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AbstractThis special issue is a collection of papers inspired by Dr. Molenaar’s work and innovations – a tribute to his passion for advancing science and his ability to ignite a spark of creativity and innovation in multiple generations of scientists. Following Dr. Molenaar’s creative breadth, the papers address a wide variety of topics – sharing of new methodological developments, ideas, and findings in idiographic science, study of intraindividual variation, behavioral genetics, model inference/identification/selection, and more. [ABSTRACT FROM AUTHOR]

The use of structural equation models for causal inference from panel data is critiqued in the causal inference literature for unnecessarily relying on a large number of parametric assumptions, and alternative methods originating from the potential outcomes framework have been recommended, such as inverse probability weighting (IPW) estimation of marginal structural models (MSMs). To better understand this criticism, we describe three phases of causal research. We explain (differences in) the assumptions that are made throughout these phases for structural equation modeling (SEM) and IPW-MSM approaches using an empirical example. Second, using simulations we compare the finite sample performance of SEM and IPW-MSM for the estimation of time-varying exposure effects on an end-of-study outcome under violations of parametric assumptions. Although increased reliance on parametric assumptions does not always translate to increased bias (even under model misspecification), researchers are still well-advised to acquaint themselves with causal methods from the potential outcomes framework. [ABSTRACT FROM AUTHOR]

Transgender and gender-diverse youth (TGDY) experience elevated rates of discrimination and victimization. Recent empirical and theoretical work with sexual minority adolescents (e.g., lesbian, gay, bisexual or pansexual) propose negative emotions result from these adverse experiences and are managed through deleterious health behaviors and that experiences of resilience contribute to positive emotions managed through promotive health behaviors. However, the role of identity-related emotions of TGDY has yet to be explored in a health framework. The current study utilizes past empirical and theoretical research to examine the construction of identity-related emotions for TGDY in the Midwest using a deductive-inductive thematic analysis with a priori and data-driven codes. Nineteen youth between the ages of 15–22 (M = 18, SD = 2.29) from two Midwestern states participated in the study. Study procedures and preliminary analysis were overseen by a TGDY advisory board. Seven discrete emotions emerged from the thematic analysis. Consistent with prior research, emotions emerged from a confluence of TGD identity experiences, perceptions of self, and the social environment. Emotions were managed through behaviors that have implications for health. Future prospective studies should test findings among a large, demographically diverse population of TGDY. Furthermore, practitioners should find ways to reduce the occurrence and impact of negative identity-related emotions while supporting the construction of positive ones. [ABSTRACT FROM AUTHOR]

Multilevel autoregressive models are popular choices for the analysis of intensive longitudinal data in psychology. Empirical studies have found a positive correlation between autoregressive parameters of affective time series and the between-person measures of psychopathology, a phenomenon known as the staging effect. However, it has been argued that such findings may represent a statistical artifact: Although common models assume normal error distributions, empirical data (for instance, measurements of negative affect among healthy individuals) often exhibit the floor effect, that is response distributions with high skewness, low mean, and low variability. In this paper, we investigated whether—and to what extent—the floor effect leads to erroneous conclusions by means of a simulation study. We describe three dynamic models which have meaningful substantive interpretations and can produce floor-effect data. We simulate multilevel data from these models, varying skewness independent of individuals' autoregressive parameters, while also varying the number of time points and cases. Analyzing these data with the standard multilevel AR(1) model we found that positive bias only occurs when modeling with random residual variance, whereas modeling with fixed residual variance leads to negative bias. We discuss the implications of our study for data collection and modeling choices [ABSTRACT FROM AUTHOR]

The Andean geography induces favorable conditions for the growth of food plants of high nutritional and functional value. Among these plants are the Andean grains, which are recognized worldwide for their nutritional attributes. The objective of this article is to show the nutritional and functional properties, as well as industrial potential, of Andean grains. Quinoa, amaranth, canihua, and Andean corn are grains that contain bioactive compounds with antioxidant, antimicrobial, and anti-inflammatory activities that benefit the health of the consumer. Numerous in vitro and in vivo studies demonstrate their functional potential. These high-Andean crops could be used industrially to add value to other functional food products. These reports suggest the inclusion of these grains in the daily diets of people and the application of their active compounds in the food industry. [ABSTRACT FROM AUTHOR]

