Quantile Plots Research Articles

Mem‐Transistor‐Based Gaussian Error–Generating Hardware for Post‐Quantum Cryptography Applications

AbstractQuantum computing can potentially hack the information encrypted by traditional cryptographic systems, leading to the development of post‐quantum cryptography (PQC) to counteract this threat. The key principle behind PQC is the “learning with errors” problem, where intentional errors make encrypted information unpredictable. Intentional errors refer to Gaussian distributed data. However, implementing Gaussian distributed errors is challenging owing to computational and memory overhead. Therefore, this study proposes a Gaussian error sampler that employs the intrinsic Gaussian properties of nanometer‐scale semiconductor devices. The proposed Gaussian error sampler significantly reduces computational and memory overhead. This work comprehensively evaluates the effectiveness of the proposed device by conducting statistical normality tests and generating quantile–quantile plots. The optimal programming voltage is identified to be −5.25 V, and the experimental results confirmed the Gaussian distribution of error data generated by the proposed module, aligning closely with software‐generated Gaussian distributions and distinct from uniform random distributions.

Comparing performances of different statistical models and multiple threshold methods in a nested association mapping population of wheat.

Nested association mapping (NAM) populations emerged as a multi-parental strategy that combines the high statistical power of biparental linkage mapping with greater allelic richness of association mapping. Several statistical models have been developed for marker-trait associations (MTAs) in genome-wide association studies (GWAS), which ranges from simple to increasingly complex models. These statistical models vary in their performance for detecting real association with the avoidance of false positives and false negatives. Furthermore, significant threshold methods play an equally important role for controlling spurious associations. In this study, we compared the performance of seven different statistical models ranging from single to multi-locus models on eight different simulated traits with varied genetic architecture for a NAM population of spring wheat (Triticum aestivum L.). The best identified model was further used to identify MTAs for 11 different agronomic and spectral reflectance traits, which were collected on the NAM population between 2014 and 2016. The "Bayesian information and linkage disequilibrium iteratively nested keyway (BLINK)" model performed better than all other models observed based on QQ plots and detection of real association in a simulated data set. The results from model comparison suggest that BLINK controls both false positives and false negatives under the different genetic architecture of simulated traits. Comparison of multiple significant threshold methods suggests that Bonferroni correction performed superior for controlling false positives and false negatives and complements the performance of GWAS models. BLINK identified 45 MTAs using Bonferroni correction of 0.05 for 11 different phenotypic traits in the NAM population. This study helps identify the best statistical model and significant threshold method for performing association analysis in subsequent NAM population studies.

Evaluation of probability distribution methods for flood frequency analysis in the Jhelum Basin of North-Western Himalayas, India

The Kashmir Valley has frequently endured devastating floods, presenting significant challenges for flood management due to unpredictable flood magnitudes and limited pre-disaster preparedness. A major difficulty in addressing these challenges is the lack of reliable flood frequency analysis (FFA) for effective planning and mitigation. This study seeks to overcome these issues by employing a rigorous quantitative analysis of annual peak discharge data over a 51-year period (1971–2021). One key challenge was the presence of low outliers, which could compromise the integrity of the data. To address this, the Multiple Grubbs-Beck test was applied to remove these outliers before conducting FFA. The study's original achievement lies in its application of multiple distribution models which include Gumbel (EV1), Generalized Extreme Variations (GEV), Log-Normal, Log Pearson III (LP III), Gamma and Normal distribution. Goodness-of-fit tests, including Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and Chi-square at the 5 % significance level, along with visualization techniques such as Probability plots (PP), Quantile plots (QQ), and Probabilistic distribution (PD) graphs, were used to identify the most suitable distribution methods. The Log Pearson Type III (LP-III) was identified as the best fit for the Sangam gauge site (Upper Jhelum), the gamma distribution for Ram Munshibagh (Middle Jhelum), and the Generalized Extreme Value (GEV) and LP-III for Asham (Lower Jhelum). For Sangam, the estimated discharges for 2, 5, 10, 50, 100, 150, 200, and 250-year return periods were 549.63, 1028.43, 1471.34, 2907.64, 3758.92, 4338.61, 4790.99, and 5167.23 cumecs, respectively, using LP-III. For Ram Munshibagh, the discharges were 602.13, 911.03, 1107.04, 1512.12, 1674.35, 1767.0, 1831.87, and 1881.74 cumecs using the gamma distribution. For Asham, the discharges were 685.8, 998.0, 1193.3, 1593.2, 1750.6, 1839.4, 1901.0, and 1948.0 cumecs using the GEV distribution. The findings indicate that the Jhelum River cannot accommodate excess discharge for return periods of 5 years or more, underscoring the need for enhanced flood management strategies.

