The role of diet on breast cancer risk is not well elucidated but animal food sources may play a role through, for example, the pathway of the insulin-like growth factor 1 system or cholesterol metabolism. The aim of this study was to evaluate the association between animal foods and the risk of postmenopausal breast cancer. This study was embedded in the Rotterdam Study, a population-based prospective cohort study of subjects aged 55 years and over (61 % female). Dietary intake of different animal foods was assessed at baseline using a validated FFQ and adjusted for energy intake using the residual method. We performed Cox proportional hazards modelling to analyse the association between the intake of the different food sources and breast cancer risk after adjustment for socio-demographic, lifestyle and metabolic factors. During a median follow-up of 17 years, we identified 199 cases of breast cancer (6·2 %) among 3209 women. After adjustment for multiple confounders, no consistent association was found between the intake of red meat intake, poultry, fish or dairy products and breast cancer risk. However, we found that egg intake was significantly associated with a higher risk of breast cancer (hazard ratioQ4 v. Q1: 1·83; 95 % CI 1·20, 2·79; Ptrend=0·01). In conclusion, this study found that dietary egg intake but no other animal foods was associated with a higher risk of postmenopausal breast cancer. Further research on the potential mechanisms underlying this association is warranted.

It is well known that deterministic two-dimensional marine ice sheets can only be stable if the grounding line is positioned at a sufficiently steep, downward sloping bedrock. When bedrock conditions favour instabilities, multiple stable ice sheet profiles may occur. Here, we employ continuation techniques to examine the sensitivity of a two-dimensional marine ice sheet to stochastic noise representing short time scale variability, either in the accumulation rate or in the sea level height. We find that in unique regimes, the position of the grounding line is most sensitive to noise in the accumulation rate and can explain excursions observed in field measurements. In the multiple equilibrium regime, there is a strong asymmetry in transition probabilities between the different ice sheet states, with a strong preference to switch to the branch with a steeper bedrock slope.

The parent–child attachment relationship plays an important role in the development of the infant's stress regulation system. However, genetic and epigenetic factors such as FK506 binding protein 51 (FKBP5) genotype and DNA methylation have also been associated with hypothalamus–pituitary–adrenal axis functioning. In the current study, we examined how parent–child dyadic regulation works in concert with genetic and epigenetic aspects of stress regulation. We study the associations of attachment, extreme maternal insensitivity, FKBP5 single nucleotide polymorphism 1360780, and FKBP5 methylation, with cortisol reactivity to the Strange Situation Procedure in 298 14-month-old infants. The results indicate that FKBP5 methylation moderates the associations of FKBP5 genotype and resistant attachment with cortisol reactivity. We conclude that the inclusion of epigenetics in the field of developmental psychopathology may lead to a more precise picture of the interplay between genetic makeup and parenting in shaping stress reactivity.

At the first meeting of the newly formed Commission on Spectrophotometry, at Paris in 1935, a thorough discussion, aided by several reports, took place on the principles of this branch of astrophysics. So it will be sufficient now to treat only such special points of theory and practice as have won interest by researches of the last few years.

The rise of Holocene (ground)water level as a function of relative sea-level rise has been extensively investigated in the western Netherlands, whereas few studies focused on the Flevo lagoon in the central Netherlands. In this study, all available 14C dates from the base of basal peat overlying the top of compaction-free Pleistocene sand in the former Flevo lagoon were evaluated in order to reconstruct water-level rise for the period 5300-2000 cal. yr BC. The present basal peat 14C data set from Flevoland consists of two subsets: (1) the largely new Almere data (41 dates) representing the southern part of the former Flevo lagoon, with 26 dates especially carried out for this study, and (2) the existing Schokland data (21 dates) representing the eastern part of the lagoon. The Schokland area is located about 50 km from the Almere area. The quality of all basal peat time-depth data was palaeo-ecologically and geologically evaluated, all 14C dates were calibrated to the same standards, and error margins of age and altitude determination were estimated. After plotting the data as error boxes in time-depth graphs, lower limit curves for water-level rise were constructed for both data sets. Comparison with the mean sea-level curve for The Netherlands (Van de Plassche, 1982) suggests that water-level rise in the Almere area between 5300 and 2000 cal. yr BC corresponded closely to the rise in mean sea level. The same holds for the Schokland area for the period 5000-4200 cal. yr BC. For the period 4200-2000 cal. yr BC, however, the Schokland data suggest water-level rise to have been slower than mean sea-level rise, leading to local water levels apparently below mean sea level, which is virtually impossible. Hypothetical explanations for this discrepancy include: errors and uncertainties in mean sea-level and local water-level reconstruction, basin subsidence and temporal differences in intra-coastal tidal damping. The presently available data are inconclusive at this point and Holocene water-level rise in the Flevo lagoon awaits further investigations.

Breeding programmes for livestock require economic weights for traits that reflect the most profitable animal in a given production system, which affect the response in each trait after selection. The profitability of sheep production systems is affected by changes in pasture growth as well as grain, meat and wool prices between seasons and across years. Annual pasture growth varies between regions within Australia’s Mediterranean climate zone from low growth with long periods of drought to high growth with shorter periods of drought. Therefore, the objective of this study was to assess whether breeding objectives need to be adapted for regions, depending on how reliable the pasture growth is across years. We modelled farms with Merino sheep bred for wool and meat in 10 regions in Western Australia. Across these 10 regions, mean annual pasture growth decreased, and the CV of annual pasture growth increased as pasture growth for regions became less reliable. We calculated economic values for nine traits, optimising management across 11 years, including variation for pasture growth and wool, meat and grain prices between and within years from 2002 to 2012. These economic values were used to calculate responses to selection for each trait for the 10 regions. We identified two potential breeding objectives, one for regions with low or high reliability and the other for regions with medium reliability of pasture growth. Breeding objectives for high or low pasture growth reliability had more emphasis on live weight traits and number of lambs weaned. Breeding objectives for medium reliability of pasture growth had more emphasis on decreasing fibre diameter. Relative economic weights for fleece weight did not change across the regions. Regions with low or high pasture reliability had similar breeding objectives and response to selection, because the relationship between the economic values and CV of pasture growth were not linear for live weight traits and the number of lambs weaned. This non-linearity was caused by differences in distribution of pasture growth between regions, particularly during summer and autumn, when ewes were pregnant, with increases in energy requirements affecting the value of lambs weaned. In addition, increasing live weight increased the intake capacity of sheep, which meant that more poor quality pasture could be consumed during summer and autumn, which had more value in regions with low and high pasture reliability. We concluded that breeding values for sheep production systems should be customised depending on the reliability of pasture growth between years.

There is evidence for health benefits from ‘Palaeolithic’ diets; however, there are a few data on the acute effects of rationally designed Palaeolithic-type meals. In the present study, we used Palaeolithic diet principles to construct meals comprising readily available ingredients: fish and a variety of plants, selected to be rich in fibre and phyto-nutrients. We investigated the acute effects of two Palaeolithic-type meals (PAL 1 and PAL 2) and a reference meal based on WHO guidelines (REF), on blood glucose control, gut hormone responses and appetite regulation. Using a randomised cross-over trial design, healthy subjects were given three meals on separate occasions. PAL2 and REF were matched for energy, protein, fat and carbohydrates; PAL1 contained more protein and energy. Plasma glucose, insulin, glucagon-like peptide-1 (GLP-1), glucose-dependent insulinotropic peptide (GIP) and peptide YY (PYY) concentrations were measured over a period of 180 min. Satiation was assessed using electronic visual analogue scale (EVAS) scores. GLP-1 and PYY concentrations were significantly increased across 180 min for both PAL1 (P= 0·001 and P< 0·001) and PAL2 (P= 0·011 and P= 0·003) compared with the REF. Concomitant EVAS scores showed increased satiety. By contrast, GIP concentration was significantly suppressed. Positive incremental AUC over 120 min for glucose and insulin did not differ between the meals. Consumption of meals based on Palaeolithic diet principles resulted in significant increases in incretin and anorectic gut hormones and increased perceived satiety. Surprisingly, this was independent of the energy or protein content of the meal and therefore suggests potential benefits for reduced risk of obesity.

In livestock, many studies have reported the results of imputation to 50k single nucleotide polymorphism (SNP) genotypes for animals that are genotyped with low-density SNP panels. The objective of this paper is to review different measures of correctness of imputation, and to evaluate their utility depending on the purpose of the imputed genotypes. Across studies, imputation accuracy, computed as the correlation between true and imputed genotypes, and imputation error rates, that counts the number of incorrectly imputed alleles, are commonly used measures of imputation correctness. Based on the nature of both measures and results reported in the literature, imputation accuracy appears to be a more useful measure of the correctness of imputation than imputation error rates, because imputation accuracy does not depend on minor allele frequency (MAF), whereas imputation error rate depends on MAF. Therefore imputation accuracy can be better compared across loci with different MAF. Imputation accuracy depends on the ability of identifying the correct haplotype of a SNP, but many other factors have been identified as well, including the number of genotyped immediate ancestors, the number of animals with genotypes at the high-density panel, the SNP density on the low- and high-density panel, the MAF of the imputed SNP and whether imputed SNP are located at the end of a chromosome or not. Some of these factors directly contribute to the linkage disequilibrium between imputed SNP and SNP on the low-density panel. When imputation accuracy is assessed as a predictor for the accuracy of subsequent genomic prediction, we recommend that: (1) individual-specific imputation accuracies should be used that are computed after centring and scaling both true and imputed genotypes; and (2) imputation of gene dosage is preferred over imputation of the most likely genotype, as this increases accuracy and reduces bias of the imputed genotypes and the subsequent genomic predictions.

