Distinguishing Cowper''s glands from neoplastic and pseudoneoplastic lesions of prostate: immunohistochemical and ultrastructural studies

beta-Glucosidase of indigo plant (Polygonum tinctorium) has a high substrate specificity for indican (indoxyl beta-D-glucoside). To examine the localization of this beta-glucosidase, we fractionated the cells of the leaves and analysed them immunocytochemically. Immunoelectron micrographs with specific antibodies against the beta-glucosidase clearly showed that the beta-glucosidase was localized in the stroma of the chloroplasts in mesophyll cells, but not in the thylakoid membrane. Chloroplasts were isolated from the crude homogenate of the fresh leaves by Percoll density gradient centrifugation and then subjected to suborganellar fractionation. beta-Glucosidase activity was specifically detected in the stromal fraction, but not in the thylakoid membrane. This was also supported by the result of an immunoblot of the fraction with anti-beta-glucosidase antibodies. The beta-glucosidase was immunocytochemically localized in the chloroplasts of mesophyll cells, but not in any chloroplasts in marginal cells of the vascular bundle or epidermal cells; ribulose 1,5-bisphosphate carboxylase (Rubisco), a typical stromal protein, was observed in all chloroplasts in these cells. These results suggest that beta-glucosidase is tissue specific in its expression in the leaves of the indigo plant.     

Detecting stable locality-aware predicates

In a large-scale locality-driven network such as in modular robotics and wireless sensor networks, knowing the state of a local area is sometimes necessary due to either interactions being local and driven by neighborhood proximity or the users being interested in the state of a certain region. We define locality-aware predicates (LAP) that aim at detecting a predicate within a specified area. We model the area of interest as the set of processes that are within a breadth-first search tree (BFST) of height k$k$ rooted at the initiator process. Although a locality-aware predicate specifies a predicate only within a local area, observing the area consistently requires considering the entire system in a consistent manner. This raises the challenge of making the complexities of the corresponding predicate detection algorithms scale-free, i.e., independent of the size of the system. Since all existing algorithms for getting a consistent view of the system require either a global snapshot of the entire system or vector clocks of the size of the system, a new solution is needed. We focus on stable LAP, which are those LAP that remain true once they become true. We propose a scale-free algorithm to detect stable LAP within a k$k$-height BFST. Our algorithm can detect both stable conjunctive LAP and stable relational LAP. In the process of designing our algorithm, we also propose the first distributed algorithm for building a BFST within an area of interest in a graph, and the first distributed algorithm for recording a consistent sub-cut within the area of interest. This paper demonstrates that LAPs are a natural fit for detecting distributed properties in large-scale distributed systems, and stable LAPs can be practically detected at low cost.     

Biomathematical study of time-dependent flow of a Carreau nanofluid through inclined catheterized arteries with overlapping stenosis

Journal of Central South University - This work is concerned with the analysis of blood flow through inclined catheterized arteries having a balloon (angioplasty) with time-variant overlapping...     

Power Control for Cognitive Radio Networks: A Game Theoretic Approach

In communication industry one of the most rapidly growing area is wireless technology and its applications. The efficient access to radio spectrum is a requirement to make this communication feasible for the users that are running multimedia applications and establishing real-time connections on an already overcrowded spectrum. In recent times cognitive radios (CR) are becoming the prime candidates for improved utilization of available spectrum. The unlicensed secondary users share the spectrum with primary licensed user in such manners that the interference at the primary user does not increase from a predefined threshold. In this paper, we propose an algorithm to address the power control problem for CR networks. The proposed solution models the wireless system with a non-cooperative game, in which each player maximize its utility in a competitive environment. The simulation results shows that the proposed algorithm improves the performance of the network in terms of high SINR and low power consumption.

     

A domain decomposition strategy for alignment of multiple biological sequences on multiprocessor platforms

Multiple Sequences Alignment (MSA) of biological sequences is a fundamental problem in computational biology due to its critical significance in wide ranging applications including haplotype reconstruction, sequence homology, phylogenetic analysis, and prediction of evolutionary origins. The MSA problem is considered NP-hard and known heuristics for the problem do not scale well with increasing numbers of sequences. On the other hand, with the advent of a new breed of fast sequencing techniques it is now possible to generate thousands of sequences very quickly. For rapid sequence analysis, it is therefore desirable to develop fast MSA algorithms that scale well with an increase in the dataset size. In this paper, we present a novel domain decomposition based technique to solve the MSA problem on multiprocessing platforms. The domain decomposition based technique, in addition to yielding better quality, gives enormous advantages in terms of execution time and memory requirements. The proposed strategy allows one to decrease the time complexity of any known heuristic of O(N)^x$O{\left(N\right)}^x$ complexity by a factor of O(1/p)^x$O{(1/p)}^x$, where N$N$ is the number of sequences, x$x$ depends on the underlying heuristic approach, and p$p$ is the number of processing nodes. In particular, we propose a highly scalable algorithm, Sample-Align-D, for aligning biological sequences using Muscle system as the underlying heuristic. The proposed algorithm has been implemented on a cluster of workstations using the MPI library. Experimental results for different problem sizes are analyzed in terms of quality of alignment, execution time and speed-up.     

