Surface Hydrogen Modeling of Super Soft X-ray Sources: Are They Supernova Ia Progenitors?

Nova explosions occur on the white dwarf (WD) component of a Cataclysmic Variable stellar system which is accreting matter lost by a companion. A Type Ia supernova explosion is thought to result when a WD, in a similar binary configuration, grows in mass to the Chandrasekhar Limit. Here, we present calculations of accretion of Solar matter, at a variety of mass accretion rates, onto hot ($2.3 \times 10^{5}$K), luminous (30L$_\odot$), massive (1.25M$_\odot$, 1.35M$_\odot$) Carbon-Oxygen WDs. In contrast to our nova simulations where the WD has a low initial luminosity and a thermonuclear runaway (TNR) occurs and ejects material, these simulations do not eject material (or only a small fraction of the accreted material) and the WD grows in mass. A hydrogen TNR does not occur because hydrogen fuses to helium in the surface layers, and we call this process Surface Hydrogen Burning (SHB). As the helium layer grows in mass, it gradually fuses either to carbon and oxygen or to more massive nuclei depending on the WD mass and mass accretion rate. If such a WD were to explode in a SN Ia event, therefore, it would show neither hydrogen nor helium in its spectrum as is observed. Moreover, the luminosities and effective temperatures of our simulations agree with the observations of some of the Super Soft X-ray Binary Sources and, therefore, our results strengthen previous speculation that some of them (CAL 83 and CAL 87 for example) are probably progenitors of SN Ia explosions. Finally, we have achieved SHB for values of the mass accretion rate that almost span the observed values of the Cataclysmic Variables.Comment: Accepted by APJL, 4 pages, 1 figure, LaTex (uses emulateapj.sty

Evaluation of the impact of a school gardening intervention on children's fruit and vegetable intake: a randomised controlled trial.

Background: Current academic literature suggests that school gardening programmes can provide an interactive environment with the potential to change children’s fruit and vegetable intake. This is the first cluster randomised controlled trial (RCT) designed to evaluate whether a school gardening programme can have an effect on children’s fruit and vegetable intake. Methods: The trial included children from 23 schools; these schools were randomised into two groups, one to receive the Royal Horticultural Society (RHS)-led intervention and the other to receive the less involved Teacher-led intervention. A 24-hour food diary (CADET) was used to collect baseline and follow-up dietary intake 18 months apart. Questionnaires were also administered to evaluate the intervention implementation. Results: A total of 641 children completed the trial with a mean age of 8.1 years (95% CI: 8.0, 8.4). The unadjusted results from multilevel regression analysis revealed that for combined daily fruit and vegetable intake the Teacher-led group had a higher daily mean change of 8 g (95% CI: −19, 36) compared to the RHS-led group -32 g (95% CI: −60, −3). However, after adjusting for possible confounders this difference was not significant (intervention effect: −40 g, 95% CI: −88, 1; p = 0.06). The adjusted analysis of process measures identified that if schools improved their gardening score by 3 levels (a measure of school gardening involvement - the scale has 6 levels from 0 ‘no garden’ to 5 ‘community involvement’), irrespective of group allocation, children had, on average, a daily increase of 81 g of fruit and vegetable intake (95% CI: 0, 163; p = 0.05) compared to schools that had no change in gardening score. Conclusions: This study is the first cluster randomised controlled trial designed to evaluate a school gardening intervention. The results have found very little evidence to support the claims that school gardening alone can improve children’s daily fruit and vegetable intake. However, when a gardening intervention is implemented at a high level within the school it may improve children’s daily fruit and vegetable intake by a portion. Improving children’s fruit and vegetable intake remains a challenging task

Survival Analysis Part I: Basic concepts and first analyses

Survival analysis is a collection of statistical procedures for data analysis where the outcome variable of interest is time until an event occurs. Because of censoring - the nonobservation of the event of interest after a period of follow-up - a proportion of the survival times of interest will often be unknown. It is assumed that those patients who are censored have the same survival prospects as those who continue to be followed, that is, the censoring is uninformative. Survival data are generally described and modelled in terms of two related functions, the survivor function and the hazard function. The survivor function represents the probability that an individual survives from the time of origin to some time beyond time t. It directly describes the survival experience of a study cohort, and is usually estimated by the KM method. The logrank test may be used to test for differences between survival curves for groups, such as treatment arms. The hazard function gives the instantaneous potential of having an event at a time, given survival up to that time. It is used primarily as a diagnostic tool or for specifying a mathematical model for survival analysis. In comparing treatments or prognostic groups in terms of survival, it is often necessary to adjust for patient-related factors that could potentially affect the survival time of a patient. Failure to adjust for confounders may result in spurious effects. Multivariate survival analysis, a form of multiple regression, provides a way of doing this adjustment, and is the subject the next paper in this series

Prediction and Testing of Biological Networks Underlying Intestinal Cancer

Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called “driver” genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections – both precedented and novel – between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21), known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc1638N+/−) or Cdkn1a (Cdkn1a−/−), followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional), then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data

Food Insecurity Prevalence Across Diverse Sites During COVID-19: A Year of Comprehensive Data

Key Findings NFACT includes 18 study sites in 15 states as well as a national poll, collectively representing a sample size of more than 26,000 people. Some sites have implemented multiple survey rounds, here we report results from 22 separate surveys conducted during the year since the COVID-19 pandemic began in March 2020. 18 out of 19 surveys in 14 sites with data for before and since the pandemic began found an increase in food insecurity since the start of the COVID-19 pandemic as compared to before the pandemic. In nearly all surveys (18/19) that measured food insecurity both before and during the pandemic, more Black, Indigenous, and People of Color (BIPOC) were classified as food insecure during the pandemic as compared to before it began. Prevalence of food insecurity for BIPOC respondents was higher than the overall population in the majority of surveys (19/20) sampling a general population. In almost all surveys (21/22), the prevalence of food insecurity for households with children was higher than the overall prevalence of food insecurity. Food insecurity prevalence was higher for households experiencing a negative job impact during the pandemic (i.e. job loss, furlough, reduction in hours) in nearly all surveys and study sites (21/22). Food insecurity prevalence in most sites was significantly higher before COVID-19 than estimates from that time period. Reporting a percent change between pre and during COVID-19 prevalence may provide additional information about the rate of change in food insecurity since the start of the pandemic, which absolute prevalence of food insecurity may not capture. Results highlight consistent trends in food insecurity outcomes since the start of the COVID-19 pandemic, across diverse study sites, methodological approaches, and time

The alpha-kinase family: an exceptional branch on the protein kinase tree

The alpha-kinase family represents a class of atypical protein kinases that display little sequence similarity to conventional protein kinases. Early studies on myosin heavy chain kinases in Dictyostelium discoideum revealed their unusual propensity to phosphorylate serine and threonine residues in the context of an alpha-helix. Although recent studies show that some members of this family can also phosphorylate residues in non-helical regions, the name alpha-kinase has remained. During evolution, the alpha-kinase domains combined with many different functional subdomains such as von Willebrand factor-like motifs (vWKa) and even cation channels (TRPM6 and TRPM7). As a result, these kinases are implicated in a large variety of cellular processes such as protein translation, Mg2+ homeostasis, intracellular transport, cell migration, adhesion, and proliferation. Here, we review the current state of knowledge on different members of this kinase family and discuss the potential use of alpha-kinases as drug targets in diseases such as cancer