9,013 research outputs found
Hybrid exciton-polaritons in a bad microcavity containing the organic and inorganic quantum wells
We study the hybrid exciton-polaritons in a bad microcavity containing the
organic and inorganic quantum wells. The corresponding polariton states are
given. The analytical solution and the numerical result of the stationary
spectrum for the cavity field are finishedComment: 3 pages, 1 figure. appear in Communications in Theoretical Physic
Distribution of Spectral Lags in Gamma Ray Bursts
Using the data acquired in the Time To Spill (TTS) mode for long gamma-ray
bursts (GRBs) collected by the Burst and Transient Source Experiment on board
the Compton Gamma Ray Observatory (BATSE/CGRO), we have carefully measured
spectral lags in time between the low (25-55 keV) and high (110-320 keV) energy
bands of individual pulses contained in 64 multi-peak GRBs. We find that the
temporal lead by higher-energy gamma-ray photons (i.e., positive lags) is the
norm in this selected sample set of long GRBs. While relatively few in number,
some pulses of several long GRBs do show negative lags. This distribution of
spectral lags in long GRBs is in contrast to that in short GRBs. This apparent
difference poses challenges and constraints on the physical mechanism(s) of
producing long and short GRBs. The relation between the pulse peak count rates
and the spectral lags is also examined. Observationally, there seems to be no
clear evidence for systematic spectral lag-luminosity connection for pulses
within a given long GRB.Comment: 20 pages, 4 figure
Petroleum generation timing and source in the Northern Longmen Shan Thrust Belt, Southwest China: Implications for multiple oil generation episodes and sources
The temporal evolution of hydrocarbons (βΌ500 million bbl) and its relationship to the orogenic events of the Longmen Shan thrust belt have been extensively debated. The hydrocarbons occur as solid bitumen, as dykes and/or coatings within/along faults/fractures, and as present-day oil seeps. Here, utilizing organic geochemistry, we demonstrate that all of the bitumen exhibit similar organo-geochemical characteristics, and were sourced from the upper Neoproterozoicβlower Cambrian Doushantuo and Qiongzhusi Formations. In contrast, the organic geochemistry of the present-day oil seeps are distinct from that of the bitumen, and suggest that the source is the Permian Dalong Formation. Bitumen Re-Os data indicate that the upper Neoproterozoicβlower Cambrian Doushantuo and Qiongzhusi Formations underwent two temporally distinct oil-generation events; initial oil generation occurred during the Late Cambrian/Early Ordovician prior to the Caledonian orogeny, and secondly during the Jurassic (βΌ165 Ma) coinciding with the Indosinian-Yanshan orogenies. In contrast, the Re-Os data of the present-day oil seeps are too similar to yield a meaningful age, although the source is considered to have undergone hydrocarbon maturation between the Triassic and Jurassic. The temporal hydrocarbon evolution in the Longmen Shan thrust belt also provides an understanding of the hydrocarbon evolution and future exploration of the adjacent petroliferous Sichuan Basin
