An eclipse of the X-ray flux from the dwarf nova OY Carinae in quiescence

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.We present a phase-resolved ROSAT HRI X-ray light curve of the dwarf nova OY Car in quiescence. The X-ray flux is eclipsed at the same time as the optical eclipse of the primary, and the region of X -ray emission is comparable in size to the white dwarf. We use subsequent optical observations to update the orbital ephemeris of the system.We thank Jakob Engelhauser for his help with the difficult scheduling of the ROSAT observations. We acknowledge the data analysis facilities provided by the Starlink Project, which is run by CCLRC on behalf of PPARC. GWP is in receipt of a University of Central Lancashire research studentship

Memory in paediatric temporal lobe epilepsy: effects of lesion type and side.

This study investigated the role of underlying pathology on memory function of children with temporal lobe epilepsy (TLE). Memory was assessed in 44 children with TLE resulting from hippocampal sclerosis (HS) or dysembryoplastic neuroepithelial tumours (DNT), and 22 control children. Delayed story and paired associate recall performance was significantly more impaired in children with HS compared to those with DNT, irrespective of the affected side. Semantic memory was impaired in both HS groups, and also in the left DNT group. These results suggest a role for type, and to a lesser extent, side of pathology in the memory profile of children with TLE

Estimating Regional Spatial and Temporal Variability of PM2.5 Concentrations Using Satellite Data, Meteorology, and Land Use Information

Background: Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters ≤ 2.5 μm (PM2.5) are often limited by sparse measurements. Satellite aerosol remote sensing data may be used to extend PM2.5 ground networks to cover a much larger area. Objectives: In this study we examined the benefits of using aerosol optical depth (AOD) retrieved by the Geostationary Operational Environmental Satellite (GOES) in conjunction with land use and meteorologic information to estimate ground-level PM2.5 concentrations. Methods: We developed a two-stage generalized additive model (GAM) for U.S. Environmental Protection Agency PM2.5 concentrations in a domain centered in Massachusetts. The AOD model represents conditions when AOD retrieval is successful; the non-AOD model represents conditions when AOD is missing in the domain. Results: The AOD model has a higher predicting power judged by adjusted R2 (0.79) than does the non-AOD model (0.48). The predicted PM2.5 concentrations by the AOD model are, on average, 0.8–0.9 μg/m3 higher than the non-AOD model predictions, with a more smooth spatial distribution, higher concentrations in rural areas, and the highest concentrations in areas other than major urban centers. Although AOD is a highly significant predictor of PM2.5, meteorologic parameters are major contributors to the better performance of the AOD model. Conclusions: GOES aerosol/smoke product (GASP) AOD is able to summarize a set of weather and land use conditions that stratify PM2.5 concentrations into two different spatial patterns. Even if land use regression models do not include AOD as a predictor variable, two separate models should be fitted to account for different PM2.5 spatial patterns related to AOD availability

Transcriptional regulation of the genes involved in protein metabolism and processing in Saccharomyces cerevisiae.

Topological analysis of large networks, which focus on a specific biological process or on related biological processes, where functional coherence exists among the interacting members, may provide a wealth of insight into cellular functionality. This work presents an unbiased systems approach to analyze genetic, transcriptional regulatory and physical interaction networks of yeast genes possessing such functional coherence to gain novel biological insight. The present analysis identified only a few transcriptional regulators amongst a large gene cohort associated with the protein metabolism and processing in yeast. These transcription factors are not functionally required for the maintenance of these tasks in growing cells. Rather, they are involved in rewiring gene transcription in response to such major challenges as starvation, hypoxia, DNA damage, heat shock or the accumulation of unfolded proteins. Indeed, only a subset of these proteins were captured empirically in the nuclear-enriched fraction of non-stressed yeast cells, suggesting that the transcriptional regulation of protein metabolism and processing in yeast is primarily concerned with maintaining cellular robustness in the face of threat by either internal or external stressors

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

Conceptualizing cultures of violence and cultural change

The historiography of violence has undergone a distinct cultural turn as attention has shifted from examining violence as a clearly defined (and countable) social problem to analysing its historically defined 'social meaning'. Nevertheless, the precise nature of the relationship between 'violence' and 'culture' is still being established. How are 'cultures of violence' formed? What impact do they have on violent behaviour? How do they change? This essay examines some of the conceptual aspects of the relationship between culture and violence. It brings together empirical research into nineteenth-century England with recent research results from other European contexts to examine three aspects of the relationship between culture and violence. These are organised under the labels 'seeing violence', 'identifying the violent' and 'changing violence'. Within a particular society, narratives regarding particular kinds of behaviour shape cultural attitudes. The notion 'violence' is thus defined in relation to physically aggressive acts as well as by being connected to other kinds of attitudes and contexts. As a result, the boundaries between physical aggression which is legitimate and that which is illegitimate (and thus 'violence') are set. Once 'violence' is defined, particular cultures form ideas about who is responsible for it: reactions to violence become associated with social arrangements such as class and gender as well as to attitudes toward the self. Finally, cultures of violence make efforts to tame or eradicate illegitimate forms of physical aggression. This process is not only connected to the development of new forms of power (e.g., new policing or punishment strategies) but also to less tangible cultural influences which aim at changing the behaviour defined as violence (in particular among the social groups identified as violent). Even if successful, this three-tiered process of seeing violence, identifying the violent and changing violence continues anew, emphasising the ways that cultures of violence develop through a continuous process of reevaluation and reinvention

