Analysis of the Biomarkers for Neurodegenerative Diseases in Aged Progranulin Deficient Mice

Neurodegenerative diseases are debilitating impairments that affect millions of people worldwide and are characterized by progressive degeneration of structure and function of the central or peripheral nervous system. Effective biomarkers for neurodegenerative diseases can be used to improve the diagnostic workup in the clinic as well as facilitate the development of effective disease-modifying therapies. Progranulin (PGRN) has been reported to be involved in various neurodegenerative disorders. Hence, in the current study we systematically compared the inflammation and accumulation of typical neurodegenerative disease markers in the brain tissue between PGRN knockout (PGRN KO) and wildtype (WT) mice. We found that PGRN deficiency led to significant neuron loss as well as activation of microglia and astrocytes in aged mice. Several characteristic neurodegenerative markers, including α-synuclein, TAR DNA-binding protein 43 (TDP-43), Tau, and β-amyloid, were all accumulated in the brain of PGRN-deficient mice as compared to WT mice. Moreover, higher aggregation of lipofuscin was observed in the brain tissue of PGRN-deficient mice compared with WT mice. In addition, the autophagy was also defective in the brain of PGRN-deficient mice, indicated by the abnormal expression level of autophagy marker LC3-II. Collectively, comprehensive assays support the idea that PGRN plays an important role during the development of neurodegenerative disease, indicating that PGRN might be a useful biomarker for neurodegenerative diseases in clinical settings.     

Extracellular Vesicles Inhibit the Response of Pancreatic Ductal Adenocarcinoma Cells to Gemcitabine and TRAIL Treatment

Pancreatic ductal adenocarcinoma remains an aggressive cancer with a low 5-year survival rate. Although gemcitabine has been a standard treatment for advanced pancreatic cancer, patients often develop resistance to this therapeutic. We have previously shown that treating pancreatic cancer cells in vitro with a combination of gemcitabine and the cytokine TRAIL significantly reduced both cell viability and survival. The data presented here demonstrate that this response to treatment is inhibited when cells are incubated with a conditioned medium derived from untreated cells. We show that this inhibition is specifically mediated by extracellular vesicles present in the conditioned medium, as seen by a significant decrease in apoptosis. Additionally, we further demonstrate that this effect can be reversed in the presence of GW4869, an inhibitor of exosome biogenesis and release. These results show that pancreatic cancer cell-derived extracellular vesicles can confer resistance to treatment with gemcitabine and TRAIL. The implications of these findings suggest that removal of EVs during treatment can improve the response of cells to gemcitabine and TRAIL treatment in vitro.     

Corrosion inhibition of aluminium in acid solutions by some imidazoline derivatives

2-Pentadecyl-1,3-imidazoline (PDI), 2-Undecyl-1,3-imidazoline (UDI), 2-Heptadecyl-1,3-imidazoline (HDI), 2-Nonyl-1,3-imidazoline (NI) were synthesized and characterized by FT-IR and NMR Studies. The corrosion inhibition properties of these compounds on aluminium in 1 M HCl and 0.5 M H₂SO₄ were investigated by weight loss, potentiodynamic polarization, electrochemical impedance and scanning electron microscopic techniques. The weight loss study showed that the inhibition efficiency increases with increase in the concentration of the inhibitor and was found to be inversely related to time and temperature while it shows no significant change with increase in acid concentration. The effectiveness of these inhibitors were in the order of UDI > NI > PDI > HDI .The values of activation energy, free energy of adsorption, heat of adsorption, enthalpy of activation and entropy of activation were also calculated to elaborate the mechanism of corrosion inhibition. The adsorption of these compounds on aluminium surface follows the Langmuir adsorption isotherm. The potentiodynamic polarization data show that the compounds studied are mixed type inhibitors. Electrochemical impedance was used to investigate the mechanism of corrosion inhibition. The surface characteristics of inhibited and uninhibited metal samples were investigated by scanning electron microscopy (SEM).     

