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Atrazine (ATZ), a widely used herbicide, disrupts mitochondrial function and lipid metabolism in the liver. Melatonin (MLT), a naturally synthesized hormone, combats mitochondrial dysfunction and alleviates lipid toxicity. However, the mechanisms behind ATZ-induced lipid metabolism toxicity and the protective effects of MLT remain unexplored. Mice were randomly assigned to four groups: control (Con), 5 mg/kg MLT, 170 mg/kg ATZ, and a cotreatment group receiving 170 mg/kg ATZ with 5 mg/kg MLT (ATZ+MLT). Additionally, we analyzed the effects of MLT and Rab8a on mRNA and proteins related to mitochondrial function and lipid metabolism disrupted by ATZ in AML12 cells. In conclusion, ATZ induced mitochondrial stress and disrupted fatty acid metabolism in mouse hepatocytes and AML12 cells. Exogenous MLT restores Rab8a levels, regulating fatty acid utilization in mitochondria and mitochondrial function. Notably, targeting Rab8a does not significantly affect mitochondrial function but prevents ATZ-induced lipid metabolism disorders in hepatocytes.

Three kinds of metal–organic frameworks possessing analogous structures were prepared by regulating the structure units of organic linkers. MOF/nickel foam electrocatalysts were formed by in situ hydrothermal growth of MOFs on a clean supported substrate nickel foam (NF), and the corresponding composites were prepared. We phosphatated them and obtained the heterojunction catalyst. Different structure units in the ligand have significant influences on the phosphating, resulting in heterogeneous materials that are not quite the same. Among them, heterogeneous materials with Co2P and NiP have the best catalytic performance. We also studied the urea oxidation reaction (UOR) properties of the materials and proved that it is feasible to improve the UOR performance of MOF/NF composites by regulating the structure units in the organic linkers. The results provided an idea for the reasonable selection of organic ligands to construct MOFs to regulate the electrocatalytic performance.

Different from conventional motion/force control strategies based on robotic dynamics, this paper presents a kinematic perspective to convert the motion/force control problem into a bi-criteria optimization problem. Specifically, the motion and force errors are formulated as an equality constraint at the kinematics level. Through a weight coefficient, the minimum infinite norm of joint velocity and the alternative kinematic index are integrated as a bi-criteria objective function. On this basis, a bi-criteria hybrid motion/force control (BHMFC) strategy is proposed with kinematic analyses on robotic manipulators. This bi-criteria kinematic strategy fulfills the potentials of robotic manipulators involving the functions of hybrid index optimization, hybrid control of motion and force, end-effector posture maintaining, and physical constraints. Furthermore, the related dynamic neural network (DNN) with theoretical analyses is presented to explore the optimal solution to the BHMFC strategy. Finally, computer simulations, physical experiments, and strategy comparisons are conducted to demonstrate the feasibility, efficiency, and superiority of the proposed BHMFC strategy. This work presents an efficient kinematic approach to address robot motion/force control problems with promising research prospects. Note to Practitioners—This paper is motivated by potential improvements of motion/force hybrid control schemes of robotic manipulators in a kinematic manner. Existing motion/force control methods typically rely on robot dynamics, which are difficult to satisfy kinematic task requirements, such as physical constraints and task optimizations. To this end, a bi-criteria hybrid motion/force control (BHMFC) strategy is proposed to achieve kinematic performance improvements in a quadratic program framework. Specifically, the designed constraints exploit the functions of hybrid control of motion and force, physical constraints, and end-effector posture maintaining. Besides, the kinematic optimization and joint velocity reduction are implemented by a bi-criteria objective function. Besides, we propose a dynamic neural network (DNN) based on Karush-Kuhn-Tucker conditions to solve the BHMFC strategy and theoretically analyze its global convergence ability and convergence rate. Simulative and experimental results show that the proposed method outperforms the traditional pseudoinverse method in terms of accurate position/force control performance and end-effector posture maintenance. In addition, computational analysis of control signals and comparisons with existing technologies highlight the feasibility and superiority of the proposed method.

Time-variant quadratic programming (TVQP) has widespread applications and often involves equality, inequality, and bound constraints. An effective solver for TVQP problems is zeroing neural network (ZNN), and nonlinear complementary problem function-based ZNN (NCP-ZNN) is a state-of-the-art ZNN solver that can handle equality and inequality constraints. However, when dealing with bound constraints, NCP-ZNN expands the dimension of the matrix and then introduces twice the number of Lagrange multipliers. To overcome this deficiency, this article develops a modified NCP-ZNN solver by introducing the first-order optimality conditions. Numerical validation is performed to substantiate the superior solving efficiency of the modified NCP-ZNN solver, which can achieve the same or lower order of residual errors compared with the original NCP-ZNN. Then, the modified NCP-ZNN solver is applied to the pose control of a redundant manipulator, demonstrating its superiority in solving practical problems.

The tracking control of redundant manipulators plays a crucial role in robotics research and generally requires accurate knowledge of models of redundant manipulators. When the model information of a redundant manipulator is unknown, the trajectory-tracking control with model-based methods may fail to complete a given task. To this end, this article proposes a data-driven neural dynamics-based model predictive control (NDMPC) algorithm, which consists of a model predictive control (MPC) scheme, a neural dynamics (ND) solver, and a discrete-time Jacobian matrix (DTJM) updating law. With the help of the DTJM updating law, the future output of the model-unknown redundant manipulator is predicted, and the MPC scheme for trajectory tracking is constructed. The ND solver is designed to solve the MPC scheme to generate control input driving the redundant manipulator. The convergence of the proposed data-driven NDMPC algorithm is proven via theoretical analyses, and its feasibility and superiority are demonstrated via simulations and experiments on a redundant manipulator. Under the drive of the proposed algorithm, the redundant manipulator successfully carries out the trajectory-tracking task without the need for its kinematics model.

