Detection Of Breaks Research Articles

Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures.

Identifying structural breaks in the dynamics of COVID-19 contagion is crucial to promptly assess policies and evaluate the effectiveness of lockdown measures. However, official data record infections after a critical and unpredictable delay. Moreover, people react to the health risks of the virus and also anticipate lockdowns. All of this makes it complex to quickly and accurately detect changing patterns in the virus’s infection dynamic. We propose a machine learning procedure to identify structural breaks in the time series of COVID-19 cases. We consider the case of Italy, an early-affected country that was unprepared for the situation, and detect the dates of structural breaks induced by three national lockdowns so as to evaluate their effects and identify some related policy issues. The strong but significantly delayed effect of the first lockdown suggests a relevant announcement effect. In contrast, the last lockdown had significantly less impact. The proposed methodology is robust as a real-time procedure for early detection of the structural breaks: the impact of the first two lockdowns could have been correctly identified just the day after they actually occurred.

Detection of DNA Double-Strand Breaks by γ-H2AX Immunodetection.

DNA double-strand breaks (DSBs) are the most deleterious type of DNA damage and a cause of genetic instability as they can lead to mutations, genome rearrangements, or loss of genetic material when not properly repaired. Eukaryotes from budding yeast to mammalian cells respond to the formation of DSBs with the immediate phosphorylation of a histone H2A isoform. The modified histone, phosphorylated in serine 139 in mammals (S129 in yeast), is named γ-H2AX. Detection of DSBs is of high relevance in research on DNA repair, aging, tumorigenesis, and cancer drug development, given the tight association of DSBs with different diseases and its potential to kill cells. DSB levels can be obtained by measuring levels of γ-H2AX in extracts of cell populations or by counting foci in individual nuclei. In this chapter some techniques to detect γ-H2AX are described.

EXPLORING NASA’S HARMONIZED LANDSAT AND SENTINEL-2 (HLS) DATASET TO MONITOR DEFORESTATION IN THE AMAZON RAINFOREST

Abstract. Deforestation is a threat to biodiversity and the world’s climate. As agriculture and mining areas grow, forest loss becomes unbearable for the environment. Consequently, monitoring deforestation is crucial for decision makers to create polices. The most reliable deforestation data about the Amazon forest is generated by the Brazil’s National Institute for Space Research (INPE) through its PRODES project. This effort is labor and time intensive because it depends on visual interpretation from experts. Additionally, frequent Amazon’s atmospheric phenomena, such as clouds, difficult image analysis which induces alternative approaches such as time series analysis. One way to increase the number of images of an area consists of using images from different satellites. NASA provides the Harmonized Landsat and Sentinel-2 (HLS) dataset solving spectral dissimilarities of satellite sensors. In this paper, the possibilities of HLS for forest monitoring are explored by applying two deforestation detection methods, Break Detection for Additive Season and Trend (BFAST) monitor and Random Forest, over four different vegetation indices, NDVI, EVI, GEMI and SAVI. The SAVI index used as input for BFAST monitor performed the best in this data setup with 95.23% for deforested pixel, 53.69% for non-deforested pixels. Although the HLS data is described as analysis ready, further pre-processing can enhance the outcome of the analysis. Especially, since the cloud and cirrus cover in the Amazon causes gaps in the dataset, a best pixel method is recommended to create patched images and thus a continuous time series as input for any land cover and land use classification.

A survey on modeling and control of thruster-assisted position mooring systems

This review presents a systematic summary of the state-of-the-art development of technological solutions, modeling, and control strategies of thruster-assisted position mooring (TAPM) systems. The survey serves as a starting point for exploring automatic control and real-time monitoring solutions proposed for TAPM systems. A brief historical background of the mooring systems is given. The kinematics and a simplified kinetic control-design model of a TAPM system are derived in accordance with established control methods, including a quasistatic linearized model for the restoring and damping forces based on low-frequency horizontal motions of the vessel. In addition, another two mooring line models, i.e., the catenary equation and the finite element method model, are presented for the purpose of higher-fidelity simulations. The basic TAPM control strategies are reviewed, including heading control, surge-sway damping, roll–pitch damping (for semisubmersibles), and line break detection and compensation. Details on the concepts of setpoint chasing for optimal positioning of a vessel at the equilibrium position are discussed based on balancing the mooring forces with the environmental loads and avoiding mooring line failure modes. One method for setpoint chasing is the use of a structural reliability index, accounting for both mean mooring line tensions and dynamic effects. Another method is the use of a lowpass filter on the position of the vessel itself, to provide a reference position. The most advanced method seems to be the use of a fault-tolerant control framework that, in addition to direct fault detection and isolation in the mooring system, incorporates minimization of either the low-frequency tensions in the mooring lines or minimization of the reliability indices for the mooring lines to select the optimal directions for the setpoint to move. A hybrid (or supervisory switching) control method is also presented, where a best-fit control law and observer law are automatically selected among a bank of control and observer algorithms based on the supervision of the sea-state and automatic switching logic.

Multiple breaks detection in financial interval-valued time series

Multiple structural breaks detection for Interval-Valued Time Series (IVTS) is undoubtedly relevant under practical perspectives and challenging under the point of view of the analysis of expert systems. In this respect, financial time series usually show high variability and outliers; moreover, they often exhibit the property of being of high frequency nature; thus, it is naturally advisable to consider them as IVTS type for a given time unit. Despite this relevance, scarce effort has been spent by scholars to apply the methodological advancements in breaks detection for IVTS to the crucial environment of financial time series. This paper contributes to fill this gap. It employs the Atheoretical Regression Trees framework – a very recent tool that is able to automatically locale multiple breaks occurring to unknown dates – to stock prices. Such a procedure is able to estimate in an efficient way the structural breaks of the considered series; at the same time, it keeps into account the main characteristics of the intervals describing the IVTS. For our purposes, we adopt a theoretical proposal of reading daily stock prices as intervals whose bounds are defined through the closing prices. Empirical experiments on the American International Group – whose daily prices have experienced structural breaks in the past – validate the theoretical model and show the usefulness of the proposed procedure.

Recent applications of the Comet Assay: A report from the International Comet Assay Workshop 2019

The International Comet Assay Workshops (ICAW) are a series of scientific conferences dealing with different aspects of the comet assay. The assay itself is a simple method for the detection of DNA strand breaks at the cellular level and can be applied to any cell type derived from different organs and tissues of eukaryotic organisms. Additionally, the comet assay is widely applied in human biomonitoring, ecotoxicology, genotoxicity testing of chemicals, but also in basic research studying the mechanisms of DNA damage and repair. The 2019 ICAW edition gathered about 80 participants with over 30 lecturers, 27 poster presentations and 2 open discussion sessions presenting the latest advances in technical developments as well as applications of the comet assay in genetic toxicology, and environmental and human biomonitoring. This report summarises the important issues that were raised and discussed during the sessions as well as a short synopsis of the papers selected for inclusion in this special issue. Based on the topics presented at the workshop, the assay with its new modifications and applications has a bright future and will for sure stay one of the most popular methods in genetic toxicology and beyond in the years to come.

Variance Homogeneity Test Based on Cumulative Wavelet Coefficients

The aim of this paper is to address the problem of variance break detection in time series in wavelet domain. The maximal overlapped discrete wavelet transform (MODWT) decomposes the series variance across scales into components known as the wavelet variances. We introduce all scale wavelet coefficients based test statistic that allows detecting a break in the homogeneity of the variance of a series through changes in the mean of wavelet variances. The statistic makes use of the traditional CUSUM (cumulative sum) based test designed to test for a break in the mean and constructed using cumulative sums of the square of wavelet coefficients. Under moments and mixing conditions, the test statistic satisfies the functional central limit theorem (FCLT) for a broad class of time series models. The overall performance of our test statistic is compared to the traditional Inclan [8] test statistic. The effectiveness of our statistic is supported by good performances reported in simulations and is as reliable as the traditional statistic. Our method provides a nonparametric test procedure that can be applied to a large class of linear and non linear models. We illustrate the practical use of our test procedure with the quarterly percentage changes in the Americans personal savings data set over the period 1970-2016. Both statistics detect a break in the variance in the second quarter of 2001.

