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2,196 results on '"Li, Yimin"'

56. Geometric Constraints on Two-electron Reduced Density Matrices

Author

Li, Yimin

Subjects
Quantum Physics, Mathematical Physics
Abstract

For many-electron systems, the second-order reduced density matrix (2-RDM) provides sufficient information for characterizing their properties of interests in physics and chemistry, ranging from total energy, magnetism, quantum correlation and entanglement to long-range orders. Theoretical prediction of the structural properties of 2-RDM is an essential endeavor in quantum chemistry, condensed matter physics and, more recently, in quantum computation. Since 1960s, enormous progresses have been made in developing RDM-based electronic structure theories and their large-scale computational applications in predicting molecular structure and mechanical, electrical and optical properties of various materials. However, for strongly correlated systems, such as high-temperature superconductors, transition-metal-based biological catalysts and complex chemical bonds near dissociation limit, accurate approximation is still out of reach by currently most sophisticated approaches. This limitation highlights the elusive structural feature of 2-RDM that determines quantum correlation in many-electron system. Here, we present a set of constraints on 2-RDM based on the basic geometric property of Hilbert space and the commutation relations of operators. Numerical examples are provided to demonstrate the pronounced violation of these constraints by the variational 2-RDMs. It is shown that, for a strongly correlated model system, the constraint violation may be responsible for a considerable portion of the variational error in ground state energy. Our findings provide new insights into the structural subtlety of many-electron 2-RDMs., Comment: 20 pages, 3 figures

Published
2020
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67. Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images

Author

Chen, Wen, Li, Yimin, Dyer, Brandon A, Feng, Xue, Rao, Shyam, Benedict, Stanley H, Chen, Quan, and Rong, Yi

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Atlases as Topic, Deep Learning, Head and Neck Neoplasms, Humans, Masticatory Muscles, Neck, Observer Variation, Radiation Dosage, Tomography, X-Ray Computed, Deep learning model, Masticatory muscles, Auto-segmentation, Oncology & Carcinogenesis, Clinical sciences, Oncology and carcinogenesis
Abstract

BackgroundImpaired function of masticatory muscles will lead to trismus. Routine delineation of these muscles during planning may improve dose tracking and facilitate dose reduction resulting in decreased radiation-related trismus. This study aimed to compare a deep learning model with a commercial atlas-based model for fast auto-segmentation of the masticatory muscles on head and neck computed tomography (CT) images.Material and methodsPaired masseter (M), temporalis (T), medial and lateral pterygoid (MP, LP) muscles were manually segmented on 56 CT images. CT images were randomly divided into training (n = 27) and validation (n = 29) cohorts. Two methods were used for automatic delineation of masticatory muscles (MMs): Deep learning auto-segmentation (DLAS) and atlas-based auto-segmentation (ABAS). The automatic algorithms were evaluated using Dice similarity coefficient (DSC), recall, precision, Hausdorff distance (HD), HD95, and mean surface distance (MSD). A consolidated score was calculated by normalizing the metrics against interobserver variability and averaging over all patients. Differences in dose (∆Dose) to MMs for DLAS and ABAS segmentations were assessed. A paired t-test was used to compare the geometric and dosimetric difference between DLAS and ABAS methods.ResultsDLAS outperformed ABAS in delineating all MMs (p  0.05). DLAS based contours had dose endpoints more closely matched with that of the manually segmented when compared with ABAS.ConclusionsDLAS auto-segmentation of masticatory muscles for the head and neck radiotherapy had improved segmentation accuracy compared with ABAS with no qualitative difference in dosimetric endpoints compared to manually segmented contours.

Published
2020

68. Inhalation of ACE2 as a therapeutic target on sex-bias differences in SARS-CoV-2 infection and variant of concern

Author

Onodera, Yu, Liang, Jady, Li, Yuchong, Griffin, Bryan, Thanabalasingam, Thenuka, Lu, Cong, Zhu, JiaYi, Liu, Mingyao, Moraes, Theo, Zheng, Wenhua, Khateeb, Jasmin, Khang, Julie, Huang, Yongbo, Jerkic, Mirjana, Nakane, Masaki, Baker, Andrew, Orser, Beverley, Chen, Ya-Wen, Wirnsberger, Gerald, Penninger, Josef M., Rotstein, Ori D., Slutsky, Arthur S., Li, Yimin, Mubareka, Samira, and Zhang, Haibo

Published
2023
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76. Clinical Enhancement in AI-Based Post-processed Fast-Scan Low-Dose CBCT for Head and Neck Adaptive Radiotherapy.

