SIGIRR, also known as the single immunoglobulin interleukin-1 receptor (IL-1R)-related molecule, is a member of the IL-1 receptor superfamily and is believed to play a pivotal role in inflammation and anti-inflammatory regulation within the body. Studies have shown that SIGIRR expression is associated with autoimmunity, inflammatory disorders, graft rejection, viral infection, thrombosis and tumour progression. Due to its unique structure and function, SIGIRR is commonly referred to as an ‘orphan receptor’, with IL-37 being the only confirmed ligand molecule for SIGIRR to date. The primary mechanism through which SIGIRR exerts its anti-inflammatory regulatory effect involves the negative modulation of the Toll-like receptor-IL-1R (TLR-IL-1R) signalling pathway. TLR-IL-1R signalling plays critical roles in immune responses triggered by microbial invasion and alterations in the tumour immune microenvironment. This article provides an overview of research findings on SIGIRR as an orphan receptor and its regulatory role in maintaining a delicate balance between natural immune activation and uncontrolled inflammatory processes under pathological conditions.

Insufficient sleep’s impact on cognitive and emotional function is well-documented, but its effects on social functioning remain understudied. This research investigates the influence of depressive symptoms on the relationship between sleep deprivation (SD) and social decision-making. Forty-two young adults were randomly assigned to either the SD or sleep control (SC) group. The SD group stayed awake in the laboratory, while the SC group had a normal night’s sleep at home. During the subsequent morning, participants completed a Trust Game (TG) in which a higher monetary offer distributed by them indicated more trust toward their partners. They also completed an Ultimatum Game (UG) in which a higher acceptance rate indicated more rational decision-making. The results revealed that depressive symptoms significantly moderated the effect of SD on trust in the TG. However, there was no interaction between group and depressive symptoms found in predicting acceptance rates in the UG. This study demonstrates that individuals with higher levels of depressive symptoms display less trust after SD, highlighting the role of depressive symptoms in modulating the impact of SD on social decision-making. Future research should explore sleep-related interventions targeting the psychosocial dysfunctions of individuals with depression.

Juvenile hormone (JH) regulates multiple physiological functions in insects including growth, metamorphosis, and reproduction. Juvenile hormone epoxide hydrolase (JHEH) and juvenile hormone esterase (JHE) are degradative enzymes that metabolise JH, and JH receptor (methoprene-tolerant, Met) functions in the regulation of female reproduction and vitellogenesis. In this study, JH titres in Coccinella septempunctata adult females were determined using ultra high-performance liquid chromatography and tandem mass spectrometry; the JH titres ranged from 0.03 to 0.16 ng g−1 in 5- to 30-day-old female adults. JHEH, JHE, and Met expression were studied in different reproductive stages of C. septempunctata females by quantitative real-time PCR. JHEH transcription levels were highest in 25-day-old female adults and were 1.93-fold higher than expression levels in 5-day-old adults. JHEH and JHE expression levels were inhibited by the addition of JH to the artificial diet. Met expression in C. septempunctata supplied with 3 μl JH in artificial diet was similar to Met transcription in females supplied with an aphid diet, and the results showed that supplementation with 3 μl JH in 582.2 g of artificial diet was the most suitable for reproductive regulation of C. septempunctata. The results of this study provide important insights for the improvement of C. septempunctata artificial diets.

We consider a test which allows students to attempt a multiple-choice question multiple times (i.e., multiple attempts). The most extreme form of multiple attempts is the answer-until-correct (AUC) procedure. Previous research has demonstrated that multiple-attempt procedures such as AUC could potentially enhance learning and increase reliability. However, for multiple-choice items, guessing is still non-ignorable. Traditional models of sequential item response theory (SIRT) could model multiple-attempt procedures but fail to take guessing into account. The purpose of this study is to propose SIRT models for multiple-choice, multiple-attempt items (SIRT-MM). First, we defined a family of SIRT-MM models to account for the idiosyncrasies of items, answer options, and examinee behavior. We also explained how these models could improve person parameter estimates by taking into account partial (mis)information of examinees. Second, we conducted model comparisons between the SIRT-MM models, the graded response model, and the nominal response model. Third, we discussed how the item and person parameters can be estimated, and evaluated item and person parameter recovery of SIRT-MM models under different conditions through a simulation study. Finally, we applied the SIRT-MM models to a real dataset and demonstrated their utility through model selection, person parameter recovery, and information functions.

