Biochemical and Structural Characterization of RCR3-AVR2: A Model for Protease-Inhibitor Interactions at the Plant-Pathogen Interface.

The tomato apoplast is a molecular battle ground for proteases and inhibitors during plant-pathogen interactions. The interaction between structurally diverse pathogen-derived inhibitors (EPIC1, EPIC2B, AVR2 and RIP1) and their target proteases (RCR3, PIP1 and C14) is an ideal system to study molecular arms-races. First we generated a collection of 52 isoforms of proteases and inhibitors in the pFLAG-ATS expression vector for expression in Escherichia coli. We summarise the expression of both the proteases and inhibitors and show that the inhibitor proteins were produced successfully, unlike the proteases. Second we expressed and purified AVR2 on a large scale and show that purified AVR2 is capable of inhibiting RCR3 and triggering the Cf2-mediated hypersensitive response (HR) in tomato demonstrating that recombinant AVR2 is functional. We next implement biophysical and structural biology tools to elucidate the secondary and tertiary structure of AVR2. The major findings are that: (i) AVR2 is a beta protein determined by circular dichroism (CD) (ii) At apoplastic pH, AVR2 exerts a conformational change associated with RCR3 inhibition determined by CD and tyrosine fluorescent spectroscopy (iii) AVR2 is a potent inhibitor of papain determined by enzyme assay using BODIPY FL casein. Attempts to crystallize AVR2 with and without epitope tags and at different concentrations and conditions failed, possibly because AVR2 is a heavily charged basic protein. Next all 13 lysines and the N-terminus of epitope-tagged AVR2 were methylated but no crystals were obtained. This methylated AVR2 still triggers HR in Cf2 tomato plants. Preliminary NMR experiments of non-methylated AVR2 showed good resolution for future structure elucidation of AVR2. Third, we tested four different heterologous expression systems (plant, bacterium, insect and yeast) to generate high quantities of active and soluble RCR3. We conclude that yeast is the best expression system to produce high amounts of soluble proRCR3 and proRCR3 is fully converted into mature RCR3 in the presence of reducing agents and acidic pH buffer. Finally, we study the role of double cysteine (Cys24, Cys25) in the catalytic site of RCR3 which is common to PLCP subclass 6 of plant PLCPs. Using agroinfiltration in Nicotiana benthamiana we produced C24A, C25A and C24AC25A mutant RCR3 proteins and we discovered that (i) Cys25 but not Cys24 is the essential catalytic residue labelled by activity-based probe MV201. (ii) Surprisingly maturation of RCR3 does not require the catalytic Cys25, indicating that other endogeneous proteases activate RCR3. (iii) Proteolytically inactive RCR3 mutants triggers Cf-2-mediated HR in the presence of AVR2 and (iv) Interestingly, ascorbate enhances the activity of the C24A mutant but not wild-type RCR3, suggesting that Cys24 in RCR3 might have a role in sensing redox potential. The autocatalytic activation of proRCR3 produced in yeast combined with the maturation of the C25A mutant inplanta suggests that RCR3 can be activated by both intramolecular and intermolecular processing

AMENDED ADAPTIVE ALGORITHM FOR CORPUS BASED IMPROVED SPEECH ENHANCEMENT

Speech enhancement objective is to improve the noisy speech signals for human perception. The intention of speech enhancement algorithm is to improve the performance of the communication, when the signal is occluded by noise. The quality and intelligibility of speech is reduced because of the presence of background noise. There are various adaptive filtering algorithms for speech enhancement. The existing least mean square and normalised least mean square algorithms have the problem of choosing the step size that guarantees the stability of the algorithm. To overcome this problem, we focus on speech enhancement by amended adaptive filtering. The proposed algorithm follows blind source separation strategy using adaptive filtering. Comparison of existing adaptive filtering algorithms with proposed algorithm justifies the amendment incorporated in this paper. Taking the objective criteria into account the algorithms has been tested for segmental signal to noise ratio (SegSNR), segmental mean square error (SegMSE), signal to noise ratio and mean square error. The proposed algorithm can be used for hand-free cell phone, hearing aids and teleconferencing systems

AMENDED ADAPTIVE ALGORITHM FOR CORPUS BASED IMPROVED SPEECH ENHANCEMENT

Speech enhancement objective is to improve the noisy speech signals for human perception. The intention of speech enhancement algorithm is to improve the performance of the communication, when the signal is occluded by noise. The quality and intelligibility of speech is reduced because of the presence of background noise. There are various adaptive filtering algorithms for speech enhancement. The existing least mean square and normalised least mean square algorithms have the problem of choosing the step size that guarantees the stability of the algorithm. To overcome this problem, we focus on speech enhancement by amended adaptive filtering. The proposed algorithm follows blind source separation strategy using adaptive filtering. Comparison of existing adaptive filtering algorithms with proposed algorithm justifies the amendment incorporated in this paper. Taking the objective criteria into account the algorithms has been tested for segmental signal to noise ratio (SegSNR), segmental mean square error (SegMSE), signal to noise ratio and mean square error. The proposed algorithm can be used for hand-free cell phone, hearing aids and teleconferencing systems

