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L R HILL

Publications and source records attributed to L R HILL.

5 recordsLinked to original sources
PubMed1965

DEOXYRIBONUCLEIC ACID BASE COMPOSITION OF SOME MEMBERS OF THE SUBGENERA BETABACTERIUM AND STREPTOBACTERIUM.

The base composition of deoxyribonucleic acid (DNA) prepared from four Betabacterium strains and four Streptobacterium strains was determined. Per cent GC values (guanine + cytosine/total bases) of the DNA were evaluated from the "melting-temperatures" (Tm) of the nucleic acids. For the Betabacterium strains, these values ranged from 44 to 51.5% GC, and those for the Streptobacterium strains ranged from 43 to 47.5% GC. The taxonomic division into these two subgenera is not, therefore, supported by these findings.

Base Composition↗
PubMed1965

AUTOMATIC CLASSIFICATION OF STAPHYLOCOCCI BY PRINCIPAL-COMPONENT ANALYSIS AND A GRADIENT METHOD.

Hill, L. R. (Università Statale, Milano, Italy), L. G. Silvestri, P. Ihm, G. Farchi, and P. Lanciani. Automatic classification of staphylococci by principal-component analysis and a gradient method. J. Bacteriol. 89:1393-1401. 1965.-Forty-nine strains from the species Staphylococcus aureus, S. saprophyticus, S. lactis, S. afermentans, and S. roseus were submitted to different taxometric analyses; clustering was performed by single linkage, by the unweighted pair group method, and by principal-component analysis followed by a gradient method. Results were substantially the same with all methods. All S. aureus clustered together, sharply separated from S. roseus and S. afermentans; S. lactis and S. saprophyticus fell between, with the latter nearer to S. aureus. The main purpose of this study was to introduce a new taxometric technique, based on principal-component analysis followed by a gradient method, and to compare it with some other methods in current use. Advantages of the new method are complete automation and therefore greater objectivity, execution of the clustering in a space of reduced dimensions in which different characters have different weights, easy recognition of taxonomically important characters, and opportunity for representing clusters in three-dimensional models; the principal disadvantage is the need for large computer facilities.

Classification↗