http:///kms.shanghaitech.edu.cn/:80 188BET金宝搏 Sun, 01 Jun 2025 16:31:08 GMT 2025-06-01T16:31:08Z http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533747 题名: 一种包括Nafamostat和K777的药物组合及其应用 作者: 杨海涛; 饶子和; 王镐锋; 刘小策; 陈新文; 肖庚富; 张磊砢; 杨琪; 姜标; 陈红莉; 彭伟; 杨秀娜 摘要: 本发明提供了一种包括Nafamostat和K777的药物组合及其应用。所述药物组合包含活性成分Nafamostat和K777。本发明的药物组合在抗冠状病毒的相关研究中能体现出显著的协同作用，在抗新冠病毒试验中，展现出10倍以上效力的提升。 Fri, 30 May 2025 17:00:18 GMT http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533747 2025-05-30T17:00:18Z http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533746 题名: 基于力反馈的接触式调平调零系统、控制方法以及终端 作者: 冯继成; 刘仕荣 摘要: 本发明提供一种基于力反馈的接触式调平调零系统、控制方法以及终端，通过基于所述力传感器实时检测的所述第一平面与第二平面之间的接触压力数据，控制多轴移动台运动调整所述第一平面与第二平面之间的相对位置，直至完成对第一平面与第二平面的调平以及调零操作，并获得调平后的三维的平面坐标系与三维的移动系统坐标系之间的偏差。本发明仅使用一个力传感器，通过碰触信号感知，结合移动系统调整既可以实现调平调零，具有结构简洁以及价格低廉的优势。本发明还能得到调平平面与移动系统的坐标偏差，通过此偏差可以实现使用移动台的移动对两平面之间的精准位移进行控制。并且针对极小面积的平面以及大面积平面均可进行调平调零。 Fri, 30 May 2025 17:00:16 GMT http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533746 2025-05-30T17:00:16Z http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533745 题名: 自由呼吸心脏脂肪分数及多弛豫参数同时定量磁共振成像方法、装置、终端及介质 作者: 齐海坤; 吕振峰 摘要: 本申请提供一种自由呼吸心脏脂肪分数及多弛豫参数同时定量磁共振成像方法、装置、终端及介质，包括：通过建立的对应心脏脂肪分数及多弛豫参数同时定量的磁共振序列，对受检对象在自由呼吸状态下的心脏组织进行多个成像层面的扫描，获得对应各个成像层面的每个心动周期的双回波图像以及心电触发信号；基于Bloch方程和心电触发信号模拟磁化矢量的演变过程，并建立字典；基于图像处理后的双回波图像，执行基于混合多回波的水脂分离操作，获得水信号图像以及脂肪分数图；通过将水信号图像与字典进行匹配的方式，获得受检对象的弛豫参数组图。本申请在受检对象自由呼吸时采集，减轻了受检对象的负担，且保证了脂肪分数定量的精度以及水脂分离的准确性。 Fri, 30 May 2025 17:00:09 GMT http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533745 2025-05-30T17:00:09Z http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533744 题名: 一种微生物单细胞测序文库的构建方法、获得的测序文库及其应用 作者: 刘一凡; 李婕; 杜亚楠 摘要: 本发明提供一种微生物单细胞测序文库的构建方法、获得的测序文库及其应用。本发明通过在微腔室中实现微生物单细胞捕获、裂解、全基因组扩增、片段化、DNA索引化；然后将微腔室溶解，将DNA置于水相体系中进行纯化和富集扩增。本发明优化裂解液配方，使微生物裂解充分，而且不具有裂解偏好性；在DNA索引化中引入包含条形码的水凝胶微球，可以显著提高水凝胶液滴和条形码微球的配对效率；而且发现通过线性扩增的方式实现微生物单细胞索引化的效果显著优于指数扩增。通过本发明的方法构建文库然后进行测序，结果显示大于95％的单细胞纯度在95％以上，微生物单细胞基因组覆盖度可以达到80％以上。 Fri, 30 May 2025 17:00:07 GMT http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533744 2025-05-30T17:00:07Z http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533743 题名: Olefins Hydrofunctionnalization; Hydroamination and Hydroalkoxylation of Olefins, Excluding Electron Deficient Olefins 作者: Ye, Yu-Meng; Li, Zhi 摘要: Asymmetric hydroamination and hydroalkoxylation of unactivated alkenes are atom-economical ways to achieve chiral α-branched amines and ethers. Using a variety of chiral metal catalysts complexed with chiral ligands, including lanthanides, Group 4 metals, and late transition metals, researchers have studied both intra- and intermolecular reactions for asymmetric hydroamination. In addition to amines, many nitrogen-containing functional groups such as amide, thiourea, and hydroxylamine can be utilized as hydroamination reagents. For asymmetric intra- and intermolecular hydroalkoxylation, a series of catalysts, including chiral Lewis acid assisted Brønsted acids and transition metals, have been developed. © 2024 Elsevier Ltd. All rights reserved. Fri, 30 May 2025 02:49:20 GMT http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533743 2025-05-30T02:49:20Z http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533742 题名: Influence of Anti-Scatter Grid on the SNR of Photon-Counting and Energy-Integrating Detectors: a Simulation Study 作者: Liu, Jiaxuan; Liu, Yanyan; Zhang, Xiaoxuan; Yu, Xiaopeng; Yan, Xinjie; Zhang, Xi; Quan, Guotao; Hsieh, Scott; Lai, Xiaochun; Wang, Wenying 摘要: Compared with energy-integrating detectors (EIDs) which use pixelated scintillators, photon-counting detectors (PCDs) employ a continuous semiconductor slab that directly converts