Identification and Characterization of Key Genes Associated with Amelogenesis

Abstract Objectives The identification of key genes associated with amelogenesis would be helpful in finding solutions to genetic disorders in oral biology. The study aimed to use in silico analysis to identify the key genes involved in tooth development associated with preameloblasts (PABs) and secretory ameloblasts (SABs). Material and Methods The data was subjected to quality analysis and uniform manifold approximation and projection analysis. To examine the distribution of the genes and identify important upregulated loci, a p-value histogram, a quantile plot, a mean difference and mean-variance plot, and a volcano plot were generated. Finally, protein-protein interaction and gene enrichment analyses were performed to determine the ontology, relevant biological processes, and molecular functions of selected genes. Results A total of 157 genes were found to be significant in the PAB versus SAB comparison. HIST1H31 revealed strong interaction with HIST1H2BM, and EXO1, ASPM, SPC25, and TTK showed strong interactions with one other. The STRING database revealed that NCAPG, CENPU, NUSAP1, HIST1H2BM, and HIST1H31 are involved in biological processes. NCAPG, CENPU, SPC25, ETV5, TTK, ETV1, FAM9A, NUSAP1, HIST1H2BM, and HIST1H31 are involved in cellular components. Conclusion The TTK, NUSAP1, CENPU, NCAPG, FAM9A, ASPM, SPC25, and HIST1H31 genes demonstrate functions in cell division. These genes might play a role in ameloblast development. These results will be useful in developing new methods to stimulate ameloblast development, which is essential for tooth regeneration and tissue engineering. However, more research is required to validate the functions of these genes and the genes with which they interact. A wide variety of genetic, epigenetic, and exogenous signaling factors regulate these genes and pathways throughout development and differentiation, cell fate, and behavior.

Speaking Stata: Quantile–quantile plots, generalized

Quantile-quantile plots in the precise sense of scatterplots showing corresponding quantiles of two variables have long been supported by official command qqplot. That command is generalized here in several ways in a new command, qqplotg. In this article, I explain the major features of qqplotg and give several examples of its use. Themes include the use of quantile-quantile plots to explore the possibilities for working on a transformed scale and the value of plotting difference between quantiles versus mean quantile or plotting position. Various historical and methodological remarks are sprinkled throughout.

Enhancing shear strength predictions of rocks using a hierarchical ensemble model

Shear strength (SS) parameters are essential for understanding the mechanical behavior of materials, particularly in geotechnical engineering and rock mechanics. This study proposes a novel hierarchical ensemble model (HEM) to predict SS parameters: cohesion (C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C$$\\end{document}) and angle of internal friction (φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi$$\\end{document}). The HEM addresses the limitations of traditional machine learning models. Its performance was validated using leave-one-out cross-validation (LOOCV) and out-of-bag (OOB) evaluation methods. The model's accuracy was assessed with R-squared correlation (R2), absolute average relative error percentage (AAREP), Taylor diagrams, and quantile–quantile plots. The computational results demonstrated that the proposed HEM outperforms previous studies using the same database. The model predicted φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi$$\\end{document} and C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C$$\\end{document} with R2 values of 0.93 and 0.979, respectively. The AAREP values were 1.96% for φ and 4.7% for C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C$$\\end{document}. These results indicate that the HEM significantly improves the prediction quality of φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi$$\\end{document} and C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C$$\\end{document}, and exhibits strong generalization capability. Sensitivity analysis revealed that σ_3maxσ3max (maximum principal stress) had the greatest impact on modeling both φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi$$\\end{document} and C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C$$\\end{document}. According to uncertainty analysis, the LOOCV and OOB had the widest uncertainty bands for the φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varphi$$\\end{document} and C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$C$$\\end{document} parameters, respectively.

