Efficient change point detection for genomic sequences of continuous measurements

Change point detection in brucellosis time series from 2010 to 2023 in Xinjiang China using the BEAST algorithm

Brucellosis is a significant global challenge, but there has been a lack of epidemiological studies on brucellosis in Xinjiang from a change point perspective. This study aims to bridge this gap by employing sequence decomposition and identifying significant change points, with datasets sourced from the Xinjiang Disease Prevention and Control Information System. This study employed the BEAST algorithm to decompose the brucellosis time series in Xinjiang from 2010 to 2023, while simultaneously identifying change points in the decomposed seasonal and trend components. The probability of four change points occurring within the seasonal component is 0.8950. And the locations where these four change points occur and the probabilities associated with each change point are August 2013 ([Formula: see text]), August 2017 ([Formula: see text]), February 2022 ([Formula: see text]), and May 2023 ([Formula: see text]), respectively. The probability of the existence of five change points in the trend factors of brucellosis in Xinjiang is highest ([Formula: see text]). The times at which these five change points occur, along with the probabilities of change at those moments, are as follows: March 2013 ([Formula: see text]), August 2015 ([Formula: see text]), July 2017 ([Formula: see text]), February 2020 ([Formula: see text]), and May 2023 ([Formula: see text]). Change point analysis holds significant utility within the field of epidemiology. These discoveries furnish pivotal insights for epidemiological investigations and the development of early warning systems tailored to brucellosis.

Estimating changepoints in extremal dependence, applied to aviation stock prices during COVID-19 pandemic

The dependence in the tails of the joint distribution of two random variables is generally assessed using χ-measure, the limiting conditional probability of one variable being extremely high given the other variable is also extremely high. This work is motivated by the structural changes in χ-measure between the daily rate of return (RoR) of the two Indian airlines, IndiGo and SpiceJet, during the COVID-19 pandemic. We model the daily maximum and minimum RoR vectors (potentially transformed) using the bivariate Hüsler-Reiss (BHR) distribution. To estimate the changepoint in the χ-measure of the BHR distribution, we explore two changepoint detection procedures based on the Likelihood Ratio Test (LRT) and Modified Information Criterion (MIC). We obtain critical values and power curves of the LRT and MIC test statistics for low through high values of χ-measure. We also explore the consistency of the estimators of the changepoint based on LRT and MIC numerically. In our data application, for RoR maxima and minima, the most prominent changepoints detected by LRT and MIC are close to the announcement of the first phases of lockdown and unlock, respectively, which are realistic; thus, our study would be beneficial for portfolio optimization in the case of future pandemic situations.

Debamestrocel multimodal effects on biomarker pathways in amyotrophic lateral sclerosis are linked to clinical outcomes

INTRODUCTION/AIMS

Biomarkers have shown promise in amyotrophic lateral sclerosis (ALS) research, but the quest for reliable biomarkers remains active. This study evaluates the effect of debamestrocel on cerebrospinal fluid (CSF) biomarkers, an exploratory endpoint.

METHODS

A total of 196 participants randomly received debamestrocel or placebo. Seven CSF samples were to be collected from all participants. Forty-five biomarkers were analyzed in the overall study and by two subgroups characterized by the ALS Functional Rating Scale-Revised (ALSFRS-R). A prespecified model was employed to predict clinical outcomes leveraging biomarkers and disease characteristics. Causal inference was used to analyze relationships between neurofilament light chain (NfL) and ALSFRS-R.

RESULTS

We observed significant changes with debamestrocel in 64% of the biomarkers studied, spanning pathways implicated in ALS pathology (63% neuroinflammation, 50% neurodegeneration, and 89% neuroprotection). Biomarker changes with debamestrocel show biological activity in trial participants, including those with advanced ALS. CSF biomarkers were predictive of clinical outcomes in debamestrocel-treated participants (baseline NfL, baseline latency-associated peptide/transforming growth factor beta1 [LAP/TGFβ1], change galectin-1, all p < .01), with baseline NfL and LAP/TGFβ1 remaining (p < .05) when disease characteristics (p < .005) were incorporated. Change from baseline to the last measurement showed debamestrocel-driven reductions in NfL were associated with less decline in ALSFRS-R. Debamestrocel significantly reduced NfL from baseline compared with placebo (11% vs. 1.6%, p = .037).

