Double incretin receptor knock-out (DIRKO) mice present with alterations of trabecular and cortical micromorphology and bone strength.

A role for gut hormone in bone physiology has been suspected. We evidenced alterations of microstructural morphology (trabecular and cortical) and bone strength (both at the whole-bone - and tissue-level) in double incretin receptor knock-out (DIRKO) mice as compared to wild-type littermates. These results support a role for gut hormones in bone physiology. INTRODUCTION: The two incretins, glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1), have been shown to control bone remodeling and strength. However, lessons from single incretin receptor knock-out mice highlighted a compensatory mechanism induced by elevated sensitivity to the other gut hormone. As such, it is unclear whether the bone alterations observed in GIP or GLP-1 receptor deficient animals resulted from the lack of a functional gut hormone receptor, or by higher sensitivity for the other gut hormone. The aims of the present study were to investigate the bone microstructural morphology, as well as bone tissue properties, in double incretin receptor knock-out (DIRKO) mice. METHODS: Twenty-six-week-old DIRKO mice were age- and sex-matched with wild-type (WT) littermates. Bone microstructural morphology was assessed at the femur by microCT and quantitative X-ray imaging, while tissue properties were investigated by quantitative backscattered electron imaging and Fourier-transformed infrared microscopy. Bone mechanical response was assessed at the whole-bone- and tissue-level by 3-point bending and nanoindentation, respectively. RESULTS: As compared to WT animals, DIRKO mice presented significant augmentations in trabecular bone mass and trabecular number whereas bone outer diameter, cortical thickness, and cortical area were reduced. At the whole-bone-level, yield stress, ultimate stress, and post-yield work to fracture were significantly reduced in DIRKO animals. At the tissue-level, only collagen maturity was reduced by 9 % in DIRKO mice leading to reductions in maximum load, hardness, and dissipated energy. CONCLUSIONS: This study demonstrated the critical role of gut hormones in controlling bone microstructural morphology and tissue properties.

Phylogenetic approach reveals that virus genotype largely determines HIV set-point viral load.

HIV virulence, i.e. the time of progression to AIDS, varies greatly among patients. As for other rapidly evolving pathogens of humans, it is difficult to know if this variance is controlled by the genotype of the host or that of the virus because the transmission chain is usually unknown. We apply the phylogenetic comparative approach (PCA) to estimate the heritability of a trait from one infection to the next, which indicates the control of the virus genotype over this trait. The idea is to use viral RNA sequences obtained from patients infected by HIV-1 subtype B to build a phylogeny, which approximately reflects the transmission chain. Heritability is measured statistically as the propensity for patients close in the phylogeny to exhibit similar infection trait values. The approach reveals that up to half of the variance in set-point viral load, a trait associated with virulence, can be heritable. Our estimate is significant and robust to noise in the phylogeny. We also check for the consistency of our approach by showing that a trait related to drug resistance is almost entirely heritable. Finally, we show the importance of taking into account the transmission chain when estimating correlations between infection traits. The fact that HIV virulence is, at least partially, heritable from one infection to the next has clinical and epidemiological implications. The difference between earlier studies and ours comes from the quality of our dataset and from the power of the PCA, which can be applied to large datasets and accounts for within-host evolution. The PCA opens new perspectives for approaches linking clinical data and evolutionary biology because it can be extended to study other traits or other infectious diseases.

Quantitative real-time polymerase chain reaction for detection of adenovirus after T cell-replete hematopoietic cell transplantation: viral load as a marker for invasive disease.

BACKGROUND: The value of adenovirus plasma DNA detection as an indicator for adenovirus disease is unknown in the context of T cell-replete hematopoietic cell transplantation, of which adenovirus disease is an uncommon but serious complication. METHODS: Three groups of 62 T cell-replete hematopoietic cell transplant recipients were selected and tested for adenovirus in plasma by polymerase chain reaction. RESULTS: Adenovirus was detected in 21 (87.5%) of 24 patients with proven adenovirus disease (group 1), in 4 (21%) of 19 patients who shed adenovirus (group 2), and in 1 (10.5%) of 19 uninfected control patients. The maximum viral load was significantly higher in group 1 (median maximum viral load, 6.3x10(6) copies/mL; range, 0 to 1.0x10(9) copies/mL) than in group 2 (median maximum viral load, 0 copies/mL; range, 0 to 1.7x10(8) copies/mL; P<.001) and in group 3 (median maximum viral load, 0 copies/mL; range 0-40 copies/mL; P<.001). All patients in group 2 who developed adenoviremia had symptoms compatible with adenovirus disease (i.e., possible disease). A minimal plasma viral load of 10(3) copies/mL was detected in all patients with proven or possible disease. Adenoviremia was detectable at a median of 19.5 days (range, 8-48 days) and 24 days (range, 9-41 days) before death for patients with proven and possible adenovirus disease, respectively. CONCLUSION: Sustained or high-level adenoviremia appears to be a specific and sensitive indicator of adenovirus disease after T cell-replete hematopoietic cell transplantation. In the context of low prevalence of adenovirus disease, the use of polymerase chain reaction of plasma specimens to detect virus might be a valuable tool to identify and treat patients at risk for viral invasive disease.

