Genomics and Epigenomics of Gestational Diabetes Mellitus: Understanding the Molecular Pathways of the Disease Pathogenesis

One of the most common complications during pregnancy is gestational diabetes mellitus (GDM), hyperglycemia that occurs for the first time during pregnancy. The condition is multifactorial, caused by an interaction between genetic, epigenetic, and environmental factors. However, the underlying mechanisms responsible for its pathogenesis remain elusive. Moreover, in contrast to several common metabolic disorders, molecular research in GDM is lagging. It is important to recognize that GDM is still commonly diagnosed during the second trimester of pregnancy using the oral glucose tolerance test (OGGT), at a time when both a fetal and maternal pathophysiology is already present, demonstrating the increased blood glucose levels associated with exacerbated insulin resistance. Therefore, early detection of metabolic changes and associated epigenetic and genetic factors that can lead to an improved prediction of adverse pregnancy outcomes and future cardio-metabolic pathologies in GDM women and their children is imperative. Several genomic and epigenetic approaches have been used to identify the genes, genetic variants, metabolic pathways, and epigenetic modifications involved in GDM to determine its etiology. In this article, we explore these factors as well as how their functional effects may contribute to immediate and future pathologies in women with GDM and their offspring from birth to adulthood. We also discuss how these approaches contribute to the changes in different molecular pathways that contribute to the GDM pathogenesis, with a special focus on the development of insulin resistance.     

Evaluation of Antioxidant and Antimicrobial Activities of Essential Oils from Carum copticum Seed and Ferula assafoetida Latex

Carum copticum and Ferula assafoetida have several medicinal properties including antispasmodic, carminative, sedative, analgesic, and antiseptic. Reactive oxygen species (ROS), reactive nitrogen species (RNS), hydrogen peroxide (H₂O₂), and thiobarbituric acid reactive substances (TBARS) scavenging activities of Carum and Ferula oils along with their antibacterial and antifungal activities were examined. Thymol (40.25%), γ‐terpinene (38.7%) and p‐cymene (15.8%) were detected as the main components of Carum oil while, β‐pinene (47.1%), α‐pinene (21.36%), and 1, 2‐dithiolane (18.6%) were the main components of Ferula oil. Inhibitory concentrations (IC50) for total radical scavenging were between 40 and 60 and 130 and 160 μg/mL of Carum and Ferula oil, respectively. Minimal inhibitory concentration (MIC) for Salmonella typhi, Escherichia coli, Staphylococcus aureus, Bacillus subtilis, Aspergillus niger, and Candida albicans were 78 ± 8, 65 ± 7, 14 ± 3, 5 ± 2, 5.6 ± 1.3, and 8.8 ± 2.2 μg/mL of Carum oil, respectively. MIC for S. typhi, E. coli, S. aureus, B. subtilis, A. niger, and C. albicans were >200, >200, 125 ± 17, 80 ± 12, 85 ± 5, and 90 ± 11 μg/mL of Ferula oil, respectively. Accordingly, Carum and Ferula oils could be used as safe and effective natural antioxidants to improve the oxidative stability of fatty foods during storage and to preserve foods against food burn pathogens. Practical Application : This study clearly demonstrates the potential of Carum and Ferula oil especially Carum oil as natural antioxidant and antimicrobial agent. The chemical composition of essential oils was identified. Thus, identification of such compounds also helps to discover of new antioxidant, antibacterial and antifungal agents for potential applications in food safety and food preservation.     

CFD modeling of flow and heat transfer in a thermosyphon

In the present study a gas/liquid two-phase flow and the simultaneous evaporation and condensation phenomena in a thermosyphon was modeled. The volume of fluid (VOF) technique was used to model the interaction between these phases. Experiments in a thermosyphon were carried out at different operating conditions. The CFD predicted temperature profile in the thermosyphon was compared with experimental measurements and a good agreement was observed. It was concluded that CFD is a useful tool to model and explain the complex flow and heat transfer in a thermosyphon.     

Lipid Class and Fatty Acid Composition of Psoralia corylifolia Seed

The purified crude lipid of Psoralia corylifolia seeds was subjected to lipid class and fatty acid analysis by thin layer and gas chromatography. The lipid classes identified were triacyl glycerol, free fatty acid, diacyl glycerol, mono acyl glycerol, hydrocarbon-waxester and polar lipid fractions. Most of the fractions were found to contain high level of C_18:1 while C_18:0, C_18:3 and C_20:0 were also found to be present in all the lipid fractions. It has been observed that the diacyl and monoacyl glycerol fractions contain significant amounts of C_14:0 and C_18:0 while the hydrocarbon-waxester fraction was rich in C_22:0. The polar lipids contain high level of C_18:3 and low level of C_18:1 as compared to other lipid fractions. The fatty acid composition of the whole oil was also determined and found to be similar to other fractions. Unidentified long chain fatty acids were also present in significant amounts in all the lipid fractions.     

