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Intervening CIV absorbers are key tracers of metal-enriched gas in galaxy halos over cosmic time. Previous studies suggest that the CIV cosmic mass density ($\Omega_{\rm CIV}$) decreases slowly over 1.5 $\lesssim z\lesssim$ 5 before declining rapidly at $z\gtrsim$ 5, but the cause of this downturn is poorly understood. We characterize the $\Omega_{\rm CIV}$ evolution over 4.3 $\lesssim z\lesssim$ 6.3 using 260 absorbers found in 42 XSHOOTER spectra of $z\sim$ 6 quasars, of which 30 come from the ESO Large Program XQR-30. The large sample enables us to robustly constrain the rate and timing of the downturn. We find that $\Omega_{\rm CIV}$ decreases by a factor of 4.8 $\pm$ 2.0 over the ~300 Myr interval between $z\sim$ 4.7 and $z\sim$ 5.8. The slope of the column density (log N) distribution function does not change, suggesting that CIV absorption is suppressed approximately uniformly across 13.2 $\leq$ log N/cm$^{-2}$ < 15.0. Assuming that the carbon content of galaxy halos evolves as the integral of the cosmic star formation rate density (with some delay due to stellar lifetimes and outflow travel times), we show that chemical evolution alone could plausibly explain the fast decline in $\Omega_{\rm CIV}$ over 4.3 $\lesssim z\lesssim$ 6.3. However, the CIV/CII ratio decreases at the highest redshifts, so the accelerated decline in $\Omega_{\rm CIV}$ at $z\gtrsim$ 5 may be more naturally explained by rapid changes in the gas ionization state driven by evolution of the UV background towards the end of hydrogen reionization., Comment: 20 pages, 8 figures, 4 tables. Published in MNRAS

Event history models are based on transition rates between states and, to define such hazards of experiencing an event, the time scale over which the process evolves needs to be identified. In many applications, however, more than one time scale might be of importance. Here we demonstrate how to model a hazard jointly over two time dimensions. The model assumes a smooth bivariate hazard function, and the function is estimated by two-dimensional P-splines. We provide an R-package TwoTimeScales for the analysis of event history data with two time scales. As an example, we model transitions from cohabitation to marriage or separation simultaneously over the age of the individual and the duration of the cohabitation. We use data from the German Family Panel (pairfam) and demonstrate that considering the two time scales as equally important provides additional insights about the transition from cohabitation to marriage or separation.

6 Supplementary Tables from Gene Expression of Parathyroid Tumors

Parathyroid tumors are heterogeneous, and diagnosis is often difficult using histologic and clinical features.We have undertaken expression profiling of 53 hereditary and sporadic parathyroid tumors to better define the molecular genetics of parathyroid tumors. A class discovery approach identified three distinct groups: (1) predominantly hyperplasia cluster, (2) HRPT2/carcinoma cluster consisting of sporadic carcinomas and benign and malignant tumors from Hyperparathyroidism-Jaw Tumor Syndrome patients, and (3) adenoma cluster consisting mainly of primary adenoma and MEN 1 tumors. Gene sets able to distinguish between the groups were identified and may serve as diagnostic biomarkers. We demonstrated, by both gene and protein expression, that Histone 1 Family 2, amyloid β precursor protein, and E-cadherin are useful markers for parathyroid carcinoma and suggest that the presence of a HRPT2 mutation, whether germ-line or somatic, strongly influences the expression pattern of these 3 genes. Cluster 2, characterized by HRPT2 mutations, was the most striking, suggesting that parathyroid tumors with somatic HRPT2 mutation or tumors developing on a background of germ-line HRPT2 mutation follow pathways distinct from those involved in mutant MEN 1-related parathyroid tumors. Furthermore, our findings likely preclude an adenoma to carcinoma progression model for parathyroid tumorigenesis outside of the presence of either a germ-line or somatic HRPT2 mutation. These findings provide insights into the molecular pathways involved in parathyroid tumorigenesis and will contribute to a better understanding, diagnosis, and treatment of parathyroid tumors.

