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交叉性失语是一组由于右利手者右侧大脑半球损伤引起的获得性语言障碍综合征，在卒 中患者中的发病率为0.38%～4.6%，包括镜像失语与非典型失语，口语表达障碍多数为非流利性失语。 交叉性失语的发病机制目前仍存在争议，越早进行康复训练，预后越好。本文从交叉性失语的发病 机制、临床特点、影像学检查及康复训练的研究进展进行综述，以提高临床医师的重视，从而为交 叉性失语的诊治提供理论依据。 Abstract: Crossed aphasia (CA) refers to aphasia occurring after right cerebral hemisphere injure in right-handed individuals. The incidence of CA in stroke patients is 0.38%-4.6%. CA includes mirror image crossed aphasia and anomalous crossed aphasia, and impaired oral expression mainly presented with non-fluent aphasia. The mechanism of CA remains unclear. Previous studies have showed that the earlier the rehabilitation, the better the prognosis. This paper reviewed the pathogenesis, clinical features, imaging characteristics and rehabilitation of CA, to provide a reference for diagnosis and treatment of CA.

【目的】探讨线粒体未折叠蛋白反应（UPR mt ）对阿尔茨海默病（AD）Aβ蛋白聚集毒性的影响。【方法】将秀丽线 虫（Caenorhabditis elegans）线粒体外膜tomm-22、内膜E04A4.5以及atfs-1基因克隆并构建L4440干扰载体，转化HT115感受 态细胞以制备 tomm-22、E04A4.5 和 atfs-1 RNA 干扰菌。研究 tomm-22 和 E04A4.5 RNA 干扰对转基因线虫 AD 疾病模型 CL4176和CL2006瘫痪进程的影响。观察tomm-22和E04A4.5 RNA干扰后野生型线虫N2寿命，检测ATFS-1信号在抑制Aβ 蛋白聚集毒性中的作用，分析tomm-22和E04A4.5 RNA干扰后转基因线虫SJ4100 UPR mt 动态变化，以及转基因线虫DA2123 自噬水平。【结果】通过对线虫线粒体tomm-22和E04A4.5基因克隆及构建RNA干扰菌建立了UPR mt 模型，证实能够抑制AD 疾病模型CL4176 Aβ蛋白聚集毒性并延缓其瘫痪进程。野生型N2在喂食tomm-22和E04A4.5 RNA 干扰菌后寿命明显缩 短，而渐进性瘫痪线虫AD模型CL2006呈现前期延缓瘫痪而后期加速瘫痪的生理现象，说明UPR mt 呈现短期缓解线粒体应 激和改善线粒体功能的调节作用。通过atfs-1 RNA干扰证实延缓线虫AD模型CL4176瘫痪进程与ATFS-1信号不直接相 关。然而，tomm-22和E04A4.5 RNA干扰后UPR mt 呈现逐渐增强的趋势，并进一步诱导自噬相关分子LGG-l的上调表达，提 示RNA干扰延缓AD瘫痪进程是通过提高线虫体内自噬活性而发挥作用。【结论】线粒体UPR mt 通过协调线粒体与细胞核之 间的信号转导能够抑制Aβ蛋白聚集毒性，有助于恢复线粒体乃至细胞内蛋白质稳态，保障细胞正常生理功能，为AD等神 经退行性疾病的早期预防和治疗提供新的靶点。

This study refers computer simulation in scientific research to as scientific simulation. Based on its narrow and broad definitions, this study divides scientific simulation into three stages: numerical computation, simulation intelligence, and science brain. Now, scientific simulation is entering the era of simulation intelligence, i.e., driven by scientific big data and artificial intelligence technology, scientific simulation is shifting from traditional numerical simulation to simulation integrated with artificial intelligence. In order to understand what the right computing system for simulation intelligence is, the design guidelines, basic methods, and key technical problems are discussed. [ABSTRACT FROM AUTHOR]

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Objective To examine the heart sound signals of four common congenital heart diseases of ventricular septal defect, atrial septal defect, patent ductus arteriosus and patent foramen with associated pulmonary hypertension and propose a deep learning-based intelligent auscultation algorithm for automatic classification of heart sound signals. Methods The algorithm in this study was based upon digital signal processing technology of converting one-dimensional temporal signal classification into a two-dimensional image classification and further applying deep neural networks for automatic classification of heart sound signals. A total of 941 heart sound data samples collected from Children's Hospital of Zhejiang University School of Medicine were employed for training, validation and testing with a ratio of 8 1 1 for training, validation and testing sets respectively. Additionally, 107 heart sound data samples gathered from a clinical screening environment were collected to validate the effectiveness of intelligent auscultation algorithm in real-world clinical applications. Results In this study, discrete wavelet transformation was utilized to denoise the heart sound signals and there was a significant improvement in model performance. Compared to model without denoising, the denoised model achieved no- table enhancements in accuracy, sensitivity, specificity and F1 score on testing set with improvements of 15. 8%,32.6%,11.1% and 27.3% respectively. Furthermore, the authors compared the performance of several common classification neural network models, including Swin_transform, Vit, Mobilenet, Resenet and Vgg, with their respective F1 scores of 0.905,0.842,0.687,0.814 and 0.864. Finally, using Swin_transform model, tests on the external dataset of 107 cases yielded an accuracy of 0.833, a sensitivity of 0.872 and a specificity of 0. 801. Conclusion This study highlights the significant impact of noise and neural network structure on the performance of automatic classification models for CHD heart sound signals. Through the application of discrete wavelet transform for denoising heart sound signals, a substantial improvement in model performance is observed. Among various common classification neural network models, Swin_transform model exhibits the best classification performance. Additionally, the validation of intelligent auscultation algorithm on an external dataset of 107 cases demonstrates its effectiveness in real-world clinical applications, yielding favorable accuracy, sensitivity and specificity results. In summary, this study demonstrates the promising potential of deep learning-based intelligent auscultation algorithms for automatic classification of CHD heart sound signals. [ABSTRACT FROM AUTHOR]