DISCUSSION

Following debamestrocel treatment, many biomarkers showed increases (anti-inflammatory/neuroprotective) or decreases (inflammatory/neurodegenerative) suggesting a possible treatment effect. Neuroinflammatory and neuroprotective biomarkers were predictive of clinical response, suggesting a potential multimodal mechanism of action. These results offer preliminary insights that need to be confirmed.

Hepatitis B time series in Xinjiang, China (2006-2021): change point detection based on the Mann-Kendall-Sneyers test

Hepatitis B is a major global challenge, but there is a lack of epidemiological research on hepatitis B incidence from a change point perspective. This study aimed to fill this gap by identifying significant change points and trends in hepatitis time series in Xinjiang, China. The datasets were obtained from the Xinjiang Information System for Disease Control and Prevention. The Mann-Kendall-Sneyers (MKS) test was used to detect change points and trend changes on the hepatitis B time series of 14 regions in Xinjiang, and the effectiveness of this method was validated by comparing it with the binary segmentation (BS) and segment regression (SR) methods. Based on the results of change point analysis, the prevention and control policies and measures of hepatitis in Xinjiang were discussed. The results showed that 8 regions (57.1%) with at least one change fell within the 95% confidence interval (CI) in all 14 regions by the MKS test, where five regions (Turpan (TP), Hami (HM), Bayingolin (BG), Kyzylsu Kirgiz (KK), Altai (AT)) were identified at one change point, two change points existed for two regions (Aksu (AK), Hotan (HT)) and three change points was detected in 1 region (Bortala (BT)). Most of the change points occurred at both ends of the sequence. More change points indicated an upward trend in the front half of the sequence, while in the latter half, many change points indicated a downward trend prominently. Finally, in comparing the results of the three change point tests, the MKS test showed a 61.5% agreement (8/13) with the BS and SR.

A Selective Review on Information Criteria in Multiple Change Point Detection

Change points indicate significant shifts in the statistical properties in data streams at some time points. Detecting change points efficiently and effectively are essential for us to understand the underlying data-generating mechanism in modern data streams with versatile parameter-varying patterns. However, it becomes a highly challenging problem to locate multiple change points in the noisy data. Although the Bayesian information criterion has been proven to be an effective way of selecting multiple change points in an asymptotical sense, its finite sample performance could be deficient. In this article, we have reviewed a list of information criterion-based methods for multiple change point detection, including Akaike information criterion, Bayesian information criterion, minimum description length, and their variants, with the emphasis on their practical applications. Simulation studies are conducted to investigate the actual performance of different information criteria in detecting multiple change points with possible model mis-specification for the practitioners. A case study on the SCADA signals of wind turbines is conducted to demonstrate the actual change point detection power of different information criteria. Finally, some key challenges in the development and application of multiple change point detection are presented for future research work.

Multiple change point analysis of hepatitis B reports in Xinjiang, China from 2006 to 2021

Objective

Hepatitis B (HB) is a major global challenge, but there has been a lack of epidemiological studies on HB incidence in Xinjiang from a change-point perspective. This study aims to bridge this gap by identifying significant change points and trends.

Method

The datasets were obtained from the Xinjiang Information System for Disease Control and Prevention. Change points were identified using binary segmentation for full datasets and a segmented regression model for five age groups.

Results

The results showed four change points for the quarterly HB time series, with the period between the first change point (March 2007) and the second change point (March 2010) having the highest mean number of HB reports. In the subsequent segments, there was a clear downward trend in reported cases. The segmented regression model showed different numbers of change points for each age group, with the 30-50, 51-80, and 15-29 age groups having higher growth rates.

Conclusion

Change point analysis has valuable applications in epidemiology. These findings provide important information for future epidemiological studies and early warning systems for HB.

