Characterization of microdevices for ferrous chloride separation for biosensing applications

Ferrous chloride has a variety of applications such as a reducing flocculation agent in waste-water treatment, especially for wastes containing chromate, in the laboratory synthesis of iron complexes and it is employed as a reducing agent in many organic syntheses. The device used for experiment was fabricated on the silicon wafer as support for two electrodes in a SU8 polymer microchannel with an inlet, for the injection of aqueous solution of ferrous chloride, and two outlets, for the two by-products of separated solutions. The various parameters of the device were measured by White Light Interferometer (WLI) and Scanning Electron Microscopy (SEM). The magnetic field created by applying different types of potential between two electrodes determined ferrous chloride to separate in ferrous oxide and chlorine (in gaseous form). If a protein is added in this solution we have the possibility to immobilize the protein on the iron particles and on the channel area. The electrical results were collected using a semiconductor system analyzer Keithley and were examined subsequently. The Fe complexes deposited on the electrodes were characterized by XRD analyses.     

InSAR validation based on GNSS measurements in Bucharest

This research aims to complex validate radar displacement products over Bucharest based on Global Navigation Satellite System or GNSS. Validation was represented by the comparison of the recent TerraSAR-X (TSX) displacement results with field GNSS data. For this purpose, 24 and 27 high-resolution images acquired from the TSX satellite between 2011 and 2014 were processed by Permanent Scatterer Inteferometry (PSI) and Small BASeline Subset (SBAS) – interferometric techniques using synthetic aperture radar (SAR) images. The images were selected from a larger pool of SAR images, after consulting weather databases, which helped eliminate the images acquired under adverse weather conditions. GNSS locations were cross-checked on high-resolution optical images and 21 ground points (including three permanent stations) were selected. All these points were (1) characterized by high coherence on radar images; (2) were on the ground; (3) were situated in large vacant spaces; and (4) far away from radar obstacles such as trees. A major challenge in achieving the research goal was to overcome the lack of permanent stations at the ground level within the area of Bucharest. Instead of permanent stations, our team designed and installed forced centring systems that remained stable over time, eliminating the instalment errors of GNSS receivers. The line-of-site (LOS) components derived from GNSS data have been compared with those of the SAR scatterers closest to the GNSS markers (never coinciding). For each location, the vertical reading was projected in the LOS of the SAR sensor. These readings were compared with the displacement values determined at the locations of permanent scatterers (PSs) within 100 m from the GNSS nodes, by eliminating all scatterers on buildings. The comparison has shown a good agreement.     

A unifying criterion for unsupervised clustering and feature selection

Exploratory data analysis methods are essential for getting insight into data. Identifying the most important variables and detecting quasi-homogenous groups of data are problems of interest in this context. Solving such problems is a difficult task, mainly due to the unsupervised nature of the underlying learning process. Unsupervised feature selection and unsupervised clustering can be successfully approached as optimization problems by means of global optimization heuristics if an appropriate objective function is considered. This paper introduces an objective function capable of efficiently guiding the search for significant features and simultaneously for the respective optimal partitions. Experiments conducted on complex synthetic data suggest that the function we propose is unbiased with respect to both the number of clusters and the number of features.     

Overhead Analysis of Scientific Workflows in Grid Environments

Scientific workflows are a topic of great interest in the grid community that sees in the workflow model an attractive paradigm for programming distributed wide-area grid infrastructures. Traditionally, the grid workflow execution is approached as a pure best effort scheduling problem that maps the activities onto the grid processors based on appropriate optimization or local matchmaking heuristics such that the overall execution time is minimized. Even though such heuristics often deliver effective results, the execution in dynamic and unpredictable grid environments is prone to severe performance losses that must be understood for minimizing the completion time or for the efficient use of high-performance resources. In this paper, we propose a new systematic approach to help the scientists and middleware developers understand the most severe sources of performance losses that occur when executing scientific workflows in dynamic grid environments. We introduce an ideal model for the lowest execution time that can be achieved by a workflow and explain the difference to the real measured grid execution time based on a hierarchy of performance overheads for grid computing. We describe how to systematically measure and compute the overheads from individual activities to larger workflow regions and adjust well-known parallel processing metrics to the scope of grid computing, including speedup and efficiency. We present a distributed online tool for computing and analyzing the performance overheads in real time based on event correlation techniques and introduce several performance contracts as quality-of-service parameters to be enforced during the workflow execution beyond traditional best effort practices. We illustrate our method through postmortem and online performance analysis of two real-world workflow applications executed in the Austrian grid environment.     

Novel self-assembled mesalamine–sucralfate complexes: preparation,characterization, and formulation aspects

Two well-known active agents, mesalamine (MES) and sucralfate (SUC), were investigated for possible utilization as fixed-dose combination product. The anti-inflammatory action of MES in association with bioadhesiveness and mucosal healing properties of SUC were considered promising for the development of a new compound containing both molecules, aimed as an improved treatment of ulcerative colitis. The present study investigates the capacity of the two active agents to interact and generate a new and stable entity via self-assembling. Spray-drying was used to co-process the two active principles from an aqueous mixture where the ratio MES:SUC was in the range 25:75, 50:50, and 75:25. The structural data (X-Ray, FTIR, SEM, DSC, and ¹H NMR) have shown that MES and SUC are interacting leading to complexes with properties differing from those of each separate active agent and from their physical blends. ¹H NMR results indicated that complexation occurred when the aqueous suspensions of drugs were mixed, prior to spray-drying. Drug–drug self-assembling was the driving mechanism in the formation of the new entity. Based on the structural data, a hypothetical structure of the complex was proposed. Co-processing of MES and SUC represents a simple and useful procedure to prepare new self-assembled compounds by valorizing the ionic interactions between the two entities. Preliminary studies with oral solid dosage forms based on MES–SUC complexes tested in vitro have shown a controlled MES release, opening the perspective of a new colon-targeted delivery system and a novel class of compounds with therapeutic application in inflammatory bowel diseases.     

GPR55 Receptor Activation by the N-Acyl Dopamine Family Lipids Induces Apoptosis in Cancer Cells via the Nitric Oxide Synthase (nNOS) Over-Stimulation

GPR55 is a GPCR of the non-CB1/CB2 cannabinoid receptor family, which is activated by lysophosphatidylinositol (LPI) and stimulates the proliferation of cancer cells. Anandamide, a bioactive lipid endocannabinoid, acts as a biased agonist of GPR55 and induces cancer cell death, but is unstable and psychoactive. We hypothesized that other endocannabinoids and structurally similar compounds, which are more hydrolytically stable, could also induce cancer cell death via GPR55 activation. We chemically synthesized and tested a set of fatty acid amides and esters for cell death induction via GPR55 activation. The most active compounds appeared to be N-acyl dopamines, especially N-docosahexaenoyl dopamine (DHA-DA). Using a panel of cancer cell lines and a set of receptor and intracellular signal transduction machinery inhibitors together with cell viability, Ca²⁺, NO, ROS (reactive oxygen species) and gene expression measurement, we showed for the first time that for these compounds, the mechanism of cell death induction differed from that published for anandamide and included neuronal nitric oxide synthase (nNOS) overstimulation with concomitant oxidative stress induction. The combination of DHA-DA with LPI, which normally stimulates cancer proliferation and is increased in cancer setting, had an increased cytotoxicity for the cancer cells indicating a therapeutic potential.