Functional Attributes and Health Benefits of Novel Prebiotic Oligosaccharides Derived from Xylan, Arabinan, and Mannan

Prebiotic oligosaccharides are produced from many different sources, with substantial differences in chemical structure, bonds between subunits, and degree of polymerization. These structural differences can materially affect microbial utilization and the dose required for efficacy. Most prebiotic oligosaccharides are based on subunits comprised of 6-carbon sugars such as glucose/fructose and alpha bonds. Newer/novel oligosaccharides are derived from 5 carbon sugars and/or connected via beta bonds. Clinical trials with xylooligosaccharides, arabinoxylanoligosaccharides, and mannooligosaccharides have shown improvements in lipids, cholesterol, management of blood glucose, weight management, and laxation, at doses typically ranging from 1 to 4 g per day. Mannooligosaccharides are also showing promise for animal health, with the potential to reduce antibiotic use. These novel prebiotics are showing promise due to greater selectivity and their ability to deliver health benefits at a lower dose compared to conventional prebiotics

Flow and thermal effects in continuous flow electrophoresis

In continuous flow electrophoresis the axial flow structure changes from a fully developed rectilinear form to one characterized by meandering as power levels are increased. The origin of this meandering is postulated to lie in a hydrodynamic instability driven by axial (and possibly lateral) temperature gradients. Experiments done at MSFC show agreement with the theory

Testing Random Effects in the Linear Mixed Model Using Approximate Bayes Factors

Deciding which predictor effects may vary across subjects is a difficult issue. Standard model selection criteria and test procedures are often inappropriate for comparing models with different numbers of random effects due to constraints on the parameter space of the variance components. Testing on the boundary of the parameter space changes the asymptotic distribution of some classical test statistics and causes problems in approximating Bayes factors. We propose a simple approach for testing random effects in the linear mixed model using Bayes factors. We scale each random effect to the residual variance and introduce a parameter that controls the relative contribution of each random effect free of the scale of the data. We integrate out the random effects and the variance components using closed form solutions. The resulting integrals needed to calculate the Bayes factor are low-dimensional integrals lacking variance components and can be efficiently approximated with Laplace’s method. We propose a default prior distribution on the parameter controlling the contribution of each random effect and conduct simulations to show that our method has good properties for model selection problems. Finally, we illustrate our methods on data from a clinical trial of patients with bipolar disorder and on data from an environmental study of water disinfection by-products and male reproductive outcomes

Participatory women’s groups and counseling through home visits to improve child growth in rural eastern India: protocol for a cluster randomised controlled trial

Background: Childhood stunting (low height-for-age) is a marker of chronic undernutrition and predicts children’s subsequent physical and cognitive development. An estimated 52 million children in India are stunted. There is a broad consensus on determinants of child undernutrition and interventions to address it, but a lack of operational research testing strategies to increase the coverage of these interventions in high burden areas. Our study aims to assess the impact, costeffectiveness, and scalability of a community intervention involving a government-proposed community-based worker to improve growth in children under two

The effects of adaptive working memory training and mindfulness meditation training on processing efficiency and worry in high worriers

Worry is the principle characteristic of generalised anxiety disorder, and has been linked to deficient attentional control, a main function of working memory (WM). Adaptive WM training and mindfulness meditation practice (MMP) have both shown potential to increase attentional control. The present study hence investigates the individual and combined effects of MMP and a dual adaptive n-back task on a non-clinical, randomised sample of high worriers. 60 participants were tested before and after seven days of training. Assessment included self-report questionnaires, as well as performance tasks measuring attentional control and working memory capacity. Combined training resulted in continued reduction in worry in the week after training, highlighting the potential of utilising n-back training as an adjunct to established clinical treatment. Engagement with WM training correlated with immediate improvements in attentional control and resilience, with worry decreasing over time. Implications of these findings and suggestions for future research are discussed

<i>In vitro</i> Characterization of Phenylacetate Decarboxylase, a Novel Enzyme Catalyzing Toluene Biosynthesis in an Anaerobic Microbial Community

Anaerobic bacterial biosynthesis of toluene from phenylacetate was reported more than two decades ago, but the biochemistry underlying this novel metabolism has never been elucidated. Here we report results of in vitro characterization studies of a novel phenylacetate decarboxylase from an anaerobic, sewage-derived enrichment culture that quantitatively produces toluene from phenylacetate; complementary metagenomic and metaproteomic analyses are also presented. Among the noteworthy findings is that this enzyme is not the well-characterized clostridial p-hydroxyphenylacetate decarboxylase (CsdBC). However, the toluene synthase under study appears to be able to catalyze both phenylacetate and p-hydroxyphenylacetate decarboxylation. Observations suggesting that phenylacetate and p-hydroxyphenylacetate decarboxylation in complex cell-free extracts were catalyzed by the same enzyme include the following: (i) the specific activity for both substrates was comparable in cell-free extracts, (ii) the two activities displayed identical behavior during chromatographic separation of cell-free extracts, (iii) both activities were irreversibly inactivated upon exposure to O2, and (iv) both activities were similarly inhibited by an amide analog of p-hydroxyphenylacetate. Based upon these and other data, we hypothesize that the toluene synthase reaction involves a glycyl radical decarboxylase. This first-time study of the phenylacetate decarboxylase reaction constitutes an important step in understanding and ultimately harnessing it for making bio-based toluene

