957 resultados para Computer Game Testing


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Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion.

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Objective In order to benefit from the obvious advantages of minimally invasive liver surgery there is a need to develop high precision tools for intraoperative anatomical orientation, navigation and safety control. In a pilot study we adapted a newly developed system for computer-assisted liver surgery (CALS) in terms of accuracy and technical feasibility to the specific requirements of laparoscopy. Here, we present practical aspects related to laparoscopic computer assisted liver surgery (LCALS). Methods Our video relates to a patient presenting with 3 colorectal liver metastases in Seg. II, III and IVa who was selected in an appropriate oncological setting for LCALS using the CAScination system combined with 3D MEVIS reconstruction. After minimal laparoscopic mobilization of the liver, a 4- landmark registration method was applied to enable navigation. Placement of microwave needles was performed using the targeting module of the navigation system and correct needle positioning was confirmed by intraoperative sonography. Ablation of each lesion was carried out by application of microwave energy at 100 Watts for 1 minute. Results To acquire an accurate (less 0.5 cm) registration, 4 registration cycles were necessary. In total, seven minutes were required to accomplish precise registration. Successful ablation with complete response in all treated areas was assessed by intraoperative sonography and confirmed by postoperative CT scan. Conclusions This teaching video demonstrates the theoretical and practical key points of LCALS with a special emphasis on preoperative planning, intraoperative registration and accuracy testing by laparoscopic methodology. In contrast to mere ultrasound-guided ablation of liver lesions, LCALS offers a more dimensional targeting and higher safety control. This is currently also in routine use to treat vanishing lesions and other difficult to target focal lesions within the liver.

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Despite major advances in the study of glioma, the quantitative links between intra-tumor molecular/cellular properties, clinically observable properties such as morphology, and critical tumor behaviors such as growth and invasiveness remain unclear, hampering more effective coupling of tumor physical characteristics with implications for prognosis and therapy. Although molecular biology, histopathology, and radiological imaging are employed in this endeavor, studies are severely challenged by the multitude of different physical scales involved in tumor growth, i.e., from molecular nanoscale to cell microscale and finally to tissue centimeter scale. Consequently, it is often difficult to determine the underlying dynamics across dimensions. New techniques are needed to tackle these issues. Here, we address this multi-scalar problem by employing a novel predictive three-dimensional mathematical and computational model based on first-principle equations (conservation laws of physics) that describe mathematically the diffusion of cell substrates and other processes determining tumor mass growth and invasion. The model uses conserved variables to represent known determinants of glioma behavior, e.g., cell density and oxygen concentration, as well as biological functional relationships and parameters linking phenomena at different scales whose specific forms and values are hypothesized and calculated based on in vitro and in vivo experiments and from histopathology of tissue specimens from human gliomas. This model enables correlation of glioma morphology to tumor growth by quantifying interdependence of tumor mass on the microenvironment (e.g., hypoxia, tissue disruption) and on the cellular phenotypes (e.g., mitosis and apoptosis rates, cell adhesion strength). Once functional relationships between variables and associated parameter values have been informed, e.g., from histopathology or intra-operative analysis, this model can be used for disease diagnosis/prognosis, hypothesis testing, and to guide surgery and therapy. In particular, this tool identifies and quantifies the effects of vascularization and other cell-scale glioma morphological characteristics as predictors of tumor-scale growth and invasion.

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BACKGROUND: The assessment of driving-relevant cognitive functions in older drivers is a difficult challenge as there is no clear-cut dividing line between normal cognition and impaired cognition and not all cognitive functions are equally important for driving. METHODS: To support decision makers, the Bern Cognitive Screening Test (BCST) for older drivers was designed. It is a computer-assisted test battery assessing visuo-spatial attention, executive functions, eye-hand coordination, distance judgment, and speed regulation. Here we compare the performance in BCST with the performance in paper and pencil cognitive screening tests and the performance in the driving simulator testing of 41 safe drivers (without crash history) and 14 unsafe drivers (with crash history). RESULTS: Safe drivers performed better than unsafe drivers in BCST (Mann-Whitney U test: U = 125.5; p = 0.001) and in the driving simulator (Student's t-test: t(44) = -2.64, p = 0.006). No clear group differences were found in paper and pencil screening tests (p > 0.05; ns). BCST was best at identifying older unsafe drivers (sensitivity 86%; specificity 61%) and was also better tolerated than the driving simulator test with fewer dropouts. CONCLUSIONS: BCST is more accurate than paper and pencil screening tests, and better tolerated than driving simulator testing when assessing driving-relevant cognition in older drivers.