Marked Point Process Secretory Events Statistically Characterize Leptin Pulsatile Dynamics.

Recent studies have highlighted leptin, a key hormone that regulates energy intake and induces satiety, due to the worldwide prevalence of obesity. In this study, we analyzed plasma leptin measurements from 18 women with premenopausal obesity before and after bromocriptine treatment. By using underlying pulses recovered through deconvolution, we modeled the leptin secretory pulses as marked point processes and applied statistical distributions to evaluate the dynamics of leptin, including the interpulse intervals and amplitudes of the secretion. We fit the generalized inverse Gaussian and lognormal distributions to the intervals and the Gaussian, lognormal, and gamma distributions to the amplitudes of pulses. We evaluated the models' goodness of fit using statistical metrics including Akaike's information criterion, Kolmogorov-Smirnov plots, and quantile-quantile plots. Our evaluation results revealed the effectiveness of these statistical distributions in modeling leptin secretion. Although the lognormal and gamma distributions performed the best based on the metrics, we found all distributions capable of accurately modeling the timing of secretory events, leading us to a better understanding of the physiology of leptin secretion and providing a basis for leptin monitoring. In terms of pulse amplitude, the evaluation metrics indicated the gamma distribution as the most accurate statistical representation. We found no statistically significant effect of bromocriptine intake on the model parameters except for one distribution model.

Uncertainty evaluation in density and viscosity of nanofluids at different temperatures using Gaussian process regression-based Monte-Carlo simulations

Uncertainty in experimental measurements is likely to be incorporated due to various limitations in measurement instruments, experimental procedures, quantity of chemicals, etc. Obtaining the true properties of nanofluids is a difficult task. Hence, the evaluation of the dynamic viscosity and density is strenuous. The present work aims to study the influence of uncertainties in temperature and concentration on density and dynamic viscosity of Graphene nanoparticle/distilled water, multiwall carbon nanotube/distilled water, and alumina/distilled water nanofluids using Gaussian Process Regression (GPR) learning algorithm guided Monte-Carlo simulations. The GPR algorithm is employed to predict the properties of the nanofluids which are then fed to Monte-Carlo simulations to predict the uncertainty. The noise and uncertainties are introduced in the mole fraction and temperature artificially and are validated using the Kolmogorov-Smirnov test and Quantile-Quantile plot. The influence of different noise levels in the data is also carried out.

Comparison of Machine Learning Models for Predicting Interstitial Glucose Using Smart Watch and Food Log

This study examines the performance of various machine learning (ML) models in predicting Interstitial Glucose (IG) levels using data from wrist-worn wearable sensors. The insights from these predictions can aid in understanding metabolic syndromes and disease states. A public dataset comprising information from the Empatica E4 smart watch, the Dexcom Continuous Glucose Monitor (CGM) measuring IG, and a food log was utilized. The raw data were processed into features, which were then used to train different ML models. This study evaluates the performance of decision tree (DT), support vector machine (SVM), Random Forest (RF), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), lasso cross-validation (LassoCV), Ridge, Elastic Net, and XGBoost models. For classification, IG labels were categorized into high, standard, and low, and the performance of the ML models was assessed using accuracy (40–78%), precision (41–78%), recall (39–77%), F1-score (0.31–0.77), and receiver operating characteristic (ROC) curves. Regression models predicting IG values were evaluated based on R-squared values (−7.84–0.84), mean absolute error (5.54–60.84 mg/dL), root mean square error (9.04–68.07 mg/dL), and visual methods like residual and QQ plots. To assess whether the differences between models were statistically significant, the Friedman test was carried out and was interpreted using the Nemenyi post hoc test. Tree-based models, particularly RF and DT, demonstrated superior accuracy for classification tasks in comparison to other models. For regression, the RF model achieved the lowest RMSE of 9.04 mg/dL with an R-squared value of 0.84, while the GNB model performed the worst, with an RMSE of 68.07 mg/dL. A SHAP analysis identified time from midnight as the most significant predictor. Partial dependence plots revealed complex feature interactions in the RF model, contrasting with the simpler interactions captured by LDA.