Automated Estimation and Tools to Extract Positions, Velocities, Breaks, and Seasonal Terms From Daily GNSS Measurements: Illuminating Nonlinear Salton Trough Deformation

AbstractThis paper describes the methods used to estimate positions, velocities, breaks, and seasonal terms from daily Global Navigation Satellite System (GNSS) measurements. Break detection and outlier removal have been automated so that decades of daily measurements from thousands of stations can be processed in a few hours. New measurements are added, and parameters are updated every week. Model parameters allow separation of interseismic, annual, coseismic, and postseismic signals. Tools available through GeoGateway (http://geo-gateway.org) allow rapid visualization and analysis of these terms for results that can be subsetted in time or space. Results show highly variable and nonlinear motion for GPS stations in southern California. The variable motion is related to seasonal motions, distributed tectonic motion, earthquakes, and postseismic motions that can continue for years. In some areas results suggest that additional processes are responsible for the observed motions. In general, following earthquakes, stations return to their long‐term motions after 2–3 years, though some exceptions occur. The use of the tools shows nonlinear motion in the Salton Trough of southern California related to the 2010 M7.2 El Mayor‐Cucapah earthquake, 2012 Brawley earthquake swarm, and a creep event on the Superstition Hills fault in 2017.

Analyzing Differences between Scenarios

Comparisons between alternative scenarios are used in many disciplines from macroeconomics to climate science to help with planning future responses. Differences between scenario paths are often interpreted as signifying likely differences between outcomes that would materialise in reality. However, even when using correctly specified statistical models of the in-sample data generation process, additional conditions are needed to sustain inferences about differences between scenario paths. We consider two questions in scenario analyses: First, does testing the difference between scenarios yield additional insight beyond simple tests conducted on the model estimated in-sample? Second, when does the estimated scenario difference yield unbiased estimates of the true difference in outcomes? Answering the first question, we show that the calculation of uncertainties around scenario differences raises difficult issues since the underlying in-sample distributions are identical for both ‘potential’ outcomes when the reported paths are deterministic functions. Under these circumstances, a scenario comparison adds little beyond testing for the significance of the perturbed variable in the estimated model. Resolving the second question, when models include multiple covariates, inferences about scenario differences depend on the relationships between the conditioning variables, especially their invariance to the interventions. Tests for invariance based on automatic detection of structural breaks can help identify in-sample invariance of models to evaluate likely constancy in projected scenarios. Applications of scenario analyses to impacts on the UK’s wage share from unemployment and agricultural growth from climate change illustrate the concepts.

Time-Varying Relationship between Crude Oil Price and Exchange Rate in the Context of Structural Breaks

This paper examines the dynamic relationship between crude oil prices and the U.S. exchange rate within the structural break detection context. Based on monthly data from January 1996 to April 2019, this paper identifies structural breaks in movements of oil price and examines the dynamic relationship between crude oil prices and the U.S. exchange rate movement by introducing the economic policy uncertainty and using the TVP-VAR (Time-Varying Parameter-Vector Auto Regression ) model. Empirical results indicate that shocks to crude oil prices have immediate and short-term impacts on movements in the exchange rate which are emphasized during the confidence intervals of structural breaks. Oil price shocks and economic policy uncertainty are interrelated and influence movements in the U.S. exchange rate. Since the U.S. dollar is the main currency of the international oil market and the U.S. has become a major exporter of crude oil, the transmission of price shocks to the U.S. exchange rate becomes complicated. In most cases, the relationship between oil prices and the U.S. exchange rate movements is negative.

