High Thermal Conductive Liquid Crystal Elastomer Nanofibers.

Liquid crystal elastomers (LCEs), consisting of polymer networks and liquid crystal mesogens, show a reversible phase change under thermal stimuli. However, the kinetic performance is limited by the inherently low thermal conductivity of the polymers. Transforming amorphous bulk into a fiber enhances thermal conductivity through the alignment of polymer chains. Challenges are present due to their rigid networks, while cross-links are crucial for deformation. Here, we employ hydrodynamic alignment to orient the LCE domains assisted by controlled in situ cross-linking and to remarkably reduce the diameter to submicrons. We report that the intrinsic thermal conductivity of LCE fibers at room temperature reaches 1.44 ± 0.32 W/m-K with the sub-100 nm diameter close to the upper limit determined in the quasi-1D regime. Combining the outstanding thermal conductivity and thin diameters, we anticipate these fibers to exhibit a rapid response and high force output in thermomechanical systems. The fabrication method is expected to apply to other cross-linked polymers.

Arabidopsis membrane protein AMAR1 interaction with type III effector XopAM triggers a hypersensitive response.

The efficient infection of plants by the bacteria Xanthomonas campestris pv. campestris (Xcc) depends on its type III effectors (T3Es). Although the functions of AvrE family T3Es have been reported in some bacteria, the member XopAM in Xcc has not been studied. As XopAM has low sequence similarity to reported AvrE-T3Es and different reports have shown that these T3Es have different targets in hosts, we investigated the functions of XopAM in the Xcc-plant interaction. Deletion of xopAM from Xcc reduced its virulence in cruciferous crops but increased virulence in Arabidopsis (Arabidopsis thaliana) Col-0, indicating that XopAM may perform opposite functions depending on the host species. We further found that XopAM is a lipase that may target the cytomembrane and that this activity might be enhanced by its membrane-targeted protein XOPAM-ACTIVATED RESISTANCE 1 (AMAR1) in Arabidopsis Col-0. The binding of XopAM to AMAR1 induced an intense hypersensitive response that restricted Xcc proliferation. Our results showed that the roles of XopAM in Xcc infection are not the same as those of other AvrE-T3Es, indicating that the functions of this type of T3E have differentiated during long-term bacterium‒host interactions.

Toward mechanical proprioception in autonomously reconfigurable kirigami-inspired mechanical systems.

Mechanical metamaterials have recently been exploited as an interesting platform for information storing, retrieval and processing, analogous to electronic devices. In this work, we describe the design and fabrication a two-dimensional (2D) multistable metamaterial consisting of building blocks that can be switched between two distinct stable phases, and which are capable of storing binary information analogous to digital bits. By changing the spatial distribution of the phases, we can achieve a variety of different configurations and tunable mechanical properties (both static and dynamic). Moreover, we demonstrate the ability to determine the phase distribution via simple probing of the dynamic properties, to which we refer as mechanical proprioception. Finally, as a simple demonstration of feasibility, we illustrate a strategy for building autonomous kirigami systems that can receive inputs from their environment. This work could bring new insights for the design of mechanical metamaterials with information processing and computing functionalities. This article is part of the theme issue 'Origami/Kirigami-inspired structures: from fundamentals to applications'.

Genome-Wide Identification and Expression Profile Analysis of the Phenylalanine Ammonia-Lyase Gene Family in Hevea brasiliensis.

The majority of the world's natural rubber comes from the rubber tree (Hevea brasiliensis). As a key enzyme for synthesizing phenylpropanoid compounds, phenylalanine ammonia-lyase (PAL) has a critical role in plant satisfactory growth and environmental adaptation. To clarify the characteristics of rubber tree PAL family genes, a genome-wide characterization of rubber tree PALs was conducted in this study. Eight PAL genes (HbPAL1-HbPAL8), which spread over chromosomes 3, 7, 8, 10, 12, 13, 14, 16, and 18, were found to be present in the genome of H. brasiliensis. Phylogenetic analysis classified HbPALs into groups I and II, and the group I HbPALs (HbPAL1-HbPAL6) displayed similar conserved motif compositions and gene architectures. Tissue expression patterns of HbPALs quantified by quantitative real-time PCR (qPCR) proved that distinct HbPALs exhibited varying tissue expression patterns. The HbPAL promoters contained a plethora of cis-acting elements that responded to hormones and stress, and the qPCR analysis demonstrated that abiotic stressors like cold, drought, salt, and H2O2-induced oxidative stress, as well as hormones like salicylic acid, abscisic acid, ethylene, and methyl jasmonate, controlled the expression of HbPALs. The majority of HbPALs were also regulated by powdery mildew, anthracnose, and Corynespora leaf fall disease infection. In addition, HbPAL1, HbPAL4, and HbPAL7 were significantly up-regulated in the bark of tapping panel dryness rubber trees relative to that of healthy trees. Our results provide a thorough comprehension of the characteristics of HbPAL genes and set the groundwork for further investigation of the biological functions of HbPALs in rubber trees.

