Map of Crimea with major cities

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Complete genome sequence and integrated protein localization and interaction map for alfalfa dwarf virus, which combines properties of both cytoplasmic and nuclear plant rhabdoviruses

Summary We have determined the full-length 14,491-nucleotide genome sequence of a new plant rhabdovirus, alfalfa dwarf virus (ADV). Seven open reading frames (ORFs) were identified in the antigenomic orientation of the negative-sense, single-stranded viral RNA, in the order 3′-N-P-P3-M-G-P6-L-5′. The ORFs are separated by conserved intergenic regions and the genome coding region is flanked by complementary 3′ leader and 5′ trailer sequences. Phylogenetic analysis of the nucleoprotein amino acid sequence indicated that this alfalfa-infecting rhabdovirus is related to viruses in the genus Cytorhabdovirus. When transiently expressed as GFP fusions in Nicotiana benthamiana leaves, most ADV proteins accumulated in the cell periphery, but unexpectedly P protein was localized exclusively in the nucleus. ADV P protein was shown to have a homotypic, and heterotypic nuclear interactions with N, P3 and M proteins by bimolecular fluorescence complementation. ADV appears unique in that it combines properties of both cytoplasmic and nuclear plant rhabdoviruses.

Topological stability through tame retractions

A smooth map is said to be stable if small perturbations of the map only differ from the original one by a smooth change of coordinates. Smoothly stable maps are generic among the proper maps between given source and target manifolds when the source and target dimensions belong to the so-called nice dimensions, but outside this range of dimensions, smooth maps cannot generally be approximated by stable maps. This leads to the definition of topologically stable maps, where the smooth coordinate changes are replaced with homeomorphisms. The topologically stable maps are generic among proper maps for any dimensions of source and target. The purpose of this thesis is to investigate methods for proving topological stability by constructing extremely tame (E-tame) retractions onto the map in question from one of its smoothly stable unfoldings. In particular, we investigate how to use E-tame retractions from stable unfoldings to find topologically ministable unfoldings for certain weighted homogeneous maps or germs. Our first results are concerned with the construction of E-tame retractions and their relation to topological stability. We study how to construct the E-tame retractions from partial or local information, and these results form our toolbox for the main constructions. In the next chapter we study the group of right-left equivalences leaving a given multigerm f invariant, and show that when the multigerm is finitely determined, the group has a maximal compact subgroup and that the corresponding quotient is contractible. This means, essentially, that the group can be replaced with a compact Lie group of symmetries without much loss of information. We also show how to split the group into a product whose components only depend on the monogerm components of f. In the final chapter we investigate representatives of the E- and Z-series of singularities, discuss their instability and use our tools to construct E-tame retractions for some of them. The construction is based on describing the geometry of the set of points where the map is not smoothly stable, discovering that by using induction and our constructional tools, we already know how to construct local E-tame retractions along the set. The local solutions can then be glued together using our knowledge about the symmetry group of the local germs. We also discuss how to generalize our method to the whole E- and Z- series.

Map of the Kryvyĭ Rih area

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Negative photo map of Jewish settlements in the Krivoy Rog district

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Sketch map of the Kryvyĭ Rih region

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Construction of a global map of human gene expression : the process, tools and analysis

This thesis studies human gene expression space using high throughput gene expression data from DNA microarrays. In molecular biology, high throughput techniques allow numerical measurements of expression of tens of thousands of genes simultaneously. In a single study, this data is traditionally obtained from a limited number of sample types with a small number of replicates. For organism-wide analysis, this data has been largely unavailable and the global structure of human transcriptome has remained unknown. This thesis introduces a human transcriptome map of different biological entities and analysis of its general structure. The map is constructed from gene expression data from the two largest public microarray data repositories, GEO and ArrayExpress. The creation of this map contributed to the development of ArrayExpress by identifying and retrofitting the previously unusable and missing data and by improving the access to its data. It also contributed to creation of several new tools for microarray data manipulation and establishment of data exchange between GEO and ArrayExpress. The data integration for the global map required creation of a new large ontology of human cell types, disease states, organism parts and cell lines. The ontology was used in a new text mining and decision tree based method for automatic conversion of human readable free text microarray data annotations into categorised format. The data comparability and minimisation of the systematic measurement errors that are characteristic to each lab- oratory in this large cross-laboratories integrated dataset, was ensured by computation of a range of microarray data quality metrics and exclusion of incomparable data. The structure of a global map of human gene expression was then explored by principal component analysis and hierarchical clustering using heuristics and help from another purpose built sample ontology. A preface and motivation to the construction and analysis of a global map of human gene expression is given by analysis of two microarray datasets of human malignant melanoma. The analysis of these sets incorporate indirect comparison of statistical methods for finding differentially expressed genes and point to the need to study gene expression on a global level.

