Quantile Plots Research Articles

Extreme Value Modelling of the Monthly South African Industrial Index (J520) Returns

This study uses Extreme Value Theory (EVT), Value-at-Risk (VaR) and Expected Shortfall (ES) analysis as a unified tool for managing extreme financial risk. The study extends the application of the generalised Pareto distribution (GPD) by modelling monthly South African Industrial Index (J520) returns (years: 1995C2018) to quantify the tail-related risk measures. The GPD is used to estimate the tail-related risk measures using the Peak over Threshold (PoT) method. Maximum Likelihood Estimates (MLE) of model parameters were obtained and the models goodness of fit was assessed graphically using Quantile-Quantile (QCQ) plots, Probability (PCP) plots, scatter plots, residuals, return levels and density plots. The findings are that the GPD provides an adequate fit to the data of excesses (extreme losses or gains). Low frequency but very high or very low returns impact on investment decisions. Calculations of the VaR and ES tail-related risk measures based on the fitted GPD model are given. The results reveal that for an investment in the South African Industrial Index (J520), the prospect of potential extreme losses is less than the prospect of potential extreme gains. There seems to be an upper bound where losses do not seem to exceed easily. The study concluded that EVT, together with VaR and ES analysis are useful tools that can be applied in practice to manage index/stock price risk and help investors improve their investment decisions and trading strategies through better quality information derived from the tools. This study contributes to empirical evidence on EVT methods that help to protect financial systems against unpredictable fluctuations and losses of extreme nature.

Foundations of Machine Learning-Based Clinical Prediction Modeling: Part V-A Practical Approach to Regression Problems.

This chapter goes through the steps required to train and validate a simple, machine learning-based clinical prediction model for any continuous outcome. We supply fully structured code for the readers to download and execute in parallel to this section, as well as a simulated database of 10,000 glioblastoma patients who underwent microsurgery, and predict survival from diagnosis in months. We walk the reader through each step, including import, checking, splitting of data. In terms of pre-processing, we focus on how to practically implement imputation using a k-nearest neighbor algorithm. We also illustrate how to select features based on recursive feature elimination and how to use k-fold cross validation. We demonstrate a generalized linear model, a generalized additive model, a random forest, a ridge regressor, and a Least Absolute Shrinkage and Selection Operator (LASSO) regressor. Specifically for regression, we discuss how to evaluate root mean square error (RMSE), mean average error (MAE), and the R2 statistic, as well as how a quantile-quantile plot can be used to assess the performance of the regressor along the spectrum of the outcome variable, similarly to calibration when dealing with binary outcomes. Finally, we explain how to arrive at a measure of variable importance using a universal, nonparametric method.

AACE2021-A-1019: How to Interpret Normality Distribution in a Hormonal Ratio: X/Y Versus Y/X

Hormonal ratios have analytical, interpretational importance. The ratio distribution depends on the mathematical position of the hormone, i.e., X/Y or Y/X). Our objective is to assess the best statistical model to interpret normality in a hormonal ratio (X/Y vs Y/X), using testosterone (T) and estradiol (E2) as numerators and denominators interchangeably. This descriptive review relied on real-life hormonal data (T and E2) and (T/E2 and E2/T) from 1198 premenopausal women. We used systematic testing approaches for normality, skewness, kurtosis, Shapiro-Wilk (S-W), Kolmogorov–Smirnov (K-S) corrected by (Lilliefors Significance Correction). We used normal Quantile-Quantile (Q-Q) plots for testing normality graphically. Log-transformation, and correlation analyses, before and after adjustment for a proposed composite factor was done. Age, body weight, and height were the proposed covariates for our distribution. We used the (Dimension Reduction) by Principal Component Analysis (PCA) to create a composite covariate (Composite Body Shape Factor). Adjustment of variables eliminates the influence of each covariate on the outcome. Then we used Parametric (Pearson’s r) and non-parametric (Spearman’s) partial and bivariate correlation analyses to test the distribution of the dependent variables (DV) after adjustment for the composite factor. Analysis of Covariance (ANCOVA) and univariate linear regression tested the DVs and the outcome. We used hirsutism as an outcome. We adjusted the DVs for the composite body shape factor to eliminate the covariates' effect. Then we applied the univariate linear regression analysis- full factorial model- to test the data during adjustment. We used Levene's test to evaluate the error distribution. The estimated marginal means were evaluated. We examined the ordinary linear regression platform in SPSS to assess the cumulative parametric change in Pearson's Coefficient of the parametric DVs after adjustment. The data lacked normality graphically (markedly right-skewed and positively leptokurtic) and on K-S and S-W. The log-transformation decreases (not eliminate) the normality deviation, which can be proven graphically but not in K-S and S-W, due to the outliers. Correlation analyses gave unsatisfactory results during adjustment for the composite factor. Parametric tests make more rigorous normality assumptions than that non-parametric tests and could be applied for testing. ANCOVA used a more complex interplay to describe normality dispersion during adjustment, to provide an explainable robust normality assumption. Additional descriptive results were obtained from the linear regression analysis, similar to correlational analysis with different significance levels. When conducting statistical analysis for any hormonal ratio of any form, the researcher must adopt more than one statistical method to robust the normality assumption of the distribution. There is no privilege of one test over another to achieve normality. However, ANCOVA and linear regression appeared to be more descriptive and more genuine in preserving the normality concept.

