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Purpose: Changes in the brain networks of patients with temporal lobe epilepsy (TLE) have been extensively explored, but the biological mechanisms underlying these alterations remain unclear. Here, we aim to identify changes in brain networks in patients with TLE and provide an accurate algorithm for distinguishing these patients from normal controls (NC) with graph-theoretical approach and advanced machine learning methods.Methods: Directed network construction was applied to resting-state functional magnetic resonance imaging (rs-fMRI) data from 55 subjects (23 TLE patients and 32 NC), and 13 directed graph measures were calculated. Two-sample t-test selected features were used as inputs to a support vector machine (SVM). The leave-one-out cross-validation method was used in measuring classification performance.Results: An accuracy of 94.55% (sensitivity = 91.30%, f1-score = 93.33%, Cohen's kappa = 0.9345) was achieved for the classification of TLE patients and NC with optimal features and SVM classifier. According to the results of the two-sample t-test results, TLE disease impacted several areas of the brain, including the temporal, parietal, occipital, posterior cingulate, angular gyrus, superior frontal gyrus, and cerebellum regions in degree centrality, flow coefficient and node efficiency. There was a significant correlation between performance IQ and the flow coefficient of the left posterior cerebellum lobe in TLE group.Conclusion: The study confirmed the validity of Granger causality analysis in constructing directed brain networks. The proposed machine learning approach based on directed graph measures may serve as a biomarker for the diagnosis of TLE to assist in the early diagnosis of TLE patients and intervention in treatment plans. [ABSTRACT FROM AUTHOR]

Soilborne plant diseases seriously limit agricultural production. Due to environmental concerns, sustainable agricultural practices must involve the use of environmentally friendly alternatives, such as biocontrol microbes (BMs), to suppress various plant diseases. However, the success of BM application largely depends on the carrier formulation. An ideal carrier that can support the survival of a BM while discouraging the growth of the target pathogen is expected to enhance the performance of the BM in plant disease control. In this study, various commercial composts and peats were evaluated as carriers for Bacillus amyloliquefaciens QL-18, a BM used to control tomato bacterial wilt (TBW) caused by the soilborne bacterial pathogen Ralstonia solanacearum. Among the evaluated carriers, rapeseed cake compost (RCC) was found to be the best carrier, acting as both a substrate and food source for B. amyloliquefaciens QL-18. RCC facilitated the growth and survival of strain QL-18 by supplying adequate levels of dissolved carbon and nitrogen. Moreover, aqueous extracts of RCC alone inhibited R. solanacearum growth, suggesting that certain RCC components are toxic to the pathogen. Greenhouse and field experiments demonstrated that RCC improved the ability of B. amyloliquefaciens QL-18 to reduce the incidence of TBW and the population of the pathogen in the tomato rhizosphere. Although other carriers maintained large populations of B. amyloliquefaciens QL-18, similar to those obtained with RCC, they were not deemed suitable carriers because they constituted a poor nutrient supply for B. amyloliquefaciens QL-18 growth; thus, they had less of an impact on disease control. The principle of BM carrier selection should be adopted in the future when developing new bio-products for the effective control of soilborne plant diseases. [ABSTRACT FROM AUTHOR]

Highlights: [•] Chitosan inhibited the growth of bacterial pathogen A. avenae subsp. avenae. [•] Antibacterial effect of chitosan depends on its concentration and incubation time. [•] Chitosan destroyed cell membrane, reduced biofilm and changed genes expression. [ABSTRACT FROM AUTHOR]

Background and objective This paper presents an automatic computer-aided diagnosis (CAD) system based on feature ranking for detection of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) data. Methods The proposed CAD system is composed of four systematic stages. First, global and local differences in the gray matter (GM) of AD patients compared to the GM of healthy controls (HCs) are analyzed using a voxel-based morphometry technique. The aim is to identify significant local differences in the volume of GM as volumes of interests (VOIs). Second, the voxel intensity values of the VOIs are extracted as raw features. Third, the raw features are ranked using a seven-feature ranking method, namely, statistical dependency (SD), mutual information (MI), information gain (IG), Pearson's correlation coefficient (PCC), t-test score (TS), Fisher's criterion (FC), and the Gini index (GI). The features with higher scores are more discriminative. To determine the number of top features, the estimated classification error based on training set made up of the AD and HC groups is calculated, with the vector size that minimized this error selected as the top discriminative feature. Fourth, the classification is performed using a support vector machine (SVM). In addition, a data fusion approach among feature ranking methods is introduced to improve the classification performance. Results The proposed method is evaluated using a data-set from ADNI (130 AD and 130 HC) with 10-fold cross-validation. The classification accuracy of the proposed automatic system for the diagnosis of AD is up to 92.48% using the sMRI data. Conclusions An automatic CAD system for the classification of AD based on feature-ranking method and classification errors is proposed. In this regard, seven-feature ranking methods (i.e., SD, MI, IG, PCC, TS, FC, and GI) are evaluated. The optimal size of top discriminative features is determined by the classification error estimation in the training phase. The experimental results indicate that the performance of the proposed system is comparative to that of state-of-the-art classification models. [ABSTRACT FROM AUTHOR]