Finite-Time H∞ Controllers Design for Stochastic Time-Delay Markovian Jump Systems with Partly Unknown Transition Probabilities.

This paper concentrates on the finite-time H∞ control problem for a type of stochastic discrete-time Markovian jump systems, characterized by time-delay and partly unknown transition probabilities. Initially, a stochastic finite-time (SFT) H∞ state feedback controller and an SFT H∞ observer-based state feedback controller are constructed to realize the closed-loop control of systems. Then, based on the Lyapunov-Krasovskii functional (LKF) method, some sufficient conditions are established to guarantee that closed-loop systems (CLSs) satisfy SFT boundedness and SFT H∞ boundedness. Furthermore, the controller gains are obtained with the use of the linear matrix inequality (LMI) approach. In the end, numerical examples reveal the reasonableness and effectiveness of the proposed designing schemes.

Nanomaterials-Enabled Physicochemical Antibacterial Therapeutics: Toward the Antibiotic-Free Disinfections.

Bacterial infection continues to be an increasing global health problem with the most widely accepted treatment paradigms restricted to antibiotics. However, the overuse and misuse of antibiotics have triggered multidrug resistance of bacteria, frustrating therapeutic outcomes, and leading to higher mortality rates. Even worse, the tendency of bacteria to form biofilms on living and nonliving surfaces further increases the difficulty in confronting bacteria because the extracellular matrix can act as a robust barrier to prevent the penetration of antibiotics and resist environmental damage. As a result, the inability to eliminate bacteria and biofilms often leads to persistent infection, implant failure, and device damage. Therefore, it is of paramount importance to develop alternative antimicrobial agents while avoiding the generation of bacterial resistance to prevent the large-scale growth of bacterial resistance. In recent years, nano-antibacterial materials have played a vital role in the antibacterial field because of their excellent physical and chemical properties. This review focuses on new physicochemical antibacterial strategies and versatile antibacterial nanomaterials, especially the mechanism and types of 2D antibacterial nanomaterials. In addition, this advanced review provides guidance on the development direction of antibiotic-free disinfections in the antibacterial field in the future.

Female-Biased Expression of R-spondin 1 in Chicken Embryonic Gonads Is Estrogen-Dependent.

The mechanism of sex determination in chickens, especially the molecular mechanism of female ovarian development, has not yet been fully elucidated. Previous studies have shown that RSPO1, which is associated with ovarian development in mammals, might have a conserved role in chickens. In this study, we systematically investigated the spatiotemporal expression pattern of RSPO1 in various tissues, especially gonads, of male and female chicken embryos using qPCR and Western blotting, and we explored its correlation with the expression of key genes in the estrogen pathway using drug treatment or gene overexpression in vivo and in vitro. Our results reveal that RSPO1 was widely expressed in all examined tissues of chicken embryos, showing a female bias in gonadal tissues at both the mRNA and protein levels. Surprisingly, RSPO1 was not differentially expressed between male and female gonadal cells with fadrozole-induced estrogen pathway blockades, and furthermore, estradiol-induced estrogen stimulation altered the expression of RSPO1. In addition, overexpression of RSPO1 in gonadal cells induced the mRNA expression of its downstream target genes, Wnt family member 4 (WNT4) and Catenin beta 1 (CTNNB1), and that of estrogen receptor α (ERα), an estrogen pathway gene. In summary, this study provided new evidence for elucidating the role of RSPO1 in ovarian development in poultry.

Preliminary Study on Expression and Function of the Chicken W Chromosome Gene MIER3 in Embryonic Gonads.

