MK2 and ETV1 Are Prognostic Factors in Esophageal Adenocarcinomas

Background. Esophageal cancer is ranked in the top ten of diagnosed tumors worldwide. Even though improvements in survival could be noticed over the last years, prognosis remains poor. ETS translocation variant 1 (ETV1) is a member of a family of transcription factors and is phosphorylated by mitogen-activated protein kinase (MAPK)-activated protein kinase 2 (MK2). Aim of this study was to evaluate the prognostic role of MK2 and ETV1 in esophageal cancer. Methods. Consecutive patients that underwent surgical resection at the department of surgery at the Medical University of Vienna between 1991 and 2012 were included into this study. After microscopic analysis, tissue micro arrays (TMAs) were created and immunohistochemistry was performed with antibodies against MK2 and ETV1. Results. 323 patients were included in this study. Clinical data was achieved from a prospective patient data base. Nuclear overexpression of MK2 was observed in 143 (44.3%) cases for nuclear staining and in 142 (44.0%) cases a cytoplasmic overexpression of MK2 was observed. Nuclear and cytoplasmic ETV1 overexpression was detected in 20 cases (6.2%) and 30 cases (9.3%), respectively. In univariate survival analysis, cMK2 and nETV1 were found to be significantly associated with patients' overall survival. Whereas overexpression of cMK2 was associated with shorter, nETV1 was associated with longer overall survival. In multivariate survival analysis, both cMK2 and nETV1 were found to be independent prognostic factors for the subgroup of EAC as well. Discussion. Expression of MK2 and ETV1 are prognostic factors in patients, with esophageal adenocarcinoma

Pseudonyms in cost-sharing games

This work initiates the study of cost-sharing mechanisms that, in addition to the usual incentive compatibility conditions, make it disadvantageous for the users to employ pseudonyms. We show that this is possible only if all serviced users pay the same price, which implies that such mechanisms do not exist even for certain subadditive cost functions. In practice, a user can increase her utility by lying in one way (misreport her willingness to pay) or another (misreport her identity). We prove also results for approximately budget-balanced mechanisms. Finally, we consider mechanisms that rely on some kind of "reputation" associated to the pseudonyms and show that they are provably better. © 2009 Springer-Verlag Berlin Heidelberg

Mainzelliste SecureEpiLinker (MainSEL): privacy-preserving record linkage using secure multi-party computation

MOTIVATION Record Linkage has versatile applications in real-world data analysis contexts, where several datasets need to be linked on the record level in the absence of any exact identifier connecting related records. An example are medical databases of patients, spread across institutions, that have to be linked on personally identifiable entries like name, date of birth or ZIP code. At the same time, privacy laws may prohibit the exchange of this personally identifiable information (PII) across institutional boundaries, ruling out the outsourcing of the record linkage task to a trusted third party. We propose to employ privacy-preserving record linkage (PPRL) techniques that prevent, to various degrees, the leakage of PII while still allowing for the linkage of related records. RESULTS We develop a framework for fault-tolerant PPRL using secure multi-party computation with the medical record keeping software Mainzelliste as the data source. Our solution does not rely on any trusted third party and all PII is guaranteed to not leak under common cryptographic security assumptions. Benchmarks show the feasibility of our approach in realistic networking settings: linkage of a patient record against a database of 10 000 records can be done in 48 s over a heavily delayed (100 ms) network connection, or 3.9 s with a low-latency connection. AVAILABILITY AND IMPLEMENTATION The source code of the sMPC node is freely available on Github at https://github.com/medicalinformatics/SecureEpilinker subject to the AGPLv3 license. The source code of the modified Mainzelliste is available at https://github.com/medicalinformatics/MainzellisteSEL. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online

The MADlib analytics library or MAD skills, the SQL.

ABSTRACT MADlib is a free, open source library of in-database analytic methods. It provides an evolving suite of SQL-based algorithms for machine learning, data mining and statistics that run at scale within a database engine, with no need for data import/export to other tools. The goal is for MADlib to eventually serve a role for scalable database systems that is similar to the CRAN library for R: a community repository of statistical methods, this time written with scale and parallelism in mind. In this paper we introduce the MADlib project, including the background that led to its beginnings, and the motivation for its open source nature. We provide an overview of the library's architecture and design patterns, and provide a description of various statistical methods in that context. We include performance and speedup results of a core design pattern from one of those methods over the Greenplum parallel DBMS on a modest-sized test cluster. We then report on two initial e↵orts at incorporating academic research into MADlib, which is one of the project's goals. MADlib is freely available at http://madlib.net, and the project is open for contributions of both new methods, and ports to additional database platforms