Prognostic significance of CD25 expression in dogs with a noninvasive diagnosis of B-cell lymphoma treated with CHOP chemotherapy.

Prior studies have identified high CD25 expression in canine diffuse large B-cell lymphoma as a negative prognostic indicator. The objective of this retrospective study was to evaluate CD25 expression as a prognostic indicator in dogs with B-cell lymphoma (BCL) diagnosed with commonly used noninvasive diagnostics (cytology and flow cytometry [FC]) and treated with CHOP chemotherapy. Lymph node aspirates from 57 dogs with cytologic diagnosis of lymphoma composed of intermediate to large lymphocytes were analysed with FC. Percentage of neoplastic B-cells expressing CD25 and median fluorescence intensity (MFI) of CD25 were measured. Relationships of CD25 percent positivity and MFI to progression free survival (PFS) and survival time were evaluated. Median survival time (MST) of all dogs was 272 days (95% CI, 196-348 days) and median PFS was 196 days (95% CI, 172-220 days). Higher percentage of B-cells positive for CD25 was associated with decreased risk of death in multivariable analysis (p = .02). Dogs with higher CD25 positivity had longer MST and PFS than dogs with lower CD25 positivity (318 days versus 176 days and 212 days versus 148 days, respectively), but these differences were not significant. CD25 MFI was not significantly associated with outcome. Based on the results of this study, the association of CD25 expression and prognosis in dogs with BCL diagnosed using noninvasive methods should be interpreted with caution. Further evaluation, with studies that include histopathologic differentiation of lymphoma subtypes, is needed.

Rotated Sphere Haar Wavelet and Deep Contractive Auto-Encoder Network With Fuzzy Gaussian SVM for Pilot's Pupil Center Detection.

How to track the attention of the pilot is a huge challenge. We are able to capture the pupil status of the pilot and analyze their anomalies and judge the attention of the pilot. This paper proposes a new approach to solve this problem through the integration of spherical Haar wavelet transform and deep learning methods. First, considering the application limitations of Haar wavelet and other wavelets in spherical signal decomposition and reconstruction, a feature learning method based on the spherical Haar wavelet is proposed. In order to obtain the salient features of the spherical signal, a rotating spherical Haar wavelet is also proposed, which has a consistent scale in the same direction between the reconstructed image and the original image. Second, in order to find a better characteristic representation of the spherical signal, a higher contractive autoencoder (HCAE) is designed for the potential representation of the spherical Haar wavelet coefficients, which has two penalty items, respectively, from Jacobian and two order items from Taylor expansion of the point x for the contract learning of sample space. Third, in order to improve the classification performance, this paper proposes a fuzzy Gaussian support vector machine (FGSVM) as the top classification tool of the deep learning model, which can punish some Gaussian noise from the output of the deep HCAE network (DHCAEN). Finally, a DHCAEN-FGSVM classifier is proposed to identify the location of the pupil center. The experimental results of the public data set and actual data show that our model is an effective method for spherical signal detection.