Change-point Analysis Method Research Articles

Study on the Evolvement Trend Process of Hydrological Elements in Luanhe River Basin, China

Over the past 50 years, there have been significant changes in the runoff process in the Luanhe River basin, exacerbating the scarcity of water resources and their spatiotemporal variability. Therefore, conducting research on the characteristics, trends, and cycles of runoff changes in the Luanhe River basin is of great theoretical and practical significance. This study selected rainfall data from the hydrological stations in Weichang, Chengde, and Qinhuangdao in the Luanhe River basin, covering the period from 1985 to 2008, as well as runoff data from the Hanjiaying, Sandaohezi, and Chengde stations. Based on linear trend regression analysis, the Mann–Kendall rank correlation test, Spearman correlation test, Mann–Kendall method, and Mann–Whitney–Pettitt change point analysis method, this study analyzed the trends in water quantity changes and their change points in the Luanhe River basin. The results of the precipitation at the Weichang and Chengde stations show a non-significant rising trend, remaining relatively stable with slightly increases. Conversely, the precipitation of Qinhuangdao Station shows a decreasing trend over time, albeit non-significant. Considering the detailed diagnostic results from both the Mann–Kendall (M-K) and MWP methods, the change point for Weichang precipitation is identified as 2007, while for Chengde, it spans from 1999 to 2002, and for Qinhuangdao, it is around 1997. The trend of the runoff series of three stations shows a significant decreasing trend and strong significance, and the change point for the annual runoff at the Hanjiaying station and the Sandaohezi station is identified as 2006, and for the Chengde station, the primary change point is 2006, with a secondary change point around 2002. The findings of this research can provide scientific references for the rational development and utilization of regional water resources.

Change Point Detection Methods for Locating Activations in Functional Neuronal Images

The most common analysis for fMRI images is activation detection, in which the purpose is to find the locations in the brain that respond to specific functions, such as visual processing or motor functions by providing related stimuli as tasks in the experiment. On the other hand, it is also important to detect the instance the activation is triggered. One of the powerful techniques that can analyze the abnormal behavior of any data is change point (CP) analysis. We suggest that CP detection algorithms also can be used to locate the activations in functional magnetic resonance imaging (fMRI) sequences, as well. Our paper presents a two-fold innovative study in that respect. First, we propose to use CP detection algorithms to locate the activations in fMRI signals as a state-of-art topic. Furthermore, we propose and compare a set of change point analysis methods, a regression-based method (RBM), a statistical method (SM), and a mean difference of double sliding windows method (MDSW)) to locate such points. Second, we apply these methods to the fMRI signals, which are acquired from the real subjects, while they were performing fMRI tasks. Proposed methods were applied to three different fMRI experiments with a motor task, a visual task, and a linguistic task. The analysis shows that the methods find activations in accordance with established methods such as statistical parametric maps (SPM). The acquired up to 94 % results also show that the proposed methods can be used effectively to locate the activation times on fMRI time series.

A new perspective on detecting performance decline: A change-point analysis based on Jensen-Shannon divergence.

A common observation in ability assessment is that the probability of an examinee giving a correct response drops for end-of-test items due to low motivation, time limits or other factors. On the test-takers' side, this change can be considered performance decline (PD), which can strongly affect test validity and bias respondents' ability estimators. Currently, there is an increasing interest in the detection of PD among researchers and practitioners. Researchers and practitioners found that PD detection fails to achieve acceptable power, which is typically below 0.55. Change-point analysis (CPA), a well-developed statistical method, can be applied to item response sequences to identify whether an abrupt change exists. Existing CPA methods cannot be directly used to detect PD because they are appropriate for two-sided alternative hypotheses. To address these issues, this research firstly develops a CPA method based on Jensen-Shannon divergence to detect PD. Additionally, existing CPA statistics were converted into one-sided statistics to accommodate PD detection. Then, a simulation study was conducted to investigate the performance of the proposed method and compare it with modified CPA statistics. Results show that the proposed CPA method can detect PD with higher power while generating a well-controlled Type-I error rate. Compared against modified CPA statistics, the proposed method exhibits an augmentation in power from 1.0% to 8.2%, with average of 5.7% and higher accuracy in locating the change point. Finally, the proposed method was applied to two real datasets to demonstrate its utility.