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We provide a novel search technique which uses a hierarchical model and a mutual information gain heuristic to efficiently prune the search space when localizing faces in images. We show exponential gains in computation over traditional sliding window approaches, while keeping similar performance levels.

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Software architecture consists of a set of design choices that can be partially expressed in form of rules that the implementation must conform to. Architectural rules are intended to ensure properties that fulfill fundamental non-functional requirements. Verifying architectural rules is often a non- trivial activity: available tools are often not very usable and support only a narrow subset of the rules that are commonly specified by practitioners. In this paper we present a new highly-readable declarative language for specifying architectural rules. With our approach, users can specify a wide variety of rules using a single uniform notation. Rules can get tested by third-party tools by conforming to pre-defined specification templates. Practitioners can take advantage of the capabilities of a growing number of testing tools without dealing with them directly.

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PURPOSE The implementation of genomic-based medicine is hindered by unresolved questions regarding data privacy and delivery of interpreted results to health-care practitioners. We used DNA-based prediction of HIV-related outcomes as a model to explore critical issues in clinical genomics. METHODS We genotyped 4,149 markers in HIV-positive individuals. Variants allowed for prediction of 17 traits relevant to HIV medical care, inference of patient ancestry, and imputation of human leukocyte antigen (HLA) types. Genetic data were processed under a privacy-preserving framework using homomorphic encryption, and clinical reports describing potentially actionable results were delivered to health-care providers. RESULTS A total of 230 patients were included in the study. We demonstrated the feasibility of encrypting a large number of genetic markers, inferring patient ancestry, computing monogenic and polygenic trait risks, and reporting results under privacy-preserving conditions. The average execution time of a multimarker test on encrypted data was 865 ms on a standard computer. The proportion of tests returning potentially actionable genetic results ranged from 0 to 54%. CONCLUSIONS The model of implementation presented herein informs on strategies to deliver genomic test results for clinical care. Data encryption to ensure privacy helps to build patient trust, a key requirement on the road to genomic-based medicine.Genet Med advance online publication 14 January 2016Genetics in Medicine (2016); doi:10.1038/gim.2015.167.

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Background: It is yet unclear if there are differences between using electronic key feature problems (KFPs) or electronic case-based multiple choice questions (cbMCQ) for the assessment of clinical decision making. Summary of Work: Fifth year medical students were exposed to clerkships which ended with a summative exam. Assessment of knowledge per exam was done by 6-9 KFPs, 9-20 cbMCQ and 9-28 MC questions. Each KFP consisted of a case vignette and three key features (KF) using “long menu” as question format. We sought students’ perceptions of the KFPs and cbMCQs in focus groups (n of students=39). Furthermore statistical data of 11 exams (n of students=377) concerning the KFPs and (cb)MCQs were compared. Summary of Results: The analysis of the focus groups resulted in four themes reflecting students’ perceptions of KFPs and their comparison with (cb)MCQ: KFPs were perceived as (i) more realistic, (ii) more difficult, (iii) more motivating for the intense study of clinical reasoning than (cb)MCQ and (iv) showed an overall good acceptance when some preconditions are taken into account. The statistical analysis revealed that there was no difference in difficulty; however KFP showed a higher discrimination and reliability (G-coefficient) even when corrected for testing times. Correlation of the different exam parts was intermediate. Conclusions: Students perceived the KFPs as more motivating for the study of clinical reasoning. Statistically KFPs showed a higher discrimination and higher reliability than cbMCQs. Take-home messages: Including KFPs with long menu questions into summative clerkship exams seems to offer positive educational effects.

