15,142 research outputs found
Worldwide Human Influenza A (H5N1) Cases
No full textThis dataset is the property of the Khazeni Lab at the Stanford University Department of Pulmonary & Critical Care Medicine. It accompanies the following papers:
Patel, Rita B., et al. "Demographic and clinical predictors of mortality from highly pathogenic avian influenza A (H5N1) virus infection: CART analysis of international cases." PLOS One 9.3 (2014).
Mathur, Maya B., et al. "Seasonal Patterns in Human A (H5N1) Virus Infection: Analysis of Global Cases." PLOS One (2014): e106171
Worldwide Human Influenza A (H5N1) Cases
No full textThis dataset is the property of the Khazeni Lab at the Stanford University Department of Pulmonary & Critical Care Medicine. It accompanies the following papers:
Patel, Rita B., et al. "Demographic and clinical predictors of mortality from highly pathogenic avian influenza A (H5N1) virus infection: CART analysis of international cases." PLOS One 9.3 (2014).
Mathur, Maya B., et al. "Seasonal Patterns in Human A (H5N1) Virus Infection: Analysis of Global Cases." PLOS One (2014): e106171
Paurocephala phalaki Mathur 1975
No full text<i>Paurocephala phalaki</i> Mathur <p> <i>Paurocephala phalaki</i> Mathur, 1975: 58. Holotype, India: Bengal, Tista village, 27 October 1965, (V. R. Phalak) (IFRI), not examined.</p> <p> <i>Description.</i> Species of the <i>chonchaiensis</i> type.</p> <p>Adult: described by Mathur (1975).</p> <p>Fifth instar larva: described by Mathur (1975).</p> <p> <i>Host plants</i>. According to Mathur (1975) the common name of the host plant in Bengal is ‘khasare’. No information was found with this local plant name, but a similar name ‘khesari’ refers to <i>Lathyrus sativus</i> L. (Fabaceae) (E. Gauda, personal communication).</p> <p> <i>Distribution</i>. India (Bengal) (Mathur, 1975), (Madras) (Kandasamy, 1986).</p> <p> <i>Comments</i>. The description provided by Mathur (1975) agrees with <i>P. bifasciata</i> Kuwayama diVering only in body setiferation. Only two long setae on genae were mentioned by Mathur (1975) for <i>P. phalaki</i> Mathur, whereas the head of <i>P. bifasciata</i> Kuwayama is completely covered by long setae. No material of the former was available for examination.</p>Published as part of <i>Mifsud, D. & Burckhardt, D., 2002, Taxonomy and phylogeny of the Old World jumping plant-louse genus Paurocephala (Insecta, Hemiptera, Psylloidea), pp. 1887-1986 in Journal of Natural History 36 (16)</i> on page 1968, DOI: 10.1080/00222930110048909, <a href="http://zenodo.org/record/5299071">http://zenodo.org/record/5299071</a>
Pauropsylla purpurescens Mathur 1975
No full text<i>Pauropsylla purpurescens</i> Mathur, 1975 <p> <b>Distribution.</b> India: Uttarakhand (Mathur 1935, as <i>Pauropsylla</i> sp.; Mathur 1975).</p> <p> <b>Host plant.</b> <i>Ficus racemosa</i> (Moraceae).</p>Published as part of <i>Burckhardt, Daniel, Sharma, Anamika & Raman, Anantanarayanan, 2018, Checklist and comments on the jumping plant-lice (Hemiptera: Psylloidea) from the Indian subcontinent, pp. 1-38 in Zootaxa 4457 (1)</i> on page 24, DOI: 10.11646/zootaxa.4457.1.1, <a href="http://zenodo.org/record/1457537">http://zenodo.org/record/1457537</a>
Diaphorina communis Mathur 1975
No full text<i>Diaphorina communis</i> Mathur, 1975 <p> <b>Distribution.</b> Bhutan (Donovan <i>et al.</i> 2012); India: Uttarakhand (Mathur 1935, as <i>Diaphorina</i> sp.; Mathur 1975; Loginova 1978, as <i>D. mathuri</i>); Nepal (Hodkinson 1986).</p> <p> <b>Host plant.</b> <i>Murraya koenigii,</i> <i>M. paniculata</i> (Rutaceae).</p>Published as part of <i>Burckhardt, Daniel, Sharma, Anamika & Raman, Anantanarayanan, 2018, Checklist and comments on the jumping plant-lice (Hemiptera: Psylloidea) from the Indian subcontinent, pp. 1-38 in Zootaxa 4457 (1)</i> on page 11, DOI: 10.11646/zootaxa.4457.1.1, <a href="http://zenodo.org/record/1457537">http://zenodo.org/record/1457537</a>
Diaphorina venata Mathur 1975
No full textDiaphorina venata Mathur, 1975 Distribution. India: Tamil Nadu (Mathur 1975). Host plant. Adults were collected on Santalum album (Santalaceae). There is no evidence for or against S. album being a host (Burckhardt et al. 2017).Published as part of Burckhardt, Daniel, Sharma, Anamika & Raman, Anantanarayanan, 2018, Checklist and comments on the jumping plant-lice (Hemiptera: Psylloidea) from the Indian subcontinent, pp. 1-38 in Zootaxa 4457 (1) on pages 11-12, DOI: 10.11646/zootaxa.4457.1.1, http://zenodo.org/record/145753
Aphalara ossiannilssoni Mathur 1975
