In this new method, a silicon ingot is connected to a positive electrode; the slicing wire is connected to a negative electrode. Material is removed by the interaction of mechanical grinding and an electrochemical reaction. In this paper, contrast experiments of EMWS and MWS were conducted based on industrialized equipment to verify the beneficial effects of the hybrid method. The experimental statistical results show that the composite processing method improved the processing qualification rate by 1.28%, and the Bow of silicon wafers was reduced by about 2.74 microns. Further testing on the surface of the silicon wafer after electrochemical action showed that obvious holes were present on the surface, and the surface hardness of the wafer decreased significantly. Therefore, the scratches on the surface of wafer sliced by EMWS were reduced; in addition, the thickness of the surface damage layer was reduced by about 9 microns. After standard texturing, the average reflectivity of the wafers sliced by EMWS was about 2–10% lower than that of the wafers sliced by MWS in the wavelength of 300–1100 nm. In this paper, the voltage parameter of the composite machining is set to 48 V; the amount of electrolyte added in each experiment is 2 L; and a good machining effect is obtained. In the future, the electric parameters and cutting fluid components will be further studied to improve the electrochemical effect.

With increasing demand in many areas, materials are constantly evolving. However, they still have numerous practical constraints. The rational design and discovery of new materials can create a huge technological and social impact. However, such rational design and discovery require a holistic, multi-stage design process, including the design of the material composition, material structure, material properties as well as process design and engineering. Such a complex exploration using traditional scientific methods is not only blind but also a huge waste of time and resources. Machine learning (ML), which is used across data to find correlations in material properties and understand the chemical properties of materials, is being considered a new way to explore the materials field. This paper reviews some of the major recent advances and applications of ML in the field of properties prediction of materials and discusses the key challenges and opportunities in this cross-cutting area.

As a multifaceted adipokine, chemerin has been found to perform functions vital for immunity, adiposity, and metabolism through its three known receptors (chemokine-like receptor 1, CMKLR1; G-protein-coupled receptor 1, GPR1; C-C motif chemokine receptor-like 2, CCRL2). Chemerin and the cognate receptors are also expressed in the hypothalamus, pituitary gland, testis, ovary, and placenta. Accumulating studies suggest that chemerin participates in normal reproduction and underlies the pathological mechanisms of certain reproductive system diseases, including polycystic ovary syndrome (PCOS), preeclampsia, and breast cancer. Herein, we present a comprehensive review of the roles of the chemerin system in multiple reproductive processes and human reproductive diseases, with a brief discussion and perspectives on future clinical applications.

Atherosclerosis is the leading cause of global morbidity and mortality. Its therapy requires research in several areas, such as diagnosis of early arteriosclerosis, improvement of the pharmacokinetics and bioavailability of rapamycin as its therapeutic agents. Here, we used the targeting peptide VHPKQHR (VHP) (or fluorescent reagent) to modify the phospholipid molecules to target vascular cell adhesion molecule-1 (VCAM-1) and loaded ultrasmall paramagnetic iron oxide (USPIO/Fe3O4) plus rapamycin (Rap) to Rap/Fe3O4@VHP-Lipo (VHPKQHR-modified magnetic liposomes coated with Rap). This nanoparticle can be used for both the diagnosis and therapy of early atherosclerosis. We designed both an ex vivo system with mouse aortic endothelial cells (MAECs) and an in vivo system with ApoE knockout mice to test the labeling and delivering potential of Rap/Fe3O4@VHP-Lipo with fluorescent microscopy, flow cytometry and MRI. Our results of MRI imaging and fluorescence imaging showed that the T2 relaxation time of the Rap/Fe3O4@VHP-Lipo group was reduced by 2.7 times and 1.5 times, and the fluorescence intensity increased by 3.4 times and 2.5 times, respectively, compared with the normal saline group and the control liposome treatment group. It showed that Rap/Fe3O4@VHP-Lipo realized the diagnosis of early AS. Additionally, our results showed that, compared with the normal saline and control liposomes treatment group, the aortic fluorescence intensity of the Rap/Fe3O4@VHP-Lipo treatment group was significantly weaker, and the T2 relaxation time was prolonged by 8.9 times and 2.0 times, indicating that the targeted diagnostic agent detected the least plaques in the Rap/Fe3O4@VHP-Lipo treatment group. Based on our results, the synthesized theragnostic Rap/Fe3O4@VHP-Lipo serves as a great label for both MRI and fluorescence bimodal imaging of atherosclerosis. It also has therapeutic effects for the early treatment of atherosclerosis, and it has great potential for early diagnosis and can achieve the same level of therapy with a lower dose of Rap.

