A novel 268 kb deletion combined with a splicing variant in IL7R causes of severe combined immunodeficiency in a Chinese family: a case report.

BACKGROUND: Severe combined immunodeficiency (SCID) is a group of fatal primary immunodeficiencies characterized by the severe impairment of T-cell differentiation. IL7R deficiency is a rare form of SCID that usually presents in the first months of life with severe and opportunistic infections, failure to thrive, and a high risk of mortality unless treated. Although recent improvements in early diagnosis have been achieved through newborn screening, few IL7R-related SCID patients had been reported in the Chinese population. CASE PRESENTATION: Here, we retrospectively analyzed a case of SCID in a 5-month-old girl with symptoms, including severe T-cell depletion, recurrent fever, oral ulcers, pneumonia, hepatosplenomegaly, bone marrow hemophagocytosis, and bacterial and viral infections. Whole-exome sequencing (WES), quantitative PCR (qPCR), and chromosome microarray analysis (CMA) were performed to identify the patient's genetic etiology. We identified a 268 kb deletion and a splicing variant, c.221 + 1G > A, in the proband. These two variants of IL7R were inherited from the father and mother. CONCLUSIONS: To our knowledge, this is the first report of whole IL7R gene deletion in combination with a pathogenic splicing variant in a patient with SCID. This deletion also expands the pathogenic variation spectrum of SCID caused by IL7R. The incorporation of exome-based copy number variant analysis makes WES a powerful molecular diagnostic technique for the clinical diagnosis of pediatric patients.

[Clinical and genetic analysis of a rare fetus with Protein C deficiency due to compound heterozygous variants of PROC gene].

OBJECTIVE: To explore the genetic etiology for a fetus with hydrocephalus and intraventricular hemorrhage. METHODS: Trio whole exome sequencing was carried out. Candidate variants were verified by Sanger sequencing of the fetus and its parents. RESULTS: The fetus was found to harbor c.818G>A (p.W273X) and c.833T>C (p.L278P) compound heterozygous variants of the PROC gene, which were respectively inherited from its mother and father. Based on the guidelines of the American College of Medical Genetics and Genomics (ACMG), both variants were predicted to be likely pathogenic (PVS1_Strong+PM2_Supporting+PP4; PM2_Supporting+PM3+PP1+PP3+PP4). CONCLUSION: The fetus was diagnosed with Protein C deficiency due to the c.818G>A (p.W273X) and c.833T>C (p.L278P) compound heterozygous variants of the PROC gene. Above finding has enriched the spectrum of PROC gene variants and enabled genetic counseling and prenatal diagnosis for the family.

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.

circRNF13, a novel N6-methyladenosine-modified circular RNA, enhances radioresistance in cervical cancer by increasing CXCL1 mRNA stability.

BACKGROUND: Circular RNAs (circRNAs) and N6-methyladenosine (m6A) have been shown to play an increasingly critical role in the development of different cancers. However, there is limited evidence on how circRNAs and m6A interact to affect the radiosensitivity of cervical cancer (CC). This study provides a mechanistic understanding of the novel m6A-regulated circRNF13 in enhancing radioresistance in CC. METHODS: Differentially expressed circRNAs were identified from radiosensitive and radioresistant CC tissues. Meanwhile, these circRNAs were subjected to methylated RNA immunoprecipitation (Me-RIP). Finally, the effects of these circRNAs on radiosensitivity were characterized. RESULTS: CircRNF13 was poorly expressed in CC patients that were sensitive to concurrent radiochemotherapy. Experiments conducted both in vitro and in vivo confirmed that the knockdown of circRNF13 potentiated the radiosensitivity of CC cells. Further mechanistic studies revealed that METTL3/YTHDF2 promoted the degradation of circRNF13 and subsequently affected the stability of CXC motif chemokine ligand 1 (CXCL1), ultimately enhancing the radiosensitivity of CC cells. CONCLUSION: This study identified circRNF13 as a novel m6A-modified circRNA and validated the METTL3/YTHDF2/circRNF13/CXCL1 axis as a potential target for CC radiotherapy.

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.

Investigating the vertical distribution of dissolved organic matter in 5-m soil profiles in farmland and typical woodland on the southern loess plateau.