Efficient search for statistically significant dependency rules in binary data

Analyzing statistical dependencies is a fundamental problem in all empirical science. Dependencies help us understand causes and effects, create new scientific theories, and invent cures to problems. Nowadays, large amounts of data is available, but efficient computational tools for analyzing the data are missing. In this research, we develop efficient algorithms for a commonly occurring search problem - searching for the statistically most significant dependency rules in binary data. We consider dependency rules of the form X->A or X->not A, where X is a set of positive-valued attributes and A is a single attribute. Such rules describe which factors either increase or decrease the probability of the consequent A. A classical example are genetic and environmental factors, which can either cause or prevent a disease. The emphasis in this research is that the discovered dependencies should be genuine - i.e. they should also hold in future data. This is an important distinction from the traditional association rules, which - in spite of their name and a similar appearance to dependency rules - do not necessarily represent statistical dependencies at all or represent only spurious connections, which occur by chance. Therefore, the principal objective is to search for the rules with statistical significance measures. Another important objective is to search for only non-redundant rules, which express the real causes of dependence, without any occasional extra factors. The extra factors do not add any new information on the dependence, but can only blur it and make it less accurate in future data. The problem is computationally very demanding, because the number of all possible rules increases exponentially with the number of attributes. In addition, neither the statistical dependency nor the statistical significance are monotonic properties, which means that the traditional pruning techniques do not work. As a solution, we first derive the mathematical basis for pruning the search space with any well-behaving statistical significance measures. The mathematical theory is complemented by a new algorithmic invention, which enables an efficient search without any heuristic restrictions. The resulting algorithm can be used to search for both positive and negative dependencies with any commonly used statistical measures, like Fisher's exact test, the chi-squared measure, mutual information, and z scores. According to our experiments, the algorithm is well-scalable, especially with Fisher's exact test. It can easily handle even the densest data sets with 10000-20000 attributes. Still, the results are globally optimal, which is a remarkable improvement over the existing solutions. In practice, this means that the user does not have to worry whether the dependencies hold in future data or if the data still contains better, but undiscovered dependencies.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

Schematic map of the Kherson region with settlements

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Schematic map of the Kherson region with no details

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Queensland Spanner Crab Fishery: Commercial quota setting for June 2016 – May 2018

The Queensland (QLD) fishery for spanner crabs primarily lands live crab for export overseas, with gross landings valued around A$5 million per year. Quota setting rules are used to assess and adjust total allowable harvest (quota) around an agreed target harvest of 1631 t and capped at a maximum of 2000 t. The quota varies based on catch rate indicators from the commercial fishery and a fishery independent survey. Quota management applies only to ‘Managed Area A’ which includes waters between Rockhampton and the New South Wales (NSW) border. This report has been prepared to inform Fisheries Queensland (Department of Agriculture and Fisheries) and stakeholders of catch trends and the estimated quota of spanner crabs in Managed Area A for the forthcoming annual quota periods (1 June 2016–31 May 2018). The quota calculations followed the methodology developed by the crab fishery Scientific Advisory Group (SAG) between November 2007 and March 2008. The QLD total reported spanner crab harvest was 1170 t for the 2015 calendar year. In 2015, a total of 55 vessels were active in the QLD fishery, down from 262 vessels at the fishery’s peak activity in 1994. Recent spanner crab harvests from NSW waters average about 125 t per year, but fell to 80 t in 2014–2015. The spanner crab Managed Area A commercial standardised catch rate averaged 0.818 kg per net-lift in 2015, 22.5% below the target level of 1.043. Compared to 2014, mean catch rates in 2015 were marginally improved south of Fraser Island. The NSW–QLD survey catch rate in 2015 was 20.541 crabs per ground-line, 33% above the target level of 13.972. This represented an increase in survey catch rates of about four crabs per groundline, compared to the 2014 survey. The QLD spanner crab total allowable harvest (quota) was set at 1923 t in the 2012-13 and 2013-14 fishing years, 1777 t in 2014-15 and 1631 t in 2015-16. The results from the current analysis rules indicate that the quota for the next two fishing years be retained at the base quota of 1631 t.

Queensland Spanner Crab Fishery : Commercial quota setting for June 2015 – May 2016

The Australian fishery for spanner crabs is the largest in the world, with the larger Queensland (QLD) sector’s landings primarily exported live overseas and GVP valued ~A$5 million per year. Spanner crabs are unique in that they may live up to 15 years, significantly more than blue swimmer crabs (Portunus armatus) and mud crabs (Scylla serrata), the two other important crab species caught in Queensland. Spanner crabs are caught using a flat net called a dilly, on which the crabs becoming entangled via the swimming legs. Quota setting rules are used to assess and adjust total allowable harvest (quota) around an agreed target harvest of 1631 t and capped at a maximum of 2000 t. The quota varies based on catch rate indicators from the commercial fishery and a fishery-independent survey from the previous two years, compared to target reference points. Quota management applies only to ‘Managed Area A’ which includes waters between Rockhampton and the New South Wales (NSW) border. This report has been prepared to inform Fisheries Queensland (Department of Agriculture and Fisheries) and stakeholders of catch trends and the estimated quota of spanner crabs in Managed Area A for the forthcoming quota period (1 June 2015–31 May 2016). The quota calculations followed the methodology developed by the crab fishery Scientific Advisory Group (SAG) between November 2007 and March 2008. The total reported spanner crab harvest was 917 t for the 2014 calendar year, almost all of which was taken from Managed Area A. In 2014, a total of 59 vessels were active in the QLD fishery, the lowest number since the peak in 1994 of 262 vessels. Recent spanner crab harvests from NSW waters have been about 125 t per year. The spanner crab Managed Area A commercial standardised catch rate averaged 0.739 kg per net-lift in 2014, 24% below the target level of 1.043. Mean catch rates declined in the commercial fishery in 2014, although the magnitude of the decreases was highest in the area north of Fraser Island. The NSW–QLD survey catch rate in 2014 was 16.849 crabs per ground-line, 22% above the target level of 13.972. This represented a decrease in survey catch rates of 0.366 crabs per ground-line, compared to the 2013 survey. The Queensland spanner crab total allowable harvest (quota) was set at 1923 t in 2012 and 2013. In 2014, the quota was calculated at the base level of 1631 t. However, given that the 2012 fisheryindependent survey was not undertaken for financial reasons, stakeholders proposed that the total allowable commercial catch (TACC) be decreased to 1777 t; a level that was halfway between the 2012/13 quota of 1923 t and the recommended base quota of 1631 t. The results from the current analysis indicate that the quota for the 2015-2016 financial year be decreased from 1777 t to the base quota of 1631 t.