Generating and evaluating a novel genetic resource in wheat in diverse environments

The principal objective of the project is to develop composite cross populations of wheat based on a wide range of key parent varieties. The parents will be selected partly on past knowledge of successful performance in terms of yield, quality and disease resistance and partly on the basis of molecular ancestry to try to ensure as wide range of diversity as possible. Following parental inter-crossing in all possible combinations, progeny population samples will be exposed to a range of widely different agricultural environments and systems through several seasons of, largely, natural selection. Performance of the population samples will be compared at different stages against both the parents grown as pure stands and as physical mixtures. Our objective is to increase the sustainability and competitiveness of organic and other extensive farming systems by developing genetically diverse wheat populations that will respond rapidly to on-farm selection for improved productivity and yield. It is well established that modern varieties of wheat perform poorly under the conditions and management options encountered in organic farming systems. This is due to a number of factors including poor competition against weeds, narrow resistance against pests and disease, inability to efficiently utilise soil bound nutrients and the lack of genetic flexibility to buffer against environmental variation. To develop a conventional, new wheat breeding programme, from start to release of adapted varieties, would take many years. The approach we propose can deliver this material quickly. This will be achieved through the production of appropriate composite-cross populations of winter wheat. The research will provide material adapted to basic organic conditions that can then be further selected on-farm. This will also be of benefit to non-organic farms as the populations will posses broad resistance to pests and disease and improved competitive ability against weeds, so minimising the need for crop protection inputs. The research will deliver a unique insight into the evolution of genetically diverse wheat populations, under a diverse range of environments, which will allow the elucidation of gene x environment interactions. In addition, it will provide information on the characters of winter wheat that confer improved productivity under a diversity of environmental conditions. Samples of the resulting winter wheat composite cross populations will be placed in the gene bank at the John Innes Centre. Overall objective: To increase the sustainability and competitiveness of both non-organic and organic farming systems by developing genetically diverse wheat populations that will respond rapidly to on-farm selection for improved productivity and yield. 1. To generate six distinct, highly heterogeneous composite-cross populations of winter wheat for further development and selection. The populations will comprise; one with parental material selected for good milling potential, one with parents selected for high yield potential and one comprising both sets of parent material. Each of these populations will then be split to either include or exclude heritable male sterility. 2. To evaluate the performance and evolution of composite-cross populations over time under a diverse range of environmental conditions and identify characteristics that confer improved productivity in these environments. 3. To track the genetic changes that accompany selection, so providing a better understanding of the assemblages of traits that underlie improved productivity in diverse environments. 4. To provide genetically diverse crop material for further selection by farmers and as a resource for future publicly funded research. 5. To disseminate the results to the scientific community and industr

Do 18-month-olds really attribute mental states to others? A critical test

In the research reported here, we investigated whether 18-month-olds would use their own past experience of visual access to attribute perception and consequent beliefs to other people. Infants in this study wore either opaque blindfolds (opaque condition) or trick blindfolds that looked opaque but were actually transparent (trick condition). Then both groups of infants observed an actor wearing one of the same blindfolds that they themselves had experienced, while a puppet removed an object from its location. Anticipatory eye movements revealed that infants who had experienced opaque blindfolds expected the actor to behave in accordance with a false belief about the object's location, but that infants who had experienced trick blindfolds did not exhibit that expectation. Our results suggest that 18-month-olds used self-experience with the blindfolds to assess the actor's visual access and to update her belief state accordingly. These data constitute compelling evidence that 18-month-olds infer perceptual access and appreciate its causal role in altering the epistemic states of other people

Ecological Risk Assessment of Queensland-managed Fisheries in the Gulf of Carpentaria

A report to the Australian Government on the ecological risk assessment requirements set out in Wildlife Trade Operation approvals for Gulf fisheries under Environment Protection and Biodiversity Conservation Act 1999 approvals

Learning the multilinear structure of visual data

Statistical decomposition methods are of paramount importance in discovering the modes of variations of visual data. Probably the most prominent linear decomposition method is the Principal Component Analysis (PCA), which discovers a single mode of variation in the data. However, in practice, visual data exhibit several modes of variations. For instance, the appearance of faces varies in identity, expression, pose etc. To extract these modes of variations from visual data, several supervised methods, such as the TensorFaces, that rely on multilinear (tensor) decomposition (e.g., Higher Order SVD) have been developed. The main drawbacks of such methods is that they require both labels regarding the modes of variations and the same number of samples under all modes of variations (e.g., the same face under different expressions, poses etc.). Therefore, their applicability is limited to well-organised data, usually captured in well-controlled conditions. In this paper, we propose the first general multilinear method, to the best of our knowledge, that discovers the multilinear structure of visual data in unsupervised setting. That is, without the presence of labels. We demonstrate the applicability of the proposed method in two applications, namely Shape from Shading (SfS) and expression transfer