The sex chromosomes of birds are designated Z and W. The male is homogamous (ZZ), and the female is heterogamous (ZW). The chicken W chromosome is a degenerate version of the Z chromosome and harbors only 28 protein-coding genes. We studied the expression pattern of the W chromosome gene MIER3 (showing differential expression during gonadogenesis) in chicken embryonic gonads and its potential role in gonadal development. The W copy of MIER3 (MIER3-W) shows a gonad-biased expression in chicken embryonic tissues which was different from its Z copy. The overall expression of MIER3-W and MIER3-Z mRNA and protein is correlated with the gonadal phenotype being higher in female gonads than in male gonads or female-to-male sex-reversed gonads. Chicken MIER3 protein is highly expressed in the nucleus, with relatively lower expression in the cytoplasm. Overexpression of MIER3-W in male gonad cells suggested its effect on the GnRH signaling pathway, cell proliferation, and cell apoptosis. MIER3 expression is associated with the gonadal phenotype. MIER3 may promote female gonadal development by regulating EGR1 and αGSU genes. These findings enrich our knowledge of chicken W chromosome genes and support a more systematic and in-depth understanding of gonadal development in chickens.

Finite-Time H∞ Control for Time-Delay Markovian Jump Systems with Partially Unknown Transition Rate via General Controllers.

This paper deals with the problems of finite-time boundedness (FTB) and H∞ FTB for time-delay Markovian jump systems with a partially unknown transition rate. First of all, sufficient conditions are provided, ensuring the FTB and H∞ FTB of systems given by linear matrix inequalities (LMIs). A new type of partially delay-dependent controller (PDDC) is designed so that the resulting closed-loop systems are finite-time bounded and satisfy a given H∞ disturbance attenuation level. The PDDC contains both non-time-delay and time-delay states, though not happening at the same time, which is related to the probability distribution of the Bernoulli variable. Furthermore, the PDDC is extended to two other cases; one does not contain the Bernoulli variable, and the other experiences a disordering phenomenon. Finally, three numerical examples are used to show the effectiveness of the proposed approaches.

Electronic Structure-Dependent Water-Dissociation Pathways of Ruthenium-Based Catalysts in Alkaline H2 -Evolution.

Ruthenium (Ru)-based catalysts have displayed compelling hydrogen evolution activities, which hold the promising potential to substitute platinum in alkaline H2 -evolution. In the challenging alkaline electrolytes, the water-dissociation process involves multistep reactions, while the profound origin and intrinsic factors of diverse Ru species on water-dissociation pathways and reaction principles remain ambiguous. Here the fundamental origin of water-dissociation pathways of Ru-based catalysts in alkaline media to be from their unique electronic structures in complex coordination environments are disclosed. These theoretical results validate that the modulated electronic structures with delocalization-localization coexistence at their boundaries between the Ru nanocluster and single-atom site have a profound influence on water-dissociation pathways, which push H2 O* migration and binding orientation during the splitting process, thus enhancing the dissociation kinetics. By creating Ru catalysts with well-defined nanocluster, single-atom site, and also complex site, the electrocatalytic data shows that both the nanocluster and single-atom play essential roles in water-dissociation, while the complex site possesses synergistically enhanced roles in alkaline electrolytes. This study discloses a new electronic structure-dependent water-dissociation pathway and reaction principle in Ru-based catalysts, thus offering new inspiration to design efficient and durable catalysts for the practical production of H2 in alkaline electrolytes.

Electrocatalytic Porphyrin/Phthalocyanine-Based Organic Frameworks: Building Blocks, Coordination Microenvironments, Structure-Performance Relationships.

Metal-porphyrins or metal-phthalocyanines-based organic frameworks (POFs), an emerging family of metal-N-C materials, have attracted widespread interest for application in electrocatalysis due to their unique metal-N4 coordination structure, high conjugated π-electron system, tunable components, and chemical stability. The key challenges of POFs as high-performance electrocatalysts are the need for rational design for porphyrins/phthalocyanines building blocks and an in-depth understanding of structure-activity relationships. Herein, the synthesis methods, the catalytic activity modulation principles, and the electrocatalytic behaviors of 2D/3D POFs are summarized. Notably, detailed pathways are given for modulating the intrinsic activity of the M-N4 site by the microenvironments, including central metal ions, substituent groups, and heteroatom dopants. Meanwhile, the topology tuning and hybrid system, which affect the conjugation network or conductivity of POFs, are also considered. Furthermore, the representative electrocatalytic applications of structured POFs in efficient and environmental-friendly energy conversion areas, such as carbon dioxide reduction reaction, oxygen reduction reaction, and water splitting are briefly discussed. Overall, this comprehensive review focusing on the frontier will provide multidisciplinary and multi-perspective guidance for the subsequent experimental and theoretical progress of POFs and reveal their key challenges and application prospects in future electrocatalytic energy conversion systems.

