Population Variance Research Articles

Venipuncture site and population affect blood analytes in head-started and free-ranging eastern box turtles (Terrapene carolina).

To compare head-started and free-ranging eastern box turtle (Terrapene carolina) blood analytes to evaluate head-starting practices and venipuncture site suitability. Eastern box turtles were head-started by Jekyll Island State Park Authority's Georgia Sea Turtle Center. Free-ranging resident box turtles on Jekyll Island State Park were captured. A physical examination and CBC were performed. Blood collection was performed from a jugular vein and the subcarapacial venous sinus. Passive integrated transponder tags were placed prior to release. Recapture and repeat assessments were attempted annually. Blood analyte data were analyzed for observational study of effects of group (head-started or free-ranging) and venipuncture site. Jugular blood samples from 15 head-started turtles (mostly immature; 88 samples) had lower total solids, lower heterophil-to-lymphocyte ratio, and higher lymphocytes than jugular samples from 24 free-ranging turtles (mostly adult; 64 samples). In head-started individuals, PCV, total solids, WBC count, and heterophils were lower in subcarapacial than jugular samples. Disparities between head-started and free-ranging turtles could indicate variance in stress, age, sex, or season. The jugular vein is the most suitable venipuncture site in comparison to the subcarapacial venous sinus, which exhibited lymphatic and/or CSF dilution. Results of the present study provide insight into variances in head-started and free-ranging box turtle populations, which must be considered for accurate hemogram interpretation. The importance of standardized protocol and serial sampling from individuals is emphasized, as values may vary by venipuncture site, population, and intrinsic factors (eg, age, sex, season) within species.

Increasing environmental fluctuations can dampen variability of endogenously cycling populations.

Understanding how populations respond to increasingly variable conditions is a major objective for natural resource managers forecasting extinction risk. The lesson from current modelling is clear: increasing environmental variability increases population abundance variability. We show that this paradigm fails to describe a broad class of empirically observed dynamics, namely endogenously driven population cycles. In contrast to the dominant paradigm, these populations can exhibit reduced long-run population variance under increasing environmental variability. We provide evidence for a mechanistic explanation of this phenomenon that relies on how stochasticity interacts with long transient dynamics present in the deterministic cycling model. This interaction stands in contrast to the often assumed additivity of stochastic and deterministic drivers of population fluctuations. We show evidence for the phenomenon in two cyclical populations: flour beetles and Canadian lynx. We quantify the impact of the phenomenon with new theory that partitions the effects of nonlinear dynamics and stochastic variation on dynamical systems. In both empirical examples, the partitioning shows that the interaction between deterministic and stochastic dynamics reduces the variance in population size. Our results highlight that previous predictions about extinction under environmental variability may prove inadequate to understand the effects of climate change in some populations.

Optimality of novel estimators for variance estimation under successive sampling

This article presents a class of estimators allied with Searls (1964) using a novel approach to estimate population variance on two occasion under successive sampling. Isaki (1983) encourages us to address the problem of estimating finite population variance in survey sampling, its application to the case of successive sampling is highly captivating, and the methodology proposed here will be useful to those involved in similar studies in the future. To the best of our knowledge, this is any authors’ first foray into this field to provide more efficient estimators than the regression type estimator. An empirical study based on real population and artificial population proves the utility of the proposed methods.

Estimation of population variance using optional randomized response technique model in the presence of measurement errors

Measurement errors are an important consideration in sample surveys. Neglecting their influence can give misleading and inaccurate results. In this paper, we propose three variance estimators using Optional Randomized Response Technique (ORRT) in the presence of measurement error. Performance of the proposed estimators are evaluated through a simulation study and a numerical example.

Role of metabolic dysfunction-associated steatotic liver disease and of its genetics on kidney function in childhood obesity.

