药物相互作用研究在新药研发和审评决策中的应用

药物相互作用改变了剂量效应关系，可能会降低疗效或增加毒性，是临床应用中合并用药治疗时重要的考虑因素。预测具有临床意义的药物相互作用是药物研发过程中获益风险评估的重要环节。本文概述了药物研发过程中药物相互作用研究的目的和意义，体内和体外研究的主要内容；梳理分析了2020年国家药品监督管理局（National Medical Products Administration, NMPA）和美国食品药品监督管理局（Food and Drug Administration, FDA）批准上市的新药药物相互作用研究情况，旨在为我国药物研发过程中药物相互作用研究及其监管审评提供参考。     

Retinal microaneurysms detection using adversarial pre-training with unlabeled multimodal images

The detection of retinal microaneurysms is crucial for the early detection of important diseases such as diabetic retinopathy. However, the detection of these lesions in retinography, the most widely available retinal imaging modality, remains a very challenging task. This is mainly due to the tiny size and low contrast of the microaneurysms in the images. Consequently, the automated detection of microaneurysms usually relies on extensive ad-hoc processing. In this regard, although microaneurysms can be more easily detected using fluorescein angiography, this alternative imaging modality is invasive and not adequate for regular preventive screening.In this work, we propose a novel deep learning methodology that takes advantage of unlabeled multimodal image pairs for improving the detection of microaneurysms in retinography. In particular, we propose a novel adversarial multimodal pre-training consisting in the prediction of fluorescein angiography from retinography using generative adversarial networks. This pre-training allows learning about the retina and the microaneurysms without any manually annotated data. Additionally, we also propose to approach the microaneurysms detection as a heatmap regression, which allows an efficient detection and precise localization of multiple microaneurysms. To validate and analyze the proposed methodology, we perform an exhaustive experimentation on different public datasets. Additionally, we provide relevant comparisons against different state-of-the-art approaches. The results show a satisfactory performance of the proposal, achieving an Average Precision of 64.90%, 31.36%, and 33.55% in the E-Ophtha, ROC, and DDR public datasets. Overall, the proposed approach outperforms existing deep learning alternatives while providing a more straightforward detection method that can be effectively applied to raw unprocessed retinal images.     

安卓平台上的医用图像信息采集系统的开发设计

为了探讨在安卓平台上构建医用图像采集系统的开发个案,分析通过以智能手机、平板电脑为核心安卓设备通过拍照获得化验单数据后进行文本识别并提交智慧医疗系统的解决方案。本文首先通过二值化算法形成低阈值图像数据,使用卷积神经元网络算法对文本进行逐一识别,使用K-means算法对识别后的单字文本进行字段记录值的整合并形成元数据库服务于其他智慧医疗系统模块。在使用9000组数据对神经元网络进行前期训练的前提下,该系统的识别准确率达到了99.5%以上。本系统具有一定的可行性,对未来智慧医疗的系统开发有实践意义。     

放射性气载流出物取样代表性分析

为获取核设施放射性气载流出物单点连续监测取样位置,提出基于随机轨道模型(DRW)的气-固多相湍流耦合方法求解代表性取样区域。借助k-epsilon湍流模型模拟连续相,并引入离散颗粒模型(DPM)模拟离散相,建立基于DRW模型的排风管道内流道气-固多相湍流耦合计算模型,计算了核设施气载流出物在管道内流道流场分布规律,分析了内流道流体气旋角、气流速度、示踪气体浓度、气溶胶粒子浓度与管道高度间的关联关系。分析结果表明,随着截面高度的增加,气旋角、气流速度变异系数(COV)、示踪气体浓度COV及示踪气体浓度最大值与平均值的偏差逐渐降低并趋于稳定,气溶胶粒子浓度COV在截面6与截面8满足取样代表性要求;基于计算流体动力学方法可快速地确定出代表性取样位置,为气载流出物单点取样现场试验提供了理论参考依据。     

尾矿库堆积坝排渗设施在某尾矿库的应用

近些年受尾矿库选址及环境保护等政策的限制，新建尾矿库的工程投资明显增加，因此在原有堆坝高度上继续加高的工程增多，接近100～200 m级的高坝尾矿库大量涌现。为增加尾矿库的稳定性，以某尾矿库工程为例，坝体浸润线埋深较高，稳定计算不能满足相关规范要求。为有效降低浸润线，在堆积坝内采用大口辐射井和飘管相结合的排渗设施。经现场实际运行，排渗设施的应用对保证坝体稳定具有十分重要的作用。     

