Gelatin-mediated gallium-doped SrHA composite coatings with sequential antimicrobial and osteogenic functions for infected bone defect repair.

In this study, gallium- and gelatin-modified strontium-doped hydroxyapatite (SrHA-Gel-Ga) bilayer coatings were prepared on titanium substrates by electrodeposition and spin-coating techniques. The results showed that gallium and gelatin were uniformly doped into the SrHA coatings, which exhibited good hydrophilicity and bioactivity. Furthermore, SrHA-Gel-Ga demonstrated good antimicrobial properties against E. coli and S. aureus, especially S. aureus. The co-doping of Sr and gelatin in the coatings was effective in mitigating the cytotoxicity of Ga. SrHA-Gel-Ga was better able to promote the adhesion, proliferation and early differentiation of MC3T3-E1 cells. This study provides a new strategy for the development of anti-infective bone repair coatings.

An Overview of Image Generation of Industrial Surface Defects.

Intelligent defect detection technology combined with deep learning has gained widespread attention in recent years. However, the small number, and diverse and random nature, of defects on industrial surfaces pose a significant challenge to deep learning-based methods. Generating defect images can effectively solve this problem. This paper investigates and summarises traditional defect generation and deep learning-based methods. It analyses the various advantages and disadvantages of these methods and establishes a benchmark through classical adversarial networks and diffusion models. The performance of these methods in generating defect images is analysed through various indices. This paper discusses the existing methods, highlights the shortcomings and challenges in the field of defect image generation, and proposes future research directions. Finally, the paper concludes with a summary.

ADS-HCSpark: A scalable HaplotypeCaller leveraging adaptive data segmentation to accelerate variant calling on Spark.

BACKGROUND: The advance of next generation sequencing enables higher throughput with lower price, and as the basic of high-throughput sequencing data analysis, variant calling is widely used in disease research, clinical treatment and medicine research. However, current mainstream variant caller tools have a serious problem of computation bottlenecks, resulting in some long tail tasks when performing on large datasets. This prevents high scalability on clusters of multi-node and multi-core, and leads to long runtime and inefficient usage of computing resources. Thus, a high scalable tool which could run in distributed environment will be highly useful to accelerate variant calling on large scale genome data. RESULTS: In this paper, we present ADS-HCSpark, a scalable tool for variant calling based on Apache Spark framework. ADS-HCSpark accelerates the process of variant calling by implementing the parallelization of mainstream GATK HaplotypeCaller algorithm on multi-core and multi-node. Aiming at solving the problem of computation skew in HaplotypeCaller, a parallel strategy of adaptive data segmentation is proposed and a variant calling algorithm based on adaptive data segmentation is implemented, which achieves good scalability on both single-node and multi-node. For the requirement that adjacent data blocks should have overlapped boundaries, Hadoop-BAM library is customized to implement partitioning BAM file into overlapped blocks, further improving the accuracy of variant calling. CONCLUSIONS: ADS-HCSpark is a scalable tool to achieve variant calling based on Apache Spark framework, implementing the parallelization of GATK HaplotypeCaller algorithm. ADS-HCSpark is evaluated on our cluster and in the case of best performance that could be achieved in this experimental platform, ADS-HCSpark is 74% faster than GATK3.8 HaplotypeCaller on single-node experiments, 57% faster than GATK4.0 HaplotypeCallerSpark and 27% faster than SparkGA on multi-node experiments, with better scalability and the accuracy of over 99%. The source code of ADS-HCSpark is publicly available at https://github.com/SCUT-CCNL/ADS-HCSpark.git .

Super-resolution reconstruction of speckle phase in depth-resolved wavelength scanning interference using the total least-squares analysis.

Depth-resolved wavelength scanning interferometry (DRWSI) is a tomographic imaging tool that employs phase measurement to visualize micro-displacement inside a sample. It is well known that the depth resolution of DRWSI is restricted by a wavelength scanning range. Recently, a nonlinear least-squares analysis (NLS) algorithm was proposed to overcome the limitation of the wavelength scanning range to achieve super-resolution; however, the NLS failed to measure speckle surfaces owing to the sensibility of initial values. To the best of our knowledge, the improvement of depth resolution on measuring a speckle surface remains an open issue for DRWSI. For this study, we redesigned the signal processing algorithm for DRWSI to refine the depth resolution when considering the case of speckle phase measurement. It is mathematically shown that the DRWSI's signal is derived as a model of total least-squares analysis (TLSA). Subsequently, a super-resolution of the speckle phase map was obtained using a singular value decomposition. Further, a numerical simulation to measure the micro-displacements for speckle surfaces was performed to validate the TLSA, and the results show that it can precisely reconstruct the displacements of layers whose depth distance is 5 µm. This study thus provides an opportunity to improve the DRWSI's depth resolution.