Hydrogen sulfide (H2S), the third gasotransmitter discovered, regulates a variety of physiological functions. Whether H2S alleviates skeletal muscle ageing by regulating autophagy has not been reported. Mice were administered 150 mg/kg/day of D‐galactose (D‐gal), and C2C12 myotubes were cultured in 20 g/L D‐gal to induce ageing. Sodium hydrosulfide (NaHS) was employed as an exogenous donor in the treatment group. The intracellular concentration of H2S was quantified by the 7‐azido‐4‐methylcoumarin fluorescence probe. The proteins involved in the ubiquitin‐mediated degradation of AMPKα1 were detected by liquid chromatography tandem mass spectrometry (LC–MS/MS) and co‐immunoprecipitation (Co‐IP). S‐sulfhydration of USP5 was tested by a biotin‐switch assay. Associated proteins were analysed by western blot. NaHS was found to effectively restore the H2S content in both ageing gastrocnemius (+91.89%, P< 0.001) and C2C12 myotubes (+27.55%, P< 0.001). In comparison to the D‐gal group, NaHS was observed to increase the mean cross‐sectional area of muscle fibres (+44.91%, P< 0.001), to decrease the collagen volume fraction of gastrocnemius (−81.32%, P= 0.001) and to reduce the β‐galactosidase‐positive area of C2C12 myotubes (−28.74%, P< 0.001). NaHS was also found to reverse the expression of muscle atrophy F box protein (MAFbx), muscle‐specific RING finger protein 1 (MuRF1), Cyclin D1 and p21 in the ageing gastrocnemius tissue (MAFbx: −31.73%, P= 0.008; MuRF1: −32.37%, P= 0.003; Cyclin D1: +45.34%, P= 0.010; p21: −25.53%, P= 0.022) and C2C12 myotubes (MAFbx: −16.38%, P< 0.001; MuRF1: −16.45%, P= 0.003; Cyclin D1: +40.23%, P< 0.001; p21: −35.85%, P= 0.026). The AMPKα1–ULK1 pathway was activated and autophagy was up‐regulated in NaHS‐treated gastrocnemius tissue (p‐AMPKα1: +61.61%, P= 0.018; AMPKα1: +30.64%, P= 0.010; p‐ULK1/ULK1: +85.87%, P= 0.005; p62: −29.07%, P< 0.001; Beclin1: +24.75%, P= 0.007; light chain 3 II/I [LC3 II/I]: +55.78%, P= 0.004) and C2C12 myotubes (p‐AMPKα1: +77.49%, P= 0.018; AMPKα1: +26.18%, P= 0.022; p‐ULK1/ULK1: +38.34%, P= 0.012; p62: −9.02%, P= 0.014; Beclin1: +13.36%, P< 0.001; LC3 II/I: +79.38%, P= 0.017; autophagy flux: +24.88%, P= 0.034) compared with the D‐gal group. The effects of NaHS on autophagy were comparable to those of acadesine and LYN‐1604, and chloroquine could reverse its effects on ageing. LC–MS/MS and Co‐IP experiments demonstrated that USP5 is a deubiquitinating enzyme of AMPKα1. Following the knockdown of USP5, the activation of AMPKα1 was decreased (p‐AMPKα1: −42.10%, P< 0.001; AMPKα1: −43.93%, P< 0.001), autophagy was inhibited (p‐ULK1/ULK1: −27.51, P= 0.001; p62: +36.00, P< 0.001; Beclin1: −22.15%, P< 0.001) and NaHS lost its ability to up‐regulate autophagy. NaHS was observed to restore the expression (gastrocnemius: +62.17%, P< 0.001; C2C12 myotubes: +37.51%, P= 0.003) and S‐sulfhydration (+53.07%, P= 0.009) of USP5 and reduce the ubiquitination of AMPKα1. H2S promotes the deubiquitination of AMPKα1 by increasing the expression and S‐sulfhydration of USP5, thereby up‐regulating autophagy and alleviating skeletal muscle ageing.

For the image-based visual servoing (IBVS) of a manipulator with an unknown structure, the unavailability of the robot Jacobian matrix impedes the accurate control of the manipulator. To solve this issue, this article proposes a data-driven IBVS (DDIBVS) scheme combining model-free learning, matrix inversion estimation, feature tracking, and joint limits. On the one hand, a data-driven learning algorithm is designed, which enables an estimated end-effector velocity to approach the real one and outputs an estimated robot Jacobian matrix. On the other hand, we consider the desired velocity information of the visual feature to improve the tracking accuracy and design an auxiliary parameter to estimate the inversion operation and address the singularity problem. On this basis, a neural dynamic controller (NDC) is developed, which possesses learning, estimation, and control capabilities. Subsequently, the effectiveness, practicability, and superiority of the proposed method are evaluated through simulations and experiments conducted on a 7-degree-of-freedom (DOF) manipulator for visual servoing tasks.

Redundant manipulators are widely used in various fields due to their multiple degrees of freedom characteristics, and their tracking control is an important problem in the field of robotics. In order to control manipulators with unknown models in practical applications, this article proposes a discrete data-driven Jacobian matrix adaptive control (DDJMAC) scheme. The scheme is composed of a discrete Jacobian matrix estimator, a discrete neural dynamics controller, and a Kalman filter. Subsequently, the convergence and robustness of the DDJMAC scheme are demonstrated by theoretical analyses. Finally, simulations, comparisons, and physical experiments are performed on redundant manipulators, and the results confirm the effectiveness, superiority, and practicality of the proposed scheme.

Cytochrome P450 monooxygenases in insects have been verified to implicated in insecticide and phytochemical detoxification metabolism. However, the regulation of P450s, which are modulated by signal-regulated transcription factors (TFs), is less well studied in insects. Here, we found that the Malpighian tubule specific P450 gene SlCYP9A75bin Spodoptera liturais induced by xenobiotics. The transgenic Drosophilabioassay and RNAi results indicated that this P450 gene contributes to α-cypermethrin, cyantraniliprole, and nicotine tolerance. In addition, functional analysis revealed that the MAPKs p38, PI3K/Akt, and JAK-STAT activate the transcription factor fushi tarazu factor 1 (FTZ-F1) to regulate CYP9A75bexpression. These findings provide mechanistic insights into the contributions of CYP9A genes to xenobiotic detoxification and support the possible involvement of different signaling pathways and TFs in tolerance to xenobiotics in insects.

Spodoptera liturais a significant agricultural pest, and its glutathione S-transferase (GST) plays a crucial role in insecticide resistance. This study aimed to investigate the relationship between the SlGSTe11gene of S. lituraand resistance to cyantraniliprole and nicotine. Transcriptome analysis revealed that SlGSTe11is highly expressed mainly in fat bodies, with a significant increase in SlGSTe11gene expression under induction by cyantraniliprole and nicotine. The ectopic expression of the SlGSTe11gene in transgenic fruit flies resulted in a 5.22-fold increase in the tolerance to cyantraniliprole. Moreover, compared to the UAS-SlGSTe11line, the Act5C-UAS>SlGSTe11line laid more eggs and had a lower mortality after nicotine exposure. RNAi-mediated inhibition of SlGSTe11gene expression led to a significant increase in the mortality of S. lituraunder cyantraniliprole exposure. In vitrometabolism experiments demonstrated that the recombinant SlGSTe11protein efficiently metabolizes cyantraniliprole. Molecular docking results indicated that SlGSTe11has a strong affinity for both cyantraniliprole and nicotine. These findings suggest that SlGSTe11is involved in the development of resistance to cyantraniliprole and nicotine in S. litura.

