Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:predictors of patient prognosis

Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly understood.Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neuro biological markers after mild traumatic brain injury.This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury.G raph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function.However,most previous mild traumatic brain injury studies using graph theory have focused on specific populations,with limited exploration of simultaneous abnormalities in structural and functional connectivity.Given that mild traumatic brain injury is the most common type of traumatic brain injury encounte red in clinical practice,further investigation of the patient characteristics and evolution of structural and functional connectivity is critical.In the present study,we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury.In this longitudinal study,we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 wee ks of injury,as well as 36 healthy controls.Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis.In the acute phase,patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network.More than 3 months of followup data revealed signs of recovery in structural and functional connectivity,as well as cognitive function,in 22 out of the 46 patients.Furthermore,better cognitive function was associated with more efficient networks.Finally,our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury.These findings highlight the importance of integrating structural and functional connectivity in unde rstanding the occurrence and evolution of mild traumatic brain injury.Additionally,exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.

Synchronization transition of a modular neural network containing subnetworks of different scales

Time delay and coupling strength are important factors that affect the synchronization of neural networks.In this study,a modular neural network containing subnetworks of different scales was constructed using the Hodgkin–Huxley(HH)neural model;i.e.,a small-scale random network was unidirectionally connected to a large-scale small-world network through chemical synapses.Time delays were found to induce multiple synchronization transitions in the network.An increase in coupling strength also promoted synchronization of the network when the time delay was an integer multiple of the firing period of a single neuron.Considering that time delays at different locations in a modular network may have different effects,we explored the influence of time delays within each subnetwork and between two subnetworks on the synchronization of modular networks.We found that when the subnetworks were well synchronized internally,an increase in the time delay within both subnetworks induced multiple synchronization transitions of their own.In addition,the synchronization state of the small-scale network affected the synchronization of the large-scale network.It was surprising to find that an increase in the time delay between the two subnetworks caused the synchronization factor of the modular network to vary periodically,but it had essentially no effect on the synchronization within the receiving subnetwork.By analyzing the phase difference between the two subnetworks,we found that the mechanism of the periodic variation of the synchronization factor of the modular network was the periodic variation of the phase difference.Finally,the generality of the results was demonstrated by investigating modular networks at different scales.

A Novel Algorithm to Estimate the Reliability of Hybrid Computer Communication Networks

In this paper, we proposed an algorithm to estimate the reliability of a hybrid computer communication network. A hybrid computer network is a network that consists of wire and wireless sub networks. The proposed algorithm is built upon using a simulation model due to the complexity of the network topology. We tested the proposed algorithm, and the results show consistency in the reliability estimates.

3-Dimensional Bag of Visual Words Framework on Action Recognition

Human motion recognition plays a crucial role in the video analysis framework.However,a given video may contain a variety of noises,such as an unstable background and redundant actions,that are completely different from the key actions.These noises pose a great challenge to human motion recognition.To solve this problem,we propose a new method based on the 3-Dimensional(3D)Bag of Visual Words(BoVW)framework.Our method includes two parts:The first part is the video action feature extractor,which can identify key actions by analyzing action features.In the video action encoder,by analyzing the action characteristics of a given video,we use the deep 3D CNN pre-trained model to obtain expressive coding information.A classifier with subnetwork nodes is used for the final classification.The extensive experiments demonstrate that our method leads to an impressive effect on complex video analysis.Our approach achieves state-of-the-art performance on the datasets of UCF101(85.3%)and HMDB51(54.5%).

Perspectives on studying molecular adaptations of amphibians in the genomic era

Understanding the genetic mechanisms underlying particular adaptations/phenotypes of organisms is one of the core issues of evolutionary biology.The use of genomic data has greatly advanced our understandings on this issue,as well as other aspects of evolutionary biology,including molecular adaptation,speciation,and even conservation of endangered species.Despite the well-recognized advantages,usages of genomic data are still limited to non-mammal vertebrate groups,partly due to the difficulties in assembling large or highly heterozygous genomes.Although this is particularly the case for amphibians,nonetheless,several comparative and population genomic analyses have shed lights into the speciation and adaptation processes of amphibians in a complex landscape,giving a promising hope for a wider application of genomics in the previously believed challenging groups of organisms.At the same time,these pioneer studies also allow us to realize numerous challenges in studying the molecular adaptations and/or phenotypic evolutionary mechanisms of amphibians.In this review,we first summarize the recent progresses in the study of adaptive evolution of amphibians based on genomic data,and then we give perspectives regarding how to effectively identify key pathways underlying the evolution of complex traits in the genomic era,as well as directions for future research.

TOPOLOGICAL CONDITIONS FOR SOLVA ILITY OF PSEUDO-TEARING SUBNETWORK

The problem for the solvability of pseudo-tearing subnetwork is one of the essentialinvestigations of network theory.The results presented would be not only mathematical conditionsbut also topological conditions for subnetwork solvability.These conditions are necessary andalmost sufficient.It should guide one intuitively to the design of accessible nodes.

Performance Analysis of Interconnected Communication Networks

As the means for communication of users in different communication networks and network resource sharing, communication network interconnection has been paid great attention to in recent years. The performance analysis of internetworks is very important in communication network interconnection, for it is the basis for the study of other problems concerned. The main contribution of this paper is a wide and deep study of performance of internetwork, the construction of systematic performance analysis method for arbitrary internetworks, and of queuing model for gateways. In this paper, the performance analysis of internetwork with arbitrary topology is studied for the first time, and a new method - Node Equivalence of Subnetworks (NES) is proposed. The key step of this method is the node equivalence of subnetworks of all kinds, so we first deeply study the node equivalence, and develop two models for node equivalence. Then we study the node equivalence of CSMA/CD and SLOTTED ALOHA systems by using these models. The results show that the NES method is a systematic method which is suitable for performance analysis of any type of internetworks. The proposed equivalence models is usable with good approximation. The queuing model of gateway is first widely studied in this paper. The queuing model for gateways with packet fragmentation and reassembly is studied for the first time and the analysis method, proposed. In the analysis, two modeling tools, Reversed Switched Poisson Process (RSPP) and Switched Determined Process (SDP), are proposed and RSPP/M/1 queue and SDP/D/1 queue, analyzed. A new method-minislot method is proposed and used for the study of SDP/D/1 queue and performance of ATM multiplexer. In this paper, the interdeparture time distribution of ATM network under some input traffic models is studied and the expression, derived. These results can be used for both performance evaluation of ATM network and the performance of ATM networks interconnected with other networks. We also propose an approximate analysis method for queuing networks with priority service and the queuing networks with packet fragmentation and reassembly.

SUBNETWORK-EXTRACTION THEOREMS AND THEIR APPLICATIONS

The active-subnetwork-extraction theorem and the passive-subnetwork-extraction theorem are derived from one of the author’s previous work. Using them to find the fully symbolic network functions, the multilevel-tearing topological analysis for active networks can be substantially simplified so that it can be conducted conveniently on a computer. Using them to find partially symbolic network functions, one finds it possible to extend not only the electrical network that can be analysed on a computer to the order that can be processed by an ordinary numerical analysis program, but also the symbolic subnetwork to the order that can be processed by an ordinary topological analysis program. So far the latter cannot be achieved with the current conventional methods, i. e. the parameter-extraction method and interpolative approach.

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network

A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network（interactome） indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy（IDCM） data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.