Identifying latent group structures in nonlinear panels

We propose a procedure to identify latent group structures in nonlinear panel data models where some regression coefficients are heterogeneous across groups but homogeneous within a group and the group number and membership are unknown. To identify the group structures, we consider the order statistics for the preliminary unconstrained consistent estimators of the regression coefficients and translate the problem of classification into the problem of break detection. Then we extend the sequential binary segmentation algorithm of Bai (1997) for break detection from the time series setup to the panel data framework. We demonstrate that our method is able to identify the true latent group structures with probability approaching one and the post-classification estimators are oracle-efficient. The method has the advantage of more convenient implementation compared with some alternative methods, which is a desirable feature in nonlinear panel applications. To improve the finite sample performance, we also consider an alternative version based on the spectral decomposition of certain estimated matrix and link the group identification issue to the community detection problem in the network literature. Simulations show that our method has good finite sample performance. We apply this method to explore how individuals’ portfolio choices respond to their financial status and other characteristics using the Netherlands household panel data from year 1993 to 2015, and find three latent groups.

Homogenizing GPS Integrated Water Vapor Time Series: Benchmarking Break Detection Methods on Synthetic Data Sets

AbstractWe assess the performance of different break detection methods on three sets of benchmark data sets, each consisting of 120 daily time series of integrated water vapor differences. These differences are generated from the Global Positioning System (GPS) measurements at 120 sites worldwide, and the numerical weather prediction reanalysis (ERA‐Interim) integrated water vapor output, which serves as the reference series here. The benchmark includes homogeneous and inhomogeneous sections with added nonclimatic shifts (breaks) in the latter. Three different variants of the benchmark time series are produced, with increasing complexity, by adding autoregressive noise of the first order to the white noise model and the periodic behavior and consecutively by adding gaps and allowing nonclimatic trends. The purpose of this “complex experiment” is to examine the performance of break detection methods in a more realistic case when the reference series are not homogeneous. We evaluate the performance of break detection methods with skill scores, centered root mean square errors (CRMSE), and trend differences relative to the trends of the homogeneous series. We found that most methods underestimate the number of breaks and have a significant number of false detections. Despite this, the degree of CRMSE reduction is significant (roughly between 40% and 80%) in the easy to moderate experiments, with the ratio of trend bias reduction is even exceeding the 90% of the raw data error. For the complex experiment, the improvement ranges between 15% and 35% with respect to the raw data, both in terms of RMSE and trend estimations.

Application of homogenization methods for Ireland's monthly precipitation records: Comparison of break detection results.

Time series homogenization for 299 of the available precipitation records for the island of Ireland (IENet) was performed. Four modern relative homogenization methods, that is, HOMER, ACMANT, CLIMATOL and AHOPS were applied to this network of station series where contiguous intact monthly records range from 30 to 70 years within the base period 1941–2010. Break detection results are compared between homogenization methods, and coincidences with available documentary information (metadata) were analysed. The lowest (highest) number of breaks were detected with HOMER (ACMANT). Large differences of break frequency were found, namely ACMANT and AHOPS detected 8 times as many breaks than HOMER, while the break frequency with CLIMATOL was intermediate. Also, the ratio of series classified to be homogeneous varies widely between the methods. It is 85% with HOMER, 60% with CLIMATOL, 31% with AHOPS, while only 22% with ACMANT. In a further experiment, all the available time series for Ireland and Northern Ireland, (910 series) were used with ACMANT and CLIMATOL to explore the stability of break frequency for the same 299 series examined in the base experiment. While overall break frequency slightly increased (by 6–13%), the break positions notably changed for individual time series. The number of breaks changed for 59% (23%) of the series with ACMANT (CLIMATOL). For the breaks detected coincidentally by at least three methods including ACMANT and CLIMATOL in the base experiment, the second experiment confirmed the break positions in 86–87% of the breaks. The consequences of these results in relation to the reliability of statistical homogenization are discussed. Sometimes markedly different step functions provide comparable good approaches. However, the accuracy of homogenized time series cannot be related directly to the instability of break detection results.

