138 research outputs found
Statistical Inference for Partially Observed Markov Processes via the R Package pomp
Partially observed Markov process (POMP) models, also known as hidden Markov
models or state space models, are ubiquitous tools for time series analysis.
The R package pomp provides a very flexible framework for Monte Carlo
statistical investigations using nonlinear, non-Gaussian POMP models. A range
of modern statistical methods for POMP models have been implemented in this
framework including sequential Monte Carlo, iterated filtering, particle Markov
chain Monte Carlo, approximate Bayesian computation, maximum synthetic
likelihood estimation, nonlinear forecasting, and trajectory matching. In this
paper, we demonstrate the application of these methodologies using some simple
toy problems. We also illustrate the specification of more complex POMP models,
using a nonlinear epidemiological model with a discrete population,
seasonality, and extra-demographic stochasticity. We discuss the specification
of user-defined models and the development of additional methods within the
programming environment provided by pomp.Comment: In press at the Journal of Statistical Software. A version of this
paper is provided at the pomp package website: http://kingaa.github.io/pom
Long-run determinants of atmospheric CO2: Granger-causality and cointegration analysis
Atmospheric concentrations of CO2 grew annually 1.12 +- 0.48 parts per million (ppm) in 1958-1984, and 1.72 +- 0.54 ppm (mean +- standard deviation) in 1985-2009, so that the rate growth is growing itself. Natural phenomena that influence short-run changes in CO2 atmospheric levels (through their influence on CO2 emissions and sinks) are stationary processes that cannot explain the growth of CO2 levels at an increasing rate. Cointegration tests show at a high level of statistical significance that the
annual increase of CO2 concentrations is roughly proportional to “human activities” as measured by the money value of the world economy and the size of the world population. We find that population and
world GDP help to predict CO2 concentrations, but CO2 concentrations do not help to predict the othervariables; that is, there is Ganger causality from population and world economic output to CO2. Though the smallness of the time series involved and the theoretical and practical issues posed by cointegration allow only for a limited confidence in these results, they have obvious major implications. For business-as-usual conditions and a world economy growing annually 3.5%—the mean annual growth of the world economy since 1960—the required world population to maintain or reduce CO2 levels would be 1.3 billion or less. For a world population of 7 billion as the present one, CO2 atmospheric levels would decrease if the global economy contracted annually 24.5% or more.http://deepblue.lib.umich.edu/bitstream/2027.42/88162/1/Long-run determinants of CO2 - A3- Dec 2011+refs.pd
Pathogenic variants in the CYP21A2 gene cause isolated autosomal dominant congenital posterior polar cataracts
Background:
Congenital cataracts are the most common cause of visual impairment worldwide. Inherited cataract is a clinically and genetically heterogeneous disease. Here we report disease-causing variants in a novel gene, CYP21A2, causing autosomal dominant posterior polar cataract. Variants in this gene are known to cause autosomal recessive congenital adrenal hyperplasia (CAH). /
Methods:
Using whole-exome sequencing (WES), we have identified disease-causing sequence variants in two families of British and Irish origin, and in two isolated cases of Asian-Indian and British origin. Bioinformatics analysis confirmed these variants as rare with damaging pathogenicity scores. Segregation was tested within the families using direct Sanger sequencing. /
Results:
A nonsense variant NM_000500.9 c.955 C > T; p.Q319* was identified in CYP21A2 in two families with posterior polar cataract and in an isolated case with unspecified congenital cataract phenotype. This is the same variant previously linked to CAH and identified as Q318* in the literature. We have also identified a rare missense variant NM_000500.9 c.770 T > C; p.M257T in an isolated case with unspecified congenital cataract phenotype. /
Conclusion:
This is the first report of separate sequence variants in CYP21A2 associated with congenital cataract. Our findings extend the genetic basis for congenital cataract and add to the phenotypic spectrum of CYP21A2 variants and particularly the CAH associated Q318* variant. CYP21A2 has a significant role in mineralo- and gluco-corticoid biosynthesis. These findings suggest that CYP21A2 may be important for extra-adrenal biosynthesis of aldosterone and cortisol in the eye lens
Multimorbidity due to novel pathogenic variants in the WFS1/RP1/NOD2 genes: autosomal dominant congenital lamellar cataract, retinitis pigmentosa and Crohn’s disease in a British family
Background: A five generation family has been analysed by whole exome sequencing (WES) for genetic associations with the multimorbidities of congenital cataract (CC), retinitis pigmentosa (RP) and Crohn’s disease (CD). //
Methods: WES was performed for unaffected and affected individuals within the family pedigree followed by bioinformatic analyses of these data to identify disease-causing variants with damaging pathogenicity scores. //
Results: A novel pathogenic missense variant in WFS1: c.1897G>C; p.V633L, a novel pathogenic nonsense variant in RP1: c.6344T>G; p.L2115* and a predicted pathogenic missense variant in NOD2: c.2104C>T; p.R702W are reported. The three variants cosegregated with the phenotypic combinations of autosomal dominant CC, RP and CD within individual family members. //
Conclusions: Here, we report multimorbidity in a family pedigree listed on a CC register, which broadens the spectrum of potential cataract associated genes to include both RP1 and NOD2
Statistical Inference for Spatiotemporal Partially Observed Markov Processes via the R Package spatPomp
We consider inference for a class of nonlinear stochastic processes with
latent dynamic variables and spatial structure. The spatial structure takes the
form of a finite collection of spatial units that are dynamically coupled. We
assume that the latent processes have a Markovian structure and that
unit-specific noisy measurements are made. A model of this form is called a
spatiotemporal partially observed Markov process (SpatPOMP). The R package
spatPomp provides an environment for implementing SpatPOMP models, analyzing
data, and developing new inference approaches. We describe the spatPomp
implementations of some methods with scaling properties suited to SpatPOMP
models. We demonstrate the package on a simple Gaussian system and on a
nontrivial epidemiological model for measles transmission within and between
cities. We show how to construct user-specified SpatPOMP models within
spatPomp
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