Accuracy Of Statistical Methods Research Articles

Genome-wide discovery of selection signatures in four Anatolian sheep breeds revealed by ddRADseq

High-density genomic data analyzed by accurate statistical methods are of potential to enlighten past breeding practices such as selection by unraveling fixed regions. In this study, four native Turkish sheep breeds (80 samples) were genotyped via 296.097 single nucleotide polymorphisms (SNPs) detected by double-digest restriction site-associated DNA (ddRADseq) library preparation combined with the Illumina HiSeq X Ten instrument in order to identify genes under selection pressure. A total of 32, 136, 133, and 119 protein-coding genes were detected under selection pressure by runs of homozygosity (ROH), integrated haplotype score (iHS), the ratio of extended haplotype homozygosity (Rsb), and fixation index (FST) approaches, respectively. Of these, a total of 129 genes were identified by at least two statistical models which overlapped with a total of 52 quantitative trait loci (QTL)-associated SNPs, known to be related to fiber diameter, milk content, body weight, carcass traits, some blood parameters, and entropion. A total of six genes under selection pressure were validated by three statistical approaches five of which are of potential to be integrated into animal breeding since they were associated with wool fiber diameter (ZNF208B), behaviors related to neurocognitive development (CBX1 and NFE2L1), adaptation to high-altitude (SDK1), and anxiety causing internal stress (GSG1L). The sixth gene (COPZ1) turned out to play an important role in coping with different types of cancer in mammals. In particular, ROH analysis uncovered significant findings that the Güney Karaman (GKR) had experienced different selection practices than the Akkaraman (AKR) breed. Moreover, some genes specifically under selection in the GKR breed turned out to be associated with olfaction (OR6K6, OR6N1, OR6N2, and OR4C16), survival during the gestation period (PRR15L), and heat stress (CDK5RAP9). The results of this study imply that GKR may become genetically different from the AKR breed at the genome level due to most probably experiencing different adaptation processes occurring in raised climatic conditions. These differences should be conserved to face future challenges, while other native Turkish sheep breeds could be monitored via genome-wide high-density SNP data to obtain deeper knowledge about the effects of natural selection.

Accurate statistical methods to cover the aspects of the increase in the incidence of kidney failure: A survey study in Ha'il -Saudi Arabia.

Chronic kidney disease (CKD) has become more common in recent decades, putting significant strain on healthcare systems worldwide. CKD is a global health issue that can lead to severe complications such as kidney failure and death. The purpose of this study was to investigate the actual causes of the alarming increase of kidney failure cases in Saudi Arabia using the supersaturated design analysis and edge design analysis. A cross-sectional questionnaire was distributed to the general population in the KSA, and data were collected using Google Forms. A total of 401 responses were received. To determine the actual causes of kidney failure, edge and supersaturated designs analysis methods were used, which resulted in statistical significance. All variables were studied from factor h1 to factor h18 related to the causes of kidney failure. The supersaturated analysis method revealed that the reasons for the increase in kidney failure cases are as follows: h9(Bad diet), h8(Recurrent urinary tract infection), h1 (Not drinking fluids), h6 (Lack of exercise), h14 (drinking from places not designated for valleys and reefs), h18 (Rheumatic diseases), h10 (Smoking and alcohol consumption), h13 (Direct damage to the kidneys), h2 (take medications), h17 (excessive intake of soft drinks), h12 (Infection), h5 (heart disease), h3 (diabetes), h4 (pressure disease), h15 (Dyes used in X-rays), and h11 (The presence of kidney stones) are all valid. The design analysis method by edges revealed that the following factors contributed to an increase in kidney failure cases: h8 (Recurrent urinary tract infection), h6 (Lack of exercise), h7 (Obesity), and h11. The findings showed that there were causes of kidney failure that led to the statistical significance, which is h8 (Recurrent urinary tract infection) and h11 (The presence of kidney stones).

