Unpaired MRI Super Resolution with Contrastive Learning

Magnetic resonance imaging (MRI) is crucial for enhancing diagnostic accuracy in clinical settings. However, the inherent long scan time of MRI restricts its widespread applicability. Deep learning-based image super-resolution (SR) methods exhibit promise in improving MRI resolution without additional cost. Due to lacking of aligned high-resolution (HR) and low-resolution (LR) MRI image pairs, unsupervised approaches are widely adopted for SR reconstruction with unpaired MRI images. However, these methods still require a substantial number of HR MRI images for training, which can be difficult to acquire. To this end, we propose an unpaired MRI SR approach that employs contrastive learning to enhance SR performance with limited HR training data. Empirical results presented in this study underscore significant enhancements in the peak signal-to-noise ratio and structural similarity index, even when a paucity of HR images is available. These findings accentuate the potential of our approach in addressing the challenge of limited HR training data, thereby contributing to the advancement of MRI in clinical applications

Computational Emotion Analysis From Images: Recent Advances and Future Directions

Emotions are usually evoked in humans by images. Recently, extensive research efforts have been dedicated to understanding the emotions of images. In this chapter, we aim to introduce image emotion analysis (IEA) from a computational perspective with the focus on summarizing recent advances and suggesting future directions. We begin with commonly used emotion representation models from psychology. We then define the key computational problems that the researchers have been trying to solve and provide supervised frameworks that are generally used for different IEA tasks. After the introduction of major challenges in IEA, we present some representative methods on emotion feature extraction, supervised classifier learning, and domain adaptation. Furthermore, we introduce available datasets for evaluation and summarize some main results. Finally, we discuss some open questions and future directions that researchers can pursue.Comment: Accepted chapter in the book "Human Perception of Visual Information Psychological and Computational Perspective

The Role of Aging in Intracerebral Hemorrhage

Intracerebral hemorrhage (ICH) is the cerebrovascular disease with the highest disability and mortality rates, causing severe damage to the health of patients and imposing a significant socioeconomic burden. Aging stands as a foremost risk factor for ICH, with a significant escalation in ICH incidence within the elderly demographic, highlighting a close association between ICH and aging. In recent years, with the acceleration of the “aging society” trend, exploring the intricate relationship between aging and ICH has become increasingly urgent and worthy of in-depth attention. We have summarized the characteristics of ICH in the elderly, reviewing how aging influences the onset and development of ICH by examining its etiology and the mechanisms of damage via ICH. Additionally, we explored the potential impacts of ICH on accelerated aging, including its effects on cognitive abilities, quality of life, and lifespan. This review aims to reveal the connection between aging and ICH, providing new ideas and insights for future ICH research

SARS-CoV-2 SUD2 and Nsp5 Conspire to Boost Apoptosis of Respiratory Epithelial Cells via an Augmented Interaction with the G-Quadruplex of BclII

ABSTRACT The molecular mechanisms underlying how SUD2 recruits other proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to exert its G-quadruplex (G4)-dependent pathogenic function is unknown. Herein, Nsp5 was singled out as a binding partner of the SUD2-N+M domains (SUD2core) with high affinity, through the surface located crossing these two domains. Biochemical and fluorescent assays demonstrated that this complex also formed in the nucleus of living host cells. Moreover, the SUD2core-Nsp5 complex displayed significantly enhanced selective binding affinity for the G4 structure in the BclII promoter than did SUD2core alone. This increased stability exhibited by the tertiary complex was rationalized by AlphaFold2 and molecular dynamics analysis. In line with these molecular interactions, downregulation of BclII and subsequent augmented apoptosis of respiratory cells were both observed. These results provide novel information and a new avenue to explore therapeutic strategies targeting SARS-CoV-2. IMPORTANCE SUD2, a unique protein domain closely related to the pathogenesis of SARS-CoV-2, has been reported to bind with the G-quadruplex (G4), a special noncanonical DNA structure endowed with important functions in regulating gene expression. However, the interacting partner of SUD2, among other SARS-CoV-2 Nsps, and the resulting functional consequences remain unknown. Here, a stable complex formed between SUD2 and Nsp5 was fully characterized both in vitro and in host cells. Moreover, this complex had a significantly enhanced binding affinity specifically targeting the Bcl2G4 in the promoter region of the antiapoptotic gene BclII, compared with SUD2 alone. In respiratory epithelial cells, the SUD2-Nsp5 complex promoted BclII-mediated apoptosis in a G4-dependent manner. These results reveal fresh information about matched multicomponent interactions, which can be parlayed to develop new therapeutics for future relevant viral disease

