Maximum Velocities in Flexion and Extension Actions for Sport.

Speed of movement is fundamental to the outcome of many human actions. A variety of techniques can be implemented in order to maximise movement speed depending on the goal of the movement, constraints, and the time available. Knowing maximum movement velocities is therefore useful for developing movement strategies but also as input into muscle models. The aim of this study was to determine maximum flexion and extension velocities about the major joints in upper and lower limbs. Seven university to international level male competitors performed flexion/extension at each of the major joints in the upper and lower limbs under three conditions: isolated; isolated with a countermovement; involvement of proximal segments. 500 Hz planar high speed video was used to calculate velocities. The highest angular velocities in the upper and lower limb were 50.0 rad·s-1 and 28.4 rad·s-1, at the wrist and knee, respectively. As was true for most joints, these were achieved with the involvement of proximal segments, however, ANOVA analysis showed few significant differences (p<0.05) between conditions. Different segment masses, structures and locations produced differing results, in the upper and lower limbs, highlighting the requirement of segment specific strategies for maximal movements.

Mining chemical information from open patents.

Linked Open Data presents an opportunity to vastly improve the quality of science in all fields by increasing the availability and usability of the data upon which it is based. In the chemical field, there is a huge amount of information available in the published literature, the vast majority of which is not available in machine-understandable formats. PatentEye, a prototype system for the extraction and semantification of chemical reactions from the patent literature has been implemented and is discussed. A total of 4444 reactions were extracted from 667 patent documents that comprised 10 weeks' worth of publications from the European Patent Office (EPO), with a precision of 78% and recall of 64% with regards to determining the identity and amount of reactants employed and an accuracy of 92% with regards to product identification. NMR spectra reported as product characterisation data are additionally captured.

OSCAR4: a flexible architecture for chemical text-mining.

The Open-Source Chemistry Analysis Routines (OSCAR) software, a toolkit for the recognition of named entities and data in chemistry publications, has been developed since 2002. Recent work has resulted in the separation of the core OSCAR functionality and its release as the OSCAR4 library. This library features a modular API (based on reduction of surface coupling) that permits client programmers to easily incorporate it into external applications. OSCAR4 offers a domain-independent architecture upon which chemistry specific text-mining tools can be built, and its development and usage are discussed.

ChemicalTagger: A tool for semantic text-mining in chemistry.

BACKGROUND: The primary method for scientific communication is in the form of published scientific articles and theses which use natural language combined with domain-specific terminology. As such, they contain free owing unstructured text. Given the usefulness of data extraction from unstructured literature, we aim to show how this can be achieved for the discipline of chemistry. The highly formulaic style of writing most chemists adopt make their contributions well suited to high-throughput Natural Language Processing (NLP) approaches. RESULTS: We have developed the ChemicalTagger parser as a medium-depth, phrase-based semantic NLP tool for the language of chemical experiments. Tagging is based on a modular architecture and uses a combination of OSCAR, domain-specific regex and English taggers to identify parts-of-speech. The ANTLR grammar is used to structure this into tree-based phrases. Using a metric that allows for overlapping annotations, we achieved machine-annotator agreements of 88.9% for phrase recognition and 91.9% for phrase-type identification (Action names). CONCLUSIONS: It is possible parse to chemical experimental text using rule-based techniques in conjunction with a formal grammar parser. ChemicalTagger has been deployed for over 10,000 patents and has identified solvents from their linguistic context with >99.5% precision.