Bayesian mixed model inference for genetic association under related samples with brain network phenotype.

Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes.

Comparative physiology and transcriptome response patterns in cold-tolerant and cold-sensitive varieties of Solanum melongena.

BACKGROUND: Climate change has led to severe cold events, adversely impacting global crop production. Eggplant (Solanum melongena L.), a significant economic crop, is highly susceptible to cold damage, affecting both yield and quality. Unraveling the molecular mechanisms governing cold resistance, including the identification of key genes and comprehensive transcriptional regulatory pathways, is crucial for developing new varieties with enhanced tolerance. RESULTS: In this study, we conducted a comparative analysis of leaf physiological indices and transcriptome sequencing results. The orthogonal partial least squares discriminant analysis (OPLS-DA) highlighted peroxidase (POD) activity and soluble protein as crucial physiological indicators for both varieties. RNA-seq data analysis revealed that a total of 7024 and 6209 differentially expressed genes (DEGs) were identified from variety "A" and variety "B", respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of DEGs demonstrated that the significant roles of starch and sucrose metabolism, glutathione metabolism, terpenoid synthesis, and energy metabolism (sucrose and starch metabolism) were the key pathways in eggplant. Weighted gene co-expression network analysis (WGCNA) shown that the enrichment of numerous cold-responsive genes, pathways, and soluble proteins in the MEgrep60 modules. Core hub genes identified in the co-expression network included POD, membrane transporter-related gene MDR1, abscisic acid-related genes, growth factor enrichment gene DELLA, core components of the biological clock PRR7, and five transcription factors. Among these, the core transcription factor MYB demonstrated co-expression with signal transduction, plant hormone, biosynthesis, and metabolism-related genes, suggesting a pivotal role in the cold response network. CONCLUSION: This study integrates physiological indicators and transcriptomics to unveil the molecular mechanisms responsible for the differences in cold tolerance between the eggplant cold-tolerant variety "A" and the cold-sensitive variety "B". These mechanisms include modulation of reactive oxygen species (ROS), elevation in osmotic carbohydrate and free proline content, and the expression of terpenoid synthesis genes. This comprehensive understanding contributes valuable insights into the molecular underpinnings of cold stress tolerance, ultimately aiding in the improvement of crop cold tolerance.

Neural signatures of second language proficiency in narrative processing.

Making sense of speech in a second language relies on multiple abilities. Differences in brain activity related to proficiency in language tasks have often been attributed to processing demands. However, during naturalistic narrative comprehension, listeners at different proficiency levels may form different representations of the same speech. We hypothesized that the intersubject synchronization of these representations could be used to measure second-language proficiency. Using a searchlight-shared response model, we found highly proficient participants showed synchronization in regions similar to those of native speakers, including in the default mode network and the lateral prefrontal cortex. In contrast, participants with low proficiency showed more synchronization in auditory cortex and word-level semantic processing areas in the temporal lobe. Moderate proficiency showed the greatest neural diversity, suggesting lower consistency in the source of this partial proficiency. Based on these synchronization differences, we were able to classify the proficiency level or predict behavioral performance on an independent English test in held-out participants, suggesting the identified neural systems represented proficiency-sensitive information that was generalizable to other individuals. These findings suggest higher second-language proficiency leads to more native-like neural processing of naturalistic language, including in systems beyond the cognitive control network or the core language network.

A random item effects generalized partial credit model with a multiple imputation-based scoring procedure.

