The World Cancer Research Fund and the American Institute for Cancer Research recommend a plant-based diet to cancer survivors, which may reduce chronic inflammation and excess adiposity associated with worse survival. We investigated associations of plant-based dietary patterns with inflammation biomarkers and body composition in the Pathways Study, in which 3659 women with breast cancer provided validated food frequency questionnaires approximately 2 months after diagnosis. We derived three plant-based diet indices: overall plant-based diet index (PDI), healthful plant-based diet index (hPDI) and unhealthful plant-based diet index (uPDI). We assayed circulating inflammation biomarkers related to systemic inflammation (high-sensitivity C-reactive protein [hsCRP]), pro-inflammatory cytokines (IL-1β, IL-6, IL-8, TNF-α) and anti-inflammatory cytokines (IL-4, IL-10, IL-13). We estimated areas (cm2) of muscle and visceral and subcutaneous adipose tissue (VAT and SAT) from computed tomography scans. Using multivariable linear regression, we calculated the differences in inflammation biomarkers and body composition for each index. Per 10-point increase for each index: hsCRP was significantly lower by 6·9 % (95 % CI 1·6%, 11·8%) for PDI and 9·0 % (95 % CI 4·9%, 12·8%) for hPDI but significantly higher by 5·4 % (95 % CI 0·5%, 10·5%) for uPDI, and VAT was significantly lower by 7·8 cm2 (95 % CI 2·0 cm2, 13·6 cm2) for PDI and 8·6 cm2 (95 % CI 4·1 cm2, 13·2 cm2) for hPDI but significantly higher by 6·2 cm2 (95 % CI 1·3 cm2, 11·1 cm2) for uPDI. No significant associations were observed for other inflammation biomarkers, muscle, or SAT. A plant-based diet, especially a healthful plant-based diet, may be associated with reduced inflammation and visceral adiposity among breast cancer survivors.

Objectives/Goals: Transmission-blocking vaccines hold promise for malaria elimination by reducing community transmission. But a major challenge that limits the development of efficacious vaccines is the vast parasite’s genetic diversity. This work aims to assess the genetic diversity of the Pfs25 vaccine candidate in complex infections across African countries. Methods/Study Population: We employed next-generation amplicon deep sequencing to identify nonsynonymous single nucleotide polymorphisms (SNPs) in 194 Plasmodium falciparum samples from four endemic African countries: Senegal, Tanzania, Ghana, and Burkina Faso. The individuals aged between 1 and 74 years, but most of them ranged from 1 to 19 years, and all presented symptomatic P. falciparum infection. The genome amplicon sequencing was analyzed using Geneious software and P. falciparum 3D7 as a reference. The SPNs were called with a minimum coverage of 500bp, and for this work, we used a very sensitive threshold of 1% variant frequency to determine the frequency of SNPs. The identified SNPs were threaded to the crystal structure of the Pfs25 protein, which allowed us to predict the impact of the novel SNP in the protein or antibody binding. Results/Anticipated Results: We identified 26 SNPs including 24 novel variants, and assessed their population prevalence and variant frequency in complex infections. Notably, five variants were detected in multiple samples (L63V, V143I, S39G, L63P, and E59G), while the remaining 21 were rare variants found in individual samples. Analysis of country-specific prevalence showed varying proportions of mutant alleles, with Ghana exhibiting the highest prevalence (44.6%), followed by Tanzania (12%), Senegal (11.8%), and Burkina Faso (2.7%). Moreover, we categorized SNPs based on their frequency, identifying dominant variants (>25%), and rare variants (Discussion/Significance of Impact: We identified additional SNPs in the Pfs25 gene beyond those previously reported. However, the majority of these newly discovered display low variant frequency and population prevalence. Further research exploring the functional implications of these variations will be important to elucidate their role in malaria transmission.

Drawing on Roman Catholic and ecumenical expertise, this article takes an honest look at the experiences and hopes of those abused. Many in the churches assume that victims seek financial compensation or legal redress. However, research indicates that many victims primarily seek truth and justice as a means of closure and that their struggles with church leadership arise when truth and justice are repeatedly withheld. This makes forgiveness near-impossible and often results in the victim being re-traumatized by the systemic re-abuse they experience. Ultimately, there is no substitute for full and genuine meeting with victims, which requires the church to lay aside its power and authority and engage with humility and proper deference to the victims abused at the hands of the church. Without such openness, the victims cannot move on, and neither can the churches.

