Functional interaction between the estrogen receptor and CTF1: analysis of the vitellogenin gene B1 promoter in yeast.

Eukaryotic gene expression depends on a complex interplay between the transcriptional apparatus and chromatin structure. We report here a yeast model system for investigating the functional interaction between the human estrogen receptor (hER) and CTF1, a member of the CTF/NFI transcription factor family. We show that a CTF1-fusion protein and the hER transactivate a synthetic promoter in yeast in a synergistic manner. This interaction requires the proline-rich transactivation domain of CTF1. When the natural estrogen-dependent vitellogenin B1 promoter is tested in yeast, CTF1 and CTF1-fusion proteins are unable to activate transcription, and no synergy is observed between hER, which activates the B1 promoter, and these factors. Chromatin structure analysis on this promoter reveals positioned nucleosomes at -430 to -270 (+/-20 bp) and at -270 to - 100 (+/-20 bp) relative to the start site of transcription. The positions of the nucleosomes remain unchanged upon hormone-dependent transcriptional activation of the promoter, and the more proximal nucleosome appears to mask the CTF/NFI site located at - 101 to -114. We conclude that a functional interaction of hER with the estrogen response element located upstream of a basal promoter occurs in yeast despite the nucleosomal organization of this promoter, whereas the interaction of CTF1 with its target site is apparently precluded by a nucleosome.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Value added logistical support service: logistics cost structure and performance in the new concept. Part 3

The aim of this study is to present an Activity-Based Costing spreadsheet tool for analyzing the logistics costs. The tool can be used both by customer-companies and logistics service providers. The study discusses the influence of different activity models on costs. Additionally this paper discusses about the logistical performance across the total supply chain This study is carried out using ananalytical research approach and literature material has been used for supplementing the concerned research approach. Cost structure analysis was based on the theory of activity-based management. This study was outlined to spare part logistics in machine-shop industry. The outlines of logistics services and logisticalperformance discussed in this report are based on the new logistics business concept (LMS-concept), which has been presented earlier in the Valssi-project. Oneof the aims of this study is to increase awareness of different activity modelson logistics costs. The report paints an overall picture about the business environment and requirements for the new logistics concept.

Modeling and developing a customer-oriented configuration process in a mass customization environment

In this thesis the main objective is to examine and model configuration system and related processes. When and where configuration information is created in product development process and how it is utilized in order-delivery process? These two processes are the essential part of the whole configuration system from the information point of view. Empirical part of the work was done as a constructive research inside a company that follows a mass customization approach. Data models and documentation are created for different development stages of the configuration system. A base data model already existed for new structures and relations between these structures. This model was used as the basis for the later data modeling work. Data models include different data structures, their key objects and attributes, and relations between. Representation of configuration rules for the to-be configuration system was defined as one of the key focus point. Further, it is examined how the customer needs and requirements information can be integrated into the product development process. Requirements hierarchy and classification system is presented. It is shown how individual requirement specifications can be connected for physical design structure via features by developing the existing base data model further.

Genetic structure and diversity of Copaifera langsdorffii Desf. in Cerrado fragments of the São Paulo State, Brazil

The loss of large areas of Cerrado (Brazilian savanna) in Brazil can lead to reduced biodiversity and to the extinction of species. Therefore, the present study aimed to investigate the genetic fragility of populations of Copaifera langsdorffii Desf exposed to different anthropic conditions in fragments of Cerrado in the state of São Paulo. The study was carried out in two Experimental Stations operated by the Forest Institute (Assis and Itirapina), in one fully protected conservation unit (Pedregulho) and in one private property (Brotas). Analyses were conducted using leaf samples from 353 adult specimens and eight pairs of microsatellite loci. The number of alleles per locus ranged from 13 to 15 in all populations, but the mean number of effective alleles was approximately half this value (7.2 to 9-1). Observed heterozygosity was significant and lower than the expected in all populations. Consequently, all populations deviated from Hardy-Weinberg expected frequencies. Fixation indexes were significant for all populations, with the Pedregulho population having the lowest value (0.189) and Itirapina having the highest (0.283). The analysis of spatial genetic structure detected family structures at distance classes of 20 to 65 m in the populations studied. No clones were detected in the populations. Estimates of effective population size were low, but the area occupied by each population studied was large enough for conservation, medium and long term. Recent reductions or bottlenecks were detected in all four populations. Mean Gst’ (genetic divergence) indicated that most of the variation was within populations. Cluster structure analysis based on the genotypes detected K= 4 clusters with distinct allele frequencies patterns. The genetic differentiation observed among populations is consistent with the hypothesis of genetic and geographic isolation. Therefore, it is essential to adopt conservation strategies that raise the gene flow between fragments.

Cloning, sequence analysis, and expression of cDNA coding for the major house dust mite allergen, Der f 1, in Escherichia coli

Our objective was to clone, express and characterize adult Dermatophagoides farinae group 1 (Der f 1) allergens to further produce recombinant allergens for future clinical applications in order to eliminate side reactions from crude extracts of mites. Based on GenBank data, we designed primers and amplified the cDNA fragment coding for Der f 1 by nested-PCR. After purification and recovery, the cDNA fragment was cloned into the pMD19-T vector. The fragment was then sequenced, subcloned into the plasmid pET28a(+), expressed in Escherichia coli BL21 and identified by Western blotting. The cDNA coding for Der f 1 was cloned, sequenced and expressed successfully. Sequence analysis showed the presence of an open reading frame containing 966 bp that encodes a protein of 321 amino acids. Interestingly, homology analysis showed that the Der p 1 shared more than 87% identity in amino acid sequence with Eur m 1 but only 80% with Der f 1. Furthermore, phylogenetic analyses suggested that D. pteronyssinus was evolutionarily closer to Euroglyphus maynei than to D. farinae, even though D. pteronyssinus and D. farinae belong to the same Dermatophagoides genus. A total of three cysteine peptidase active sites were found in the predicted amino acid sequence, including 127-138 (QGGCGSCWAFSG), 267-277 (NYHAVNIVGYG) and 284-303 (YWIVRNSWDTTWGDSGYGYF). Moreover, secondary structure analysis revealed that Der f 1 contained an a helix (33.96%), an extended strand (17.13%), a ß turn (5.61%), and a random coil (43.30%). A simple three-dimensional model of this protein was constructed using a Swiss-model server. The cDNA coding for Der f 1 was cloned, sequenced and expressed successfully. Alignment and phylogenetic analysis suggests that D. pteronyssinus is evolutionarily more similar to E. maynei than to D. farinae.

Study on period search methods of variable stars: Application to ASAS and CRTS databases

