Finding the K Nearest Objects over Time Dependent Road Networks

2018 ◽  
pp. 334-349
Author(s):  
Muxi Leng ◽  
Yajun Yang ◽  
Junhu Wang ◽  
Qinghua Hu ◽  
Xin Wang
Keyword(s):  
Road Networks ◽  
Time Dependent ◽  
Over Time

scholarly journals A Novel Index Method for K Nearest Object Query over Time-Dependent Road Networks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Yajun Yang ◽  
Hanxiao Li ◽  
Junhu Wang ◽  
Qinghua Hu ◽  
Xin Wang ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Real Life ◽  
Road Networks ◽  
Secondary Level ◽  
Location Based Services ◽  
Time Dependent ◽  
Index Method ◽  
Balanced Tree ◽  
Changes Over Time ◽  
Over Time

Knearest neighbor (kNN) search is an important problem in location-based services(LBS) and has been well studied on static road networks. However, in real world, road networks are often time-dependent; i.e., the time for traveling through a road always changes over time. Most existing methods forkNN query build various indexes maintaining the shortest distances for some pairs of vertices on static road networks. Unfortunately, these methods cannot be used for the time-dependent road networks because the shortest distances always change over time. To address the problem ofkNN query on time-dependent road networks, we propose a novel voronoi-based index in this paper. Furthermore, we propose a novel balanced tree, namedV-tree, which is a secondary level index on voronoi-based index to make our querying algorithm more efficient. Moreover, we propose an algorithm for preprocessing time-dependent road networks such that the waiting time is not necessary to be considered. We confirm the efficiency of our method through experiments on real-life datasets.

scholarly journals Detection of Acetaminophen and Its Glucuronide in Fingerprint by SALDI Mass Spectrometry Using Zeolite and Study of Time-Dependent Changes in Detected Ion Amount

Analytica ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 66-75
Author(s):  
Toshiki Horikoshi ◽  
Chihiro Kitaoka ◽  
Yosuke Fujii ◽  
Takashi Asano ◽  
Jiawei Xu ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Glucuronic Acid ◽  
Laser Desorption ◽  
The Body ◽  
Time Dependent ◽  
Laser Desorption Ionization ◽  
Fingerprint Analysis ◽  
Desorption Ionization ◽  
Acid Conjugate ◽  
Over Time

The ingredients of an antipyretic (acetaminophen, AAP) and their metabolites excreted into fingerprint were detected by surface-assisted laser desorption ionization (SALDI) mass spectrometry using zeolite. In the fingerprint taken 4 h after AAP ingestion, not only AAP but also the glucuronic acid conjugate of AAP (GAAP), caffeine (Caf), ethenzamide (Eth), salicylamide (Sala; a metabolite of Eth), and urea were detected. Fingerprints were collected over time to determine how the amounts of AAP and its metabolite changed with time, and the time dependence of the peak intensities of protonated AAP and GAAP was measured. It was found that the increase of [GAAP+H]+ peak started later than that of [AAP+H]+ peak, reflecting the metabolism of AAP. Both AAP and GAAP reached maximum concentrations approximately 3 h after ingestion, and were excreted from the body with a half-life of approximately 3.3 h. In addition, fingerprint preservation was confirmed by optical microscopy, and fingerprint shape was retained even after laser irradiation of the fingerprint. Our method may be used in fingerprint analysis.

scholarly journals Risk Factors for Prostate Cancer Detection After a Negative Biopsy: A Novel Multivariable Longitudinal Approach

2010 ◽  
Vol 28 (10) ◽  
pp. 1714-1720 ◽  
Author(s):  
Peter H. Gann ◽  
Angela Fought ◽  
Ryan Deaton ◽  
William J. Catalona ◽  
Edward Vonesh
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Specific Antigen ◽  
Time Dependent ◽  
Negative Biopsy ◽  
Gland Volume ◽  
Piecewise Exponential ◽  
Over Time ◽  
Negative Biopsies

