Reconstitution of membrane proteins:a GPCR as an example

Membrane proteins are the gatekeepers to the cell and are essential to the function of all cells, controlling the flow of molecules and information across the cell membrane. Much effort has been put into the development of systems for studying membrane proteins in simplified environments that nevertheless mimic their native lipid environment. After isolation and production of purified membrane proteins in detergent, it is often necessary to reconstitute them into a lipid structure such as liposome, nanodisc, or lipodisq. Each of these has the advantage of returning the protein to a defined lipid environment, and the choice of system depends on the application. Regardless of the system to be used, the fundamental process involves the removal of detergent and incorporation of the protein into a stable lipid system. This chapter details methodologies we have developed, mainly focussed on the model G protein-coupled receptor (GPCR) neurotensin receptor 1, and the GPCR-homologue and model, bacteriorhopdopsin

PARENTS, AND NETWORKS ORDINANCES: DETECTION OF STRUCTURE IN THE INTERACTIVE

Neste artigo apresentamos três abordagens usuais para a detecção de padrões em comunidades de plantas e animais que interagem entre si por meio de processos ecológicos como a polinização, a frugivoria ou a herbivoria. Modelos estruturais simples revelam padrões de interação em gradientes, compartimentados ou aninhados; padrões intermediários entre um gradiente e compartimentos também são possíveis. De forma semelhante, o aninhamento no interior de compartimentos gera ainda um modelo estrutural combinado. Os padrões de interação podem ser visualizados e analisados sob a forma de matrizes, redes bipartidas ou gráficos de ordenação obtidos através de uma Análise de Correspondência. Neste trabalho, propomos que as diferenças entre os padrões de interação observados em comunidades representam resultados de diferentes processos ecológicos e evolutivos que atuam sobre tais comunidades. De maneira geral, a compartimentação deveria refletir o histórico da coevolução e os limites impostos às espécies presentes na comunidade, ao passo que diferenças na abundância e na capacidade de dispersão dessas espécies podem gerar uma estrutura aninhada. Portanto, ao invés de ser testada para um modelo estrutural a priori, a estrutura de comunidades ecológicas deve ser confrontada com uma gama inteira de padrões possíveis. Esperamos que as abordagens para a detecção de estruturas em comunidades interativas aqui apresentadas facilitem a elaboração de hipóteses ecológicas mais abrangentes e melhor formuladas.In this paper we present a comprehensive approach to detect structural patterns in interactive communities of plant and animal species, linked by ecological processes such as pollination, frugivory or herbivory. Simple structural models can reveal gradient, compartmented or nested patterns of interaction; intermediate patterns between a gradient and compartments are also possible. Of special potential interest is a combined model, in which nested structures are embedded within compartments. Interaction patterns can be visualized and analyzed in different ways, either as matrices, as bipartite networks or as multivariate sets through correspondence analysis or other ordination procedures. We also propose that differences among patterns represent outcomes of distinct evolutionary and ecological processes that will be especially relevant in highly diversified communities. In general, compartmentation should reflect coevolutionary histories and constraints, whereas differences in species abundances or dispersal rates may generate nestedness. Hence, instead of choosing one model a priori, to be empirically verified, community structure should be probed for a suite of patterns. The comprehensive approach for detecting community structure that we advocate should help to improve ecological hypotheses on compositional patterns in interactive communities, as well as their attendant empirical tests in actual communities.

The contribution of poor and rural populations to national trends in reproductive, maternal, newborn, and child health coverage: analyses of cross-sectional surveys from 64 countries

Background Coverage levels for essential interventions aimed at reducing deaths of mothers and children are increasing steadily in most low-income and middle-income countries. We assessed how much poor and rural populations in these countries are benefiting from national-level progress. Methods We analysed trends in a composite coverage indicator (CCI) based on eight reproductive, maternal, newborn, and child health interventions in 209 national surveys in 64 countries, from Jan 1, 1994, to Dec 31, 2014. Trends by wealth quintile and urban or rural residence were fitted with multilevel modelling. We used an approach akin to the calculation of population attributable risk to quantify the contribution of poor and rural populations to national trends. Findings From 1994 to 2014, the CCI increased by 0·82 percent points a year across all countries; households in the two poorest quintiles had an increase of 0·99 percent points a year, which was faster than that for the three wealthiest quintiles (0·68 percent points). Gains among poor populations were faster in lower-middle-income and uppermiddle- income countries than in low-income countries. Globally, national level increases in CCI were 17·5% faster than they would have been without the contribution of the two poorest quintiles. Coverage increased more rapidly annually in rural (0·93 percent points) than urban (0·52 percent points) areas. Interpretation National coverage gains were accelerated by important increases among poor and rural mothers and children. Despite progress, important inequalities persist, and need to be addressed to achieve the Sustainable Development Goals

Bayesian Bootstrap Inference for the ROC Surface

Accurate diagnosis of disease is of great importance in clinical practice and medical research. The receiver operating characteristic (ROC) surface is a popular tool for evaluating the discriminatory ability of continuous diagnostic test outcomes when there exist three ordered disease classes (e.g., no disease, mild disease, advanced disease). We propose the Bayesian bootstrap, a fully nonparametric method, for conducting inference about the ROC surface and its functionals, such as the volume under the surface. The proposed method is based on a simple, yet interesting, representation of the ROC surface in terms of placement variables. Results from a simulation study demonstrate the ability of our method to successfully recover the true ROC surface and to produce valid inferences in a variety of complex scenarios. An application to data from the Trail Making Test to assess cognitive impairment in Parkinson's disease patients is provided