Evidence linked metabolic associated steatotic liver disease (MASLD) to kidney damage with the potential contribution of the I148M variant of the Patatin-like phospholipase containing domain 3 (PNPLA3) gene. We aimed at investigating the relationship of MASLD and of its genetics with kidney function in children with obesity. A comprehensive evaluation including genotyping for the I148M PNPLA3 polymorphism was performed in 1037 children with obesity. Fatty liver (FL) was assessed by liver ultrasound. According to MASLD criteria, subjects with obesity but without FL were included in group 1, while patients with obesity and FL (encompassing one MASLD criterion) were clustered into group 2. Group 3 included patients with obesity, FL, and metabolic dysregulation (encompassing >1 MASLD criterion). Alanine transaminase levels significantly increased while estimated glomerular filtration rate (eGFR) significantly reduced from group 1 to 3. Group 3 showed a higher percentage of carriers of the I148M allele of the PNPLA3 gene compared to other groups (p < 0.0001). Carriers of group 2 and of group 3 showed reduced eGFR levels than noncarriers of group 2 (p = 0.04) and of group 3 (p = 0.02), respectively. A general linear model for eGFR variance in the study population showed an inverse association of eGFR with both MASLD and PNPLA3 genotypes (p = 0.011 and p = 0.02, respectively). An inverse association of eGFR with MASLD was also confirmed only in carriers (p = 0.006). The coexistence of more than 1 MASLD criterion in children with obesity seems to adversely affect kidney function. The PNPLA3 I148M allele further impacts on this association.

Efficient Handling of Non-response in Sample Surveys: Development of Ratio-type Variance Estimators

The present study has been under taken to address the issue of presence and absence of non-response problems in Sample surveys. We have developed some new efficient ratio-type variance estimators for the computation of the finite population variance in presence and absence of non-response, by incorporating Positional parameters as auxiliary information. The expression for mean square errors of proposed estimators has been derived up to the first order of approximation. To test the efficiency of new developed estimators, practical demonstration has been carried to ascertain the performance of suggested Estimators.

New Modified Efficient Variance Estimators for the Estimation of Population Variance in Survey Sampling

For efficient and precise estimate of population variance, we have proposed some new ratio type variance estimators in the current research work by incorporating the linear combination of conventional and non-conventional parameters of auxiliary information. Bias and mean square error have been computed up to the first order of approximation. Numerical demonstration has been carried out to test the efficiency of new proposed estimators against existing estimators.

Variance estimation using memory type estimators based on EWMA statistic for time scaled surveys in stratified sampling

In this article, we have proposed memory-type exponential and non-exponential estimators for population variance based on exponentially weighted moving average (EWMA) statistic in stratified sampling. We drive mathematical expressions for mean square errors of the proposed estimators using Taylor and exponential expansions. Our analysis demonstrates that the proposed memory-type estimators outperform the conventional estimators under stratification, particularly, when the information of previous sample is utilized. The performances of the proposed estimators are evaluated mathematically by deriving the conditions in which the memory-type estimators would perform better than their corresponding conventional estimators under stratification. Through an extensive simulation, we evaluated the performance of the proposed estimators across various population parameters, revealing their enhanced efficiency in time-scaled survey. Additionally, a real data application is also used to support the mathematical findings, confirming the practical utility of the proposed estimators. The results of numerical study underscore the importance of the use of previous sampled information which significantly improves the accuracy and reliability of the proposed estimator for variance estimation for time-scaled surveys.

A new modified estimator of population variance in calibrated survey sampling

In survey statistics, estimating and reducing population variation is crucial. These variations can occur in any sampling design, including stratified random sampling, where stratum weights may increase the variance of estimators. Calibration techniques, which use additional auxiliary information, can help mitigate this issue. This paper examines three calibration-based estimators—calibration variance, calibration ratio, and calibration exponential ratio estimators—within the framework of stratified random sampling. The study generates data from normal, gamma, and exponential distributions to test these estimators. Results demonstrate that the proposed calibration estimators offer more accurate estimates of population variance and outperform existing methods in estimating population variance under stratified random sampling, providing more accurate and reliable estimates.

Impact of genotype × environment interaction and selection history on genomic prediction in maize (Zea mays L.)