Detecting pattern transitions in psychological time series - A validation study on the Pattern Transition Detection Algorithm (PTDA).

With the increasing use of real-time monitoring procedures in clinical practice, psychological time series become available to researchers and practitioners. An important interest concerns the identification of pattern transitions which are characteristic features of psychotherapeutic change. Change Point Analysis (CPA) is an established method to identify the point where the mean and/or variance of a time series change, but changes of other and more complex features cannot be detected by this method. In this study, an extension of the CPA, the Pattern Transition Detection Algorithm (PTDA), is optimized and validated for psychological time series with complex pattern transitions. The algorithm uses the convergent information of the CPA and other methods like Recurrence Plots, Time Frequency Distributions, and Dynamic Complexity. These second level approaches capture different aspects of the primary time series. The data set for testing the PTDA (300 time series) is created by an instantaneous control parameter shift of a simulation model of psychotherapeutic change during the simulation runs. By comparing the dispersion of random change points with the real change points, the PTDA determines if the transition point is significant. The PTDA reduces the rate of false negative and false positive results of the CPA below 5% and generalizes its application to different types of pattern transitions. RQA quantifiers also can be used for the identification of nonstationary transitions in time series which was illustrated by using Determinism and Entropy. The PTDA can be easily used with Matlab and is freely available at Matlab File Exchange (https://www.mathworks.com/matlabcentral/fileexchange/80380-pattern-transition-detection-algorithm-ptda).

Study on stage method of reservoir flood season

Due to the influence of the monsoon, river runoff in most of China’s river basins is mainly concentrated in the flood season. The distribution is extremely uneven during the year and between the year. It needs to be fully utilized by the reservoir to bring serious challenges to the daily management. Therefore, analyzing the seasonal variation of storm floods, scientifically and rationally staging the flood season can not only give full play to the comprehensive benefits of the reservoir, but also be an inevitable requirement for coordinating reservoir flood control and power generation. Aiming at the several commonly used methods of flood season staging, the case study is carried out, and the applicable conditions are analyzed. Based on these, three new staging methods are proposed. Among them, the improved change point analysis method adopts the linear and nonlinear joint fitting, which improves the commonly used mean value calculation method. In theory, the non-uniformity and seasonality of the reservoir water are more carefully considered, and the flood season is considered to some extent. The distribution law and continuity of floods along the time course; the projection method has a similar theoretical system with the vector statistical method based on the annual maximum sampling method, both of which are based on the intensity of the points, the difference is the point of the latter is projected on the circumference, the partitioning result is selected by manual experience, and the human influence factor is large. The new method is to project the point on the straight line, and quantitatively gives the partitioning method, and is also applicable to the maximum flow of different sequences. The case of the same time, the dynamic clustering method divides the sample set into several subsets according to the similarity between samples, and has the advantage that the calculation amount is small and the genus of the sample can be changed compared with the system clustering method. The research results can provide reference for the reasonable adjustment of the reservoir flood season.

Distributed estimation and its fast algorithm for change-point in location models*

Change point detection has been widely used in quality control, earthquake disaster prediction and other fields. Existing change-point analysis methods rarely take into account the computational complexity, memory requirements and privacy issues under large data size. In this paper, we propose a distributed fast algorithm for change-point estimation when data are divided into many computers. Based on a subsequence data stored in one single machine, we get a change-point pre-estimator which is used to construct an interval covering the true change point with large probability, and then search the change-point more precisely on this interval among all machines. The final estimator by the above algorithm is proved to have consistency and limiting distribution with the same performance under the data-centralized case. The effectiveness of our algorithm is verified by sufficient numerical experiments which show that the asymptotic properties of our method are very close to that of traditional one, but with much less computation time.

Change Point Analysis for Detecting Vaccine Safety Signals.

