Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields

Producción CientíficaSite-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively

Tumor Necrosis Factor Alpha (TNFα) Gene Promoter Polymorphisms and Haplotypes are Associated with the Febrile Seizure (FS) and TNFα Serum Levels: Association of TNFα SNPs and febrile seizure

ObjectivesFebrile seizure is a neuroinflammatory disease involving feverinduced seizures affecting children in the early stages of life. TNFα is a pro-inflammatory cytokine reported to be elevated in FS. Specific promoter variants of TNFα could be associated with its elevated cytokine expression and susceptibility to FS. The present study analyzed the association of specific TNFα variants, including TNFα-238 G/A (a genetic variant; G: Guanine, A: Adenine) (rs361525), TNFα -308 G/A (rs1800629), and TNFα -376 G/A (rs1800750) promoter polymorphisms, with FS susceptibility, and TNFα serum levels in an Iranian population Materials & MethodsSixty-eight FS patients and 136 controls were enrolled. The SSPPCR method was utilized to analyze TNFα promoter genotypes. This research also confirmed the genotyping results by sequencing samples of ten patients and normal controls. ResultsThe GG (a genetic sequence; G: Guanine) genotype of -238 SNP was associated with the increased risk of FS [OR = 12.65, 95% CI (2.83-56.60), P-value = 0.0012]. The AA (a genetic sequence; A: Adenine) genotype in the-308 region was increased in patients with FS and associated with the disease [OR = 4.62, 95% CI (1.46-14.56), P-value = 0.028]. The increased occurrence of heterozygous AG in the -376 SNP among control groups has been linked to a decreased risk of FS [OR = 0.22This study revealed that AGA (a genetic sequence; G: Guanine, A: Adenine) (-238/ -308/ -376) haplotype with the highest frequency in controls was associated with a decreased risk of FS, while GAA (a  genetic sequence; G: Guanine, A: Adenine) (-238/ -308/ -376) carriers were more susceptible to FS., 95% CI (0.11-0.43), P-value = 0.0001]. ConclusionThe current study suggested that TNFα gene promoter variants at rs361525, rs1800629, and rs1800750 could be associated with the susceptibility to FS and altered serum levels of TNFα.