Active fault-tolerant control for the dual-valve hydraulic system with unknown dead-zone.

This paper proposes a method for high-performance motion control of the dual-valve hydraulic system subject to parameter and model uncertainties, unknown proportional valve dead-zone, and servo valve fault. By constructing a detailed dual-valve fault system model (DFSM), a disturbance observer-based adaptive robust fault-tolerant controller is proposed via the backstepping method. This controller integrates a model-based fault detection algorithm for real-time fault monitoring and subsequent controller reconfiguration. Additionally, the DFSM-based adaptive robust control (ARC) technique is applied to handle the unknown dead-zone problem and other nonlinearities, ensuring precise control. Once the servo valve fault occurs, a nonlinear observer estimates the fault and collaborates with the ARC to establish a reconfigured controller, thereby maintaining motion control. The effectiveness of the proposed method has been experimentally verified.

A Torque Control Strategy for a Robotic Dolphin Platform Based on Angle of Attack Feedback.

Biological fish can always sense the state of water flow and regulate the angle of attack in time, so as to maintain the highest movement efficiency during periodic flapping. The biological adjustment of the caudal fin's angle of attack (AoA) depends on the contraction/relaxation of the tail muscles, accompanying the variation in tail stiffness. During an interaction with external fluid, it helps to maintain the optimal angle of attack during movement, to improve the propulsion performance. Inspired by this, this paper proposes a tail joint motion control scheme based on AoA feedback for the high-speed swimming of bionic dolphins. Firstly, the kinematic characteristics of the designed robot dolphin are analyzed, and the hardware basis is clarified. Second, aiming at the deficiency of the tail motor, which cannot effectively cooperate with the waist joint motor during high-frequency movement, a compensation model for the friction force and latex skin-restoring force is designed, and a joint angle control algorithm based on fuzzy inference is proposed to realize the tracking of the desired joint angle for the tail joint in torque mode. In addition, a tail joint closed-loop control scheme based on angle of attack feedback is proposed to improve the motion performance. Finally, experiments verify the effectiveness of the proposed motion control scheme.

Dynamic pruning group equivariant network for motor imagery EEG recognition.

Introduction: The decoding of the motor imaging electroencephalogram (MI-EEG) is the most critical part of the brain-computer interface (BCI) system. However, the inherent complexity of EEG signals makes it challenging to analyze and model them. Methods: In order to effectively extract and classify the features of EEG signals, a classification algorithm of motor imagery EEG signals based on dynamic pruning equal-variant group convolutional network is proposed. Group convolutional networks can learn powerful representations based on symmetric patterns, but they lack clear methods to learn meaningful relationships between them. The dynamic pruning equivariant group convolution proposed in this paper is used to enhance meaningful symmetric combinations and suppress unreasonable and misleading symmetric combinations. At the same time, a new dynamic pruning method is proposed to dynamically evaluate the importance of parameters, which can restore the pruned connections. Results and Discussion: The experimental results show that the pruning group equivariant convolution network is superior to the traditional benchmark method in the benchmark motor imagery EEG data set. This research can also be transferred to other research areas.

1,4-Bisvinylbenzene-Bridged BODIPY Dimers for Fluorescence Imaging in the Second Near-Infrared Window.

Herein, we report a class of 1,4-bisvinylbenzene-bridged BODIPY dimers with fluorescence emission in the second near-infrared window (NIR-II, 1000-1700 nm). These dyes show excellent NIR-II fluorescence properties and can be easily functionalized to achieve good water-solubility or tumor-targeting ability. In vivo imaging results demonstrate that these dyes have high resolution and deep-penetration NIR-II imaging ability, which enable them to be used as promising NIR-II imaging agents.

Blood-Brain Barrier Permeable Photoacoustic Probe for High-Resolution Imaging of Nitric Oxide in the Living Mouse Brain.

