Simulation of heat transfer on the peristaltic flow of a Jeffrey-six constant fluid in a diverging tube

Peristaltic flow of a Jeffrey-six constant fluid in a nonuniform tube is investigated under the assumption of long wavelength and low Reynolds number approximations. The dimensionless quantities are used to simplify momentum and energy equations assuming that fluid physical/rheological properties remain constant. Regular perturbation method is invoked to find an analytical solution for the velocity and temperature field. The variation of pressure rise and frictional forces with the different parameters is also examined numerically. Results are also presented graphically at the end of the article.     

Geometric modeling of the human normal cerebral arterial system

We propose an anatomy-based approach for an efficient construction of a three-dimensional human normal cerebral arterial model from segmented and skeletonized angiographic data. The centerline-based model is used for an accurate angiographic data representation. A vascular tree is represented by tubular segments and bifurcations whose construction takes into account vascular anatomy. A bifurcation is defined quantitatively and the algorithm calculating it is given. The centerline is smoothed by means of a sliding average filter. As the vessel radius is sensitive to quality of data as well as accuracy of segmentation and skeletonization, radius outlier removal and radius regression algorithms are formulated and applied. In this way, the approach compensates for some inaccuracies introduced during segmentation and skeletonization. To create the frame of vasculature, we use two different topologies: tubular and B-subdivision based. We also propose a technique to prevent vessel twisting. The analysis of the vascular model is done on a variety of data containing 258 vascular segments and 131 bifurcations. Our approach gives acceptable results from anatomical, topological and geometrical standpoints as well as provides fast visualization and manipulation of the model. The approach is applicable for building a reference cerebrovascular atlas, developing applications for simulation and planning of interventional radiology procedures and vascular surgery, and in education.     

Process modeling and optimization of industrial ethylene oxide reactor by integrating support vector regression and genetic algorithm

This article presents an artificial intelligence‐based process modeling and optimization strategies, namely support vector regression–genetic algorithm (SVR‐GA) for modeling and optimization of catalytic industrial ethylene oxide (EO) reactor. In the SVR‐GA approach, an SVR model is constructed for correlating process data comprising values of operating and performance variables. Next, model inputs describing process operating variables are optimized using Genetic Algorithm (GAs) with a view to maximize the process performance. The GA possesses certain unique advantages over the commonly used gradient‐based deterministic optimization algorithms The SVR‐GA is a new strategy for chemical process modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics, etc.) is not required. Using SVR‐GA strategy, a number of sets of optimized operating conditions leading to maximized EO production and catalyst selectivity were obtained. The optimized solutions when verified in actual plant resulted in a significant improvement in the EO production rate and catalyst selectivity.     

Automatic quantification of changes in bone in serial MR images of joints

Recent innovations in drug therapies have made it highly desirable to obtain sensitive biomarkers of disease progression that can be used to quantify the performance of candidate disease modifying drugs. In order to measure potential image-based biomarkers of disease progression in an experimental model of rheumatoid arthritis (RA), we present two different methods to automatically quantify changes in a bone in in-vivo serial magnetic resonance (MR) images from the model. Both methods are based on rigid and nonrigid image registration to perform the analysis. The first method uses segmentation propagation to delineate a bone from the serial MR images giving a global measure of temporal changes in bone volume. The second method uses rigid body registration to determine intensity change within a bone, and then maps these into a reference coordinate system using nonrigid registration. This gives a local measure of temporal changes in bone lesion volume. We detected significant temporal changes in local bone lesion volume in five out of eight identified candidate bone lesion regions, and significant difference in local bone lesion volume between male and female subjects in three out of eight candidate bone lesion regions. But the global bone volume was found to be fluctuating over time. Finally, we compare our findings with histology of the subjects and the manual segmentation of bone lesions.     