A robust method for comparing two treatments in a confirmatory clinical trial via multivariate time-to-event methods that jointly incorporate information from longitudinal and time-to-event data

We consider regulatory clinical trials that required a pre-specified method for the comparison of two treatments for chronic diseases (e.g. Chronic Obstructive Pulmonary Disease) in which patients suffer deterioration in a longitudinal process until death occurs. We define a composite endpoint structure that encompasses both the longitudinal data for deterioration and the time-to-event data for death, and use multivariate time-to-event methods to assess treatment differences on both data structures simultaneously, without a need for parametric assumptions or modeling. Our method is straightforward to implement, and simulations show the method has robust power in situations in which incomplete data could lead to lower than expected power for either the longitudinal or survival data. We illustrate the method on data from a study of chronic lung disease

Elevation of gangliosides in four brain regions from Parkinson’s disease patients with a GBA mutation

A number of genetic risk factors have been identified over the past decade for Parkinson's Disease (PD), with variants in GBA prominent among them. GBA encodes the lysosomal enzyme that degrades the glycosphingolipid, glucosylceramide (GlcCer), with the activity of this enzyme defective in Gaucher disease. Based on the ill-defined relationship between glycosphingolipid metabolism and PD, we now analyze levels of various lipids by liquid chromatography/electrospray ionization-tandem mass spectrometry in four brain regions from age- and sex-matched patient samples, including idiopathic PD, PD patients with a GBA mutation and compare both to control brains (n = 21 for each group) obtained from individuals who died from a cause unrelated to PD. Of all the glycerolipids, sterols, and (glyco)sphingolipids (251 lipids in total), the only lipid class which showed significant differences were the gangliosides (sialic acid-containing complex glycosphingolipids), which were elevated in 3 of the 4 PD-GBA brain regions. There was no clear correlation between levels of individual gangliosides and the genetic variant in Gaucher disease [9 samples of severe (neuronopathic), 4 samples of mild (non-neuronopathic) GBA variants, and 8 samples with low pathogenicity variants which have a higher risk for development of PD]. Most brain regions, i.e. occipital cortex, cingulate gyrus, and striatum, did not show a statistically significant elevation of GlcCer in PD-GBA. Only one region, the middle temporal gyrus, showed a small, but significant elevation in GlcCer concentration in PD-GBA. We conclude that changes in ganglioside, but not in GlcCer levels, may contribute to the association between PD and GBA mutations

[Accepted Manuscript] Smartphone tool to collect repeated 24 h dietary recall data in Nepal.

To outline the development of a smartphone-based tool to collect thrice-repeated 24 h dietary recall data in rural Nepal, and to describe energy intakes, common errors and researchers' experiences using the tool. We designed a novel tool to collect multi-pass 24 h dietary recalls in rural Nepal by combining the use of a CommCare questionnaire on smartphones, a paper form, a QR (quick response)-coded list of foods and a photographic atlas of portion sizes. Twenty interviewers collected dietary data on three non-consecutive days per respondent, with three respondents per household. Intakes were converted into nutrients using databases on nutritional composition of foods, recipes and portion sizes. Dhanusha and Mahottari districts, Nepal. Pregnant women, their mothers-in-law and male household heads. Energy intakes assessed in 150 households; data corrections and our experiences reported from 805 households and 6765 individual recalls. Dietary intake estimates gave plausible values, with male household heads appearing to have higher energy intakes (median (25th-75th centile): 12 079 (9293-14 108) kJ/d) than female members (8979 (7234-11 042) kJ/d for pregnant women). Manual editing of data was required when interviewers mistook portions for food codes and for coding items not on the food list. Smartphones enabled quick monitoring of data and interviewer performance, but we initially faced technical challenges with CommCare forms crashing. With sufficient time dedicated to development and pre-testing, this novel smartphone-based tool provides a useful method to collect data. Future work is needed to further validate this tool and adapt it for other contexts

Assessing variance components in multilevel linear models using approximate Bayes factors: A case study of ethnic disparities in birthweight

Racial/ethnic disparities in birthweight are a large source of differential morbidity and mortality worldwide and have remained largely unexplained in epidemiologic models. We assess the impact of maternal ancestry and census tract residence on infant birth weights in New York City and the modifying effects of race and nativity by incorporating random effects in a multilevel linear model. Evaluating the significance of these predictors involves the test of whether the variances of the random effects are equal to zero. This is problematic because the null hypothesis lies on the boundary of the parameter space. We generalize an approach for assessing random effects in the two-level linear model to a broader class of multilevel linear models by scaling the random effects to the residual variance and introducing parameters that control the relative contribution of the random effects. After integrating over the random effects and variance components, the resulting integrals needed to calculate the Bayes factor can be efficiently approximated with Laplace’s method