ZAP-70 restoration in mice by in vivo thymic electroporation.

Viral and non-viral vectors have been developed for gene therapy, but their use is associated with unresolved problems of efficacy and safety. Efficient and safe methods of DNA delivery need to be found for medical application. Here we report a new monopolar system of non-viral electro-gene transfer into the thymus in vivo that consists of the local application of electrical pulses after the introduction of the DNA. We assessed the proof of concept of this approach by correcting ZAP-70 deficient severe combined immunodeficiency (SCID) in mice. The thymic electro-gene transfer of the pCMV-ZAP-70-IRES-EGFP vector in these mice resulted in rapid T cell differentiation in the thymus with mature lymphocytes detected by three weeks in secondary lymphoid organs. Moreover, this system resulted in the generation of long-term functional T lymphocytes. Peripheral reconstituted T cells displayed a diversified T cell receptor (TCR) repertoire, and were responsive to alloantigens in vivo. This process applied to the thymus could represent a simplified and effective alternative for gene therapy of T cell immunodeficiencies.

Monotonically normal topological vector spaces are stratifiable

We prove that for any Hausdorff topological vector space E over the field R there exists A subset of E such that E is homeomorphic to a subset of A x R and A x R is homeomorphic to a subset of E. Using this fact we prove that E is monotonically normal if and only if E is stratifiable.

Support vector machine classification for large data sets via minimum enclosing ball clustering

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.

Experimental assessment of oyster transfers as a vector for macroalgal introductions

Introduction of non-indigenous species can alter marine communities and ecosystems. In shellfish farming, transfer of livestock, especially oysters, is a common practice and potentially constitutes a pathway for non-indigenous introductions. Many species of seaweeds are believed to have been accidentally introduced in association with these transfers, but there is little direct evidence.

Hull fouling on commercial ships as a vector of macroalgal introduction

Hull fouling is thought to have been the vector of introduction for many algal species. We studied ships arriving at a Mediterranean harbour to clarify the present role of commercial cargo shipping in algal introductions. A total of 31 macroalgal taxa were identified from 22 sampled hulls. The majority of records (58%) were of species with a known cosmopolitan geographical distribution. Due to a prevalence of cosmopolitan species and a high turnover of fouling communities, species composition of assemblages did not appear to be influenced by the area of origin, length of ship or age of coating. In the light of the present results, hull fouling on standard trading commercial vessels does not seem to pose a significant risk for new macroalgal species introductions. However, a high proportion of non-cosmopolitan species found on a ship with non-toxic coating may modify this assessment, especially in the light of the increasing use of such coatings and the potential future changes in shipping routes.

A New Concept of Interprofessional Education in Planning Programmes: Reflections on Healthy Urban Planning Project

Although widely debated, some of the defining professional characteristics of planners appear to be competencies in co-ordination, mediation and multidisciplinary working. Despite this, there is little pedagogical reflection on how interprofessional skills are promoted in planning programmes. This paper reflects on the experience of bringing together undergraduate students from medicine and planning to explore the concept of Healthy Urban Planning in a real life context of an urban motorway extension. This reveals a number of unexpected outcomes of such collaboration and points to the value of promoting interprofessional education, both as a way of increasing interest in some of the key challenges now facing society and in order to induce greater professional reflection amongst our students.

A New Concept of Interprofessional Education in Planning Programmes: Reflections on a Healthy Urban Planning Project

Although widely debated, some of the defining professional characteristics of planners appear to be competencies in co-ordination, mediation and multidisciplinary working. Despite this, there is little pedagogical reflection on how interprofessional skills are promoted in planning programmes. This paper reflects on the experience of bringing together undergraduate students from medicine and planning to explore the concept of Healthy Urban Planning in a real life context of an urban motorway extension. This reveals a number of unexpected outcomes of such collaboration and points to the value of promoting interprofessional education, both as a way of increasing interest in some of the key challenges now facing society and in order to induce greater professional reflection amongst students.

A hierarchical multiclass support vector machine incorporated with holistic triple learning units

This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs a decision tree up to a depth of A leaf node of the decision tree is allowed to be placed with a holistic triple learning unit whose generalisation abilities are assessed and approved. Meanwhile, the remaining nodes in the decision tree each accommodate a standard binary SVM classifier. The holistic triple classifier is a regression model trained on three classes, whose training algorithm is originated from a recently proposed implementation technique, namely the least-squares support vector machine (LS-SVM). A major novelty with the holistic triple classifier is the reduced number of support vectors in the solution. For the resultant HTL-SVM, an upper bound of the generalisation error can be obtained. The time complexity of training the HTL-SVM is analysed, and is shown to be comparable to that of training the one-versus-one (1-vs.-1) SVM, particularly on small-scale datasets. Empirical studies show that the proposed HTL-SVM achieves competitive classification accuracy with a reduced number of support vectors compared to the popular 1-vs-1 alternative.