Image Analysis Enhanced Event Detection from Geo-Tagged Tweet Streams
Ontology highlight
ABSTRACT:
SUBMITTER: Lauw H
PROVIDER: S-EPMC7206246 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:In dynamic environments, robots require instantaneous detection of moving events with microseconds of latency. This task, known as moving event detection, is typically achieved using event cameras. While light detection and ranging (LiDAR) sensors are essential for robots due to their dense and accurate depth measurements, their use in event detection has not been thoroughly explored. Current approaches involve accumulating LiDAR points into frames and detecting object-level motions, resulting in a latency of tens to hundreds of milliseconds. We present a different approach called M-detector, which determines if a point is moving immediately after its arrival, resulting in a point-by-point detection with a latency of just several microseconds. M-detector is designed based on occlusion principles and can be used in different environments with various types of LiDAR sensors. Our experiments demonstrate the effectiveness of M-detector on various datasets and applications, showcasing its superior accuracy, computational efficiency, detection latency, and generalization ability.
| S-EPMC10771495 | biostudies-literature
Project description:We live in a world where digital trails of different forms of human activities compose big urban data, allowing us to detect many aspects of how people experience the city in which they live or come to visit. In this study we propose to enhance urban planning by taking into a consideration individual preferences using information from an unconventional big data source: dataset of geo-tagged photographs that people take in cities which we then use as a measure of urban attractiveness. We discover and compare a temporal behavior of residents and visitors in ten most photographed cities in the world. Looking at the periodicity in urban attractiveness, the results show that the strongest periodic patterns for visitors are usually weekly or monthly. Moreover, by dividing cities into two groups based on which continent they belong to (i.e., North America or Europe), it can be concluded that unlike European cities, behavior of visitors in the US cities in general is similar to the behavior of their residents. Finally, we apply two indices, called "dilatation attractiveness index" and "dilatation index", to our dataset which tell us the spatial and temporal attractiveness pulsations in the city. The proposed methodology is not only important for urban planning, but also does support various business and public stakeholder decision processes, concentrated for example around the question how to attract more visitors to the city or estimate the impact of special events organized there.
| S-EPMC5148589 | biostudies-literature
Project description:Microscopy imaging experiments generate vast amounts of data, and there is a high demand for smart acquisition and analysis methods. This is especially true for transmission electron microscopy (TEM) where terabytes of data are produced if imaging a full sample at high resolution, and analysis can take several hours. One way to tackle this issue is to collect a continuous stream of low resolution images whilst moving the sample under the microscope, and thereafter use this data to find the parts of the sample deemed most valuable for high-resolution imaging. However, such image streams are degraded by both motion blur and noise. Building on deep learning based approaches developed for deblurring videos of natural scenes we explore the opportunities and limitations of deblurring and denoising images captured from a fast image stream collected by a TEM microscope. We start from existing neural network architectures and make adjustments of convolution blocks and loss functions to better fit TEM data. We present deblurring results on two real datasets of images of kidney tissue and a calibration grid. Both datasets consist of low quality images from a fast image stream captured by moving the sample under the microscope, and the corresponding high quality images of the same region, captured after stopping the movement at each position to let all motion settle. We also explore the generalizability and overfitting on real and synthetically generated data. The quality of the restored images, evaluated both quantitatively and visually, show that using deep learning for image restoration of TEM live image streams has great potential but also comes with some limitations.
| S-EPMC7850474 | biostudies-literature
Project description:BackgroundVisual inspection for tongue analysis is a diagnostic method in traditional Chinese medicine (TCM). Owing to the variations in tongue features, such as color, texture, coating, and shape, it is difficult to precisely extract the tongue region in images. This study aims to quantitatively evaluate tongue diagnosis via automatic tongue segmentation.MethodsExperiments were conducted using a clinical image dataset provided by the Laboratory of Traditional Medical Syndromes, Shanghai University of TCM. First, a clinical tongue image was refined by a saliency window. Second, we initialized the tongue area as the upper binary part and lower level set matrix. Third, a double geo-vector flow (DGF) was proposed to detect the tongue edge and segment the tongue region in the image, such that the geodesic flow was evaluated in the lower part, and the geo-gradient vector flow was evaluated in the upper part.ResultsThe performance of the DGF was evaluated using 100 images. The DGF exhibited better results compared with other representative studies, with its true-positive volume fraction reaching 98.5%, its false-positive volume fraction being 1.51%, and its false-negative volume fraction being 1.42%. The errors between the proposed automatic segmentation results and manual contours were 0.29 and 1.43% in terms of the standard boundary error metrics of Hausdorff distance and mean distance, respectively.ConclusionsBy analyzing the time complexity of the DGF and evaluating its performance via standard boundary and area error metrics, we have shown both efficiency and effectiveness of the DGF for automatic tongue image segmentation.
