Systemic multicompartmental effects of the gut microbiome on mouse metabolic phenotypes

To characterize the impact of gut microbiota on host metabolism, we investigated the multicompartmental metabolic profiles of a conventional mouse strain (C3H/HeJ) (n=5) and its germ-free (GF) equivalent (n=5). We confirm that the microbiome strongly impacts on the metabolism of bile acids through the enterohepatic cycle and gut metabolism (higher levels of phosphocholine and glycine in GF liver and marked higher levels of bile acids in three gut compartments). Furthermore we demonstrate that (1) well-defined metabolic differences exist in all examined compartments between the metabotypes of GF and conventional mice: bacterial co-metabolic products such as hippurate (urine) and 5-aminovalerate (colon epithelium) were found at reduced concentrations, whereas raffinose was only detected in GF colonic profiles. (2) The microbiome also influences kidney homeostasis with elevated levels of key cell volume regulators (betaine, choline, myo-inositol and so on) observed in GF kidneys. (3) Gut microbiota modulate metabotype expression at both local (gut) and global (biofluids, kidney, liver) system levels and hence influence the responses to a variety of dietary modulation and drug exposures relevant to personalized health-care investigations.

Modulating self-assembly of a nanotape-forming peptide amphiphile with an oppositely charged surfactant

A peptide amphiphile (PA) C16-KTTKS, containing a pentapeptide headgroup based on a sequence from procollagen I attached to a hexadecyl lipid chain, self-assembles into extended nanotapes in aqueous solution. The tapes are based on bilayer structures, with a 5.2 nm spacing. Here, we investigate the effect of addition of the oppositely charged anionic surfactant sodium dodecyl sulfate (SDS) via AFM, electron microscopic methods, small-angle X-ray scattering and X-ray diffraction among other methods. We show that addition of SDS leads to a transition from tapes to fibrils, via intermediate states that include twisted ribbons. Addition of SDS is also shown to enhance the development of remarkable lateral ‘‘stripes’’ on the nanostructures, which have a 4 nm periodicity. This is ascribed to counterion condensation. The transition in the nanostructure leads to changes in macroscopic properties, in particular a transition from sol to gel is noted on increasing SDS (with a further reentrant transition to sol on further increase of SDS concentration). Formation of a gel may be useful in applications of this PA in skincare applications and we show that this can be controlled via development of a network of fine stranded fibrils.

Self-assembly of Fmoc-tetrapeptides based on the RGDS cell adhesion motif

Self-assembly in aqueous solution has been investigated for two Fmoc [Fmoc ¼ N-(fluorenyl)-9-methoxycarbonyl] tetrapeptides comprising the RGDS cell adhesion motif from fibronectin or the scrambled sequence GRDS. The hydrophobic Fmoc unit confers amphiphilicity on the molecules, and introduces aromatic stacking interactions. Circular dichroism and FTIR spectroscopy show that the self-assembly of both peptides at low concentration is dominated by interactions among Fmoc units, although Fmoc-GRDS shows b-sheet features, at lower concentration than Fmoc-RGDS. Fibre X-ray diffraction indicates b-sheet formation by both peptides at sufficiently high concentration. Strong alignment effects are revealed by linear dichroism experiments for Fmoc-GRDS. Cryo-TEM and smallangle X-ray scattering (SAXS) reveal that both samples form fibrils with a diameter of approximately 10 nm. Both Fmoc-tetrapeptides form self-supporting hydrogels at sufficiently high concentration. Dynamic shear rheometry enabled measurements of the moduli for the Fmoc-GRDS hydrogel, however syneresis was observed for the Fmoc-RGDS hydrogel which was significantly less stable to shear. Molecular dynamics computer simulations were carried out considering parallel and antiparallel b-sheet configurations of systems containing 7 and 21 molecules of Fmoc-RGDS or Fmoc-GRDS, the results being analyzed in terms of both intermolecular structural parameters and energy contributions.

The effects of age, enactment, and cue-action relatedness on memory for intentions in the Virtual Week task

The current study investigated the influence of encoding modality and cue-action relatedness on prospective memory (PM) performance in young and older adults using a modified version of the Virtual Week task. Participants encoded regular and irregular intentions either verbally or by physically performing the action during encoding. For half of the intentions there was a close semantic relation between the retrieval cue and the intended action, while for the remaining intentions the cue and action were semantically unrelated. For irregular tasks, both age groups showed superior PM for related intentions compared to unrelated intentions in both encoding conditions. While older adults retrieved fewer irregular intentions than young adults after verbal encoding, there was no age difference following enactment. Possible mechanisms of enactment and relatedness effects are discussed in the context of current theories of event-based PM.