The Relative Prevalence of Wave Packets and Coherent Structures in the Inertial and Kinetic Ranges of Turbulence as Seen by Solar Orbiter

The Solar Orbiter (SO) mission provides the opportunity to study the evolution of solar wind turbulence. We use SO observations of nine extended intervals of homogeneous turbulence to determine when turbulent magnetic field fluctuations may be characterized as: (i) wave packets and (ii) coherent structures (CSs). We perform the first systematic scale-by-scale decomposition of the magnetic field using two wavelets known to resolve wave packets and discontinuities, the Daubechies 10 (Db10) and Haar, respectively. The probability distribution functions (PDFs) of turbulent fluctuations on small scales exhibit stretched tails, becoming Gaussian at the outer scale of the cascade. Using quantile–quantile plots, we directly compare the wavelet fluctuations PDFs, revealing three distinct regimes of behavior. Deep within the inertial range (IR) both decompositions give essentially the same fluctuation PDFs. Deep within the kinetic range (KR) the PDFs are distinct as the Haar decompositions have larger variance and more extended tails. On intermediate scales, spanning the IR–KR break, the PDF is composed of two populations: a core of common functional form containing ∼97% of fluctuations, and tails that are more extended for the Haar decompositions than the Db10 decompositions. This establishes a crossover between wave-packet (core) and CS (tail) phenomenology in the IR and KR, respectively. The range of scales where the PDFs are two-component is narrow at 0.9 au (4–16 s) and broader (0.5–8 s) at 0.4 au. As CS and wave–wave interactions are both candidates to mediate the turbulent cascade, these results offer new insights into the distinct physics of the IR and KR.

A Novel Tool for the Rapid and Transparent Verification of Reference Intervals in Clinical Laboratories.

Background/Objectives: We present a software package called reflimR (Version 1.0.6), which enables rapid and transparent verification of reference intervals from routine laboratory measurements. Our method makes it easy to compare the results with specified target values and facilitates the interpretation of deviations using traffic light colors. Methods: The algorithm includes three procedural steps: (a) definition of an appropriate distribution model, based on Bowley's quartile skewness, (b) iterative truncation, based on a modified boxplot method to obtain the central 95% of presumably inconspicuous results, and (c) extrapolation of reference limits from a truncated normal quantile-quantile plot. Results: All algorithms have been combined into one consolidated library, which can be called in the R environment with a single command reflim (x). Using an example dataset included in the package, we demonstrate that our method can be applied to mixed data containing a substantial proportion of pathological values. It leads to similar results as the direct guideline approach as well as the more sophisticated indirect refineR software package. As compared to the latter, reflimR works much faster and needs smaller datasets for robust estimates. For the interpretation of the results, we present an intuitive color scheme based on tolerance ranges (permissible uncertainty of laboratory results). We show that a relatively high number of published reference limits require careful reevaluation. Conclusions: The reflimR package closes the gap between direct guideline methods and the more sophisticated indirect refineR method. We recommend reflimR for the rapid routine verification of large amounts of reference limits and refineR for a careful analysis of unclear or doubtful results from this check.

An extreme value analysis of water levels at the Akosombo dam, Ghana

The Akosombo Dam is the largest dam in Ghana and is linked to the world's largest man-made lake by surface area. The top of flood control pool of the dam has been breached a number of times, so it is of interest to know the corresponding probability. The paper fits the generalized extreme value distribution to the extreme water levels – with all three of its parameters (including the shape parameter) accounting for various linear trends, seasonality and cyclic trends with respect to time, the first time such a model has been fitted. The fitted model contains in total 50 parameters. It provided an adequate fit, as evaluated by probability plots, quantile plots, and the Kolmogorov-Smirnov test. It is used to provide return level estimates as well as probabilities of the top of flood control pool of the dam being breached.

From Delays to Densities: Exploring Data Uncertainty through Speech, Text, and Visualization

AbstractUnderstanding and communicating data uncertainty is crucial for making informed decisions in sectors like finance and healthcare. Previous work has explored how to express uncertainty in various modes. For example, uncertainty can be expressed visually with quantile dot plots or linguistically with hedge words and prosody. Our research aims to systematically explore how variations within each mode contribute to communicating uncertainty to the user; this allows us to better understand each mode's affordances and limitations. We completed an exploration of the uncertainty design space based on pilot studies and ran two crowdsourced experiments examining how speech, text, and visualization modes and variants within them impact decision‐making with uncertain data. Visualization and text were most effective for rational decision‐making, though text resulted in lower confidence. Speech garnered the highest trust despite sometimes leading to risky decisions. Results from these studies indicate meaningful trade‐offs among modes of information and encourage exploration of multimodal data representations.