Drug Response Prediction of Liver Cancer Cell Line Using Deep Learning

Cancer is the second deadliest human disease worldwide with high mortality rate. Rehabilitation and treatment of this disease requires precise and automatic assessment of effective drug response and control system. Prediction of treated and untreated cancerous cell line is one of the most challenging problems for precise and targeted drug delivery and response. A novel approach is proposed for prediction of drug treated and untreated cancer cell line automatically by employing modified Deep neural networks. Human hepatocellular carcinoma (HepG2) cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our lab. Prediction models are developed by modifying ResNet101 and exploiting the transfer learning concept. Last three layers of ResNet101 are re-trained for the identification of drug treated cancer cells. Transfer learning approach in an appropriate choice especially when there is limited amount of annotated data. The proposed technique is validated on acquired 203 fluorescent microscopy images of human HepG2 cells treated with drug functionalized cobalt ferrite@barium titanate (CFO@BTO) magnetoelectric nanoparticles in vitro. The developed approach achieved high prediction with accuracy of 97.5% and sensitivity of 100% and outperformed other approaches. The high performance reveals the effectiveness of the approach. It is scalable and fully automatic prediction approach which can be extended for other similar cell diseases such as lung, brain tumor and breast cancer.     

Electro‐Oxidation of Ni42 Steel: A Highly Active Bifunctional Electrocatalyst

Janus type water‐splitting catalysts have attracted highest attention as a tool of choice for solar to fuel conversion. AISI Ni42 steel is upon harsh anodization converted into a bifunctional electrocatalyst. Oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) are highly efficiently and steadfast catalyzed at pH 7, 13, 14, 14.6 (OER) and at pH 0, 1, 13, 14, 14.6 (HER), respectively. The current density taken from long‐term OER measurements in pH 7 buffer solution upon the electro‐activated steel at 491 mV overpotential (η) is around four times higher (4 mA cm^?2) in comparison with recently developed OER electrocatalysts. The very strong voltage–current behavior of the catalyst shown in OER polarization experiments at both pH 7 and at pH 13 are even superior to those known for IrO₂‐RuO₂. No degradation of the catalyst is detected even when conditions close to standard industrial operations are applied to the catalyst. A stable Ni‐, Fe‐oxide based passivating layer sufficiently protects the bare metal for further oxidation. Quantitative charge to oxygen (OER) and charge to hydrogen (HER) conversion are confirmed. High‐resolution XPS spectra show that most likely γ?NiO(OH) and FeO(OH) are the catalytic active OER and NiO is the catalytic active HER species.     

Computer-aided process intensification of natural gas to methanol process

The recent revolution in shale gas has presented opportunities for distributed manufacturing of key commodity chemicals, such as methanol, from methane. However, the conventional methane-to-methanol process is energy intensive which negatively affects the profitability and sustainability. We report an intensified process configuration that is both economically attractive and environmentally sustainable. This flowsheet is systematically discovered using the building block-based representation and optimization methodology. The new process configuration utilizes membrane-assisted reactive separations and can have as much as 190% higher total annual profit compared to a conventional configuration. Additionally, it has 57% less CO₂-equivalent greenhouse gas emission. Such drastic improvement highlights the advantages of building block-based computer-aided process intensification method.     

Impact of HHO gas enrichment and high purity biodiesel on the performance of a 315 cc diesel engine

Biodiesel and oxyhydrogen (HHO) gas have shown promising results in improving engine performance and emissions. In this work, the effects of HHO gas and 5% biodiesel blends (B5) and their combined use in a 315 cc diesel engine have been analyzed. Biodiesel is produced by base catalyzed transesterification and cleaned by emulsification. Its calculated cetane index (CCI) was 61.4. HHO gas is produced from electrolysis of concentrated potassium hydroxide solution. The use of 5% biodiesel blend resulted in a significant rise of 9.4% in the brake thermal efficiency (BTE) and a maximum reduction of 8.19% in the brake specific fuel consumption (BSFC). HHO enrichment of diesel and biodiesel at 2.81 L/min through the intake manifold improved the torque and power by an average of over 3%. HHO addition also improved the BTE of diesel by a maximum of 3.67%. The combination of high CCI biodiesel fuel and HHO creates a mixture that has shortened the ignition delay (ID) to the point that adverse effects were observed due to the premature combustion as shown by the average decrease in the BTE of 2.97% compared to B5. Thus, B5, on its own, is found to be the optimum fuel under these conditions.     