Study on variable stars is an important topic of modern astrophysics. After the invention of powerful telescopes and high resolving powered CCD’s, the variable star data is accumulating in the order of peta-bytes. The huge amount of data need lot of automated methods as well as human experts. This thesis is devoted to the data analysis on variable star’s astronomical time series data and hence belong to the inter-disciplinary topic, Astrostatistics. For an observer on earth, stars that have a change in apparent brightness over time are called variable stars. The variation in brightness may be regular (periodic), quasi periodic (semi-periodic) or irregular manner (aperiodic) and are caused by various reasons. In some cases, the variation is due to some internal thermo-nuclear processes, which are generally known as intrinsic vari- ables and in some other cases, it is due to some external processes, like eclipse or rotation, which are known as extrinsic variables. Intrinsic variables can be further grouped into pulsating variables, eruptive variables and flare stars. Extrinsic variables are grouped into eclipsing binary stars and chromospheri- cal stars. Pulsating variables can again classified into Cepheid, RR Lyrae, RV Tauri, Delta Scuti, Mira etc. The eruptive or cataclysmic variables are novae, supernovae, etc., which rarely occurs and are not periodic phenomena. Most of the other variations are periodic in nature. Variable stars can be observed through many ways such as photometry, spectrophotometry and spectroscopy. The sequence of photometric observa- xiv tions on variable stars produces time series data, which contains time, magni- tude and error. The plot between variable star’s apparent magnitude and time are known as light curve. If the time series data is folded on a period, the plot between apparent magnitude and phase is known as phased light curve. The unique shape of phased light curve is a characteristic of each type of variable star. One way to identify the type of variable star and to classify them is by visually looking at the phased light curve by an expert. For last several years, automated algorithms are used to classify a group of variable stars, with the help of computers. Research on variable stars can be divided into different stages like observa- tion, data reduction, data analysis, modeling and classification. The modeling on variable stars helps to determine the short-term and long-term behaviour and to construct theoretical models (for eg:- Wilson-Devinney model for eclips- ing binaries) and to derive stellar properties like mass, radius, luminosity, tem- perature, internal and external structure, chemical composition and evolution. The classification requires the determination of the basic parameters like pe- riod, amplitude and phase and also some other derived parameters. Out of these, period is the most important parameter since the wrong periods can lead to sparse light curves and misleading information. Time series analysis is a method of applying mathematical and statistical tests to data, to quantify the variation, understand the nature of time-varying phenomena, to gain physical understanding of the system and to predict future behavior of the system. Astronomical time series usually suffer from unevenly spaced time instants, varying error conditions and possibility of big gaps. This is due to daily varying daylight and the weather conditions for ground based observations and observations from space may suffer from the impact of cosmic ray particles. Many large scale astronomical surveys such as MACHO, OGLE, EROS, xv ROTSE, PLANET, Hipparcos, MISAO, NSVS, ASAS, Pan-STARRS, Ke- pler,ESA, Gaia, LSST, CRTS provide variable star’s time series data, even though their primary intention is not variable star observation. Center for Astrostatistics, Pennsylvania State University is established to help the astro- nomical community with the aid of statistical tools for harvesting and analysing archival data. Most of these surveys releases the data to the public for further analysis. There exist many period search algorithms through astronomical time se- ries analysis, which can be classified into parametric (assume some underlying distribution for data) and non-parametric (do not assume any statistical model like Gaussian etc.,) methods. Many of the parametric methods are based on variations of discrete Fourier transforms like Generalised Lomb-Scargle peri- odogram (GLSP) by Zechmeister(2009), Significant Spectrum (SigSpec) by Reegen(2007) etc. Non-parametric methods include Phase Dispersion Minimi- sation (PDM) by Stellingwerf(1978) and Cubic spline method by Akerlof(1994) etc. Even though most of the methods can be brought under automation, any of the method stated above could not fully recover the true periods. The wrong detection of period can be due to several reasons such as power leakage to other frequencies which is due to finite total interval, finite sampling interval and finite amount of data. Another problem is aliasing, which is due to the influence of regular sampling. Also spurious periods appear due to long gaps and power flow to harmonic frequencies is an inherent problem of Fourier methods. Hence obtaining the exact period of variable star from it’s time series data is still a difficult problem, in case of huge databases, when subjected to automation. As Matthew Templeton, AAVSO, states “Variable star data analysis is not always straightforward; large-scale, automated analysis design is non-trivial”. Derekas et al. 2007, Deb et.al. 2010 states “The processing of xvi huge amount of data in these databases is quite challenging, even when looking at seemingly small issues such as period determination and classification”. It will be beneficial for the variable star astronomical community, if basic parameters, such as period, amplitude and phase are obtained more accurately, when huge time series databases are subjected to automation. In the present thesis work, the theories of four popular period search methods are studied, the strength and weakness of these methods are evaluated by applying it on two survey databases and finally a modified form of cubic spline method is intro- duced to confirm the exact period of variable star. For the classification of new variable stars discovered and entering them in the “General Catalogue of Vari- able Stars” or other databases like “Variable Star Index“, the characteristics of the variability has to be quantified in term of variable star parameters.

