Improvement of Riboflavin Production by Lactobacillus fermentum Isolated from Yogurt

The riboflavin production by Lactobacillus fermentum isolated from yogurt was improved by inactivation of folE gene. Guanosine triphosphate (GTP) serves as the precursor for both folate as well as riboflavin biosynthesis. The folE gene of Lb. fermentum, which codes for GTP cyclohydrolase I was inactivated by the insertion of erythromycin resistance gene cassette through recombination and the riboflavin production by the parental and mutant strains were estimated. Inactivation of the folE gene involved in folate biosynthesis pathway resulted in enhanced level of riboflavin production by Lb. fermentum, since more amount of GTP molecules are made available for the GTP cyclohydrolase II mediated riboflavin biosynthesis pathway. In a chemically defined medium, the Lb. fermentum produced 2.29 mg L^?1 of riboflavin at 24 h of fermentation. Whereas the folE disrupted mutant Lb. fermentum GKJFE produced approximately 50% more riboflavin (3.49 mg L^?1) than that of the parental strain at 72 h of fermentation.     

Folic acid fortification of parboiled rice: Multifactorial analysis and kinetic investigation

Folic acid fortification of parboiled rice has been systematically studied to obtain quantitative insights into the role of key process variables. Parboiling was conducted with brown rice soaked at 70 °C for 1, 2 and 3 h with four different fortificant concentrations added and dried parboiled rice was milled for three durations (i.e. 0, 60 and 120 s). Both residual folate concentration in treated parboiled rice and pH of the soaking water after soaking stages were measured. Multifactorial model was developed to describe the residual folate retention behaviour and suggested that both soaking and milling were significant factors in folic acid fortification. The optimum soaking time was deduced to be 1.97 h. Folate retention rate followed a 1st order kinetics while the rates of natural rice hydrolysis and folate uptake were both time-dependent.     

Weak Generalized Closed World Assumption

Explicit representation of negative information in logic programs is not feasible in many applications such as deductive databases and artificial intelligence. Defining default rules which allow implicit inference of negated facts from positive information encoded in a logic program has been an attractive alternative to the explicit representation approach. There is, however, a difficulty associated with implicit default rules. Default rules such as the CWA and the GCWA, which closely model logical negation, are in general computationally intractable. This has led to the development of weaker definitions of negation such as the Negation-As-Failure (NF) and the Support-For-Negation (SN) rules which are computationally simpler. These are sound implementations of the CWA and the GCWA, respectively. In this paper, we define an alternative rule of negation based upon the fixpoint definition of the GCWA. This rule, called the Weak Generalized Closed World Assumption (WGCWA), is a weaker definition of the GCWA that allows us to implement a sound negation rule, called the Negation-As-Finite-Failure (NAFF), similar to the NF-rule and less cumbersome than the SN-rule. We present three definitions of the NAFF. Two declarative definitions similar to those for the NF-rule and one procedural definition based on SLI-resolution.     

Context-Enhanced Representation Learning for Single Image Deraining

International Journal of Computer Vision - Perception of content and structure in images with rainstreaks or raindrops is challenging, and it often calls for robust deraining algorithms to remove...     

Multi-level classification of emphysema in HRCT lung images

Emphysema is a common chronic respiratory disorder characterised by the destruction of lung tissue. It is a progressive disease where the early stages are characterised by a diffuse appearance of small air spaces, and later stages exhibit large air spaces called bullae. A bullous region is a sharply demarcated region of emphysema. In this paper, it is shown that an automated texture-based system based on co-training is capable of achieving multiple levels of emphysema extraction in high-resolution computed tomography (HRCT) images. Co-training is a semi-supervised technique used to improve classifiers that are trained with very few labelled examples using a large pool of unseen examples over two disjoint feature sets called views. It is also shown that examples labelled by experts can be incorporated within the system in an incremental manner. The results are also compared against “density mask”, currently a standard approach used for emphysema detection in medical image analysis and other computerized techniques used for classification of emphysema in the literature. The new system can classify diffuse regions of emphysema starting from a bullous setting. The classifiers built at different iterations also appear to show an interesting correlation with different levels of emphysema, which deserves more exploration.

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Complexity of computing with extended propositional logic programs

In this paper we introduce the notion of anF-program, whereF is a collection of formulas. We then study the complexity of computing withF-programs.F-programs can be regarded as a generalization of standard logic programs. Clauses (or rules) ofF-programs are built of formulas fromF. In particular, formulas other than atoms are allowed as building blocks ofF-program rules. Typical examples ofF are the set of all atoms (in which case the class of ordinary logic programs is obtained), the set of all literals (in this case, we get the class of logic programs with classical negation [9]), the set of all Horn clauses, the set of all clauses, the set of all clauses with at most two literals, the set of all clauses with at least three literals, etc. The notions of minimal and stable models [16, 1, 7] of a logic program have natural generalizations to the case ofF-programs. The resulting notions are called in this paperminimal andstable answer sets. We study the complexity of reasoning involving these notions. In particular, we establish the complexity of determining the existence of a stable answer set, and the complexity of determining the membership of a formula in some (or all) stable answer sets. We study the complexity of the existence of minimal answer sets, and that of determining the membership of a formula in all minimal answer sets. We also list several open problems.This work was partially supported by National Science Foundation under grant IRI-9012902.This work was partially supported by National Science Foundation under grant CCR-9110721.     

Synthesis of guar‐gum‐stabilized nanosized silver clusters with γ radiation

Gamma ray induced synthesis of Ag clusters in the aqueous medium has been carried out using natural polysaccharide guar gum as a stabilizer. The results showed that guar gum is very effective in binding the Ag clusters and restricts their size in the nano region. The surface plasmon resonance band in the wavelength range of 410–425 nm of visible region has confirmed the formation of Ag clusters. The size of the clusters is governed by the concentration of both, i.e. precursor Ag⁺ and guar gum. The clusters were characterized by TEM and XRD, which shows that the size of clusters is in the range of 10–30 nm. The results have shown that silver clusters stabilized by guar gum are stable in acid media and alkaline media is not a desired media for the synthesis. From thermogravimetric analysis it has been demonstrated that incorporation of nano sized Ag clusters within guar gum improves the thermal properties of irradiated guar gum. To the best of our knowledge, the use of guar gum as a stabilizer in the gamma ray induced synthesis of Ag nano clusters is being reported first time in the literature. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009     

A communication‐efficient framework for outlier‐free data reporting in data‐gathering sensor networks

In this paper, we address the problem of reducing the communication cost and hence the energy costs incurred in data‐gathering applications of a sensor network. Environmental data depicts a huge amount of correlation in both the spatial and temporal domains. We exploit these temporal–spatial correlations to address the aforementioned problem. More specifically, we propose a framework that partitions the physical sensor network topology into a number of feature regions. Each sensor node builds a data model that represents the underlying structure of the data. A representative node in each feature region communicates only the model coefficients to the sink, which then uses them to answer queries. The temporal and spatial similarity has special meaning in outlier cleaning too. We use a modified z‐score technique to precisely label the outliers and use the spatial similarity to confirm whether the outliers are due to a true change in the phenomenon under study or due to faulty sensor nodes. Copyright © 2008 John Wiley & Sons, Ltd.