Anticancer Activity of Anthopleura anjunae Oligopeptides in Prostate Cancer DU-145 Cells.

Anthopleura anjunae anti-tumor peptide (AAP-H) is a pentapeptide from the sea anemone Anthopleura anjunae with an amino acid sequence of Tyr-Val-Pro-Gly-Pro that is obtained by alkaline protease enzymatic hydrolysis extraction. In this study, we investigated the inhibitory effects of AAP-H on prostate cancer DU-145 cell proliferation using a methylthiazolyldiphenyl-tetrazolium bromide assay. Cell morphology was analyzed by hematoxylin-eosin staining, acridine orange/ethidium bromide fluorescence staining, Hoechst 33258 fluorescence staining, and scanning electron microscopy. The mitochondrial membrane potential was determined by flow cytometry following JC-1 staining. The cell apoptosis rate was measured by Annexin V-fluorescein isothiocyanate and propidium iodide staining followed by flow cytometric analysis, and the expression of apoptosis-associated proteins was assayed by Western blotting. The results demonstrated that AAP-H induced significant reductions in the number of viable cells and increased cell death in both a dose-dependent and time-dependent manner, with an IC50 of approximately 9.605 mM, 7.910 mM, and 2.298 mM at 24 h, 48 h, and 72 h, respectively. The morphologic characteristics of apoptotic cells were observed after treatment with AAP-H. The mitochondrial membrane potential was markedly decreased, and apoptosis increased after AAP-H treatment. Pro-apoptotic proteins, such as Bax, cytochrome-C, caspase-3, and caspase-9 were increased, but Bcl-2 was decreased. These findings suggest that AAP-H has moderate inhibitory effects on prostate cancer DU-145 cells, and the mechanism might involve the mitochondria-mediated apoptotic pathway. Therefore, AAP-H is a candidate anti-prostate cancer drug or health-care food.

Low-Complexity High-Order Propagator Method for Near-Field Source Localization.

In this paper, an efficient high-order propagator method is proposed to localize near-field sources. We construct a specific non-Hermitian matrix based on the high-order cumulant of the received signals. With its columns and rows, we can obtain two subspaces orthogonal to all the columns of two steering matrices, respectively, with which the estimation of the directions of arrival (DOA) and ranges of near-field sources can be achieved. Different from other methods, the proposed method needs only one matrix for estimating two parameters separately, therefore leading to a smaller computational burden. Simulation results show that the proposed method achieves the same performance as the other high order statistics-based methods with a lower complexity.

Adaptive Critic Learning-Based Optimal Bipartite Consensus for Multiagent Systems With Prescribed Performance.

Developing a distributed bipartite optimal consensus scheme while ensuring user-predefined performance is essential in practical applications. Existing approaches to this problem typically require a complex controller structure due to adopting an identifier-actor-critic framework and prescribed performance cannot be guaranteed. In this work, an adaptive critic learning (ACL)-based optimal bipartite consensus scheme is developed to bridge the gap. A newly designed error scaling function, which defines the user-predefined settling time and steady accuracy without relying on the initial conditions, is then integrated into a cost function. The backstepping framework combines the ACL and integral reinforcement learning (IRL) algorithm to develop the adaptive optimal bipartite consensus scheme, which contributes a critic-only controller structure by removing the identifier and actor networks in the existing methods. The adaptive law of the critic network is derived by the gradient descent algorithm and experience replay to minimize the IRL-based residual error. It is shown that a compute-saving learning mechanism can achieve the optimal consensus, and the error variables of the closed-loop system are uniformly ultimately bounded (UUB). Besides, in any bounded initial condition, the evolution of bipartite consensus is limited to a user-prescribed boundary under bounded initial conditions. The illustrative simulation results validate the efficacy of the approach.

Variational Distillation for Multi-View Learning.

Information Bottleneck (IB) provides an information-theoretic principle for multi-view learning by revealing the various components contained in each viewpoint. This highlights the necessity to capture their distinct roles to achieve view-invariance and predictive representations but remains under-explored due to the technical intractability of modeling and organizing innumerable mutual information (MI) terms. Recent studies show that sufficiency and consistency play such key roles in multi-view representation learning, and could be preserved via a variational distillation framework. But when it generalizes to arbitrary viewpoints, such strategy fails as the mutual information terms of consistency become complicated. This paper presents Multi-View Variational Distillation (MV 2D), tackling the above limitations for generalized multi-view learning. Uniquely, MV 2D can recognize useful consistent information and prioritize diverse components by their generalization ability. This guides an analytical and scalable solution to achieving both sufficiency and consistency. Additionally, by rigorously reformulating the IB objective, MV 2D tackles the difficulties in MI optimization and fully realizes the theoretical advantages of the information bottleneck principle. We extensively evaluate our model on diverse tasks to verify its effectiveness, where the considerable gains provide key insights into achieving generalized multi-view representations under a rigorous information-theoretic principle.