Microstructure evolution, mechanical response and strengthening models for TA15 titanium alloy during thermal processes: A brief review

TA15 titanium alloy is widely used in aerospace industry in terms of its high specific strength, good thermal stability, and excellent corrosion resistance. To further improve its mechanical properties, thermal deformation combined with heat treatment is always used, during which the microstructure evolution and mechanical response are absolutely complex leading to the difficulty in control of final mechanical properties. Therefore, in-depth research on the relationship between microstructure and mechanical response of TA15 titanium alloy during these processes is of great significance. At present, less relevant review has been reported. In this article, the effects of process parameters such as heating temperature, strain rate, deformation amount, deformation mode, deformation path, cooling method and soaking time on the microstructure and mechanical properties of TA15 alloy, namely tensile performance and damage tolerance properties have been discussed. In addition, some strengthening models suitable for TA15 titanium alloy have been analyzed. In conclusion, different microstructure morphologies formed in various processing processes will display diverse mechanical performances in which the direct relationship between the yield strength and microstructure morphology can be established through distinct calculation models but with some limitations. So, in future works, microstructure and mechanical properties should be optimized further through adjusting processing parameters. Moreover, strengthening models remains to be modified by adopting more accurate statistical methods and considering more factors. Finally, establishing the relationship between damage tolerance properties such as fracture toughness, creep performance and LCF property and microstructure features should be considered more for TA15 alloy.

Machine learning score focused only on echocardiographic data to predict in-hospital outcomes in ICCU patients. A study from the ADDICT ICCU cohort

Abstract Background While several studies have shown the strong prognostic value of several transthoracic echocardiography (TTE) parameters in patients admitted in intensive cardiac care unit (ICCU), the interest of a severity score integrating these parameters has not been established. Machine learning (ML) represents an innovative and accurate statistical method to embed TTE parameters into a scoring system. Purpose To investigate the feasibility and accuracy of ML integrating TTE parameters measured at admission in ICCU to predict in-hospital major adverse cardiovascular events (MACE) and to compare its performance with existing scores. Methods In April 2021, 1,499 patients admitted to 39 French ICCU were included in the ADDICT-ICCU Cohort (NCT05063097). A comprehensive TTE was performed within the first 24 hours of admission. Twenty TTE parameters were systematically collected. ML involved automated feature selection by Boruta to select variables and prevent model overfitting. Several ML models (LASSO, Random Forest and XGBoost) were then trained on 70% of patients and evaluated on the other 30% as internal validation. Their performances were compared against standard logistic regression model, using receiver operating characteristics (ROC) curves, precision-recall (PR) curves and area-under-the curves (AUC). The primary composite outcome was in-hospital MACEs defined by all-cause mortality, cardiogenic shock (requiring medical or mechanical haemodynamic support) and resuscitated cardiac arrest. The external validation cohort of the ML score was performed in another center on 300 patients. Results Of 1,499 consecutive patients screened (63±15 years, 70% male), MACEs occurred in 67 patients (4.5%) including: 27 (1.8%) deaths, 38 (2.53%) cardiogenic shocks, and 14 (0.9%) resuscitated cardiac arrests. To build the ML models, 5 TTE parameters were selected as being the most important in predicting MACEs: LVOT VTI, E/e’ ratio, sPAP, TAPSE, and LVEF. The XGBoost model showed the best performance compared with the other ML models (AUROC for XGBoost: 0.83, Random-forest: 0.81, LASSO regression 0.79 and to a regression model: 0.76). Using the XGBoost model as our final ML-score, SHAP values for the parameters were: 0.047 for LVOT VTI, 0.041 for E/e’ ratio, 0.028 for SPAP, 0.021 for TAPSE and 0.01 for LVEF. Our ML-score exhibited a higher AUC compared with existing scores (TIMI, GRACE, qSOFA, Acute-HF) (Figure 1), and an incremental prognostic value for predicting in-hospital MACEs over and above clinical or biological data (Chi-square 59.72 p<0.001; C-Index 0.8 [0.71;0.89], p=0.012) (Figure 2). Conclusion The ML score including only TTE parameters exhibited a higher prognostic value to predict in-hospital MACE compared with any other existing traditional methods in patients admitted in ICCU. This ML score could be implemented in clinical practice given its ease of use (only 5 TTE parameters to analyze) and its prognostic implication.Figure 1.PerformanceFigure 2.Prognostic value

ICU-Acquired Colonization and Infection Related to Multidrug-Resistant Bacteria in COVID-19 Patients: A Narrative Review.

