Transitional Interface: Concept, Issues and Framework

Transitional Interfaces have emerged as a new way to interact and collaborate between different interaction spaces such as Reality, Virtual Reality and Augmented Reality. In this paper we explore this concept further. We introduce a descriptive model of the concept, its collaborative aspect and how it can be generalized to describe natural and continuous transitions between contexts (e.g. across space, scale, viewpoint, and representation)

The usability attributes and evaluation measurements of mobile media AR (augmented reality)

This research aims to develop a tool for creating user-based design interfaces in mobile augmented reality (MAR) education. To develop a design interface evaluation tool, previous literature was examined for key design elements in the educational usage of MAR. The evaluation criteria identified were presence, affordance, and usability. The research used a focus group interview with 7 AR experts to develop a basic usability evaluation checklist, which was submitted to factor analysis for reliability by 122 experts in practice and academia. Based on this checklist, a MAR usability design interface test was conducted with seven fourth-grade elementary students. Then, it conducted follow-up structured interviews and questionnaires. This resulted in 29 questions being developed for the MAR interface design checklist.ope

MARS spectral molecular imaging of lamb tissue: data collection and image analysis

Spectral molecular imaging is a new imaging technique able to discriminate and quantify different components of tissue simultaneously at high spatial and high energy resolution. Our MARS scanner is an x-ray based small animal CT system designed to be used in the diagnostic energy range (20 to 140 keV). In this paper, we demonstrate the use of the MARS scanner, equipped with the Medipix3RX spectroscopic photon-processing detector, to discriminate fat, calcium, and water in tissue. We present data collected from a sample of lamb meat including bone as an illustrative example of human tissue imaging. The data is analyzed using our 3D Algebraic Reconstruction Algorithm (MARS-ART) and by material decomposition based on a constrained linear least squares algorithm. The results presented here clearly show the quantification of lipid-like, water-like and bone-like components of tissue. However, it is also clear to us that better algorithms could extract more information of clinical interest from our data. Because we are one of the first to present data from multi-energy photon-processing small animal CT systems, we make the raw, partial and fully processed data available with the intention that others can analyze it using their familiar routines. The raw, partially processed and fully processed data of lamb tissue along with the phantom calibration data can be found at [http://hdl.handle.net/10092/8531].Comment: 11 pages, 6 fig

DNA/RNA: Building Blocks of Life Under UV Irradiation

International audienceDuring the last 10 years, intense experimental and theoretical work has proven the existence of ultrafast nonradiative decay routes for UV-excited monomeric nucleic acid bases, accounting for their high photostability. This mechanism has been explained by the occurrence of easily accessible conical intersections connecting the first excited ππ* state with the ground state. However, recent studies of substituent and solvent effects indicate that the situation is more complicated than what was initially thought, notably by the presence of dark excited states. Moreover, the actual shape of the excited-state potential energy surface may induce nonexponential dynamics. Further efforts are needed in order to clarify how various environmental factors affect the structural and dynamical aspects of the nucleic acid base excited states

Integrative MicroRNA and Proteomic Approaches Identify Novel Osteoarthritis Genes and Their Collaborative Metabolic and Inflammatory Networks

BACKGROUND: Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development. METHODOLOGY/PRINCIPAL FINDINGS: In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103) and proteins (PPARA, BMP7, IL1B) to be highly correlated with Body Mass Index (BMI). Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic and clinical data provides a detailed picture of how a network state is correlated with disease and furthermore leads to the development of new treatments. This strategy will help to improve the understanding of the pathogenesis of multifactorial diseases such as osteoarthritis and provide possible novel therapeutic targets

Integrative MicroRNA and Proteomic Approaches Identify Novel Osteoarthritis Genes and Their Collaborative Metabolic and Inflammatory Networks

BACKGROUND: Osteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development. METHODOLOGY/PRINCIPAL FINDINGS: In this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103) and proteins (PPARA, BMP7, IL1B) to be highly correlated with Body Mass Index (BMI). Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic and clinical data provides a detailed picture of how a network state is correlated with disease and furthermore leads to the development of new treatments. This strategy will help to improve the understanding of the pathogenesis of multifactorial diseases such as osteoarthritis and provide possible novel therapeutic targets

Type II and VI collagen in nasal and articular cartilage and the effect of IL-1α on the distribution of these collagens

The distribution of type II and VI collagen was immunocytochemically investigated in bovine articular and nasal cartilage. Cartilage explants were used either fresh or cultured for up to 4 weeks with or without interleukin 1α (IL-1α). Sections of the explants were incubated with antibodies for both types of collagen. Microscopic analyses revealed that type II collagen was preferentially localized in the interchondron matrix whereas type VI collagen was primarily found in the direct vicinity of the chondrocytes. Treatment of the sections with hyaluronidase greatly enhanced the signal for both types of collagen. Also in sections of explants cultured with IL-1α a higher level of labeling of the collagens was found. This was apparent without any pre-treatment with hyaluronidase. Under the influence of IL-1α the area positive for type VI collagen that surrounded the chondrocytes broadened. Although the two collagens in both types of cartilage were distributed similarly, a remarkable difference was the higher degree of staining of type VI collagen in articular cartilage. Concomitantly we noted that digestion of this type of cartilage hardly occurred in the presence of IL-1α whereas nasal cartilage was almost completely degraded within 18 days of culture. Since type VI collagen is known to be relatively resistant to proteolysis we speculate that the higher level of type VI collagen in articular cartilage is important in protecting cartilage from digestion

LivePhantom: Retrieving Virtual World Light Data to Real Environments.

To achieve realistic Augmented Reality (AR), shadows play an important role in creating a 3D impression of a scene. Casting virtual shadows on real and virtual objects is one of the topics of research being conducted in this area. In this paper, we propose a new method for creating complex AR indoor scenes using real time depth detection to exert virtual shadows on virtual and real environments. A Kinect camera was used to produce a depth map for the physical scene mixing into a single real-time transparent tacit surface. Once this is created, the camera's position can be tracked from the reconstructed 3D scene. Real objects are represented by virtual object phantoms in the AR scene enabling users holding a webcam and a standard Kinect camera to capture and reconstruct environments simultaneously. The tracking capability of the algorithm is shown and the findings are assessed drawing upon qualitative and quantitative methods making comparisons with previous AR phantom generation applications. The results demonstrate the robustness of the technique for realistic indoor rendering in AR systems