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In this article, the adaptive neural backstepping control approaches are designed for uncertain stochastic nonlinear systems with full-state constraints. According to the symmetry of constraint boundary, two cases of controlled systems subject to symmetric and asymmetric constraints are studied, respectively. Then, corresponding adaptive neural controllers are developed by virtue of backstepping design procedure and the learning ability of radial basis function neural network (RBFNN). It is worth mentioning that the integral Barrier Lyapunov function (IBLF), as an effective tool, is first applied to solve the above constraint problems. As a result, the state constraints are avoided from being transformed into error constraints via the proposed schemes. In addition, based on Lyapunov stability analysis, it is demonstrated that the errors can converge to a small neighborhood of zero, the full states do not exceed the given constraint bounds, and all signals in the closed-loop systems are semiglobally uniformly ultimately bounded (SGUUB) in probability. Finally, the numerical simulation results are provided to exhibit the effectiveness of the proposed control approaches.

A reliable, efficient, and simple control is presented and validated for a quarter-car active suspension system equipped with an electro-hydraulic actuator. Unlike the existing techniques, this control does not use any function approximation, e.g., neural networks (NNs) or fuzzy-logic systems (FLSs), while the unmolded dynamics, including the hydraulic actuator behavior, can be accommodated effectively. Hence, the heavy computational costs and tedious parameter tuning phase can be remedied. Moreover, both the transient and steady-state suspension performance can be retained by incorporating prescribed performance functions (PPFs) into the control implementation. This guaranteed performance is particularly useful for guaranteeing the safe operation of suspension systems. Apart from theoretical studies, some practical considerations of control implementation and several parameter tuning guidelines are suggested. Experimental results based on a practical quarter-car active suspension test-rig demonstrate that this control can obtain a superior performance and has better computational efficiency over several other control methods.

Xishuangbanna (XIS) cucumber (Cucumis sativus L. var. xishuangbannanesis) is a semiwild variety originated from low latitude tropic area, and therefore show extreme cold sensitivity and heat tolerance. Here, we mapped the quantitative trait loci (QTLs) that control cold-sensitivity and heat-tolerance of XIS cucumber seedlings. By bulked-segregant analysis (BSA), we identified three QTLs (HTT1.1, HTT3.1 and HTT3.2, in a total length of 11.98 Mb) for heat tolerance and two QTLs (LTT6.1 and LTT6.2, in a total length of 8.74 Mb) for cold sensitivity. The QTL LTT6.1 was then narrowed down to a length of 641 kb. Based on structural variants (SVs) and single nucleotide polymorphism (SNP), we found the LTT6.1 is covered by a high divergent region including a 50-kb deletion in XIS49 genome, which affect the gene structure of lipase ABHD6 (Csa_6G032560). Accordingly, there is a very big difference in lipid composition but not other osmoprotectants like free amino acids and fatty acids between XIS49 and cultivated cucumber CL. Moreover, we calculated the composite likelihood ratio (CLR) and identified selective sweeps from 115 resequencing data, and found that lipid- and fatty acid-related processes is a major aspect of the domestication of XIS group cucumber. LTT6.1 is such a special region positioned nearby lipid-related selective sweeps. These studies above suggested that lipids-related domestication of XIS cucumbers should account for their extreme cold sensitivity.

IntroductionViral diseases have always been intricate and persistent issues throughout the world and there is a lack of holistic discoveries regarding the molecular dysregulations of virus-host interactions. The temporal proteomics strategy can identify various differentially expressed proteins and offer collaborated interaction networks under pathological conditions.MethodHerein, temporal proteomics at various hours post infection of Vero cells were launched to uncover molecular alternations during vaccinia virus (VACV)-induced cell migration. Different stages of infection were included to differentiate gene ontologies and critical pathways at specific time points of infection via bioinformatics.ResultsBioinformatic results showed functional and distinct ontologies and pathways at different stages of virus infection. The enrichment of interaction networks and pathways verified the significances of the regulation of actin cytoskeleton and lamellipodia during VACV-induced fast cell motility.DiscussionThe current results offer a systematic proteomic profiling of molecular dysregulations at different stages of VACV infection and potential biomedical targets for treating viral diseases.

