Massive Dirac fermions and spin physics in an ultrathin film of topological insulator

We study transport and optical properties of the surface states which lie in the bulk energy gap of a thin-film topological insulator. When the film thickness is comparable with the surface state decay length into the bulk, the tunneling between the top and bottom surfaces opens an energy gap and form two degenerate massive Dirac hyperbolas. Spin dependent physics emerges in the surface bands which are vastly different from the bulk behavior. These include the surface spin Hall effects, spin dependent orbital magnetic moment, and spin dependent optical transition selection rule which allows optical spin injection. We show a topological quantum phase transition where the Chern number of the surface bands changes when varying the thickness of the thin film.Comment: 7 pages, 5 figure

The connection domain in reverse transcriptase facilitates the in vivo annealing of tRNA(Lys3 )to HIV-1 genomic RNA

The primer tRNA for reverse transcription in HIV-1, tRNA(Lys3), is selectively packaged into the virus during its assembly, and annealed to the viral genomic RNA. The ribonucleoprotein complex that is involved in the packaging and annealing of tRNA(Lys )into HIV-1 consists of Gag, GagPol, tRNA(Lys), lysyl-tRNA synthetase (LysRS), and viral genomic RNA. Gag targets tRNA(Lys )for viral packaging through Gag's interaction with LysRS, a tRNA(Lys)-binding protein, while reverse transcriptase (RT) sequences within GagPol (the thumb domain) bind to tRNA(Lys). The further annealing of tRNA(Lys3 )to viral RNA requires nucleocapsid (NC) sequences in Gag, but not the NC sequences GagPol. In this report, we further show that while the RT connection domain in GagPol is not required for tRNA(Lys3 )packaging into the virus, it is required for tRNA(Lys3 )annealing to the viral RNA genome

Image Denoising via Style Disentanglement

Image denoising is a fundamental task in low-level computer vision. While recent deep learning-based image denoising methods have achieved impressive performance, they are black-box models and the underlying denoising principle remains unclear. In this paper, we propose a novel approach to image denoising that offers both clear denoising mechanism and good performance. We view noise as a type of image style and remove it by incorporating noise-free styles derived from clean images. To achieve this, we design novel losses and network modules to extract noisy styles from noisy images and noise-free styles from clean images. The noise-free style induces low-response activations for noise features and high-response activations for content features in the feature space. This leads to the separation of clean contents from noise, effectively denoising the image. Unlike disentanglement-based image editing tasks that edit semantic-level attributes using styles, our main contribution lies in editing pixel-level attributes through global noise-free styles. We conduct extensive experiments on synthetic noise removal and real-world image denoising datasets (SIDD and DND), demonstrating the effectiveness of our method in terms of both PSNR and SSIM metrics. Moreover, we experimentally validate that our method offers good interpretability

Spectrophotometric determination of yeast RNA with neutral red

The interaction of neutral red (NR) with yeast RNA (yRNA) was studied by UV-Vis spectrophotometry to develop a simple spectrophotometric method for the determination yRNA. NR exhibited a maximum absorption peak at 528 nm in a Britton-Robinson (B-R) buffer solution of pH 4.0. After the addition of yRNA into NR solution, the absorbance value was greatly decreased and no new absorption peaks appeared. The interaction conditions such as the buffer pH, reaction time, etc. were carefully studied. Under the optimal conditions the decrease in absorbance value was proportional to the yRNA concentration in the range from 0.2 to 20.0 mg L-1 when 8.0 × 10-5 M NR was employed. The detection limit was calculated as 0.78 mg L-1 (3σ) and three synthetic samples were determined satisfactorily. A binding ratio of NR to yRNA was found to be 1:1 by the molar ratio method. KEY WORDS: Neutral red, Yeast RNA, Interaction, UV-Vis spectrophotometry   Bull. Chem. Soc. Ethiop. 2008, 22(3), 441-444