Education can be viewed as a control theory problem in which students seek ongoing exogenous input—either through traditional classroom teaching or other alternative training resources—to minimize the discrepancies between their actual and target (reference) performance levels. Using illustrative data from $n=784$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$n=784$$\end{document} Dutch elementary school students as measured using the Math Garden, a web-based computer adaptive practice and monitoring system, we simulate and evaluate the outcomes of using off-line and finite memory linear quadratic controllers with constraintsto forecast students’ optimal training durations. By integrating population standards with each student’s own latent change information, we demonstrate that adoption of the control theory-guided, person- and time-specific training dosages could yield increased training benefits at reduced costs compared to students’ actual observed training durations, and a fixed-duration training scheme. The control theory approach also outperforms a linear scheme that provides training recommendations based on observed scores under noisy and the presence of missing data. Design-related issues such as ways to determine the penalty cost of input administration and the size of the control horizon window are addressed through a series of illustrative and empirically (Math Garden) motivated simulations.

The monotone homogeneity model (MHM—also known as the unidimensional monotone latent variable model) is a nonparametric IRT formulation that provides the underpinning for partitioning a collection of dichotomous items to form scales. Ellis (Psychometrika 79:303–316, 2014, doi:10.1007/s11336-013-9341-5) has recently derived inequalities that are implied by the MHM, yet require only the bivariate (inter-item) correlations. In this paper, we incorporate these inequalities within a mathematical programming formulation for partitioning a set of dichotomous scale items. The objective criterion of the partitioning model is to produce clusters of maximum cardinality. The formulation is a binary integer linear program that can be solved exactly using commercial mathematical programming software. However, we have also developed a standalone branch-and-bound algorithm that produces globally optimal solutions. Simulation results and a numerical example are provided to demonstrate the proposed method.

An IRT model with a parameter-driven process for change is proposed. Quantitative differences between persons are taken into account by a continuous latent variable, as in common IRT models. In addition, qualitative interindividual differences and autodependencies are accounted for by assuming within-subject variability with respect to the parameters of the IRT model. In particular, the parameters of the IRT model are governed by an unobserved or “hidden'” homogeneous Markov process. The model includes the mixture linear logistic test model (Mislevy & Verhelst, 1990), the mixture Rasch model (Rost, 1990), and the Saltus model (Wilson, 1989) as specific instances. The model is applied to a longitudinal experiment on discontinuity in conservation acquisition (van der Maas, 1993).

Dynamic programming methods for matrix permutation problems in combinatorial data analysis can produce globally-optimal solutions for matrices up to size 30×30, but are computationally infeasible for larger matrices because of enormous computer memory requirements. Branch-and-bound methods also guarantee globally-optimal solutions, but computation time considerations generally limit their applicability to matrix sizes no greater than 35×35. Accordingly, a variety of heuristic methods have been proposed for larger matrices, including iterative quadratic assignment, tabu search, simulated annealing, and variable neighborhood search. Although these heuristics can produce exceptional results, they are prone to converge to local optima where the permutation is difficult to dislodge via traditional neighborhood moves (e.g., pairwise interchanges, object-block relocations, object-block reversals, etc.). We show that a heuristic implementation of dynamic programming yields an efficient procedure for escaping local optima. Specifically, we propose applying dynamic programming to reasonably-sized subsequences of consecutive objects in the locally-optimal permutation, identified by simulated annealing, to further improve the value of the objective function. Experimental results are provided for three classic matrix permutation problems in the combinatorial data analysis literature: (a) maximizing a dominance index for an asymmetric proximity matrix; (b) least-squares unidimensional scaling of a symmetric dissimilarity matrix; and (c) approximating an anti-Robinson structure for a symmetric dissimilarity matrix.

