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

OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition

Recently, visually-situated text parsing (VsTP) has experienced notable advancements, driven by the increasing demand for automated document understanding and the emergence of Generative Large Language Models (LLMs) capable of processing document-based questions. Various methods have been proposed to address the challenging problem of VsTP. However, due to the diversified targets and heterogeneous schemas, previous works usually design task-specific architectures and objectives for individual tasks, which inadvertently leads to modal isolation and complex workflow. In this paper, we propose a unified paradigm for parsing visually-situated text across diverse scenarios. Specifically, we devise a universal model, called OmniParser, which can simultaneously handle three typical visually-situated text parsing tasks: text spotting, key information extraction, and table recognition. In OmniParser, all tasks share the unified encoder-decoder architecture, the unified objective: point-conditioned text generation, and the unified input & output representation: prompt & structured sequences. Extensive experiments demonstrate that the proposed OmniParser achieves state-of-the-art (SOTA) or highly competitive performances on 7 datasets for the three visually-situated text parsing tasks, despite its unified, concise design. The code is available at https://github.com/AlibabaResearch/AdvancedLiterateMachinery.Comment: CVPR 202

Dietary melatonin attenuates age-related changes in morphology and in levels of key proteins in globus pallidus of mouse brain.

The ability of melatonin treatment of aged animals to partially restore the pattern of gene expression characterizing the younger animal has been frequently reported. The current study examines the effect of melatonin upon age-related changes of some key proteins relevant to the aging process. Male B6C3F1 mice, aged 5.5 months and 23.4 months were used as a model for aging and half of each group received a diet supplemented with 40-ppm (w/w) melatonin for 9.3 weeks. Protein components of the globus pallidus were studied including glial fibrillary acidic protein (GFAP), NF-κB, protein disulfide isomerase (PDI), and Nissl staining. Some age-related changes were in an upward direction (GFAP and NF-κB), while others were depressed with age (PDI and intensity of Nissl staining). However, in either case, melatonin treatment of aged mice generally altered these parameters so that they came to more closely resemble the levels found in younger animals. The extent of this reversal to a more youthful profile, ranged from complete (for NF-κB) to very minor (for Nissl staining and PDI). Overall, these findings are in accord with prior data on the effect of melatonin on cortical gene expression and confirm the value of melatonin as a means of retarding events associated with senescence

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