Study on endotoxin quantitative detector / 重庆医学

Objective To develop a quantitative,highly sensitive,low detection limit and fast endotoxin(ET) biosensing detection system by utilizing the piezoelectric crystal's damping effect(non-mass effect) in the liquid phase environment.Methods The sensor probe of better performance (eliminating the quality load effects and improving the liquid solid coupling effect) was obtained by using the smoothing processing and hydrophilic processing of a quartz crystal sensor probe surface,thus the stability and repeatability of measurement were increased and the low detection limit of measurement was decreased.The whole measurement system was made into a miniature and automated endotoxin detection system by using CPLD device and LabView software to realize the automatic collection,and analysis and results display of measurement data.Results The good linear relationship existed between different concentrations of endotoxin and the stable value of frequency shift.Conclusion By the established data of frequency shift,the endotoxin concentration can be obtained accurately and conveniently.

Feature selection using feature dissimilarity measure and density-based clustering: Application to biological data.

Reduction of dimensionality has emerged as a routine process in modelling complex biological systems. A large number of feature selection techniques have been reported in the literature to improve model performance in terms of accuracy and speed. In the present article an unsupervised feature selection technique is proposed, using maximum information compression index as the dissimilarity measure and the well-known density-based cluster identification technique DBSCAN for identifying the largest natural group of dissimilar features. The algorithm is fast and less sensitive to the user-supplied parameters. Moreover, the method automatically determines the required number of features and identifies them. We used the proposed method for reducing dimensionality of a number of benchmark data sets of varying sizes. Its performance was also extensively compared with some other well-known feature selection methods.

Assesment of Cocoa Genotypes for Quantitative Pod Traits.

Yield related traits in 20 cocoa genotypes were investigated to determine suitable parental genotypes for yield improvement programmes in cocoa. Fifteen uniformly ripe pods were collected for pod and bean characteristic assessment from twenty genotypes in an existing cocoa hybrid trial research plot laid out in a randomized complete block design with six replications at the Cocoa Research Institute of Nigeria (CRIN), Ibadan, Nigeria. Seven quantitative data on the pods were subjected to statistical analysis. The 20 genotypes differed significantly (P < 0.001) for the seven traits. Performances of the genotypes ranged as: pod weight (175.40 – 620.50 g), pod length (11.30 – 20.10 cm), pod width (6.37 – 8.90 cm), pod thickness (0.73 – 1.65 cm), number of beans per pod (20 - 52), weight of beans per pod (27.33–119.67 g) and dry weight of hundred beans (52.33 – 115 g). Positive and significant (P < 0.001) correlation existed between pod weight and length, pod width, pod thickness and weight of beans per pod. The range of broad sense heritability was between 56.13 (number of beans per pod) to 81.76 (dry weight of hundred beans). About 86% of the total variation was explained by the first three principal component axes and four distinct groups emerged from the clustering technique. Results show significant (P<0.05) intra-cluster variability of the seven traits and that choosing genotypes G3 (T65/7 x T9/15), G5 (P7 x T60/887), G6 (P7 x PA150), G15 (T86/2 x T22/28) and G16 (T82/27 x T12/11) as parents in future yield improvement programmes will enhance cocoa productivity in Nigeria.

Potential and Merits of the Eigenvalues Method to Simulate the Conjunctive Use of Groundwater and Surface Water.

It is necessary predict the effect of aquifer stresses in surface water and wetlands and consider the mutual effects that are produced by the conjunctive use of surface water and groundwater. This was originally made with very simple idealized analytical methods. The next development was the application of finite differences or finite elements numerical models, but poses problems when the model has to be run many times to analyze different management alternatives. When aquifer behavior is linear, as in confined, semiconfined, or unconfined aquifers with not too large changes in its saturated thickness, it is possible to apply the superposition strategy through influence functions. That has simplified significantly modeling and improved the effectiveness of management models. However, for large models, long modeling periods and a large number of alternatives, it is needed to handle and store many influence functions and to consider and store all the previous stresses. In that case, the eigenvalue method can be a more appropriated option. This approach solves the spatially discretized flow equation explicitly and continuously in time, obtaining modal orthogonal components through very simple explicit state equations in function of time. To reduce the computational load, the simulation can be simplified with appropriate truncation using only dominant modes of the components at the expense of a small error. Efficient methods have been developed to get the modal components as well as to perform truncation with limited errors.

Application of the principal-component analysis in the evaluation of three grass varieties / Aplicación del análisis por componentes principales en la evaluación de tres variedades de pasto / Aplicação da análise de componentes principais na avaliação de três variedades de capim

Objective: demonstrate how to use the principal-component analysis to reduce dimensionality in assessing three varieties of Ryegrass (Lolium sp. L.), namely, tetraploid hybrid (Foster), annual diploid (Southern Star), and annual tetraploid (Beefbuilder). Both the statistical properties and programming using the SAS statistical package are also highlighted. Results: The variables that defined the main factor for the three grass varieties were: height from the floor, middle width of the fully elongated last leaf, and biomass.

Objetivo: mostrar una aplicación del análisis de componentes principales en la reducción de la dimensionalidad de variables derivadas de la evaluación agronómica de tres variedades de pasto. Métodos: los pastos evaluados fueron Ryegrass (Lolium sp. L.), híbrido tetraploide (Foster), anual diploide (Southern Star) y anual tetraploide (Beef Builder). Igualmente se destacan las propiedades estadísticas y la forma de programación en el paquete estadístico SAS. Resultados: se observó que las variables que definieron el factor principal para las tres variedades fueron: altura desde el piso, ancho de la parte media de la última hoja completamente elongada y biomasa.

O objetivo principal deste trabalho foi mostrar uma aplicação da análise de componentes principais na redução da dimensionalidade de variáveis derivadas da avaliação agronômica de três variedades de capim azevém (Lolium sp. L), a saber, híbrido tetraplóide (Foster), anual diplóide(Southern Star) e anual tetraploide (BeefBuilder). Também se destacam as propriedades estatísticas e a programação no pacote estatístivo SAS. Resultados: como um resultado notável do processo de pesquisa foi observado que as variáveis que definiram o principal fator para as três variedades foram: altura do solo, a largura da parte meia da última folha totalmente alongada e a biomassa.

Image Reconstruction of Eigenvalue of Diffusion Principal Axis Using Diffusion Tensor Imaging

PURPOSE: The objective of this work to construct eigenvalue maps that have information of magnitude of three primary diffusion directions using diffusion tensor images. MATERIALS AND METHODS: To construct eigenvalue maps, we used a 3.0T MRI scanner. We also compared the Moore-Penrose pseudo-inverse matrix method and the SVD (single value decomposition) method to calculate magnitude of three primary diffusion directions. Eigenvalue maps were constructed by calculating of magnitude of three primary diffusion directions. We did investigate the relationship between eigenvalue maps and fractional anisotropy map. RESULTS: Using Diffusion Tensor Images by diffusion tensor imaging sequence, we did construct eigenvalue maps of three primary diffusion directions. Comparison between eigenvalue maps and Fractional Anisotropy map shows what is difference of Fractional Anisotropy value in brain anatomy. Furthermore, through the simulation of variable eigenvalues, we confirmed changes of Fractional Anisotropy values by variable eigenvalues. And Fractional anisotropy was not determined by magnitude of each primary diffusion direction, but it was determined by combination of each primary diffusion direction. CONCLUSION: By construction of eigenvalue maps, we can confirm what is the reason of fractional anisotropy variation by measurement the magnitude of three primary diffusion directions on lesion of brain white matter, using eigenvalue maps and fractional anisotropy map.