Deep Learning-Based Dynamic Region of Interest Autofocus Method for Grayscale Image.

In the field of autofocus for optical systems, although passive focusing methods are widely used due to their cost-effectiveness, fixed focusing windows and evaluation functions in certain scenarios can still lead to focusing failures. Additionally, the lack of datasets limits the extensive research of deep learning methods. In this work, we propose a neural network autofocus method with the capability of dynamically selecting the region of interest (ROI). Our main work is as follows: first, we construct a dataset for automatic focusing of grayscale images; second, we transform the autofocus issue into an ordinal regression problem and propose two focusing strategies: full-stack search and single-frame prediction; and third, we construct a MobileViT network with a linear self-attention mechanism to achieve automatic focusing on dynamic regions of interest. The effectiveness of the proposed focusing method is verified through experiments, and the results show that the focusing MAE of the full-stack search can be as low as 0.094, with a focusing time of 27.8 ms, and the focusing MAE of the single-frame prediction can be as low as 0.142, with a focusing time of 27.5 ms.

Sirt1 Deficiency Promotes Age-Related AF Through Enhancing Atrial Necroptosis by Activation of RIPK1 Acetylation.

BACKGROUND: Aging is one of the most potent risk determinants for the onset of atrial fibrillation (AF). Sirts (sirtuins) have been implicated in the pathogenesis of cardiovascular disease, and their expression declines with aging. However, whether Sirts involved in age-related AF and its underlying mechanisms remain unknown. The present study aims to explore the role of Sirts in age-related AF and delineate the underlying molecular mechanisms. METHODS: Sirt1 levels in the atria of both elderly individuals and aging rats were evaluated using quantitative real-time polymerase chain reaction and Western blot analysis. Mice were engineered to specifically knockout Sirt1 in the atria and right ventricle (Sirt1mef2c/mef2c). Various techniques, such as echocardiography, atrial electrophysiology, and protein acetylation modification omics were employed. Additionally, coimmunoprecipitation was utilized to substantiate the interaction between Sirt1 and RIPK1 (receptor-interacting protein kinase 1). RESULTS: We discerned that among the diverse subtypes of sirtuin proteins, only Sirt1 expression was significantly diminished in the atria of elderly people and aged rats. The Sirt1mef2c/mef2c mice exhibited an enlarged atrial diameter and heightened vulnerability to AF. Acetylated proteomics and cell experiments identified that Sirt1 deficiency activated atrial necroptosis through increasing RIPK1 acetylation and subsequent pseudokinase MLKL (mixed lineage kinase domain-like protein) phosphorylation. Consistently, necroptotic inhibitor necrosulfonamide mitigated atrial necroptosis and diminished both the atrial diameter and AF susceptibility of Sirt1mef2c/mef2c mice. Resveratrol prevented age-related AF in rats by activating atrial Sirt1 and inhibiting necroptosis. CONCLUSIONS: Our findings first demonstrated that Sirt1 exerts significant efficacy in countering age-related AF by impeding atrial necroptosis through regulation of RIPK1 acetylation, highlighting that the activation of Sirt1 or the inhibition of necroptosis could potentially serve as a therapeutic strategy for age-related AF.

Robust Principal Component Analysis Based on Fuzzy Local Information Reservation.

Principal Component Analysis (PCA) aims to acquire the principal component space containing the essential structure of data, instead of being used for mining and extracting the essential structure of data. In other words, the principal component space contains not only information related to the essential structure of data but also some unrelated information. This frequently occurs when the intrinsic dimensionality of data is unknown or when it has complex distribution characteristics such as multi-modalities, manifolds, etc. Therefore, it is unreasonable to identify noise and useful information based solely on reconstruction error. For this reason, PCA is unsuitable as a preprocessing technique for most applications, especially in noisy environment. To solve this problem, this paper proposes robust PCA based on fuzzy local information reservation (FLIPCA). By analyzing the impact of reconstruction error on sample discriminability, FLIPCA provides a theoretical basis for noise identification and processing. This not only greatly improves its robustness but also extends its applicability and effectiveness as a data preprocessing technique. Meanwhile, FLIPCA maintains consistent mathematical descriptions with traditional PCA while having few adjustable hyperparameters and low algorithmic complexity. Finally, we conducted comprehensive experiments on synthetic and real-world datasets, which substantiated the superiority of our proposed algorithm.

Infant age inversely correlates with gut carriage of resistance genes, reflecting modifications in microbial carbohydrate metabolism during early life.

The infant gut microbiome is increasingly recognized as a reservoir of antibiotic resistance genes, yet the assembly of gut resistome in infants and its influencing factors remain largely unknown. We characterized resistome in 4132 metagenomes from 963 infants in six countries and 4285 resistance genes were observed. The inherent resistome pattern of healthy infants (N = 272) could be distinguished by two stages: a multicompound resistance phase (Months 0-7) and a tetracycline-mupirocin-ß-lactam-dominant phase (Months 8-14). Microbial taxonomy explained 40.7% of the gut resistome of healthy infants, with Escherichia (25.5%) harboring the most resistance genes. In a further analysis with all available infants (N = 963), we found age was the strongest influencer on the resistome and was negatively correlated with the overall resistance during the first 3 years (p < 0.001). Using a random-forest approach, a set of 34 resistance genes could be used to predict age (R 2 = 68.0%). Leveraging microbial host inference analyses, we inferred the age-dependent assembly of infant resistome was a result of shifts in the gut microbiome, primarily driven by changes in taxa that disproportionately harbor resistance genes across taxa (e.g., Escherichia coli more frequently harbored resistance genes than other taxa). We performed metagenomic functional profiling and metagenomic assembled genome analyses whose results indicate that the development of gut resistome was driven by changes in microbial carbohydrate metabolism, with an increasing need for carbohydrate-active enzymes from Bacteroidota and a decreasing need for Pseudomonadota during infancy. Importantly, we observed increased acquired resistance genes over time, which was related to increased horizontal gene transfer in the developing infant gut microbiome. In summary, infant age was negatively correlated with antimicrobial resistance gene levels, reflecting a composition shift in the gut microbiome, likely driven by the changing need for microbial carbohydrate metabolism during early life.

MMCA-NET: A Multimodal Cross Attention Transformer Network for Nasopharyngeal Carcinoma Tumor Segmentation Based on a Total-Body PET/CT System.

Nasopharyngeal carcinoma (NPC) is a malignant tumor primarily treated by radiotherapy. Accurate delineation of the target tumor is essential for improving the effectiveness of radiotherapy. However, the segmentation performance of current models is unsatisfactory due to poor boundaries, large-scale tumor volume variation, and the labor-intensive nature of manual delineation for radiotherapy. In this paper, MMCA-Net, a novel segmentation network for NPC using PET/CT images that incorporates an innovative multimodal cross attention transformer (MCA-Transformer) and a modified U-Net architecture, is introduced to enhance modal fusion by leveraging cross-attention mechanisms between CT and PET data. Our method, tested against ten algorithms via fivefold cross-validation on samples from Sun Yat-sen University Cancer Center and the public HECKTOR dataset, consistently topped all four evaluation metrics with average Dice similarity coefficients of 0.815 and 0.7944, respectively. Furthermore, ablation experiments were conducted to demonstrate the superiority of our method over multiple baseline and variant techniques. The proposed method has promising potential for application in other tasks.

Fructo-oligosaccharide supplementation enhances the growth of nursing dairy calves while stimulating the persistence of Bifidobacterium and hindgut microbiome's maturation.

The colonization and development of the gut microbiome in dairy calves play a crucial role in their overall health and future productivity. Despite the widely proposed benefits of inulin-related products on the host, there is insufficient information about how supplementing fructo-oligosaccharides (FOS) affects the colonization and development of the gut microbiome in calves. In a randomized intervention trial involving newborn male Holstein dairy calves, we investigated the effect of FOS on the calf hindgut microbiome, short-chain fatty acids (SCFA), growth performance, and the incidence of diarrhea. The daily administration of FOS exhibited a time-dependent increase in the ADG and the concentration of SCFA. Concurrently, FOS delayed the natural decline of Bifidobacterium, promoting the maturation and stabilization of the hindgut microbiome. These findings not only contribute to a theoretical understanding of the judicious application of prebiotics but also hold significant practical implications for the design of early life dietary interventions in the rearing of dairy calves.