Rodney Irwin Green
Ontology highlight
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PROVIDER: S-EPMC1122866 | biostudies-literature | 2002 Apr
REPOSITORIES: biostudies-literature
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Project description: Not available
| S-EPMC261786 | biostudies-literature
Project description:Dr. Irwin Fridovich (Ph.D., 1955) is recognized here as a Redox Pioneer because as first/last author he has published at least 1 paper on antioxidant/redox biology that has been cited over 1000 times and has published at least 10 papers each cited over 100 times. In collaboration with his graduate student, Joe McCord, Dr. Fridovich discovered the activity of superoxide dismutase (SOD). Subsequently, he and his colleagues demonstrated that the enzyme is ubiquitous among aerobic biota and comprises a critical defense against oxidative stress. With coworkers, Dr. Fridovich identified the first physiological targets of superoxide, the iron-sulfur clusters of dehydratases. They also showed that SOD is just one of several strategies by which cells fend off oxidative stress. It is now clear that organisms are chronically exposed to endogenous superoxide; further, microbes, plants, and mammals all employ superoxide as a weapon to poison their competitors. Thus, the achievement of Fridovich's laboratory was not only the seminal discovery of SOD but also the painstaking work over the subsequent decades that illuminated its place in biology.
| S-EPMC3026652 | biostudies-literature
Project description:The endemic Australian genus Actenomeros Winterton & Irwin, 1999b is reviewed. Three species are transferred from Nanexila Winterton & Irwin, 1999a: Actenomeros aureilineata (Winterton & Irwin, 1999a) comb. n., Actenomeros intermedia (Winterton & Irwin, 1999a) comb. n. and Actenomeros paradoxa (Winterton & Irwin, 1999a) comb. n. A new species (Actenomeros budawangsp. n.) is described and figured from New South Wales. A key to species is presented.
| S-EPMC3192440 | biostudies-literature
Project description:ObjectivesTo investigate whether routinely collected data from hospital episode statistics could be used to identify the gynaecologist Rodney Ledward, who was suspended in 1966 and was the subject of the Ritchie inquiry into quality and practice within the NHS.DesignA mixed scanning approach was used to identify seven variables from hospital episode statistics that were likely to be associated with potentially poor performance. A blinded multivariate analysis was undertaken to determine the distance (known as the Mahalanobis distance) in the seven indicator multidimensional space that each consultant was from the average consultant in each year. The change in Mahalanobis distance over time was also investigated by using a mixed effects model.SettingNHS hospital trusts in two English regions, in the five years from 1991-2 to 1995-6. Population Gynaecology consultants (n = 143) and their hospital episode statistics data.Main outcome measureWhether Ledward was a statistical outlier at the 95% level.ResultsThe proportion of consultants who were outliers in any one year (at the 95% significance level) ranged from 9% to 20%. Ledward appeared as an outlier in three of the five years. Our mixed effects (multi-year) model identified nine high outlier consultants, including Ledward.ConclusionIt was possible to identify Ledward as an outlier by using hospital episode statistics data. Although our method found other outlier consultants, we strongly caution that these outliers should not be overinterpreted as indicative of "poor" performance. Instead, a scientific search for a credible explanation should be undertaken, but this was outside the remit of our study. The set of indicators used means that cancer specialists, for example, are likely to have high values for several indicators, and the approach needs to be refined to deal with case mix variation. Even after allowing for that, the interpretation of outlier status is still as yet unclear. Further prospective evaluation of our method is warranted, but our overall approach may be potentially useful in other settings, especially where performance entails several indicator variables.
| S-EPMC556335 | biostudies-literature
Project description:Senna spectabilis (DC.) H.S. Irwin & Barneby is a popular ornamental tree as well as a traditional medical plant in Cameroon. In this study, we sequenced and annotated the complete chloroplast genome of S. spectabilis and reconstructed the phylogenetic relationship of the tribe Cassieae. The length of the chloroplast genome was determined to be 162,754 bp, containing a pair of inverted repeats of 25,413 bp which separated by a small single-copy (SSC) region of 20,161 bp and a large single-copy (LSC) region of 91,767 bp. The cp genome encodes 128 genes, including 83 protein-coding genes, 37 tRNA genes, and eight rRNA genes. The percentage of total GC content of this genome was 35.7%. The phylogenetic analysis indicates that S. spectabilis with the sampled Senna species formed a well-supported monophyletic clade.
| S-EPMC7782949 | biostudies-literature
Project description:Vossian Antonomasia (VA) is a well-known stylistic device based on attributing a certain property to a person by relating them to another person who is famous for this property. Although the morphological and semantic characteristics of this phenomenon have long been the subject of linguistic research, little is known about its distribution. In this paper, we describe end-to-end approaches for detecting and extracting VA expressions from large news corpora in order to study VA more broadly. We present two types of approaches: binary sentence classifiers that detect whether or not a sentence contains a VA expression, and sequence tagging of all parts of a VA on the word level, enabling their extraction. All models are based on neural networks and outperform previous approaches, best results are obtained with a fine-tuned BERT model. Furthermore, we study the impact of training data size and class imbalance by adding negative (and possibly noisy) instances to the training data. We also evaluate the models' performance on out-of-corpus and real-world data and analyze the ability of the sequence tagging model to generalize in terms of new entity types and syntactic patterns.
| S-EPMC9218426 | biostudies-literature