PURPOSE: Random item effects item response theory (IRT) models have received much attention for more than a decade. However, more research is needed on random item effects IRT models for polytomous data. Additionally, to improve the utility of this new class of IRT models, the scoring issue must be addressed. METHODS: We proposed a new random item effects generalized partial credit model (GPCM), which considers both random person and random item and category-specific effects. In addition, we introduced a multiple imputation (MI)-based scoring procedure that applies to various random item effects IRT models. To evaluate the proposed model and scoring procedure, we analyzed data from a Quality of Life (QoL) scale for the Chronically Mentally III and conducted a preliminary simulation study. RESULTS: In the empirical data analysis, we found that patient scores generated based on the proposed model and scoring procedure were almost identical to those obtained through the conventional GPCM and scoring method. However, the standard errors (SEs) associated with the scores were slightly larger when the proposed approach was utilized. In the simulation study, we observed adequate recovery of the model parameters and patient scores. CONCLUSION: The proposed model and MI-based scoring procedure contribute to the literature. The proposed model substantially reduces the number of free parameters in comparison to a conventional GPCM, which can be desired when sample sizes are small, e.g., special populations. In addition, the MI-based scoring procedure addresses the scoring issue and can be easily extended for scoring with other random item effects IRT models.

Psychometric properties of the health literacy instrument in Brazil (HLS-EU-BR47).

BACKGROUND: Health literacy (HL) is a key component of health promotion and sustainability and contributes to well-being. Despite its global relevance, HL is an under-researched topic in South America but is now debuting its exploration in Brazil. To leverage its benefits for South America, the mere translation of validated tools into Portuguese is insufficient. Rather, it is necessary to examine their validity. This study aims to assess the psychometric properties of the European Health Literacy Questionnaire (HLS-EU-BR47) using the Item Response Theory (IRT) in a population-based sample of adults in Brazil. METHODS: A cross-sectional online study was conducted across Brazil and included 1028 participants aged 18 years and above (80% women). Cronbach's alpha, McDonald's omega, factor analysis, graded responses model, Item Characteristic Curve, HL levels based on this, HL standard calculation, IRT, and regular score correlation were computed. RESULTS: The instrument exhibit high reliability (Cronbach's alpha 0.95). Factor analysis yielded one factor. IRT was appropriate for data analysis because it allowed quality evaluation of items and constructed a scale to quantify HL. The 47 items and latent features of respondents in the same unit of measurement are positioned in the construction of the HLS-EU-BR47 instrument. The percentages of individuals at each HL level, calculated using IRT, were found to be comparable to those obtained through the standard computation, e.g., 3.2% of people reported very low HL versus 10.8% inadequate HL, 56.2% reported low HL versus 39.5% problematic HL, 31.1% had moderate HL versus 30.1% sufficient HL, and 9.5% had high HL versus 19.7% with excellent HL. The mean HL scores were comparable between women and men (33.9 vs. 33.7, P = 0.36). CONCLUSION: This study provides new evidence of the validity of a widely used HL instrument for the population of South America (in this case, Brazil). This tool can be utilized by citizens, health professionals, and regional/national policymakers to inform the development of initiatives to assess and improve the HL of individuals, groups, and communities. Further studies are needed to confirm and extend the findings and to explore the influence of local cultures and practices in the vast Brazilian territory on HL.

Does the exponential Wells-Riley model provide a good fit for human coronavirus and rhinovirus? A comparison of four dose-response models based on human challenge data.

The risk assessments during the COVID-19 pandemic were primarily based on dose-response models derived from the pooled datasets for infection of animals susceptible to SARS-CoV. Despite similarities, differences in susceptibility between animals and humans exist for respiratory viruses. The two most commonly used dose-response models for calculating the infection risk of respiratory viruses are the exponential and the Stirling approximated ß-Poisson (BP) models. The modified version of the one-parameter exponential model or the Wells-Riley model was almost solely used for infection risk assessments during the pandemic. Still, the two-parameter (α and ß) Stirling approximated BP model is often recommended compared to the exponential dose-response model due to its flexibility. However, the Stirling approximation restricts this model to the general rules of ߠ≫ 1 and α â‰ª ß, and these conditions are very often violated. To refrain from these requirements, we tested a novel BP model by using the Laplace approximation of the Kummer hypergeometric function instead of the conservative Stirling approximation. The datasets of human respiratory airborne viruses available in the literature for human coronavirus (HCoV-229E) and human rhinovirus (HRV-16 and HRV-39) are used to compare the four dose-response models. Based on goodness-of-fit criteria, the exponential model was the best fitting model for the HCoV-229E (k = 0.054) and for HRV-39 datasets (k = 1.0), whereas the Laplace approximated BP model followed by the exact and Stirling approximated BP models are preferred for both the HRV-16 (α = 0.152 and ß = 0.021 for Laplace BP) and the HRV-16 and HRV-39 pooled datasets (α = 0.2247 and ß = 0.0215 for Laplace BP).