Vertex-deformed square microcavity lasers with a connected output waveguide are proposed to support dual-mode lasing. Two opposite circular vertices are introduced to enlarge the mode interval between the fundamental and the first order transverse modes, and pure dual-mode lasing is realized due to the suppression of high-order transverse modes. For a deformed 30- $\mu $ m-side-length square microcavity laser with a 1.5- $\mu $ m-wide waveguide, dual-mode lasing is verified with intervals from 0.46 to 1.60 nm by controlling the circular vertices radius. The autocorrelation traces prove that the proposed microlasers emit dual modes synchronously and have potential applications in sub-THz wave generation.

The augmented Sylvester equation, as a comprehensive equation, is of great significance and its special cases (e.g., Lyapunov equation, Sylvester equation, Stein equation) are frequently encountered in various fields. It is worth pointing out that the current research on simultaneously eliminating the lagging error and handling noises in the nonstationary complex-valued field is rather rare. Therefore, this article focuses on solving a nonstationary complex-valued augmented Sylvester equation (NCASE) in real time and proposes two modified recurrent neural network (RNN) models. The first proposed modified RNN model possesses gradient search and velocity compensation, termed as RNN-GV model. The superiority of the proposed RNN-GV model to traditional algorithms including the complex-valued gradient-based RNN (GRNN) model lies in completely eliminating the lagging error when employed in the nonstationary problem. The second model named complex-valued integration enhanced RNN-GV with the nonlinear acceleration (IERNN-GVN) model is proposed to adapt to a noisy environment and accelerate the convergence process. Besides, the convergence and robustness of these two proposed models are proved via theoretical analysis. Simulative results on an illustrative example and an application to the moving source localization coincide with the theoretical analysis and illustrate the excellent performance of the proposed models.

Metal contaminants from surface water pollution events often enter hyporheic zones, under certain conditions, they may be released back into streams, causing secondary pollution to the water quality. The present study investigated the effects of adsorption, permeability, and anisotropy of sediment beds on the release of zinc ions (Zn2+) from the hyporheic zone into overlying turbulent flows using large-eddy simulations (LES). The volume-averaged Navier-Stokes equations and advection-diffusion equation with adsorption term were used to describe the sediment in-flow, adsorption, and convective diffusion of Zn2+within the sediment layer. The effects of sediment permeability on the Zn2+concentration distribution and mass transfer processes were investigated by time-averaged statistics of flow and concentration fields. The results show that adsorption becomes stronger as the pH value increases, leading to a slow increase in Zn2+concentration in the overlying water layer and reaching a lower steady-state concentration. Higher overall permeability of the sediment layer can enhance mass and momentum exchange near the sediment-water interface (SWI), and intensify the release of Zn2+from the sediment layer into the overlying water. As the wall-normal permeability of the sediment layer increases, the normal turbulent intensity strengthens, momentum transport enhances, the wall-normal Zn2+concentration flux increases, the effective diffusion coefficient increases, and the concentration in the overlying water increases.

Recently, recurrent neural networks have been extensively utilized to address a time-dependent system of linear equations (TDSLEs) with inequality systems. Nevertheless, these existing studies only limit the variable without considering constraints on its derivatives, which may be challenging to accomplish a given task in practical applications when additional constraints are introduced. Beyond that, the matrix pseudoinverse is performed, and non-negative slack variables are introduced in the solution process, which increases the model’s complexity and leads to a high computational burden. To remedy these deficiencies, this article makes improvements via proposing a novel recurrent neural dynamics (RND) model for solving the TDSLEs with constraints on the variable and its derivatives. Specifically, such a model neither needs to compute the pseudoinverse of a matrix nor to introduce non-negative slack variables, thereby enhancing its computational efficiency and accuracy. Corresponding theoretical analysis is provided to ensure its convergence performance. Finally, numerical results, comparisons with other models, and applications to single and multiple robots are provided, which substantiates the availability and meliority of the pseudoinverse-free RND model for disposing of the TDSLEs with constraints on the variable and its derivatives.

In recent years, the discrete-time recurrent neural network (DTRNN) model has received growing attention. This fully benefits from the recurrent neural networks (RNNs) that not only have plenty of advantages for solving computing problems in the real-time tracking control but also have the remarkable potential of parallel processing and nonlinear processing. However, there is a general lack of research on the applicability of DTRNN model to handle parallel robot. In addition, the precision is always an important point in real-time tracking control, and most of existing studies generally lack the elaborate researches on the precision analyses. In this article, the corresponding DTRNN model (i.e., general five-instant discretization (FID) formula DTRNN model) with parameter selection method is established. As one of the important theoretical contributions, the dominant term of truncation error of discretization formula and the conditions of maintaining precision of corresponding DTRNN model are proved from the mathematical view strictly. Besides, the influence of the selected parameter for the precision of such a DTRNN model is also analyzed. Finally, the above theoretical analyses are verified in the tracking control experiments of the Stewart platform, which is a widely used and representative parallel robot.

In industrial applications where computational resources are finite and data noises are prevalent, the representation power of deep neural networks (DNNs) is crucial. Traditional network structures often require a significant increase in the parameter amount to enhance the representation power, making it difficult to achieve effective representation under parameter amount constraints. In order to alleviate this problem, this work leverages the ordinary differential equation (ODE) interpretation of deep residual networks, elucidating the relationship between the fine-grained connectivity modes of blocks in DNNs and the representation power. We build a bridge from the order of numerical methods and the order of ODEs to the representation power of DNNs. Besides, we show that higher-order ODEs can be approximated by k-step methods incorporating trainable coefficients. Empirically, we validate our theoretical insights by demonstrating the superior representation power of our proposed network structures through enhanced performance on industrial tasks, such as surface defect detection, critical temperature prediction of superconductors, and image classification under noises. The proposed method provides a new approach to the design of network structures for robust and accurate DNNs, enhancing the representation power with a negligible number of additional parameters. The code is publicly available at https://github.com/LongJin-lab/Order-and-Representation-Power.

