Reliabilities of Intraindividual Variability Indicators with Autocorrelated Longitudinal Data: Implications for Longitudinal Study Designs

<p>Intraindividual variability can be measured by the intraindividual standard deviation (), intraindividual variance (), estimated <i>h</i>th-order autocorrelation coefficient (), and mean square successive difference (). Unresolved issues exist in the research on reliabilities of intraindividual variability indicators: (1) previous research only studied conditions with 0 autocorrelations in the longitudinal responses; (2) the reliabilities of and have not been studied. The current study investigates reliabilities of , , , , and the intraindividual mean, with autocorrelated longitudinal data. Reliability estimates of the indicators were obtained through Monte Carlo simulations. The impact of influential factors on reliabilities of the intraindividual variability indicators is summarized, and the reliabilities are compared across the indicators. Generally, all the studied indicators of intraindividual variability were more reliable with a more reliable measurement scale and more assessments. The reliabilities of were generally lower than those of and , the reliabilities of were usually between those of and unless the scale reliability was large and/or the interindividual standard deviation in autocorrelation coefficients was large, and the reliabilities of the intraindividual mean were generally the highest. An R function is provided for planning longitudinal studies to ensure sufficient reliabilities of the intraindividual indicators are achieved.</p

A Bayesian Power Analysis Procedure Considering Uncertainty in Effect Size Estimates from a Meta-analysis

<p>In conventional frequentist power analysis, one often uses an effect size estimate, treats it as if it were the true value, and ignores uncertainty in the effect size estimate for the analysis. The resulting sample sizes can vary dramatically depending on the chosen effect size value. To resolve the problem, we propose a hybrid Bayesian power analysis procedure that models uncertainty in the effect size estimates from a meta-analysis. We use observed effect sizes and prior distributions to obtain the posterior distribution of the effect size and model parameters. Then, we simulate effect sizes from the obtained posterior distribution. For each simulated effect size, we obtain a power value. With an estimated power distribution for a given sample size, we can estimate the probability of reaching a power level or higher and the expected power. With a range of planned sample sizes, we can generate a power assurance curve. Both the conventional frequentist and our Bayesian procedures were applied to conduct prospective power analyses for two meta-analysis examples (testing standardized mean differences in example 1 and Pearson's correlations in example 2). The advantages of our proposed procedure are demonstrated and discussed.</p

A Comparison of Multilevel Imputation Schemes for Random Coefficient Models: Fully Conditional Specification and Joint Model Imputation with Random Covariance Matrices

Literature addressing missing data handling for random coefficient models is particularly scant, and the few studies to date have focused on the fully conditional specification framework and “reverse random coefficient” imputation. Although it has not received much attention in the literature, a joint modeling strategy that uses random within-cluster covariance matrices to preserve cluster-specific associations is a promising alternative for random coefficient analyses. This study is apparently the first to directly compare these procedures. Analytic results suggest that both imputation procedures can introduce bias-inducing incompatibilities with a random coefficient analysis model. Problems with fully conditional specification result from an incorrect distributional assumption, whereas joint imputation uses an underparameterized model that assumes uncorrelated intercepts and slopes. Monte Carlo simulations suggest that biases from these issues are tolerable if the missing data rate is 10% or lower and the sample is composed of at least 30 clusters with 15 observations per group. Furthermore, fully conditional specification tends to be superior with intraclass correlations that are typical of crosssectional data (e.g., ICC = .10), whereas the joint model is preferable with high values typical of longitudinal designs (e.g., ICC = .50).</p

A new model predictive control approach integrating physical and data-driven modelling for improved energy performance of district heating substations

District heating (DH) substations play a crucial role in ensuring the efficient and effective distribution of thermal energy necessary to provide space heating for buildings. However, optimizing their operation for energy savings while still ensuring indoor comfort poses significant challenges due to the complex dynamics of building demand and the inertia of building envelopes. To address these challenges, this study introduces a novel model predictive control (MPC) approach that combines a reduced-order physical model with a machine learning-based data-driven model to jointly optimize the operation parameters of a DH substation. In this approach, a reduced-order physical model is first used to capture essential operational principles and energy behaviors of the DH substations and generate candidate solutions for the control of the DH substations. Then, a data-driven model is constructed by integrating a Long Short-Term Memory model and a Back-propagation Neural Network, leveraging historical operational data of the DH substation concerned. The data-driven model is further formulated into a data-driven MPC framework to identify optimal control solutions from all candidates provided by the physical model. To evaluate the proposed approach, a data-driven surrogate model is developed using real operational data. Comparative analysis against the original fuzzy rule-based control strategy and a pure data-driven strategy demonstrates a substantial reduction in heat consumption of 4.77% and 19.47%, respectively. Moreover, compared with using a reduced-order physical model alone, this approach achieves additional benefits in reducing the energy consumption of the DH substation and minimizing indoor temperature fluctuations within the end-users

