De facto and official exchange rate regimes in transition economies

This paper provides an empirical investigation on the discrepancies between official exchange rate regimes and de facto exchange rate policies in transition economies. Since official and de facto regime choices are not independent of each other, we adopt a bivariate probit model to describe the joint determination of the two regime choices. After finding the important determinants of both regime choices, we use a univariate probit model to describe the determination of regime discrepancies. We find that errors in the selection of official regimes as well as the macroeconomic developments calling for conflicting adjustments in exchange rate regimes are important determinants of regime discrepancies. --

Fear of floating and fear of pegging: An empirical anaysis of de facto exchange rate regimes in developing countries

This paper uses a panel probit model with simultaneous equations to explain the joint determination of de facto and de jure exchange rate regimes in developing countries since 1980. We also derive an ordered-choice panel probit model to explain the causes of discrepancies between the two regime choices. Both models are estimated using simulation-based maximum likelihood methodsl. The results of the simultaneous equations model suggest that the two regime choices are dependent of each other and exhibit considerable state dependence. The ordered probit model provides evidence that regime discrepancies reflect an error-correction mechanism, and the discrepancies are persistent over time. --de facto exchange rate regimes,developing countries,simultaneous equations model,simulated maximum likelihood

The choice of exchange rate regimes: An empirical analysis for transition economies

We analyze the choice of exchange rate regimes of the 25 transition economies in Europe and the CIS after 1990. The empirical results show that the traditional Optimum Currency Area considerations provide relevant guidance for the exchange rate regime choices in these countries. Moreover, regime choices are influenced by inflation rates, cumulative inflation differentials, and the availability of international reserves. That is, macroeconomic stabilization and the ability to commit to a credible exchange rate peg play important roles in the determination of exchange rate regime choices. Large government deficits have ambiguous effects; they increase the likelihood of moving from a flexible exchange rate to an intermediate peg as well as the likelihood of moving from a fixed to an intermediate peg. --

Systems Microbiology: From Genomes to Ecosystems

Twenty-first century microbiology faces several grand challenges, e.g., linking structure to functions, mechanisms controlling extremely high diversity, information scaling from genomes to ecosystems, modeling simulation and predictions. With the recent advances of omics technologies, microbiologists have begun to tackle some of these challenges. In this talk, I will report the most recently progresses in these areas at the Institute for Environmental Genomics, with respect to genomic technologies, global microbial diversity and biogeography of wastewater treatment plants, climate warming, community assembly and network tool development, and ecosystem modeling

Stochastic Community Assembly: Does It Matter in Microbial Ecology?

Understanding the mechanisms controlling community diversity, functions, succession, and biogeography is a central, but poorly understood, topic in ecology, particularly in microbial ecology. Although stochastic processes are believed to play nonnegligible roles in shaping community structure, their importance relative to deterministic processes is hotly debated. The importance of ecological stochasticity in shaping microbial community structure is far less appreciated. Some of the main reasons for such heavy debates are the difficulty in defining stochasticity and the diverse methods used for delineating stochasticity. Here, we provide a critical review and synthesis of data from the most recent studies on stochastic community assembly in microbial ecology. We then describe both stochastic and deterministic components embedded in various ecological processes, including selection, dispersal, diversification, and drift. We also describe different approaches for inferring stochasticity from observational diversity patterns and highlight experimental approaches for delineating ecological stochasticity in microbial communities. In addition, we highlight research challenges, gaps, and future directions for microbial community assembly research

The unseen world: environmental microbial sequencing and identification methods for ecologists

Archaea, bacteria, microeukaryotes, and the viruses that infect them (collectively “microorganisms”) are foundational components of all ecosystems, inhabiting almost every imaginable environment and comprising the majority of the planet’s organismal and evolutionary diversity. Microorganisms play integral roles in ecosystem functioning; are important in the biogeochemical cycling of carbon (C), nitrogen (N), sulfur (S), phosphorus (P), and various metals (eg Barnard et al. 2005); and may be vital to ecosystem responses to large-scale climatic change (Mackelprang et al. 2011). Rarely found alone, microorganisms often form complex communities that are dynamic in space and time (Martiny et al. 2006). For these and other reasons, ecologists and environmental scientists have become increasingly interested in understanding microbial dynamics in ecosystems. Ecological studies of microbes in the environment generally focus on determining which organisms are present and what functional roles they are playing or could play. Rapid advances in molecular and bioinformatic approaches over the past decade have dramatically reduced the difficulty and cost of addressing such questions (Figure 1; WebTable 1). Yet the range of methodologies currently in use and the rapid pace of their ongoing development can be daunting for researchers unaccustomed to these technologies