A large proportion of ICU-acquired infections are related to multidrug-resistant bacteria (MDR). Infections caused by these bacteria are associated with increased mortality, and prolonged duration of mechanical ventilation and ICU stay. The aim of this narrative review is to report on the association between COVID-19 and ICU-acquired colonization or infection related to MDR bacteria. Although a huge amount of literature is available on COVID-19 and MDR bacteria, only a few clinical trials have properly evaluated the association between them using a non-COVID-19 control group and accurate design and statistical methods. The results of these studies suggest that COVID-19 patients are at a similar risk of ICU-acquired MDR colonization compared to non-COVID-19 controls. However, a higher risk of ICU-acquired infection related to MDR bacteria has been reported in several studies, mainly ventilator-associated pneumonia and bloodstream infection. Several potential explanations could be provided for the high incidence of ICU-acquired infections related to MDR. Immunomodulatory treatments, such as corticosteroids, JAK2 inhibitors, and IL-6 receptor antagonist, might play a role in the pathogenesis of these infections. Additionally, a longer stay in the ICU was reported in COVID-19 patients, resulting in higher exposure to well-known risk factors for ICU-acquired MDR infections, such as invasive procedures and antimicrobial treatment. Another possible explanation is the surge during successive COVID-19 waves, with excessive workload and low compliance with preventive measures. Further studies should evaluate the evolution of the incidence of ICU-acquired infections related to MDR bacteria, given the change in COVID-19 patient profiles. A better understanding of the immune status of critically ill COVID-19 patients is required to move to personalized treatment and reduce the risk of ICU-acquired infections. The role of specific preventive measures, such as targeted immunomodulation, should be investigated.

A large scale artificial dataset to evaluate the impact of on-board monitoring set-ups on the Heat Transfer Coefficient assessment

At the current state, the majority of residential buildings are underperforming in terms of energy. To assess the current state of the building sector and bridge the gap between the designed and operative energy demand, quantifying the as-built envelope behaviour is essential. For this purpose the Heat Transfer Coefficient is used as a stationary performance indicator, however, prevailing assessment methods are impractical as they are performed on individual buildings. To extend the assessment range to urban scale statistics-based modelling techniques can be applied to limited and on-board collected data. In this step, where the accuracy of statistical methods and different input scenarios are investigated, simulated data is more suitable since there is no influence of measurement inaccuracy and the expected outcome is known. As part of this research, a comprehensive dataset to assess the HTC estimation methodology is created. The artificial dataset is generated using urban building energy models representative of the Belgian residential building stock. The generated artificial datasets resemble ideal measurement campaigns where inputs can be systematically selected which supports the aim of determining the impact of available monitoring setups. In this work, black-box AutoRegressive with eXogenous input statistical models were applied and the results show that, considering the assumed modelling limitations, with ideal monitoring setups 85% of the estimates fall within 10% of the target.

Research on Self-Supervised Building Information Extraction with High-Resolution Remote Sensing Images for Photovoltaic Potential Evaluation

With the rapid development of the energy industry and the growth of the global energy demand in recent years, the development of the photovoltaic industry has become increasingly significant. However, the development of the PV industry is constrained by high land costs, and land in central cities and industrial areas is often very expensive and unsuitable for the installation of PV equipment in large areas. With this background knowledge, the key to evaluating the PV potential is by counting the rooftop information of buildings, and an ideal solution for extracting building rooftop information is from remote sensing satellite images using the deep learning method; however, the deep learning method often requires large-scale labeled samples, and the labeling of remote sensing images is often time-consuming and expensive. To reduce the burden of data labeling, models trained on large datasets can be used as pre-trained models (e.g., ImageNet) to provide prior knowledge for training. However, most of the existing pre-trained model parameters are not suitable for direct transfer to remote sensing tasks. In this paper, we design a pseudo-label-guided self-supervised learning (PGSSL) semantic segmentation network structure based on high-resolution remote sensing images to extract building information. The pseudo-label-guided learning method allows the feature results extracted by the pretext task to be more applicable to the target task and ultimately improves segmentation accuracy. Our proposed method achieves better results than current contrastive learning methods in most experiments and uses only about 20–50% of the labeled data to achieve comparable performance with random initialization. In addition, a more accurate statistical method for building density distribution is designed based on the semantic segmentation results. This method addresses the last step of the extraction results oriented to the PV potential assessment, and this paper is validated in Beijing, China, to demonstrate the effectiveness of the proposed method.