The structure of the mixed OH+H2O overlayer on Pt{111}

The structure of the mixed p(3x3)-(3OH+3H(2)O) phase on Pt{111} has been investigated by low-energy electron diffraction-IV structure analysis. The OH+H2O overlayer consists of hexagonal rings of coplanar oxygen atoms interlinked by hydrogen bonds. Lateral shifts of the O atoms away from atop sites result in different O-O separations and hexagons with only large separations (2.81 and 3.02 angstrom) linked by hexagons with alternating separations of 2.49 and 2.81/3.02 A. This unusual pattern is consistent with a hydrogen-bonded network in which water is adsorbed in cyclic rings separated by OH in a p(3x3) structure. The topmost two layers of the Pt atoms relax inwards with respect to the clean surface and both show vertical buckling of up to 0.06 angstrom. In addition, significant shifts away from the lateral bulk positions have been found for the second layer of Pt atoms. (C) 2005 American Institute of Physics.

The surface geometries of the medium and high coverage carbon monoxide structures c(2 × 4)–(2CO) and p(root7- x root7)R19°–(4CO) structure

The surface geometries of the p (root7- x root7)R19degrees-(4CO) and c(2 x 4)-(2CO) layers on Ni {111} and the clean Ni {111} surface were determined by low energy electron diffraction structure analysis. For the clean surface small but significant contractions of d(12) and d(23) (both 2.02 Angstrom) were found with respect to the bulk interlayer distance (2.03 Angstrom). In the c(2 x 4)-(2CO) structure these distances are expanded, with values of d(12) = 2.08 Angstrom and d(23) = 2.06 Angstrom and buckling of 0.08 and 0.02 Angstrom, respectively, in the first and second layer. CO resides near hcp and fcc hollow sites with relatively large lateral shifts away from the ideal positions leading to unequal C-Ni bond lengths between 1.76 and 1.99 Angstrom. For the p(root7- x root7-)R19'-(4CO) layer two best fit geometries were found, which agree in most of their atomic positions, except for one out of four CO molecules, which is either near atop or between bridge and atop. The remaining three molecules reside near hcp and fcc sites, again with large lateral deviations from their ideal positions. The average C Ni bond length for these molecules is, however, the same as for CO on hollow sites at low coverage. The average CNi bond length at hollow sites, the interlayer distances, and buckling in the first Ni layer are similar to the c(2 x 4)(2CO) geometry, only the buckling in the second layer (0.08 Angstrom) is significantly larger. Lateral and vertical shifts of the Ni atoms in the first layer lead to unsymmetric environments for the CO molecules, which can be regarded as an imprint of the chiral p(root7- x root7-)R19degrees lattice geometry onto the substrate.

A simplified, high-yield synthesis, and crystal structure of [tris{bis(2,4,6-triisopropylphenyl)tin}]

The species [{Sn(C2H2iPr3-2,4,6)2}3] has been obtained in a simple, essentially quantitative, synthesis from SnCl2 and ArLi in diethyl ether at low temperature. The crystal structure analysis confirms the trimeric nature of the molecular units but reveals some unusual features. The crystal contains the unusual feature of an asymmetric unit that consists of three units of [{SnAr2}3] in P21/c; the molecular unit is a scalene triangle, showing high consistency between the three molecules, in contrast to analogous trimeric species of silicon or germanium. The SnSn bonds are lengthened (average value 2.942 Å) owing to steric crowding.

The synthesis and properties of [Fe3(CO)11(RCN)] derivatives, and a high-yield general route to mono- and di-substituted derivatives of [Fe3(CO)12]; the crystal and molecular structure of [Fe3(CO)11(NCC6H4-2-Me)]

The clusters [Fe3(CO)11(RCN)] (1: R = Me, C3H5, C6H5, or C6H4-2-Me) have been prepared at low temperature from [Fe3(CO)12] and RCN in the presence of Me3NO. Compounds 1 react essentially quantitatively with a wide range of two-electron donors, L, (viz.: CO, PPh3, P(OMe)3, PPh2H, PPh2Me, PF3, CyNC (Cy = cyclohexyl), P(OEt)3, SbPh3, PBu3, AsPh3, or SnR2 (R = CH(SiMe3)2)) to give [Fe3(CO)11L] (2). In some cases (2), on treatment with Me3NO and then L′ (L′ = a second two-electron donor) yields [Fe3(CO)10LL′] in high yield. The crystal and molecular structures of 1 (L = NCC6H4Me-2) have been determined by a full single crystal structure analysis, and shown to have an axial nitrile coordinated at the unique iron atom, with two CO groups bridging the other two metal atoms.