Ecotoxicity of malathion pesticide and its genotoxic effects over the biomarker comet assay in Daphnia magna.

Malathion is a highly toxic organophosphate insecticide, being one of the most widely used in the world and is generally used for insect control in food production. Thus, ecotoxicological studies have been used to verify its toxic effects on aquatic organisms, such as Daphnia magna and biomarkers, as the comet assay. The comet assay is a microgel electrophoresis method for the detection and quantification of DNA strand breaks in individual cells. Cells were obtained from Daphnia magna after disaggregation of newborn organisms, exposed at concentrations of 0.23μgL-1 and 0.47μgL-1 for 48h. Malathion has shown to cause damage to DNA of the exposed organisms. It was also observed the need of further studies to standardize the comet assay technique for Daphnia magna, once methodologies used present several differences.

Simulation-based training to enhance process safety in offshore energy operations: Process tracing through eye-tracking

Systemic risks and human errors are the root causes of process safety incidents in offshore operations. Human factor amounts for more than 70 % of offshore incidents and among those 75 % of them are due to perceptual errors involving rich information displays. Offshore drilling console is one such area where a display of hundreds of parameters and alarms maintains a constant cognitive load on the driller and upon his slight oblivion, severe consequences can follow. One way to study these errors is through a cognitive approach that focuses on the information accusation patterns rather than a behaviorist approach of focusing on the decision outcomes. In this research, we attempt to apply process tracing through eye-tracking methodology to look at information acquisition patterns of participants during a well control simulation. The simulation was designed on a commercial well control drilling simulator to observe the difference in performances by tracking information acquisition patterns of expert and novice participants. Fourteen (n=14) participants were paired into eight teams with each comprising of a driller and a supervisor. Eye-tracking glasses were used to record the fixation and saccade pattern of the participants over the simulator console and displays. For the analysis phase, four time of interests (TOIs) were selected as drilling break detection, kick detection, kick control, and kick circulation. For these TOIs the fixation and saccades pattern of participants on various areas of interest (AOIs) were analyzed through heat and gaze plots. Significant differences in information acquisition patterns of the novice and expert participants were linked to their performance to reveal valuable insights. Such insights are useful in identifying the dominating factors behind performance and can be used for targeted feedback to improve well control training and evaluation.

Robust multivariate change point analysis based on data depth

AbstractModern methods for detecting changes in the scale or covariance of multivariate distributions rely primarily on testing for the constancy of the covariance matrix. These depend on higher‐order moment conditions, and also do not work well when the dimension of the data is large or even moderate relative to the sample size. In this paper, we propose a nonparametric change point test for multivariate data using rankings obtained from data depth measures. As the data depth of an observation measures its centrality relative to the sample, changes in data depth may signify a change of scale of the underlying distribution, and the proposed test is particularly responsive to detecting such changes. We provide a full asymptotic theory for the proposed test statistic under the null hypothesis that the observations are stable, and natural conditions under which the test is consistent. The finite sample properties are investigated by means of a Monte Carlo simulation, and these along with the theoretical results confirm that the test is robust to heavy tails, skewness and high dimensionality. The proposed methods are demonstrated with an application to structural break detection in the rate of change of pollutants linked to acid rain measured in Turkey lake, a lake in central Ontario, Canada. Our test suggests a change in the rate of acid rain in the late 1980s/early 1990s, which coincides with clean air legislation in Canada and the US. The Canadian Journal of Statistics 48: 417–446; 2020 © 2020 Statistical Society of Canada

Profile-Splitting Linearized Bregman Iterations for Trend Break Detection Applications

Trend break detection is a fundamental problem that materializes in many areas of applied science, where being able to identify correctly, and in a timely manner, trend breaks in a noisy signal plays a central role in the success of the application. The linearized Bregman iterations algorithm is one of the methodologies that can solve such a problem in practical computation times with a high level of accuracy and precision. In applications such as fault detection in optical fibers, the length N of the dataset to be processed by the algorithm, however, may render the total processing time impracticable, since there is a quadratic increase on the latter with respect to N. To overcome this problem, the herewith proposed profile-splitting methodology enables blocks of data to be processed simultaneously, with significant gains in processing time and comparable performance. A thorough analysis of the efficiency of the proposed methodology stipulates optimized parameters for individual hardware units implementing the profile-splitting. These results pave the way for high performance linearized Bregman iteration algorithm hardware implementations capable of efficiently dealing with large datasets.