A new time-varying coefficient regression approach for analyzing infectious disease data

Since the beginning of the global pandemic of Coronavirus (SARS-COV-2), there has been many studies devoted to predicting the COVID-19 related deaths/hospitalizations. The aim of our work is to (1) explore the lagged dependence between the time series of case counts and the time series of death counts; and (2) utilize such a relationship for prediction. The proposed approach can also be applied to other infectious diseases or wherever dynamics in lagged dependence are of primary interest. Different from the previous studies, we focus on time-varying coefficient models to account for the evolution of the coronavirus. Using two different types of time-varying coefficient models, local polynomial regression models and piecewise linear regression models, we analyze the province-level data in Canada as well as country-level data using cumulative counts. We use out-of-sample prediction to evaluate the model performance. Based on our data analyses, both time-varying coefficient modeling strategies work well. Local polynomial regression models generally work better than piecewise linear regression models, especially when the pattern of the relationship between the two time series of counts gets more complicated (e.g., more segments are needed to portray the pattern). Our proposed methods can be easily and quickly implemented via existing R packages.

Statistical Picking of Multivariate Waveforms

In this paper, we propose a new approach based on the fitting of a generalized linear regression model in order to detect points of change in the variance of a multivariate-covariance Gaussian variable, where the variance function is piecewise constant. By applying this new approach to multivariate waveforms, our method provides simultaneous detection of change points in functional time series. The proposed approach can be used as a new picking algorithm in order to automatically identify the arrival times of P- and S-waves in different seismograms that are recording the same seismic event. A seismogram is a record of ground motion at a measuring station as a function of time, and it typically records motions along three orthogonal axes (X, Y, and Z), with the Z-axis being perpendicular to the Earth's surface and the X- and Y-axes being parallel to the surface and generally oriented in North-South and East-West directions, respectively. The proposed method was tested on a dataset of simulated waveforms in order to capture changes in the performance according to the waveform characteristics. In an application to real seismic data, our results demonstrated the ability of the multivariate algorithm to pick the arrival times in quite noisy waveforms coming from seismic events with low magnitudes.

Multiple change point estimation of trends in Covid-19 infections and deaths in India as compared with WHO regions

The present study aims at estimating the multiple change points for the time series data of COVID-19 confirmed cases and deaths and trend estimation within the estimated multiple change points (MCP) in India as compared with WHO regions. The data were described using descriptive statistical measures, and for the estimation of change point's E-divisive procedure was employed. Further, the trend within the estimated change points was tested using Sen's slope and Mann Kendal tests. India, along with the African Region, American region, and South East Asia regions experienced a significant surge in the fresh cases up to the 5th Change point. Among the WHO regions, The American region was the worst hit by the pandemic in case of fresh cases and deaths. While the European region experienced an early negative trend of fresh cases during the 3rd and 4th change point, but later the situation reversed by the 5th (7th July 2020) and 6th (6th August 2020) change point. The trend of deaths in India and the South-East Asia Region was similar, and global deaths had a negative trend from the 4th (17th May 2020) Change point onwards. The change points were estimated with prefixed significance level α < 0.002. Infections and deaths were positively significant for India and SEARO region across change points. Infection was significant at every 30 days interval across other WHO regions, and any delay in the infections was due to the interventions. The European region is expected to have a second wave of positive infections during the 5th and 6th change points though the early two change points were negatively significant. The study highlights the efficacy of change point analysis in understanding the dynamics of covid-19 cases in India and across the world. It further helps to develop effective public health strategies.

Efficient change-points detection for genomic sequences via cumulative segmented regression

MOTIVATION

Knowing the number and the exact locations of multiple change points in genomic sequences serves several biological needs. The cumulative-segmented algorithm (cumSeg) has been recently proposed as a computationally efficient approach for multiple change-points detection, which is based on a simple transformation of data and provides results quite robust to model mis-specifications. However, the errors are also accumulated in the transformed model so that heteroscedasticity and serial correlation will show up, and thus the variations of the estimated change points will be quite different, while the locations of the change points should be of the same importance in the original genomic sequences.

RESULTS

In this study, we develop two new change-points detection procedures in the framework of cumulative segmented regression. Simulations reveal that the proposed methods not only improve the efficiency of each change point estimator substantially but also provide the estimators with similar variations for all the change points. By applying these proposed algorithms to Coriel and SNP genotyping data, we illustrate their performance on detecting copy number variations.

