A Bayesian Framework for Parameter Estimation in Dynamical Models with Applications to Forecasting

Mathematical models in Biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real system.
Proper handling of such uncertainties, is key to the successful usage of models to predict experimental or field observations. This problem has been addressed over the years by many tools for model calibration an parameter estimation. In this article we present a general framework for uncertainty analysis and parameter estimation which is designed to handle uncertainties associated with the modeling of dynamic biological systems while remaining agnostic as to the type of model used. We apply the framework to two Influenza transmission models: one deterministic and the other stochastic. The results show that the framework can be applied without modifications to the two types of models and that it performs equally well on both. We also discuss the application of the framework to calibrate models for forecasting purposes.
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Modelling, Estimation and Visualization of Multivariate Dependence for Risk Management

Dependence modelling and estimation is a key issue in the assessment of portfolio risk. When measuring extreme risk in terms of the Value-at-Risk, the multivariate normal model with linear correlation as its natural dependence measure is by no means an ideal model. We suggest a large class of models and a new dependence function which allows us to capture the complete extreme dependence structure of a portfolio. We also present a simple nonparametric estimation procedure. To show our new method at work we apply it to a financial data set of zero coupon swap rates and estimate the extreme dependence in the data

Dependence Estimation and Visualization in Multivariate Extremes with Applications to Financial Data

We investigate extreme dependence in a multivariate setting with special emphasis on financial applications. We introduce a new dependence function which allows us to capture the complete extreme dependence structure and present a nonparametric estimation procedure. The new dependence function is compared with existing measures including the spectral measure and other devices measuring extreme dependence. We also apply our method to a financial data set of zero coupon swap rates and estimate the extreme dependence in the data

The Exosome Subunit Rrp44 Plays a Direct Role in RNA Substrate Recognition

The exosome plays key roles in RNA maturation and surveillance, but it is unclear how target RNAs are identified. We report the functional characterization of the yeast exosome component Rrp44, a member of the RNase II family. Recombinant Rrp44 and the purified TRAMP polyadenylation complex each specifically recognized tRNAiMet lacking a single m1A58 modification, even in the presence of a large excess of total tRNA. This tRNA is otherwise mature and functional in translation in vivo but is presumably subtly misfolded. Complete degradation of the hypomodified tRNA required both Rrp44 and the poly(A) polymerase activity of TRAMP. The intact exosome lacking only the catalytic activity of Rrp44 failed to degrade tRNAiMet, showing this to be a specific Rrp44 substrate. Recognition of hypomodified tRNAiMet by Rrp44 is genetically separable from its catalytic activity on other substrates, with the mutations mapping to distinct regions of the protein

Assessing household vulnerability to climate change: The case of farmers in the Nile Basin of Ethiopia

Vulnerability to climate extremes, Nile Basin of Ethiopia, Minimum daily income, Climate change,

Increased DNA Copy Number Variation Mosaicism in Elderly Human Brain.

Aging is a complex process strongly determined by genetics. Previous reports have shown that the genome of neuronal cells displays somatic genomic mosaicism including DNA copy number variations (CNVs). CNVs represent a significant source of genetic variation in the human genome and have been implicated in several disorders and complex traits, representing a potential mechanism that contributes to neuronal diversity and the etiology of several neurological diseases and provides new insights into the normal, complex functions of the brain. Nonetheless, the features of somatic CNV mosaicism in nondiseased elderly brains have not been investigated. In the present study, we demonstrate a highly significant increase in the number of CNVs in nondiseased elderly brains compared to the blood. In two neural tissues isolated from paired postmortem samples (same individuals), we found a significant increase in the frequency of deletions in both brain areas, namely, the frontal cortex and cerebellum. Also, deletions were found to be significantly larger when present only in the cerebellum. The sizes of the variants described here were in the 150-760 kb range, and importantly, nearly all of them were present in the Database of Genomic Variants (common variants). Nearly all evidence of genome structural variation in human brains comes from studies detecting changes in single cells which were interpreted as derived from independent, isolated mutational events. The observations based on array-CGH analysis indicate the existence of an extensive clonal mosaicism of CNVs within and between the human brains revealing a different type of variation that had not been previously characterized