Genetic evaluation and selection in Jatropha curcas through Frequentist and Bayesian inferences

An accurate and efficient statistical method for genetic evaluation is a key requirement for progress in any breeding program. Thus, the present study aimed to evaluate the performance of Frequentist and Bayesian inferences for repeated measures analysis in Jatropha curcas breeding. To this end, 730 individuals from 73 half-sib families were evaluated for grain yield trait, over six crop years. Frequentist and Bayesian analyses were made considering repeatability models with different residual variance structures. Variance components were estimated through restricted maximum likelihood (REML) and Markov Chain Monte Carlo (MCMC). Genetic values were predicted through best linear unbiased prediction (BLUP) and estimated through MCMC. Variance components and genetic and non-genetic parameters estimated by the Frequentist inference presented values similar to those estimated by the Bayesian inference. The selective accuracy presented high magnitude (0.84) by the Frequentist and Bayesian inferences, indicating high reliability. Confidence and highest posterior density (HPD) intervals were similar for the genetic parameters, however the HPD intervals range was slightly short. This study highlighted the importance of testing the residual variance structure and pointed out that the Frequentist and Bayesian inferences presented similar results when using non-informative prior. Then, both inferences can be efficiently applied in Jatropha curcas breeding.

Subjective assessment of frequency distribution histograms and consequences on reference interval accuracy for small sample sizes: A computer‐simulated study

Inaccuracy in estimating reference intervals (RIs) is a problem with small sample sizes. This study aimed to identify the most accurate statistical methods to estimate RIs based on sample size and population distribution shape. We also studied the accuracy of sample frequency distribution histograms to retrieve the original population distribution and compared strategies based on the histogram and goodness-of-fit test. The statistical methods that best enhanced accuracy were determined for various sample sizes (n=20-60) and population distributions (Gaussian, log-normal, and left-skewed) were determined by repeated-measures ANOVA and posthoc analyses. Frequency distribution histograms were built from 900 samples of five different sizes randomly extracted from six simulated populations. Three reviewers classified the population distributions from visual assessments of a sample histogram, and the classification error rate was calculated. RI accuracy was compared among the strategies based on the histograms and goodness-of-fit tests. The parametric, nonparametric, and robust methods enhanced lower reference limit estimation accuracy for Gaussian, log-normal, and left-skewed distributions, respectively. The parametric, nonparametric bootstrap, and nonparametric methods enhanced the upper limit estimation accuracy for Gaussian, log-normal, and left-skewed distributions, respectively. Regardless of sample size, sample histogram assessments properly classified the original population distribution 71% to 93.9% of the time, depending on the reviewers. In this study, the strategy based on histograms assessed by the statistician was significantly more precise and accurate than the strategy based on the goodness-of-fit test (P<0.001). A strategy based on histograms might enhance the accuracy of RI estimations. However, relevant inter-reviewer variations in histogram interpretation were detected. Factors affecting inter-reviewer variations should be further explored.

Path analysis under multiple-trait BLUP: application in the study of interrelationships among traits related to cotton fiber length

&#x0D; Multi-trait best linear unbiased prediction (BLUP) is, generally, the most appropriate method to genetic evaluation because it considers the genetic and residual correlations among traits and conduct to higher selection accuracy. Thus, the present study aimed to identify traits correlated to the fiber length via path analysis under multi-trait BLUP for the cotton breeding. To this end, thirty-six elite lines were evaluated in three environments and phenotyped for many traits related to fiber quality and agronomic traits. Variance components were estimated via residual maximum likelihood (REML). The genetic correlation coefficients among traits were obtained through mixed model output, and to graphically express these results a correlation network was built. Subsequently, we performed path analysis considering fiber length as a principal dependent variable. Genetic parameters obtained by multi-trait BLUP model indicate that the phenotypic variance for most traits is mostly composed of residual effects, which reinforces the need for using more accurate statistical methods such as multi-trait BLUP. The results found for genetic correlations and path analysis under multi-trait BLUP reveal the difficulty of selection based on important fiber quality traits, especially fiber length, since most traits show very low cause-and-effect relationship, and other important traits present undesirable cause-and-effect relationship.&#x0D; Highlights&#x0D; &#x0D; Multiple-trait BLUP is the most appropriate method to predict genetic values.&#x0D; This is the first study in cotton to perform path analysis under multiple-trait BLUP.&#x0D; The findings of this study indicate that there is no genotype presenting all desirable traits.&#x0D;