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This paper presents the development history and specification of a 3D game engine titled "Spark Engine". The term "engine" is used to describe a complex graphics software suite that streamlines application development and provides efficient rendering functionality. A game engine specifically provides tools to simplify game development. Spark Engine is fully shader driven and is built on top of Microsoft's XNA Framework. It is a reusable and flexible platform that can be used to build any type of graphics application ranging from gaming to simulation. The engine was released as open source software under the New BSD License with an interest in furthering its development.

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Electricity markets in the United States presently employ an auction mechanism to determine the dispatch of power generation units. In this market design, generators submit bid prices to a regulation agency for review, and the regulator conducts an auction selection in such a way that satisfies electricity demand. Most regulators currently use an auction selection method that minimizes total offer costs ["bid cost minimization" (BCM)] to determine electric dispatch. However, recent literature has shown that this method may not minimize consumer payments, and it has been shown that an alternative selection method that directly minimizes total consumer payments ["payment cost minimization" (PCM)] may benefit social welfare in the long term. The objective of this project is to further investigate the long term benefit of PCM implementation and determine whether it can provide lower costs to consumers. The two auction selection methods are expressed as linear constraint programs and are implemented in an optimization software package. Methodology for game theoretic bidding simulation is developed using EMCAS, a real-time market simulator. Results of a 30-day simulation showed that PCM reduced energy costs for consumers by 12%. However, this result will be cross-checked in the future with two other methods of bid simulation as proposed in this paper.

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Objective. To explore (1) the association between "club drug" use and unprotected anal intercourse (UAI) and (2) the association between binge drug use and UAI among HIV seronegative men who have sex with men (MSM) seeking HIV/STD testing at a local clinic in Houston. ^ Study design. A sub-sample of 297 HIV seronegative MSM from a cross-sectional study of drug and sexual behavior in Houston was conducted in 2006. Patients who were seeking HIV/STD testing at a local MSM-identified STD clinic were recruited for an anonymous computer-assisted interview. Analysis of identified secondary data consisted of self-reported information about demographic characteristics, use of drugs, and sexual behaviors. ^ Results. With new and casual sex partners, there was a strong and statistically significant association between use of "club drugs" and UAI. No association between binge drug use and UAI was evident. Men aware of HIV seropositivity or unaware of the HIV serostatus of their primary partner were less likely to report UAI. ^ Conclusion. These data suggest that in the Houston area, HIV-negative MSM club drug users, particularly multiple drug users, are at higher risk of UAI than comparable MSMs who do not use club drugs. Episode-level data regarding binge use of these and other drugs, and UAI should be collected in future studies to explore their relationship. The 'new partner' category should be added to sex partner types to measure sex and drug use behaviors in future studies.^ Keywords. HIV-negative MSM; club drugs; unprotected anal intercourse; binge drug use. ^

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The objective of this thesis is model some processes from the nature as evolution and co-evolution, and proposing some techniques that can ensure that these learning process really happens and useful to solve some complex problems as Go game. The Go game is ancient and very complex game with simple rules which still is a challenge for the Artificial Intelligence. This dissertation cover some approaches that were applied to solve this problem, proposing solve this problem using competitive and cooperative co-evolutionary learning methods and other techniques proposed by the author. To study, implement and prove these methods were used some neural networks structures, a framework free available and coded many programs. The techniques proposed were coded by the author, performed many experiments to find the best configuration to ensure that co-evolution is progressing and discussed the results. Using co-evolutionary learning processes can be observed some pathologies which could impact co-evolution progress. In this dissertation is introduced some techniques to solve pathologies as loss of gradients, cycling dynamics and forgetting. According to some authors, one solution to solve these co-evolution pathologies is introduce more diversity in populations that are evolving. In this thesis is proposed some techniques to introduce more diversity and some diversity measurements for neural networks structures to monitor diversity during co-evolution. The genotype diversity evolved were analyzed in terms of its impact to global fitness of the strategies evolved and their generalization. Additionally, it was introduced a memory mechanism in the network neural structures to reinforce some strategies in the genes of the neurons evolved with the intention that some good strategies learned are not forgotten. In this dissertation is presented some works from other authors in which cooperative and competitive co-evolution has been applied. The Go board size used in this thesis was 9x9, but can be easily escalated to more bigger boards.The author believe that programs coded and techniques introduced in this dissertation can be used for other domains.