Significant Change Points Research Articles

Scale dependency of lidar‐derived forest structural diversity

AbstractLidar‐derived forest structural diversity (FSD) metrics—including measures of forest canopy height, vegetation arrangement, canopy cover (CC), structural complexity and leaf area and density—are increasingly used to describe forest structural characteristics and can be used to infer many ecosystem functions. Despite broad adoption, the importance of spatial resolution (grain and extent) over which these structural metrics are calculated remains largely unconsidered. Often researchers will quantify FSD at the spatial grain size of the process of interest without considering the scale dependency or statistical behaviour of the FSD metric employed.We investigated the appropriate scale of inference for eight lidar‐derived spatial metrics—CC, canopy relief ratio, foliar height diversity, leaf area index, mean and median canopy height, mean outer canopy height, and rugosity (RT)‐‐representing five FSD categories—canopy arrangement, CC, canopy height, leaf area and density, and canopy complexity. Optimal scale was determined using the representative elementary area (REA) concept whereby the REA is the smallest grain size representative of the extent. Structural metrics were calculated at increasing canopy spatial grain (from 5 to 1000 m) from aerial lidar data collected at nine different forested ecosystems including sub‐boreal, broadleaf temperate, needleleaf temperate, dry tropical, woodland and savanna systems, all sites are part of the National Ecological Observatory Network within the conterminous United States. To identify the REA of each FSD metric, we used changepoint analysis via segmented or piecewise regression which identifies significant changepoints for both the magnitude and variance of each metric.We find that using a spatial grain size between 25 and 75 m sufficiently captures the REA of CC, canopy arrangement, canopy leaf area and canopy complexity metrics across multiple forest types and a grain size of 30–150 m captures the REA of canopy height metrics. However, differences were evident among forest types with higher REA necessary to characterize CC in evergreen needleleaf forests, and canopy height in deciduous broadleaved forests.These findings indicate the appropriate range of spatial grain sizes from which inferences can be drawn from this set of FSD metrics, informing the use of lidar‐derived structural metrics for research and management applications.

An empirical study on assessment of trend analysis: Food grain production in India

Considering the importance of the food grains to be specified-rice, wheat, coarse cereals, and pulses-is vital in developing the Indian economy. This paper carries out change point detection and trend analysis for the annual historical series of food grains in India. The change points were identified by the methods of the Pettitt?s test (PT), the Buishand?s range test (BRT), and the standard normal homogeneity test (SNHT). On the other hand, the Mann-Kendall (MK) test was used to determine the presence and absence of trends in food grain production, and their slopes were assessed by the Sen?s slope estimator. The change point analysis result shows significant change points for food grains at a 95% level. The discrete change points were observed from 1982 to 2009, but the ideal change point, i.e., 1987, was captured in all food grains. In India?s history, agricultural production in the year 1987 was vulnerable due to drought, and thus it is strong evidence of the need to consider methods based on proven facts. The MK test results reveal that the trend in food grain production was statistically significant and had an upward direction in all three segmentation periods after 1987.

Trends and Variabilities in Rainfall and Streamflow: A Case Study of the Nilwala River Basin in Sri Lanka

Rainfall is one of the dominating climatic parameters that affect water availability. Trend analysis is of paramount significance to understand the behavior of hydrological and climatic variables over a long timescale. The main aim of the present study was to identify trends and analyze existing linkages between rainfall and streamflow in the Nilwala River Basin (NRB) of Southern Sri Lanka. An investigation of the trends, detection of change points and streamflow alteration, and linkage between rainfall and streamflow were carried out using the Mann–Kendall test, Sen’s slope test, Pettitt’s test, indicators of hydrological alteration (IHA), and Pearson’s correlation test. Selected rainfall-related extreme climatic indices, namely, CDD, CWD, PRCPTOT, R25, and Rx5, were calculated using the RClimdex software. Trend analysis of rainfall data and extreme rainfall indices demonstrated few statistically significant trends at the monthly, seasonal, and annual scales, while streamflow data showed non-significant trends, except for December. Pettitt’s test showed that Dampahala had a higher number of statistically significant change points among the six rainfall stations. The Pearson coefficient correlation showed a strong-to–very-strong positive relationship between rainfall and streamflow. Generally, both rainfall and streamflow showed non-significant trend patterns in the NRB, suggesting that rainfall had a higher impact on streamflow patterns in the basin. The historical trends of extreme climatic indices suggested that the NRB did not experience extreme climates. The results of the present study will provide valuable information for water resource planning, flood and disaster mitigation, agricultural operations planning, and hydropower generation in the NRB.

