Macrophage formation of angiostatin during inflammation. A byproduct of the activation of plasminogen

Angiostatin is a potent inhibitor of tumor angiogenesis and the growth of metastatic foci. Recent studies have indicated that neoplastic cells can generate angiostatin directly or in cooperation with tumor-associated macrophages. In studies reported here, we determined whether angiostatin is generated in mice under non-neoplastic settings. Utilizing murine RAW264.7 macrophages and thioglycollate-elicited peritoneal macrophages, we demonstrate that angiostatin-like fragments are generated as a byproduct of the proteolytic regulation of membrane-bound plasmin. Plasmin proteolysis and subsequent loss in membrane-bound plasmin activity requires active plasmin but was unaffected by inhibitors of metalloproteinases. Lysine binding fragments of plasmin, isolated from macrophage-conditioned media utilizing affinity chromatography, appeared as a major (48 kDa) and two minor bands (42 and 50 kDa) in SDS-polyacrylamide gel electrophoresis and were immunoreactive with anti-kringle 1-3 IgG. Each peptide begins with Lys77 and contains the entire sequence of angiostatin. The affinity isolated plasmin fragments inhibited bFGF-induced endothelial cell proliferation. Lavage fluid recovered from the peritoneal cavities of mice previously injected with thioglycollate contained angiostatin-like plasmin fragments similar to those generated in vitro. This is the first demonstration that angiostatin-like plasmin fragments are generated in a non-neoplastic inflammatory setting. Thus, in addition to regulating pericellular plasmin activity, proteolysis of plasmin generates inactive kringle-containing fragments expressing angiostatic properties.     

Effects of Inhibition of Carotenoids on the Lipids Synthesis by Micrococcus colpogenes

Effect of glucose concentration and the inhibition of carotenoids on the lipids synthesis by Micrococcus colpogenes were studied. It was observed that the maximum lipid production was obtained when 2 % glucose was used as carbon source. The inhibition of carotenoids with diphenylaminc (25 mg/L) showed 37 % increase in the lipid synthesis.     

Harmonic and intermodulation performance in an R-LED series network

Analytical expressions are obtained for predicting the harmonic and intermodulation performance of R-LED series networks. These expressions are in terms of the ordinary Bessel functions with arguments depenedent on the modulation index.     

Wireless transmission of HD video using H.264 compression over UWB channel

With the technological advancement, entertainment has become revolutionized and the high-definition (HD) video has become an integral feature of our modern amusement system. The demand for wireless transmission of HD video is rapidly rising for its ubiquitous nature, easy installation and relocation. Such wireless transmissions of HD video streams require very high bandwidth. The ultra-wideband (UWB) offers a large bandwidth, and short-range high-speed data transmission at low cost and low power consumption. In this paper, we present the feasibility study to transmit HD video wirelessly using H.264/AVC compression over the UWB communication channel. Simulations are carried out by controlling key H.264/AVC encoder parameters such as, in-loop deblocking filter, group of pictures, and quantization parameter. Based on the analysis, an optimum setting of these parameters is proposed for different bandwidth requirements, as well as acceptable video quality. The bandwidth achieved is restricted between 1.5 and 20?Mbps with a minimum reconstruction quality of 34?dB. The HD bit stream is then transmitted over the UWB communication channel and the demonstration shows that the overall encoder performance is satisfactory with the transmission bit-error-rate (BER) in the range of 10^?5?C10^?8.     

The effect of composition,substrate temperature and annealing on the paramagnetic centres in SiO x /SnO co-evaporated thin films

The effects of composition and substrate temperature during evaporation and annealing, on the electron spin resonance spectra of co-evaporated SiO _x /SnO (300 nm) thin films have been investigated. The spin density is observed to decrease with increasing SnO content. This effect is attributed to the saturation of some of the paramagnetic defects such as dangling bonds and other structural defects like E₁ centres. The decrease in spin density in the composite thin films of SiO _x /SnO of constant thickness and composition with annealing and with increase in the substrate temperature during evaporation, is related to the conversion of some of the paramagnetic E₁ centres to E₁ (probable non-paramagnetic precursor of E₁) centres as well as to the saturation of some of the dangling bonds.     