The final phase of the reionization process can be probed by rest-frame UV absorption spectra of quasars at z>6, shedding light on the properties of the diffuse intergalactic medium within the first Gyr of the Universe. The ESO Large Programme "XQR-30: the ultimate XSHOOTER legacy survey of quasars at z~5.8-6.6" dedicated ~250 hours of observations at the VLT to create a homogeneous and high-quality sample of spectra of 30 luminous quasars at z~6, covering the rest wavelength range from the Lyman limit to beyond the MgII emission. Twelve quasar spectra of similar quality from the XSHOOTER archive were added to form the enlarged XQR-30 sample, corresponding to a total of ~350 hours of on-source exposure time. The median effective resolving power of the 42 spectra is R~11400 and 9800 in the VIS and NIR arm, respectively. The signal-to-noise ratio per 10 km/s pixel ranges from ~11 to 114 at $\lambda \simeq 1285$ \AA rest frame, with a median value of ~29. We describe the observations, data reduction and analysis of the spectra, together with some first results based on the E-XQR-30 sample. New photometry in the H and K bands are provided for the XQR-30 quasars, together with composite spectra whose characteristics reflect the large absolute magnitudes of the sample. The composite and the reduced spectra are released to the community through a public repository, and will enable a range of studies addressing outstanding questions regarding the first Gyr of the Universe., Comment: 21 pages, 10 figures. Final version accepted by MNRAS

We propose an iterative algorithm to select the smoothing parameters in additive quantile regression, wherein the functional forms of the covariate effects are unspecified and expressed via B-spline bases with difference penalties on the spline coefficients. The proposed algorithm relies on viewing the penalized coefficients as random effects from the symmetric Laplace distribution, and it turns out to be very efficient and particularly attractive with multiple smooth terms. Through simulations we compare our proposal with some alternative approaches, including the traditional ones based on minimization of the Schwarz Information Criterion. A real-data analysis is presented to illustrate the method in practice.

We present a novel method for the estimation of variance parameters in generalised linear mixed models. The method has its roots in Harville (1977)'s work, but it is able to deal with models that have a precision matrix for the random-effect vector that is linear in the inverse of the variance parameters (i.e., the precision parameters). We call the method SOP (Separation of Overlapping Precision matrices). SOP is based on applying the method of successive approximations to easy-to-compute estimate updates of the variance parameters. These estimate updates have an appealing form: they are the ratio of a (weighted) sum of squares to a quantity related to effective degrees of freedom. We provide the sufficient and necessary conditions for these estimates to be strictly positive. An important application field of SOP is penalised regression estimation of models where multiple quadratic penalties act on the same regression coefficients. We discuss in detail two of those models: penalised splines for locally adaptive smoothness and for hierarchical curve data. Several data examples in these settings are presented., MTM2014-55966-P MTM2014-52184-P

This is a practical guide to P-splines, a simple, flexible and powerful tool for smoothing. P-splines combine regression on B-splines with simple, discrete, roughness penalties. They were introduced by the authors in 1996 and have been used in many diverse applications. The regression basis makes it straightforward to handle non-normal data, like in generalized linear models. The authors demonstrate optimal smoothing, using mixed model technology and Bayesian estimation, in addition to classical tools like cross-validation and AIC, covering theory and applications with code in R. Going far beyond simple smoothing, they also show how to use P-splines for regression on signals, varying-coefficient models, quantile and expectile smoothing, and composite links for grouped data. Penalties are the crucial elements of P-splines; with proper modifications they can handle periodic and circular data as well as shape constraints. Combining penalties with tensor products of B-splines extends these attractive properties to multiple dimensions. An appendix offers a systematic comparison to other smoothers.