Evaluation of machine learning models for automatic detection of DNA double strand breaks after irradiation using a γH2AX foci assay.

Ionizing radiation induces amongst other the most critical type of DNA damage: double-strand breaks (DSBs). Efficient repair of such damage is crucial for cell survival and genomic stability. The analysis of DSB associated foci assays is often performed manually or with automatic systems. Manual evaluation is time consuming and subjective, while most automatic approaches are prone to changes in experimental conditions or to image artefacts. Here, we examined multiple machine learning models, namely a multi-layer perceptron classifier (MLP), linear support vector machine classifier (SVM), complement naive bayes classifier (cNB) and random forest classifier (RF), to correctly classify γH2AX foci in manually labeled images containing multiple types of artefacts. All models yielded reasonable agreements to the manual rating on the training images (Matthews correlation coefficient >0.4). Afterwards, the best performing models were applied on images obtained under different experimental conditions. Thereby, the MLP model produced the best results with an F1 Score >0.9. As a consequence, we have demonstrated that the used approach is sufficient to mimic manual counting and is robust against image artefacts and changes in experimental conditions.

<p>NVP-BSK805, an Inhibitor of JAK2 Kinase, Significantly Enhances the Radiosensitivity of Esophageal Squamous Cell Carcinoma in vitro and in vivo</p>

PurposeRadiotherapy is one major curative treatment modality for esophageal squamous cell carcinoma (ESCC) patients. This study aimed to find out small-molecular kinase inhibitors, which can significantly enhance the radiosensitivity of ESCC in vitro and in vivo.Materials and MethodsNinety-three kinase inhibitors were tested for their radiosensitizing effect in ESCC cells through high-content screening. The radiosensitizing effect of kinase inhibitors was investigated in vitro by detection of DNA double-strand breaks (DSBs) and clonogenic survival assay. By the establishment of xenograft tumor models in BALB/c nude mice, the radiosensitizing effect of kinase inhibitors was investigated in vivo.ResultsAmong the 93 kinase inhibitors tested, we found NVP-BSK805, an inhibitor of JAK2 kinase, significantly radiosensitized ESCC cells through enhancing DSBs, inhibiting DNA damage repair and arresting cell cycle in G2/M or G0/G1 phase. After treatment with NVP-BSK805, ESCC cells showed decreased clonogenic survival and delayed tumor growth in vivo. JAK2 kinase was highly expressed in tumor tissues of ESCC patients, while rarely expressed in matched normal esophageal epithelial tissues. Survival analysis revealed JAK2 kinase as a prognostic factor of ESCC patients treated with chemoradiotherapy.ConclusionOur study discovered JAK2 kinase as an attractive target to enhance the radiosensitivity of ESCC cells in vitro and in vivo.

A bootstrap bias correction of long run fourth order moment estimation in the CUSUM of squares test

ABSTRACTThe aim of this paper is to propose a bias correction of the estimation of the long run fourth order moment in the CUSUM of squares test proposed in Sansó et al. [Testing for change in the unconditional variance of financial time series. Rev Econ Financ. 2004;4(1):32–53] for the detection of structural breaks in financial data. The correction is made by using the stationary bootstrap. The choice of this resampling technique is justified by the stationarity and weak dependence of the time series under the assumptions which ensure the existence of the limiting distribution of the test statistic, under the null hypothesis. Monte Carlo experiments have been implemented to evaluate the effect of the proposed bias correction considering two particular data generating processes, the GARCH(1,1) and the log-Normal Stochastic Volatility. The effectiveness of the bias correction has been evaluated also on real data sets.