Leaching and fractionation of phosphorus in intensive greenhouse vegetable production soils

Greenhouse vegetable production systems use excessive phosphorus (P) fertilizer. This study is set out to look into the P fractionation, mobility, and risk of P leaching in ten greenhouse soils. The mean P concentrations in leachates varied from 0.4 to 1.6 mg l-1 (mean of 30 days of soil leaching). Between 5.7 and 31.0 mg kg-1 of P was leached from soils during 30 days of column leaching. Organic matter (OM) and Olsen-extractable P (Olsen P) correlated strongly with cumulative P leached after 5, 10, 15, 20, 25, and 30 days of leaching. The high correlation between OM and Olsen P with cumulative P leached at 5 days of leaching suggests that in future leaching experiments, the leaching period should be extended to 5 days of leaching. The first two P fractions correlated significantly with the total P leached in the primary days of leaching. The pH had little effect on P leaching but had a significant impact on soluble and exchangeable P fraction, suggesting that P mobility would increase in these calcareous greenhouse vegetable soils as pH rose. The calculated change point (194 mg kg-1) was high, indicating that a high percentage (40%) of the studied greenhouse soils had exceeded the change point. In conclusion, due to the high degree of P saturation and change point in greenhouse vegetable soils, P mobilization is a significant risk, and the findings can be used to provide future direction for fertilizing greenhouse vegetable soils.

Randomness accelerates the dynamic clearing process of the COVID-19 outbreaks in China

During the implementation of strong non-pharmaceutical interventions (NPIs), more than one hundred COVID-19 outbreaks induced by different strains in China were dynamically cleared in about 40 days, which presented the characteristics of small scale clustered outbreaks with low peak levels. To address how did randomness affect the dynamic clearing process, we derived an iterative stochastic difference equation for the number of newly reported cases based on the classical stochastic SIR model and calculate the stochastic control reproduction number (SCRN). Further, by employing the Bayesian technique, the change points of SCRNs have been estimated, which is an important prerequisite for determining the lengths of the exponential growth and decline phases. To reveal the influence of randomness on the dynamic zeroing process, we calculated the explicit expression of the mean first passage time (MFPT) during the decreasing phase using the relevant theory of first passage time (FPT), and the main results indicate that random noise can accelerate the dynamic zeroing process. This demonstrates that powerful NPI measures can rapidly reduce the number of infected people during the exponential decline phase, and enhanced randomness is conducive to dynamic zeroing, i.e. the greater the random noise, the shorter the average clearing time is. To confirm this, we chose 26 COVID-19 outbreaks in various provinces in China and fitted the data by estimating the parameters and change points. We then calculated the MFPTs, which were consistent with the actual duration of dynamic zeroing interventions.

Segmented correspondence curve regression for quantifying covariate effects on the reproducibility of high-throughput experiments

High-throughput biological experiments are essential tools for identifying biologically interesting candidates in large-scale omics studies. The results of a high-throughput biological experiment rely heavily on the operational factors chosen in its experimental and data-analytic procedures. Understanding how these operational factors influence the reproducibility of the experimental outcome is critical for selecting the optimal parameter settings and designing reliable high-throughput workflows. However, the influence of an operational factor may differ between strong and weak candidates in a high-throughput experiment, complicating the selection of parameter settings. To address this issue, we propose a novel segmented regression model, called segmented correspondence curve regression, to assess the influence of operational factors on the reproducibility of high-throughput experiments. Our model dissects the heterogeneous effects of operational factors on strong and weak candidates, providing a principled way to select operational parameters. Based on this framework, we also develop a sup-likelihood ratio test for the existence of heterogeneity. Simulation studies show that our estimation and testing procedures yield well-calibrated type I errors and are substantially more powerful in detecting and locating the differences in reproducibility across workflows than the existing method. Using this model, we investigated an important design question for ChIP-seq experiments: How many reads should one sequence to obtain reliable results in a cost-effective way? Our results reveal new insights into the impact of sequencing depth on the binding-site identification reproducibility, helping biologists determine the most cost-effective sequencing depth to achieve sufficient reproducibility for their study goals.

