Search Results (1,953)

The integration of a spatial process model into an environmental modeling framework can enhance the model’s capabilities. This paper describes a general methodology for integrating environmental models into the Object Modeling System (OMS) regardless of the model’s complexity, the programming language, and the operating system used. We present the integration of the GEOtop model into the OMS version 3.0 and illustrate its application in a small watershed. OMS is an environmental modeling framework that facilitates model development, calibration, evaluation, and maintenance. It provides innovative techniques in software design such as multithreading, implicit parallelism, calibration and sensitivity analysis algorithms, and cloud-services. GEOtop is a physically based, spatially distributed rainfall-runoff model that performs three-dimensional finite volume calculations of water and energy budgets. Executing GEOtop as an OMS model component allows it to: (1) interact directly with the open-source geographical information system (GIS) uDig-JGrass to access geo-processing, visualization, and other modeling components; and (2) use OMS components for automatic calibration, sensitivity analysis, or meteorological data interpolation. A case study of the model in a semi-arid agricultural catchment is presented for illustration and proof-of-concept. Simulated soil water content and soil temperature results are compared with measured data, and model performance is evaluated using goodness-of-fit indices. This study serves as a template for future integration of process models into OMS. Full article

Visual sensor networks (VSNs) can be widely applied in security surveillance, environmental monitoring, smart rooms, etc. However, with the increased number of camera nodes in VSNs, the volume of the visual information data increases significantly, which becomes a challenge for storage, processing and transmitting the visual data. The state-of-the-art video compression standard, high efficiency video coding (HEVC), can effectively compress the raw visual data, while the higher compression rate comes at the cost of heavy computational complexity. Hence, reducing the encoding complexity becomes vital for the HEVC encoder to be used in VSNs. In this paper, we propose a fast coding unit (CU) depth decision method to reduce the encoding complexity of the HEVC encoder for VSNs. Firstly, the content property of the CU is analyzed. Then, an early CU depth decision method and a low complexity distortion calculation method are proposed for the CUs with homogenous content. Experimental results show that the proposed method achieves 71.91% on average encoding time savings for the HEVC encoder for VSNs. Full article

Magnetic labeling of stem cells enables their noninvasive detection by magnetic resonance imaging (MRI). In practical terms, most MRI studies have been limited to the visualization of local engraftment because other sources of endogenous hypointense contrast complicate the interpretation of systemic (whole-body) cell distribution. In addition, MRI cell tracking is inherently nonquantitative in nature. We report herein on the potential of magnetic particle imaging (MPI) as a novel tomographic technique for noninvasive “hot-spot” imaging and quantification of stem cells using superparamagnetic iron oxide (SPIO) tracers. Neural and mesenchymal stem cells, representing small and larger cell bodies, were labeled with 3 different SPIO tracer formulations, including 2 preparations (Feridex and Resovist) that have previously been used in clinical MRI celltracking studies. Magnetic particle spectroscopy measurements demonstrated a linear correlation between MPI signal and iron content for both free particles in homogeneous solution and for internalized and aggregated particles in labeled cells over a wide range of concentrations. The overall MPI signal ranged from 1 × 10⁻³ to 3 × 10⁻⁴ Am²/g Fe, which was equivalent to 2 × 10⁻¹⁴ to 1 × 10⁻¹⁵ Am² per cell, indicating that cell numbers can be quantified with MPI analogous to the use of radiotracers in nuclear medicine or fluorine tracers in 19F MRI. When SPIO-labeled cells were transplanted in the mouse brain, they could be readily detected by MPI at a detection threshold of about 5 × 104 cells, with MPI/MRI overlays showing an excellent agreement between the hypointense MRI areas and MPI hot spots. The calculated tissue MPI signal ratio for 100,000 vs 50,000 implanted cells was 2.08. Hence, MPI can potentially be further developed for quantitative and easy-to-interpret, tracer-based noninvasive cell imaging, preferably with MRI as an adjunct anatomical imaging modality. Full article