Self-assembly formation of nanoparticles (NPs) is a central theme in nanotechnology that has garnered significant attention in both academia and industry. The size of self-assembled NPs plays crucial role in their physicochemical properties. It has long been known that when macromolecules in a solvent being exposed to an anti-solvent, they can spontaneously form NPs of varying sizes through static diffusion (mixing rate = 0) and dynamic convection (mixing rate > 0). However, the impact of solvent mixing rate on the size of self-assembled NPs remains a mystery. Here, for the first time, we mathematically and experimentally prove that Reynolds number (Re), which quantifies fluid turbulence, is decisive on the self-assembly formation of NPs, by exponentially influencing the way kinetic energy of precursor molecules converting into surface energy of subsequent NPs. We surprisingly find that various self-assembly systems share a very similar critical value of Re about 1,000 ~ 1,200, which predetermines whether self-assembly occurs following a low or high energy dissipation pathway. This new framework further enables us to quantitatively determine energy conversion efficiency of a self-assembly process, measure surface tension of NP in a complex system, and predict NP size at arbitrary Re (including 0), which cannot be achieved in the past due to the limits of technology.

Leukocyte cell-derived chemotaxin-2 (LECT2, also named ChM-II), initially identified as a chemokine mediating neutrophil migration, is a multifunctional secreted factor involved in diverse physiological and pathological processes. The high sequence similarity of LECT2 among different vertebrates makes it possible to explore its functions by using comparative biology. LECT2 is associated with many immune processes and immune-related diseases via its binding to cell surface receptors such as CD209a, Tie1, and Met in various cell types. In addition, the misfolding LECT2 leads to the amyloidosis of several crucial tissues (kidney, liver, and lung, etc.) by inducing the formation of insoluble fibrils. However, the mechanisms of LECT2-mediated diverse immune pathogenic conditions in various tissues remain to be fully elucidated due to the functional and signaling heterogeneity. Here, we provide a comprehensive summary of the structure, the “double-edged sword” function, and the extensive signaling pathways of LECT2 in immune diseases, as well as the potential applications of LECT2 in therapeutic interventions in preclinical or clinical trials. This review provides an integrated perspective on the current understanding of how LECT2 is associated with immune diseases, with the aim of facilitating the development of drugs or probes against LECT2 for the theranostics of immune-related diseases.

Background Unresolved taxonomic classification and paraphyly pervade the flatworm class Monogenea: the class itself may be paraphyletic and split into Polyopisthocotylea and Monopisthocotylea; there are some indications that the monopisthocotylean order Dactylogyridea may also be paraphyletic; single-gene markers and some morphological traits indicate that the family Ancyrocephalidae is paraphyletic and intertwined with the family Dactylogyridae. Methods To attempt to study the relationships of Ancyrocephalidae and Monopisthocotylea using a phylogenetic marker with high resolution, we sequenced mitochondrial genomes of two fish ectoparasites from the family Dactylogyridae: Dactylogyrus simplex and Dactylogyrus tuba. We conducted phylogenetic analyses using three datasets and three methods. Datasets were ITS1 (nuclear) and nucleotide and amino acid sequences of almost complete mitogenomes of almost all available Monopisthocotylea mitogenomes. Methods were maximum likelihood (IQ-TREE), Bayesian inference (MrBayes) and CAT-GTR (PhyloBayes). Results Both mitogenomes exhibited the ancestral gene order for Neodermata, and both were compact, with few and small intergenic regions and many and large overlaps. Gene sequences were remarkably divergent for nominally congeneric species, with only trnI exhibiting an identity value > 80%. Both mitogenomes had exceptionally low A + T base content and AT skews. We found evidence of pervasive compositional heterogeneity in the dataset and indications that base composition biases cause phylogenetic artefacts. All six mitogenomic analyses produced unique topologies, but all nine analyses produced topologies that rendered Ancyrocephalidae deeply paraphyletic. Mitogenomic data consistently resolved the order Capsalidea as nested within the Dactylogyridea. Conclusions The analyses indicate that taxonomic revisions are needed for multiple Polyopisthocotylea lineages, from genera to orders. In combination with previous findings, these results offer conclusive evidence that Ancyrocephalidae is a paraphyletic taxon. The most parsimonious solution to resolve this is to create a catch-all Dactylogyridae sensu lato clade comprising the current Ancyrocephalidae, Ancylodiscoididae, Pseudodactylogyridae and Dactylogyridae families, but the revision needs to be confirmed by another marker with a sufficient resolution. Graphical Abstract