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目的 探讨标准化病人的教学模式对卒中后言语障碍、吞咽困难教学效果的影响。 方法 将全国继续教育项目——全国语言障碍、吞咽困难培训班的学员中接受标准化病人教学模式 者作为实验组，未采用标准化教学模式教学者作为对照组，进行教学满意度调查问卷。对调查问卷 的8个问题进行统计学分析，比较实验组和对照组的教学效果。 结果 实验组对培训老师的授课能力满意度、培训内容掌握程度、教师授课能力和教材内容的认可 度、专家授课内容实用性的认可度、专家授课内容的深度、对培训班授课的综合评价等项目的满意度 均比对照组高，差异有统计学意义。 结论 标准化病人教学方法在全国继续教育项目——全国言语障碍、吞咽困难培训班中教学效果 显著，是值得推荐的教学模式。 Abstract: Objective To explore the effect of standardized patients teaching model on aphasia and dysphagia after stroke. Methods Those who received the standardized patients teaching model among the students of the National Continuing Education Program-National Aphasia and Dysphagia Training Course were assigned to the experimental group. Those not receiving the standardized teaching model as the control group, and a questionnaire on teaching satisfaction was conducted. Statistical analysis was performed on the eight questions of the questionnaire, to compare the teaching effects of the experimental group and the control group. Results The satisfaction with the training ability of the teachers, the degree of mastery of the training content, the recognition of the teachers' teaching ability and the content of the teaching material, the recognition of the practical content of the experts' lecture, the depth of the content of the experts' lecture, and the comprehensive evaluation of the training courses, etc. in the experimental group were all higher than that of the control group and the differences were statistically significant. Conclusion The standardized patients teaching method has a significant teaching effect in the National Continuing Education Progrom-National Aphasia and Dysphagia Training Course. This teaching mothod is worthy of recommendation.

Bacillus cereus（ B. cereus） is a facultative anaerobic gram-positive bacterium, which is widely distributed in nature. Some B. cereus strains are opportunistic pathogens which can cause food poisoning. B. cereus exists in two forms （free or biofilm） in the environment, and the formation of biofilm increases the difficulty of prevention and control of this bacterium. In this paper, the composition, regulatory mechanism and removal methods of B. cereus biofilm formation are reviewed, in order to provide a reference for the research of B. cereus biofilm and its control and prevention. [ABSTRACT FROM AUTHOR]

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Glucose monitoring is an important part of diabetes management. For over a century, diabetes monitoring technology has developed from the initial urine glucose test, to the later blood glucose test, and finally to the current continuous glucose monitoring (CGM), which is evolving in a more convenient, accurate, minimally invasive, and even non-invasive direction. CGM refers to the technology that continuously measures glucose concentrations in the subcutaneous interstitial fluid by glucose sensors. It can detect hyperglycemia and hypoglycemia that are not easily recognized by traditional monitoring methods. Using the huge amounts of glucose data generated by CGM technology, diabetes management is expected to be more targeted, with glucose control more accurate. In this context, novel measure of glucose control represented by time in range (TIR) has been popularized, which can provide comprehensive information including hyperglycemia, hypoglycemia, and glucose fluctuation. Hence, the modern approach to glucose control should focus not only on glycosylated hemoglobin, but also pay attention to new metrics such as TIR. In the future, more mature, minimally invasive and even non-invasive glucose monitoring technologies that are comfortable, stable and highly accurate should be further developed to greatly improve the experience and enthusiasm of the patients in blood glucose monitoring. Meanwhile, closed-loop insulin infusion system should be further developed, to truly realize individualized and automated glucose control, as well as further improvement of glucose control in the patients with diabetes. [ABSTRACT FROM AUTHOR]

