Outlier Detection and Comparison of Origin-Destination Flows Using Data Depth

Advances in location-aware technology have resulted in massive trajectory data. Origin-destination (OD) trajectories provide rich information on urban flow and transport demand. This study describes a new method for detecting OD flows outliers and conducting hypothesis testing between two OD flow datasets in terms of the variations of spatial extent, that is, spread. The proposed method is based on data depth, which measures the centrality and outlyingness of a point with respect to a given dataset in R^d. Based on the center-outward ordering property, the proposed method analyzes the underlying characteristics of OD flows, such as location, outlyingness, and spread. The ability of the method to detect OD anomalies is compared with that of the Mahalanobis distance approach, and an F-test is used to verify the difference in scale. Empirical evaluation has demonstrated that our method effectively identifies OD flows outliers in an interactive way. Furthermore, the method can provide new perspectives such as spatial extent by considering the overall structure of data when comparing two different OD flows in terms of scale

Hepatocyte growth factor suppresses vascular endothelial growth factor-induced expression of endothelial ICAM-1 and VCAM-1 by inhibiting the nuclear factor-kappaB pathway

Vascular endothelial growth factor (VEGF) and hepatocyte growth factor (HGF) are potent angiogenic factors that have been used clinically to induce angiogenesis. However, concerns have been raised about VEGF because of its proinflammatory actions, which include enhancing the adhesion of leukocytes to endothelial cells. We have examined the possible antiinflammatory effects of HGF on the vasculature. HGF, unlike VEGF, did not alter leukocyte adhesion to endothelial cells. Instead it inhibited VEGF-induced leukocyte-endothelial cell interactions and the endothelial expression of intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1). In a skin inflammation model, VEGF-treated mice showed a significant increase of leukocytes infiltrated or adherent to the luminal surface of blood vessels, as compared with vehicle- or HGF-treated mice. The VEGF effect was markedly suppressed by coadministration of HGF. RT-PCR and promoter analysis revealed that HGF downregulated VEGF-mediated expression of ICAM-1 and VCAM-1 at the transcriptional level. Furthermore, these inhibitory effects coincided with suppression of IkappaB kinase activity, and this in turn prevented the activation of the inflammatory transcription factor NF-kappaB. Taken together, our results demonstrate that HGF suppresses VEGF-induced inflammation presumably by inhibiting the endothelial NF-kappaB pathway. This suggests that combined treatment with HGF and VEGF could be superior to treatment with either factor alone for enhancing therapeutic angiogenesis while avoiding inflammation

Development and external validation of a deep learning algorithm for prognostication of cardiovascular outcomes

Background and Objectives: We aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD), compared to Cox hazard regression. Methods: Two CVD prediction models were developed from National Health Insurance Service-Health Screening Cohort (NHIS-HEALS): A Cox regression model and a DL model. Performance of each model was assessed in the internal and 2 external validation cohorts in Koreans (National Health Insurance Service-National Sample Cohort; NHIS-NSC) and in Europeans (Rotterdam Study). A total of 412,030 adults in the NHIS-HEALS; 178,875 adults in the NHIS-NSC; and the 4,296 adults in Rotterdam Study were included. Results: Mean ages was 52 years (46% women) and there were 25,777 events (6.3%) in NHIS-HEALS during the follow-up. In internal validation, the DL approach demonstrated a C-statistic of 0.896 (95% confidence interval, 0.886-0.907) in men and 0.921 (0.908-0.934) in women and improved reclassification compared with Cox regression (net reclassification index [NRI], 24.8% in men, 29.0% in women). In external validation with NHIS-NSC, DL demonstrated a C-statistic of 0.868 (0.860-0.876) in men and 0.889 (0.876-0.898) in women, and improved reclassification compared with Cox regression (NRI, 24.9% in men, 26.2% in women). In external validation applied to the Rotterdam Study, DL demonstrated a C-statistic of 0.860 (0.824-0.897) in men and 0.867 (0.830-0.903) in women, and improved reclassification compared with Cox regression (NRI, 36.9% in men, 31.8% in women). Conclusions: A DL algorithm exhibited greater discriminative accuracy than Cox model approaches

