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This article describes a set of proposed measures for characterizing areas within a virtual terrain in terms of their attributes and their relationships with other areas for incorporating game designers’ intent in gameplay requirement-based terrain generation. Examples of such gameplay elements include vantage point, strongholds, chokepoints and hidden areas. Our measures are constructed on characteristics of an isovist, that is, the volume of visible space at a local area and the connectivity of areas within the terrain. The calculation of these measures is detailed, in particular we introduce two new ways to accurately and efficiently calculate the 3D isovist volume. Unlike previous research that has mainly focused on aesthetic-based terrain generation, the proposed measures address a gap in gameplay requirement-based terrain generation – the need for a flexible mechanism to automatically parameterise specified areas and their associated relationships, capturing semantic knowledge relating to high level user intent associated with specific gameplay elements within the virtual terrain. We demonstrate applications of using the measures in an evolutionary process to automatically generate terrains that include specific gameplay elements as defined by a game designer. This is significant as this shows that the measures can characterize different gameplay elements and allow gameplay elements consistent with the designers’ intents to be generated and positioned in a virtual terrain without the need to specify low-level details at a model or logic level, hence leading to higher productivity and lower cost.

Fabry disease (FD) is a rare X-linked disorder of glycosphingolipid metabolism caused by pathogenic variants within the alpha-galactosidase A (GLA) gene, often leading to neurological manifestations including stroke. Multiple screening programs seeking GLA variants among stroke survivors lacked detailed phenotype description, making the interpretation of the detected variant’s pathogenicity difficult. Here, we describe detailed clinical characteristics of GLA variant carriers identified by a nationwide stroke screening program in the Czech Republic. A total of 23 individuals with 8 different GLA variants were included in the study. A comprehensive diagnostic workup was performed by a team of FD specialists. The investigation led to the suggestion of phenotype reclassification for the G325S mutation from late-onset to classical. A novel variant R30K was found and was classified as a variant of unknown significance (VUS). The typical manifestation in our FD patients was a stroke occurring in the posterior circulation with an accompanying pathological finding in the cerebrospinal fluid. Moreover, we confirmed that cornea verticillata is typically associated with classical variants. Our findings underline the importance of detailed phenotype description and data sharing in the correct identification of pathogenicity of gene variants detected by high-risk-population screening programs.

We present a software package for analysing body positions of a subject when they are lying or sleeping in their bed. The software is designed to interface to inexpensive sensors, such as the Microsoft Kinect, and is thus suitable for monitoring at the subjects own home, rather than a dedicated sleep lab. The system is invariant to bed clothing and levels of ambient lighting. Analysis time for a single night session is under five minutes, a significant improvement over the 30–60 min analysis time reported in the literature. Keywords: Lying position, Body position, Automated analysis

The automatic generation of behavioural models for intelligent agents in military simulation and experimentation remains a challenge. Genetic Algorithms are a global optimization approach which is suitable for addressing complex problems where locating the global optimum is a difficult task. Unlike traditional optimisation techniques such as hill-climbing or derivatives-based methods, Genetic Algorithms are robust for addressing highly multi-modal and discontinuous search landscapes. In this paper, we outline a simheuristic GA-based approach for automatic generation of finite state machine based behavioural models of intelligent agents, where the aim is the identification of novel combat tactics. Rather than evolving states, the proposed approach evolves a sequence of transitions. We also discuss workable starting points for the use of Genetic Algorithms for such scenarios, shedding some light on the associated design and implementation difficulties. Keywords: Simheuristics, Genetic algorithms, Multiagent simulations, Stochastic combinatorial optimization, Finite state machines

BACKGROUND AND PURPOSE: Potential differences between primary progressive and relapsing remitting multiple sclerosis are the subject of ongoing controversial discussions. The aim of this work was to determine whether and how primary-progressive and relapsing-remitting multiple sclerosis subtypes differ regarding conventional MR imaging parameters, cerebral iron deposits, and their association with clinical status. MATERIALS AND METHODS: We analyzed 24 patients with primary-progressive MS, 80 with relapsing-remitting MS, and 20 healthy controls with 1.5T MR imaging for assessment of the conventional quantitative parameters: T2 lesion load, T1 lesion load, brain parenchymal fraction, and corpus callosum volume. Quantitative susceptibility mapping was performed to estimate iron concentration in the deep gray matter. RESULTS: Decreased susceptibility within the thalamus in relapsing-remitting MS compared with primary-progressive MS was the only significant MR imaging difference between these MS subtypes. In the relapsing-remitting MS subgroup, the Expanded Disability Status Scale score was positively associated with conventional parameters reflecting white matter lesions and brain atrophy and with iron in the putamen and caudate nucleus. A positive association with putaminal iron and the Expanded Disability Status Scale score was found in primary-progressive MS. CONCLUSIONS: Susceptibility in the thalamus might provide additional support for the differentiation between primary-progressive and relapsing-remitting MS. That the Expanded Disability Status Scale score was associated with conventional MR imaging parameters and iron concentrations in several deep gray matter regions in relapsing-remitting MS, while only a weak association with putaminal iron was observed in primary-progressive MS suggests different driving forces of disability in these MS subtypes.

