Structural and functional characterization of itaconyl-CoA hydratase and citramalyl-CoA lyase involved in itaconate metabolism of Pseudomonas aeruginosa.

Itaconate is a key anti-inflammatory/antibacterial metabolite in pathogen-macrophage interactions that induces adaptive changes in Pseudomonas aeruginosa-exposed airways. However, the impact and mechanisms underlying itaconate metabolism remain unclear. Our study reveals that itaconate significantly upregulates the expression of pyoverdine in P. aeruginosa and enhances its tolerance to tobramycin. Notably, the enzymes responsible for efficient itaconate metabolism, PaIch and PaCcl, play crucial roles in both utilizing itaconate and clearing its toxic metabolic intermediates. By using protein crystallography and molecular dynamics simulations analyses, we have elucidated the unique catalytic center and substrate-binding pocket of PaIch, which contribute to its highly efficient catalysis. Meanwhile, analysis of PaCcl has revealed how interactions between domains regulate the conformational changes of the active sites and binding pockets, influencing the catalytic process. Overall, our research uncovers the significance and mechanisms of PaIch and PaCcl in the efficient metabolism of itaconate by P. aeruginosa.

The value of multimodality MR in T staging evaluation after neoadjuvant therapy for rectal cancer.

BACKGROUND: Surgery is the preferred treatment for rectal cancer, but surgical treatment alone sometimes does not achieve satisfactory results. OBJECTIVE: To explore the value of multimodal Magnetic Resonance (MR) images in evaluating T staging of rectal cancer after neoadjuvant therapy and to compare and analyze with pathological results. METHODS: This study retrospectively analyzed 232 patients with stage T3, T4 rectal cancer between January 1, 2017 and October 31, 2022. MR examination was performed within 3 days before surgery. Different MR sequences were used for mrT staging of rectal cancer after neoadjuvant therapy and compared with pathological pT staging. The accuracy of different MR sequences in evaluating T staging of rectal cancer was calculated, and the consistency between the two was analyzed by kappa test. The sensitivity, specificity, negative predictive value and positive predictive value of different MR sequences in evaluating rectal cancer invading mesorectal fascia after neoadjuvant therapy were calculated. RESULTS: A total of 232 patients with rectal cancer were included in the study. The accuracy of high-resolution T2 WI in evaluating T staging of rectal cancer after neoadjuvant therapy was 49.57%, and the Kappa value was 0.261. The accuracy of high-resolution T2WI combined with diffusion weighted imaging (DWI) in evaluating T staging of rectal cancer after neoadjuvant therapy was 61.64%, and the Kappa value was 0.411. The accuracy of high-resolution combined with DCE-MR images in evaluating T staging of rectal cancer after neoadjuvant therapy was 80.60%, and the Kappa value was 0.706. The sensitivity and specificity of high-resolution t2-weighted imaging (HR-T2WI) combined with dynamic contrast-enhancement magnetic resonance (DCE-MR) in evaluating the invasion of mesorectal fascia were 83.46% and 95.33%, respectively. CONCLUSION: Compared with HR-T2WI combined with DWI images for mrT staging of rectal cancer after neoadjuvant chemoradiotherapy (N-CRT), HR-T2WI combined with DCE-M has the highest accuracy in evaluating mrT staging of rectal cancer after neoadjuvant therapy (80.60%), and has a high consistency with pathological pT staging. It is the best sequence for T staging of rectal cancer after neoadjuvant therapy. At the same time, the sequence has high sensitivity and specificity in evaluating mesorectal fascia invasion, which can provide accurate perioperative information for the formulation of surgical plan.

Rectified Wasserstein Generative Adversarial Networks for Perceptual Image Restoration.

Wasserstein generative adversarial network (WGAN) has attracted great attention due to its solid mathematical background, i.e., to minimize the Wasserstein distance between the generated distribution and the distribution of interest. In WGAN, the Wasserstein distance is quantitatively evaluated by the discriminator, also known as the critic. The vanilla WGAN trained the critic with the simple Lipschitz condition, which was later shown less effective for modeling complex distributions, like the distribution of natural images. We try to improve the WGAN training by introducing pairwise constraint on the critic, oriented to image restoration tasks. In principle, pairwise constraint is to suggest the critic assign a higher rating to the original (real) image than to the restored (generated) image, as long as such a pair of images are available. We show that such pairwise constraint may be implemented by rectifying the gradients in WGAN training, which leads to the proposed rectified Wasserstein generative adversarial network (ReWaGAN). In addition, we build interesting connections between ReWaGAN and the perception-distortion tradeoff. We verify ReWaGAN on two representative image restoration tasks: single image super-resolution (4× and 8×) and compression artifact reduction, where our ReWaGAN not only beats the vanilla WGAN consistently, but also outperforms the state-of-the-art perceptual quality-oriented methods significantly. Our code and models are publicly available at https://github.com/mahaichuan/ReWaGAN.

aiWave: Volumetric Image Compression With 3-D Trained Affine Wavelet-Like Transform.