Optimal coordination of directional overcurrent relays using hybrid fractional computing with gravitational search strategy

The protection system has a vital role in subsystems of the power network including the generation, transmission and distribution system. This system combines several types of protection relays including directional over current relay, differential relay, distance relay, earth faults relay, inverse definite minimum time (IDMT) relay for the protection of line, generators bus bars, transformers, line to ground faults and feeder lines of the power system. In this research paper, the overall performance of the protection system is improved by reducing the overall operational time of directional over current relays (DOCRs) through the optimal coordination between the primary and backup over current protective relays while keeping the time multiplier setting (TMS), pickup tap setting (PTS) and coordination time of interval (CTI) within the allowable limits. A new optimization strategy, namely the FPSOGSA, is developed by integrating the concept of fractional calculus inside the mathematical model of canonical particle swarm optimization and combining with traditional gravitational search algorithm to enhance the optimizer characteristics and tested on coordination problem of DOCRs in standard power systems including the IEEE 3, 8 and 15 bus networks. The results, yielded by proposed algorithm and their comparison with other well-known recently established counterparts, reveal that the synergy of FC, PSO and GSA enhance the performance of the optimizer by combining the individual strengths of these tools and finding the best global solution. The consistency, stability and reliability of FPSOGSA is endorsed by comprehensive statistical analysis based on the empirical cumulative distribution function, histograms, boxplot illustrations, quantile–quantile plots, minimum fitness evolution in each independent simulation.

Evaluation of Plotting Position Formulae for Pearson Type 3 Distribution in Three Hydrological Stations on the Niger River

In this study, eight unbiased plotting position formulae recommended for Pearson Type 3 distribution were evaluated by comparing the simulated series of each formula with the annual maximum series (AMS) of River Niger at Baro, Koroussa and Shintaku hydrological stations, each having data length of 51years, 53 years and 58 years respectively.
 The parameters of Pearson Type 3 distribution were computed by the method of moments with corrections for skewness. While the fitting of Pearson Type 3 distribution proceeds with the development of flood – return period (Q-T) relationship, followed by application of the derived Q- T relation to compute simulated discharges for comparison with AMS of the study stations.
 The plotting position formulae were evaluated on the basis of optimum values of the statistically goodness-of-fit of probability plot correlation coefficient (PPCC), relative root mean square error (RRMSE), percent bias (PBIAS), mean absolute error (MAE) and Nash-sutcliffe efficiency (NSE), across the stations.
 The plotting position formulae were ranked on scale of 1 to 8. Thus a plotting formula that best simulates the empirical observations using the goodness-of-measures was scored “1” and so on. The individual scores per plotting position were summed across the gof tests to obtain the total score. 
 The study show that Chegodayev is the best plotting position formula for Baro, Weibull is the best plotting position Formula for Kourassou and Shintaku hydrological stations. The overall performances of the eight plotting position formulae across the hydrological stations show that weibull distribution is the overall best having scored 27, seconded by Chegodayev with 30 and thirdly, Beard with 38.
 The Pearson Type 3 distribution had been found one of the best probability distribution model of flood flow in Nigeria and this study was conducted to gain in-depth knowledge of the distribution. Finally, this study recommends extension of the studies to Log-Pearson Type 3 distribution.

