FGFR2 mutations are rare across histologic subtypes of ovarian cancer

OBJECTIVE: Ovarian cancer is the leading cause of death from gynecologic malignancies in the Western world. Fibroblast growth factor receptor (FGFR) signaling has been implicated to play a role in ovarian tumorigenesis. Mutational activation of one member of this receptor family, FGFR2, is a frequent event in endometrioid endometrial cancer. Given the similarities in the histologic and molecular genetics of ovarian and endometrial cancers, we hypothesized that activating FGFR2 mutations may occur in a subset of endometrioid ovarian tumors, and possibly other histotypes. METHODS: Six FGFR2 exons were sequenced in 120 primary ovarian tumors representing the major histologic subtypes. RESULTS: FGFR2 mutation was detected at low frequency in endometrioid (1/46, 2.2%) and serous (1/41, 2.4%) ovarian cancer. No mutations were detected in clear cell, mucinous, or mixed histology tumors or in the ovarian cancer cell lines tested. Functional characterization of the FGFR2 mutations confirmed that the mutations detected in ovarian cancer result in receptor activation. CONCLUSIONS: Despite the low incidence of FGFR2 mutations in ovarian cancer, the two FGFR2 mutations identified in ovarian tumors (S252W, Y376C) overlap with the oncogenic mutations previously identified in endometrial tumors, suggesting activated FGFR2 may contribute to ovarian cancer pathogenesis in a small subset of ovarian tumors.

A large change in temperature between neighbouring days increases the risk of mortality

Background: Previous studies have found high temperatures increase the risk of mortality in summer. However, little is known about whether a sharp decrease or increase in temperature between neighbouring days has any effect on mortality. Method: Poisson regression models were used to estimate the association between temperature change and mortality in summer in Brisbane, Australia during 1996–2004 and Los Angeles, United States during 1987–2000. The temperature change was calculated as the current day’s mean temperature minus the previous day’s mean. Results: In Brisbane, a drop of more than 3 °C in temperature between days was associated with relative risks (RRs) of 1.157 (95% confidence interval (CI): 1.024, 1.307) for total non external mortality (NEM), 1.186 (95%CI: 1.002, 1.405) for NEM in females, and 1.442 (95%CI: 1.099, 1.892) for people aged 65–74 years. An increase of more than 3 °C was associated with RRs of 1.353 (95%CI: 1.033, 1.772) for cardiovascular mortality and 1.667 (95%CI: 1.146, 2.425) for people aged < 65 years. In Los Angeles, only a drop of more than 3 °C was significantly associated with RRs of 1.133 (95%CI: 1.053, 1.219) for total NEM, 1.252 (95%CI: 1.131, 1.386) for cardiovascular mortality, and 1.254 (95%CI: 1.135, 1.385) for people aged ≥75 years. In both cities, there were joint effects of temperature change and mean temperature on NEM. Conclusion : A significant change in temperature of more than 3 °C, whether positive or negative, has an adverse impact on mortality even after controlling for the current temperature.

Pattern recognition using invariants defined from higher order spectra : 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants