Synchronization of Chaotic Neural Networks with Leakage Delay and Mixed Time-Varying Delays via Sampled-Data Control

This paper investigates the synchronization problem for neural networks with leakage delay and both discrete and distributed time-varying delays under sampled-data control. By employing the Lyapunov functional method and using the matrix inequality techniques, a delay-dependent LMIs criterion is given to ensure that the master systems and the slave systems are synchronous. An example with simulations is given to show the effectiveness of the proposed criterion

RNAi-based Gene Therapy for Blood Genetic Diseases

Therapies for blood genetic diseases can be divided into different categories, including chemotherapy, radiotherapy, gene therapy, and hematopoietic stem cell transplantation. Among these treatments, gene targeting is progressively becoming a therapeutic alternative that offers the possibility of a permanent cure for certain blood genetic diseases. In recent years, gene therapy has played a more important role in curing genetic blood disorders. RNA interference (RNAi) is one of the directions for gene therapy, which was intensively studied in the past decades for its potentials in the treatment of diseases. In order to provide useful references and prospective directions for further studies concerning RNAi-based gene therapy for blood genetic diseases, current RNAi-based gene therapies for several typical blood genetic diseases have been summarized and discussed in this chapter

Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI

Brain network analysis is a useful approach to studying human brain disorders because it can distinguish patients from healthy people by detecting abnormal connections. Due to the complementary information from multiple modal neuroimages, multimodal fusion technology has a lot of potential for improving prediction performance. However, effective fusion of multimodal medical images to achieve complementarity is still a challenging problem. In this paper, a novel hierarchical structural-functional connectivity fusing (HSCF) model is proposed to construct brain structural-functional connectivity matrices and predict abnormal brain connections based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). Specifically, the prior knowledge is incorporated into the separators for disentangling each modality of information by the graph convolutional networks (GCN). And a disentangled cosine distance loss is devised to ensure the disentanglement's effectiveness. Moreover, the hierarchical representation fusion module is designed to effectively maximize the combination of relevant and effective features between modalities, which makes the generated structural-functional connectivity more robust and discriminative in the cognitive disease analysis. Results from a wide range of tests performed on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database show that the proposed model performs better than competing approaches in terms of classification evaluation. In general, the proposed HSCF model is a promising model for generating brain structural-functional connectivities and identifying abnormal brain connections as cognitive disease progresses.Comment: 4 figure

Aerosols Monitored by Satellite Remote Sensing

Aerosols, small particles suspended in the atmosphere, affect the air quality and climate change. Their distributions can be monitored by satellite remote sensing. Many images of aerosol properties are available from websites as the by-products of the atmospheric correction of the satellite data. Their qualities depend on the accuracy of the atmospheric correction algorithms. The approaches of the atmospheric correction for land and ocean are different due to the large difference of the ground reflectance between land and ocean. A unified atmospheric correction (UAC) approach is developed to improve the accuracy of aerosol products over land, similar to those over ocean. This approach is developed to estimate the aerosol scattering reflectance from satellite data based on a lookup table (LUT) of in situ measured ground reflectance. The results show that the aerosol scattering reflectance can be completely separated from the satellite measured radiance over turbid waters and lands. The accuracy is validated with the mean relative errors of 22.1%. The vertical structures of the aerosols provide a new insight into the role of aerosols in regulating Earth\u27s weather, climate, and air quality

PredT4SE-Stack: Prediction of Bacterial Type IV Secreted Effectors From Protein Sequences Using a Stacked Ensemble Method

Gram-negative bacteria use various secretion systems to deliver their secreted effectors. Among them, type IV secretion system exists widely in a variety of bacterial species, and secretes type IV secreted effectors (T4SEs), which play vital roles in host-pathogen interactions. However, experimental approaches to identify T4SEs are time- and resource-consuming. In the present study, we aim to develop an in silico stacked ensemble method to predict whether a protein is an effector of type IV secretion system or not based on its sequence information. The protein sequences were encoded by the feature of position specific scoring matrix (PSSM)-composition by summing rows that correspond to the same amino acid residues in PSSM profiles. Based on the PSSM-composition features, we develop a stacked ensemble model PredT4SE-Stack to predict T4SEs, which utilized an ensemble of base-classifiers implemented by various machine learning algorithms, such as support vector machine, gradient boosting machine, and extremely randomized trees, to generate outputs for the meta-classifier in the classification system. Our results demonstrated that the framework of PredT4SE-Stack was a feasible and effective way to accurately identify T4SEs based on protein sequence information. The datasets and source code of PredT4SE-Stack are freely available at http://xbioinfo.sjtu.edu.cn/PredT4SE_Stack/index.php

Enhanced but highly variable bioturbation around seamounts in the northwest Pacific

Abstract(#br)Seamounts are a unique ecosystem in marine environment, but the relevant understanding is limited. In this study, sedimentation and bioturbation around the Pako Guyot of Magellan, and the Lamont, Scripps, Arnold, and Pot Guyots of Marcus-Wake seamounts in the northwest Pacific were evaluated using 230 Th ex and 210 Pb ex as tracers. Our results showed that the linear sedimentation rate and the mass accumulation rate ranged from 0.12 to 2.50 mm/ka and from 0.06 to 1.14 kg/m 2 /ka with averages of 1.27 ± 0.80 mm/ka and 0.49 ± 0.30 kg/m 2 /ka respectively. The accumulation flux of organic carbon in surface sediments was estimated to be 0.10-4.52 gC/m 2 /ka. The bioturbation coefficients ranged from 1.01 to 27.1 cm 2 /a with an average of 10.8 ± 9.2 cm 2 /a, which is higher than those in abyssal sediments or predicted by traditional empirical equations. The enhanced bioturbation supports the view that seamounts are hotspots for pelagic benthic organisms. The bioturbation intensity showed a great variability with the maximum around 40 km away from the edge of seamount summit. The bioturbation coefficient correlated positively with sedimentation rate and accumulation flux of organic carbon in surface sediments, indicating that the supply of organic matter is a main driving force for enhanced bioturbation around the seamounts. The increase in sedimentary organic matter promotes the activities of benthic organisms. More research is needed to gain a deep understanding of bioturbation in seamounts in the context of future climate change

Stability analysis of impulsive stochastic Cohen–Grossberg neural networks with mixed time delays

This is the post print version of the article. The official published version can be obtained from the link - Copyright 2008 Elsevier LtdIn this paper, the problem of stability analysis for a class of impulsive stochastic Cohen–Grossberg neural networks with mixed delays is considered. The mixed time delays comprise both the time-varying and infinite distributed delays. By employing a combination of the M-matrix theory and stochastic analysis technique, a sufficient condition is obtained to ensure the existence, uniqueness, and exponential p-stability of the equilibrium point for the addressed impulsive stochastic Cohen–Grossberg neural network with mixed delays. The proposed method, which does not make use of the Lyapunov functional, is shown to be simple yet effective for analyzing the stability of impulsive or stochastic neural networks with variable and/or distributed delays. We then extend our main results to the case where the parameters contain interval uncertainties. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. An example is given to show the effectiveness of the obtained results.This work was supported by the Natural Science Foundation of CQ CSTC under grant 2007BB0430, the Scientific Research Fund of Chongqing Municipal Education Commission under Grant KJ070401, an International Joint Project sponsored by the Royal Society of the UK and the National Natural Science Foundation of China, and the Alexander von Humboldt Foundation of Germany

All-optical format conversion-based flexible optical interconnection using nonlinear MZI with nested-pump assisted NOLM

An all-optical format conversion (AOFC) scheme of star-m-ary quadrature amplitude modulation (star-mQAM) based on a nonlinear Mach-Zehnder interferometer (MZI) with nested-pump assisted nonlinear optical loop mirror (nested-PA-NOLM) is proposed and numerically simulated. In this scheme, input multi-Gbps star-8QAM signals can be converted into three quadrature phase shift keying (QPSK) signals (namely QPSK-A, -B and -C) through the PA-NOLM under different input power of the signal and the pump. The nonlinear MZI is formed by two PA-NOLMs of the upper and the lower arms, the former and the latter 3-dB optical couplers (OCs), a directional variable optical attenuator (VOA) in the upper arm and a directional variable phase shifter (VPS) in the lower arm. A VOA and a VPS are used to adjust the power ratio (PR) and relative phase shift (RPS) between any two of QPSK-A, -B and -C. When any two adjusted signals in QPSK-A, -B and -C are coherently superposed, the aggregated star-8QAM signal can be extracted again. Furthermore, the proposed scheme can also be used to convert the 20 Gbps bipolar 4-ary pulse amplitude modulation (PAM4) signal into two 10 Gbps BPSK signals and a 20 Gbps QPSK signal. When the proposed scheme is combined with the phase-sensitive amplification (PSA), it can also be used to convert one 16QAM into two QPSK signals. The scheme performance is analyzed via constellation diagrams, power waveforms, the error vector magnitude (EVM) and the bit error rate (BER) of the optical signals. The scheme can not only be deployed in optical gateways to connect optical networks using different modulation formats, but also has a potential applied advantage in security information transmission between different optical networks