Manifestation of spatially varying demarcations in Indian rainfall trends through change-point analysis (1901-2020)

The rainfall over the Indian region, governed majorly by the monsoonal flow, is a point of research in the perspective of climate change. In this paper, we compute the change points in the rainfall series at every grid of the India Meteorological Department (IMD) daily gridded rainfall data for a period of 120 years (1901 to 2020). The map shows clearly demarcated regions indicating different zones, where the rainfall statistics have altered at different periods. It is observed that in a major part of central India, the shift in rainfall intensity is mainly associated with the time frame 1955-1965; in the Indo-Gangetic plain, the changes are found to be more recent (1990), while the latest changes (post 2000) are observed particularly for North Eastern region and some parts along the East Indian coast. The changeover years are significant at a 95% confidence level for most part of the Indian landmass. The causes may be surmised due to moisture transport from the Arabian Sea (Central India), the presence of aerosol (Gangetic Plain), and the possible revival of monsoon due to land-ocean gradient (Eastern coast and North East India). This is the first-ever study which provides a comprehensive daily rainfall change point map over India using 120 years of gridded station data.

Case study of rainfall and temperature assessment through trend and homogeneity analyses in Vadodara and Chhotaudepur district of Gujarat State, India

This study aims to assess the climate change impact on the rainfall and temperature data of the Vadodara-Chhotaudepur district of India and to focus on the environmental challenges related to the rainfall and temperature in the present state of development, land use, industrialization, and urbanization. The study utilized nine trend analysis methods, namely linear regression (LR), Sen's robust slope estimator (SS), Mann-Kendall test (MK), Spearman's rank correlation (SRC), the trend-free pre-whitening (TFPW), variance correction approach by Hamed and Rao (1998) with MK test (MK-CF1), variance correction approach by Yue and Wang (2004) with MK test (MK-CF2), block bootstrap with MK test (BBS-MK), and graphical method as innovative trend analysis (ITA), applied on monthly, annual, and seasonal scales. Additionally, the study also employed four homogeneity analysis methods, including Pettitt's test, standard normal homogeneity test (SNHT), Buishand's test, and Von Neumann Ratio test (VNRTs). The IMD (Indian meteorological department) gridded long-term rainfall data from 1901 to 2019 and temperature data from 1951 to 2019 are used in the present study to assess the homogeneity and trends of the data series. Results showed a warming trend of maximum temperature (MaxT) and minimum temperature (MinT) at the monthly, annual, and seasonal time scale and significant (at 5%) warming trend in annual MinT in the entire study area. Annual rainfall showed negative trend in the study area with significant (at 5%) negative trend in eastern deforested area western industrial and area adjacent to thermal power station. The change point is detected in annual rainfall time series in eastern forest area in 1959 and western area in 1983, i.e., after development of the industries and commissioning of thermal power station in the western study area. The trend rate of MaxT and MinT has been recognized as 0.004 °C/year and 0.019 °C/year, respectively, for the data period of 1951 to 2019. The annual rainfall trend rate has been observed as -0.743 mm/year for the data range 1901 to 2019. All trend analysis methods revealed consistent results except MK-CF2 method, which portraits greater number of significant trends in trend analysis methods.

On the association between COVID-19 vaccination levels and incidence and lethality rates at a regional scale in Spain

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19), has led to the deepest global health and economic crisis of the current century. This dramatic situation has forced the public health authorities and pharmaceutical companies to develop anti-COVID-19 vaccines in record time. Currently, almost 80% of the population are vaccinated with the required number of doses in Spain. Thus, in this paper, COVID-19 incidence and lethality rates are analyzed through a segmented spatio-temporal regression model that allows studying if there is an association between a certain vaccination level and a change (in mean) in either the incidence or the lethality rates. Spatial dependency is included by considering the Besag-York-Mollié model, whereas natural cubic splines are used for capturing the temporal structure of the data. Lagged effects between the exposure and the outcome are also taken into account. The results suggest that COVID-19 vaccination has not allowed yet (as of September 2021) to observe a consistent reduction in incidence levels at a regional scale in Spain. In contrast, the lethality rates have displayed a declining tendency which has associated with vaccination levels above 50%.

Estimating disease onset from change points of markers measured with error

Huntington disease is an autosomal dominant, neurodegenerative disease without clearly identified biomarkers for when motor-onset occurs. Current standards to determine motor-onset rely on a clinician's subjective judgment that a patient's extrapyramidal signs are unequivocally associated with Huntington disease. This subjectivity can lead to error which could be overcome using an objective, data-driven metric that determines motor-onset. Recent studies of motor-sign decline-the longitudinal degeneration of motor-ability in patients-have revealed that motor-onset is closely related to an inflection point in its longitudinal trajectory. We propose a nonlinear location-shift marker model that captures this motor-sign decline and assesses how its inflection point is linked to other markers of Huntington disease progression. We propose two estimating procedures to estimate this model and its inflection point: one is a parametric method using nonlinear mixed effects model and the other one is a multi-stage nonparametric approach, which we developed. In an empirical study, the parametric approach was sensitive to correct specification of the mean structure of the longitudinal data. In contrast, our multi-stage nonparametric procedure consistently produced unbiased estimates regardless of the true mean structure. Applying our multi-stage nonparametric estimator to Neurobiological Predictors of Huntington Disease, a large observational study of Huntington disease, leads to earlier prediction of motor-onset compared to the clinician's subjective judgment.

