The NEWMEDS rodent touchscreen test battery for cognition relevant to schizophrenia.

RATIONALE: The NEWMEDS initiative (Novel Methods leading to New Medications in Depression and Schizophrenia, http://www.newmeds-europe.com ) is a large industrial-academic collaborative project aimed at developing new methods for drug discovery for schizophrenia. As part of this project, Work package 2 (WP02) has developed and validated a comprehensive battery of novel touchscreen tasks for rats and mice for assessing cognitive domains relevant to schizophrenia. OBJECTIVES: This article provides a review of the touchscreen battery of tasks for rats and mice for assessing cognitive domains relevant to schizophrenia and highlights validation data presented in several primary articles in this issue and elsewhere. METHODS: The battery consists of the five-choice serial reaction time task and a novel rodent continuous performance task for measuring attention, a three-stimulus visual reversal and the serial visual reversal task for measuring cognitive flexibility, novel non-matching to sample-based tasks for measuring spatial working memory and paired-associates learning for measuring long-term memory. RESULTS: The rodent (i.e. both rats and mice) touchscreen operant chamber and battery has high translational value across species due to its emphasis on construct as well as face validity. In addition, it offers cognitive profiling of models of diseases with cognitive symptoms (not limited to schizophrenia) through a battery approach, whereby multiple cognitive constructs can be measured using the same apparatus, enabling comparisons of performance across tasks. CONCLUSION: This battery of tests constitutes an extensive tool package for both model characterisation and pre-clinical drug discovery.This work was supported by the Innovative Medicine Initiative Joint Undertaking under grant agreement no. 115008 of which resources are composed of EFPIA in-kind contribution and financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013). The authors thank Charlotte Oomen for valuable comments on the manuscript.This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s00213-015-4007-

Subsurface Characterization with Support Vector Machines

A typical subsurface environment is heterogeneous, consists of multiple materials (geologic facies), and is often insufficiently characterized by data. The ability to delineate geologic facies and to estimate their properties from sparse data is essential for modeling physical and biochemical processes occurring in the subsurface. We demonstrate that the support vector machine is a viable and efficient tool for lithofacies delineation, and we compare it with a geostatistical approach. To illustrate our approach, and to demonstrate its advantages, we construct a synthetic porous medium consisting of two heterogeneous materials and then estimate boundaries between these materials from a few selected data points. Our analysis shows that the error in facies delineation by means of support vector machines decreases logarithmically with increasing sampling density.We also introduce and analyze the use of regression support vector machines to estimate the parameter values between points where the parameter is sampled