Mid-air haptics for shape recognition of virtual objects

This paper presents an experiment in which participants had to discriminate three mid-air haptic shapes (circle, square, point) by reporting whether the haptic stimulus (e.g., circle on the palm of the hand) was compatible with an image (e.g., a circle) or a word (e.g., “circle”) displayed on a screen. Results indicate that only the “point” stimulus was appreciably recognised and discriminated in terms of accuracy and time needed for the identification. Accuracy increased with repetition, and response time decreased, suggesting a learning effect. The comparison between visual and textual labels shows that for the haptic point stimulus there is no significant difference but a tendency to have greater accuracy with images than with texts, while the opposite result is found for the circle stimulus. This outcome suggests the need for new experiments focused of the effect of visual/textual labels to make the recognition/discrimination tasks of haptic stimuli easier. Practitioner Summary: Three haptic shapes were presented with images or texts, matching or not the stimuli. The point was easy to recognise, while the circle and the square were difficult to discriminate against each other. Visual/textual labels bring contradictory results for different shapes.

Experimental investigation on statistical characteristics of cell voltage distribution for a PEMFC stack under dynamic driving cycle

The proton exchange membrane fuel cell (PEMFC) stack consists of individual cells in series. Its operating life is subjected to performance of the weakest cell because of the short-board effect, thus voltage uniformity during dynamic long-running process is significant to its durability. In this work, based on a 1044 h aging experiment on a 6.55 kW PEMFC stack under dynamic driving cycle, voltage uniformity is analyzed. In a single cycle, voltage uniformity becomes worse with the increase of loading current and there are some local maxima of voltage coefficient variation (Cv) at the moment of loading or unloading step. Cv value at higher current is greater and increases faster with cycles. At the end of experiment, Cv at 135 A is more than 6%. Besides, skewness (Sk) is used to evaluate the skew direction and degree of cell voltage data in a cycle. In most cycles, Sk values at 34.22 A are above 0 and Sk values at 59.70 A and 135 A are below 0. After the Box-Cox transformation, which is used to improve symmetry of data and reduce Sk, the cell voltage data have passed the verifications of normal fitting, probability-probability plot and quantile-quantile plot. Therefore, it is found that cell voltage data tend to obey skewed normal distribution, which is of positive significance for improving voltage uniformity and durability of PEMFC stack.

Nurses' exposure to verbal abuse at a university hospital in Crete, Greece

Background & Aim: The current study aimed to investigate workplace bullying, specifically verbal abuse of working nurses, and its consequences on the physical and psychological levels. Methods & Materials: A cross-sectional study was designed and implemented in 151 nurses working at a university hospital. The survey was conducted in 2018 at a university hospital in Greece, and the Verbal Abuse Scale (G-AVQ v.0/2015) was used to assess the verbal abuse level. Multiple linear regression analysis was applied, and distributions were tested for their symmetry using Blom's method (QQ plot). Results: 88.1% were women as the mean age of all was 41.7 years and 16.1 years the mean length of employment. Almost all nurses (94.7%) reported experiencing verbal abuse, as 77.6% reported that the perpetrator was a patient's relative (p<0.05). Among cognitive assessment, coping, severity of effects, and strength of feeling, coping had the highest mean value the highest mean value, while severity of effects had the lowest (2.52 vs. 1.83, p<0.05). Multiple linear regression analysis showed that frequency of verbal abuse type has a significant correlation with higher coping (β=0.28, p=0.007) and strength of feeling values (β=0.29, p=0.010). Similarly, the severity of verbal abuse type has a significant correlation with higher strength of feeling values (β=0.30, p=0.014). Conclusion: Almost all the nurses reported experiencing verbal abuse as the 'patients' relatives were the main perpetrators. Although the nurses seem to cope with verbal abuse successfully, this fact is described as stressful. Moreover, the frequency, severity of verbal abuse, and consequences are correlated to the emotions that arise from it.

TIGER: The gene expression regulatory variation landscape of human pancreatic islets.

SUMMARYGenome-wide association studies (GWASs) identified hundreds of signals associated with type 2 diabetes (T2D). To gain insight into their underlying molecular mechanisms, we have created the translational human pancreatic islet genotype tissue-expression resource (TIGER), aggregating >500 human islet genomic datasets from five cohorts in the Horizon 2020 consortium T2DSystems. We impute genotypes using four reference panels and meta-analyze cohorts to improve the coverage of expression quantitative trait loci (eQTL) and develop a method to combine allele-specific expression across samples (cASE). We identify >1 million islet eQTLs, 53 of which colocalize with T2D signals. Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identify eight cASE colocalizations, among which we found a T2D-associated SLC30A8 variant. We make all data available through the TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translates into therapeutic insight and precision medicine for T2D.