With the implementation of the 'Grain-for-Green' program on the Chinese Loess Plateau (CLP), drought-tolerant deep-rooted plants have been increasingly introduced to the northwest in China. However, the vertical features of dissolved organic matter (DOM) in deep soil profiles on CLP during the 'Grain-for-Green' program is still not well understood. In the study, ultraviolet-visible (UV-Vis) spectroscopy and three-dimensional fluorescence excitation-emission matrices (3D-EEMs) with parallel factor analysis (PARAFAC) were used to characterize DOM in 5-m profile of farmland and forestland (Pinus tabulaeformis and Robinia pseudoacacia) in the southern CLP. The results demonstrated that the average dissolved organic carbon (DOC) content of the surface layer of farmland (119.3 mg kg-1 soil) was lower than that of forestland (Pinus tabulaeformis 175.5 mg kg-1 soil; Robinia pseudoacacacia 166.4 mg kg-1 soil). The DOC content gradually decreased with increasing soil depth and reached stability after 2 m depth. Three substances, including tryptophan-like substances (C1) and two humic acid-like substances (C2, C3), were detected from all samples. Tryptophan-like substances (C1) significantly increased with soil depth while humic acid-like substances (C2, C3) significantly decreased particularly in farmland. The humic acid-like content of surface soils (Robinia pseudoacacia) was relatively higher, but the difference between the two vegetation soils was not significant. The freshness index (ß/α) values of DOM as well as biological index (BIX) values were significantly higher in farmland than that in forestland, and the humification index (HIX) values were lower than in forestland soils, indicating that the change of soil DOM in farmland was more active than that in forestland and more dependent on local terrestrial sources. These results could contribute to a better understanding of the vertical distribution and features of soil DOM during the 'Grain-for-Green' program of CLP.

A new view into three-dimensional excitation-emission matrix fluorescence spectroscopy for dissolved organic matter.

Three-dimensional excitation-emission matrix fluorescence spectroscopy (3D EEMs) has been extensively used for dissolved organic matter (DOM) characterization. However, the application of 3D EEMs is constantly limited by issues such as contradictory component identification, confusing interpretation of spectral indicators, and inability to establish biodegradability. In this study, some improvements were proposed by investigating the 3D EEMs, spectral indicators, and degradability of the standard and representative DOM. To overcome the unclear identification of DOM components, it was recommended to partition 3D EEMs into three subareas: aromatic protein (New-I), humic-like (New-II), and soluble microbial by-product-like (New-III). Significant strong positive correlations (ρ = 0.727, P < 0.001) were observed between fluorescence index (FI) and biological index (BIX), and (R = 0.809, P < 0.001) humification index (HIX) and specific ultraviolet absorbance of 254 nm (SUVA254). Except for FI (R = -0.483, P = 0.023), no other spectral indicators (P > 0.05) were found to be significantly correlated with molecular weight. As thence results, the FI and HIX were the most suitable indicators for evaluating DOM. The half-life (20 < 21 < 26 < 29 < 46 days) revealed that the degradability of individual DOM components was in the order of tyrosine > tryptophan > fulvic acid > protein > humic acid. The degradation dynamics were governed by first-order decay kinetics (R2 = 0.91-0.99). This study clarified the fluorescence properties and degradability of DOM, as well as the reliability of spectral indicators. The degradation performance of individual DOM components engaged in the carbon cycling process was revealed, paving the path for further applications of 3D EEMs in DOM research.

Fulvic-polyphosphate composite embedded in ZnO nanorods (FA-APP@ZnO) for efficient P/Zn nutrition for peas (Pisum sativum L.).

A nano-fertilizer (FA-APP@ZnO) was designed and prepared based on the copolymer of fulvic acid (FA) and ammonium polyphosphate (APP) with ZnO nanorods embedded, to tackle the antagonism between phosphorus (P) and zinc (Zn) in fertilization. FA-APP@ZnO was confirmed to revert the precipitability of H2PO4 - and Zn2+ into a synergistic performance, where FA and APP can disperse ZnO nanorods, and in return, ZnO catalyzes the hydrolysis of the absorbed APP. The hydrolysis rate constant of pyrophosphates consequently increased 8 times. The dry biomass of pea (Pisum sativum L.) under the FA-APP@ZnO hydroponics for 7 days increased by 119%, as compared with the situation employing the conventional NH4H2PO4 and ZnSO4 compound fertilizer. Moreover, the uptake of seedlings for P and Zn was enhanced by 54% and 400%, respectively. The accelerated orthophosphate release due to ZnO catalysis and the well-dispersed ZnO nanorods enabled by APP met the urgent demand for P and Zn nutrients for peas, especially at their vigorous seedling stage. This work would provide a new idea for constructing nano-platforms to coordinate the incompatible P and Zn nutrients for the improvement of agronomic efficiency.

Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA.

Epilepsy is a neurological disorder that is characterized by transient and unexpected electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is associated with the primary interest of the evaluation and auxiliary diagnosis of epileptic patients. The aim of this study is to establish a hybrid model with improved particle swarm optimization (PSO) and a genetic algorithm (GA) to determine the optimal combination of features for epileptic seizure detection. First, the second-order difference plot (SODP) method was applied, and ten geometric features of epileptic EEG signals were derived in each frequency band (δ, θ, α and ß), forming a high-dimensional feature vector. Secondly, an optimization algorithm, AsyLnCPSO-GA, combining a modified PSO with asynchronous learning factor (AsyLnCPSO) and the genetic algorithm (GA) was proposed for feature selection. Finally, the feature combinations were fed to a naïve Bayesian classifier for epileptic seizure and seizure-free identification. The method proposed in this paper achieved 95.35% classification accuracy with a tenfold cross-validation strategy when the interfrequency bands were crossed, serving as an effective method for epilepsy detection, which could help clinicians to expeditiously diagnose epilepsy based on SODP analysis and an optimization algorithm for feature selection.

(20S) Ginsenoside Rh2-Activated, Distinct Apoptosis Pathways in Highly and Poorly Differentiated Human Esophageal Cancer Cells.

Ginsenoside Rh2 (G-Rh2), a rare ginsenoside isolated from red ginseng, has considerable anti-cancer activity and induces apoptosis in a variety of cancer cells, but its activity in esophageal cancer cells is unclear. In this study, we examined the cytotoxic activity of (20S) G-Rh2 in highly differentiated esophageal squamous ECA109 cells and poorly differentiated esophageal squamous TE-13 cells. (20S) G-Rh2 exerted intense cytotoxicity in ECA109 and TE-13 cells with an IC50 of 2.9 and 3.7 µg/mL, respectively. After treatment with G-Rh2, Bcl-2, and Bcl-xL, the two main anti-apoptosis Bcl-2 family proteins upregulated, and Bax and Bak, the two key pro-apoptosis proteins translocated to mitochondria in both cell lines. At the same time, cytochrome c and Smac released from mitochondria, followed by caspase-9 activation, indicating that a mitochondria-mediated intrinsic apoptosis pathway was activated in both cell lines upon treatment with (20S) G-Rh2. It is noteworthy that (20S) G-Rh2 upregulated the transcription and protein expression of two death receptors, Fas and DR5, and subsequently activated Caspase-8 in the TE-13 cells but not in the ECA109 cells. Taken together, we demonstrated the potent anti-esophageal cancer cell activity of (20S) G-Rh2 and showed its working mechanism in two differentiated esophageal cancer cells, which can provide important evidence for developing an effective strategy for anti-esophageal cancer treatment.

Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals.

Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver's attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain's information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain's local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue.

Single-atom palladium anchored N-doped carbon towards oxygen electrocatalysis for rechargeable Zn-air batteries.

Herein, an atomically dispersed palladium catalyst on a hierarchical porous structure of N-doped carbon (Pd1/N-C) is prepared using a facile freeze-drying-assisted strategy. Freeze-drying methods not only suppress the aggregation of Pd atoms but also successfully produce abundant nanopores. HAADF-STEM confirms that Pd single atoms are uniformly anchored on the N-C surface. The Pd1/N-C electrocatalyst enhances the ORR and OER activity and durability compared to N-C and Pd-NPs/N-C. Rechargeable Zn-air batteries (ZABs) based on novel Pd1/N-C exhibit a peak power density of 113.7 mW cm-2 and maintain a voltage efficiency of 64.0% after 495 cycles at a discharge current density of 5 mA cm-2. Besides, two ZABs in series can supply an LED light for at least 170 h.

[Analysis of TUBB2B gene variant in a fetus with complex cortical dysplasia with other brain malformations-7].