Statistical downscaling of precipitation in northwestern Iran using a hybrid model of discrete wavelet transform, artificial neural networks, and quantile mapping

Downscaling of daily precipitation from Global Circulation Models (GCMs)-predictors at a station level, especially in arid and semi-arid regions, has remained a formidable challenge yet. The current study aims at proposing a coupled model of Discrete Wavelet Transform (DWT), Artificial Neural Networks (ANNs), and Quantile Mapping (QM) for statistical downscaling of daily precipitation. Given the historic (1978–2005) and future (2006–2100) predictors of eight-selected GCMs under Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5, a viable DWT-ANN model was developed for each station. Subsequently, we linked QM to DWT-ANN for bias correction and drizzle effect postprocessing of the DWT-ANN-historic/future projected precipitation. The skill of DWT-ANN-QM was demonstrated using various evaluation metrics, including Taylor diagram, Quantile–Quantile plot, Empirical Cumulative Distribution Function, and wet/dry spell analysis. We appraise the efficacy of the coupled model at 12 weather stations over the Gharehsoo River Basin (GRB) in northwestern Iran. Compared to the observed wet/dry spells, the dry-spells were better simulated via DWT-ANN-QM rather than the wet-spells wherein length and exceedance probability of the spells were overestimated. Results indicated that the future precipitation across the GRB will rise, on average, from 10 to 17% depending on weather station. Seasonal spatial distribution of the middle future (2041–2070) precipitation illustrated that an increase for fall and winter, especially, is expected, whereas the amount of the future spring and summer precipitation is projected to be declined. Having been developed and tested in a semi-arid basin, the efficacy of the coupled model should be further assessed in more humid climates.

Estimating Inhaled Nitrogen Dioxide from the Human Biometric Response

Breathing clean air is crucial for maintaining good human health. The air we inhale can significantly impact our physical and mental well-being, influenced by parameters such as particulate matter and gases (e.g. carbon dioxide, carbon monoxide, and nitrogen dioxide). Building on previous research that explored the effects of particulate matter (PM) in specific environments, analyzed using biometric indicators and machine learning models; this work focuses on the effects and estimation of inhaled nitrogen dioxide (NO<sub>2</sub>). This study involved a cyclist equipped with sensors to monitor various biometric parameters. In addition, an electric car following the cyclist measured the ambient NO<sub>2</sub> levels using an onboard sensor. A total of 329 biometric variables have been taken into account, of which 320 biometric variables are cognitive responses extracted using an electroencephalogram (EEG) and 9 biometric variables are physiological responses extracted using several sensors. Inhaled NO<sub>2</sub> levels are first estimated initially by making use of all 329 variables, then using 9 physiological responses and finally using only 6 of the 9 physiological responses. The study also uses a ranking method to pinpoint which biometric variables most significantly estimate inhaled NO<sub>2</sub> levels. Furthermore, it investigates the linear and non-linear relationship between certain variables and inhaled NO<sub>2</sub>. The general precision of the prediction for the data set was moderate, as indicated by the coefficient of determination (R<sup>2</sup>) and the root mean square error (RMSE) between the true and estimated values of NO<sub>2</sub> to be 0.35 and 5.41 ppb, respectively, in the test set. A higher accuracy in the prediction of lower values of NO<sub>2</sub> levels was qualitatively observed using a scatter diagram and a Quantile-Quantile plot where the data were more plentiful. For more robust conclusions, additional data and refined machine learning models are necessary.

Quadratic resampling method for the long-term [formula omitted]-ray background spectra using an HPGe detector

We developed a novel quadratic resampling method for summing up γ-ray spectra with different calibration parameters. We investigated a long-term environmental background γ-ray spectrum by summing up 114 spectra measured using a 30% HPGe detector between 2017 and 2021. Gain variations in different measurement periods shift γ-ray peak positions by a fractional pulse-height bin size up to around 2 keV. The resampling method was applied to measure low-level background γ-ray peaks in the γ-ray spectrum in a wide energy range from 50 keV to 3 MeV. We additionally document temporal variations in the activities of major γ-ray peaks, such as 40K (1461 keV), 208Tl (2615 keV), and other typical nuclides, along with contributions from cosmic rays. The normal distribution of γ-ray background count rates, as evidenced by quantile–quantile plots, indicates consistent data collection throughout the measurement period. Consequently, we assert that the quadratic resampling method for accumulating γ-ray spectra surpasses the linear method (Bossew, 2005) in various aspects.