each x-ray photon to an electric signal, yielding high detector quantum efficiency. While the benefits of an anti-scatter grid (ASG) have been well investigated for EIDs, the use of ASG on PCDs may be less advantageous due to the reduction in geometric detection efficiency. Through simulation studies, we compared the influence of various ASG designs, z-collimation widths, and phantom sizes on the SNR for both detector types. Preliminary results indicate that PCDs in low-scatter scenarios may achieve higher SNR with a lower-dimension ASG design, while a 2D ASG consistently enhances the SNR for EIDs. These findings suggest that PCD systems might require different ASG strategies compared to traditional EID systems to maximize performance. © 2025 SPIE. Fri, 30 May 2025 02:43:34 GMT http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533742 2025-05-30T02:43:34Z http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533741 题名: Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 作者: Ma, Jiquan; Teng, Yujun; Chen, Geng; Jiang, Haotian; Zhang, Kai; Liu, Feihong; Rekik, Islem; Shen, Dinggang 摘要: Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Fri, 30 May 2025 02:43:32 GMT http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533741 2025-05-30T02:43:32Z http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533740 题名: Is Vanilla Bayesian Optimization Enough for High-Dimensional Architecture Design Optimization? 作者: Gao, Yuanhang; Luo, Donger; Bai, Chen; Yu, Bei; Geng, Hao; Sun, Qi; Zhuo, Cheng 摘要: In the tide of explosive development in artificial intelligence (AI), the design of AI System-on-Chips (SoCs) is an urgently pressing issue that needs to be addressed. The application of Design Space Exploration (DSE) methods is paramount in pursuing a sound microarchitecture design and improving the quality of results. However, the high-dimensional design parameters and huge design space, which normally occur in the complicated SoCs for Large Language Model (LLM) tasks, pose a great challenge to existing techniques. In this paper, a novel and explainable Bayesian optimization-based framework MCT-Explorer is proposed. A Monte Carlo Tree Search (MCTS)-based method is utilized to analyze the importance of design parameters, guide the sampling directions, mitigate low-quality performance modeling issues, and further improve optimization efficiency. Besides, an information-guided multi-objective optimization function is adopted to balance the multiple metrics (e.g., Cycle. Area, and Power) for SoC design. Our approach can provide guiding opinions and deeper insights for parameter optimization, thus transcending previous arts and achieving an explainable model. Experiment results demonstrate the extraordinary performance of our framework in various high-dimensional (up to hundreds of parameters) and complicated LLM SoC designs. © 2024 Copyright is held by the owner/author(s). Fri, 30 May 2025 02:43:30 GMT http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533740 2025-05-30T02:43:30Z http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533739 题名: One-Step Material Decomposition for Photon-Counting CT Using Implicit Neural Representation and Physics-Guided Model 作者: Qin, Wenhui; Yu, Xiaopeng; Liu, Zhentao; Zhong, Tao; Zhang, Yikun; Ji, Xu; Wang, Wenying; Cui, Zhiming; Quan, Guatao; Chen, Yang; Lai, Xiaochun 摘要: Photon-counting computed tomography (PCCT) demonstrates significant potential for advancing clinical applications through its ability to precisely measure individual X-ray photon energies, enabling enhanced material and tissue differentiation. The technique employs multi-energy bin