This paper concentrates upon the event-driven controller design problem for a class of nonlinear single input single output (SISO) parametric systems with full state constraints. A varying threshold for the triggering mechanism is exploited, which makes the communication more flexible. Moreover, from the viewpoint of energy conservation and consumption reduction, the system capability becomes better owing to the contribution of the proposed event triggered mechanism. In the meantime, the developed control strategy can avoid the Zeno behavior since the lower bound of the sample time is provided. The considered plant is in a lower-triangular form, in which the match condition is not satisfied. To ensure that all the states to retain in a predefined region, a barrier Lyapunov function (BLF) based adaptive control law is developed. Due to the existence of the parametric uncertainties, an adaptive algorithm is presented as an estimated tool. All the signals appearing in the closed-loop systems are then proven to be uniformly ultimately bounded (UUB). Meanwhile, the output of the system can track a given signal as far as possible. In the end, the effectiveness of the proposed approach is validated by an aircraft wing rock motion system.

This paper develops an adaptive output feedback control for a class of functional constraint systems with unmeasurable states and unknown dead zone input. The constraint is a series of functions closely linked to state variables and time, which is not achieved in current research results and is more general in practical systems. Furthermore, a fuzzy approximator based adaptive backstepping algorithm is designed and an adaptive state observer with time-varying functional constraints (TFC) is constructed to estimate the unmeasurable states of the control system. Relying on the relevant knowledge of dead zone slopes, the issue of non-smooth dead-zone input is successfully solved. The time-varying integral barrier Lyapunov functions (iBLFs) are employed to guarantee that the states of the system remain within the constraint interval. By Lyapunov stability theory, the adopted control approach can ensure the stability of the system. Finally, the feasibility of the considered method is conformed via a simulation experiment.

In this article, an integral barrier Lyapunov-function (IBLF)-based adaptive tracking controller is proposed for a class of switched nonlinear systems under the arbitrary switching rule, in which the unknown terms are approximated by radial basis function neural networks (RBFNNs). The IBLF method is used to solve the problem of state constraint. This method constrains states directly and avoids the verification of feasibility conditions. In addition, a completely unknown control gain is considered, which makes it impossible to directly apply previous existing methods. To offset the effect of the unknown control gain, the lower bound of the control gain is added into the barrier Lyapunov function, and a regulating term is introduced into the controller. The proposed control strategy realizes three control objectives: 1) all the signals in the resulting system are bounded; 2) the system output tracks the reference signal to a arbitrarily small compact set; and 3) all the constraint conditions for system states are not violated. Finally, a simulation example is used to show the effectiveness of the proposed method.

In this paper, the formation control problem of PDE-based multi-agent systems (MASs) is discussed. Firstly, the MASs are developed on a one-dimensional chain topology based on the polar coordinate system, and the dynamics of MASs is simulated using the spatial-varying coefficient wave equation. Secondly, a boundary control scheme is proposed by combining PDE-backstepping technique and the Volterra integral transformation. The well-posedness of kernel function is proved by using the iterative and inductive methods. Then, the stability of the closed-loop system is proved by using Lyapunov direct method. Finally, the PDE model is discretized using the finite difference method, and the distributed cooperative control protocol is obtained, in which the followers only need to know the location information of themselves and their neighbors. With this control protocol, leaders drive the MAS to stabilize in the desired formation. Both theoretical analysis and numerical simulation prove that the proposed control scheme is effective.

Spatially resolved profiling of miRNAs was realized in migrating cells using enzyme-free signal-amplification nanoprobes, in which distinct migration modes of single living cells are driven by precisely engineered microchips.

