A Nomogram Model Identifies Eosinophilic Frequencies to Powerfully Discriminate Kawasaki Disease From Febrile Infections.

Background: Kawasaki disease (KD) is a form of systemic vasculitis that occurs primarily in children under the age of 5 years old. No single laboratory data can currently distinguish KD from other febrile infection diseases. The purpose of this study was to establish a laboratory data model that can differentiate between KD and other febrile diseases caused by an infection in order to prevent coronary artery complications in KD. Methods: This study consisted of a total of 800 children (249 KD and 551 age- and gender-matched non-KD febrile infection illness) as a case-control study. Laboratory findings were analyzed using univariable, multivariable logistic regression, and nomogram models. Results: We selected 562 children at random as the model group and 238 as the validation group. The predictive nomogram included high eosinophil percentage (100 points), high C-reactive protein (93 points), high alanine transaminase (84 points), low albumin (79 points), and high white blood cell (64 points), which generated an area under the curve of 0.873 for the model group and 0.905 for the validation group. Eosinophilia showed the highest OR: 5.015 (95% CI:-3.068-8.197) during multiple logistic regression. The sensitivity and specificity in the validation group were 84.1 and 86%, respectively. The calibration curves of the validation group for the probability of KD showed near an agreement to the actual probability. Conclusion: Eosinophilia is a major factor in this nomogram model and had high precision for predicting KD. This report is the first among the existing literature to demonstrate the important role of eosinophil in KD by nomogram.

[Accuracy improvement of spectral classification of crop using microwave backscatter data].

In the present study, VV polarization microwave backscatter data used for improving accuracies of spectral classification of crop is investigated. Classification accuracy using different classifiers based on the fusion data of HJ satellite multi-spectral and Envisat ASAR VV backscatter data are compared. The results indicate that fusion data can take full advantage of spectral information of HJ multi-spectral data and the structure sensitivity feature of ASAR VV polarization data. The fusion data enlarges the spectral difference among different classifications and improves crop classification accuracy. The classification accuracy using fusion data can be increased by 5 percent compared to the single HJ data. Furthermore, ASAR VV polarization data is sensitive to non-agrarian area of planted field, and VV polarization data joined classification can effectively distinguish the field border. VV polarization data associating with multi-spectral data used in crop classification enlarges the application of satellite data and has the potential of spread in the domain of agriculture.

[Review of crop residue fractional cover monitoring with remote sensing].

Crop residue, as an important element of agro-ecosystem, can influence the flow of nutrients, carbon, water, and energy in agro-ecosystem. As a crucial indicator of distribution of crop residue, crop residue fractional cover is a key parameter of agro-ecosystem carbon cycle process model. Since remote sensing can easily obtain quantities of data, many researches were carried out on monitoring crop residue fractional cover with remote sensing. The present paper summarizes crop residue fractional cover estimation methods and latest progress in remote sensing, and these methods are classified into five categories according to the differences in methodologies and data sources. The principle of every method is described and compared. The advantages and shortages are also discussed and analyzed. Eventually, this paper points out some methods that should be improved, and presents the prospects of crop residue fractional cover estimation in the future.

[A review of classification methods of remote sensing imagery].

Remote sensing data classification is an important way of information extraction and a hot research topic of remote sensing technique. Classification method of remote sensing data is an important issue, and effective selection of appropriate classifier is especially significant for improving classification accuracy. Along with the development of remote sensing technique, traditional parametric classifier is difficult to meet accuracy requirement, leading to the rapid development of intelligent algorithm based non-parametric classifiers. Recently, combined classifiers become a new hot topic for its ability of utilizing complement information of single classifier. In the present paper, characters and advantages of different classifiers as well as the research prospect are analyzed. The paper provides a scientific reference for the development of remote sensing data classification technique.

[Study on spectral reflectance characteristics of hemp canopies].

Hemp (Cannabis sativa L.) is a special economic crop and widely used in many field. It is significative for the government to master the information about planting acreage and spatial distribution of hemp for hemp industrial policy decision in China. Remote sensing offers a potential way of monitoring large area for the cultivation of hemp. However, very little study on the spectral properties of hemp is available in the scientific literature. In the present study, the spectral reflectance characteristics of hemp canopy were systematically analyzed based on the spectral data acquired with ASD FieldSpec portable spectrometer. The wavebands and its spectral resolution for discriminating hemp from other plants were identified using difference analysis. The major differences in canopy reflectance of hemp and other plants were observed near 530, 552, 734, 992, 1 213, 1 580 and 2 199 nm, and the maximal difference is near 734 nm. The spectral resolution should be 30 nm or less in visible and near infrared regions, and 50 nm or less in middle infrared regions.