Fruit Shop Tool: Fruit Classification and Recognition using Deep Learning

Fruit image classification and recognition is a challenging application of computer vision. The computer vision system is used to recognize a fruit based on artificial neural networks. Deep neural network is widely used for various classification problems. In this paper Convolutional Neural Network (CNN) is used to recognize the fruits. The dataset contains 1877 images of ten categories which are used for the experimental purpose. CNN is constructed with sixteen layers which are used to extract the features from images and Support Vector Machine (SVM) classifier is used for classification. The proposed system has the classification accuracy of 99.2% and the recognition accuracy of 99.02%

Studies on Nymphaea pubescens willd. (Nymphaeaceae) - a plant drug of aquatic flora interest.

The present study was aimed at establishing scientific validation with supporting data of the aquatic plant Nymphaea pubescens Willd family Nymphaeaceae on its pharmacognosy, phytochemistry and pharmacological activities. The study for the first time designed to focus on the pharmacognostic standardization, systematic isolation of the phytoconstituents, identification by spectroscopic interpretation including 2D NMR studies and subjecting the same for physico-chemical analysis like Lipinski rule of five, pharmacodynamic and pharmacokinetic parameters, screening the plant extracts and the isolated compounds for the target based antidiabetic activity in the type II diabetic animal model enzymatically and also at the receptor level, anti-cancer activity and In-vitro antioxidant activity in order to establish and standardize the folklore claims. The pharmacognostic standardization helps to differentiate between the species and adulterants or substitutes. The study revealed the macroscopic, microscopic identification, physico-chemical constants, powder and fluorescence analytical datas that provides the standardizing protocol for Nymphaea pubescens. Thoroughly investigated the literature, presence of macrocyclic flavonol Nympholide A and Nympholide B in Nymphaea lotus and Nuciferine in Nelumbo nucifera were isolated and reported. But so far no such compounds were isolated and scientifically proved from Nymphaea pubescens. The phytochemical studies of the aquatic plant for the first time focused mainly on identification and isolation of two aliphatic compounds 10-oxoundecanoic acid and 14-oxopentadec-9-enoic acid, one aporphine based alkaloid Nuciferine and one flavonol Quercetin. The studies supported by TLC, HPTLC, UV, IR, 1H NMR, 13C NMR, 13C DEPT-135 NMR, HMBC 2D NMR, Mass spectroscopic and melting point data’s. In addition the isolated compounds are subjected for physico-chemical analysis by feeding the structure of the compound in the database ACD/ilabs. The physico-chemical studies revealed all the isolated compounds exhibits drug likeness property. The literature has thus so far documented no pharmacological and systematic study has been attempted to confirm the traditional practice of using Nymphaea pubescens in the treatment of diabetes and cancer. The pharmacological studies of the root and rhizome showed that the ethanolic extract acts mechanistically by increasing the glycolytic enzymes, antiapoptotic protein Bcl-2 expression and decreasing the gluconeogenic enzymes, proapoptotic protein expression in hepatic cells. The isolated compounds 10-oxoundecanoic acid, 14-oxopentadec-9-enoic acid and Nuciferine inhibit the Protein tyrosine phosphatase 1B receptor involved in the insulin signaling deactivation pathway. The ethanolic extract from the root and rhizome of Nymphaea pubescens screened for the first time for antidiabetic activity in the type II diabetes induced animal models and the compounds 10-oxoundecanoic acid, 14-oxopentadec-9-enoic acid and Nuciferine was isolated from root and rhizome and molecularly docked for the first time. The ethyl acetate fraction from ethanolic flower extract of N.pubescens showed significant anticancer and anti-oxidant activity and the activities may be due to the presence of Quercetin which is isolated and reported for the first time. We conclude that the aquatic plant Nymphaea pubescens is scientifically proved by isolating the compounds 10-oxoundecanoic acid, 14-oxopentadec-9-enoic acid, Nuciferine and Quercetin and validating by physico-chemical property analysis, chemotaxonomical analysis, molecular studies of the extracts for the antidiabetic activity, molecular docking for the isolated compounds with the receptor Protein Tyrosine Phosphatase 1B, biological assay for active fraction in the DAL induced animal model and its free radical scavenging effect, since scientific validation is the hour of the day. The aquatic plant Nymphaea pubescens also gives the chemotaxonomical significance due to the presence of aporphine based alkaloid Nuciferine and flavonol Quercetin to the Nymphaeaceae family. In future, large scale isolation of 10-oxoundecanoic acid, 14-oxopentadec-9-enoic acid and Nuciferine and screening the isolated compounds for in-vivo antidiabetic activity including apoptotic studies, in-vivo assay for PTP1B, QSAR studies may provide potent lead molecules for the treatment of type II diabetes mellitus

