Allogeneic Uterus Transplantation in Baboons

Preclinical report on allogeneic uterus transplantation in non-human primates

Study question Is it possible to perform allogeneic uterus transplantation (UTx) with a donation from a live donor in a non-human primate species and what immunosuppression is needed to prevent rejection? Summary answer Allogeneic UTx in the baboon is a donor- and recipient-safe surgical procedure; immunosuppression with induction therapy and a triple protocol should be used. What is known already UTx may become a treatment for absolute uterine factor infertility. Autologous UTx models have been developed in non-human primates with reports on long-term survival of the uterine grafts. STUDY DESIGN, SIZEAND DURATION: This experimental study included 18 female baboons as uterus donors and 18 f…

Passive millimeter wave image classification with large scale Gaussian processes

Passive Millimeter Wave Images (PMMWIs) are being increasingly used to identify and localize objects concealed under clothing. Taking into account the quality of these images and the unknown position, shape, and size of the hidden objects, large data sets are required to build successful classification/detection systems. Kernel methods, in particular Gaussian Processes (GPs), are sound, flexible, and popular techniques to address supervised learning problems. Unfortunately, their computational cost is known to be prohibitive for large scale applications. In this work, we present a novel approach to PMMWI classification based on the use of Gaussian Processes for large data sets. The proposed…

Efficient remote sensing image classification with Gaussian processes and Fourier features

This paper presents an efficient methodology for approximating kernel functions in Gaussian process classification (GPC). Two models are introduced. We first include the standard random Fourier features (RFF) approximation into GPC, which largely improves the computational efficiency and permits large scale remote sensing data classification. In addition, we develop a novel approach which avoids randomly sampling a number of Fourier frequencies, and alternatively learns the optimal ones using a variational Bayes approach. The performance of the proposed methods is illustrated in complex problems of cloud detection from multispectral imagery.