Design of Deep Convolution Neural Networks for categorical signature classification of raw panchromatic satellite images

Multimedia Tools and Applications - Remote Sensing categorical signature classification has gained significant implications on spatial resolution image analysis due to differences in the...     

Technological Aspects of Synthesizing Ceramic Coatings by Flow-Through Micro-Arc Oxidation

Refractories and Industrial Ceramics - The paper introduces a promising technology for utilizing a traditional scheme for implementing a flow-through micro-arc oxidation method to restore localized...     

Influence of Titanium Excess in a Ti–Si Reaction Mixture on the Synthesis of a Composite in a Combustion Wave

Theoretical Foundations of Chemical Engineering - On the basis of the classic concepts of the theory of solid-phase combustion, for the first time, a model with a detailed scheme of chemical...     

Modeling of Ignition and Combustion of a Coflowing Hydrogen Jet in a Supersonic Air Flow

Combustion, Explosion, and Shock Waves - Results of a numerical study of mixing, ignition, and combustion of a cold hydrogen jet propagating along the lower wall of a channel parallel to a...     

Three dimensional reconstruction of brain tumor along with space occupying in lesions

Multimedia Tools and Applications - The three-dimensional models of brain tumors serve as diagnostic assistance for physicians, surgeons, and radiologists. The proposed system establishes an...     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

Effect of Drosophila suzukii on Blueberry VOCs: Chemical Cues for a Pupal Parasitoid,Trichopria anastrephae

Journal of Chemical Ecology - Biocontrol agents such as parasitic wasps use long-range volatiles and host-associated cues from lower trophic levels to find their hosts. However, this chemical...     

Syndecan-1 (CD138), Carcinomas and EMT

Cell surface proteoglycans are known to be important regulators of many aspects of cell behavior. The principal family of transmembrane proteoglycans is the syndecans, of which there are four in mammals. Syndecan-1 is mostly restricted to epithelia, and bears heparan sulfate chains that are capable of interacting with a large array of polypeptides, including extracellular matrix components and potent mediators of proliferation, adhesion and migration. For this reason, it has been studied extensively with respect to carcinomas and tumor progression. Frequently, but not always, syndecan-1 levels decrease as tumor grade, stage and invasiveness and dedifferentiation increase. This parallels experiments that show depletion of syndecan-1 can be accompanied by loss of cadherin-mediated adhesion. However, in some tumors, levels of syndecan-1 increase, but the characterization of its distribution is relevant. There can be loss of membrane staining, but acquisition of cytoplasmic and/or nuclear staining that is abnormal. Moreover, the appearance of syndecan-1 in the tumor stroma, either associated with its cellular component or the collagenous matrix, is nearly always a sign of poor prognosis. Given its relevance to myeloma progression, syndecan-1-directed antibody—toxin conjugates are being tested in clinical and preclinical trials, and may have future relevance to some carcinomas.