To establish the relationship between ultraviolet-B radiation and squamous cell carcinoma (SCC), basal cell carcinoma (BCC), and actinic keratosis (AK), a cross-sectional prevalence survey was performed in a sample of 808 white, male watermen 30 years of age and older residing in the Eastern Shore of Maryland. A measure of personal cumulative ultraviolet-B exposure was determined for each subject from data collected through interviews and field and laboratory measurements. A personal interview elicited skin type, medication history, and other factors. Clinical diagnoses and histologic confirmation were done for current and previously removed skin tumors. The ratio of subjects with SCC to subjects with BCC was approximately 1:1; however, the ratio of BCC to SCC was 1.25:l because BCC cases were more prone to multiple lesions. Watermen with SCC or AK but not BCC had higher average annual ultraviolet-B doses than age-matched controls. This was particularly marked in watermen younger than 60 years of age. Logistic regression showed that an older age, childhood freckling, and blue eyes significantly increased the risk of the development of all three types of skin tumor. Ease of sunburning was associated with BCC and AK, but not with SCC. Watermen in the upper quartile of cumulative ultraviolet-B exposure had a 2.5 times higher risk for the development of SCC when compared with the lower 3 quartiles. This suggests that high levels of ultraviolet-B exposure are important in SCC occurrence. The risk of AK developing was 1.5 times higher for those whose cumulative ultraviolet-B exposure exceeded the median. The relationship of BCC to cumulative ultraviolet-B exposure was not clear and this suggests that different etiologic mechanisms operate for SCC and BCC. Cancer 65:2811-2817,1990. From the

Unusual clinical and histopathological variants of SS described in the literature are similarly encountered in our cohort of patients, with some features being more common than others. We highlight and discuss some unique clinical and histopathological observations seen in our patients with SS.

Learning from positive and unlabeled examples (PU learning) has been investigated in recent years as an alternative learning model for dealing with situations where negative training examples are not available. It has many real world applications, but it has yet to be applied in the data stream environment where it is highly possible that only a small set of positive data and no negative data is available. An important challenge is to address the issue of concept drift in the data stream environment, which is not easily handled by the traditional PU learning techniques. This paper studies how to devise PU learning techniques for the data stream environment. Unlike existing data stream classification methods that assume both positive and negative training data are available for learning, we propose a novel PU learning technique LELC (PU Learning by Extracting Likely positive and negative micro-Clusters) for document classification. LELC only requires a small set of positive examples and a set of unlabeled examples which is easily obtainable in the data stream environment to build accurate classifiers. Experimental results show that LELC is a PU learning method that can effectively address the issues in the data stream environment with significantly better speed and accuracy on capturing concept drift than the existing state-of-the-art PU learning techniques.

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