Magia : robust automated modeling and image processing platform for PET neuroinformatics

Generating uptake estimates from PET images requires several steps: frame-to-frame alignment, coregistration with MRI, delineation of reference region, kinetic modelling, spatial normalization and smoothing. Here we introduce Magia that can automatically perform each of these steps. Magia runs on MATLAB and processes and stores PET data in a standardized manner, facilitating neuroinformatics approaches also for PET imaging. Magia produces uptake estimates at voxel and ROI level. Given specified metadata, Magia processes studies independent of each other using one of eight analysis branches. For reference region studies, Magia automatically generates the reference regions using FreeSurfer. Currently, Magia supports SRTM, SUV-ratio, Patlak and FUR analyses. Other models can be added based on demand. Magia is free to use and can be downloaded from GitHub. Magia can generate tracer specific reference regions automatically from MRI using FreeSurfer. We validated automatic reference region generation for four PET tracers: [11C]carfentanil, [11C]raclopride, [11C]MADAM and [11C]PiB. For each tracer we chose 30 subjects from our previous projects. Five neuroscientists delineated manually tracer specific reference regions according to written and visual instructions. In the validation process we compared the new automatic method to the traditional manual method. Comparison of outcome measures (BPND or SUVR) between the methods was our primary validation metric. The validation process also included the comparison of anatomical similarities, time-activity curves and radioactivity concentrations of reference regions between the methods. No significant differences in outcome measures were observed for [11C]carfentanil and [11C]PiB. For [11C]raclopride and [11C]MADAM Magia derived outcome measures were positively biased compared to manual measures. The bias correlated negatively with BPND and in high-binding areas the bias was under 10%. Magia generates reliable reference regions for these four PET tracers. Magia has robust scalability and together with centralized database (Aivo) future bigdata analyses will become possible. Limitations: Magia processes PET studies independently and it is therefore not an optimal tool for analysing challenge studies. Also, if analysis requires plasma input it must still be generated elsewhere prior to analysis with Magia

From a Domain Analysis to the Specification and Detection of Code and Design Smells

Code and design smells are recurring design problems in software systems that must be identified to avoid their possible negative consequences\ud on development and maintenance. Consequently, several smell detection\ud approaches and tools have been proposed in the literature. However,\ud so far, they allow the detection of predefined smells but the detection\ud of new smells or smells adapted to the context of the analysed systems\ud is possible only by implementing new detection algorithms manually.\ud Moreover, previous approaches do not explain the transition from\ud specifications of smells to their detection. Finally, the validation\ud of the existing approaches and tools has been limited on few proprietary\ud systems and on a reduced number of smells. In this paper, we introduce\ud an approach to automate the generation of detection algorithms from\ud specifications written using a domain-specific language. This language\ud is defined from a thorough domain analysis. It allows the specification\ud of smells using high-level domain-related abstractions. It allows\ud the adaptation of the specifications of smells to the context of\ud the analysed systems.We specify 10 smells, generate automatically\ud their detection algorithms using templates, and validate the algorithms\ud in terms of precision and recall on Xerces v2.7.0 and GanttProject\ud v1.10.2, two open-source object-oriented systems.We also compare\ud the detection results with those of a previous approach, iPlasma

Leveraging Semantic Web Service Descriptions for Validation by Automated Functional Testing

Recent years have seen the utilisation of Semantic Web Service descriptions for automating a wide range of service-related activities, with a primary focus on service discovery, composition, execution and mediation. An important area which so far has received less attention is service validation, whereby advertised services are proven to conform to required behavioural specifications. This paper proposes a method for validation of service-oriented systems through automated functional testing. The method leverages ontology-based and rule-based descriptions of service inputs, outputs, preconditions and effects (IOPE) for constructing a stateful EFSM specification. The specification is subsequently utilised for functional testing and validation using the proven Stream X-machine (SXM) testing methodology. Complete functional test sets are generated automatically at an abstract level and are then applied to concrete Web services, using test drivers created from the Web service descriptions. The testing method comes with completeness guarantees and provides a strong method for validating the behaviour of Web services

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works

JWalk: a tool for lazy, systematic testing of java classes by design introspection and user interaction

Popular software testing tools, such as JUnit, allow frequent retesting of modified code; yet the manually created test scripts are often seriously incomplete. A unit-testing tool called JWalk has therefore been developed to address the need for systematic unit testing within the context of agile methods. The tool operates directly on the compiled code for Java classes and uses a new lazy method for inducing the changing design of a class on the fly. This is achieved partly through introspection, using Java’s reflection capability, and partly through interaction with the user, constructing and saving test oracles on the fly. Predictive rules reduce the number of oracle values that must be confirmed by the tester. Without human intervention, JWalk performs bounded exhaustive exploration of the class’s method protocols and may be directed to explore the space of algebraic constructions, or the intended design state-space of the tested class. With some human interaction, JWalk performs up to the equivalent of fully automated state-based testing, from a specification that was acquired incrementally

Development and deployment of a microfluidic platform for water quality monitoring

There is an increasing demand for autonomous sensor devices which can provide reliable data on key water quality parameters at a higher temporal and geographical resolution than is achievable using current approaches to sampling and monitoring. Microfluidic technology, in combination with rapid and on-going developments in the area of wireless communications, has significant potential to address this demand due to a number of advantageous features which allow the development of compact, low-cost and low-powered analytical devices. Here we report on the development of a microfluidic platform for water quality monitoring. This system has been successfully applied to in-situ monitoring of phosphate in environmental and wastewater monitoring applications. We describe a number of the technical and practical issues encountered and addressed during these deployments and summarise the current status of the technology