Quantifying the impact of climate change and human activities on the eco-hydrological regimes of the Weihe River Basin, Northwest China

Abstract Climate change and anthropogenic interventions have obviously altered the eco-hydrological regimes. A quantitative evaluation and attribution of the eco-hydrological alterations are urgently required. In this study, we evaluated the various attributions of eco-hydrological regimes in the Weihe River Basin (WRB). Firstly, the trends and change-point analysis of hydrological elements were examined, and the natural streamflow was reproduced based on the variable infiltration capacity model. Then, the most ecologically relevant hydrological indicators (ERHIs) were selected and combined with the eco-deficit and eco-surplus indicators to assess the degree of eco-hydrological regime alterations. Finally, the relative contributions to eco-hydrological alterations were quantified using the ‘simulated–observed comparison’ method. The results showed that (1) the streamflow of the WRB exhibited significant decreasing trends (p < 0.01), and a significant change point (p < 0.01) of the streamflow series was identified in 1990. (2) Seven representative indicators of hydrological alteration were selected as ERHIs. (3) During the human-induced period (1991–2017), human activities were the dominant factors in the eco-hydrological alterations as well as the variations of the ERHI indexes and the eco-deficit and eco-surplus metrics. Overall, the proposed framework may improve the understanding of the driving forces of eco-hydrological regime alterations under a changing environment.

Spatiotemporal variability of rainfall erosivity and its teleconnection with atmospheric circulation in monsoon-driven climate region

Understanding the spatiotemporal variability of annual, monthly, and seasonal rainfall erosivity (RE) is crucial for determining the risk of soil erosion and developing soil conservation plans. However, little knowledge is available on RE at the monthly, seasonal, and annual scales in monsoon-driven climate regions like Bangladesh. Therefore, we aimed to assess the spatiotemporal variability of RE in Bangladesh from 1980 to 2017 and analyze teleconnection of RE with large-scale atmospheric circulation indices. The detrended fluctuation analysis (DFA) and cross wavelet analysis were employed to evaluate the potential future trends and interpret the periodic cycles in the RE, respectively. The results demonstrate that mean annual RE values fluctuated from 12574.16 MJ.mm.ha-1.h-1 to 123914.76 MJ.mm.ha-1.h-1, while RE has been lower in recent decades than in the earlier decades. The mean annual RE is decreasing all over the country, and significant abrupt change points occurred in the early 2000 s. The highest mean annual RE was observed in the southernmost part of Bangladesh and the lowest in the western part of the country. The results of DFA indicated that the RE trend would sustain its present trend in the upcoming period, except for May and July. The effects of Niño3.4 SST and IOD, SASMI, and EASMI have almost no to minimal influence on the RE trend in Bangladesh. However, a noticeable impact was found on the monthly scale, which could be why the observed intra-annual variability of RE was observed. We found that the average RE has reduced significantly with increasing distance from the sea, latitude, and elevation in Bangladesh. The study found that increasing geopotential height in the summer and decreasing geopotential height in the winter with enough moisture divergence, decreasing convective precipitation, decreasing large-scale rain rate, decreasing specific cloud liquid water content, and decreasing explicit rainwater content all contributed to RE variability and change in the country.