Empirical Evaluation of a New Method for Calculating Signal to Noise Ratio (SNR) for Microarray Data Analysis

Signal-to-noise-ratio (SNR) thresholds for microarray data analysis were experimentally determined with an oligonucleotide array that contained perfect match (PM) and mismatch (MM) probes based upon four genes from Shewanella oneidensis MR-1. A new SNR calculation, called signal to both standard deviations ratio (SSDR) was developed, and evaluated along with other two methods, signal to standard deviation ratio (SSR), and signal to background ratio (SBR). At a low stringency, the thresholds of SSR, SBR, and SSDR were 2.5, 1.60 and 0.80 with oligonucleotide and PCR amplicon as target templates, and 2.0, 1.60 and 0.70 with genomic DNA as target templates. Slightly higher thresholds were obtained at the high stringency condition. The thresholds of SSR and SSDR decreased with an increase in the complexity of targets (e.g., target types), and the presence of background DNA, and a decrease in the composition of targets, while SBR remained unchanged under all situations. The lowest percentage of false positives (FP) and false negatives (FN) was observed with the SSDR calculation method, suggesting that it may be a better SNR calculation for more accurate determination of SNR thresholds. Positive spots identified by SNR thresholds were verified by the Student t-test, and consistent results were observed. This study provides general guidance for users to select appropriate SNR thresholds for different samples under different hybridization conditions

Selection of optimal oligonucleotide probes for microarrays using multiple criteria, global alignment and parameter estimation

The oligonucleotide specificity for microarray hybridization can be predicted by its sequence identity to non-targets, continuous stretch to non-targets, and/or binding free energy to non-targets. Most currently available programs only use one or two of these criteria, which may choose ‘false’ specific oligonucleotides or miss ‘true’ optimal probes in a considerable proportion. We have developed a software tool, called CommOligo using new algorithms and all three criteria for selection of optimal oligonucleotide probes. A series of filters, including sequence identity, free energy, continuous stretch, GC content, self-annealing, distance to the 3′-untranslated region (3′-UTR) and melting temperature (T(m)), are used to check each possible oligonucleotide. A sequence identity is calculated based on gapped global alignments. A traversal algorithm is used to generate alignments for free energy calculation. The optimal T(m) interval is determined based on probe candidates that have passed all other filters. Final probes are picked using a combination of user-configurable piece-wise linear functions and an iterative process. The thresholds for identity, stretch and free energy filters are automatically determined from experimental data by an accessory software tool, CommOligo_PE (CommOligo Parameter Estimator). The program was used to design probes for both whole-genome and highly homologous sequence data. CommOligo and CommOligo_PE are freely available to academic users upon request

Understanding and predicting synthetic lethal genetic interactions in Saccharomyces cerevisiae using domain genetic interactions

Genetic interactions have been widely used to define functional relationships between proteins and pathways. In this study, we demonstrated that yeast synthetic lethal genetic interactions can be explained by the genetic interactions between domains of those proteins. The domain genetic interactions rarely overlap with the domain physical interactions from iPfam database and provide a complementary view about domain relationships. Moreover, we found that domains in multidomain yeast proteins contribute to their genetic interactions differently. The domain genetic interactions help more precisely define the function related to the synthetic lethal genetic interactions, and then help understand how domains contribute to different functionalities of multidomain proteins. Using the probabilities of domain genetic interactions, we were able to predict novel yeast synthetic lethal genetic interactions. Furthermore, we had also identified novel compensatory pathways from the predicted synthetic lethal genetic interactions. Our study significantly improved the understanding of yeast mulitdomain proteins, the synthetic lethal genetic interactions and the functional relationships between proteins and pathways.Comment: 36 page, 4 figure