Modelling the cohort effect in CBD models using a piecewise linear approach

This paper discusses a new pattern of mortality model which is built on the form and knowledge of the two-factor mortality model named after its designers Cairns, Blake and Dowd (2006). This model – the CBD model – is widely used and has been extended by the authors in a number of ways, including by the use of a cohort effect. In this paper, we propose a range of new parsimonious approaches to model the cohort effect. Instead of adding a cohort factor to an age-period model we model the effect by building discontinuities into the pattern of rates within each year. The fit of the resulting models is close to that available from the best of the CBD derivatives

The volatility of consumption and output with increasing industrialization

Consumption is more volatile than output in developing countries while it is less volatile than output in developed economies. This paper shows that the relatively large home sector in developing economies contributes to this difference, and the driving force for this difference is technology. Thus this paper suggests that volatile market consumption is almost inevitable at the start of industrialization, when the technology level in the market sector is just above that of the home sector.Consumption volatility; Industralization

Secretory RING finger proteins function as effectors in a grapevine galling insect.

BackgroundAll eukaryotes share a conserved network of processes regulated by the proteasome and fundamental to growth, development, or perception of the environment, leading to complex but often predictable responses to stress. As a specialized component of the ubiquitin-proteasome system (UPS), the RING finger domain mediates protein-protein interactions and displays considerable versatility in regulating many physiological processes in plants. Many pathogenic organisms co-opt the UPS through RING-type E3 ligases, but little is known about how insects modify these integral networks to generate novel plant phenotypes.ResultsUsing a combination of transcriptome sequencing and genome annotation of a grapevine galling species, Daktulosphaira vitifoliae, we identified 138 putatively secretory protein RING-type (SPRINGs) E3 ligases that showed structure and evolutionary signatures of genes under rapid evolution. Moreover, the majority of the SPRINGs were more expressed in the feeding stage than the non-feeding egg stage, in contrast to the non-secretory RING genes. Phylogenetic analyses indicated that the SPRINGs formed clusters, likely resulting from species-specific gene duplication and conforming to features of arthropod host-manipulating (effector) genes. To test the hypothesis that these SPRINGs evolved to manipulate cellular processes within the plant host, we examined SPRING interactions with grapevine proteins using the yeast two-hybrid assay. An insect SPRING interacted with two plant proteins, a cellulose synthase, CSLD5, and a ribosomal protein, RPS4B suggesting secretion reprograms host immune signaling, cell division, and stress response in favor of the insect. Plant UPS gene expression during gall development linked numerous processes to novel organogenesis.ConclusionsTaken together, D. vitifoliae SPRINGs represent a novel gene expansion that evolved to interact with Vitis hosts. Thus, a pattern is emerging for gall forming insects to manipulate plant development through UPS targeting

Cross-screening in observational studies that test many hypotheses

We discuss observational studies that test many causal hypotheses, either hypotheses about many outcomes or many treatments. To be credible an observational study that tests many causal hypotheses must demonstrate that its conclusions are neither artifacts of multiple testing nor of small biases from nonrandom treatment assignment. In a sense that needs to be defined carefully, hidden within a sensitivity analysis for nonrandom assignment is an enormous correction for multiple testing: in the absence of bias, it is extremely improbable that multiple testing alone would create an association insensitive to moderate biases. We propose a new strategy called "cross-screening", different from but motivated by recent work of Bogomolov and Heller on replicability. Cross-screening splits the data in half at random, uses the first half to plan a study carried out on the second half, then uses the second half to plan a study carried out on the first half, and reports the more favorable conclusions of the two studies correcting using the Bonferroni inequality for having done two studies. If the two studies happen to concur, then they achieve Bogomolov-Heller replicability; however, importantly, replicability is not required for strong control of the family-wise error rate, and either study alone suffices for firm conclusions. In randomized studies with a few hypotheses, cross-split screening is not an attractive method when compared with conventional methods of multiplicity control, but it can become attractive when hundreds or thousands of hypotheses are subjected to sensitivity analyses in an observational study. We illustrate the technique by comparing 46 biomarkers in individuals who consume large quantities of fish versus little or no fish.Comment: 33 pages, 2 figures, 5 table