The fusion-active HIV-1 gp41 core structure is a stable sixhelix bundle (6-HB) formed by its N-and C-terminal heptadrepeat sequences (NHR and CHR). A highly conserved, deep hydrophobic cavity on the surface of the N-helical trimer is important for stability of the 6-HB and serves as an ideal target for developing anti-human immunodeficiency virus (HIV) fusion inhibitors. We have recently identified several small molecule HIV-1 fusion inhibitors that bind to the gp41 cavity through hydrophobic and ionic interactions and block the gp41 6-HB formation. Molecular docking analysis reveals that these small molecules fit inside the hydrophobic cavity and interact with positively charged residue Lys 574 to form a conserved salt bridge. In this study, the functionality of Lys 574 has been finely characterized by mutational analysis and biophysical approaches. We found that substitutions of Lys 574 with non-conserved residues (K574D, K574E, and K574V) could completely abolish virus infectivity. With a set of wildtype and mutant N36 peptides derived from the NHR sequence as a model, we demonstrated that non-conservative Lys 574 substitutions severely impaired the stability and conformation of 6-HBs as detected by circular dichroism spectroscopy, native polyacrylamide gel electrophoresis, and enzyme-linked immunosorbent assay. The binding affinity of N36 mutants bearing non-conservative Lys 574 substitutions to the peptide C34 derived from the CHR sequence dramatically decreased as measured by isothermal titration calorimetry. These substitutions also significantly reduced the potency of N-peptides to inhibit HIV-1 infection. Collectively, these data suggest that conserved Lys 574 plays a critical role in 6-HB formation and HIV-1 infectivity, and may serve as an important target for designing anti-HIV drugs. Entry of human immunodeficiency virus type 1 (HIV-1)3 into target cells is mediated by its envelope glycoprotein (Env), a type I transmembrane protein consisting of surface subunit gp120 and non-covalently associated transmembrane subunit gp41 (1). Sequential binding of HIV-1 gp120 to its cell receptor CD4 and a coreceptor (CCR5 or CXCR4) can trigger a series of conformational rearrangements in gp41 to mediate fusion between viral and cellular membranes (2-4). Structurally, the gp41 ectodomain contains the N-terminal heptad-repeat sequence (NHR) and C-terminal heptad-repeat sequence (CHR), which are adjacent to the fusion peptide and the transmembrane segment, respectively (Fig. 1A). Crystallographic analyses demonstrated that the NHR and CHR associate to form a stable six-helix bundle (6-HB), representing a fusionactive gp41 core structure, in which three N-helices form an interior, parallel coiled-coil trimer, whereas three C-helices pack in an oblique, antiparallel manner into the highly conserved, deep hydrophobic cavity on the surface of the N-helical trimer (Fig. 1B) ) penetrate into the cavity causing an extensive interaction with the hydrophobic residues in the cavity. Considerable evidence imply that interheli...

Ground based optical telescopes are seriously affected by atmospheric turbulence induced aberrations. Understanding properties of these aberrations is important both for instruments design and image restoration methods development. Because the point spread function can reflect performance of the whole optic system, it is appropriate to use the point spread function to describe atmospheric turbulence induced aberrations. Assuming point spread functions induced by the atmospheric turbulence with the same profile belong to the same manifold space, we propose a non-parametric point spread function -PSF-NET. The PSF-NET has a cycle convolutional neural network structure and is a statistical representation of the manifold space of PSFs induced by the atmospheric turbulence with the same profile. Testing the PSF-NET with simulated and real observation data, we find that a well trained PSF-NET can restore any short exposure images blurred by atmospheric turbulence with the same profile. Besides, we further use the impulse response of the PSF-NET, which can be viewed as the statistical mean PSF, to analyze interpretation properties of the PSF-NET. We find that variations of statistical mean PSFs are caused by variations of the atmospheric turbulence profile: as the difference of the atmospheric turbulence profile increases, the difference between statistical mean PSFs also increases. The PSF-NET proposed in this paper provides a new way to analyze atmospheric turbulence induced aberrations, which would be benefit to develop new observation methods for ground based optical telescopes.

The point spread function reflects the state of an optical telescope and it is important for data post-processing methods design. For wide field small aperture telescopes, the point spread function is hard to model, because it is affected by many different effects and has strong temporal and spatial variations. In this paper, we propose to use the denoising autoencoder, a type of deep neural network, to model the point spread function of wide field small aperture telescopes. The denoising autoencoder is a pure data based point spread function modelling method, which uses calibration data from real observations or numerical simulated results as point spread function templates. According to real observation conditions, different levels of random noise or aberrations are added to point spread function templates, making them as realizations of the point spread function, i.e., simulated star images. Then we train the denoising autoencoder with realizations and templates of the point spread function. After training, the denoising autoencoder learns the manifold space of the point spread function and can map any star images obtained by wide field small aperture telescopes directly to its point spread function, which could be used to design data post-processing or optical system alignment methods.

Wide field small aperture telescopes are working horses for fast sky surveying. Transient discovery is one of their main tasks. Classification of candidate transient images between real sources and artifacts with high accuracy is an important step for transient discovery. In this paper, we propose two transient classification methods based on neural networks. The first method uses the convolutional neural network without pooling layers to classify transient images with low sampling rate. The second method assumes transient images as one dimensional signals and is based on recurrent neural networks with long short term memory and leaky ReLu activation function in each detection layer. Testing with real observation data, we find that although these two methods can both achieve more than 94% classification accuracy, they have different classification properties for different targets. Based on this result, we propose to use the ensemble learning method to further increase the classification accuracy to more than 97%.

Texture is one of the most obvious characteristics in solar images and it is normally described by texture features. Because textures from solar images of the same wavelength are similar, we assume texture features of solar images are multi-fractals. Based on this assumption, we propose a pure databased image restoration method: with several high resolution solar images as references, we use the Cycle-Consistent Adversarial Network to restore burred images of the same steady physical process, in the same wavelength obtained by the same telescope. We test our method with simulated and real observation data and find that our method can improve the spatial resolution of solar images, without loss of any frames. Because our method does not need paired training set or additional instruments, it can be used as a post-processing method for solar images obtained by either seeing limited telescopes or telescopes with ground layer adaptive optic system.