It is important to detect signals of abrupt changes in adverse event reporting in order to notice public safety concerns and take prompt action, especially for vaccines under national immunization programs. In this study, we assessed the applicability of change point analysis (CPA) for signal detection in vaccine safety surveillance. The performances of three CPA methods, namely Bayesian change point analysis, Taylor’s change point analysis (Taylor-CPA), and environmental time series change point detection (EnvCpt), were assessed via simulated data with assumptions for the baseline number of events and degrees of change. The analysis was validated using the Korea Adverse Event Reporting System (KAERS) database. In the simulation study, the Taylor-CPA method exhibited better results for the detection of a change point (accuracy of 96% to 100%, sensitivity of 7% to 100%, specificity of 98% to 100%, positive predictive value of 25% to 85%, negative predictive value of 96% to 100%, and balanced accuracy of 53% to 100%) than the other two CPA methods. When the CPA methods were applied to reports of syncope or dizziness following human papillomavirus (HPV) immunization in the KAERS database, Taylor-CPA and EnvCpt detected a change point (Q2/2013), which was consistent with actual public safety concerns. CPA can be applied as an efficient tool for the early detection of vaccine safety signals.

The use of scaling properties to detect relevant changes in financial time series: A new visual warning tool

The dynamical evolution of multiscaling in financial time series is investigated using time-dependent Generalized Hurst Exponents (GHE), Hq, for various values of the parameter q. Using Hq, we introduce a new visual methodology to algorithmically detect critical changes in the scaling of the underlying complex time-series. The methodology involves the degree of multiscaling at a particular time instance, the multiscaling trend which is calculated by the Change-Point Analysis method, and a rigorous evaluation of the statistical significance of the results. Using this algorithm, we have identified particular patterns in the temporal co-evolution of the different Hq time-series. These GHE patterns, distinguish in a statistically robust way, not only between time periods of uniscaling and multiscaling, but also among different types of multiscaling: symmetric multiscaling (M) and asymmetric multiscaling (A). Asymmetric multiscaling can also be robustly divided into three other subcategories. We apply the visual methodology to time-series comprising of daily close prices of four stock market indices: two major ones (S&P 500 and Tokyo-NIKKEI) and two peripheral ones (Athens Stock Exchange general Index and Bombay-SENSEX). Results show that multiscaling varies greatly with time: time periods of strong multiscaling behavior and time periods of uniscaling behavior are interchanged while transitions from uniscaling to multiscaling behavior occur before critical market events, such as stock market bubbles. Moreover, particular asymmetric multiscaling patterns appear during critical stock market eras and provide useful information about market conditions. In particular, they can be used as ’fingerprints’ of a turbulent market period as well as provide warning signals for an upcoming stock market ’bubble’. The applied visual methodology also appears to distinguish between exogenous and endogenous stock market crises, based on the observed patterns before the actual events. The visual methodology is sufficiently general to be applicable for the description of the dynamical evolution of multiscaling properties of any complex system.

Impact of soil and water conservation measures and precipitation on streamflow in the middle and lower reaches of the Hulu River Basin, China

Precipitation variation and soil and water conservation (SWC) measures mostly determine the dynamics of streamflow in the Hulu River Basin. In particular, SWC measures play an essential role in controlling streamflow reduction and soil loss in the Hulu River Basin. The objective of this study is to quantitatively explore the effect of SWC measures and precipitation on streamflow changes in the middle and lower reaches of the Hulu River Basin. In this study, a long-term measured annual streamflow data from two major hydrological stations was used to analyze variations in streamflow in this basin. The Mann-Kendall trend test and the change-point analysis method were applied to analyze the trend of streamflow datasets. The results indicated that the annual streamflow in the middle and lower basin of the Hulu River (1975–2016) had a similar decreasing trend to streamflow change of the entire basin (1960–2016), with the abrupt change point of annual streamflow occurred in the year 1986. In addition, the streamflow exhibited a slightly increasing trend due to the more precipitation increase in the basin throughout 1999–2016. From 1999 to 2016, the impact of precipitation and SWC measures on streamflow is quantitatively assessed by using a multiple linear regression model. The result showed that SWC measures could effectively delay and reduce streamflow. The analysis of the regression coefficients suggested that SWC measures had a positive ecological effect on streamflow decrease, whereas precipitation had a significantly positive effect on streamflow increase. The precipitation had a higher contribution to streamflow changes than SWC measures. For the change of annual streamflow, precipitation contributed 79.6%, the Grain-for-Green program and check dams contributed 13.4% and 7%, respectively. A comparison of the impact of SWC measures on streamflow showed that the Grain-for-Green program measure had a more significant impact on streamflow reduction in the middle and lower reaches of the Hulu River Basin. In conclusion, these results can guide future water resource planning and management, and the allocation of SWC measures in the entire Hulu River Basin.

