Economic growth, income distribution, and poverty in Poland during transition

The authors attempt to analyze the linkages between macroeconomic policies and economic growth variables, their movement over time, and their impact on poverty in the case of Poland. Poland, a middle-income country, is of particular interest because its data sources allow for a relatively detailed analysis of such developments, and the macroeconomic environment and the economic growth variables show a relatively sizable degree of variance. In addition, Poland has struggled in the past few years to reduce poverty while still experiencing positive economic growth. The authors show that in Poland, poverty-reducing growth depends heavily on the ability of the economy to generate jobs. During the early years of transition, net job growth was positive, while after the Russian crisis of 1998, productivity gains were accomplished mostly through labor shedding, henceforth, increasing poverty in Poland. In addition, the authors identify how fiscal and social protection policies affect income distribution and poverty in Poland.Economic Theory&Research,Environmental Economics&Policies,Services&Transfers to Poor,Poverty Impact Evaluation,Economic Conditions and Volatility,Inequality,Governance Indicators,Environmental Economics&Policies,Achieving Shared Growth,Economic Theory&Research

FLORA: a novel method to predict protein function from structure in diverse superfamilies

Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2–3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (α, β, αβ) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues