Quantitative PET Imaging and Clinical Parameters as Predictive Factors for Patients With Cervical Carcinoma: Implications of a Prediction Model Generated Using Multi-Objective Support Vector Machine Learning.

Purpose: Quantitative features from pre-treatment positron emission tomography (PET) have been used to predict treatment outcomes for patients with cervical carcinoma. The purpose of this study is to use quantitative PET imaging features and clinical parameters to construct a multi-objective machine learning predictive model.

Materials/methods: Seventy-five patients with stage IB2-IVA disease treated at our institution from 2009-2012 were analyzed.

A functional role for transposases in a large eukaryotic genome.

Despite comprising much of the eukaryotic genome, few transposons are active, and they usually confer no benefit to the host. Through an exaggerated process of genome rearrangement, Oxytricha trifallax destroys 95% of its germline genome during development. This includes the elimination of all transposon DNA.