Long-term Variability of Summer Heavy Rainfall in the Seoul Metropolitan Area

The long-term variability of summer heavy rainfall in the Seoul metropolitan area was investigated in this study for the period of 1970-2018. The study period was divided into two phases; first phase from 1970 to 1994 and the second phase was 1995 to 2018. Long-term variability of summer heavy rainfall was examined using the change-point analysis method. Annual mean heavy rainfall amounts showed increasing trends in Seoul and Incheon during summer monsoon season (June to August). Results revealed that the changes in frequency and amount of heavy rainfall were observed larger in the months of July and August as compare to June during the second phase. The upper-level trough was prominently developed at the west of the Seoul metropolitan area and the core region of the upper-level jet was shifted to the east of the area during second phase. The western North Pacific subtropical high was expanded westward and moisture flux flowed along the southwesterly wind, resultant increasing moisture supply. The temperature and humidity tended to increase recently at the lower and mid-levels.

A change-point analysis procedure based on weighted residuals to detect back random responding.

Careless or inattentive responding is frequently observed in questionnaire or survey data, which jeopardizes test validity and the generalizability of research findings. It is therefore very important to detect such response behavior. The most frequently encountered type of careless response behavior is back random responding (BRR). Literature suggests that BRR is challenging to detect, with reported power of detection around .5 or lower. Change point analysis (CPA), which is a widely used statistical process control method, can be applied to item response data to detect if aberrant behavior exists in a response pattern. Existing CPA methods, however, may not be suitable for detecting BRR, because the change may not be directional. In this article we propose a weighted-residual-based CPA procedure to detect BRR behavior. The performance of the proposed method was evaluated in a comprehensive simulation study and compared against 3 existing CPA methods. Results indicated that the proposed residual-based CPA procedure can detect BRR behavior with high power for tests of 20 items or longer, while keeping the Type-I error rate well under control. Compared with the 3 existing CPA methods, it leads to comparable empirical Type-I error but a gain in power of 17%-42%. An empirical study further illustrated the utility of the proposed method to detect BRR with a real dataset. Implications of the proposed method, its limitations, as well as future directions are provided at the end. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Detecting Multiple Change Points Using Adaptive Regression Splines With Application to Neural Recordings.

Time series, as frequently the case in neuroscience, are rarely stationary, but often exhibit abrupt changes due to attractor transitions or bifurcations in the dynamical systems producing them. A plethora of methods for detecting such change points in time series statistics have been developed over the years, in addition to test criteria to evaluate their significance. Issues to consider when developing change point analysis methods include computational demands, difficulties arising from either limited amount of data or a large number of covariates, and arriving at statistical tests with sufficient power to detect as many changes as contained in potentially high-dimensional time series. Here, a general method called Paired Adaptive Regressors for Cumulative Sum is developed for detecting multiple change points in the mean of multivariate time series. The method's advantages over alternative approaches are demonstrated through a series of simulation experiments. This is followed by a real data application to neural recordings from rat medial prefrontal cortex during learning. Finally, the method's flexibility to incorporate useful features from state-of-the-art change point detection techniques is discussed, along with potential drawbacks and suggestions to remedy them.