Inference about the number of contributors to a DNA mixture: Comparative analyses of a Bayesian network approach and the maximum allele count method.

In the forensic examination of DNA mixtures, the question of how to set the total number of contributors (N) presents a topic of ongoing interest. Part of the discussion gravitates around issues of bias, in particular when assessments of the number of contributors are not made prior to considering the genotypic configuration of potential donors. Further complication may stem from the observation that, in some cases, there may be numbers of contributors that are incompatible with the set of alleles seen in the profile of a mixed crime stain, given the genotype of a potential contributor. In such situations, procedures that take a single and fixed number contributors as their output can lead to inferential impasses. Assessing the number of contributors within a probabilistic framework can help avoiding such complication. Using elements of decision theory, this paper analyses two strategies for inference on the number of contributors. One procedure is deterministic and focuses on the minimum number of contributors required to 'explain' an observed set of alleles. The other procedure is probabilistic using Bayes' theorem and provides a probability distribution for a set of numbers of contributors, based on the set of observed alleles as well as their respective rates of occurrence. The discussion concentrates on mixed stains of varying quality (i.e., different numbers of loci for which genotyping information is available). A so-called qualitative interpretation is pursued since quantitative information such as peak area and height data are not taken into account. The competing procedures are compared using a standard scoring rule that penalizes the degree of divergence between a given agreed value for N, that is the number of contributors, and the actual value taken by N. Using only modest assumptions and a discussion with reference to a casework example, this paper reports on analyses using simulation techniques and graphical models (i.e., Bayesian networks) to point out that setting the number of contributors to a mixed crime stain in probabilistic terms is, for the conditions assumed in this study, preferable to a decision policy that uses categoric assumptions about N.

Adaptively weighted maximum likelihood estimation of discrete distributions

SummaryDiscrete data arise in various research fields, typically when the observations are count data.I propose a robust and efficient parametric procedure for estimation of discrete distributions. The estimation is done in two phases. First, a very robust, but possibly inefficient, estimate of the model parameters is computed and used to indentify outliers. Then the outliers are either removed from the sample or given low weights, and a weighted maximum likelihood estimate (WML) is computed.The weights are determined via an adaptive process such that if the data follow the model, then asymptotically no observation is downweighted.I prove that the final estimator inherits the breakdown point of the initial one, and that its influence function at the model is the same as the influence function of the maximum likelihood estimator, which strongly suggests that it is asymptotically fully efficient.The initial estimator is a minimum disparity estimator (MDE). MDEs can be shown to have full asymptotic efficiency, and some MDEs have very high breakdown points and very low bias under contamination. Several initial estimators are considered, and the performances of the WMLs based on each of them are studied.It results that in a great variety of situations the WML substantially improves the initial estimator, both in terms of finite sample mean square error and in terms of bias under contamination. Besides, the performances of the WML are rather stable under a change of the MDE even if the MDEs have very different behaviors.Two examples of application of the WML to real data are considered. In both of them, the necessity for a robust estimator is clear: the maximum likelihood estimator is badly corrupted by the presence of a few outliers.This procedure is particularly natural in the discrete distribution setting, but could be extended to the continuous case, for which a possible procedure is sketched.RésuméLes données discrètes sont présentes dans différents domaines de recherche, en particulier lorsque les observations sont des comptages.Je propose une méthode paramétrique robuste et efficace pour l'estimation de distributions discrètes. L'estimation est faite en deux phases. Tout d'abord, un estimateur très robuste des paramètres du modèle est calculé, et utilisé pour la détection des données aberrantes (outliers). Cet estimateur n'est pas nécessairement efficace. Ensuite, soit les outliers sont retirés de l'échantillon, soit des faibles poids leur sont attribués, et un estimateur du maximum de vraisemblance pondéré (WML) est calculé.Les poids sont déterminés via un processus adaptif, tel qu'asymptotiquement, si les données suivent le modèle, aucune observation n'est dépondérée.Je prouve que le point de rupture de l'estimateur final est au moins aussi élevé que celui de l'estimateur initial, et que sa fonction d'influence au modèle est la même que celle du maximum de vraisemblance, ce qui suggère que cet estimateur est pleinement efficace asymptotiquement.L'estimateur initial est un estimateur de disparité minimale (MDE). Les MDE sont asymptotiquement pleinement efficaces, et certains d'entre eux ont un point de rupture très élevé et un très faible biais sous contamination. J'étudie les performances du WML basé sur différents MDEs.Le résultat est que dans une grande variété de situations le WML améliore largement les performances de l'estimateur initial, autant en terme du carré moyen de l'erreur que du biais sous contamination. De plus, les performances du WML restent assez stables lorsqu'on change l'estimateur initial, même si les différents MDEs ont des comportements très différents.Je considère deux exemples d'application du WML à des données réelles, où la nécessité d'un estimateur robuste est manifeste : l'estimateur du maximum de vraisemblance est fortement corrompu par la présence de quelques outliers.La méthode proposée est particulièrement naturelle dans le cadre des distributions discrètes, mais pourrait être étendue au cas continu.

