On the spheroidized carbide dissolution and elemental partitioning in a high carbon bearing steel 100Cr6

We report on the characterization of high carbon bearing steel 100Cr6 using electron microscopy and atom probe tomography in combination with multi-component diffusion simulations (DICTRA). Scanning electron micrographs show that around 14 vol.% spheroidized carbides are formed during soft annealing and only 3 vol.% remain after dissolution into the austenitic matrix by austenitization at 1123 K (850 {\deg}C) for 300 s. The spheroidized particles are identified as (Fe, Cr)3C by transmission electron microscopy. Atom probe analyses reveal the redistribution and partitioning behaviors of elements, i.e. C, Si, Mn, Cr, Fe in both, the spheroidized carbides and the bainitic matrix in the sample isothermally heat-treated at 773 K (500 {\deg}C) after austenitization. A homogeneous distribution of C and gradual gradient of Cr was detected within the spheroidized carbides. Due to its limited diffusivity in (Fe, Cr)3C, Cr exhibits a maximum concentration at the surface of spheroidized carbides (16 at.%) and decreases gradually from surface towards the core down to a level of about 2 at.%. The atom probe results also indicate that the partially dissolved spheroidized carbides during austenitization may serve as nucleation sites for intermediate temperature cementite within bainite, which results in a relatively softer surface and harder core in spheroidized particles. This microstructure may contribute to the good wear resistance and fatigue propertie

Overexpression of SoCYP85A1 Increases the Accumulation of Castasterone and Confers Enhanced Black Shank Tolerance in Tobacco Through Modulation of the Antioxidant Enzymes’ Activities

Black shank caused by Phytophthora nicotianae is one of the most devastating diseases in tobacco production. In this study, we characterized a novel cytochromic resistance gene, SoCYP85A1, from spinach, which was upregulated in response to P. nicotianae infection. Overexpression of SoCYP85A1 in tobacco resulted in remarkable resistance to pathogen inoculation, with diverse resistance levels in different transgenic lines. Meanwhile, a significant accumulation of castasterone (CS) was detected in transgenic plants when challenged with the pathogen. Moreover, activities of antioxidant enzymes were enhanced by SoCYP85A1 in the transgenic lines as compared to those in the wild types inoculated with P. nicotianae. In addition, the alteration of CS content resulted in interference of phytohormone homeostasis. Overall, these results demonstrate that SoCYP85A1 can participate in the defense response to P. nicotianae through the involvement of defense enzymes and by interaction with certain phytohormones. Our findings suggest that SoCYP85A1 could be used as a potential candidate gene for improving resistance to black shank disease in tobacco and other economic crops

A Unified GAN Framework Regarding Manifold Alignment for Remote Sensing Images Generation

Generative Adversarial Networks (GANs) and their variants have achieved remarkable success on natural images. However, their performance degrades when applied to remote sensing (RS) images, and the discriminator often suffers from the overfitting problem. In this paper, we examine the differences between natural and RS images and find that the intrinsic dimensions of RS images are much lower than those of natural images. As the discriminator is more susceptible to overfitting on data with lower intrinsic dimension, it focuses excessively on local characteristics of RS training data and disregards the overall structure of the distribution, leading to a faulty generation model. In respond, we propose a novel approach that leverages the real data manifold to constrain the discriminator and enhance the model performance. Specifically, we introduce a learnable information-theoretic measure to capture the real data manifold. Building upon this measure, we propose manifold alignment regularization, which mitigates the discriminator's overfitting and improves the quality of generated samples. Moreover, we establish a unified GAN framework for manifold alignment, applicable to both supervised and unsupervised RS image generation tasks

Unbiased Image Synthesis via Manifold-Driven Sampling in Diffusion Models

Diffusion models are a potent class of generative models capable of producing high-quality images. However, they can face challenges related to data bias, favoring specific modes of data, especially when the training data does not accurately represent the true data distribution and exhibits skewed or imbalanced patterns. For instance, the CelebA dataset contains more female images than male images, leading to biased generation results and impacting downstream applications. To address this issue, we propose a novel method that leverages manifold guidance to mitigate data bias in diffusion models. Our key idea is to estimate the manifold of the training data using an unsupervised approach, and then use it to guide the sampling process of diffusion models. This encourages the generated images to be uniformly distributed on the data manifold without altering the model architecture or necessitating labels or retraining. Theoretical analysis and empirical evidence demonstrate the effectiveness of our method in improving the quality and unbiasedness of image generation compared to standard diffusion models

Learning to Sample Tasks for Meta Learning

Through experiments on various meta-learning methods, task samplers, and few-shot learning tasks, this paper arrives at three conclusions. Firstly, there are no universal task sampling strategies to guarantee the performance of meta-learning models. Secondly, task diversity can cause the models to either underfit or overfit during training. Lastly, the generalization performance of the models are influenced by task divergence, task entropy, and task difficulty. In response to these findings, we propose a novel task sampler called Adaptive Sampler (ASr). ASr is a plug-and-play task sampler that takes task divergence, task entropy, and task difficulty to sample tasks. To optimize ASr, we rethink and propose a simple and general meta-learning algorithm. Finally, a large number of empirical experiments demonstrate the effectiveness of the proposed ASr.Comment: 10 pages, 7 tables, 3 figure

Present and Future: Crosstalks Between Polycystic Ovary Syndrome and Gut Metabolites Relating to Gut Microbiota

Polycystic ovary syndrome (PCOS) is a common disease, affecting 8%–13% of the females of reproductive age, thereby compromising their fertility and long-term health. However, the pathogenesis of PCOS is still unclear. It is not only a reproductive endocrine disease, dominated by hyperandrogenemia, but also is accompanied by different degrees of metabolic abnormalities and insulin resistance. With a deeper understanding of its pathogenesis, more small metabolic molecules, such as bile acids, amino acids, and short-chain fatty acids, have been reported to be involved in the pathological process of PCOS. Recently, the critical role of gut microbiota in metabolism has been focused on. The gut microbiota-related metabolic pathways can significantly affect inflammation levels, insulin signaling, glucose metabolism, lipid metabolism, and hormonal secretions. Although the abnormalities in gut microbiota and metabolites might not be the initial factors of PCOS, they may have a significant role in the pathological process of PCOS. The dysbiosis of gut microbiota and disturbance of gut metabolites can affect the progression of PCOS. Meanwhile, PCOS itself can adversely affect the function of gut, thereby contributing to the aggravation of the disease. Inhibiting this vicious cycle might alleviate the symptoms of PCOS. However, the role of gut microbiota in PCOS has not been fully explored yet. This review aims to summarize the potential effects and modulative mechanisms of the gut metabolites on PCOS and suggests its potential intervention targets, thus providing more possible treatment options for PCOS in the future

Bone marrow mesenchymal stem cells in premature ovarian failure: Mechanisms and prospects

Premature ovarian failure (POF) is a common female reproductive disorder and characterized by menopause, increased gonadotropin levels and estrogen deficiency before the age of 40 years old. The etiologies and pathogenesis of POF are not fully clear. At present, hormone replacement therapy (HRT) is the main treatment options for POF. It helps to ameliorate perimenopausal symptoms and related health risks, but can’t restore ovarian function and fertility fundamentally. With the development of regenerative medicine, bone marrow mesenchymal stem cells (BMSCs) have shown great potential for the recovery of ovarian function and fertility based on the advantages of abundant sources, high capacity for self-renewal and differentiation, low immunogenicity and less ethical considerations. This systematic review aims to summarize the possible therapeutic mechanisms of BMSCs for POF. A detailed search strategy of preclinical studies and clinical trials on BMSCs and POF was performed on PubMed, MEDLINE, Web of Science and Embase database. A total of 21 studies were included in this review. Although the standardization of BMSCs need more explorations, there is no doubt that BMSCs transplantation may represent a prospective therapy for POF. It is hope to provide a theoretical basis for further research and treatment for POF