Re-delineating mountainous areas with three topographic parameters in Mainland Southeast Asia using ASTER global digital elevation model data

Tropical mountainous areas not only provide substantial carbon storage and play an important role in global biological diversity, but also provide basic livelihood for a large number of poor ethnic minorities. However, there is no unified and explicit definition for mountainous areas. The local elevation range (LER) is a crucial structural parameter for delineating mountainous areas. However, current LER products are limited by the subjective selection of an optimum statistical window or coarser spatial resolution of topographical data. In this study, we presented an approach using thresholds for three topographic parameters, elevation, slope, and LER, derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) to redelineate the vast mountainous areas of mainland Southeast Asia (MSEA). The mean change-point analysis method was applied to determine the optimum statistical window of the 1 arc second (approximately 30 m)-resolution GDEM LER. The results showed that: First, the optimum statistical window is 38 × 38 cell units (width × height) in a rectangular neighborhood, or an area of about 1.30 km2 for calculating GDEM LER in MSEA. Second, the LER of more than 80% of the area ranges from 30 m to 499 m in MSEA. The LERs in the northern and northwestern MSEA are greater than their counterparts in the south and east. Third, the area of the re-delineated mountainous areas was 83.52 × 104 km2, about 38.71% of the total area. Spatially, the mountainous areas are mainly distributed in the north and northeast of MSEA. The re-delineated 30-m resolution map of the mountainous areas will serve as a topographical dataset for monitoring mountainrelated land surface changes in MSEA. The parameter-modified mountain extraction procedure can be expanded to delineate global mountainous areas.

Benefits of combining change-point analysis with disproportionality analysis in pharmacovigilance signal detection.

Change-point analysis (CPA) is a powerful method to analyse pharmacovigilance data but it has never been used on the disproportionality metric. To optimize signal detection investigating the interest of time-series analysis in pharmacovigilance and the benefits of combining CPA with the proportional reporting ratio (PRR). We investigated the couple benfluorex and aortic valve incompetence (AVI) using the French National Pharmacovigilance and EudraVigilance databases: CPA was applied on monthly counts of reports and the lower bound of monthly computed PRR (PRR-). We stated a CPA hypothesis that the substance-event combination is more likely to be a signal when the 2 following criteria are fulfilled: PRR- is greater than 1 with at least 5 cases, and CPA method detects at least 2 successive change points of PRR- which made consecutively increasing segments. We tested this hypothesis by 95 test cases identified from a drug safety reference set and 2 validated signals from EudraVigilance database: CPA was applied on PRR-. For benfluorex and AVI, change points detected by CPA on PRR- were more meaningful compared with monthly counts of reports: More change points detected and detected earlier. In the reference set, 14 positive controls satisfied CPA hypothesis, 6 positive controls only met first requirements, 3 negative controls only met first requirement, and 2 validated signals satisfied CPA hypothesis. The combination of CPA and PRR represents a significant advantage in detecting earlier signals and reducing false-positive signals. This approach should be confirmed in further studies.

Calculating shock arrival in expansion tubes and shock tunnels using Bayesian changepoint analysis

To understand the flow conditions generated in expansion tubes and shock tunnels, shock speeds are generally calculated based on shock arrival times at high-frequency wall-mounted pressure transducers. These calculations require that the shock arrival times are obtained accurately. This can be non-trivial for expansion tubes especially because pressure rises may be small and shock speeds high. Inaccurate shock arrival times can be a significant source of uncertainty. To help address this problem, this paper investigates two separate but complimentary techniques. Principally, it proposes using a Bayesian changepoint detection method to automatically calculate shock arrival, potentially reducing error and simplifying the shock arrival finding process. To compliment this, a technique for filtering the raw data without losing the shock arrival time is also presented and investigated. To test the validity of the proposed techniques, tests are performed using both a theoretical step change with different levels of noise and real experimental data. It was found that with conditions added to ensure that a real shock arrival time was found, the Bayesian changepoint analysis method was able to automatically find the shock arrival time, even for noisy signals.

Predictive Characteristics of Elevated 1-Hour Glucose Challenge Test Results for Gestational Diabetes.

