Stimulating the Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling

Image denoising is a fundamental problem in computational photography, where achieving high-quality perceptual performance with low distortion is highly demanding. Current methods either struggle with perceptual performance or suffer from significant distortion. Recently, the emerging diffusion model achieves state-of-the-art performance in various tasks, and its denoising mechanism demonstrates great potential for image denoising. However, stimulating diffusion models for image denoising is not straightforward and requires solving several critical problems. On the one hand, the input inconsistency hinders the connection of diffusion models and image denoising. On the other hand, the content inconsistency between the generated image and the desired denoised image introduces additional distortion. To tackle these problems, we present a novel strategy called Diffusion Model for Image Denoising (DMID) by understanding and rethinking the diffusion model from a denoising perspective. Our DMID strategy includes an adaptive embedding method that embeds the noisy image into a pre-trained diffusion model, and an adaptive ensembling method that reduces distortion in the denoised image. Our DMID strategy achieves state-of-the-art performance on all distortion-based and perceptual metrics, for both Gaussian and real-world image denoising.Comment: 10 pages,7 figure

Physics-guided Noise Neural Proxy for Low-light Raw Image Denoising

Low-light raw image denoising plays a crucial role in mobile photography, and learning-based methods have become the mainstream approach. Training the learning-based methods with synthetic data emerges as an efficient and practical alternative to paired real data. However, the quality of synthetic data is inherently limited by the low accuracy of the noise model, which decreases the performance of low-light raw image denoising. In this paper, we develop a novel framework for accurate noise modeling that learns a physics-guided noise neural proxy (PNNP) from dark frames. PNNP integrates three efficient techniques: physics-guided noise decoupling (PND), physics-guided proxy model (PPM), and differentiable distribution-oriented loss (DDL). The PND decouples the dark frame into different components and handles different levels of noise in a flexible manner, which reduces the complexity of the noise neural proxy. The PPM incorporates physical priors to effectively constrain the generated noise, which promotes the accuracy of the noise neural proxy. The DDL provides explicit and reliable supervision for noise modeling, which promotes the precision of the noise neural proxy. Extensive experiments on public low-light raw image denoising datasets and real low-light imaging scenarios demonstrate the superior performance of our PNNP framework

Stage-specific transcriptomes of the Mussel Mytilus coruscus reveals the developmental Program for the Planktonic to Benthic Transition

Many marine invertebrate larvae undergo complex morphological and physiological changes during the planktonic—benthic transition (a.k.a. metamorphosis). In this study, transcriptome analysis of different developmental stages was used to uncover the molecular mechanisms underpinning larval settlement and metamorphosis of the mussel, Mytilus coruscus. Analysis of highly upregulated differentially expressed genes (DEGs) at the pediveliger stage revealed enrichment of immune-related genes. The results may indicate that larvae co-opt molecules of the immune system to sense and respond to external chemical cues and neuroendocrine signaling pathways forecast and trigger the response. The upregulation of adhesive protein genes linked to byssal thread secretion indicates the anchoring capacity required for larval settlement arises prior to metamorphosis. The results of gene expression support a role for the immune and neuroendocrine systems in mussel metamorphosis and provide the basis for future studies to disentangle gene networks and the biology of this important lifecycle transformation.info:eu-repo/semantics/publishedVersio

Common reduction of the Raf kinase inhibitory protein in clear cell renal cell carcinoma

Despite the recent progress in our understanding of clear cell renal cell carcinomas (ccRCCs), the etiology of ccRCC remains unclear. We reported here a prevailing reduction of the raf kinase inhibitory protein (RKIP) in ccRCC. In our examination of more than 600 ccRCC patients by western blot and immunohistochemistry, RKIP was significantly reduced in 80% of tumors. Inhibition of RKIP transcription in ccRCC occurs to greater levels than VHL transcription based on the quantification analysis of their transcripts in six large datasets of DNA microarray available in Oncomine™ with the median rank of suppression being 582 and 2343 for RKIP and VHL, respectively. Collectively, the magnitude of RKIP reduction and the levels of its downregulation match those of VHL. Furthermore, RKIP displays tumor suppressing activity in ccRCC. While modulation of RKIP expression did not affect the proliferation of A498 and 786-0 ccRCC cells and neither their ability to form xenograft tumors in NOD/SCID mice, ectopic expression or knockdown of RKIP inhibited or enhanced A498 and 786-0 ccRCC cell invasion, respectively. This was associated with robust changes in vimentin expression, a marker of EMT. Taken together, we demonstrate here that downregulation of RKIP occurs frequently at a rate that reaches that of VHL, suggesting RKIP being a critical tumor suppressor for ccRCC. This is consistent with RKIP being a tumor suppressor for other cancers

