A study on the defluoridation in water by using natural soil

Removal of excess fluoride (F−) from the water has been attempted by several authors by using different materials both natural and artificial. The main aim of this paper was to attempt the fluoride removal by using the locally available red soil adopting column method. The red soil was mixed in different proportion with sand in order to increase the porosity and permeability property of the medium. It was optimized for 4:1 ratio of red soil to sand and it was used for the following experiment. The experiment was conducted in 11 batches for a period of about 9,213 min. Fresh standard solution of F was used in each batch, prepared from Orion 1,000 ppm solution. The samples were collected and analyzed for pH, EC (Electrical Conductivity) and HCO3. Rate of flow of water and efficiency of adsorption were calculated and compared with the fluoride removal capacities of the medium. The medium used for the fluoride removal was subjected to FTIR analysis before and after the experiment. The variation of IR spectrum before and after treatment signifies the changes in the OH bonding between Al and Fe ions present in the soil. The variation in pH decreased during the course of defluoridation. Higher F removal was noted when flow rate was lesser. An attempt on the regeneration of the fluoride adsorbed soil was also made and found to be effective

Acetylation and phosphorylation of human TFAM regulate TFAM-DNA interactions via contrasting mechanisms

Mitochondrial transcription factor A (TFAM) is essential for the maintenance, expression and transmission of mitochondrial DNA (mtDNA). However, mechanisms for the post-translational regulation of TFAM are poorly understood. Here, we show that TFAM is lysine acetylated within its high-mobility-group box 1, a domain that can also be serine phosphorylated. Using bulk and single-molecule methods, we demonstrate that site-specific phosphoserine and acetyllysine mimics of human TFAM regulate its interaction with non-specific DNA through distinct kinetic pathways. We show that higher protein concentrations of both TFAM mimics are required to compact DNA to a similar extent as the wild-type. Compaction is thought to be crucial for regulating mtDNA segregation and expression. Moreover, we reveal that the reduced DNA binding affinity of the acetyl-lysine mimic arises from a lower on-rate, whereas the phosphoserine mimic displays both a decreased on-rate and an increased off-rate. Strikingly, the increased off-rate of the phosphoserine mimic is coupled to a significantly faster diffusion of TFAM on DNA. These findings indicate that acetylation and phosphorylation of TFAM can fine-tune TFAM-DNA binding affinity, to permit the discrete regulation of mtDNA dynamics. Furthermore, our results suggest that phosphorylation could additionally regulate transcription by altering the ability of TFAM to locate promoter sites

A comparison of the effects of physical and chemical mutagens in sesame (Sesamum indicum L.)

Three sesame genotypes (Rama, SI 1666 and IC 21706) were treated with physical (γ-rays: 200 Gy, 400 Gy or 600 Gy) or chemical (ethyl methane sulphonate, EMS: 0.5%, 1.0%, 1.5% or 2.0%) mutagens and their mutagenic effectiveness and efficiency were estimated in the M 2 generation. The M 3 generation was used to identify the most effective mutagen and dose for induction of mutations. The average effectiveness of EMS was much higher than γ-rays. The lowest dose of γ-rays (200 Gy) and the lowest concentration of EMS (0.5%) showed the highest mutagenic efficiency in all genotypes. Analysis of the M 3 generation data based on parameters such as the variance ratio and the difference in residual variances derived from the model of Montalván and Ando indicated that 0.5% concentration of EMS was the most effective treatment for inducing mutations

Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity

Cotton in the new millennium: advances, economics, perceptions and problems

Cotton is the most significant natural fibre and has been a preferred choice of the textile industry and consumers since the industrial revolution began. The share of man-made fibres, both regenerated and synthetic fibres, has grown considerably in recent times but cotton production has also been on the rise and accounts for about half of the fibres used for apparel and textile goods. To cotton’s advantage, the premium attached to the presence of cotton fibre and the general positive consumer perception is well established, however, compared to commodity man-made fibres and high performance fibres, cotton has limitations in terms of its mechanical properties but can help to overcome moisture management issues that arise with performance apparel during active wear. This issue of Textile Progress aims to: i. Report on advances in cotton cultivation and processing as well as improvements to conventional cotton cultivation and ginning. The processing of cotton in the textile industry from fibre to finished fabric, cotton and its blends, and their applications in technical textiles are also covered. ii. Explore the economic impact of cotton in different parts of the world including an overview of global cotton trade. iii. Examine the environmental perception of cotton fibre and efforts in organic and genetically-modified (GM) cotton production. The topic of naturally-coloured cotton, post-consumer waste is covered and the environmental impacts of cotton cultivation and processing are discussed. Hazardous effects of cultivation, such as the extensive use of pesticides, insecticides and irrigation with fresh water, and consequences of the use of GM cotton and cotton fibres in general on the climate are summarised and the effects of cotton processing on workers are addressed. The potential hazards during cotton cultivation, processing and use are also included. iv. Examine how the properties of cotton textiles can be enhanced, for example, by improving wrinkle recovery and reducing the flammability of cotton fibre

Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review

Structure-based virtual screening (SBVS) has been widely applied in early-stage drug discovery. From a problem-centric perspective, we reviewed the recent advances and applications in SBVS with a special focus on docking-based virtual screening. We emphasized the researchers’ practical efforts in real projects by understanding the ligand-target binding interactions as a premise. We also highlighted the recent progress in developing target-biased scoring functions by optimizing current generic scoring functions toward certain target classes, as well as in developing novel ones by means of machine learning techniques

A Novel Hyperspectral Image Classification Technique Using Deep Multi-Dimensional Recurrent Neural Network

In this paper, a novel Multi-Dimensional Recurrent Deep Neural Network is proposed for classifying hyperspectral images. Deep Learning Networks have developed rapidly with applications in several fields including computer vision, healthcare, bioinformatics and machine learning. Multi-Dimensional Recurrent Deep Neural Networks are a special case of directed acyclic graph networks in which standard Recurrent Neural Networks are realized by giving recurrent connections along all spatio-temporal dimensions of the data and the recurrent connection size is equal to the dimension of the data. In this work, two Recurrent Neural Networks are replaced by one Multi-Dimensional Recurrent Deep Neural Network to learn middle-level visual patterns and spatial dependencies between them. In the last stage, fully connected layers are used to learn a global image representation. Due to the recurrent connections, this method is robust to local distortions such as image rotation and shear. Without suffering from scaling problems, it brings additional advantages over Recurrent Neural Networks to multi-dimensional data. This paper investigates hyperspectral image classification with the proposed network and the results have been validated with hyperspectral datasets namely Pavia University and Salinas images. There is an improvement in the classification accuracy of this newly proposed method in comparison to classical methods like Support Vector Machine, Convolutional Neural Network (CNN) and Recurrent Convolutional Neural Network (RCNN)

INTELLIGENT WATER DROP ALGORITHM POWERED BY TABU SEARCH TO ACHIEVE NEAR OPTIMAL SOLUTION FOR GRID SCHEDULING

ABSTRACT Grid computing is a network of computer resources where every resources are shared, turning a computer network into a powerful super computers. In which, Grid Scheduling is a non linear multi-objective problem. In this paper, intelligent water drop algorithm is hybridized with Tabu Search algorithm to solve scheduling problem in computational grid. The proposed algorithm named EIWD-TS is a meta-heuristic algorithm based on swarm intelligence. The optimization objective of this research is to find the near optimal solution considering multiple objectives namely makespan, slowdown ratio, failure rate and resource utilization of grid scheduling. The result of the proposed model of this paper is tested with PSA (Parameter Sweep Application) dataset and the results are compared with Risky-MinMin (RMM), Preemptive-MinMin (PMM), Particle Swarm Optimization (PSO) and IWD. Experimental evaluation shows that the EIWD-TS algorithm has good convergence property and better in quality of solution than other algorithms reported in recent literature