Screening for mutagens in complex environmental mixtures, such as surface water or industrial wastewater, is gradually being accepted as a routine method in environmental monitoring programs. In the present work, the simplified Allium cepa root assay was utilized to evaluate the possible cytotoxic effect of sewage effluent on root tip cells of A. cepa L. Sewage effluents were collected from Kunnamkulam market. Treatements were done in five different time durations such as 3, 6, 12, 24, and 48 hours. Allium cepa exposed to sewage effluents resulted to cells with chromosomal aberration. The Mitotic index was decreased when the time durations were increased. The mitotic index of the control was 49.65±3.35. The mitotic index of the sewage effluent in 3 hr was 42.28±3.37. From this a gradual decrease in the mitotic index when the duration of time increased. The mitotic index 42.15±3.17, 32.27±3.04, 31.65±3.19 and 28.19±2.87 are 6 h, 12 hr, 24 h and 48 h respectively chromosomal abnormalities such as bridges, fragments, vagrant chromosomes, sticky chromosomes, C-mitosis, spindle abnormalities and disoriented chromosomes were observed. The impacts of using sewage effluent as fertilizer on the environment are difficult to predict. So they may be treated to remove its toxic particles prior to its release into the environment. Toxicity or genotoxicity bioassays should be an integral tool in the evaluation of waste water toxicity.
The problem of child health management and development has been well studied. There are number of methods available for the problem of child health development but suffers to achieve higher performance. To improve the performance, an efficient real time child infant mortality analysis
for improved health development using multi feature covariance measure (MFCM). The method maintains number of data records of various child and infants from the age of 1 month to 15 years. For each child or infant, the method maintains continuous records of their health diagnosis. Using the
data maintained, the method identifies and groups them according to the cause of death. Using the cluster generated, the method estimates health factor influence (HFI) for different features. Based on the value of HFI, a set of features which has higher HFI are selected and used to generate
analysis. Further the method generates a prediction result on the future mortality and the reasons. The method improves the performance of mortality prediction and increases the accuracy also.
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