IoT enabled environmental toxicology for air pollution monitoring using AI techniques.

In past decades, the industrial and technological developments have increased exponentially and accompanied by non-judicial and un-sustainable utilization of non-renewable resources. At the same time, the environmental branch of toxicology has gained significant attention in understanding the effect of toxic chemicals on human health. Environmental toxic agents cause several diseases, particularly high risk among children, pregnant women, geriatrics and clinical patients. Since air pollution affects human health and results in increased morbidity and mortality increased the toxicological studies focusing on industrial air pollution absorbed by the common people. Therefore, it is needed to design an automated Environmental Toxicology based Air Pollution Monitoring System. To resolve the limitations of traditional monitoring system and to reduce the overall cost, this paper designs an IoT enabled Environmental Toxicology for Air Pollution Monitoring using Artificial Intelligence technique (ETAPM-AIT) to improve human health. The proposed ETAPM-AIT model includes a set of IoT based sensor array to sense eight pollutants namely NH3, CO, NO2, CH4, CO2, PM2.5, temperature and humidity. The sensor array measures the pollutant level and transmits it to the cloud server via gateways for analytic process. The proposed model aims to report the status of air quality in real time by using cloud server and sends an alarm in the presence of hazardous pollutants level in the air. For the classification of air pollutants and determining air quality, Artificial Algae Algorithm (AAA) based Elman Neural Network (ENN) model is used as a classifier, which predicts the air quality in the forthcoming time stamps. The AAA is applied as a parameter tuning technique to optimally determine the parameter values of the ENN model. In-order to examine the air quality monitoring performance of the proposed ETAPM-AIT model, an extensive set of simulation analysis is performed and the results are inspected in 5, 15, 30 and 60 min of duration respectively. The experimental outcome highlights the optimal performance of the proposed ETAPM-AIT model over the recent techniques.

Functional annotation of mulberry (Morus spp.) transcriptome, differential expression of genes related to growth and identification of putative genic SSRs, SNPs and InDels.

Growth is a complex trait associated with mulberry leaf yield and controlled by several genes. In this study, we have explored the molecular basis underlying growth using Transcriptome profiling of contrasting genotypes. A total of 66.6 Mbp of primary transcriptomes from high growth (HGG)-Jalalgarah-3 and M. laevigata (H) and, low growth genotypes (LGG)-Harmutty and Vadagaraparai-2; resulting in 24210, 27998, 28085 and 28764 final transcripts respectively. Out of the 34096 pooled transcripts, 20249 transcripts matched with at least one sequence of the non-redundant database. Functional annotation resulted in the categorization of 18970 transcripts into 3 gene ontology (GO) terms and 7440 were assigned to 23 Kyoto encyclopaedia of genes and genomes (KEGG) pathway. Based on the differentially expressed genes and gene enrichment analysis, over expression of photosynthetic related transcripts in HGG and defence related transcripts in LGG were noted. Simple sequence repeats were mined from unique transcripts and the most abundant motifs were tri- (1883) followed by di- (1710), tetra- (192), penta- (68) and hexa- (40) repeats. Further, a total of 390897 high quality SNPs and 8081 InDels were identified by mapping onto Morus notabilis reference genome. The study provides an insight into the expression of genes involved in growth and further research on utilization in gentic improvement of the crop.