Abstract The cross-sectional correlation is frequently used to summarize psychological data, and can be considered the basis for many statistical techniques. However, the work of Peter Molenaar on ergodicity has raised concerns about the meaning and utility of this measure, especially when the interest is in discovering general laws that apply to (all) individuals. Through using Cattell’s databox and adopting a multilevel perspective, this paper provides a closer look at the cross-sectional correlation, with the goal to better understand its meaning when ergodicity is absent. An analytical expression is presented that shows the cross-sectional correlation is a function of the between-person correlation (based on person-specific means), and the within-person correlation (based on individuals’ temporal deviations from their person-specific means). Two curiosities related to this expression of the cross-sectional correlation are elaborated on, that is: a) the difference between the within-person correlation and the (average) person-specific correlation; and b) the unexpected scenarios that can arise because the cross-sectional correlation is a weighted sum rather than a weighted average of the between-person and within-person correlations. Seven specific examples are presented to illustrate various ways in which these two curiosities may combine; R code is provided, which allows researchers to investigate additional scenarios. [ABSTRACT FROM AUTHOR]

Food-to-food fortification (FtFF) with moringa leaf (iron source) and/or baobab fruit (citric acid and ascorbic acid source) (each 13–15 g/100 g porridge dry basis (db)) was studied to improve iron and zinc nutritive quality in African-type wholegrain maize-based porridges using in vitro dialysability assay. Moringa FtFF decreased percentage and total bioaccessible iron and zinc, by up to 84% and 45%, respectively. Moringa was very high in calcium, approximately 3% db and calcium–iron–phytate complexes inhibit iron bioavailability. Baobab FtFF increased percentage and total bioaccessible iron and zinc, especially in porridges containing carrot + mango (β-carotene source) and conventionally fortified with FeSO4, by up to 111% and 60%, respectively. The effects were similar to those when ascorbic and citric acids were added as mineral absorption enhancers. While moringa FtFF could be inhibitory to iron and zinc bioavailability in cereal-based porridges, baobab fruit FtFF could improve their bioavailability, especially in combination with conventional iron fortification. [ABSTRACT FROM AUTHOR]

A new synthetic methodology has been developed for the synthesis of N-alkyl and N-aryl arenesulfinamides. The methodology involved reacting arenesulfinic acids (R = -Me, -H, -Cl) with 1,1'-carbonyldiimidazole (CDI) to form the reactive intermediate, an arenesulfinylimidazole. This intermediate was then reacted with both primary and secondary amines to yield the corresponding N-alkyl sulfinamides in yields up to 90%. While the overall yields ranged from 52% to 90%, the level of diastereoselection with racemic or enantiomerically enriched amines only reached a level of 54:46 favoring the major diastereomer as determined by analysis of the 500 MHz 1H NMR spectra. A series of aniline derivatives were also investigated as coupling partners and were found to form the N-aryl arenesulfinamide in good yield. [ABSTRACT FROM AUTHOR]

The random intercept cross-lagged panel model (RI-CLPM) is rapidly gaining popularity in psychology and related fields as a structural equation modeling (SEM) approach to longitudinal data. It decomposes observed scores into within-unit dynamics and stable, between-unit differences. This paper discusses three extensions of the RI-CLPM that researchers may be interested in, but are unsure of how to accomplish: (a) including stable, person-level characteristics as predictors and/or outcomes; (b) specifying a multiple-group version; and (c) including multiple indicators. For each extension, we discuss which models need to be run in order to investigate underlying assumptions, and we demonstrate the various modeling options using a motivating example. We provide fully annotated code for lavaan (R-package) and Mplus on an accompanying website. [ABSTRACT FROM AUTHOR]

We sought to determine whether design of carbohydrate-based microspheres to have different digestion rates, while retaining the same material properties, could modulate gastric emptying through the ileal brake. Microspheres made to have three slow digestion rates and a rapidly digested starch analogue (maltodextrin) were administrated to rats by gavage and starch contents in the stomach, proximal and distal small intestine, and caecum were measured 2 h post-gavage. A stepwise increase in the amount of starch retained in the stomach was found for microspheres with incrementally slower rates of digestion. Postprandial glycaemic and insulinaemic responses were incrementally lower for the different microspheres than for the rapidly digestible control. A second-meal effect was observed for slowly digestible starch (SDS) microspheres compared to glucose. Thus, dietary slowly digestible carbohydrates were designed to elicit incremental significant changes in gastric emptying, glycaemic and insulinaemic responses, and they may be a means to trigger the ileal brake. [ABSTRACT FROM AUTHOR]