Considering a dyad as a dynamic system whose current state depends on its past state has allowed researchers to investigate whether and how partners influence each other. Some researchers have also focused on how differences between dyads in their interaction patterns are related to other differences between them. A promising approach in this area is the model that was proposed by Gottman and Murray, which is based on nonlinear coupled difference equations. In this paper, it is shown that their model is a special case of the threshold autoregressive (TAR) model. As a consequence, we can make use of existing knowledge about TAR models with respect to parameter estimation, model alternatives and model selection. We propose a new estimation procedure and perform a simulation study to compare it to the estimation procedure developed by Gottman and Murray. In addition, we include an empirical example based on interaction data of three dyads.

Several authors have touted the p-median model as a plausible alternative to within-cluster sums of squares (i.e., K-means) partitioning. Purported advantages of the p-median model include the provision of “exemplars” as cluster centers, robustness with respect to outliers, and the accommodation of a diverse range of similarity data. We developed a new simulated annealing heuristic for the p-median problem and completed a thorough investigation of its computational performance. The salient findings from our experiments are that our new method substantially outperforms a previous implementation of simulated annealing and is competitive with the most effective metaheuristics for the p-median problem.

This paper is about fitting multivariate normal mixture distributions subject to structural equation modeling. The general model comprises common factor and structural regression models. The introduction of covariance and mean structure models reduces the number of parameters to be estimated in fitting the mixture and enables one to investigate a variety of substantive hypotheses concerning the differences between the components in the mixture. Within the general model, individual parameters can be subjected to equality, nonlinear and simple bounds constraints. Confidence intervals are based on the inverse of the Hessian and on the likelihood profile. Several illustrations are given and results of a simulation study concerning the confidence intervals are reported.

The emergence of computer-based assessments has made response times, in addition to response accuracies, available as a source of information about test takers’ latent abilities. The development of substantively meaningful accounts of the cognitive process underlying item responses is critical to establishing the validity of psychometric tests. However, existing substantive theories such as the diffusion model have been slow to gain traction due to their unwieldy functional form and regular violations of model assumptions in psychometric contexts. In the present work, we develop an attention-based diffusion model based on process assumptions that are appropriate for psychometric applications. This model is straightforward to analyse using Gibbs sampling and can be readily extended. We demonstrate our model’s good computational and statistical properties in a comparison with two well-established psychometric models.

Although the K-means algorithm for minimizing the within-cluster sums of squared deviations from cluster centroids is perhaps the most common method for applied cluster analyses, a variety of other criteria are available. The p-median model is an especially well-studied clustering problem that requires the selection of p objects to serve as cluster centers. The objective is to choose the cluster centers such that the sum of the Euclidean distances (or some other dissimilarity measure) of objects assigned to each center is minimized. Using 12 data sets from the literature, we demonstrate that a three-stage procedure consisting of a greedy heuristic, Lagrangian relaxation, and a branch-and-bound algorithm can produce globally optimal solutions for p-median problems of nontrivial size (several hundred objects, five or more variables, and up to 10 clusters). We also report the results of an application of the p-median model to an empirical data set from the telecommunications industry.

The clique partitioning problem (CPP) requires the establishment of an equivalence relation for the vertices of a graph such that the sum of the edge costs associated with the relation is minimized. The CPP has important applications for the social sciences because it provides a framework for clustering objects measured on a collection of nominal or ordinal attributes. In such instances, the CPP incorporates edge costs obtained from an aggregation of binary equivalence relations among the attributes. We review existing theory and methods for the CPP and propose two versions of a new neighborhood search algorithm for efficient solution. The first version (NS-R) uses a relocation algorithm in the search for improved solutions, whereas the second (NS-TS) uses an embedded tabu search routine. The new algorithms are compared to simulated annealing (SA) and tabu search (TS) algorithms from the CPP literature. Although the heuristics yielded comparable results for some test problems, the neighborhood search algorithms generally yielded the best performances for large and difficult instances of the CPP.

A Bayesian approach for simultaneous optimization of test-based decisions is presented using the example of a selection decision for a treatment followed by a mastery decision. A distinction is made between weak and strong rules where, as opposed to strong rules, weak rules use prior test scores as collateral data. Conditions for monotonicity of optimal weak and strong rules are presented. It is shown that under mild conditions on the test score distributions and utility functions, weak rules are always compensatory by nature.