Analysis of 6851 foot and ankle injuries from 2010-2017 in an emergency department

HintergrundSprunggelenk- und Fußverletzungen gehören zu den häufigsten Krankheitsbildern in der Traumatologie. Ziel dieser Studie war es, die demografischen Veränderungen von Patienten mit Fuß- und Sprunggelenkverletzungen, das zeitliche Auftreten und die Entwicklung über die Jahre in einer Notaufnahme zu untersuchen.Material und MethodenRetrospektive Datenauswertung von Patienten, welche aufgrund einer Fuß- und Sprunggelenkverletzung in der Notaufnahme im Zeitraum von 2010 bis 2017 behandelt wurden. Die Patienten wurden mittels ICD-Codes identifiziert und die demografischen Veränderungen, das zeitliche Auftreten und die Entwicklung über die Jahre analysiert. Mittels Quantil-Quantil-Diagramm wurden kontinuierliche Variablen auf Normalverteilung getestet und, falls zutreffend, mittels t‑Test oder nichtparametrischem Mann-Whitney-U-Test auf Signifikanz überprüft.ErgebnisseInsgesamt wurden 6581 Fuß- und Sprunggelenkverletzungen in die Analyse eingeschlossen. Das mittlere Alter von Patienten mit einer Fußfraktur war 39 ± 17,4 Jahre und mit einer Sprunggelenkfraktur 47 ± 19,2 Jahre (p < 0,001). Ligamentäre Verletzungen des Sprunggelenks traten insbesondere bei jüngeren Patienten in den Sommermonaten auf. Die Altersverteilung bei Sprunggelenkfrakturen zeigte für Männer einen Häufigkeitsgipfel zwischen dem 30. und 39. Lebensjahr und bei Frauen ein vermehrtes Auftreten ab dem 50. Lebensjahr. In der Altersgruppe zwischen 65 und 75 Jahren stellten Trimalleolarfrakturen die zweithäufigste Sprunggelenkfraktur dar. Frakturen des Fußes betrafen am häufigsten Patienten im Alter von 20 bis 29 Jahren, wobei bei Frauen ein 2. Häufigkeitsgipfel zwischen dem 50. und 59. Lebensjahr zu beobachten war.SchlussfolgerungLigamentäre Verletzungen des Sprunggelenks sind häufige Krankheitsbilder in der Notaufnahme und betreffen v. a. jüngere Patienten. Sprunggelenkfrakturen zeigten für Frauen eine bimodale Altersverteilung, und insbesondere Bi- und Trimalleolarfrakturen waren vermehrt bei Frauen ab dem 65. Lebensjahr zu beobachten. Aufgrund der erhöhten Prävalenz von Fuß- und Sprunggelenkfrakturen bei älteren Frauen sollte in diesem Patientenkollektiv eine weitere Osteoporoseabklärung veranlasst werden.

Homogeneity tests for functional data based on depth-depth plots with chemical applications

One of the standard problems in statistics is determining if two samples come from the same population, that is, testing homogeneity for two samples. In this paper, we propose homogeneity tests in the context of functional data, adopting an idea from multivariate analysis corresponding to the depth-depth plot. This plot is a multivariate generalization of the quantile-quantile plot. We propose some statistics based on the depth-depth plot, and use bootstrapping to approximate their null distributions. We conduct simulations to state the empirical size and power of the proposed tests, obtaining better results than other homogeneity tests considered in the literature. We detect that our test has very high power in relation to other competing tests. We employ many different depths based on what is proposed in the literature to see which is more suitable for this kind of homogeneity testing. Finally, we illustrate the obtained results with chemical heterogeneous data to show potential applications, getting consistent results.

