Aquilegia, Vol. 26 No. 1, January-February 2002: Newsletter of the Colorado Native Plant Society

https://epublications.regis.edu/aquilegia/1091/thumbnail.jp

From Vampire to Apollo: William Blake's Ghosts of the Flea (c. 1819-20)

Varley’s Zodiacal Physiognomy and Blake’s Visionary Heads are the two mainstays of a project which involved séance-like meetings at Varley’s house. While the lights were still on, Varley’s guests would have listened to the stories about the flea. With The Ghost of a Flea in front of them, the recitals of the flea’s pompous speeches, combined with the fact that it was just a ghost who leered after human blood, Varley’s guests may have laughed very heartily, if not in front of him then behind his back. Each evening followed the same protocol. When the lights were off, Varley would call out a name and Blake would look around, suddenly exclaiming ‘There he is!’ and start drawing. The flea is the most striking of the Visionary Heads, though it is not the only head which exists in different versions. If appearance is elemental to any kind of judgement of one human being of another, then Blake deliberately confused Varley. By working up the sketch, he played on Varley’s expectations; he presented him with an extraordinary and very puzzling painting, The Ghost of a Flea. But why, if Blake could have chosen any monster, did he settle on the ghost of a flea

Co-occurrence of diabetes and hopelessness predicts adverse prognosis following percutaneous coronary intervention

We examined the impact of co-occurring diabetes and hopelessness on 3-year prognosis in percutaneous coronary intervention patients. Consecutive patients (n = 534) treated with the paclitaxel-eluting stent completed a set of questionnaires at baseline and were followed up for 3-year adverse clinical events. The incidence of 3-year death/non-fatal myocardial infarction was 3.5% in patients with no risk factors (neither hopelessness nor diabetes), 8.2% in patients with diabetes, 11.2% in patients with high hopelessness, and 15.9% in patients with both factors (p = 0.001). Patients with hopelessness (HR: 3.28; 95% CI: 1.49-7.23) and co-occurring diabetes and hopelessness (HR: 4.89; 95% CI: 1.86-12.85) were at increased risk of 3-year adverse clinical events compared to patients with no risk factors, whereas patients with diabetes were at a clinically relevant but not statistically significant risk (HR: 2.40; 95% CI: 0.82-7.01). These results remained, adjusting for baseline characteristics an

Extracting expression modules from perturbational gene expression compendia

<p>Abstract</p> <p>Background</p> <p>Compendia of gene expression profiles under chemical and genetic perturbations constitute an invaluable resource from a systems biology perspective. However, the perturbational nature of such data imposes specific challenges on the computational methods used to analyze them. In particular, traditional clustering algorithms have difficulties in handling one of the prominent features of perturbational compendia, namely partial coexpression relationships between genes. Biclustering methods on the other hand are specifically designed to capture such partial coexpression patterns, but they show a variety of other drawbacks. For instance, some biclustering methods are less suited to identify overlapping biclusters, while others generate highly redundant biclusters. Also, none of the existing biclustering tools takes advantage of the staple of perturbational expression data analysis: the identification of differentially expressed genes.</p> <p>Results</p> <p>We introduce a novel method, called ENIGMA, that addresses some of these issues. ENIGMA leverages differential expression analysis results to extract expression modules from perturbational gene expression data. The core parameters of the ENIGMA clustering procedure are automatically optimized to reduce the redundancy between modules. In contrast to the biclusters produced by most other methods, ENIGMA modules may show internal substructure, i.e. subsets of genes with distinct but significantly related expression patterns. The grouping of these (often functionally) related patterns in one module greatly aids in the biological interpretation of the data. We show that ENIGMA outperforms other methods on artificial datasets, using a quality criterion that, unlike other criteria, can be used for algorithms that generate overlapping clusters and that can be modified to take redundancy between clusters into account. Finally, we apply ENIGMA to the Rosetta compendium of expression profiles for <it>Saccharomyces cerevisiae </it>and we analyze one pheromone response-related module in more detail, demonstrating the potential of ENIGMA to generate detailed predictions.</p> <p>Conclusion</p> <p>It is increasingly recognized that perturbational expression compendia are essential to identify the gene networks underlying cellular function, and efforts to build these for different organisms are currently underway. We show that ENIGMA constitutes a valuable addition to the repertoire of methods to analyze such data.</p

Why Some Women Might Want ‘Missed-Period Pills’

Professor Joanna Erdman was interviewed for this opinion piece in The New York Times by Patrick Adams. She is quoted in paragraphs 19-20

Why Some Women Might Want ‘Missed-Period Pills’

Professor Joanna Erdman was interviewed for this opinion piece in The New York Times by Patrick Adams. She is quoted in paragraphs 19-20

Evaluation of CisBio ELISA for Chromogranin A Measurement

Background: Chromogranin A (CgA) is a nonspecific marker for the presence of neuroendocrine tumors and neuroendocrine differentiation. The objective of this study was to evaluate the performance of the CisBio CgA ELISA. Methods: Precision, linearity, limit of blank, and recovery of the CisBio CgA ELISA were evaluated. Seventy waste serum samples obtained from the clinical laboratory at Memorial Sloan Kettering Cancer Center were analyzed by the CisBio CgA ELISA. Results were compared to those obtained from a reference laboratory that used a proprietary ELISA for serum CgA measurement. Paired waste plasma samples were also collected from 24 of these patients to assess possible differences between CgA in serum and plasma. Finally, a preliminary reference range study was performed with samples from healthy volunteers in serum (n = 60) and plasma (n = 60). Results: Within-run and between-run precision ranged from 3.0% to 5.1% and 4.8% to 12.9%, respectively. The limit of blank was 2.4 ng/mL. Recovery ranged from 88% to 102%. A statistically significant bias was observed when the CisBio CgA assay results were compared to those of a reference laboratory. Comparison of the 2 assays yielded a slope of 9.05, intercept of -18.0, and a correlation coefficient of 0.955. CgA values in serum correlated well to values measured in plasma. Conclusions: The analytical performance of the CisBio CgA ELISA was acceptable. However, CgA results are method-specific owing to lack of standardization and use of different antibodies. This lack of standardization results in several challenges for the clinical laboratory when evaluating a CgA assay