Linking environmental with biological data: Low sampling frequencies of chemical pollutants and nutrients in rivers reduce the reliability of model results

Linking environmental and biological data using ecological models can provide crucial knowledge about the effects of water quality parameters on freshwater ecosystems. However, a model can only be as reliable as its input data. Here, the influence of sampling frequency of temporal variable environmental input data on the reliability of model results when linked to biological data was investigated using Threshold Indicator Taxa Analysis (TITAN) and species sensitivity distributions (SSDs). Large-scale biological data from benthic macroinvertebrates and matching water quality data including four metals and four nutrients of up to 559 site-year combinations formed the initial data sets. To compare different sampling frequencies, the initial water quality data sets (n = 12 samples per year, set as reference) were subsampled (n = 10, 8, 6, 4, 2 and 1), annual mean values calculated and used as input data in the models. As expected, subsampling significantly reduced the reliability of the environmental input data across all eight substances. For TITAN, the use of environmental input data with a reduced reliability led to a considerable (1) loss of information because valid taxa were no longer identified, (2) gain of unreliable taxon-specific change points due to false positive taxa, and (3) bias in the change point estimation. In contrast, the reliability of the SSD results appeared to be much less reduced. However, closer examination of the SSD input data indicated that existing effects were masked by poor model performance. The results confirm that the sampling frequency of water quality data significantly influences the reliability of model results when linked with biological data. For studies limited to low sampling frequencies, the discussion provides recommendations on how to deal with low sampling frequencies of temporally variable water quality data when using them in TITAN, in SSDs, and in other ecological models.

Analysis of the early COVID-19 epidemic curve in Germany by regression models with change points

We analysed the coronavirus disease 2019 epidemic curve from March to the end of April 2020 in Germany. We use statistical models to estimate the number of cases with disease onset on a given day and use back-projection techniques to obtain the number of new infections per day. The respective time series are analysed by a trend regression model with change points. The change points are estimated directly from the data. We carry out the analysis for the whole of Germany and the federal state of Bavaria, where we have more detailed data. Both analyses show a major change between 9 and 13 March for the time series of infections: from a strong increase to a decrease. Another change was found between 25 March and 29 March, where the decline intensified. Furthermore, we perform an analysis stratified by age. A main result is a delayed course of the pandemic for the age group 80 + resulting in a turning point at the end of March. Our results differ from those by other authors as we take into account the reporting delay, which turned out to be time dependent and therefore changes the structure of the epidemic curve compared to the curve of newly reported cases.

Verification of abrupt and gradual shifts in Iranian precipitation and temperature data with statistical methods and stations metadata

Climate time series may exhibit abrupt or gradual shift, due to non-climatic changes (e.g., the station relocation) or actual climate change of a region. This study presented a step-by-step methodology for detecting the climatic and non-climatic changes in annual precipitation ([Formula: see text]) and maximum ([Formula: see text]) and minimum ([Formula: see text]) air temperature data related to 37 weather stations across Iran, using the statistical methods and stations metadata. All data cover the common period of 1961-2014. Abrupt changes in climate data were detected using the non-parametric Pettitt test and the piecewise linear regression model, the gradual changes using the non-parametric Mann-Kendall (MK) test, and the magnitude of trends using the Sen's slope estimator. In addition, a two-sample t-test was used to consider whether means of the climate data have been significantly changed in the presence of change points recorded in stations metadata. Results indicated overall increasing trends in [Formula: see text] and[Formula: see text], with more increasing rate for [Formula: see text]. In case of precipitation, most stations indicated non-significant decreasing/increasing trends while six of them showed significant decreasing trends. The detected breaking points, mainly in [Formula: see text] were concurrent with the years of change in the original locations of 6 out of 37 stations. It was specified that the unreliable stations' data intensified the trend orientation and magnitude of climatic variables compared to the reliable ones. In addition, increasing rates in [Formula: see text] and [Formula: see text] (decreasing rate in[Formula: see text]) for the stations located in the urban areas were larger (smaller) than those in the non-urban areas. This research revealed the necessity of metadata for accurate interpretation of results obtained from the statistical methods. The study suggests to the Iranian climate researchers to employ with caution a homogeneous length of series rather than total inhomogeneous length of series.