MassExplorer: a computational tool for analyzing desorption electrospray ionization mass spectrometry data

In the last few years, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) has been increasingly used for simultaneous detection of thousands of metabolites and lipids from human tissues and biofluids. To successfully find the most significant differences between two sets of DESI-MSI data (e.g., healthy vs disease) requires the application of accurate computational and statistical methods that can pre-process the data under various normalization settings and help identify these changes among thousands of detected metabolites. Here, we report MassExplorer, a novel computational tool, to help pre-process DESI-MSI data, visualize raw data, build predictive models using the statistical lasso approach to select for a sparse set of significant molecular changes, and interpret selected metabolites. This tool, which is available for both online and offline use, is flexible for both chemists and biologists and statisticians as it helps in visualizing structure of DESI-MSI data and in analyzing the statistically significant metabolites that are differentially expressed across both sample types. Based on the modules in MassExplorer, we expect it to be immediately useful for various biological and chemical applications in mass spectrometry. MassExplorer is available as an online R-Shiny application or Mac OS X compatible standalone application. The application, sample performance, source code and corresponding guide can be found at: https://zarelab.com/research/massexplorer-a-tool-to-help-guide-analysis-of-mass-spectrometry-samples/. Supplementary data are available at Bioinformatics online.

Utilizing Beta Regression in Predicting the Underlying Factors of Motorcycle Rider Behavior

Introduction: Motorcyclists have the highest proportion of casualty toll caused by street accidents in Iran, and they endanger themselves and others by those risky behaviors. Health and safety education will not be sufficient without knowing the causes of such behaviors. Since no studies have been carried out based on accurate statistical methods on bounded response variables for motorcyclists' high-risk behaviors in Iran, this study aimed to predict MRBQ by ADHD and the underlying predictors using the Beta Regression as an alternative strategy. Methods: The present sectional study included 311 Motorcyclists randomly selected using a cluster sampling method in Bukan city to evaluating the relationship between the limited response MRBQ with ADHD and its subscales. We used an innovative beta regression method for the analysis and carried out unadjusted and adjusted modeling. Results: Direct and significant relationships were observed between MRBQ score and ADHD score and its subscales, including (DSS score) (coefficients ranged over 0.01 to 0.6, All P&lt;0.05). Additionally, the riding period (coefficients ranged over -0.32 to -0.48, P&lt;0.05), hours of riding (coefficients ranged over: 0.31 to 0.34, P&lt;0.05), using the helmet (coefficients: 0.26 to 0.31, P&lt;0.05), and sub-accident (coefficients ranged over 0.35 to 0.37, P&lt;0.05) also turned out to be significant predictors of MRBQ score. Conclusion: ADHD score and riding parameters can be taken into account when contriving actions on the motorcycle rider behaviors as measured by MRBQ.

Validation of PHASE for deriving N-acetyltransferase 2 haplotypes in the Western Cape mixed ancestry population.

BackgroundThere is a shortage of data on the accuracy of statistical methods for the prediction of N-acetyltransferase 2 (NAT2) haplotypes in the mixed ancestry population of the Western Cape.ObjectiveThis study aimed to identify the NAT2 haplotypes and assess the accuracy of PHASE version 2.1.1 in assigning NAT2 haplotypes to a mixed ancestry population from the Western Cape.MethodsThis study was conducted between 2013 and 2016. The NAT2 gene was amplified and sequenced from the DNA of 100 self-identified mixed ancestry participants. Haplotyping was performed by molecular and computational techniques. Agreement was assessed between the two techniques.ResultsHaplotypes were assigned to 93 samples, of which 67 (72%) were ambiguous. Haplotype prediction by PHASE demonstrated 94.6% agreement (kappa 0.94, p < 0.001) with those assigned using molecular techniques. Five haplotype combinations (from 10 chromosomes) were incorrectly predicted, four of which were flagged as uncertain by the PHASE software. Only one resulted in the assignment of an incorrect acetylation phenotype (intermediate to slow), although the software flagged this for further analysis. The most common haplotypes were NAT2*4 (28%) followed by NAT2*5B (27.4%), NAT2*6A (21.5%) and NAT2*12A (7.5%). Four rare single nucleotide variants (c.589C>T, c.622T>C, c.809T>C and c.387C>T) were detected.ConclusionPHASE accurately predicted the phenotype in 92 of 93 samples (99%) from genotypic data in our mixed ancestry sample population, and is therefore a suitable alternative to molecular methods to individualise isoniazid therapy in this high burden tuberculosis setting.