The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker. Full article

As digital terrain models are indispensable for visualizing and modeling geographic processes, terrain information content is useful for terrain generalization and representation. For terrain generalization, if the terrain information is considered, the generalized terrain may be of higher fidelity. In other words, the richer the terrain information at the terrain surface, the smaller the degree of terrain simplification. Terrain information content is also important for evaluating the quality of the rendered terrain, e.g., the rendered web terrain tile service in Google Maps (Google Inc., Mountain View, CA, USA). However, a unified definition and measures for terrain information content have not been established. Therefore, in this paper, a definition and measures for terrain information content from Digital Elevation Model (DEM, i.e., a digital model or 3D representation of a terrain’s surface) data are proposed and are based on the theory of map information content, remote sensing image information content and other geospatial information content. The information entropy was taken as the information measuring method for the terrain information content. Two experiments were carried out to verify the measurement methods of the terrain information content. One is the analysis of terrain information content in different geomorphic types, and the results showed that the more complex the geomorphic type, the richer the terrain information content. The other is the analysis of terrain information content with different resolutions, and the results showed that the finer the resolution, the richer the terrain information. Both experiments verified the reliability of the measurements of the terrain information content proposed in this paper. Full article

Kansenone is a triterpene from the root of the traditional Chinese medicine, Euphorbia kansui. However, kansenone exerts serious toxicity, but the exact mechanism was not clear. In this work, the effects of kansenone on cell proliferation, cell cycle, cell damage, and cell apoptosis were investigated. The suppression of cell proliferation was assessed via the colorimetric MTT assay, and cell morphology was visualized via inverted microscopy after IEC-6 cells were incubated with different concentrations of kansenone. Reactive oxygen species (ROS), superoxide dismutase (SOD) and malondialdehyde (MDA) content were detected for evaluating cell damage. RNase/propidium iodide (PI) labeling for evaluation of cell cycle distribution was performed by flow cytometry analysis. Annexin V-fluorescein isothiocyanate (FITC)/PI and Hoechst 33342/Annexin V-FITC/PI staining assay for cell apoptosis detection were performed using confocal laser scanning microscopy and high content screening. Moreover, apoptosis induction was further confirmed by transmission electron microscope (TEM) and JC-1 mitochondrial membrane potential, western blot and RT-PCR analysis. The results demonstrated that kansenone exerted high cytotoxicity, induced cell arrest at G0/G1 phase, and caused mitochondria damage. In addition, kansenone could up-regulate the apoptotic proteins Bax, AIF, Apaf-1, cytochrome c, caspase-3, caspase-9, caspase-8, FasR, FasL, NF-κB, and TNFR1 mRNA expression levels, and down-regulate the anti-apoptotic Bcl-2 family proteins, revealing that kansenone induces apoptosis through both the death receptor and mitochondrial pathways. Full article

α-Linolenic acid (ALA) is the precursor of docosahexaenoic acid (DHA) in humans, which is fundamental for brain and visual function. Western diet provides low ALA and DHA, which is reflected in low DHA in maternal milk. Chia oil extracted from chia (Salvia hispanica L.), a plant native to some Latin American countries, is high in ALA (up to 60%) and thereby is an alternative to provide ALA with the aim to reduce DHA deficits. We evaluated the modification of the fatty acid profile of milk obtained from Chilean mothers who received chia oil during gestation and nursing. Forty healthy pregnant women (22–35 years old) tabulated for food consumption, were randomly separated into two groups: a control group with normal feeding (n = 21) and a chia group (n = 19), which received 16 mL chia oil daily from the third trimester of pregnancy until the first six months of nursing. The fatty acid profile of erythrocyte phospholipids, measured at six months of pregnancy, at time of delivery and at six months of nursing, and the fatty acid profile of the milk collected during the first six months of nursing were assessed by gas-chromatography. The chia group, compared to the control group, showed (i) a significant increase in ALA ingestion and a significant reduction of linoleic acid (LA) ingestion, no showing modification of arachidonic acid (AA), eicosapentaenoic acid (EPA) and DHA; (ii) a significant increase of erythrocyte ALA and EPA and a reduction of LA. AA and DHA were not modified; (iii) a increased milk content of ALA during the six months of nursing, whereas LA showed a decrease. AA and EPA were not modified, however DHA increased only during the first three months of nursing. Consumption of chia oil during the last trimester of pregnancy and the first three months of nursing transiently increases the milk content of DHA. Full article

Visible and near-infrared hyperspectral imaging covering spectral range of 380–1030 nm as a rapid and non-destructive method was applied to estimate the soluble protein content of oilseed rape leaves. Average spectrum (500–900 nm) of the region of interest (ROI) of each sample was extracted, and four samples out of 128 samples were defined as outliers by Monte Carlo-partial least squares (MCPLS). Partial least squares (PLS) model using full spectra obtained dependable performance with the correlation coefficient (r_p) of 0.9441, root mean square error of prediction (RMSEP) of 0.1658 mg/g and residual prediction deviation (RPD) of 2.98. The weighted regression coefficient (Bw), successive projections algorithm (SPA) and genetic algorithm-partial least squares (GAPLS) selected 18, 15, and 16 sensitive wavelengths, respectively. SPA-PLS model obtained the best performance with r_p of 0.9554, RMSEP of 0.1538 mg/g and RPD of 3.25. Distribution of protein content within the rape leaves were visualized and mapped on the basis of the SPA-PLS model. The overall results indicated that hyperspectral imaging could be used to determine and visualize the soluble protein content of rape leaves. Full article