Polymorphisms of melatonin receptor genes and their associations with egg production traits in Shaoxing duck

Objective In birds, three types of melatonin receptors (MTNR1A, MTNR1B, and MTNR1C) have been cloned. Previous researches have showed that three melatonin receptors played an essential role in reproduction and ovarian physiology. However, the association of polymorphisms of the three receptors with duck reproduction traits and egg quality traits is still unknown. In this test, we chose MTNR1A, MTNR1B, and MTNR1C as candidate genes to detect novel sequence polymorphism and analyze their association with egg production traits in Shaoxing duck, and detected their mRNA expression level in ovaries. Methods In this study, a total of 785 duck blood samples were collected to investigate the association of melatonin receptor genes with egg production traits and egg quality traits using a direct sequencing method. And 6 ducks representing two groups (3 of each) according to the age at first eggs (at 128 days of age or after 150 days of age) were carefully selected for quantitative real-time polymerase chain reaction. Results Seven novel polymorphisms (MTNR1A: g. 268C>T, MTNR1B: g. 41C>T, and g. 161T>C, MTNR1C: g. 10C>T, g. 24A>G, g. 108C>T, g. 363 T>C) were detected. The single nucleotide polymorphism (SNP) of MTNR1A (g. 268C>T) was significantly linked with the age at first egg (pT and egg production traits: total egg numbers at 34 weeks old of age and age at first egg. In addition, the mRNA expression level of MTNR1A in ovary was significantly higher in late-mature group than in early-mature group, while MTNR1C showed a contrary tendency (p<0.05). Conclusion These results suggest that identified SNPs in MTNR1A and MTNR1C may influence the age at first egg and could be considered as the candidate molecular marker for identify early maturely traits in duck selection and improvement

A Nationwide Study of Maternal Exposure To Ambient Ozone and Term Birth Weight In the United States

Background: Maternal exposure to ozone (O3) may cause systemic inflammation and oxidative stress and contribute to fetal growth restriction. We sought to estimate the association between maternal exposure to O3 and term birth weight and term small for gestational age (SGA) in the United States (US). Methods: We conducted a nationwide study including 2,179,040 live term singleton births that occurred across 453 populous counties in the contiguous US in 2002. Daily county-level concentrations of O3 data were estimated using a Bayesian fusion model. We used linear regression to estimate the association between O3 exposure and term birth weight and logistic regression to estimate the association between O3 exposure and term SGA during each trimester of the pregnancy and the entire pregnancy after adjusting for maternal characteristics, infant sex, season of conception, ambient temperature, county poverty rate, and census region. We additionally used distributed lag models to identify the critical exposure windows by estimating the monthly and weekly associations. Results: A 10 parts per billion (ppb) increase in O3 over the entire pregnancy was associated with a lower term birth weight (-7.6 g; 95 % CI: −8.8 g, −6.4 g) and increased risk of SGA (odds ratio = 1.030; 95 % CI: 1.020, 1.040). The identified critical exposure windows were the 13th- 25th and 32nd −37th gestational weeks for term birth weight and 13th- 25th for term SGA. We found the association was more pronounced among mothers who were non-Hispanic Black, unmarried, or had lower education level. Conclusions: Among US singleton term births, maternal exposure to O3 was associated with lower rates of fetal growth, and the 13th- 25th gestational weeks were the identified critical exposure windows

Tailoring magnetic behavior of CoFeMnNiX (X = Al, Cr, Ga, and Sn) high entropy alloys by metal doping

Magnetic materials with excellent performances are desired for functional applications. Based on the high-entropy effect, a system of CoFeMnNiX (X = Al, Cr, Ga, and Sn) magnetic alloys are designed and investigated. The dramatic change in phase structures from face-centered-cubic (FCC) to ordered body-centered-cubic (BCC) phases, caused by adding Al, Ga, and Sn in CoFeMnNiX alloys, originates from the potent short-range chemical order in the liquid state predicted by ab initio molecular dynamics (AIMD) simulations. This phase transition leads to the significant enhancement of the saturation magnetization (Ms), e.g., the CoFeMnNiAl alloy has Ms of 147.86 Am2/kg. First-principles density functional theory (DFT) calculations on the electronic and magnetic structures reveal that the anti-ferromagnetism of Mn atoms in CoFeMnNi is suppressed especially in the CoFeMnNiAl HEA because Al changes the Fermi level and itinerant electron-spin coupling that lead to ferromagnetism