Background With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, PhenoFam. Results PhenoFam performs gene set enrichment analysis by employing structural and functional information on families of protein domains as annotation terms. Our tool is designed to analyse complete sets of results from quantitative high-throughput studies (gene expression microarrays, functional RNAi screens, etc.) without prior pre-filtering or hits-selection steps. PhenoFam utilizes Ensembl databases to link a list of user-provided identifiers with protein features from the InterPro database, and assesses whether results associated with individual domains differ significantly from the overall population. To demonstrate the utility of PhenoFam we analysed a genome-wide RNA interference screen and discovered a novel function of plexins containing the cytoplasmic RasGAP domain. Furthermore, a PhenoFam analysis of breast cancer gene expression profiles revealed a link between breast carcinoma and altered expression of PX domain containing proteins. Conclusions PhenoFam provides a user-friendly, easily accessible web interface to perform GSEA based on high-throughput data sets and structural-functional protein information, and therefore aids in functional annotation of genes.

Background With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, PhenoFam. Results PhenoFam performs gene set enrichment analysis by employing structural and functional information on families of protein domains as annotation terms. Our tool is designed to analyse complete sets of results from quantitative high-throughput studies (gene expression microarrays, functional RNAi screens, etc.) without prior pre-filtering or hits-selection steps. PhenoFam utilizes Ensembl databases to link a list of user-provided identifiers with protein features from the InterPro database, and assesses whether results associated with individual domains differ significantly from the overall population. To demonstrate the utility of PhenoFam we analysed a genome-wide RNA interference screen and discovered a novel function of plexins containing the cytoplasmic RasGAP domain. Furthermore, a PhenoFam analysis of breast cancer gene expression profiles revealed a link between breast carcinoma and altered expression of PX domain containing proteins. Conclusions PhenoFam provides a user-friendly, easily accessible web interface to perform GSEA based on high-throughput data sets and structural-functional protein information, and therefore aids in functional annotation of genes.

El objetivo de esta investigación fue examinar la relación entre los factores críticos de éxito del perfil de implementación del proyecto (PIP) con los criterios para medir el éxito del proyecto, y el grado en que varían las relaciones al ser moderadas por la experiencia, la certificación en gestión de proyectos, y el estilo de liderazgo transformacional del director de proyecto. El estudio fue no experimental, de naturaleza cuantitativa, y realizado en Perú y Ecuador para el sector de tecnologías de información (TI). Los datos fueron obtenidos por encuestas y modelados utilizando ecuaciones estructurales. Los resultados muestran que los factores críticos que explicaron el éxito fueron la aceptación del cliente, el equipo del proyecto, y la resolución de problemas. La experiencia del director de proyecto no es significativa y la certificación no incide en el éxito. Se concluye que durante el desarrollo de un proyecto es necesario combinar varios estilos de liderazgo. / The aim of this study was to examine the relationships between the critical success factors of the project implementation profile (PIP) with project success criteria and with the degree to which relationships vary when moderated by experience, management’s project approval, and the transformational leadership style of the project’s manager. This study was non-experimental, quantitative in nature, and was conducted in Peru and Ecuador in the information technology (IT) sector. Data were gathered through surveys and were modeled by using structural equations. The results showed that the critical factors that explained success were customer acceptance, project team, and problem solving. Project manager experience was not significant and certification had no relevance. It is concluded that during project development it is necessary to combine various leadership styles. Keywords: project success; critical; factors; criteria; transformational leadership style; certificatio

Although current augmented, virtual, and mixed reality (AR/VR/MR) systems are facing advanced and immersive experience in the entertainment industry with countless media forms. Theses systems suffer a lack of correct direct and indirect illumination modeling where the virtual objects render with the same lighting condition as the real environment. Some systems are using baked GI, pre-recorded textures, and light probes that are mostly accomplished offline to compensate for precomputed real-time global illumination (GI). Thus, illumination information can be extracted from the physical scene for interactively rendering the virtual objects into the real world which produces a more realistic final scene in real-time. This work approaches the problem of visual coherence in AR by proposing a system that detects the real-world lighting conditions in dynamic scenes, then uses the extracted illumination information to render the objects added to the scene. The system covers several major components to achieve a more realistic augmented reality outcome. First, the detection of the incident light (direct illumination) from the physical scene with the use of computer vision techniques based on the topological structural analysis of 2D images using a live-feed 360^o camera instrumented on an AR device that captures the entire radiance map. Also, the physics-based light polarization eliminates or reduces false-positive lights such as white surfaces, reflections, or glare which negatively affect the light detection process. Second, the simulation of the reflected light (indirect illumination) that bounce between the real-world surfaces to be rendered into the virtual objects and reflect their existence in the virtual world. Third, defining the shading characteristic/properties of the virtual object to depict the correct lighting assets with a suitable shadow casting. Fourth, the geometric properties of real-scene including plane detection, 3D surface reconstruction, and simple meshing are incorporated with the virtual scene for more realistic depth interactions between the real and virtual objects. These components are developed methods which assumed to be working simultaneously in real-time for photo-realistic AR. The system is tested with several lighting conditions to evaluate the accuracy of the results based on the error incurred between the real/virtual objects casting shadow and interactions. For system efficiency, the rendering time is compared with previous works and research. Further evaluation of human perception is conducted through a user study. The overall performance of the system is investigated to reduce the cost to a minimum.