Sedimentary ancient DNA metabarcoding delineates the contrastingly temporal change of lake cyanobacterial communities

Harmful cyanobacterial blooms consisting of toxic taxa can produce a wide variety of toxins to threaten water quality, ecosystem functions and services. Of greater concern was the changing patterns of cyanobacterial assemblage were not well understood due to the lack of long-term monitoring data over the temporal scale. Biodiversity change in cyanobacterial community and paleoenvironmental variables over the past 170 years in Lake Chenghai were investigated based on sedimentary ancient DNA metabarcoding and traditional paleolimnological analysis. The results showed species richness and homogenization of cyanobacterial assemblage increased in the most recent decades, which were synchronized with the growth of artificial fertilization and decline in precipitation. Cyanobacterial co-occurrence network analysis revealed more complex interactions and weak community stability after the change point of ∼1987, while the rare cyanobacterial genera such as Anabaena, Planktothrix, Oscillatoria and Microcystis were identified to be keystone taxa affecting cyanobacterial assemblage. Furthermore, an increase of toxin-producing cyanobacterial taxa was significantly and positively associated with TN and TP, as well as TN/IP and TN/TP, which was verified by quantitative real-time PCR of mcyA and rpoC1 genes. Threshold in total nitrogen (TN) concentration should be targeted no more than 0.60 mg/L to alleviate nuisance cyanobacterial blooms in Lake Chenghai. These findings reinforce the comprehensive understanding for the long-term dynamics of cyanobacterial assemblage responding to environmental change, which could contribute to proactively regulate environmental conditions for avoiding undesirable ecological consequences.

Predicting viral exposure response from modeling the changes of co-expression networks using time series gene expression data

BACKGROUND

Deciphering the relationship between clinical responses and gene expression profiles may shed light on the mechanisms underlying diseases. Most existing literature has focused on exploring such relationship from cross-sectional gene expression data. It is likely that the dynamic nature of time-series gene expression data is more informative in predicting clinical response and revealing the physiological process of disease development. However, it remains challenging to extract useful dynamic information from time-series gene expression data.

RESULTS

We propose a statistical framework built on considering co-expression network changes across time from time series gene expression data. It first detects change point for co-expression networks and then employs a Bayesian multiple kernel learning method to predict exposure response. There are two main novelties in our method: the use of change point detection to characterize the co-expression network dynamics, and the use of kernel function to measure the similarity between subjects. Our algorithm allows exposure response prediction using dynamic network information across a collection of informative gene sets. Through parameter estimations, our model has clear biological interpretations. The performance of our method on the simulated data under different scenarios demonstrates that the proposed algorithm has better explanatory power and classification accuracy than commonly used machine learning algorithms. The application of our method to time series gene expression profiles measured in peripheral blood from a group of subjects with respiratory viral exposure shows that our method can predict exposure response at early stage (within 24 h) and the informative gene sets are enriched for pathways related to respiratory and influenza virus infection.

CONCLUSIONS

The biological hypothesis in this paper is that the dynamic changes of the biological system are related to the clinical response. Our results suggest that when the relationship between the clinical response and a single gene or a gene set is not significant, we may benefit from studying the relationships among genes in gene sets that may lead to novel biological insights.

Assessing the non-stationarity of low flows and their scale-dependent relationships with climate and human forcing

It is necessary to assess the non-stationarity of a hydrological series under changing environments. This study aimed to determine the validity of the stationarity of low flow series in terms of trends and possible change points, as well as the time-scale that is responsible for the production of trends and change points in low flow series. Further, we investigated how climatic variables affect low flow variations by studying their scale-dependent relationships. The modified Mann-Kendall trend test, heuristic segmentation method, discrete wavelet transform, and Pearson correlation coefficient were co-utilized to achieve these objectives. The Wei River Basin (WRB), a typical Loess Plateau region in China, was selected as the case study. Results showed significantly decreasing trends and change points in the low flow series, indicating that its stationarity assumption is invalid. The 2-year and 4-year events were the most important time-scales contributing to the trend of the original low flow series, and the 8-year periodic scale was the most influential frequency component for change point generation. Additionally, the strongest scale-dependent relationships among high frequency components (2-year and 4-year scales) of the low flow series and climatic variables (precipitation, potential evaporation, and soil moisture) demonstrated the importance of climatic factors for driving the trends of a low flow series. In contrast, human activities, including water withdrawals and water and soil conservation projects showed strong influences on the non-stationarity of low flows via affecting the low frequency component (8-year frequency and approximate components). These findings contribute to a better understanding temporal variations of low flow and their responses to changing environments, and the results also would be helpful for local water resources management as well as agricultural and ecological sustainable development.

