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Abstract The hormone receptor (HR) status plays a significant role in breast cancer, serving as the primary guide for treatment decisions and closely correlating with prognosis. This study aims to investigate the predictive value of radiomics analysis in long-axis and short-axis ultrasound planes for distinguishing between HR-positive and HR-negative breast cancers. A cohort of 505 patients from two hospitals was stratified into discovery (Institute 1, 416 patients) and validation (Institute 2, 89 patients) cohorts. A comprehensive set of 788 ultrasound radiomics features was extracted from both long-axis and short-axis ultrasound planes, respectively. Utilizing least absolute shrinkage and selection operator (LASSO) regression analysis, distinct models were constructed for the long-axis and short-axis data. Subsequently, radiomics scores (Rad-scores) were computed for each patient. Additionally, a combined model was formulated by integrating data from long-axis and short-axis Rad-scores along with clinical factors. The diagnostic efficacy of all models was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The long-axis and short-axis models, consisting of 11 features and 15 features, respectively, were established, yielding AUCs of 0.743 and 0.751 in the discovery cohort, and 0.795 and 0.744 in the validation cohort. The calculated long-axis and short-axis Rad-scores exhibited significant differences between HR-positive and HR-negative groups across all cohorts (all p

The closed coal mine water is mostly characterized by high iron. After mine closure and flooding, residual iron-prone devices and iron-bearing minerals form a “dual-source” iron pollution system in mine water, contributing to the release of iron in different periods after mine closure and creating environmental risks in groundwater. In order to clarify the process and reaction mechanism of “dual-source” iron release in closed coal mine water, to characterize the “dual-source” release mode of iron pollution, and compare the release rate, based on the principle of electrochemical simulation, the working electrodes were prepared by using pyrite and mining bolt as the simulated “dual-source”, and the redox reaction process and iron release mechanism in the acid mine water under the coexistence of “dual-source” at the early stage of coal mine closure were simulated using electrochemical methods such as cyclic voltammetry and polarization as well as X-ray photoelectron spectroscopy (XPS) material surface characterization techniques. The results showed that the dissolved oxygen content was an important inhibitory factor affecting the redox reaction of pyrite and bolt in acid mine drainage. The oxidation mechanism of pyrite and bolt is different, the passivation effect occurs on the surface of pyrite during oxidation, and the final oxidation products of both materials are Fe3+ and \begin{document}$ { {\rm{SO}}_{4}^{2-}}$\end{document}. Pyrite releases iron mainly through the oxidation reaction of Fe2+ on the mineral surface. Bolt released iron mainly through the reaction of iron and its oxidation products on the surface of the material with the acid substances in the solution, and the oxide reacted preferentially over the monomer. In the simulated aerobic (DO=7.0 mg/L) acid mine drainage, the annual corrosion rates of pyrite and bolt reached 8.3636 mm/a and 7.8314 mm/a, respectively, and the annual iron release rates reached 1.2240 g/a and 3.9395 g/a, respectively. In the simulated underground anoxic (DO=3.5 mg/L) acid mine drainage, the annual corrosion rates of pyrite and bolt reached 0.7324 mm/a and 0.3642 mm/a, respectively, and the estimated annual iron release rates reached 0.1072 g/a and 0.1832 g/a, respectively. The integrated electrochemical parameters and static iron release experiments showed that the total iron release rate ratios were both dual-source > bolt > pyrite under the conditions of sufficient or lack of dissolved oxygen.