The glycemic carbohydrates we consume are currently viewed in an unfavorable light in both the consumer and medical research worlds. In significant part, these carbohydrates, mainly starch and sucrose, are looked upon negatively due to their rapid and abrupt glucose delivery to the body which causes a high glycemic response. However, dietary carbohydrates which are digested and release glucose in a slow manner are recognized as providing health benefits. Slow digestion of glycemic carbohydrates can be caused by several factors, including food matrix effect which impedes α-amylase access to substrate, or partial inhibition by plant secondary metabolites such as phenolic compounds. Differences in digestion rate of these carbohydrates may also be due to their specific structures (e.g. variations in degree of branching and/or glycosidic linkages present). In recent years, much has been learned about the synthesis and digestion kinetics of novel α-glucans (i.e. small oligosaccharides or larger polysaccharides based on glucose units linked in different positions by α-bonds). It is the synthesis and digestion of such structures that is the subject of this review. [ABSTRACT FROM AUTHOR]

No abstract available for this article. [ABSTRACT FROM PUBLISHER]

Opinion. Looks at the study carried out on serial costs and collection development at Louisiana State University (LSU) Libraries. Methodology used in the study; Suggestion of a strategies to analyse serial needs; Provision of statistical information; Results from the study.

Discusses the need to reexamine the role and form of collection development policy as libraries stepped into an arena of rapid change. Forces behind change in libraries; Types of programmatic shifts; Circulation measures; Comparison of circulation and patron categories; Factors affecting change; System of guidelines for Inter Library Borrowing; Refocusing collecting strategy.

Reports on efforts to re-design library serials collections at the Louisiana State University (LSU) in Baton Rouge in 1993. Serials cancellations at LSU since 1987; Use of document delivery services from UNCOVER; Shifts in the range of materials provided by LSU's libraries.

Presents an overview of the issues surrounding the library collection development. Rising cost of published materials; Increase in published information; Question of ownership of information; Shift of responsibility for selection and paying for library purchases; Implications of the Aqueduct agenda and `University Policy Regarding Faculty Publication in Scholarly Journals.'

No abstract available for this article. [ABSTRACT FROM PUBLISHER]

No abstract available for this article. [ABSTRACT FROM PUBLISHER]

No abstract available for this article. [ABSTRACT FROM PUBLISHER]

No abstract available for this article. [ABSTRACT FROM PUBLISHER]

With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use composite scores of positive and negative affect and apply a multilevel vector autoregressive model to allow for individual differences in means, autoregressions, and cross-lagged effects. Then we extend the model to include random residual variances and covariance, and finally we investigate whether prior depression affects later depression scores through the random effects of the daily diary measures. We end with discussing several urgent—but mostly unresolved—issues in the area of dynamic multilevel modeling. [ABSTRACT FROM AUTHOR]

This article presents dynamic structural equation modeling (DSEM), which can be used to study the evolution of observed and latent variables as well as the structural equation models over time. DSEM is suitable for analyzing intensive longitudinal data where observations from multiple individuals are collected at many points in time. The modeling framework encompasses previously published DSEM models and is a comprehensive attempt to combine time-series modeling with structural equation modeling. DSEM is estimated with Bayesian methods using the Markov chain Monte Carlo Gibbs sampler and the Metropolis-Hastings sampler. We provide a detailed description of the estimation algorithm as implemented in the Mplus software package. DSEM can be used for longitudinal analysis of any duration and with any number of observations across time. Simulation studies are used to illustrate the framework and study the performance of the estimation method. Methods for evaluating model fit are also discussed. [ABSTRACT FROM AUTHOR]

Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male’s emotion dynamics over time, but not in the female’s—which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system. [ABSTRACT FROM AUTHOR]