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear

Malaria is a serious worldwide disease, caused by a bite of a female Anopheles mosquito. The parasite transferred into complex life round in which it is grown and reproduces into the human body. The detection and recognition of Plasmodium species are possible and efficient through a process called staining (Giemsa). The staining process slightly colorizes the red blood cells (RBCs) but highlights Plasmodium parasites, white blood cells and artifacts. Giemsa stains nuclei, chromatin in blue tone and RBCs in pink color. It has been reported in numerous studies that manual microscopy is not a trustworthy screening technique when performed by nonexperts. Malaria parasites host in RBCs when it enters the bloodstream. This paper presents segmentation of Plasmodium parasite from the thin blood smear points on region growing and dynamic convolution based filtering algorithm. After segmentation, malaria parasite classified into four Plasmodium species: Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, and Plasmodium malaria. The random forest and K‐nearest neighbor are used for classification base on local binary pattern and hue saturation value features. The sensitivity for malaria parasitemia (MP) is 96.75% on training and testing of the proposed approach while specificity is 94.59%. Beside these, the comparisons of the two features are added to the proposed work for classification having sensitivity is 83.60% while having specificity is 94.90% through random forest classifier based on local binary pattern feature.     

Current developments in chemistry,coordination, structure and biological aspects of 1-(acyl/aroyl)-3- (substituted)thioureas: advances Continue …

1-(acyl/aroyl)-3-(substituted)thioureas are exciting structures in the fields of organic synthesis, material sciences, and biomedical research. Of particular significance is the fact that acyl thiourea derivatives have been demonstrated to be highly exceptional ligands and their coordination compounds have extensively been investigated in the synthesis of pharmaceuticals and agrochemical ingredients. Moreover, these structural motifs are enjoying the prestigious status as versatile synthons in heterocyclic syntheses. Therefore, the progress to introduce functionalization on the backbone of 1-(acyl/aroyl)-3-(substituted)thioureas is highly desirable. This account summarizes our efforts to capture recent documented achievements by various research groups.     

Stagnation flow of hybrid nanoparticles with MHD and slip effects

The flow of hybrid nanoparticles with significant physical parameters with different base fluids in the presence of Biot number, velocity slip, and MHD effects has not been explored so far, particularly for a circular cylinder. Therefore, the current report is presented to offer a numerical solution for hybrid nanoparticles with base fluids (water and ethylene glycerol) via a circular cylinder. The physical situation is interpreted in terms of partial differential equations and is converted into ordinary differential equations after applying the similarity transformation. The results are presented in both tabular and graphical forms. The impact of physical parameters on velocity distribution is examined through graphs. The comparative results of hybrid nanoparticles for distinct base fluids as ethylene glycol and water are proposed and the hybrid nanoparticles with base fluid water seems to be greater than that of the hybrid nanoparticles with base fluid EG. The temperature profile of hybrid nanoparticles is found to be a decreasing function with growth in velocity slip parameter but an opposite trend is noted in case of nanoparticles . The skin friction and Nusselt number augmented for the increase in magnetic field, velocity slip, and nanoparticle while it shows a decreasing trend toward thermal slip parameter. For the both cases, improvement in Biot number helps enhance the heat transfer constantly.     

E7766, a Macrocycle-Bridged Stimulator of Interferon Genes (STING) Agonist with Potent Pan-Genotypic Activity

A strategy for creating potent and pan-genotypic stimulator of interferon genes (STING) agonists is described. Locking a bioactive U-shaped conformation of cyclic dinucleotides by introducing a transannular macrocyclic bridge between the nucleic acid bases leads to a topologically novel macrocycle-bridged STING agonist (MBSA). In addition to substantially enhanced potency, the newly designed MBSAs, exemplified by clinical candidate E7766 , exhibit broad pan-genotypic activity in all major human STING variants. E7766 is shown to have potent antitumor activity with long lasting immune memory response in a mouse liver metastatic tumor model. Two complementary stereoselective synthetic routes to E7766 are also described.