CFD and artificial neural network modeling of two-phase flow pressure drop

A large number of experiments in a 2 cm diameter and 6 m length tube were carried out in order to study the two-phase flow regimes and pressure drops in it. The two-phase flow in the experimental tube was modeled using commercial CFD code, Fluent 6.2. An Artificial Neural Network (ANN) with three inputs including gas and liquid velocities and tube slope was designed and trained to predict average pressure drop across the tube. The comparison between CFD and ANN predictions of pressure drops with experimental measurements shows that the CFD results are more accurate than the ANN evaluations for new conditions.     

A hybrid lithium‐ion battery model for system‐level analyses

We describe an advanced lithium‐ion battery model for system‐level analyses such as electric vehicle fleet simulation or distributed energy storage applications. The model combines an empirical multi‐parameter model and an artificial neural network with particular emphasis on thermal effects such as battery internal heating. The model is fast and can accurately describe constant current charging and discharging of a battery cell at a variety of ambient temperatures. Comparison to a commonly used linear kilowatt‐hour counter battery model indicates that a linear model overestimates the usable capacity of a battery at low temperatures. We highlight the importance of including internal heating in a battery model at low temperatures, as more capacity is available when internal heating is taken into account. Copyright © 2016 John Wiley & Sons, Ltd.     

Variation in structural and dielectric properties of co-precipitated nanoparticles strontium ferrites due to value of pH

Nanoparticles of strontium ferrites with nominal composition SrFe₁₂O₁₉ were prepared by co-precipitation method, by decreasing pH from 13 to 8 with a regular step of 1. The secondary phase of α-Fe₂O₃ was increased with the decrease in pH. The crystallite size estimated from X-rays diffraction data was in the range 52-70 nm, which is much smaller than that already reported. Most of the particles formed had hexagonal structure, as observed by the scanning electron microscopy. Particle size and dielectric loss were increased where as dc electrical resistivity and dielectric constant were decreased with decrease in pH. The results show that the material synthesized with higher pH is phase pure and is potentially more suitable for high frequency applications.     

A genetic programming model for bankruptcy prediction: Empirical evidence from Iran

Prediction of corporate bankruptcy is a phenomenon of increasing interest to investors/creditors, borrowing firms, and governments alike. Timely identification of firms’ impending failure is indeed desirable. By this time, several methods have been used for predicting bankruptcy but some of them suffer from underlying shortcomings. In recent years, Genetic Programming (GP) has reached great attention in academic and empirical fields for efficient solving high complex problems. GP is a technique for programming computers by means of natural selection. It is a variant of the genetic algorithm, which is based on the concept of adaptive survival in natural organisms. In this study, we investigated application of GP for bankruptcy prediction modeling. GP was applied to classify 144 bankrupt and non-bankrupt Iranian firms listed in Tehran stock exchange (TSE). Then a multiple discriminant analysis (MDA) was used to benchmarking GP model. Genetic model achieved 94% and 90% accuracy rates in training and holdout samples, respectively; while MDA model achieved only 77% and 73% accuracy rates in training and holdout samples, respectively. McNemar test showed that GP approach outperforms MDA to the problem of corporate bankruptcy prediction.     

An analysis of peristaltic transport for flow of a Jeffrey fluid

Summary The peristaltic mechanism of a Jeffrey fluid in a circular tube is investigated. The rheological effects and compressibility of the fluid are taken into account. The modeled equations are solved using perturbation technique when the ratio of the wave amplitude to the radius of the pore is small. In the second order approximation, a net flow due to a travelling wave is obtained and effects of Reynolds number, relaxation and retardation times, compressibility of the fluid and tube radius are studied. It is noticed that for the Jeffrey fluid the back flow only occurs for large values of the relaxation time and small values of the retardation time (less than 10 in the present analysis). Another interesting observation is that oscillatory behavior of the net flow rate in the Jeffrey fluid is less than that of a Maxwell fluid. Several results of other fluid models can be deduced as the limiting cases of our situation.