Causal Effects of Paternity Leave on Children and Parents

In this paper we use a parental leave reform directed towards fathers to identify the causal effects of paternity leave on children’s and parents’ outcomes. We document that paternity leave causes fathers to become more important for children’s cognitive skills. School performance at age 16 increases for children whose father is relatively higher educated than the mother. We find no evidence that fathers’ earnings and work hours are affected by paternity leave. Contrary to expectation, mothers’ labor market outcomes are adversely affected by paternity leave. Our findings do therefore not suggest that paternity leave shifts the gender balance at home in a way that increases mothers’ time and/or effort spent at market work.parental leave, labor supply, child development

Constraints on the evolution of azole resistance in plant pathogenic fungi

The durability of azole fungicides in controlling agriculturally important pathogenic fungi is unique amongst modern single site fungicides. Today, azoles are still relied on to control pathogens of many crops including cereals, fruits and vegetables, canola and soybeans. Significantly, this widespread use continues despite many reports of azole-resistant fungal strains. In this review, recent reports of azole resistance and the mechanisms associated with resistant phenotypes are discussed. The example of the complex evolution of the azole target sterol 14-demethylase (CYP51) enzyme in modern European populations of the wheat pathogen Mycosphaerella graminicola is used to describe the quantitative and epistatic effects on fungicide sensitivity and enzyme function of target site mutations, and to explore the hypothesis that constraints on CYP51 evolution have ensured the longevity of azoles. In addition, the threats posed by alternative resistance mechanisms causing cross-resistance to all azoles or even unrelated fungicides are discussed, and postulations are made on how using new genomic technologies to gain a greater understanding of azole resistance evolution should enhance the ability to control azole-resistant strains of plant pathogenic fungi in the future

Human Substantia Nigra Neurons Encode Unexpected Financial Rewards

The brain's sensitivity to unexpected outcomes plays a fundamental role in an organism's ability to adapt and learn new behaviors. Emerging research suggests that midbrain dopaminergic neurons encode these unexpected outcomes. We used microelectrode recordings during deep brain stimulation surgery to study neuronal activity in the human substantia nigra (SN) while patients with Parkinson's disease engaged in a probabilistic learning task motivated by virtual financial rewards. Based on a model of the participants' expected reward, we divided trial outcomes into expected and unexpected gains and losses. SN neurons exhibited significantly higher firing rates after unexpected gains than unexpected losses. No such differences were observed after expected gains and losses. This result provides critical support for the hypothesized role of the SN in human reinforcement learning

Application of quasi-Monte Carlo methods to PDEs with random coefficients -- an overview and tutorial

This article provides a high-level overview of some recent works on the application of quasi-Monte Carlo (QMC) methods to PDEs with random coefficients. It is based on an in-depth survey of a similar title by the same authors, with an accompanying software package which is also briefly discussed here. Embedded in this article is a step-by-step tutorial of the required analysis for the setting known as the uniform case with first order QMC rules. The aim of this article is to provide an easy entry point for QMC experts wanting to start research in this direction and for PDE analysts and practitioners wanting to tap into contemporary QMC theory and methods.Comment: arXiv admin note: text overlap with arXiv:1606.0661

Hot new directions for quasi-Monte Carlo research in step with applications

This article provides an overview of some interfaces between the theory of quasi-Monte Carlo (QMC) methods and applications. We summarize three QMC theoretical settings: first order QMC methods in the unit cube $[0,1]^s$ and in $\mathbb{R}^s$, and higher order QMC methods in the unit cube. One important feature is that their error bounds can be independent of the dimension $s$ under appropriate conditions on the function spaces. Another important feature is that good parameters for these QMC methods can be obtained by fast efficient algorithms even when $s$ is large. We outline three different applications and explain how they can tap into the different QMC theory. We also discuss three cost saving strategies that can be combined with QMC in these applications. Many of these recent QMC theory and methods are developed not in isolation, but in close connection with applications

Cost-Effectiveness of Cranberry Capsules to Prevent Urinary Tract Infection in Long-Term Care Facilities: Economic Evaluation with a Randomized Controlled Trial

Analysis and support of clinical decision makin

Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm

This paper presents a model to simulate built-up expansion and densification based on a combination of a non-ordered multinomial logistic regression (MLR) and cellular automata (CA). The probability for built-up development is assessed based on (i) a set of built-up development causative factors and (ii) the land-use of neighboring cells. The model considers four built-up classes: non built-up, low-density, medium-density and high-density built-up. Unlike the most commonly used built-up/urban models which simulate built-up expansion, our approach considers expansion and the potential for densification within already built-up areas when their present density allows it. The model is built, calibrated, and validated for Wallonia region (Belgium) using cadastral data. Three 100 × 100 m raster-based built-up maps for 1990, 2000, and 2010 are developed to define one calibration interval (1990–2000) and one validation interval (2000 − 2010). The causative factors are calibrated using MLR whereas the CA neighboring effects are calibrated based on a multi-objective genetic algorithm. The calibrated model is applied to simulate the built-up pattern in 2010. The simulated map in 2010 is used to evaluate the model’s performance against the actual 2010 map by means of fuzzy set theory. According to the findings, land-use policy, slope, and distance to roads are the most important determinants of the expansion process. The densification process is mainly driven by zoning, slope, distance to different roads and richness index. The results also show that the densification generally occurs where there are dense neighbors whereas areas with lower densities retain their densities over time