The leader‐following consensus problem of a class of non‐strict feedback multi‐agent systems with unknown disturbances, this paper develops a novel adaptive event‐triggered control strategy ground on the consideration of the full state constraints. Combining the dynamic surface control and event triggering mechanism, the unknown external disturbances are estimated by designing a disturbance observer. In order to deal with unknown functions, this paper employs the neural networks. On purpose of keeping the output variable within a constraint boundary, the barrier Lyapunov function is chosen during the design course. Then, through the backstepping method and the Lyapunov stability theorem, it is proved that all signals in the closed‐loop systems are bounded, and the control performance of the closed‐loop system is ensured by selecting design parameters reasonably. Eventually, the availability of the control strategy is guaranteed through experimental example.

In this article, the adaptive sliding mode (ASM) control scheme of half-car active suspension systems with prescribed performance is studied. Because of the affected by model uncertainty, time-varying parameter, pavement roughness excitation, etc., the study of suspension systems can be regarded as the multivariable nonlinear control problem. First of all, the prescribed performance function (PPF) is applied to constrain the displacement and pitch angle of the suspension systems to ensure the transient and steady-state suspension responses. Second, an integral terminal sliding mode control method with strong robustness is put forward, which can make the system converge rapidly in a finite-time when it is far from the equilibrium point, solve the singularity problem in the control process, and reduce the chattering phenomenon in the traditional sliding mode control. Then, the neural networks (NNs) approximation characteristics are used to deal with unknown items in the design of the controller, and the Lyapunov stability theory is employed to analyze the stability of the closed-loop system. In the end, the comparative simulation results demonstrate the feasibility and effectiveness of the proposed control scheme.

The conventional machine learning algorithm for analyzing ultrasonic signals to detect structural defects necessarily identifies and extracts either time- or frequency-domain features manually, which has problems in reliability and effectiveness. This work proposes a novel approach by combining convolutional neural networks (CNNs) and wavelet transform to analyze the laser-generated ultrasonic signals for detecting the width of subsurface defects accurately. The novelty of this work is to convert the laser ultrasonic signals into the scalograms (images) via wavelet transform, which are subsequently utilized as the image input for the pretrained CNN to extract the defect features automatically to quantify the width of defects, avoiding the necessity and inaccuracy induced by artificial feature selection. The experimentally validated numerical model that simulates the interaction of laser-generated ultrasonic waves with subsurface defects is first established, which is further utilized to generate adequate laser ultrasonic signals for training the CNN model. A total number of 3104 data are obtained from simulation and experiments, with 2480 simulated signals for training the CNN model and the remaining 620 simulated data together with 4 experimental signals for verifying the performance of the proposed algorithm. This approach achieves the prediction accuracy of 98.5% on validation set, particularly with the prediction accuracy of 100% for the four experimental data. This work proves the feasibility and reliability of the proposed method for quantifying the width of subsurface defects and can be further expanded as a universal approach to various other defects detection, such as defect locations and shapes.

Aiming at a class of switched uncertain nonlinear strict-feedback systems under the action of average dwell time switching signal, this paper proposes a novel adaptive neural network output feedback tracking control based on the consideration of the full state constraints. The controller is proposed based on neural networks. One of the key characteristics of the system discussed is that the state variables cannot be measured and the system states need to be kept within the constraint ranges. For the sake of estimating the unmeasured states, the observer is constructed. In order to ensure all states which are within the time-varying boundary, the tangent barrier Lyapunov function (BLF-Tan) is selected in the design process. The boundedness of the closed-loop signals with average dwell time is guaranteed by the designed controllers and all the states limit in their constrained sets. It has been proved that the output tracking error converge to a small neighborhood of zero. In addition, the significance of the presented control strategy is verified and tested by a simulation example.