Eutrophication is considered to be a significant threat to estuaries and coastal waters. Various localized studies on the world’s oceans have recognized and confirmed that the Forel-Ule Color Index (FUI) or optical measurements are proportional to several water quality variables based on the relatively clear Chl-a-based waters. However, the application potential of FUI in the turbid estuary with complex optics has not been explored. In this study, we selected the coastal waters in the northern Liaodong Bay as the study area, using the field hyperspectral reflectances (Rrs) collected in 2018 to correct the hue angle and verify the Sentinel-2 images algorithm of FUI by in situ FUI in 2019–2020. The results show that there is a good agreement (R2 = 0.81, RMSE = 1.32, MAPE = 1.25%). Trophic Level Index (TLI) was used to evaluate the eutrophication status. The relationship between the in situ FUI and TLI collected in 2018 was discussed based on the difference in the dominant components of waters, while a number of non-algae suspended solids in the estuaries and coastal waters led to the overestimation of eutrophication based on FUI. The R(560)–R(704) (when FUI is between 11 and 15) and R(665)/R(704) (when FUI is between 19 and 21) was employed to distinguish total suspended matter (TSM)-dominated systems in the FUI-based eutrophication assessment. Based on the analysis, a new approach to assessing the eutrophication of coastal waters in Liaodong Bay was developed, which proved to have good accuracy by the field data in 2019 and 2020 (accuracy is 79%). Finally, we used Sentinel-2 images from Google Earth from 2019 to 2020 and locally processed data from 2018 to analyze the FUI spatial distribution and spatial and temporal statistics of the trophic status in the northern Liaodong Bay. The results show that the northern Liaodong Bay always presented the distribution characteristics of high inshore and low outside, high in the southeast and low in the northwest. The nutrient status is the worst in spring and summer.

Multi-phase motors and generators are regarded with great fault tolerance capability, especially on open-circuit faults. Various mathematics analytical methods are applied for their fault control. In this paper, a fault-tolerant control strategy with asymmetric phase current for the open-circuit faults with arbitrary phases in the six-phase PMSM (six-phase permanent magnetic synchronous motor, 6P-PMSM) system, is proposed for better electrical and dynamical performance of the machine. An innovative mathematical model for PMSM under one to four-phase-open circuit faults are established considering the asymmetry of the machine. Combining with time-varying relations in machines’ working conditions, targeted decoupling transformation matrixes of every kind of open-circuit faults are settled by voltage equations under different faults. Modified control strategy with a connection between the neutral point and the inverter’s DC side is presented, which aims at increasing the system redundancy and reducing the amplitude of phase currents. Besides, improved control loops with two layers are put forward as well, with which the PMSM system acquires fewer harmonics in phase current and smoother electromagnetic torque. Simulation and experimental results of open-circuit faults are provided for verification of the theoretical analysis.