Background The tumor burden within the axillary lymph nodes (ALNs) constitutes a pivotal factor in breast cancer, serving as the primary determinant for treatment decisions and exhibiting a close correlation with prognosis.Objective This study aimed to investigate the potential of ultrasound-based radiomics and clinical characteristics in non-invasively distinguishing between low tumor burden (1-2 positive nodes) and high tumor burden (more than 2 positive nodes) in patients with node-positive breast cancer.Methods A total of 215 patients with node-positive breast cancer, who underwent preoperative ultrasound examinations, were enrolled in this study. Among these patients, 144 cases were allocated to the training set, 37 cases to the validation set, and 34 cases to the testing set. Postoperative histopathology was used to determine the status of ALN tumor burden. The region of interest for breast cancer was delineated on the ultrasound image. Nine models were developed to predict high ALN tumor burden, employing a combination of three feature screening methods and three machine learning classifiers. Ultimately, the optimal model was selected and tested on both the validation and testing sets. In addition, clinical characteristics were screened to develop a clinical model. Furthermore, Shapley additive explanations (SHAP) values were utilized to provide explanations for the machine learning model.Results During the validation and testing sets, the models demonstrated area under the curve (AUC) values ranging from 0.577 to 0.733 and 0.583 to 0.719, and accuracies ranging from 64.9% to 75.7% and 64.7% to 70.6%, respectively. Ultimately, the Boruta_XGB model, comprising five radiomics features, was selected as the final model. The AUC values of this model for distinguishing low from high tumor burden were 0.828, 0.715, and 0.719 in the training, validation, and testing sets, respectively, demonstrating its superiority over the clinical model.Conclusions The developed radiomics models exhibited a significant level of predictive performance. The Boruta_XGB radiomics model outperformed other radiomics models in this study.

Battery management systems (BMS) in electric vehicles (EVs) require robust communication interfaces to accurately monitor and control lithium-ion battery cells. The communication interface of daisy chain architecture has attracted more and more attention because of its lower deployment cost compared with the communication architecture of the controller area network (CAN). However, in automotive electronics, in order to meet safety standards, it is difficult to implement circuits that are highly robust to electromagnetic interference, which has also attracted many practitioners to study it. The problem of partial electromagnetic interference and clock jitter can be addressed not only by employing methods commonly used in the field of analog signals, but also by leveraging algorithms utilized in the domain of digital signals. This paper proposes an EMI-immune daisy chain interface circuit with a PID-based clock-data-recovery (CDR) algorithm, utilizing either a capacitor or a transformer as an isolator. The system includes a transmitter, an active receiver, a wake-up receiver, and the corresponding algorithm digital circuits, achieving a successful transmission under 39-dBm DPI injection. Simulation results demonstrate a CMTI of 100kV/ $\mu $ s and low power consumption. The proposed CDR algorithm enables effective operation in the presence of clock frequency offset and EMI between chips. This paper thoroughly examines the underlying principles of the algorithm, supplemented by comprehensive test environments and accompanying data visuals.

Rationale and Objectives: We aimed to develop and validate prediction models for histological grade of invasive breast carcinoma (BC) based on ultrasound radiomics features and clinical characteristics. Materials and Methods: A number of 383 patients with invasive BC were retrospectively enrolled and divided into a training set (207 patients), internal validation set (90 patients), and external validation set (86 patients). Ultrasound radiomics features were extracted from all the eligible patients. The Boruta method was used to identify the most useful features. Seven classifiers were adopted to developed prediction models. The output of the classifier with best performance was labeled as the radiomics score (Rad-score) and the classifier was selected as the Rad-score model. A combined model combining clinical factors and Rad-score was developed. The performance of the models was evaluated using receiver operating characteristic curve. Results: Seven radiomics features were selected from 788 candidate features. The logistic regression model performing best among the 7 classifiers in the internal and external validation sets was considered as Rad-score model, with areas under the receiver operating characteristic curve (AUC) values of 0.731 and 0.738. The tumor size was screened out as the risk factor and the combined model was developed, with AUC values of 0.721 and 0.737 in the internal and external validation sets. Furthermore, the 10-fold cross-validation demonstrated that the 2 models above were reliable and stable. Conclusion: The Rad-score model and combined model were able to predict histological grade of invasive BC, which may enable tailored therapeutic strategies for patients with BC in routine clinical use.