Molybdenum Oxycarbide Supported Rh-Clusters with Modulated Interstitial C-O Microenvironments for Promoting Hydrogen Evolution.

Tuning the microenvironment and electronic structure of support materials is essential strategy to induce electron transfer between supports and active centers, which is of great importance in optimizing catalytic kinetics. In this study, the molybdenum oxycarbide supported Rh-clusters are synthesized with modulated interstitial C-O microenvironments (Rh/MoOC) for promoting efficient hydrogen evolution in water splitting. Both electronic structure characterizations and theoretical calculations uncover the apparent charge transfer from Rh to MoOC, which optimizes the d-band center, H2 O adsorption energy, and hydrogen binding energy, thus enhancing its intrinsic hydrogen-evolving activities. In addition, the co-occurrence of interstitial C and O atoms in MoOC supports plays a vital role in the dissociation reaction of water during the hydrogen-evolving process. Impressively, the Rh/MoOC exhibits excellent hydrogen-evolving activities in terms of exceptional turnover frequency values (11.4 and 39.41 H2 s-1 in alkaline and acidic media) and mass activities (21.3 and 73.87 A mg-1 in alkaline and acidic media) at an overpotential of 100 mV, which is more than 40 times higher than that of the benchmark commercial Rh/C catalysts. This work sheds new light on designing water dissociation materials that surpasses most of the reported catalysts.

A network-based method for identifying cancer driver genes based on node control centrality.

Cancer is one of the major contributors to human mortality and has a serious influence on human survival and health. In biomedical research, the identification of cancer driver genes (cancer drivers for short) is an important task; cancer drivers can promote the progression and generation of cancer. To identify cancer drivers, many methods have been developed. These computational models only identify coding cancer drivers; however, non-coding drivers likewise play significant roles in the progression of cancer. Hence, we propose a Network-based Method for identifying cancer Driver Genes based on node Control Centrality (NMDGCC), which can identify coding and non-coding cancer driver genes. The process of NMDGCC for identifying driver genes mainly includes the following two steps. In the first step, we construct a gene interaction network by using mRNAs and miRNAs expression data in the cancer state. In the second step, the control centrality of the node is used to identify cancer drivers in the constructed network. We use the breast cancer dataset from The Cancer Genome Atlas (TCGA) to verify the effectiveness of NMDGCC. Compared with the existing methods of cancer driver genes identification, NMDGCC has a better performance. NMDGCC also identifies 295 miRNAs as non-coding cancer drivers, of which 158 are related to tumorigenesis of BRCA. We also apply NMDGCC to identify driver genes related to the different breast cancer subtypes. The result shows that NMDGCC detects many cancer drivers of specific cancer subtypes.

Origin and Acceleration of Insoluble Li2 S2 -Li2 S Reduction Catalysis in Ferromagnetic Atoms-based Lithium-Sulfur Battery Cathodes.

Accelerating insoluble Li2 S2 -Li2 S reduction catalysis to mitigate the shuttle effect has emerged as an innovative paradigm for high-efficient lithium-sulfur battery cathodes, such as single-atom catalysts by offering high-density active sites to realize in situ reaction with solid Li2 S2 . However, the profound origin of diverse single-atom species on solid-solid sulfur reduction catalysis and modulation principles remains ambiguous. Here we disclose the fundamental origin of Li2 S2 -Li2 S reduction catalysis in ferromagnetic elements-based single-atom materials to be from their spin density and magnetic moments. The experimental and theoretical studies disclose that the Fe-N4 -based cathodes exhibit the fastest deposition kinetics of Li2 S (226 mAh g-1 ) and the lowest thermodynamic energy barriers (0.56 eV). We believe that the accelerated Li2 S2 -Li2 S reduction catalysis enabled via spin polarization of ferromagnetic atoms provides practical opportunities towards long-life batteries.

