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Identifying high-risk intracranial plaques is significant for the treatment and prevention of stroke.To develop a high-risk plaque model using three-dimensional (3D) high-resolution magnetic resonance imaging (HRMRI) based radiomics features and machine learning.136 patients with documented symptomatic intracranial artery stenosis and available HRMRI data were included. Among these patients, 136 and 92 plaques were identified as symptomatic and asymptomatic plaques, respectively. A conventional model was developed by recording and quantifying the radiological plaque characteristics. Radiomics features from T1-weighted images (T1WI) and contrast-enhanced T1WI (CE-T1WI) were used to construct a high-risk plaque model with linear support vector classification (linear SVC). The radiological and radiomics features were combined to build a combined model. Receiver operating characteristic (ROC) curves were used to evaluate these models.Plaque length, burden, and enhancement were independently associated with clinical symptoms and were included in the conventional model, which had an AUC of 0.853 vs. 0.837 in the training and test sets. While the radiomics and the combined model showed an improved AUC: 0.923 vs. 0.925 for the training sets and 0.906 vs. 0.903 in the test sets. Both the radiomics model (p = 0.024, p = 0.018) and combined model (p = 0.042, p = 0.049) outperformed the conventional model in the two sets, whereas the performance of the combined model was not significantly different from that of the radiomics model in the two sets (p = 0.583 and p = 0.606).The radiomics model based on 3D HRMRI can accurately differentiate symptomatic from asymptomatic intracranial arterial plaques and significantly outperforms the conventional model.

Introduction: Diabetes markedly affects the formation and development of intracranial atherosclerosis. The study was aimed at evaluating whether radiomics features can help distinguish plaques primarily associated with diabetes. Materials and Methods: We retrospectively analyzed patients who were admitted to our center because of acute ischemic stroke due to intracranial atherosclerosis between 2016 and 2022. Clinical data, blood biomarkers, conventional plaque features and plaque radiomics features were collected for all patients. Odds ratios (ORs) with 95% confidence intervals (CIs) were determined from logistic regression models. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to describe diagnostic performance. The DeLong test was used to compare differences between models. Results: Overall, 157 patients (115 men; mean age, 58.7 ± 10.7 years) were enrolled. Multivariate logistic regression analysis showed that plaque length (OR: 1.17; 95% CI: 1.07–1.28) and area (OR: 1.13; 95% CI: 1.02–1.24) were independently associated with diabetes. On combining plaque length and area as a conventional model, the AUCs of the training and validation cohorts for identifying diabetes patients were 0.789 and 0.720, respectively. On combining radiomics features on T1WI and contrast-enhanced T1WI sequences, a better diagnostic value was obtained in the training and validation cohorts (AUC: 0.889 and 0.861). The DeLong test showed the model combining radiomics and conventional plaque features performed better than the conventional model in both cohorts (p < 0.05). Conclusions: The use of radiomics features of intracranial plaques on hrMRI can effectively distinguish culprit plaques with diabetes as the primary pathological cause, which will provide new avenues of research into plaque formation and precise treatment.