BNPdensity: Bayesian nonparametric mixture modelling in R

SummaryRobust statistical data modelling under potential model mis‐specification often requires leaving the parametric world for the nonparametric. In the latter, parameters are infinite dimensional objects such as functions, probability distributions or infinite vectors. In the Bayesian nonparametric approach, prior distributions are designed for these parameters, which provide a handle to manage the complexity of nonparametric models in practice. However, most modern Bayesian nonparametric models seem often out of reach to practitioners, as inference algorithms need careful design to deal with the infinite number of parameters. The aim of this work is to facilitate the journey by providing computational tools for Bayesian nonparametric inference. The article describes a set of functions available in the R package BNPdensity in order to carry out density estimation with an infinite mixture model, including all types of censored data. The package provides access to a large class of such models based on normalised random measures, which represent a generalisation of the popular Dirichlet process mixture. One striking advantage of this generalisation is that it offers much more robust priors on the number of clusters than the Dirichlet. Another crucial advantage is the complete flexibility in specifying the prior for the scale and location parameters of the clusters, because conjugacy is not required. Inference is performed using a theoretically grounded approximate sampling methodology known as the Ferguson &amp; Klass algorithm. The package also offers several goodness‐of‐fit diagnostics such as QQ plots, including a cross‐validation criterion, the conditional predictive ordinate. The proposed methodology is illustrated on a classical ecological risk assessment method called the species sensitivity distribution problem, showcasing the benefits of the Bayesian nonparametric framework.

Elementary integrate-and-fire process underlies pulse amplitudes in Electrodermal activity.

Electrodermal activity (EDA) is a direct read-out of sweat-induced changes in the skin’s electrical conductance. Sympathetically-mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process, which yields an inverse Gaussian model as the inter-pulse interval distribution. We have previously showed that the inter-pulse intervals in EDA follow an inverse Gaussian distribution. However, the statistical structure of EDA pulse amplitudes has not yet been characterized based on the physiology. Expanding upon the integrate-and-fire nature of sweat glands, we hypothesized that the amplitude of an EDA pulse is proportional to the excess volume of sweat produced compared to what is required to just reach the surface of the skin. We modeled this as the difference of two inverse Gaussian models for each pulse, one which represents the time required to produce just enough sweat to rise to the surface of the skin and one which represents the time requires to produce the actual volume of sweat. We proposed and tested a series of four simplifications of our hypothesis, ranging from a single difference of inverse Gaussians to a single simple inverse Gaussian. We also tested four additional models for comparison, including the lognormal and gamma distributions. All models were tested on EDA data from two subject cohorts, 11 healthy volunteers during 1 hour of quiet wakefulness and a different set of 11 healthy volunteers during approximately 3 hours of controlled propofol sedation. All four models which represent simplifications of our hypothesis outperformed other models across all 22 subjects, as measured by Akaike’s Information Criterion (AIC), as well as mean and maximum distance from the diagonal on a quantile-quantile plot. Our broader model set of four simplifications offered a useful framework to enhance further statistical descriptions of EDA pulse amplitudes. Some of the simplifications prioritize fit near the mode of the distribution, while others prioritize fit near the tail. With this new insight, we can summarize the physiologically-relevant amplitude information in EDA with at most four parameters. Our findings establish that physiologically based probability models provide parsimonious and accurate description of temporal and amplitude characteristics in EDA.

Quantile–Quantile Embedding for distribution transformation and manifold embedding with ability to choose the embedding distribution

We propose a new embedding method, named Quantile–Quantile Embedding (QQE), for distribution transformation and manifold embedding with the ability to choose the embedding distribution. QQE, which uses the concept of quantile–quantile plot from visual statistical tests, can transform the distribution of data to any theoretical desired distribution or empirical reference sample. Moreover, QQE gives the user a choice of embedding distribution in embedding the manifold of data into the low dimensional embedding space. It can also be used for modifying the embedding distribution of other dimensionality reduction methods, such as PCA, t-SNE, and deep metric learning, for better representation or visualization of data. We propose QQE in both unsupervised and supervised forms. QQE can also transform a distribution to either an exact reference distribution or its shape. We show that QQE allows for better discrimination of classes in some cases. Our experiments on different synthetic and image datasets show the effectiveness of the proposed embedding method.