AVAILABILITY AND IMPLEMENTATION

The proposed algorithms are implemented in R program and the codes are provided in the online supplementary material.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Estimation and inference for multikink expectile regression with longitudinal data

In this article, we investigate parameter estimation, kink points testing and statistical inference for a longitudinal multikink expectile regression model. The estimators for the kink locations and regression coefficients are obtained by using a bootstrap restarting iterative algorithm to avoid local minima. A backward selection procedure based on a modified BIC is applied to estimate the number of kink points. We theoretically demonstrate the number selection consistency of kink points and the asymptotic normality of all estimators. In particular, the estimators of kink locations are shown to achieve root-n consistency. A weighted cumulative sum type statistic is proposed to test the existence of kink effects at a given expectile, and its limiting distributions are derived under both the null and the local alternative hypotheses. The traditional Wald-type and cluster bootstrap confidence intervals for kink locations are also constructed. Simulation studies show that the proposed estimators and test have desirable finite sample performance in both homoscedastic and heteroscedastic errors. Two applications to the Nation Growth, Lung and Health Study and Capital Bike sharing dataset in Washington D.C. are also presented. The R codes for simulation studies and the real data are available at https://github.com/wangleink/MKER.

Detection of multiple change points in a Weibull accelerated failure time model using sequential testing

With improvements to cancer diagnoses and treatments, incidences and mortality rates have changed. However, the most commonly used analysis methods do not account for such distributional changes. In survival analysis, change point problems can concern a shift in a distribution for a set of time-ordered observations, potentially under censoring or truncation. We propose a sequential testing approach for detecting multiple change points in the Weibull accelerated failure time model, since this is sufficiently flexible to accommodate increasing, decreasing, or constant hazard rates and is also the only continuous distribution for which the accelerated failure time model can be reparameterized as a proportional hazards model. Our sequential testing procedure does not require the number of change points to be known; this information is instead inferred from the data. We conduct a simulation study to show that the method accurately detects change points and estimates the model. The numerical results along with real data applications demonstrate that our proposed method can detect change points in the hazard rate.

Non-parametric treatment time-lag effect estimation

In general, the change point problem considers inference of a change in distribution for a set of time-ordered observations. This has applications in a large variety of fields, and can also apply to survival data. In survival analysis, most existing methods compare two treatment groups for the entirety of the study period. Some treatments may take a length of time to show effects in subjects. This has been called the time-lag effect in the literature, and in cases where time-lag effect is considerable, such methods may not be appropriate to detect significant differences between two groups. In this paper, we propose a novel non-parametric approach for estimating the point of treatment time-lag effect by using an empirical divergence measure. Theoretical properties of the estimator are studied. The results from the simulated data and the applications to real data examples support our proposed method.

Detecting multiple generalized change-points by isolating single ones

We introduce a new approach, called Isolate-Detect (ID), for the consistent estimation of the number and location of multiple generalized change-points in noisy data sequences. Examples of signal changes that ID can deal with are changes in the mean of a piecewise-constant signal and changes, continuous or not, in the linear trend. The number of change-points can increase with the sample size. Our method is based on an isolation technique, which prevents the consideration of intervals that contain more than one change-point. This isolation enhances ID's accuracy as it allows for detection in the presence of frequent changes of possibly small magnitudes. In ID, model selection is carried out via thresholding, or an information criterion, or SDLL, or a hybrid involving the former two. The hybrid model selection leads to a general method with very good practical performance and minimal parameter choice. In the scenarios tested, ID is at least as accurate as the state-of-the-art methods; most of the times it outperforms them. ID is implemented in the R packages IDetect and breakfast, available from CRAN.

SUPPLEMENTARY INFORMATION

The online version supplementary material available at 10.1007/s00184-021-00821-6.

Using GC Content to Compare Recombination Patterns on the Sex Chromosomes and Autosomes of the Guppy, Poecilia reticulata, and Its Close Outgroup Species