Hongquan Guo,1,&ast; Wei Xu,1,2,&ast; Xiaohao Zhang,3,&ast; Shuai Zhang,4 Zheng Dai,5 Shun Li,6 Yi Xie,3 Yingle Li,1 Jianzhong Xue,7 Xinfeng Liu1 1Department of Neurology, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, Jiangsu, 210002, People’s Republic of China; 2Department of Neurology, Affiliated Changsha Central Hospital, University of South China, Changsha, Hunan, 410000, People’s Republic of China; 3Department of Neurology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, People’s Republic of China; 4Department of Neurology, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, 225001, People’s Republic of China; 5Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, Jiangsu, 214023, People’s Republic of China; 6Department of Cerebrovascular Disease Treatment Center, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, 210002, People’s Republic of China; 7Department of Neurology, Changshu No.2 People’s Hospital, Changshu, Jiangsu, 215500, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xinfeng LiuDepartment of Neurology, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, No. 305 East Zhongshan Road, Nanjing, 210002, Jiangsu Province, People’s Republic of ChinaTel +86 2584801861Fax +86 2584805169Email xfliu2@vip.163.comJianzhong XueDepartment of Neurology, Changshu No.2 People’s Hospital, No. 18 Taishan Road, Changshu, Jiangsu Province, People’s Republic of ChinaTel +86 13506247572Email xjz2021@sina.comBackground and Aims: A reliable predictive score system to identify the risk of symptomatic intracranial hemorrhage (sICH) after intravenous thrombolysis (IVT) in acute ischemic stroke patients is of great essence. We aimed to develop a nomogram for predicting the risk of sICH after IVT in Chinese patients.Methods: We recruited acute ischemic stroke patients who were treated with IVT from five advanced stroke centers in China from April 2014 to November 2020. sICH was diagnosed according to the European Cooperative Acute Stroke Study II (ECASS-II) definition. Multivariable logistic regression was performed to construct the best-fit nomogram. The discrimination and calibration of the nomogram were evaluated by the area under the receiver operating characteristic curve (AUC-ROC) and calibration plot.Results: A total of 1200 patients were enrolled, of whom 66 (5.5%) developed sICH. In the multivariate logistic regression model, atrial fibrillation (odds ratio [OR] 3.25; 95% confidence interval [CI], 1.89− 5.60; P < 0.001), baseline glucose level (OR, 1.13; 95% CI, 1.07− 1.20; P < 0.001), neutrophil to lymphocyte ratio (OR, 1.05; 95% CI, 1.01− 1.09; P = 0.024) and baseline National Institute of Health Stroke Scale (NIHSS) (OR, 1.07; 95% CI, 1.04− 1.10; P < 0.001) were independent predictors for sICH and were used to generate the nomogram. The nomogram demonstrated good discrimination as the AUC-ROC value was 0.788 (95% CI, 0.737– 0.840). The calibration plot revealed good calibration.Conclusion: The nomogram consisted of atrial fibrillation, baseline glucose level, neutrophil to lymphocyte ratio, and NIHSS score may predict the risk of sICH in Chinese acute ischemic stroke patients treated with IVT.Keywords: stroke, intravenous thrombolysis, symptomatic intracranial hemorrhage, nomogram, predict

We assessed the value of computed tomography (CT) and automated Alberta Stroke Program Early CT Score (ASPECTS) with net water uptake (NWU) to predict stroke onset time. Two-hundred forty stroke patients with anterior circulation large-vessel occlusion were included. CT-ASPECTS-NWU values were calculated by comparing the mean Hounsfield units of affected ASPECTS regions with unaffected contralateral regions. The correlation between ASPECTS-NWU and stroke onset to CT time was assessed. ASPECTS-NWU predictive values were calculated to identify a stroke onset to CT time of within 4.5/6 h. A correlation existed between stroke onset to CT time and ASPECTS-NWU (r = 0.65, p

Traditional research studies on interdependent networks with groups ignore the relationship between nodes in dependency groups. In real-world networks, nodes in the same group may support each other through cooperation and tend to fail or survive together. In this paper, based on the framework of group percolation, a cascading failure model on interdependent networks with cooperative dependency groups under targeted attacks is proposed, and the effect of group size distributions on the robustness of interdependent networks is investigated. The mutually giant component and phase transition point of networks with different group size distributions are analyzed. The effectiveness of the theory is verified through simulations. Results show that the robustness of interdependent networks with cooperative dependency groups can be enhanced by increasing the heterogeneity between groups under targeted attacks. The theory can well predict the numerical simulation results. This model provides some theoretical guidance for designing robust interdependent systems in real world.

