Effect of prenatal and postnatal exposure to ethanol on rat central nervous system gangliosides and glycosidases

We investigated the effect of maternal alcohol consumption on cell number, gangliosides and ganglioside catabolizing enzymes in the central nervous system (CNS) of the offspring. Virgin female rats of the Charles Foster strain were given 15% (v/v) ethanol in drinking water one month prior to conception and during gestation and lactation. At 21 days postnatal age, the offspring were sacrificed and the brains were separated into cerebrum, cerebellum and brain stem to investigate possible regional variations. Compared to controls, wet weight of cerebrum, cerebellum and brain stem, and of spinal cord was decreased in the pups exposed to alcohol. DNA and protein contents were also found to be lowered in all the CNS regions of the pups exposed to alcohol. Conversely, maternal alcohol consumption was found to increase the concentration and the content of total gangliosideN-acetylneuraminic (NANA) in CNS of the pups. In addition, alcohol treatment was found to induce alterations in the proportions of individual ganglioside fractions. Interestingly, these alterations are somewhat different than those observed in the neonatal brain and spinal cord of the pups subjected to prenatal alcohol exposure. The alterations in the proportions of ganglioside fractions were shown to be region-specific. Maternal alcohol consumption resulted in decreased activities of sialidase, β-galactosidase, β-glucosidase and β-hexosaminidase. The results suggest that the alcohol-associated increases in ganglioside concentration may be at least partly due to the decreased activities of ganglioside catabolizing enzymes.     

Thermal and flow properties of oils from salmon heads

Thermal and flow properties of unrefined oils from the heads of red or pink salmon were evaluated. Major thermal degradation of the salmon oils occurred between 200 and 450°C. Red and pink salmon oils were completely decomposed at 533 and 668°C, respectively. The phase transition of salmon oils occurred over a wide range of temperatures. The melting points of −69.6 to −0.36°C and −64.7 to 20.8°C were observed for red and pink salmon oils, respectively. The enthalpy was 40 j/g for red salmon oil and 39 j/g for pink salmon oil. Specific heat capacity ranges of 0.8 to 1.6 and 1.3 to 2.3 j/g/°C were observed for red and pink salmon oils, respectively. Both salmon oils exhibited Newtonian flow behavior. Red salmon oil required higher magnitudes of energy (kj·mol⁻¹) to flow than pink salmon oil. The viscosity of salmon oils was temperature-dependent and could be predicted by the Arrhenius equation.     

Hydrogen-transfer catalytic activity of minerals common to coal

The hydrogenation/dehydrogenation catalytic activity of minerals commonly present in coal was investigated. The extent of reaction undergone by the model hydrogen-transfer system tetralin/1,2-dihydronaphthalene/naphthalene at 400 °C in the presence of these minerals was used to measure the catalytic activity. For the four most active minerals, the order of catalytic activity per gram of mineral, for a given range of particle size, is limonites > pyrites > diaspore > magnetites. The pure chemical analog of the limonites, Fe₂O₃, was found to have catalytic activity similar to that of the limonites. Heating the minerals at 400 °C before running the reaction was found to decrease these catalytic activities, despite the surface-area increases resulting from the annealing process. The catalytic activity per unit area of mineral was determined and used as an index of the catalytic activity of the reactive sites for these heterogeneous systems. The order of decreasing site catalytic activity for the more active minerals is pyrites > magnetites > diaspore > limonites. The observed mineral/ chemical compound catalytic activities indicate that the more reactive mineral hydrogenation/ dehydrogenation catalytic sites are composed of iron compounds.     

Characteristics and compositions ofCarissa spinarum,Leucaena leucocephala andPhysalis minima seeds and oils

The seeds and extracted oils ofCarissa spinarum (Apocynaceae), (I),Leucaena leucocephala (Leguminosae) (II) andPhysalis minima (Solanaceae) (III) were analyzed for characteristics and compositions. The seeds of I, II and III contained 22.4, 6.4 and 40.0% oil and 10.1, 27.6 and 17.9% protein, respectively. The oils of I, II and III had, respectively, iodine values 70.1, 113.5 and 122.5; saponification values 186, 188 and 189; unsaponifiable matter 5.2, 2.5 and 0.8%, and the following fatty acid compositions (area %): palmitic 12.6, 14.2, 10.5; stearic 7.6, 6.1, 8.6; oleic 72.7, 20.1, 17.3; linoleic 5.2, 53.8, 61.4; linolenic 0.9, 1.8, 0.0, and arachidic 1.0, 2.3, 0.0. II contained 1.7% lignoceric acid. III contained small amounts of hexadecenoic (0.1%), epoxy (0.6%) and hydroxy (1.5%) fatty acids.     