Objective The objective was to estimate the optimal screen-positive 1-hour 50 g glucose challenge test (GCT) threshold for gestational diabetes (GDM) and predictive characteristics of increasing screen-positive GCT threshold values (135–199 mg/dL) for GDM. Study Design It is a secondary analysis of a multicenter mild GDM study. At 24 to 30 weeks' gestation, women with elevated GCT (135–199 mg/dL) completed a diagnostic 3-hour oral glucose tolerance test (OGTT). A novel change-point analysis method was used to compare the GDM rates for the adjacent GCT values, delineating categories of changing risk such that values within categories have equal risk for GDM. Positive (PPV) and negative (NPV) predictive values for GDM were computed for increasing GCT cut-offs. Results In 7,280 women with both GCT (135–199 mg/dL) and OGTT results, 4 GDM risk-equivalent GCT categories were identified with escalations at 144, 158, and 174 mg/dL (all p-values <0.05). The PPV for GDM increased from 33 to 64% as GCT increased from 135 to 199 mg/dL, while the NPV decreased from 80 to 67%. PPVs were only 20% and 61% for risk-equivalent categories of 135 to 143 mg/dL and 174 to 199 mg/dL, respectively. Conclusion Elevated GCT cut-off values between 135 and 143 mg/dL may carry equivalent GDM risk. No threshold GCT value <199 mg/dL alone sufficiently predicts GDM.

艾比湖区域景观格局空间特征与地表水质的关联分析

PDF HTML阅读 XML下载 导出引用 引用提醒 艾比湖区域景观格局空间特征与地表水质的关联分析 DOI: 10.5846/stxb201701100078 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41361045，41130531）；国家自然科学基金-新疆联合基金（U1503302）；新疆维吾尔自治区研究生教育改革创新项目（XJGRI2016014）；2016年新疆大学大学生创新训练计划重点项目（201610755057）资助 Correlation analysis between the spatial characteristics of land use/cover-landscape pattern and surface-water quality in the Ebinur Lake area Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:为进一步明确区域土地利用/覆被-景观格局对河流水质影响的空间尺度。选择新疆艾比湖区域为研究对象，以25个水质采样点为中心，建立5种尺度的河流缓冲区并提取不同尺度下的土地利用/覆被-景观格局数据。首先，通过主成分分析获得水环境的主要水质变量。其次，利用冗余分析（RDA）方法探讨研究区不同宽度缓冲区土地利用/覆被-景观格局对河流水质的影响，获得水质管理的有效缓冲区。最后，引入突变点分析方法进一步寻找导致水质变量沿景观梯度突变的特定位置。结果表明：（1）土地利用/覆被-景观格局在不同宽度缓冲区内对河流水质的影响不同。4 km缓冲区土地利用/覆被-景观格局对区域水质有较强的分异解释能力，因此4 km缓冲区的景观格局合理配置对河流水质管理尤为重要。（2）通过偏RDA分析发现4 km缓冲区中，影响区域水质的主要环境变量为景观水平斑块密度、类型水平耕地斑块密度和森林聚集度。（3）在偏RDA分析的基础上，对4 km缓冲区内的景观指数进行突变点分析研究，发现区域景观水平斑块密度为90-105 m/hm2，类型水平耕地的斑块密度ED值在90-110 m/hm2、林草地的AI值在70%-90%，是艾比湖区域水质保护的最佳突变值，该值为艾比湖区域水质保护的阈值。本研究从多个角度对新疆艾比区域的土地利用/覆被-景观格局对区域水质的影响进行定量分析，揭示该地区景观生态变化的规律，为区域景观格局优化和土地合理规划提供理论依据，并对改善"丝绸之路经济带"生态环境、实现资源可持续利用具有重要的现实意义和理论价值。 Abstract:To understand the effect of land use/cover-landscape on water quality, this study selected the Xinjiang Ebinur Lake region as the study area. We took 25 water sampling points to build five buffer zones, and combined land use/cover types with water-quality monitoring data, selected land-use types, and landscape index (landscape level) to monitor water quality. We tried to study the effects of a wide range of buffer land use/cover-landscape patterns on water quality indexes using the redundancy analysis method. Subsequently, we analyzed the specific location of the water-quality variables along the landscape gradient using the change-point analysis method. The results indicated:(1) the landscape pattern has different effects on the river-water quality indexes in different buffer zones. A 4 km buffer of land use/cover landscape pattern seems to be a threshold for differentiating the regional water quality. (2) The major environmental variables affecting the water quality of the lake area are the patch density, the patch density, and the degree of aggregation in a buffer of 4 km. (3) When the edge density (ED), of cultivation was 90-105 m/hm2, and the aggregation of forest and grassland index of water was 70%-90%, and the water quality in this region appeared to be better protected. This study performed the quantitative analysis of the land use/landscape pattern changes from multiple perspectives in the Ebinur Lake area, revealing the main regularities of the landscape ecology variation in the region. This study provides the theoretical basis for the optimization of regional landscape pattern and reasonable land planning. It improves the "Silk Road Economic-One Belt and One Road" initiative of the ecological environment and sustainable utilization of resources in the Ebinur Lake area in Xinjiang. 参考文献 相似文献 引证文献