Enhancement in adsorption potential of microplastics in sewage sludge for metal pollutants after the wastewater treatment process
Β© 2019 Elsevier Ltd Microplastics (MPs) as new pollutants of environmental concern have been widely detected in sewage sludge, and may act as significant vectors for metal pollutants due to their adsorption property. Our findings show that Cd, Pb, and Co, but not Ni, contents in sewage sludge are lower than that of corresponding metal irons adsorbed on sludge-based MPs, indicating that the MPs accumulate such metal pollutants as Cd in the sludge samples. In contrast to virgin MPs, sludge-based MPs are one order of magnitude higher adsorption capacity for Cd, which reaches up to 2.523 mg gβ1, implying that there is a considerable enhancement in adsorption potential of the MPs for metals after the wastewater treatment process. SEM analysis shows that sludge-based MPs have rougher and more porous surface than virgin MPs, and FTIR spectra reveal that functional groups such as CβO and OβH are found on sludge-based MPs. Further, two-dimensional FTIR correlation spectroscopy indicates that CβO and NβH functional groups play a vital role in the process that sludge-based MPs adsorb Cd, which are not found in virgin MPs. The results imply that increased adsorption potentials of the sludge-based MPs to Cd are attributed to changes in the MP physicochemical properties during wastewater treatment process. In addition, such factors as pH value, and sludge inorganic and organic components also have an effect on the MP adsorption to Cd. Principal component analysis shows that the MPs could be divided into three categories, i.e. polyamide, rubbery MPs (polyethylene and polypropylene) and glassy MPs (polyvinyl chloride and polystyrene). Their adsorption potentials to Cd follow the decreasing order: polyamide > rubbery MPs > glassy MPs. In summary, these findings indicate that MPs may exert an important influence on fate and transport of metal pollutants during sewage sludge treatment process, which deserves to be further concerned
Applying Neural Network based on Fuzzy Cluster Pre-processing to Thermal Error Modeling for Coordinate Boring Machine