Short-term office temperature forecasting through a data-driven approach integrated with bidirectional gated recurrent neural network

Accurate indoor temperature forecasting can facilitate the energy savings, thus improving the building energy efficiency. Based on the discontinuous sensor data collection in this research, a novel gated recurrent neural network model, bidirectional Gated Recurrent Unit (Bi-GRU) is developed, achieving superior performances and optimizing the forecasting accuracy, compared to other state-of-the-art models. The developed data-driven approach based on integrating data pre-processing, feature engineering, Kalman time-series smoothing, and neural network models is applied for indoor temperature forecasting, calibrated on two test scenarios. The prediction results are examined by error performance metrics, and the relative prediction residuals are visually compared in time-series plots and quantile–quantile (Q-Q) plots. Kalman smoothing has effectively improved the forecasting accuracy of regression models by reducing their prediction errors and upgrading the prediction accuracies with approximately 4 ∼ 9 % and 6 ∼ 19 % in single-step- and multi-step-ahead forecasting tasks, respectively. Bi-GRU with Kalman smoothing is verified to be the most efficacious forecasting solution by yielding the accuracy of 94.28 % and 91.58 % in Test scenario I, and 91.58 % and 83.82 % in Test scenario II for single-step- and multi-step-ahead predictions, respectively.

Breathe Easy, an automated respiratory data pipeline for waveform characteristic analysis

Whole body barometric flow through plethysmography is used to study respiratory output in rodent genetic and disease models. Turn-key commercial and custom systems are both used to gather multiple data streams for respiratory and metabolic parameters. Comprehensive and accurate data analysis is crucial to modelling congenital, pathogenic, and degenerative diseases. However, such studies produce large amounts of data that are time-consuming and diffcult to analyze. The investigator may use cumbersome hand annotation subject to observer bias or a series of software products to manage raw data, calculated data, statistical analysis, and graphing. Commercial software, however, is prohibitively expensive or bundled with turn-key systems limiting options for bespoke respiratory measurement systems. A lack of tools for high-throughput analysis of respiratory datasets remains a major challenge. Here, we present Breathe Easy, a novel open-source pipeline for processing raw recordings and associated metadata into operative outcomes, publication-worthy graphs, and robust statistical analyses including QQ and residual plots for assumption queries and data transformations. This pipeline uses a facile graphical user interface (GUI) for uploading data files, setting waveform feature thresholds, and defining experimental variables. After loading the GUI a, python-based program, Signal Analysis Selection and Segmentation Integration (SASSI) takes the user-settings and selects all quantitatively defined, quality breaths from each period of interest in the signal files. After SASSI, the STatistics and Graph Generator (STAGG) takes those breaths and performs linear mixed effects modeling based on user selections for each dependent variable of interest. Furthermore, our software can integrate non-respiratory experimental outcomes including quantified behavioral or gene expression data to test for breathing related associations. To validate breath selection and generate optimized default values, results from Breathe Easy were compared to manual selection by experts, which represents the current standard in the field. We show high quality breath selection and a significant reduction in quality of breaths that were rejected by the software. We also demonstrate Breathe Easy’s utility and capacity by examining a 2-year longitudinal study of an Alzheimer’s Disease mouse model to assess contributions of forebrain pathology in disordered breathing (600+ files; 1TB data). We show that, at least with this model of AD, there does not seem to be an association between forebrain pathology and respiratory outcomes. Breathe Easy offers a facile and highly customizable platform for automated handling and analysis of animal respiratory and metabolic data that sets the stage for high-throughput studies and machine learning approaches on large data sets. NIH: 1F32HL160073-01A1, R01HL130249 44617-S4. BCM McNair Scholar Program, March of Dimes Basil O'Connor Research Award, Parker B. Francis Fellowship, CJ Foundation for SIDS. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

What Is the Representation of Sexual and Gender Minority Identities Among Orthopaedic Professionals in the United States?

