Evaluation and optimization of food-grade tannin acyl hydrolase production by a probiotic Lactobacillus plantarum strain in submerged and solid state fermentation

Tannin acyl hydrolase (tannase) production by Lactobacillus plantarum MTCC1407 was studied in submerged and solid-state fermentation process. Sequential optimization strategy using Plackett–Burman screening and response surface methodology was adopted to optimize the submerged fermentation process. Eight medium components were evaluated initially by Plackett–Burman two level factorial designs to identify the most significant parameters that affect the tannase production. The significant variables affecting tannase production were found to be tannic acid, glucose and MnSO₄·7H₂O. These factors were further optimized by response surface methodology. Maximum tannase activity of 9.13 U ml^?1 was observed at 30 h using the following medium composition (g l^?1): tannic acid, 13.16; glucose, 1.5; NH₄Cl, 1.0; CaCl₂·2H₂O, 1.0; K₂HPO₄, 0.5; KH₂PO₄, 0.5; MgSO4·7H₂O, 0.5 and MnSO₄·7H₂O, 0.03. Among the various carbon sources examined for tannase production by L. plantarum, glucose and tannic acid combination was found to be decisive for enhancing tannase yield. Solid state fermentation was conducted using various solid substrates and agricultural residues. Maximum tannase activity of 5.319 U gds^?1 was obtained using coffee husk as substrate.     

Analysis of homodimeric protein interfaces by graph-spectral methods

The quaternary structures impart structural and functional credibilityto proteins. In a multi-subunit protein, it is important tounderstand the factors that drive the association or dissociationof the subunits. It is a well known fact that both hydrophobicand charged interactions contribute to the stability of theprotein interface. The interface residues are also known tobe highly conserved. Though they are buried in the oligomer,these residues are either exposed or partially exposed in themonomer. It is felt that a systematic and objective method ofidentifying interface clusters and their analysis can significantlycontribute to the identification of a residue or a collectionof residues important for oligomerization. Recently, we haveapplied the techniques of graph-spectral methods to a varietyof problems related to protein structure and folding. A majoradvantage of this methodology is that the problem is viewedfrom a global protein topology point of view rather than localizedregions of the protein structure. In the present investigation,we have applied the methods of graph-spectral analysis to identifyside chain clusters at the interface and the centers of theseclusters in a set of homodimeric proteins. These clusters areanalyzed in terms of properties such as amino acid composition,accessibility to solvent and conservation of residues. Interestingresults such as participation of charged and aromatic residueslike arginine, glutamic acid, histidine, phenylalanine and tyrosine,consistent with earlier investigations, have emerged from theseanalyses. Important additional information is that the residuesinvolved are a part of a cluster(s) and that they are sequentiallydistant residues which have come closer to each other in thethree-dimensional structure of the protein. These residues caneasily be detected using our graph-spectral algorithm. Thismethod has also been used to identify important residues (`hotspots') in dimerization and also to detect dimerization siteson the monomer. The residues predicted using the present algorithmhave correlated well with the experiments indicating the efficacyof this method in predicting residues involved in dimer stability.     

Growth of PbSe Quantum Dots Within High‐Index Lead‐Phosphate Glass for Infrared Saturable Absorbers

PbSe quantum dots (QDs) were grown in high‐refractive‐index low‐melting‐temperature lead‐phosphate glass. The lowest energy exciton transition of the QDs was tuned over a wide range within the infrared spectral region (0.93–2.75 μm) by a controlled heat treatment. The measured QD radius ranged between 2 and 5.3 nm, with a time (t) dependence of t^0.29 for long dwelling times during the heat treatment, indicating that the QD growth mechanism tends to follow Lifshitz‐Slyozov‐Wagner theory. The QD saturable absorber behavior at 1.2 μm had a measured saturation fluence of ~2.1 μJ/cm².     