Joint evolution of dispersal and inbreeding load.

Inbreeding avoidance is often invoked to explain observed patterns of dispersal, and theoretical models indeed point to a possibly important role. However, while inbreeding load is usually assumed constant in these models, it is actually bound to vary dynamically under the combined influences of mutation, drift, and selection and thus to evolve jointly with dispersal. Here we report the results of individual-based stochastic simulations allowing such a joint evolution. We show that strongly deleterious mutations should play no significant role, owing to the low genomic mutation rate for such mutations. Mildly deleterious mutations, by contrast, may create enough heterosis to affect the evolution of dispersal as an inbreeding-avoidance mechanism, but only provided that they are also strongly recessive. If slightly recessive, they will spread among demes and accumulate at the metapopulation level, thus contributing to mutational load, but not to heterosis. The resulting loss of viability may then combine with demographic stochasticity to promote population fluctuations, which foster indirect incentives for dispersal. Our simulations suggest that, under biologically realistic parameter values, deleterious mutations have a limited impact on the evolution of dispersal, which on average exceeds by only one-third the values expected from kin-competition avoidance.

Quadrant/octant sequencing and the role of coherent structures in bed load sediment entrainment

To permit the tracking of turbulent flow structures in an Eulerian frame from single-point measurements, we make use of a generalization of conventional two-dimensional quadrant analysis to three-dimensional octants. We characterize flow structures using the sequences of these octants and show how significance may be attached to particular sequences using statistical mull models. We analyze an example experiment and show how a particular dominant flow structure can be identified from the conditional probability of octant sequences. The frequency of this structure corresponds to the dominant peak in the velocity spectra and exerts a high proportion of the total shear stress. We link this structure explicitly to the propensity for sediment entrainment and show that greater insight into sediment entrainment can be obtained by disaggregating those octants that occur within the identified macroturbulence structure from those that do not. Hence, this work goes beyond critiques of Reynolds stress approaches to bed load entrainment that highlight the importance of outward interactions, to identifying and prioritizing the quadrants/octants that define particular flow structures. Key Points <list list-type=''bulleted'' id=''jgrf20196-list-0001''> <list-item id=''jgrf20196-li-0001''>A new method for analysing single point velocity data is presented <list-item id=''jgrf20196-li-0002''>Flow structures are identified by a sequence of flow states (termed octants) <list-item id=''jgrf20196-li-0003''>The identified structure exerts high stresses and causes bed-load entrainment

Sociodemographic, behavioral and genetic determinants of allostatic load in a Swiss population-based study.

Allostatic load (AL) is a marker of physiological dysregulation which reflects exposure to chronic stress. High AL has been related to poorer health outcomes including mortality. We examine here the association of socioeconomic and lifestyle factors with AL. Additionally, we investigate the extent to which AL is genetically determined. We included 803 participants (52% women, mean age 48±16years) from a population and family-based Swiss study. We computed an AL index aggregating 14 markers from cardiovascular, metabolic, lipidic, oxidative, hypothalamus-pituitary-adrenal and inflammatory homeostatic axes. Education and occupational position were used as indicators of socioeconomic status. Marital status, stress, alcohol intake, smoking, dietary patterns and physical activity were considered as lifestyle factors. Heritability of AL was estimated by maximum likelihood. Women with a low occupational position had higher AL (low vs. high OR=3.99, 95%CI [1.22;13.05]), while the opposite was observed for men (middle vs. high OR=0.48, 95%CI [0.23;0.99]). Education tended to be inversely associated with AL in both sexes(low vs. high OR=3.54, 95%CI [1.69;7.4]/OR=1.59, 95%CI [0.88;2.90] in women/men). Heavy drinking men as well as women abstaining from alcohol had higher AL than moderate drinkers. Physical activity was protective against AL while high salt intake was related to increased AL risk. The heritability of AL was estimated to be 29.5% ±7.9%. Our results suggest that generalized physiological dysregulation, as measured by AL, is determined by both environmental and genetic factors. The genetic contribution to AL remains modest when compared to the environmental component, which explains approximately 70% of the phenotypic variance.