Cutting edge: influence of the TCR Vbeta domain on the selection of semi-invariant NKT cells by endogenous ligands.

Invariant Valpha14 (Valpha14i) NKT cells are a murine CD1d-dependent regulatory T cell subset characterized by a Valpha14-Jalpha18 rearrangement and expression of mostly Vbeta8.2 and Vbeta7. Whereas the TCR Vbeta domain influences the binding avidity of the Valpha14i TCR for CD1d-alpha-galactosylceramide complexes, with Vbeta8.2 conferring higher avidity binding than Vbeta7, a possible impact of the TCR Vbeta domain on Valpha14i NKT cell selection by endogenous ligands has not been studied. In this study, we show that thymic selection of Vbeta7(+), but not Vbeta8.2(+), Valpha14i NKT cells is favored in situations where endogenous ligand concentration or TCRalpha-chain avidity are suboptimal. Furthermore, thymic Vbeta7(+) Valpha14i NKT cells were preferentially selected in vitro in response to CD1d-dependent presentation of endogenous ligands or exogenously added self ligand isoglobotrihexosylceramide. Collectively, our data demonstrate that the TCR Vbeta domain influences the selection of Valpha14i NKT cells by endogenous ligands, presumably because Vbeta7 confers higher avidity binding.

Immigration and the school system

Immigration is an important problem in many societies, and it has wide-ranging effects on the educational systems of host countries. There is a now a large empirical literature, but very little theoretical work on this topic. We introduce a model of family immigration in a framework where school quality and student outcomes are determined endogenously. This allows us to explain the selection of immigrants in terms of parental motivation and the policies which favor a positive selection. Also, we can study the effect of immigration on the school system and how school quality may self-reinforce immigrants' and natives' choices.

The generator problem for Z-stable C*-algebras

The generator problem was posed by Kadison in 1967, and it remains open until today. We provide a solution for the class of C*-algebras absorbing the Jiang-Su algebra Z tensorially. More precisely, we show that every unital, separable, Z-stable C*-algebra A is singly generated, which means that there exists an element x є A that is not contained in any proper sub-C*- algebra of A. To give applications of our result, we observe that Z can be embedded into the reduced group C*-algebra of a discrete group that contains a non-cyclic, free subgroup. It follows that certain tensor products with reduced group C*-algebras are singly generated. In particular, C*r (F ∞) ⨂ C*r (F ∞) is singly generated.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Exogenous selection shapes germination behaviour and seedling traits of populations at different altitudes in a Senecio hybrid zone.

BACKGROUND AND AIMS: The Senecio hybrid zone on Mt Etna, Sicily, is characterized by steep altitudinal clines in quantitative traits and genetic variation. Such clines are thought to be maintained by a combination of 'endogenous' selection arising from genetic incompatibilities and environment-dependent 'exogenous' selection leading to local adaptation. Here, the hypothesis was tested that local adaptation to the altitudinal temperature gradient contributes to maintaining divergence between the parental species, S. chrysanthemifolius and S. aethnensis. METHODS: Intra- and inter-population crosses were performed between five populations from across the hybrid zone and the germination and early seedling growth of the progeny were assessed. KEY RESULTS: Seedlings from higher-altitude populations germinated better under low temperatures (9-13 °C) than those from lower altitude populations. Seedlings from higher-altitude populations had lower survival rates under warm conditions (25/15 °C) than those from lower altitude populations, but also attained greater biomass. There was no altitudinal variation in growth or survival under cold conditions (15/5 °C). Population-level plasticity increased with altitude. Germination, growth and survival of natural hybrids and experimentally generated F(1)s generally exceeded the worse-performing parent. CONCLUSIONS: Limited evidence was found for endogenous selection against hybrids but relatively clear evidence was found for divergence in seed and seedling traits, which is probably adaptive. The combination of low-temperature germination and faster growth in warm conditions might enable high-altitude S. aethnensis to maximize its growth during a shorter growing season, while the slower growth of S. chrysanthemifolius may be an adaptation to drought stress at low altitudes. This study indicates that temperature gradients are likely to be an important environmental factor generating and maintaining adaptive divergence across the Senecio hybrid zone on Mt Etna.