DPV: a taxonomy for utilizing deep learning as a prediction technique for various types of cancers detection

Multimedia Tools and Applications - Deep learning (DL) is a type of machine learning capable of processing large quantities of data to provide analytic results based on a particular...     

Low intensity visual data improvement using DWT and LWT based regularizer

Image Completion plays a vital role in compressed sensing, machine learning, and computer vision applications. The Rank Minimization algorithms are used to perform the image completion. The major problem with rank minimization algorithms is the loss of information in the recovered image at high corruption ratios. To overcome this problem Lifting wavelet transform based Rank Minimization (LwRM), and Discrete wavelet transform based Rank Minimization (DwRM) methods are proposed, which can recover the image, if the corrupted observations are more than 80%. The evaluation of the proposed methods are accomplished by Full Reference Image Quality Assessment (FRIQA) and No Reference Image Quality Assessment (NR-IQA) metrics. The simulation results of proposed methods are superior to state-of-the-art methods.

     

A novel secure solution of using mixed reality in data transmission for bowel and jaw surgical telepresence: enhanced rivest cipher RC6 block cipher

Security threats are crucial challenges that deter Mixed reality (MR) communication in medical telepresence. This research aims to improve the security by reducing the chances of types of various attacks occurring during the real-time data transmission in surgical telepresence as well as reduce the time of the cryptographic algorithm and keep the quality of the media used. The proposed model consists of an enhanced RC6 algorithm in combination. Dynamic keys are generated from the RC6 algorithm mixed with RC4 to create dynamic S-box and permutation table, preventing various known attacks during the real-time data transmission. For every next session, a new key is created, avoiding possible reuse of the same key from the attacker. The results obtained from our proposed system are showing better performance compared to the state of art. The resistance to the tested attacks is measured throughout the entropy, Pick to Signal Noise Ratio (PSNR) is decreased for the encrypted image than the state of art, structural similarity index (SSIM) closer to zero. The execution time of the algorithm is decreased for an average of 20%. The proposed system is focusing on preventing the brute force attack occurred during the surgical telepresence data transmission. The paper proposes a framework that enhances the security related to data transmission during surgeries with acceptable performance.

     

Localized swelling of Fe-Cr-Ni alloy rollers in high-temperature applications

Localized swelling has been observed in 24Cr-24Ni-Nb steel transportation rollers used in the normalizing furnace of a plate mill after prolonged service at high temperature. Due to high localized thermal and mechanical stresses, the chromia layer formed on the roller surface ruptures, exposing the roller substrate to furnace oxygen. Oxidation of second-phase carbides results in the formation of carbon monoxide at very high partial pressure. This leads to formation of voids, leading in turn to localized swelling of the roller material.     

Updating a 1950s-generation aluminum smelter

Hindalco’s aluminum electrolysis cells were initially installed in 1962, and the technology was based on 1950s-generation pots. Although Hindalco expanded its aluminums melting capacity from 20,000 tonnes per year to 175,000 tonnes per year, the basic design of the pots remained unchanged. In view of energy price increases, and to keep pace with the latest developments in aluminum smelting technology, Hindalco undertook efforts to modernize its facilities. In spite of numerous constraints, the Hindalco smelter has been able to achieve performance nearly equivalent to that of 1980s-generation pots by retrofitting new technologies. This has resulted in considerable savings in electrical energy consumption and raw materials usage.     

Pedestrian Detection and Tracking Using Deformable Part Models and Kalman Filtering

Abstract： Pedestrian detection techniques are important and challenging especially for complex real world scenes. They can be used for ensuring pedestrian safety, ADASs （advance driver assistance systems） and safety surveillance systems. In this paper, we propose a novel approach for multi-person tracking-by-detection using deformable part models in Kalman filtering framework. The Kalman filter is used to keep track of each person and a unique label is assigned to each tracked individual. Based on this approach, people can enter and leave the scene randomly. We test and demonstrate our results on Caltech Pedestrian benchmark, which is two orders of magnitude larger than any other existing datasets and consists of pedestrians varying widely in appearance, pose and scale. Complex situations such as people occluded by each other are handled gracefully and individual persons can be tracked correctly after a group of people split. Experiments confirm the real-time performance and robustness of our system, working in complex scenes. Our tracking model gives a tracking accuracy of 72.8% and a tracking precision of 82.3%. We can further reduce false positives by 2.8%, using Kalman filtering.