Volumetric image compression has become an urgent task to effectively transmit and store images produced in biological research and clinical practice. At present, the most commonly used volumetric image compression methods are based on wavelet transform, such as JP3D. However, JP3D employs an ideal, separable, global, and fixed wavelet basis to convert input images from pixel domain to frequency domain, which seriously limits its performance. In this paper, we first design a 3-D trained wavelet-like transform to enable signal-dependent and non-separable transform. Then, an affine wavelet basis is introduced to capture the various local correlations in different regions of volumetric images. Furthermore, we embed the proposed wavelet-like transform to an end-to-end compression framework called aiWave to enable an adaptive compression scheme for various datasets. Last but not least, we introduce the weight sharing strategies of the affine wavelet-like transform according to the volumetric data characteristics in the axial direction to reduce the number of parameters. The experimental results show that: 1) when cooperating our trained 3-D affine wavelet-like transform with a simple factorized entropy coding module, aiWave performs better than JP3D and is comparable in terms of encoding and decoding complexities; 2) when adding a context module to remove signal redundancy further, aiWave can achieve a much better performance than HEVC.

End-to-End Optimized Versatile Image Compression With Wavelet-Like Transform.

Built on deep networks, end-to-end optimized image compression has made impressive progress in the past few years. Previous studies usually adopt a compressive auto-encoder, where the encoder part first converts image into latent features, and then quantizes the features before encoding them into bits. Both the conversion and the quantization incur information loss, resulting in a difficulty to optimally achieve arbitrary compression ratio. We propose iWave++ as a new end-to-end optimized image compression scheme, in which iWave, a trained wavelet-like transform, converts images into coefficients without any information loss. Then the coefficients are optionally quantized and encoded into bits. Different from the previous schemes, iWave++ is versatile: a single model supports both lossless and lossy compression, and also achieves arbitrary compression ratio by simply adjusting the quantization scale. iWave++ also features a carefully designed entropy coding engine to encode the coefficients progressively, and a de-quantization module for lossy compression. Experimental results show that lossy iWave++ achieves state-of-the-art compression efficiency compared with deep network-based methods; on the Kodak dataset, lossy iWave++ leads to 17.34 percent bits saving over BPG; lossless iWave++ achieves comparable or better performance than FLIF. Our code and models are available at https://github.com/mahaichuan/Versatile-Image-Compression.

The interaction of sodium dodecyl sulfate with trypsin: Multi-spectroscopic analysis, molecular docking, and molecular dynamics simulation.

The bioactivity of enzymes is sensitive to certain factors in their application environment, such as the pH, temperature, ionic strength, and additives, which can alter the native conformation of enzymes. To determine the mechanism by which the interaction of SDS influences the structure and activity of trypsin, molecular docking, molecular dynamics simulations, DSC, and multi-spectroscopic measures including UV absorption, fluorescence, and circular dichroism were used. The results show that the hydrolytic activity towards casein could be dramatically restrained by SDS. UV absorption, CD, and fluorescence spectra demonstrated the formation of a trypsin-SDS complex. Thermodynamic parameters and molecular docking data revealed that the binding process was spontaneous, and that the main binding forces between SDS and trypsin were hydrogen bonds and van der Waals forces. In addition, molecular docking predicted that the binding site of SDS on trypsin was located at the active center. Molecular dynamic simulations showed that treatment with SDS resulted in the structure of trypsin becoming unstable and unfolded near its active center. This work provides insights into the interaction of SDS with trypsin on the molecular level and is beneficial to understanding of how SDS affects the conformation and activity of trypsin in application processes.

Preparation of ZnO nanoparticle loaded amidoximated wool fibers as a promising antibiofouling adsorbent for uranium(vi) recovery.

In this study, nano-ZnO loaded amidoxime-functionalized wool fibers (wool-AO@ZnO) were synthesized by radiation-induced copolymerization and in situ co-precipitation as a novel adsorbent with good antibiofouling properties for uranium recovery. The prepared adsorbent was characterized by SEM-EDS and FT-IR spectroscopy. Batch adsorption experiments showed that the prepared wool-AO@ZnO have a good uranium adsorption property at pH 6.0-9.0. Meanwhile, wool-AO@ZnO displayed a good inhibition of aerobic bacteria such as S. aureus and E. coli; of anaerobic bacteria; of sulfate reducing bacteria; of fungus C. albicans and of mildew Aspergillus niger. After four cyclic cultivations with microorganisms, the inhibition rate of wool-AO@ZnO to E. coli and S. aureus still remains at 77.8% and 84.9%, respectively; and a good adsorption capacity for uranium(vi) was maintained. The prepared wool-AO@ZnO could be a promising antibiofouling adsorbent for the recovery of uranium(vi) from seawater.