Purpose To introduce a novel approach for the time-dependent quantification of risk factors for prostate cancer (PCa) detection after an initial negative biopsy. Patients and Methods Data for 1,871 men with initial negative biopsies and at least one follow-up biopsy were available. Piecewise exponential regression models were developed to quantify hazard ratios (HRs) and define cumulative incidence curves for PCa detection for subgroups with specific patterns of risk factors over time. Factors evaluated included age, race, serum prostate-specific antigen (PSA) concentration, PSA slope, digital rectal examination, dysplastic glands or prostatitis on biopsy, ultrasound gland volume, urinary symptoms, and number of negative biopsies. Results Four hundred sixty-five men had PCa detected, after a mean follow-up time of 2.8 years. All of the factors were independent predictors of PCa detection except for PSA slope, as a result of its correlation with time-dependent PSA level, and race. PSA (HR = 3.90 for > 10 v 2.5 to 3.9 ng/mL), high-grade prostatic intraepithelial neoplasia/atypical glands (HR = 2.97), gland volume (HR = 0.39 for > 50 v < 25 mL), and number of repeat biopsies (HR = 0.36 for two v zero repeat biopsies) were the strongest predictors. Men with high-risk versus low-risk event histories had a 20-fold difference in PCa detection over 5 years. Conclusion Piecewise exponential models provide an approach to longitudinal analysis of PCa risk that allows clinicians to see the interplay of risk factors as they unfold over time for individual patients. With these models, it is possible to identify distinct subpopulations with dramatically different needs for monitoring and repeat biopsy.

scholarly journals A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making

2018 ◽  
Vol 38 (8) ◽  
pp. 904-916 ◽  
Author(s):  
Aasthaa Bansal ◽  
Patrick J. Heagerty
Keyword(s):  
Decision Making ◽  
Statistical Methods ◽  
Sensitivity And Specificity ◽  
Prognostic Model ◽  
Dynamic Decision Making ◽  
Time Dependent ◽  
Survival Models ◽  
Biliary Cirrhosis ◽  
Time Varying ◽  
Over Time

Many medical decisions involve the use of dynamic information collected on individual patients toward predicting likely transitions in their future health status. If accurate predictions are developed, then a prognostic model can identify patients at greatest risk for future adverse events and may be used clinically to define populations appropriate for targeted intervention. In practice, a prognostic model is often used to guide decisions at multiple time points over the course of disease, and classification performance (i.e., sensitivity and specificity) for distinguishing high-risk v. low-risk individuals may vary over time as an individual’s disease status and prognostic information change. In this tutorial, we detail contemporary statistical methods that can characterize the time-varying accuracy of prognostic survival models when used for dynamic decision making. Although statistical methods for evaluating prognostic models with simple binary outcomes are well established, methods appropriate for survival outcomes are less well known and require time-dependent extensions of sensitivity and specificity to fully characterize longitudinal biomarkers or models. The methods we review are particularly important in that they allow for appropriate handling of censored outcomes commonly encountered with event time data. We highlight the importance of determining whether clinical interest is in predicting cumulative (or prevalent) cases over a fixed future time interval v. predicting incident cases over a range of follow-up times and whether patient information is static or updated over time. We discuss implementation of time-dependent receiver operating characteristic approaches using relevant R statistical software packages. The statistical summaries are illustrated using a liver prognostic model to guide transplantation in primary biliary cirrhosis.

scholarly journals A joint modeling approach for longitudinal microbiome data improves ability to detect microbiome associations with disease

2020 ◽  
Vol 16 (12) ◽  
pp. e1008473
Author(s):  
Pamela N. Luna ◽  
Jonathan M. Mansbach ◽  
Chad A. Shaw
Keyword(s):  
Joint Modeling ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Time Dependent ◽  
Time To Event ◽  
Modeling Framework ◽  
Microbial Composition ◽  
Disease Outcomes ◽  
Microbiome Data ◽  
Over Time