IntroductionThe molecular subtype plays a significant role in breast carcinoma (BC), which is the main indicator to guide treatment and is closely associated with prognosis. The aim of this study was to investigate the feasibility and efficacy of an ultrasound-based radiomics nomogram in preoperatively discriminating the luminal from non-luminal type in patients with BC.MethodsA total of 264 BC patients who underwent routine ultrasound examination were enrolled in this study, of which 184 patients belonged to the training set and 80 patients to the test set. Breast tumors were delineated manually on the ultrasound images and then radiomics features were extracted. In the training set, the T test and least absolute shrinkage and selection operator (LASSO) were used for selecting features, and the radiomics score (Rad-score) for each patient was calculated. Based on the clinical risk features, Rad-score, and combined clinical risk features and Rad-score, three models were established, respectively. The performances of the models were validated with receiver operator characteristic (ROC) curve and decision curve analysis.ResultsIn all, 788 radiomics features per case were obtained from the ultrasound images. Through radiomics feature selection, 11 features were selected to constitute the Rad-score. The area under the ROC curve (AUC) of the Rad-score for predicting the luminal type was 0.828 in the training set and 0.786 in the test set. The nomogram comprising the Rad-score and US-reported tumor size showed AUCs of the training and test sets were 0.832 and 0.767, respectively, which were significantly higher than the AUCs of the clinical model in the training and test sets (0.691 and 0.526, respectively). However, there was no significant difference in predictive performance between the Rad-score and nomogram.ConclusionBoth the Rad-score and nomogram can be applied as useful, noninvasive tools for preoperatively discriminating the luminal from non-luminal type in patients with BC. Furthermore, this study might provide a novel technique to evaluate molecular subtypes of BC.

ObjectiveThe aim of this study was to develop and validate an ultrasound-based radiomics nomogram model by integrating the clinical risk factors and radiomics score (Rad-Score) to predict the Ki-67 status in patients with breast carcinoma.MethodsUltrasound images of 284 patients (196 high Ki-67 expression and 88 low Ki-67 expression) were retrospectively analyzed, of which 198 patients belonged to the training set and 86 patients to the test set. The region of interest of tumor was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analysis by using the independent sample t test and least absolute shrinkage and selection operator (LASSO) algorithm. The support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction model based on the selected features. The classifier with the highest AUC value was selected to convert the output of the results into the Rad-Score and was regarded as Rad-Score model. In addition, the logistic regression method was used to integrate Rad-Score and clinical risk factors to generate the nomogram model. The leave group out cross-validation (LGOCV) method was performed 200 times to verify the reliability and stability of the nomogram model.ResultsSix classifier models were established based on the 15 non-zero coefficient features. Among them, the LR classifier achieved the best performance in the test set, with the area under the receiver operating characteristic curve (AUC) value of 0.786, and was obtained as the Rad-Score model, while the XGB performed the worst (AUC, 0.615). In multivariate analysis, independent risk factor for high Ki-67 status was age (odds ratio [OR] = 0.97, p = 0.04). The nomogram model based on the age and Rad-Score had a slightly higher AUC than that of Rad-Score model (AUC, 0.808 vs. 0.798) in the test set, but no statistical difference (p = 0.144, DeLong test). The LGOCV yielded a median AUC of 0.793 in the test set.ConclusionsThis study proposed a convenient, clinically useful ultrasound radiomics nomogram model that can be used for the preoperative individualized prediction of the Ki-67 status in patients with BC.

