Hybrid Impulsive Control for Closed Quantum Systems

The state transfer problem of a class of nonideal quantum systems is investigated. It is known that traditional Lyapunov methods may fail to guarantee convergence for the nonideal case. Hence, a hybrid impulsive control is proposed to accomplish a more accurate convergence. In particular, the largest invariant sets are explicitly characterized, and the convergence of quantum impulsive control systems is analyzed accordingly. Numerical simulation is also presented to demonstrate the improvement of the control performance

Exponential Synchronization Analysis and Control for Discrete-Time Uncertain Delay Complex Networks with Stochastic Effects

The exponential synchronization for a class of discrete-time uncertain complex networks with stochastic effects and time delay is investigated by using the Lyapunov stability theory and discrete Halanay inequality. The uncertainty arises from the difference of the nodes’ reliability in the complex network. Through constructing an appropriate Lyapunov function and applying inequality technique, some synchronization criteria and two control methods are obtained to ensure the considered complex network being exponential synchronization. Finally, a numerical example is provided to show the effectiveness of our proposed methods

LGBMDF: A cascade forest framework with LightGBM for predicting drug-target interactions

Prediction of drug-target interactions (DTIs) plays an important role in drug development. However, traditional laboratory methods to determine DTIs require a lot of time and capital costs. In recent years, many studies have shown that using machine learning methods to predict DTIs can speed up the drug development process and reduce capital costs. An excellent DTI prediction method should have both high prediction accuracy and low computational cost. In this study, we noticed that the previous research based on deep forests used XGBoost as the estimator in the cascade, we applied LightGBM instead of XGBoost to the cascade forest as the estimator, then the estimator group was determined experimentally as three LightGBMs and three ExtraTrees, this new model is called LGBMDF. We conducted 5-fold cross-validation on LGBMDF and other state-of-the-art methods using the same dataset, and compared their Sn, Sp, MCC, AUC and AUPR. Finally, we found that our method has better performance and faster calculation speed

Guests mediated supramolecule-modified gold nanoparticles network for mimic enzyme application

1434-1441Supramolecules mediated porous metal nanostructures are meaningful materials because of their specific properties and wide range of applications. Here, we describe a general and simple strategy for building Au-networks based on the guest-induced 3D assembly of Au nanoparticles (Au-NPs) resulted in host-guest interaction resolved sulfonatocalix[4]arene (pSC4)-modified Au-NPs aggregate. The diverse guest molecules induced different porous network structures resulting in their different oxidize ability toward glucose. Among three different kinds of guest, hexamethylenediamine-pSC4-Au-NPs have high sensitivity, wide linear range and good stability. By surface characterization and calculating the electrochemical properties of the Au-NPs networks modified glassy carbon electrodes, the giving Au-NPs network reveals good porosity, high surface areas and increased conductance and electron transfer for the electrocatalysis. The synthesized nano-structures afford fast transport of glucose and ensure contact with a larger reaction surface due to high surface area. The fabricated sensor provides a platform for developing a more stable and efficient glucose sensor based on supramolecules mediated Au-NPs networks

Hybrid Impulsive Control for Closed Quantum Systems

The state transfer problem of a class of nonideal quantum systems is investigated. It is known that traditional Lyapunov methods may fail to guarantee convergence for the non-ideal case. Hence, a hybrid impulsive control is proposed to accomplish a more accurate convergence. In particular, the largest invariant sets are explicitly characterized, and the convergence of quantum impulsive control systems is analyzed accordingly. Numerical simulation is also presented to demonstrate the improvement of the control performance

Downregulation of Long Non-coding RNA FALEC Inhibits Gastric Cancer Cell Migration and Invasion Through Impairing ECM1 Expression by Exerting Its Enhancer-Like Function

Long non-coding RNAs (lncRNAs) have been shown to play important roles in many human diseases. However, their functions and mechanisms in tumorigenesis and development remain largely unknown. Here, we demonstrated that focally amplified lncRNA in epithelial cancer (FALEC) was upregulated and significantly correlated with lymph node metastasis, TNM stage in gastric cancer (GC). Further experiments revealed that FALEC knockdown significantly inhibited GC cells migration and invasion in vitro. Mechanistic investigations demonstrated that small interfering RNA-induced silencing of FALEC decreased expression of the nearby gene extracellular matrix protein 1 (ECM1) in cis. Additionally, ECM1 and FALEC expression were positively correlated, and high levels of ECM1 predicted shorter survival time in GC patients. Our results suggest that the downregulation of FALEC significantly inhibited the migration and invasion of GC cells through impairing ECM1 expression by exerting an enhancer-like function. Our work provides valuable information and a novel promising target for developing new therapeutic strategies in GC

Cascade Forest-Based Model for Prediction of RNA Velocity

In recent years, single-cell RNA sequencing technology (scRNA-seq) has developed rapidly and has been widely used in biological and medical research, such as in expression heterogeneity and transcriptome dynamics of single cells. The investigation of RNA velocity is a new topic in the study of cellular dynamics using single-cell RNA sequencing data. It can recover directional dynamic information from single-cell transcriptomics by linking measurements to the underlying dynamics of gene expression. Predicting the RNA velocity vector of each cell based on its gene expression data and formulating RNA velocity prediction as a classification problem is a new research direction. In this paper, we develop a cascade forest model to predict RNA velocity. Compared with other popular ensemble classifiers, such as XGBoost, RandomForest, LightGBM, NGBoost, and TabNet, it performs better in predicting RNA velocity. This paper provides guidance for researchers in selecting and applying appropriate classification tools in their analytical work and suggests some possible directions for future improvement of classification tools