Proton Nuclear Magnetic Resonance-Based Method for the Quantification of Epoxidized Methyl Oleate

Epoxidized methyl esters (EMO) with their high oxirane ring reactivity, acts as a raw material in the synthesis of various industrial chemicals including polymers, stabilizers, plasticizers, glycols, polyols, carbonyl compounds, biolubricants etc. EMO has been generally quantified by the gas chromatography (GC) and high-performance liquid chromatography (HPLC) techniques. Taking into the account of the limitations of these techniques, two qHNMR-based equations have been proposed for the quantification of EMO in the mixture of EMO and methylesters (MO). The validity of the proposed method was determined using standard mixtures of MO and EMO having different molar concentrations. The developed equations have been applied on the samples of EMO prepared from oleic acid in two-step process viz., esterification followed by epoxidation. The qHNMR-based EMO quantification showed acceptable agreement with the results obtained from HPLC analysis.     

PRATIT: a CNN-based emotion recognition system using histogram equalization and data augmentation

Multimedia Tools and Applications - Emotions are spontaneous feelings that are accompanied by fluctuations in facial muscles, which leads to facial expressions. Categorization of these facial...     

Targets (Metabolic Mediators) of Therapeutic Importance in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC), an extremely aggressive invasive cancer, is the fourth most common cause of cancer-related death in the United States. The higher mortality in PDAC is often attributed to the inability to detect it until it has reached advanced stages. The major challenge in tackling PDAC is due to its elusive pathology, minimal effectiveness, and resistance to existing therapeutics. The aggressiveness of PDAC is due to the capacity of tumor cells to alter their metabolism, utilize the diverse available fuel sources to adapt and grow in a hypoxic and harsh environment. Therapeutic resistance is due to the presence of thick stroma with poor angiogenesis, thus making drug delivery to tumor cells difficult. Investigating the metabolic mediators and enzymes involved in metabolic reprogramming may lead to the identification of novel therapeutic targets. The metabolic mediators of glucose, glutamine, lipids, nucleotides, amino acids and mitochondrial metabolism have emerged as novel therapeutic targets. Additionally, the role of autophagy, macropinocytosis, lysosomal transport, recycling, amino acid transport, lipid transport, and the role of reactive oxygen species has also been discussed. The role of various pro-inflammatory cytokines and immune cells in the pathogenesis of PDAC and the metabolites involved in the signaling pathways as therapeutic targets have been previously discussed. This review focuses on the therapeutic potential of metabolic mediators in PDAC along with stemness due to metabolic alterations and their therapeutic importance.     

3D study of temperature drop behavior of subsonic rarefied gas flow in microchannel

3D Numerical study of temperature variation for subsonic rarefied gas flow in a microchannel is carried out using an in-house MPI-based parallelized DSMC code. The temperature drop in the microchannel decreases with an increase in the aspect ratio whereas it increases with an increase in the pressure ratio, the cross-aspect ratio (CAR), and the Knudsen number. 3D and 2D simulations results are compared and effect of the CAR and Knudsen number are brought out. Finally, a correlation that predicts the temperature drop is formulated along with a list of conditions that ensures a near isothermal flow.     

A novel binary gaining–sharing knowledge-based optimization algorithm for feature selection

Neural Computing and Applications - To obtain the optimal set of features in feature selection problems is the most challenging and prominent problem in machine learning. Very few human-related...     

A novel region-based multimodal image fusion technique using improved dictionary learning

Recently, the sparse representation (SR) based algorithms have gained much attention from the researchers in the area of image fusion (IF). The building of a compact discriminative dictionary plays a vital role in the sparse-based IF techniques. In this context, an efficient multimodal IF method based on improved dictionary learning is investigated. The key contributions of this paper are: (a) An improved KSVD algorithm is suggested for the dictionary learning process, (b) to reduce the computational time, only the informative patches are selected using energy feature, and (c) a novel region-based fusion scheme is suggested for the first time for the problem on hand. The suggested technique is tested with a number of multimodal images from Harvard Medical School brain database. The results are compared with state-of-the-art multiscale transform-based methods and modified SR-based methods. Unlike earlier methods, our proposed technique generates an adaptive dictionary through selection of informative patches only. This results in a compact dictionary with improved computational efficiency. The experimental results reveal that our approach outperforms other methods. The potential application of the suggested method could be in pathological images for follow-up study and better treatment planning.     

Formulation of solid self-nanoemulsifying drug delivery systems using N-methyl pyrrolidone as cosolvent

Atorvastatin calcium (ATRC) is a poor water soluble drug used for treatment of hypercholesterolemia. This research is aimed to improve solubility and dissolution rate of ATRC by formulating into solid self-nanoemulsifying drug delivery system (S-SNEDDS) using N-methyl pyrrolidone (NMP) as cosolvent. Solubility of ATRC was determined in various vehicles. Ternary phase diagrams were constructed to identify stable nanoemulsion region. SNEDDS formulations were evaluated for robustness to dilution, thermodynamic stability study, % transmittance, self-emulsification time, globule size and transmission electron microscopy. The optimized liquid SNEDDS showed robust to all dilutions exhibiting no signs of phase separation or precipitation for 24?h. Liquid SNEDDS was transformed into S-SNEDDS using different adsorbents. Differential scanning calorimetry and scanning electron microscopy studies unravel the transformation of native crystalline state to amorphous state/solubilized state. In vitro dissolution study of S-SNEDDS was found to be significantly higher in comparison to that from plain drug, irrespective of pH (p?ex vivo permeation studies showed a 4.45-fold improvement in apparent permeability coefficient (P_app) from S-SNEDDS compared to plain drug. In conclusion, S-SNEDDS prepared using NMP as cosolvent provides an effective approach for improved oral delivery of ATRC.