Breaking the color-reddening degeneracy in type Ia supernovae

A new method to study the intrinsic color and luminosity of type Ia supernovae (SNe Ia) is presented. A metric space built using principal component analysis (PCA) on spectral series SNe Ia between -12.5 and +17.5 days from B maximum is used as a set of predictors. This metric space is built to be insensitive to reddening. Hence, it does not predict the part of color excess due to dust-extinction. At the same time, the rich variability of SN Ia spectra is a good predictor of a large fraction of the intrinsic color variability. Such metric space is a good predictor of the epoch when the maximum in the B-V color curve is reached. Multivariate Partial Least Square (PLS) regression predicts the intrinsic B band light-curve and the intrinsic B-V color curve up to a month after maximum. This allows to study the relation between the light curves of SNe Ia and their spectra. The total-to-selective extinction ratio RV in the host-galaxy of SNe Ia is found, on average, to be consistent with typical Milky-Way values. This analysis shows the importance of collecting spectra to study SNe Ia, even with large sample publicly available. Future automated surveys as LSST will provide a large number of light curves. The analysis shows that observing accompaning spectra for a significative number of SNe will be important even in the case of "normal" SNe Ia.Comment: 11 pages, 11 figure

Epigenetics, Nutrition, and Infant Health

The field of epigenetics is currently garnering a great deal of interest, exploring how our very molecular makeup in the form of modifications to the genome can be altered by factors as diverse as aging, disease, nutrition, stress, alcohol, and exposure to pollutants. Epigenetic changes have previously been implicated in the etiology of a variety of diseases, notably in the development of certain cancers, and inherited growth disorder syndromes, but the exploration of epigenetics’ role in fetal programming is still in its infancy. This chapter focuses on how nutritional exposures during pregnancy may affect the infant epigenome, and the impact that such modifications may have on the long-term health of the child. We start by describing some keys concepts in epigenetics and discuss windows of epigenetic plasticity in the context of the developmental origins of health and disease (DOHaD) hypothesis. We then review some of the key mechanisms by which nutrition can affect the epigenome, with a particular focus on the role of one-carbon metabolism. We finish by outlining some of the child health outcomes that have been linked to epigenetic dysregulation, and discuss possible next steps that need to be realized if insights into the basic science of epigenetics are to be translated into tangible public health benefits

Maternal nutritional status, C1 metabolism and offspring DNA methylation: a review of current evidence in human subjects.

: Evidence is growing for the long-term effects of environmental factors during early-life on later disease susceptibility. It is believed that epigenetic mechanisms (changes in gene function not mediated by DNA sequence alteration), particularly DNA methylation, play a role in these processes. This paper reviews the current state of knowledge of the involvement of C1 metabolism and methyl donors and cofactors in maternal diet-induced DNA methylation changes in utero as an epigenetic mechanism. Methyl groups for DNA methylation are mostly derived from the diet and supplied through C1 metabolism by way of choline, betaine, methionine or folate, with involvement of riboflavin and vitamins B6 and B12 as cofactors. Mouse models have shown that epigenetic features, for example DNA methylation, can be altered by periconceptional nutritional interventions such as folate supplementation, thereby changing offspring phenotype. Evidence of early nutrient-induced epigenetic change in human subjects is scant, but it is known that during pregnancy C1 metabolism has to cope with high fetal demands for folate and choline needed for neural tube closure and normal development. Retrospective studies investigating the effect of famine or season during pregnancy indicate that variation in early environmental exposure in utero leads to differences in DNA methylation of offspring. This may affect gene expression in the offspring. Further research is needed to examine the real impact of maternal nutrient availability on DNA methylation in the developing fetus

Clinical validity assessment of a breast cancer risk model combining genetic and clinical information

_Background:_ The extent to which common genetic variation can assist in breast cancer (BCa) risk assessment is unclear. We assessed the addition of risk information from a panel of BCa-associated single nucleotide polymorphisms (SNPs) on risk stratification offered by the Gail Model.

_Methods:_ We selected 7 validated SNPs from the literature and genotyped them among white women in a nested case-control study within the Women’s Health Initiative Clinical Trial. To model SNP risk, previously published odds ratios were combined multiplicatively. To produce a combined clinical/genetic risk, Gail Model risk estimates were multiplied by combined SNP odds ratios. We assessed classification performance using reclassification tables and receiver operating characteristic (ROC) curves. 

_Results:_ The SNP risk score was well calibrated and nearly independent of Gail risk, and the combined predictor was more predictive than either Gail risk or SNP risk alone. In ROC curve analysis, the combined score had an area under the curve (AUC) of 0.594 compared to 0.557 for Gail risk alone. For reclassification with 5-year risk thresholds at 1.5% and 2%, the net reclassification index (NRI) was 0.085 (Z = 4.3, P = 1.0×10^-5^). Focusing on women with Gail 5-year risk of 1.5-2% results in an NRI of 0.195 (Z = 3.8, P = 8.6×10^−5^).