Biofouling is the undesirable accumulation of living organisms and their metabolites on submerged surfaces. Biofouling begins with adhesion of biomacromolecules and/or microorganisms and can lead to the subsequent formation of biofilms that are predominantly regulated by chemical signals, such as cyclic dinucleotides and quorum-sensing molecules. Biofilms typically release chemical cues that recruit or repel other invertebrate larvae and algal spores. As such, harnessing the biochemical mechanisms involved is a promising avenue for controlling biofouling. Here, we discuss how chemical signaling affects biofilm formation and dispersion in model species. We also examine how this translates to marine biofouling. Both inductive and inhibitory effects of chemical cues from biofilms on macrofouling are also discussed. Finally, we outline promising mitigation strategies by targeting chemical signaling to foster biofilm dispersion or inhibit biofouling.

Influenza A virus (IAV) is a widespread pathogen that poses a significant threat to human health, causing pandemics with high mortality and pathogenicity. Given the emergence of increasingly drug-resistant strains of IAV, currently available antiviral drugs have been reported to be inadequate to meet clinical demands. Therefore, continuous exploration of safe, effective and broad-spectrum antiviral medications is urgently required. Here, we found that the small molecule compound J1 exhibited low toxicity both in vitro and in vivo. Moreover, J1 exhibits broad-spectrum antiviral activity against enveloped viruses, including IAV, respiratory syncytial virus (RSV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), human coronavirus OC43 (HCoV-OC43), herpes simplex virus type 1 (HSV-1) and HSV-2. In this study, we explored the inhibitory effects and mechanism of action of J1 on IAV in vivo and in vitro. The results showed that J1 inhibited infection by IAV strains, including H1N1, H7N9, H5N1 and H3N2, as well as by oseltamivir-resistant strains. Mechanistic studies have shown that J1 blocks IAV infection mainly through specific interactions with the influenza virus hemagglutinin HA2 subunit, thereby blocking membrane fusion. BALB/c mice were used to establish a model of acute lung injury (ALI) induced by IAV. Treatment with J1 increased survival rates and reduced viral titers, lung index and lung inflammatory damage in virus-infected mice. In conclusion, J1 possesses significant anti-IAV effects in vitro and in vivo, providing insights into the development of broad-spectrum antivirals against future pandemics.

Byline: Jian. Wei, Yuefeng. Hu, Jianan. Yu, Chao. Yin, Guang. Chen, Long. Jin Aims: To investigate the predictive value of hepatic venous pressure gradient (HVPG) and the efficacy and significance [...]

Phytophagous insects are more predisposed to evolve insecticide resistance than other insect species due to the “preadaptation hypothesis”. Cytochrome P450 monooxygenases have been strongly implicated in insecticide and phytochemical detoxification in insects. In this study, RNA-seq results reveal that P450s of Spodoptera litura, especially the CYP3 clan, are dominant in cyantraniliprole, nicotine, and gossypol detoxification. The expression of a Malpighian tubule-specific P450 gene, SlCYP9A75a, is significantly upregulated in xenobiotic treatments except α-cypermethrin. The gain-of-function and loss-of-function analyses indicate that SlCYP9A75acontributes to cyantraniliprole, nicotine, and α-cypermethrin tolerance, and SlCYP9A75a is capable of binding to these xenobiotics. This study indicates the roles of inducible SlCYP9A75ain detoxifying man-made insecticides and phytochemicals and may provide an insight into the development of cross-tolerance in omnivorous insects.

Di-2-ethylhexyl phthalate (DEHP) is frequently used as a plasticizer to enhance the plasticity and durability of agricultural products, which pose adverse effects to human health and the environment. Aquaporin 1 (AQP1) is a main water transport channel protein and is involved in the maintenance of intestinal integrity. However, the impact of DEHP exposure on gut health and its potential mechanisms remain elusive. Here, we determined that DEHP exposure induced a compromised duodenum structure, which was concomitant with mitochondrial structural injury of epithelial cells. Importantly, DEHP exposure caused duodenum inflammatory epithelial cell damage and strong inflammatory response accompanied by activating the TLR4/MyD88/NF-κB signaling pathway. Mechanistically, DEHP exposure directly inhibits the expression of AQP1 and thus leads to an inflammatory response, ultimately disrupting duodenum integrity and barrier function. Collectively, our findings uncover the role of AQP1 in phthalate-induced intestinal disorders, and AQP1 could be a promising therapeutic approach for treating patients with intestinal disorders or inflammatory diseases.

Atrazine (ATR) is a widely used herbicide worldwide that can cause kidney damage in humans and animals by accumulation in water and soil. Lycopene (LYC), a carotenoid with numerous biological activities, plays an important role in kidney protection due to its potent antioxidant and anti-inflammatory effects. The current study sought to investigate the role of interactions between mtDNA and the cGAS-STING signaling pathway in LYC mitigating PANoptosis and inflammation in kidneys induced by ATR exposure. In our research, 350 mice were orally administered LYC (5 mg/kg BW/day) and ATR (50 or 200 mg/kg BW/day) for 21 days. Our results reveal that ATR exposure induces a decrease in mtDNA stability, resulting in the release of mtDNA into the cytoplasm through the mPTP pore and the BAX pore and the mobilization of the cGAS-STING pathway, thereby inducing renal PANoptosis and inflammation. LYC can inhibit the above changes caused by ATR. In conclusion, LYC inhibited ATR exposure-induced histopathological changes, renal PANoptosis, and inflammation by inhibiting the cGAS-STING pathway. Our results demonstrate the positive role of LYC in ATR-induced renal injury and provide a new therapeutic target for treating renal diseases in the clinic.

The involvement of M2-like tumor-associated macrophages (TAMs) in the advancement and treatment of cancer has been widely documented. This study aimed to develop a new signature associated with M2-like TAMs to predict the prognosis and treatment response in individuals diagnosed with breast cancer (BC). Weighted gene co-expression network analysis (WGCNA) was used to identity for M2-like TAM-related modular genes. The M2-like TAM-related modular subtype was identified using unsupervised clustering. WGCNA identified 722 M2-like TAM genes, 204 of which were associated with recurrence-free survival (RFS). Patients in cluster 1 exhibited upregulated cancer-related pathways, a higher proportion of triple-negative breast cancer (TNBC) subtypes, lower expression of immune checkpoints, and worse prognosis. Cluster 2 was characterized by upregulated immune-related pathways, a higher proportion of luminal A subtypes, and higher expression of immune checkpoints. A prognostic signature was created and confirmed using an independent dataset. A well-built nomogram can accurately forecast the survival outcomes for every individual. Furthermore, patients classified as low-risk exhibited a more favorable outlook, elevated tumor microenvironment (TME) score, and superior reaction to immunotherapy. In conclusion, we discovered 2 different types of M2-like TAMs and developed a prognostic signature revealing the diversity of M2-like TAMs in BC and their correlation with immune status and prognosis. This feature can predict the prognosis and immunotherapeutic effects of BC and offer novel concepts and approaches for tailoring BC treatment.