Changes in the composition of the microbiome over time are associated with myriad human illnesses. Unfortunately, the lack of analytic techniques has hindered researchers’ ability to quantify the association between longitudinal microbial composition and time-to-event outcomes. Prior methodological work developed the joint model for longitudinal and time-to-event data to incorporate time-dependent biomarker covariates into the hazard regression approach to disease outcomes. The original implementation of this joint modeling approach employed a linear mixed effects model to represent the time-dependent covariates. However, when the distribution of the time-dependent covariate is non-Gaussian, as is the case with microbial abundances, researchers require different statistical methodology. We present a joint modeling framework that uses a negative binomial mixed effects model to determine longitudinal taxon abundances. We incorporate these modeled microbial abundances into a hazard function with a parameterization that not only accounts for the proportional nature of microbiome data, but also generates biologically interpretable results. Herein we demonstrate the performance improvements of our approach over existing alternatives via simulation as well as a previously published longitudinal dataset studying the microbiome during pregnancy. The results demonstrate that our joint modeling framework for longitudinal microbiome count data provides a powerful methodology to uncover associations between changes in microbial abundances over time and the onset of disease. This method offers the potential to equip researchers with a deeper understanding of the associations between longitudinal microbial composition changes and disease outcomes. This new approach could potentially lead to new diagnostic biomarkers or inform clinical interventions to help prevent or treat disease.

scholarly journals Mortality Risk and The Progression of The Electronic Frailty Index and Hospital Frailty Risk Score Over Time

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Joe Hollinghurst ◽  
Alan Watkins
Keyword(s):  
Risk Score ◽  
Frailty Index ◽  
Time Frame ◽  
Time Dependent ◽  
Cox Models ◽  
Hazard Ratios ◽  
Increased Risk ◽  
The Matrix ◽  
Time Dependent Covariates ◽  
Over Time

IntroductionThe electronic Frailty Index (eFI) and the Hospital Frailty Risk Score (HFRS) have been developed in primary and secondary care respectively. Objectives and ApproachOur objective was to investigate how frailty progresses over time, and to include the progression of frailty in a survival analysis.To do this, we performed a retrospective cohort study using linked data from the Secure Anonymised Information Linkage Databank, comprising 445,771 people aged 65-95 living in Wales (United Kingdom) on 1st January 2010. We calculated frailty, using both the eFI and HFRS, for individuals at quarterly intervals for 8 years with a total of 11,702,242 observations. ResultsWe created a transition matrix for frailty states determined by the eFI (states: fit, mild, moderate, severe) and HFRS (states: no score, low, intermediate, high), with death as an absorbing state. The matrix revealed that frailty progressed over time, but that on a quarterly basis it was most likely that an individual remained in the same state. We calculated Hazard Ratios (HRs) using time dependent Cox models for mortality, with adjustments for age, gender and deprivation. Independent eFI and HFRS models showed increased risk of mortality as frailty severity increased. A combined eFI and HFRS revealed the highest risk was primarily determined by the HFRS and revealed further subgroups of individuals at increased risk of an adverse outcome. For example, the HRs (95% Confidence Interval) for individuals with an eFI as fit, mild, moderate and severe with a high HFRS were 18.11 [17.25,19.02], 20.58 [19.93,21.24], 21.45 [20.85,22.07] and 23.04 [22.34,23.76] respectively with eFI fit and no HFRS score as the reference category. ConclusionFrailty was found to vary over time, with progression likely in the 8-year time-frame analysed. We refined HR estimates of the eFI and HFRS for mortality by including time dependent covariates.

scholarly journals Reconstructing the Scene: New Views of Supernovae and Progenitors from the SNSPOL Project