A novel method for the classification of Alzheimer's disease from normal controls using magnetic resonance imaging

Alzheimer's disease (AD) is the most prevalent form of dementia. Although fewer people, who suffer from AD are correctly and promptly diagnosed, due to a lack of knowledge of its cause and unavailability of treatment, AD is more manageable if the symptoms of mild cognitive impairment (MCI) are in an early stage. In recent years, computer‐aided diagnosis has been widely used for the diagnosis of AD. The main motive of this paper is to improve the classification and prediction accuracy of AD. In this paper, a novel approach is developed to classify MCI, normal control (NC), and AD using structural magnetic resonance imaging (sMRI) from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset (50 AD, 50 NC, 50 MCI subjects). FreeSurfer is used to process these MRI data and obtain cortical features such as volume, surface area, thickness, white matter (WM), and intrinsic curvature of the brain regions. These features are modified by normalizing each cortical region's features using the absolute maximum value of that region's features from all subjects in each group of MCI, NC, and AD independently. A total of 420 features are obtained. To address the curse of dimensionality, the obtained features are reduced to 30 features using a sequential feature selection technique. Three classifiers, namely the twin support vector machine (TSVM), least squares TSVM (LSTSVM), and robust energy‐based least squares TSVM (RELS‐TSVM), are used to evaluate the classification accuracy from the obtained features. Five‐fold and 10‐fold cross‐validation are used to validate the proposed method. Experimental results show an accuracy of 100% for the studied database. The proposed approach is innovative due to its higher classification accuracy compared to methods in the existing literature.     

The Bivariate Transmuted Family of Distributions: Theory and Applications

The bivariate distributions are useful in simultaneous modeling of two random variables. These distributions provide a way to model models. The bivariate families of distributions are not much widely explored and in this article a new family of bivariate distributions is proposed. The new family will extend the univariate transmuted family of distributions and will be helpful in modeling complex joint phenomenon. Statistical properties of the new family of distributions are explored which include marginal and conditional distributions, conditional moments, product and ratio moments, bivariate reliability and bivariate hazard rate functions. The maximum likelihood estimation (MLE) for parameters of the family is also carried out. The proposed bivariate family of distributions is studied for the Weibull baseline distributions giving rise to bivariate transmuted Weibull (BTW) distribution. The new bivariate transmuted Weibull distribution is explored in detail. Statistical properties of the new BTW distribution are studied which include the marginal and conditional distributions, product, ratio and conditional momenst. The hazard rate function of the BTW distribution is obtained. Parameter estimation of the BTW distribution is also done. Finally, real data application of the BTW distribution is given. It is observed that the proposed BTW distribution is a suitable fit for the data used.     

A review on fault detection and diagnosis techniques: basics and beyond

Safety and reliability are absolutely important for modern sophisticated systems and technologies. Therefore, malfunction monitoring capabilities are instilled in the system for detection of the incipient faults and anticipation of their impact on the future behavior of the system using fault diagnosis techniques. In particular, state-of-the-art applications rely on the quick and efficient treatment of malfunctions within the equipment/system, resulting in increased production and reduced downtimes. This paper presents developments within Fault Detection and Diagnosis (FDD) methods and reviews of research work in this area. The review presents both traditional model-based and relatively new signal processing-based FDD approaches, with a special consideration paid to artificial intelligence-based FDD methods. Typical steps involved in the design and development of automatic FDD system, including system knowledge representation, data-acquisition and signal processing, fault classification, and maintenance related decision actions, are systematically presented to outline the present status of FDD. Future research trends, challenges and prospective solutions are also highlighted.

     

Adoption of human metabolic processes as Data Quality Based Models

The Journal of Supercomputing - The buildup of huge data within business intelligence is essential because such data includes complete conceptual and technological stack in addition to raw and...     

A dynamic variability management approach working with agile product line engineering practices for reusing features

The Journal of Supercomputing - Agile software development (ASD) and software product line (SPL) have shown significant benefits for software engineering processes and practices. Although both...