Analyzing the psychometric properties of the PHQ-9 using item response theory in a Chinese adolescent population.

BACKGROUND: People are more likely to fall victim to depression during adolescence since it is a period of rapid biopsychosocial transformation. Despite this, most depression research has concentrated on clinical issues, and evaluating depressive symptoms in teenagers is not as widespread. This study used item response theory (IRT) to examine the psychometric properties of the Patient Health Report scale (PHQ-9) in Chinese adolescents. Meanwhile, item function difference tests were used to check whether there were differences in depression symptoms in this group based on education and gender. METHODS: In this research, the PHQ-9 was employed as a measurement tool, and 5958 valid data points were obtained from 12 secondary schools in China (Mage = 13.484; SDage = 1.627; range 11-19 years; 52.17% boys). RESULTS: IRT shows that all items of the PHQ-9 satisfy monotonicity, unidimensionality and local independence and that they have good psychometric properties. Furthermore, DIF analysis revealed gender and educational disparities in adolescent depressive symptoms. CONCLUSION: The study indicates that the PHQ-9 possesses favourable psychometric properties for use in Chinese adolescents. As a result, it serves as a valuable tool for effectively screening depressive symptoms in adolescents. It provides a foundation for prioritizing the development of secondary school students' physical and mental health.

ARRIVE Together: A Qualitative Process Evaluation of the New Jersey State Police Co-responding Pilot Program.

Law enforcement personnel are often first to respond to calls involving behavioral health emergencies. However, encounters with law enforcement are more dangerous and lethal for people with behavioral health conditions. Co-responding models, wherein law enforcement and behavioral health professionals respond to calls together, are among the top programs developed to improve responding to behavioral health crises. The current study describes a qualitative process evaluation of a co-responding pilot program in New Jersey: "Alternative Responses to Reduce Instances of Violence & Escalation" (ARRIVE Together). The evaluation centered on the experience of the co-responding team as to their perceptions of specific deployments and of the program implementation overall. Semi-structured interviews were conducted following 10 consecutive encounters (three interviews per encounter; February-March 2022). Transcripts were transcribed and thematically analyzed by two trained researchers independently. Once thematically analyzed, researchers determined a consensus and developed a SWOT analysis report. Thematic analysis produced six major themes: communication, staffing, training, resources, community outreach, and deployments with minors. Overall, participants were enthusiastic about the program, but they shared numerous observations about ways in which the program could be improved. Sample size, the brief follow-up window, and lack of generalizability to other contexts were among the most limiting factors. Further research should include an effectiveness evaluation and extend to urban and suburban communities and communities of color. Future research should also explore after-response affects including accessibility to follow-up care. The current study gives insight into piloting a co-responding model for approaching behavioral health crisis calls.

Fast, Zero-Reference Low-Light Image Enhancement with Camera Response Model.