_Conclusions:_ Combining clinical risk factors and validated common genetic risk factors results in improvement in classification of BCa risks in white, postmenopausal women. This may have implications for informing primary prevention and/or screening strategies. Future research should assess the clinical utility of such strategies.
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A model analysis of climate and CO2 controls on tree growth and carbon allocation in a semi-arid woodland

Many studies have failed to show an increase in the radial growth of trees in response to increasing atmospheric CO2 concentration [CO2] despite the expected enhancement of photosynthetic rates and water-use efficiency at high [CO2]. A global light use efficiency model of photosynthesis, coupled with a generic carbon allocation and tree-growth model based on mass balance and tree geometry principles, was used to simulate annual ring-width variations for the gymnosperm Callitris columellaris in the semi-arid Great Western Woodlands, Western Australia, over the past 100 years. Parameter values for the tree-growth model were derived from independent observations except for sapwood specific respiration rate, fine-root turnover time, fine-root specific respiration rate and the ratio of fine-root mass to foliage area (ζ), which were calibrated to the ring-width measurements by Bayesian optimization. This procedure imposed a strong constraint on ζ. Modelled and observed ring-widths showed quantitatively similar, positive responses to total annual photosynthetically active radiation and soil moisture, and similar negative responses to vapour pressure deficit. The model also produced enhanced radial growth in response to increasing [CO2] during recent decades, but the data do not show this. Recalibration in moving 30-year time windows produced temporal shifts in the estimated values of ζ, including an increase by ca 12% since the 1960s, and eliminated the [CO2]-induced increase in radial growth. The potential effect of CO2 on ring-width was thus shown to be small compared to effects of climate variability even in this semi-arid climate. It could be counteracted in the model by a modest allocation shift, as has been observed in field experiments with raised [CO2]

Climate versus carbon dioxide controls on biomass burning: a model analysis of the glacial–interglacial contrast

Climate controls fire regimes through its influence on the amount and types of fuel present and their dryness. CO2 concentration constrains primary production by limiting photosynthetic activity in plants. However, although fuel accumulation depends on biomass production, and hence on CO2 concentration, the quantitative relationship between atmospheric CO2 concentration and biomass burning is not well understood. Here a fire-enabled dynamic global vegetation model (the Land surface Processes and eXchanges model, LPX) is used to attribute glacial–interglacial changes in biomass burning to an increase in CO2, which would be expected to increase primary production and therefore fuel loads even in the absence of climate change, vs. climate change effects. Four general circulation models provided last glacial maximum (LGM) climate anomalies – that is, differences from the pre-industrial (PI) control climate – from the Palaeoclimate Modelling Intercomparison Project Phase~2, allowing the construction of four scenarios for LGM climate. Modelled carbon fluxes from biomass burning were corrected for the model's observed prediction biases in contemporary regional average values for biomes. With LGM climate and low CO2 (185 ppm) effects included, the modelled global flux at the LGM was in the range of 1.0–1.4 Pg C year-1, about a third less than that modelled for PI time. LGM climate with pre-industrial CO2 (280 ppm) yielded unrealistic results, with global biomass burning fluxes similar to or even greater than in the pre-industrial climate. It is inferred that a substantial part of the increase in biomass burning after the LGM must be attributed to the effect of increasing CO2 concentration on primary production and fuel load. Today, by analogy, both rising CO2 and global warming must be considered as risk factors for increasing biomass burning. Both effects need to be included in models to project future fire risks

Ecosystem effects of CO2 concentration: evidence from past climates

Atmospheric CO2 concentration has varied from minima of 170-200 ppm in glacials to maxima of 280-300 ppm in the recent interglacials. Photosynthesis by C-3 plants is highly sensitive to CO2 concentration variations in this range. Physiological consequences of the CO2 changes should therefore be discernible in palaeodata. Several lines of evidence support this expectation. Reduced terrestrial carbon storage during glacials, indicated by the shift in stable isotope composition of dissolved inorganic carbon in the ocean, cannot be explained by climate or sea-level changes. It is however consistent with predictions of current process-based models that propagate known physiological CO2 effects into net primary production at the ecosystem scale. Restricted forest cover during glacial periods, indicated by pollen assemblages dominated by non-arboreal taxa, cannot be reproduced accurately by palaeoclimate models unless CO2 effects on C-3-C-4 plant competition are also modelled. It follows that methods to reconstruct climate from palaeodata should account for CO2 concentration changes. When they do so, they yield results more consistent with palaeoclimate models. In conclusion, the palaeorecord of the Late Quaternary, interpreted with the help of climate and ecosystem models, provides evidence that CO2 effects at the ecosystem scale are neither trivial nor transient