AbstractBreeders made remarkable progress in improving productivity and stability of cultivars. Breeding progress relies on selecting favorable alleles for performance and stability to produce productive varieties across diverse environments. In this study, we analyzed the Genomes to Fields Initiative 2018–2019 genotype by environment interaction (G × E) dataset, focusing on three populations of double haploid (DH) lines derived from crossing inbrexpired Plant Variety Protection (ex‐PVP) inbred line PHW65 with inbred lines PHN11, Mo44, and MoG. PHW65 is an Iodent/Lancaster‐type inbred; PHN11 is an Iodent type ex‐PVP line; Mo44 is a tropical‐derived inbred; and MoG is an agronomically poor line derived from the variety Mastadon. Hybrids were produced by crossing the resulting DHs with Stiff Stalk testers PHT69 and LH195. The study's objective was to determine the donor inbreds' relative value and understand the impact of selection history on genomic prediction. We conducted a two‐stage analysis to compare hybrid performance and G × E variance of the populations. G × E variance for yield was significantly lower in the PHW65 × PHN11 population relative to the PHW65 × MoG population. The reduced G × E variance of the PHN11 population led to increased indirect prediction accuracy (when training and testing data are drawn from the same population but different environments). In cross‐validation, the PHN11 population had the greatest indirect prediction accuracy 45% of the time, followed by the Mo44 population (30%) and the MoG population (25%). Results demonstrate that prediction accuracy was greater in the population with the longest history of selection for favorable alleles (PHN11), contributing to greater yield stability.

Assessing Alkaline Phosphatase Reference Intervals Using Gamma-Glutamyl Transferase as a Surrogate for Normal Liver Function

Abstract Limited studies assess reference intervals (RIs) for hepatic enzymes, particularly for alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT). In the US, ALP and GGT RIs were established on Roche assays using a European cohort of ≤ 450 patients that may not reflect population variance. Further, age- and sex-specific variation in ALP and GGT may warrant tiered RIs, but evidence to justify this approach is limited. We aimed to evaluate the manufacturer’s RIs by comparing them to indirectly estimated RIs (IRIs). 217,333 AST, ALT, ALP, and GGT results (Roche c702) performed between 1/1/2019-12/31/2022 on outpatients ≥21 years were extracted from the laboratory information system. The first ALP for each patient was identified, and the earliest AST, ALT, and GGT drawn within 7 days was included. This approach was repeated for GGT. Concordance and correlation were calculated using Weighted Cohen’s Kappa and Spearman’s rank correlation coefficient respectively. {refineR} was applied to patients with “normal” liver function (defined as normal AST, ALT for all IRIs, plus normal GGT for ALP IRIs, and ALP for GGT IRIs). IRIs were estimated for all males, all females, females ≤50, and females &amp;gt;50 years and compared to the method specific manufacturer RI (ALP: 40-129 Units/L for males and 35-104 Units/L for females; GGT: 8-61 Units/L for males and 5-36 Units/L for females). Of the 71,783 outpatients, 54.9% were female, 74% self-identified as white and 19% as black. The median age was 58 (IQR: 43-68 years), the median ALP concentration was 77 (62-97 Units/L), and the median GGT concentration was 28 (17-57 Units/L). A moderate agreement (Kappa = 0.48, 95% confidence interval: 0.46-0.51) and positive correlation (r = 0.51; p&amp;lt;0.0001) between ALP and GGT was observed. For ALP and GGT, no significant differences were observed between the lower limit of the IRIs and the current RI. The upper limit of estimated IRIs was 132 [95% confidence interval: 117-147 Units/L] for patients with normal AST/ALT/GGT (n = 1596). For males, the sex-specific upper limit was 120 [101-140 Units/L] and for females, was 123 [110-136 Units/L]. Age stratified upper limits for females were 120 [101-140 Units/L], and 109 [97-121 Units/L] for ≤50 and &amp;gt;50 years, respectively. The upper limit for all patients with normal AST/ALT/ALP (n = 2,446) was 62 [55-70 Units/L]. For males, all females, females ≤50 and &amp;gt;50 years was 58 [46-69 Units/L], 40 [39-51 Units/L], 31 [23-39 Units/L], and 56 [41-71 Units/L]. Manufacturer RIs agreed well with the IRIs for most subsets, but differed for ALP IRIs of all females with normal hepatobilliary function and in GGT for all females and females &amp;gt;50 years. Further investigation of the impact of preselecting patient populations on IRIs and a direct RI study on select patient subsets is worth investigation.