Rheumatoid arthritis (RA) is characterized by the chronic synovial inflammation and bone destruction, which eventually cause joint deformity along with physical disability. Aberrant autoimmune T cell activation underpins RA pathogenesis, but the molecular cues that boost autoimmune T cell program remain elusive. Herein, we report that protein disulfide-isomerase A3 (PDIA3) serves as a critical intrinsic regulator to orchestrate Th1 and Th17 differentiation in RA setting. PDIA3 was remarkably upregulated in CD4 T cells in arthritic mice and positively correlated with key clinic parameters including C-reactive protein (CRP) and Disease Activity Scores 28 (DAS28) in RA patients. Depletion of Pdia3 in CD4 T cells protected mice against RA induction, while CD4 T cells deficient in Pdia3 were featured by the attenuated Th1 and Th17 polarization. Mechanistically, synovial fibroblasts (SFs) derived Wnt5a acts on CD4 T cells to enhance NFAT activity; then, NFAT directly binds to the Pdia3 promoter to facilitate its transcription. Intriguingly, PDIA3 did not exert its function in the endoplasmic reticulum (ER) of CD4 T cells, rather bolstered Th1 and Th17 program mainly through forming a complex with either STAT1 or PKM2 in the nucleus, by which it acted as a transcription coactivator. In accordance, pharmacological inhibition of PDIA3 attenuated collagen-induced arthritis in mice. Collectively, our data unveiled a positive feedback loop in the synovial niche where highly expressed PDIA3 promotes pathogenic CD4 T cell polarization during RA pathogenesis, and targeting PDIA3 to block the crosstalk between SFs and CD4 T cells could be a viable strategy against RA.

Breast cancer (BC) is a kind of malignant cancer in women, and it has become the most diagnosed cancer worldwide since 2020. Histone methylation is a common biological epigenetic modification mediating varieties of physiological and pathological processes. Lysine-specific demethylase 1 (LSD1), a first identified histone demethylase, mediates the removal of methyl groups from histones H3K4me1/2 and H3K9me1/2 and plays a crucial role in varieties of cancer progression. It is also specifically amplified in breast cancer and contributes to BC tumorigenesis and drug resistance via both demethylase and non-demethylase manners. This review will provide insight into the overview structure of LSD1, summarize its action mechanisms in BC, describe the therapeutic potential of LSD1 inhibitors in BC, and prospect the current opportunities and challenges of targeting LSD1 for BC therapy.

A multi-point flexible straightening process characterized by reciprocating bending is proposed. Specifically, the process is analyzed in terms of deformation mechanism and verified by numerical simulations and physical experiments of the straightening of a series of metal profiles with different materials and initial shapes. Further, the relationship between the bending radius and the times of reciprocating bending required to unify the curvature is discussed, and the distribution of residual stress after straightening is analyzed. The results show that the reciprocating bending process can eliminate the difference of the initial curvature, make the curvature of each section tend to be uniform; the times of reciprocating bending to reach the uniform curvature decreases with the decrease of bending radius. The straightness of the straightened profile obtained from the experiment and simulation is less than 0.2%, demonstrating a good feasibility of this method.

In this paper, an adaptive neural network (NN) control scheme is developed for a class of stochastic nonlinear systems with time-varying full state constraints. In the controller design, RBF NNs are employed to approximate the unknown terms, and the backtracking technique is introduced to overcome the restriction of matching conditions. At the same time, tangent type time-varying barrier Lyapunov functions (tan-TVBLFs) are constructed to ensure the full state constraints are never violated, where tan-TVBLFs are beneficial to integrate constraint analysis into a common method. Furthermore, the Lyapunov stability theory is used to prove that all closed-loop signals are semiglobal uniformly ultimately bounded in probability and error signals remain in the compact set do not violate the time-varying constraints. A simulation example will be used to exhibit the effectiveness of the proposed control scheme.

In this article, an adaptive neural network (NN) decentralized output-feedback control design is studied for the uncertain strict-feedback large-scale interconnected nonlinear systems with nonconstant virtual and control gains. NNs are utilized to approximate the unknown nonlinear functions, and the immeasurable states are estimated via designing an NN decentralized state observer. By constructing the logarithm Lyapunov functions, an observer-based NN adaptive decentralized backstepping output-feedback control is developed in the framework of the decentralized backstepping control. The proposed adaptive decentralized backstepping output-feedback control can make that the closed-loop system is semiglobally uniformly ultimately bounded (SGUUB) and that the tracking and observer errors converge to a small neighborhood of the origin. The most important contribution of this article is that it removes the restrictive assumption in the existing results that both virtual and control gain functions in each subsystem must be constants. A numerical simulation example is provided to validate the effectiveness of the proposed control method and theory.