| S-EPMC3922256 | biostudies-literature
Project description:BackgroundEngaging influential stakeholders in meaningful exchange is essential for pharmaceutical companies aiming to improve care. At a time where opportunities for face-to-face engagement are limited, the ability to interact, learn and generate actionable insights through digital channels such as Twitter, is of considerable value.AimThe aim of this study was to evaluate digital engagement among global diabetes mellitus researchers.Materials and methodsWe identified every global tweet (20,614,515) and scientific publication (44,135) regarding diabetes mellitus from 1 August 2018 to 1 August 2020. Through author matching we combined datasets, resulting in a list of digitally active scientific authors. Generalised linear modelling identified factors predicting their digital engagement.FindingsGlobally, 2686 diabetes researchers used Twitter to discuss the management of diabetes mellitus, posting 110,346 diabetes-related tweets. As Twitter followers increased, so did tweet frequency (p < 0.001), retweets (p < 0.001) and replies (p < 0.001) to their content. Publication count (overall/per month) and proportion of first/last authorships were unrelated to tweet frequency and the likelihood of being retweeted or replied to (p > 0.05). Those with the most academic co-authors were significantly less likely to tweet than those with smaller networks (< 50; p = 0.001). Finally, those publishing most frequently on specific themes, including insulin (p = 0.041) and paediatrics (p < 0.001), were significantly more likely to tweet about these themes.ConclusionAcademic expertise and seniority cannot be assumed as proxies for digital influence. Those aiming to promote science and obtain digital insights regarding condition management should consider looking beyond well-known 'key opinion leaders' to perhaps lesser known 'digital opinion leaders' with smaller academic networks, who are likely to specialise in the delivery of highly specific content to captive audiences.
| S-EPMC8650740 | biostudies-literature
Project description:The design of modern scientific experiments requires the control and monitoring of many different data streams. However, the serial execution of programming instructions in a computer makes it a challenge to develop software that can deal with the asynchronous, parallel nature of scientific data. Here we present Bonsai, a modular, high-performance, open-source visual programming framework for the acquisition and online processing of data streams. We describe Bonsai's core principles and architecture and demonstrate how it allows for the rapid and flexible prototyping of integrated experimental designs in neuroscience. We specifically highlight some applications that require the combination of many different hardware and software components, including video tracking of behavior, electrophysiology and closed-loop control of stimulation.
| S-EPMC4389726 | biostudies-literature
Project description:The massively and rapidly spreading disinformation on social network platforms poses a serious threat to public safety and social governance. Therefore, early and accurate detection of rumors in social networks is of vital importance before they spread on a large scale. Considering the small-world property of social networks, the source tweet-word graph is decomposed from the global graph of rumors, and a rumor detection method based on graph attention network of source tweet-word graph is proposed to fully learn the structure of rumor propagation and the deep representation of text contents. Specifically, the proposed model can adequately capture the contextual semantic association representation of source tweets during the propagation and extract semantic features. For the data sparseness of the early stage of information dissemination, text attention mechanism based on opinion similarity can aggregate and capture more tweet propagation structure features to help improve the efficiency of early detection of rumors. Through the analysis of the experimental results on real public datasets, the rumor detection performance of the proposed method is better than that of other baseline methods. Especially in the early rumor detection tasks, the proposed method can detect rumors with an accuracy of nearly 90% in the early stage of information dissemination. And it still has good robustness with noise interference.
