Process Optimization for Mass Production of Marine Yeast Candida sp. S 27 and its Nutritional Characterization

The main source of protein for human and animal consumption is from the agricultural sector, where the production is vulnerable to diseases, fluctuations in climatic conditions and deteriorating hydrological conditions due to water pollution. Therefore Single Cell Protein (SCP) production has evolved as an excellent alternative. Among all sources of microbial protein, yeast has attained global acceptability and has been preferred for SCP production. The screening and evaluation of nutritional and other culture variables of microorganisms are very important in the development of a bioprocess for SCP production. The application of statistical experimental design in bioprocess development can result in improved product yields, reduced process variability, closer confirmation of the output response to target requirements and reduced development time and overall cost.The present work was undertaken to develop a bioprocess technology for the mass production of a marine yeast, Candida sp.S27. Yeasts isolated from the offshore waters of the South west coast of India and maintained in the Microbiology Laboratory were subjected to various tests for the selection of a potent strain for biomass production. The selected marine yeast was identified based on ITS sequencing. Biochemical/nutritional characterization of Candida sp.S27 was carried out. Using Response Surface Methodology (RSM) the process parameters (pH, temperature and salinity) were optimized. For mass production of yeast biomass, a chemically defined medium (Barnett and Ingram, 1955) and a crude medium (Molasses-Yeast extract) were optimized using RSM. Scale up of biomass production was done in a Bench top Fermenter using these two optimized media. Comparative efficacy of the defined and crude media were estimated besides nutritional evaluation of the biomass developed using these two optimized media.

Studies on Reproductive Biology euui Mass Larval Rearing of Selected Species of Macrobrach

In India scientific commercial farming of Macrobrachium is yet to get a start, the major constraint being the lack of seed. Seed collection from the natural sources alone may not be sufficient, and in such cases seed production in hatcheries is inevitable. This necessitates knowledge of the reproductive biology of the species technology development for brood stock and larval rearing, and the optimum conditions required for larval development to undertake large scale seed production.The present study was taken up with the major objectives of developing a proper, simple and feasible technology for the larval rearing of Macrobrachium, with special emphasis on. mass larval rearing suitable for commercial level operations.

Mass Transport In The Equatorial Indian Ocean

In the equatorial oceans, the meridional currents are far less energetic than their zonal counterparts. The response of the Equatorial Indian Ocean to the seasonal reversals in the zonal wind field. is quite interesting and unique. A modest attempt, considering the shortcomings in the hydrographic data availability and distribution, is made to evaluate the variability in the zonal transport of mass. in_ both space and time. The peculiarities in its hydrological regime imposed upon by the seasonally varying winds is best appreciated when compared with the quasi permanent circulation characteristics of the Pacific and Atlanti'c.The major features of the tequatorial mass transport is outlined in the introductory chapter of this thesis for the Pacific and Atlantic Mass transport studies in the Indian Ocean, as can be seen from the earlier studies, gis“ the least known and understood, though could have captured the attention of both the experimentalist and the theoretician alike. owing to its complexity. Since in the Indian Ocean, the studies on the zonal mass transport are limited and are confined to the equator only, an attempt has been made to compute the mass transport extending from 5 N to 20 S.

Mass production of nitrifying bacterial consortia for the rapid establishment of nitrification in saline recirculating aquaculture systems

Two distinct nitrifying bacterial consortia, namely an ammonia oxidizing non-penaeid culture (AMO NPCU-1) and an ammonia oxidizing penaeid culture (AMOPCU-1), have been mass produced in a nitrifying bacterial consortia production unit (NBCPU). The consortia, maintained at 4 C were activated and cultured in a 2 l fermentor initially. At this stage the net biomass (0.105 and 0.112 g/l), maximum specific growth rate (0.112 and 0.105/h) and yield coefficients (1.315 and 2.08) were calculated respectively, for AMONPCU-1 and AMOPCU-1 on attaining stationary growth phase. Subsequently on mass production in a 200 l NBCPU under optimized culture conditions, the total amounts of NH4 ?–N removed by AMONPCU-1 and AMOPCU-1 were 1.948 and 1.242 g/l within 160 and 270 days, respectively. Total alkalinity reduction of 11.7–14.4 and 7.5–9.1 g/l were observed which led to the consumption of 78 and 62 g Na2CO3. The yield coefficient and biomass of AMONPCU-1 were 0.67 and 125.3 g/l and those of AMOPCU-1 were 1.23 and 165 g/l. The higher yield coefficient and growth rate of AMOPCU-1 suggest better energy conversion efficiency and higher CO2 fixation potential. Both of the consortia were dominated by Nitrosomonas-like organisms. The consortia may find application in the establishment of nitrification within marine and brackish water culture systems.