We demonstrate a simple, cost-effective method to enhance the photoluminescence intensity of monolayer MoS2. A hexagonal symmetric Au metasurface, made by polystyrene nanosphere lithography and metal coating, is developed to enhance the photoluminescence intensity of monolayer MoS2. By using nanospheres of different sizes, the localized surface plasmon resonances of the Au metasurfaces can be effectively tuned. By transferring monolayer MoS2 onto the Au metasurface, the photoluminescence signal of the monolayer MoS2 can be significantly enhanced up to 12-fold over a square-centimeter area. The simple, large-area, cost-effective fabrication technique could pave a new way for plasmon-enhanced light-mater interactions of atomically thin two-dimensional materials.

This article presents an adaptive output feedback approach of nonlinear multi-input-multi-output (MIMO) systems with time-varying state constraints and unmeasured states. An adaptive approximator is designed to approximate the unknown nonlinear functions existing in the state-constrained systems with immeasurable states. To deal with the tracking problem of such systems, a state observer with time-varying barrier Lyapunov functions (BLFs) is introduced in the controller design procedure. The backstepping design with time-varying BLFs is utilized to guarantee that all system states remain within the time-varying-constrained interval. The constant constraint is only the special case of the time-varying constraint which is more general in the real systems. The proposed control approach guarantees that all signals in the closed-loop systems are bounded and the tracking errors converge to a bounded compact set, and time-varying full-state constraints are never violated. A simulation example is given to confirm the feasibility of the presented control approach in this article.

Osteosarcoma is one of the most frequent primary sarcoma of bone among adolescents. Early diagnosis of osteosarcoma is the key factor to achieve high survival rate of patients. Nevertheless, traditional histological biopsy is highly invasive and associated with the risk of arousing tumor spread. Herein, we develop a method integrating microfluidics and surface-enhanced Raman spectroscopy (SERS) to isolate plasma-derived exosomes and profile multiple exosomal biomarkers for the diagnosis of osteosarcoma. The method showed highly efficient isolation of exosomes directly from human plasma and can profile exosomes based on protein biomarkers, with the detection limit down to 2 exosomes per μL. The whole assay can be performed in 5 h and only consumed 50 μL of plasma for one analysis. With the method, we analyzed the level of three protein biomarkers, i.e., CD63, vimentin (VIM) and epithelial cell adhesion molecule (EpCAM), on plasma-derived exosomes from 20 osteosarcoma patients and 20 heathy controls. Significantly higher levels of CD63, VIM and EpCAM were observed on plasma exosomes from the osteosarcoma patients compared to the healthy controls. Based on the level of the exosomal biomarkers, a classification model was built for the rapid diagnosis of osteosarcoma, with the sensitivity, specificity and accuracy of 100%, 90% and 95%, respectively. The proposed method does not require complex operations nor expensive equipment, and has great promise in clinical diagnosis of cancer as a liquid biopsy technique.

Breast cancer (BC) is a common malignancy that mainly occurred in women and it has become the most diagnosed cancer annually since 2020. Berberine (BBR), an alkaloid extracted from the Berberidacea family, has been found with broad pharmacological bioactivities including anti-inflammatory, anti-diabetic, anti-hypertensive, anti-obesity, antidepressant, and anticancer effects. Mounting evidence shows that BBR is a safe and effective agent with good anticancer activity against BC. However, its detailed underlying mechanism in BC treatment remains unclear. Here, we will provide the evidence for BBR in BC therapy and summarize its potential mechanisms. This review briefly introduces the source, metabolism, and biological function of BBR and emphasizes the therapeutic effects of BBR against BC via directly interacting with effector proteins, transcriptional regulatory elements, miRNA, and several BBR-mediated signaling pathways. Moreover, the novel BBR-based therapeutic strategies against BC improve biocompatibility and water solubility, and the efficacies of BBR are also briefly discussed. Finally, the status of BBR in BC treatment and future research directions is also prospected.