Exploring the low redshift universe: two parametric models for effective pressure

Astrophysical observations have put unprecedentedly tight constraints on cosmological theories. The $\Lambda$CDM model, mathematically simple and fits observational data-sets well, is preferred for explaining the behavior of universe. But many basic features of the dark sectors are still unknown, which leaves rooms for various nonstandard cosmological hypotheses. As the pressure of cosmological constant dark energy is unvarying, ignoring contributions from radiation and curvature terms at low redshift, the effective pressure keeps constant. In this paper, we propose two parametric models for non-constant effective pressure in order to study the tiny deviation from $\Lambda$CDM at low redshift. We recover our phenomenological models in the scenarios of quintessence and phantom fields, and explore the behavior of scalar field and potential. We constrain our model parameters with SNe Ia and BAO observations, and detect subtle hints of $\omega_{de}<-1$ from the data fitting results of both models, which indicates possibly a phantom dark energy scenario at present.Comment: 11 pages, 24 figure

Current and promising therapies based on the pathogenesis of Graves’ ophthalmopathy

Graves’ ophthalmopathy (GO) is a hyperthyroidism-related and immune-mediated disease that poses a significant threat to human health. The pathogenesis of GO primarily involves T cells, B cells, and fibroblasts, suggesting a pivotal role for the thyrotropin-antibody-immunocyte-fibroblast axis. Traditional treatment approaches for Graves’ disease (GD) or GO encompass antithyroid drugs (ATDs), radioactive iodine, and beta-blockers. However, despite decades of treatment, there has been limited improvement in the global incidence of GO. In recent years, promising therapies, including immunotherapy, have emerged as leading contenders, demonstrating substantial benefits in clinical trials by inhibiting the activation of immune cells like Th1 and B cells. Furthermore, the impact of diet, gut microbiota, and metabolites on GO regulation has been recognized, suggesting the potential of non-pharmaceutical interventions. Moreover, as traditional Chinese medicine (TCM) components have been extensively explored and have shown effective results in treating autoimmune diseases, remarkable progress has been achieved in managing GO with TCM. In this review, we elucidate the pathogenesis of GO, summarize current and prospective therapies for GO, and delve into the mechanisms and prospects of TCM in its treatment

Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping

Designing deep learning algorithms for gland segmentation is crucial for automatic cancer diagnosis and prognosis, yet the expensive annotation cost hinders the development and application of this technology. In this paper, we make a first attempt to explore a deep learning method for unsupervised gland segmentation, where no manual annotations are required. Existing unsupervised semantic segmentation methods encounter a huge challenge on gland images: They either over-segment a gland into many fractions or under-segment the gland regions by confusing many of them with the background. To overcome this challenge, our key insight is to introduce an empirical cue about gland morphology as extra knowledge to guide the segmentation process. To this end, we propose a novel Morphology-inspired method via Selective Semantic Grouping. We first leverage the empirical cue to selectively mine out proposals for gland sub-regions with variant appearances. Then, a Morphology-aware Semantic Grouping module is employed to summarize the overall information about the gland by explicitly grouping the semantics of its sub-region proposals. In this way, the final segmentation network could learn comprehensive knowledge about glands and produce well-delineated, complete predictions. We conduct experiments on GlaS dataset and CRAG dataset. Our method exceeds the second-best counterpart over 10.56% at mIOU.Comment: MICCAI 2023 Accepte

AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation

Semi-supervised semantic segmentation (SSSS) has been proposed to alleviate the burden of time-consuming pixel-level manual labeling, which leverages limited labeled data along with larger amounts of unlabeled data. Current state-of-the-art methods train the labeled data with ground truths and unlabeled data with pseudo labels. However, the two training flows are separate, which allows labeled data to dominate the training process, resulting in low-quality pseudo labels and, consequently, sub-optimal results. To alleviate this issue, we present AllSpark, which reborns the labeled features from unlabeled ones with the channel-wise cross-attention mechanism. We further introduce a Semantic Memory along with a Channel Semantic Grouping strategy to ensure that unlabeled features adequately represent labeled features. The AllSpark shed new light on the architecture level designs of SSSS rather than framework level, which avoids increasingly complicated training pipeline designs. It can also be regarded as a flexible bottleneck module that can be seamlessly integrated into a general transformer-based segmentation model. The proposed AllSpark outperforms existing methods across all evaluation protocols on Pascal, Cityscapes and COCO benchmarks without bells-and-whistles. Code and model weights are available at: https://github.com/xmed-lab/AllSpark.Comment: Accepted by CVPR 2024; correct typos; this is not the camera-ready versio

V2V Routing in VANET Based on Fuzzy Logic and Reinforcement Learning

To ensure the transmission quality of real-time communications on the road, the research of routing protocol is crucial to improve effectiveness of data transmission in Vehicular Ad Hoc Networks (VANETs). The existing work Q-Learning based routing algorithm, QLAODV, is studied and its problems, including slow convergence speed and low accuracy, are found. Hence, we propose a new routing algorithm FLHQRP by considering the characteristics of real-time communication in VANETs in the paper. The virtual grid is introduced to divide the vehicle network into clusters. The node’s centrality and mobility, and bandwidth efficiency are processed by the Fuzzy Logic system to select the most suitable cluster head (CH) with the stable communication links in the cluster. A new heuristic function is also proposed in FLHQRP algorithm. It takes cluster as the environment state of heuristic Q-learning, by considering the delay to guide the forwarding process of the CH. This can speed up the learning convergence, and reduce the impact of node density on the convergence speed and accuracy of Q-learning. The problem of QLAODV is solved in the proposed algorithm since the experimental results show that FLHQRP has many advantages on delivery rate, end-to-end delay, and average hops in different network scenarios

Dissecting the genome-wide evolution and function of R2R3-MYB transcription factor family in Rosa chinensis

Rosa chinensis, an important ancestor species of Rosa hybrida, the most popular ornamental plant species worldwide, produces flowers with diverse colors and fragrances. The R2R3-MYB transcription factor family controls a wide variety of plant-specific metabolic processes, especially phenylpropanoid metabolism. Despite their importance for the ornamental value of flowers, the evolution of R2R3-MYB genes in plants has not been comprehensively characterized. In this study, 121 predicted R2R3-MYB gene sequences were identified in the rose genome. Additionally, a phylogenomic synteny network (synnet) was applied for the R2R3-MYB gene families in 35 complete plant genomes. We also analyzed the R2R3-MYB genes regarding their genomic locations, Ka/Ks ratio, encoded conserved motifs, and spatiotemporal expression. Our results indicated that R2R3-MYBs have multiple synteny clusters. The RcMYB114a gene was included in the Rosaceae-specific Cluster 54, with independent evolutionary patterns. On the basis of these results and an analysis of RcMYB114a-overexpressing tobacco leaf samples, we predicted that RcMYB114a functions in the phenylpropanoid pathway. We clarified the relationship between R2R3-MYB gene evolution and function from a new perspective. Our study data may be relevant for elucidating the regulation of floral metabolism in roses at the transcript level