Genetic and physical mapping of the guppy (Poecilia reticulata) have shown that recombination patterns differ greatly between males and females. Crossover events occur evenly across the chromosomes in females, but in male meiosis they are restricted to the tip furthest from the centromere of each chromosome, creating very high recombination rates per megabase, as in pseudoautosomal regions of mammalian sex chromosomes. We used GC content to indirectly infer recombination patterns on guppy chromosomes, based on evidence that recombination is associated with GC-biased gene conversion, so that genome regions with high recombination rates should be detectable by high GC content. We used intron sequences and third positions of codons to make comparisons between sequences that are matched, as far as possible, and are all probably under weak selection. Almost all guppy chromosomes, including the sex chromosome (LG12), have very high GC values near their assembly ends, suggesting high recombination rates due to strong crossover localization in male meiosis. Our test does not suggest that the guppy XY pair has stronger crossover localization than the autosomes, or than the homologous chromosome in the close relative, the platyfish (Xiphophorus maculatus). We therefore conclude that the guppy XY pair has not recently undergone an evolutionary change to a different recombination pattern, or reduced its crossover rate, but that the guppy evolved Y-linkage due to acquiring a male-determining factor that also conferred the male crossover pattern. We also identify the centromere ends of guppy chromosomes, which were not determined in the genome assembly.

A Novel Online Change Point Detection Using an Approximate Random Blanket and the Line Process Energy

In this paper, we focus on the problem of change point detection in piecewise constant signals. This problem is central to several applications such as human activity analysis, speech or image analysis and anomaly detection in genetics. We present a novel window-sliding algorithm for an online change point detection. The proposed approach considers a local blanket of a global Markov Random Field (MRF) representing the signal and its noisy observation. For each window, we define and solve the local energy minimization problem to deduce the gradient on each edge of the MRF graph. The gradient is then processed by an activation function to filter the weak features and produce the final jumps. We demonstrate the effectiveness of our method by comparing its running time and several detection metrics with state of the art algorithms.

Neurons with Complex Karyotypes Are Rare in Aged Human Neocortex

A subset of human neocortical neurons harbors complex karyotypes wherein megabase-scale copy-number variants (CNVs) alter allelic diversity. Divergent levels of neurons with complex karyotypes (CNV neurons) are reported in different individuals, yet genome-wide and familial studies implicitly assume a single brain genome when assessing the genetic risk architecture of neurological disease. We assembled a brain CNV atlas using a robust computational approach applied to a new dataset (>800 neurons from 5 neurotypical individuals) and to published data from 10 additional neurotypical individuals. The atlas reveals that the frequency of neocortical neurons with complex karyotypes varies widely among individuals, but this variability is not readily accounted for by tissue quality or CNV detection approach. Rather, the age of the individual is anti-correlated with CNV neuron frequency. Fewer CNV neurons are observed in aged individuals than in young individuals.

Discriminant Analysis of Lung Cancer Using Nonlinear Clustering of Copy Numbers

Background: Patient survival is not optimal for non-small cell lung cancer (NSCLC) patients, recurrence rate is high, and hence, early detection is crucial to increase the patient's survival. Gene-cancer mapping intends to discover associated genes with cancers and due to advances in high-throughput genotyping, screening for disease loci on a genome-wide scale is now possible. DNA copy numbers can potentially be used to identify cancer from normal cells in early detection of cancer.Methods: We use a nonlinear clustering method, so-called kernel K-means to separate cancer from normal samples. Kernel K-means is applied to the copy numbers obtained for each chromosome to cluster 63 paired cancer-blood samples (total of 126 samples) into two groups. Clustering performance is evaluated using true and false-positive rates, true and false-negative rates, and a nonlinear criterion, normalized mutual information (NMI).Results: Copy numbers of paired cancer-blood samples for 63 NSCLC patients are used in this study. Kernel K-means was applied to cluster 126 samples in two groups using copy numbers on each chromosome separately. The clustering results for 22 chromosomes are evaluated and discriminant power of them in identifying cancer is computed. We identified the top five and bottom five chromosomes based on their discriminant power.Conclusions: The results reveal high discriminant power of chromosomes 8, 5, 1, 3, and 19 for identifying cancer with the highest sensitivity of 75% yielded by chromosome 5. Bottom 5 chromosomes 9, 6, 4, 13, and 21 show low discriminant power with the accuracy of below 54% where true cancer and normal samples are grouped into substantially overlapping groups using copy numbers. This indicates the similarities of copy numbers obtained for cancer and normal samples on these chromosomes.