Low-light images are prevalent in intelligent monitoring and many other applications, with low brightness hindering further processing. Although low-light image enhancement can reduce the influence of such problems, current methods often involve a complex network structure or many iterations, which are not conducive to their efficiency. This paper proposes a Zero-Reference Camera Response Network using a camera response model to achieve efficient enhancement for arbitrary low-light images. A double-layer parameter-generating network with a streamlined structure is established to extract the exposure ratio K from the radiation map, which is obtained by inverting the input through a camera response function. Then, K is used as the parameter of a brightness transformation function for one transformation on the low-light image to realize enhancement. In addition, a contrast-preserving brightness loss and an edge-preserving smoothness loss are designed without the requirement for references from the dataset. Both can further retain some key information in the inputs to improve precision. The enhancement is simplified and can reach more than twice the speed of similar methods. Extensive experiments on several LLIE datasets and the DARK FACE face detection dataset fully demonstrate our method's advantages, both subjectively and objectively.

Drug combinations screening using a Bayesian ranking approach based on dose-response models.

Drug combinations have been of increasing interest in recent years for the treatment of complex diseases such as cancer, as they could reduce the risk of drug resistance. Moreover, in oncology, combining drugs may allow tackling tumor heterogeneity. Identifying potent combinations can be an arduous task since exploring the full dose-response matrix of candidate combinations over a large number of drugs is costly and sometimes unfeasible, as the quantity of available biological material is limited and may vary across patients. Our objective was to develop a rank-based screening approach for drug combinations in the setting of limited biological resources. A hierarchical Bayesian 4-parameter log-logistic (4PLL) model was used to estimate dose-response curves of dose-candidate combinations based on a parsimonious experimental design. We computed various activity ranking metrics, such as the area under the dose-response curve and Bliss synergy score, and we used the posterior distributions of ranks and the surface under the cumulative ranking curve to obtain a comprehensive final ranking of combinations. Based on simulations, our proposed method achieved good operating characteristics to identifying the most promising treatments in various scenarios with limited sample sizes and interpatient variability. We illustrate the proposed approach on real data from a combination screening experiment in acute myeloid leukemia.

A state response measurement model for problem-solving process data.

In computer simulation-based interactive tasks, different people make different response processes to the same tasks, resulting in various action sequences. These sequences contain rich information, not only about respondents, but also about tasks. In this study, we propose a state response (SR) measurement model with a Bayesian approach for analyzing the process sequences, which assumes that each action made is determined by the individual's problem-solving ability and the easiness of the current problem state. This model is closer to reality compared with the action sub-model (referred to as DC model) of Chen's (2020) continuous-time dynamic choice (CTDC) measurement model that defines the easiness parameter only at the task level and ignores the task's process characteristics. The simulation study showed that the SR model performed well in parameter estimation. Moreover, the estimation accuracy of the SR model was quite similar to that of the DC model when state easiness parameters were equal within the task, but was much higher when within-task state easiness parameters were unequal. For the empirical data from the Program for International Student Assessment 2012, the SR model showed better model fit than the DC model. The estimates for state easiness parameters within each task were obviously different and made sense for characterizing task steps, further demonstrating the rationality of the proposed SR model.

Accelerated reproduction is not an adaptive response to early-life adversity in wild baboons.

In humans and other long-lived species, harsh conditions in early life often lead to profound differences in adult life expectancy. In response, natural selection is expected to accelerate the timing and pace of reproduction in individuals who experience some forms of early-life adversity. However, the adaptive benefits of reproductive acceleration following early adversity remain untested. Here, we test a recent version of this theory, the internal predictive adaptive response (iPAR) model, by assessing whether accelerating reproduction following early-life adversity leads to higher lifetime reproductive success. We do so by leveraging 48 y of continuous, individual-based data from wild female baboons in the Amboseli ecosystem in Kenya, including prospective, longitudinal data on multiple sources of nutritional and psychosocial adversity in early life; reproductive pace; and lifetime reproductive success. We find that while early-life adversity led to dramatically shorter lifespans, individuals who experienced early adversity did not accelerate their reproduction compared with those who did not experience early adversity. Further, while accelerated reproduction predicted increased lifetime reproductive success overall, these benefits were not specific to females who experienced early-life adversity. Instead, females only benefited from reproductive acceleration if they also led long lives. Our results call into question the theory that accelerated reproduction is an adaptive response to both nutritional and psychosocial sources of early-life adversity in baboons and other long-lived species.

