The effect of co-encapsulation of Lactobacillus rhamnosus GG ATCC 53103 with inulin on alginate/chitosan matrix: the viability in fermented soy blend and simulated digestive system

This research verified the ability of Lactobacillus rhamnosus encapsulated with inulin to tolerate the simulated digestive system and their viability in a soy blend. Probiotic encapsulated in alginate-chitosan matrix without inulin presented a better encapsulation efficiency (80.92%) than encapsulation with inulin (57.39%). On the 28th day, the count of probiotics decreased by 3.42 and 1.99 logarithmic cycles of free and encapsulated cells without inulin, respectively. In contrast, the microorganisms encapsulated with inulin showed an increase of 1.26 logs CFU g⁻¹. During gastrointestinal simulation, cell counts decreased by 0.78, 1.55 and 1.95 CFU g⁻¹ logs for encapsulated cells without inulin, free and encapsulated with inulin, respectively. Sensory panellists liked the fermented soy blend with encapsulated lactobacilli, and this result shows the possibility to create new probiotic foods of plant origin. Therefore, the alginate/chitosan matrix can be considered adequate for the encapsulation of L. rhamnosus. The inulin reduces the encapsulation efficiency and increases the cell loss in gastrointestinal simulation. Considering cellular losses, the best option for preparing a fermented soy blend is to use L. rhamnosus encapsulated without inulin.     

Simulating pavement structural condition using artificial neural networks

This article aims to investigate the feasibility of incorporating of an artificial neural network (ANN) as an innovative technique for modelling the pavement structural condition, into pavement management systems. For the development of the ANN, strain assessment criteria are set in order to characterise the structural condition of flexible asphalt pavements with regards to fatigue failure. This initial task is directly followed with the development of an ANN model for the prediction of strains primarily based on in situ field gathered data and not through the usage of synthetic databases. For this purpose, falling weight deflectometer (FWD) measurements were systematically conducted on a highway network, with ground-penetrating radar providing the required pavement thickness data. The FWD data (i.e. deflections) were back-analysed in order to assess strains that would be utilised as output data in the process of developing the ANN model. A paper exercise demonstrates how the developed ANN model combined with the suggested conceptual approach for characterising pavement structural condition with regard to strain assessment could make provisions for pavement management activities, categorising network pavement sections according to the need for maintenance or rehabilitation. Preliminary results indicate that the ANN technique could help assist policy decision makers in deriving optimum strategies for the planning of pavement infrastructure maintenance.     

Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases

There has been an increasing prevalence of neurodegenerative diseases with the rapid increase in aging societies worldwide. Biomarkers that can be used to detect pathological changes before the development of severe neuronal loss and consequently facilitate early intervention with disease-modifying therapeutic modalities are therefore urgently needed. Diffusion magnetic resonance imaging (MRI) is a promising tool that can be used to infer microstructural characteristics of the brain, such as microstructural integrity and complexity, as well as axonal density, order, and myelination, through the utilization of water molecules that are diffused within the tissue, with displacement at the micron scale. Diffusion tensor imaging is the most commonly used diffusion MRI technique to assess the pathophysiology of neurodegenerative diseases. However, diffusion tensor imaging has several limitations, and new technologies, including neurite orientation dispersion and density imaging, diffusion kurtosis imaging, and free-water imaging, have been recently developed as approaches to overcome these constraints. This review provides an overview of these technologies and their potential as biomarkers for the early diagnosis and disease progression of major neurodegenerative diseases.     

Development of surrogate models for the prediction of the flow around an aircraft propeller

In the present work, the derivation of two surrogate models (SMs) for modelling the flow around a propeller for small aircrafts is presented. Both methodologies use derived functions based on computations with the detailed propeller geometry. The computations were performed using k–ω shear stress transport for modelling turbulence. In the SMs, the modelling of the propeller was performed in a computational domain of disk-like geometry, where source terms were introduced in the momentum equations. In the first SM, the source terms were polynomial functions of swirl and thrust, mainly related to the propeller radius. In the second SM, regression analysis was used to correlate the source terms with the velocity distribution through the propeller. The proposed SMs achieved faster convergence, in relation to the detail model, by providing also results closer to the available operational data. The regression-based model was the most accurate and required less computational time for convergence.     

p‐TCOs für Transparente Elektronik

p‐type TCOs for use in transparent electronics In this article, a new type of semiconducting material is introduced. Transparent conducting thin films are already established in our everydaylife. But the type of these so‐called ?TCOs”? is dominated by electron transport, that means they are n‐type. To get transparent p‐n‐junctions and therefore transparent electronics, the missing part is a transparent p‐type material. Researchers all over the world have found different approach to produce this new generation of materials. At the Fraunhofer Institute for Surface Engineering and Thin Films (IST) in Braunschweig it is now possible to produce these kind of thin films as well.     

