Pro-labour myometrial gene expression: are preterm labour and term labour the same?

Preterm labour (PTL) is the most important cause of neonatal morbidity and mortality. While some causes have been identified, the mechanisms involved remain elusive. This study investigates whether term labour (TL) is an appropriate model for PTL by examining pro-labour gene expression, using quantitative rtPCR, and protein synthesis, using Western analysis, in preterm and term myometrial samples obtained from the upper and lower uterine segments before and after the onset of labour. In the lower segment, the levels of prostaglandin H synthase type-2 (PGHS-2), interleukin-1beta (IL-1beta), IL-6 and IL-8 mRNA expression were significantly higher in TL compared with PTL samples. Compared with non-labour controls, the expression of IL-1beta and IL-8 mRNA was increased in both PTL and TL samples and the expression of PGHS-2 and IL-6 mRNA was increased in TL samples only. In the upper segment, there were no differences between PTL and TL samples and the mRNA expression of PGHS-2 and IL-1beta was increased in TL compared with term no labour samples. No effect of PTL or TL was seen on either oxytocin receptor or connexin-43 mRNA expression or protein levels. The multiple regression analysis and studies in primary cultures of uterine myocytes suggest that the inflammatory cytokines, IL-1beta and tumour necrosis factor-alpha, are the most important regulators of PGHS-2 and IL-8. Our data show that preterm and term labouring myometrium are significantly different and that the most marked labour-induced changes in gene expression are in the lower segment. These changes may occur in response to the release of inflammatory cytokines by the labour-associated inflammatory infiltration.     

Dopant induced polarity inversion in polar ZnO nanorods

Highly oriented and polarity controlled ZnO nanorod thin films are deposited by chemical bath deposition where the preferred polarity depends on the anionic nature of the Zn salt. Significant differences are noticed in magnesium (Mg) doping concentration on Zn- and O-polar ZnO nanorods. Higher doping concentration is achieved in Zn polar nanorods, which is characterized with different spectroscopic techniques. A noteworthy contribution in morphological changes is noted upon by Mg doping on both Zn- and O-polar ZnO nanorods. Polarity inversion of the individual nanorods are found at certain doping condition, characterized by chemical etching rate measurements and Schottky Barrier Height determination of the individual nanorod.     

Design of ANN Based Non-Linear Network Using Interconnection of Parallel Processor

Suspicious mass traffic constantly evolves, making network behaviour tracing and structure more complex. Neural networks yield promising results by considering a sufficient number of processing elements with strong interconnections between them. They offer efficient computational Hopfield neural networks models and optimization constraints used by undergoing a good amount of parallelism to yield optimal results. Artificial neural network (ANN) offers optimal solutions in classifying and clustering the various reels of data, and the results obtained purely depend on identifying a problem. In this research work, the design of optimized applications is presented in an organized manner. In addition, this research work examines theoretical approaches to achieving optimized results using ANN. It mainly focuses on designing rules. The optimizing design approach of neural networks analyzes the internal process of the neural networks. Practices in developing the network are based on the interconnections among the hidden nodes and their learning parameters. The methodology is proven best for nonlinear resource allocation problems with a suitable design and complex issues. The ANN proposed here considers more or less 46k nodes hidden inside 49 million connections employed on full-fledged parallel processors. The proposed ANN offered optimal results in real-world application problems, and the results were obtained using MATLAB.     

Familiarity effects in the construction of facial-composite images using modern software systems

We investigate the effect of target familiarity on the construction of facial composites, as used by law enforcement to locate criminal suspects. Two popular software construction methods were investigated. Participants were shown a target face that was either familiar or unfamiliar to them and constructed a composite of it from memory using a typical 'feature' system, involving selection of individual facial features, or one of the newer 'holistic' types, involving repeated selection and breeding from arrays of whole faces. This study found that composites constructed of a familiar face were named more successfully than composites of an unfamiliar face; also, naming of composites of internal and external features was equivalent for construction of unfamiliar targets, but internal features were better named than the external features for familiar targets. These findings applied to both systems, although benefit emerged for the holistic type due to more accurate construction of internal features and evidence for a whole-face advantage. STATEMENT OF RELEVANCE: This work is of relevance to practitioners who construct facial composites with witnesses to and victims of crime, as well as for software designers to help them improve the effectiveness of their composite systems.     