projections for material decomposition'a complex nonlinear, nonconvex inverse problem that is often compromised by physical non-idealities, including charge-sharing and pulse pile-up effects in photon-counting detector (PCD) and application-specific integrated circuit (ASIC) responses. Material decomposition algorithms are categorized into three types: image-domain, projection-domain, and one-step inversion decomposition. One-step inversion decomposition, though computationally intensive, provides a more comprehensive solution by integrating decomposition and reconstruction within a unified optimization framework. Recent advances in Implicit Neural Representation (INR) have introduced novel approaches to address the reconstruction inverse problem, offering a promising framework for one-step material decomposition. This study implements INR methodology for one-step inversion decomposition by developing a direct mapping between position distributions and basic material length fractions, while incorporating a physics-guided detector model to enhance system response characterization and decomposition accuracy. Experimental results from water and Gammex phantom studies validate the method's capability for one-step material decomposition. However, further optimization is necessary to enhance decomposition accuracy and mitigate artifacts visible in narrow clinical display windows to meet diagnostic quality standards. © 2025 SPIE. Fri, 30 May 2025 02:43:23 GMT http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533739 2025-05-30T02:43:23Z http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533738 题名: Revolutionizing Disease Diagnosis with simultaneous functional PET/MR and Deeply Integrated Brain Metabolic, Hemodynamic, and Perfusion Networks 作者: Wang, Luoyu; Tao, Yitian; Yang, Qing; Liang, Yan; Liu, Siwei; Shi, Hongcheng; Shen, Dinggang; Zhang, Han 摘要: Simultaneous functional PET/MR (sf-PET/MR) presents a cutting-edge multimodal neuroimaging technique. It provides an unprecedented opportunity for concurrently monitoring and integrating multifaceted brain networks built by spatiotemporally covaried metabolic activity, neural activity, and cerebral blood flow (perfusion). Albeit high scientific/clinical values, short in hardware accessibility of PET/MR hinders its applications, let alone modern AI-based PET/MR fusion models. Our objective is to develop a clinically feasible AI-based disease diagnosis model trained on comprehensive sf-PET/MR data with the power of, during inferencing, allowing single modality input (e.g., PET only) as well as enforcing multimodal-based accuracy. To this end, we propose MX-ARM, a multimodal MiXture-of-experts Alignment and Reconstruction Model. It is modality detachable and exchangeable, allocating different multi-layer perceptrons dynamically ("mixture of experts") through learnable weights to learn respective representations from different modalities. Such design will not sacrifice model performance in uni-modal situation. To fully exploit the inherent complex and nonlinear relation among modalities while producing fine-grained representations for uni-modal inference, a modal alignment module is utilized to line up a dominant modality (e.g., PET) with representations of auxiliary modalities (MR). We further adopt multimodal reconstruction to promote the quality of learned features. Experiments on precious multimodal sf-PET/MR data for Mild Cognitive Impairment diagnosis showcase the efficacy of MX-ARM toward clinically feasible precision medicine. © 2025 IEEE. Fri, 30 May 2025 02:43:21 GMT http:///kms.shanghaitech.edu.cn/:80/handle/2MSLDSTB/533738 2025-05-30T02:43:21Z