Removal of Cu(II) by biosorption onto coconut shell in fixed-bed column systems

Biosorption of Cu(II) onto coconut shell, an agricultural biomaterial, was studied in a fixed-bed column. The Cu(II) biosorption column had the best performance at 10 mg L^?1 inlet Cu(II) concentration, 10 mL min^?1 flow rate and 20 cm bed depth. The equilibrium uptake of Cu(II) amounted to 7.25 mg g^?1. The simulation of the breakthrough curve was successful with the BDST and Yoon–Nelson models, but the entire breakthrough curve was best predicted by the Clark model. The design of a fixed bed column for Cu(II) removal from wastewater by biosorption onto coconut shell can be done based on these models.     

Hydrogen-bond induced side-chain liquid crystalline polymers based on nicotinic acid derivatives

Supramolecular side-chain liquid crystalline poly(acrylate)s have been prepared by self-assembly of H-bond donor and acceptor complexes through intermolecular complementary hydrogen bond formation. Poly[4-(m-acryloyloxyalkyloxy)benzoic acid]s [m = 6 (P1) and 8 (P2)] were employed as polymer components. Liquid crystalline nicotinic acid derivatives (C1, C2, C3, and C4) were used as complementary H-bond donor/acceptor counterparts. The liquid crystalline properties of the nicotinic acid derivatives, the polymers and their complexes were investigated by DSC and POM. Methylene spacers present at the terminal position of the nicotinic acid derivatives played a key role in mesophase arrangements. The columnar phase exhibited by the nicotinic acid derivatives completely disappeared in the H-bonded complexes to afford a nematic phase, thereby substantiating the complex formation.     

Catalytic decomposition of nitrous oxide over calcined cobalt aluminum hydrotalcites

Catalytic decomposition of nitrous oxide has been carried out over calcined cobalt aluminum hydrotalcites of general formula [Co_1−xAl_x(OH)₂[CO₃]_x/2 H₂O where x = 0.25–0.33 at 50 Torr (1 Torr = 133 Pa) initial pressure of N₂O in a static glass recirculatory reactor (130 cc) in the temperature range 150–280°C. All catalysts showed a first order dependence in N₂O without significant oxygen inhibition. The activity increased with an increase in cobalt concentration present in the sample. The catalyst precursor synthesized under low supersaturation (LS) exhibited a higher activity than the precursor synthesized by sequential precipitation (SP) method. The observed trend in the activity is explained based on the surface concentration of cobalt, determined by XPS and matrix effects. Prior to catalytic studies, the fresh and calcined samples were characterized by various physicochemical techniques such as XRD, FT–IR, TG–DSC, TEM (with EDAX) and BET surface area measurements.     

Adapting funding processes for agile IT projects: an empirical investigation

Agile software development that provides software development organizations, the ability to respond to changes in turbulent business environments, has been gaining wide adoption. Agile software development projects are characterized by ‘just enough’ planning and lack of upfront commitment to scope, cost, and schedule. These characteristics pose conflicting demands on managers responsible for making funding decisions, because traditional approaches to funding IT projects are often based on well-defined scope, cost, and schedule. These conflicts demand the adaptation of traditional funding processes to suit to agile projects. We draw from Adaptive Structuration Theory to understand the nature of conflicts between traditional IT project funding processes and the dynamic nature of agile projects, and how these conflicts are addressed by practices that are appropriated in the process of social interaction between funding decision makers and development teams. On the basis of a multisite case study, we present a framework that explains how organizations adapt traditional IT funding approaches to accommodate the unique characteristics of agile IT projects.     