There is substantial corroborating evidence that orthopaedic surgery has historically been the least diverse of all medical and surgical specialties in terms of race, ethnicity, and sex. Growing recognition of this deficit and the benefits of a diverse healthcare workforce has motivated policy changes to improve diversity. To measure progress with these efforts, it is important to understand the existing representation of sexual and gender minorities among orthopaedic professionals. (1) What proportion of American Academy of Orthopaedic Surgeons (AAOS) members reported their identity as a sexual or gender minority? (2) What demographic factors are associated with the self-reporting of one's sexual orientation and gender identity? The AAOS published the updated membership questionnaire in January 2022 to collect information from new and existing society members regarding age and race or ethnicity and newly added categories of gender identity, sexual orientation, and pronouns. The questionnaire was updated with input from a committee of orthopaedic surgeons and researchers to ensure face validity. The AAOS provided a deidentified dataset that included the variables of interest: membership type, gender identity, sexual orientation, pronouns, age, race, and ethnicity. Of 35,427 active AAOS members, 47% (16,652) updated their membership questionnaire. To answer our first study question, we calculated the prevalence of participants who self-reported as lesbian, gay, bisexual, transgender, queer, or another sexual or gender minority identity (LGBTQ+) and other demographic characteristics of the 16,652 respondents. Categorical demographic data are described using frequencies and proportions. Median and IQR were used to describe the central tendency and variability. To answer our second study question, we conducted a stratified analysis to compare demographic characteristics between those who self-reported LGBTQ+ identity and those who did not. Visual methods (quantile-quantile plots) and statistical tests (Kolmogorov-Smirnov and Shapiro Wilk) confirmed that the age of AAOS member was not normally distributed. Therefore, a Kruskal Wallis test was used to determine the statistical associations between age and self-reported LGBTQ+ status. Chi-square tests were used to determine bivariate statistical associations between categorical demographic characteristics and self-reported LGBTQ+ status. A multivariable logistic regression model was developed to identify the independent demographic characteristics associated with respondents who self-reported LGBTQ+ identity. Further stratified analyses were not conducted to protect the anonymity of AAOS members. An alpha level of 5% was established a priori to define statistical significance. Overall, 3% (109 of 3679) and fewer than 1% (3 of 16,182) of the AAOS members (surgeons, clinicians, allied healthcare providers, and researchers) who updated their membership profiles reported identifying as a sexual (lesbian, gay, bisexual, queer) or gender minority (nonbinary or transgender), respectively. No individual self-identified as transgender. Five percent (33 of 603) of women and 3% (80 of 3042) of men self-identified as a sexual minority (such as lesbian, gay, bisexual, or queer). AAOS members who self-identified as LGBTQ+ were younger (OR 0.99 [95% confidence interval (CI) 0.98 to 0.99]; p < 0.001), less likely to self-identify as women (OR 0.86 [95% CI 0.767 to 0.954]; p < 0.001), less likely to be underrepresented in medicine (OR 0.49 [95% CI 0.405 to 0.599]; p < 0.001), and less likely to be an emeritus or honorary member (OR 0.75 [95% CI 0.641 to 0.883]; p < 0.003). The proportion of self-reported LGBTQ+ AAOS members is lower than the 7% of the general US population. The greater proportion of younger AAOS members reporting this information suggests progress in the pursuit of a more-diverse field. The study findings support standardized collection of sexual orientation and gender identity data to better identify and address diversity gaps. As orthopaedic surgery continues to transform to reflect the diversity of musculoskeletal patients, all orthopaedic professionals (surgeons, clinicians, allied healthcare providers, and researchers), regardless of their identities, are essential in the mission to provide equitable and informed orthopaedic care. Sexual and gender minority individuals may serve as important mentors to the next generations of orthopaedic professionals; individuals from nonminority groups should serve as important allies in achieving this goal.

Shared genetic aetiology of Alzheimer’s disease and age-related macular degeneration by APOC1 and APOE genes

BackgroundAlzheimer’s disease (AD) and age-related macular degeneration (AMD) share similar pathological features, suggesting common genetic aetiologies between the two. Investigating gene associations between AD and AMD may provide useful insights...