Detecting and explaining long‐term changes in river water quality in south‐eastern Australia

AbstractUnderstanding the temporal changes in river water quality is important for catchment water quality management. This study aims to detect and attribute long‐term trends and abrupt changes in river water quality. We used 26 years of water quality data (1994–2020) collected from 102 river monitoring sites across Victoria, south‐eastern Australia. We analysed six water quality constituents that are of key concerns for Australian catchment management, namely: electrical conductivity (EC), total suspended solids, nitrate‐nitrite, total Kjeldahl nitrogen, total phosphorous and filtered reactive phosphorus. To detect trends and abrupt changes in water quality at each site, a Bayesian ensemble modelling approach was applied, namely, the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST). To explain water quality trends, we then built multivariate regressions to link water quality with streamflow and seasonality, and then compared alternative model structures with and without a change in the regression relationships informed by the changes detected by BEAST. Among the six constituents studied, EC shows the most distinct systematic trends, with 21 sites having a significant increase followed by a non‐significant trend; within the 21 sites, 14 had a significant change point in EC around Year 2010. The regression analyses between water quality and streamflow suggested that the observed systematic change in EC could be largely related to reduced streamflow during the Millennium drought, which greatly impacted the climate and hydrology of south‐eastern Australia over the first decade of 2000. The results of this study can help inform the design of effective mitigation strategies and avoid further degradation of water quality across Victoria. Besides, our trend analysis and attribution approaches are applicable to water quality time series in other regions for robust trend analysis and change point detection.

Quantitative Evaluation of Aerosol Generation During In-Office Flexible Laryngoscopy

Despite growing scientific knowledge and research, it is still unknown if office flexible laryngoscopy (FL) is aerosol generating and thereby potentially increases the risk of SARS-CoV-2 transmission. The limited literature that exists is conflicting, precluding formal conclusions. To determine whether FL is aerosol generating. This prospective cohort study included 134 patients seen in the otolaryngology clinic at a single tertiary care academic institution between February and May 2021. Two optical particle sizer instruments were used, quantifying particles ranging from 0.02 μm to 5 μm. Measurements were taken every 30 seconds, with sample periods of 15 seconds throughout the patient encounter. Instruments were located 12 inches from the patient's nares. Timing of events was recorded, including the start and end of physical examination, topical spray administration, start and end of laryngoscopy, and other potential aerosol-generating events (eg, coughing, sneezing). Data analysis was performed from February to May 2021. Office examination and office FL. Bayesian online change point detection (OCPD) algorithm was used to detect significant change points (CPs) in this time-series data. The primary outcome was significant CP after FL compared with baseline physiologic variations, such as breathing and phonation. Data were collected from 134 patients between February and May 2021. Ninety-one encounters involved FL. Of this group, 51 patients (56%) wore no mask over their mouth during FL. There was no statistically significant CP in either visits involving FL or visits where FL was not performed. Use of nasal spray did not result in CP in aerosol levels. Overall, neither the number of people present in the examination room, masks over patients' mouth, the duration of the visit, nor the duration of FL were associated with mean aerosol counts, regardless of the exposure. For larger aerosol sizes (≥1 μm), however, rooms with higher air exchange rates had significantly higher reductions in mean aerosol counts for visits involving FL. The findings of this cohort study support that FL, including topical spray administration, is not a significant aerosol-generating procedure. The Bayesian OCPD model has a promising application for future aerosol studies in otolaryngology.

Lumpy Skin Disease Outbreaks in Africa, Europe, and Asia (2005-2022): Multiple Change Point Analysis and Time Series Forecast.

LSD is an important transboundary disease affecting the cattle industry worldwide. The objectives of this study were to determine trends and significant change points, and to forecast the number of LSD outbreak reports in Africa, Europe, and Asia. LSD outbreak report data (January 2005 to January 2022) from the World Organization for Animal Health were analyzed. We determined statistically significant change points in the data using binary segmentation, and forecast the number of LSD reports using auto-regressive moving average (ARIMA) and neural network auto-regressive (NNAR) models. Four significant change points were identified for each continent. The year between the third and fourth change points (2016–2019) in the African data was the period with the highest mean of number of LSD reports. All change points of LSD outbreaks in Europe corresponded with massive outbreaks during 2015–2017. Asia had the highest number of LSD reports in 2019 after the third detected change point in 2018. For the next three years (2022–2024), both ARIMA and NNAR forecast a rise in the number of LSD reports in Africa and a steady number in Europe. However, ARIMA predicts a stable number of outbreaks in Asia, whereas NNAR predicts an increase in 2023–2024. This study provides information that contributes to a better understanding of the epidemiology of LSD.

Predictors of Treatment Completion in an Inpatient Substance Use Treatment Service in India.

Treatment completion is associated with a better outcome in substance use disorders. We examined the rates of treatment completion and its predictors in patients admitted to specialized addiction treatment settings over a 13-year period. Ours was a retrospective cohort study. We included consecutive 2850 patients admitted to the inpatient treatment between January 2007 and December 2019. We divided the patients into 2 groups: completed versus premature discontinuation of treatment. The predictor variables were based on previous research, clinical experience, and availability of the digital record. The number of patients who completed and discontinued treatments was 1873 (72.6%) and 707 (27.4%), respectively. The inpatient treatment discontinuation rate varied widely during the study period (18% in 2007 and 41% in 2012). The average rate of treatment discontinuation was 27%. The change-point analysis showed 5 statistically significant change points in the years 2008, 2010, 2012, 2014, and 2016. Patients who were prescribed medications for alcohol and opioid dependence and those who were on opioid agonist treatment had 4.7 and 6.3 higher odds of completing inpatient treatment than those who were not on medication. Patients with physical and psychiatric comorbidities had higher odds of treatment completion. Patients with a primary diagnosis of opioid dependence had lower odds of treatment completion than those with alcohol dependence. The rates of discontinuation may vary with concurrent changes in the treatment policies. Awareness of the risk factors and policy measures that may improve treatment completion must aid in informed decision making.

Bayesian dynamic programming approach for tracking time-varying model properties in SHM

Structural health monitoring (SHM) quite often involves continually tracking the temporal variation of some properties of interest, where statistical information about variations under normal situation can be obtained and potential anomalies may be detected for further attention. Structural-related properties such as natural frequency and stiffness often need to be identified with a physics-based model that relates them to measured data. Conventional approaches empirically divide data into non-overlapping segments with equal lengths and identify the model parameters within each segment based on a time-invariant model. Potentially time-varying model properties are then investigated based on the variation of identified results from one data segment to another. Challenges do exist, e.g., how to choose segment length to balance modeling error and identification accuracy, how to set criterion for anomaly detection, etc., which should be addressed with proper extraction of probabilistic information from data. In this work, the SHM problem is formulated as a Bayesian model selection problem, where the data can be ‘partitioned’ in an arbitrary manner, whose optimal choice, and hence points of significant change, are determined together with the model inference process by maximizing the probabilistic evidence supported by data. An efficient algorithm based on dynamic programming is proposed to determine the optimal partitioning and associated piecewise-constant properties, which is otherwise computationally prohibitive. The methodology is applied to tracking modal properties, e.g., natural frequency and damping ratio, of structures using output-only ambient vibration data. It is investigated with synthetic data and then applied to field data of a tall building during a typhoon event.

Impact of the cervical cancer awareness months on public interest in Japan: A Google Trends analysis, 2012–2021

The immunization and screening rates for human papillomavirus in Japan are lower than those in other countries. We aimed to evaluate the impact of cervical cancer awareness months on public attention using Google Trends analysis. Between 2012 and 2021, we analyzed the trends in relative search volumes (RSVs) for “Shikyuu-keigan” (cervical cancer in English) in Japan, during the cervical cancer awareness month (CCAM) in January and cervical cancer prevention awareness enhancement month (CCPAEM) in November. We performed a joinpoint regression analysis to identify a statistically significant trend change point. Additionally, we compared the mean RSVs of each awareness month with the rest of the year. Significant trend change points were observed, but none were found in CCAM and CCPAEM periods. Comparison of mean RSVs among CCAM, CCPAEM, and the rest of the months did not suggest any significant increases in RSVs during these awareness periods. In conclusion, CAM and CCPAEM did not raise public interest in cervical cancer in Japan. Although the results are based on internet users, the findings might suggest a need to develop a more effective and attractive approach to achieve the 90-70-90 targets of cervical cancer prevention by 2030.