Graded Response Model Analysis and Computer Adaptive Test Simulation of the Depression Anxiety Stress Scale 21: Evaluation and Validation Study.

BACKGROUND: The Depression Anxiety Stress Scale 21 (DASS-21) is a mental health screening tool with conflicting studies regarding its factor structure. No studies have yet attempted to develop a computer adaptive test (CAT) version of it. OBJECTIVE: This study calibrated items for, and simulated, a DASS-21 CAT using a nonclinical sample. METHODS: An evaluation sample (n=580) was used to evaluate the DASS-21 scales via confirmatory factor analysis, Mokken analysis, and graded response modeling. A CAT was simulated with a validation sample (n=248) and a simulated sample (n=10,000) to confirm the generalizability of the model developed. RESULTS: A bifactor model, also known as the "quadripartite" model (1 general factor with 3 specific factors) in the context of the DASS-21, displayed good fit. All scales displayed acceptable fit with the graded response model. Simulation of 3 unidimensional (depression, anxiety, and stress) CATs resulted in an average 17% to 48% reduction in items administered when a reliability of 0.80 was acceptable. CONCLUSIONS: This study clarifies previous conflicting findings regarding the DASS-21 factor structure and suggests that the quadripartite model for the DASS-21 items fits best. Item response theory modeling suggests that the items measure their respective constructs best between 0θ and 3θ (mild to moderate severity).

Development and validation of the first adaptive test of emotion perception in music.

ABSTRACTThe Musical Emotion Discrimination Task (MEDT) is a short, non-adaptive test of the ability to discriminate emotions in music. Test-takers hear two performances of the same melody, both played by the same performer but each trying to communicate a different basic emotion, and are asked to determine which one is "happier", for example. The goal of the current study was to construct a new version of the MEDT using a larger set of shorter, more diverse music clips and an adaptive framework to expand the ability range for which the test can deliver measurements. The first study analysed responses from a large sample of participants (N = 624) to determine how musical features contributed to item difficulty, which resulted in a quantitative model of musical emotion discrimination ability rooted in Item Response Theory (IRT). This model informed the construction of the adaptive MEDT. A second study contributed preliminary evidence for the validity and reliability of the adaptive MEDT, and demonstrated that the new version of the test is suitable for a wider range of abilities. This paper therefore presents the first adaptive musical emotion discrimination test, a new resource for investigating emotion processing which is freely available for research use.

Measurement invariance of the Liebowitz Social Anxiety Scale-Self-Report.

OBJECTIVE: The Liebowitz Social Anxiety Scale-Self Report (LSAS-SR) is a self-report measure of social anxiety (SA), which has shown adequate psychometric properties across cultures. However, no study has systematically evaluated its measurement invariance (MI) between (a) individuals with and without a diagnosis of social anxiety disorder (SAD) and (b) males and females. The current study addresses this issue. METHODS: We collected data on 257 (158 females) Italian individuals diagnosed with SAD and 356 (232 females) community-dwelling adults. RESULTS: We initially found support for the unidimensionality of the Italian LSAS-SR measurement model in all samples. Using the Graded Response Model, we obtained evidence of partial MI and differential item functioning between community-dwelling and SAD-diagnosed individuals and evidence of strong MI between male and female participants. CONCLUSIONS: The results of this study suggest that the Italian LSAS-SR measures the same trait in the same way across the symptom continuum and sexes, making it a psychometrically sound tool for assessment, screening, and research purposes.

A Modified Net Reclassification Improvement Statistic.

