基于国家专利的中药复方治疗化学性肝损伤用药规律研究

目的分析国家专利治疗化学性肝损伤的组方用药规律,为新药研发及相关专利的申请提供参考。方法以国家知识产权局中国专利公布公告网站收录的授权专利为资料来源,使用Microsoft Excel 2018建立数据库,对中药频次与分类进行统计分析;采用SPSS Statistic 21.0和SPSS Modeler 15.0对高频中药(频次≥10)进行性味归经分析、系统聚类分析、关联度与网络分析。结果纳入专利100项,涉及中药186味,以葛根最为常见;高频中药15味,药味以甘为主,药性以温、平为主,多归脾、肝经;聚类分析得到7组中药组合;二项、三项和四项关联规则分别为6、24、2项,各项组合分别为6、16、2项;核心药物为葛根、五味子、丹参、甘草、山楂、枸杞子、黄芪、柴胡、姜黄和枳椇子。结论中药复方治疗化学性肝损伤常用补虚、解表、化瘀、活血、渗湿及利水药;临证多采用疏肝理气之剂,适当伍以柔润之品。     

Troubled-Cell Indicator Based on Mean Value Property for Hybrid WENO Schemes

In this paper, we introduce a new type of troubled-cell indicator to improve hybrid weighted essentially non-oscillatory (WENO) schemes for solving the hyperbolic conservation laws. The hybrid WENO schemes selectively adopt the high-order linear upwind scheme or the WENO scheme to avoid the local characteristic decompositions and calculations of the nonlinear weights in smooth regions. Therefore, they can reduce computational cost while maintaining non-oscillatory properties in non-smooth regions. Reliable troubled-cell indicators are essential for efficient hybrid WENO methods. Most of troubled-cell indicators require proper parameters to detect discontinuities precisely, but it is very difficult to determine the parameters automatically. We develop a new troubled-cell indicator derived from the mean value theorem that does not require any variable parameters. Additionally, we investigate the characteristics of indicator variable; one of the conserved properties or the entropy is considered as indicator variable. Detailed numerical tests for 1D and 2D Euler equations are conducted to demonstrate the performance of the proposed indicator. The results with the proposed troubled-cell indicator are in good agreement with pure WENO schemes. Also the new indicator has advantages in the computational cost compared with the other indicators.     

The Legal Challenge of Abortion Stigma—and Government Restrictions on the Practice of Medicine

During the 2016 election, Donald Trump won conservative support by promising that he would, if elected, nominate “pro-life” justices to the U.S. Supreme Court. Whether President Trump makes good on his campaign promise to restrict abortion rights may come down to competing impulses of the chief justice, John Roberts. These dueling dispositions—from the man whom many see as the new “swing justice”—hold the key to a blockbuster new case that legal historians call “the most unpredictable the Supreme Court has been on abortion in decades.” The case, June Medical Services v. Russo, turns on arduous new requirements that Louisiana has imposed on facilities and clinicians that provide abortion. But the case is not just about abortion access. The Court will have to decide whether a clinic has a right to challenge the law in the first place.     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.     

希腊医疗现状与中医药发展前景分析＊

希腊历史悠久，是现代西方医学的发祥地。希腊位于陆上和海上丝绸之路的交汇点，一直是我国“一带一路”建设的重要合作伙伴。对中医药海外发展而言，希腊已成为中医药进入欧洲市场的重要门户。本文通过综合多方资料，阐明希腊医疗现状，并对中医药的发展前景进行分析，为中医药在希腊的进一步发展提供参考。     

Relationship between retail food outlets near public schools and adolescent obesity in New York City

Using objectively-measured height and weight data from academic years 2009–2013 (n = 1,114,010 student-year observations), we estimated the association between the food outlet in closest proximity to schools and the likelihood of obesity among New York City public high school students. Obesity risk was higher for students with a corner store as the nearest option to schools, regardless of whether other food outlet types were located within a quarter mile or a half mile of schools (i.e., benchmarks for zoning policies). Policymakers may want to consider introducing healthier food options near schools, in conjunction with programs to support changes within corner stores.     

Moment-Based Multi-Resolution HWENO Scheme for Hyperbolic Conservation Laws

In this paper, a high-order moment-based multi-resolution Hermite weighted essentially non-oscillatory (HWENO) scheme is designed for hyperbolic conservation laws. The main idea of this scheme is derived from our previous work [J. Comput. Phys., 446 (2021) 110653], in which the integral averages of the function and its first order derivative are used to reconstruct both the function and its first order derivative values at the boundaries. However, in this paper, only the function values at the Gauss-Lobatto points in the one or two dimensional case need to be reconstructed by using the information of the zeroth and first order moments. In addition, an extra modification procedure is used to modify those first order moments in the troubled-cells, which leads to an improvement of stability and an enhancement of resolution near discontinuities. To obtain the same order of accuracy, the size of the stencil required by this moment-based multi-resolution HWENO scheme is still the same as the general HWENO scheme and is more compact than the general WENO scheme. Moreover, the linear weights are not unique and are independent of the node position, and the CFL number can still be 0.6 whether for the one or two dimensional case, which has to be 0.2 in the two dimensional case for other HWENO schemes. Extensive numerical examples are given to demonstrate the stability and resolution of such moment-based multi-resolution HWENO scheme.     

李东垣学术思想及《脾胃论》用药规律浅析

[目的]探索李东垣《脾胃论》学术思想及其用药规律,以期指导临床。[方法]运用文献法对《脾胃论》进行研究,总结归纳其学术思想,并采用数据挖掘的方法对其所记载的方剂进行系统整理,通过Excel进行中药分类统计,最后分析总结其使用规律。[结果]《脾胃论》主要学术思想为脾胃为元气之本“,火与元气不两立”;脾胃为气机升降之枢纽;内伤脾胃,百病由生;顾护胃气。全篇共记载方剂63首,中药101味。单味中药使用频次,最多的为炙甘草,人参、陈皮次之;从功能分类来看,补气药使用频次最多,其次为解表药与理气药;从药物配伍来看,共有32首方剂中补气药与理气药同用,其中陈皮与炙甘草联用次数高达21次,陈皮与人参20次,陈皮与黄芪15次。[结论]《脾胃论》是李东垣脾胃学术思想之集大成者,其用药组方以补气、升阳、理气为主,重视调达气机。以补气药缓补脾胃之气为多,同用风药以达到升发脾阳、胜湿、开郁之效,并佐以理气药,使其补而不滞,其学术思想及遣方用药特点值得研究与借鉴。     

基于数据挖掘探讨吴寿善治疗慢性乙型病毒性肝炎用药规律

目的：基于中医医疗与临床科研信息共享系统,运用数据挖掘技术探讨湖北中医大师吴寿善教授 中医药治疗慢性乙型病毒性肝炎的用药规律。方法：应用结构化电子病历采集767例吴寿善接诊慢性乙型病 毒性肝炎门诊患者的临床资料,通过数据集成、汇交、整合后,利用数据分类、关联、聚类规则方法对临床资料进 行分析。结果：①确定259个处方中药物的使用频次前五位药物分别是茯苓、茵陈、陈皮、丹参、连翘;②药物配 伍方面,关联度最高的配对分别为茯苓-甘草-白术、茯苓-丹参-白术、茵陈-茯苓-甘草等;③挖掘核心处方时, 发现蒿芩清胆汤、大安丸、半夏白术天麻汤等经典方加减使用频次最高。结论：通过数据挖掘方法,我们发现吴 寿善教授以中医药治疗慢性乙型病毒性肝炎在治则治法上,主要从毒痰瘀虚进行辨证施治。