Development and verification of prediction models for preventing cardiovascular diseases

Objectives Cardiovascular disease (CVD) is one of the major causes of death worldwide. For improved accuracy of CVD prediction, risk classification was performed using national time-series health examination data. The data offers an opportunity to access deep learning (RNN-LSTM), which is widely known as an outstanding algorithm for analyzing time-series datasets. The objective of this study was to show the improved accuracy of deep learning by comparing the performance of a Cox hazard regression and RNN-LSTM based on survival analysis. Methods and findings We selected 361,239 subjects (age 40 to 79 years) with more than two health examination records from 2002–2006 using the National Health Insurance System-National Health Screening Cohort (NHIS-HEALS). The average number of health screenings (from 2002–2013) used in the analysis was 2.9 ± 1.0. Two CVD prediction models were developed from the NHIS-HEALS data: a Cox hazard regression model and a deep learning model. In an internal validation of the NHIS-HEALS dataset, the Cox regression model showed a highest time-dependent area under the curve (AUC) of 0.79 (95% CI 0.70 to 0.87) for in females and 0.75 (95% CI 0.70 to 0.80) in males at 2 years. The deep learning model showed a highest time-dependent AUC of 0.94 (95% CI 0.91 to 0.97) for in females and 0.96 (95% CI 0.95 to 0.97) in males at 2 years. Layer-wise Relevance Propagation (LRP) revealed that age was the variable that had the greatest effect on CVD, followed by systolic blood pressure (SBP) and diastolic blood pressure (DBP), in that order. Conclusion The performance of the deep learning model for predicting CVD occurrences was better than that of the Cox regression model. In addition, it was confirmed that the known risk factors shown to be important by previous clinical studies were extracted from the study results using LRP

Acupuncture for persistent allergic rhinitis: a multi-centre, randomised, controlled trial protocol

<p>Abstract</p> <p>Background</p> <p>Allergic rhinitis is one of the most common health complaints worldwide. Complementary and alternative medical approaches have been employed to relieve allergic rhinitis symptoms and to avoid the side effects of conventional medication. Acupuncture has been widely used to treat patients with allergic rhinitis, but the available evidence of its effectiveness is insufficient. Our objective is to evaluate the effectiveness of acupuncture in patients in Korea and China with persistent allergic rhinitis compared to sham acupuncture treatment or waitlist control.</p> <p>Methods</p> <p>This study consists of a multi-centre (two centres in Korea and two centres in China), randomised, controlled trial with three parallel arms (active acupuncture, sham acupuncture, and waitlist group). The active acupuncture and sham acupuncture groups will receive real or sham acupuncture treatment, respectively, three times per week for a total of 12 sessions over four weeks. Post-treatment follow-up will be performed a month later to complement these 12 acupuncture sessions. Participants in the waitlist group will not receive real or sham acupuncture treatments during this period but will only be required to keep recording their symptoms in a daily diary. After four weeks, the same treatment given to the active acupuncture group will be provided to the waitlist group.</p> <p>Discussion</p> <p>This trial will provide evidence for the effectiveness of acupuncture as a treatment for persistent allergic rhinitis. The primary outcome between groups is a change in the self-reported total nasal symptom score (i.e., nasal obstruction, rhinorrhea, sneezing, and itching) from baseline at the fourth week. Secondary outcome measures include the Rhinitis Quality of Life Questionnaire score and total non-nasal symptom score (i.e., headache, itching, pain, eye-dropping). The quantity of conventional relief medication used during the follow-up period is another secondary outcome measure.</p> <p>Trial registration</p> <p>Current Controlled Trials ISRCTN90807007</p