The distance to the Vela pulsar gauged with HST parallax oservations

The distance to the Vela pulsar (PSR B0833-45) has been traditionally assumed to be 500 pc. Although affected by a significant uncertainty, this value stuck to both the pulsar and the SNR. In an effort to obtain a model free distance measurement, we have applied high resolution astrometry to the pulsar V~23.6 optical counterpart. Using a set of five HST/WFPC2 observations, we have obtained the first optical measurement of the annual parallax of the Vela pulsar. The parallax turns out to be 3.4 +/- 0.7 mas, implying a distance of 294(-50;+76) pc, i.e. a value significantly lower than previously believed. This affects the estimate of the pulsar absolute luminosity and of its emission efficiency at various wavelengths and confirms the exceptionally high value of the N_e towards the Vela pulsar. Finally, the complete parallax data base allows for a better measurement of the Vela pulsar proper motion (mu_alpha(cos(delta))=-37.2 +/- 1.2 mas/yr; mu_delta=28.2 +/- 1.3 mas/yr after correcting for the peculiar motion of the Sun) which, at the parallax distance, implies a transverse velocity of ~65 km/s. Moreover, the proper motion position angle appears specially well aligned with the axis of symmetry of the X-ray nebula as seen by Chandra. Such an alignment allows to assess the space velocity of the Vela pulsar to be ~81 km/s.Comment: LaTeX, 21 pages, 5 figures. Accepted for publication in Ap

The PML-RAR alpha transcript in long-term follow-up of acute promyelocytic leukemia patients

Background and Objectives. Detection of PML-RAR alpha transcripts by RT-PCR is now established as a rapid and sensitive method for diagnosis of acute promyelocytic leukemia (APL), Although the majority of patients in longterm clinical remission are negative by consecutive reverse transcription polymerase chain reaction (RT-PCR) assays, negative tests are still observed in patients who ultimately relapse. Conversion from negative to positive PCR has been observed after consolidation and found to be a much stronger predictor of relapse. This study reports on 47 APL patients to determine the correlation between minimal residual disease (MRD) status and clinical outcome in our cohort of patients. Design and Methods. The presence of PML-RAR alpha t transcripts was investigated in 47 APL patients (37 adults and 10 children) using a semi-nested reverse transcriptase-polymerase chain reaction to evaluate the prognostic value of RT-PCR tests. Results. All patients achieved complete clinical remission (CCR) following induction treatment with all-trans retinoic acid (ATRA) and chemotherapy (CHT) or ATRA alone. Patients were followed up between 2 and 117.6 months (median: 37 months). Relapses occurred in 11 patients (9 adults and 2 children) between 11.4 and 19 months after diagnosis (median: 15.1 months) while 36 patients (28 adults and 8 children) remained in CCR, Seventy-five percent of patients carried the PML-RARa long isoform (bcr 1/2) which also predominated among the relapsed cases (9 of 11) but did not associate with any adverse outcome (p = 0.37), For the purpose of this analysis, minimal residual disease tests were clustered into four time-intervals: 0-2 months, 3-5 months, 5-9 months and 10-24 months. Interpretation and Conclusions. Children showed persisting disease for longer than adults during the first 2 months of treatment, At 2 months, 10 (50%) of 20 patients who remained in CCR and 4 (80%) of 5 patients who subsequently relapsed were positive. Patients who remained in CCR had repeatedly negative results beyond 5.5 months from diagnosis. A positive MRD test preceded relapse in 3 of 4 tested patients. The ability of a negative test to predict CCR (predictive negative value, PNV) was greater after 6 months (> 83%), while the ability of a positive test to predict relapse (predictive positive value, PPV) was most valuable only beyond 10 months (100%). This study (i) highlights the prognostic value of RT-PCR monitoring after treatment of APL patients but only from the end of treatment, (ii) shows an association between conversion to a positive test and relapse and (iii) suggests that PCR assessments should be carried out at 3-month intervals to provide a more accurate prediction of hematologic relapses but only after the end of treatment, (C) 2001, Ferrata Storti Foundatio

Causal relationships vs. emergent patterns in the global controls of fire frequency

Global controls on month-by-month fractional burnt area (2000–2005) were investigated by fitting a generalised linear model (GLM) to Global Fire Emissions Database (GFED) data, with 11 predictor variables representing vegetation, climate, land use and potential ignition sources. Burnt area is shown to increase with annual net primary production (NPP), number of dry days, maximum temperature, grazing-land area, grass/shrub cover and diurnal temperature range, and to decrease with soil moisture, cropland area and population density. Lightning showed an apparent (weak) negative influence, but this disappeared when pure seasonal-cycle effects were taken into account. The model predicts observed geographic and seasonal patterns, as well as the emergent relationships seen when burnt area is plotted against each variable separately. Unimodal relationships with mean annual temperature and precipitation, population density and gross domestic product (GDP) are reproduced too, and are thus shown to be secondary consequences of correlations between different controls (e.g. high NPP with high precipitation; low NPP with low population density and GDP). These findings have major implications for the design of global fire models, as several assumptions in current models – most notably, the widely assumed dependence of fire frequency on ignition rates – are evidently incorrect