Currently, engineered skin substitutes (ESS) are unable to regenerate cutaneous appendages. Recent studies have shown that skin-derived precursors (SKPs), which are extensively available, have the potential to induce hair follicle neogenesis. Here, we demonstrate that ESS consisting of culture-expanded SKPs and epidermal stem cells (Epi-SCs) reconstitute the skin with hair follicle regeneration after grafting into nude mice. SKPs seeded in a C-GAG matrix proliferated and expressed higher levels of hair induction signature genes—such as Akp2, Sox2, CD133 and Bmp6—compared to dermal fibroblasts. Moreover, when ESS prepared by seeding a mixture of culture-expanded murine SKPs and human adult Epi-SCs into a C-GAG matrix was grafted into full-thickness skin wounds in nude mice, black hairs were generated within 3 weeks. Immunofluorescence analysis showed that the SKPs were localized to the dermal papillae of the newly-formed hair follicle. Our results indicate that SKPs can serve as the hair-inductive cells in ESS to furnish it with hair genesis potential

Because of extensive cultivation areas, various cultivars, nonstandard naming notations, and morphology similarity among relative cultivars, adulteration and associated business fraud may happen in the marketplaces of prickly ash pericarps due to higher financial gain and high-frequency trading. This study presents variations in the chemical components and contents of different prickly ash species from different plantations. Alkylamide profiling of pericarps derived from Zanthoxylum armatum, Z. bungeanum, and some relative Zanthoxylum species from 72 plantations across China were tested using ultra-performance liquid chromatography. Then, several chemometrics were applied to classify the prickly ash pericarps to reveal potential indicators that distinguish prickly ash pericarps and to identify the key factors that affect pericarp alkylamide profiling. The dominating alkylamides in the prickly ash pericarps were Z. piperitum (ZP)-amide C (0–20.64 mg/g) and ZP-amide D (0–30.43 mg/g). Alkylamide profiling of prickly ash pericarps varied significantly across species and geographical variations. ZP-amide D in prickly ash pericarps was identified as a potential indicator to distinguish prickly ash species. Longitude and aluminum content in soils were identified as key factors that affected alkylamide profiling of prickly ash pericarps. This study provides a useful tool to classify prickly ash species based on pericarp alkylamide profiling and to determine the key influence factors on pericarp alkylamide variations.

Zanthoxylum plants, important aromatic plants, have attracted considerable attention in the food, pharmacological, and industrial fields because of their potential health benefits, and they are easily accessible because of the wild distribution in most parts of China. The chemical components vary with inter and intraspecific variations, ontogenic variations, and climate and soil conditions in compositions and contents. To classify the relationships between different Zanthoxylum species and to determine the key factors that influence geographical variations in the main components of the plant, the fatty acid composition and content of 72 pericarp samples from 12 cultivation regions were measured and evaluated. Four fatty acids, palmitic acid (21.33–125.03 mg/g), oleic acid (10.66–181.37 mg/g), linoleic acid (21.98–305.32 mg/g), and linolenic acid (0.06–218.84 mg/g), were the most common fatty acid components in the Zanthoxylum pericarps. Fatty acid profiling of Zanthoxylum pericarps was significantly affected by Zanthoxylum species and geographical variations. Stearic acid and oleic acid in pericarps were typical fatty acids that distinguished Zanthoxylum species based on the result of discriminant analysis (DA). Palmitic acid, palmitoleic acid, trans-13-oleic acid, and linoleic acid were important differential indicators in distinguishing given Zanthoxylum pericarps based on the result of orthogonal partial least squares discriminant analysis (OPLS-DA). In different Zanthoxylum species, the geographical influence on fatty acid variations was diverse. This study provides information on how to classify the Zanthoxylum species based on pericarp fatty acid compositions and determines the key fatty acids used to classify the Zanthoxylum species.