Multifaceted Catalytic ROS-Scavenging via Electronic Modulated Metal Oxides for Regulating Stem Cell Fate.

Developing reactive oxygen species (ROS)-scavenging nanostructures to protect and regulate stem cells has emerged as an intriguing strategy for promoting tissue regeneration, especially in trauma microenvironments or refractory wounds. Here, an electronic modulated metal oxide is developed via Mn atom substitutions in Co3 O4 nanocrystalline (Mn-Co3 O4 ) for highly efficient and multifaceted catalytic ROS-scavenging to reverse the fates of mesenchymal stem cells (MSCs) in oxidative-stress microenvironments. Benefiting from the atomic Mn-substitution and charge transfer from Mn to Co, the Co site in Mn-Co3 O4 displays an increased ratio of Co2+ /Co3+ and improved redox properties, thus enhancing its intrinsic and broad-spectrum catalytic ROS-scavenging activities, which surpasses most of the currently reported metal oxides. Consequently, the Mn-Co3 O4 can efficiently protect the MSCs from ROS attack and rescue their functions, including adhesion, spreading, proliferation, and osteogenic differentiation. This work not only establishes an efficient material for catalytic ROS-scavenging in stem-cell-based therapeutics but also provides a new avenue to design biocatalytic metal oxides via modulation of electronic structure.

NEXGB: A Network Embedding Framework for Anticancer Drug Combination Prediction.

Compared to single-drug therapy, drug combinations have shown great potential in cancer treatment. Most of the current methods employ genomic data and chemical information to construct drug-cancer cell line features, but there is still a need to explore methods to combine topological information in the protein interaction network (PPI). Therefore, we propose a network-embedding-based prediction model, NEXGB, which integrates the corresponding protein modules of drug-cancer cell lines with PPI network information. NEXGB extracts the topological features of each protein node in a PPI network by struc2vec. Then, we combine the topological features with the target protein information of drug-cancer cell lines, to generate drug features and cancer cell line features, and utilize extreme gradient boosting (XGBoost) to predict the synergistic relationship between drug combinations and cancer cell lines. We apply our model on two recently developed datasets, the Oncology-Screen dataset (Oncology-Screen) and the large drug combination dataset (DrugCombDB). The experimental results show that NEXGB outperforms five current methods, and it effectively improves the predictive power in discovering relationships between drug combinations and cancer cell lines. This further demonstrates that the network information is valid for detecting combination therapies for cancer and other complex diseases.

NESM: a network embedding method for tumor stratification by integrating multi-omics data.

Tumor stratification plays an important role in cancer diagnosis and individualized treatment. Recent developments in high-throughput sequencing technologies have produced huge amounts of multi-omics data, making it possible to stratify cancer types using multiple molecular datasets. We introduce a Network Embedding method for tumor Stratification by integrating Multi-omics data. Network Embedding method for tumor Stratification by integrating Multi-omics pregroup the samples, integrate the gene features and somatic mutation corresponding to cancer types within each group to construct patient features, and then integrate all groups to obtain comprehensive patient information. The gene features contain network topology information, because it is extracted by integrating deoxyribonucleic acid methylation, messenger ribonucleic acid expression data, and protein-protein interactions through network embedding method. On the one hand, a supervised learning method Light Gradient Boosting Machine is used to classify cancer types based on patient features. When compared with other 3 methods, Network Embedding method for tumor Stratification by integrating Multi-omics has the highest AUC in most cancer types. The average AUC for stratifying cancer types is 0.91, indicating that the patient features extracted by Network Embedding method for tumor Stratification by integrating Multi-omics are effective for tumor stratification. On the other hand, an unsupervised clustering algorithm Density-Based Spatial Clustering of Applications with Noise is utilized to divide single cancer subtypes. The vast majority of the subtypes identified by Network Embedding method for tumor Stratification by integrating Multi-omics are significantly associated with patient survival.

scVAEBGM: Clustering Analysis of Single-Cell ATAC-seq Data Using a Deep Generative Model.