Nonstationary flood and low flow frequency analysis in the upper reaches of Huaihe River Basin, China, using climatic variables and reservoir index as covariates

Conventional water infrastructure designs for flood and low flows are usually based on the assumption of stationarity of extreme events. However, recent evidence suggests that the influences of climate variability and human activities have made the hypothesis of stationarity questionable. In this study, we used the generalized additive models for location, scale, and shape (GAMLSS) to construct a nonstationary model in which the parameters of the selected distributions were modelled as a function of climatic variables (i.e., climate indices and precipitation) and/or the reservoir index (RI). The nonstationary models were then used to analyse annual flood and low flow frequency at four hydrological stations in the upper reaches of the Huaihe River Basin, including Dapoling (DPL), Changtaiguan (CTG), Zhuganpu (ZGP), and Xixian (XX) stations. Annual floods were represented by the maximum daily streamflow in each year, and low flows were represented by the 95th quantile of the daily streamflow (Q95) in each year. The change point and trend analysis revealed that the flood series of the ZGP station and the low flow series of the DPL and XX stations exhibited significant downward and upward trends (p < 0.1)), respectively. The low flow series of the ZGP station showed a significant change point in 1980 (p < 0.1). GAMLSS modelling results showed that, in comparison with stationary models, nonstationary models that included precipitation and the Arctic Oscillation climate index as covariates for the gamma distribution location parameter provided a superior description of the flood series at the four stations. Nonstationary models that incorporated precipitation and/or RI as covariates for the Weibull distribution parameters fit the low flow series better than stationary models at all stations. Furthermore, we found that nonstationary models outperformed stationary models in terms of flood frequency analysis, covering all flood observation points and capturing the generally decreasing trend in flood series, as well as a decrease in the scatter of estimated flood value magnitudes. For the low flow frequency analysis, the comparison results showed that the nonstationary and stationary models performed identically for the DPL, CTG, and XX stations, where no significant change point was detected. However, for the ZGP station, where a significant change point was detected, the nonstationary models performed better than the stationary models and could accurately capture the changes in the magnitude of the estimated low flow values before and after the change point. Overall, the proposed nonstationary model can serve as a tool for nonstationary frequency analysis of flood and low flow series under the influence of climate variability and reservoir regulations, thus providing a reference for regional water infrastructure design.

National reduction in cerebral arteriovenous malformation treatment correlated with increased rupture incidence

BackgroundRecently, there has been a shift in management of unruptured cerebral arteriovenous malformations (AVMs) following studies suggesting that medical management alone was superior to interventional therapy.ObjectiveTo evaluate the influence of...

Secular changes in metamorphism and metamorphic cooling rates track the evolving plate-tectonic regime on Earth

When disturbed, dynamic emergent systems, such as tectonics on Earth, may transition from one stable state to another if the perturbation is sufficiently large. Here, we identify such state shifts through examination of statistically significant change points in the time series of metamorphic pressure–temperature and cooling rate data. Change points occur in the mid-Paleoproterozoic, the Mesoproterozoic, the early and late Paleozoic and the Cenozoic. To compare the timing of change points with mantle geodynamics, we interrogate the time series of calculated mantle potential temperature, which yields a statistically significant drop of &gt;130°C in the mid-Paleoproterozoic, indicating a change in the mechanism of mantle cooling. We interpret changes in the mid-Paleoproterozoic to relate to the global emergence of stable subduction, orogenesis associated with the formation of the Nuna megacontinent, and slab breakoff, consistent with the operation of a distinctive style of Proterozoic plate tectonics. By contrast, changes from the dawn of the Cambrian onward may relate to the large volume of sediments supplied to trenches following multiple Cryogenian and Paleozoic glaciations. Sediments acted as a lubricant, allowing deeper subduction, transport of lower continental plates to mantle depths and faster metamorphic cooling rates, features characteristic of modern plate tectonics. Supplementary material: A dataset of metamorphic cooling rates is available at https://doi.org/10.6084/m9.figshare.c.5943418

Analysis of changes in large‐scale circulation patterns driving extreme precipitation events over the central‐eastern China

AbstractTo an extent, large‐scale circulation situations and moisture transport are responsible for extreme precipitation occurrence. The aim of our study is to investigate the possible modifications of circulation patterns (CPs) in driving extreme precipitation over the central‐eastern China (CEC). The self‐organizing map (SOM) and event synchronization methods are used to link the extreme precipitation events with CPs. Results show that 23% of rain gauges have a significant change point (at the 90% confidence level) in annual extreme precipitation from 1960 to 2015. Based on the identified change points, we classified the data into two periods, that is, 1960–1989 and 1990–2015. Overall, CPs characterized by obvious positive anomalies of 500 hPa geopotential height over the Eastern Eurasia continent and negative values over the surrounding oceans are highly synchronized with extreme precipitation events. During 1990–2015, the predominant CPs are more related to the extreme precipitation with enhanced event synchronization. We found that the CP changes produce an increase in extreme precipitation frequency from 1960–1989 to 1990–2015.

