More on Identification on Detailed Wage Decompositions

Revised: 2006-05.-- Published as an article in: The Review of Economics and Statistics, 2004, vol. 86, issue 4, pp. 1034-1036.

3D line drawing from point clouds using chromatic stereo and shading

1 PDF document (8 pp., English).-- Contributed to: VSMM'08: 14th International Conference on Virtual Systems and Multimedia (Limassol, Cyprus, Oct 20-25, 2008)

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).

Whales, dolphins, and porpoises of the eastern North Pacific and adjacent Arctic waters: a guide to their identification

This is an identification guide for cetaceans (whales, dolphins, and porpoises), that was designed to assist laymen in identifying cetaceans encountered in eastern North Pacific and Arctic waters. It was intended for use by ongoing cetacean observer programs. This is a revision of an earlier guide with the same title published in 1972 by the Naval Undersa Center and the National Marine Fisheries Service. It includes sections on identifying cetaceans at sea as well as stranded animals on shore. Species accounts are divided by body size and presence or lack of a dorsal fin. Appendices include illustrations of tags on whales, dolphins, and porpoises, by Larry Hobbs; how to record data from observed cetaceans at sea and for stranded cetaceans; and a list of cetacean names in Japanese and Russian. (Document contains 245 pages - file takes considerable time to open)

Whales, dolphins, and porpoises of the western North Atlantic: a guide to their identification

This is an identification guide for cetaceans (whales, dolphins, and porpoises). It was designed to assist laypersons in identifying cetaceans encountered in the western North Atlantic Ocean and was intended for use by ongoing cetacean observer programs. This publication includes sections on identifying cetaceans at sea as well as stranded animals on shore. Species accounts are divided by body size and presence or lack of a dorsal fin. Appendices cover tags used on cetacean species; how to record and report cetacean observations at see and for stranded cetaceans; and a list of contacts for reporting cetacean strandings. (Document pdf contains 183 pages - file takes considerable time to open)

A Simplified Evaluation Procedure of the Scale of Fluctuation Combined Recurrence Averaging and Weighted Linear Regression

The divergence of properties from one location to another within a soil mass is termed spatial variability, which traditionally includes three parameters the mean, the standard deviation, and the scale of fluctuation, in order to stochastically describe a soil property. Among them, determining the scale of fluctuation in the evaluation of spatial variability of soil profiles is not easy due to soil condition complexity. A simplified procedure is presented in the paper to determine the scale of fluctuation combined recurrence averaging and weighted linear regression. The alternative approach utilizes widely usable spreadsheet to solve the problem more directly and efficiently.