Homocysteine (Hcy) is a sulphur-containing nonessential amino acid derived from the intermediate metabolites of methionine. Methionine is obtained from dietary proteins, such as poultry, meat, eggs, seafood, and dairy products. Abnormalities in Hcy metabolic pathways, deficiencies in dietary methionine, folate, and vitamins B12, B6, and B2and genetic defects, polymorphisms, or mutations in Hcy metabolism-related enzymes may lead to an increase in plasma Hcy levels. Generally, a plasma Hcy level higher than 10 or 15 μmol/L has been defined as hyperhomocysteinemia (HHcy). An individual with essential hypertension complicated with HHcy is considered to have H-type hypertension (HTH). Currently, HHcy is considered a novel independent risk factor for various cardiovascular diseases. To provide a useful reference for clinicians, the research progress on Hcy, HHcy, and HTH in recent years was systematically reviewed here, with a focus on the source and metabolic pathways of Hcy, plasma Hcy levels and influencing factors, detection methods for plasma Hcy levels, relationship between Hcy concentration and hypertension, pathogenesis of HTH, cardiovascular complications of HTH, and treatment of HTH.

The cotton aphid, Aphis gossypii, is a polyphagous pest that stunts host plant growth via direct feeding or transmitting plant virus. Due to the long-term application of insecticides, A. gossypiihas developed different levels of resistance to numerous insecticides. We found that five field populations had evolved multiple resistances to neonicotinoids. To explore the resistance mechanism mediated by uridine diphosphate glycosyltransferases (UGTs), two upregulated UGT genes in these five strains, UGT350C3and UGT344L7, were selected for functional analysis of their roles in neonicotinoid detoxification. Transgenic Drosophilabioassay results indicated that compared with the control lines, the UGT350C3and UGT344L7overexpression lines were more tolerant to thiamethoxam, imidacloprid, and dinotefuran. Knockdown of UGT350C3and UGT344L7significantly increased A. gossypiisensitivity to thiamethoxam, imidacloprid, and dinotefuran. Molecular docking analysis demonstrated that these neonicotinoids could bind to the active pockets of UGT350C3 and UGT344L7. This study provides functional evidence of neonicotinoid detoxification mediated by UGTs and will facilitate further work to identify strategies for preventing the development of neonicotinoid resistance in insects.

Cadmium (Cd) is a transition metal ion that is extremely harmful to human and animal biological systems. Cd is a toxic substance that can accumulate in the food chain and cause various health issues. Sulforaphane (SFN) is a natural bioactive compound with potent antioxidant properties. In our study, 80 1 day-old chicks were fed with Cd (140 mg/kg BW/day) and/or SFN (50 mg/kg BW/day) for 90 days. The blood–thymus barrier (BTB) is a selective barrier separating T-lymphocytes from blood and cortical capillaries in the thymus cortex. Our research revealed that Cd could destroy the BTB by downregulating Wnt/β-catenin signaling and induce immunodeficiency, leading to irreversible injury to the immune system. The study emphasizes the health benefits of SFN in the thymus. SFN could ameliorate Cd-triggered BTB dysfunction and pyroptosis in the thymus tissues. SFN modulated the PI3K/AKT/FOXO1 axis, improving the level of claudin-5 (CLDN5) in the thymus to alleviate BTB breakdown. Our findings indicated the toxic impact of Cd on thymus, and BTB could be the specific target of Cd toxicity. The finding also provides evidence for the role of SFN in maintaining thymic homeostasis for Cd-related health issues.

Developing new pharmaceuticals is a costly and time-consuming endeavor fraught with significant safety risks. A critical aspect of drug research and disease therapy is discerning the existence of interactions between drugs and proteins. The evolution of deep learning (DL) in computer science has been remarkably aided in this regard in recent years. Yet, two challenges remain: (i) balancing the extraction of profound, local cohesive characteristics while warding off gradient disappearance and (ii) globally representing and understanding the interactions between the drug and target local attributes, which is vital for delivering molecular level insights indispensable to drug development. In response to these challenges, we propose a DL network structure, MolLoG, primarily comprising two modules: local feature encoders (LFE) and global interactive learning (GIL). Within the LFE module, graph convolution networks and leap blocks capture the local features of drug and protein molecules, respectively. The GIL module enables the efficient amalgamation of feature information, facilitating the global learning of feature structural semantics and procuring multihead attention weights for abstract features stemming from two modalities, providing biologically pertinent explanations for black-box results. Finally, predictive outcomes are achieved by decoding the unified representation via a multilayer perceptron. Our experimental analysis reveals that MolLoG outperforms several cutting-edge baselines across four data sets, delivering superior overall performance and providing satisfactory results when elucidating various facets of drug–target interaction predictions.

Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) is an invasive alien species that causes major losses in the Chinese tomato industry. Historically, insecticides have been used extensively to control T. absoluta, but resistant populations have been frequently detected. Biological control represents a sustainable and effective method with which to prevent and control T. absoluta. Egg parasitoids from the family of Trichogrammatidae (Hymenoptera) have been identified as potential biological option for controlling of T. absoluta. In the present study, we aimed to identify local dominant Trichogramma species that exhibited the best biological control ability against T. absoluta in China. To accomplish this, we compared the selectivity and number of offspring of three Trichogramma species to T. absoluta by performing host preference experiments in the laboratory. We also evaluated the fitness of Trichogramma against T. absoluta by assessing the parasitic ability and behavior of their offspring on different hosts. Compared with Trichogramma chilonis and Trichogramma dendrolimi, Trichogramma ostriniae exhibited a significantly greater preference to T. absoluta eggs and produced a higher number of offspring. The emergence rate and proportion of female T. ostriniae parasitizing the eggs of T. absoluta was higher than that of the other two Trichogramma species, the rate of T. ostriniae offspring deformity was only 3.00%. In addition, T. ostriniae has shortest immature stage and the longest adult stage among the three species. In conclusion T. ostriniae can be a promising native biological control agent against T. absoluta in China.