Nicotinic Acid Increases Adiponectin Secretion from Differentiated Bovine Preadipocytes through G-Protein Coupled Receptor Signaling

The transition period in dairy cows (3 weeks prepartum until 3 weeks postpartum) is associated with substantial mobilization of energy stores, which is often associated with metabolic diseases. Nicotinic acid (NA) is an antilipolytic and lipid-lowering compound used to treat dyslipidaemia in humans, and it also reduces non-esterified fatty acids in cattle. In mice the G-protein coupled receptor 109A (GPR109A) ligand NA positively affects the secretion of adiponectin, an important modulator of glucose and fat metabolism. In cattle, the corresponding data linking NA to adiponectin are missing. Our objective was to examine the effects of NA on adiponectin and AMPK protein abundance and the expression of mRNAs of related genes such as chemerin, an adipokine that enhances adiponectin secretion in vitro. Differentiated bovine adipocytes were incubated with pertussis toxin (PTX) to verify the involvement of GPR signaling, and treated with 10 or 15 µM NA for 12 or 24 h. NA increased adiponectin concentrations (p ≤ 0.001) and the mRNA abundances of GPR109A (p ≤ 0.05) and chemerin (p ≤ 0.01). Pre-incubation with PTX reduced the adiponectin response to NA (p ≤ 0.001). The NA-stimulated secretion of adiponectin and the mRNA expression of chemerin in the bovine adipocytes were suggestive of GPR signaling-dependent improved insulin sensitivity and/or adipocyte metabolism in dairy cows.     

From dynamic capabilities to ERP enabled business improvements: The mediating effect of the implementation project

This study explores whether an Enterprise Resource Planning (ERP) implementation project mediates the relationship between dynamic pre-adoption capabilities and ERP-enabled business improvements. The results from our field survey of large ERP adopters in Austria suggest that the effects of the two out of three selected dynamic capabilities are dependent on the properties of the underlying organizational transformation project. While external information acquisition and IT governance capabilities are fully mediated by the performance of the ERP implementation project, decision making only directly impacts business capabilities. These results further our understanding about the role of the implementation project for ERP value creation and the different natures of capability relationships. We call for more research on co-presence conditions related to dynamic capabilities and IT transformation project performance.     

Influence of water on the generation of Strecker aldehydes from dry processed foods

Completeness of extraction, losses during isolation and, in particular, the formation of new constituents are challenges during the enrichment of volatiles for GC analysis. Especially the isolation of volatiles by simultaneous steam distillation/extraction has long been known to generate artifacts due to the hot water treatment. However, even a simple treatment of dry foods with cold water may already lead to the release of odour-active volatiles. After treatment of chocolate or cocoa beans with water, the concentrations of the four Strecker aldehydes 2- and 3-methylbutanal, 3-(methylthio)propanal and phenylacetaldehyde were increased by factors between 10 and 100. Also in other dry processed foods, such as malt or crackers, the concentrations of the Strecker aldehydes were substantially increased upon water addition. Although a physical release of the aldehydes weakly bound to, e.g. proteins or starch is possible, in a further experiment, it could be shown that a yet unknown precursor of 3-(methylthio)propanal and phenylacetaldehyde could be isolated by solvent extraction from caramalt and Munich malt. As a consequence, the presence or absence of water during the work-up procedure might either lead to an over- or underestimation of the relevance of the respective aroma compounds, which might also effect aroma formation and perception during the process of eating.     

Two-way Bayesian hierarchical phylogenetic models: An application to the co-evolution of gp120 and gp41 during and after enfuvirtide treatment

Enfuvirtide (ENF) is a fusion inhibitor that prevents the entry of HIV virions into target cells. Studying the characteristics of viral evolution during treatment and after a treatment interruption can lend insight into the mechanisms of viral evolution and fitness. Although interruption of anti-HIV therapy often results in rapid emergence of an archived “wild-type” virus population, previous work from our group indicates that when only ENF is interrupted, viral gp41 continues to evolve forward and resistance mutations are lost due to back-mutation and remodeling of the envelope protein. To examine the co-evolution of gp120 and gp41 during ENF interruption we extend the Bayesian Hierarchical Phylogenetic model (HPM). Current HPMs enforce conditional independence across all outcomes while biologically all gene regions within a patient should return the same tree unless recombination confers an evolutionary selective advantage. A two-way-interaction HPM is proposed that provides middle ground between these two extremes and allows us to test for differences in evolutionary pressures across gene regions in multiple patients simultaneously. When the model is applied to a well-characterized cohort of HIV-infected patients interrupting ENF we find that across patients, the virus continued to evolve forward in both gene regions. Overall, the hypothesis of independence over dependence between the gene regions is supported. Models that allow for the examination of co-evolution over time will be increasingly important as more therapeutic classes are developed, each of which may impact other through novel and complex mechanisms.     

All‐subsets regression under effect heredity restrictions for experimental designs with complex aliasing

We consider an all‐subsets regression method for models under effect heredity restrictions for experimental designs with complex aliasing, whose number of potential main effects and two‐factor interactions exceed the number of runs. In this paper, we present an algorithm that systematically attempts to fit all such models. We illustrate the algorithm with two published experiments. Copyright © 2009 John Wiley & Sons, Ltd.