A surge in research has occurred because of current developments in single-cell technologies. Above all, single-cell Assay for Transposase-Accessible Chromatin with high throughput sequencing (scATAC-seq) is a popular approach of analyzing chromatin accessibility differences at the level of single cell, either within or between groups. As a result, it is critical to examine cell heterogeneity at a previously unseen level and to identify both recognized and unknown cell types. However, with the ever-increasing number of cells engendered by technological development and the characteristics of the data, such as high noise, sparsity and dimension, challenges in distinguishing cell types have emerged. We propose scVAEBGM, which integrates a Variational Autoencoder (VAE) with a Bayesian Gaussian-mixture model (BGM) to process and analyze scATAC-seq data. This method combines and takes benefits of a Bayesian Gaussian mixture model to estimate the number of cell types without determining the cluster number in a beforehand. In other words, the size of the clusters is inferred from the data, thus avoiding biases introduced by subjective assessments when manually determining the size of the clusters. Additionally, the method is more robust to noise and can better represent single-cell data in lower dimensions. We also create a further clustering strategy. It is indicated by experiments that further clustering based on the already completed clustering can improve the clustering accuracy again. We test on six public datasets, and scVAEBGM outperforms various dimension reduction baselines. In downstream applications, scVAEBGM can reveal biological cell types.

Tunable Structured MXenes With Modulated Atomic Environments: A Powerful New Platform for Electrocatalytic Energy Conversion.

Owing to their rich surface chemistry, high conductivity, tunable bandgap, and thermal stability, structured 2D transition-metal carbides, nitrides, and carbonitrides (MXenes) with modulated atomic environments have emerged as efficient electrochemical energy conversion systems in the past decade. Herein, the most recent advances in the engineering of tunable structured MXenes as a powerful new platform for electrocatalytic energy conversion are comprehensively summarized. First, the state-of-the-art synthetic and processing methods, tunable nanostructures, electronic properties, and modulation principles of engineering MXene-derived nanoarchitectures are focused on. The current breakthroughs in the design of catalytic centers, atomic environments, and the corresponding structure-performance correlations, including termination engineering, heteroatom doping, defect engineering, heterojunctions, and alloying, are discussed. Furthermore, representative electrocatalytic applications of structured MXenes in energy conversion systems are also summarized. Finally, the challenges in and prospects for constructing MXene-based electrocatalytic materials are also discussed. This review provides a leading-edge understanding of the engineering of various MXene-based electrocatalysts and offers theoretical and experimental guidance for prospective studies, thereby promoting the practical applications of tunable structured MXenes in electrocatalytic energy conversion systems.

Tailoring Bond Microenvironments and Reaction Pathways of Single-Atom Catalysts for Efficient Water Electrolysis.

Single-atom sites (SASs) are commonly stabilized and influenced by neighboring atoms in the host; disclosing the structure-reactivity relationships of SASs in water electrolysis is one of the grand challenges originating from the tremendous wealth of support materials with complex structures. Through a multidisciplinary view of the design principles, synthesis strategies, characterization techniques, and theoretical analysis of structure-performance correlations, this timely Review is dedicated to summarizing the most recent progress in tailoring bond microenvironments on different supports and discussing the reaction pathways and performance advantages of different SAS structures for water electrolysis. The essence and mechanisms of how SAS structures influence electrocatalysis and the critical requirements for future developments are discussed. Finally, the challenges and perspectives are also provided to stimulate the practical, widespread utilization of SAS catalysts in water-splitting electrolyzers.

Robust Finite-Time Stability for Uncertain Discrete-Time Stochastic Nonlinear Systems with Time-Varying Delay.

The main concern of this paper is finite-time stability (FTS) for uncertain discrete-time stochastic nonlinear systems (DSNSs) with time-varying delay (TVD) and multiplicative noise. First, a Lyapunov-Krasovskii function (LKF) is constructed, using the forward difference, and less conservative stability criteria are obtained. By solving a series of linear matrix inequalities (LMIs), some sufficient conditions for FTS of the stochastic system are found. Moreover, FTS is presented for a stochastic nominal system. Lastly, the validity and improvement of the proposed methods are shown with two simulation examples.