Low-dose radiation from CT examination induces DNA double-strand breaks and detectable changes of DNA methylation in peripheral blood cells

Radiation burden from CT examinations increases rapidly with the increased clinical use frequency. Previous studies have disclosed the association between radiation exposure and increased double-strand breaks (DSBs) and changes in DNA methylation. However, whether the induced DSBs by CT examination recover within 24h and whether a CT examination induces detectable gene-specific methylation changes are still unclear. The aim of the present study was to analyze γ-H2AX in the peripheral blood lymphocyte (PBL) of healthy adults before and after CT examination and to discover the differentially methylated positions (DMPs) along with an analysis of DNA methylation changes caused by CT examination. Peripheral blood samples of 4 ml were drawn from 20 healthy volunteers at three time points: before CT examination, after CT examination 1h, and after CT examination 24h. γ-H2AX immunofluorescence and Illumina Infinium Human Methylation EPIC BeadChip (850k BeadChip) were used respectively for the test of DSBs and the epigenome-wide DNA methylation analysis. Linear mixed-effect (LME) models were used to evaluate the impacts of doses represented by different parameters and foci on genome-wide DNA methylation. The number of γ-H2AX foci per cell at 1h showed linear dose–responses for the radiation doses represented by CT index volume (CTDIvol), dose length product (DLP), and blood absorbed dose, respectively. Residual γ-H2AX foci was observed after CT examination at 24h (p  Residual γ-H2AX foci exist after CT examination at 24h. The DNA methylation changes induced by CT examination may not recover within 24h. The DNA methylation had been changed as early as at 1h. The PAX5-related CpG site may be a potential biomarker of low-dose radiation. The biological effects and the cancer risks of CT examination are still unclear. The present study is an effort to document the CT scan-induced events in 24h in vivo. The CT scanning area should be strictly limited, and non-essential repeated operations shouldn’t be performed within 24h.</p

Ploidy changes in pathogenic fungi.

(A) Colony and cellular morphologies of haploid and diploid forms of C. albicans (SC5314), C. auris (BJCA001), and C. glabrata (FK83). Cells were plated on YPD medium (BJCA001 and FK83 cells) or SCD medium (SC5314 cells) containing 5 μg/mL phloxine B and incubated at 30°C for 4 days. Scale bar for colonies, 5 mm; scale bar for cells, 5 μm. (B) Schematic diagram of ploidy changes. Fungal cells may undergo ploidy changes spontaneously or in response to environmental stresses. Cells of lower ploidy (e.g., haploid) can adopt a higher ploidy (diploid, tetraploid, or polyploid), which are subsequently able to return to the lower ploidy state through chromosome non-disjunction events that lead to concerted chromosome loss. Fungal cells are also able to switch between euploid and aneuploid states through gain or loss of chromosomes. Aneuploid and non-baseline ploidy states are often unstable and can give rise to additional genetic variants.</p

Role of α-pheromone and the Ste2 receptor in same-sex mating in <i>C</i>. <i>tropicalis</i>.

(A) Synthetic α-pheromone induces the expression of MFA1, MFα, STE2, and STE3 in a cells. WT a cells were grown on Lee’s GlcNAc medium (pH 8.5) for four days and the resulting opaque cells inoculated into liquid Lee’s GlcNAc medium (pH 8.5) for 48-hour growth at 25°C. Cells were then treated with or without synthetic α-pheromone (100 μΜ) in liquid Lee’s GlcNAc medium (pH 8.5) for six hours. The relative expression levels of MFA1, MFα, STE2, and STE3 were examined using real-time PCR assays. The expression level of ACT1 was used for normalization. **, indicates significant difference (Pt-test). Strains used: GH1374h (WT). (B) The α-factor receptor Ste2 is required for “a x a” same-sex mating when α cells (CAY4149) are present as helper cells. Mating condition: Lee’s GlcNAc medium (pH 8.5) at 25°C for seven days. Strains used: WT x WT: CAY2060 x GH1374h; ste2/ste2 x ste2/ste2: CAY2200 x CAY2202; WT x ste2/ste2: CAY2060 x CAY2200; STE2R x STE2R: CAY2247 x CAY2246; WT x STE2R: GH1374h x CAY2246. STE2R is a ste2/ste2 strain in which one copy of STE2 was been restored.</p

