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Biomedical subjects

M Siddall

Publications and source records attributed to M Siddall.

4 recordsLinked to original sources
PubMed2003

Sea-level fluctuations during the last glacial cycle.

The last glacial cycle was characterized by substantial millennial-scale climate fluctuations, but the extent of any associated changes in global sea level (or, equivalently, ice volume) remains elusive. Highstands of sea level can be reconstructed from dated fossil coral reef terraces, and these data are complemented by a compilation of global sea-level estimates based on deep-sea oxygen isotope ratios at millennial-scale resolution or higher. Records based on oxygen isotopes, however, contain uncertainties in the range of +/-30 m, or +/-1 degrees C in deep sea temperature. Here we analyse oxygen isotope records from Red Sea sediment cores to reconstruct the history of water residence times in the Red Sea. We then use a hydraulic model of the water exchange between the Red Sea and the world ocean to derive the sill depth-and hence global sea level-over the past 470,000 years (470 kyr). Our reconstruction is accurate to within +/-12 m, and gives a centennial-scale resolution from 70 to 25 kyr before present. We find that sea-level changes of up to 35 m, at rates of up to 2 cm yr(-1), occurred, coincident with abrupt changes in climate.

Animals↗
PubMed2002

Characteristic attributes in cancer microarrays.

Rapid advances in genome sequencing and gene expression microarray technologies are providing unprecedented opportunities to identify specific genes involved in complex biological processes, such as development, signal transduction, and disease. The vast amount of data generated by these technologies has presented new challenges in bioinformatics. To help organize and interpret microarray data, new and efficient computational methods are needed to: (1) distinguish accurately between different biological or clinical categories (e.g., malignant vs. benign), and (2) identify specific genes that play a role in determining those categories. Here we present a novel and simple method that exhaustively scans microarray data for unambiguous gene expression patterns. Such patterns of data can be used as the basis for classification into biological or clinical categories. The method, termed the Characteristic Attribute Organization System (CAOS), is derived from fundamental precepts in systematic biology. In CAOS we define two types of characteristic attributes ('pure' and 'private') that may exist in gene expression microarray data. We also consider additional attributes ('compound') that are composed of expression states of more than one gene that are not characteristic on their own. CAOS was tested on three well-known cancer DNA microarray data sets for its ability to classify new microarray samples. We found CAOS to be a highly accurate and robust class prediction technique. In addition, CAOS identified specific genes, not emphasized in other analyses, that may be crucial to the biology of certain types of cancer. The success of CAOS in this study has significant implications for basic research and the future development of reliable methods for clinical diagnostic tools.

Acute Disease↗