Introduction: Imaging-based early warning indicators and feasible stratification of acute ischemic stroke (AIS) patients with hemorrhagic transformation (HT), especially high-risk patients with parenchymal hematoma (PH), are crucial in determining subsequent treatment strategies. This study combined automated ASPECTS software with noncontrast CT (NCCT) and CTA source image (CTASI) attenuation changes using Hounsfield unit (HU) values to predict HT and PH in patients with AIS. Materials and Methods: We retrospectively enrolled 172 consecutive patients with anterior circulation large-vessel occlusion between 2016 and 2020. Univariate and multivariate logistic regression and receiver operating characteristic (ROC) analyses were used to investigate the relationship between NCCT and CTASI-ASPECTS-HU, as well as other clinical and radiological parameters of HT and PH. Univariate and multivariate logistic regression analyses were performed to explore risk factors for HT or PH, and an ROC curve was used to evaluate their diagnostic values. Results: A multivariate analysis showed that CTASI-ASPECTS-HU and NIHSS score were independent predictors of HT (CTASI-ASPECTS-HU: odds ratio (OR), 2.22; 95% CI, 1.01–4.92; NIHSS: OR, 1.07; 95% CI, 1.02–1.13) and PH (CTASI-ASPECTS-HU: OR, 6.51; 95% CI, 2.29–18.50; NIHSS: OR, 1.07; 95% CI, 1.01–1.13). According to ROC analysis, CTASI-ASPECTS-HU >0.09 identified HT (area under the curve, 0.70; sensitivity, 70.15%; specificity, 61.90%), and CTASI-ASPECTS-HU >0.10 identified PH (area under the curve, 0.79; sensitivity, 76.19%; specificity, 73.33%). The area under the curve for predicting HT or PH increased when CTASI-ASPECTS-HU was combined with NIHSS score (HT: area under the curve, 0.74; sensitivity, 73.13%; specificity, 70.48%; PH: area under the curve, 0.81; sensitivity, 85.71%; specificity, 72.38%). Conclusion: CTASI-ASPECTS-HU is a reliable radiological predictor of HT and PH in patients with AIS. Its predictive efficacy is moderately improved when combined with NIHSS score.

We assessed the value of computed tomography (CT) and automated Alberta Stroke Program Early CT Score (ASPECTS) with net water uptake (NWU) to predict stroke onset time.Two-hundred forty stroke patients with anterior circulation large-vessel occlusion were included. CT-ASPECTS-NWU values were calculated by comparing the mean Hounsfield units of affected ASPECTS regions with unaffected contralateral regions. The correlation between ASPECTS-NWU and stroke onset to CT time was assessed. ASPECTS-NWU predictive values were calculated to identify a stroke onset to CT time of within 4.5/6 h.A correlation existed between stroke onset to CT time and ASPECTS-NWU (r = 0.65, p 0.001), which was affected by collateral status and infarct location. The area under the receiver operating characteristic (ROC) curve (AUC) for distinguishing a stroke onset to CT time of within 4.5 h was 0.837 (95% confidence interval [CI] 0.784-0.881; optimal cutoff 7%; sensitivity 87.10%; specificity 62.36%). The multi-index AUC was 0.884 (95% CI 0.837-0.922). The AUC for distinguishing a stroke onset to CT time of within 6 h was 0.836 (95% CI 0.783-0.880; optimal cutoff 9%; sensitivity 72.73%; specificity 81.16%). The multi-index AUC was 0.881 (95% CI 0.834-0.920).ASPECTS-NWU may be used to determine stroke onset time in patients with unwitnessed or wake-up stroke.