Identifying Similarities and Disparities Between DNA Copy Number Changes in Cancer and Matched Blood Samples

Background: Non-small cell lung cancer (NSCLC) is the first cause of cancer-related mortality for men and women in the United States. In spite of curative resection in early-stage, patient survival is not optimal and recurrence rate is high. Consequently, early detection and staging is essential to increase the patient's survival.Methods: Copy number (CN) changes in cancer populations have been broadly investigated to identify CN gains and deletions associated with cancer. In contrast, in this research, we quantify the similarities and disparities between cancer and paired peripheral blood samples using maximal information coefficient (MIC). We then detect the spatial locations with substantially high and the spatial locations with very low MICs in each chromosome. These locations can potentially help with early diagnosis, treatment, and prevention of cancer by identifying the similarities and disparities between cancer and healthy tissues.Results: Lung cancer data used in this project contains CN pairs for cancer and blood (non-involved) samples for 63 subjects. MIC was obtained to quantify the relation (linear or nonlinear) between cancer-blood pair samples for 63 subjects at each location for each chromosome. MIC values above a high threshold and MIC values below a low threshold were located. Among them top five (with lowest MIC's and with highest MIC's) were identified for each chromosome. For these identified locations, a high MIC score indicates high similarity between blood (non-involved) and cancer samples, while a low MIC score shows lack of similarity between the two samples.Conclusions: The results showed that a few chromosomes have a large number of MICs exceeding a high threshold. These locations can potentially be used to identify early indicators of NSCLC. In contrast, second group of chromosomes have several locations with small MICs which are potential candidates to develop biomarkers for discriminating cancer from the matched blood sample. Moreover, there is a third group of chromosomes with a large number of MICs exceeding a high threshold and a large set of MICs below a low threshold. These locations can help with both finding early indicators of cancer and developing biomarkers for discriminating cancer from non-involved tissue.

The Photorhabdus asymbiotica virulence cassettes deliver protein effectors directly into target eukaryotic cells

Photorhabdus is a highly effective insect pathogen and symbiont of insecticidal nematodes. To exert its potent insecticidal effects, it elaborates a myriad of toxins and small molecule effectors. Among these, the Photorhabdus Virulence Cassettes (PVCs) represent an elegant self-contained delivery mechanism for diverse protein toxins. Importantly, these self-contained nanosyringes overcome host cell membrane barriers, and act independently, at a distance from the bacteria itself. In this study, we demonstrate that Pnf, a PVC needle complex associated toxin, is a Rho-GTPase, which acts via deamidation and transglutamination to disrupt the cytoskeleton. TEM and Western blots have shown a physical association between Pnf and its cognate PVC delivery mechanism. We demonstrate that for Pnf to exert its effect, translocation across the cell membrane is absolutely essential.

Appointment utilization as a trigger for palliative care introduction: A retrospective cohort study

BACKGROUND

Chronic kidney disease palliative care guidelines would benefit from more diverse and objectively defined health status measures.

AIM

The aim is to identify high-risk patients from administrative data and facilitate timely and uniform palliative care involvement.

DESIGN

It is a retrospective cohort study.

SETTING/PARTICIPANTS

In total, 45,368 Veterans, with chronic kidney disease Stage 3, 4, or 5, were monitored for up to 6 years and categorized into three groups, based on whether they died, started dialysis, or avoided both outcomes.

RESULTS

Patient's appointment utilization was a significant predictor for both outcomes. It separated individuals into low, medium, and high appointment utilizers. Among the low appointment utilizers, the risk of death did not change significantly, while the risk of dialysis increased. Medium appointment utilizers had a stable risk of death and a decreasing risk of dialysis. Significant appointment utilization (above 31 visits during the baseline year) helped high-risk patients avoid both outcomes of interest-death and dialysis.

CONCLUSION

Our model could justify the creation of a novel palliative care introduction trigger, as patients with medium demand for care may benefit from additional palliative care evaluation. The trigger could facilitate the uniformization of conservative treatment preparations. It could prompt messages to a managing physician when a patient crosses the threshold between low and medium appointment utilization. It may also aid in system-level policy development. Furthermore, our results highlight the benefit of significant appointment utilization among high-risk patients.