Predicting surface quality of γ-TiAl produced by additive manufacturing process using response surface method

Electron beam melting (EBM) has been found to be a promising technology for producing complex shaped parts from gamma titanium aluminide alloys (γ-TiAl). The parts produced by this process are projected to have dimensions very close to the desired final shapes. However, the surface roughness of the parts produced by EBM is excessively rough. In many applications, it is necessary to improve the quality of manufactured parts using a convenient post process. This paper determines process parameters of end milling when it is used as a post process for the parts produced by EBM. Design of experiments has been used to study the effect of the selected input parameters of end milling (spindle speed, feed rate, depth of cut and coolant type) on the surface roughness of γ-TiAl parts. Response surface methodology is used to develop a predictive model for surface roughness. Effects of the selected milling process are investigated. This paper also optimizes the selected process parameters to minimize the value of the obtained surface roughness.     

On Plastic Deformation Behavior of Cryorolled AA8090 Alloy

AA8090 alloy was rolled up to 50 and 75 % reductions at both liquid nitrogen (LNR) and room temperatures (RTR). Both hardness and tensile behavior were evaluated on rolled samples. Optical microscope, TEM and EBSD were used for detailed microstructural examination of rolled samples. Williamson-Hall peak broadening analysis on X-ray diffraction data was made to evaluate crystallite size, lattice strain and dislocation density. An enhanced tensile strength was evidenced in LNR samples when compared to RTR samples without sacrificing ductility, which was ascribed to the higher density of dislocations in LNR samples than RTR samples. A large number of dislocation tangled regions along with ultrafine grain structure were evidenced through TEM and EBSD. Significant fraction of special boundaries in combination with increased fraction of texture components like S, Brass, Cu and Goss would be another reason for enhanced properties in LNR conditions than that of RTR. These components were observed to be strengthened with increased rolling reduction. Work hardening behavior clearly evidenced the variation in amount of work hardening and recovery phenomenon. It showed large variation in recovery in the case of 50 % reduction than that of 75 % reduction, which was attributed to significantly higher density of dislocations in 75 % rolled samples in RTR and LNR.     

Deformation and water loss from solvent filled microcapsules under compressive loads

Microcapsules filled with liquid solvents for CO₂ absorption can be easily deformed due to their elastic polymer shells. We present a combination of experiments and model predictions to demonstrate that modest compressive forces can lead to significant capsule deformation and performance issues for this enabling technology. Contrary to expectations based on Raoult's law, capsules containing aqueous carbonate solution were found to lose water to flows of humidified nitrogen in centimeter-scale packed beds. Water loss increased with gas velocity, suggesting compression was responsible for mass transfer, an interpretation supported by microscope images of deformed and broken capsules. A model for compression induced mass transfer under packed/fluidized bed operating conditions was developed and validated with the experimental data for a range of conditions (gas velocities, temperatures, humidities). Design criteria for future generations of microcapsules that will more effectively resist compression are evaluated.     

Accuracy improving guidelines for network anomaly detection systems

An unprecedented growth in computer and communication systems in the last two decades has resulted in a proportional increase in the number and sophistication of network attacks. In particular, the number of previously-unseen attacks has increased exponentially in the last few years. Due to the rapidly evolving nature of network attacks, a considerable paradigm shift has taken place in the intrusion detection community. The main focus is now on Network Anomaly Detection Systems (NADSs) which model and flag deviations from normal/benign behavior of a network and can hence detect previously-unseen attacks. Contemporary NADS borrow concepts from a variety of theoretical fields (e.g., Information theory, stochastic and machine learning, signal processing, etc.) to model benign behavior. These NADSs, however, fall short of achieving acceptable performance levels as therefore widespread commercial deployments. Thus, in this paper, we firstly evaluate the performance of eight prominent network-based anomaly detectors under malicious portscan attacks to identify which NADSs perform better than others and why. These NADSs are evaluated on three criteria: accuracy (ROC curves), scalability (with respect to varying normal and attack traffic rates, and deployment points) and detection delay. These criteria are evaluated using two independently collected datasets with complementary strengths. We then propose novel methods and promising guidelines to improve the accuracy and scalability of existing and future anomaly detectors. Experimental analysis of the proposed guidelines is also presented for the proof of concept.