Automatic detection and severity classification of diabetic retinopathy

Multimedia Tools and Applications - Diabetic retinopathy (DR) is a leading cause of preventable blindness caused by damaged blood vessels in the eye, if not treated early on. The aim of this...     

Lexicon-based approach outperforms Supervised Machine Learning approach for Urdu Sentiment Analysis in multiple domains

Web is facilitating people to express their views and opinions on different topics through reviews and blogs. Effective advantages can be reaped from these reviews and blogs by fusing the sentiment knowledge. In this research, Sentiment Analysis of Urdu blogs from multiple domains is done by using the two widely used approaches i.e. the Lexicon-based approach and the Supervised Machine Learning approach. Three well known classifiers i.e. Support Vector Machine, Decision Tree and K Nearest Neighbor are used in case of Supervised Machine Learning approach whereas a wide coverage Urdu Sentiment Lexicon and an efficient Urdu Sentiment Analyzer are used in Lexicon-based approach. In both the approaches the information are fused from two sources to successfully perform Sentiment Analysis. In case of Lexicon-based approach, the two sources are the wide coverage Urdu Sentiment Lexicon and the efficient Urdu Sentiment Analyzer. In case of Supervised Machine Learning approach, the two sources are the un-annotated data and annotated data along with important attributes. After performing Sentiment Analysis using both the approaches, the results are observed carefully and on the basis of experiments performed in this research, it is concluded that the Lexicon-based approach outperforms Supervised Machine Learning approach not only in terms of Accuracy, Precision, Recall and F-measure but also in terms of economy of time and efforts used.     

Going Green: making reality match ambition for sustainable coil coatings

The European Commission has launched a €79 billion research and innovation program with the aim of bringing Europe to the forefront of bio-sustainable material research and development and encouraging activities which contribute to the EU’s stated sustainability strategies. This is creating exciting opportunities as new, more sustainable raw materials become available for use in many different industries including coatings for pre-painted applications. This article focuses on one sustainability approach—the framework for strategic sustainable development highlighting not only long-term goals for fully sustainable coil coatings but describing various pathways currently open for sustainable development of coil coatings. It also describes some of the general challenges formulators face trying to reduce dependence on petroleum-based and high environmental impact raw materials. Two examples are used from coatings’ R and D to illustrate these points. Analysis of weathering of bio-based exterior durable products by a variety of techniques is described. Techniques such as AFM, CFM, and PAS-FTIR have been chosen to compliment the traditional assessment techniques for weathered coatings, as a means to understand the links between macro-changes in topography and appearance with the micron-scale physical and chemical changes both on the surface and in the bulk of the exposed film. This work has demonstrated that by careful selection of bio-based monomers, coatings can be formulated with equivalent durability performance to existing petroleum-based coatings at the same time as introducing more sustainable content into the paint film. The second example is an evaluation of the structure—property relationships of bio-sustainable resin monomers aimed at the white goods market, where a high degree of formability and resistance to staining and marking is required. Experimental design-based studies show potential to reduce petroleum-based aromatic acid content without a compromise on film T_g, among other properties.     

Detection of adulteration by caprine body fat and mixtures of caprine body fat and groundnut oil in bovine and buffalo ghee using differential scanning calorimetry

Differential scanning calorimetry was used to detect adulteration of pure ghee with caprine body fat when added singly (at 5, 10 and 15%) and in combination with groundnut oil (GNO) (at 5, 10 and 15%). Samples were analysed for transition behaviour in terms of crystallising and melting curves. When compared to pure ghee, adulterated ghee samples showed a shift in the midrange temperature of thermal curves, indicating the presence of foreign fats. The results revealed that the detection of adulteration was possible at the lowest level of the study (5%), irrespective of the nature of the adulterants.     

Numerical and Experimental Investigation of the Melt Casting of Explosives

Melt casting of energetic materials is investigated, and a numerical model is formulated for the analysis of the coupled fluid flow, heat transfer, and stress fields involved in this phase‐change process. The numerical model is based on a conservative multi block control volume method. The SIMPLE algorithm is employed along with an enthalpy method approach to model the solidification process. Results from the model are verified against analytical solutions, experimental results, and published numerical results for simplified cases. In the melt casting of RDX‐binder mixtures, the very high viscosity of the melt limits the influence of melt convection. The impacts of different cooling conditions on the velocity, temperature and stress distributions, as well as on the solidification time, are discussed. The present model can be used to improve the quality of cast explosives, by optimizing and controlling the processing conditions.