Enhancement of long-range magnetic order by magnetic field in superconducting La2CuO(4+y)

We report a detailed study, using neutron scattering, transport and magnetization measurements, of the interplay between superconducting (SC) and spin density wave (SDW) order in La2CuO(4+y). Both kinds of order set in below the same critical temperature. However, the SDW order grows with applied magnetic field, whereas SC order is suppressed. Most importantly, the field dependence of the SDW Bragg peak intensity has a cusp at zero field, as predicted by a recent theory of competing SDW and SC order. This leads us to conclude that there is a repulsive coupling between the two order parameters. The question of whether the two kinds of order coexist or microscopically phase separate is discussed.Comment: Version accepted for publication in Phys. Rev. B. Improved discussion in connection with the muSR result

Seasonal drought limits tree species across the Neotropics

Within the tropics, the species richness of tree communities is strongly and positively associated with precipitation. Previous research has suggested that this macroecological pattern is driven by the negative effect of water-stress on the physiological processes of most tree species. This process implies that the range limits of taxa are defined by their ability to occur under dry conditions, and thus in terms of species distributions it predicts a nested pattern of taxa distribution from wet to dry areas. However, this ‘dry-tolerance’ hypothesis has yet to be adequately tested at large spatial and taxonomic scales. Here, using a dataset of 531 inventory plots of closed canopy forest distributed across the Western Neotropics we investigated how precipitation, evaluated both as mean annual precipitation and as the maximum climatological water deficit, influences the distribution of tropical tree species, genera and families. We find that the distributions of tree taxa are indeed nested along precipitation gradients in the western Neotropics. Taxa tolerant to seasonal drought are disproportionally widespread across the precipitation gradient, with most reaching even the wettest climates sampled; however, most taxa analysed are restricted to wet areas. Our results suggest that the ‘dry tolerance’ hypothesis has broad applicability in the world's most species-rich forests. In addition, the large number of species restricted to wetter conditions strongly indicates that an increased frequency of drought could severely threaten biodiversity in this region. Overall, this study establishes a baseline for exploring how tropical forest tree composition may change in response to current and future environmental changes in this region

Discovery and fine-mapping of height loci via high-density imputation of GWASs in individuals of African ancestry

Although many loci have been associated with height in European ancestry populations, very few have been identified in African ancestry individuals. Furthermore, many of the known loci have yet to be generalized to and fine-mapped within a large-scale African ancestry sample. We performed sex-combined and sex-stratified meta-analyses in up to 52,764 individuals with height and genome-wide genotyping data from the African Ancestry Anthropometry Genetics Consortium (AAAGC). We additionally combined our African ancestry meta-analysis results with published European genome-wide association study (GWAS) data. In the African ancestry analyses, we identified three novel loci (SLC4A3, NCOA2, ECD/FAM149B1) in sex-combined results and two loci (CRB1, KLF6) in women only. In the African plus European sex-combined GWAS, we identified an additional three novel loci (RCCD1, G6PC3, CEP95) which were equally driven by AAAGC and European results. Among 39 genome-wide significant signals at known loci, conditioning index SNPs from European studies identified 20 secondary signals. Two of the 20 new secondary signals and none of the 8 novel loci had minor allele frequencies (MAF) < 5%. Of 802 known European height signals, 643 displayed directionally consistent associations with height, of which 205 were nominally significant (p < 0.05) in the African ancestry sex-combined sample. Furthermore, 148 of 241 loci contained ≤20 variants in the credible sets that jointly account for 99% of the posterior probability of driving the associations. In summary, trans-ethnic meta-analyses revealed novel signals and further improved fine-mapping of putative causal variants in loci shared between African and European ancestry populations

Mating type, mefenoxam sensitivity, and pathotype diversity in Phytophthora infestans isolates from tomato in Brazil

The objective of this work was to characterize 79 Phytophthora infestans isolates collected in tomato (Solanum lycopersicum) fields, as to mating type, mefenoxam sensitivity, and pathotype composition. The isolates were sampled in 2006 and 2007 in seven Brazilian states as well as in the Distrito Federal. They were characterised as to mating type (n=79), sensitivity to fungicide mefenoxam (n=79), and virulence to three major resistance genes Ph-1, Ph-2, and Ph-3/Ph-4 (n=62). All isolates were of the mating type A1. Resistant isolates were detected in all sampled states, and its average frequency was superior to 50%. No difference was detected in pathotype diversity, neither between subpopulations collected in 2006 and 2007 nor between isolates grouped as resistant or intermediately sensitive to mefenoxam. All major resistance genes were overcome at different frequencies: Ph-1, 88.7%; Ph-2, 64.5%; and Ph-3/Ph-4, 25.8%. Isolates with virulence genes able to overcome all major resistance genes were detected at low frequencies. Tomato breeding programs in Brazil must avoid the development of cultivars with resistance based exclusively on major genes

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations