Search

ABSTRACT A large number of transcriptome studies generate important data and information for the study of pathogenic mechanisms of pathogens, including Vibrio cholerae. V. cholerae transcriptome data include RNA-seq and microarray: microarray data mainly include clinical human and environmental samples, and RNA-seq data mainly focus on laboratory processing conditions, including different stresses and experimental animals in vivo. In this study, we integrated the data sets of both platforms using Rank-in and the Limma R package normalized Between Arrays function, achieving the first cross-platform transcriptome data integration of V. cholerae. By integrating the entire transcriptome data, we obtained the profiles of the most active or silent genes. By transferring the integrated expression profiles into the weighted correlation network analysis (WGCNA) pipeline, we identified the important functional modules of V. cholerae in vitro stress treatment, gene manipulation, and in vitro culture as DNA transposon, chemotaxis and signaling, signal transduction, and secondary metabolic pathways, respectively. The analysis of functional module hub genes revealed the uniqueness of clinical human samples; however, under specific expression patterning, the Δhns, ΔoxyR1 strains, and tobramycin treatment group showed high expression profile similarity with human samples. By constructing a protein-protein interaction (PPI) interaction network, we discovered several unreported novel protein interactions within transposon functional modules. IMPORTANCE We used two techniques to integrate RNA-seq data for laboratory studies with clinical microarray data for the first time. The interactions between V. cholerae genes were obtained from a global perspective, as well as comparing the similarity between clinical human samples and the current experimental conditions, and uncovering the functional modules that play a major role under different conditions. We believe that this data integration can provide us with some insight and basis for elucidating the pathogenesis and clinical control of V. cholerae.

To investigate the description and evolution of the damage state of rock-like brittle materials, the physical meaning of each elastic modulus method parameter based on the strain equivalence hypothesis and the limitations of the model application are discussed. The modulus change during the triaxial cyclic loading and unloading test of limestone is studied. Moreover, the defects of the unloading modulus substitution method and the statistical damage evolution model in damage evolution analysis are discussed. The results show the existing elastic modulus method can only be used to reflect the damage evolution process of rock under uniaxial compression, and the unloading modulus substitution method cannot correctly describe the damage state and its evolution law. In addition, the statistical damage constitutive model can only be regarded as a theoretical self-consistent solution under the numerical range [0, 1] of the statistical damage evolution model. Based on the above research, a damage characterization variable and its evolution model considering the effects of damage strain threshold are proposed. Additionally, the constitutive model below the damage strain threshold and the damage constitutive model above the damage strain threshold are established, respectively. The sensitivity of model parameters is also analyzed in this study. The final results show that the proposed model and method can not only reasonably explain the damage mechanism of rocks under triaxial compression, but also accurately simulate the full stress-strain process, which is rationable and feasible.

BackgroundThe typical enhancement patterns of hepatocellular carcinoma (HCC) on contrast-enhanced ultrasound (CEUS) are hyper-enhanced in the arterial phase and washed out during the portal venous and late phases. However, atypical variations make a differential diagnosis both challenging and crucial. We aimed to investigate whether machine learning-based ultrasonic signatures derived from CEUS images could improve the diagnostic performance in differentiating focal nodular hyperplasia (FNH) and atypical hepatocellular carcinoma (aHCC).Patients and MethodsA total of 226 focal liver lesions, including 107 aHCC and 119 FNH lesions, examined by CEUS were reviewed retrospectively. For machine learning-based ultrasomics, 3,132 features were extracted from the images of the baseline, arterial, and portal phases. An ultrasomics signature was generated by a machine learning model. The predictive model was constructed using the support vector machine method trained with the following groups: ultrasomics features, radiologist’s score, and combination of ultrasomics features and radiologist’s score. The diagnostic performance was explored using the area under the receiver operating characteristic curve (AUC).ResultsA total of 14 ultrasomics features were chosen to build an ultrasomics model, and they presented good performance in differentiating FNH and aHCC with an AUC of 0.86 (95% confidence interval [CI]: 0.80, 0.89), a sensitivity of 76.6% (95% CI: 67.5%, 84.3%), and a specificity of 80.5% (95% CI: 70.6%, 85.9%). The model trained with a combination of ultrasomics features and the radiologist’s score achieved a significantly higher AUC (0.93, 95% CI: 0.89, 0.96) than that trained with the radiologist’s score (AUC: 0.84, 95% CI: 0.79, 0.89, P < 0.001). For the sub-group of HCC with normal AFP value, the model trained with a combination of ultrasomics features, and the radiologist’s score remain achieved the highest AUC of 0.92 (95% CI: 0.87, 0.96) compared to that with the ultrasomics features (AUC: 0.86, 95% CI: 0.74, 0.89, P < 0.001) and radiologist’s score (AUC: 0.86, 95% CI: 0.79, 0.91, P < 0.001).ConclusionsMachine learning-based ultrasomics performs as well as the staff radiologist in predicting the differential diagnosis of FNH and aHCC. Incorporating an ultrasomics signature into the radiologist’s score improves the diagnostic performance in differentiating FNH and aHCC.

This article presents a ±45° dual‐band dual‐polarized filtering antenna. The filtering antenna element is a dual‐polarized patch antenna that loads a parasitic patch with two rectangle slots to achieve compact multi‐array. Multi‐array consists of three 1×2 subarrays arranged, which are designed for 1.8/2.1 GHz FDD band applications. Very close‐arranged antennas exhibit radiation pattern invariance as well as in‐band radiation property. Radiation patterns are conformal with a compact subarray distance of 45 mm (0.29λ0), which is 56% smaller than that of the array using traditional filtering elements. Moreover, the total width is only 180 mm for three subarray‐composed antenna arrays. The average gain is 8.7 dBi with a low profile of 0.069λ0. The proposed array exhibits the filtering property of the subarrays with a very simple structure. [ABSTRACT FROM AUTHOR]