Coppicing shifts CO2 stimulation of poplar productivity to above-ground pools: a synthesis of leaf to stand level results from the POP/EUROFACE experiment

A poplar short rotation coppice (SRC) grown for the production of bioenergy can combine carbon (C) storage with fossil fuel substitution. Here, we summarize the responses of a poplar (Populus) plantation to 6 yr of free air CO2 enrichment (POP/EUROFACE consisting of two rotation cycles). We show that a poplar plantation growing in nonlimiting light, nutrient and water conditions will significantly increase its productivity in elevated CO2 concentrations ([CO2]). Increased biomass yield resulted from an early growth enhancement and photosynthesis did not acclimate to elevated [CO2]. Sufficient nutrient availability, increased nitrogen use efficiency (NUE) and the large sink capacity of poplars contributed to the sustained increase in C uptake over 6 yr. Additional C taken up in high [CO2] was mainly invested into woody biomass pools. Coppicing increased yield by 66% and partly shifted the extra C uptake in elevated [CO2] to above-ground pools, as fine root biomass declined and its [CO2] stimulation disappeared. Mineral soil C increased equally in ambient and elevated [CO2] during the 6 yr experiment. However, elevated [CO2] increased the stabilization of C in the mineral soil. Increased productivity of a poplar SRC in elevated [CO2] may allow shorter rotation cycles, enhancing the viability of SRC for biofuel production.

Eliciting consumer preferences for certified animal-friendly foods: can elements of the theory of planned behavior improve choice experiment analysis?

Models used in neoclassical economics assume human behaviour to be purely rational. On the other hand, models adopted in social and behavioural psychology are founded on the ‘black box’ of human cognition. In view of these observations, this paper aims at bridging this gap by introducing psychological constructs in the well established microeconomic framework of choice behaviour based on random utility theory. In particular, it combines constructs developed employing Ajzen’s theory of planned behaviour with Lancaster’s theory of consumer demand for product characteristics to explain stated preferences over certified animal-friendly foods. To reach this objective a web survey was administered in the largest five EU-25 countries: France, Germany, Italy, Spain and the UK. Findings identify some salient cross-cultural differences between northern and southern Europe and suggest that psychological constructs developed using the Ajzen model are useful in explaining heterogeneity of preferences. Implications for policy makers and marketers involved with certified animal-friendly foods are discussed.

Simulated annealing technique for fast learning of SOM networks

The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input datasets. In this paper, we present an application of the simulated annealing procedure to the SOM learning algorithm with the aim to obtain a fast learning and better performances in terms of quantization error. The proposed learning algorithm is called Fast Learning Self-Organized Map, and it does not affect the easiness of the basic learning algorithm of the standard SOM. The proposed learning algorithm also improves the quality of resulting maps by providing better clustering quality and topology preservation of input multi-dimensional data. Several experiments are used to compare the proposed approach with the original algorithm and some of its modification and speed-up techniques.