Alternations in the brain nitric oxide (NO) homeostasis are associated with a variety of neurodegeneration diseases; therefore, high-resolution imaging of NO in the brain is essential for understanding pathophysiological processes. However, currently available NO probes are unsuitable for this purpose due to their poor ability to cross the blood-brain barrier (BBB) or to image in deep tissues with spatial resolution. Herein, we developed a photoacoustic (PA) probe with BBB crossing ability to overcome this obstacle. The probe shows a highly selective ratiometric response toward NO, which enables the probe to image NO with micron resolution in the whole brain of living mice. Using three-dimensional PA imaging, we demonstrated that the probe could be used to visualize the detailed NO distribution in varying depth cross-sections (0-8 mm) of the living Parkinson's disease (PD) mouse brain. We also investigated the therapeutic properties of natural polyphenols in the PD mouse brain using the probe as an imaging agent and suggested the potential of the probe for screening therapeutic agents. This study provides a promising imaging agent for imaging of NO in the mouse brain with high resolution. We anticipate that these findings may open up new possibilities for understanding the biological functions of NO in the brain and the development of new imaging agents for the diagnosis and treatment of brain diseases.

Natural organic-inorganic hybrid structure enabled green biomass adhesive with desirable strength, toughness and mildew resistance.

Plant based proteins are green, sustainable, and renewable materials that show the potential to replace traditional formaldehyde resin. High performance plywood adhesives exhibit high water resistance, strength, toughness, and desirable mildew resistance. Adding petrochemical-based crosslinkers is not economically viable or environmentally benign; this chemical crosslinking strategy makes the imparted high strength and toughness less attractive. Herein, a green approach based on natural organic-inorganic hybrid structure enhancement is proposed. The design of soybean meal-dialdehyde chitosan-amine modified halloysite nanotubes (SM-DACS-HNTs@N) adhesive with desirable strength and toughness enhanced by covalent bonding (Schiff base) crosslinking and toughened by surface-modified nanofillers is demonstrated. Consequently, the prepared adhesive showed a wet shear strength of 1.53 MPa and work of debonding of 389.7 mJ, which increased by 146.8 % and 276.5 %, respectively, due to the cross-linking effect of organic DACS and toughening effect of inorganic HNTs@N. The introduction of DACS and Schiff base generation enhanced the antimicrobial property of the adhesive and increased the mold resistance of the adhesive and plywood. In addition, the adhesive has good economic benefits. This research creates new opportunities for developing biomass composites with desirable performance.

Quantifying the Leaping Motion Using a Self-Propelled Bionic Robotic Dolphin Platform.

Kinematic analysis of leaping motions can provide meaningful insights into unraveling the efficient and agile propulsive mechanisms in dolphin swimming. However, undisturbed kinematic examination of live dolphins has been very scarce due to the restriction of close-up biological observation with a motion capture system. The main objective of this study is to quantify the leaping motion of a self-propelled bionic robotic dolphin using a combined numerical and experimental method. More specifically, a dynamic model was established for the hydrodynamic analysis of a changeable submerged portion, and experimental data were then employed to identify hydrodynamic parameters and validate the effectiveness. The effects of wave-making resistance were explored, indicating that there is a varying nonlinear relationship between power and speed at different depths. In addition, the wave-making resistance can be reduced significantly when swimming at a certain depth, which leads to a higher speed and less consumed power. Quantitative estimation of leaping motion is carried out, and the results suggest that with increase of the exiting velocity and angle, the maximum height of the center of mass (CM) increases as well; furthermore, a small exiting angle usually requires a much larger exiting velocity to achieve a complete exiting motion. These findings provide implications for optimizing motion performance, which is an integral part of underwater operations in complex aquatic environments.

Discovery of BODIPY J-aggregates with absorption maxima beyond 1200 nm for biophotonics.

Organic dyes with absorption maxima in the second near-infrared window (NIR-II; 1000 to 1700 nm) are of great interest in biophotonics. However, because of the lack of appropriate molecular scaffolds, current research in this field is limited to cyanine dyes, and developing NIR-II-absorbing organic dyes for biophotonics remains an immense challenge. Here, we rationally designed an ethenylene-bridged BODIPY scaffold featuring excellent J-aggregation capabilities and revealed that the bridging ethylene unit is crucial for intermolecular J-coupling regulation. By integrating the electron-donating groups into the scaffold, we obtained a BODIPY dye, BisBDP2, with a J-aggregate absorption maximum of around 1300 nm. BisBDP2 J-aggregates show excellent photothermal performance, including intense photoacoustic response, and a high photothermal conversion efficiency value of 63%. In vivo results demonstrate the potential of J-aggregates for photoacoustic imaging and photothermal ablation of deep-seated tumors. This study will speed up the exploration of NIR-II-absorbing J-aggregates for future biophotonic applications.