Change-point analysis (CPA) is a well-established statistical method to detect abrupt changes, if any, in a sequence of data. In this paper, we propose a procedure based on CPA to detect test speededness. This procedure is not only able to classify examinees into speeded and non-speeded groups, but also identify the point at which an examinee starts to speed. Identification of the change point can be very useful. First, it informs decision makers of the appropriate length of a test. Second, by removing the speeded responses, instead of the entire response sequence of an examinee suspected of speededness, ability estimation can be improved. Simulation studies show that this procedure is efficient in detecting both speeded examinees and the speeding point. Ability estimation is dramatically improved by removing speeded responses identified by our procedure. The procedure is then applied to a real dataset for illustration purpose.

Item cloning is increasingly used to generate slight differences in tasks for use in psychological experiments and educational assessments. This paper investigates the psychometric issues that arise when item cloning introduces variation into the difficulty parameters of the item clones. Four models are proposed and evaluated in simulation studies with conditions representing possible types of variation due to item cloning. Depending on the model specified, unaccounted variance in the item clone difficulties propagates to other parameters in the model, causing specific and predictable patterns of bias. Person parameters are largely unaffected by the choice of model, but for inferences related to the item parameters, the choice is critical and can even be leveraged to identify problematic item cloning.

The paper clarifies the relationship among several information matrices for the maximum likelihood estimates (MLEs) of item parameters. It shows that the process of calculating the observed information matrix also generates a related matrix that is the middle piece of a sandwich-type covariance matrix. Monte Carlo results indicate that standard errors (SEs) based on the observed information matrix are robust to many, but not all, conditions of model/distribution misspecifications. SEs based on the sandwich-type covariance matrix perform most consistently across conditions. Results also suggest that SEs based on other matrices are either not consistent or perform not as robust as those based on the sandwich-type covariance matrix or the observed information matrix.

Computerized adaptive testing (CAT) is a mode of testing which enables more efficient and accurate recovery of one or more latent traits. Traditionally, CAT is built upon Item Response Theory (IRT) models that assume unidimensionality. However, the problem of how to build CAT upon latent class models (LCM) has not been investigated until recently, when Tatsuoka (J. R. Stat. Soc., Ser. C, Appl. Stat. 51:337–350, 2002) and Tatsuoka and Ferguson (J. R. Stat., Ser. B 65:143–157, 2003) established a general theorem on the asymptotically optimal sequential selection of experiments to classify finite, partially ordered sets. Xu, Chang, and Douglas (Paper presented at the annual meeting of National Council on Measurement in Education, Montreal, Canada, 2003) then tested two heuristics in a simulation study based on Tatsuoka’s theoretical work in the context of computerized adaptive testing. One of the heuristics was developed based on Kullback–Leibler information, and the other based on Shannon entropy. In this paper, we showcase the application of the optimal sequential selection methodology in item selection of CAT that is built upon cognitive diagnostic models. Two new heuristics are proposed, and are compared against the randomized item selection method and the two heuristics investigated in Xu et al. (Paper presented at the annual meeting of National Council on Measurement in Education, Montreal, Canada, 2003). Finally, we show the connection between the Kullback–Leibler-information-based approaches and the Shannon-entropy-based approach, as well as the connection between algorithms built upon LCM and those built upon IRT models.

In this paper we propose an upward correction to the standard error (SE) estimation of \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$\hat{\theta}_{\mathrm{ML}}$\end{document}, the maximum likelihood (ML) estimate of the latent trait in item response theory (IRT). More specifically, the upward correction is provided for the SE of \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$\hat{\theta}_{\mathrm{ML}}$\end{document} when item parameter estimates obtained from an independent pretest sample are used in IRT scoring. When item parameter estimates are employed, the resulting latent trait estimate is called pseudo maximum likelihood (PML) estimate. Traditionally, the SE of \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$\hat{\theta}_{\mathrm{ML}}$\end{document} is obtained on the basis of test information only, as if the item parameters are known. The upward correction takes into account the error that is carried over from the estimation of item parameters, in addition to the error in latent trait recovery itself. Our simulation study shows that both types of SE estimates are very good when θ is in the middle range of the latent trait distribution, but the upward-corrected SEs are more accurate than the traditional ones when θ takes more extreme values.

