Extracellular β-glucosidase Production by a Marine Aspergillus sydowii BTMFS 55 under Solid State Fermentation Using Statistical Experimental Design

A potential fungal strain producing extracellular β-glucosidase enzyme was isolated from sea water and identified as ^ëéÉêJ Öáääìë=ëóÇçïáá BTMFS 55 by a molecular approach based on 28S rDNA sequence homology which showed 93% identity with already reported sequences of ^ëéÉêÖáääìë=ëóÇçïáá in the GenBank. A sequential optimization strategy was used to enhance the production of β-glucosidase under solid state fermentation (SSF) with wheat bran (WB) as the growth medium. The two-level Plackett-Burman (PB) design was implemented to screen medium components that influence β-glucosidase production and among the 11 variables, moisture content, inoculums, and peptone were identified as the most significant factors for β-glucosidase production. The enzyme was purified by (NH4)2SO4 precipitation followed by ion exchange chromatography on DEAE sepharose. The enzyme was a monomeric protein with a molecular weight of ~95 kDa as determined by SDS-PAGE. It was optimally active at pH 5.0 and 50°C. It showed high affinity towards éNPG and enzyme has a hã and sã~ñ of 0.67 mM and 83.3 U/mL, respectively. The enzyme was tolerant to glucose inhibition with a há of 17 mM. Low concentration of alcohols (10%), especially ethanol, could activate the enzyme. A considerable level of ethanol could produce from wheat bran and rice straw after 48 and 24 h, respectively, with the help of p~ÅÅÜ~êçãóÅÉë=ÅÉêÉîáëá~É in presence of cellulase and the purified β-glucosidase of ^ëéÉêÖáääìë=ëóÇçïáá BTMFS 55.

A Framework of Statistical Machine Translator from English to Malayalam

In this paper we describe the methodology and the structural design of a system that translates English into Malayalam using statistical models. A monolingual Malayalam corpus and a bilingual English/Malayalam corpus are the main resource in building this Statistical Machine Translator. Training strategy adopted has been enhanced by PoS tagging which helps to get rid of the insignificant alignments. Moreover, incorporating units like suffix separator and the stop word eliminator has proven to be effective in bringing about better training results. In the decoder, order conversion rules are applied to reduce the structural difference between the language pair. The quality of statistical outcome of the decoder is further improved by applying mending rules. Experiments conducted on a sample corpus have generated reasonably good Malayalam translations and the results are verified with F measure, BLEU and WER evaluation metrics

Design, Development and Realization of Quadratic Volterra Filters for Selected Applications

The basic concepts of digital signal processing are taught to the students in engineering and science. The focus of the course is on linear, time invariant systems. The question as to what happens when the system is governed by a quadratic or cubic equation remains unanswered in the vast majority of literature on signal processing. Light has been shed on this problem when John V Mathews and Giovanni L Sicuranza published the book Polynomial Signal Processing. This book opened up an unseen vista of polynomial systems for signal and image processing. The book presented the theory and implementations of both adaptive and non-adaptive FIR and IIR quadratic systems which offer improved performance than conventional linear systems. The theory of quadratic systems presents a pristine and virgin area of research that offers computationally intensive work. Once the area of research is selected, the next issue is the choice of the software tool to carry out the work. Conventional languages like C and C++ are easily eliminated as they are not interpreted and lack good quality plotting libraries. MATLAB is proved to be very slow and so do SCILAB and Octave. The search for a language for scientific computing that was as fast as C, but with a good quality plotting library, ended up in Python, a distant relative of LISP. It proved to be ideal for scientific computing. An account of the use of Python, its scientific computing package scipy and the plotting library pylab is given in the appendix Initially, work is focused on designing predictors that exploit the polynomial nonlinearities inherent in speech generation mechanisms. Soon, the work got diverted into medical image processing which offered more potential to exploit by the use of quadratic methods. The major focus in this area is on quadratic edge detection methods for retinal images and fingerprints as well as de-noising raw MRI signals