Trans-omics analyses revealed key epigenetic genes associated with overall survival in secondary progressive multiple sclerosis.

BACKGROUND: Secondary progressive multiple sclerosis (SPMS) is the second most common presentation of multiple sclerosis (MS) and is characterized by a gradually deteriorating disease with or without relapses. Approximately 80% of patients with relapsing-remitting MS (RRMS) develop SPMS within 20 years. Epidemiological investigations have revealed an average 7-year life expectancy decrease (more severe in progressive subtypes) in patients with MS. Studies have focused on the neurodegenerative pathogenesis of SPMS; and epigenetic changes have been associated with disease progression in neurodegenerative disorders. However, the evidence for the association between epigenetic changes and SPMS is scarce. Thus, in this study we aimed to identify the key epigenetic genes in SPMS. METHODS: We downloaded DNA methylation and gene expression matrices from the Gene Expression Omnibus (GEO) database. We used bioinformatic analyses to identify key epigenetic genes associated with overall survival (OS) in patients with SPMS. RESULTS: We found 49 differentially methylated positions (DMPs) between the SPMS and control GSE40360 datasets. We used the wANNOVAR server to obtain 64 methylated genes. We merged the gene expression datasets (GSE131282 and GSE135511) in the NetworkAnalyst platform and found 12,442 differentially-expressed genes (DEGs) between SPMS and controls using the Fisher's method, fixed effect model, Vote counting, and direct merging methods. Moreover, we identified 21 epigenetic genes (all hyper-methylated) after an integrating analysis of DMPs and DEGs of patients with SPMS. We established an epigenetic gene signature associated with the OS of patients with SPMS including six hyper-methylated genes (ITGA6, PPP1R16B, RNF126, ABHD8, FOXK1, and SLC6A19) based on the LASSO-Cox method. The calculated individual risk scores were associated with Oss, and we divided patients into high- and low-risk groups on the basis of the mean cut-off value. The six key epigenetic genes were significantly associated with gender, disease duration, and age at death via Spearman correlation analyses. In addition, survival analyses revealed a significant OS difference between high- and low-risk groups. The ROC curves indicated good performance for this predictive model. CONCLUSION: We identified 21 hyper-methylated genes in patients with SPMS via an integrated analysis of DNA methylation and gene expression datasets. We identified a six-epigenetic gene signature that predicts the individual OS with good accuracy. These results indicated that epigenetic modifications play a vital role in the disease progression of SPMS.

Bioinformatic Analysis of Coexpressed Differentially Expressed Genes and Potential Targets for Intracerebral and Subarachnoid Hemorrhage.

BACKGROUND: Secondary brain injury following intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH) is life threatening, and effective therapeutic strategies are lacking. This study aimed to understand the molecular pathogenesis of ICH- or SAH-induced secondary brain injury and provide insights regarding potential therapeutic options. METHODS: Original data of tissue microarray studies were downloaded from the Gene Expression Omnibus database. We identified the differentially expressed genes (DEGs) for each disease and common DEGs between ICH and SAH. Functional enrichment analyses were then analyzed, and a protein-protein interaction network was constructed to strictly select hub genes. Additionally, immune infiltration analyses were used to identify the common differently distributed cells in both diseases. Finally, animal model microarrays were used for external validation. RESULTS: We identified 158 common DEGs. The common DEGs were significantly enriched in cytotoxicity and inflammation pathways. The top 10 hub genes were then filtered through the protein-protein interaction networks. Moreover, natural regulatory T, T helper 17, and dendritic cells and monocytes and macrophages were identified as common differentially distributed immune cells. Additionally, target microRNAs and related drugs of hub genes were predicted. CONCLUSIONS: This study identified a variety of key genes and their respective molecular functions involved in both ICH and SAH for better understanding of the cytotoxic and inflammatory pathogenesis of secondary brain injury. The predicted targeted microRNAs and related drugs of hub genes not only could provide insights into the novel therapeutic strategies, but also could aid in future studies and drug discovery.

