Farm size and growth in field crop and Dairy farms in France, Hungary and Slovenia

The aim of this article is to investigate the relationship between size and farm growth. The existing theories of the association between size and farm growth give mixed results by countries and over time. This paper pursues a twofold objective: on one hand, to test the validity of Gibrat's Law for French, Hungarian and Slovenian specialized dairy and crop farms during the pre- and post-accession period to the European Union membership. Dairy and crops farms are prevailing in the farming structure of these countries. Using Farm Accountancy Data Network datasets makes it necessary to avoid biases due to heterogeneous structures across the farming systems. Thus we use quantile regressions to control for farm size related heterogeneity in the samples. On the other hand, the main novelty of this paper is the comparative analysis of the relationship between farm size and farm growth between transition Hungarian and Slovenian and non-transition French farming sectors, characterized by rather different farm structures. The results reject the validity of Gibrat's Law for crop farms in Hungary and to a lesser extent in France, and for French and Slovenian dairy farms. We provide evidence that smaller farms grew faster than larger ones over the studied period 2001-2007 for France, 2001-2008 for Hungary, and 2004-2008 for Slovenia. Conversely, the results for Slovenia suggest that the rate of growth of crop farms in terms of its land is independent from its size

Exponential and power-law probability distributions of wealth and income in the United Kingdom and the United States

We present the data on wealth and income distributions in the United Kingdom, as well as on the income distributions in the individual states of the USA. In all of these data, we find that the great majority of population is described by an exponential distribution, whereas the high-end tail follows a power law. The distributions are characterized by a dimensional scale analogous to temperature. The values of temperature are determined for the UK and the USA, as well as for the individual states of the USA.Comment: 8 pages, 6 figures, elsart.cls. Submitted to Physica A, proceedings of NATO workshop Applications of Physics in Economic Modeling, Prague, February 2001. V.2: minor stylistic expansio

Power Law Distribution of the Frequency of Demises of U.S Firms

Both theoretical and applied economics have a great deal to say about many aspects of the firm, but the literature on the extinctions, or demises, of firms is very sparse. We use a publicly available data base covering some 6 million firms in the US and show that the underlying statistical distribution which characterises the frequency of firm demises - the disappearances of firms as autonomous entities - is closely approximated by a power law. The exponent of the power law is, intriguingly, close to that reported in the literature on the extinction of biological species.Comment: 8 pages, 2 figure

Predicted and Verified Deviations from Zipf's law in Ecology of Competing Products

Zipf's power-law distribution is a generic empirical statistical regularity found in many complex systems. However, rather than universality with a single power-law exponent (equal to 1 for Zipf's law), there are many reported deviations that remain unexplained. A recently developed theory finds that the interplay between (i) one of the most universal ingredients, namely stochastic proportional growth, and (ii) birth and death processes, leads to a generic power-law distribution with an exponent that depends on the characteristics of each ingredient. Here, we report the first complete empirical test of the theory and its application, based on the empirical analysis of the dynamics of market shares in the product market. We estimate directly the average growth rate of market shares and its standard deviation, the birth rates and the "death" (hazard) rate of products. We find that temporal variations and product differences of the observed power-law exponents can be fully captured by the theory with no adjustable parameters. Our results can be generalized to many systems for which the statistical properties revealed by power law exponents are directly linked to the underlying generating mechanism

Analyses of the radiation of birnaviruses from diverse host phyla and of their evolutionary affinities with other double-stranded RNA and positive strand RNA viruses using robust structure-based multiple sequence alignments and advanced phylogenetic methods

BACKGROUND: Birnaviruses form a distinct family of double-stranded RNA viruses infecting animals as different as vertebrates, mollusks, insects and rotifers. With such a wide host range, they constitute a good model for studying the adaptation to the host. Additionally, several lines of evidence link birnaviruses to positive strand RNA viruses and suggest that phylogenetic analyses may provide clues about transition. RESULTS: We characterized the genome of a birnavirus from the rotifer Branchionus plicalitis. We used X-ray structures of RNA-dependent RNA polymerases and capsid proteins to obtain multiple structure alignments that allowed us to obtain reliable multiple sequence alignments and we employed “advanced” phylogenetic methods to study the evolutionary relationships between some positive strand and double-stranded RNA viruses. We showed that the rotifer birnavirus genome exhibited an organization remarkably similar to other birnaviruses. As this host was phylogenetically very distant from the other known species targeted by birnaviruses, we revisited the evolutionary pathways within the Birnaviridae family using phylogenetic reconstruction methods. We also applied a number of phylogenetic approaches based on structurally conserved domains/regions of the capsid and RNA-dependent RNA polymerase proteins to study the evolutionary relationships between birnaviruses, other double-stranded RNA viruses and positive strand RNA viruses. CONCLUSIONS: We show that there is a good correlation between the phylogeny of the birnaviruses and that of their hosts at the phylum level using the RNA-dependent RNA polymerase (genomic segment B) on the one hand and a concatenation of the capsid protein, protease and ribonucleoprotein (genomic segment A) on the other hand. This correlation tends to vanish within phyla. The use of advanced phylogenetic methods and robust structure-based multiple sequence alignments allowed us to obtain a more accurate picture (in terms of probability of the tree topologies) of the evolutionary affinities between double-stranded RNA and positive strand RNA viruses. In particular, we were able to show that there exists a good statistical support for the claims that dsRNA viruses are not monophyletic and that viruses with permuted RdRps belong to a common evolution lineage as previously proposed by other groups. We also propose a tree topology with a good statistical support describing the evolutionary relationships between the Picornaviridae, Caliciviridae, Flaviviridae families and a group including the Alphatetraviridae, Nodaviridae, Permutotretraviridae, Birnaviridae, and Cystoviridae families