Based on quantile regression (QR) and kernel density estimation (KDE), a framework for probability density forecasting of short-term wind speed is proposed in this study. The empirical mode decomposition (EMD) technique is implemented to reduce the noise of raw wind speed series. Both linear QR (LQR) and nonlinear QR (NQR, including quantile regression neural network (QRNN), quantile regression random forest (QRRF), and quantile regression support vector machine (QRSVM)) models are, respectively, utilized to study the de-noised wind speed series. An ensemble of conditional quantiles is obtained and then used for point and interval predictions of wind speed accordingly. After various experiments and comparisons on the real wind speed data at four wind observation stations of China, it is found that the EMD-LQR-KDE and EMD-QRNN-KDE generally have the best performance and robustness in both point and interval predictions. By taking conditional quantiles obtained by the EMD-QRNN-KDE model as the input, probability density functions (PDFs) of wind speed at different times are obtained by the KDE method, whose bandwidth is optimally determined according to the normal reference criterion. It is found that most actual wind speeds lie near the peak of predicted PDF curves, indicating that the probabilistic density prediction by EMD-QRNN-KDE is believable. Compared with the PDF curves of the 90% confidence level, the PDF curves of the 80% confidence level usually have narrower wind speed ranges and higher peak values. The PDF curves also vary with time. At some times, they might be biased, bimodal, or even multi-modal distributions. Based on the EMD-QRNN-KDE model, one can not only obtain the specific PDF curves of future wind speeds, but also understand the dynamic variation of density distributions with time. Compared with the traditional point and interval prediction models, the proposed QR-KDE models could acquire more information about the randomness and uncertainty of the actual wind speed, and thus provide more powerful support for the decision-making work.

Spacers are important components in reinforced concrete structures to provide cover between the steel reinforcement and the formwork. Cementitious spacers are of particular interest for coastal engineering structures, as they are compatible with cement-based chloride-resistant high-performance concrete compared to plastic and steel spacers. However, the cementitious spacer–concrete interface was found to be highly porous and microcracked. This study investigated the effect of surface treatment on the chloride ion transport at the cementitious spacer–concrete interface. A surface treatment technique for potential mass production was introduced and the state-of-practice tests of the hardened concrete were modified to evaluate the performance of the spacer–concrete composite specimens. The results showed that the surface treatment on a cementitious spacer improved the bonding between the spacer and concrete at the interface. The surface treatment of the spacer improved the compressive strength and the chloride resistance of the composite specimen locally compared to those without surface treatment. The advantage of surface treatment on the chloride resistance was partially represented in either the diffusion coefficient or the column electric flux. The maximum chloride ion penetration depth at the spacer–concrete interface was recommended as an additional proxy for the evaluation of the chloride resistance performance of composite specimens.

The same action takes different time in different cases. This difference will affect the accuracy of action recognition to a certain extent. We propose an end-to-end deep neural network called “Multi-Term Attention Networks” (MTANs), which solves the above problem by extracting temporal features with different time scales. The network consists of a Multi-Term Attention Recurrent Neural Network (MTA-RNN) and a Spatio-Temporal Convolutional Neural Network (ST-CNN). In MTA-RNN, a method for fusing multi-term temporal features are proposed to extract the temporal dependence of different time scales, and the weighted fusion temporal feature is recalibrated by the attention mechanism. Ablation research proves that this network has powerful spatio-temporal dynamic modeling capabilities for actions with different time scales. We perform extensive experiments on four challenging benchmark datasets, including the NTU RGB+D dataset, UT-Kinect dataset, Northwestern-UCLA dataset, and UWA3DII dataset. Our method achieves better results than the state-of-the-art benchmarks, which demonstrates the effectiveness of MTANs.

Influenza B virus (IBV) is a respiratory pathogen that infects humans and causes seasonal influenza epidemics. However, cellular response to IBV infection in humans and mechanisms of host-mediated restriction of IBV replication are not thoroughly understood. In this study, we used next-generation sequencing (NGS) to perform transcriptome profiling of IBV-infected human lung epithelial A549 cells at 0, 6, 12, and 24 h post infection (hpi) and characterized the cellular gene expression dynamics. We observed that more than 4000 host genes were differentially regulated during the study period, which included up regulation of genes encoding proteins, having a role in the innate antiviral immune responses, immune activation, cellular metabolism, autophagy, and apoptosis, as well as down regulation of genes involved in mitosis and cell proliferation. Further analysis of RNA-Seq data coupled with RT-qPCR validation collectively showed that double-strand RNA recognition pathways, including retinoic acid-inducible gene I (RIG-I) and Toll-like receptor 3 (TLR3), were substantially activated following IBV infection. Taken together, these results provide important initial insights into the intimate interaction between IBV and lung epithelial cells, which can be further explored towards elucidation of the cellular mechanisms in restriction or elimination of IBV infections in humans.

