Girolando breed genetic improvement program - Sire summary - Progeny test results - July/2015.

The Girolando breed progeny test was established in 1997, as a result of the partnership between Girolando and Embrapa Dairy Cattle. In 2007, the Programa de Melhoramento Genético da Raça Girolando? PMGG (Genetic Improvement Program of the Girolando Breed) was implemented. Besides interacting with previously existing initiatives of the Girolando Breeders Association, such as the genealogical register service, the progeny test and the dairy control service, the PMGG launched the Linear Evaluation System (SLAG). The main objectives of the PMGG comprises identification of genetically superior individuals, the technically-oriented multiplication of genetics, the evaluation of economic traits and the promotion of sustainable dairy activities. The program have yielded impressive results. The Girolando breed semen sales increases faster than any other breed in Brazil.

A genetic algorithm based economic dispatch (GAED) with environmental constraint optimisation

The role of renewable energy in power systems is becoming more significant due to the increasing cost of fossil fuels and climate change concerns. However, the inclusion of Renewable Energy Generators (REG), such as wind power, has created additional problems for power system operators due to the variability and lower predictability of output of most REGs, with the Economic Dispatch (ED) problem being particularly difficult to resolve. In previous papers we had reported on the inclusion of wind power in the ED calculations. The simulation had been performed using a system model with wind power as an intermittent source, and the results of the simulation have been compared to that of the Direct Search Method (DSM) for similar cases. In this paper we report on our continuing investigations into using Genetic Algorithms (GA) for ED for an independent power system with a significant amount of wind energy in its generator portfolio. The results demonstrate, in line with previous reports in the literature, the effectiveness of GA when measured against a benchmark technique such as DSM.

Integrated genetic analysis systems

The overall objective of this thesis is to integrate a number of micro/nanotechnologies into integrated cartridge type systems to implement such biochemical protocols. Instrumentation and systems were developed to interface such cartridge systems: (i) implementing microfluidic handling, (ii) executing thermal control during biochemical protocols and (iii) detection of biomolecules associated with inherited or infectious disease. This system implements biochemical protocols for DNA extraction, amplification and detection. A digital microfluidic chip (ElectroWetting on Dielectric) manipulated droplets of sample and reagent implementing sample preparation protocols. The cartridge system also integrated a planar magnetic microcoil device to generate local magnetic field gradients, manipulating magnetic beads. For hybridisation detection a fluorescence microarray, screening for mutations associated with CFTR gene is printed on a waveguide surface and integrated within the cartridge. A second cartridge system was developed to implement amplification and detection screening for DNA associated with disease-causing pathogens e.g. Escherichia coli. This system incorporates (i) elastomeric pinch valves isolating liquids during biochemical protocols and (ii) a silver nanoparticle microarray for fluorescent signal enhancement, using localized surface plasmon resonance. The microfluidic structures facilitated the sample and reagent to be loaded and moved between chambers with external heaters implementing thermal steps for nucleic acid amplification and detection. In a technique allowing probe DNA to be immobilised within a microfluidic system using (3D) hydrogel structures a prepolymer solution containing probe DNA was formulated and introduced into the microfluidic channel. Photo-polymerisation was undertaken forming 3D hydrogel structures attached to the microfluidic channel surface. The prepolymer material, poly-ethyleneglycol (PEG), was used to form hydrogel structures containing probe DNA. This hydrogel formulation process was fast compared to conventional biomolecule immobilization techniques and was also biocompatible with the immobilised biomolecules, as verified by on-chip hybridisation assays. This process allowed control over hydrogel height growth at the micron scale.

Genetic engineering of mesenchymal stem cells and its application in human disease therapy.

