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A predefined-time (PT) tracking adaptive control method is studied for non-affine pure-feedback nonlinear systems, with an emphasis on its practical application in robotic exoskeleton technology. A novel PT neural networks control algorithm is implemented, by leveraging the approximation capabilities of neural networks, backstepping technique, barrier functions and Mean Value Theorem. The neural networks are used to approximate the unknown nonlinearities inherent in the system’s control dynamics, while the adaptive law is meticulously designed based on the PT Lyapunov stability criterion. By Lyapunov PT theory, the developed methodology guarantees the system’s convergence within a pre-established time, therefore offering enhanced performance over conventional fixed-time control methodologies. Simulation results validate the efficacy of this proposed control approach, demonstrating its practical implications for controlling robotic exoskeletons under state constraints, thus validating its potential for real-world applications.

Deep learning models have achieved success in image recognition and have shown great potential for interpretation of ground penetrating radar (GPR) data. However, training reliable deep learning models requires massive labeled data, which are usually not easy to obtain due to the high costs of data acquisition and field validation. This paper proposes an improved least square generative adversarial networks (LSGAN) model which employs the loss functions of LSGAN and convolutional neural networks (CNN) to generate GPR images. This model can generate high-precision GPR data to address the scarcity of labelled GPR data. We evaluate the proposed model using Frechet Inception Distance (FID) evaluation index and compare it with other existing GAN models and find it outperforms the other two models on a lower FID score. In addition, the adaptability of the LSGAN-generated images for GPR data augmentation is investigated by YOLOv4 model, which is employed to detect rebars in field GPR images. It is verified that inclusion of LSGAN-generated images in the training GPR dataset can increase the target diversity and improve the detection precision by 10%, compared with the model trained on the dataset containing 500 field GPR images.

MicroRNAs are a class of small non-coding, endogenous RNAs involved in cancer development and progression. MicroRNA-221 (mir-221) has been reported to have both an oncogenic and tumor-suppressive role in human tumors, but the role of miR-221 in ovarian cancer is poorly understood. In the present study, the expression levels of miR-221 and the apoptosis protease activating factor 1 (APAF1) protein in 63 samples of ovarian cancer tissues and the cell lines, IOSE25, A2780, OVCAR3, SKOV3 and 3AO were detected by quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and western blot analysis, respectively. Cell proliferation was measured using Cell Counting kit-8 (CCK-8); cell migration and invasion were detected using a Transwell assay; cell apoptosis was evaluated by flow cytometry and Hoechst staining, and a luciferase assay was performed to verify a putative target site of miR-221 in the 3′-UTR of APAF1 mRNA. Expression of miR-221 was upregulated in ovarian cancer tissues. Patients with increased miR-221 expression levels had a reduced disease-free survival (P=0.0014) and overall survival (P=0.0058) compared with those with low miR-221 expression. Transfection of SKOV3 and A2780 cell lines with miR-221 inhibitor induced APAF1 protein expression, suppressed cell proliferation and migration and promoted tumor cell apoptosis. In conclusion, the APAF1 gene was confirmed as a direct target of miR-221 and overexpression of APAF1 suppressed ovarian cancer cell proliferation and induced cell apoptosis in vitro. These findings indicate that miR-221-APAF1 should be studied further as a potential new diagnostic or prognostic biomarker for ovarian cancer.

This paper investigates the adaptive neural network (NN) tracking control problem for stochastic nonlinear systems with multiple actuator constraints and full-state constraints. The issue of system full-state constraints is tackled by a generalized barrier Lyapunov function (GBLF), and the output constraints of the system are considered to be in the form of time-varying functions, which are more in line with the needs of real physical systems. The NN approximation technique is utilized to overcome the influence of the uncertainty term on controller design due to randomness. Based on the backstepping technique, a neural adaptive fixed-time tracking control strategy is designed. Under the designed control strategy, the tracking accuracy of the controlled system can reach the expectation in a fixed time. The multi-actuator constraints are converted into a generalized mathematical model to simplify the controller design process. Using the characteristics of the hyperbolic tangent function, a new function called practical virtual control signal is designed using the virtual control signal as the input. Due to the saturation constraint property of the hyperbolic tangent function, it is theoretically ensured that no state of the system exceeds the constraints through to the new form of the virtual controller. Using the adaptive controller constructed in this paper, the controlled system is semi-global fixed-time stabilized in probability (SGFSP). Finally, the effectiveness of the proposed control strategy is further verified by simulation examples.