Amphiphilic rod–coil compounds have excellent photophysical properties and can be assembled into supramolecular nanostructures of different sizes in water or polar solvents. Herein, we synthesized the amphiphilic compounds 2N-DSA, 4N-DSA, and 6N-DSAwith 9,10-distyrylanthracene (DSA) as the core and a naphthalene unit as the terminal group that connected DSA through a tetraethylene glycol chain. These compounds have excellent aggregation-induced emission (AIE) properties in aqueous solution and are assembled into worm-like fragments or different sizes of spherical assemblies, defending the volume ratio of the rod to coil segments. Notably, the donor–acceptor interaction between DSA and electron- deficient compounds 2,4,6-trinitrophenol (TNP), 2,4,5,7-tetranitrofluorenone (TNF), and tetraethylene glycol dinitrobenzoate (TGDNB) forms a charge transfer complex, which can be used as a nanoreactor to improve the yield of the Suzuki coupling reaction about 8–10 times. The experimental results reveal that the synergy effect of the donor–acceptor, intermolecular π–π stacking, and hydrophobic–hydrophilic interactions significantly influences the morphology of aggregates and the efficiency of the nanoreactor.

Di-(2-ethylhexyl) phthalate (DEHP), as the most common phthalate, has been extensively used as a plasticizer to improve the plasticity of agricultural products, which pose severe harm to human health. Mitochondrial dynamics and endoplasmic reticulum (ER) homeostasis are indispensable for maintaining mitochondria-associated ER membrane (MAM) integrity. In this study, we aimed to explore the effect of DEHP on the nervous system and its association with the ER–mitochondria interaction. Here, we showed that DEHP caused morphological changes, motor deficits, cognitive impairments, and blood–brain barrier disruption in the brain. DEHP triggered ER stress, which is mainly mediated by protein kinase R-like endoplasmic reticulum kinase (PERK) signaling. Moreover, DEHP-induced mitofusin-2 (Mfn2) downregulation results in imbalance of the mitochondrial dynamics. Interestingly, DEHP exposure impaired MAMs by inhibiting the Mfn2-PERK interaction. Above all, this study elucidates the disruption of the Mfn2-PERK axis-mediated ER–mitochondria interaction as a phthalate-induced neurotoxicity that could be potentially developed as a novel therapy for neurological diseases.

The interfacial structure plays an important role in the mechanical properties of magnesium matrix composite (MMCs) reinforced with graphene nanosheet (GNS) due to their poor wettability with the Mg matrix. An interface design strategy was proposed to form the semi-coherent interfacial structure with superior bonding strength. The lattice mismatch and interfacial bonding strength between Mg/rare earth oxide/carbon were utilized as key characteristics to evaluate the interfacial structure. Lanthanum oxide (La2O3) was selected as the intermediate candidate due to its low lattice mismatch and high interfacial bonding strength. To identify the interfacial structure of Mg/La2O3/graphene, first-principles calculations were conducted to calculate the ideal work of separation and electronic structure of the interfaces. Results demonstrated the presence of strong ionic and covalent interactions at the interface, which theoretically verified the strong interfacial bonding strength among Mg/La2O3/graphene interfaces. To experimentally validate the interface strength, MMCs with the interface structure of Mg/La2O3/GNS were developed. The formation of in-situ La2O3led to the successful attainment of semi-coherent structures between Mg/La2O3and La2O3/GNS, resulting in high strength and good ductility of the composite. Overall, this work proposes a new approach to interface design in MMCs with an enhancement of mechanical properties.

Optimal intestinal health and functionality are essential for animal health and performance, and simultaneously intestinal nutrient transporters and intestinal peptides are also involved in appetite and feed intake control mechanisms. Given the potential of essential oil (EO) in improving animal performance and improving feed palatability, we hypothesized that dietary supplementation of cinnamaldehyde and carvacrol could improve performance and appetite of nursery pigs by modulating intestinal health and microbiota. Cinnamaldehyde (100 mg/kg), carvacrol (100 mg/kg), and their mixtures (including 50 mg/kg cinnamaldehyde and 50 mg/kg carvacrol) were supplemented into the diets of 240 nursery pigs for 42 d, and data related to performance were measured. Thereafter, the influence of EO on intestinal health, appetite and gut microbiota and their correlations were explored. EO supplementation increased (P < 0.05) the body weight, average daily gain (ADG) and average daily feed intake (ADFI) of piglets, and reduced (P < 0.05) diarrhea rates in nursery pigs. Furthermore, EO increased (P < 0.05) the intestinal absorption area and the abundance of tight junction proteins, and decreased (P < 0.05) intestinal permeability and local inflammation. In terms of intestinal development and the mucus barrier, EO promoted intestinal development and increased (P < 0.05) the number of goblet cells. Additionally, we found that piglets in the EO-supplemented group had upregulated (P < 0.05) levels of transporters and digestive enzymes in the intestine, which were significantly associated with daily gain and feed utilization. In addition, EO supplementation somewhat improved appetite in nursery pigs, increased the diversity of the gut microbiome and the abundance of beneficial bacteria, and there was a correlation between altered bacterial structure and appetite-related hormones. These findings indicate that EO is effective in promoting growth performance and nutrient absorption as well as in regulating appetite by improving intestinal health and bacterial structure.

The expression of P450 genes is regulated by trans-regulatory factors or cis-regulatory elements and influences how endogenous or xenobiotic substances are metabolized in an organism’s tissues. In this study, we showed that overexpression of the cytochrome P450 gene, CYP6CY22, led to resistance to cyantraniliprole in Aphis gossypii. The expression of CYP6CY22increased in the midgut and remaining carcass of the CyR strain, and after repressing the expression of CYP6CY22, the mortality of cotton aphids increased 2.08-fold after exposure to cyantraniliprole. Drosophilaectopically expressing CYP6CY22exhibited tolerance to cyantraniliprole and cross-tolerance to xanthotoxin, quercetin, 2-tridecanone, tannic acid, and nicotine. Moreover, transcription factor CF2–II(XM_027994540.2) is transcribed only as the splicing variant isoform CF2–II-AS, which was found to be 504 nucleotides shorter than CF2–IIin A. gossypii. RNAi and yeast one-hybrid (Y1H) results indicated that CF2–II-ASpositively regulates CYP6CY22and binds to cis-acting element p (−851/-842) of CYP6CY22to regulate its overexpression. The above results indicated that CYP6CY22was regulated by the splicing isoform CF2-II-AS, which will help us further understand the mechanism of transcriptional adaption of cross-tolerance between synthetic insecticides and plant secondary metabolites mediated by P450s.