A space-embedding strategy for anomaly detection in multivariate time series

Anomaly detection of time series has always been a hot topic in academia and industry. However, many existing multivariant time series methods suffer from common challenges due to increased dimensionality. In this study, we developed a space-embedding strategy for anomaly detection in multivariant time series (SES-AD). As a hybrid model, SES-AD did not directly search the discords from the original time series, but projected the raw sequence to a lower dimensional space so that the significant abrupt change points in the new space can be easily captured from the dissimilarity vector. Finally, the potential abnormities were determined by a statistical strategy. To verify the performance of our method, SES-AD was applied to an extensive number of multivariate time series. The experimental results suggest that SES-AD is more efficient than five existing approaches. Overall, the SES-AD model is suitable for solving anomaly detection for high-dimensional datasets and guarantees computational effectiveness and accuracy.

Change-point analysis of lambda-cyhalothrin efficacy against soybean aphid (Aphis glycines Matsumura): identifying practical resistance from field efficacy trials.

BackgroundSoybean aphid (Aphis glycines Matsumura) remains the most economically important arthropod pest of soybean in the Upper Midwest Region of the USA. Soybean aphid resistance to the pyrethroid insecticides emerged in 2015; however, the reduction in the efficacy of field applications of pyrethroid insecticides has not been quantified. Based on time‐series data from insecticide efficacy trials at two locations, a novel approach of continuous two‐phase change point‐regression models was used to indicate whether a change in percent control had occurred, and to provide an indication of when and to what degree the percent control had changed.ResultsAt both locations examined in this study, a significant change point for percent control of λ‐cyhalothrin was detected in 2014, thus marking the onset of practical resistance in the soybean aphid. Percent control decreased at a rate of 4.30% and 19.90% per year at these locations. By contrast, percent control for chlorpyrifos remained high over time with no significant change point.ConclusionThis research demonstrates that retrospective time‐series analysis of insecticide efficacy data can identify the onset and magnitude of practical resistance in the field. This further validates and compliments the other lines of evidence related to pyrethroid resistance in soybean aphid. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Decreasing flood hazard evaluated in Turkey using nonstationary models

AbstractAn assessment of changing flood hazard in the river basins of Turkey is currently lacking. This study evaluates the drivers of flood flows using distributional regression models. The generalized additive models for location, scale, and shape (GAMLSS), a flexible framework well‐suited to probabilistic nonstationary modeling, is applied to annual instantaneous maximum flows (AIMF) from 12 gauging stations located in the upper, middle and lower parts of the Euphrates basin, the most important river basin in the Middle East. First, the Pettitt test was used to detect abrupt changes in the AIMF data series, and the Mann–Kendall test to evaluate temporal trends. Significant change points for two stations and significant decreasing temporal changes for seven stations were observed. Three strictly positive, continuous distributions (namely, Gamma, Lognormal, and Weibull) were then fitted to the AIMF data series and the distribution parameters were specified as functions of both time and physically based covariates (precipitation, temperature, and climate oscillation indices). Among the candidate distributions, the Lognormal was found to be the most appropriate for describing flood flows across the basin. Annual precipitation, seasonal temperature and annual oscillation indices were the most relevant covariates. Among the climate indices, the East Atlantic Oscillation was most relevant in the lower part of the basin, the Southern and North Atlantic Oscillations in the middle, and the East Atlantic‐West Russia pattern in the upper part. The models were then employed to estimate and compare historical stationary flood quantiles with nonstationary quantiles (identified with physically based covariates) under various return periods (10, 20, 50, 100, and 200 years). Results indicate that flood processes can be better described by employing distributional regression models with physical covariates and this study can serve as a reference for regional water resources management in the basin and possibly for other river basins.