Synthesis and crystal structure of a novel octa-aqua bridged star-shaped Ni4K complex

A penta-nuclear. star-shaped hetero-metallic compound containing a unique Ni4KO8 core has been synthesized. The X-ray single crystal structure analysis reveals that in the complex, [K(Ni(LH)(2))(4)(OH2)(8)](Br)(ClO4)(8)center dot 11H(2)O (LH=(CH3)(2)HN+(CH2)(3)N=CHC6H4O-) the eight coordinate central K+ ion is encapsulated by four terminal [Ni(LH)(2)](2+) units through the double water bridges between K+ and each Ni(II) ions.

Genetic structure and linkage disequilibrium in landrace populations of barley in Sardinia

Multilocus digenic linkage disequilibria (LD) and their population structure were investigated in eleven landrace populations of barley (Hordeum vulgare ssp. vulgare L.) in Sardinia, using 134 dominant simple-sequence amplified polymorphism markers. The analysis of molecular variance for these markers indicated that the populations were partially differentiated (F ST = 0.18), and clustered into three geographic areas. Consistent with this population pattern, STRUCTURE analysis allocated individuals from a bulk of all populations into four genetic groups, and these groups also showed geographic patterns. In agreement with other molecular studies in barley, the general level of LD was low (13 % of locus pairs, with P < 0.01) in the bulk of 337 lines, and decayed steeply with map distance between markers. The partitioning of multilocus associations into various components indicated that genetic drift and founder effects played a major role in determining the overall genetic makeup of the diversity in these landrace populations, but that epistatic homogenising or diversifying selection was also present. Notably, the variance of the disequilibrium component was relatively high, which implies caution in the pooling of barley lines for association studies. Finally, we compared the analyses of multilocus structure in barley landrace populations with parallel analyses in both composite crosses of barley on the one hand and in natural populations of wild barley on the other. Neither of these serves as suitable mimics of landraces in barley, which require their own study. Overall, the results suggest that these populations can be exploited for LD mapping if population structure is controlled.

Population structure and inbreeding effects on growth traits of Santa Ines sheep in Brazil

The aim of this study was to describe the population structure, inbreeding and to quantify their effect for different weights, of Santa Ines sheep. For this reason, 6490 data of production and 17,097 animals in the pedigree data set were utilized to evaluate birth weight (BW), weight at 60 days (W60) and weight at 180 days (W180). The genetic structure analysis of the population was realized by the software ENDOG (v.4.6.), resulting in some level of inbreeding for 21.72% of the animals in the pedigree data, being 41.02% the maximum value, and average of 10.74% for the inbred individuals. The population average inbreeding was 2.33% and the average relatedness was 0.73%. The effective number of ancestors was 156 animals and the effective number of founders was 211 individuals. A significant depressive effect of the inbreeding can be verified for all traits. The monitored parameters related with the genetic variability on this population must be constant in order to prevent the decrease in the genetic progress. The utilization of a program for directed mating in the present flock is an appropriate alternative to keep the level of inbreeding under control. (C) 2010 Elsevier B.V. All rights reserved.

Expression and purification of human respiratory syncytial virus recombinant fusion protein

The Human Respiratory Syncytial Virus (HRSV) fusion protein (F) was expressed in Escherichia call BL21A using the pET28a vector at 37 degrees C. The protein was purified from the soluble fraction using affinity resin. The structural quality of the recombinant fusion protein and the estimation of its secondary structure were obtained by circular dichroism. Structural models of the fusion protein presented 46% of the helices in agreement with the spectra by circular dichroism analysis. There are only few studies that succeeded in expressing the HRSV fusion protein in bacteria. This is a report on human fusion protein expression in E. call and structure analysis, representing a step forward in the development of fusion protein F inhibitors and the production of antibodies. (c) 2008 Elsevier Inc. All rights reserved.