Glycoproteins play important roles in maintaining normal cell functions depending on their glycosylations. Our previous study indicated that the abundance of glycoproteins recognized by concanavalin A (ConA) was increased in human hepatic stellate cells (HSCs) following activation by transforming growth factor-β1 (TGF-β1); however, little is known about the ConA-binding glycoproteins (CBGs) of HSCs. In this study, we employed a targeted glycoproteomics approach using lectin-magnetic particle conjugate-based liquid chromatography-tandem mass spectrometry to compare CBG profiles between LX-2 HSCs with and without activation by TGF-β1, with the aim of discovering novel CBGs and determining their possible roles in activated HSCs. A total of 54 and 77 proteins were identified in the quiescent and activated LX-2 cells, respectively. Of the proteins identified, 14.3% were glycoproteins and 73.3% were novel potential glycoproteins. Molecules involved in protein processing in the endoplasmic reticulum (e.g., calreticulin) and calcium signaling (e.g., 1-phosphatidylinositol-4,5-bisphosphate phosphodiesterase β-2 [PLCB2]) were specifically identified in activated LX-2 cells. Additionally, PLCB2 expression was upregulated in the cytoplasm of the activated LX-2 cells, as well as in the hepatocytes and sinusoidal cells of liver cirrhosis tissues. In conclusion, the results of this study may aid future investigations to find new molecular mechanisms involved in HSC activation and antifibrotic therapeutic targets.

Psychological stress (PS) disturbs the reproductive endocrine system and promotes male infertility, but the underlying pathogenic mechanisms have not been extensively studied. This study aimed to uncover the mechanisms of PS-induced male reproductive related abnormalities subjected to a ‘terrified sound’ exposure. Male rats subjected to PS displayed slow growth, decreased sperm quality, abnormal levels of the reproductive endocrine hormones, decreased expression of the reproductive-related proteins androgen-binding protein (ABP) and bromodomain-containing protein (BRDT), increased apoptosis in the testis, and accompanied by elevated levels of β-endorphin (β-EP). These effects were reversed by naloxone. Furthermore, PS-induced β-EP could promote mu opioid receptor (MOR) activation and ensure intracellular p38 MAPK phosphorylation and then lead to Leydig cells (LCs) apoptosis. The current result showed that β-EP was a key factor to PS-induced male infertility.

In recent years, almost all of the current top-performing object detection networks use CNN (convolutional neural networks) features. State-of-the-art object detection networks depend on CNN features. In this work, we add feature fusion in the object detection network to obtain a better CNN feature, which incorporates well deep, but semantic, and shallow, but high-resolution, CNN features, thus improving the performance of a small object. Also, the attention mechanism was applied to our object detection network, AF R-CNN (attention mechanism and convolution feature fusion based object detection), to enhance the impact of significant features and weaken background interference. Our AF R-CNN is a single end to end network. We choose the pre-trained network, VGG-16, to extract CNN features. Our detection network is trained on the dataset, PASCAL VOC 2007 and 2012. Empirical evaluation of the PASCAL VOC 2007 dataset demonstrates the effectiveness and improvement of our approach. Our AF R-CNN achieves an object detection accuracy of 75.9% on PASCAL VOC 2007, six points higher than Faster R-CNN.