No full text<i>Aphalara ossiannilssoni</i> Mathur, 1975 <p> <b>Distribution.</b> India: West Bengal (Mathur 1975).</p> <p> <b>Host plant.</b> <i>Polygonum microcephalum</i> (Polygonaceae).</p>Published as part of <i>Burckhardt, Daniel, Sharma, Anamika & Raman, Anantanarayanan, 2018, Checklist and comments on the jumping plant-lice (Hemiptera: Psylloidea) from the Indian subcontinent, pp. 1-38 in Zootaxa 4457 (1)</i> on page 3, DOI: 10.11646/zootaxa.4457.1.1, <a href="http://zenodo.org/record/1457537">http://zenodo.org/record/1457537</a>
A relative evaluation of multiclass image classification by support vector machines
No full textSupport vector machines (SVMs) have considerable potential as classifiers of remotely sensed data. A constraint on their application in remote sensing has been their binary nature, requiring multiclass classifications to be based upon a large number of binary analyses. Here, an approach for multiclass classification of airborne sensor data by a single SVM analysis is evaluated against a series of classifiers that are widely used in remote sensing, with particular regard to the effect of training set size on classification accuracy. In addition to the SVM, the same datasets were classified using a discriminant analysis, decision tree, and multilayer perceptron neural network. The accuracy statements of the classifications derived from the different classifiers were compared in a statistically rigorous fashion that accommodated for the related nature of the samples used in the analyses. For each classification technique, accuracy was positively related with the size of the training set. In general, the most accurate classifications were derived from the SVM approach, and with the largest training set the SVM classification was significantly (p < 0.05)more accurate (93.75%) than that derived from the discriminant analysis (90.00%) and decision tree algorithms (90.31%). Although each classifier could yield a very accurate classification, > 90% correct, the classifiers differed in the ability to correctly label individual cases and so may be suitable candidates for an ensemble-based approach to classification
Mathur and VanderWeele's d (R code)
No full textR code (computer code) to calculate Mathur and VanderWeele's (2020) d, SE, and confidence intervals.Equations provided in reference below. Mathur, M.B., & VanderWeele, T.J. (2020). A simple, interpretable conversion from Pearson’s correlation to Cohen’s for d continuous exposures. Epidemiology, 31(2), e16–e18. https://doi.org/10.1097/ ede.0000000000001105</p
Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification
No full textConventional approaches to training a supervised image classification aim to fully describe all of the classes spectrally. To achieve a complete description of each class in feature space, a large training set is typically required. It is not, however, always necessary to have training statistics that provide a complete and representative description of the classes, especially if using nonparametric classifiers. For classification by a support vector machine, only the training samples that are support vectors, which lie on part of the edge of the class distribution in feature space, are required; all other training samples provide no contribution to the classification analysis. If regions likely to furnish support vectors can be identified in advance of the classification, it may be possible to intelligently select useful training samples. The ability to target useful training samples may allow accurate classification from small training sets. This potential for intelligent training sample collection was explored for the classification of agricultural crops from multispectral satellite sensor data. With a conventional approach to training, only a quarter of the training samples acquired actually made a positive contribution to the analysis and allowed the crops to be classified to a high accuracy (92.5%). The majority of the training set, therefore, was unnecessary as it made no contribution to the analysis. Using ancillary information on soil type, however, it would be possible to constrain the training sample acquisition process. By limiting training sample acquisition only to regions with a specific soil type, it was possible to use a small training set to classify the data without loss of accuracy. Thus, a small number of intelligently selected training samples may be used to classify a data set as accurately as a larger training set derived in a conventional manner. The results illustrate the potential to direct training data acquisition strategies to target the most useful training samples to allow efficient and accurate image classification. <br/
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