Logarithmic rate dependence in deforming granular materials

Rate-independence for stresses within a granular material is a basic tenet of many models for slow dense granular flows. By contrast, logarithmic rate dependence of stresses is found in solid-on-solid friction, in geological settings, and elsewhere. In this work, we show that logarithmic rate-dependence occurs in granular materials for plastic (irreversible) deformations that occur during shearing but not for elastic (reversible) deformations, such as those that occur under moderate repetitive compression. Increasing the shearing rate, \Omega, leads to an increase in the stress and the stress fluctuations that at least qualitatively resemble what occurs due to an increase in the density. Increases in \Omega also lead to qualitative changes in the distributions of stress build-up and relaxation events. If shearing is stopped at t=0, stress relaxations occur with \sigma(t)/ \sigma(t=0) \simeq A \log(t/t_0). This collective relaxation of the stress network over logarithmically long times provides a mechanism for rate-dependent strengthening.Comment: 4 pages, 5 figures. RevTeX

The transcriptional co-factor RIP140 regulates mammary gland development by promoting the generation of key mitogenic signals

Nuclear receptor interacting protein (Nrip1), also known as RIP140, is a co-regulator for nuclear receptors that plays an essential role in ovulation by regulating the expression of the epidermal growth factor-like family of growth factors. Although several studies indicate a role for RIP140 in breast cancer, its role in the development of the mammary gland is unclear. By using RIP140-null and RIP140 transgenic mice, we demonstrate that RIP140 is an essential factor for normal mammary gland development and that it functions by mediating oestrogen signalling. RIP140-null mice exhibit minimal ductal elongation with no side-branching, whereas RIP140-overexpressing mice show increased cell proliferation and ductal branching with age. Tissue recombination experiments demonstrate that RIP140 expression is required in both the mammary epithelial and stromal compartments for ductal elongation during puberty and that loss of RIP140 leads to a catastrophic loss of the mammary epithelium, whereas RIP140 overexpression augments the mammary basal cell population and shifts the progenitor/differentiated cell balance within the luminal cell compartment towards the progenitors. For the first time, we present a genome-wide global view of oestrogen receptor-α (ERα) binding events in the developing mammary gland, which unravels 881 ERα binding sites. Unbiased evaluation of several ERα binding sites for RIP140 co-occupancy reveals selectivity and demonstrates that RIP140 acts as a co-regulator with ERα to regulate directly the expression of amphiregulin (Areg), the progesterone receptor (Pgr) and signal transducer and activator of transcription 5a (Stat5a), factors that influence key mitogenic pathways that regulate normal mammary gland development

Decreasing intensity of open-ocean convection in the Greenland and Iceland seas

The air–sea transfer of heat and fresh water plays a critical role in the global climate system. This is particularly true for the Greenland and Iceland seas, where these fluxes drive ocean convection that contributes to Denmark Strait overflow water, the densest component of the lower limb of the Atlantic Meridional Overturning Circulation (AMOC). Here we show that the wintertime retreat of sea ice in the region, combined with different rates of warming for the atmosphere and sea surface of the Greenland and Iceland seas, has resulted in statistically significant reductions of approximately 20% in the magnitude of the winter air–sea heat fluxes since 1979. We also show that modes of climate variability other than the North Atlantic Oscillation (NAO) are required to fully characterize the regional air–sea interaction. Mixed-layer model simulations imply that further decreases in atmospheric forcing will exceed a threshold for the Greenland Sea whereby convection will become depth limited, reducing the ventilation of mid-depth waters in the Nordic seas. In the Iceland Sea, further reductions have the potential to decrease the supply of the densest overflow waters to the AMOC

Fast uncertainty quantification of tracer distribution in the brain interstitial fluid with multilevel and quasi Monte Carlo

Efficient uncertainty quantification algorithms are key to understand the propagation of uncertainty -- from uncertain input parameters to uncertain output quantities -- in high resolution mathematical models of brain physiology. Advanced Monte Carlo methods such as quasi Monte Carlo (QMC) and multilevel Monte Carlo (MLMC) have the potential to dramatically improve upon standard Monte Carlo (MC) methods, but their applicability and performance in biomedical applications is underexplored. In this paper, we design and apply QMC and MLMC methods to quantify uncertainty in a convection-diffusion model of tracer transport within the brain. We show that QMC outperforms standard MC simulations when the number of random inputs is small. MLMC considerably outperforms both QMC and standard MC methods and should therefore be preferred for brain transport models.Comment: Multilevel Monte Carlo, quasi Monte Carlo, brain simulation, brain fluids, finite element method, biomedical computing, random fields, diffusion-convectio