Collecting Experiments. Making Big Data Biology. By Bruno J. Strasser. Chicago University Press, 2019. Pp. 392. Price USD 45.00. ISBN 9780226635040

CSD Communications of the Cambridge Structural Database

The Cambridge Structural Database (CSD) is a collection of over one million experimental three-dimensional structures obtained through crystallographic analyses. These structures are determined by crystallographers worldwide and undergo curation and enhancement by scientists at the Cambridge Crystallographic Data Centre (CCDC) prior to their addition to the database. Though the CSD is substantial and contains widespread chemical diversity across organic and metal-organic compounds, it is estimated that a significant proportion of crystal structures determined are not published or shared through the peer-reviewed journal mechanism. To help overcome this, scientists can publish structures directly through the database as CSD Communications and these structural datasets are made publicly available alongside structures associated with scientific articles. CSD Communications contribute to the collective crystallographic knowledge as nearly two thirds are novel structures that are not otherwise available in the scientific literature. The primary benefits of sharing data through CSD Communications include the long-term preservation of scientific data, the strengthening of a widely data-mined world repository (the CSD), and the opportunity for scientists to receive recognition for their work through a formal and citable data publication. All CSD Communications are assigned unique digital object identifiers (DOIs). Contributions as CSD Communications currently comprise about 3.89% of the total CSD entries. Each individual CSD Communication is free to view and retrieve from the CCDC website.

Correlation between Collateral Compensation and Homocysteine Levels in Patients with Acute Cerebral Infarction after Intravenous Thrombolysis Based on Medical Big Data

Homocysteine is an amino acid present in plasma, which is an important intermediate product in the metabolism of methionine and cysteine. Acute cerebral infarction (CI) is called acute CI of stroke. It is one of the most common diseases in neurology and has a serious impact, affecting people's lives. This article is aimed at studying the effect of data mining algorithms based on medical big data and the improved apriori algorithm on the analysis of the correlation between collateral compensation and homocysteine levels in patients with acute CI. This article proposes that there are many factors in patients with acute CI, among which are collateral compensation and homocysteine levels that are not easily determined. From the data in the tables in the experiment of this article, it can be seen that the collateral circulation of patients with acute CI is 8%, and the collateral circulation of patients without acute CI is 35%. The results indicate that both collateral compensation and homocysteine levels affect patients with acute CI. The higher the homocysteine level, the greater the probability of acute CI, and the better the state of collateral circulation, the less likely it is to suffer from acute CI.