Phylogenetic diversity, significance and future prospects of heterotrophic bacteria associated with marine microalgae

Control of the unwanted microorganisms is essential in all aspects of life, and microbial diseases must be treated in humans, animals, and plants. Emergence of antibiotic resistant bacteria and the need for novel, antimicrobial compounds led to the exploration of new habitats to screen the production of bioactive substances (Gram et al. 2010). Nature provides a treasure-trove of chemicals that can be used in chemical manufacturing processes, or developed into drugs for the treatment of human disease. New environmental niches provide a new source of microbes and potential for novel compound production (Sfanos et al. 2005). Advances in natural product chemistry are expected to rely ever more on interdisciplinary research, and this is particularly important for marine microbial products. The marine environment contains over 80% of world’s plant and animal species (Jha et al. 2004). Marine natural products from the microbes have lagged behind those from macroorganisms. Therefore, marine microorganisms deserve more and more organized attention by the natural products chemists (Pietra 1997). Marine microorganisms are valuable resources due to the production of a wide range of natural products with potential biotechnological and pharmaceutical application (Gram et al. 2010). Antibiotic production by marine bacteria has been documented for a long time. However, there is a paucity of information dealing with the isolation and purification of the active inhibitory substances (Barja et al. 1989)

Phylogenetic diversity of culturable bacteria in Chaetoceros gracilis mass culture system of a marine finfish hatchery

Microalgae, a major live feed in aquaculture always coexist with associated bacteria. Hence a better understanding of algal-bacterial interaction is essential for maintaining a stable environment in intensive larval rearing tanks. Therefore, herein we attempted to determine the phylogenetic diversity of culturable bacteria associated with microalgal production system of a marine finfish hatchery with special reference to Chaetoceros gracilis mass culture. The sequencing of 16S rDNA of representative from each phylotypes revealed that the associated microflora belong to the classes Gammaproteo bacteria, Alphaproteo bacteria, and Bacilli. In particular, members of Marinobacter genus showed higher degree of association followed by Leisingera, Alteromonas, Nautella, Halomonas and Ruegeria. The association of bacterial groups belonging to the genera Idiomarina, Albidovulum and Staphylococcus were also detected. The variation of bacterial diversity in microalgal habitat with changes in environmental conditions was also discussed in the present work. In overall, the present study gives a greater insight to the algal microhabitat which would be vital for improving stability, productivity, sustainability and reliability of large scale microalgal cultivation and their feeding to the target aquaculture species

Monoclonal antibodies to mycobacterial DNA gyrase A inhibit DNA supercoiling activity

DNA gyrase is an essential type II topoisomerase found in bacteria. We have previously characterized DNA gyrase from Mycobacterium tuberculosis and Mycobacterium smegmatis. In this study, several monoclonal antibodies were generated against the gyrase A subunit (GyrA) of M. smegmatis. Three, MsGyrA:C3, MsGyrA:H11 and MsGyrA:E9, were further analyzed for their interaction with the enzyme. The monoclonal antibodies showed high degree of cross-reactivity with both fast-growing and slow-growing mycobacteria. In contrast, none recognized Escherichia coli GyrA. All the three monoclonal antibodies were of IgG1 isotype falling into two distinct types with respect to epitope recognition and interaction with the enzyme. MsGyrA:C3 and MsGyrA:H11 IgG, and their respective Fab fragments, inhibited the DNA supercoiling activity catalyzed by mycobacterial DNA gyrase. The epitope for the neutralizing monoclonal antibodies appeared to involve the region towards the N-terminus (residues 351-415) of the enzyme in a conformation-dependent manner. These monoclonal antibodies would serve as valuable tools for structure-function analysis and immunocytological studies of mycobacterial DNA gyrase. In addition, they would be useful for designing peptide inhibitors against DNA gyrase

Removal of heavy metals Pb(II), Cd(II) and Cu(II) from waste waters using synthesized chromium doped nickel oxide nano particles

Adsorption of metal ions Pb(II), Cd(II) and Cu(II) are observed on chromium doped nickel nano metal oxide. Chromium doped nickel oxide was synthesized by combustion technique using glycine as a fuel. In this work, transmission electronic microscopy (TEM), scanning electronic microscopy (SEM), X-ray diffraction (XRD) and  Brunauer–Emmett–Teller (BET) are applied to study the composition and structure of chromium doped nickel oxide. Batch equilibrium experiments were performed to study the removal efficiency of heavy metal ions. The effects of different parameters such as contact time, pH and adsorbent dose on the adsorption process were investigated. The adsorption kinetics and adsorption isotherms studies were also performed. The formation of hydroxide on the surface of chromium doped nickel oxide could be the main factor of cations removal due to its high adsorption affinity for aqueous solutes. Chromium doped nickel oxide may offer a potential remediation method for the removal of metals from water and environment.               KEY WORDS: Adsorption isotherms, BET, Adsorption efficiency, Batch equilibrium, Adsorption kinetics Bull. Chem. Soc. Ethiop. 2018, 32(2), 225-238.DOI: https://dx.doi.org/10.4314/bcse.v32i2.

Hadron energy response of the Iron Calorimeter detector at the India-based Neutrino Observatory

The results of a Monte Carlo simulation study of the hadron energy response for the magnetized Iron CALorimeter detector, ICAL, proposed to be located at the India-based Neutrino Observatory (INO) is presented. Using a GEANT4 modeling of the detector ICAL, interactions of atmospheric neutrinos with target nuclei are simulated. The detector response to hadrons propagating through it is investigated using the hadron hit multiplicity in the active detector elements. The detector response to charged pions of fixed energy is studied first, followed by the average response to the hadrons produced in atmospheric neutrino interactions using events simulated with the NUANCE event generator. The shape of the hit distribution is observed to fit the Vavilov distribution, which reduces to a Gaussian at high energies. In terms of the parameters of this distribution, we present the hadron energy resolution as a function of hadron energy, and the calibration of hadron energy as a function of the hit multiplicity. The energy resolution for hadrons is found to be in the range 85% (for 1GeV) -- 36% (for 15 GeV).Comment: 14 pages, 10 figures (24 eps files

Flood Prediction using MLP, CATBOOST and Extra-Tree Classifier

Flooding can be one of the many devastating natural catastrophes, resulting in the annihilation of life and damaging property. Additionally, it can harm farmland and kill growing crops and trees. Nowadays, rivers and lakes are being destroyed, and the natural water reservoirs are converted into development sites and buildings. Due to this, even just a bit of rain can cause a flood. To minimize the number of fatalities, property losses, and other flood-related issues, an early flood forecast is necessary. Therefore, machine learning methods can be used for the prediction of floods.To forecast the frequency of floods brought on by rainfall, a forecasting system is built using rainfall data. The dataset is trained using various techniques like the MLP classifier, the CatBoost classifier, and the Extra-Tree classifier to predict the occurrence of floods. Finally, the three models' performances are compared and the best model for flood prediction is presented. The MLP, Extra-Tree, and CatBoost models achieved accuracy of 94.5%, 97.9%, and 98.34%, respectively, and it is observed that CatBoost performed well with high accuracy to predict the occurrence of floods