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A 12-year-old neutered male domestic shorthair cat was presented with a 3-year history of multiple nonpruritic, ulcerated, plaque-like skin lesions but no other clinical signs. A systemic examination revealed mild lymphadenopathy. Histopathologic analysis of the skin lesions revealed multicentric basosquamous carcinoma (BSC). Immunohistochemical analysis, PCR, and sequencing detected Felis catus papillomavirus type 2 (FcaPV-2) within the tumors. As BSC is rare in cats, clinical behavior has not been established. To our knowledge, this is the first case report to demonstrate detection of FcaPV-2 within a BSC in a domestic cat.

Near infrared spectroscopy (NIR), is recently applied to analysis of food, and it gives non-destructive and highly reproducible results. The objectives of this study were to assay protein, lipid and starch content in Alaska pollack surimi and to investigate application in seafood. NIR spectra showed the absorbance signal at 1,510 nm, 2,050 nm, 2,170 nm and 2,180 nm for protein, and signal were increased with protein content. Standard error of equation (SEE) was 0,296 and standard error of prediction (SEP) was 0.327. In analysis of lipid in Alaska pollack surimi by NIR, near infrared spectra of lyophilized Alaska pollack surimi were scanned and the signals of absorbance from C-H functional groups in lipid were identified at 1,730 nm, 1,740 nm and 2,300 nm and these signals were risen as Increasing lipid contents of Alaska pollack surimi as samely protein. SEE and SEP were 0.319, 0,353, respectively. In starch analysis using NIR the signals of starch distinctly changed at wavelength of 1,450 nm and 1,950 nm. Collected values of SEE and SEP were 0.304 and 0.318, respectively and the range of errors was in starch contents.

This paper proposes a dimension reduction technique for estimation in linear mixed models. Specifically, we show that in a linear mixed model, the maximum-likelihood (ML) problem can be rewritten as a substantially simpler optimization problem which presents at least two main advantages: the number of variables in the simplified problem is lower and the search domain of the simplified problem is a compact set. Whereas the former advantage reduces the computational burden, the latter permits the use of stochastic optimization methods well qualified for closed bounded domains. The developed dimension reduction technique makes the computation of ML estimates, for fixed effects and variance components, feasible with large computational savings. Computational experience is reported here with the results evidencing an overall good performance of the proposed technique.