Development of support vector regression (SVR)-based model for prediction of circulation rate in a vertical tube thermosiphon reboiler

The hydrodynamics and heat transfer in a thermosiphon reboiler interact with each other making the process very complex. Prediction of the rates of heat transfer and thermally induced flow are the primary requirements for the design of thermosiphon reboilers. The objective of this study was to develop, for the first time, a unified data-driven model, for the prediction of circulation rate in a thermosiphon reboiler for different pure components with wide variation in thermo-physical properties and operating parameters, using support vector regression (SVR)-based modeling technique. In the present work, 148 experimental data points from accessible sources, including the author's own study were used. First, a multiple regression (MR) model for circulation rate (in the form of Reynolds number) was developed as a function of dimensionless parameters namely, Peclet number for boiling (Pe_b), Subcooling number (K_sub), and the Lockhart–Martinelli parameter (X_tt), followed by the formulation of an SVR-based model. Statistical analysis revealed that the proposed generalized SVR-based model had high prediction accuracy with an average absolute relative error (AARE) of 3.82%, root mean square error (RMSE) of 0.0717, leave-one-out cross validation (Q²_LOO) of 0.9975 and mean relative error (MRE) of 0.0288 on the training data. Corresponding values of 6.11% AARE, 0.0816 RMSE, 0.9991 leave-one-out cross validation on test data (Q²_ext) and 0.0541 MRE were obtained for the test data. A comparison of the SVR-based correlation was made with the MR model and with some selected empirical correlations in the literature. It was observed that the proposed SVR-based model significantly exhibited an enhanced prediction and generalization performance.     

Data-driven approximation of thermodynamic phase equilibria

We present a new data-driven approach for both accurate and computationally efficient approximation of vapor liquid equilibria (VLE) models. Our method is able to provide guaranteed enclosure to limit the approximation errors over the entire domain of interest, all just by sampling only at select points. The approximation relies on a mixed-integer linear programming (MILP) formulation that exploits vertex polyhedral properties of theoretically guaranteed lower and upper bounds to enclose nonlinear and nonconvex equations of state (EOS) and empirical models. Another advantage is that, unlike traditional full simulation-based data-driven approaches, we do not solve nonlinear system of equations ( f ( x ) = 0) for sampling. Instead of looking for only feasible samples, we evaluate f ( x ) over x -domain. This functional evaluation eliminates the need for computationally-demanding full-scale simulations and the associated convergence issues. We demonstrate excellent performance of the proposed MILP formulation in predicting the solubility of hydrofluorocarbon (HFC) refrigerants in ionic liquids (IL).     

Pectin recovery from apple pomace: physico-chemical and functional variation based on methyl-esterification

This study explored the valorisation of apple pomace by extracting pectin with both high and low degrees of esterification (DM). Two types of pectin were extracted with a DM of 43.29% being low methoxyl (LM) pectic polysaccharide and high methoxyl (HM) pectin with a DM of 65.88%. HM pectin was characterised by a WHC and OHC of 1.3 g g⁻¹ and 0.4 g g⁻¹, respectively, compared to 0.32 g g⁻¹ and 0.14 g g⁻¹, respectively, for LM pectic polysaccharide. HM pectin possessed greater purity indicated by 69.18% GalA, whereas LM pectic polysaccharide possessed 41.26% GalA. HM pectin revealed better functionality in terms of high emulsifying activity which was concentration-dependent, and emulsion stability than LM pectic polysaccharide. The solubility of HM pectin was 90.8%, which is preferable for various industrial applications. LM pectic polysaccharide was characterised by a higher % radical-scavenging activity in a concentration-dependent manner. XRD analysis revealed LM pectic polysaccharide to possess higher crystallinity (53%) and SEM analysis revealed its surface to be rough and coarse.