The use of stem cells for tissue regeneration and repair is advancing both at the bench and bedside. Stem cells isolated from bone marrow are currently being tested for their therapeutic potential in a variety of clinical conditions including cardiovascular injury, kidney failure, cancer, and neurological and bone disorders. Despite the advantages, stem cell therapy is still limited by low survival, engraftment, and homing to damage area as well as inefficiencies in differentiating into fully functional tissues. Genetic engineering of mesenchymal stem cells is being explored as a means to circumvent some of these problems. This review presents the current understanding of the use of genetically engineered mesenchymal stem cells in human disease therapy with emphasis on genetic modifications aimed to improve survival, homing, angiogenesis, and heart function after myocardial infarction. Advancements in other disease areas are also discussed.

Detecting separate time scales in genetic expression data.

BACKGROUND: Biological processes occur on a vast range of time scales, and many of them occur concurrently. As a result, system-wide measurements of gene expression have the potential to capture many of these processes simultaneously. The challenge however, is to separate these processes and time scales in the data. In many cases the number of processes and their time scales is unknown. This issue is particularly relevant to developmental biologists, who are interested in processes such as growth, segmentation and differentiation, which can all take place simultaneously, but on different time scales. RESULTS: We introduce a flexible and statistically rigorous method for detecting different time scales in time-series gene expression data, by identifying expression patterns that are temporally shifted between replicate datasets. We apply our approach to a Saccharomyces cerevisiae cell-cycle dataset and an Arabidopsis thaliana root developmental dataset. In both datasets our method successfully detects processes operating on several different time scales. Furthermore we show that many of these time scales can be associated with particular biological functions. CONCLUSIONS: The spatiotemporal modules identified by our method suggest the presence of multiple biological processes, acting at distinct time scales in both the Arabidopsis root and yeast. Using similar large-scale expression datasets, the identification of biological processes acting at multiple time scales in many organisms is now possible.

An immunoglobulin C kappa-reactive single chain antibody fusion protein induces tolerance through receptor editing in a normal polyclonal immune system.

Understanding immune tolerance mechanisms is a major goal of immunology research, but mechanistic studies have generally required the use of mouse models carrying untargeted or targeted antigen receptor transgenes, which distort lymphocyte development and therefore preclude analysis of a truly normal immune system. Here we demonstrate an advance in in vivo analysis of immune tolerance that overcomes these shortcomings. We show that custom superantigens generated by single chain antibody technology permit the study of tolerance in a normal, polyclonal immune system. In the present study we generated a membrane-tethered anti-Igkappa-reactive single chain antibody chimeric gene and expressed it as a transgene in mice. B cell tolerance was directly characterized in the transgenic mice and in radiation bone marrow chimeras in which ligand-bearing mice served as recipients of nontransgenic cells. We find that the ubiquitously expressed, Igkappa-reactive ligand induces efficient B cell tolerance primarily or exclusively by receptor editing. We also demonstrate the unique advantages of our model in the genetic and cellular analysis of immune tolerance.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Live Imaging of Host-Parasite Interactions in a Zebrafish Infection Model Reveals Cryptococcal Determinants of Virulence and Central Nervous System Invasion.

