Summary Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest—namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial—ENTHUSE M1—in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39–4·62, p<0·0001; reference model: 2·56, 1·85–3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified...

This article proposes a novel framework for the real-time capture, assessment, and visualization of ballet dance movements as performed by a student in an instructional, virtual reality (VR) setting. The acquisition of human movement data is facilitated by skeletal joint tracking captured using the popular Microsoft (MS) Kinect camera system, while instruction and performance evaluation are provided in the form of 3D visualizations and feedback through a CAVE virtual environment, in which the student is fully immersed. The proposed framework is based on the unsupervised parsing of ballet dance movement into a structured posture space using the spherical self-organizing map (SSOM). A unique feature descriptor is proposed to more appropriately reflect the subtleties of ballet dance movements, which are represented as gesture trajectories through posture space on the SSOM. This recognition subsystem is used to identify the category of movement the student is attempting when prompted (by a virtual instructor) to perform a particular dance sequence. The dance sequence is then segmented and cross-referenced against a library of gestural components performed by the teacher. This facilitates alignment and score-based assessment of individual movements within the context of the dance sequence. An immersive interface enables the student to review his or her performance from a number of vantage points, each providing a unique perspective and spatial context suggestive of how the student might make improvements in training. An evaluation of the recognition and virtual feedback systems is presented.

ABSTRACT:With the increased exposure to tourists, historical monuments are at an ever-growing risk of disappearing. Building Information Modelling (BIM) offers a process of digitally documenting of all the features that are made or incorporated into the building over its life-span, thus affords unique opportunities for information preservation. BIM of historical buildings are called Historical Building Information Models (HBIM). This involves documenting a building in detail throughout its history. Geomatics professionals have the potential to play a major role in this area as they are often the first professionals involved on construction development sites for many Architectural, Engineering, and Construction (AEC) projects. In this work, we discuss how to establish an architectural database of a heritage site, digitally reconstruct, preserve and then interact with it through an immersive environment that leverages BIM for exploring historic buildings. The reconstructed heritage site under investigation was constructed in the early 15 th century. In our proposed approach, the site selection was based on many factors such as architectural value, size, and accessibility. The 3D model is extracted from the original collected and integrated data (Image-based, range-based, CAD modelling, and land survey methods), after which the elements of the 3D objects are identified by creating a database using the BIM software platform (Autodesk Revit). The use of modern and widely accessible game engine technology (Unity3D) is explored, allowing the user to fully embed and interact with the scene using handheld devices. The details of implementing an integrated pipeline between HBIM, GIS and augmented and virtual reality (AVR) tools and the findings of the work are presented.

In natural vision both stimulus features and cognitive/affective factors influence an observer's attention. However, the relationship between stimulus-driven (bottom-up) and cognitive/affective (top-down) factors remains controversial: How well does the classic visual salience model account for gaze locations? Can emotional salience counteract strong visual stimulus signals and shift attention allocation irrespective of bottom-up features? Here we compared Itti and Koch's [2000] and Spectral Residual (SR) visual salience model and explored the impact of visual salience and emotional salience on eye movement behavior, to understand the competition between visual salience and emotional salience and how they affect gaze allocation in complex scenes viewing. Our results show the insufficiency of visual salience models in predicting fixation. Emotional salience can override visual salience and can determine attention allocation in complex scenes. These findings are consistent with the hypothesis that cognitive/affective factors play a dominant role in active gaze control.

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