Topological properties and connectivity patterns in brain networks of patients with refractory epilepsy combined with intracranial electrical stimulation

ObjectiveAlthough intracranial electrical stimulation has emerged as a treatment option for various diseases, its impact on the properties of brain networks remains challenging due to its invasive nature. The combination of intracranial electrical stimulation and whole-brain functional magnetic resonance imaging (fMRI) in patients with refractory epilepsy (RE) makes it possible to study the network properties associated with electrical stimulation. Thus, our study aimed to investigate the brain network characteristics of RE patients with concurrent electrical stimulation and obtain possible clinical biomarkers.MethodsOur study used the GRETNA toolbox, a graph theoretical network analysis toolbox for imaging connectomics, to calculate and analyze the network topological attributes including global measures (small-world parameters and network efficiency) and nodal characteristics. The resting-state fMRI (rs-fMRI) and the fMRI concurrent electrical stimulation (es-fMRI) of RE patients were utilized to make group comparisons with healthy controls to identify the differences in network topology properties. Network properties comparisons before and after electrode implantation in the same patient were used to further analyze stimulus-related changes in network properties. Modular analysis was used to examine connectivity and distribution characteristics in the brain networks of all participants in study.ResultsCompared to healthy controls, the rs-fMRI and the es-fMRI of RE patients exhibited impaired small-world property and reduced network efficiency. Nodal properties, such as nodal clustering coefficient (NCp), betweenness centrality (Bc), and degree centrality (Dc), exhibited differences between RE patients (including rs-fMRI and es-fMRI) and healthy controls. The network connectivity of RE patients (including rs-fMRI and es-fMRI) showed reduced intra-modular connections in subcortical areas and the occipital lobe, as well as decreased inter-modular connections between frontal and subcortical regions, and parieto-occipital regions compared to healthy controls. The brain networks of es-fMRI showed a relatively weaker small-world structure compared to rs-fMRI.ConclusionThe brain networks of RE patients exhibited a reduced small-world property, with a tendency toward random networks. The network connectivity patterns in RE patients exhibited reduced connections between cortical and subcortical regions and enhanced connections among parieto-occipital regions. Electrical stimulation can modulate brain network activity, leading to changes in network connectivity patterns and properties

Research on Fault Parameters Modeling Approach of Aircraft IDG

The essence of the faults of the aircraft IDG (Integrated Drive Generator) is the change of its internal structure parameters. In this paper, mathematical models of the exciter and the main generator in aircraft IDG are constructed and the relationship between the parameter change and the faults can be observed directly through the mathematical models. The mathematical models are simulated in MATLAB/Simulink. After modifying certain fault parameters, the relevant fault waveform of aircraft IDG can be acquired

Controllable Synthesis of Na3V2(PO4)3/C Nanofibers as Cathode Material for Sodium-Ion Batteries by Electrostatic Spinning

Na3V2(PO4)3/C nanofibers are prepared by a pre-reduction assisted electrospinning method. In order to maintain the perfect fibrous architecture of the Na3V2(PO4)3/C samples after calcining, a series of heat treatment parameters are studied in detail. It is found that the heat treatment process shows important influence on the morphology and electrochemical performance of Na3V2(PO4)3/C composite nanofibers. Under the calcining conditions of 800°C for 10 h with a heating rate of 2.5°C min−1, the well-crystallized uniform Na3V2(PO4)3/C nanofibers with excellent electrochemical performances are successfully obtained. The initial discharge specific capacities of the nanofibers at 0.05, 1, and 10C are 114.0, 106.0, and 77.9 mAh g−1, respectively. The capacity retention still remains 97.0% after 100 cycles at 0.05C. This smooth, uniform, and continuous Na3V2(PO4)3/C composite nanofibers prepared by simple electrospinning method, is expected to be a superior cathode material for sodium-ion batteries

RNA-Seq Mapping and Detection of Gene Fusions with a Suffix Array Algorithm

High-throughput RNA sequencing enables quantification of transcripts (both known and novel), exon/exon junctions and fusions of exons from different genes. Discovery of gene fusions–particularly those expressed with low abundance– is a challenge with short- and medium-length sequencing reads. To address this challenge, we implemented an RNA-Seq mapping pipeline within the LifeScope software. We introduced new features including filter and junction mapping, annotation-aided pairing rescue and accurate mapping quality values. We combined this pipeline with a Suffix Array Spliced Read (SASR) aligner to detect chimeric transcripts. Performing paired-end RNA-Seq of the breast cancer cell line MCF-7 using the SOLiD system, we called 40 gene fusions among over 120,000 splicing junctions. We validated 36 of these 40 fusions with TaqMan assays, of which 25 were expressed in MCF-7 but not the Human Brain Reference. An intra-chromosomal gene fusion involving the estrogen receptor alpha gene ESR1, and another involving the RPS6KB1 (Ribosomal protein S6 kinase beta-1) were recurrently expressed in a number of breast tumor cell lines and a clinical tumor sample

Enhanced Oil Recovery for ASP Flooding Based on Biorthogonal Spatial-Temporal Wiener Modeling and Iterative Dynamic Programming

Because of the mechanism complexity, coupling, and time-space characteristic of alkali-surfactant-polymer (ASP) flooding, common methods are very hard to be implemented directly. In this paper, an iterative dynamic programming (IDP) based on a biorthogonal spatial-temporal Wiener modeling method is developed to solve the enhanced oil recovery for ASP flooding. At first, a comprehensive mechanism model for the enhanced oil recovery of ASP flooding is introduced. Then the biorthogonal spatial-temporal Wiener model is presented to build the relation between inputs and states, in which the Wiener model is expanded on a set of spatial basis functions and temporal basis functions. After inferring the necessary condition of solutions, these basis functions are determined by the snapshot method. Furthermore, a theorem is proved to identify parameters in the Wiener model. Combined with the least square estimation (LSE), all unknown parameters are determined. In addition, the ARMA model is applied to build the model between states and outputs, whose parameters are identified by recursive least squares (RLS). Thus, the whole modeling process for ASP flooding is finished. At last, the IDP algorithm is applied to solve the enhanced oil recovery problem for ASP flooding based on the identification model to obtain the optimal injection strategy. Simulations on the ASP flooding with four injection wells and nine production wells show the accuracy and effectiveness of the proposed method