Various strategies for developing APOBEC3G protectors to circumvent human immunodeficiency virus type 1.

Host restriction factor APOBEC3G (A3G) efficiently restricts Vif-deficient HIV-1 by being packaged with progeny virions and causing the G to A mutation during HIV-1 viral DNA synthesis as the progeny virus infects new cells. HIV-1 expresses Vif protein to resist the activity of A3G by mediating A3G degradation. This process requires the self-association of Vif in concert with A3G proteins, protein chaperones, and factors of the ubiquitination machinery, which are potential targets to discover novel anti-HIV drugs. This review will describe compounds that have been reported so far to inhibit viral replication of HIV-1 by protecting A3G from Vif-mediated degradation.

Attention-Driven Graph Neural Network for Deep Face Super-Resolution.

With the help of convolutional neural networks (CNNs), deep learning-based methods have achieved remarkable performance in face super-resolution (FSR) task. Despite their success, most of the existing methods neglect non-local correlations of face images, leaving much room for improvement. In this paper, we introduce a novel end-to-end trainable attention-driven graph neural network (AD-GNN) for more discriminative feature extraction and feature relation modeling. This is achieved by two major components. The first component is a cross-scale dynamic graph (CDG) block. The CDG block considers cross-scale relationships of patches in distant areas and employs two dynamic graphs to construct enhanced features. The second component is a series of channel attention and spatial dynamic graph (CASDG) blocks. A CASDG block has a channel-wise attention unit and a spatial-aware dynamic graph (SDG) unit. The SDG unit extracts informative features by exploring spatial non-local self-similarity information of the patches using dynamic graph convolution. Using these two components, facial details can be effectively reconstructed with the help of information supplemented by similar but spatially remote patches and structural information of faces. Extensive experiments on two public benchmarks demonstrate the superiority of AD-GNN over the state-of-the-art FSR methods.

Effect of calcium oxide on chromium solidification during the melting of hazardous waste incineration fly ash.

Thermal treatment technology considerably affects the harmlessness of fly ash (FA), but highly toxic heavy metals, such as Cr, attract considerable attention. In this study, we investigated the influence of CaO dosage at 600°C-1200 °C on the curing effect of Cr during FA thermal treatment based on the combination effect of CaO. Static, dynamic, and continuous sequential leachings were performed for the sintered products. Results showed that the leaching concentration of Cr decreased by approximately 91% when CaO dosage was 8.57%, and the difference in the residual state was the main reason for the difference in the leaching behavior of Cr. The proportion of the residual state in the sintered products increased from 35.16% to 64.01%. The transition between Cr2O3, Cr5O12, and CaCr2O4 is the fundamental reason for the leaching behavior of Cr and the change in the residual state. This study provides a scientific basis for preventing and controlling heavy metal pollution and optimizing environmental supervision in the FA thermal treatment process.

RFormer: Transformer-Based Generative Adversarial Network for Real Fundus Image Restoration on a New Clinical Benchmark.

Ophthalmologists have used fundus images to screen and diagnose eye diseases. However, different equipments and ophthalmologists pose large variations to the quality of fundus images. Low-quality (LQ) degraded fundus images easily lead to uncertainty in clinical screening and generally increase the risk of misdiagnosis. Thus, real fundus image restoration is worth studying. Unfortunately, real clinical benchmark has not been explored for this task so far. In this paper, we investigate the real clinical fundus image restoration problem. Firstly, We establish a clinical dataset, Real Fundus (RF), including 120 low- and high-quality (HQ) image pairs. Then we propose a novel Transformer-based Generative Adversarial Network (RFormer) to restore the real degradation of clinical fundus images. The key component in our network is the Window-based Self-Attention Block (WSAB) which captures non-local self-similarity and long-range dependencies. To produce more visually pleasant results, a Transformer-based discriminator is introduced. Extensive experiments on our clinical benchmark show that the proposed RFormer significantly outperforms the state-of-the-art (SOTA) methods. In addition, experiments of downstream tasks such as vessel segmentation and optic disc/cup detection demonstrate that our proposed RFormer benefits clinical fundus image analysis and applications.

Development of novel androgen receptor antagonists based on the structure of darolutamide.

Androgen signaling pathway plays an important role in the occurrence and development of prostate cancer (PCa), and anti-androgen drugs are one of the most effective therapies for PCa. Darolutamide 4 (ODM-201) is a promising second- generation antiandrogen because of its unique chemical structure and good activity against androgen receptor (AR). Herein, the structure-activity relationship of ODM-201 was studied, and 37 analogues were synthesized. Half of them exhibited similar or better anti-AR transcriptional activity compared to ODM-201. In addition, the inhibitory activity of compound 28t against the two resistant mutants (AR-F876L and AR-T877A) was superior to that of ODM-201. This study provides a new clue for the further optimization of ODM-201 and the development of anti-CRPC drugs.

MDAN: Mirror Difference Aware Network for Brain Stroke Lesion Segmentation.

Brain stroke lesion segmentation is of great importance for stroke rehabilitation neuroimaging analysis. Due to the large variance of stroke lesion shapes and similarities of tissue intensity distribution, it remains a challenging task. To help detect abnormalities, the anatomical symmetries of brain magnetic resonance (MR) images have been widely used as visual cues for clinical practices. However, most methods for brain images segmentation do not fully utilize structural symmetry information. This paper presents a novel mirror difference aware network (MDAN) for stroke lesion segmentation. The network uses an encoder-decoder architecture, aiming at holistically exploiting the symmetries of image features. Specifically, a differential feature augmentation (DFA) module is developed in the encoding path to highlight the semantically pathological asymmetries of features in abnormalities. In the DFA module, a Siamese contrastive supervised loss is designed to enhance discriminative features, and a mirror position-based difference augmentation (MDA) module is used to further magnify the discrepancy. Moreover, mirror feature fusion (MFF) modules are applied to efficiently fuse and transfer the information both of the original input and the horizontally flipped features to the decoding path. Extensive experiments on the Anatomical Tracings of Lesions After Stroke (ATLAS) dataset show the proposed MDAN outperforms the state-of-the-art methods.

Preparation and characterization of highly lipophilic modified potato starch by ultrasound and freeze-thaw treatments.

In order to explore the potential application of combined physical treatment in producing highly lipophilic modified starch, the effects of ultrasound combined with freeze-thaw treatment on the microstructure and physicochemical properties of potato starch were investigated. The samples treated by combined treatment had the roughest structure and the oil adsorptive capacity value increased from 59.62% (native starch, NS) to 80.2% (7 cycles of ultrasound-freeze-thaw treatment starch, 7UT-FTS). Compared to NS, the crystalline type and chemical groups of modified starches did not change, but the relative crystallinity, enthalpy change, and paste viscosity decreased to varying degrees, while the gelatinization temperature increased. The digestibility of raw modified starch was higher than that of NS, but this phenomenon disappeared after gelatinization. 7UT-FTS showed better resist-digestibility than NS after encapsulating oil. Hence, this would be an efficient and environmentally friendly way to produce modified starch with safety, highly lipophilic and heat resistance.