Anion-Bridged Dual Hydrogen Bond Enabled Concerted Addition of Phenol to Glycal.

A noncovalent organocatalytic concerted addition of phenol to glycal is developed for the stereoselective and regioselective construction of biologically important phenolic 2-deoxyglycosides, featuring wide substrate tolerance. The method relies on an anion-bridged dual hydrogen bond interaction which is experimentally proved by Nuclear Magnetic Resonance (NMR), Ultraviolet and visible (UV-vis), and fluorescence analysis. Experimental evidence including kinetic analysis, Kinetic Isotope Effect (KIE) studies, linear free energy relationship, Hammett plot, and density functional theory (DFT) calculations is provided for a concerted mechanism where a high-energy oxocarbenium ion is not formed. In addition, the potential utility of this method is further demonstrated by the synthesis of biologically active glycosylated flavones. The benchmarking studies demonstrate significant advances in this newly developed method compared to previous approaches.

A novel time series prediction method based on pooling compressed sensing echo state network and its application in stock market.

In the prediction of time series, the echo state network (ESN) exhibits exclusive strengths and a unique training structure. Based on ESN model, a pooling activation algorithm consisting noise value and adjusted pooling algorithm is proposed to enrich the update strategy of the reservoir layer in ESN. The algorithm optimizes the distribution of reservoir layer nodes. And the nodes set will be more matched to the characteristics of the data. In addition, we introduce a more efficient and accurate compressed sensing technique based on the existing research. The novel compressed sensing technique reduces the amount of spatial computation of methods. The ESN model based on the above two techniques overcomes the limitations in traditional prediction. In the experimental part, the model is validated with different chaotic time series as well as multiple stocks, and the method shows its efficiency and accuracy in prediction.

Finite-time parameter identification of fractional-order time-varying delay neural networks based on synchronization.

We research the finite-time parameter identification of fractional-order time-varying delay neural networks (FTVDNNs) based on synchronization. First, based on the fractional-order Lyapunov stability theorem and feedback control idea, we construct a synchronous controller and some parameter update rules, which accomplish the synchronization of the drive-response FTVDNNs and complete the identification of uncertain parameters. Second, the theoretical analysis of the synchronization method is carried out, and the stable time is calculated. Finally, we give two examples for simulation verification. Our method can complete the synchronization of the FTVDNNs in finite time and identify uncertain parameters while synchronizing.

Predefined-Time Stability/Synchronization of Coupled Memristive Neural Networks With Multi-Links and Application in Secure Communication.

This paper explores the realization of a predefined-time synchronization problem for coupled memristive neural networks with multi-links (MCMNN) via nonlinear control. Several effective conditions are obtained to achieve the predefined-time synchronization of MCMNN based on the controller and Lyapunov function. Moreover, the settling time can be tunable based on a parameter designed by the controller, which is more flexible than fixed-time synchronization. Then based on the predefined-time stability criterion and the tunable settling time, we propose a secure communication scheme. This scheme can determine security of communication in the aspect of encrypting the plaintext signal with the participation of multi-links topology and coupled form. Meanwhile, the plaintext signals can be recovered well according to the given new predefined-time stability theorem. Finally, numerical simulations are given to verify the effectiveness of the obtained theoretical results and the feasibility of the secure communication scheme.

Gradually Increased Interhemispheric Functional Connectivity During One Night of Sleep Deprivation.

BACKGROUND: It is well known that circadian rhythms and sleep homeostasis contribute to a pronounced trough in sleepiness and behavioral performance at night. However, the underlying neuroimaging mechanisms remain unclear. How brain-function connectivity is modulated during sleep deprivation (SD) has been rarely examined. METHODS: By increasing the number of scanning sessions during SD, the current study used voxel-mirrored homotopic connectivity (VMHC) to investigate dynamic changes in interhemispheric communication during one night of SD. Every 2 hours from 10 pm to 06 am (session 1, 10 pm; session 2, 12 am; session 3, 2 am; session 4, 4 am; session 5, 6 am), functional magnetic resonance-imaging data and Stanford Sleepiness Scale (SSS) scores were collected from 36 healthy participants with intermediate chronotype. Dynamic changes in SSS scores and VMHC were determined using one-way repeated-measure ANOVA with the false discovery-rate method to correct for multiple comparisons. RESULTS: Significant time effects for VMHC were found mainly in the bilateral thalamus, bilateral superior temporal gyrus, and bilateral precentral gyrus. SSS scores and VMHC in these areas were both found to be monotonously increased during SD. Furthermore, significant positive associations were found between SSS valu and VMHC values in the left superior temporal and right superior gyri. CONCLUSION: These findings might represent the dynamic modulation of circadian rhythm merely or the interaction effects of both circadian rhythm and sleep homeostasis on interhemispheric connectivity within the thalamus, default-mode network, and sensorimotor network. Our study provides more comprehensive information on how SD regulates brain connectivity between hemispheres and adds new evidence of neuroimaging correlates of increased sleepiness after SD.

Ferrocene-containing hybrids as potential anticancer agents: Current developments, mechanisms of action and structure-activity relationships.

Cancer is one of the most fatal threatens to human health throughout the world. The major challenges in the control and eradication of cancers are the continuous emergency of drug-resistant cancer and the low specificity of anticancer agents, creating an urgent need to develop novel anticancer agents. Organometallic compounds especially ferrocene derivatives possess remarkable structural and mechanistic diversity, inherent stability towards air, heat and light, low toxicity, low cost, reversible redox, ligand exchange, and catalytic properties, making them promising drug candidates for cancer therapy. Ferrocifen, a ferrocene-phenol hybrid, has demonstrated promising anticancer properties on drug-resistant cancers. Currently, Ferrocifen is in pre-clinical trial against cancers. Obviously, ferrocene moiety is a useful template for the development of novel anticancer agents. This review will provide an overview of ferrocene-containing hybrids with potential application in the treatment of cancers covering articles published between 2010 and 2020. The mechanisms of action, the critical aspects of design and structure-activity relationships are also discussed.

Adaptive trajectory tracking of wheeled mobile robot based on fixed-time convergence with uncalibrated camera parameters.

The problem of fixed-time trajectory tracking control for a wheeled mobile robot with uncalibrated camera parameters is considered. The fixed-time adaptive trajectory tracking control laws are developed. Firstly, the dynamics mode of robot system with unknown parameters is introduced and the tracking error system is given. Secondly, the problem of trajectory tracking is transformed into the problem of fixed-time stabilization for the error system by using the state and input transformations. Then, the fixed-time controller is designed based on nonsingular recursive terminal sliding mode control method, which enables the actual system to track the reference trajectory in fixed time. Finally, the simulation examples verify the validity of the conclusions.

Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays.

This paper mainly studies the globally fixed-time synchronization of a class of coupled neutral-type neural networks with mixed time-varying delays via discontinuous feedback controllers. Compared with the traditional neutral-type neural network model, the model in this paper is more general. A class of general discontinuous feedback controllers are designed. With the help of the definition of fixed-time synchronization, the upper right-hand derivative and a defined simple Lyapunov function, some easily verifiable and extensible synchronization criteria are derived to guarantee the fixed-time synchronization between the drive and response systems. Finally, two numerical simulations are given to verify the correctness of the results.