Nonparametric change point estimation for survival distributions with a partially constant hazard rate

We present a new method for estimating a change point in the hazard function of a survival distribution assuming a constant hazard rate after the change point and a decreasing hazard rate before the change point. Our method is based on fitting a stump regression to p values for testing hazard rates in small time intervals. We present three real data examples describing survival patterns of severely ill patients, whose excess mortality rates are known to persist far beyond hospital discharge. For designing survival studies in these patients and for the definition of hospital performance metrics (e.g. mortality), it is essential to define adequate and objective end points. The reliable estimation of a change point will help researchers to identify such end points. By precisely knowing this change point, clinicians can distinguish between the acute phase with high hazard (time elapsed after admission and before the change point was reached), and the chronic phase (time elapsed after the change point) in which hazard is fairly constant. We show in an extensive simulation study that maximum likelihood estimation is not robust in this setting, and we evaluate our new estimation strategy including bootstrap confidence intervals and finite sample bias correction.

Stochastic Modelling of Air Pollution Impacts on Respiratory Infection Risk

The impact of air pollution on people's health and daily activities in China has recently aroused much attention. By using stochastic differential equations, variation in a 6 year long time series of air quality index (AQI) data, gathered from air quality monitoring sites in Xi'an from 15 November 2010 to 14 November 2016 was studied. Every year the extent of air pollution shifts from being serious to not so serious due to alterations in heat production systems. The distribution of such changes can be predicted by a Bayesian approach and the Gibbs sampler algorithm. The intervals between changes in a sequence indicate when the air pollution becomes increasingly serious. Also, the inflow rate of pollutants during the main pollution periods each year has an increasing trend. This study used a stochastic SEIS model associated with the AQI to explore the impact of air pollution on respiratory infections. Good fits to both the AQI data and the numbers of influenza-like illness cases were obtained by stochastic numerical simulation of the model. Based on the model's dynamics, the AQI time series and the daily number of respiratory infection cases under various government intervention measures and human protection strategies were forecasted. The AQI data in the last 15 months verified that government interventions on vehicles are effective in controlling air pollution, thus providing numerical support for policy formulation to address the haze crisis.

Influences of environmental factors on biomass of phytoplankton in the northern part of Tai Lake, China, from 2000 to 2012

Long-term (2000 to 2012) monthly data on communities of phytoplankton, and environmental variables were measured in water collected from Meiliang Bay and Wuli Lake of Tai Lake, China. Redundancy analysis (RDA) was conducted to explore relationships between the phytoplankton communities and environmental variables. Change points for concentrations of nutrients, which serve as early warnings of state shifts in lacustrine ecosystems, were identified using the Threshold Indicator Taxa Analysis (TITAN). The biomass of phytoplankton was positively correlated with the concentrations of total phosphorus (TP), suspended solids (SS), water temperature (WT), and pH but negatively correlated with the N/P ratio (by mass) and Secchi disk depth (SD). Furthermore, TP, rather than other factors, was a controlling factor limiting the primary production of phytoplankton in most of this region. The change points for concentrations of TP controlling the occurrences of sensitive and tolerant taxa were 56.1 and 103.5 μg TP/L, respectively. These results imply that an abrupt change in this lacustrine ecosystem has occurred in most parts of the study area, and the turbid state of this lake can be altered by reducing TP loading. This study provides an alternative ecological method for exploring the production of algal blooms and could advance the understanding of HABs.

chngpt: threshold regression model estimation and inference

Background

Threshold regression models are a diverse set of non-regular regression models that all depend on change points or thresholds. They provide a simple but elegant and interpretable way to model certain kinds of nonlinear relationships between the outcome and a predictor.

Results

The R package chngpt provides both estimation and hypothesis testing functionalities for four common variants of threshold regression models. All allow for adjustment of additional covariates not subjected to thresholding. We demonstrate the consistency of the estimating procedures and the type 1 error rates of the testing procedures by Monte Carlo studies, and illustrate their practical uses using an example from the study of immune response biomarkers in the context of Mother-To-Child-Transmission of HIV-1 viruses.

Conclusion

chngpt makes several unique contributions to the software for threshold regression models and will make these models more accessible to practitioners interested in modeling threshold effects.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1863-x) contains supplementary material, which is available to authorized users.

Robust bent line regression

We introduce a rank-based bent linear regression with an unknown change point. Using a linear reparameterization technique, we propose a rank-based estimate that can make simultaneous inference on all model parameters, including the location of the change point, in a computationally efficient manner. We also develop a score-like test for the existence of a change point, based on a weighted CUSUM process. This test only requires fitting the model under the null hypothesis in absence of a change point, thus it is computationally more efficient than likelihood-ratio type tests. The asymptotic properties of the test are derived under both the null and the local alternative models. Simulation studies and two real data examples show that the proposed methods are robust against outliers and heavy-tailed errors in both parameter estimation and hypothesis testing.

