Variable Precision Rough Set Model for Incomplete Information Systems and Its Beta-Reducts

As the original rough set model is quite sensitive to noisy data, Ziarko proposed the variable precision rough set (VPRS) model to deal with noisy data and uncertain information. This model allowed for some degree of uncertainty and misclassification in the mining process. In this paper, the variable precision rough set model for an incomplete information system is proposed by combining the VPRS model and incomplete information system, and the beta-lower and beta-upper approximations are defined. Considering that classical VPRS model lacks a feasible method to determine the precision parameter beta when calculating the beta-reducts, we present an approach to determine the parameter beta. Then, by calculating discernibility matrix and discernibility functions based on beta-lower approximation, the beta-reducts and the generalized decision rules are obtained. Finally, a concrete example is given to explain the validity and practicability of beta-reducts which is proposed in this paper

Self-training with dual uncertainty for semi-supervised medical image segmentation

In the field of semi-supervised medical image segmentation, the shortage of labeled data is the fundamental problem. How to effectively learn image features from unlabeled images to improve segmentation accuracy is the main research direction in this field. Traditional self-training methods can partially solve the problem of insufficient labeled data by generating pseudo labels for iterative training. However, noise generated due to the model's uncertainty during training directly affects the segmentation results. Therefore, we added sample-level and pixel-level uncertainty to stabilize the training process based on the self-training framework. Specifically, we saved several moments of the model during pre-training, and used the difference between their predictions on unlabeled samples as the sample-level uncertainty estimate for that sample. Then, we gradually add unlabeled samples from easy to hard during training. At the same time, we added a decoder with different upsampling methods to the segmentation network and used the difference between the outputs of the two decoders as pixel-level uncertainty. In short, we selectively retrained unlabeled samples and assigned pixel-level uncertainty to pseudo labels to optimize the self-training process. We compared the segmentation results of our model with five semi-supervised approaches on the public 2017 ACDC dataset and 2018 Prostate dataset. Our proposed method achieves better segmentation performance on both datasets under the same settings, demonstrating its effectiveness, robustness, and potential transferability to other medical image segmentation tasks. Keywords: Medical image segmentation, semi-supervised learning, self-training, uncertainty estimatio

Identification of dominant propagation paths based on sub-synchronous oscillation using branch oscillation energy distribution coefficient

The large-scale integration of wind power into the power grid can cause a new type of sub-synchronous power oscillation, different from traditional thermal power generation. The oscillation energy will spread extensively in the grid, causing power oscillation and even grid-cascading events. To address this issue, this article proposes a method for quantitatively analyzing the propagation characteristics of oscillation energy based on branch oscillation energy. Firstly, analyzing the oscillation energy shared by different branches in the network based on transient energy function. Next, a method is proposed to identify the dominant propagation path of sub-synchronous oscillation by defining the oscillation energy of branches under the dominant oscillation mode and the oscillation energy distribution coefficient of each branch. The oscillation partition set formed by the dominant propagation path can be used to locate the high-risk oscillation area of the system. Finally, the effectiveness of the method proposed in this paper for studying the wide-area propagation characteristics of sub-synchronous oscillations was verified through time-domain simulation analysis

Tomato Chlorosis Virus Infection Facilitates \u3cem\u3eBemisia tabaci\u3c/em\u3e MED Reproduction by Elevating \u3cem\u3eVitellogenin\u3c/em\u3e Expression

Transmission of plant pathogenic viruses mostly relies on insect vectors. Plant virus could enhance its transmission by modulating the vector. Previously, we showed that feeding on virus infected plants can promote the reproduction of the sweet potato whitefly, Bemisia tabaci MED (Q biotype). In this study, using a whitefly-Tomato chlorosis virus (ToCV)-tomato system, we investigated how ToCV modulates B. tabaci MED reproduction to facilitate its spread. Here, we hypothesized that ToCV-infected tomato plants would increase B. tabaci MED fecundity via elevated vitellogenin (Vg) gene expression. As a result, fecundity and the relative expression of B. tabaci MED Vg was measured on ToCV-infected and uninfected tomato plants on days 4, 8, 12, 16, 20 and 24. The role of Vg on B. tabaci MED reproduction was examined in the presence and absence of ToCV using dietary RNAi. ToCV infection significantly increased B. tabaci MED fecundity on days 12, 16 and 20, and elevated Vg expression on days 8, 12 and 16. Both ovarian development and fecundity of B. tabaci MED were suppressed when Vg was silenced with or without ToCV infection. These combined results suggest that ToCV infection increases B. tabaci MED fecundity via elevated Vg expression

NSs, the Silencing Suppressor of Tomato Spotted Wilt Orthotospovirus, Interferes with JA-Regulated Host Terpenoids Expression to Attract \u3cem\u3eFrankliniella occidentalis\u3c/em\u3e

Tomato spotted wilt orthotospovirus (TSWV) causes serious crop losses worldwide and is transmitted by Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae). NSs protein is the silencing suppressor of TSWV and plays an important role in virus infection, cycling, and transmission process. In this research, we investigated the influences of NSs protein on the interaction of TSWV, plants, and F. occidentalis with the transgenic Arabidopsis thaliana. Compared with the wild-type Col-0 plant, F. occidentalis showed an increased number and induced feeding behavior on transgenic Arabidopsis thaliana expressing exogenous NSs. Further analysis showed that NSs reduced the expression of terpenoids synthesis-related genes and the content of monoterpene volatiles in Arabidopsis. These monoterpene volatiles played a repellent role in respect to F. occidentalis. In addition, the expression level of plant immune-related genes and the content of the plant resistance hormone jasmonic acid (JA) in transgenic Arabidopsis were reduced. The silencing suppressor of TSWV NSs alters the emission of plant volatiles and reduces the JA-regulated plant defenses, resulting in enhanced attractiveness of plants to F. occidentalis and may increase the transmission probability of TSWV

Macrophage polarization and metabolism in atherosclerosis

: Atherosclerosis is a chronic inflammatory disease characterized by the accumulation of fatty deposits in the inner walls of vessels. these plaques restrict blood flow and lead to complications such as heart attack or stroke. the development of atherosclerosis is influenced by a variety of factors, including age, genetics, lifestyle, and underlying health conditions such as high blood pressure or diabetes. atherosclerotic plaques in stable form are characterized by slow growth, which leads to luminal stenosis, with low embolic potential or in unstable form, which contributes to high risk for thrombotic and embolic complications with rapid clinical onset. In this complex scenario of atherosclerosis, macrophages participate in the whole process, including the initiation, growth and eventually rupture and wound healing stages of artery plaque formation. macrophages in plaques exhibit high heterogeneity and plasticity, which affect the evolving plaque microenvironment, e.g., leading to excessive lipid accumulation, cytokine hyperactivation, hypoxia, apoptosis and necroptosis. the metabolic and functional transitions of plaque macrophages in response to plaque microenvironmental factors not only influence ongoing and imminent inflammatory responses within the lesions but also directly dictate atherosclerotic progression or regression. In this review, we discuss the origin of macrophages within plaques, their phenotypic diversity, metabolic shifts, and fate and the roles they play in the dynamic progression of atherosclerosis. It also describes how macrophages interact with other plaque cells, particularly T cells. ultimately, targeting pathways involved in macrophage polarization may lead to innovative and promising approaches for precision medicine. further insights into the landscape and biological features of macrophages within atherosclerotic plaques may offer valuable information for optimizing future clinical treatment for atherosclerosis by targeting macrophages

Spermidine endows macrophages anti-inflammatory properties by inducing mitochondrial superoxide-dependent AMPK activation, Hif-1α upregulation and autophagy.

Distinct metabolic programs, either energy-consuming anabolism or energy-generating catabolism, were required for different biological functions. Macrophages can adopt different immune phenotypes in response to various cues and exhibit anti- or pro-inflammatory properties relying on catabolic pathways associated with oxidative phosphorylation (OXPHOS) or glycolysis. Spermidine, a natural polyamine, has been reported to regulate inflammation through inducing anti-inflammatory (M2) macrophages. However, the underlying mechanisms remain elusive. We show here that the M2-polarization induced by spermidine is mediated by mitochondrial reactive oxygen species (mtROS). The levels of mitochondrial superoxide and H2O2 were markedly elevated by spermidine. Mechanistically, mtROS were found to activate AMP-activated protein kinase (AMPK), which in turn enhanced mitochondrial function. Furthermore, hypoxia-inducible factor-1α (Hif-1α) was upregulated by the AMPK activation and mtROS and was required for the expression of anti-inflammatory genes and induction of autophagy. Consistent with previous report that autophagy is required for the M2 polarization, we found that the M2 polarization induced by spermidine was also mediated by increased autophagy. The macrophages treated with spermidine in vitro were found to ameliorate Dextran Sulfate Sodium (DSS)-induced inflammatory bowel disease (IBD) in mice. Thus, spermidine can elicit an anti-inflammatory program driven by mtROS-dependent AMPK activation, Hif-1α stabilization and autophagy induction in macrophages. Our studies revealed a critical role of mtROS in shaping macrophages into M2-like phenotype and provided novel information for management of inflammatory disease by spermidine

Evaluation of exclusive bus lanes in a tri-modal road network incorporating carpooling behavior

This paper considers the evaluation of exclusive bus lanes (EBLs) in the road network with three travel modes: bus, solo driving, and carpooling. A tri-modal transportation network equilibrium model is developed to analyze the effects of EBLs under three different policies: (i) no EBLs (called Policy 1); (ii) EBLs can only be used by bus (called Policy 2); and (iii) EBLs can be used by both bus and carpooling modes (called Policy 3). By taking into account both EBLs setting scheme and bus frequencies, a combinatorial optimization model is proposed to test the performance of the tri-modal transportation system. In a traffic corridor case with single O-D pair, numerical results show that travel demand levels will remarkably influence the total system costs under different policies. The effects of average carpooling occupancy and mode choice parameters on travelers’ choice behavior are analyzed. Finally, a tri-modal network with nineteen links is used to illustrate that the system could be more efficient when different EBLs policies are adopted on different links