Summary Species occurrence is influenced by environmental conditions and the presence of other species. Current approaches for multispecies occupancy modelling are practically limited to two interacting species and often require the assumption of asymmetric interactions. We propose a multispecies occupancy model that can accommodate two or more interacting species. We generalize the single‐species occupancy model to two or more interacting species by assuming the latent occupancy state is a multivariate Bernoulli random variable. We propose modelling the probability of each potential latent occupancy state with both a multinomial logit and a multinomial probit model and present details of a Gibbs sampler for the latter. As an example, we model co‐occurrence probabilities of bobcat (Lynx rufus), coyote (Canis latrans), grey fox (Urocyon cinereoargenteus) and red fox (Vulpes vulpes) as a function of human disturbance variables throughout 6 Mid‐Atlantic states in the eastern United States. We found evidence for pairwise interactions among most species, and the probability of some pairs of species occupying the same site varied along environmental gradients; for example, occupancy probabilities of coyote and grey fox were independent at sites with little human disturbance, but these two species were more likely to occur together at sites with high human disturbance. Ecological communities are composed of multiple interacting species. Our proposed method improves our ability to draw inference from such communities by permitting modelling of detection/non‐detection data from an arbitrary number of species, without assuming asymmetric interactions. Additionally, our proposed method permits modelling the probability two or more species occur together as a function of environmental variables. These advancements represent an important improvement in our ability to draw community‐level inference from multiple interacting species that are subject to imperfect detection.

Key words and phrases: Bayesian inference; conditional reference prior; frequentist coverage; Markov process; mean squared error; phylogenetic reconstruction; prior for branch length.MSC 2000: Primary 62M05; secondary 62F15. Abstract:The authors consider Bayesian analysis for continuous-time Markov chain models based on a conditional reference prior. For such models, inference of the elapsed time between chain observations depends heavily on the rate of decay of the prior as the elapsed time increases. Moreover, improper priors on the elapsed time may lead to improper posterior distributions. In addition, an infinitesimal rate matrix also characterizes this class of models. Experts often have good prior knowledge about the parameters of this matrix. The authors show that the use of a proper prior for the rate matrix parameters together with the conditional reference prior for the elapsed time yields a proper posterior distribution. The authors also demonstrate that, when compared to analyses based on priors previously proposed in the literature, a Bayesian analysis on the elapsed time based on the conditional reference prior possesses better frequentist properties. The type of prior thus represents a better default prior choice for estimation software. Analyse bayésienne des tempsécoulés dans une chaîne de Markov en temps continuRésumé : Les auteurs s'intéressentà l'emploi d'une loi a priori de référence conditionnelle dans l'analyse bayésienne des modèles de chaînes de Markov en temps continu. Dans ce cadre, l'inférence sur le tempś ecoulé entre les observations de la chaîne dépend fortement du taux de dégradation de la loi a priori au fil du temps. De plus, une loi a priori impropre sur le tempsécoulé peut conduireà une loi a posteriori impropre. Par ailleurs, ce type de modèle est aussi caractérisé par une matrice de taux infinitésimaux. Les experts ont souvent une bonne connaissance a priori des paramètres de cette matrice. Les auteurs montrent que l'emploi d'une loi a priori intégrable pour les paramètres de la matrice et d'une loi a priori de référence conditionnelle pour le tempsécoulé conduità une loi a posteriori intégrable. Les auteurs font aussi valoir que par rapport aux analyses fondées sur les lois a priori existantes, une analyse bayésienne du tempsécoulé fondée sur une loi a priori de référence conditionnelle possède de meilleures propriétés fréquentistes. Ce type de loi a priori constitue donc un meilleur choix par défaut pour les logiciels d'estimation.

The ventricular-subventricular zone harbors neural stem cells (NSCs) that can differentiate into neurons, astrocytes, and oligodendrocytes. This process requires loss of stem cell properties and gain of characteristics associated with differentiated cells. miRNAs function as important drivers of this transition; miR-124, -128, and -137 are among the most relevant ones and have been shown to share commonalities and act as proneurogenic regulators. We conducted biological and genomic analyses to dissect their target repertoire during neurogenesis and tested the hypothesis that they act cooperatively to promote differentiation. To map their target genes, we transfected NSCs with antagomiRs and analyzed differences in their mRNA profile throughout differentiation with respect to controls. This strategy led to the identification of 910 targets for miR-124, 216 for miR-128, and 652 for miR-137. The target sets show extensive overlap. Inspection by gene ontology and network analysis indicated that transcription factors are a major component of these miRNAs target sets. Moreover, several of these transcription factors form a highly interconnected network. Sp1 was determined to be the main node of this network and was further investigated. Our data suggest that miR-124, -128, and -137 act synergistically to regulate Sp1 expression. Sp1 levels are dramatically reduced as cells differentiate and silencing of its expression reduced neuronal production and affected NSC viability and proliferation. In summary, our results show that miRNAs can act cooperatively and synergistically to regulate complex biological processes like neurogenesis and that transcription factors are heavily targeted to branch out their regulatory effect. STEM CELLS 2016;34:220-232 SIGNIFICANCE STATEMENTWe demonstrate here, perhaps for the first time, that miRNAs (miR-124, -128 and -137) can act cooperatively to regulate a complex biological problem such as neurogenesis. They do so by targeting overlapping sets of genes. Moreover, our genomic analyses determined that transcription factors are the main component of their target sets. Our results indicate additional associations between the three analyzed miRNA and the targeted TFs. Based on described ChIP analysis, we suggest that miR-124, -128 and -137 and their targeted TFs could act on overlapping and related target sets producing different functional outcomes that ultimately would influence the balance self-renewal/differentiation.

No abstract

This paper presents an application of probabilistic methodologies to evaluate the reserve requirements of generating systems with large amounts of renewable energy sources. The idea is to investigate the behavior of reliability indices, including those from the well-being analysis, when the major portion of the renewable sources comes from the wind power. Renewable in this work mainly comprises hydroelectric, minihydroelectric and wind power sources. Case studies on configurations of the Portuguese and Spanish generating systems are presented and discussed.

No abstract

We introduce a new class of dynamic multiscale models for spatiotemporal processes arising from Gaussian areal data. Specifically, we use nested geographical structures to decompose the original process into multiscale coefficients which evolve through time following state space equations. Our approach naturally accommodates data that are observed on irregular grids as well as heteroscedasticity. Moreover, we propose a multiscale spatiotemporal clustering algorithm that facilitates estimation of the nested geographical multiscale structure. In addition, we present a singular forward filter backward sampler for efficient Bayesian estimation. Our multiscale spatiotemporal methodology decomposes large data analysis problems into many smaller components and thus leads to scalable and highly efficient computational procedures. Finally, we illustrate the utility and flexibility of our dynamic multiscale framework through two spatiotemporal applications. The first example considers mortality ratios in the state of Missouri whereas the second example examines agricultural production in Espírito Santo State, Brazil.

No abstract