Long-term Variability Properties and Periodicity Analysis for Blazars

In this paper, the compiled long-term optical and infrared measurements of some blazars are used to analyze the variation properties and the optical data are used to search for periodicity evidence in the lightcurve by means of the Jurkevich technique and the discrete correlation function (DCF) method. Following periods are found: 4.52-year for 3C 66A; 1.56 and 2.95 years for AO 0235+164; 14.4, 18.6 years for PKS 0735+178; 17.85 and 24.7 years for PKS 0754+100; 5.53 and 11.75 for OJ 287. 4.45, and 6.89 years for PKS 1215; 9 and 14.84 years for PKS 1219+285; 2.0, 13.5 and 22.5 for 3C273; 7.1 year for 3C279; 6.07 for PKS 1308+326; 3.0 and 16.5 years for PKS 1418+546; 2.0 and 9.35 years for PKS 1514-241; 18.18 for PKS 1807+698; 4.16 and 7.0 for 2155-304; 14 and 20 years for BL Lacertae. Some explanations have been discussed.Comment: 10 pages, 2 table, no figure, a proceeding paper for Pacific Rim Conference on Stellar Astrophysics, Aug. 1999, HongKong, Chin

Weakening of the stratospheric polar vortex by Arctic sea-ice loss

Successive cold winters of severely low temperatures in recent years have had critical social and economic impacts on the mid-latitude continents in the Northern Hemisphere. Although these cold winters are thought to be partly driven by dramatic losses of Arctic sea-ice, the mechanism that links sea-ice loss to cold winters remains a subject of debate. Here, by conducting observational analyses and model experiments, we show how Arctic sea-ice loss and cold winters in extra-polar regions are dynamically connected through the polar stratosphere. We find that decreased sea-ice cover during early winter months (November-December), especially over the Barents-Kara seas, enhances the upward propagation of planetary-scale waves with wavenumbers of 1 and 2, subsequently weakening the stratospheric polar vortex in mid-winter (January-February). The weakened polar vortex preferentially induces a negative phase of Arctic Oscillation at the surface, resulting in low temperatures in mid-latitudes.open11167174Ysciescopu

Protein expression and purification of integrin I domains and IgSF ligands for crystallography

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An intelligent system by fuzzy reliability algorithm in fault tree analysis for nuclear power plant probabilistic safety assessment

© Imperial College Press. Fault tree analysis for nuclear power plant probabilistic safety assessment is an intricate process. Personal computer-based software systems have therefore been developed to conduct this analysis. However, all existing fault tree analysis software systems only accept quantitative data to characterized basic event reliabilities. In real-world applications, basic event reliabilities may not be represented by quantitative data but by qualitative justifications. The motivation of this work is to develop an intelligent system by fuzzy reliability algorithm in fault tree analysis, which can accept not only quantitative data but also qualitative information to characterized reliabilities of basic events. In this paper, a newly-developed system called InFaTAS-NuSA is presented and its main features and capabilities are discussed. To benchmark the applicability of the intelligent concept implemented in InFaTAS-NuSA, a case study is performed and the analysis results are compared to the results obtained from a well-known fault tree analysis software package. The results confirm that the intelligent concept implemented in InFaTAS-NuSA can be very useful to complement conventional fault tree analysis software systems

Arsenite-Induced Alterations of DNA Photodamage Repair and Apoptosis After Solar-Simulation UVR in Mouse Keratinocytes in Vitro