The safety of risers in hang-off states is a vital challenge in offshore oil and gas engineering. A new hang-off system installed on top of risers is proposed for improving the security of risers. This approach leads to a challenging problem: coupling the dynamics of risers with a new hang-off system combined with multiple structures and complex constraints. To accurately analyze the dynamic responses of the coupled system, a coupled dynamic model is established based on the Euler–Bernoulli beam-column theory and penalty function method. A comprehensive analysis method is proposed for coupled dynamic analysis by combining the finite element method and the Newmark βmethod. An analysis program is also developed in MATLAB for dynamic simulation. The simulation results show that the dynamic performances of the risers at the top part are significantly improved by the new hang-off system, especially the novel design, which includes the centralizer and articulation joint. The bending moment and lateral deformation of the risers at the top part decrease, while the hang-off joint experiences a great bending moment at the bottom of the lateral restraint area which requires particular attention in design and application. The platform navigation speed range under the safety limits of risers expands with the new hang-off system in use.

This brief investigates a distributed $k$ -winner-take-all $(k$ WTA) network that effectively eliminates the lagging error problem. On this basis, a special case that the communication topology of robots is weight-unbalanced in the distributed $k$ WTA problem is considered for the first time. Then a distributed weight-unbalanced $k$ WTA (DWU- $k$ WTA) model is proposed with the aid of a consensus estimator. Theoretical analyses substantiate the usability and exponential convergence of the proposed DWU- $k$ WTA model in encountering directed weight-unbalanced graphs. A simulation based on a multi-robot system further verifies the feasibility of the proposed model in dealing with dynamic task allocation problems.

ABSTRACTAsphalt concrete is one of the widespread pavement materials. The multilayer asphalt concrete pavement (MACP) has strong heat-absorption capacity. The thermal behavior of the MACP can affect the energy exchange [...]

ABSTRACTFibre-reinforced polymer (FRP) bars and seawater sea-sand concrete (SWSSC) is a very promising new combination. When seawater and sea sand are used, not only the tensile reinforcements but also the [...]

Hepatocellular carcinoma (HCC) is a concerning liver cancer with rising incidence and mortality rates worldwide. The effectiveness of traditional therapies in managing advanced HCC is limited, necessitating the development of new therapeutic strategies. Immune checkpoint inhibitors (ICIs) have emerged as a promising strategy for HCC management. By preventing tumor cells from evading immune surveillance through immunological checkpoints, ICIs can restore the immune system’s ability to target and eliminate tumors. While ICIs show promise in enhancing the immune response against malignancies, challenges such as drug resistance and adverse reactions hinder their efficacy. To address these challenges, developing individualized ICI treatment strategies is critical. Combining targeted therapy and immunotherapy holds the potential for comprehensive therapeutic effects. Additionally, biomarker-based individualized ICI treatment strategies offer promise in predicting treatment response and guiding personalized patient care. Future research should explore emerging ICI treatment methods to optimize HCC immunotherapy. This review provides an overview of ICIs as a new treatment for HCC, demonstrating some success in promoting the tumor immune response. However, drug resistance and adverse reactions remain important considerations that must be addressed. As tailored treatment plans evolve, the prospect of immunotherapy for HCC is expected to grow, offering new opportunities for improved patient outcomes.

Manipulability optimization plays a crucial role in the kinematic control of redundant manipulators, as it reduces their risks of entering a singular state. However, manipulability is a nonlinear and nonconvex function with respect to joint angles. The existing kinematic schemes either do not consider the manipulability optimization or require transforming the nonconvex problem into a convex one, which may affect achieving the optimal value of manipulability. Furthermore, obstacle avoidance is rarely considered in the existing manipulability optimization methods. To address these limitations, this article proposes a manipulability optimization with obstacle avoidance constraints (MOOAC) scheme. Subsequently, a metaheuristic-based recurrent neural network (MRNN) model is constructed, which can directly handle a nonlinear and nonconvex problem with constraints and ensure achieving the global optimal with probability 1. In addition, the proposed MOOAC scheme is solved by the MRNN model at the joint angle level, which can handle the limits of joint angle and joint velocity without reducing the feasible region of decision variables. Computer simulations and physical experiments are provided to demonstrate the accuracy and superiority of the proposed scheme.

Salicylic acid (SA) is a phytohormone required for plant growth and defense signaling. There are two major SA biosynthesis pathways in plants: the isochorismate synthase (ICS) pathway and the phenylalanine ammonia-lyase (PAL) pathway. It has been demonstrated in several plant species, including the model plant Arabidopsis, that SA is derived predominantly from the ICS pathway. Here, we employed the CRISPR/Cas9 system to generate ICSknockout mutants in rice (Oryza sativaL.). The Osicsmutants display severe growth defects, and are completely devoid of phylloquinone, an isochorismate-derived product. The growth defects of Osicscan be rescued through exogenous application of 1,4-dihydroxy-2-naphthoic acid (NA), a precursor of phylloquinone. Remarkably, the basal SA levels are not altered in the Osicsmutants. Our findings support a role of OsICS in the biosynthesis of phylloquinone, and imply that SA biosynthesis in rice may occur through an alternative route other than the ICS pathway.

When the structure information of a redundant manipulator is unknown, motion control methods that do not rely on its model are attractive. Due to the numerous advantages of model predictive control (MPC), such as the direct handling of constraints, this article proposes a model-free MPC algorithm for redundant manipulators with unknown structure information. In this article, a cerebellum-inspired model based on the echo state network (ESN) is employed to replace the kinematic model of the redundant manipulator, and an MPC algorithm based on the cerebellum model and neural dynamics (ND) approach is developed. Unlike existing studies, this work considers both performance optimization and system constraints of the redundant manipulator, and can achieve high-precision prediction and tracking by designing an online training algorithm for the cerebellum model. Furthermore, this article proposes an ND-based correction algorithm to modify the prediction model and an ND solver to solve the MPC scheme. Theoretical analyses confirm the convergence of both the ND-based correction algorithm and ND solver. Simulation and experimental results consistently demonstrate that the proposed cerebellum-inspired MPC (CIMPC) algorithm is effective and outperforms comparison algorithms in terms of tracking performance.