Lombard effect onset times reveal the speed of vocal plasticity in a songbird

Animals that use vocal signals to communicate often compensate for interference and masking from background noise by raising the amplitude of their vocalisations. This response has been termed the Lombard effect. However, despite more than a century of research little is known how quickly animals can adjust the amplitude of their vocalisations after the onset of noise. The ability to respond quickly to increases in noise levels would allow animals to avoid signal masking and ensure their calls continue to be heard, even if they are interrupted by sudden bursts of high amplitude noise. We tested how quickly singing male canaries (Serinus canaria) exhibit the Lombard effect by exposing them to short playbacks of white noise and measuring the speed of their responses. We show that canaries exhibit the Lombard effect in as little as 300 ms after the onset of noise and are also able to increase the amplitude of their songs mid-song and mid-phrase without pausing. Our results demonstrate high vocal plasticity in this species and suggest that birds are able to adjust the amplitude of their vocalisations very rapidly to ensure they can still be heard even during sudden changes in background noise levels.

Empirical search for factors affecting mean particle size of PLGA microspheres containing macromolecular drugs

BACKGROUND AND OBJECTIVES

Poly(lactic-co-glycolic acid) (PLGA) has become one of the most promising in design, development, and optimization for medical applications polymers. PLGA-based multiparticulate dosage forms are usually prepared as microspheres where the size is from 5 to 100 µm, depending on the route of administration. The main objectives of the study were to develop a predictive model of mean volumetric particle size and on its basis extract knowledge of PLGA containing proteins forming behaviour.

METHODS

In the present study, a model for the prediction of mean volumetric particle size developed by an rgp package of R environment is presented. Other tools like fscaret, monmlp, fugeR, MARS, SVM, kNNreg, Cubist, randomForest and piecewise linear regression are also applied during the data mining procedure.

RESULTS

The feature selection provided by the fscaret package reduced the original input vector from a total of 295 input variables to 10, 16 and 19. The developed models had good predictive ability, which was confirmed by a normalized root-mean-square error (NRMSE) of 6.8 to 11.1% in 10-fold cross validation training procedure. Moreover, the best models were validated using external experimental data. The superior predictiveness had a model obtained by rgp in the form of a classical equation with a normalized root-mean-squared error (NRMSE) of 6.1%.

CONCLUSIONS

A new approach is proposed for computational modelling of the mean particle size of PLGA microspheres and rules extraction from tree-based models. The feature selection leads to revealing chemical descriptor variables which are important in predicting the size of PLGA microspheres. In order to achieve better understanding in the relationships between particle size and formulation characteristics, the surface analysis method and rules extraction procedures were applied.

GeneBreak: detection of recurrent DNA copy number aberration-associated chromosomal breakpoints within genes

Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. 'GeneBreak' is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, 'GeneBreak' collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, 'GeneBreak', is implemented in R ( www.cran.r-project.org) and is available from Bioconductor ( www.bioconductor.org/packages/release/bioc/html/GeneBreak.html).

GeneBreak: detection of recurrent DNA copy number aberration-associated chromosomal breakpoints within genes

Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. 'GeneBreak' is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, 'GeneBreak' collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, 'GeneBreak', is implemented in R ( www.cran.r-project.org) and is available from Bioconductor ( www.bioconductor.org/packages/release/bioc/html/GeneBreak.html).

Detection of Local DNA Copy Number Changes in Lung Cancer Population Analyses Using A Multi-Scale Approach

Emerging advances in genomic sequencing have prompted the development of new computational methods for studying the genomic sources of human diseases. This paper presents a recent statistical approach for detection of local regions with significant copy number alterations (CNAs) in lung cancer population. Mapping such regions is of interest as they are potentially associated with lung cancer. Conventional application of multiple testing methods corresponds to testing for CNAs at each probe separately and thresholding the t-statistics as test statistics. Due to the large number of probes, this approach often fails to detect CNA regions. In contrast, the proposed method uses the heights of located peaks and improves the detection power. This is achieved by taking advantage of the spatial structure in the data as well as reducing the number of tests in the multiple comparisons problem. In copy number analysis, it is common to apply segmentation or change detection tools to each individual genomic sample. However, since segmentation results vary among subjects, it becomes difficult to find the common genomic regions in population analyses. Our approach solves this problem by performing the analysis using summary statistics to study at population level directly. Hence, the region detection is performed on the summary t-statistic map. The proposed method is applied to lung cancer data and shows promise for detection of local regions with significant CNAs.