The continuous net reclassification improvement (NRI) statistic is a popular model change measure that was developed to assess the incremental value of new factors in a risk prediction model. Two prominent statistical issues identified in the literature call the utility of this measure into question: (1) it is not a proper scoring function and (2) it has a high false positive rate when testing whether new factors contribute to the risk model. For binary response regression models, these subjects are interrogated and a modification of the continuous NRI, guided by the likelihood-based score residual, is proposed to address these issues. Within a nested model framework, the modified NRI may be viewed as a distance measure between two risk models. An application of the modified NRI is illustrated using prostate cancer data.

Modeling item-level heterogeneous treatment effects: A tutorial with the glmer function from the lme4 package in R.

Recent advancements in education scholarship have introduced Item Response Theory (IRT) models to address treatment heterogeneity at the assessment item level. These models for item-level heterogeneous treatment effects (IL-HTE) enable detailed analyses of treatments that may have varying impacts on individual items within an assessment. This article offers a comprehensive tutorial for applied researchers interested in implementing IL-HTE analysis in R, utilizing the lme4 package. Using empirical data from a second-grade reading comprehension assessment as a running example, this tutorial emphasizes model-building strategies, interpretation techniques, visualization methods, and extensions. By following this tutorial, researchers will gain practical insights into utilizing IL-HTE analysis for enhanced understanding and interpretation of treatment effects at the item level.

Comparing the prediction performance of item response theory and machine learning methods on item responses for educational assessments.

To obtain more accurate and robust feedback information from the students' assessment outcomes and to communicate it to students and optimize teaching and learning strategies, educational researchers and practitioners must critically reflect on whether the existing methods of data analytics are capable of retrieving the information provided in the database. This study compared and contrasted the prediction performance of an item response theory method, particularly the use of an explanatory item response model (EIRM), and six supervised machine learning (ML) methods for predicting students' item responses in educational assessments, considering student- and item-related background information. Each of seven prediction methods was evaluated through cross-validation approaches under three prediction scenarios: (a) unrealized responses of new students to existing items, (b) unrealized responses of existing students to new items, and (c) missing responses of existing students to existing items. The results of a simulation study and two real-life assessment data examples showed that employing student- and item-related background information in addition to the item response data substantially increases the prediction accuracy for new students or items. We also found that the EIRM is as competitive as the best performing ML methods in predicting the student performance outcomes for the educational assessment datasets.

An Optimized Schwarz Method for the Optical Response Model Discretized by HDG Method.

An optimized Schwarz domain decomposition method (DDM) for solving the local optical response model (LORM) is proposed in this paper. We introduce a hybridizable discontinuous Galerkin (HDG) scheme for the discretization of such a model problem based on a triangular mesh of the computational domain. The discretized linear system of the HDG method on each subdomain is solved by a sparse direct solver. The solution of the interface linear system in the domain decomposition framework is accelerated by a Krylov subspace method. We study the spectral radius of the iteration matrix of the Schwarz method for the LORM problems, and thus propose an optimized parameter for the transmission condition, which is different from that for the classical electromagnetic problems. The numerical results show that the proposed method is effective.

A semiparametric model for between-subject attributes: Applications to beta-diversity of microbiome data.

The human microbiome plays an important role in our health and identifying factors associated with microbiome composition provides insights into inherent disease mechanisms. By amplifying and sequencing the marker genes in high-throughput sequencing, with highly similar sequences binned together, we obtain operational taxonomic units (OTUs) profiles for each subject. Due to the high-dimensionality and nonnormality features of the OTUs, the measure of diversity is introduced as a summarization at the microbial community level, including the distance-based beta-diversity between individuals. Analyses of such between-subject attributes are not amenable to the predominant within-subject-based statistical paradigm, such as t-tests and linear regression. In this paper, we propose a new approach to model beta-diversity as a response within a regression setting by utilizing the functional response models (FRMs), a class of semiparametric models for between- as well as within-subject attributes. The new approach not only addresses limitations of current methods for beta-diversity with cross-sectional data, but also provides a premise for extending the approach to longitudinal and other clustered data in the future. The proposed approach is illustrated with both real and simulated data.