This paper studies changes of standard errors (SE) of the normal-distribution-based maximum likelihood estimates (MLE) for confirmatory factor models as model parameters vary. Using logical analysis, simplified formulas and numerical verification, monotonic relationships between SEs and factor loadings as well as unique variances are found. Conditions under which monotonic relationships do not exist are also identified. Such functional relationships allow researchers to better understand the problem when significant factor loading estimates are expected but not obtained, and vice versa. What will affect the likelihood for Heywood cases (negative unique variance estimates) is also explicit through these relationships. Empirical findings in the literature are discussed using the obtained results.

Person fit statistics are frequently used to detect aberrant behavior when assuming an item response model generated the data. A common statistic, $l_z$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$l_z$$\end{document}, has been shown in previous studies to perform well under a myriad of conditions. However, it is well-known that $l_z$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$l_z$$\end{document} does not follow a standard normal distribution when using an estimated latent trait. As a result, corrections of $l_z$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$l_z$$\end{document}, called $l_z^{\ast }$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$l_z^*$$\end{document}, have been proposed in the literature for specific item response models. We propose a more general correction that is applicable to many types of data, namely survey or tests with multiple item types and underlying latent constructs, which subsumes previous work done by others. In addition, we provide corrections for multiple estimators of $\theta$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\theta $$\end{document}, the latent trait, including MLE, MAP and WLE. We provide analytical derivations that justifies our proposed correction, as well as simulation studies to examine the performance of the proposed correction with finite test lengths. An applied example is also provided to demonstrate proof of concept. We conclude with recommendations for practitioners when the asymptotic correction works well under different conditions and also future directions.

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

While estimation bias is a primary concern in psychological and educational measurement, the standard error is of equal importance in linking key aspects of the assessment structure, especially when the assessment goal concerns the classification of individuals into categories (e.g., master/non-mastery). In this paper, we show analytically how standard error of ability estimates affects expected classification accuracy and consistency when the decision is binary. When standard error decreases, the conditional classification accuracy and consistency increase. Given an examinee population and a cut score, smaller standard error over the entire latent trait continuum guarantees higher overall expected classification accuracy and consistency. We were also able to show the interrelationship between standard error, the expected classification consistency, and reliability. Utilizing the relationship between standard error and expected classification accuracy and consistency, we derive the upper bounds of the overall expected classification accuracy and consistency of a fixed-length computerized adaptive test. The lower bound of the expected classification accuracy and consistency is also derived given a number of stopping rules of variable-length computerized adaptive testing. Implications of these analytical results on operational tests are discussed.

We report on an experimental study of turbulent Rayleigh–Bénard convection with asymmetric top and bottom plates. The plates are covered with pyramid-shaped roughness elements whose aspect ratios are $\lambda =1$ or $\lambda =4$. In the low-Rayleigh-number regime ($Ra<1.9\times 10^9$), the heat transport efficiencies in the asymmetric cells, characterized by the Nusselt number, are smaller than those measured in a symmetric $\lambda = 1$ cell and are greater than those for a symmetric $\lambda = 4$ cell, whereas in the high-Rayleigh-number regime ($Ra>1.9\times 10^9$), the Nusselt numbers of the asymmetric cells are, in turn, greater than those for the symmetric cell with $\lambda = 1$ and smaller than those for the symmetric cell with $\lambda = 4$. In addition, the heat transports of individual plates are studied based on the temperature drops across both halves of the cell. In the low-$Ra$ regime, the $\lambda =1$ plate shows higher heat transfer than the $\lambda =4$ plate, while for the high-$Ra$ regime, the $\lambda =4$ plate shows a higher heat transport ability. In both regimes, the individual Nusselt number of the plate with lower heat transfer is insensitive to the topology of the other plate. Besides, it is found that the symmetry of the centre temperature distribution is robust to the symmetry breaking of boundary topographies. For the $Ra$ range explored, a weak temperature inversion is observed in the bulk of asymmetric rough cells. Finally, we remark that the temperature fluctuation at the cell centre and the Reynolds number associated with the large-scale circulation show universal power laws in terms of the flux Rayleigh number as $\sigma _{T_{c}}\sim Ra_F^{0.68}$ and $Re_{LSC}\sim Ra_F^{0.36}$, respectively.