AbstractTo investigate the effect of the thermal characteristics of a motorized spindle system on the precision of a machine tool, a thermal error model for spindle axial expansion and radial thermal declination is proposed. With precision CNC coordinate boring machine as an object, using the five-point method to calibrate spindle system thermal errors by the eddy current sensors for axial thermal elongation and radial thermal tilted values, and temperatures of measurement points are obtained by the PT100. The relationships between the rotational speed and temperature field, thermal errors are analyzed. Then fuzzy clustering analysis method is used to group and optimize the temperature variables, selecting the variables for thermal error-sensitive. Finally the MIMO artificial neural network approach is established for the spindle axial thermal elongation and radial thermal drifts. The results indicated that the model prediction accuracy could reach 86% with perfect generalization ability under different cutting conditions, providing a theoretical model and thermal characteristic parameters for both thermal error compensation and thermal equilibrium design
VEGF-siRNA silencing induces apoptosis, inhibits proliferation and suppresses vasculogenic mimicry in osteosarcoma in vitro
Aim: To inhibit the gene expression of vascular endothelial growth factor (VEGF) with the RNA interference (RNAi) technique and explore its influence on the apoptosis, proliferation, vasculogenic mimicry of osteosarcoma in vitro. Methods: PSilencer-VEGF plasmid transfected into osteosarcoma cell line of MG63 was recombinated by gene cloning, and VEGF expression was examined by Western blotting after transfection. The biological features of transfected MG63 including proliferation, apoptosis and vasculogenic mimicry, were assessed by MTT assay, flowcytometer, annexin V-FITC/PI, light microscopy and HE staining. Results: The sequence of pSilencer-VEGF plasmid was confirmed by DNA sequencing. Sequence-specific siRNA targeting VEGF significantly decreases its expression in MG63 cells. Cell growth was inhibited and early apoptosis was induced compared with non-siRNA tansfected cells. Decreased VEGF expression was associated with reduced vasculogenic mimicry of MG63 cells. Conclusions: VEGF gene might be closely related to the apoptosis and proliferation of osteosarcoma cells, and appears to be crucial for formation of vasculogenic mimicry in osteosarcoma. Inhibition of VEGF expression by sequence-specific siRNA may become a novel approach for the treatment of VEGF-enriched neoplasma such as osteosarcoma.Π¦Π΅Π»Ρ: ΠΈΠ½Π³ΠΈΠ±ΠΈΡΠΎΠ²Π°ΡΡ Π΅ΠΊΡΠΏΡΠ΅ΡΡΠΈΡ ΡΠ°ΠΊΡΠΎΡΠ° ΡΠΎΡΡΠ° ΡΠ½Π΄ΠΎΡΠ΅Π»ΠΈΡ ΡΠΎΡΡΠ΄ΠΎΠ² (VEGF) Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΌΠ΅ΡΠΎΠ΄Π° Π ΠΠ ΠΈΠ½ΡΠ΅ΡΡΠ΅ΡΠ΅Π½ΡΠΈΠΈ (RNAi) ΠΈ
ΠΈΠ·ΡΡΠΈΡΡ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π²Π²Π΅Π΄Π΅Π½ΠΈΡ ΡΠ°ΠΉΠ»Π΅Π½ΡΠΈΠ½Π³Π° siRNA-VEGF Π½Π° Π°ΠΏΠΎΠΏΡΠΎΠ·, ΠΏΡΠΎΠ»ΠΈΡΠ΅ΡΠ°ΡΠΈΡ, Π²Π°ΡΠΊΡΠ»ΡΡΠ½ΡΡ ΠΌΠΈΠΌΠΈΠΊΡΠΈΡ ΠΊΠ»Π΅ΡΠΎΠΊ ΠΎΡΡΠ΅ΠΎΡΠ°ΡΠΊΠΎΠΌΡ in