IFKMHC: Implicit Fuzzy K-Means Model for High-Dimensional Data Clustering.

The graph-information-based fuzzy clustering has shown promising results in various datasets. However, its performance is hindered when dealing with high-dimensional data due to challenges related to redundant information and sensitivity to the similarity matrix design. To address these limitations, this article proposes an implicit fuzzy k-means (FKMs) model that enhances graph-based fuzzy clustering for high-dimensional data. Instead of explicitly designing a similarity matrix, our approach leverages the fuzzy partition result obtained from the implicit FKMs model to generate an effective similarity matrix. We employ a projection-based technique to handle redundant information, eliminating the need for specific feature extraction methods. By formulating the fuzzy clustering model solely based on the similarity matrix derived from the membership matrix, we mitigate issues, such as dependence on initial values and random fluctuations in clustering results. This innovative approach significantly improves the competitiveness of graph-enhanced fuzzy clustering for high-dimensional data. We present an efficient iterative optimization algorithm for our model and demonstrate its effectiveness through theoretical analysis and experimental comparisons with other state-of-the-art methods, showcasing its superior performance.

A versatile and practical method for regioselective synthesis of polysubstituted furanonaphthoquinones.

An efficient and attractive synthesis of a series of novel poly-functionalized phosphorus zwitterions was achieved via three-component reactions of the corresponding functional nucleophiles, aldehydes, and Bu(3)P in the presence of acidic promoter. These polysubstituted zwitterions could regioselectively undergo further transformations to synthetically important furanonaphthoquinones and related products via the intramolecular Wittig reaction. These methods could have potential application in synthetic and pharmaceutical chemistry for its facilitation and easily accessible commercial materials.

Robust H∞ Control for Semilinear Parabolic Distributed Parameter Systems With External Disturbances via Mobile Actuators and Sensors.

This article presents a robust H∞ feedback compensator design approach for semilinear parabolic distributed parameter systems (DPSs) with external disturbances via mobile actuators and sensors. An H∞ performance constraint is introduced to deal with the external disturbances from the model and measurement noise. Two types of feedback compensators are designed in terms of the collocated and noncollocated mobile actuators and sensors. By the Lyapunov direct technique, some sufficient conditions based on LMI constraints are proposed for the exponential stability under H∞ performance constraints in the L2 -norm. Moreover, the open-loop and closed-loop well-posedness of the semilinear DPSs with external disturbances are analyzed via the C0 -semigroup theory approach. Finally, extensive numerical simulation results for semilinear DPSs with external disturbances via collocated and noncollocated mobile actuators and sensors are shown to verify the effectiveness of the proposed method.

Multistep Multiagent Reinforcement Learning for Optimal Energy Schedule Strategy of Charging Stations in Smart Grid.

An efficient energy scheduling strategy of a charging station is crucial for stabilizing the electricity market and accommodating the charging demand of electric vehicles (EVs). Most of the existing studies on energy scheduling strategies fail to coordinate the process of energy purchasing and distribution and, thus, cannot balance the energy supply and demand. Besides, the existence of multiple charging stations in a complex scenario makes it difficult to develop a unified schedule strategy for different charging stations. In order to solve these problems, we propose a multiagent reinforcement learning (MARL) method to learn the optimal energy purchasing strategy and an online heuristic dispatching scheme to develop a energy distribution strategy in this article. Unlike the traditional scheduling methods, the two proposed strategies are coordinated with each other in both temporal and spatial dimensions to develop the unified energy scheduling strategy for charging stations. Specifically, the proposed MARL method combines the multiagent deep deterministic policy gradient (MADDPG) principles for learning purchasing strategy and a long short-term memory (LSTM) neural network for predicting the charging demand of EVs. Moreover, a multistep reward function is developed to accelerate the learning process. The proposed method is verified by comprehensive simulation experiments based on real data of the electricity market in Chicago. The experiment results show that the proposed method can achieve better performance than other state-of-the-art energy scheduling methods in the charging market in terms of the economic profits and users' satisfaction ratio.

Reinforcement learning based adaptive optimal control for constrained nonlinear system via a novel state-dependent transformation.

