Methods to Construct a Step-By-Step Beginner’s Guide (BG) to Decision Analytic Cost Effectiveness Modelling.

Background: Although guidance on good research practice in health economic modelling is widely available, there is still a need for a simpler instructive resource which could guide a beginner modeller alongside modelling for the first time. Aim: To develop a Beginner’s Guide to be used as a hand-held guide contemporaneous to the model development process. Methods: A systematic review of best practice guidelines was used to construct a framework of steps undertaken during the model development process. Focused methods reviews supplemented this framework. Consensus was obtained amongst a group of model developers to review and finalise the content of the preliminary Beginner’s Guide. The final Beginner’s Guide was used to develop cost effectiveness models. Results: Thirty two best practice guidelines were data extracted, synthesised and critically evaluated to identify steps for model development which formed a framework for the Beginner’s Guide. Within five phases of model development, eight broad submethods were identified and nineteen methodological reviews were conducted to develop the content of the draft Beginner’s Guide. Two rounds of consensus agreement were undertaken to reach agreement on the final Beginner’s Guide. To assess fitness for purpose (ease of use and completeness), models were developed independently and by the researcher using the Beginner’s Guide. Conclusion: A combination of systematic review, methods reviews, consensus agreement and validation was used to construct a step-by-step Beginner’s Guide for developing decision analytical cost effectiveness models. The final Beginner’s Guide is a step-by-step resource to accompany the model development process from understanding the problem to be modelled, model conceptualisation, model implementation, and model checking through to reporting of the model results

Merging fragments of classical logic

We investigate the possibility of extending the non-functionally complete logic of a collection of Boolean connectives by the addition of further Boolean connectives that make the resulting set of connectives functionally complete. More precisely, we will be interested in checking whether an axiomatization for Classical Propositional Logic may be produced by merging Hilbert-style calculi for two disjoint incomplete fragments of it. We will prove that the answer to that problem is a negative one, unless one of the components includes only top-like connectives.Comment: submitted to FroCoS 201

Neo: an object model for handling electrophysiology data in multiple formats

Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs. A common representation of the core data would improve interoperability and facilitate data-sharing. To that end, we propose here a language-independent object model, named “Neo,” suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language. In addition to representing electrophysiology data in memory for the purposes of analysis and visualization, the Python implementation provides a set of input/output (IO) modules for reading/writing the data from/to a variety of commonly used file formats. Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB. Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation. For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualization. Software for neurophysiology data analysis and visualization built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in neurophysiology.EC/FP7/269921/EU/Brain-inspired multiscale computation in neuromorphic hybrid systems/BrainScaleSDFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen SystemenBMBF, 01GQ1302, Nationaler Neuroinformatik Knote

Deliverable Raport D4.6 Tools for generating QMRF and QPRF reports

Scientific reports carry significant importance for the straightforward and effective transfer of knowledge, results and ideas. Good practice dictates that reports should be well-structured and concise. This deliverable describes the reporting services for models, predictions and validation tasks that have been integrated within the eNanoMapper (eNM) modelling infrastructure. Validation services have been added to the Jaqpot Quattro (JQ) modelling platform and the nano-lazar read-across framework developed within WP4 to support eNM modelling activities. Moreover, we have proceeded with the development of reporting services for predictions and models, respectively QPRF and QMRF reports. Therefore, in this deliverable, we first describe the three validation schemes created, namely training set split, cross- and external validation in detail and demonstrate their functionality both on API and UI levels. We then proceed with the description of the read across functionalities and finally, we present and describe the QPRF and QMRF reporting services

Beam Test of Silicon Strip Sensors for the ZEUS Micro Vertex Detector

For the HERA upgrade, the ZEUS experiment has designed and installed a high precision Micro Vertex Detector (MVD) using single sided micro-strip sensors with capacitive charge division. The sensors have a readout pitch of 120 microns, with five intermediate strips (20 micron strip pitch). An extensive test program has been carried out at the DESY-II testbeam facility. In this paper we describe the setup developed to test the ZEUS MVD sensors and the results obtained on both irradiated and non-irradiated single sided micro-strip detectors with rectangular and trapezoidal geometries. The performances of the sensors coupled to the readout electronics (HELIX chip, version 2.2) have been studied in detail, achieving a good description by a Monte Carlo simulation. Measurements of the position resolution as a function of the angle of incidence are presented, focusing in particular on the comparison between standard and newly developed reconstruction algorithms.Comment: 41 pages, 21 figures, 2 tables, accepted for publication in NIM