Serum Cystatin C as a Potential Predictor of the Severity of Multiple System Atrophy With Predominant Cerebellar Ataxia: A Case-Control Study in Chinese Population.

Objective: Multiple system atrophy (MSA) is a serious neurodegenerative disease that is charactered by progressive neurological disability. The aim of this study was to investigate the correlation of serum oxidant factors with the severity of MSA. Methods: A total of 52 MSA patients and 52 age- and gender- matched healthy subjects were retrospectively enrolled in this study. Enzymatic colorimetric methods were used to assay the concentrations of uric acid (UA), serum creatinine (Scr), blood urea nitrogen (BUN), and cystatin C (Cys-C). Disease severity was evaluated by the Unified Multiple System Atrophy Rating Scale (UMSARS). The disease progression rate was defined by the change in UMSARS-IV (global disability score, GDS) over a 1-year period. Results: Comparisons between the two groups revealed that there were no significant differences in terms of serum Scr (70.81 ± 13.88 vs. 70.92 ± 14.19 µmol/L, p = 0.967). However, the serum levels of the other three biomarkers were significantly higher in the MSA patients (UA: 325.31 ± 84.92 vs. 291.19 ± 64.14 µmol/L, p = 0.023; BUN: 5.68 ± 1.67 vs. 4.60 ± 1.24 mmol/L, p < 0.001; Cys-C: 0.96 ± 0.15 vs. 0.89 ± 0.14 mg/L, p = 0.024). In addition, Pearson correlation analyses revealed that only serum Cys-C was significantly correlated to GDS (r = 0.281, p = 0.044). Subgroup analysis further demonstrated that serum Cys-C was the only factor that was positively associated with the disease severity in patients with MSA and predominant cerebellar ataxia (MSA-C) (r = 0.444, p = 0.018); there was no significant association in MSA patients with predominant Parkinsonism (MSA-P) (r = 0.118, p = 0.582). MSA-C patients with severe disability were shown to express higher serum levels of Cys-C than patients with mild disability (1.03 ± 0.13 vs. 0.88 ± 0.12 mg/L, p = 0.009). Finally, Kaplan-Meier plots revealed a significant difference in the 5-year probability of survival from severe disability between MSA-C patients with high- and low-concentrations of serum Cys-C (Log-rank test: X2 = 4.154, p = 0.042). ROC curve analysis confirmed that serum Cys-C exhibits good performance as a biomarker (AUC = 0.847). Conclusion: Our research indicated that oxidative stress plays a vital role in MSA. Serum Cys-C represents a potential prognostic biomarker to evaluate the severity of disease in patients with MSA-C.

Identification of immune-associated gene signature and immune cell infiltration related to overall survival in progressive multiple sclerosis.

BACKGROUND: Delayed diagnosis and noneffective treatment contribute to the shorter life expectancy in patients with progressive multiple sclerosis (PMS). Studies demonstrate the key role of autoimmunity in PMS, but the prognostic value of immune-associated factors remains unknown. Thus, this study aimed to develop an immune-associated gene (IAG) signature related to overall survival (OS) and conduct an immune cell infiltration analysis using PMS data. METHODS: The differentially expressed IAGs were identified based on gene expression profiles (from the Gene Expression Omnibus database) and IAGs (from the ImmPort database). Univariate and least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analyses were used to develop the IAG signature related to OS. Kaplan-Meier analyses were conducted, and receiver operating characteristic (ROC) curves were generated to assess the performance. Additionally, the differential distribution of immune cells was identified by Wilcoxon rank-sum tests and correlations with IAGs were analyzed using Spearman correlation analyses. Moreover, univariate and multivariate Cox regression analyses were used to identify the independent prognostic factors to develop a prognostic nomogram. RESULTS: The training group, consisting of 57 PMS lesions and 52 control tissues, was obtained through batch normalization to remove the inter-batch difference. A total of 206 differentially expressed IAGs were identified, and 38 of them were associated with OS. Thereafter, a 4-IAG signature was constructed to calculate the risk score and thus classify PMS patients into high- and low-risk groups according to mean risk score. Patients in the high-risk group had a lower survival time than those in the low-risk group. The Kaplan-Meier plots and ROC curves demonstrated a good performance in both the training and internal validation groups. Additionally, five differentially abundant immune cell types were identified and their relationships with IAGs were analyzed. Finally, risk score, cortical region, and naive B cells were identified as independent prognostic factors, and a nomogram incorporating these factors was developed to predict the OS in PMS. CONCLUSION: The novel IAG signature may be a reliable tool for assisting neurologists in predicting the OS for PMS patients in clinical settings. These findings may facilitate personalized treatment and provide insights into the complex mechanism of PMS.