Healthy child, healthy future - Speech and language therapy: Factsheet for parents 5. Does my child have a voice problem?

This factsheet describes voice disorders such as 'hoarseness' in children and what parents can do to help their child with a voice problem.

The problem of Antimicrobial Resistance in the food chain

Antimicrobial resistance (AMR) associated with the food chain is currently a subject of major interest to many food chain stakeholders. In response safefood commissioned this report to update our knowledge of this area and to raise awareness of the issue. Its primary focus is on the food chain where it impacts consumer health. This review will inform and underpin any future action to be taken by safefood with regard to AMR.

Mechanisms governing lentivirus integration site selection.

An essential step of the life cycle of retroviruses is the stable insertion of a copy of their DNA genome into the host cell genome, and lentiviruses are no exception. This integration step, catalyzed by the viral-encoded integrase, ensures long-term expression of the viral genes, thus allowing a productive viral replication and rendering retroviral vectors also attractive for the field of gene therapy. At the same time, this ability to integrate into the host genome raises safety concerns regarding the use of retroviral-based gene therapy vectors, due to the genomic locations of integration sites. The availability of the human genome sequence made possible the analysis of the integration site preferences, which revealed to be nonrandom and retrovirus-specific, i.e. all lentiviruses studied so far favor integration in active transcription units, while other retroviruses have a different integration site distribution. Several mechanisms have been proposed that may influence integration targeting, which include (i) chromatin accessibility, (ii) cell cycle effects, and (iii) tethering proteins. Recent data provide evidence that integration site selection can occur via a tethering mechanism, through the recruitment of the lentiviral integrase by the cellular LEDGF/p75 protein, both proteins being the two major players in lentiviral integration targeting.

The Road to Recovery: A New Approach to Tackling Scotland's Drug Problem

The Scottish National Drugs Strategy requires the 22 regional Drug Action Teams to prepare and submit to the Scottish Executive annual action plans for tackling drug misuse in their areas. These plans should address national and local priorities, including their contribution to the achievement of national targets. These comprise three parts: Part A provides an overview of the DAT structures and working; Part B provides detailed information on current local services and Part C reports plans for 2003/04.This resource was contributed by The National Documentation Centre on Drug Use.

Liver Transplantation for Neuroendocrine Tumors in Europe-Results and Trends in Patient Selection: A 213-Case European Liver Transplant Registry Study.

OBJECTIVE:: The purpose of this study was to assess outcomes and indications in a large cohort of patients who underwent liver transplantation (LT) for liver metastases (LM) from neuroendocrine tumors (NET) over a 27-year period. BACKGROUND:: LT for NET remains controversial due to the absence of clear selection criteria and the scarcity and heterogeneity of reported cases. METHODS:: This retrospective multicentric study included 213 patients who underwent LT for NET performed in 35 centers in 11 European countries between 1982 and 2009. One hundred seven patients underwent transplantation before 2000 and 106 after 2000. Mean age at the time of LT was 46 years. Half of the patients presented hormone secretion and 55% had hepatomegaly. Before LT, 83% of patients had undergone surgical treatment of the primary tumor and/or LM and 76% had received chemotherapy. The median interval between diagnosis of LM and LT was 25 months (range, 1-149 months). In addition to LT, 24 patients underwent major resection procedures and 30 patients underwent minor resection procedures. RESULTS:: Three-month postoperative mortality was 10%. At 5 years after LT, overall survival (OS) was 52% and disease-free survival was 30%. At 5 years from diagnosis of LM, OS was 73%. Multivariate analysis identified 3 predictors of poor outcome, that is, major resection in addition to LT, poor tumor differentiation, and hepatomegaly. Since 2000, 5-year OS has increased to 59% in relation with fewer patients presenting poor prognostic factors. Multivariate analysis of the 106 cases treated since 2000 identified the following predictors of poor outcome: hepatomegaly, age more than 45 years, and any amount of resection concurrent with LT. CONCLUSIONS:: LT is an effective treatment of unresectable LM from NET. Patient selection based on the aforementioned predictors can achieve a 5-year OS between 60% and 80%. However, use of overly restrictive criteria may deny LT to some patients who could benefit. Optimal timing for LT in patients with stable versus progressive disease remains unclear.