Background Ovarian cancer is a common gynecological malignancy among female patients and poses a serious threat to women’s health. Although it has been established that Fos-like antigen 2 (FOSL2) is linked to ovarian cancer (OC), its exact role in the development of OC remains unknown. Objective This article aims to investigate the role of FOSL2 in ovarian cancer development. Methods FOSL2 expression in ovarian carcinoma and adjacent tissues was assessed using real-time fluorescent quantitative PCR and western blot. We constructed OE/sh-FOSL2 plasmids and Caspase-1 specific inhibitors (Yvad-CMK) and transfected A 2780 cells with them to identify the relevant cell functions. Furthermore, we used western blot assay to determine the changes in expression of apoptosis-associated speck-like protein containing a CARD (ASC), cysteine aspartate-specific proteasezymogen procaspase 1 (pro-caspase-1), cysteinyl aspartate-specific proteinase-1 (caspase-1), interleukin-1β precursor (pro-IL-1β), interleukin-1β (IL-1β), interleukin-18 precursor (pro-IL-18), and interleukin-18 (IL-18). In addition, we measured the concentration of IL-1β and IL-18 using an enzyme-linked immunosorbent assay (ELISA). Moreover, Tthe level of lactate dehydrogenase (LDH) in the cell supernatant was measured by LDH release assay kit. Results The expression of FOSL2 was significantly higher compared with the surrounding tissues. The proliferation, migration, and invasion of A2780 cells were enhanced after transfection with OE-FOSL2 plasmids; however, the cell apoptosis was significantly decreased. When FOSL2 was overexpressed, the inflammasome-associated proteins such as ASC, caspase-1, IL-1β, and IL-18 were downregulated. Furthermore, FOSL2 induced apoptosis and activated the production of inflammasomes in A2780 cells. Co-therapy with Yvad-CMK and substantially inhibited apoptosis and activation of inflammasomes. Conclusions Inhibition of FOSL2 promotes the apoptosis of OC cells by mediating the formation of an inflammasome.

The influence of particle size on the abrasive flow machining mechanism and processing limit was studied by establishing the processing model of different particle size. When many scholars studied the mechanism of abrasive flow machining in the past, they only established a machining model and ignored the effect of abrasive particle size. By comparing the size of different granularity abrasive particles and the workpiece surface in contact with each other, the large granularity abrasive particle processing model and the small granularity abrasive particle processing model were put forward, and their processing mechanism was analyzed respectively. On this basis, the reason of machining limit in different particle size abrasive processing process is explained. Finally, the theoretical analysis and experimental results were compared to verify the correctness of the processing mechanism and theoretical analysis of processing limit of abrasive with different granularity, and an optimal experimental scheme was proposed to break through the original processing limit.

This paper investigates the problem of the adaptive neural network tracking control of nonlinear cyber–physical systems (CPSs) subject to unknown deception attacks with prescribed performance. The considered system is under the influence of unknown deception attacks on both actuator and sensor networks, making the research problem challenging. The outstanding contribution of this paper is that a new anti-deception attack-prescribed performance tracking control scheme is proposed through a special coordinate transformation and funnel function, combined with backstepping and bounded estimation methods. The transient performance of the system can be ensured by the prescribed performance control scheme, which makes the indicators of the controlled system, such as settling time and tracking accuracy, able to be pre-assigned offline according to the task needs, and the applicability of the prescribed performance is tested by selecting different values of the settling time (0.5 s, 1 s, 1.5 s, 2 s, 2.5 s, and 3 s). In addition, to save the computational and communication resources of the CPS, this paper uses a finite-time differentiator to approximate the virtual control law differentiation to avoid “complexity explosion” and a switching threshold event triggering mechanism to save the communication resources for data transmission. Finally, the effectiveness of the proposed control strategy is further verified by an electromechanical system simulation example.