Energy metabolism characteristic with risk of secondary bacterial infection in patients with hepatitis B virus-related chronic liver disease

Objective: To investigate and analyze the energy metabolism characteristics and the correlation between energy metabolism and the risk of secondary bacterial infection in patients with hepatitis B virus-related chronic liver disease (HBV-CLD). Methods: Data of 183 cases admitted to the Mengchao Hepatobiliary Hospital of Fujian Medical University from November 2017 to November 2020 were retrospectively analyzed. 79 cases of chronic hepatitis B, 51 cases of hepatitis B-related liver cirrhosis, and 53 cases of hepatitis B-related liver failure were collected. Among them patients with liver failure and decompensated liver cirrhosis were defined as severe liver disease group. The Quark RMR indirect calorimetry (COSMED Corporation, Italy) was used to exam the patients' energy metabolism condition, and the incidences of secondary bacterial infection of the patients during hospitalization were recorded. Shapiro-Wilk test and normal QQ plot were used to analyze the normal distribution of continuous variable data, which was consistent with the normal distribution and was described by mean ± standard deviation. In addition, if it did not conform to the normal distribution, the median and interquartile distance were used to describe it. Levene's test was used to test the homogeneity of variance of the data, which was consistent with the normal distribution. The t-test was used to compare the means of the two groups of samples. One-way analysis of variance was used to compare the mean values of the three groups of samples, and then the Tukey's test was used to compare the two groups. If the variance was uneven or did not conform to the normal distribution, the Wilcoxon rank sum test was used to compare the differences between the two groups. Kruskal-Wallis test (H test) was used to compare the differences between the three groups of samples, and then the Dunnett's test (Z test) was used for comparison between the two groups. Categorical variable data were analyzed using chi-square test. Logistic regression analysis was used to screen independent risk factors, and the criteria for variable inclusion (P < 0.05). Results: The respiratory entropy (RQ) and non-protein respiratory entropy (npRQ) of the three groups had statistically significant difference (P < 0.05). Among them, the RQ and npRQ of the chronic hepatitis B group were higher than hepatitis B-related liver cirrhosis group and hepatitis B-related liver failure group. There were statistically significant differences in fat oxidation rate (FAT%) and carbohydrate oxidation rate (CHO%) between the three groups (P < 0.05). Compared with hepatitis B-related liver cirrhosis group and hepatitis B-related liver failure group, chronic hepatitis B group (P < 0.05) had lower FAT% and higher CHO%. There were no statistically significant differences in the measured and predicted resting energy expenditure and protein oxidation rate (PRO%) between the three groups. The incidence of secondary bacterial infection in patients with severe liver disease was 48.39% (45/93). Compared with the non-infected group, the RQ and npRQ values ​​of the infected group were significantly decreased (P < 0.05), while FAT% was significantly increased (P < 0.05). Logistic regression analysis showed that glutamyltransferase, cholesterol, and npRQ were independent risk factors for secondary bacterial infections in patients with severe liver disease. Glutamyltransferase elevation, and cholesterol and npRQ depletion had suggested an increased risk of secondary bacterial infection. Subgroup analysis of patients with hepatitis B-related liver failure also showed that compared with non-infected group, RQ value and npRQ value of secondary bacterial infection group were significantly decreased (P < 0.05), while FAT% was significantly increased (P < 0.05). Conclusion: Patients with hepatitis B virus-related chronic liver disease generally have abnormal energy metabolism. Low RQ, npRQ, CHO% and high FAT% are related to the severity of the disease; while npRQ reduction is related to the risk of secondary bacterial infection in patients with severe liver disease, and thus can be used as a clinical prognostic indicator.

From consumer to enterprise grade: How the choice of four UAS impacts point cloud quality

AbstractUncrewed aerial systems (UAS), combined with structure‐from‐motion photogrammetry, has already proven to be very powerful for a wide range of geoscience applications and different types of UAS are used for scientific and commercial purposes. However, the impact of the UAS used on the accuracy of the point clouds derived is not fully understood, especially for the quantitative analysis of geomorphic changes in complex terrain. Therefore, in this study, we aim to quantify the magnitude of systematic and random error in digital elevation models derived from four commonly used UAS (XR6/Sony α6000, Inspire 2/X4s, Phantom 4 Pro+, Mavic Pro) following different flight patterns. The vertical error of each elevation model is evaluated through comparison with 156 GNSS reference points and the normal distribution and spatial correlation of errors are analysed. Differences in mean errors (−0.4 to −1.8 cm) for the XR6, Inspire 2 and Phantom 4 Pro are significant but not relevant for most geomorphological applications. The Mavic Pro shows lower accuracies with mean errors up to 4.3 cm, thus showing a higher influence of random errors. QQ plots revealed a deviation of errors from a normal distribution in almost all data. All UAS data except Mavic Pro exhibit a pure nugget semivariogram, suggesting spatially uncorrelated errors. Compared to the other UAS, the Mavic Pro data show trends (i.e. differences increase with distance across the survey—doming) and the range of semivariances is 10 times greater. The lower accuracy of Mavic Pro can be attributed to the lower GSD at the same flight altitude and most likely, the rolling shutter sensor has an effect on the accuracy of the camera calibration. Overall, our study shows that accuracies depend highly on the chosen data sampling strategy and that the survey design used here is not suitable for calibrating all types of UAS camera equally.

