Non-industrial private forest landowners’ choices of timber management strategies.

The factors affecting the non-industrial, private forest landowners' (hereafter referred to using the acronym NIPF) strategic decisions in management planning are studied. A genetic algorithm is used to induce a set of rules predicting potential cut of the landowners' choices of preferred timber management strategies. The rules are based on variables describing the characteristics of the landowners and their forest holdings. The predictive ability of a genetic algorithm is compared to linear regression analysis using identical data sets. The data are cross-validated seven times applying both genetic algorithm and regression analyses in order to examine the data-sensitivity and robustness of the generated models. The optimal rule set derived from genetic algorithm analyses included the following variables: mean initial volume, landowner's positive price expectations for the next eight years, landowner being classified as farmer, and preference for the recreational use of forest property. When tested with previously unseen test data, the optimal rule set resulted in a relative root mean square error of 0.40. In the regression analyses, the optimal regression equation consisted of the following variables: mean initial volume, proportion of forestry income, intention to cut extensively in future, and positive price expectations for the next two years. The R2 of the optimal regression equation was 0.34 and the relative root mean square error obtained from the test data was 0.38. In both models, mean initial volume and positive stumpage price expectations were entered as significant predictors of potential cut of preferred timber management strategy. When tested with the complete data set of 201 observations, both the optimal rule set and the optimal regression model achieved the same level of accuracy.

Genomic alterations in Myeloproliferative Neoplasms and Myelodysplastic Syndromes

Myeloproliferative neoplasms (MPN) and myelodysplastic syndromes (MDS) are a heterogeneous group of clonal hematopoietic disorders whose etiology and molecular pathogenesis are poorly understood. During the past decade, enormous developments in microarray technology and bioinformatics methods have made it possible to mine novel molecular alterations in a large number of malignancies, including MPN and MDS, which has facilitated the detection of new prognostic, predictive and therapeutic biomarkers for disease stratification. By applying novel microarray techniques, we profiled copy number alterations and microRNA (miRNA) expression changes in bone marrow aspirate and blood samples. In addition, we set up and validated an miRNA expression test for bone marrow core biopsies in order to utilize the large archive material available in many laboratories. We also tested JAK2 mutation status and compare it with the in vitro growth pattern of hematologic progenitors cells. In the study focusing on 100 MPN cases, we detected a Janus kinase 2 (JAK2) mutation in 71 cases. We observed spontaneous erythroid colony growth in all mutation-positive cases in addition to nine mutation negative cases. Interestingly, seven JAK2V167F negative ET cases showed spontaneous megakaryocyte colony formation, one case of which also harbored a myeloproliferative leukemia virus oncogene (MPL) mutation. We studied copy number alterations in 35 MPN and 37 MDS cases by using oligonucleotide-based array comparative hybridization (array CGH). Only one essential thrombocythemia (ET) case presented copy number alterations in chromosomes 1q and 13q. In contrast, MDS cases were characterized by numerous novel cryptic chromosomal aberrations with the most common copy number losses at 5q21.3q33.1 and 7q22.1q33, while the most common copy number gain was trisomy 8. As for the study of the bone marrow core biopsy samples, we showed that even though these samples were embedded in paraffin and underwent decalcification, they were reliable sources of miRNA and suitable for array expression analysis. Further, when studying the miRNA expression profiles of the 19 MDS cases, we found that, compared to controls, two miRNAs (one human Epstein-Barr virus (miR-BART13) miRNA and one human (has-miR-671-5p) miRNA) were downregulated, whereas two other miRNAs (hsa-miR-720 and hsa-miR-21) were upregulated. However, we could find no correlation between copy number alterations and microRNA expression when integrating these two data. This thesis brings to light new information about genomic changes implicated in the development of MPN and MDS, and also underlines the power of applying genome-wide array screening techniques in neoplasias. Rapid advances in molecular techniques and the integration of different genomic data will enable the discovery of the biological contexts of many complex disorders, including myeloid neoplasias.

