Vector analysis, a text-book for the use of students of mathematics and physics,

An unabridged and unaltered republication of the second edition published by Charles Scribner's Sons in 1909.

Relative importance of various host plants of the beet leafhopper in southern Idaho /

Literature cited: p. 11.

A taxonomic revision of the leafhopper genus Flexamia and a new related genus (Homoptera, cicadellidae) /

Includes bibliographical references (p. 51-52) and index.

Life history, host plants, and migrations of the beet leafhopper in the western United States,

"Literature cited": p. 119-122.

The leafhopper vectors of phytopathogenic viruses (Homoptera, Cicadellidae); taxonomy, biology, and virus transmission,

Bibliography: p. 318-368.

Nematosporaceae (Hemiascomycetidae) : taxonomy, pathogenicity, distribution, and vector relations /

Includes bibliographical references (p. 61-71).

Ecology of the green peach aphid as a vector of beet western yellows virus of sugarbeets /

Issued Aug. 1979.

Vector electrocardiography.

Mode of access: Internet.

Fiji disease resistance in sugarcane: Relationship to cultivar preference in field populations of the planthopper vector Perkinsiella saccharicida

Populations of the planthopper vector Perkinsiella saccharicida on sugarcane cultivars resistant (cvs Q110 and Q87), moderately resistant (cvs Q90 and Q124) and susceptible (evs NCo310 and Q 102) to Fiji disease with known field resistance scores were monitored on the plant (2000-2001) and ratoon (2001-2002) crops. In both crops, the vector population remained very low, reaching its peak in the autumn. The vector population was significantly higher on cultivars susceptible to Fiji disease than on cultivars moderately resistant and resistant to Fiji disease. The number of R saccharicida adults, nymphs and oviposition sites per plant increased with the increase in the Fiji disease susceptibility. The results suggest that under low vector density, cultivar preference by the planthopper vector mediates Fiji disease resistance in sugarcane. To obtain resistance ratings in the glasshouse that reflect field resistance, glasshouse-screening trials should be conducted under both low and high vector densities, and the cultivar preference of the planthopper vector recorded along with Fiji disease incidence.

The specification of vector autoregressive moving average models

In this paper we propose a new identification method based on the residual white noise autoregressive criterion (Pukkila et al. , 1990) to select the order of VARMA structures. Results from extensive simulation experiments based on different model structures with varying number of observations and number of component series are used to demonstrate the performance of this new procedure. We also use economic and business data to compare the model structures selected by this order selection method with those identified in other published studies.

SVMtm: Support vector machines to predict transmembrane segments

A new method has been developed for prediction of transmembrane helices using support vector machines. Different coding schemes of protein sequences were explored, and their performances were assessed by crossvalidation tests. The best performance method can predict the transmembrane helices with sensitivity of 93.4% and precision of 92.0%. For each predicted transmembrane segment, a score is given to show the strength of transmembrane signal and the prediction reliability. In particular, this method can distinguish transmembrane proteins from soluble proteins with an accuracy of similar to99%. This method can be used to complement current transmembrane helix prediction methods and can be Used for consensus analysis of entire proteomes . The predictor is located at http://genet.imb.uq.edu.au/predictors/ SVMtm. (C) 2004 Wiley Periodicals, Inc.

Use of the Vector (TM) scaling unit in supportive periodontal therapy: a subjective patient evaluation

Background: Patient discomfort is one reason for poor compliance with supportive periodontal therapy (SPT). The aim of this study was to compare the levels of discomfort during SPT, using the Vector (TM) system and treatment with a conventional ultrasonic scaler. Methods: Forty-six patients with an SPT programme were debrided using both the Vector (TM) system and a conventional piezo-electric scaler (Sirona (TM)) in a split mouth design. A visual analogue scale was used to evaluate of pain scores upon completion of treatment. A verbal response scale(VRS) was used to assess discomfort, vibration and noise associated with the scaling system, as well as the volume and taste of the coolant used by these systems. Results: Patients instrumented with the Vector (TM) system experienced approximately half the amount of pain compared with the conventional ultrasonic scaling system. The VRS showed that the Vector (TM) system caused less discomfort than the conventional ultrasonic scaling system when assessed for pain, vibration, noise and volume of coolant. These findings were all statistically significant. There was, however, no statistically significant difference between the two systems when assessed for taste. Conclusion: During SPT the Vector (TM) system caused reduced discomforting sensations compared with conventional methods and may be useful in improving compliance with SPT programmes.

Better prediction of protein contact number using a support vector regression analysis of amino acid sequence

Background: Protein tertiary structure can be partly characterized via each amino acid's contact number measuring how residues are spatially arranged. The contact number of a residue in a folded protein is a measure of its exposure to the local environment, and is defined as the number of C-beta atoms in other residues within a sphere around the C-beta atom of the residue of interest. Contact number is partly conserved between protein folds and thus is useful for protein fold and structure prediction. In turn, each residue's contact number can be partially predicted from primary amino acid sequence, assisting tertiary fold analysis from sequence data. In this study, we provide a more accurate contact number prediction method from protein primary sequence. Results: We predict contact number from protein sequence using a novel support vector regression algorithm. Using protein local sequences with multiple sequence alignments (PSI-BLAST profiles), we demonstrate a correlation coefficient between predicted and observed contact numbers of 0.70, which outperforms previously achieved accuracies. Including additional information about sequence weight and amino acid composition further improves prediction accuracies significantly with the correlation coefficient reaching 0.73. If residues are classified as being either contacted or non-contacted, the prediction accuracies are all greater than 77%, regardless of the choice of classification thresholds. Conclusion: The successful application of support vector regression to the prediction of protein contact number reported here, together with previous applications of this approach to the prediction of protein accessible surface area and B-factor profile, suggests that a support vector regression approach may be very useful for determining the structure-function relation between primary sequence and higher order consecutive protein structural and functional properties.

Predictive vaccinology: Optimisation of predictions using support vector machine classifiers

Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 super-type molecules with excellent accuracy, even for molecules where no binding data are currently available.