A-093 Assessing Alkaline Phosphatase Reference Intervals Using Gamma-Glutamyl Transferase as a Surrogate for Normal Liver Function

Abstract Background There are limited published studies assessing reference intervals (RIs) for hepatic enzymes, particularly for alkaline phosphatase (ALP) and gamma-glutamyl transferase (GGT). In the US, ALP and GGT RIs were established on Roche assays using a European cohort of ≤ 450 patients that may not reflect population variance across practice settings. Further, age- and sex-specific variation in ALP and GGT may warrant tiered RIs, but evidence to justify this approach is limited. We aimed to evaluate the manufacturer’s RIs by comparing them to indirectly estimated RIs (IRIs) from routine clinical data. Methods 217,333 AST, ALT, ALP, and GGT results (Roche c702) performed between 1/1/2019-12/31/2022 on outpatients ≥21 years were extracted from the laboratory information system. The first ALP for each patient was identified, and the earliest AST, ALT, and GGT drawn within 7 days was included. This approach was repeated for GGT. Concordance and correlation were calculated using Weighted Cohen’s Kappa and Spearman’s rank correlation coefficient respectively. {refineR}, an algorithm for calculating IRIs, was applied to patients with “normal” liver function (defined as normal AST, ALT for all IRIs, plus normal GGT for ALP IRIs, and ALP for GGT IRIs). IRIs were estimated for all males, all females, females ≤50, and females &amp;gt;50 years and compared to the method specific manufacturer RI (ALP: 40-129 Units/L for males and 35-104 Units/L for females; GGT: 8-61 Units/L for males and 5-36 Units/L for females). Results Of the 71,783 outpatients, 54.9% were female, 74% self-identified as white and 19% as black. The median age was 58 (IQR: 43-68 years), the median ALP concentration was 77 (62-97 Units/L), and the median GGT concentration was 28 (17-57 Units/L). A moderate agreement between ALP and GGT was observed (Kappa = 0.48, 95% confidence interval: 0.46-0.51). A positive correlation between ALP and GGT was observed r = 0.51 (p&amp;lt;0.0001). For ALP, no significant differences were observed between the lower limit of the IRIs and the current RI. The upper limit of estimated IRIs was 132 [95% confidence interval: 117-147 Units/L] for patients with normal AST/ALT/GGT (n = 1596). For males, the sex-specific upper limit was 120 [101-140 Units/L] and for females, was 123 [110-136 Units/L]. Age stratified upper limits for females were 120 [101-140 Units/L], and 109 [97-121 Units/L] for ≤50 and &amp;gt;50 years, respectively. For GGT, no significant differences were observed for the lower limits. The upper limit for all patients with normal AST/ALT/ALP (n = 2,446) was 62 [55-70 Units/L]. For males, all females, females ≤50 and &amp;gt;50 years was 58 [46-69 Units/L], 40 [39-51 Units/L], 31 [23-39 Units/L], and 56 [41-71 Units/L]. Conclusions For the majority of patient subsets, the manufacturer RI agreed well with the IRIs. However, significant differences in IRIs were observed for the upper limit of ALP IRIs of all females with normal hepatobilliary function and in GGT for all females and females &amp;gt;50 years. Further investigation of the impact of preselecting patient populations on IRI estimation and a direct RI study on select patient subsets is worth investigation.