vitro. ΠΠ΅ΡΠΎΠ΄Ρ: ΠΏΠ»Π°Π·ΠΌΠΈΠ΄Π° PSilencer-VEGF, ΡΠ΅ΠΊΠΎΠΌΠ±ΠΈΠ½ΠΈΡΠΎΠ²Π°Π½Π½Π°Ρ Π·Π° ΡΡΠ΅Ρ ΠΊΠ»ΠΎΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ΅ΡΠΊΠΎΠΉ siRNA ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ,
Π±ΡΠ»Π° ΡΡΠ°Π½ΡΡΠ΅ΡΠΈΡΠΎΠ²Π°Π½Π° Π² ΠΊΠ»Π΅ΡΠΎΡΠ½ΡΡ Π»ΠΈΠ½ΠΈΡ ΠΎΡΡΠ΅ΠΎΡΠ°ΡΠΊΠΎΠΌΡ MG63. ΠΠΊΡΠΏΡΠ΅ΡΡΠΈΡ VEGF ΠΏΠΎΡΠ»Π΅ ΡΡΠ°Π½ΡΡΠ΅ΠΊΡΠΈΠΈ ΠΏΡΠΎΠ²Π΅ΡΡΠ»ΠΈ Ρ ΠΏΠΎΠΌΠΎΡΡΡ
ΠΠ΅ΡΡΠ΅ΡΠ½-Π±Π»ΠΎΡ Π°Π½Π°Π»ΠΈΠ·Π°. ΠΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΡΡΠ°Π½ΡΡΠ΅ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΊΠ»Π΅ΡΠΎΠΊ MG63, Π° ΠΈΠΌΠ΅Π½Π½ΠΎ ΠΏΡΠΎΠ»ΠΈΡΠ΅ΡΠ°ΡΠΈΡ, Π°ΠΏΠΎΠΏΡΠΎΠ· ΠΈ
Π²Π°ΡΠΊΡΠ»ΡΡΠ½ΡΡ ΠΌΠΈΠΌΠΈΠΊΡΠΈΡ, ΠΈΠ·ΡΡΠ°Π»ΠΈ Ρ ΠΏΠΎΠΌΠΎΡΡΡ MTΠ’-Π°Π½Π°Π»ΠΈΠ·Π°, ΠΏΡΠΎΡΠΎΡΠ½ΠΎΠΉ ΡΠΈΡΠΎΡΠ»ΡΠΎΡΠΈΠΌΠ΅ΡΡΠΈΠΈ, Π°Π½Π½Π΅ΠΊΡΠΈΠ½Π° V-FITC/ΠΏΡΠΎΠΏΠΈΠ΄ΠΈΡΠΌ ΠΉΠΎΠ΄ΠΈΠ΄Π°,
ΡΠ²Π΅ΡΠΎΠ²ΠΎΠΉ ΠΌΠΈΠΊΡΠΎΡΠΊΠΎΠΏΠΈΠΈ ΠΈ ΠΎΠΊΡΠ°ΡΠΈΠ²Π°Π½ΠΈΡ Π³Π΅ΠΌΠ°ΡΠΎΠΊΡΠΈΠ»ΠΈΠ½ΠΎΠΌ ΠΈ ΡΠΎΠ·ΠΈΠ½ΠΎΠΌ (HE). Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ: ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΡ, ΠΊΠ»ΠΎΠ½ΠΈΡΠΎΠ²Π°Π½Π½Π°Ρ
Π² ΠΏΠ»Π°Π·ΠΌΠΈΠ΄Ρ pSilencer-VEGF, ΠΏΡΠΎΠ²Π΅ΡΠ΅Π½Π° ΠΏΡΠΈ ΡΠ΅ΠΊΠ²Π΅Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΠΠ. Π‘ΠΏΠ΅ΡΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΊ VEGF ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ siRNA
Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΡΠ½ΠΈΠΆΠ°Π»ΠΈ Π΅Π³ΠΎ ΡΠΊΡΠΏΡΠ΅ΡΡΠΈΡ ΠΊΠ»Π΅ΡΠΊΠ°ΠΌΠΈ MG63. Π ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ Ρ ΠΊΠ»Π΅ΡΠΊΠ°ΠΌΠΈ, ΡΡΠ°Π½ΡΡeΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌΠΈ ΠΏΠ»Π°Π·ΠΌΠΈΠ΄ΠΎΠΉ Π±Π΅Π· siRNA,
ΡΠΎΡΡ ΠΊΠ»Π΅ΡΠΎΠΊ Ρ PSilencer-VEGF Π±ΡΠ» ΠΈΠ½Π³ΠΈΠ±ΠΈΡΠΎΠ²Π°Π½, Π° ΡΠ°ΠΊΠΆΠ΅ Π² Π½ΠΈΡ
Π±ΡΠ» ΠΈΠ½Π΄ΡΡΠΈΡΠΎΠ²Π°Π½ ΡΠ°Π½Π½ΠΈΠΉ Π°ΠΏΠΎΠΏΡΠΎΠ·. ΠΠ½Π³ΠΈΠ±ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠΊΡΠΏΡΠ΅ΡΡΠΈΠΈ
VEGF Π±ΡΠ»ΠΎ Π°ΡΡΠΎΡΠΈΠΈΡΠΎΠ²Π°Π½ΠΎ ΡΠΎ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ΠΌ Π²Π°ΡΠΊΡΠ»ΡΡΠ½ΠΎΠΉ ΠΌΠΈΠΌΠΈΠΊΡΠΈΠΈ ΠΊΠ»Π΅ΡΠΎΠΊ MG63. ΠΡΠ²ΠΎΠ΄Ρ: Π³Π΅Π½ VEGF ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΡΠ΅ΡΠ½ΠΎ ΡΠ²ΡΠ·Π°Π½
Ρ ΡΠ΅Π³ΡΠ»ΡΡΠΈΠ΅ΠΉ Π°ΠΏΠΎΠΏΡΠΎΠ·Π° ΠΈ ΠΏΡΠΎΠ»ΠΈΡΠ΅ΡΠ°ΡΠΈΠΈ ΠΊΠ»Π΅ΡΠΎΠΊ ΠΎΡΡΠ΅ΠΎΡΠ°ΡΠΊΠΎΠΌΡ ΠΈ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΊΠ»ΡΡΠ΅Π²ΡΠΌ ΡΠ΅Π³ΡΠ»ΡΡΠΎΡΠΎΠΌ Π²Π°ΡΠΊΡΠ»ΡΡΠ½ΠΎΠΉ ΠΌΠΈΠΌΠΈΠΊΡΠΈΠΈ.
ΠΠ½Π³ΠΈΠ±ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠΊΡΠΏΡΠ΅ΡΡΠΈΠΈ VEGF Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ΅ΡΠΊΠΈΡ
siRNA ΠΌΠΎΠΆΠ½ΠΎ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡ ΠΊΠ°ΠΊ Π½ΠΎΠ²ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΎΠΏΡΡ
ΠΎΠ»Π΅ΠΉ,
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡΡΠΈΡ
ΡΡ ΠΏΠΎΠ²ΡΡΠ΅Π½Π½ΠΎΠΉ ΡΠΊΡΠΏΡΠ΅ΡΡΠΈΠ΅ΠΉ VEGF, ΡΠ°ΠΊΠΈΡ
ΠΊΠ°ΠΊ ΠΎΡΡΠ΅ΠΎΡΠ°ΡΠΊΠΎΠΌΠ°
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