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Abstract Purpose To investigate the value of multi-parametric MRI-based radiomics for preoperative prediction of lung metastases from soft tissue sarcoma (STS). Methods In total, 122 patients with clinicopathologically confirmed STS who underwent pretreatment T1-weighted contrast-enhanced (T1-CE) and T2-weighted fat-suppressed (T2FS) MRI scans were enrolled between Jul. 2017 and Mar. 2021. Radiomics signatures were established by calculating and selecting radiomics features from the two sequences. Clinical independent predictors were evaluated by statistical analysis. The radiomics nomogram was constructed from margin and radiomics features by multivariable logistic regression. Finally, the study used receiver operating characteristic (ROC) and calibration curves to evaluate performance of radiomics models. Decision curve analyses (DCA) were performed to evaluate clinical usefulness of the models. Results The margin was considered as an independent predictor (p

Abstract Black rot, caused by Xanthomonas campestris pv. campestris (Xcc) significantly affects the production of cabbage and other cruciferous vegetables. Plant antioxidant system plays an important role in pathogen invasion and is one of the main mechanisms underlying resistance to biological stress. Therefore, it is important to study the resistance mechanisms of the cabbage antioxidant system during the early stages of Xcc. In this study, 108 CFU/mL (OD600 = 0.1) Xcc race1 was inoculated on “zhonggan 11” cabbage using the spraying method. The effects of Xcc infection on the antioxidant system before and after Xcc inoculation (0, 1, 3, and 5 d) were studied by physiological indexes determination, transcriptome and metabolome analyses. We concluded that early Xcc infection can destroy the balance of the active oxygen metabolism system, increase the generation of free radicals, and decrease the scavenging ability, leading to membrane lipid peroxidation, resulting in the destruction of the biofilm system and metabolic disorders. In response to Xcc infection, cabbage clears a series of reactive oxygen species (ROS) produced during Xcc infection via various antioxidant pathways. The activities of antioxidant enzymes such as superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) increased after Xcc infection, and the ROS scavenging rate increased. The biosynthesis of non-obligate antioxidants, such as ascorbic acid (AsA) and glutathione (GSH), is also enhanced after Xcc infection. Moreover, the alkaloid and vitamin contents increased significantly after Xcc infection. We concluded that cabbage could resist Xcc invasion by maintaining the stability of the cell membrane system and improving the biosynthesis of antioxidant substances and enzymes after infection by Xcc. Our results provide theoretical basis and data support for subsequent research on the cruciferous vegetables resistance mechanism and breeding to Xcc.

Abstract Background As the second largest leafy vegetable, cabbage (Brassica oleracea L. var. capitata) is grown globally, and the characteristics of the different varieties, forms, and colors of cabbage may differ. In this study, five analysis methods—variance analysis, correlation analysis, cluster analysis, principal component analysis, and comprehensive ranking—were used to evaluate the quality indices (soluble protein, soluble sugar, and nitrate), antioxidant content (vitamin C, polyphenols, and flavonoids), and mineral (K, Ca, Mg, Cu, Fe, Mn, and Zn) content of 159 varieties of four forms (green spherical, green oblate, purple spherical, and green cow heart) of cabbage. Results The results showed that there are significant differences among different forms and varieties of cabbage. Compared to the other three forms, the purple spherical cabbage had the highest flavonoid, K, Mg, Cu, Mn, and Zn content. A scatter plot of the principal component analysis showed that the purple spherical and green cow heart cabbage varieties were distributed to the same quadrant, indicating that their quality indices and mineral contents were highly consistent, while those of the green spherical and oblate varieties were irregularly distributed. Overall, the green spherical cabbage ranked first, followed by the green cow heart, green oblate, and purple spherical varieties. Conclusions Our results provide a theoretical basis for the cultivation and high-quality breeding of cabbage.

The return of agricultural waste to the field is one of the most effective strategies of increasing crop yield, improving the soil’s physicochemical properties, and improving the soil rhizosphere environment. In the present study, sheep manure (SM), cow manure (CM), tail vegetable (TV), mushroom residue (MR), and corn straw (CS) were used as raw materials, and no fertilization (CK1) and local commercial organic fertilizer (CK2) treatments were used as controls. Eight composts were set up using specific mass ratios of different compost materials. After fermentation, field experiments were conducted to determine the cabbage yield, soil’s physicochemical properties, and soil rhizosphere conditions. The eight composts increased the soil organic matter and nutrient contents significantly. Among the eight fermentation formulas, T6 (CM:CS:TV:SM = 1:1:2:6), T7 (MR:CS:TV:SM = 1:1:2:6), and T8 (CM:MR:CS:TV:SM = 1:1:1:2:5) were relatively effective. Therefore, high-throughput sequencing was performed on T6, T7, T8, CK1, and CK2. T6, T7, and T8 exhibited increased relative abundance of Proteobacteria, Actinomycetes, and Firmicutes, while the Acidobacteria abundance was decreased. In addition, Ascomycota’s and Basidiomycetes’ relative abundance decreased, and the oil chytrid and mortierella increased. The microbial community structure was affected significantly by pH, electrical conductivity, available potassium, available nitrogen, and organic matter. In general, the three composts increased yield by improving the soil’s physicochemical properties, fertility, and microbial community structure. Among them, T6 had the most significant effect and is the optimal formula for use as a local organic cabbage fertilizer, and it could facilitate sustainable agricultural development.

As one of the important sources of human nutrition, Brassicaceae vegetables are widely grown worldwide. Black rot caused by Xanthomonas campestris pv. campestris (Xcc) seriously affects the quality and yield of Brassicaceae vegetables. Therefore, it is important to study control methods of Xcc for Brassicaceae vegetable production. This paper reviews the physical, chemical, and biological control methods of Xcc in Brassicaceae vegetables developed in recent years, and the underlying mechanisms of control methods are also discussed. Based on our current knowledge, future research directions for Xcc control are also proposed. This review also provides a reference basis for the control of Xcc in the field cultivation of Brassicaceae vegetables.

Aiming at the limitations of a single High Resolution Range Profile (HRRP) in recognition, this paper proposes a Time step Correlation-based Feature Fusion (TCFF) method. This method calculates the covariance matrix at the time step of the two features extracted by the two channels, and assigns different weights to the time step according to the strength of the covariance correlation for feature fusion. The experimental results on the simulated ship target HRRP dataset show that the feature fusion method can achieve better recognition performance than the single channel model. Compared with simple feature fusion such as element addition and element contacting, it can also achieve better recognition results.

The traditional High-Resolution Range Profile (HRRP) target recognition method has difficulty automatically extracting target deep features, and has low recognition accuracy under low training samples. To solve these problems, a ship recognition method is proposed based on the lightweight Transformer model. The model enhances the representation of key features by embedding Recurrent Neural Networks (RNN) into Transformer’s encoder. The Group Linear Transformations (GLTs) are introduced into Transformer to reduce the number of parameters in the model, and stable features are extracted through linear intergroup dimensional transformations. The adaptive gradient clipping algorithm is combined with the Stochastic Gradient Descent (SGD) optimizer to allow the gradient to change dynamically with the training process and to improve the training speed and generalization ability of the model. Experimental results on the simulated dataset show that multi-layer model stacking can effectively extract deep features of targets and raise recognition accuracy. At the same time, the lightweight Transformer model can maintain good recognition performance with low parameters and low training samples.