Bayesian two-part bent-cable Tobit models with skew distributions: Application to AIDS studies

This paper presents a new development of a bent-cable two-part Tobit model to identify both phasic patterns and mixture of advancing (to AIDS) and non-advancing patients of HIV. In identification of such phasic patterns, estimation of a transition period for the development of drug resistance to antiretroviral (ARV) drug or therapy is carried out using longitudinal data that have a gradual change from a declining phase to an increasing phase. In addition to phasic changes, there are also problems of skewness and left-censoring in the response variable because of a lower limit of detection. A relatively large percentage of data below limit of detection are recorded more than expected under an assumed skew-distribution. To properly accommodate these features, we present an extension of the random effects bent-cable Tobit model that incorporates a mixture of true undetectable observations and those values from a skew-normal distribution for a response with left-censoring, skewness and phasic patterns. The proposed methods are illustrated using real data from an AIDS clinical study.

Proportional hazards model with a change point for clustered event data

In many epidemiology studies, family data with survival endpoints are collected to investigate the association between risk factors and disease incidence. Sometimes the risk of the disease may change when a certain risk factor exceeds a certain threshold. Finding this threshold value could be important for disease risk prediction and diseases prevention. In this work, we propose a change-point proportional hazards model for clustered event data. The model incorporates the unknown threshold of a continuous variable as a change point in the regression. The marginal pseudo-partial likelihood functions are maximized for estimating the regression coefficients and the unknown change point. We develop a supremum test based on robust score statistics to test the existence of the change point. The inference for the change point is based on the m out of n bootstrap. We establish the consistency and asymptotic distributions of the proposed estimators. The finite-sample performance of the proposed method is demonstrated via extensive simulation studies. Finally, the Strong Heart Family Study dataset is analyzed to illustrate the methods.

Bacterioplankton community responses to key environmental variables in plateau freshwater lake ecosystems: A structural equation modeling and change point analysis

Elevated environmental pressures negatively affect the bacterial community structure. However, little knowledge about the nonlinear responses of spatially related environmental variable across multiple plateau lake ecosystems on bacterioplankton communities has been gathered. Here, we used 454 pyrosequencing of 16S rRNA genes to study the associations of bacterial communities in terms of environmental characteristics as well as the potentially ecological threshold-inducing shifts of the bacterial community structure along the key environmental variables based on hypothesized structural equation models and the SEGMENTED method in 21 plateau lakes. Our results showed that water transparency was the major driving force and that total nitrogen was more significant than total phosphorus in determining the taxon composition of the bacterioplankton community. Significant community threshold estimates for bacterioplankton were observed at 7.36 for pH and 25.6% for the percentage of the agricultural area, while the remarkable change point of the cyanobacteria community structure responding to pH was at 7.74. Furthermore, the findings indicated that increasing nutrient loads can induce a distinct shift in dominance from Proteobacteria to Cyanobacteria, as well as a sharp decrease and adjacent increase when crossing the change point for Actinobacteria and Bacteroidetes along the gradient of the agricultural area.

Statistical Assessment of QC Metrics on Raw LC-MS/MS Data

Data quality assessment is important for reproducibility of proteomics experiments and reusability of proteomics data. We describe a set of statistical tools to routinely visualize and examine the quality control (QC) metrics obtained for raw LC-MS/MS data on different instrument types and mass spectrometers. The QC metrics used here are the identification free QuaMeter metrics. Statistical assessments introduced include (a) principal component analysis, (b) dissimilarity measures, (c) T ²-chart for quality control, and (d) change point analysis. We demonstrate the workflow by a step-by-step assessment of a subset of Study 5 for the Clinical Proteomics Technology Assessment for Cancer (CPTAC) using our R functions.

Vegetation community change in Atlantic oak woodlands along a nitrogen deposition gradient

