Image-based state-of-the-art techniques for the identification and classification of brain diseases: a review.

Detection and classification methods have a vital and important role in identifying brain diseases. Timely detection and classification of brain diseases enable an accurate identification and effective management of brain impairment. Brain disorders are commonly most spreadable diseases and the diagnosing process is time-consuming and highly expensive. There is an utmost need to develop effective and advantageous methods for brain diseases detection and characterization. Magnetic resonance imaging (MRI), computed tomography (CT), and other various brain imaging scans are used to identify different brain diseases and disorders. Brain imaging scans are the efficient tool to understand the anatomical changes in brain in fast and accurate manner. These different brain imaging scans used with segmentation techniques and along with machine learning and deep learning techniques give maximum accuracy and efficiency. This paper focuses on different conventional approaches, machine learning and deep learning techniques used for the detection, and classification of brain diseases and abnormalities. This paper also summarizes the research gap and problems in the existing techniques used for detection and classification of brain disorders. Comparison and evaluation of different machine learning and deep learning techniques in terms of efficiency and accuracy are also highlighted in this paper. Furthermore, different brain diseases like leukoariaosis, Alzheimer's, Parkinson's, and Wilson's disorder are studied in the scope of machine learning and deep learning techniques.

Revealing the Variation and Stability of Bacterial Communities in Tomato Rhizosphere Microbiota.

Microorganisms that colonize the plant rhizosphere can contribute to plant health, growth and productivity. Although the importance of the rhizosphere microbiome is known, we know little about the underlying mechanisms that drive microbiome assembly and composition. In this study, the variation, assembly and composition of rhizobacterial communities in 11 tomato cultivars, combined with one cultivar in seven different sources of soil and growing substrate, were systematically investigated. The tomato rhizosphere microbiota was dominated by bacteria from the phyla Proteobacteria, Bacteroidetes, and Acidobacteria, mainly comprising Rhizobiales, Xanthomonadales, Burkholderiales, Nitrosomonadales, Myxococcales, Sphingobacteriales, Cytophagales and Acidobacteria subgroups. The bacterial community in the rhizosphere microbiota of the samples in the cultivar experiment mostly overlapped with that of tomato cultivar MG, which was grown in five natural field soils, DM, JX, HQ, QS and XC. The results supported the hypothesis that tomato harbors largely conserved communities and compositions of rhizosphere microbiota that remains consistent in different cultivars of tomato and even in tomato cultivar grown in five natural field soils. However, significant differences in OTU richness (p < 0.0001) and bacterial diversity (p = 0.0014 < 0.01) were observed among the 7 different sources of soil and growing substrate. Two artificial commercial nutrient soils, HF and CF, resulted in a distinct tomato rhizosphere microbiota in terms of assembly and core community compared with that observed in natural field soils. PERMANOVA of beta diversity based on the combined data from the cultivar and soil experiments demonstrated that soil (growing substrate) and plant genotype (cultivar) had significant impacts on the rhizosphere microbial communities of tomato plants (soil, F = 22.29, R2 = 0.7399, p < 0.001; cultivar, F = 2.04, R2 = 0.3223, p = 0.008). Of these two factors, soil explained a larger proportion of the compositional variance in the tomato rhizosphere microbiota. The results demonstrated that the assembly process of rhizosphere bacterial communities was collectively influenced by soil, including the available bacterial sources and biochemical properties of the rhizosphere soils, and plant genotype.