Modified Calibration Variance Estimators in the Presence of Non- response and Measurement Error

Samples from a population that can be divided into smaller groups are taken using a technique called stratified sampling. When subpopulations within the total population differ, it may be beneficial to sample each stratum (subpopulation) separately in sample surveys. Government organizations, independent consultants, and applied statisticians all frequently use this crucial strategy. There are many problems encountered by survey statisticians in estimating the population variance of the study variable. These problems include the presence of outliers in data collected for analysis, non-response, and measurement errors occurring during the survey. Shahzad et al. [1] developed variance estimators by addressing the problem of outliers using the L-moment and calibration approach. However, they do not consider the situation of non-response and measurement errors. This paper addresses these problems by proposing modified variance estimators in the presence of non-response and measurement errors. The properties (Biases and MSEs) were derived up to the first order of approximation using the Taylor series approach. The efficiency conditions of the modified estimators over the existing estimators considered in the study were established. The result of simulation studies revealed that the estimators are efficient.

Except-Extremes Ranked Set Sampling for Estimating the Population Variance with Two Applications of Real Data Sets

The ranked set sampling (RSS) procedure was initially established by McIntyre (1952) for estimating the mean of forage and pasture yield as more precise than simple random sampling (SRS). Recently, Aldrabseh and Ismail (2023) suggested the except extreme RSS (EERSS) approach as a modification to RSS for estimating the population mean. In this paper, a new estimator of the population variance is proposed using the EERSS method. The mean squared error and bias equations of the new estimator are derived. When the underlying distribution is non-symmetric, a simulation study is conducted to evaluate the suggested estimator relative to SRS and RSS, based on the same number of measured units, in terms of the relative precision and bias values for several sample sizes. For symmetric distributions, the exact values of the bias and relative precision of the EERSS variance estimator are evaluated. Two real datasets are utilized to illustrate the performance of the suggested variance estimator. It is found that the EERSS variance estimator is more efficient than the SRS estimator and more precise than RSS in most cases, especially for small set sizes.

Dual Transformation of Auxiliary Variables by Using Outliers in Stratified Random Sampling

To estimate the finite population variance of the study variable, this paper proposes an improved class of efficient estimators using different transformations. When both the minimum and maximum values of the auxiliary variable are known and the ranks of the auxiliary variable are associated with the study variable, these estimators are particularly useful. Therefore, the precision of the estimators can be effectively improved through the utilization of these rankings. We examine the properties of the proposed class of estimators, including bias and mean squared error (MSE), using a first-order approximation through a stratified random sampling method. To determine the performances and validate the findings mathematically, a simulation study is carried out. Based on the results, the proposed class of estimators performs better in terms of the mean squared error (MSE) and percent relative efficiency (PRE) as compared to other estimators in all scenarios. Furthermore, in order to prove that the performances of the improved class of estimators are better than those of the existing estimators, three data sets are examined in the application section.

The evolution of data treatment tools in single-particle and single-cell ICP-MS analytics.

Single-particle inductively coupled plasma-mass spectrometry (sp-ICP-MS) is one of the most powerful tools in the thriving field of nanomaterial analysis. Along the same lines, single-cell ICP-MS (sc-ICP-MS) has become an invaluable tool in the study of the variances of cell populations down to a per-cell basis. Their importance and application fields have been listed numerous times, across various reports and reviews. However, not enough attention has been paid to the immense and ongoing development of the tools that are currently available to the analytical community for the acquisition, and more importantly, the treatment of single-particle and single-cell-related data. Due to the ever-increasing demands of modern research, the efficient and dependable treatment of the data has become more important than ever. In addition, the field of single-particle and single-cell analysis suffers due to a large number of approaches for the generated data-with varying levels of specificity and applicability. As a result, finding the appropriate tool or approach, or even comparing results, can be challenging. This article will attempt to bridge these gaps, by covering the evolution and current state of the tools at the disposal of sp-ICP-MS users.