N6-Methyladenosine RNA modification in cerebrospinal fluid as a novel potential diagnostic biomarker for progressive multiple sclerosis.

BACKGROUND: Progressive multiple sclerosis (PMS) is an uncommon and severe subtype of MS that worsens gradually and leads to irreversible disabilities in young adults. Currently, there are no applicable or reliable biomarkers to distinguish PMS from relapsing-remitting multiple sclerosis (RRMS). Previous studies have demonstrated that dysfunction of N6-methyladenosine (m6A) RNA modification is relevant to many neurological disorders. Thus, the aim of this study was to explore the diagnostic biomarkers for PMS based on m6A regulatory genes in the cerebrospinal fluid (CSF). METHODS: Gene expression matrices were downloaded from the ArrayExpress database. Then, we identified differentially expressed m6A regulatory genes between MS and non-MS patients. MS clusters were identified by consensus clustering analysis. Next, we analyzed the correlation between clusters and clinical characteristics. The random forest (RF) algorithm was applied to select key m6A-related genes. The support vector machine (SVM) was then used to construct a diagnostic gene signature. Receiver operating characteristic (ROC) curves were plotted to evaluate the accuracy of the diagnostic model. In addition, CSF samples from MS and non-MS patients were collected and used for external validation, as evaluated by an m6A RNA Methylation Quantification Kit and by real-time quantitative polymerase chain reaction. RESULTS: The 13 central m6A RNA methylation regulators were all upregulated in MS patients when compared with non-MS patients. Consensus clustering analysis identified two clusters, both of which were significantly associated with MS subtypes. Next, we divided 61 MS patients into a training set (n = 41) and a test set (n = 20). The RF algorithm identified eight feature genes, and the SVM method was successfully applied to construct a diagnostic model. ROC curves revealed good performance. Finally, the analysis of 11 CSF samples demonstrated that RRMS samples exhibited significantly higher levels of m6A RNA methylation and higher gene expression levels of m6A-related genes than PMS samples. CONCLUSIONS: The dynamic modification of m6A RNA methylation is involved in the progression of MS and could potentially represent a novel CSF biomarker for diagnosing MS and distinguishing PMS from RRMS in the early stages of the disease.

Development and Validation of a Nomogram to Predict the Individual Future Stroke Risk for Adult Patients With Moyamoya Disease: A Multicenter Retrospective Cohort Study in China.

