Novel arginase inhibitors

The present invention relates to novel arginase inhibitors of formula (I). These novel compounds are useful in the treatment of diseases that are associated with arginase activity, such as asthma, allergic rhinitis and COPD (chronic obstructive pulmonary disease)

SYNTHESIS AND CHARACTERIZATION OF SUPPORTED SOLID ACID CATALYSTS FOR CONVERSION OF GREEN SEED CANOLA OIL TO BIODIESEL

Biodiesel, as a bioenergy source, is traditionally produced using alkaline catalysts which are limited to the use of refined vegetable oil feedstocks. The process tends to be environmentally non-benign because of undesired side reactions. It is important that this bioenergy is harnessed in a sustainable way. Heterogeneous solid acid catalysts can promote transesterification and esterification reactions from low-quality, unrefined feedstock without any side reactions like saponification or hydrolysis of triglycerides and minimize the effluent load downstream, thus, making them more desirable alternatives. The use of reactive and stable solid acid catalysts is one way of making the process sustainable. The primary aim of this research was to develop solid acid catalysts comprised of heteropolyacid like tungstophosphoric acid (HPW) and ordered mesoporous aluminosilicates such as MAS-7 and MAS-9. The work plan for this research was divided into five phases. In phase one, commercial γ-alumina was tested for the heterogenization of HPW. HPW loading of 45 wt % was found to be optimum giving a methyl ester yield of 90.0 ± 2.8 wt % for biodiesel synthesis from refined canola oil under optimized reaction conditions that is 10 wt % of the catalyst loading, 17.5 methanol to canola oil molar ratio, 4 MPa, and 200°C for 10 hr. In case of biodiesel production from unrefined green seed canola oil, the methyl ester yield observed was 74.0 ± 1.9 % at the above reaction conditions. Porous oxide material with acidic properties was determined to be suitable for heterogenization of HPW. However, complete disintegration of HPW at loadings of high concentrations (55 and 65 wt %) was observed on the γ-Al2O3 surface and hence, reduced the catalytic activity. Therefore, to tailor their properties mesoporous aluminosilicates such as MAS-7 and MAS-9 were used as supports in the next phase. The introduction of silica into the alumina framework has been shown to improve the textural and hydrothermal properties of alumina and hence in phase two, mesoporous aluminosilicates (MAS-7 and MAS-9) were selected as substitutes for γ-alumina as supports. A series of 5-45 wt % HPW on MAS-7/MAS-9 catalysts was prepared by a wet impregnation technique. Detailed insights into the surface chemistry of HPW supported on MAS catalysts were obtained with the help of Raman, Infrared, 29Si magic-angle spinning and cross-polarization/MAS nuclear magnetic resonance (NMR), and X-ray absorption spectroscopy. The catalytic activities were strongly correlated with the surface chemistry of the HPW supported on MAS-7 and MAS-9 catalysts. HPW supported on MAS-7 and MAS-9 exhibited favourable catalytic activity with a methyl ester yield of 76.5−88.7 wt % and stability sufficient for the simultaneous esterification and transesterification of low grade green seed canola oil. Based on this study, it was found that the MAS-7 and MAS-9 could serve as viable supports for the heterogenization of HPW. Therefore, the main goal in phase three was to enhance catalytic performance by tuning the textural characteristics of these materials. This was achieved by the direct incorporation of HPW into aluminosilicates resulting in HPW-MAS-7 and HPW-MAS-9 composites. These composites were obtained via a facile one-step assembly between positively charged ZSM-5 precursors and negatively charged PW12O403− species in the presence of the block copolymer. The textural characteristics of the composites were improved by introduction of HPW after the addition of an inorganic precursor to the template, leading to a material with a high BET surface area. Being novel heterogeneous solid acid catalysts, the activities of the composites were determined for biodiesel production from unrefined green seed canola oil, and yielded 95.4 ± 1.4 wt % of methyl esters in 10 h at 180 °C with 5.5 wt % of catalyst and a 15.5:1 methanol to oil molar ratio. Due to the high catalytic activity of these synthesized catalysts, it was decided to model the catalytic behaviour and investigate the techno-economic feasibility of scaling up this reaction for industrial application. In the fourth phase, kinetic modelling, mechanistic and thermodynamic studies were undertaken using an optimized HPW-MAS-9 catalyst. The experimental data were fitted to a pseudo-homogeneous model (PH) and adsorption-based models such as Eley-Rideal (ER) Langmuir–Hinshelwood-Hougen–Watson (LHHW). The activity coefficients of the reactant and product species were estimated using the UNIQUAC method. The Eley-Rideal reaction pathway with the surface reaction of adsorbed methanol as the rate controlling step, was found to be a reliable representation of the observed kinetics. In the fifth phase, techno-economic feasibility and life cycle analyses were conducted based on the results obtained in phase three. The economic analysis was performed using both deterministic and stochastic models. With the inclusion of tax incentives, the biodiesel selling price is approximately equal to $ 1.2 /kg and has a positive net present value (NPV) and an internal rate of return 25%. The results of these investigations revealed that the valorisation of green seed canola oil can be achieved by using HPW-MAS-9 catalyst; provided that there are appropriate incentives for the production of biodiesel. This research demonstrated that HPW-MAS-9 was not only an efficient heterogeneous catalyst for biodiesel synthesis, but also a material with tunable physicochemical properties that have potential relevance for industrial catalysis

