HeatGSNs: Integrating Eigenfilters and Low-Pass Graph Heat Kernels into Graph Spectral Convolutional Networks for Brain Tumor Segmentation and Classification.

Recent studies on graph representation learning in brain tumor learning tasks have garnered significant interest by encoding and learning inherent relationships among the geometric features of tumors. There are serious class imbalance problems that occur on brain tumor MRI datasets. Impressive deep learning models like CNN- and Transformer-based can easily address this problem through their complex model architectures with large parameters.

Variation in brain connectivity during motor imagery and motor execution in stroke patients based on electroencephalography.

Objective: The objective of this study was to analyze the changes in connectivity between motor imagery (MI) and motor execution (ME) in the premotor area (PMA) and primary motor cortex (MA) of the brain, aiming to explore suitable forms of treatment and potential therapeutic targets.

Methods: Twenty-three inpatients with stroke were selected, and 21 right-handed healthy individuals were recruited. EEG signal during hand MI and ME (synergy and isolated movements) was recorded.

The effects of microcapsules on radiation-induced brain injury by regulating the gut microenvironment.

A radiation-induced brain injury (RIBI) is a major adverse event following radiotherapy of malignant tumors. RIBI would affect cognitive function, leading to a series of complications and even death. However, the pathogenesis of RIBI is still unclear, and it still lacks specific therapeutic drugs.

Cannabidiol-loaded biomimetic macrophage membrane vesicles against post-traumatic stress disorder assisted by ultrasound.

Post-traumatic stress disorder (PTSD), which normally follows psychological trauma, has been increasingly studied as a brain disease. However, the blood-brain barrier (BBB) prevents conventional drugs for PTSD from entering the brain. Our previous studies proved the effectiveness of cannabidiol (CBD) against PTSD, but low water solubility, low brain targeting efficiency and poor bioavailability restricted its applications.

Quercetin inclusion complex gels ameliorate radiation-induced brain injury by regulating gut microbiota.

Radiation-induced brain injury (RIBI) is a serious adverse effect of radiotherapy. RIBI has garnered considerable clinical attention owing to its powerful effects on brain function and cognition; however, no effective treatment is available. The microbiota-gut-brain axis theory is a novel concept of treating RIBI by regulating gut microbiota.

The Improved Brain-Targeted Drug Delivery of Edaravone Temperature-Sensitive Gels by Ultrasound for γ-ray Radiation-Induced Brain Injury.

Radiation-induced brain injury (RBI) is a common neurological disease caused by ionizing radiation (IR). Edaravone (EDA) is a free radical scavenger, has the potential to treat RBI. EDA loaded temperature-sensitive gels (TSGs) were prepared for subcutaneous injection to improve inconvenient administration of intravenous infusion.

EEG decoding method based on multi-feature information fusion for spinal cord injury.

To develop an efficient brain-computer interface (BCI) system, electroencephalography (EEG) measures neuronal activities in different brain regions through electrodes. Many EEG-based motor imagery (MI) studies do not make full use of brain network topology. In this paper, a deep learning framework based on a modified graph convolution neural network (M-GCN) is proposed, in which temporal-frequency processing is performed on the data through modified S-transform (MST) to improve the decoding performance of original EEG signals in different types of MI recognition.

Nasal Delivery of Cinnarizine Thermo- and Ion-Sensitive In Situ Hydrogels for Treatment of Microwave-Induced Brain Injury.

(1) Background: When the body is exposed to microwave radiation, the brain is more susceptible to damage than other organs. However, few effective drugs are available for the treatment of microwave-induced brain injury (MIBI) because most drugs are difficult to cross the blood-brain barrier (BBB) to reach the brain. (2) Methods: Nasal cinnarizine inclusion complexes with thermo-and ion-sensitive hydrogels (cinnarizine ISGs) were prepared to treat MIBI and the characteristics of the inclusion complexes and their thermo-and ion-sensitive hydrogels were evaluated.

Therapeutic carrier based on solanesol and hyaluronate for synergistic tumor treatment.

The administration of nanodrugs can lead to metabolism related systemic toxicity due to the use of inert carriers in large quantities. Carrier materials that offer therapeutic effects are therefore a promising means of addressing this limitation. Herein, a hyaluronate-based nanocarrier was prepared from hyaluronic acid (HA) and solanesol.

Parallel-slipped π-π electron-donor-acceptor in adsorption process: Molecular dynamics simulation.

Molecular dynamics simulation was used to study the adsorption of single wall carbon nanotubes (SCNT) in levofloxacin (LEV) solutions of different concentrations by Radial distribution function, mean square displacement and interaction energy. The results showed that levofloxacin molecules were adsorbed around the carbon nanotubes. The adsorption effect of large concentration solution was not as good as that of low concentration solution because of agglomeration.

A novel biochar electrode for efficient electroreduction of nitrate: Selective and regulation of halogen.