Understanding the Bias Introduced in Quantum Dot Blinking Using Change Point Analysis

The fluorescence intermittency of single CdSe/CdS quantum dots (QDs) with different shell sizes is studied using the conventional bin and threshold method and the statistically more rigorous method, change point analysis (CPA). The on-state truncation time (τc) is a critical value used to interpret the dynamics of charge trapping in single QDs; however, changing the bin size and threshold in blink traces significantly modifies τc. Herein, we use the CPA method to minimize the bias that binning and thresholding introduces and find that a widely used assumption that there is only one on and one off state is questionable. We observe that 12 out of 17 QDs exhibit more than two intensity levels and find that the τc values of individual levels differ from the values obtained when the levels are combined, i.e., when one assumes there is only one on and one off state as in the conventional bin and threshold method. For instance, one QD has τc values of 0.5 (0.1) and 2.0 (0.2) s from two different intensity levels...

Calculation of the ELISA's cut-off based on the change-point analysis method for detection of Trypanosoma cruzi infection in Bolivian dogs in the absence of controls.

In ELISAs, sera of individuals infected by Trypanosoma cruzi show absorbance values above a cut-off value. The cut-off is generally computed by means of formulas that need absorbance readings of negative (and sometimes positive) controls, which are included in the titer plates amongst the unknown samples. When no controls are available, other techniques should be employed such as change-point analysis. The method was applied to Bolivian dog sera processed by ELISA to diagnose T. cruzi infection. In each titer plate, the change-point analysis estimated a step point which correctly discriminated among known positive and known negative sera, unlike some of the six usual cut-off formulas tested. To analyse the ELISAs results, the change-point method was as good as the usual cut-off formula of the form “mean + 3 standard deviation of negative controls”. Change-point analysis is therefore an efficient alternative method to analyse ELISA absorbance values when no controls are available.

Outbreak definition by change point analysis: a tool for public health decision?

BackgroundMost studies of epidemic detection focus on their start and rarely on the whole signal or the end of the epidemic. In some cases, it may be necessary to retrospectively identify outbreak signals from surveillance data. Our study aims at evaluating the ability of change point analysis (CPA) methods to locate the whole disease outbreak signal. We will compare our approach with the results coming from experts’ signal inspections, considered as the gold standard method.MethodsWe simulated 840 time series, each of which includes an epidemic-free baseline (7 options) and a type of epidemic (4 options). We tested the ability of 4 CPA methods (Max-likelihood, Kruskall-Wallis, Kernel, Bayesian) methods and expert inspection to identify the simulated outbreaks. We evaluated the performances using metrics including delay, accuracy, bias, sensitivity, specificity and Bayesian probability of correct classification (PCC).ResultsA minimum of 15 h was required for experts for analyzing the 840 curves and a maximum of 25 min for a CPA algorithm. The Kernel algorithm was the most effective overall in terms of accuracy, bias and global decision (PCC = 0.904), compared to PCC of 0.848 for human expert review.ConclusionsFor the aim of retrospectively identifying the start and end of a disease outbreak, in the absence of human resources available to do this work, we recommend using the Kernel change point model. And in case of experts’ availability, we also suggest to supplement the Human expertise with a CPA, especially when the signal noise difference is below 0.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-016-0271-x) contains supplementary material, which is available to authorized users.