Purported health benefits of whole grain foods in lowering risk of obesity, type 2 diabetes, cardiovascular disease, and cancer are supported by epidemiological studies and scientific researches. Bioactive components including dietary fibers, phytochemicals, and various micronutrients present in the bran and germ are commonly considered as the basis for such benefits. Endosperm starch, as the major constituent of whole grains providing glucose to the body, has been less investigated regarding its nutritional property and contribution to the value of whole grain foods. Nutritional quality of starch is associated with its rate of digestion and glucose absorption. In whole grain foods, starch digestion and glucose delivery may vary depending on the form in which the food is delivered, some with starch being rapidly and others slowly digested. Furthermore, there are other inherent factors in whole grain products, such as phenolic compounds and dietary fibers, that may moderate glycemic profiles. A good understanding of the nutritional properties of whole grain starch is important to the development of food processing technologies to maximize their health benefits. [ABSTRACT FROM PUBLISHER]

Markov modeling presents an attractive analytical framework for researchers who are interested in state-switching processes occurring within a person, dyad, family, group, or other system over time. Markov modeling is flexible and can be used with various types of data to study observed or latent state-switching processes, and can include subject-specific random effects to account for heterogeneity. We focus on the application of mixed Markov models to intensive longitudinal data sets in psychology, which are becoming ever more common and provide a rich description of each subject’s process. We examine how specifications of a Markov model change when continuous random effect distributions are included, and how mixed Markov models can be used in the intensive longitudinal research context. Advantages of Bayesian estimation are discussed and the approach is illustrated by two empirical applications. [ABSTRACT FROM PUBLISHER]

Streptococcus mutans-derived exopolysaccharides are virulence determinants in the matrix of biofilms that cause caries. Extracellular DNA (eDNA) and lipoteichoic acid (LTA) are found in cariogenic biofilms, but their functions are unclear. Therefore, strains ofS. mutanscarrying single deletions that would modulate matrix components were used: eDNA – ∆lytSand ∆lytT; LTA – ∆dltAand ∆dltD; and insoluble exopolysaccharide – ΔgtfB. Single-species (parental strainS. mutansUA159 or individual mutant strains) and mixed-species (UA159 or mutant strain,Actinomyces naeslundiiandStreptococcus gordonii) biofilms were evaluated. Distinct amounts of matrix components were detected, depending on the inactivated gene. eDNA was found to be cooperative with exopolysaccharide in early phases, while LTA played a larger role in the later phases of biofilm development. The architecture of mutant strains biofilms was distinct (vsUA159), demonstrating that eDNA and LTA influence exopolysaccharide distribution and microcolony organization. Thus, eDNA and LTA may shape exopolysaccharide structure, affecting strategies for controlling pathogenic biofilms. [ABSTRACT FROM PUBLISHER]

This article describes the general time-intensive longitudinal latent class modeling framework implemented in Mplus. For each individual a latent class variable is measured at each time point and the latent class changes across time follow a Markov process (i.e., a hidden or latent Markov model), with subject-specific transition probabilities that are estimated as random effects. Such a model for single-subject data has been referred to as the regime-switching state-space model. The latent class variable can be measured by continuous or categorical indicators, under the local independence condition, or more generally by a class-specific structural equation model or a dynamic structural equation model. We discuss the Bayesian estimation based on Markov chain Monto Carlo, which allows modeling with arbitrary long time series data and many random effects. The modeling framework is illustrated with several simulation studies. [ABSTRACT FROM PUBLISHER]

In this article, we show that the underlying dimensions obtained when factor analyzing cross-sectional data actually form a mix of within-person state dimensions and between-person trait dimensions. We propose a factor analytical model that distinguishes between four independent sources of variance: common trait, unique trait, common state, and unique state. We show that by testing whether there is weak factorial invariance across the trait and state factor structures, we can tackle the fundamental question first raised by Cattell; that is, are within-person state dimensions qualitatively the same as between-person trait dimensions? Furthermore, we discuss how this model is related to other trait-state factor models, and we illustrate its use with two empirical data sets. We end by discussing the implications for cross-sectional factor analysis and suggest potential future developments. [ABSTRACT FROM AUTHOR]

