Cytogenetic analyses of clastogens and aneuploidogens by immunofluorescent and micronucleus methods.

The cytogenetic effect of various genotoxic compounds was analysed in cytokinesis-blocked human peripheral lymphocytes and V 79 cells by using immunofluorescence and micronucleus methods. Cytochalasin-B (CYB) induced a maximum number of binucleated cells at 3-4 {dollar}\\mu{dollar}g/ml without any genotoxic effect. Comparative studies between binucleated (CB) and mononucleated micronucleus assays show that the micronucleus (MN) frequency in CB method was higher than those in the mononucleated cells (P {dollar}\u3c{dollar} 0.05). The MN frequencies in cultures treated with ethylene dibromide (EDB) and vincristine sulfate (VS) were scored. VS caused significantly higher MN frequencies than controls (P {dollar}\u3c{dollar} 0.01) at 24 h and decreased later dramatically with time. VS induced a large number of micronucleated cells with multiple MN. Continuous presence of VS in cultures caused a severe decrease of nuclear division index (NDI). EDB induced MN in binucleated cells in both 4 h and continuously treated cells. EDB induced very few micronucleated cells with multiple MN and it did not affect nuclear division. Immunofluorescent staining of kinetochores in MN was found to be a useful technique for distinguishing clastogenic and aneuploidogenic agents. Within the base line MN (2%) 50 to 58% of cells contained kinetochore-positive (Kc+) MN, indicating the origin of MN from both acentric and centric chromatid(s)/chromosome(s). Mitomycin C (MMC) induced a significantly higher number of MN than controls (P {dollar}\u3c{dollar} 0.01) and the majority of micronucleated cells contained kinetochore-negative (Kc{dollar}-{dollar}) MN, indicating that MMC is a clastogenic agent. VS also produced statistically significant number of MN (P {dollar}\u3c{dollar} 0.01). However, the majority of micronucleated cells contained Kc+ MN indicating that VS is an aneuploidogen. Cigarette smoke condensate (CSC) induced a statistically significant number of MN (P {dollar}\u3c{dollar} 0.01). CSC induced both Kc+ (P {dollar}\u3c{dollar} 0.01) and Kc{dollar}-{dollar} MN (P {dollar}\u3c{dollar} 0.05) indicating that CSC is both a clastogenic and an aneuploidogenic agent. These results suggest that immunofluorescent staining of kinetochores in binucleated cells is capable of distinguishing between clastogenic and aneuploidogenic agents

Ultrastructural changes in tomato infected with tomato leaf curl virus, a whitefly-transmitted geminivirus

The ultrastructural modifications in nucleus of tomato infected with tomato leaf curl virus (TLCV) included hypertrophy of the nucleolus, segregation of nucleolar components into discrete granular and fibrillar regions, appearance of electron-dense particles associated ribbon-like structures, and presence of virus particles as either loosely compacted or hexagonally close-packed symmetrical arrays. The virus particles were isometric, about 18-20 nm in diameter. In the lumen of sieve elements, virus particles occasionally formed aggregates that were cylindrically arranged and occurred in pairs. Among the organelles other than the nucleus, virus particles were found in the plastids of sieve elements. In the chloroplasts of TLCV-infected cells, considerable disturbances in the internal organization were observed. In the most severe form of degeneration, the thylakoid system was fragmented and disorganized. In some chloroplasts starch grains were abnormally large. Excessive accumulation of osmiophilic bodies in degenerating chloroplasts was prominent

Construction of LC-MS maps of root exudates in cotton (Gossypium hirsutum L.) seedlings

Root exudates composition and pattern of biochemical expression is genotype specific and highly influenced by both by abiotic and biotic factors. During this investigation, various attempts made to standardize the techniques to construct LC-MS maps using cotton as a plant system. Construction of root exudates maps by LC-MS analysis found as very unique and having high utility in genotype identification through genotypic maps, detecting the presence/absence of specific chemicals of interest, and for rhizosphere engineering. As expected each sample (root exudates of a particular genotype) produced very distinct peaks-spectra. Each peak in the peak-spectral map (Y-axis) provides very useful information, the peak intensity (peak height), which represents the percent of each chemical/analyte present in the sample. The total number of peaks in each spectrum indicates the number of biochemicals present in that sample. The root exudates samples were probed in both positive and negative LC-MS mode, since some acidic compounds could not be detected in positive mode. The peaks displayed in the negative mode spectra maps indicates most of them are belong to the compounds in acidic groups. This distinction also provides additional chemical diversity and chemical specificity to include in the genotypic maps. By this way, the diversity present in all these parameters for each cotton genotype was included and the information presented was used to establish a very high-resolution maps. These peak spectral maps directly depend on the biochemicals produced by a specific genotype and genetically controlled; therefore, they can be called as genotypic maps or root exudates maps. Keywords: Root Exudates, Silica sand, Liquid Chromatography mass spectroscopy (LC-MS)</jats:p

An efficient security framework for intrusion detection and prevention in internet-of-things using machine learning technique

Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%