Screen of Non-annotated Small Secreted Proteins of \u3ci\u3ePseudomonas syringae\u3c/i\u3e Reveals a Virulence Factor That Inhibits Tomato Immune Proteases

Pseudomonas syringae pv. tomato DC3000 (PtoDC3000) is an extracellular model plant pathogen, yet its potential to produce secreted effectors that manipulate the apoplast has been under investigated. Here we identified 131 candidate small, secreted, non-annotated proteins from the PtoDC3000 genome, most of which are common to Pseudomonas species and potentially expressed during apoplastic colonization. We produced 43 of these proteins through a custom-made gateway-compatible expression system for extracellular bacterial proteins, and screened them for their ability to inhibit the secreted immune protease C14 of tomato using competitive activity-based protein profiling. This screen revealed C14-inhibiting protein-1 (Cip1), which contains motifs of the chagasin-like protease inhibitors. Cip1 mutants are less virulent on tomato, demonstrating the importance of this effector in apoplastic immunity. Cip1 also inhibits immune protease Pip1, which is known to suppress PtoDC3000 infection, but has a lower affinity for its close homolog Rcr3, explaining why this protein is not recognized in tomato plants carrying the Cf-2 resistance gene, which uses Rcr3 as a co-receptor to detect pathogen-derived protease inhibitors. Thus, this approach uncovered a protease inhibitor of P. syringae, indicating that also P. syringae secretes effectors that selectively target apoplastic host proteases of tomato, similar to tomato pathogenic fungi, oomycetes and nematodes

Screen of Non-annotated Small Secreted Proteins of \u3ci\u3ePseudomonas syringae\u3c/i\u3e Reveals a Virulence Factor That Inhibits Tomato Immune Proteases

Pseudomonas syringae pv. tomato DC3000 (PtoDC3000) is an extracellular model plant pathogen, yet its potential to produce secreted effectors that manipulate the apoplast has been under investigated. Here we identified 131 candidate small, secreted, non-annotated proteins from the PtoDC3000 genome, most of which are common to Pseudomonas species and potentially expressed during apoplastic colonization. We produced 43 of these proteins through a custom-made gateway-compatible expression system for extracellular bacterial proteins, and screened them for their ability to inhibit the secreted immune protease C14 of tomato using competitive activity-based protein profiling. This screen revealed C14-inhibiting protein-1 (Cip1), which contains motifs of the chagasin-like protease inhibitors. Cip1 mutants are less virulent on tomato, demonstrating the importance of this effector in apoplastic immunity. Cip1 also inhibits immune protease Pip1, which is known to suppress PtoDC3000 infection, but has a lower affinity for its close homolog Rcr3, explaining why this protein is not recognized in tomato plants carrying the Cf-2 resistance gene, which uses Rcr3 as a co-receptor to detect pathogen-derived protease inhibitors. Thus, this approach uncovered a protease inhibitor of P. syringae, indicating that also P. syringae secretes effectors that selectively target apoplastic host proteases of tomato, similar to tomato pathogenic fungi, oomycetes and nematodes

Amended Adaptive Algorithm for Corpus Based Improved Speech Enhancement

Speech enhancement objective is to improve the noisy speech signals for human perception. The intention of speech enhancement algorithm is to improve the performance of the communication, when the signal is occluded by noise. The quality and intelligibility of speech is reduced because of the presence of background noise. There are various adaptive filtering algorithms for speech enhancement. The existing least mean square and normalised least mean square algorithms have the problem of choosing the step size that guarantees the stability of the algorithm. To overcome this problem, we focus on speech enhancement by amended adaptive filtering. The proposed algorithm follows blind source separation strategy using adaptive filtering. Comparison of existing adaptive filtering algorithms with proposed algorithm justifies the amendment incorporated in this paper. Taking the objective criteria into account the algorithms has been tested for segmental signal to noise ratio (SegSNR), segmental mean square error (SegMSE), signal to noise ratio and mean square error. The proposed algorithm can be used for hand-free cell phone, hearing aids and teleconferencing systems