This study aimed to identify genetic evaluation models (GEM) to accurately select cattle for milk production when only limited data are available. It is based on a data set from the Pakistani Sahiwal progeny testing programme which includes records from five government herds, each consisting of 100 to 350 animals, with lactation records dating back to 1968. Different types of GEM were compared, namely: (1) multivariate v. repeatability model when using the first three lactations, (2) an animal v. a sire model, (3) different fixed effects models to account for effects such as herd, year and season; and (4) fitting a model with genetic parameters fixed v. estimating the genetic parameters as part of the model fitting process. Two methods were used for the comparison of models. The first method used simulated data based on the Pakistani progeny testing system and compared estimated breeding values with true breeding values. The second method used cross-validation to determine the best model in subsets of actual Australian herd-recorded data. Subsets were chosen to reflect the Pakistani data in terms of herd size and number of herds. Based on the simulation and the cross-validation method, the multivariate animal model using fixed genetic parameters was generally the superior GEM, but problems arise in determining suitable values for fixing the parameters. Using mean square error of prediction, the best fixed effects structure could not be conclusively determined. The simulation method indicated the simplest fixed effects structure to be superior whereas in contrast, the cross-validation method on actual data concluded that the most complex one was the best. In conclusion it is difficult to propose a universally best GEM that can be used in any data set of this size. However, some general recommendations are that it is more appropriate to estimate the genetic parameters when evaluating for selection purposes, the animal model was superior to the sire model and that in the Pakistani situation the repeatability model is more suitable than a multivariate.

We investigated the extent to which the geographic proximity of adult children affected the relocations of older people in the Netherlands in 2008. A major contribution of this study is the examination of the differentiation between relocation to care institutions and elsewhere. Data from the Dutch population register linked to complementary datasets were analysed for nearly one million inhabitants aged 75 and above, using multinomial logistic regression models to estimate the effects of intergenerational proximity and of other factors on the propensity to relocate to an institution and elsewhere. An interaction of proximity with partnership status as an indicator of the presence of an important care provider was considered. We found that older people were less likely to move elsewhere when their children were living very close by, and were more likely to do so when their children were living farther away. Having children living close was negatively associated with the likelihood of moving to a care institution. Very close proximity had an additional negative effect on the propensity of older people with a partner to relocate elsewhere whereas the negative effect was less for older people without a partner on moving elsewhere. Our findings did, however, show that (recently) widowed people were more likely to move elsewhere when their children were living more than 40 kilometres away.

This work was part of the EU RobustMilk project. In this work package, we have focused on two aspects of robustness, micro- and macro-environmental sensitivity and applied these to somatic cell count (SCC), one aspect of milk quality. We showed that it is possible to combine both categorical and continuous descriptions of the environment in one analysis of genotype by environment interaction. We also developed a method to estimate genetic variation in residual variance and applied it to both simulated and a large field data set of dairy cattle. We showed that it is possible to estimate genetic variation in both micro- and macro-environmental sensitivity in the same data, but that there is a need for good data structure. In a dairy cattle example, this would mean at least 100 bulls with at least 100 daughters each. We also developed methods for improved genetic evaluation of SCC. We estimated genetic variance for some alternative SCC traits, both in an experimental herd data and in field data. Most of them were highly correlated with subclinical mastitis (>0.9) and clinical mastitis (0.7 to 0.8), and were also highly correlated with each other. We studied whether the fact that animals in different herds are differentially exposed to mastitis pathogens could be a reason for the low heritabilities for mastitis, but did not find strong evidence for that. We also created a new model to estimate breeding values not only for the probability of getting mastitis but also for recovering from it. In a progeny-testing situation, this approach resulted in accuracies of 0.75 and 0.4 for these two traits, respectively, which means that it is possible to also select for cows that recover more quickly if they get mastitis.