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To solve the green fruit recognition problem of persimmons and apples, a green fruit segmentation algorithm based on optimized SOLO (Segmenting Objects by Locations) was proposed in this study to achieve accurate segmentation of green fruits in complex environments. The proposed algorithm was a single-stage instance segmentation method, which avoided the disadvantage that detection before segmentation in two-stage methods relied on detection performance. By introducing the concept of instance category, each pixel in the instance was assigned a category according to the location and size of the instance, therefore, the instance segmentation was transformed into a classification problem. This study takes green persimmons and green apples as the research objects. The image collection locations are Shandong Normal University (Changqing Lake Campus) Houshan and the Longwangshan Apple Production Base in Fushan District, Yantai City, Shandong Province. The acquisition device is a Canon EOS 80D SLR camera with an image resolution of 6 000×4 000 pixels. Collect under natural light during the day (7:00-17:00) and under LED light at night (19:00-22:00). A total of 568 images of green persimmons and 498 images of green apples were collected in the experiment, including nighttime, overlap, backlighting, forward light, shading, and after rain. The collected images were annotated by LabelMe software and then were made into a dataset in COCO format. Specifically, first, split-attention networks (ResNeSt) were used to extract features of the target fruit as the backbone network of optimized SOLO, which enhanced the transfer, reuse, and fusion of features in the front and back layers. Then ResNeSt and Feature Pyramid Network (FPN) were combined to solve the multi-scale problem of green fruits. Because FPN defined allocation strategies for different scale features and assigned them to the pyramid levels optimally. Finally, the image features extracted by the ResNeSt+FPN structure were utilized for the subsequent prediction. The optimized SOLO segmentation algorithm was divided into two branches: category prediction and mask generation. While the semantic category was predicted by the category prediction branch, the object instance was segmented by the mask generation branch, in this way, the target fruit segmentation was completed. The experimental results showed that the average recall and precision of the optimized SOLO segmentation algorithm reached 94.84% and 96.16%, respectively, with an average segmentation time of 0.14 s per green target fruit image on Graphics Processing Unit (GPU). Besides, compared with four algorithms, which were the optimized Mask R-CNN fruit recognition algorithm, SOLO, Mask Region Convolutional Neural Network (Mask R-CNN), and Fully Convolutional Instance-aware Semantic Segmentation (FCIS), the recall of the optimized SOLO segmentation algorithm in this study was improved by 1.63, 1.74, 2.23, and 6.52 percentage points, the precision was improved by 1.10, 1.47, 2.61, and 6.75 percentage points, respectively, and the segmentation times were reduced by 0.06, 0.04, 0.11, and 0.13 s, respectively. The relevant results show that the green fruit optimization SOLO segmentation algorithm proposed by the study can meet the real-time performance of green fruit segmentation and improve the accuracy of green fruit segmentation. This research algorithm can provide theoretical reference for segmentation of other target fruits and vegetables to extend the application of orchard yield measurement and robot harvesting. [ABSTRACT FROM AUTHOR]

The posture of target fruit is ever-changing in the complex orchard environment. Some target fruits are homochromatic with background, and the limited number of samples have brought great challenges to accurately detect the target, due mainly to the difficulty of collecting some environmental data. Therefore, the detection needs to meet the high requirements of intelligent yield measurement and automatic harvesting, in terms of both accuracy and efficiency. In this study, an efficient detection model was proposed for the green target fruits suitable for small samples under different light conditions and fruit postures. The optimized transformer network was also employed in this model. Firstly, the convolutional neural network (CNN) was used to extract image features. The transformer encoder was input after feature dimension reduction and positional encoding. Multi-head attention and feed-forward network (FFN) were then selected to obtain the encoder output. Secondly, the transformer decoder processed the input using multi-head attention and feed-forward network. The positional encoding was then added to each link. After that, the outputs were generated with different data sizes. The bounding boxes were much larger than the actual objects after prediction, indicating a low missing rate of green target fruit after decoder settlement. Finally, the feed-forward network (FFN) was utilized to predict the detection. The training of detection model was mostly used sufficient samples to avoid overfitting in the training process for higher generalization of the model. Bootstrapping was also introduced to repeatedly mapping the original data for several times. As such, the expanding dataset was utilized to meet the high requirement of larger samples for the higher accuracy of detection mode in the training process. Transfer learning was selected to significantly improve the training efficiency of the model, while, accelerate the convergence of the network. The apple and persimmon datasets were made separately for the model training. The experimental results show that the training efficiency of the model was greatly improved by more than 13% after migration learning. An excellent illustration of features transferability increased the speed and efficiency of detection, as the difference decreased between the pre-training and target task. Transfer learning was adopted to improve the efficiency of the model, where the model converged faster and was better suitable for the complex orchard environment. The new model can widely be expected to effectively realize the detection of green target fruit in the complex orchard environment with multiple postures, illumination, and scenes, indicating better generalization ability and robustness. The accuracies of detection were 93.27% and 91.35%, respectively, when testing green persimmons and green apples. Consequently, the new optimized model presented the best performance, compared with the conventional. The finding can also provide a sound theoretical reference for the target detection of green fruits and vegetables in the intelligent yield measurement and automated harvesting in orchards. [ABSTRACT FROM AUTHOR]

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The primal-dual interior point method requires strict initial feasible points in solving linear programming problems. The degree function is given based on the criterion of the initial feasible point in the primal-dual interior point method，and the fitness function in the evolutionary algorithm is designated by the degree function. The initial feasible point selection of the primal-dual interior point method for linear programming based on the evolutionary computation is proposed. It is taken by the harmony search. Numerical experiments show that the algorithm can find an initial feasible point of most given problems，thus validating the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

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