A robust model for finding optimal evolutionary trees

Constructing evolutionary trees for species sets is a fundamental problem in computational biology. One of the standard models assumes the ability to compute distances between every pair of species, and seeks to find an edge-weighted treeT in which the distanced _ij ^T in the tree between the leaves ofT corresponding to the speciesi andj exactly equals the observed distance,d _ij . When such a tree exists, this is expressed in the biological literature by saying that the distance function or matrix isadditive, and trees can be constructed from additive distance matrices in0(n ²) time. Real distance data is hardly ever additive, and we therefore need ways of modeling the problem of finding the best-fit tree as an optimization problem.In this paper we present several natural and realistic ways of modeling the inaccuracies in the distance data. In one model we assume that we have upper and lower bounds for the distances between pairs of species and try to find an additive distance matrix between these bounds. In a second model we are given a partial matrix and asked to find if we can fill in the unspecified entries in order to make the entire matrix additive. For both of these models we also consider a more restrictive problem of finding a matrix that fits a tree which is not only additive but alsoultrametric. Ultrametric matrices correspond to trees which can be rooted so that the distance from the root to any leaf is the same. Ultrametric matrices are desirable in biology since the edge weights then indicate evolutionary time. We give polynomial-time algorithms for some of the problems while showing others to be NP-complete. We also consider various ways of fitting a given distance matrix (or a pair of upper- and lower-bound matrices) to a tree in order to minimize various criteria of error in the fit. For most criteria this optimization problem turns out to be NP-hard, while we do get polynomial-time algorithms for some.Supported by DIMACS under NSF Contract STC-88-09648.Supported by NSF Grant CCR-9108969.This work was begun while this author was visiting DIMACS in July and August 1992, and was supported in part by the U.S. Department of Energy under Contract DE-AC04-76DP00789.     

Investigation of liquid crystalline and photocrosslinkable poly[4‐x‐phenyl‐4′‐(m‐methacryloyloxyalkyloxy)cinnamate]s

Three series of liquid‐crystalline‐cum‐photocrosslinkable polymers were synthesized from 4‐x‐phenyl‐4′‐(m‐methacryloyloxyalkyloxy)cinnamates (x = ? H, ? OCH₃ and ? CN; m = 6, 8 and 10) by free radical solution polymerization using azobisisobutyronitrile as an initiator in tetrahydrofuran at 60 °C. All the monomers and polymers were characterized using intrinsic viscosity, and FTIR, ¹H NMR and ¹³C NMR spectroscopy. The liquid crystalline behavior of these polymers was examined using a hot stage optical polarizing microscope. All the polymers exhibited liquid crystalline behavior. The hexamethylene spacer‐containing polymers exhibited grainy textures; in contrast, the octamethylene and decamethylene spacer‐containing polymers showed nematic textures. Differential scanning calorimetry data confirmed the liquid crystalline property of the polymers. Thermogravimetric analysis revealed that all the polymers were stable between 236 and 344 °C in nitrogen atmosphere and underwent degradation thereafter. As the methylene chain length increases in the polymer side‐chain, the thermal stability and char yield of the polymers decrease. The photocrosslinking property of the polymers was investigated using the technique of exposing the polymer solution to UV light and using UV spectroscopy. The crosslinking reaction proceeds via 2π–2π cycloaddition reactions of the ? CH?CH? of the pendant cinnamate ester. The polymers containing electron‐releasing substituents (? OCH₃) showed faster crosslinking than the unsubstituted polymers and those containing electron‐withdrawing substituents (? CN). Copyright © 2007 Society of Chemical Industry     

Deep Learning with Optimal Hierarchical Spiking Neural Network for Medical Image Classification

Medical image classification becomes a vital part of the design of computer aided diagnosis (CAD) models. The conventional CAD models are majorly dependent upon the shapes, colors, and/or textures that are problem oriented and exhibited complementary in medical images. The recently developed deep learning (DL) approaches pave an efficient method of constructing dedicated models for classification problems. But the maximum resolution of medical images and small datasets, DL models are facing the issues of increased computation cost. In this aspect, this paper presents a deep convolutional neural network with hierarchical spiking neural network (DCNN-HSNN) for medical image classification. The proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical images. In addition, region growing segmentation technique is involved to determine the infected regions in the medical image. Moreover, NADAM optimizer with DCNN based Capsule Network (CapsNet) approach is used for feature extraction and derived a collection of feature vectors. Furthermore, the shark smell optimization algorithm (SSA) based HSNN approach is utilized for classification process. In order to validate the better performance of the DCNN-HSNN technique, a wide range of simulations take place against HIS2828 and ISIC2017 datasets. The experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different measures. Please type your abstract here.