Novel Biocompatible Honey Hydrogel Wound Healing Sponge for Chronic Ulcers

There is an ever-present need for non-allergenic antibacterial and antifungal wound dressing with a superior healing property for chronic ulcers. Among the entire modern wound healing dressings, hydrogel has a good capacity to donate moisture or absorb exudate and thereby providing a moist environment to facilitate wound healing process and at the same time protect the wound too. In the present study, povidne iodine loaded acrylamide based biocompatible biodegradable hydrogel dressings incorporating alginate, chitosan and gelatin showed good fluid absorbance capacity. The addition of honey showed improved tensile strength and moisture absorbance capacity of the hydrogel sponge. Apart from tensile strength, all the formulations were evaluated and compared for thickness, % elongation, folding endurance, swelling ratio, % of drug loading, thrombus formation, haemolysis assay and dispersion characteristics. Hydrogel containing chitosan and alginate showed better results in terms of tensile strength 4323gm/mm2, drug loading (27.17 %), thrombus formation (0.002 gm), drug release (97.99 %) and other parameters compared to gelatin based hydrogel. Wound healing study using well established wistar rat model showed complete healing of wound i.e. 98.28 % within 12 days. Povidone-Iodine and honey loaded acrylamide hydrogel with chitosan and alginate presented a very promising wound healing dressing. This honey hydrogel dressing can be a good alternative for infected chronic wounds and diabetic foot ulcers

Novel Biocompatible Honey Hydrogel Wound Healing Sponge for Chronic Ulcers

There is an ever-present need for non-allergenic antibacterial and antifungal wound dressing with a superior healing property for chronic ulcers. Among the entire modern wound healing dressings, hydrogel has a good capacity to donate moisture or absorb exudate and thereby providing a moist environment to facilitate wound healing process and at the same time protect the wound too. In the present study, povidne iodine loaded acrylamide based biocompatible biodegradable hydrogel dressings incorporating alginate, chitosan and gelatin showed good fluid absorbance capacity. The addition of honey showed improved tensile strength and moisture absorbance capacity of the hydrogel sponge. Apart from tensile strength, all the formulations were evaluated and compared for thickness, % elongation, folding endurance, swelling ratio, % of drug loading, thrombus formation, haemolysis assay and dispersion characteristics. Hydrogel containing chitosan and alginate showed better results in terms of tensile strength 4323gm/mm2, drug loading (27.17 %), thrombus formation (0.002 gm), drug release (97.99 %) and other parameters compared to gelatin based hydrogel. Wound healing study using well established wistar rat model showed complete healing of wound i.e. 98.28 % within 12 days. Povidone-Iodine and honey loaded acrylamide hydrogel with chitosan and alginate presented a very promising wound healing dressing. This honey hydrogel dressing can be a good alternative for infected chronic wounds and diabetic foot ulcers

Artificial Neural Networks and Guided Gene Expression Programming to Predict Wall Pressure Spectra Beneath Turbulent Boundary Layers

This study evaluates the efficacy of two machine learning (ML) techniques, namely artificial neural networks (ANN) and gene expression programming (GEP) that use data-driven modeling to predict wall pressure spectra (WPS) underneath turbulent boundary layers. Different datasets of WPS from experiments and high-fidelity numerical simulations covering a wide range of pressure gradients and Reynolds numbers are considered. For both ML methods, an optimal hyperparameter environment is identified that yields accurate predictions. ANN is observed to be faster and more accurate than GEP with an order of magnitude lower training time and logarithmic mean squared error ($lMSE$), despite a higher memory consumption. Novel training schemes are devised to address the shortcomings of GEP. These include (a) ANN-assisted GEP to reduce the noise in the training data, (b) exploiting the low and high-frequency trends to guide the GEP search, and (c) a stepped training strategy where the chromosomes are first trained on the canonical datasets followed by the datasets with complex features. When compared to the baseline scheme, these training strategies accelerated convergence and resulted in models with superior accuracy ($\approx 30\%$ reduction in the median $lMSE$) and higher reliability ($\approx 75\%$ reduction in the spread of $lMSE$ in the interquartile range). The final GEP models captured the complex trends of WPS across varying flow conditions and pressure gradients, surpassing the accuracy of Goody's model

Identification of Diabetic Retinopathy in Retinal Images using Support Vector Machine

Abstract -Retinal vessel segmentation algorithms are a fundamental component of automatic retinal disease screening systems. It helps in not only detecting the retinal diseases but also can help to recover that disease in time like diabetic retinopathy, retinopathy of prematurity (ROP) etc. Supervised Retinal vessel segmentation algorithms are most widely research and studied by researcher. As more research work is published on supervised algorithm we are decided to work on this paper. In this work we are simply examines the supervised blood vessel segmentation methodologies in two dimensional retinal images acquired from a fundus camera along with a survey along with provide the most of the databases which are locally present for this work. The aim of this paper is to review and analyze the supervised retinal vessel extraction algorithms, techniques and methodologies, giving a brief description, highlighting the key points and the performance measures given by the different authors in systematic form. We trying to provide the reader a framework for the existing research; to introduce the all supervised retinal vessel segmentation algorithms along with databases which are locally present over for work and future directions and summarize the survey. The performance of algorithms is compared and analyzed on two publicly available databases (DRIVE and STARE) of retinal images using a number of measures which include accuracy, true positive rate, false positive rate, sensitivity, specificity and area under receiver operating characteristic (ROC) curve