Fast distributed approximation algorithms for vertex cover and set cover in anonymous networks

We present a distributed algorithm that finds a maximal edge packing in O(Δ + log* W) synchronous communication rounds in a weighted graph, independent of the number of nodes in the network; here Δ is the maximum degree of the graph and W is the maximum weight. As a direct application, we have a distributed 2-approximation algorithm for minimum-weight vertex cover, with the same running time. We also show how to find an f-approximation of minimum-weight set cover in O(f2k2 + fk log* W) rounds; here k is the maximum size of a subset in the set cover instance, f is the maximum frequency of an element, and W is the maximum weight of a subset. The algorithms are deterministic, and they can be applied in anonymous networks.

Income inequality in the process of economic development : An empirical approach

This thesis studies the effect of income inequality on economic growth. This is done by analyzing panel data from several countries with both short and long time dimensions of the data. Two of the chapters study the direct effect of inequality on growth, and one chapter also looks at the possible indirect effect of inequality on growth by assessing the effect of inequality on savings. In Chapter two, the effect of inequality on growth is studied by using a panel of 70 countries and a new EHII2008 inequality measure. Chapter contributes on two problems that panel econometric studies on the economic effect of inequality have recently encountered: the comparability problem associated with the commonly used Deininger and Squire s Gini index, and the problem relating to the estimation of group-related elasticities in panel data. In this study, a simple way to 'bypass' vagueness related to the use of parametric methods to estimate group-related parameters is presented. The idea is to estimate the group-related elasticities implicitly using a set of group-related instrumental variables. The estimation results with new data and method indicate that the relationship between income inequality and growth is likely to be non-linear. Chapter three incorporates the EHII2.1 inequality measure and a panel with annual time series observations from 38 countries to test the existence of long-run equilibrium relation(s) between inequality and the level of GDP. Panel unit root tests indicate that both the logarithmic EHII2.1 inequality measure and the logarithmic GDP per capita series are I(1) nonstationary processes. They are also found to be cointegrated of order one, which implies that there is a long-run equilibrium relation between them. The long-run growth elasticity of inequality is found to be negative in the middle-income and rich economies, but the results for poor economies are inconclusive. In the fourth Chapter, macroeconomic data on nine developed economies spanning across four decades starting from the year 1960 is used to study the effect of the changes in the top income share to national and private savings. The income share of the top 1 % of population is used as proxy for the distribution of income. The effect of inequality on private savings is found to be positive in the Nordic and Central-European countries, but for the Anglo-Saxon countries the direction of the effect (positive vs. negative) remains somewhat ambiguous. Inequality is found to have an effect national savings only in the Nordic countries, where it is positive.

Sensitivity of wetland methane emissions to model assumptions: application and model testing against site observations

Abstract. Methane emissions from natural wetlands and rice paddies constitute a large proportion of atmospheric methane, but the magnitude and year-to-year variation of these methane sources is still unpredictable. Here we describe and evaluate the integration of a methane biogeochemical model (CLM4Me; Riley et al., 2011) into the Community Land Model 4.0 (CLM4CN) in order to better explain spatial and temporal variations in methane emissions. We test new functions for soil pH and redox potential that impact microbial methane production in soils. We also constrain aerenchyma in plants in always-inundated areas in order to better represent wetland vegetation. Satellite inundated fraction is explicitly prescribed in the model because there are large differences between simulated fractional inundation and satellite observations. A rice paddy module is also incorporated into the model, where the fraction of land used for rice production is explicitly prescribed. The model is evaluated at the site level with vegetation cover and water table prescribed from measurements. Explicit site level evaluations of simulated methane emissions are quite different than evaluating the grid cell averaged emissions against available measurements. Using a baseline set of parameter values, our model-estimated average global wetland emissions for the period 1993–2004 were 256 Tg CH4 yr−1, and rice paddy emissions in the year 2000 were 42 Tg CH4 yr−1. Tropical wetlands contributed 201 Tg CH4 yr−1, or 78 % of the global wetland flux. Northern latitude (>50 N) systems contributed 12 Tg CH4 yr−1. We expect this latter number may be an underestimate due to the low high-latitude inundated area captured by satellites and unrealistically low high-latitude productivity and soil carbon predicted by CLM4. Sensitivity analysis showed a large range (150–346 Tg CH4 yr−1) in predicted global methane emissions. The large range was sensitive to: (1) the amount of methane transported through aerenchyma, (2) soil pH (± 100 Tg CH4 yr−1), and (3) redox inhibition (± 45 Tg CH4 yr−1).