OBJECTIVE: To explore the genetic basis for a fetus with dysgenesis of corpus callosum and other brain malformations. METHODS: Whole exome sequencing was carried out for the fetus and its parents. Suspected pathogenic variants were verified by Sanger sequencing. RESULTS: A novel de novo missense variant c.758T>A (p.L253Q) of the TUBB2B gene was identified, which was unreported previously. Based on the guidelines from the American College of Medical Genetics, the c.758T>A variant was predicted to be likely pathogenic. Bioinformatics analysis predicted that the leucine at position 253 was highly conserved among various species, and the c.758T>A variant may impact the formation of hydrogen bonds between Leu253 and Asp249 and Met257 residues, which in turn may affect the combination of GTP/GDP and function of the TUBB2B protein. CONCLUSION: The c.758T>A variant of the TUBB2B gene probably underlay the fetal malformations in this Chinese family. Above discovery has enriched the spectrum of TUBB2B gene variants and provided a basis for genetic counseling and prenatal diagnosis.

[Variation analysis of EPG5 gene in a Vici syndrome family].

OBJECTIVE: To explore the genetic etiology of Vici syndrome in a Chinese family. METHODS: Whole exome sequencing (WES) technology was used to detect gene variants in a fetus of abnormal ultrasonic structure without abnormalities in routine chromosome karyotype analysis and SNP-array. Sanger sequencing and bioinformatics prediction were performed for the suspected variants of the fetus and parents. RESULTS: The fetus and the elder sister have carried c. 2427delC (p.T809fs) and c.1886A>T (p.E629V) compound heterozygous variants of the EPG5 gene, which were respectively inherited from their mother and father. Neither variant was reported previously. According to ACMG guidelines, the c.2427delC variant was predicted as pathogenic, while the c.1886A>T variant was of uncertain significance. PolyPhen-2 and PROVEAN software indicated that c.1886A>T variant was probably damaging. CONCLUSION: The c.2427delC and c.1886A>T variants of the EPG5 gene probably underlie the pathogenesis of the Vici syndrome in this family. Above finding has enriched the variational spectrum of EPG5 gene and provided a basis for genetic counseling and prenatal diagnosis for the family.

[Genetic analysis of a rare fetus with mandibulofacial dysostosis Guion-Almeida type].

OBJECTIVE: To delineate the clinical and genetic features of a fetus with micrognathia, low-set ears, microtia, polyhydramnios and anechoic stomach by ultrasonography. METHODS: Whole exome sequencing (WES) was carried out to detect genetic variant in the fetus, for which routine chromosomal karyotyping and chromosomal microarray analysis (CMA) yielded no positive finding. Candidate variants were verified by Sanger sequencing and bioinformatic analysis. RESULTS: WES revealed that the fetus has carried a de novo nonsense c.2302C>T (p.Q768X) variant in exon 23 of the EFTUD2 gene, which was detected in neither parent. The variant was unreported previously and may lead to premature termination of the translation of EFTUD2 protein at the 768th amino acid. Bioinformatic analysis predicted the amino acid to be highly conserved and may alter the structure and function of the EFTUD2 protein. CONCLUSION: The c.2302C>T variant of the EFTUD2 gene probably underlay the mandibulofacial dysostosis Guion-Almeida type in the fetus. Discovery of the novel variant has enriched variant spectrum of the EFTUD2 gene and provided a basis for genetic counseling and prenatal diagnosis for the family.

Spike-sorting analysis of neural electrical signals evoked by acupuncture based on model.

Acupuncturing the Zusanli (ST 36) point with different types of manual acupuncture manipulations (MAs) and different frequencies can evoke a lot of neural response activities in spinal dorsal root neurons. The action potential is the basic unit of communication in the neural response process. With the rapid development of the electrode acquisition technology, we can simultaneously obtain neural electrical signals of multiple neurons in the target area. So it is crucial to extract spike trains of each neuron from raw recorded data. To solve the problem of variability of the spike waveform, this paper adopts a optimization algorithm based on model to improve the wave-cluster algorithm, which can provide higher accuracy and reliability. Further, through this optimization algorithm, we make a statistical analysis on spike events evoked by different MAs. Results suggest that numbers of response spikes under reinforcing manipulations are far more than reducing manipulations, which mainly embody in synchronous spike activities.