CLOI-NET: Class segmentation of industrial facilities’ point cloud datasets

Shape segmentation from point cloud data is a core step of the digital twinning process for industrial facilities. However, it is also a very labor intensive step, which counteracts the perceived value of the resulting model. The state-of-the-art method for automating cylinder detection can detect cylinders with 62% precision and 70% recall, while other shapes must then be segmented manually and shape segmentation is not achieved. This performance is promising, but it is far from drastically eliminating the manual labor cost. We argue that the use of class segmentation deep learning algorithms has the theoretical potential to perform better in terms of per point accuracy and less manual segmentation time needed. However, such algorithms could not be used so far due to the lack of a pre-trained dataset of laser scanned industrial shapes as well as the lack of appropriate geometric features in order to learn these shapes. In this paper, we tackle both problems in three steps. First, we parse the industrial point cloud through a novel class segmentation solution (CLOI-NET) that consists of an optimized PointNET++ based deep learning network and post-processing algorithms that enforce stronger contextual relationships per point. We then allow the user to choose the optimal manual annotation of a test facility by means of active learning to further improve the results. We achieve the first step by clustering points in meaningful spatial 3D windows based on their location. Then, we apply a class segmentation deep network, and output a probability distribution of all label categories per point and improve the predicted labels by enforcing post-processing rules. We finally optimize the results by finding the optimal amount of data to be used for training experiments. We validate our method on the largest richly annotated dataset of the most important to model industrial shapes (CLOI) and yield 82% average accuracy per point, 95.6% average AUC among all classes and estimated 70% labor hour savings in class segmentation. This proves that it is the first to automatically segment industrial point cloud shapes with no prior knowledge at commercially viable performance and is the foundation for efficient industrial shape modeling in cluttered point clouds.     

灭火器的定位和有效性辨识研究

智慧消防是智慧城市建设的重要组成部分，在消防工作创新发展中发挥重要作用。灭火器作为扑救初期火灾的最普遍、最便捷和最广泛使用的灭火设施，在初期火灾的扑救和火灾防控中发挥重要的作用，其有效性和完好性是扑救初期火灾的必要条件。基于物联网及大数据的优势，建立消防灭火设施的定位与有效性辨识系统，并介绍系统的关键技术、网络架构、技术特征。此系统可对灭火器等消防灭火设施的伺应状态、完好性、有效性等功能特性进行跟踪巡检和辨识，以实现对灭火器的实时监控和智能化管理，促进消防设施建设的完善和优化，增强灭火系统的可靠性，充分发挥智慧消防技术在智慧城市建设中的优势。     

节能型浮式生产储油装置惰气系统

为最大限度地利用FPSO上热站锅炉的烟气,减少FPSO上惰气系统能源消耗,对惰气系统配置及控制方式进行了改进。即惰气系统采用1台烟气模式、1台柴油模式,并与之对应配置2套完全独立的惰气总管,和1套公共控制系统。惰气系统工作时,烟气模式优先运行,烟气量不足时,柴油模式运行,产生的惰气作为烟气模式的补充。采用这种惰气系统,不但对FPSO热介质加热炉产生的烟气可以充分利用,减少柴油消耗量,节约惰气系统燃料成本,而且可以同时进行外输及洗舱作业,提高生产时率。     

Plasma gasification of the medical waste

In terms of infection control in hospitals, especially the Covid-19 pandemic that we are living in, it has revealed the necessity of proper disposal of medical waste. The increasing amount of medical waste with the pandemic is straining the capacity of incineration facilities or storage areas. Converting this waste to energy with gasification technologies instead of incineration is also important for sustainability. This study investigates the gasification characteristics of the medical waste in a novel updraft plasma gasifier with numerical simulations in the presence of the plasma reactions. Three different medical waste samples, chosen according to the carbon content and five different equivalence ratios (ER) ranging from 0.1 to 0.5 are considered in the simulations to compare the effects of different chemical compositions and waste feeding rates on hydrogen (H₂) content and syngas production. The outlet properties of a 10 kW microwave air plasma generator are used to define the plasma inlet in the numerical model and the air flow rate is held constant for all cases. Results showed that the maximum H₂ production can be obtained with ER = 0.1 for all waste samples.