Background: Studies exploring the predictive performance of major risk factors associated with future stroke events are insufficient, and a useful tool to predict individual risk is not available. Therefore, personalized advice for preventing future stroke in patients with moyamoya disease (MMD) cannot provide evidence-based recommendations. The aim of this study was to develop a novel nomogram with reliable validity to predict the individual risk of future stroke for adult MMD patients. Methods: This study included 450 patients from seven medical centers between January 2013 and December 2018. Follow-ups were performed via clinical visits and/or telephone interviews from initial discharge to December 2019. The cohort was randomly assigned to a training set (2/3, n = 300) for nomogram development and a test set (1/3, n = 150) for external validation. The Kaplan-Meier analyses and receiver operating characteristic (ROC) curves were applied to assess the clinical benefits of this nomogram. Results: Diabetes mellitus, a family history of MMD, a past history of stroke or transient ischemic attack, clinical manifestation, and treatment were identified as major risk factors via the least absolute shrinkage and selection operator (LASSO) method. A nomogram including these predictors was established via a multivariate Cox regression model, which displayed excellent discrimination [Harrell's concordance index (C-index), 0.85; 95% confidence interval (CI): 0.75-0.96] and calibration. In the external validation, the nomogram was found to have good discrimination (C-index, 0.81; 95% CI: 0.68-0.94) and calibration. In the subgroup analysis, this predictive nomogram also showed great performance in both ischemic-type (C-index, 0.90; 95% CI: 0.77-1.00) and hemorrhagic-type MMD (C-index, 0.72; 95% CI: 0.61-0.83). Furthermore, the nomogram was shown to have potential in clinical practice through Kaplan-Meier analyses and ROC curves. Conclusions: We developed a novel nomogram incorporating several clinical characteristics with relatively good accuracy, which may have considerable potential for evaluating individual future stroke risk and providing useful management recommendations for adult patients with MMD in clinical practice.

Novelty Detection and Online Learning for Chunk Data Streams.

Datastream analysis aims at extracting discriminative information for classification from continuously incoming samples. It is extremely challenging to detect novel data while incrementally updating the model efficiently and stably, especially for high-dimensional and/or large-scale data streams. This paper proposes an efficient framework for novelty detection and incremental learning for unlabeled chunk data streams. First, an accurate factorization-free kernel discriminative analysis (FKDA-X) is put forward through solving a linear system in the kernel space. FKDA-X produces a Reproducing Kernel Hilbert Space (RKHS), in which unlabeled chunk data can be detected and classified by multiple known-classes in a single decision model with a deterministic classification boundary. Moreover, based on FKDA-X, two optimal methods FKDA-CX and FKDA-C are proposed. FKDA-CX uses the micro-cluster centers of original data as the input to achieve excellent performance in novelty detection. FKDA-C and incremental FKDA-C (IFKDA-C) using the class centers of original data as their input have extremely fast speed in online learning. Theoretical analysis and experimental validation on under-sampled and large-scale real-world datasets demonstrate that the proposed algorithms make it possible to learn unlabeled chunk data streams with significantly lower computational costs and comparable accuracies than the state-of-the-art approaches.

Full-length transcriptome analysis of Misgurnus anguillicaudatus.

Faithful analysis of transcripts represents a critical component of the gene discovery, genomic characterization and annotation process in species without a reference genome. The present study generated 28,001 full-length transcripts of Misgurnus anguillicaudatus using the Pacific Bioscience (PacBio) single-molecule real-time (SMRT) sequencing system. A total of 77,346 consensus isoforms were identified, and 18,991 complete open reading frames (ORFs) detected. In total, 1345 lncRNAs were identified with high-confidence, with additional identification of a number of well-known transcription factors. The present study may facilitate additional exploration of the genomic signatures of M. anguillicaudatus.

Characterization of Myostain (MSTN) and Myogenic Differentiation Antigen (MyoD) and the Effect of Dexamethasone on Their Expression in Large-Scale Loach Paramisgurnus dabryanus.

Myostatin (MSTN) and myogenic differentiation antigen (MyoD) play an essential role in specification and differentiation of skeletal muscle. However, the role of stress in the regulation of MyoD and MSTN has not been fully revealed and more evidence should be provided. Here, we reported the cloning and expressional analyses of MSTN and MyoD in Large-scale Loach Paramisgurnus dabryanus (hereafter PdMSTN and PdMyoD). Injecting individuals with 0, 60, 600, and 1,200 µg/kg dexamethasone (DXM) for five consecutive days resulted in a dose-dependent change of PdMSTN and PdMyoD expression. The expression of PdMSTN was upregulated with increasing DXM concentrations, while PdMyoD expression was downregulated. The changes in the expression of these genes at different time points for 10 consecutive days were studied after individuals were treated with 600 µg/kg DXM. Compared with the control group, PdMSTN expression decreased and PdMyoD expression increased before 12 h, and both PdMSTN and PdMyoD expression levels increased at 24 h, which was significantly higher than those in control group. At a prolonged treatment of 5-10 d, expression levels of PdMSTN and PdMyoD had significantly reduced. The results indicate that both PdMyoD and PdMSTN are involved in DXM-induced stress in Large-scale Loach.