A Novel Missense Variant in the Gene PPP2R5D Causes a Rare Neurodevelopmental Disorder with Increased Phenotype.

PPP2R5D-related neurodevelopmental disorder, which is mainly caused by de novo missense variants in the PPP2R5D gene, is a rare autosomal dominant genetic disorder with about 100 patients and a total of thirteen pathogenic variants known to exist globally so far. Here, we present a 24-month-old Chinese boy with developmental delay and other common clinical characteristics of PPP2R5D-related neurodevelopmental disorder including hypotonia, macrocephaly, intellectual disability, speech impairment, and behavioral abnormality. Trio-whole exome sequencing (WES) and Sanger sequencing were performed to identify the causal gene variant. The pathogenicity of the variant was evaluated using bioinformatics tools. We identified a novel pathogenic variant in the PPP2R5D gene (c.620G>T, p.Trp207Leu). The variant is located in the variant hotspot region of this gene and is predicted to cause PPP2R5D protein dysfunction due to an increase in local hydrophobicity and unstable three-dimensional structure. We report a novel pathogenic variant of PPP2R5D associated with PPP2R5D-related neurodevelopmental disorder from a Chinese family. Our findings expanded the phenotypic and mutational spectrum of PPP2R5D-related neurodevelopmental disorder.

Spiking Correlation Analysis of Synchronous Spikes Evoked by Acupuncture Mechanical Stimulus.

Acupuncturing the ST36 acupoint can evoke the response of the sensory nervous system, which is translated into output electrical signals in the spinal dorsal root. Neural response activities, especially synchronous spike events, evoked by different acupuncture manipulations have remarkable differences. In order to identify these network collaborative activities, we analyze the underlying spike correlation in the synchronous spike event. In this paper, we adopt a log-linear model to describe network response activities evoked by different acupuncture manipulations. Then the state-space model and Bayesian theory are used to estimate network spike correlations. Two sets of simulation data are used to test the effectiveness of the estimation algorithm and the model goodness-of-fit. In addition, simulation data are also used to analyze the relationship between spike correlations and synchronous spike events. Finally, we use this method to identify network spike correlations evoked by four different acupuncture manipulations. Results show that reinforcing manipulations (twirling reinforcing and lifting-thrusting reinforcing) can evoke the third-order spike correlation but reducing manipulations (twirling reducing and lifting-thrusting reducing) does not. This is the main reason why synchronous spikes evoked by reinforcing manipulations are more abundant than reducing manipulations.

Model Predictive Control for Seizure Suppression Based on Nonlinear Auto-Regressive Moving-Average Volterra Model.

This article investigates a closed-loop brain stimulation method based on model predictive control strategy to suppress epileptic seizures. A neural mass model (NMM), exhibiting the normal and various epileptic seizures by changing physiologically meaningful parameters, is used as a black-box model of the brain. Based on system identification, an auto-regressive moving-average Volterra model is established to reveal the relationship between stimulation and neuronal responses. Then, the model predictive control strategy is implemented based the Volterra model, which can generate an optimal stimulation waveform to eliminate epileptiform waves. The computational simulation results indicate the proposed closed-loop control strategy can optimize the stimulation waveform without particular knowledge of the physiological properties of the system. The robustness of the proposed control strategy to system disturbances makes it more appropriate for future clinical application.

Robust closed-loop control of spike-and-wave discharges in a thalamocortical computational model of absence epilepsy.

In this paper, we investigate the abatement of spike-and-wave discharges in a thalamocortical model using a closed-loop brain stimulation method. We first explore the complex states and various transitions in the thalamocortical computational model of absence epilepsy by using bifurcation analysis. We demonstrate that the Hopf and double cycle bifurcations are the key dynamical mechanisms of the experimental observed bidirectional communications during absence seizures through top-down cortical excitation and thalamic feedforward inhibition. Then, we formulate the abatement of epileptic seizures to a closed-loop tracking control problem. Finally, we propose a neural network based sliding mode feedback control system to drive the dynamics of pathological cortical area to track the desired normal background activities. The control system is robust to uncertainties and disturbances, and its stability is guaranteed by Lyapunov stability theorem. Our results suggest that the seizure abatement can be modeled as a tracking control problem and solved by a robust closed-loop control method, which provides a promising brain stimulation strategy.