This study aimed to use the automated Alberta Stroke Program Early CT Score (ASPECTS) software to assess the value of different CT modalities (non-contrast CT, CT angiography [CTA]-arterial, CTA-venous, and arterial- and venous-phase mismatch-ASPECTS) in predicting the final infarct extent and clinical outcome in large-vessel occlusion stroke.This retrospective study included patients with large-vessel occlusion stroke who underwent reperfusion therapy during 2015 to 2019. Correlations between different CT-ASPECTS modalities and follow-up CT-ASPECTS and outcome were determined using Spearman rank correlation coefficient. Receiver operating characteristic curve analysis was used to assess the ability of different CT-ASPECTS modalities to identify patients with good outcomes.One hundred and thirty-five patients were included. We found almost-perfect correlation between CTA-venous-ASPECTS and follow-up CT-ASPECTS (r = 0.92; 95% CI: 0.89-0.95), better than that in other CT modalities. The 90-day modified Rankin scale (mRS) score substantially correlated with CTA-venous-ASPECTS (r = -0.64; 95% CI: -0.73 to -0.52). The ROC curve analysis showed CTA-venous-ASPECTS had the highest area under the curve (AUC: 0.82; 95% CI: 0.75-0.89; P 0.001), followed by mismatch-ASPECTS (AUC: 0.75; 95% CI: 0.65-0.85; P 0.001). When emphasizing the sensitivity for identifying patients with good outcomes, the best cut-off point of mismatch-ASPECTS was -3 with the highest sensitivity (91.30%).CTA-venous-ASPECTS is a reliable tool to predict the infarct extent and outcome. Furthermore, mismatch-ASPECTS may represent images in different angiographic phases and was sensitive for prognosis prediction.

Computation offloading decision in Multi-access Edge Computing (MEC) may expose users' characteristics and reveal users' privacy. In this paper a privacy-preserving computation offloading method based on privacy entropy is proposed. Firstly, the privacy exposure risk caused by the characteristics of tasks' offloading frequencies is studied, and privacy entropy based on the deviation of tasks' offloading frequencies is proposed as a quantitative analysis metric. Then, the privacy restriction is introduced into the offloading decision model with optimal energy consumption under delay constraint, ensuring that the privacy entropy does not exceed the privacy threshold, and a privacy-preserving computation offloading method based on privacy entropy is established. Finally, based on the genetic algorithm the offloading decisions that satisfy the privacy constraint and the optimal energy consumption target are solved, and the optimal decisions are used to train the artificial neural network's parameters to quickly solve the optimal decision in each timeslot. Simulation results show that the method can effectively reduce the average energy consumption of the terminal and make the offloading frequencies of tasks always meet the privacy constraint.

The transformation of parameter tensors for anisotropic medium in different coordinate systems is derived. The electric field for a magnetized cold plasma sphere and the general expression of scattering field from anisotropic target are obtained. The functional relations of differential scattering cross section and the RCS for the magnetized plasma sphere are presented. Simulation results are in agree with that in the literatures, which shows the method used and results obtained are correct and the results provide a theoretical base for anisotropic target identification etc.

The size distribution and canting angle distribution for non-spherical rain drops are taken into account. Utilizing the Muller matrix, the backscattering power densities are computed at different rain rate and different polarization in millimeter waves band, according the rainfall condition in Xi'an, China. Finally the results obtained are discussed.

A atmosphere structure constant Cn2 model, which varies with height, is presented based on Karasawa's amplitude scintillation model, existing ITU-R Cn2 model for optical and Xi'an area meteorologic measured relative humidity data in cloud cover, at 30∼50GHz. In this Cn2 model it is considered that relative humidity u varies with height. The log-amplitude scintillation deviation σχcalculated in terms of the Cn2 model compare with values predicted by means of STNV2 model applied to Italsat channels. It is emerged that the calculation results based on the Cn2 model are distinctly smaller then the values predicted by STNV2 model. Their differences increase with decrease of u, because the water vapor content in the atmosphere at continental climate Xi'an area is smaller then the one at Italy, which is oceanic cilmate. When the water vapor in atmosphere approach saturation, the calculation results agree almost with prediction results. Therefore, it is shown that the Cn2 model is usable and is applied at the continental climate area as Xi'an area.

A method of determining the rationality for raindrop size distribution is first presented. The relative volume occupied by raindrops is derived. The attenuation and cross-polar discrimination induced by rainfall at millimeter waves are investigated with the incoming expression and a brief analysis is presented.