Existing schemes for state-constrained systems either impose feasibility conditions or ignore the optimality. In this article, an adaptive optimal control scheme for the strict-feedback nonlinear system is proposed, which benefits from two design steps. Firstly, a novel nonlinear state-dependent function (NSDF) is formulated to equivalently transform the system into a non-constrained one to deal with state constraints without the requirements on feasibility conditions. Secondly, an adaptive optimal control scheme is designed for the non-constrained system, in which reinforcement learning (RL) is utilized to yield the optimal controller in each designing procedure. Updating rules of the actor and critic neural network are driven by the modified adaptive laws, used to approximate the optimal virtual and actual controllers. It is proved that all the signals in the closed-loop system are bounded and the output tracking error converges to an adjustable neighborhood of the origin not affected by the proposed NSDF. Two simulation examples are presented illustrating the effectiveness of the proposed scheme.

Perturbed Progressive Learning for Semisupervised Defect Segmentation.

Recently, with the development of intelligent manufacturing, the demand for surface defect inspection has been increasing. Deep learning has achieved promising results in defect inspection. However, due to the rareness of defect data and the difficulties of pixelwise annotation, the existing supervised defect inspection methods are too inferior to be implemented in practice. To solve the problem of defect segmentation with few labeled data, we propose a simple and efficient method for semisupervised defect segmentation (SSDS), named perturbed progressive learning (PPL). On the one hand, PPL decouples the predictions of student and teacher networks as well as alleviates overfitting on noisy pseudo-labels. On the other hand, PPL encourages consistency across various perturbations in a broader stagewise scope, alleviating drift caused by the noisy pseudo-labels. Specifically, PPL contains two training stages. In the first stage, the teacher network gives the unlabeled data with pseudo-labels that are divided into the easy and hard groups. The labeled data and the unlabeled data in the easy group with their perturbation are both used to train for a better-performing student network. In the second stage, the unlabeled data in the hard group are predicted by the obtained student network, so the refined pseudo-labeled data are enlarged. All the pseudo-labeling data and labeled data with their perturbation are used to retrain the student network, progressively improving the defect feature representation. We build a mobile screen defect dataset (MSDD-3) with three classes of defects. PPL is implemented on MSDD-3 as well as other public datasets. Extensive experimental results demonstrate that PPL significantly surpasses the state-of-the-art methods across all evaluation partition protocols.

Reinforcement Learning-Based Adaptive Optimal Control for Nonlinear Systems With Asymmetric Hysteresis.

This article investigates the adaptive optimal tracking problem for a class of nonlinear affine systems with asymmetric Prandtl-Ishlinskii (PI) hysteresis nonlinearities based on actor-critic (A-C) learning mechanisms. Considering the huge obstacles arising from the uncertainty of hysteresis nonlinearity in actuators, we develop a scheme for the conflict between the construction of Hamilton functions and hysteresis nonlinearity. The actuator hysteresis forces the input into a hysteresis delay, thus preventing the Hamilton function from getting the current moment's input instantly and thus making optimization impossible. In the first step, an inverse model is constructed to compensate for the hysteresis model with a shift factor. In the second step, we compensate for the control input by designing a feedback controller and incorporating the estimation and approximation errors into the Hamilton error. Optimal control, the other part of the actual control input, is obtained by taking partial derivatives of the Hamiltonian function after the nonlinearities have been circumvented. At the end, a simulation is given to validate the developed solution.

Learning All-In Collaborative Multiview Binary Representation for Clustering.

Multiview clustering via binary representation has attracted intensive attention due to its effectiveness in handling large-scale multiple view data. However, these kind of clustering approaches usually ignore a very important potential high-order correlation in discrete representation learning. In this article, we propose a novel all-in collaborative multiview binary representation for clustering (AC-MVBC) framework, where multiview collaborative binary representation and clustering structure are learned in a joint manner. Specifically, using a new type of tensor low-rank constraint, the high-order collaborations, i.e., cross-view and inner view collaborations, can be effectively captured in our model. Moreover, by incorporating the Bregman discrepancy, the projective consistency among different views can be guaranteed to achieve a more powerful binary representation. An efficient optimization algorithm is also proposed to solve the objective function with fast convergence empirically. Experimental results on several challenge datasets demonstrate that the proposed method has achieved highly competent performance compared with the state-of-the-art multiview clustering (MVC) methods while maintaining low computational and memory requirements.

Comprehensive Analysis of Cuproptosis-Related Genes in Prognosis and Immune Infiltration of Hepatocellular Carcinoma Based on Bulk and Single-Cell RNA Sequencing Data.