Magnetic vitrimer-based soft robotics.

Magnetically responsive elastomers, consisting of elastomer embedded with magnetic particles, can produce fast and reversible actuation when subjected to a magnetic field. They have been extensively explored to construct versatile remotely controllable soft robots. Nevertheless, the magnetically induced actuation strain in elastomers is typically small, which limits its broad applications. Recently, magnetic particles have been mixed with viscous fluids to enable giant magnetically induced deformations. However, their response speed is slow and the actuation is usually irreversible. In this work, we have developed a magnetic vitrimer (MV), with magnetic particles mixed with the polymer network containing abundant dynamic covalent bonds. At room temperature, the MV behaves like a regular magnetically responsive elastomer. When the temperature is elevated to the exchange reaction temperature of the dynamic covalent bonds, the material behaves like a viscous magnetically responsive fluid, which can produce large deformations. The embedded magnetic particles and the vitrimer matrix also make the material self-healable without requiring any direct touch. We have demonstrated that with the guidance of an externally applied magnetic field, a MV-based soft robot can pass through a confined space, dramatically change its configuration, self-heal without any contact, catch, secure and release a fast-moving object, and move along a planned path.

Ectopic Expression of HbRPW8-a from Hevea brasiliensis Improves Arabidopsis thaliana Resistance to Powdery Mildew Fungi (Erysiphe cichoracearum UCSC1).

The RPW8s (Resistance to Powdery Mildew 8) are atypical broad-spectrum resistance genes that provide resistance to the powdery mildew fungi. Powdery mildew of rubber tree is one of the serious fungal diseases that affect tree growth and latex production. However, the RPW8 homologs in rubber tree and their role of resistance to powdery mildew remain unclear. In this study, four RPW8 genes, HbRPW8-a, b, c, d, were identified in rubber tree, and phylogenetic analysis showed that HbRPW8-a was clustered with AtRPW8.1 and AtRPW8.2 of Arabidopsis. The HbRPW8-a protein was localized on the plasma membrane and its expression in rubber tree was significantly induced upon powdery mildew infection. Transient expression of HbRPW8-a in tobacco leaves induced plant immune responses, including the accumulation of reactive oxygen species and the deposition of callose in plant cells, which was similar to that induced by AtRPW8.2. Consistently, overexpression of HbRPW8-a in Arabidopsis thaliana enhanced plant resistance to Erysiphe cichoracearum UCSC1 and Pseudomonas syringae pv. tomato DC30000 (PstDC3000). Moreover, such HbRPW8-a mediated resistance to powdery mildew was in a salicylic acid (SA) dependent manner. Taken together, we demonstrated a new RPW8 member in rubber tree, HbRPW8-a, which could potentially contribute the resistance to powdery mildew.

HbLFG1, a Rubber Tree (Hevea brasiliensis) Lifeguard Protein, Can Facilitate Powdery Mildew Infection by Suppressing Plant Immunity.

Powdery mildew causes substantial losses in crop and economic plant yields worldwide. Although powdery mildew infection of rubber trees (Hevea brasiliensis), caused by the biotrophic fungus Erysiphe quercicola, severely threatens natural rubber production, little is known about the mechanism by which E. quercicola adapts to H. brasiliensis to invade the host plant. In barley and Arabidopsis thaliana, lifeguard (LFG) proteins, which have topological similarity to BAX INHIBITOR-1, are involved in host plant susceptibility to powdery mildew infection. In this study, we characterized an H. brasiliensis LFG protein (HbLFG1) with a focus on its function in regulating defense against powdery mildew. HbLFG1 gene expression was found to be upregulated during E. quercicola infection. HbLFG1 showed conserved functions in cell death inhibition and membrane localization. Expression of HbLFG1 in Nicotiana benthamiana leaves and A. thaliana Col-0 was demonstrated to significantly suppress callose deposition induced by conserved pathogen-associated molecular patterns chitin and flg22. Furthermore, we found that overexpression of HbLFG1 in H. brasiliensis mesophyll protoplasts significantly suppressed the chitin-induced burst of reactive oxygen species. Although A. thaliana Col-0 and E. quercicola displayed an incompatible interaction, Col-0 transformants overexpressing HbLFG1 were shown to be susceptible to E. quercicola. Collectively, the findings of this study provide evidence that HbLFG1 acts as a negative regulator of plant immunity that facilitates E. quercicola infection in H. brasiliensis.