PurposeThe clinical practice of elastosonography for the detection of salivary gland tumors is still a controversial issue. The objective of this meta-analysis was to evaluate the effect of elastosonography for the diagnosis of salivary gland tumors and to compare the diagnostic value of elastosonography and conventional ultrasound in the diagnosis of salivary gland tumors.MethodsA comprehensive literature search through PubMed, EMBASE, and Cochrane Library was carried out from inception to November 2021. Two researchers independently extracted the data from the enrolled papers using a standard data extraction form. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated to evaluate the diagnostic performance of elastosonography. The Quality Assessment of Diagnostic Accuracy Studies—2 (QUADAS-2) tool was utilized to evaluate the quality of each included study. Meta-DiSc version 1.4, Review Manager 5.3, and StataSE 15 were used.ResultsSixteen studies with a total of 1105 patients with 1146 lesions were included in this meta-analysis. The pooled sensitivity, specificity, PLR, NLR, and DOR of elastosonography for the differentiation between benign and malignant salivary gland tumors were 0.73 (95%CI, 0.66–0.78), 0.64 (95%CI, 0.61–0.67), 2.83 (95%CI, 1.97–4.07), 0.45 (95%CI, 0.32–0.62), and 9.86 (95%CI, 4.49–21.62), respectively, with an AUC of 0.82. Four studies provided data regarding the conventional ultrasound for the differentiation between benign and malignant salivary gland tumors. The pooled sensitivity, specificity, and DOR were 0.62 (95%CI, 0.50–0.73), 0.93 (95%CI, 0.90–0.96), and 25.07 (95%CI, 4.28–146.65), respectively. The meta-regression and subgroup analyses found that assessment methods were associated with significant heterogeneity, and quantitative or semiquantitative elastosonography performed better than the qualitative one.ConclusionsElastosonography showed a limited value for diagnosing malignant salivary gland tumors; it could be considered as a supplementary diagnostic technology to conventional ultrasound, and quantitative or semiquantitative elastosonography was superior to the qualitative one.

(1) Objective: To evaluate the performance of ultrasound-based radiomics in the preoperative prediction of human epidermal growth factor receptor 2-positive (HER2+) and HER2− breast carcinoma. (2) Methods: Ultrasound images from 309 patients (86 HER2+ cases and 223 HER2− cases) were retrospectively analyzed, of which 216 patients belonged to the training set and 93 patients assigned to the time-independent validation set. The region of interest of the tumors was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analyses using the intra-class correlation coefficient (ICC), Mann–Whitney U test, and the least absolute shrinkage and selection operator (LASSO) algorithm. The radiomics score (Rad-score) for each patient was calculated through a linear combination of the nonzero coefficient features. The support vector machine (SVM), K nearest neighbors (KNN), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction models based on the Rad-score. A clinical model based on significant clinical features was also established. In addition, the logistic regression method was used to integrate Rad-score and clinical features to generate the nomogram model. The leave-one-out cross validation (LOOCV) method was used to validate the reliability and stability of the model. (3) Results: Among the seven classifier models, the LR achieved the best performance in the validation set, with an area under the receiver operating characteristic curve (AUC) of 0.786, and was obtained as the Rad-score model, while the RF performed the worst. Tumor size showed a statistical difference between the HER2+ and HER2− groups (p = 0.028). The nomogram model had a slightly higher AUC than the Rad-score model (AUC, 0.788 vs. 0.786), but no statistical difference (Delong test, p = 0.919). The LOOCV method yielded a high median AUC of 0.790 in the validation set. (4) Conclusion: The Rad-score model performs best among the seven classifiers. The nomogram model based on Rad-score and tumor size has slightly better predictive performance than the Rad-score model, and it has the potential to be utilized as a routine modality for preoperatively determining HER2 status in BC patients non-invasively.

Fundamental understandings on the bitumen fracture mechanism are vital to improve the mixture design of asphalt concrete. In this paper, a diffuse interface model, namely, phase-field method is used for modeling the quasi-brittle fracture in bitumen. This method describes the microstructure using a phase-field variable which assumes one in the intact solid and negative one in the crack region. Only the elastic energy will directly contribute to cracking. To account for the growth of cracks, a nonconserved Allen-Cahn equation is adopted to evolve the phase-field variable. Numerical simulations of fracture are performed in bituminous materials with the consideration of quasi-brittle properties. It is found that the simulation results agree well with classic fracture mechanics.

Recent research shows that macro-scale cracking in asphalt binder may originate from its intrinsic defects at the nano-scale. In this paper, a molecular dynamics (MD) simulation was conducted to evaluate the nucleation of natural defects in asphalt. The asphalt microstructure was modeled using an ensemble of three different types of molecules to represent a constituent species: asphaltenes, naphthene aromatics and saturates, where the weight proportion of 20:60:20 was used to create an asphalt-like ensemble of molecules. Tension force was then applied on the molecular boundaries to study the crack initiation and propagation. It was discovered that the natural distribution of atoms at microscale would affect the intrinsic defects in asphalt and further influence crack initiation and propagation in asphalt.