ArtBank: Artistic Style Transfer with Pre-trained Diffusion Model and Implicit Style Prompt Bank

Artistic style transfer aims to repaint the content image with the learned artistic style. Existing artistic style transfer methods can be divided into two categories: small model-based approaches and pre-trained large-scale model-based approaches. Small model-based approaches can preserve the content strucuture, but fail to produce highly realistic stylized images and introduce artifacts and disharmonious patterns; Pre-trained large-scale model-based approaches can generate highly realistic stylized images but struggle with preserving the content structure. To address the above issues, we propose ArtBank, a novel artistic style transfer framework, to generate highly realistic stylized images while preserving the content structure of the content images. Specifically, to sufficiently dig out the knowledge embedded in pre-trained large-scale models, an Implicit Style Prompt Bank (ISPB), a set of trainable parameter matrices, is designed to learn and store knowledge from the collection of artworks and behave as a visual prompt to guide pre-trained large-scale models to generate highly realistic stylized images while preserving content structure. Besides, to accelerate training the above ISPB, we propose a novel Spatial-Statistical-based self-Attention Module (SSAM). The qualitative and quantitative experiments demonstrate the superiority of our proposed method over state-of-the-art artistic style transfer methods. Code is available at https://github.com/Jamie-Cheung/ArtBank

Transcriptome Analysis Suggests That Chromosome Introgression Fragments from Sea Island Cotton (Gossypium barbadense) Increase Fiber Strength in Upland Cotton (Gossypium hirsutum)

As high-strength cotton fibers are critical components of high quality cotton, developing cotton cultivars with high-strength fibers as well as high yield is a top priority for cotton development. Recently, chromosome segment substitution lines (CSSLs) have been developed from high-yield Upland cotton (Gossypium hirsutum) crossed with high-quality Sea Island cotton (G. barbadense). Here, we constructed a CSSL population by crossing CCRI45, a high-yield Upland cotton cultivar, with Hai1, a Sea Island cotton cultivar with superior fiber quality. We then selected two CSSLs with significantly higher fiber strength than CCRI45 (MBI7747 and MBI7561), and one CSSL with lower fiber strength than CCRI45 (MBI7285), for further analysis. We sequenced all four transcriptomes at four different time points postanthesis, and clustered the 44,678 identified genes by function. We identified 2200 common differentially-expressed genes (DEGs): those that were found in both high quality CSSLs (MBI7747 and MBI7561), but not in the low quality CSSL (MBI7285). Many of these genes were associated with various metabolic pathways that affect fiber strength. Upregulated DEGs were associated with polysaccharide metabolic regulation, single-organism localization, cell wall organization, and biogenesis, while the downregulated DEGs were associated with microtubule regulation, the cellular response to stress, and the cell cycle. Further analyses indicated that three genes, XLOC_036333 [mannosyl-oligosaccharide-α-mannosidase (MNS1)], XLOC_029945 (FLA8), and XLOC_075372 (snakin-1), were potentially important for the regulation of cotton fiber strength. Our results suggest that these genes may be good candidates for future investigation of the molecular mechanisms of fiber strength formation and for the improvement of cotton fiber quality through molecular breeding

Hate crimes

The number of the candidate genes in each function categories of the KOG annotation. (XLSX 11 kb