2016 ◽  
Vol 12 (S329) ◽  
pp. 54-58
Author(s):  
Jennifer L. Hoffman ◽  
G. Grant Williams ◽  
Douglas C. Leonard ◽  
Christopher Bilinski ◽  
Luc Dessart ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Electron Scattering ◽  
Time Dependent ◽  
Complex Velocity ◽  
Circumstellar Material ◽  
Radiative Transfer Models ◽  
The Future ◽  
Geometrical Information ◽  
Physical Phenomena ◽  
Over Time

AbstractBecause polarization encodes geometrical information about unresolved scattering regions, it provides a unique tool for analyzing the 3-D structures of supernovae (SNe) and their surroundings. SNe of all types exhibit time-dependent spectropolarimetric signatures produced primarily by electron scattering. These signatures reveal physical phenomena such as complex velocity structures, changing illumination patterns, and asymmetric morphologies within the ejecta and surrounding material. Interpreting changes in polarization over time yields unprecedentedly detailed information about supernovae, their progenitors, and their evolution.Begun in 2012, the SNSPOL Project continues to amass the largest database of time-dependent spectropolarimetric data on SNe. I present an overview of the project and its recent results. In the future, combining such data with interpretive radiative transfer models will further constrain explosion mechanisms and processes that shape SN ejecta, uncover new relationships among SN types, and probe the properties of progenitor winds and circumstellar material.

scholarly journals Environmental Surveillance Can Dynamically Track Ecological Changes in Enteroviruses

2019 ◽  
Vol 85 (24) ◽  
Author(s):  
Hiroki Ozawa ◽  
Hiromu Yoshida ◽  
Shuzo Usuku
Keyword(s):  
Multidimensional Scaling ◽  
Large Scale ◽  
Local Community ◽  
Amino Acid Sequences ◽  
Time Dependent ◽  
Small Scale ◽  
Dependent Manner ◽  
Environmental Surveillance ◽  
Ecological Changes ◽  
Over Time

ABSTRACT Environmental surveillance can be used to trace enteroviruses shed from human stool using a sewer network that is independent of symptomatic or asymptomatic infection. In this study, the local transmission of enteroviruses was analyzed using two wastewater treatment plants, which were relatively close to each other (15 km), designated as sentinels. Influent was collected at both sentinels once a month from 2013 to 2016, and viruses were isolated. Using neutralizing tests with type-specific polyclonal antisera and molecular typing, 933 isolates were identified as enteroviruses. Our results showed that the frequency of virus isolation varied for each serotype at the two sentinels in a time-dependent manner. Because echovirus 11 (Echo11) and coxsackievirus B5 isolates showed a high frequency and were difficult to distinguish, they were further grouped into various lineages based on the VP1 amino acid sequences. The prevalence of each lineage was visualized using multidimensional scaling. The results showed that Echo11 isolates of the same lineage were isolated continuously, similar to coxsackievirus B5 isolates of three lineages. Conversely, Echo1, Echo13, Echo18, Echo19, Echo20, Echo29, and Echo33 were isolated only once each. Our findings suggested that if an enterovirus is imported into the population, it may result in small-scale transmission, whereas if there are initially many infected individuals, it may be possible for the virus to spread to a wide area, beyond the local community, over time. In addition, our findings could provide insights into risk assessment of transmission for importation of poliovirus in polio-free countries and regions. IMPORTANCE In this study, we showed that environmental enterovirus surveillance can be used to monitor the propagation of nonpolio enteroviruses in addition to poliovirus detection. Since epidemiological studies of virus transmission based on the past were performed using specimens from humans, there were limitations to research design, such as specimen collection for implementation on a large-scale target population. However, environmental monitoring can dynamically track the ecological changes in enteroviruses in the region by monitoring viruses in chronological order and targeting the population within the area by monitoring viruses over time. We observed differences in the transmission of echovirus 11 and coxsackievirus B5 in the region according to lineage in a time-dependent manner and with a multidimensional scaling pattern.

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