Mitogen-activated protein kinase cascade is evolutionarily conserved signal transduction module involved in transducing extracellular signals to the nucleus for appropriate cellular adjustment. Thi...

Mitogen-Activated Protein Kinase Kinase Kinases (MAPKKKs) are important components of MAPK cascades, which are universal signal transduction modules and play important role in plant growth and development. In the sequenced Arabidopsis genome 80 MAPKKKs were identified and currently being analysed for its role in different stress. In rice, economically important monocot cereal crop only five MAPKKKs were identified so far. In this study using computational analysis of sequenced rice genome we have identified 75 MAPKKKs. EST hits and full-length cDNA sequences (from KOME or Genbank database) of 75 MAPKKKs supported their existence. Phylogenetic analyses of MAPKKKs from rice and Arabidopsis have classified them into three subgroups, which include Raf, ZIK and MEKK. Conserved motifs in the deduced amino acid sequences of rice MAPKKKs strongly supported their identity as members of Raf, ZIK and MEKK subfamilies. Further expression analysis of the MAPKKKs in MPSS database revealed that their transcripts were differentially regulated in various stress and tissue-specific libraries.

The recent spark in the interest for the usage of nano zero valent iron (nZVI) as a remediation tool for contaminated land and groundwater is mainly due to its higher reactivity in comparison to micro ZVI, cost effectiveness, and potential to treat a broad range of contaminants. The authors review the recent developments and approaches made on synthesis on nZVI, strucuture and characterization of nZVI, the challenges faced in the transport of nZVI in the subsurface environment, and the augmentation of the motility of nZVI. The effective use of nZVI in remediating organic pollutants (halogenated organic compounds, pharmaceutical waste, and azo dyes) and inorganic pollutants (Ni²⁺, PO₄³⁻, Co²⁺, Cu²⁺) carried out in recent studies is discussed. The potential risks and limitations of this emerging nanotechnology are also addressed.

Freshwater quality and quantity are some of the fundamental requirements for sustaining human life and civilization. The Water Quality Index is the most extensively used parameter for determining water quality worldwide. However, the traditional approach for the calculation of the WQI is often complex and time consuming since it requires handling large data sets and involves the calculation of several subindices. We investigated the performance of artificial intelligence techniques, including particle swarm optimization (PSO), a naive Bayes classifier (NBC), and a support vector machine (SVM), for predicting the water quality index. We used an SVM and NBC for prediction, in conjunction with PSO for optimization. To validate the obtained results, groundwater water quality parameters and their corresponding water quality indices were found for water collected from the Pindrawan tank area in Chhattisgarh, India. Our results show that PSO–NBC provided a 92.8% prediction accuracy of the WQI indices, whereas the PSO–SVM accuracy was 77.60%. The study’s outcomes further suggest that ensemble machine learning (ML) algorithms can be used to estimate and predict the Water Quality Index with significant accuracy. Thus, the proposed framework can be directly used for the prediction of the WQI using the measured field parameters while saving significant time and effort.