Our laboratory has shown that arsenite markedly increased the cancer rate caused by solar-simulation ultraviolet radiation (UVR) in the hairless mouse skin model. In the present study, we investigated how arsenite affected DNA photodamage repair and apoptosis after solar-simulation UVR in the mouse keratinocyte cell line 291.03C. The keratinocytes were treated with different concentrations of sodium arsenite (0.0, 2.5, 5.0 μM) for 24 hr and then were immediately irradiated with a single dose of 0.30 kJ/m(2) UVR. At 24 hr after UVR, DNA photoproducts [cyclobutane pyrimidine dimers (CPDs) and 6–4 photoproducts (6-4PPs)] and apoptosis were measured using the enzyme-linked immunosorbent assay and the two-color TUNEL (terminal deoxynucleotide transferase dUTP nick end labeling) assay, respectively. The results showed that arsenite reduced the repair rate of 6-4PPs by about a factor of 2 at 5.0 μM and had no effect at 2.5 μM. UVR-induced apoptosis at 24 hr was decreased by 22.64% at 2.5 μM arsenite and by 61.90% at 5.0 μM arsenite. Arsenite decreased the UVR-induced caspase-3/7 activity in parallel with the inhibition of apoptosis. Colony survival assays of the 291.03C cells demonstrate a median lethal concentration (LC(50)) of arsenite of 0.9 μM and a median lethal dose (LD(50)) of UVR of 0.05 kJ/m(2). If the present results are applicable in vivo, inhibition of UVR-induced apoptosis may contribute to arsenite’s enhancement of UVR-induced skin carcinogenesis

Universal Relations for a Fermi Gas Close to a p-Wave Interaction Resonance

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Acarbose: A New Option in the Treatment of Ulcerative Colitis by Increasing Hydrogen Production

Acarbose，which is clinically widely used to treat Type 2 Diabetes，is thought to act at the small intestine by competitively inhibiting enzymes that delay the release of glucose from complex carbohydrates, thereby specifically reducing post prandial glucose excursion. The major side-effect of treatment with acarbose, flatulence, occurs when undigested carbohydrates are fermented by colonic bacteria, resulting in considerable amount of hydrogen. We propose that enteric benefits of acarbose is partly attributable to be their ability to neutralise oxidative stress via increased production of H2 in the gastrointestinal tract. Therefore, symptoms of ulcerative colitis in human beings can be ameliorated by acarbose

Physiological Response of Soybean Genotypes to Water Limiting Conditions

This article has been retracted by the editor of the African Crop Science Journal. Soybean (Glycine max L. Merr.) is the most important source of protein as well as vegetable oil world wide. It suffers variously from water shortage at all stages of growth. This study was conducted to explore the physiological responses of soybean genotypes to water limiting conditions. Seven days old seedlings of C01, C08, C27, W01, W06 and W08 drought-sensitive, and C12 and W05 drought-tolerant genotypes used in the study were transplanted in Polyvinyl chloride (PVC) pipes, filled with a soil mixture. Fifty percent of the plants were left unwatered when the second trifoliate leaves were halfway to growth. The rest (50%) were watered daily and considered as the control. Results showed that as soil water content diminished, plant stem elongation, stomatal conductance, relative water content (%RWC), water potential, osmotic potential and turgor pressure of stressed plants declined in all genotypes. The declining trends of those parameters were significantly different, to a great extent, in the drought tolerant genotypes, from the susceptible ones. Key Words: Glycine max, stomatal conductanc

Wavelength-multiplexed duplex transceiver based on III-V/Si hybrid integration for off-chip and on-chip optical interconnects

A six-channel wavelength-division-multiplexed optical transceiver with a compact footprint of 1.5 x 0.65 mm(2) for off-chip and on-chip interconnects is demonstrated on a single silicon-on-insulator chip. An arrayed waveguide grating is used as the (de)multiplexer, and III-V electroabsorption sections fabricated by hybrid integration technology are used as both modulators and detectors, which also enable duplex links. The 30-Gb/s capacity for each of the six wavelength channels for the off-chip transceiver is demonstrated. For the on-chip interconnect, an electrical-to-electrical 3-dB bandwidth of 13 GHz and a data rate of 30 Gb/s per wavelength are achieved

Deep Learning in Lane Marking Detection: A Survey

Lane marking detection is a fundamental but crucial step in intelligent driving systems. It can not only provide relevant road condition information to prevent lane departure but also assist vehicle positioning and forehead car detection. However, lane marking detection faces many challenges, including extreme lighting, missing lane markings, and obstacle obstructions. Recently, deep learning-based algorithms draw much attention in intelligent driving society because of their excellent performance. In this paper, we review deep learning methods for lane marking detection, focusing on their network structures and optimization objectives, the two key determinants of their success. Besides, we summarize existing lane-related datasets, evaluation criteria, and common data processing techniques. We also compare the detection performance and running time of various methods, and conclude with some current challenges and future trends for deep learning-based lane marking detection algorithm