Study on period search methods of variable stars: Application to ASAS and CRTS databases

Study on variable stars is an important topic of modern astrophysics. After the invention of powerful telescopes and high resolving powered CCD’s, the variable star data is accumulating in the order of peta-bytes. The huge amount of data need lot of automated methods as well as human experts. This thesis is devoted to the data analysis on variable star’s astronomical time series data and hence belong to the inter-disciplinary topic, Astrostatistics. For an observer on earth, stars that have a change in apparent brightness over time are called variable stars. The variation in brightness may be regular (periodic), quasi periodic (semi-periodic) or irregular manner (aperiodic) and are caused by various reasons. In some cases, the variation is due to some internal thermo-nuclear processes, which are generally known as intrinsic vari- ables and in some other cases, it is due to some external processes, like eclipse or rotation, which are known as extrinsic variables. Intrinsic variables can be further grouped into pulsating variables, eruptive variables and flare stars. Extrinsic variables are grouped into eclipsing binary stars and chromospheri- cal stars. Pulsating variables can again classified into Cepheid, RR Lyrae, RV Tauri, Delta Scuti, Mira etc. The eruptive or cataclysmic variables are novae, supernovae, etc., which rarely occurs and are not periodic phenomena. Most of the other variations are periodic in nature. Variable stars can be observed through many ways such as photometry, spectrophotometry and spectroscopy. The sequence of photometric observa- xiv tions on variable stars produces time series data, which contains time, magni- tude and error. The plot between variable star’s apparent magnitude and time are known as light curve. If the time series data is folded on a period, the plot between apparent magnitude and phase is known as phased light curve. The unique shape of phased light curve is a characteristic of each type of variable star. One way to identify the type of variable star and to classify them is by visually looking at the phased light curve by an expert. For last several years, automated algorithms are used to classify a group of variable stars, with the help of computers. Research on variable stars can be divided into different stages like observa- tion, data reduction, data analysis, modeling and classification. The modeling on variable stars helps to determine the short-term and long-term behaviour and to construct theoretical models (for eg:- Wilson-Devinney model for eclips- ing binaries) and to derive stellar properties like mass, radius, luminosity, tem- perature, internal and external structure, chemical composition and evolution. The classification requires the determination of the basic parameters like pe- riod, amplitude and phase and also some other derived parameters. Out of these, period is the most important parameter since the wrong periods can lead to sparse light curves and misleading information. Time series analysis is a method of applying mathematical and statistical tests to data, to quantify the variation, understand the nature of time-varying phenomena, to gain physical understanding of the system and to predict future behavior of the system. Astronomical time series usually suffer from unevenly spaced time instants, varying error conditions and possibility of big gaps. This is due to daily varying daylight and the weather conditions for ground based observations and observations from space may suffer from the impact of cosmic ray particles. Many large scale astronomical surveys such as MACHO, OGLE, EROS, xv ROTSE, PLANET, Hipparcos, MISAO, NSVS, ASAS, Pan-STARRS, Ke- pler,ESA, Gaia, LSST, CRTS provide variable star’s time series data, even though their primary intention is not variable star observation. Center for Astrostatistics, Pennsylvania State University is established to help the astro- nomical community with the aid of statistical tools for harvesting and analysing archival data. Most of these surveys releases the data to the public for further analysis. There exist many period search algorithms through astronomical time se- ries analysis, which can be classified into parametric (assume some underlying distribution for data) and non-parametric (do not assume any statistical model like Gaussian etc.,) methods. Many of the parametric methods are based on variations of discrete Fourier transforms like Generalised Lomb-Scargle peri- odogram (GLSP) by Zechmeister(2009), Significant Spectrum (SigSpec) by Reegen(2007) etc. Non-parametric methods include Phase Dispersion Minimi- sation (PDM) by Stellingwerf(1978) and Cubic spline method by Akerlof(1994) etc. Even though most of the methods can be brought under automation, any of the method stated above could not fully recover the true periods. The wrong detection of period can be due to several reasons such as power leakage to other frequencies which is due to finite total interval, finite sampling interval and finite amount of data. Another problem is aliasing, which is due to the influence of regular sampling. Also spurious periods appear due to long gaps and power flow to harmonic frequencies is an inherent problem of Fourier methods. Hence obtaining the exact period of variable star from it’s time series data is still a difficult problem, in case of huge databases, when subjected to automation. As Matthew Templeton, AAVSO, states “Variable star data analysis is not always straightforward; large-scale, automated analysis design is non-trivial”. Derekas et al. 2007, Deb et.al. 2010 states “The processing of xvi huge amount of data in these databases is quite challenging, even when looking at seemingly small issues such as period determination and classification”. It will be beneficial for the variable star astronomical community, if basic parameters, such as period, amplitude and phase are obtained more accurately, when huge time series databases are subjected to automation. In the present thesis work, the theories of four popular period search methods are studied, the strength and weakness of these methods are evaluated by applying it on two survey databases and finally a modified form of cubic spline method is intro- duced to confirm the exact period of variable star. For the classification of new variable stars discovered and entering them in the “General Catalogue of Vari- able Stars” or other databases like “Variable Star Index“, the characteristics of the variability has to be quantified in term of variable star parameters.