The assembly of snRNP cores, in which seven Sm proteins, D1/D2/F/E/G/D3/B, form a ring around the nonameric Sm site of snRNAs, is the early step of spliceosome formation and essential to eukaryotes. It is mediated by the PMRT5 and SMN complexes sequentially in vivo. SMN deficiency causes neurodegenerative disease spinal muscular atrophy (SMA). How the SMN complex assembles snRNP cores is largely unknown, especially how the SMN complex achieves high RNA assembly specificity and how it is released. Here we show, using crystallographic and biochemical approaches, that Gemin2 of the SMN complex enhances RNA specificity of SmD1/D2/F/E/G via a negative cooperativity between Gemin2 and RNA in binding SmD1/D2/F/E/G. Gemin2, independent of its N-tail, constrains the horseshoe-shaped SmD1/D2/F/E/G from outside in a physiologically relevant, narrow state, enabling high RNA specificity. Moreover, the assembly of RNAs inside widens SmD1/D2/F/E/G, causes the release of Gemin2/SMN allosterically and allows SmD3/B to join. The assembly of SmD3/B further facilitates the release of Gemin2/SMN. This is the first to show negative cooperativity in snRNP assembly, which provides insights into RNA selection and the SMN complex's release. These findings reveal a basic mechanism of snRNP core assembly and facilitate pathogenesis studies of SMA.

Low-dose X-CT scanning method effectively reduces radiation hazards, however, reducing the radiation dose will introduce noise and artifacts during the projection process, resulting in a decrease in the quality of the reconstructed image. To address this problem, we combined 2D variational modal decomposition and dictionary learning. We proposed a low-dose CT (LDCT) image denoising algorithm based on an improved K-SVD algorithm with image decomposition. The dictionary obtained by K-SVD training lacks consideration of image structure information. To address this problem, we employ the two-dimensional variational mode decomposition (2D-VMD) method to decompose the image into distinct modal components. Through the adaptive learning of dictionaries based on the characteristics of each modal component, independent denoising processing is applied to each component, avoiding the loss of structural and detailed information in the image. In addition, we introduce the regularized orthogonal matching pursuit algorithm (ROMP) and dictionary atom optimization method to improve the sparse representation ability of the dictionary and reduce the impact of noise atoms on denoising performance. The experiments show that the proposed method outperforms other denoising methods regarding peak signal-to-noise ratio and structural similarity. The proposed method maintains the denoised image details and structural information while removing LDCT image noise and artifacts. The image quality after denoising is significantly improved and facilitates more accurate detection and analysis of lesion areas.

In the process of digital micromirror device (DMD) digital mask projection lithography, the lithography efficiency will be enhanced greatly by path planning of pattern transfer. This paper proposes a new dual operator and dual population ant colony (DODPACO) algorithm. Firstly, load operators and feedback operators are used to update the local and global pheromones in the white ant colony, and the feedback operator is used in the yellow ant colony. The concept of information entropy is used to regulate the number of yellow and white ant colonies adaptively. Secondly, take eight groups of large-scale data in TSPLIB as examples to compare with two classical ACO and six improved ACO algorithms; the results show that the DODPACO algorithm is superior in solving large-scale events in terms of solution quality and convergence speed. Thirdly, take PCB production as an example to verify the time saved after path planning; the DODPACO algorithm is used for path planning, which saves 34.3% of time compared with no path planning, and is about 1% shorter than the suboptimal algorithm. The DODPACO algorithm is applicable to the path planning of pattern transfer in DMD digital mask projection lithography and other digital mask lithography.

Photons trapped in the form of waveguide (WG) modes associated with the organic–organic interface and in the form of surface plasmon polariton (SPP) modes associated with the metallic electrode–organic interface result in a large energy loss in organic light-emitting devices (OLEDs). Introducing gratings onto the metallic electrode is especially crucial for recovering the power lost to the associated SPP modes. In our research, we demonstrate the efficient outcoupling of SPP modes in TE mode by two-dimensional (2D) grating, which cannot excited in one-dimensional (1D) grating OLED. This causes a 62.5% increase in efficiency from 2D grating OLED than 1D grating OLED. The efficient outcoupling of the WG and SPP modes is verified by the numerical simulation of both the emission spectra and the field distribution.

In the compressed sensing (CS) reconstruction algorithms, the problems of overestimation and large redundancy of candidate atoms will affect the reconstruction accuracy and probability of the algorithm when using Sparsity Adaptive Matching Pursuit (SAMP) algorithm. In this paper, we propose an improved SAMP algorithm based on a double threshold, candidate set reduction, and adaptive backtracking methods. The algorithm uses the double threshold variable step-size method to improve the accuracy of sparsity judgment and reduces the undetermined atomic candidate set in the small step stage to enhance the stability. At the same time, the sparsity estimation accuracy can be improved by combining with the backtracking method. We use a Gaussian sparse signal and a measured shock wave signal of the 15psi range sensor to verify the algorithm performance. The experimental results show that, compared with other iterative greedy algorithms, the overall stability of the DBCSAMP algorithm is the strongest. Compared with the SAMP algorithm, the estimated sparsity of the DBCSAMP algorithm is more accurate, and the reconstruction accuracy and operational efficiency of the DBCSAMP algorithm are greatly improved.

We demonstrate highly flexible and efficient organic light-emitting devices (OLEDs) using an ultra-smooth NAN anode. A template-stripping process was employed to create an ultra-smooth NAN anode on a photopolymer substrate. The flexible OLEDs obtained by this method maintained good electroluminescent properties and mechanical stability after bending. The efficiency of the flexible OLEDs was improved by 30.6% compared with conventional OLEDs deposited on PET/ITO substrate due to the enhanced hole injection from the ultra-smooth anode.