New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

GEMS will monitor air quality over Asia at unprecedented spatial and temporal resolution from GEO for the first time, providing column measurements of aerosol, ozone and their precursors (nitrogen dioxide, sulfur dioxide and formaldehyde). Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in late 2019 - early 2020 to monitor Air Quality (AQ) at an unprecedented spatial and temporal resolution from a Geostationary Earth Orbit (GEO) for the first time. With the development of UV-visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO and aerosols) can be obtained. To date, all the UV-visible satellite missions monitoring air quality have been in Low Earth orbit (LEO), allowing one to two observations per day. With UV-visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be onboard the GEO-KOMPSAT-2 satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager (GOCI)-2. These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA&apos;s TEMPO and ESA&apos;s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS)

Efficient, Decentralized Detection of Qualitative Spatial Events in a Dynamic Scalar Field

This paper describes an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to the critical points (i.e., peak, pits and passes) and edges of the surface network derived from the field. Four fundamental types of event (appearance, disappearance, movement and switch) are defined. Our algorithm is designed to rely purely on qualitative information about the neighborhoods of nodes in the sensor network and does not require information about nodes’ coordinate positions. Experimental investigations confirm that our algorithm is efficient, with O(n) overall communication complexity (where n is the number of nodes in the sensor network), an even load balance and low operational latency. The accuracy of event detection is comparable to established centralized algorithms for the identification of critical points of a surface network. Our algorithm is relevant to a broad range of environmental monitoring applications of sensor networks

Qualitative characteristics of fields monitored by a resource-constrained geosensor network

© 2014 Dr. Myeonghun JeongThis research investigates efficient monitoring of the qualitative characteristics of, and changes to spatial fields monitored by a geosensor network (GSN). A spatial field maps the locations of nodes in a GSN to a scalar property (e.g., temperature, humidity, pollutant). This research aims to monitor the states and events of scalar fields. However, resource-constraints and limited spatial granularity in GSNs require new decentralized approaches to designing scalable and accurate algorithms. Thus, algorithms in this research can operate in the network without centralized control, important in emerging resource-constrained spatial computing environments. Further, the algorithms in this thesis operate without any coordinate information, making them suitable for applications where a positioning system is unavailable or unreliable. First, this thesis investigates the characteristics of fields, based on thresholded regions. In particular, the research explores the design and evaluation of a family of new algorithms for determining the topological relations between static thresholded regions. The algorithms are based on efficient, decentralized (in-network) variants of conventional 4-intersection and intersection and difference models. While all four algorithms are shown to have overall communication complexity O(n), where n is the number of nodes in the GSN, and optimal load balance O(1), the algorithms differ in the level of topological detail they can detect; the types of regions they can monitor; and in the constant factors for communication complexity. The research also demonstrates the impact of finite granularity observations on the correctness of the query results. Next, a monitored field is represented as a surface network. A decentralized and coordinate-free algorithm is developed to identify critical points (peaks, pits, and passes) and a surface network connecting those critical points. Empirical evaluation shows that the algorithm can improve the accuracy of critical point identification when compared with the current state-of-the-art decentralized algorithm, and matches the accuracy of a centralized algorithm for peaks and pits. The algorithm is efficient, requiring O(n) overall communication complexity. Further, empirical investigations of the algorithm across a range of simulations demonstrate improved load balance when compared with an existing decentralized algorithm. While the previous two topics deal with the atemporal characteristics of fields, the third topic presents an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to a surface network derived from a monitored field. Four fundamental types of event (appearance, disappearance, movement, and switch) are defined. Experimental investigations confirm that the algorithm is efficient, with O(n) overall communication complexity, an even load balance, and low operational latency. The accuracy of event detection is comparable with established centralized algorithms for the identification of critical points. The results of this research have wide applications to environmental monitoring using qualitative representation: thresholded regions or surface networks. For example, specific applications of this thesis might include identifying where high temperature and low soil humidity areas overlap in a vineyard, to help the vineyard managers to optimize the use of water; and monitoring where are the peaks in nitrogen levels on a meadow, to help farmers to efficiently irrigate or fertilize for grazing cattle