Prior research has focused on comparing clinical judgment with geriatric assessments in identifying potentially unfit patients or the prognostic value of geriatric impairments. In this study, we set out to compare and combine geriatric impairments with clinical judgment in predicting mortality and non-completion of chemotherapy in older patients with a hematological malignancy. Between March 2004 and August 2014, a multi-dimensional geriatric assessment was performed in consecutive patients aged ≥65 years diagnosed with a hematological malignancy at the Innsbruck university hospital. Associations between geriatric assessment, clinical judgment (derived from initial treatment decision) and outcome (mortality, non-completion) were analyzed. Patient sample consisted of 157 patients, of which 37% was 80 + years of age. Aggressive non-Hodgkin lymphoma (29%), acute myeloid leukemia (27%) and myelodysplastic syndromes (20%) were most common diagnoses. Prevalence of an impaired geriatric assessment (i.e. impairments in ≥2 of eight assessed domains) was 71% and similar for patients receiving standard (71%) or attenuated treatment (72%). Clinical judgment was the strongest predictor of mortality (p = 0.003); addition of geriatric assessment allowed for further stratification of patients with a good (>75% one year survival), intermediate (50–60%) and poor prognosis (<30%). Cumulative burden of geriatric impairments further increased risk of mortality. Clinical judgment was greatly superior to geriatric assessment in identifying patients at risk for non-completion. The combination of clinical judgment and geriatric assessment allows for better prognostic stratification of patients compared to clinical judgment alone. [ABSTRACT FROM PUBLISHER]

Multilevel autoregressive models are especially suited for modeling between-person differences in within-person processes. Fitting these models with Bayesian techniques requires the specification of prior distributions for all parameters. Often it is desirable to specify prior distributions that have negligible effects on the resulting parameter estimates. However, the conjugate prior distribution for covariance matrices—the Inverse-Wishart distribution—tends to be informative when variances are close to zero. This is problematic for multilevel autoregressive models, because autoregressive parameters are usually small for each individual, so that the variance of these parameters will be small. We performed a simulation study to compare the performance of three Inverse-Wishart prior specifications suggested in the literature, when one or more variances for the random effects in the multilevel autoregressive model are small. Our results show that the prior specification that uses plug-in ML estimates of the variances performs best. We advise to always include a sensitivity analysis for the prior specification for covariance matrices of random parameters, especially in autoregressive models, and to include a data-based prior specification in this analysis. We illustrate such an analysis by means of an empirical application on repeated measures data on worrying and positive affect. [ABSTRACT FROM PUBLISHER]

Background: Currently over 55% of end-stage renal disease (ESRD) patients are aged ≥60 years and patients >75 years represent the fastest growing segment of the dialysis population. We aimed to assess whether the Groningen frailty indicator (GFI) can be used to distinguish fit older ESRD patients, likely able to tolerate and benefit from dialysis, from frail older patients who need further evaluation with a geriatrician’s comprehensive assessment.Methods: All patients aged ≥65 years visiting the pre-dialysis unit at the Gelre hospital between 2007 and 2013 were included and underwent the GFI (n = 65). Patients with GFI ≥ 4 (frail) were referred for geriatric consultation (n = 13). Results of the GFI and nephrologists’ evaluation were compared with geriatrician’s assessment. Survival rates and outcomes after one year of follow up were recorded.Results: Twenty patients (32%) were identified as frail. Of the problems identified by the geriatrician in 13 patients, 55% were not reported in the nephrologists’ notes. The first year after inclusion, 30% of patients with a GFI ≥ 4 died, compared to 9% of fit patients (p = 0.04). Moreover, 90% of frail patients had been hospitalized one or more times, compared to 53% in the fit group (p = 0.005).Conclusion: Although the GFI can be a useful instrument to identify ESRD patients at risk, both the GFI and the nephrologists’ assessment failed to identify specific geriatric impairments. Further research is needed to develop a specific frailty indicator for ESRD patients and to determine the value and effect of a comprehensive geriatric assessment in ESRD patients. [ABSTRACT FROM PUBLISHER]

In this article we consider a multilevel first-order autoregressive [AR(1)] model with random intercepts, random autoregression, and random innovation variance (i.e., the level 1 residual variance). Including random innovation variance is an important extension of the multilevel AR(1) model for two reasons. First, between-person differences in innovation variance are important from a substantive point of view, in that they capture differences in sensitivity and/or exposure to unmeasured internal and external factors that influence the process. Second, using simulation methods we show that modeling the innovation variance as fixed across individuals, when it should be modeled as a random effect, leads to biased parameter estimates. Additionally, we use simulation methods to compare maximum likelihood estimation to Bayesian estimation of the multilevel AR(1) model and investigate the trade-off between the number of individuals and the number of time points. We provide an empirical illustration by applying the extended multilevel AR(1) model to daily positive affect ratings from 89 married women over the course of 42 consecutive days. [ABSTRACT FROM PUBLISHER]