A novel dual-polarization and dual-scan (DPDS) classification algorithm is developed for clutter detection in weather radar observations. Two consecutive scans of dual-polarization radar echoes are jointly processed to estimate auto- and cross-correlation functions. Discriminants are then defined and estimated in order to separate clutter from weather based on their physical and statistical properties. An optimal Bayesian classifier is used to make a decision on clutter presence from the estimated discriminant functions. The DPDS algorithm is applied to the data collected with the KOUN polarimetric radar and compared with the existing detection methods. It is shown that the DPDS algorithm yields a higher probability of detection and lower false alarm rate in clutter detection.

Objective: Long noncoding RNA growth arrest-specific transcript 5 (lncRNA GAS5) is involved in various kinds of cancer. However, the role of lncGAS5 in the development of ovarian cancer remains unclear. In the present study, we explored the cellular mechanism and clinical value of lncRNA GAS5 in ovarian cancer. Methods: Quantitative real-time PCR was used to detect mRNA level of lncRNA GAS5 in 20 ovarian cancer tissues. The effect of lncRNA GAS5 on cell proliferation was performed using CCK-8 assay. Cell apoptosis was evaluated by flow cytometry. Western blotting was used to detect the protein level of lncRNA GAS5 potential target. Standard sandwich ELISA was used to quantify the level of inflammatory cytokines. The cells with stable expression of lncRNA GAS5 were injected into nude mice to study the effect of lncRNA GAS5 on tumorigenesis in vivo. Results: The expression of lncRNA GAS5 was significantly decreased in ovarian cancer tissues. Decrease in lncRNA GAS5 expression resulted in increased cell proliferation and colony formation and reduced ovarian cancer cell apoptosis. In contrast, exogenous overexpression of lncRNA GAS5 in ovarian cancer cells inhibited proliferation, colony formation, and apoptosis in ovarian cancer cells. In addition, the role of lncRNA GAS5 in ovarian cancer was associated with inflammasome formation and pyroptosis. Conclusion: These results suggested that lncRNA GAS5 acts as tumor suppressor and could be used as a potential treatment target for diagnosis and therapy of ovarian cancer.

Classical swine fever virus (CSFV) causes a highly contagious disease that leads to significant economic losses in the pig industry worldwide. However, there is a paucity of knowledge on the accurate genotyping of CSFV isolates in south China. This study genotyped the E2 gene of 14 CSFV strains isolated during 2008–2010 from domestic pigs in different districts of south China. Phylogenetic analyses revealed that all of the 14 CSFV isolates were clustered into genetic subgroup 1.1. This contrasts with most parts of China, where group 2 isolates are predominant. Furthermore, the positive selection pressures acting on the Erns and E2 envelope protein genes of CSFV were assessed and a site-by-site analysis of the dN/dS ratio was performed to identify specific codons that undergo diversification under positive selection. While no significant evidence for positive selection was observed in Erns, two positively selected sites at amino acid residues 49 and 72 in the E2 encoding region were identified. Our results revealed that a predominance of subgroup 1.1 CSFV isolates is currently circulating in some districts of south China, which appear to be unrelated to the Chinese C-strain vaccine. Moreover, the envelope protein gene, E2, has undergone positive selection in 14 CSFV strains and two positively selected sites have been identified in this study. Understanding the molecular epidemiology and functional importance of these positively selected amino acid positions could help to predict possible changes in virulence, the development of vaccines and disease control.