Background: Studies on prognostic potential and tumor immune microenvironment (TIME) characteristics of cuproptosis-related genes (CRGs) in hepatocellular carcinoma (HCC) are limited. Methods: A multigene signature model was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The cuproptosis-related multivariate cox regression analysis and bulk RNA-seq-based immune infiltration analysis were performed. The results were verified using two cohorts. The enrichment of CRGs in T cells based on single-cell RNA sequencing (scRNA-seq) was performed. Real-time polymerase chain reaction (RT-PCR) and multiplex immunofluorescence staining were performed to verify the reliability of the conclusions. Results: A four-gene risk scoring model was constructed. Kaplan−Meier curve analysis showed that the high-risk group had a worse prognosis (p < 0.001). The time-dependent receiver operating characteristic (ROC) curve showed that the OS risk score prediction performance was good. These results were further confirmed in the validation queue. Meanwhile, the Tregs and macrophages were enriched in the cuproptosis-related TIME of HCC. Conclusions: The CRGs-based signature model could predict the prognosis of HCC. Treg and macrophages were significantly enriched in cuproptosis-related HCC, which was associated with the depletion of proliferating T cells.

Labeled-Robust Regression: Simultaneous Data Recovery and Classification.

Rank minimization is widely used to extract low-dimensional subspaces. As a convex relaxation of the rank minimization, the problem of nuclear norm minimization has been attracting widespread attention. However, the standard nuclear norm minimization usually results in overcompression of data in all subspaces and eliminates the discrimination information between different categories of data. To overcome these drawbacks, in this article, we introduce the label information into the nuclear norm minimization problem and propose a labeled-robust principal component analysis (L-RPCA) to realize nuclear norm minimization on multisubspace data. Compared with the standard nuclear norm minimization, our method can effectively utilize the discriminant information in multisubspace rank minimization and avoid excessive elimination of local information and multisubspace characteristics of the data. Then, an effective labeled-robust regression (L-RR) method is proposed to simultaneously recover the data and labels of the observed data. Experiments on real datasets show that our proposed methods are superior to other state-of-the-art methods.

Prescribed Performance Adaptive Neural Compensation Control for Intermittent Actuator Faults by State and Output Feedback.

Due to the existing effects of intermittent jumps of unknown parameters during operation, effectively establishing transient and steady-state tracking performances in control systems with unknown intermittent actuator faults is very important. In this article, two prescribed performance adaptive neural control schemes based on command-filtered backstepping are developed for a class of uncertain strict-feedback nonlinear systems. Under the condition of system states being available for feedback, the state feedback control scheme is investigated. When the system states are not directly measured, a cascade high-gain observer is designed to reconstruct the system states, and in turn, the output feedback control scheme is presented. Since the projection operator and modified Lyapunov function are, respectively, used in the adaptive law design and stability analysis, it is proven that both schemes can not only ensure the boundedness of all closed-loop signals but also confine the tracking errors within prescribed arbitrarily small residual sets for all the time even if there exist the effects of intermittent jumps of unknown parameters. Thus, the prescribed system transient and steady-state performances in the sense of the tracking errors are established. Furthermore, we also prove that the tracking performance under output feedback is able to recover the tracking performance under state feedback as the observer gain decreases. Simulation studies are done to verify the effectiveness of the theoretical discussions.

Hypoxia associated multi-omics molecular landscape of tumor tissue in patients with hepatocellular carcinoma.

The present study was designed to update the knowledge about hypoxia-related multi-omic molecular landscape in hepatocellular carcinoma (HCC) tissues. Large-size HCC datasets from multiple centers were collected. The hypoxia exposure of tumor tissue from patients in 10 HCC cohorts was estimated using a novel HCC-specific hypoxia score system constructed in our previous study. A comprehensive bioinformatical analysis was conducted to compare hypoxia-associated multi-omic molecular features in patients with a high hypoxia score to a low hypoxia score. We found that patients with different exposure to hypoxia differed significantly in transcriptomic, genomic, epigenomic, and proteomic alterations, including differences in mRNA, microRNA (miR), and long non-coding RNA (lncRNA) expression, differences in copy number alterations (CNAs), differences in DNA methylation levels, differences in RNA alternative splicing events, and differences in protein levels. HCC survival- associated molecular events were identified. The potential correlation between molecular features related to hypoxia has also been explored, and various networks have been constructed. We revealed a particularly comprehensive hypoxia-related molecular landscape in tumor tissues that provided novel evidence and perspectives to explain the role of hypoxia in HCC. Clinically, the data obtained from the present study may enable the development of individualized treatment or management strategies for HCC patients with different levels of hypoxia exposure.