Atlantic old sessile oak woodlands are of high conservation importance in Europe, listed in the European Union (EU) Habitats Directive Annex I, and known for their rich bryophyte communities. Their conservation status ranges from unfavourable to bad across their known distribution, which is predominantly within the UK and Ireland, but also extends into Iberia and Brittany. The objectives of this study were to determine if nitrogen (N) deposition, a known driver of terrestrial biodiversity loss, was a significant predictor of community composition in old sessile oak woodlands (i.e., EU Habitats Directive Annex I class: 91A0), and to identify significant changes in individual plant species and community-level abundance (i.e., change points) along an N deposition gradient. Relevé data from 260 Irish oak woodland plots were evaluated using Canonical Correspondence Analysis (CCA) and Threshold Indicator Taxa ANalysis (TITAN). Nitrogen deposition accounted for 14% of the explainable variation in the dataset (inertia = 0.069, p < 0.005). A community scale change point of 13.2 kg N ha(-1) yr(-1) was indicated by TITAN, which falls within the current recommended critical load (CL) range for acidophilous Quercus-dominated (oak) woodlands (10-15 kg N ha(-1) yr(-1)). The results suggest that the current CL is sufficient for maintaining a core group of indicator species in old sessile oak woodlands, but many nutrient sensitive species may disappear even at the CL range minimum.

Detecting a trend change in cross-border epidemic transmission

A method for a system of Langevin equations is developed for detecting a trend change in cross-border epidemic transmission. The equations represent a standard epidemiological SIR compartment model and a meta-population network model. The method analyzes a time series of the number of new cases reported in multiple geographical regions. The method is applicable to investigating the efficacy of the implemented public health intervention in managing infectious travelers across borders. It is found that the change point of the probability of travel movements was one week after the WHO worldwide alert on the SARS outbreak in 2003. The alert was effective in managing infectious travelers. On the other hand, it is found that the probability of travel movements did not change at all for the flu pandemic in 2009. The pandemic did not affect potential travelers despite the WHO alert.

An Unconditional Test for Change Point Detection in Binary Sequences with Applications to Clinical Registries

OBJECTIVES

Methods for change point (also sometimes referred to as threshold or breakpoint) detection in binary sequences are not new and were introduced as early as 1955. Much of the research in this area has focussed on asymptotic and exact conditional methods. Here we develop an exact unconditional test.

METHODS

An unconditional exact test is developed which assumes the total number of events as random instead of conditioning on the number of observed events. The new test is shown to be uniformly more powerful than Worsley's exact conditional test and means for its efficient numerical calculations are given. Adaptions of methods by Berger and Boos are made to deal with the issue that the unknown event probability imposes a nuisance parameter. The methods are compared in a Monte Carlo simulation study and applied to a cohort of patients undergoing traumatic orthopaedic surgery involving external fixators where a change in pin site infections is investigated.

RESULTS

The unconditional test controls the type I error rate at the nominal level and is uniformly more powerful than (or to be more precise uniformly at least as powerful as) Worsley's exact conditional test which is very conservative for small sample sizes. In the application a beneficial effect associated with the introduction of a new treatment procedure for pin site care could be revealed.

CONCLUSIONS

We consider the new test an effective and easy to use exact test which is recommended in small sample size change point problems in binary sequences.

Soil and phosphorus accretion rates in sub-tropical wetlands: Everglades Stormwater Treatment Areas as a case example

Wetlands are known to serve as sinks for particulate matter and associated nutrients and contaminants. Consequently rate of soil accretion is critical for continued performance of wetlands to provide ecosystem services including water quality improvement and reduce excess contaminant loads into downstream waters. Here we demonstrate a new technique to determine rate of soil accretion in selected subtropical treatment wetlands located in southern USA. We also report changes in soil accretion rates and subsequent phosphorus (P) removal efficiency with increasing operational history of these treatment wetlands. Utilizing discernible signatures preserved within the soil depth profiles, 'change points' (CP) that corresponded to specific events in the life history of a wetland were determined. The CP was observed as an abrupt transition in the physico-chemical properties of soil as a manifestation of prevailing historical conditions (e.g. startup of treatment wetlands in this case). Vertical depth of CP from the soil surface was equivalent to the depth of recently accreted soil (RAS) and used for soil accretion rate calculations. Annual soil and P accretion rates determined using CP technique (CPT) in studied wetlands ranged from 1.0±0.3 to 1.7±0.8 cm yr(-1) and 1.3±0.6 to 3.3±2 g m(-2) yr(-1), respectively. There was no difference in RAS depth between emergent and submerged aquatic vegetation communities found at the study location. Our results showed that soil and P accretion rates leveled off after 10 yr of treatment wetlands' operation. On comparison, soil accretion rates and RAS depth determined by CPT were commensurate with that measured by other techniques. CPT can be easily used where a reliable record of wetland establishment date or some significant alteration/perturbation is available. This technique offers a relatively simple alternative to determine vertical accretion rates in free-water surface wetlands.