In the analysis of biological samples, control over experimental design and data acquisition procedures alone cannot ensure well-conditioned 1H NMR spectra with maximal information recovery for data analysis. A third major element affects the accuracy and robustness of results: the data pre-processing/pre-treatment for which not enough attention is usually devoted, in particular in metabolomic studies. The usual approach is to use proprietary software provided by the analytical instruments' manufacturers to conduct the entire pre-processing strategy. This widespread practice has a number of advantages such as a user-friendly interface with graphical facilities, but it involves non-negligible drawbacks: a lack of methodological information and automation, a dependency of subjective human choices, only standard processing possibilities and an absence of objective quality criteria to evaluate pre-processing quality. This paper introduces PepsNMR to meet these needs, an R package dedicated to the whole processing chain prior to multivariate data analysis, including, among other tools, solvent signal suppression, internal calibration, phase, baseline and misalignment corrections, bucketing and normalisation. Methodological aspects are discussed and the package is compared to the gold standard procedure with two metabolomic case studies. The use of PepsNMR on these data shows better information recovery and predictive power based on objective and quantitative quality criteria. Other key assets of the package are workflow processing speed, reproducibility, reporting and flexibility, graphical outputs and documented routines.

Embryonic growth is often impaired in miscarriages. It is postulated that derangements in embryonic growth result in abnormalities of the embryonic curvature. This study aims to create first trimester reference charts of the human embryonic curvature and investigate differences between ongoing pregnancies and miscarriages. Weekly ultrasonographic scans from ongoing pregnancies and miscarriages were used from the Rotterdam periconceptional cohort and a cohort of recurrent miscarriages. In 202 ongoing pregnancies and 33 miscarriages, first trimester crown rump length and total arch length were measured to assess the embryonic curvature. The results show that the total arch length increases and shows more variation with advanced gestation. The crown rump length/total arch length ratio shows a strong increase from 8+0 to 10+0 weeks and flattening thereafter. No significant difference was observed between the curvature of embryos of ongoing pregnancies and miscarriages. The majority of miscarried embryos could not be measured. Therefore, this technique is too limited to recommend the measurement of the embryonic curvature in clinical practice.

The effective dimension of a model is a useful measure of its complexity. In linear models, the trace of the so-called hat matrix is a convenient choice. It has strong links to variance estimation in mixed models, and it suggests straightforward partial effective dimensions for additive model components. Efron (2004) casts doubt on the trace of the hat matrix and advocates an alternative definition of the effective dimension, based on a covariance formula. Unfortunately, he uses the robust lowess smoother, which is strongly nonlinear. This blurs the issue and invalidates his conclusions. I show that the problems disappear if a linear smoother is being used. The computation of the trace of the hat matrix is much more efficient than using the covariance formula, which needs bootstrapping.

In the field of cardio-thoracic surgery, valve function is monitored over time after surgery. The motivation for our research comes from a study which includes patients who received a human tissue valve in the aortic position. These patients are followed prospectively over time by standardized echocardiographic assessment of valve function. Loss of follow-up could be caused by valve intervention or the death of the patient. One of the main characteristics of the human valve is that its durability is limited. Therefore, it is of interest to obtain a prognostic model in order for the physicians to scan trends in valve function over time and plan their next intervention, accounting for the characteristics of the data. Several authors have focused on deriving predictions under the standard joint modeling of longitudinal and survival data framework that assumes a constant effect for the coefficient that links the longitudinal and survival outcomes. However, in our case, this may be a restrictive assumption. Since the valve degenerates, the association between the biomarker with survival may change over time. To improve dynamic predictions, we propose a Bayesian joint model that allows a time-varying coefficient to link the longitudinal and the survival processes, using P-splines. We evaluate the performance of the model in terms of discrimination and calibration, while accounting for censoring.