Dust storms and turbulence consist of a random medium system, its effects on milimeter waves propagation are studied. Attenuation of millimeter waves, its phase shift and cross- polar discrimination are presented. Results show that dust storms mainly effects XPD and phase shift of millimeter waves, turbulence chiefly produces attenuation, in mediocre fluctuation.

Attenuation of millimeter waves induced by sand and dust storms along horizontal paths is analyzed and its expressions applied to earth-space paths is presented. Graphs of the specific attenuation and attenuation versus height and visibility are gotten for two kinds of particle size distribution, namely exponential, lognormal. Results obtained are briefly discussed.

The reflection and transmission of an incident Gaussian beam by a uniaxial anisotropic slab are investigated, by expanding the incident Gaussian beam, reflected beam, internal beam as well as transmitted beam in terms of cylindrical vector wave functions. The unknown expansion coefficients are determined by virtue of the boundary conditions. For a localized beam model, numerical results are provided for the normalized field intensity distributions, and the propagation characteristics are discussed concisely.

Twitter is emerging social media. There is an important practical significance to study its communication effect in many aspects of the Marketing Management, Public Sentiment, Hot Extraction and so on. The retweeting scale is an important indicator to reflect the communication effect. Based on analyzing the factors, a prediction algorithm based on SVM was proposed for retweeting with five features: publisher influence, acceptor activity, interest level, content importance and intimacy. Furthermore, the prediction algorithm for retweeting scale was proposed based above, the method to evaluate the prediction accuracy was given. At last the experiment with Twitter data showed a good result that the prediction accuracy of retweeting scale was up to 86.63%.

Within the generalized Lorenz–Mie theory framework, an analytic solution to Gaussian beam scattering by a rotationally uniaxial anisotropic sphere is presented. The scattered fields as well as the fields within the anisotropic sphere are expanded in terms of infinite series with spherical vector wave functions by using an appropriate expansion of the incident Gaussian beam. The unknown expansion coefficients are determined from a system of linear equations derived from the boundary conditions. Numerical results of the normalized differential scattering cross section are shown, and the scattering characteristics are discussed concisely.

Based on the generalized Lorenz-Mie theory (GLMT) that provides the general framework and expansion of the incident shaped beam in terms of cylindrical vector wave functions, an analytic solution to the electromagnetic scattering by coated infinite cylinders is constructed, for arbitrary incidence of a shaped beam. As an example, for a tightly focused Gaussian beam propagating perpendicular to the cylinder axis, the scattering characteristics that obviously demonstrate the three-dimensional nature are described in detail.

Analysis of log-amplitude scintillation due to troposphere clear-air turbulent and meteorological parameters variation is shown at microwaves (MW) on slant paths, based on ITU-R turbulence atmosphere structure parameter, temperature and relative humidity along vertical path. Comparisons of Karasawa model based on the data obtained from a low-elevation microwave propagation experiment and ITU-R Recommendation model prediction results with evaluation results are shown and discussed. The results show that although the relative humidity effected on optical refractive index of a cell is not principally element at optical wave band, at microwave and millimeter-wave band, the relative humidity and temperature is the major factor impacted on log-amplitude scintillation. Hence, the variations of temperature and relative humidity with height, which can be obtained by experiment (or test) and weather observation method is important for low-elevation satellite communication and microwave remote sensing. A atmosphere structure constant Cn2 model, which varies with height, is presented based on ITU-R and Karasawa amplitude scintillation model, existing ITU-R Cn2 model for optical and meteorologic measured relative humidity and temperature data, at 10~30GHz. In this Cn2 model it is considered that relative humidity and temperature varies with height. The log-amplitude scintillation deviation calculated in terms of the Cn2 model based on humidity and temperature vertical profile compare with values predicted by means of ITU-R and Ortgies model applied to Italsat channels. It is emerged that the calculation results based on the Cn2 model agree almost with prediction results by ITU-R and Ortgies model at 10~30GHz and there is an advantage that relative humidity and temperature varied with height has be considered in the Cn2 model. Therefore, it is shown that the Cn2 model is usable and is more practical.