Gapless Neutron Superfluidity Can Explain the Late Time Cooling of Transiently Accreting Neutron Stars.

The current interpretation of the observed late time cooling of transiently accreting neutron stars in low-mass x-ray binaries during quiescence requires the suppression of neutron superfluidity in their crust at variance with recent ab initio many-body calculations of dense matter. Focusing on the two emblematic sources KS 1731-260 and MXB 1659-29, we show that their thermal evolution can be naturally explained by considering the existence of a neutron superflow driven by the pinning of quantized vortices. Under such circumstances, we find that the neutron superfluid can be in a gapless state in which the specific heat is dramatically increased compared to that in the classical BCS state assumed so far, thus delaying the thermal relaxation of the crust. We perform neutron-star cooling simulations taking into account gapless superfluidity, and we obtain excellent fits to the data, thus reconciling astrophysical observations with microscopic theories. The imprint of gapless superfluidity on other observable phenomena is briefly discussed.

Optimization of multi-environment trials for genomic selection based on crop models.

KEY MESSAGE: We propose a statistical criterion to optimize multi-environment trials to predict genotype × environment interactions more efficiently, by combining crop growth models and genomic selection models. Genotype × environment interactions (GEI) are common in plant multi-environment trials (METs). In this context, models developed for genomic selection (GS) that refers to the use of genome-wide information for predicting breeding values of selection candidates need to be adapted. One promising way to increase prediction accuracy in various environments is to combine ecophysiological and genetic modelling thanks to crop growth models (CGM) incorporating genetic parameters. The efficiency of this approach relies on the quality of the parameter estimates, which depends on the environments composing this MET used for calibration. The objective of this study was to determine a method to optimize the set of environments composing the MET for estimating genetic parameters in this context. A criterion called OptiMET was defined to this aim, and was evaluated on simulated and real data, with the example of wheat phenology. The MET defined with OptiMET allowed estimating the genetic parameters with lower error, leading to higher QTL detection power and higher prediction accuracies. MET defined with OptiMET was on average more efficient than random MET composed of twice as many environments, in terms of quality of the parameter estimates. OptiMET is thus a valuable tool to determine optimal experimental conditions to best exploit MET and the phenotyping tools that are currently developed.

Genome-wide association analysis to identify chromosomal regions determining components of earliness in wheat.

The modification of flowering date is considered an important way to escape the current or future climatic constraints that affect wheat crops. A better understanding of its genetic bases would enable a more efficient and rapid modification through breeding. The objective of this study was to identify chromosomal regions associated with earliness in wheat. A 227-wheat core collection chosen to be highly contrasted for earliness was characterized for heading date. Experiments were conducted in controlled conditions and in the field for 3 years to break down earliness in the component traits: photoperiod sensitivity, vernalization requirement and narrow-sense earliness. Whole-genome association mapping was carried out using 760 molecular markers and taking into account the five ancestral group structure. We identified 62 markers individually associated to earliness components corresponding to 33 chromosomal regions. In addition, we identified 15 other significant markers and seven more regions by testing marker pair interactions. Co-localizations were observed with the Ppd-1, Vrn-1 and Rht-1 candidate genes. Using an independent set of lines to validate the model built for heading date, we were able to explain 34% of the variation using the structure and the significant markers. Results were compared with already published data using bi-parental populations giving an insight into the genetic architecture of flowering time in wheat.

Europe-wide reduction in primary productivity caused by the heat and drought in 2003.

Future climate warming is expected to enhance plant growth in temperate ecosystems and to increase carbon sequestration. But although severe regional heatwaves may become more frequent in a changing climate, their impact on terrestrial carbon cycling is unclear. Here we report measurements of ecosystem carbon dioxide fluxes, remotely sensed radiation absorbed by plants, and country-level crop yields taken during the European heatwave in 2003. We use a terrestrial biosphere simulation model to assess continental-scale changes in primary productivity during 2003, and their consequences for the net carbon balance. We estimate a 30 per cent reduction in gross primary productivity over Europe, which resulted in a strong anomalous net source of carbon dioxide (0.5 Pg C yr(-1)) to the atmosphere and reversed the effect of four years of net ecosystem carbon sequestration. Our results suggest that productivity reduction in eastern and western Europe can be explained by rainfall deficit and extreme summer heat, respectively. We also find that ecosystem respiration decreased together with gross primary productivity, rather than accelerating with the temperature rise. Model results, corroborated by historical records of crop yields, suggest that such a reduction in Europe's primary productivity is unprecedented during the last century. An increase in future drought events could turn temperate ecosystems into carbon sources, contributing to positive carbon-climate feedbacks already anticipated in the tropics and at high latitudes.

Variabilities in µQCT-based FEA of a tumoral bone mice model.

A finite element analysis based on Micro-Quantitative Computed Tomography (µQCT) is a method with high potential to improve fracture risk prediction. However, the segmentation process and model generation are generally not automatized in their entirety. Even with a rigorous protocol, the operator might add uncertainties during the creation of the model. The aim of this study was to evaluate a µQCT-based model of mice tumoral and sham tibias in terms of the variabilities induced by the operator and sensitivity to operator-dependent variables (such as model orientation or length). Two different operators generated finite element (FE) models from µCT images of 8 female Balb/c nude mice tibias aged 10 weeks old with bone tumors induced in the right tibia and with sham injection in the left. From these models, predicted failure load was determined for two different boundary conditions: fixed support and spherical joints. The difference between the predicted and experimental failure load of both operators was large (-122% to 93%). The difference in the predicted failure load between operators was less for the spherical joints boundary conditions (9.8%) than for the fixed support (58.3%), p < 0.001, whereas varying the orientation of bone tibia caused more variability for the fixed support boundary condition (44.7%) than for the spherical joints (9.1%), p < 0.002. Varying tibia length had no significant effect, regardless of boundary conditions (<4%). When using the same mesh and same orientation, the difference between operators is non-significant (<6%) for each model. This study showed that the operator influences the failure load assessed by a µQCT-based finite element model of the tumoral and sham mice tibias. The results suggest that automation is needed for better reproducibility.

Estimation of lungs' hypoplasia on postoperative chest x-rays in congenital diaphragmatic hernia.

Since we stopped inserting ipsilateral underwater chest drains after surgical reduction of the herniated contents in congenital diaphragmatic hernia, both lungs are allowed to expand at their own pace, making it possible to estimate their degree of hypoplasia on postoperative chest x-rays. Thirty-nine consecutive series of postoperative chest x-rays were examined by an independent reviewer in a blind manner and classified into three groups: none to mild pulmonary hypoplasia (group I, 10 cases); moderate to severe (group II, 20 cases); and very severe hypoplasia (group III, 9 cases). Correlations were done among groups and available anatomical and clinical data. Eight patients out of 9 survived in spite of very severe hypoplasia on postoperative chest x-rays, suggesting that indexes of severity may be misleading when they mistake pulmonary hypoplasia for the cause of death.