Quantifying individual variation in reaction norms using random regression models fitted through Legendre polynomials: application in eucalyptus breeding

ABSTRACT An accurate, efficient and informative statistical method for analyses of genotype × environment (G × E) interactions is a key requirement for progress in any breeding program. Thus, the objective of this study was to quantify individual variation in reaction norms using random regression models fitted through Legendre polynomials in eucalyptus (Eucalyptus spp.) breeding. To this end, a data set with 215 eucalyptus clones of different species and hybrids evaluated in four environments for diameter at breast height (DBH) and Pilodyn penetration (PP) was used. Variance components were estimated by restricted maximum likelihood, and genetic values were predicted by best linear unbiased prediction. The best-fitted model for DBH and PP was indicated by the Akaike information criterion, and the significance of the genotype effects was tested using the likelihood ratio test. Genetic variability between eucalyptus clones and very high accuracies (rg^g ≥ 0.90) were detected for both traits. Reaction norms and eigenfunctions generated genetic insights into G × E interactions. This is the first study that quantified individual variation in reaction norms using random regression models fitted through Legendre polynomials in eucalyptus breeding and demonstrated the great potential of this technique.

Three-Dimensional Volumetric Analysis of Venous Malformations for Assessing the Effectiveness of Percutaneous Sclerotherapy

ABSTRACT BACKGROUND Percutaneous sclerotherapy can be used to successfully treat venous malformations (VMs) of the head, neck, and limbs. However, the standard curative effect of sclerotherapy has rarely been analyzed, and there is currently no accurate statistical method to measure the volume of VMs after sclerotherapy. Here, we propose a novel three-dimensional (3D) reconstruction method to evaluate this effect. OBJECTIVE To test the feasibility of 3D software (MIMICS 19.0) to evaluate the treatment effect of sclerotherapy. METHODS This retrospective study included patients with VMs on the head, neck, and limbs who were treated with ethanol sclerotherapy or foam sclerotherapy every 8 weeks. MIMICS 19.0 was used to calculate the performance of the lesion after treatment and measure the VM volumes before and after the treatment. The effect of the clinical treatment dose on the lesion was evaluated, and the treatment effect of each patient was recorded. The relationship between the number of treatments and the reduced volume of VMs was analyzed. RESULTS Based on the MIMICS-calculated regions of interest (ROI), we found that 1 mL of ethanol reduced the lesion by 473 mm3 and that one dosage of foam (1 mL of polidocanol and 4 mL air content) reduced the lesion by 2 138 mm3, demonstrating that the foam sclerosing agent exhibited greater efficacy in this study. CONCLUSIONS The MIMICS 3D volume reconstruction method can effectively and safely evaluate the efficacy of sclerotherapy and provide a preoperative evaluation. This method is simple, accurate, and feasible.

Copula Based Spatial Analysis of Drought Return Period in Southwest of Iran

In the past years, Khuzestan province which is located in the southwest of Iran has experienced severe droughts. Drought can be explained by its characteristics known as duration or severity. However, combination of the two features by probabilistic approach is appeared to be a well improved method to describe the phenomena. The aim of this study is to provide a more accurate statistical method of determining drought based on simultaneous analysis of two drought characteristics. Here, precipitation data from twenty stations were used to determine drought characteristics, by Standardized Precipitation Index (SPI). Joint probability function of two variables were built via copula functions. The drought return period was calculated in the form of two scenarios. The first scenario is, based on an assumption that drought is recognized by at least one of the two specific characteristics. Drought in the second scenario is distinguished by the two characteristics in a joint probabilistic form. According to research results, there was no significant difference between the north and south of Khuzestan in the study of single characteristics of drought. While analyzing two characteristics of the drought, the return period in the north was shorter than the south. The return period of droughts in the east was always shorter than in the west. The drought return period varies from 30 to 52 months and 50 to 87 months for the first and second scenarios, respectively.