Programmable actuation of liquid crystal elastomers via"living" exchange reaction.

Liquid crystal elastomers (LCEs) have recently been intensively explored in various applications as soft actuating materials and opto-mechanical coupling materials. Since the orientation of mesogens in the elastomer largely determines its properties, various methods of programming the alignment of mesogens in LCEs have recently been developed. However, most of the methods either involve complex fabrication processes or have intrinsic limitations in their applicability. In this article, we synthesize a LCE with dynamic disulfide bonds and propose a facile room-temperature mechanical programming method via a "living" exchange reaction without requiring additional stimuli and catalysts. We also, for the first time, reveal the "living" exchange reaction of disulfide bonds at room temperature in a bulk polymer. Such a "living" exchange reaction relies on the significantly prolonged lifetime of free radicals generated during the breakage of disulfide bonds, which is counterintuitive and in contrast to most previous understandings. Thanks to the disulfide bond, the patterned orientation of the mesogens in the LCE is erasable and reprogrammable. In addition, the synthesized LCE also exhibits a shape memory effect, enabling the design of deployable soft actuating devices.

Evaluation of antifungal metabolites activity from bacillus licheniformis OE-04 against Colletotrichum gossypii.

Anthracnose disease in the cotton plant caused by fungal pathogen Colletotrichum gossypii. It is supposed to be most critical diseases in the cotton crop as it causes infection and leads to complete damaging of the cotton crop by infecting the leaves, stems, and bolls in the field. The disease control is challenging due to the absence of an effective fungicide without damaging the farmer health and environment. So the series of experiments were designed to assess the antagonistic activity of biosurfactant released by strain Bacillus licheniformis OE-04 against the anthracnose causing agent in cotton and this strain was screened out from forty eight strain of rhizobacteria. We also estimated the heat stability and pH range and toxicity of biosurfactant produced by strain 0E-04. The results showed that biosurfactant has maximum antifungal activity against C. gossypii. In vitro study concluded that the biosurfactant can reduce fungal activity by inhibiting the spore germination of C. gossypii. Moreover, the biosurfactant also has wide pH and temperature range. We observed Antifungal activity of biosurfactant at 5 to 10 pH range and temperature range was also wide from room temperature to 100 °C. We also observed the toxicity of biosurfactant produced by Bacillus licheniformis against zebra fish (Danio rerio). We were noticed that biosurfactant have least harmful effect with maximum concentration. The study confirmed that biosurfactant of Bacillus licheniformis have high pH and heat stability range with least harmful effects so it can be a good replacement of chemical pesticides for cotton anthracnose control.

Development of SNP-Based Markers to Identify Colletotrichum gossypii in Upland Cotton.

Colletotrichum gossypii is a causal agent of anthracnose in upland cotton (Gossypii hirsutum). C. gossypii belongs to the C. gloeosporioides species complex. However, the high level of genetic similarity among the species within the C. gloeosporioides species complex makes the development of a diagnostic assay for C. gossypii challenging. Furthermore, the spore size and cultural characteristics of C. gossypii from different geographical areas can vary. In this study, we examined the morphological variance, growth pattern, and pathogenicity of C. gossypii and developed a molecular diagnostic assay to detect C. gossypii in cotton plants from different regions of China. To overcome any ambiguity in morphological and pathogenic characteristics, a set of primers targeting the ß-tubulin (TUB) gene of C. gossypii was designed. Single-nucleotide polymorphisms (SNP) were used to identify C. gossypii at the species level using DNA sequence alignments of the TUB gene. The SPSCG/F and SPSCG/R primer pair only amplified C. gossypii, and was able to amplify C. gossypii in mixtures of other Colletotrichum spp., even when DNA concentrations were up to 10 times lower than that of the other species. This is the first report of the development of SNP-based markers for the specific identification of C. gossypii.

Porous double network gels with high toughness, high stretchability and fast solvent-absorption.

Using the freeze-drying method, we fabricated porous double network gels with high toughness, high stretchability and fast solvent-absorption. When the freezing temperature was -20 °C and the freezing time was 24 hours, pores with diameters around 300 µm could form in the gel. When the freezing temperature was lowered to -196 °C and the freezing time was reduced to 10 minutes, monodisperse pores with diameters around 15 µm could form in the gel. We found out that both porous gels fabricated under different conditions could absorb solvent much more and much faster than a nonporous gel. Furthermore, we found that the rupturing strain, stiffness and strength of the porous double network gels were all comparable to the nonporous double network gel when containing the same amount of solvent. The unique combination of the mechanical properties of the porous double network gels might motivate new explorations of gels in practical applications.