The multifaceted history of the Walker Circulation during the Plio-Pleistocene

The Walker Circulation (WC) is an east-west trending band of atmospheric circulation cells along the equator and the predominant controller of heat and moisture transport in the tropics. Its variability is closely linked to the sea-surface temperature (SST) changes across the Pacific, the Indian and the Atlantic Oceans and can have pronounced effects on the humidity regimes of the adjacent continents. In recent years, the evolution of the WC during the Plio- and Pleistocene epochs has been intensely studied in the context of the effectiveness of the tropics in modulating global climate change (e.g., the intensification of Northern Hemisphere glaciation).However, the onset of the modern WC pattern as well as its global impact during the Plio- and Pleistocene is controversially assessed in the literature. For its onset, previous studies have suggested dates ranging between 2.4 and 0.8 million years ago (Myr), while its argued impact ranges from – crucially influencing the increase of Northern Hemisphere ice sheet growth by channelling heat and moisture from the tropics into the high latitudes to – having no effect on global ice volume changes. In order to achieve a comprehensive understanding of the spatiotemporal evolution of the WC during this time frame, we statistically analysed 30 globally distributed SST records covering the low and high latitudes between 3.5 and 1.5 Myr, encompassing the Late Pliocene to Early Pleistocene. We utilized a statistical change-point regression model to determine significant change points in the SST evolution of the (sub)-tropics and high latitudes that potentially relate to changes in the WC. We find that the WC experienced a multifaceted evolution between the Late Pliocene and the Early Pleistocene with significant transitional steps at ∼2.7 and ∼2.1 - Ma. Our results suggest after the Late Pliocene, a pre-modern WC set in, which was characterized by a progressively strengthened Pacific Walker Cell alongside a weakened Indian Ocean Walker Cell. This change was potentially triggered by the constriction of the Indonesian seaway, an important transmitter between the Pacific and Indian Ocean. The ensuing mode of the WC intensified until ∼2.1 Myr, when SST values around the global scale signalled a progressive strengthening of the Indian Walker Cell in phase with the progressive strengthening of the Pacific and Atlantic Cells. Our findings indicate that a shift from a pre-modern to a modern-like WC potentially only occurred during the mid-Pleistocene.

165 National Reduction in Cerebral Arteriovenous Malformation Treatment Correlated with Increased Rupture Incidence

INTRODUCTION: In 2014 “A Randomized Trial of Unruptured Brain Arteriovenous Malformations” (ARUBA) concluded that medical management alone was superior to interventional therapy for adult patients with unruptured brain arteriovenous malformations (AVMs). METHODS: 154,297 weighted AVM admissions were identified in the United States from 2003 to 2017 in the National Inpatient Sample. Annual AVM intervention and rupture rates were computed and multivariable logistic regression assessed likelihood of AVM intervention pre- and post-ARUBA. Segmented regression identified significant change-points and fitted segmented linear models for annual intervention and rupture rates. Pearson and Spearman coefficients examined correlation between annual AVM intervention and rupture rates. RESULTS: For unruptured AVMs, intervention proportion and likelihood of receiving intervention decreased following ARUBA (28.1% to 22.3%, P &lt; 0.0001; adjusted odds ratio 0.857, 95% CI 0.751-0.977, P = 0.02). Ruptured AVM admissions increased from 14.7% to 18.6% after ARUBA (P &lt; 0.0001). Between 2003 and 2017, segmented linear regression identified one significant change-point in unruptured AVM intervention rate between 2014 and 2015. Average annual percent change for rupture incidence and intervention rate increased by 0.49% (P=0.0001) and decreased by 1.17% (P = 0.0001), respectively. Annual AVM intervention rates had a strong inverse correlation with annual AVM rupture incidence (Pearson coefficient = -0.82, P = 0.0002). In 2017, the annual AVM rupture rate (20.6%) surpassed the annual AVM intervention rate (19.7%) for the first time. CONCLUSION: Following ARUBA, the odds of intervention for unruptured AVMs have decreased while ruptured AVM incidence has increased. These findings are inversely correlated suggesting that less unruptured AVM treatments may lead to a substantial increase in AVM rupture incidence.