Augmented reality, augmented television and second screen are cutting edge technologies that provide end users extra and enhanced information related to certain events in real time. This enriched information helps users better understand such events, at the same time providing a more satisfactory experience. In the present paper, we apply this main idea to human–robot interaction (HRI), to how users and robots interchange information. The ultimate goal of this paper is to improve the quality of HRI, developing a new dialog manager system that incorporates enriched information from the semantic web. This work presents the augmented robotic dialog system (ARDS), which uses natural language understanding mechanisms to provide two features: (i) a non-grammar multimodal input (verbal and/or written) text; and (ii) a contextualization of the information conveyed in the interaction. This contextualization is achieved by information enrichment techniques that link the extracted information from the dialog with extra information about the world available in semantic knowledge bases. This enriched or contextualized information (information enrichment, semantic enhancement or contextualized information are used interchangeably in the rest of this paper) offers many possibilities in terms of HRI. For instance, it can enhance the robot’s pro-activeness during a human–robot dialog (the enriched information can be used to propose new topics during the dialog, while ensuring a coherent interaction). Another possibility is to display additional multimedia content related to the enriched information on a visual device. This paper describes the ARDS and shows a proof of concept of its applications. Full article

Green exercise research often reports psychological health outcomes without rigorously controlling exercise. This study examines effects of visual exercise environments on directed attention, perceived exertion and time to exhaustion, whilst measuring and controlling the exercise component. Participants completed three experimental conditions in a randomized counterbalanced order. Conditions varied by video content viewed (nature; built; control) during two consistently-ordered exercise bouts (Exercise 1: 60% VO2peakInt for 15-mins; Exercise 2: 85% VO2peakInt to voluntary exhaustion). In each condition, participants completed modified Backwards Digit Span tests (a measure of directed attention) pre- and post-Exercise 1. Energy expenditure, respiratory exchange ratio and perceived exertion were measured during both exercise bouts. Time to exhaustion in Exercise 2 was also recorded. There was a significant time by condition interaction for Backwards Digit Span scores (F_2,22 = 6.267, p = 0.007). Scores significantly improved in the nature condition (p < 0.001) but did not in the built or control conditions. There were no significant differences between conditions for either perceived exertion or physiological measures during either Exercise 1 or Exercise 2, or for time to exhaustion in Exercise 2. This was the first study to demonstrate effects of controlled exercise conducted in different visual environments on post-exercise directed attention. Via psychological mechanisms alone, visual nature facilitates attention restoration during moderate-intensity exercise. Full article

This paper describes a novel strategy for the visualization of hyperspectral imagery based on the analysis of image pixel pairwise distances. The goal of this approach is to generate a final color image with excellent interpretability and high contrast at the cost of distorting a few pairwise distances. Specifically, the principle of equal variance is introduced to divide all hyperspectral bands into three subgroups and to ensure the energy is distributed uniformly between them, as in natural color images. Then, after detecting both normal and outlier pixels, these three subgroups are mapped into three color components of the output visualization using two different mapping (i.e., dimensionality reduction) schemes for the two types of pixels. The widely-used multidimensional scaling (MDS) is used for normal pixels and a new objective function, taking into account the weighting of pairwise distances, is presented for the outlier pixels. The pairwise distance weighting is designed such that small pairwise distances between the outliers and their respective neighbors are emphasized and large deviations are suppressed. This produces an image with high contrast and good interpretability while retaining the detailed information content. The proposed algorithm is compared with several state-of-the-art visualization techniques and evaluated on the well-known AVIRIS hyperspectral images. The effectiveness of the proposed strategy is substantiated both visually and quantitatively. Full article

BioCyc.org is a genome and metabolic pathway web portal covering 5500 organisms, including Homo sapiens, Arabidopsis thaliana, Saccharomyces cerevisiae and Escherichia coli. These organism-specific databases have undergone variable degrees of curation. The EcoCyc (Escherichia coli Encyclopedia) database is the most highly curated; its contents have been derived from 27,000 publications. The MetaCyc (Metabolic Encyclopedia) database within BioCyc is a “universal” metabolic database that describes pathways, reactions, enzymes and metabolites from all domains of life. Metabolic pathways provide an organizing framework for analyzing metabolomics data, and the BioCyc website provides computational operations for metabolomics data that include metabolite search and translation of metabolite identifiers across multiple metabolite databases. The site allows researchers to store and manipulate metabolite lists using a facility called SmartTables, which supports metabolite enrichment analysis. That analysis operation identifies metabolite sets that are statistically over-represented for the substrates of specific metabolic pathways. BioCyc also enables visualization of metabolomics data on individual pathway diagrams and on the organism-specific metabolic map diagrams that are available for every BioCyc organism. Most of these operations are available both interactively and as programmatic web services. Full article

With the rapid increase of 3-dimensional (3D) content, considerable research related to the 3D human factor has been undertaken for quantitatively evaluating visual discomfort, including eye fatigue and dizziness, caused by viewing 3D content. Various modalities such as electroencephalograms (EEGs), biomedical signals, and eye responses have been investigated. However, the majority of the previous research has analyzed each modality separately to measure user eye fatigue. This cannot guarantee the credibility of the resulting eye fatigue evaluations. Therefore, we propose a new method for quantitatively evaluating eye fatigue related to 3D content by combining multimodal measurements. This research is novel for the following four reasons: first, for the evaluation of eye fatigue with high credibility on 3D displays, a fuzzy-based fusion method (FBFM) is proposed based on the multimodalities of EEG signals, eye blinking rate (BR), facial temperature (FT), and subjective evaluation (SE); second, to measure a more accurate variation of eye fatigue (before and after watching a 3D display), we obtain the quality scores of EEG signals, eye BR, FT and SE; third, for combining the values of the four modalities we obtain the optimal weights of the EEG signals BR, FT and SE using a fuzzy system based on quality scores; fourth, the quantitative level of the variation of eye fatigue is finally obtained using the weighted sum of the values measured by the four modalities. Experimental results confirm that the effectiveness of the proposed FBFM is greater than other conventional multimodal measurements. Moreover, the credibility of the variations of the eye fatigue using the FBFM before and after watching the 3D display is proven using a t-test and descriptive statistical analysis using effect size. Full article

We studied changes in articular calcified cartilage (ACC) and subchondral bone (SCB) in the third carpal bones (C3) of Standardbred racehorses with naturally-occurring repetitive loading-induced osteoarthritis (OA). Two osteochondral cores were harvested from dorsal sites from each of 15 post-mortem C3 and classified as control or as showing early or advanced OA changes from visual inspection. We re-examined X-ray micro-computed tomography (µCT) image sets for the presence of high-density mineral infill (HDMI) in ACC cracks and possible high-density mineralized protrusions (HDMP) from the ACC mineralizing (tidemark) front (MF) into hyaline articular cartilage (HAC). We hypothesized and we show that 20-µm µCT resolution in 10-mm diameter samples is sufficient to detect HDMI and HDMP: these are lost upon tissue decalcification for routine paraffin wax histology owing to their predominant mineral content. The findings show that µCT is sufficient to discover HDMI and HDMP, which were seen in 2/10 controls, 6/9 early OA and 8/10 advanced OA cases. This is the first report of HDMI and HDMP in the equine carpus and in the Standardbred breed and the first to rely solely on µCT. HDMP are a candidate cause for mechanical tissue destruction in OA. Full article

Research in the area of spatial cognition demonstrated that references to landmarks are essential in the communication and the interpretation of wayfinding instructions for human being. In order to detect landmarks, a model for the assessment of their salience has been previously developed by Raubal and Winter. According to their model, landmark salience is divided into three categories: visual, structural, and semantic. Several solutions have been proposed to automatically detect landmarks on the basis of these categories. Due to a lack of relevant data, semantic salience has been frequently reduced to objects’ historical and cultural significance. Social dimension (i.e., the way an object is practiced and recognized by a person or a group of people) is systematically excluded from the measure of landmark semantic salience even though it represents an important component. Since the advent of mobile Internet and smartphones, the production of geolocated content from social web platforms—also described as geosocial data—became commonplace. Actually, these data allow us to have a better understanding of the local geographic knowledge. Therefore, we argue that geosocial data, especially Social Location Sharing datasets, represent a reliable source of information to precisely measure landmark semantic salience in urban area. Full article