| S-EPMC9273096 | biostudies-literature
Project description:To investigate the undesired irradiations outside of the treatment field by electron streams in air (air-electron-stream) during magnetic-resonance image-guided radiation therapy (MR-IGRT). A custom-made support phantom adjusting angles between the beam central axis (CAX) and the phantom surface (termed phantom-angles), were used. Using the ViewRay system, a rectangular parallelepiped phantom placed on the support phantom, was irradiated with field sizes of 6.3 cm × 6.3 cm (FS6.3) and 12.6 cm × 12.6 cm (FS12.6) at gantry angles of 0°, 30°, and 330°, and phantom-angles of 10°, 20°, and 30°. For each beam delivery, the isocenter was located at the center of mass of the phantom and 3 Gy was delivered to the isocenter (prescription dose = 3 Gy). The doses given by the air-electron-streams were measured using the EBT3 films on the panels placed orthogonal to the direction of the magnetic field at distances of 10 and 17 cm from CAX. Two dose distributions per irradiation were measured on the panel facing the phantom surface of the incident beam (front panel) and on the panel facing the phantom surface of the beam exit (end panel). We investigated the doses by the air-electron-streams by calculating the average doses inside the circles drawn around a point of the maximum dose with radii of x cm (DRx) from the dose distributions on the panels (x = 1-5 cm). The largest value of DRx was DR1 (1.64 Gy, 55% of the prescription dose) at 10 cm distance from CAX, with FS12.6, at 30° phantom-angle and 330° gantry angle. The average difference of the DR1 at the end panels (FS12.6) between the calculations and measurements was 1.36 Gy. The average global gamma passing rate with 3%/3 mm on the dose distributions at the end panels (FS12.6) was 40.3%. The calculated dose distributions on both panels were not coincident with the measured dose distributions. The Spearman's rank correlation coefficients between the projected areas and the DRx values were always higher than 0.75 (all with p < 0.001). The doses by the air-electron-streams increased with the projected areas of the cross-sections of the treatment beams on the panels.
| S-EPMC6519819 | biostudies-literature
Project description:Affordable point-of-care (POC) CD4 + T lymphocyte counting techniques have been developed as alternatives to flow cytometry-based instruments caring for patients with human immunodeficiency virus (HIV)-1. However, POC CD4 enumeration technologies can be inaccurate. Here, we developed a microparticle-based visual detector of CD4 + T lymphocytes (ImmunoSpin) using microparticles conjugated with anti-CD4 antibodies, independent of microfluidic or fluorescence detection systems. Visual enumeration of CD4 + T cells under conventional light microscope was accurate compared to flow cytometry. Microparticle-tagged CD4 + T cells were well-recognized under a light microscope. ImmunoSpin showed very good precision (coefficients of variation of ImmunoSpin were ≤ 10%) and high correlation with clinical-grade flow cytometry for the enumeration of CD4 + T cells (y = 0.4232 + 0.9485 × for the %CD4 + T cell count, R2 = 0.99). At thresholds of 200 and 350 cells/µL, there was no misclassification of the ImmunoSpin system compared to the reference flow cytometry. ImmunoSpin showed clear differential classification of CD4 + T lymphocytes from granulocytes and monocytes. Because non-fluorescence microparticle-tags and cytospin slides are used in ImmunoSpin, they can be applied to an automatic digital image analyzer. Slide preparation allows long-term storage, no analysis time limitations, and image transfer in remote areas.
| S-EPMC8616869 | biostudies-literature
Project description:Insecticide use in urban areas results in the detection of these compounds in streams following stormwater runoff at concentrations likely to cause toxicity for stream invertebrates. In this 2013 study, stormwater runoff and streambed sediments were analyzed for 91 pesticides dissolved in water and 118 pesticides on sediment. Detections included 33 pesticides, including insecticides, fungicides, herbicides, degradates, and a synergist. Patterns in pesticide occurrence reveal transport of dissolved and sediment-bound pesticides, including pyrethroids, from upland areas through stormwater outfalls to receiving streams. Nearly all streams contained at least one insecticide at levels exceeding an aquatic-life benchmark, most often for bifenthrin and (or) fipronil. Multiple U.S. EPA benchmark or criterion exceedances occurred in 40 % of urban streams sampled. Bed sediment concentrations of bifenthrin were highly correlated (p?<?0.001) with benthic invertebrate assemblages. Non-insects and tolerant invertebrates such as amphipods, flatworms, nematodes, and oligochaetes dominated streams with relatively high concentrations of bifenthrin in bed sediments, whereas insects, sensitive invertebrates, and mayflies were much more abundant at sites with no or low bifenthrin concentrations. The abundance of sensitive invertebrates, % EPT, and select mayfly taxa were strongly negatively correlated with organic-carbon normalized bifenthrin concentrations in streambed sediments. Our findings from western Clackamas County, Oregon (USA), expand upon previous research demonstrating the transport of pesticides from urban landscapes and linking impaired benthic invertebrate assemblages in urban streams with exposure to pyrethroid insecticides.
| S-EPMC4869748 | biostudies-literature