Identification of geographical origins of Cordyceps based on data of amino acids with self-organizing map neural network / 中国中药杂志

In this study, data of amino acids of Cordyceps samples from Qinghai and Tibet was analyzed with self-organizing map neural network. A model of XY-Fused network was established with the content of 8 major amino acids and total amino acids for the identification of geographical origins of Cordyceps from Qinghai and Tibet. It had the prediction accuracy of 83.3% for the test set. In addition, data mining indicated that methionine was a special kind of amino acid in Cordyceps which could serve as a marker to identify its geographical origins. On this basis, the content ratio of methionine to total amino acids was proposed to be a quantifiable indicator to distinguish Cordyceps from Qinghai and Tibet.

A self-organizing maps classifier structure for brain computer interfaces

AbstractIntroductionBrain Computer Interfaces provide an alternative communication path to severe paralyzed people and uses electrical signals related to brain activity in order to identify the user’s intention. In this paper a classifier based on a Self-Organizing Map is introduced.MethodsElectroencephalography signal is used on this work as a source for the user’s intention. This signal represents the brain activity and is processed in order to extract the frequency features presented to the classifier, which uses a Self-Organizing Map and a series of probability masks in order to identify the correct class.ResultsThe proposed structure was evaluated using a dataset of Electroencephalography with three mental tasks. The system was able to identify the different states of the users intention with an accuracy of 71.21% for a three-class problem using only 25 neurons for one of the users.ConclusionThe classifier proposed in this paper has an accuracy that is around the value of similar works in the literature, using the same data, but using a small time window for the classification, meaning the system can have a better time response for the user.

Classification of Atmospheric Individual Aerosol Particles Sampled by Time-of-flight Mass Spectrometry Using Self-Organizing Map / 分析化学

Large amount of data including chemical composition and size information of individual particles would be generated in the measurement of aerosol particles using atmospheric aerosol time-of-flight mass spectrometry ( ATOFMS ) . Our home-made ATOFMS was used to measure the indoor individual aerosol particles in real-time for 24 h, and the obtained mass spectrometric data were clustering analysis by self-organizing map ( SOM ) because of its ability of vector quantization and data dimensionality reduction. 20 classification results were got which includedCalcium-Containing,Salt+Secondary particles,Secondary particles,Organic Amines,K+-Rich Organics andSoil particles, etc. Compared with previous mass spectrometric methods, SOM is a natural visualization tool, more classification results can be obtained. This classification information would be useful to assess the response and toxicity of atmospheric aerosol particles and identify the origin of atmospheric aerosol particles.

13C NMR spectral data and molecular descriptors to predict the antioxidant activity of flavonoids / Dados espectrais de RMN 13C e descritores moleculares para prever a atividade antioxidante dos flavonóides

Tissue damage due to oxidative stress is directly linked to development of many, if not all, human morbidity factors and chronic diseases. In this context, the search for dietary natural occurring molecules with antioxidant activity, such as flavonoids, has become essential. In this study, we investigated a set of 41 flavonoids (23 flavones and 18 flavonols) analyzing their structures and biological antioxidant activity. The experimental data were submitted to a QSAR (quantitative structure-activity relationships) study. NMR 13C data were used to perform a Kohonen self-organizing map study, analyzing the weight that each carbon has in the activity. Additionally, we performed MLR (multilinear regression) using GA (genetic algorithms) and molecular descriptors to analyze the role that specific carbons and substitutions play in the activity.

Danos aos tecidos devido ao estresse oxidativo estão diretamente ligados ao desenvolvimento de muitos, senão todos, os fatores de sedentarismo e de doenças crônicas. Neste contexto, a busca de moléculas naturais, que participam da nossa dieta e que possuam atividade antioxidante, flavonóides, torna-se de grande interesse. Neste estudo, nós investigamos um conjunto de 41 flavonóides (23 flavonas e 18 flavonóis), relacionando suas estruturas e atividade antioxidante. Os dados experimentais foram submetidos à análise de QSAR (relações quantitativas estrutura-atividade). Dados de RMN 13C foram utilizados para realizar um estudo do mapa auto-organizável de Kohonen, analisando o peso que cada carbono tem na atividade. Além disso, realizamos uma MLR (regressão múltipla) usando GA (algoritmos genéticos) e descritores moleculares para avaliar a influência de carbonos e substituições na atividade.