The transcriptome of lung tumor-infiltrating dendritic cells reveals a tumor-supporting phenotype and a microRNA signature with negative impact on clinical outcome

Targeting immunomodulatory pathways has ushered a new era in lung cancer therapy. Further progress requires deeper insights into the biology of immune cells in th

Methodological issues in cross-cultural research

Regardless of whether the research goal is to establish cultural universals or to identify and explain cross-cultural differences, researchers need measures that are comparable across different cultures when conducting cross-cultural studies. In this chapter, we describe two major strategies for enhancing cross-cultural comparability. First, we discuss a priori methods to ensure the comparability of data in cross-cultural surveys. In particular, we review findings on cross-cultural differences based on the psychology of survey response and provide suggestions on how to deal with these cultural differences in the survey design stage. Second, we discuss post hoc methods to ascertain data comparability and enable comparisons in the presence of threats to equivalence

Enhanced selectivity and search speed for method development using one-segment-per-component optimization strategies

Linear gradient programs are very frequently used in reversed phase liquid chromatography to enhance the selectivity compared to isocratic separations. Multi-linear gradient programs on the other hand are only scarcely used, despite their intrinsically larger separation power. Because the gradient-conformity of the latest generation of instruments has greatly improved, a renewed interest in more complex multi-segment gradient liquid chromatography can be expected in the future, raising the need for better performing gradient design algorithms. We explored the possibilities of a new type of multi-segment gradient optimization algorithm, the so-called "one-segment-per-group-of-components" optimization strategy. In this gradient design strategy, the slope is adjusted after the elution of each individual component of the sample, letting the retention properties of the different analytes auto-guide the course of the gradient profile. Applying this method experimentally to four randomly selected test samples, the separation time could on average be reduced with about 40% compared to the best single linear gradient. Moreover, the newly proposed approach performed equally well or better than the multi-segment optimization mode of a commercial software package. Carrying out an extensive in silico study, the experimentally observed advantage could also be generalized over a statistically significant amount of different 10 and 20 component samples. In addition, the newly proposed gradient optimization approach enables much faster searches than the traditional multi-step gradient design methods. © 2014 Elsevier B.V