Identification of Powdery Mildew Responsive Genes in Hevea brasiliensis through mRNA Differential Display.

Powdery mildew is an important disease of rubber trees caused by Oidium heveae B. A. Steinmann. As far as we know, none of the resistance genes related to powdery mildew have been isolated from the rubber tree. There is little information available at the molecular level regarding how a rubber tree develops defense mechanisms against this pathogen. We have studied rubber tree mRNA transcripts from the resistant RRIC52 cultivar by differential display analysis. Leaves inoculated with the spores of O. heveae were collected from 0 to 120 hpi in order to identify pathogen-regulated genes at different infection stages. We identified 78 rubber tree genes that were differentially expressed during the plant-pathogen interaction. BLAST analysis for these 78 ESTs classified them into seven functional groups: cell wall and membrane pathways, transcription factor and regulatory proteins, transporters, signal transduction, phytoalexin biosynthesis, other metabolism functions, and unknown functions. The gene expression for eight of these genes was validated by qRT-PCR in both RRIC52 and the partially susceptible Reyan 7-33-97 cultivars, revealing the similar or differential changes of gene expressions between these two cultivars. This study has improved our overall understanding of the molecular mechanisms of rubber tree resistance to powdery mildew.

Embedded Physical Intelligence in Liquid Crystalline Polymer Actuators and Robots.

Responsive materials possess the inherent capacity to autonomously sense and respond to various external stimuli, demonstrating physical intelligence. Among the diverse array of responsive materials, liquid crystalline polymers (LCPs) stand out for their remarkable reversible stimuli-responsive shape-morphing properties and their potential for creating soft robots. While numerous reviews have extensively detailed the progress in developing LCP-based actuators and robots, there exists a need for comprehensive summaries that elucidate the underlying principles governing actuation and how physical intelligence is embedded within these systems. This review provides a comprehensive overview of recent advancements in developing actuators and robots endowed with physical intelligence using LCPs. This review is structured around the stimulus conditions and categorizes the studies involving responsive LCPs based on the fundamental control and stimulation logic and approach. Specifically, three main categories are examined: systems that respond to changing stimuli, those operating under constant stimuli, and those equip with learning and logic control capabilities. Furthermore, the persisting challenges that need to be addressed are outlined and discuss the future avenues of research in this dynamic field.

A PM2.5 pollution-level adaptive air filtration system based on elastic filters for reducing energy consumption.

Exacerbated by human activities and natural events, air pollution poses severe health risks, requiring effective control measures to ensure healthy living environments. Traditional filtration systems that employ high-efficiency particulate air (HEPA) filters are capable of effectively removing particulate matter (PM) in indoor environments. However, these systems often work without considering the fluctuations in air pollution levels, leading to high energy consumption. This study proposed a novel PM2.5 pollution-level adaptive air filtration system that combined elastic thermoplastic polyurethane (TPU) filters and an Internet of Things (IoT) system. The developed system can effectively adjust its filtration performance (i.e., pressure drop and PM2.5 filtration efficiency) in response to real-time air quality conditions by mechanically altering the structures of TPU filters. Furthermore, while operating in varied pollution conditions, the proposed system demonstrated remarkable reductions in pressure drop without notably compromising the pollution control capability. Finally, the energy consumption of the pollution-level adaptive air filtration system was estimated when applied in mechanical ventilation systems in different cities (Hong Kong, Beijing, and Xi'an) with various pollution conditions. The results revealed that, compared to a traditional fixed system, the annual energy consumption could be reduced by up to ∼26.4 % in Hong Kong.

Programmable responsive metamaterials for mechanical computing and robotics.

Unconventional computing based on mechanical metamaterials has been of growing interest, including how such metamaterials might process information via autonomous interactions with their environment. Here we describe recent efforts to combine responsive materials with nonlinear mechanical metamaterials to achieve stimuli-responsive mechanical logic and computation. We also describe some key challenges and opportunities in the design and construction of these devices, including the lack of comprehensive computational tools, and the challenges associated with patterning multi-material mechanisms.

Integrated physiology, transcriptome and proteome analyses highlight the potential roles of multiple hormone-mediated signaling pathways involved in tapping panel dryness in rubber tree.