A novel design for randomized immuno-oncology clinical trials with potentially delayed treatment effects

The semi-parametric proportional hazards model is widely adopted in randomized clinical trials with time-to-event outcomes, and the log-rank test is frequently used to detect a potential treatment effect. Immuno-oncology therapies pose unique challenges to the design of a trial as the treatment effect may be delayed, which violates the proportional hazards assumption, and the log-rank test has been shown to markedly lose power under the non-proportional hazards setting. A novel design and analysis approach for immuno-oncology trials is proposed through a piecewise treatment effect function, which is capable of detecting a potentially delayed treatment effect. The number of events required for the trial will be determined to ensure sufficient power for both the overall log-rank test without a delayed effect and the test beyond the delayed period when such a delay exists. The existence of a treatment delay is determined by a likelihood ratio test with resampling. Numerical results show that the proposed design adequately controls the Type I error rate, has a minimal loss in power under the proportional hazards setting and is markedly more powerful than the log-rank test with a delayed treatment effect.

Modeling longitudinal data with a random change point and no time-zero: applications to inference and prediction of the labor curve

In some longitudinal studies the initiation time of the process is not clearly defined, yet it is important to make inference or do predictions about the longitudinal process. The application of interest in this article is to provide a framework for modeling individualized labor curves (longitudinal cervical dilation measurements) where the start of labor is not clearly defined. This is a well-known problem in obstetrics where the benchmark reference time is often chosen as the end of the process (individuals are fully dilated at 10 cm) and time is run backwards. This approach results in valid and efficient inference unless subjects are censored before the end of the process, or if we are focused on prediction. Providing dynamic individualized predictions of the longitudinal labor curve prospectively (where backwards time is unknown) is of interest to aid obstetricians to determine if a labor is on a suitable trajectory. We propose a model for longitudinal labor dilation that uses a random-effects model with unknown time-zero and a random change point. We present a maximum likelihood approach for parameter estimation that uses adaptive Gaussian quadrature for the numerical integration. Further, we propose a Monte Carlo approach for dynamic prediction of the future longitudinal dilation trajectory from past dilation measurements. The methodology is illustrated with longitudinal cervical dilation data from the Consortium of Safe Labor Study.

Investigation of phase shifts for different period lengths in the genomes of C. elegans, D. melanogaster and S. cerevisiae

We describe a new mathematical method for finding very diverged short tandem repeats containing a single indel. The method involves comparison of two frequency matrices: a first matrix for a subsequence before shift and a second one for a subsequence after it. A measure of comparison is based on matrix similarity. The approach developed was applied to analysis of the genomes of Caenorhabditis elegans, Drosophila melanogaster and Saccharomyces cerevisiae. They were investigated regarding the presence of tandem repeats having repeat length equal to 2 - 11 nucleotides except equal to 3, 6 and 9 nucleotides. A number of phase shift regions for these genomes was approximately 2.2 × 10(4), 1.5 × 10(4) and 1.7 × 10(2), respectively. Type I error was less than 5%. The mean length of fuzzy periodicity and phase shift regions was about 220 nucleotides. The regions of fuzzy periodicity having single insertion or deletion occupy substantial parts of the genomes: 5%, 3% and 0.3%, respectively. Only less than 10% of these regions have been detected previously. That is, the number of such regions in the genomes of C. elegans, D. melanogaster and S. cerevisiae is dramatically higher than it has been revealed by any known methods. We suppose that some found regions of fuzzy periodicity could be the regions for protein binding.

The effect of Operation 24 Hours on reducing collision in the City of Edmonton

In the City of Edmonton, in order to reduce the prevalence of collisions, the Operation 24 Hours program (OPS24) was developed by using existing police and transportation services resources. The program uses traditional manned police speed enforcement method, which are supplemented by traffic safety messages displayed on permanent and mobile dynamic messaging signs (DMS). In this paper, collision data analysis was performed by looking at the daily number of collisions from 2008 to 2011 that covers 28 Operation 24 Hours (OPS24) events. The objective of the collision data analysis is to analyze if there is a reduction in collision frequencies after OPS24 was held and examined how long the collision reduction effect last. Weather factors such as temperature, thickness of snow, and wind gust have been considered by many as a great influence on collision occurrences, especially in a city with long and cold winter such as Edmonton. Therefore, collision modeling was performed by considering these external weather factors. To analyze the linear and periodic trend of different collision types (injury, fatal, and property damage only (PDO)) and examine the influence of weather factors on collisions, negative binomial time series model that accounts for seasonality and weather factors was used to model daily collision data. The modeling also considered collision proportion to account for missing traffic volume data; the Gaussian time series model that accounts for seasonality and weather factors was used to model collision proportion. To estimate the collision trend and test for changes in collision levels before/after OPS24, interrupted time series model with segmented regression was used. While for estimating how long the effect of the OPS24 last, change point method was applied.