Author

Chen, Wen, Li, Yimin, Yuan, Nimu, Qi, Jinyi, Dyer, Brandon A, Sensoy, Levent, Benedict, Stanley H, Shang, Lu, Rao, Shyam, and Rong, Yi

Subjects
adaptive radiotherapy, cone beam CT, deep convolutional neural network, head and neck cancer, image quality, Cancer, Dental/Oral and Craniofacial Disease, Biomedical Imaging, Clinical Research
Abstract

Purpose: To assess image quality and uncertainty in organ-at-risk segmentation on cone beam computed tomography (CBCT) enhanced by deep-learning convolutional neural network (DCNN) for head and neck cancer. Methods: An in-house DCNN was trained using forty post-operative head and neck cancer patients with their planning CT and first-fraction CBCT images. Additional fifteen patients with repeat simulation CT (rCT) and CBCT scan taken on the same day (oCBCT) were used for validation and clinical utility assessment. Enhanced CBCT (eCBCT) images were generated from the oCBCT using the in-house DCNN. Quantitative imaging quality improvement was evaluated using HU accuracy, signal-to-noise-ratio (SNR), and structural similarity index measure (SSIM). Organs-at-risk (OARs) were delineated on o/eCBCT and compared with manual structures on the same day rCT. Contour accuracy was assessed using dice similarity coefficient (DSC), Hausdorff distance (HD), and center of mass (COM) displacement. Qualitative assessment of users' confidence in manual segmenting OARs was performed on both eCBCT and oCBCT by visual scoring. Results: eCBCT organs-at-risk had significant improvement on mean pixel values, SNR (p < 0.05), and SSIM (p < 0.05) compared to oCBCT images. Mean DSC of eCBCT-to-rCT (0.83 ± 0.06) was higher than oCBCT-to-rCT (0.70 ± 0.13). Improvement was observed for mean HD of eCBCT-to-rCT (0.42 ± 0.13 cm) vs. oCBCT-to-rCT (0.72 ± 0.25 cm). Mean COM was less for eCBCT-to-rCT (0.28 ± 0.19 cm) comparing to oCBCT-to-rCT (0.44 ± 0.22 cm). Visual scores showed OAR segmentation was more accessible on eCBCT than oCBCT images. Conclusion: DCNN improved fast-scan low-dose CBCT in terms of the HU accuracy, image contrast, and OAR delineation accuracy, presenting potential of eCBCT for adaptive radiotherapy.

Published
2020

80. Guidelines for the diagnosis, treatment, prevention and control of infections caused by carbapenem-resistant gram-negative bacilli

Author

Zeng, Mei, Xia, Jun, Zong, Zhiyong, Shi, Yi, Ni, Yuxing, Hu, Fupin, Chen, Yijian, Zhuo, Chao, Hu, Bijie, Lv, Xiaoju, Li, Jiabin, Liu, Zhengyin, Zhang, Jing, Yang, Wenjie, Yang, Fan, Yang, Qiwen, Zhou, Hua, Li, Xin, Wang, Jianhua, Li, Yimin, Ren, Jian'an, Chen, Baiyi, Chen, Dechang, Wu, Anhua, Guan, Xiangdong, Qu, Jieming, Wu, Depei, Huang, Xiaojun, Qiu, Haibo, Xu, Yingchun, Yu, Yunsong, and Wang, Minggui

Published
2023
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83. Cancer Mortality and Trend among Community-living Residents in Central Pudong New Area, Shanghai, 2007—2020

Author

Jie XU, Wei WANG, Xiaoqiong ZHANG, Juanping LI, Yimin CHEN, Li WANG, Guifen MA

Subjects
neoplasms, mortality, demographic aging, trend analysis, vital statistics, data collection, Medicine
Abstract

Background The percentage of older population of the total population is increasing in developed areas of developing countries, and the impact and contribution of population composition factors towards cancer mortality are still unclear. Objective To analyze the epidemiological characteristics and trend of cancer mortality among community-living residents in central Pudong New Area of Shanghai from 2007 to 2020. Methods From January to December 2021, we retrospectively collected the death data during 2007 to 2020 of registered permanent residents living in Dongming Road Community of Pudong New Area from the all-cause death monitoring database, and population composition data from Pudong New Area Public Security Bureau. We used the crude mortality rate (CMR) , standardized mortality rate (SMR, with reference to the SMR of the general Chinese population in 2010) , and average annual percent change (AAPC) to analyze the status and trends of cancer mortality, and used the decomposition method to estimate the contribution rates of demographic and non-demographic factors to the change of cancer mortality. Results From 2007 to 2020, a total of 2 446 cancer-induced deaths were reported by the Dongming Road Community, accounting for 28.48% of the total reported deaths (8 589 cases) . The CMR of cancers was 268.56/105, in which the CMR due to lung cancer, colorectal cancer, and gastric cancer was 69.61/105, 33.05/105 and 31.84/105, respectively. The SMR of cancers was 71.46/105 , in which the SMR due to lung cancer, colorectal cancer, and gastric cancer was 33.12/105, 15.46/105, and 15.29/105, respectively. During this period, the CMR for cancers among men, women and the whole population remained stable (Z=1.82, -0.54, 0.87, P>0.05) . The CMR and SMR in men were higher than those in women (u=6.69, P0.05) . As in the whole population, it showed a downward trend year by year (AAPC=-1.73%, Z=-3.09, P=0.010) . Furthermore, the CMR for cancers in those aged 65-79 showed a downward trend gradually (AAPC=-4.17%, Z=-1.80, P