In recent years, the application of deep neural networks to human behavior recognition has become a hot topic. Although remarkable achievements have been made in the field of image recognition, there are still many problems to be solved in the area of video. It is well known that convolutional neural networks require a fixed size image input, which not only limits the network structure but also affects the recognition accuracy. Although this problem has been solved in the field of images, it has not yet been broken through in the field of video. To address the input problem of fixed size video frames in video recognition, we propose a three-dimensional (3D) densely connected convolutional network based on spatial pyramid pooling (3D-DenseNet-SPP). As the name implies, the network structure is mainly composed of three parts: 3DCNN, DenseNet, and SPPNet. Our models were evaluated on a KTH dataset and UCF101 dataset separately. The experimental results showed that our model has better performance in the field of video-based behavior recognition in comparison to the existing models.

We present an occlusion-aware unsupervised neural network for jointly learning three low-level vision tasks from monocular videos: depth, optical flow, and camera motion. The system consists of three different predicting sub-networks simultaneously coupled by combined loss terms and is capable of computing each task independently on test samples. Geometric constraints extracted from scene geometry which have traditionally been used in bundle adjustment or pose-graph optimization are formed as various self-supervisory signals during our end-to-end learning approach. Different from prior works, our image reconstruction loss also takes account of optical flow. Moreover, we impose novel 3D flow consistency constraints over the predictions of all the three tasks. By explicitly modeling occlusion and taking utilization of both 2D and 3D geometry relationships, abundant geometric constraints are formed over estimated outputs, enabling the system to capture both low-level representations and high-level cues to infer thinner scene structures. Empirical evaluation on the KITTI dataset demonstrates the effectiveness and improvement of our approach: (1) monocular depth estimation outperforms state-of-the-art unsupervised methods and is comparable to stereo supervised ones; (2) optical flow prediction ranks top among prior works and even beats supervised and traditional ones especially in non-occluded regions; (3) pose estimation outperforms established SLAM systems under comparable input settings with a reasonable margin.

Elastography can be used to estimate the regional shear modulus of a tendon. This can advance our knowledge on the impact of patellar alignment and regional patellar tendon stiffness. This is important as patellar tendon abnormality is mainly found in the medial portion of the tendon in subjects with proximal patellar tendinopathy. This paper aims to assess the effect of patellar displacement on differential modulation on the shear modulus of the patellar tendon. Shear modulus is captured on the medial and lateral half of the patella tendon using the Axiplorer® ultrasound unit in conjunction with a 4–15 MHz, 50 mm linear transducer with the patellar being positioned in its resting, medio- and laterally displaced positions on 40 adults (19 females, 21 males). When the patellar is displaced laterally, the shear modulus is significantly increased at the medial half in both genders but decreased at the lateral half only in females. Conclusions: Elastography detects changes in regional tendon stiffness associated with alteration in patellar positions. The modulation on the shear modulus is gender and region specific.

Marine invertebrates, such as sponges, tunicates and cnidarians (zoantharians and scleractinian corals), form functional assemblages, known as holobionts, with numerous microbes. This type of species-specific symbiotic association can be a repository of myriad valuable low molecular weight organic compounds, bioactive peptides and enzymes. The zoantharian Protopalythoa variabilis (Cnidaria: Anthozoa) is one such example of a marine holobiont that inhabits the coastal reefs of the tropical Atlantic coast and is an interesting source of secondary metabolites and biologically active polypeptides. In the present study, we analyzed the entire holo-transcriptome of P. variabilis, looking for enzyme precursors expressed in the zoantharian-microbiota assemblage that are potentially useful as industrial biocatalysts and biopharmaceuticals. In addition to hundreds of predicted enzymes that fit into the classes of hydrolases, oxidoreductases and transferases that were found, novel enzyme precursors with multiple activities in single structures and enzymes with incomplete Enzyme Commission numbers were revealed. Our results indicated the predictive expression of thirteen multifunctional enzymes and 694 enzyme sequences with partially characterized activities, distributed in 23 sub-subclasses. These predicted enzyme structures and activities can prospectively be harnessed for applications in diverse areas of industrial and pharmaceutical biotechnology.