Considerations for the use of functional principal components analysis in sports biomechanics: examples from on-water rowing

The proliferation of new biomechanical technology in laboratory and field settings facilitates the capture of data-sets consisting of complex time-series. An understanding of the appropriate statistical approaches for analysing and interpreting these data-sets is required and the functional data analysis (FDA) family of statistical techniques has emerged in the biomechanical literature. Given the use of FDA is currently in its infancy with biomechanical data, this paper will form the first of a two part series aiming to address practical issues surrounding the application of FDA techniques in biomechanics. This work focuses on functional principal components analysis (fPCA), which is explored using existing literature and sample data from an on-water rowing database. In particular methodological considerations for the implementation of fPCA such as temporal normalisation of data, removal of unwanted forms of variation in a data-set and documented methods for preserving the original temporal properties within a set of curves are explored in detail as a part of this review. Limitations and strengths of the technique are outlined and recommendations are provided to encourage the appropriate use of fPCA within the field of applied sports biomechanics.     

Bivariate functional principal components analysis: considerations for use with multivariate movement signatures in sports biomechanics

Sporting performance is often investigated through graphical observation of key technical variables that are representative of whole movements. The presence of differences between athletes in such variables has led to terms such as movement signatures being used. These signatures can be multivariate (multiple time-series observed concurrently), and also be composed of variables measured relative to different scales. Analytical techniques from areas of statistics such as Functional Data Analysis (FDA) present a practical alternative for analysing multivariate signatures. When applied to concurrent bivariate time-series multivariate functional principal components analysis (referred to as bivariate fPCA or bfPCA in this paper) has demonstrated preliminary application in biomechanical contexts. Despite this, given the infancy of bfPCA in sports biomechanics there are still necessary considerations for its use with non-conventional or complex bivariate structures. This paper focuses on the application of bfPCA to the force-angle graph in on-water rowing, which is a bivariate structure composed of variables with different units. A normalisation approach is proposed to investigate and standardise differences in variability between the two variables. The results of bfPCA applied to the non-normalised data and normalised data are then compared. Considerations and recommendations for the application of bfPCA in this context are also provided.     

The NBER Orange Book Dataset: A user’s guide

This paper introduces a newly digitized, open-access version of the Food and Drug Administration’s “Orange Book”—a linkage between approved small-molecule drugs and the patents that protect them. The Orange Book also reports any applicable regulatory exclusivity that prevents competitive entry. We summarize the Orange Book’s coverage and discuss the opportunities and challenges associated with using these data for research. Empirical validations against various administrative datasets suggest that Orange Book records are, largely, complete and accurate. We conclude with a specific use case—calculating legal exclusivity periods for drugs—to highlight the types of choices that researchers must make when using this resource.     

United States National Library of Medicine Drug Information Portal

The Drug Information Portal is a free Web resource from the National Library of Medicine (NLM) that provides a user-friendly gateway to current information for more than 15,000 drugs. The site guides users to related resources of NLM, the National Institutes of Health (NIH), and other government agencies. Current drug-related information regarding consumer health, clinical trials, AIDS, MeSH pharmacological actions, MEDLINE/PubMed biomedical literature, and physical properties and structure is easily retrieved by searching on a drug name. A varied selection of focused topics in medicine and drugs is also available from displayed subject headings. This column provides background information about the Drug Information Portal, as well as search basics.     

基于时间序列的参数模型和Fisher判别分析的滚动轴承故障诊断方法

提出了基于时间序列参数模型和Fisher判别分析的滚动轴承故障诊断方法。该方法通过对轴承振动信号建立自回归模型,将自回归模型的特征参数作为特征向量,然后采用Fisher判别分析方法对轴承状态进行分类与识别,实验结果验证了所用方法的有效性。     

Measuring the knowledge translation and convergence in pharmaceutical innovation by funding-science-technology-innovation linkages analysis

We propose a backward tracking model for measuring knowledge transfer in the whole translational research spectrum. Using the drugs-patents-papers-grants backward linkages, we try to figure out the funding-science-technology-innovation translational pattern and ponder some policy implications on e.g., which priority areas and knowledge convergence level are more likely to generate new drugs. The drug-patent linkage data was accessed through the USFDA Orange Book, covering a drug's active ingredient, formulation, or methods of use for approved indications. It will take about 10 years from the application of earliest patent to the approval of the new drug. Also such high-value patents in FDA Orange Book tend to cite scientific knowledge published on average 10–15 years ago. The technology linkage of new drugs was relatively stable while the science linkage of technology inventions increased rapidly. Among the scientific papers cited by drug patents, private-institution originated papers are only a quarter of the public. By linking theses scientific papers with funding sources, we found a large majority (90%) are public-funded and only a very small part are private-funded or public-private joint-funded. Our study also indicates the importance of research on such fields as pharmacology, chemistry (including medicinal chemistry, biochemistry, and organic chemistry), molecular biology, neurosciences, and immunology on new drugs innovation. There is no obvious relationship between “basicness” and linkages to the resulting patents’ impact and to drugs innovation. A balanced basic research and applied research maybe essential for fostering drug innovation because it is a complete chain translating from basic discovery to clinical evidence then to clinical practice. In order to achieve successful pharmaceutical innovation, rather than focusing on only technology, convergence with science at moderate levels (maybe 1/3) is suggested.     

基于核PCA和FDA的凝汽器非线型过程监控和故障诊断

提出了基于核主元分析和Fisher判别分析相结合的非线性统计过程监控和故障诊断新方法.该方法首先利用非线性核函数将数据从原始空间映射到高维空间,然后在高维空间中利用线性Fisher判别分析法提取数据最优的核Fisher判别矢量和特征矢量,通过计算特征矢量之间的欧式距离来实现过程监控.若系统发生故障,则根据当前故障的判别矢量和历史故障数据集中所含故障的最优核Fisher判别矢量的相似度进行故障诊断.所提出的方法能有效的捕获过程变量之间的非线性关系,汽轮机特征故障数据集仿真试验验证了该方法的有效性.