A novel biochar electrode Bio-FeO/CF used for electroreduction of nitrate was prepared by the hydrothermal synthesis method. The results showed that the growth of spherical FeO on the surface of smooth biochar can significantly increase the nitrate reduction rate. Besides, the presence of Cl and Br in the solution could promote the conversion of NH to N, thereby regulating the element nitrogen in the solution.

Combating the Infodemic: A Chinese Infodemic Dataset for Misinformation Identification.

Misinformation posted on social media during COVID-19 is one main example of infodemic data. This phenomenon was prominent in China when COVID-19 happened at the beginning. While a lot of data can be collected from various social media platforms, publicly available infodemic detection data remains rare and is not easy to construct manually.

Insights on the mechanism of Fe doped ZnO for tightly-bound extracellular polymeric substances tribo-catalytic degradation: The role of hydration layers at the interface.

The control of interfacial microbial pollution is of great significance for water safety. Herein, the tribo-catalysis ability of zinc oxide (ZnO) has been investigated, which can realize the control of tightly-bound extracellular polymeric substances (T-EPS) in water under dark environment. The DFT calculation proves the Fe doping introduces the impurity level and decreases the work function from 5.

An Updated Functional Annotation of Protein-Coding Genes in the Cucumber Genome.

Although the cucumber reference genome and its annotation were published several years ago, the functional annotation of predicted genes, particularly protein-coding genes, still requires further improvement. In general, accurately determining orthologous relationships between genes allows for better and more robust functional assignments of predicted genes. As one of the most reliable strategies, the determination of collinearity information may facilitate reliable orthology inferences among genes from multiple related genomes.

The comparison of alternative splicing among the multiple tissues in cucumber.

Background: Alternative splicing (AS) is an important post-transcriptional process. It has been suggested that most AS events are subject to tissue-specific regulation. However, the global dynamics of AS in different tissues are poorly explored.

A fast sequence assembly method based on compressed data structures.

Assembling a large genome using next generation sequencing reads requires large computer memory and a long execution time. To reduce these requirements, a memory and time efficient assembler is presented from applying FM-index in JR-Assembler, called FMJ-Assembler, where FM stand for FMR-index derived from the FM-index and BWT and J for jumping extension. The FMJ-Assembler uses expanded FM-index and BWT to compress data of reads to save memory and jumping extension method make it faster in CPU time.

Accurate reconstruction for DNA sequencing by hybridization based on a constructive heuristic.

Sequencing by hybridization is a promising cost-effective technology for high-throughput DNA sequencing via microarray chips. However, due to the effects of spectrum errors rooted in experimental conditions, an accurate and fast reconstruction of original sequences has become a challenging problem. In the last decade, a variety of analyses and designs have been tried to overcome this problem, where different strategies have different trade-offs in speed and accuracy.

An improved multi-label classification method and its application to functional genomics.

In this paper, a multi-label classification method based on label ranking and delicate boundary Support Vector Machine (SVM) is proposed for solving the functional genomics applications. Firstly, an improved probabilistic SVM with delicate decision boundary is used as scoring approach to obtain a proper label rank. Secondly, an instance-dependent thresholding strategy is proposed to decide classification results.

Poisson approach to clustering analysis of regulatory sequences.

The presence of similar patterns in regulatory sequences may aid users in identifying co-regulated genes or inferring regulatory modules. By modelling pattern occurrences in regulatory regions with Poisson statistics, this paper presents a log likelihood ratio statistics-based distance measure to calculate pair-wise similarities between regulatory sequences. We employed it within three clustering algorithms: hierarchical clustering, Self-Organising Map, and a self-adaptive neural network.

A graph-based evolutionary algorithm: Genetic Network Programming (GNP) and its extension using reinforcement learning.

This paper proposes a graph-based evolutionary algorithm called Genetic Network Programming (GNP). Our goal is to develop GNP, which can deal with dynamic environments efficiently and effectively, based on the distinguished expression ability of the graph (network) structure. The characteristics of GNP are as follows.

A study of evolutionary multiagent models based on symbiosis.

Multiagent Systems with Symbiotic Learning and Evolution (Masbiole) has been proposed and studied, which is a new methodology of Multiagent Systems (MAS) based on symbiosis in the ecosystem. Masbiole employs a method of symbiotic learning and evolution where agents can learn or evolve according to their symbiotic relations toward others, i.e.

Propagation and control of stochastic signals through universal learning networks.

The way of propagating and control of stochastic signals through Universal Learning Networks (ULNs) and its applications are proposed. ULNs have been already developed to form a superset of neural networks and have been applied as a universal framework for modeling and control of non-linear large-scale complex systems. However, the ULNs cannot deal with stochastic variables.

A functions localized neural network with branch gates.

In this paper, a functions localized network with branch gates (FLN-bg) is studied, which consists of a basic network and a branch gate network. The branch gate network is used to determine which intermediate nodes of the basic network should be connected to the output node with a gate coefficient ranging from 0 to 1. This determination will adjust the outputs of the intermediate nodes of the basic network depending on the values of the inputs of the network in order to realize a functions localized network.