Disentangling the modes of variation in unlabelled data

Statistical methods are of paramount importance in discovering the modes of variation in visual data. The Principal Component Analysis (PCA) is probably the most prominent method for extracting a single mode of variation in the data. However, in practice, visual data exhibit several modes of variations. For instance, the appearance of faces varies in identity, expression, pose etc. To extract these modes of variations from visual data, several supervised methods, such as the TensorFaces relying on multilinear (tensor) decomposition (e.g., Higher Order SVD) have been developed. The main drawbacks of such methods is that they require both labels regarding the modes of variations and the same number of samples under all modes of variations (e.g., the same face under different expressions, poses etc.). Therefore, their applicability is limited to well-organised data, usually captured in well-controlled conditions. In this paper, we propose a novel general multilinear matrix decomposition method that discovers the multilinear structure of possibly incomplete sets of visual data in unsupervised setting (i.e., without the presence of labels). We also propose extensions of the method with sparsity and low-rank constraints in order to handle noisy data, captured in unconstrained conditions. Besides that, a graph-regularised variant of the method is also developed in order to exploit available geometric or label information for some modes of variations. We demonstrate the applicability of the proposed method in several computer vision tasks, including Shape from Shading (SfS) (in the wild and with occlusion removal), expression transfer, and estimation of surface normals from images captured in the wild

Pneumococcal Serotype-Specific Antibodies Persist through Early Childhood after Infant Immunization: Follow-Up from a Randomized Controlled Trial

Background: In a previous UK multi-center randomized study 278 children received three doses of 7-valent (PCV-7) or 13- valent (PCV-13) pneumococcal conjugate vaccine at 2, 4 and 12 months of age. At 13 months of age, most of these children had pneumococcal serotype-specific IgG concentrations $0.35 mg/ml and opsonophagocytic assay (OPA) titers$8. Methods: Children who had participated in the original study were enrolled again at 3.5 years of age. Persistence of immunity following infant immunization with either PCV-7 or PCV-13 and the immune response to a PCV-13 booster at preschool age were investigated. Results: In total, 108 children were followed-up to the age of 3.5 years and received a PCV-13 booster at this age. At least 76% of children who received PCV-7 or PCV-13 in infancy retained serotype-specific IgG concentrations $0.35 mg/ml against each of 5/7 shared serotypes. For serotypes 4 and 18C, persistence was lower at 22–42$0.35 mg/ml against each of 4/6 of the additional PCV-13 serotypes; for serotypes 1 and 3 this proportion was 45% and 52%. In the PCV-7 group these percentages were significantly lower for serotypes 1, 5 and 7F. A pre-school PCV-13 booster was highly immunogenic and resulted in low rates of local and systemic adverse effects. Conclusion: Despite some decline in antibody from 13 months of age, these data suggest that a majority of pre-school children maintain protective serotype-specific antibody concentrations following conjugate vaccination at 2, 4 and 12 months of age. Trial Registration: ClinicalTrials.gov NCT0109547

Learning the multilinear structure of visual data

Statistical decomposition methods are of paramount im- portance in discovering the modes of variations of visual data. Probably the most prominent linear decomposition method is the Principal Component Analysis (PCA), which discovers a single mode of variation in the data. However, in practice, visual data exhibit several modes of variations. For instance, the appearance of faces varies in identity, ex- pression, pose etc. To extract these modes of variations from visual data, several supervised methods, such as the Ten- sorFaces, that rely on multilinear (tensor) decomposition (e.g., Higher Order SVD) have been developed. The main drawbacks of such methods is that they require both labels regarding the modes of variations and the same number of samples under all modes of variations (e.g., the same face under different expressions, poses etc.). Therefore, their ap- plicability is limited to well-organised data, usually cap- tured in well-controlled conditions. In this paper, we pro- pose the first general multilinear method, to the best of our knowledge, that discovers the multilinear structure of visual data in unsupervised setting. That is, without the presence of labels. We demonstrate the applicability of the proposed method in two applications, namely Shape from Shading (SfS) and expression transfer

Hydropyrolysis of high molecular weight organic matter in Murchison

Hydropyrolysis of the Murchison macromolecular material releases polyaromatic compounds including phenanthrene, carbazole, fluoranthene, pyrene, chrysene, perylene, benzoperylene and coronene units with varying degrees of alklyation

Administration of the GnRH-targeted immunocontraceptive vaccine ‘GonaCon™’ to the tammar wallaby, Macropus eugenii: side effects and welfare implications

Snape, M.A., Hinds, L.A. Miller, L.A