We investigated the extent and nature of multivariate statistical inferential procedures used in eight European psychology journals covering a range of content (i.e., clinical, social, health, personality, organizational, developmental, educational, and cognitive). Multivariate methods included those found in popular texts that focused on prediction, group difference, and advanced modeling: multiple regression, logistic regression, analysis of covariance, multivariate analysis of variance, factor or principal component analysis, structural equation modeling, multilevel modeling, and other methods. Results revealed that an average of 57% of the articles from these eight journals involved multivariate analyses with a third using multiple regression, 17% using structural modeling, and the remaining methods collectively comprising about 50% of the analyses. The most frequently occurring inferential procedures involved prediction weights, dichotomouspvalues, figures with data, and significance tests with very few articles involving confidence intervals, statistical mediation, longitudinal analyses, power analysis, or meta-analysis. Contributions, limitations and future directions are discussed. [ABSTRACT FROM PUBLISHER]

A collection ofN-acyl andN-carbamoyl succinimides were prepared by acylation of succinimide with acyl chlorides or by ethylene dichloride (EDC) coupling of carboxylic acids. The x-ray crystal structures ofN-benzoyl andN-p-nitrobenzoyl succinimides were determined. TheN-acyl succinimides were effective in acylating primary amines, a secondary amine, and an aromatic amine. Supplemental materials are available for this article. Go to the publisher's online edition ofSynthetic Communications®to view the free supplemental file. [ABSTRACT FROM AUTHOR]

This article shows that the mean and covariance structure of the predetermined autoregressive latent trajectory (ALT) model are very flexible. As a result, the shape of the modeled growth curve can be quite different from what one might expect at first glance. This is illustrated with several numerical examples that show that, for example, a linear trajectory might be present among the model predicted scores even though no latent change parameter was included in the model. In addition, 2 examples are given that show that the predetermined ALT model can fit to data generated by models with model structures that are rather different from that of the ALT model itself. The practical relevance of these findings is demonstrated using an empirical example. We end by providing recommendations for researchers considering the use of the predetermined ALT model. [ABSTRACT FROM AUTHOR]

State-space modeling techniques have been compared to structural equation modeling (SEM) techniques in various contexts but their unique strengths have often been overshadowed by their similarities to SEM. In this article, we provide a comprehensive discussion of these 2 approaches' similarities and differences through analytic comparisons and numerical simulations, with a focus on their use in representing intraindividual dynamics and interindividual differences. To demonstrate the respective strengths and weaknesses of the 2 approaches in representing these 2 aspects, we simulated data under (a) a cross-sectional common factor model, (b) a latent difference score model with random effects in intercept and slope, and (c) a bivariate dynamic factor analysis model with auto- and cross-regression parameters. Possible ways in which SEM and state-space modeling can be utilized as complementary tools in representing human developmental and other related processes are discussed. [ABSTRACT FROM AUTHOR]

Starch is the major glycemic carbohydrate in foods, and its nutritional property is related to its rate and extent of digestion and absorption in the small intestine. A classification of starch into rapidly digestible starch (RDS), slowly digestible starch (SDS), and resistant starch (RS) based on the in vitro Englyst test is used to specify the nutritional quality of starch. Both the RDS and RS fractions have been extensively studied while there are only limited studies on the intermediate starch fraction of SDS, particularly regarding its structural basis and slow digestion mechanism. The current understanding of SDS including its concept, measurement method, structural basis and mechanism, physiological consequences, and approaches to make SDS is reviewed. An in vivo method of extended glycemic index (EGI) is proposed to evaluate its metabolic effect and related health consequences. [ABSTRACT FROM AUTHOR]

The article focuses on a study about the applicability and action of starch-entrapped biopolymer microspheres as customized, novel, slowly digestible carbohydrates to obtain desired glycemic responses. Based on the scanning electron microscopy, cooked starch entrapped in the dense biopolymer matrix is digested layer by layer from the outside to the inside of the microsphere. The researchers concluded that starch-entrapped microspheres can be used in studying the postprandial metabolic consequences of slowly digestible carbohydrates.