UNLABELLED: The human fungal pathogen Cryptococcus neoformans is capable of infecting a broad range of hosts, from invertebrates like amoebas and nematodes to standard vertebrate models such as mice and rabbits. Here we have taken advantage of a zebrafish model to investigate host-pathogen interactions of Cryptococcus with the zebrafish innate immune system, which shares a highly conserved framework with that of mammals. Through live-imaging observations and genetic knockdown, we establish that macrophages are the primary immune cells responsible for responding to and containing acute cryptococcal infections. By interrogating survival and cryptococcal burden following infection with a panel of Cryptococcus mutants, we find that virulence factors initially identified as important in causing disease in mice are also necessary for pathogenesis in zebrafish larvae. Live imaging of the cranial blood vessels of infected larvae reveals that C. neoformans is able to penetrate the zebrafish brain following intravenous infection. By studying a C. neoformans FNX1 gene mutant, we find that blood-brain barrier invasion is dependent on a known cryptococcal invasion-promoting pathway previously identified in a murine model of central nervous system invasion. The zebrafish-C. neoformans platform provides a visually and genetically accessible vertebrate model system for cryptococcal pathogenesis with many of the advantages of small invertebrates. This model is well suited for higher-throughput screening of mutants, mechanistic dissection of cryptococcal pathogenesis in live animals, and use in the evaluation of therapeutic agents. IMPORTANCE: Cryptococcus neoformans is an important opportunistic pathogen that is estimated to be responsible for more than 600,000 deaths worldwide annually. Existing mammalian models of cryptococcal pathogenesis are costly, and the analysis of important pathogenic processes such as meningitis is laborious and remains a challenge to visualize. Conversely, although invertebrate models of cryptococcal infection allow high-throughput assays, they fail to replicate the anatomical complexity found in vertebrates and, specifically, cryptococcal stages of disease. Here we have utilized larval zebrafish as a platform that overcomes many of these limitations. We demonstrate that the pathogenesis of C. neoformans infection in zebrafish involves factors identical to those in mammalian and invertebrate infections. We then utilize the live-imaging capacity of zebrafish larvae to follow the progression of cryptococcal infection in real time and establish a relevant model of the critical central nervous system infection phase of disease in a nonmammalian model.

Plastid microsatellites for the study of genetic variability in the widespread Cephalanthera longifolia, C. damasonium and C. rubra (Neottieae, Orchidaceae), and cross-amplification in other Cephalanthera species

Plastid microsatellite loci developed for Cephalanthera longifolia were used to examine the level of genetic variation within and between populations of the three widespread Cephalanthera species (C. damasonium, C. longifolia and C. rubra). The most detailed sampling was in C. longifolia (42 localities from Ireland to China; 147 individuals). Eight haplotypes were detected. One was detected in the vast majority of individuals and occurred from Ireland to Iran. Three others were only found in Europe (Ireland to Italy, England to Italy and Austria to Croatia). Two were only found in the Middle East and two only in Asia. In C. damasonium, 21 individuals from 10 populations (England to Turkey) were sampled. Only one haplotype was detected. In C. rubra, 34 individuals from eight populations (England to Turkey) were sampled. Although it was not possible to amplify all loci for all samples of this species, nine haplotypes were detected. Short alleles for the trnS-trnG region found in two populations of C. rubra were characterized by sequencing and were caused by deletions of 26 and 30 base pairs. At this level of sampling, it appears that C. rubra shows the greatest genetic variability. Cephalanthera longifolia, C. rubra and C. damasonium have previously been characterized as outbreeding, outbreeding with facultative vegetative reproduction and inbreeding, respectively. Patterns of genetic variation here are discussed in the light of these reproductive system differences. The primers used in these three species of Cephalanthera were also demonstrated to amplify these loci in another five species (C. austiniae, C. calcarata, C. epipactoides, C. falcata and C. yunnanensis). Although it is sometimes treated as a synonym of C. damasonium, the single sample of C. yunnanensis from China had a markedly different haplotype from that found in C. damasonium. All three loci were successfully amplified in two achlorophyllous, myco-heterotrophic species, C. austinae and C. calcarata. © 2010 The Linnean Society of London.

Exploring genetic alternative concepts for FUELGEN

This paper describes new crossover operators and mutation strategies for the FUELGEN system, a genetic algorithm which designs fuel loading patterns for nuclear power reactors. The new components are applications of new ideas from recent research in genetic algorithms. They are designed to improve the performance of FUELGEN by using information in the problem as yet not made explicit in the genetic algorithm's representation. The paper introduces new developments in genetic algorithm design and explains how they motivate the proposed new components.

Predominance of genetic monogamy by females in a hammerhead shark, Sphyrna tiburo: Implications for shark conservation.