Cobalt-Based Double Catalytic Sites on Mesoporous Carbon as Reversible Polysulfide Catalysts for Fast-Kinetic Li-S Batteries.

Li-S batteries are considered to be the most promising next-generation advanced energy-storage systems. However, the sluggish reaction kinetics and the "shuttle effect" of lithium polysulfides (LiPSs) severely limit their battery performances. To overcome the complex and multiphase sulfur redox chemistry of LiPSs, in this study, we propose a new type of cobalt-based double catalytic sites (DCSs) codoped mesoporous carbon to immobilize and reversibly catalyze the LiPS intermediates in the cycling process, thus eliminating the shuttle effect and improving the charge-discharge kinetics. The theoretical calculation shows that the well-designed DCS configuration endows LiPSs with both strong and weak binding capabilities, which will facilitate the synergistic and reversible catalytic conversion. Furthermore, the experimental results also confirm that the DCS structure shows significantly enhanced catalytic kinetics than the single catalytic sites. The Li-S battery equipped with the DCS structure displays an extremely high discharge capacity of 918 mA h g-1 at a current density of 0.2 C and can reach a capacity of 867 mA h g-1 after 200 cycles with an ultralow capacity attenuation rate of 0.028% for each cycle. This study opens new avenues to address the catalytic requirements both in discharging and charging processes.

Synthesis and Electronic Modulation of Nanostructured Layered Double Hydroxides for Efficient Electrochemical Oxygen Evolution.

Water electrolysis is considered to be one of the most promising technologies to produce clean fuels. However, its extensive realization critically depends on the progress in cost-effective and high-powered oxygen evolution reaction (OER) electrocatalysts. As a member of the big family of two-dimensional (2D) materials, nanostructured layered double hydroxides (nLDHs) have made significant processes and continuous breakthroughs for OER electrocatalysis. In this Review, the advancements in designing nLDHs for OER in recent years were discussed with a unique focus on their electronic modulations and in situ analysis on catalytic processes. After a brief discussion on different synthetic methodologies of nLDHs, including "bottom-up" and "top-down" approaches, the general strategies to enhance the catalytic performances of nLDHs reported so far were summarized, including compositional substitution, heteroatom doping, vacancy engineering, and amorphous/crystalline engineering. Furthermore, the in situ OER processes and mechanism analysis on engineering efficient nLDHs electrocatalysts were discussed. Finally, the research trends, perspectives, and challenges on designing nLDHs were also carefully outlined. This progress Review may offer enlightening experimental/theoretical guidance for designing highly catalytic active nLDHs and provide new directions to promote their future prosperity for practical utilization in water splitting.

Electron-Donating Effect Enabled Simultaneous Improvement on the Mechanical and Self-Healing Properties of Bromobutyl Rubber Ionomers.

Due to the dynamic nature of networks and high mobility of molecular chains, self-healing elastomers are usually confronted with the trade-off between self-healing efficiency and mechanical properties. Herein, a self-healing ionomer with both high mechanical performance and high self-healing efficiency has been successfully developed by grafting bromobutyl rubber (BIIR) with pyridine-based derivatives. Interestingly, the substituents on the pyridine ring can be used to regulate the interaction forces of ionic clusters and molecular dynamics. The electron-donating effect of the substituents facilitates stable π-π stacking between pyridyl ions, inducing the formation of regular and large ion aggregates, thereby improving the mechanical strength of the ionomer. Meanwhile, the plasticizing effect of the substituents reduces the activation energy and relaxation temperature of the ionic aggregates, thus endowing the ionomer with a high self-healing efficiency. As a result, the ionomer shows tensile strength as high as 8.1 ± 0.3 MPa under room temperature and self-healing efficiency of 100 ± 3% at 60 °C. Therefore, this strategy can be easily extended to other halogen-containing polymers, leading to a novel class of self-healing ionomers that hold great promise in diverse applications.