Can molecular dynamics simulations help in discriminating correct from erroneous protein 3D models?

<p>Abstract</p> <p>Background</p> <p>Recent approaches for predicting the three-dimensional (3D) structure of proteins such as <it>de novo </it>or fold recognition methods mostly rely on simplified energy potential functions and a reduced representation of the polypeptide chain. These simplifications facilitate the exploration of the protein conformational space but do not permit to capture entirely the subtle relationship that exists between the amino acid sequence and its native structure. It has been proposed that physics-based energy functions together with techniques for sampling the conformational space, e.g., Monte Carlo or molecular dynamics (MD) simulations, are better suited to the task of modelling proteins at higher resolutions than those of models obtained with the former type of methods. In this study we monitor different protein structural properties along MD trajectories to discriminate correct from erroneous models. These models are based on the sequence-structure alignments provided by our fold recognition method, FROST. We define correct models as being built from alignments of sequences with structures similar to their native structures and erroneous models from alignments of sequences with structures unrelated to their native structures.</p> <p>Results</p> <p>For three test sequences whose native structures belong to the all-<it>α</it>, all-<it>β </it>and <it>αβ </it>classes we built a set of models intended to cover the whole spectrum: from a perfect model, i.e., the native structure, to a very poor model, i.e., a random alignment of the test sequence with a structure belonging to another structural class, including several intermediate models based on fold recognition alignments. We submitted these models to 11 ns of MD simulations at three different temperatures. We monitored along the corresponding trajectories the mean of the Root-Mean-Square deviations (RMSd) with respect to the initial conformation, the RMSd fluctuations, the number of conformation clusters, the evolution of secondary structures and the surface area of residues. None of these criteria alone is 100% efficient in discriminating correct from erroneous models. The mean RMSd, RMSd fluctuations, secondary structure and clustering of conformations show some false positives whereas the residue surface area criterion shows false negatives. However if we consider these criteria in combination it is straightforward to discriminate the two types of models.</p> <p>Conclusion</p> <p>The ability of discriminating correct from erroneous models allows us to improve the specificity and sensitivity of our fold recognition method for a number of ambiguous cases.</p

Flexible Alignments for Protein Threading

We present a new local alignment method for the protein threading problem. Local sequence-sequence alignments are widely used to find functionally important regions in families of proteins. However, to the best of our knowledge, no local sequence-structure alignment algorithm has been described in the literature. Here we model local alignments as Mixed Integer Programming (MIP) models. These models permit to align a part of a protein structure onto a protein sequence in order to detect local similarities. The paper describes two MIP models, compares and analyzes their performance by using ILOG CPLEX 10 solver

Power-law distributions from additive preferential redistributions

We introduce a non-growth model that generates the power-law distribution with the Zipf exponent. There are N elements, each of which is characterized by a quantity, and at each time step these quantities are redistributed through binary random interactions with a simple additive preferential rule, while the sum of quantities is conserved. The situation described by this model is similar to those of closed $N$-particle systems when conservative two-body collisions are only allowed. We obtain stationary distributions of these quantities both analytically and numerically while varying parameters of the model, and find that the model exhibits the scaling behavior for some parameter ranges. Unlike well-known growth models, this alternative mechanism generates the power-law distribution when the growth is not expected and the dynamics of the system is based on interactions between elements. This model can be applied to some examples such as personal wealths, city sizes, and the generation of scale-free networks when only rewiring is allowed.Comment: 12 pages, 4 figures; Changed some expressions and notations; Added more explanations and changed the order of presentation in Sec.III while results are the sam