Outliers are typically regarded as data anomalies that should be discarded. However, dynamic or “innovative” outliers can be appropriately utilized to capture unusual but substantively meaningful shifts in a system's dynamics. We extend De Jong and Penzer's 1998 approach for representing outliers in single-subject state-space models to a group-based framework. The group-based model enables model predictions concerning the group as a whole while individualized change points are incorporated to capture discrete shifts in each individual's trajectory. An illustrative example using cognitive performance data from N = 38 older adults over 60 days is presented. Results indicated that a subset of older adults showed sudden shifts in learning dynamics in several forms, including sudden jumps in level, slope, and linkage between perceived control and actual performance level. The broader implications of incorporating innovative outliers into studies of intraindividual change are discussed. [ABSTRACT FROM AUTHOR]

This study examined the use of a whole-cell biocatalyst to transesterify triglycerides, including high-Free Fatty Acid (FFA) waste greases, in a water-containing system. The whole-cell biocatalyst derived from Rhizopus oryzae (ATCC10260) was grown and reacted at room temperature without immobilization. The effectiveness of improving biodiesel yield through alteration of reaction temperature, additional alcohol, and additional transesterification reaction was also examined. Results showed that whole-cell biocatalyst was able to produce biodiesel with a yield of about 75% for virgin canola oil, 80% for waste vegetable oil and 55% for brown grease with a 72-hr transesterification reaction using no excess methanol. Elevating the reaction temperature to 35°C significantly diminished the yield. An additional dose of methanol with another 24 hours of reaction time or a second 72-hr reaction resulted in biodiesel yield approaching 90% and only 3% residual glycerides (mono-, di- and tri-glycerides). These results suggest that whole-cell biocatalysts are able to transesterify waste oils or greases that are high in FFA and contain water. Brown (trap) grease and similar degraded or complex greases may be good candidates for further whole-cell biocatalyst research. [ABSTRACT FROM AUTHOR]

Four methods for estimating a dynamic factor model, the direct autoregressive factor score (DAFS) model, are evaluated and compared. The first method estimates the DAFS model using a Kalman filter algorithm based on its state space model representation. The second one employs the maximum likelihood estimation method based on the construction of a block-Toeplitz covariance matrix in the structural equation modeling framework. The third method is built in the Bayesian framework and implemented using Gibbs sampling. The fourth is the least squares method, which also employs the block-Toeplitz matrix. All 4 methods are implemented in currently available software. The simulation study shows that all 4 methods reach appropriate parameter estimates with comparable precision. Differences among the 4 estimation methods and related software are discussed. [ABSTRACT FROM AUTHOR]

This research examined the effect of using separate hydrolysis and methanolysis reactions for biodiesel production using a whole-cell biocatalysts derived from Rhizopus Oryzae (ATCC 10260). Biodiesel yield from separate hydrolysis and methanolysis was compared to transesterification reactions where both hydrolysis and methanolysis reactions occur in the same reactor. All reactions were conducted at room temperature. The effect of substituting ethanol for methanol was also studied. Separating the hydrolysis and methanolysis reactions did not significantly increase biodiesel yields; however, this approach successfully converted about 99% of triglycerides into fatty acid methyl esters (FAME) and free fatty acids (FFA). Use of ethanol in place of methanol did not significantly improve the biodiesel yield. However, there is evidence that ethanol may either esterify FFA more quickly than methanol, or result in a more stable ester. The best biodiesel yield was about 90% when a transesterification reaction using methanol was followed by one hydrolysis and one ethanolysis reaction; however, this is only slightly higher than the 88% biodiesel yield of two transesterification reactions in series (using methanol as alcohol). [ABSTRACT FROM AUTHOR]

The article presents a commentary on the article about the concept of invariance in factor analytic practice. It offers a differentiation from the psychological theory used in this study. The author focuses on theoretical measurement, substantive, and other empirical issues with significance to the discussion of invariance.

Current aerospace parametric cost models predict space project costs using regression equations. The equations relate the historical cost of projects to technical variables such as weight and power. While improvements in parametric cost modeling have been made, there is still significant variability in the estimates. This is particularly true when estimating the costs for complex systems. Part of this variability may be due to the models' inability to capture the impacts of the engineering management considerations required to develop these systems. This article lays the groundwork for the introduction of engineering management variables into current cost models to improve their predictive capabilities. [ABSTRACT FROM AUTHOR]

Results obtained with interindividual techniques in a representative sample of a population are not necessarily generalizable to the individual members of this population. In this article the specific condition is presented that must be satisfied to generalize from the interindividual level to the intraindividual level. A way to investigate whether this condition is satisfied is by means of multivariate time series analysis. More generally, time series analysis can be used to investigate psychological processes situated within individuals. In this article we consider a well established class of multivariate stationary time series models that may be used to study the intraindividual covariance structure. We demonstrate the application of some of these models with an empirical example consisting of state measurements of behavior associated with the Five Factor Model of Personality. We illustrate how one can investigate whether individuals are similar with respect to their intraindividual structure of variation, and whether this structure is similar to the structure of interindividual variation. [ABSTRACT FROM AUTHOR]

Longitudinal structural equation modeling has generally addressed the time-dependent covariance structure of a relatively small number of repeated measures, T, observed in a relatively large representative sample, N. In contrast, the literature on autoregressive moving average modeling is usually directed at a single realization comprising many observations, that is, N = 1, and T > 50. This article deals with autoregressive moving average-based structural equation modeling of time series data, in the situation that N is small, T is intermediate, and T > N. The aims of this article are to (a) give a brief overview of the development of alternative formulations of the likelihood function to obtain estimates of autoregressive moving average parameters, in particular the formulation that lies at the basis of Mélard's algorithm; (b) show the equivalence between the likelihood function to obtain estimates for these parameters, and the raw data likelihood method that can be used in structural equation modeling programs like Mx, and demonstrate this equivalence through simulation experiments; and (c) provide illustrations of this use of Mx with real data. [ABSTRACT FROM AUTHOR]

Several papers have been devoted to the use of structural equation modeling (SEM) software in fitting autoregressive moving average (ARMA) models to a univariate series observed in a single subject. Van Buuren (1997) went beyond specification and examined the nature of the estimates obtained with SEM software. Although the results were mixed, he concluded that these parameter estimates resemble true maximum likelihood (ML) estimates. Molenaar (1999) argued that the negative findings for pure moving average models might be due to the absence of invertibility constraints in Van Buuren's simulation experiment. The aim of this article is to (a) reexamine the nature of SEM estimates of ARMA parameters; (b) replicate Van Buuren's simulation experiment in light of Molenaar's comment; and (c) examine the behavior of the log-likelihood ratio test. We conclude that estimates of ARMA parameters obtained with SEM software are identical to those obtained by univariate stochastic model preliminary estimation, and are not true ML estimates. Still, these estimates, which may be viewed as moment estimates, have the same asymptotic properties as ML estimates for pure autoregressive (AR) processes. For pure moving average (MA) processes, they are biased and less efficient. The estimates from SEM software for mixed processes seem to have the same asymptotic properties as ML estimates. Furthermore, the log-likelihood ratio is reliable for pure AR processes, but this is not the case for pure MA processes. For mixed processes, the behavior of the log-likelihood ratio varies, and in this case these statistics should be handled with caution. [ABSTRACT FROM AUTHOR]

Comments on the cost-effectiveness of using electronic publishing solutions in libraries. Concerns over the lack of standards for measuring the cost effectiveness of the medium; Importance of resolving cost-related issues such as defining terms, analyzing statistics and protecting user confidentiality.

Through the discovery of opaque‐2 maize with improved amino acid profile and the continuous efforts to improve its kernel quality and cultivation characteristics, Quality Protein Maize (QPM) evolved with the goal of replacing common maize and improving the protein quality of maize‐centered diets, especially those of children. The nutritional benefits of QPM, however, have been questioned based on the notion that even in poor communities, the amino acid deficiencies in maize are nearly always made up by other foods consumed along with it. An extension of this argument is that protein intakes of the maize‐consuming populations are adequate and high quality protein varieties are not needed. This position, even though correct in describing the status of many poor populations that are relatively “better‐off”, is contestable for the “worse‐off” with limited access to high quality protein foods. An argument is presented that there exist poor, high maize‐consuming populations that are at risk of protein inadequacy and would benefit from QPM. [ABSTRACT FROM PUBLISHER]

Examines the use of self-generated drawings to solve arithmetic word problems. Description of two intervention studies; Improvement of problem solutions after the intervention; Nature of difficulties children experience in solving arithmetic word problems.