Crystal structure and function analysis of 6-phosphogluconate dehydrogenase in Mycobacterium tuberculosis.

6-phosphogluconate dehydrogenase (6PGDH) is an essential enzyme in energy metabolism and redox reactions, and represents a potential drug target for the development of therapies targeting trypanosomes, plasmodium, or other pathogens. Tuberculosis, caused by Mycobacterium tuberculosis, is a contagious disease that severely affects human health, with approximately one-third of the world's population infected. However, the protein structure, exact oligomeric state, and catalytic mechanism of 6PGDH in Mycobacterium tuberculosis (Mt6PGDH) have remained largely unknown. In this study, we successfully purified and determined the structure of Mt6PGDH, revealing its function as a tetramer in both solution and crystal states. Through structural comparisons, we clarified the tetramer formation mechanism and the oligomeric organization of short-chain 6PGDHs. Additionally, we identified key residues for coenzyme recognition and catalytic activity. This work not only deepens our understanding of the enzymatic function of Mt6PGDH but also lays a foundation for the development of drugs targeting this enzyme.

LDCT image denoising algorithm based on two-dimensional variational mode decomposition and dictionary learning.

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.

Fast high quality computational ghost imaging based on saliency variable sampling detection.

Fast computational ghost imaging with high quality and ultra-high-definition resolution reconstructed images has important application potential in target tracking, biological imaging and other fields. However, as far as we know, the resolution (pixels) of the reconstructed image is related to the number of measurements. And the limited resolution of reconstructed images at low measurement times hinders the application of computational ghost imaging. Therefore, in this work, a new computational ghost imaging method based on saliency variable sampling detection is proposed to achieve high-quality imaging at low measurement times. This method physically variable samples the salient features and realizes compressed detection of computational ghost imaging based on the salient periodic features of the bucket detection signal. Numerical simulation and experimental results show that the reconstructed image quality of our method is similar to the compressed sensing method at low measurement times. Even at 500 (sampling rate 0.76 % ) measurement times, the reconstructed image of the method still has the target features. Moreover, the 2160 × 4096 (4K) pixels ultra-high-definition resolution reconstructed images can be obtained at only a sampling rate of 0.11 % . This method has great potential value in real-time detection and tracking, biological imaging and other fields.

Increasing the Light Extraction Efficiency of Organic Light-Emitting Devices by Electrochemically Corroded Patterned Substrates.

A substrate with microstructure can increase the light extraction efficiency of OLEDs. However, the present preparation methods for micro- and nanostructures are not suited for broad-area manufacturing. In this research, we suggested an electrochemical etching approach to patterning Si substrates and effectively generated a vast area of micro-/nanostructures on the surface of Si. We created OLEDs using this patterned substrate. It was discovered through this study that when the current density is 100 mA/cm2, the brightness increases by 1.67 times and the efficiency increases by 1.43 times, over a planar equivalent. In the future, this electrochemical etching process for patterned silicon substrates might give rise to a new approach to the large-scale manufacture of microstructured silicon substrates.