The Bayesian approach has become increasingly popular because it allows to fit quite complex models to data via Markov chain Monte Carlo sampling. However, it is also recognized nowadays that Markov chain Monte Carlo sampling can become computationally prohibitive when applied to a large data set. We encountered serious computational difficulties when fitting an hierarchical model to longitudinal glaucoma data of patients who participate in an ongoing Dutch study. To overcome this problem, we applied and extended a recently proposed two-stage approach to model these data. Glaucoma is one of the leading causes of blindness in the world. In order to detect deterioration at an early stage, a model for predicting visual fields (VFs) in time is needed. Hence, the true underlying VF progression can be determined, and treatment strategies can then be optimized to prevent further VF loss. Because we were unable to fit these data with the classical one-stage approach upon which the current popular Bayesian software is based, we made use of the two-stage Bayesian approach. The considered hierarchical longitudinal model involves estimating a large number of random effects and deals with censoring and high measurement variability. In addition, we extended the approach with tools for model evaluation. Copyright © 2017 John Wiley & Sons, Ltd.

Introduction Offspring exposed to preeclampsia (PE) show an increased risk of cardiovascular disease in adulthood. We hypothesize that this is mediated by a disturbed vascular development of the placenta, umbilical cord and fetus. Therefore, we investigated associations between early-onset PE (EOPE), late-onset PE (LOPE) and features of placental and newborn vascular health. Methods We performed a nested case-control study in The Rotterdam Periconceptional Cohort, including 30 PE pregnancies (15 EOPE, 15 LOPE) and 218 control pregnancies (164 uncomplicated controls, 54 complicated controls including 28 fetal growth restriction, 26 preterm birth) and assessed macroscopic and histomorphometric outcomes of the placenta and umbilical cord. Results A significant association was observed between PE and a smaller umbilical vein area and wall thickness, independent of gestational age and birth weight. In EOPE we observed significant associations with a lower weight, length and width of the placenta, length of the umbilical cord, and thickness and wall area of the umbilical vein and artery. These associations attenuated after gestational age and birth weight adjustment. In LOPE a significant association with a larger placental width and smaller umbilical vein wall thickness was shown, independent of gestational age and birth weight. Discussion Our study suggests that PE is associated with a smaller umbilical cord vein area and wall thickness, independent of gestational age and birth weight, which may serve as a proxy of disturbed cardiovascular development in the newborn. Follow-up studies are needed to ultimately predict and lower the risk of cardiovascular disease in offspring exposed to PE.

Supplemental Digital Content is available in the text., During an infectious disease outbreak, timely information on the number of new symptomatic cases is crucial. However, the reporting of new cases is usually subject to delay due to the incubation period, time to seek care, and diagnosis. This results in a downward bias in the numbers of new cases by the times of symptoms onset towards the current day. The real-time assessment of the current situation while correcting for underreporting is called nowcasting. We present a nowcasting method based on bivariate P-spline smoothing of the number of reported cases by time of symptoms onset and delay. Our objective is to predict the number of symptomatic-but-not-yet-reported cases and combine these with the already reported symptomatic cases into a nowcast. We assume the underlying two-dimensional reporting intensity surface to be smooth. We include prior information on the reporting process as additional constraints: the smooth surface is unimodal in the reporting delay dimension, is (almost) zero at a predefined maximum delay and has a prescribed shape at the beginning of the outbreak. Parameter estimation is done efficiently by penalized iterative weighted least squares. We illustrate our method on a large measles outbreak in the Netherlands. We show that even with very limited information the method is able to accurately predict the number of symptomatic-but-not-yet-reported cases. This results in substantially improved monitoring of new symptomatic cases in real time.

Representing the conditional mean in Poisson regression directly as a sum of smooth components can provide a realistic model of the data generating process. Here, we present an approach that allows such an additive decomposition of the expected values of counts. The model can be formulated as a penalized composite link model and can, therefore, be estimated by a modified iteratively weighted least-squares algorithm. Further shape constraints on the smooth additive components can be enforced by additional penalties, and the model is extended to two dimensions. We present two applications that motivate the model and demonstrate the versatility of the approach.