A coupled process of same- and opposite-sex mating generates polyploidy and genetic diversity in <i>Candida tropicalis</i>

<div><p>Sexual reproduction is a universal mechanism for generating genetic diversity in eukaryotes. Fungi exhibit diverse strategies for sexual reproduction both in nature and in the laboratory. In this study, we report the discovery of same-sex (homothallic) mating in the human fungal pathogen <i>Candida tropicalis</i>. We show that same-sex mating occurs between two cells carrying the same mating type (<i>MTL</i><b>a</b>/<b>a</b> or α/α) and requires the presence of pheromone from the opposite mating type as well as the receptor for this pheromone. In ménage à trois mating mixes (i.e., “<b>a</b> x <b>a</b> + α helper” or “α x α + <b>a</b> helper” mixes), pheromone secreted by helper strains promotes diploid <i>C</i>. <i>tropicalis</i> cells to undergo same-sex mating and form tetraploid products. Surprisingly, however, the tetraploid mating products can then efficiently mate with cells of the opposite mating type to generate hexaploid products. The unstable hexaploid progeny generated from this coupled process of same- and opposite-sex mating undergo rapid chromosome loss and generate extensive genetic variation. Phenotypic analysis demonstrated that the mating progeny-derived strains exhibit diverse morphologies and phenotypes, including differences in secreted aspartic proteinase (Sap) activity and susceptibility to the antifungal drugs. Thus, the coupling of same- and opposite-sex mating represents a novel mode to generate polyploidy and genetic diversity, which may facilitate the evolution of new traits in <i>C</i>. <i>tropicalis</i> and adaptation to changing environments.</p></div

Morphological diversity of progeny generated from the coupled process of same- and opposite-sex mating.

<p>Progeny strains with different ploidies from the “<b>a</b> x <b>a</b> + α helper” experiment were patched onto YPD and incubated at 30°C overnight. Cells were subsequently plated on Lee’s glucose (pH 6.8, with 5 μg/mL of phloxine B) and incubated at 25°C for three days. Colony and cellular images were taken. Colonies with different appearances are numbered and the corresponding cellular morphologies are shown. Diploid parental strains (CAY2060, GH1374h and CAY4149) served as controls. Scale bar for cells, 10 μm; Scale bar for colonies, 1 mm.</p

Opposite sex cells induce same-sex mating of <i>C</i>. <i>tropicalis</i> in a sandwich-culture mode.

<p><b>a</b>- or α-cells were patched onto Lee’s GlcNAc medium (pH 8.5) in a sandwich mode as indicated. Cells were cultured at 25°C for seven days. Cells of the “<b>a</b> x <b>a</b>” or <b>“</b>α x α” mating mixture were replated onto selection plates for the growth of parental (SCD-His or -Arg) or mating progeny cells (SCD-His-Arg). Mating efficiencies were calculated and are indicated in the corresponding figures. In the schematics, orange dots represent α-pheromone secreted by helper α cells, and blue dots indicate <b>a</b>-pheromone secreted by helper <b>a</b> cells. Strains used: “<b>a</b> x <b>a</b>” mating mixture: CAY2060 x GH1374h; <b>“</b>α <b>x</b> α” mating mixture: CAY2061 x CAY2063; helper <b>a</b> cells: CAY3741; helper α cells: CAY4149. P, mating projection. Scale bar, 10 μM. (A) α cells secrete α-pheromone and induce <b>“a x a”</b> same-sex mating. (B) The patches of helper <b>a</b> cells serve as the control for (A). (C) <b>a</b> cells secrete <b>a</b>-pheromone and induce <b>“</b>α x α<b>”</b> same-sex mating. (D) The patches of helper α cells served as the control for (C). No mating projections were observed in controls (B and D).</p