Based on frequency synchronization theory of the second-order non-uniform Kuramoto model, a novel approach for power system transient stability analysis is put forward by establishing the correspondence between the classic power system model and the second-order non-uniform Kuramoto model. This method relates network parameters with the region of attraction of the disturbed system’s stable equilibrium and thus the transient stability information of the disturbed system can be obtained by comparing the initial configuration with trapping region of the stable equilibrium of the disturbance-canceling system. The application of our approach to single machine infinite bus system shows that this method features a fast computation speed. It can determine the transient stability of the system when a certain perturbation acts on as well as offer the stability margin of the disturbed system, which is of great importance for practical use.
Abstract Background To develop and validate a conventional MRI-based radiomic model for predicting prognosis in patients with IDH wild-type glioblastoma (GBM) and reveal the biological underpinning of the radiomic phenotypes. Methods A total of 801 adult patients (training set, N = 471; internal validation set, N = 239; external validation set, N = 91) diagnosed with IDH wild-type GBM were included. A 20-feature radiomic risk score (Radscore) was built for overall survival (OS) prediction by univariate prognostic analysis and least absolute shrinkage and selection operator (LASSO) Cox regression in the training set. GSEA and WGCNA were applied to identify the intersectional pathways underlying the prognostic radiomic features in a radiogenomic analysis set with paired MRI and RNA-seq data (N = 132). The biological meaning of the conventional MRI sequences was revealed using a Mantel test. Results Radscore was demonstrated to be an independent prognostic factor (P < 0.001). Incorporating the Radscore into a clinical model resulted in a radiomic-clinical nomogram predicting survival better than either the Radscore model or the clinical model alone, with better calibration and classification accuracy (a total net reclassification improvement of 0.403, P < 0.001). Three pathway categories (proliferation, DNA damage response, and immune response) were significantly correlated with the prognostic radiomic phenotypes. Conclusion Our findings indicated that the prognostic radiomic phenotypes derived from conventional MRI are driven by distinct pathways involved in proliferation, DNA damage response, and immunity of IDH wild-type GBM.
The lymphocyte-specific protein tyrosine kinase (LCK) is a critical target in leukemia treatment. However, potential off-target interactions involving LCK can lead to unintended consequences. This underscores the importance of accurately predicting the inhibitory reactions of drug molecules with LCK during the research and development stage. To address this, we introduce an advanced ensemble machine learning technique designed to estimate the binding affinity between molecules and LCK. This comprehensive method includes the generation and selection of molecular fingerprints, the design of the machine learning model, hyperparameter tuning, and a model ensemble. Through rigorous optimization, the predictive capabilities of our model have been significantly enhanced, raising test R2 values from 0.644 to 0.730 and reducing test RMSE values from 0.841 to 0.732. Utilizing these advancements, our refined ensemble model was employed to screen an MCE -like drug library. Through screening, we selected the top ten scoring compounds, and tested them using the ADP-Glo bioactivity assay. Subsequently, we employed molecular docking techniques to further validate the binding mode analysis of these compounds with LCK. The exceptional predictive accuracy of our model in identifying LCK inhibitors not only emphasizes its effectiveness in projecting LCK-related safety panel predictions but also in discovering new LCK inhibitors. For added user convenience, we have also established a webserver, and a GitHub repository to share the project.
The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n = 92) and evaluated on a testing cohort (n = 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.
Different from common thin-walled workpiece, in the process of milling of large-size thin-walled workpiece chatter in the axial direction along the spindle is also likely to happen because of the low stiffness of the workpiece in this direction. An analytical method for prediction of stability lobes of milling of large-size thin-walled workpiece is presented in this paper. In the method, not only frequency response function of the tool point but also frequency response function of the workpiece is considered.
Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.
<b><i>Background:</i></b> Cyclin-dependent kinase (CDK) 4/6 inhibitors have been advocated for adjuvant therapy of metastatic hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)− breast cancer (BC). However, the efficiency of adding CDK 4/6 inhibitors to neoadjuvant therapy was not unequivocal. <b><i>Objective:</i></b> The aim of the study was to evaluate the efficiency and toxicity of neoadjuvant CDK 4/6 inhibitors + endocrine therapy (ET) versus neoadjuvant endocrine monotherapy or standard neoadjuvant chemotherapy in HR+/HER2− BC. <b><i>Method:</i></b> We searched PubMed, the Cochrane Library, Web of Science, and Embase online databases for randomized controlled trials and single-arm studies written in English until April 2021. <b><i>Results:</i></b> Five studies comparing CDK 4/6 inhibitors + ET as neoadjuvant treatments to ET alone and 2 studies comparing neoadjuvant CDK 4/6 inhibitors + ET to neoadjuvant chemotherapy were analysed. Neoadjuvant CDK 4/6 inhibitors + ET improved the rate of complete cell cycle arrest (CCCA: central Ki67 < 2.7%, odds ratio [OR] = 7.91, 95% confidence interval [CI] = 4.81–13.03, <i>p</i> < 0.001), increased the risk of adverse events (AEs; especially ≥3 AEs; AEs of all grades: OR = 9.10, 95% CI = 2.39–34.58, <i>p</i> = 0.001; AEs ≥3: OR = 12.24, 95% CI = 4.17–35.88, <i>p</i> < 0.001), led to no significant differences in pathological complete response (pCR) in patients with BC (OR = 0.34, 95% CI = 0.04–2.85, <i>p</i> = 0.318) compared to endocrine monotherapy. Moreover, subgroup analysis showed that the 3 types of CDK 4/6 inhibitors all improved the rate of CCCA (ribociclib: OR = 10.31, 95% CI = 3.59–29.61, <i>p</i> < 0.001; palbociclib: OR = 7.39, 95% CI = 1.26–43.40, <i>p</i> = 0.027, and abemaciclib: OR = 8.28, 95% CI = 3.41–20.11, <i>p</i> < 0.001). Compared to neoadjuvant chemotherapy, neoadjuvant CDK 4/6 inhibitors plus ET decreased the risk of AEs ≥3 (OR = 0.50, 95% CI = 0.29–0.87, <i>p</i> = 0.015) and showed similar ability to reach pCR (OR = 0.50, 95% CI = 0.12–2.07, <i>p</i> = 0.342) and reduce the residual cancer burden (RCB, RCB 0–1: OR = 0.47, 95% CI = 0.18–1.22, <i>p</i> = 0.121; RCB 2–3: OR = 2.30, 95% CI = 0.89–5.91, <i>p</i> = 0.084). <b><i>Conclusions:</i></b> The results suggested that combination therapy had increased efficacy and toxicity compared to endocrine monotherapy and showed similar efficacy to and better safety than neoadjuvant chemotherapy.
Intracellular Ca2+ transients have been shown to be induced by ultrasound in various types of cells and Ca2+ plays an important role in cell recovery after sonoporation. To achieve a complete understanding of Ca2+ dynamics during insonation and get clues for suitable parameters of ultrasound to accelerate its clinical application, a new model of ultrasound-induced Ca2+ dynamics has been developed. In the model, effects of ultrasound stimulation on calcium influx and mobilization have been numerically investigated with an assumed linear relation between the low-level ultrasound intensity and induced membrane strain density. The modeling results reproduced the characteristics of elevated intracellular Ca2+ transients induced by ultrasound, showing a biphasic response of intracellular [Ca2+] for about 3 minutes. Numerical results suggested that ultrasound intensity should be between 40 and 1200 mW/cm2 to induce recoverable Ca2+ transients. Stimulation above this intensity range may cause cell damage. This range of intensity changes with cell types. Keywords: Calcium influx, calcium transient, modeling, ultrasound.
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