Nowadays, many dynamic systems show the characteristics of multiple-input-multiple-output and nonlinearity, which usually involve the solution of time-dependent nonlinear equations. This article focuses on the accurate solution of time-dependent nonlinear equations with periodic noises considered. In the hardware or numerical implementations of an actual system, inevitable internal disturbances or external factors caused by the changeable scene may greatly affect the accuracy of the solution. Most of the existing time-dependent anti-noise neural dynamic (ND) models can effectively suppress some constant noise, but they are not satisfactory when facing some periodic noise with high frequency. To solve this problem, a characteristics-capturing ND (CCND) model is designed, which considers the periodic noise from the perspective of harmonic expansion so as to effectively capture harmonic characteristics and eliminate them. Theoretical analysis proves the robustness of the CCND model with periodic noise disturbance considered. Moreover, numerical simulations and robotic experiments further confirm the effectiveness of the CCND model.

Autonomous vehicle control systems are unavoidably influenced by diverse noise perturbations from the unpredictable external environment and internal system. In this consideration, based on the model predictive control (MPC) strategy, a perturbation-resistant neural dynamics (PRND) controller equipped with the noise-suppression ability for the path-tracking control of autonomous vehicles is newly designed in this paper, under the framework of artificial systems, computational experiments, and parallel execution (ACP). In addition, theoretical analyses show that the proposed ACP-incorporated PRND controller can behave with exponential convergence and strong robustness under different noise scenarios. Lastly, computational experiments are conducted and parallelly executed on the CarSim-Simulink platform and E-Car physical platform to demonstrate the effectiveness and superiority of the proposed controller. Overall, this paper provides a new perspective for designing neural-dynamics-based controllers for autonomous vehicles, thereby guaranteeing reliable control performance and effectively resisting noise perturbations.

The trajectory tracking plays a vital role in unmanned driving technology. Although traditional control schemes may yield satisfactory outcomes in dealing with simple linear tasks, they may fall short when handling dynamic systems with time-varying characteristics or lack of ability to complete a given task with the disturbance of noise. Therefore, a predictive control scheme under the framework of artificial systems, computational experiments, and parallel execution (ACP) is proposed. Within the ACP framework, the scheme integrates a model predictive control (MPC) controller and a physical-informed neural network (PINN) model to tackle intricate trajectory tracking tasks effectively with noise considered. Moreover, soft constraints that can enhance model robustness and improve solution efficiency are considered in the scheme. Then, theoretical analyses on the PINN model are provided with rigorous mathematical proofs. Finally, experiments and comparisons with existing works are conducted to illustrate the effectiveness and superiority of the constructed PINN model for MPC-based trajectory tracking of vehicles.

A robust and high-precision localization algorithm is crucial for the valid running of wireless sensor networks. However, the conventional localization algorithms hardly overcome the shortcoming of low accuracy. Concerning this problem, a novel coevolutionary noise-suppressing Newton method (CNSNM) is proposed, which combines the advantages of the noise-suppressing Newton method (NSNM) and particle swarm optimization (PSO) algorithm to enhance its accuracy and robustness. Specifically, the PSO algorithm conducts a global search using all beacon nodes, and the NSNM carries out local searches with different beacon node groupings, respectively. When the two algorithms get their solutions, NSNM utilizes the solution of the PSO algorithm to correct its search starting point and distance errors. Simultaneously, the PSO algorithm uses the best solution of NSNM to correct its distance errors, and then the next search begins until the termination conditions are satisfied. Simulation results show that the proposed method has the advantages of high accuracy and noise-suppressing performance. Further, since the CNSNM does not require the sensor to be equipped with specific measuring components, does not depend on GPS signals, and has low-computational burden, it possesses potential in the generalized application of real-world scenarios.

In combination with a robotic arm, a mobile platform can fulfill complicated functions but may encounter singularity problems. To solve them, this work investigates the kinematic control of a mobile robotic arm (MRA) that is based on a redundant arm mounted on a mobile robot. Specifically, a quadratic program-based biobjective (QPBO) scheme is proposed, which integrates manipulability optimization and the minimum velocity infinite-norm. Accordingly, a modified neural dynamics (MND) is founded with its stability substantiated by Lyapunov stability analyses theoretically. Furthermore, simulations, comparisons, and experiments are carried out on an MRA, from which the feasibility, effectiveness, and superiority of QPBO are validated. In conclusion, the proposed scheme is of great significance for enhancing the manipulability of MRA while reducing the maximum joint velocity.

An unqualified six-cylinder heavy truck crankshaft has been studied to investigate the cause of magnetic particle testing defects on the rod journals. Large-sized long-striped MnS inclusions are regarded as the major cause for the magnetic particle testing failure because they have been detected in situ under the magnetic particle indications. Through the observation of macroscopic structures of the rod journals and corresponding counterweight blocks, it is found that for the 1# and 3# rod journals, the center metal of the original hot-rolled bar has been extruded to the inboard edge of the rod journals and large-sized long-striped MnS inclusions are exposed on the surface after flash removal, leading to the failure of magnetic particle testing. As for the 2# rod journal, the center metal of the original bar has not been extruded to the surface and MnS inclusions on the rod journal surface are small in size, few in number, resulting in passing the magnetic particle testing. If the quality of the hot-rolled bars fluctuates, it is more recommended to apply magnetic particle testing on samples at the center of bars before forging to evaluate the severity of defects caused by the long-striped MnS inclusions for fear of the scrap of the final crankshafts.

Nonlinear dynamics in semiconductor lasers have been realized by external optical injection, optical feedback, optoelectronic feedback, and mutual coupling. In this article, an experimental demonstration of nonlinear dynamics in a solitary whispering-gallery mode microcavity laser is achieved based on the internal mode interaction. A natural hybrid quadrilateral mode exists in a circular-sided octagonal microcavity, which is coupled by two independent orbital modes formed by the sub-adjacent total reflection. By designing a spatially selective injection area along the field distribution of one quadrilateral mode, the hybrid quadrilateral mode is decoupled into two quadrilateral modes. The frequency offset between two decoupled modes is tuned through the non-uniform injection current. The interaction between the decoupled modes in the isolated-selectively-injected microlaser facilitates abundant nonlinear dynamics including mode locking, four-wave mixing, period-two oscillation and weak chaotic state. Our scheme provides a method for self-generated nonlinear dynamics, and paves the way for mode engineering of tailoring lasing actions in microcavity lasers.