Currently, one of the most serious threats to rubber tree is the tapping panel dryness (TPD) that greatly restricts natural rubber production. Over-tapping or excessive ethephon stimulation is regarded as the main cause of TPD occurrence. Although extensive studies have been carried out, the molecular mechanism underlying TPD remains puzzled. An attempt was made to compare the levels of endogenous hormones and the profiles of transcriptome and proteome between healthy and TPD trees. Results showed that most of endogenous hormones such as jasmonic acid (JA), 1-aminocyclopropanecarboxylic acid (ACC), indole-3-acetic acid (IAA), trans-zeatin (tZ) and salicylic acid (SA) in the barks were significantly altered in TPD-affected rubber trees. Accordingly, multiple hormone-mediated signaling pathways were changed. In total, 731 differentially expressed genes (DEGs) and 671 differentially expressed proteins (DEPs) were identified, of which 80 DEGs were identified as putative transcription factors (TFs). Further analysis revealed that 12 DEGs and five DEPs regulated plant hormone synthesis, and that 16 DEGs and six DEPs were involved in plant hormone signal transduction pathway. Nine DEGs and four DEPs participated in rubber biosynthesis and most DEGs and all the four DEPs were repressed in TPD trees. All these results highlight the potential roles of endogenous hormones, signaling pathways mediated by these hormones and rubber biosynthesis pathway in the defense response of rubber trees to TPD. The present study extends our understanding of the nature and mechanism underlying TPD and provides some candidate genes and proteins related to TPD for further research in the future.

A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network.

Background: The convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification. Objective: The aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD. Methods: First, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer. Results: Finally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold cross-validation strategy. It was found that the classification accuracy of combined method achieved 98.869%. Conclusion: These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional connections, and characterize brain functional abnormalities, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD.

Construction and analysis of the tapping panel dryness-related lncRNA/circRNA-miRNA-mRNA ceRNA network in latex of Hevea brasiliensis.

Tapping panel dryness (TPD) results in a severe reduction in latex yield in Hevea brasiliensis. However, the molecular regulatory mechanisms of TPD occurrence are still largely unclear. In this study, whole-transcriptome sequencing was carried out on latex from TPD and healthy trees. In total, 7078 long noncoding RNAs (lncRNAs), 3077 circular RNAs (circRNAs), 4956 miRNAs, and 25041 mRNAs were identified in latex, among which 435 lncRNAs, 68 circRNAs, 320 miRNAs, and 1574 mRNAs were differentially expressed in the latex of TPD trees. GO and KEGG analyses indicated that plant hormone signal transduction, MAPK signaling pathway, and ubiquitin-mediated proteolysis were the key pathways associated with TPD onset. Phytohormone profiling revealed significant changes in the contents of 28 hormonal compounds, among which ACC, ABA, IAA, GA, and JA contents were increased, while SA content was reduced in TPD latex, suggesting that hormone homeostasis is disrupted in TPD trees. Furthermore, we constructed a TPD-related competitive endogenous RNA (ceRNA) regulatory network of lncRNA/circRNA-miRNA-mRNA with 561 edges and 434 nodes (188 lncRNAs, 5 circRNAs, 191 miRNAs, and 50 mRNAs) and identified two hub lncRNAs (MSTRG.11908.1 and MSTRG.8791.1) and four hub miRNAs (hbr-miR156, miR156-x, miRf10477-y, and novel-m0452-3p). Notably, the lncRNA-miR156/157-SPL module containing three hubs probably plays a crucial role in TPD onset. The expression of network hubs and the lncRNA-miR156/157-SPL module were further validated by qRT-PCR. Our results reveal the TPD-associated ceRNA regulatory network of lncRNA/circRNA-miRNA-mRNA in latex and lay a foundation for further investigation of molecular regulatory mechanisms for TPD onset in H. brasiliensis.

A modular strategy for distributed, embodied control of electronics-free soft robots.

Robots typically interact with their environments via feedback loops consisting of electronic sensors, microcontrollers, and actuators, which can be bulky and complex. Researchers have sought new strategies for achieving autonomous sensing and control in next-generation soft robots. We describe here an electronics-free approach for autonomous control of soft robots, whose compositional and structural features embody the sensing, control, and actuation feedback loop of their soft bodies. Specifically, we design multiple modular control units that are regulated by responsive materials such as liquid crystal elastomers. These modules enable the robot to sense and respond to different external stimuli (light, heat, and solvents), causing autonomous changes to the robot's trajectory. By combining multiple types of control modules, complex responses can be achieved, such as logical evaluations that require multiple events to occur in the environment before an action is performed. This framework for embodied control offers a new strategy toward autonomous soft robots that operate in uncertain or dynamic environments.

A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network.

Background: Most patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients. Objective: This study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition. Methods: First, considering the network interactions in different frequency bands (δ, θ, α, ß, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective. Results: Finally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy. Conclusion: These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.