The q–q Boxplot

Boxplots have become an extremely popular display of distribution summaries for collections of data, especially when we need to visualize summaries for several collections simultaneously. The whiskers in the boxplot show only the extent of the tails for most of the data (with outside values denoted separately); more detailed information about the shape of the tails, such as skewness and “weight” relative to a standard reference distribution, is much better displayed via quantile–quantile (q-q) plots. We incorporate the q-q plot’s tail information into the traditional boxplot by replacing the boxplot’s whiskers with the tails from a q-q plot, and display these tails with confidence bands for the tails that would be expected from the tails of the reference distribution. We describe the construction of the “q-q boxplot” and demonstrate its advantages over earlier proposed boxplot modifications on data from economics and neuroscience, which illustrate the q-q boxplots’ effectiveness in showing important tail behavior especially for large datasets. The package qqboxplot (an extension to the ggplot2 package) is available for the R programming language. Supplementary files for this article are available online.

Is There an Association Between Bone Microarchitecture and Fracture in Patients who were Treated for High-grade Osteosarcoma? A Controlled Study at Long-term Follow-up Using High-resolution Peripheral Quantitative CT.

Neoadjuvant chemotherapy in patients with primary osteosarcoma improves survival rates, but it also causes side effects in various organs including bone. Low bone mineral density (BMD) can occur owing partly to chemotherapy or limited mobility. This can cause a higher risk of fractures compared with those who do not receive such treatment. Changes in BMD alone cannot explain the propensity of fractures. Studying microarchitectural changes of bone might help to understand the effect. (1) Do patients who were treated for osteosarcoma (more than 20 years previously) have low BMD? (2) Do these patients experience more fractures than controls who do not have osteosarcoma? (3) What differences in bone microarchitecture are present between patients treated for high-grade osteosarcoma and individuals who have never had osteosarcoma? We contacted 48 patients who were treated for osteosarcoma and who participated in an earlier study. These patients underwent multimodal treatment including chemotherapy more than 20 years ago. Of the original patient group, 60% (29 of 48) were missing, leaving 40% (19 of 48) available for inclusion in this study; all 19 agreed to participate. There were nine men and 10 women with a mean age of 46 ± 4 years and a mean time from surgery to examination of 28 ± 3 years. BMD was measured by dual-energy x-ray absorptiometry, and any fracture history was assessed using a questionnaire. Additionally, high-resolution peripheral quantitative CT was performed to compare the groups in terms of microarchitectural changes, such as cortical and trabecular area, cortical and trabecular thickness, cortical porosity, and endocortical perimeter. Participants in the control group were selected from a cohort consisting of a population-based random sample of 499 healthy adult women and men. Osteoporosis or low BMD was not an exclusion criterion for entering this study; however, the patients in the control group were selected based on a normal BMD (that is, T score > -1.0 at both the spine and hip). Also, the participants were matched based on age and sex. Differences between patients and controls were assessed using the Wilcoxon rank sum test for continuous variables and a chi-square test for categorical variables. A multiple regression analysis was performed. Model assumptions were checked using histograms and quantile-quantile plots of residuals. Twelve of 19 patients who were treated for osteosarcoma had either osteopenia (eight patients) or osteoporosis (four patients). More patients with osteosarcoma reported sustaining fractures (11 of 19 patients) than did control patients (2 of 19 controls; p < 0.001). Among all microarchitectural parameters, only the endocortical perimeter was increased in patients compared with the control group (75 ± 15 mm versus 62 ± 18 mm; p = 0.04); we found no differences between the groups in terms of cortical and trabecular area, cortical and trabecular thickness, or cortical porosity. Although patients who were treated for osteosarcoma had osteopenic or osteoporotic BMD and a higher proportion of patients experienced fractures than did patients in the control group, we could not confirm differences in microarchitectural parameters using high-resolution peripheral quantitative CT. Therefore, it seems that bone geometry and microstructural parameters are not likely the cause of the increased proportion of fractures observed in our patients who were treated for osteosarcoma. Until we learn more about the bone changes associated with chemotherapy in patients with osteosarcoma, we recommend that patients undergo regular BMD testing, and we recommend that physicians consider osteoporosis treatment in patients with low BMD. These data might provide the impetus for future multicenter prospective studies examining the association between chemotherapy and bone microarchitecture. Level III, therapeutic study.