Circular piecewise regression with applications to cell-cycle data

Applications of circular regression models appear in many different fields such as evolutionary psychology, motor behavior, biology, and, in particular, in the analysis of gene expressions in oscillatory systems. Specifically, for the gene expression problem, a researcher may be interested in modeling the relationship among the phases of cell-cycle genes in two species with differing periods. This challenging problem reduces to the problem of constructing a piecewise circular regression model and, with this objective in mind, we propose a flexible circular regression model which allows different parameter values depending on sectors along the circle. We give a detailed interpretation of the parameters in the model and provide maximum likelihood estimators. We also provide a model selection procedure based on the concept of generalized degrees of freedom. The model is then applied to the analysis of two different cell-cycle data sets and through these examples we highlight the power of our new methodology.

Efficient and Accurate OTU Clustering with GPU-Based Sequence Alignment and Dynamic Dendrogram Cutting

De novo clustering is a popular technique to perform taxonomic profiling of a microbial community by grouping 16S rRNA amplicon reads into operational taxonomic units (OTUs). In this work, we introduce a new dendrogram-based OTU clustering pipeline called CRiSPy. The key idea used in CRiSPy to improve clustering accuracy is the application of an anomaly detection technique to obtain a dynamic distance cutoff instead of using the de facto value of 97 percent sequence similarity as in most existing OTU clustering pipelines. This technique works by detecting an abrupt change in the merging heights of a dendrogram. To produce the output dendrograms, CRiSPy employs the OTU hierarchical clustering approach that is computed on a genetic distance matrix derived from an all-against-all read comparison by pairwise sequence alignment. However, most existing dendrogram-based tools have difficulty processing datasets larger than 10,000 unique reads due to high computational complexity. We address this difficulty by developing two efficient algorithms for CRiSPy: a compute-efficient GPU-accelerated parallel algorithm for pairwise distance matrix computation and a memory-efficient hierarchical clustering algorithm. Our experiments on various datasets with distinct attributes show that CRiSPy is able to produce more accurate OTU groupings than most OTU clustering applications.

Improved Variable Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B Infection Relevant Factors in Community Residents

OBJECTIVES

In epidemiological studies, it is important to identify independent associations between collective exposures and a health outcome. The current stepwise selection technique ignores stochastic errors and suffers from a lack of stability. The alternative LASSO-penalized regression model can be applied to detect significant predictors from a pool of candidate variables. However, this technique is prone to false positives and tends to create excessive biases. It remains challenging to develop robust variable selection methods and enhance predictability.

MATERIAL AND METHODS

Two improved algorithms denoted the two-stage hybrid and bootstrap ranking procedures, both using a LASSO-type penalty, were developed for epidemiological association analysis. The performance of the proposed procedures and other methods including conventional LASSO, Bolasso, stepwise and stability selection models were evaluated using intensive simulation. In addition, methods were compared by using an empirical analysis based on large-scale survey data of hepatitis B infection-relevant factors among Guangdong residents.

RESULTS

The proposed procedures produced comparable or less biased selection results when compared to conventional variable selection models. In total, the two newly proposed procedures were stable with respect to various scenarios of simulation, demonstrating a higher power and a lower false positive rate during variable selection than the compared methods. In empirical analysis, the proposed procedures yielding a sparse set of hepatitis B infection-relevant factors gave the best predictive performance and showed that the procedures were able to select a more stringent set of factors. The individual history of hepatitis B vaccination, family and individual history of hepatitis B infection were associated with hepatitis B infection in the studied residents according to the proposed procedures.

CONCLUSIONS

The newly proposed procedures improve the identification of significant variables and enable us to derive a new insight into epidemiological association analysis.

Hybrid algorithms for multiple change-point detection in biological sequences

Array comparative genomic hybridization (aCGH) is one of the techniques that can be used to detect copy number variations in DNA sequences in high resolution. It has been identified that abrupt changes in the human genome play a vital role in the progression and development of many complex diseases. In this study we propose two distinct hybrid algorithms that combine efficient sequential change-point detection procedures (the Shiryaev-Roberts procedure and the cumulative sum control chart (CUSUM) procedure) with the Cross-Entropy method, which is an evolutionary stochastic optimization technique to estimate both the number of change-points and their corresponding locations in aCGH data. The proposed hybrid algorithms are applied to both artificially generated data and real aCGH experimental data to illustrate their usefulness. Our results show that the proposed methodologies are effective in detecting multiple change-points in biological sequences of continuous measurements.