PDM 2006: Proceedings of the International Workshop on Data Mining

Recently major processor manufacturers have announced a dramatic shift in their paradigm to increase computing power over the coming years. Instead of focusing on faster clock speeds and more powerful single core CPUs, the trend clearly goes towards multi core systems. This will also result in a paradigm shift for the development of algorithms for computationally expensive tasks, such as data mining applications. Obviously, work on parallel algorithms is not new per se but concentrated efforts in the many application domains are still missing. Multi-core systems, but also clusters of workstations and even large-scale distributed computing infrastructures provide new opportunities and pose new challenges for the design of parallel and distributed algorithms. Since data mining and machine learning systems rely on high performance computing systems, research on the corresponding algorithms must be on the forefront of parallel algorithm research in order to keep pushing data mining and machine learning applications to be more powerful and, especially for the former, interactive. To bring together researchers and practitioners working in this exciting field, a workshop on parallel data mining was organized as part of PKDD/ECML 2006 (Berlin, Germany). The six contributions selected for the program describe various aspects of data mining and machine learning approaches featuring low to high degrees of parallelism: The first contribution focuses the classic problem of distributed association rule mining and focuses on communication efficiency to improve the state of the art. After this a parallelization technique for speeding up decision tree construction by means of thread-level parallelism for shared memory systems is presented. The next paper discusses the design of a parallel approach for dis- tributed memory systems of the frequent subgraphs mining problem. This approach is based on a hierarchical communication topology to solve issues related to multi-domain computational envi- ronments. The forth paper describes the combined use and the customization of software packages to facilitate a top down parallelism in the tuning of Support Vector Machines (SVM) and the next contribution presents an interesting idea concerning parallel training of Conditional Random Fields (CRFs) and motivates their use in labeling sequential data. The last contribution finally focuses on very efficient feature selection. It describes a parallel algorithm for feature selection from random subsets. Selecting the papers included in this volume would not have been possible without the help of an international Program Committee that has provided detailed reviews for each paper. We would like to also thank Matthew Otey who helped with publicity for the workshop.

Control of strand registry by attachment of PEG chains to amyloid peptides influences nanostructure

The self-assembly in aqueous solution of PEG-peptide conjugates comprising a model amyloid peptide sequence FFKLVFF that contains the Ab(16–20) KLVFF motif is investigated. X-ray diffraction reveals different packing motifs dependent on PEG chain length. This is correlated to remarkable differences in self-assembled nanostructures. The control of strand registry points to a subtle interplay between aromatic stacking, electrostatic and amphiphilic interactions.

The vulnerable subject of negligence law

The approach taken by English courts to the duty of care question in negligence has been subject to harsh criticism in recent years. This article examines this fundamental issue in tort law, drawing upon Canadian and Australian jurisprudence by way of comparison. From this analysis, the concept of vulnerability is developed as a productive means of understanding the duty of care. Vulnerability is of increasing interest in legal and political theory and it is of particular relevance to the law of negligence. In addition to aiding doctrinal coherence, vulnerability – with its focus on relationships and care – has the potential to broaden the way in which the subject of tort law is conceived because it challenges dominant assumptions about autonomy as being prior to the relationships on which it is dependent.

Optimizing layer-by-layer deposition of interpolymer complexes on solid substrates using Biacore

The layer-by-layer deposition of polymers onto surfaces allows the fabrication of multilayered materials for a wide range of applications, from drug delivery to biosensors. This work describes the analysis of complex formation between poly(acrylic acid) and methylcellulose in aqueous solutions using Biacore, a surface plasmon resonance analytical technique, traditionally used to examine biological interactions. This technique characterized the layer-by-layer deposition of these polymers on the surface of a Biacore sensor chip. The results were subsequently used to optimize the experimental conditions for sequential layer deposition on glass slides. The role of the solution pH and poly(acrylic acid) molecular weight on the formation of interpolymer multilayered coatings was researched, and showed that the optimal deposition of the polymer complexes was achieved at pHs ≤2.5 with a poly(acrylic acid) molecular weight of 450 kDa.

The mechanical properties of amniotic membrane influence its effect as a biomaterial for ocular surface repair

The human amniotic membrane (AM) is a tissue of fetal origin and has proven to be clinically useful as a biomaterial in the management of various ocular surface disorders including corneal stem cell transplantation. However, its success rate displays a degree of clinical unpredictability. We suggest that the measured variability inAMstiffness offers an explanation for the poor clinical reproducibility when it is used as a substrate for stem cell expansion and transplantation. Corneal epithelial stem cells were expanded upon AM samples possessing different mechanical stiffness. To investigate further the importance of biological substrate stiffness on cell phenotype we replaced AM with type I collagen gels of known stiffness. Substrate stiffness was measured using shear rheometry and surface topography was characterized using scanning electron microscopy and atomic force microscopy. The differentiation status of epithelial cells was examined using RT-PCR, immunohistochemistry and Western blotting. The level of corneal stem cell differentiation was increased in cells expanded upon AM with a high dynamic elastic shear modulus and cell expansion on type I collagen gels confirmed that the level of corneal epithelial stem cell differentiation was related to the substrate’s mechanical properties. In this paper we provide evidence to show that the preparatory method of AM for clinical use can affect its mechanical properties and that these measured differences can influence the level of differentiation within expanded corneal epithelial stem cells.