New Techniques for Estimating Finite Population Variance Using Ranks of Auxiliary Variable in Two-Stage Sampling

This article presents a new set of estimators designed to estimate the finite population variance of a study variable in two-phase sampling. These estimators utilize the information about extreme values and ranks of an auxiliary variable. Through a first-order approximation, we investigate the properties of these estimators, including biases and mean squared errors (MSEs). Furthermore, a comprehensive simulation study is conducted to assess their performance and validate our theoretical insights. Results demonstrate that our proposed class of estimators performs better in terms of percent relative efficiency (PRE) across various simulation scenarios compared to existing estimators. In addition, in the application section, we utilize three data sets to further validate the performance of our proposed estimators against conventional unbiased variance estimators, ratio and regression estimators, as well as other existing methods.

Default Bayes Factors for Testing the (In)equality of Several Population Variances

Testing the (in)equality of variances is an important problem in many statistical applications. We develop default Bayes factor tests to assess the (in)equality of two or more population variances, as well as a test for whether a population variance equals a specific value. The resulting test can be used to check assumptions for commonly used procedures such as the t-test or ANOVA, or test substantive hypotheses concerning variances directly. We show that our Bayes factor fulfills a number of desiderata. Researchers may have directed hypotheses such as σ12>σ22, they may want to extend H0 to have a null-region, or wish to combine hypotheses about equality with hypotheses about inequality, for example σ12=σ22>(σ32,σ42). We extend our Bayes factor test to allow for these deviations from our proposed default and illustrate it on a number of practical examples. Our procedure is implemented in the R package bfvartest.

Robust Estimation of Variance Using Supplementary Information

This study introduces robust exponential ratio-type estimators for finite population variance, incorporating the minimum covariance determinant (M.C.D.) and minimum volume ellipsoid (M.V.E.) robust covariance matrices in the context of simple random sampling. We utilize a first-order approximation to derive expressions for the bias and mean square error (M.S.E.) of these proposed estimators. Through a comprehensive review of existing literature, it is evident that these robust estimators outperform other alternatives in terms of efficiency. Both the M.C.D. and M.V.E. methods are known for their resilience to outliers, making them particularly effective when such anomalies are present in the data. Simulation studies and empirical results consistently demonstrate that the proposed robust exponential ratio-type estimators yield lower mean square errors compared to traditional estimators under simple random sampling. Additionally, the efficiency of these estimators is further validated through simulated studies and real-world data sets. Theoretical analysis and numerical evidence collectively affirm that the proposed class of estimators consistently outperforms competing methods across various scenarios.

Brain transcriptomic signatures for mood disorders and suicide phenotypes: an anterior insula and subgenual ACC network postmortem study.

Mood disorders affect over ten percent of humans, but studies dissecting the brain anatomical and molecular neurobiological mechanisms underlying mood (dys)functions have not consistently identified the patterns of pathological changes in relevant brain regions. Recent studies have identified pathological changes in the anterior insula (Ant-Ins) and subgenual anterior cingulate (sgACC) brain network in mood disorders, in line with this network's role in regulating mood/affective feeling states. Here, we applied whole-tissue RNA-sequencing measures of differentially expressed genes (DEGs) in mood disorders versus (vs.) psychiatrically unaffected controls (controls) to identify postmortem molecular pathological markers for mood disorder phenotypes. Using data-driven factor analysis of the postmortem phenotypic variables to determine relevant sources of population variances, we identified DEGs associated with mood disorder-related diagnostic phenotypes by combining gene co-expression, differential gene expression, and pathway-enrichment analyses. We found downregulation/under expression of inflammatory, and protein synthesis-related genes associated with psychiatric morbidity (i.e., all co-occurring mental disorders and suicide outcomes/death by suicide) in Ant-Ins, in contrasts to upregulation of synaptic membrane and ion channel-related genes with increased psychiatric morbidity in sgACC. Our results identified a preponderance of downregulated metabolic, protein synthesis, inflammatory, and synaptic membrane DEGs associated with suicide outcomes in relation to a factor representing longevity in the Ant-Ins and sgACC (AIAC) network. Our study revealed a critical brain network molecular repertoire for mood disorder phenotypes, including suicide outcomes and longevity, and provides a framework for defining dosage-sensitive (i.e., downregulated vs. upregulated) molecular signatures for mood disorder phenotypic complexity and pathological outcomes.