There is growing interest in the mating systems of sharks and their relatives (Class Chondrichthyes) because these ancient fishes occupy a key position in vertebrate phylogeny and are increasingly in need of conservation due to widespread overexploitation. Based on precious few genetic and field observational studies, current speculation is that polyandrous mating strategies and multiple paternity may be common in sharks as they are in most other vertebrates. Here, we test this hypothesis by examining the genetic mating system of the bonnethead shark, Sphyrna tiburo, using microsatellite DNA profiling of 22 litters (22 mothers, 188 embryos genotyped at four polymorphic loci) obtained from multiple locations along the west coast of Florida. Contrary to expectations based on the ability of female S. tiburo to store sperm, the social nature of this species and the 100% multiple paternity observed in two other coastal shark species, over 81% of sampled bonnethead females produced litters sired by a single male (i.e. genetic monogamy). When multiple paternity occurred in S. tiburo, there was an indication of increased incidence in larger mothers with bigger litters. Our data suggest that sharks may exhibit complex genetic mating systems with a high degree of interspecific variability, and as a result some species may be more susceptible to loss of genetic variation in the face of escalating fishing pressure. Based on these findings, we suggest that knowledge of elasmobranch mating systems should be an important component of conservation and management programmes for these heavily exploited species.

Reproductive isolation and genetic differentiation of ferox trout from sympatric brown trout in Loch Aweand Loch Laggan, Scotland

Molecular marker studies reported here, involving allozymes, mitochondrial DNA and microsatellites, demonstrate that ferox brown trout Salmo trutta in Lochs Awe and Laggan, Scotland, are reproductively isolated and genetically distinct from co-occurring brown trout. Ferox were shown to spawn primarily, and possibly solely, in a single large river in each lake system making them particularly vulnerable to environmental changes. Although a low level of introgression seems to have occurred with sympatric brown trout, possibly as a result of human-induced habitat alterations and stocking, ferox trout in these two lakes meet the requirements for classification as a distinct biological, phylogenetic and morphological species. It is proposed that the scientific name Salmo ferox Jardine, 1835, as already applied to Lough Melvin (Ireland) ferox, should be extended to Awe and Laggan ferox.

Aircraft Cost Modelling using the Genetic Causal Technique within a Systems Engineering Approach

The paper is primarily concerned with the modelling of aircraft manufacturing cost. The aim is to establish an integrated life cycle balanced design process through a systems engineering approach to interdisciplinary analysis and control. The cost modelling is achieved using the genetic causal approach that enforces product family categorisation and the subsequent generation of causal relationships between deterministic cost components and their design source. This utilises causal parametric cost drivers and the definition of the physical architecture from the Work Breakdown Structure (WBS) to identify product families. The paper presents applications to the overall aircraft design with a particular focus on the fuselage as a subsystem of the aircraft, including fuselage panels and localised detail, as well as engine nacelles. The higher level application to aircraft requirements and functional analysis is investigated and verified relative to life cycle design issues for the relationship between acquisition cost and Direct Operational Cost (DOC), for a range of both metal and composite subsystems. Maintenance is considered in some detail as an important contributor to DOC and life cycle cost. The lower level application to aircraft physical architecture is investigated and verified for the WBS of an engine nacelle, including a sequential build stage investigation of the materials, fabrication and assembly costs. The studies are then extended by investigating the acquisition cost of aircraft fuselages, including the recurring unit cost and the non-recurring design cost of the airframe sub-system. The systems costing methodology is facilitated by the genetic causal cost modeling technique as the latter is highly generic, interdisciplinary, flexible, multilevel and recursive in nature, and can be applied at the various analysis levels required of systems engineering. Therefore, the main contribution of paper is a methodology for applying systems engineering costing, supported by the genetic causal cost modeling approach, whether at a requirements, functional or physical level.

System oriented neural networks - Problem formulation, methodology and application

A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The objective is to improve the transparency of the neural networks, i.e. to produce physically meaningful