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Selective surface modification of biobased fibers affords effective individualization and functionalization into nanomaterials, as exemplified by the TEMPO-mediated oxidation. However, such a route leads to changes of the native surface chemistry, affecting interparticle interactions and limiting the development of potential supermaterials. Here we introduce a methodology to extract elementary cellulose fibrils by treatment of biomass with N-succinylimidazole, achieving regioselective surface modification of C6-OH, which can be reverted using mild post-treatments. No polymer degradation, cross-linking, nor changes in crystallinity occur under the mild processing conditions, yielding cellulose nanofibrils bearing carboxyl moieties, which can be removed by saponification. The latter offers a significant opportunity in the reconstitution of the chemical and structural interfaces associated with the native states. Consequently, 3D structuring of native elementary cellulose nanofibrils is made possible with the same supramolecular features as the biosynthesized fibers, which is required to unlock the full potential of cellulose as a sustainable building block.

The conceptual and methodological framework that underpins approximate Bayesian computation (ABC) is targeted primarily towards problems in which the likelihood is either challenging or missing. ABC uses a simulation- based non-parametric estimate of the likelihood of a summary statistic and assumes that the generation of data from the model is computationally cheap. This chapter reviews two alternative approaches for estimating the intractable likelihood, with the goal of reducing the necessary model simulations to pro- duce an approximate posterior. The first of these is a Bayesian version of the synthetic likelihood (SL), initially developed by Wood (2010), which uses a multivariate normal approximation to the summary statistic likelihood. Using the parametric approximation as opposed to the non-parametric approximation of ABC, it is possible to reduce the number of model simulations required. The second likelihood approximation method we consider in this chapter is based on the empirical likelihood (EL), which is a non-parametric technique and involves maximising a likelihood constructed empirically under a set of moment constraints. Mengersen et al. (2013) adapt the EL framework so that it can be used to form an approximate posterior for problems where ABC can be applied, that is, for models with intractable likelihoods. However, unlike ABC and the Bayesian SL (BSL), the Bayesian EL ( BC el ) approach can be used to completely avoid model simulations in some cases. The BSL and BCel methods are illustrated on models of varying complexity.

Indirect inference (II) is a classical method for estimating the parameter of a complex model when the likelihood is unavailable or too expensive to evaluate. The idea was popularised several years prior to the main developments in ABC by Gourieroux et al. (1993); Smith (1993), where interest was in calibrating complex time series models used in financial applications. The II method became a very popular approach in the econometrics literature (e.g. Smith (1993); Monfardini (1998); Dridi et al. (2007)) in a similar way to the ubiquitous application of ABC to models in population genetics. However, the articles by Jiang and Turnbull (2004) and Heggland and Frigessi (2004) have allowed the II approach to be known and appreciated by the wider statistical community. In its full generality, the II approach can be viewed as a classical method to estimate the parameter of a statistical model on the basis of a so-called indirect or auxiliary summary of the observed data (Jiang and Turnbull, 2004). A special case of II is the simulated method of moments (McFadden, 1989), where the auxiliary statistic is a set of sample moments. In this spirit, the traditional ABC method may be viewed as a Bayesian version of II, where prior information about the parameter may be incorporated and updated using the information about the parameter contained in the summary statistic. However, much of the II literature has concentrated on developing the summary statistic from an alternative parametric auxiliary model that is analytically and/or computationally more tractable. The major focus of this book chapter is on approximate Bayesian methods that harness such an auxiliary model. These are referred to as parametric Bayesian indirect inference (pBII) methods by Drovandi et al. (2014a)...

The aim of EU project MyNewGut is to contribute to future public health-related recommendations supported by new insight in gut microbiome and nutrition-host relationship. In this Opinion Paper, we first revisit the concept of dietary fiber, taking into account their interaction with the gut microbiota. This paper also summarizes the main effects of dietary fibers with prebiotic properties in intervention studies in humans, with a particular emphasis on the effects of arabinoxylans and arabinoxylo-oligosaccharides on metabolic alterations associated with obesity. Based on the existing state of the art and future development, we elaborate the steps required to propose dietary guidelines related to dietary fibers, taking into account their interaction with the gut microbiota.

The gut microbiota coexists in partnership with the human host through adaptations to environmental and physiological changes that help maintain dynamic homeostatic healthy states. Break-down of this delicate balance under sustained exposure to stressors (e.g. unhealthy diets) can, however, contribute to the onset of disease. Diet is a key modifiable environmental factor that modulates the gut microbiota and its metabolic capacities that, in turn, could impact human physiology. On this basis, the diet and the gut microbiota could act as synergistic forces that provide resilience against disease or that speed the progress from health to disease states. Associations between unhealthy dietary patterns, non-communicable diseases and intestinal dysbiosis can be explained by this hypothesis. Translational studies showing that dietary-induced alterations in microbial communities recapitulate some of the pathological features of the original host further support this notion. In this introductory paper by the European project MyNewGut, we briefly summarize the investigations conducted to better understand the role of dietary patterns and food components in metabolic and mental health and the specificities of the microbiome-mediating mechanisms. We also discuss how advances in the understanding of the microbiome's role in dietary health effects can help to provide acceptable scientific grounds on which to base dietary advice for promoting healthy living.

The Dipeptidyl peptidase-4 (DPP-4) activity influences metabolic, behavioral and intestinal disorders through the cleavage of key hormones and peptides. Some studies describe the existence of human DPP-4 homologs in commensal bacteria, for instance in Prevotella or Lactobacillus. However, the role of the gut microbiota as a source of DPP-4-like activity has never been investigated. Through the comparison of the DPP-4 activity in the cecal content of germ-free mice (GFM) and gnotobiotic mice colonized with the gut microbiota of a healthy subject, we bring the proof of concept that a significant DPP-4-like activity occurs in the microbiota. By analyzing the existing literature, we propose that DPP-4-like activity encoded by the intestinal microbiome could constitute a novel mechanism to modulate protein digestion as well as host metabolism and behavior.