Genome wide association study of response to interval and continuous exercise training: the Predict-HIIT study

BackgroundLow cardiorespiratory fitness (V̇O2peak) is highly associated with chronic disease and mortality from all causes. Whilst exercise training is recommended in health guidelines to improve V̇O2peak, there is considerable inter-individual variability in the V̇O2peak response to the same dose of exercise. Understanding how genetic factors contribute to V̇O2peak training response may improve personalisation of exercise programs. The aim of this study was to identify genetic variants that are associated with the magnitude of V̇O2peak response following exercise training.MethodsParticipant change in objectively measured V̇O2peak from 18 different interventions was obtained from a multi-centre study (Predict-HIIT). A genome-wide association study was completed (n = 507), and a polygenic predictor score (PPS) was developed using alleles from single nucleotide polymorphisms (SNPs) significantly associated (P < 1 × 10–5) with the magnitude of V̇O2peak response. Findings were tested in an independent validation study (n = 39) and compared to previous research.ResultsNo variants at the genome-wide significance level were found after adjusting for key covariates (baseline V̇O2peak, individual study, principal components which were significantly associated with the trait). A Quantile–Quantile plot indicates there was minor inflation in the study. Twelve novel loci showed a trend of association with V̇O2peak response that reached suggestive significance (P < 1 × 10–5). The strongest association was found near the membrane associated guanylate kinase, WW and PDZ domain containing 2 (MAGI2) gene (rs6959961, P = 2.61 × 10–7). A PPS created from the 12 lead SNPs was unable to predict V̇O2peak response in a tenfold cross validation, or in an independent (n = 39) validation study (P > 0.1). Significant correlations were found for beta coefficients of variants in the Predict-HIIT (P < 1 × 10–4) and the validation study (P < × 10–6), indicating that general effects of the loci exist, and that with a higher statistical power, more significant genetic associations may become apparent.ConclusionsOngoing research and validation of current and previous findings is needed to determine if genetics does play a large role in V̇O2peak response variance, and whether genomic predictors for V̇O2peak response trainability can inform evidence-based clinical practice.Trial registration Australian New Zealand Clinical Trials Registry (ANZCTR), Trial Id: ACTRN12618000501246, Date Registered: 06/04/2018, http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374601&isReview=true.

Discovery of Candidate DNA Methylation Cancer Driver Genes.

Epigenetic alterations, such as promoter hypermethylation, may drive cancer through tumor suppressor gene inactivation. However, we have limited ability to differentiate driver DNA methylation (DNAme) changes from passenger events. We developed DNAme driver inference-MethSig-accounting for the varying stochastic hypermethylation rate across the genome and between samples. We applied MethSig to bisulfite sequencing data of chronic lymphocytic leukemia (CLL), multiple myeloma, ductal carcinoma in situ, glioblastoma, and to methylation array data across 18 tumor types in TCGA. MethSig resulted in well-calibrated quantile-quantile plots and reproducible inference of likely DNAme drivers with increased sensitivity/specificity compared with benchmarked methods. CRISPR/Cas9 knockout of selected candidate CLL DNAme drivers provided a fitness advantage with and without therapeutic intervention. Notably, DNAme driver risk score was closely associated with adverse outcome in independent CLL cohorts. Collectively, MethSig represents a novel inference framework for DNAme driver discovery to chart the role of aberrant DNAme in cancer. SIGNIFICANCE: MethSig provides a novel statistical framework for the analysis of DNA methylation changes in cancer, to specifically identify candidate DNA methylation driver genes of cancer progression and relapse, empowering the discovery of epigenetic mechanisms that enhance cancer cell fitness.This article is highlighted in the In This Issue feature, p. 2113.