The Importance of Sample Weights and Plausible Values in Large-Scale Assessments

International large-scale assessments such as PISA (The Programme for International Student Assessment), PIAAC (The Programme for the International Assessment of Adult Competencies) and TIMSS (Trends in International Mathematics Science Study), play a key role in determining educational policies besides their primary objectives of measuring, evaluating and monitoring the educational process. Therefore, it is critical to analyze the data gathered from the large scale assessments using scientifically accurate statistical methods as the results have the potential to influence millions of stakeholders through major policy changes. Analysis of these data that consists of hundreds of different genuine variables requires expertise and using specific methods. This study illustrates issues to be considered while analyzing PISA, PIAAC and TIMSS data by presenting relevant syntax and exemplifying the possible incorrect results that might be encountered. In Turkey, there are very limited courses that focus on large scale data analysis. Workshops are also very limited to reach major groups. The aim of this study is to raise awareness related to sample weights and plausible values. Comparative findings of the study showed that without using sample weights and plausible values there is a high probability to get incorrect results. In this study, t-test and multiple regression analyses conducted by IDB Analyzer and multilevel regression analysis by Mplus were exemplified.

Objectifying processes: The use of geometric morphometrics and multivariate analyses on Acheulean tools

Nowadays, the fruitful discussion regarding the morphological variability of handaxes during the Middle Pleistocene has reached a decisive moment with the use of more accurate statistical methods, such as geometric morphometrics (GM) and multivariate analyses (MA). This paper presents a preliminary methodological approach for checking the utility of these new approaches on the analysis of the tools’ shape. It goes beyond the simple description of morphology and isolates the variables which define the final morphology of a tool. We compared two Middle Pleistocene sites, Boxgrove and Swanscombe, which are morphologically very different. Then, we applied the GM analysis on 1) 2D images, with two semi-landmark distributions: 28 semi-landmarks, specially concentrated on the tip and butt, and 60 equally spaced points; and 2) on 3D models using new software (AGMT3-D Software) including 5000 semi-landmarks. The more points used to define the tool’s outline, the more accurate will be the interpretation of the variables affecting shape. On the other hand, if the semi-landmarks are localized on specific sectors of the tool, a bias is created, by concentrating on those sectors, rather than the general tool shape. The 3D models offer a new dimension on the shape analysis, as their results mean the combination of plan-shape, profile-shape and the tool’s topography.

Research Progress on Human Age Estimation Based on DNA Methylation.

In forensic science practice, age is an important individual information, and one of the indicators to be considered first to depict features of the suspect. Recently, DNA methylation has become a research hotspot in age estimation because of its hig accuracy and stability. New progress has been made in specificity of DNA methylation sites, age estimation in multiple tissues, DNA methylation age estimation of minors, sensitivity of age estimation, forensic practical applications, etc. At the same time, several studies also established more accurate statistical modeling methods, eliminated differences between different detection platforms, found appropriate number of sites in models and analyzed the influence of environment and diseases. This review summarizes these to provide references.

On the application of a novel linear mixture model on laser‐induced breakdown spectroscopy: Implications for Mars

AbstractAs the exploration of Mars and other solar system bodies becomes more prevalent, the importance of accurate methods in chemical analyses has increased. The use of laser‐induced breakdown spectroscopy (LIBS) in such analyses requires that well understood and accurate statistical methods exist for appropriate interpretation of resulting spectra. Many multivariate techniques have been developed for the elemental quantification of LIBS; however, each still has its limitations. In an endeavor to improve upon existing methodologies, a new algorithm is proposed using the ChemCam preflight calibration dataset and a dataset from the characterization of a LIBS/Raman sensor prototype developed at York University. The algorithm which was developed in this work is a linear mixture model within a submodel clustering framework. The cross validation and test results of the model on both datasets were reported using various metrics for each element under consideration (root mean square error, relative standard deviation, and R2 value). The algorithm was subsequently compared with other well established chemometric models on both datasets, such as principal component regression, partial least square regression, and ordinary least squares regression. Further validation of the algorithm was achieved by comparing the results presented herein to previously published results on the ChemCam data. The samples in each dataset are highly representative of Martian geology, which, given the overwhelming success of the algorithm on both datasets, suggests that subsequent implementation of the proposed algorithm on larger databases may have significant implications for Martian geochemical analyses and for planetary exploration as a whole.