Assessment of microsatellite instability status for the prediction of metachronous recurrence after initial endoscopic submucosal dissection for early gastric cancer

The technique of endoscopic submucosal dissection (ESD) has been developed for en bloc resection of early gastric cancer (EGC); however, little is known about the risk of metachronous cancer in the remnant stomach after initial ESD. In this study, we investigated the correlation between microsatellite instability (MSI) status and the incidence of metachronous recurrence of gastric cancer. According to the genetic/molecular background determined with MSI status and expression levels of hMLH1 and p53 tumour suppressor, 110 EGCs removed with ESD were subclassified into three groups: the mutator/MSI-type (8%), suppressor/p53-type (45%) and unclassified type (47%). Interestingly, patients with the mutator/MSI-type tumour had a high incidence (67%) of metachronous recurrence of gastric cancer within a 3-year observation after initial ESD, which was significantly higher than those with the suppressor/p53-type and unclassified type tumours (P<0.01). Although we investigated mucin phenotypes, there was no correlation between mucin phenotype and the recurrence of EGC. These findings suggest that subclassification of molecular pathological pathways in EGCs is required for the assessment of patients with a high risk of recurrent gastric cancer. The information delivered from our investigation is expected to be of value for decisions about therapy and surveillance after ESD

Toroidal flow measurements of impurity ions in QUEST ECH plasmas using multiple viewing chords emission spectroscopy

A spectroscopic system with multiple viewing chords was developed for QUEST (Q-shu University Experiment with Steady-State Spherical Tokamak) to measure the spatial distribution of ion toroidal velocities in discharges sustained by electron cyclotron resonance heating (ECH). Twenty-four viewing chords were aligned in the midplane and C III emission line spectra were measured for three types of ECH discharge under different magnetic field configurations. By applying an inversion method to the measured spectra, we evaluated the radial distributions of C2+ ion emissivity, temperature, and toroidal velocity. The error in the evaluated velocity was estimated to be less than 5 km/s. It was found that the velocity depends on the magnetic field configuration

Towards learning and verifying invariants of cyber-physical systems by code mutation

Cyber-physical systems (CPS), which integrate algorithmic control with physical processes, often consist of physically distributed components communicating over a network. A malfunctioning or compromised component in such a CPS can lead to costly consequences, especially in the context of public infrastructure. In this short paper, we argue for the importance of constructing invariants (or models) of the physical behaviour exhibited by CPS, motivated by their applications to the control, monitoring, and attestation of components. To achieve this despite the inherent complexity of CPS, we propose a new technique for learning invariants that combines machine learning with ideas from mutation testing. We present a preliminary study on a water treatment system that suggests the efficacy of this approach, propose strategies for establishing confidence in the correctness of invariants, then summarise some research questions and the steps we are taking to investigate them.Comment: Short paper accepted by the 21st International Symposium on Formal Methods (FM 2016