The scanning and computerized processing of images hadits birth in 1956 at the National Bureau of Standards (NBS,nowNationalInstituteofStandardsandTechnology(NIST))[1].Image enhancementalgorithms weresome of the first tobe developed [2]. Half a century later, literally thousands ofimage processing algorithms have been published. Some ofthese have been specific to certain applications such as theenhancement of latent fingerprints, whilst others have beenmore generic in nature, applicable to all, yet master of none.The scope of these algorithms is fairly expansive, rangingfrom automatically extracting and delineating regions of in-terest such as in the case of segmentation, to improving theperceived quality of an image, by means of image enhance-ment. Since the early years of image processing, as in manysubfields of software design, there has been a portion of thedesign process dedicated to algorithm testing. Testing is theprocessofdeterminingwhetherornotaparticularalgorithmhassatisfieditsspecificationsrelatingtocriteriasuchasaccu-racy and robustness. A major limitation in the design of im-ageprocessingalgorithmsliesinthedifficultyindemonstrat-ingthatalgorithmsworktoanacceptablemeasureofperfor-mance. The purpose of algorithm testing is two-fold. Firstlyit provides either a qualitative or a quantitative method ofevaluating an algorithm. Secondly, it provides a comparativemeasure of the algorithm against similar algorithms, assum-ing similar criteria are used. One of the greatest caveats indesigning algorithms incorporating image processing is howtoconceivethecriteriausedtoanalyzetheresults.Dowede-signacriterionwhichmeasuressensitivity,robustness,orac-curacy? Performance evaluation in the broadest sense refersto a measure of some required behavior of an algorithm,whether it is achievable accuracy, robustness, or adaptabil-ity. It allows the intrinsic characteristics of an algorithm tobe emphasized, as well as the evaluation of its benefits andlimitations.More often than not though, such testing has been lim-ited in its scope. Part of this is attributable to the actual lackof formal process used in performance evaluation of im-age processing algorithms, from the establishment of testingregimes, to the design of metrics. Selection of an appropri-ate evaluation methodology is dependent on the objectiveof the task. For example, in the context of image enhance-ment, requirements are essentially different for screen-basedenhancementandenhancementwhichisembeddedwithinasubalgorithm. Screen-based enhancement is usually assessedinasubjectivemanner,whereaswhenanalgorithmisencap-sulated within a larger system, subjective evaluation is notavailable,andthealgorithmitselfmustdeterminethequalityof a processed image. Very few approaches to the evaluationofimageprocessingalgorithmscanbefoundintheliterature,although the concept has been around for decades. A signif-icant difficulty which arises in the evaluation of algorithmsis finding suitable metrics which provide an objective mea-sure of performance. A performance metric is a meaningfuland computable measure used for quantitatively evaluatingthe performance of any algorithm. Consider the process ofassessing image quality. There is no single quantitative met-ric which correlates well with image quality as perceived bythe human visual system. The process of analyzing failure isintrinsically coupled with the process of performance evalu-ation.Inordertoascertainwhetheranalgorithmfailsornot,youhavetodefinethecharacteristicsofsuccess.Failureanal-ysisistheprocessofdeterminingwhyanalgorithmfailsdur-ing testing. The knowledge generated is then fed back to thedesign process in order to engender refinements in the algo-rithm.Thisisadifficultprocessinapplicationssuchasimageenhancement primarily because there is usually no referenceimagewhichcanbeusedasan“ideal”image.Theassessmentofimagequalityplaysanimportantroleinapplicationssuchas consumer electronics. Metrics could be used to monitororoptimizeimagequalityindigitalcameras,benchmarkandevaluate image enhancement algorithms. There is no singlemetric that correlates well with image quality as perceivedby the human visual system. Selection of an appropriate