SSREnricher: a computational approach for large-scale identification of polymorphic microsatellites based on comparative transcriptome analysis.

Microsatellite (SSR) markers are the most popular markers for genetic analyses and molecular selective breeding in plants and animals. However, the currently available methods to develop SSRs are relatively time-consuming and expensive. One of the most factors is low frequency of polymorphic SSRs. In this study, we developed a software, SSREnricher, which composes of six core analysis procedures, including SSR mining, sequence clustering, sequence modification, enrichment containing polymorphic SSR sequences, false-positive removal and results output and multiple sequence alignment. After running of transcriptome sequences on this software, a mass of polymorphic SSRs can be identified. The validation experiments showed almost all markers (>90%) that were identified by the SSREnricher as putative polymorphic markers were indeed polymorphic. The frequency of polymorphic SSRs identified by SSREnricher was significantly higher (P < 0.05) than that of traditional and HTS approaches. The software package is publicly accessible on GitHub (https://github.com/byemaxx/SSREnricher).

Efficacy and Safety of Antiplatelet Agents for Adult Patients With Ischemic Moyamoya Disease.

Background: The use of antiplatelet agents in ischemic moyamoya disease (MMD) is controversial. This study aimed to investigate the effectiveness and safety of antiplatelet therapy compared with conservative treatment and surgical revascularization in ischemic MMD patients. Methods: Ischemic MMD patients were retrospectively enrolled from eight clinical sites from January 2013 to December 2018. Follow-up was performed through clinical visits and/or telephone interviews from first discharge to December 2019. The primary outcome was the episodes of further ischemic attacks, and the secondary outcome was the individual functional status. Risk factors for future stroke were identified by the LASSO-Cox regression model. Propensity score matching was applied to assemble a cohort of patients with similar baseline characteristics using the TriMatch package. Results: Among 217 eligible patients, 159 patients were included in the analyses after a 1:1:1 propensity score matching. At a mean follow-up of 33 months, 12 patients (7.5%) developed further incident cerebral ischemic events (surgical:antiplatelet:conservative = 1:3:8; p = 0.030), 26 patients (16.4%) developed a poor functional status (surgical:antiplatelet:conservative = 7:12:7; p = 0.317), and 3 patients (1.8%) died of cerebral hemorrhage (surgical:antiplatelet:conservative = 1:2:0; p = 0.361). The survival curve showed that the risk of further cerebral ischemic attacks was lowest with surgical revascularization, while antiplatelet therapy was statistically significant for preventing recurrent risks compared with conservative treatment (χ2 = 8.987; p = 0.011). No significant difference was found in the functional status and bleeding events. The LASSO-Cox regression model revealed that a family history of MMD (HR = 6.93; 95% CI: 1.28-37.52; p = 0.025), a past history of stroke or transient ischemic attack (HR = 4.35; 95% CI: 1.09-17.33; p = 0.037), and treatment (HR = 0.05; 95% CI: 0.01-0.32; p = 0.001) were significantly related to the risk of recurrent strokes. Conclusions: Antiplatelet agents were effective and safe in preventing further cerebral ischemic attacks in adult patients with ischemic MMD. They may be a replacement therapy for patients with surgical contraindications and for patients prior to revascularization.

BlastGUI: A Python-based Cross-platform Local BLAST Visualization Software.

Many researchers use BLAST+ software to build databases and perform sequence alignments. However, command-line operations are required and this created a barrier for researchers with weak computer skills. Therefore, we designed BlastGUI, a Local BLAST visualization software based on Python programming language, to enable users to build databases and perform sequence filtering and sequence alignment through a graphical user interface. The operation visualization, automatic sequence filtering, and cross-platform utilization significantly facilitate biological data analysis and information mining. Our designed software is freely available under the terms of the GNU GPLv3 at https://github.com/byemaxx/BlastGUI.