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Stroke is the second major diseases leading to death, among which ischemic stroke is the most common. The key to the treatment of ischemic stroke replies on early diagnosis and treatment. With the development of technology, artificial intelligence (AI) has shown great application value in the early screening, lesion detection, ischemic penumbra evaluation, vascular occlusion judgment, treatment assistance and prognosis prediction of ischemic stroke, which is expected to further improve the diagnostic efficiency and therapeutic effect of ischemic stroke.

Reviews contain rich user and item information,which helps to alleviate the problem of data sparsity.However,the existing recommendation model based on reviews is not sufficient and effective enough to mine the review texts,and most of them ignore the migration of user interest over time and the item description documents containing the item attribute,which makes the recommendation result not accurate enough.In this paper,a deep semantic mining based recommendation model (DSMR) is proposed.By mining the semantic information of review texts and item description documents in depth,user characteristics and item attributes can be extracted more accurately,so as to realize more accurate recommendation.Firstly,the BERT pre-training model is used to process the comment text and item description document,and the user characteristics and item attributes are excavated deeply,which effectively alleviated the problems of data sparse and item cold start.Then,the forward LSTM is used to pay attention to the change of user preferences over time,and more accurate recommendations are obtained.Finally,in the model training stage,the experimental data are randomly selected from 1 to 5 points at 1:1:1:1:1 to ensure the same amount of data for each score value,so as to make the results more accurate and the model more robust.Experiments on four commonly used Amazon open datasets show that the root mean square error (RMSE) of DSMR is at least 11.95% lower than the two classical recommendation models based only on rating data,and it is better than the three new recommendation models based only on review text,and 5.1% lower than the optimal model.

Objective To establish a platform by using carotid MRI and computational fluid dynamics (CFD) to assess hemodynamic changes around carotid atherosclerotic plaques. Methods Thirteen patients with carotid atherosclerosis were recruited in this study. Six volunteers were regarded as normal controls. All the patients and volunteers underwent carotid MRI and contrast-enhanced magnetic resonance angiography (CEMRA). Carotid MRI was used to visualize the plaque structures and components. All plaques were divided into different types according to plaque components. CEMRA images were used to obtain three-dimensional (3D) models of carotid bifurcations, whose boundary conditions were specified using CFD front-end software, and then the mesh file of the 3D models were obtained to import to CFD software to visualize hemodynamic maps, including wall shear stress (WSS), static pressure and blood velocity. Results Fifteen diseased internal carotid arteries (ICAs) were assessed. According to the MRI appearance of the plaques, the types of these plaques were from IV-V to VIII. All of these were vulnerable plaques which caused irregular stenosis of ICAs. The WSS, static pressure and blood velocity were (79.86 ± 57.83) Pa, (- 7586.81 ± 9313.83) Pa, (2.76±1.81) m/s, respectively in the diseased ICAs group and (2.52 ± 0.58) Pa, (- 71.65 ± 30.89) Pa, (0.21 ± 0.06) m/s, respectively in the normal control group. In the diseased ICAs group WSS was elevated heterogenously and static pressure was decreased heterogenously near the plaques. The blood velocity near the plaques was increased but still streamlined. Statistical significant differences were shown for WSS, static pressure and blood velocity between 2 groups (P = 0.000, for all). Conclusions The platform combining MRI and CFD can be used to analyze the plaque structures and fluid dynamic change near the plaques, suggesting hemodynamics plays an important role in the plaque progression and vulnerability. [ABSTRACT FROM AUTHOR]

The "Songshan" supercomputer system is a new generation of heterogeneous supercomputer cluster independently developed by China.The CPU and DCU accelerators it carries are all independently developed by my country.In order to expand the scientific computing ecology of the platform and verify the feasibility of quantum computing research on this platform,the paper uses a heterogeneous programming model to implement a heterogeneous version of the quantum Fourier transform simulation on the "Songshan" supercomputer system.The computing hotspots of the program are allocated to run on the DCU;then MPI is used to enable multiple processes on a single computing node to realize the concurrent data transmission and calculation of the DCU accelerator;finally,the hiding of computing and communication prevents the DCU from being in the middle of data transmission.The experiment implements a 44 Qubits-scale quantum Fourier transform simulation on a supercomputing system for the first time.The results show that the heterogeneous version of the quantum Fourier transform module makes full use of the computing resources of the DCU accelerator and achieves 11.594 compared to the traditional CPU version.The speedup ratio is high,and it has good scalability on the cluster.This implementation method provides a reference for the simulation implementation and optimization of other quantum algorithms on the "Songshan" supercomputer system.