Parsec-scale jet properties of the gamma-ray quasar 3C 286

The quasar 3C~286 is one of two compact steep spectrum sources detected by the {\it Fermi}/LAT. Here, we investigate the radio properties of the parsec(pc)-scale jet and its (possible) association with the $\gamma$-ray emission in 3C~286. The Very Long Baseline Interferometry (VLBI) images at various frequencies reveal a one-sided core--jet structure extending to the southwest at a projected distance of $\sim$1 kpc. The component at the jet base showing an inverted spectrum is identified as the core, with a mean brightness temperature of $2.8\times 10^{9}$~K. The jet bends at about 600 pc (in projection) away from the core, from a position angle of $-135^\circ$ to $-115^\circ$. Based on the available VLBI data, we inferred the proper motion speed of the inner jet as $0.013 \pm 0.011$ mas yr$^{-1}$ ($\beta_{\rm app} = 0.6 \pm 0.5$), corresponding to a jet speed of about $0.5\,c$ at an inclination angle of $48^\circ$ between the jet and the line of sight of the observer. The brightness temperature, jet speed and Lorentz factor are much lower than those of $\gamma$-ray-emitting blazars, implying that the pc-scale jet in 3C~286 is mildly relativistic. Unlike blazars in which $\gamma$-ray emission is in general thought to originate from the beamed innermost jet, the location and mechanism of $\gamma$-ray emission in 3C~286 may be different as indicated by the current radio data. Multi-band spectrum fitting may offer a complementary diagnostic clue of the $\gamma$-ray production mechanism in this source.Comment: 9 pages, 4 figures, accept for publication in MNRA

Joint measurement of multiple noncommuting parameters

Although quantum metrology allows us to make precision measurements beyond the standard quantum limit, it mostly works on the measurement of only one observable due to the Heisenberg uncertainty relation on the measurement precision of noncommuting observables for one system. In this paper, we study the schemes of joint measurement of multiple observables which do not commute with each other using the quantum entanglement between two systems. We focus on analyzing the performance of a SU(1,1) nonlinear interferometer on fulfilling the task of joint measurement. The results show that the information encoded in multiple noncommuting observables on an optical field can be simultaneously measured with a signal-to-noise ratio higher than the standard quantum limit, and the ultimate limit of each observable is still the Heisenberg limit. Moreover, we find a resource conservation rule for the joint measurement

Characteristics of yak platelet derived growth factors-alpha gene and its expression in brain tissues

Background: Platelet derived growth factors (PDGFs) are key components of autocrine and paracrine signalling, both of which play important roles in mammalian developmental processes. PDGF expression levels also relate to oxygen levels. The characteristics of yak PDGFs, which are indigenous to hypoxic environments, have not been clearly described until the current study. Materials and methods: We amplified the open reading frame encoding yak (Bos grunniens) platelet derived growth factor-alpha (PDGFA) from a yak skin tissue cDNA library by reverse transcriptase polymerase chain reaction (PCR) using specific primers and Sanger dideoxy sequencing. Expression of PDGFA mRNA in different portions of yak brain tissue (cerebrum, cerebellum, hippocampus, and spinal cord) was detected by quantitative real-time PCR (qRT-PCR). PDGFA protein expression levels and its location in different portions of the yak brain were evaluated by western blot and immunohistochemistry. Results: We obtained a yak PDGFA 755 bp cDNA gene fragment containing a 636 bp open reading frame, encoding 211 amino acids (GenBank: KU851801). Phylogenetic analysis shows yak PDGFA to be well conserved, having 98.1% DNA sequence identity to homologous Bubalus bubalus and Bos taurus PDGFA genes. However, 8 nucleotides in the yak DNA sequence and 4 amino acids in the yak protein sequence differ from the other two species. PDGFA is widely expressed in yak brain tissue, and furthermore, PDGFA expression in the cerebrum and cerebellum are higher than in the hippocampus and spinal cord (p > 0.05). PDGFA was observed by immunohistochemistry in glial cells of the cerebrum, cerebellum, and hippocampus, as well as in pyramidal cells of the cerebrum, and Purkinje cell bodies of the hippocampus, but not in glial cells of the spinal cord. Conclusions: The PDGFA gene is well conserved in the animal kingdom; however, the yak PDGFA gene has unique characteristics and brain expression patterns specific to this high elevation species

Local competition-based superpixel segmentation algorithm in remote sensing

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Remote sensing technologies have been widely applied in urban environments’ monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the “salt and pepper” phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive