Search results for " Probability and Uncertainty"
showing 10 items of 578 documents
Whole genome sequencing data and de novo draft assemblies for 66 teleost species
2017
Teleost fishes comprise more than half of all vertebrate species, yet genomic data are only available for 0.2% of their diversity. Here, we present whole genome sequencing data for 66 new species of teleosts, vastly expanding the availability of genomic data for this important vertebrate group. We report on de novo assemblies based on low-coverage (9–39×) sequencing and present detailed methodology for all analyses. To facilitate further utilization of this data set, we present statistical analyses of the gene space completeness and verify the expected phylogenetic position of the sequenced genomes in a large mitogenomic context. We further present a nuclear marker set used for phylogenetic…
A global occurrence database of the Atlantic blue crab Callinectes sapidus
2021
The Atlantic blue crab Callinectes sapidus is a portunid native to the western Atlantic, from New England to Uruguay. The species was introduced in Europe in 1901 where it has become invasive; additionally, a significant northward expansion has been emphasized in its native range. Here we present a harmonized global compilation of C. sapidus occurrences from native and non-native distribution ranges derived from online databases (GBIF, BISON, OBIS, and iNaturalist) as well as from unpublished and published sources. The dataset consists of 40,388 geo-referenced occurrences, 39,824 from native and 564 from non-native ranges, recorded in 53 countries. The implementation of quality controls imp…
Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests
2021
We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points x affects another set of points y but not vice versa. We use the model to investigate the effect of large trees to the locations of seedlings. In the model, every point in x has a parametric influence kernel or signal, which together form an influence field. Conditionally on the parameters, the influence field acts as a spatial covariate in the intensity of the model, and the intensity itself is a non-linear function of the parameters. Points outside the observation window may affect the influence field inside the window. We propose an edge correction to account for this missing data. The par…
Variational Approximations for Generalized Linear Latent Variable Models
2017
Generalized linear latent variable models (GLLVMs) are a powerful class of models for understanding the relationships among multiple, correlated responses. Estimation, however, presents a major challenge, as the marginal likelihood does not possess a closed form for nonnormal responses. We propose a variational approximation (VA) method for estimating GLLVMs. For the common cases of binary, ordinal, and overdispersed count data, we derive fully closed-form approximations to the marginal log-likelihood function in each case. Compared to other methods such as the expectation-maximization algorithm, estimation using VA is fast and straightforward to implement. Predictions of the latent variabl…
The role of noise on the steady state distributions of phytoplankton populations
2016
The spatio-temporal behaviour of total chlorophyll concentration is investigated in the middle of the Tyrrhenian Sea by using a stochastic approach. The study is based on a reaction-diffusion-taxis model, which is used to analyse the dynamics of five phytoplankton groups, responsible for about 80% of the total chlorophyll a inside the euphotic zone of the water column. The analysis is performed by considering: (i) the intraspecific competition of the phytoplanktonic groups for limiting factors, i.e. light intensity and nutrient concentration, (ii) the seasonal changes of environmental variables, and (iii) the random fluctuations of the components of the velocity field and temperature. Speci…
Sustainable growth and environmental catastrophes
2017
Abstract In the standard AK growth model we introduce the threat of an ecological catastrophe and study the consequences for the economic variables in the long-run. We extend the basic framework by considering two environmental externalities: the first one is local and gives account of the marginal damage from emissions flow; the second one is aggregate, or global, and relates to the extreme damage which may happen if the accumulated stock of pollutants is on the threshold of a worldwide catastrophe. In this context dominated by market failures, we focus on the socially optimal solution and the search of conditions for sustainability. We identify the efficient balanced growth path, which ma…
Intermittent targeted therapies and stochastic evolution in patients affected by chronic myeloid leukemia
2016
Front line therapy for the treatment of patients affected by chronic myeloid leukemia (CML) is based on the administration of tyrosine kinase inhibitors, namely imatinib or, more recently, axitinib. Although imatinib is highly effective and represents an example of a successful molecular targeted therapy, the appearance of resistance is observed in a proportion of patients, especially those in advanced stages. In this work, we investigate the appearance of resistance in patients affected by CML, by modeling the evolutionary dynamics of cancerous cell populations in a simulated patient treated by an intermittent targeted therapy. We simulate, with the Monte Carlo method, the stochastic evolu…
Two-Stage Bayesian Approach for GWAS With Known Genealogy
2019
Genome-wide association studies (GWAS) aim to assess relationships between single nucleotide polymorphisms (SNPs) and diseases. They are one of the most popular problems in genetics, and have some peculiarities given the large number of SNPs compared to the number of subjects in the study. Individuals might not be independent, especially in animal breeding studies or genetic diseases in isolated populations with highly inbred individuals. We propose a family-based GWAS model in a two-stage approach comprising a dimension reduction and a subsequent model selection. The first stage, in which the genetic relatedness between the subjects is taken into account, selects the promising SNPs. The se…
Small RNA-seq analysis of circulating miRNAs to identify phenotypic variability in Friedreich's ataxia patients.
2018
AbstractFriedreich’s ataxia (FRDA; OMIM 229300), an autosomal recessive neurodegenerative mitochondrial disease, is the most prevalent hereditary ataxia. In addition, FRDA patients have shown additional non-neurological features such as scoliosis, diabetes, and cardiac complications. Hypertrophic cardiomyopathy, which is found in two thirds of patients at the time of diagnosis, is the primary cause of death in these patients. Here, we used small RNA-seq of microRNAs (miRNAs) purified from plasma samples of FRDA patients and controls. Furthermore, we present the rationale, experimental methodology, and analytical procedures for dataset analysis. This dataset will facilitate the identificatio…
L1-Penalized Censored Gaussian Graphical Model
2018
Graphical lasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. Typical examples are data generated by polymerase chain reactions and flow cytometer. The combination of censoring and high-dimensionality make inference of the underlying genetic networks from these data very challenging. In this article, we propose an $\ell_1$-penalized Gaussian graphical model for censored data and derive two EM-like algorithm…