Visual Basic在油藏数值模拟中的应用

随着油藏数值模拟技术的发展以及油藏数值模拟软件的不断改进和完善,油藏数值模拟软件在油田开发中的应用越来越广泛。对油藏数值模拟软件计算出的数据进行整理不仅枯燥而且花费了大量时间。本文利用Visual Basic语言编制了处理ECLIPSE软件生成的油气田开发指标数据软件(RSMAN)。该软件方便了油气田开发指标的整理和汇总,软件界面友好,操作简单。

Lo visual como construcción. Desarrollo de un modelo pictórico de representación de la imagen

903 páginas, bibliografía en páginas 854-895, glosario en páginas 896-903

Identification of a biomarker panel for colorectal cancer diagnosis

Background: Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries. Methods: A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60- mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables. Results: After an exhaustive process of pre-processing to ensure data quality–lost values imputation, probes quality, data smoothing and intraclass variability filtering–the final dataset comprised a total of 8, 104 probes. Next, a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples. Conclusions: We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997 and 0.955).

Portador de deficiência visual [gravação de som] : guia legal

Síntetiza a legislação federal referente ou aplicável à pessoa portadora de deficiência visual, comentando as disposições civis e penais pertinentes. Seu objetivo é facilitar ao portador de deficiência visual o conhecimento dos seu direitos, requisito primeiro para assegurar-lhe o exercício pleno da cidadania. Apresenta dados sobre os portadores de deficiência visual no Brasil e no mundo, trata da legislação brasileira já disponível em braile e traz a listagem dos órgãos federais de apoio ao portador de deficiência visual.