Published
2022
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97. Correction to: Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

Author

Ke, Lu, Lin, Jiajia, Doig, Gordon S., van Zanten, Arthur R. H., Wang, Yang, Xing, Juan, Zhang, Zhongheng, Chen, Tao, Zhou, Lixin, Jiang, Dongpo, Shi, Qindong, Lin, Jiandong, Liu, Jun, Cheng, Aibin, Liang, Yafeng, Gao, Peiyang, Sun, Junli, Liu, Wenming, Yang, Zhenyu, Zhang, Rumin, Xing, Wei, Zhang, An, Zhou, Zhigang, Zhou, Tingfa, Liu, Yang, Tong, Fei, Wang, Qiuhui, Pan, Aijun, Huang, Xiaobo, Fan, Chuming, Lu, Weihua, Shi, Dongwu, Wang, Lei, Li, Wei, Gu, Liming, Xie, Yingguang, Sun, Rongqing, Guo, Feng, Han, Lin, Zhou, Lihua, Zheng, Xiangde, Shan, Feng, Liu, Jianbo, Ai, Yuhang, Qu, Yan, Li, Liandi, Li, Hailing, Pan, Zhiguo, Xu, Donglin, Zou, Zhiqiang, Gao, Yan, Yang, Chunli, Kou, Qiuye, Zhang, Xijing, Wu, Jinglan, Qian, Chuanyun, Zhang, Weixing, Zhang, Minjie, Zong, Yuan, Qin, Bingyu, Zhang, Fusen, Zhai, Zhe, Sun, Yun, Chang, Ping, Yu, Bo, Yu, Min, Yuan, Shiying, Deng, Yijun, Zhao, Liyun, Zang, Bin, Li, Yuanfei, Zhou, Fachun, Chen, Xiaomei, Shao, Min, Wu, Weidong, Wu, Ming, Zhang, Zhaohui, Li, Yimin, Guo, Qiang, Wang, Zhiyong, Gong, Yuanqi, Song, Yunlin, Qian, Kejian, Feng, Yongjian, Fu, Baocai, Liu, Xueyan, Li, Zhiping, Gong, Chuanyong, Sun, Cheng, Yu, Jian, Tang, Zhongzhi, Huang, Linxi, Ma, Biao, He, Zhijie, Zhou, Qingshan, Yu, Rongguo, Tong, Zhihui, and Li, Weiqin

Published
2022
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99. Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

Author

Ke, Lu, Lin, Jiajia, Doig, Gordon S., van Zanten, Arthur R. H., Wang, Yang, Xing, Juan, Zhang, Zhongheng, Chen, Tao, Zhou, Lixin, Jiang, Dongpo, Shi, Qindong, Lin, Jiandong, Liu, Jun, Cheng, Aibin, Liang, Yafeng, Gao, Peiyang, Sun, Junli, Liu, Wenming, Yang, Zhenyu, Zhang, Rumin, Xing, Wei, Zhang, An, Zhou, Zhigang, Zhou, Tingfa, Liu, Yang, Tong, Fei, Wang, Qiuhui, Pan, Aijun, Huang, Xiaobo, Fan, Chuming, Lu, Weihua, Shi, Dongwu, Wang, Lei, Li, Wei, Gu, Liming, Xie, Yingguang, Sun, Rongqing, Guo, Feng, Han, Lin, Zhou, Lihua, Zheng, Xiangde, Shan, Feng, Liu, Jianbo, Ai, Yuhang, Qu, Yan, Li, Liandi, Li, Hailing, Pan, Zhiguo, Xu, Donglin, Zou, Zhiqiang, Gao, Yan, Yang, Chunli, Kou, Qiuye, Zhang, Xijing, Wu, Jinglan, Qian, Chuanyun, Zhang, Weixing, Zhang, Minjie, Zong, Yuan, Qin, Bingyu, Zhang, Fusen, Zhai, Zhe, Sun, Yun, Chang, Ping, Yu, Bo, Yu, Min, Yuan, Shiying, Deng, Yijun, Zhao, Liyun, Zang, Bin, Li, Yuanfei, Zhou, Fachun, Chen, Xiaomei, Shao, Min, Wu, Weidong, Wu, Ming, Zhang, Zhaohui, Li, Yimin, Guo, Qiang, Wang, Zhiyong, Gong, Yuanqi, Song, Yunlin, Qian, Kejian, Feng, Yongjian, Fu, Baocai, Liu, Xueyan, Li, Zhiping, Gong, Chuanyong, Sun, Cheng, Yu, Jian, Tang, Zhongzhi, Huang, Linxi, Ma, Biao, He, Zhijie, Zhou, Qingshan, Yu, Rongguo, Tong, Zhihui, and Li, Weiqin

Published
2022
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