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Meta-analyses of bipolar disorder (BD) genome-wide association studies (GWAS) have identified several genome-wide significant signals in European-ancestry samples, but so far account for little of the inherited risk. We performed a meta-analysis of ∼750 000 high-quality genetic markers on a combined sample of ∼14 000 subjects of European and Asian-ancestry (phase I). The most significant findings were further tested in an extended sample of ∼17 700 cases and controls (phase II). The results suggest novel association findings near the genes TRANK1 ( LBA1 ), LMAN2L and PTGFR . In phase I, the most significant single nucleotide polymorphism (SNP), rs9834970 near TRANK1 , was significant at the P =2.4 × 10 −11 level, with no heterogeneity. Supportive evidence for prior association findings near ANK3 and a locus on chromosome 3p21.1 was also observed. The phase II results were similar, although the heterogeneity test became significant for several SNPs. On the basis of these results and other established risk loci, we used the method developed by Park et al. to estimate the number, and the effect size distribution, of BD risk loci that could still be found by GWAS methods. We estimate that >63 000 case–control samples would be needed to identify the ∼105 BD risk loci discoverable by GWAS, and that these will together explain <6% of the inherited risk. These results support previous GWAS findings and identify three new candidate genes for BD. Further studies are needed to replicate these findings and may potentially lead to identification of functional variants. Sample size will remain a limiting factor in the discovery of common alleles associated with BD.

Francis McMahon and colleagues report a meta-analysis of genome-wide association data sets for major mood disorders, including bipolar disorder and major depressive disorder. The major mood disorders, which include bipolar disorder and major depressive disorder (MDD), are considered heritable traits, although previous genetic association studies have had limited success in robustly identifying risk loci. We performed a meta-analysis of five case-control cohorts for major mood disorder, including over 13,600 individuals genotyped on high-density SNP arrays. We identified SNPs at 3p21.1 associated with major mood disorders (rs2251219, P = 3.63 × 10 −8 ; odds ratio = 0.87; 95% confidence interval, 0.83–0.92), with supportive evidence for association observed in two out of three independent replication cohorts. These results provide an example of a shared genetic susceptibility locus for bipolar disorder and MDD.

Correction to: Molecular Psychiatry advance online publication, 20 December 2011; doi:10.1038/mp.2011.157 Following the online publication of this article, the authors noted the following co-authors were not included: A Farmer6, P McGuffin6, I Craig6, C Lewis6, G Hosang6, S Cohen-Woods6, JB Vincent7, JL Kennedy7 and J Strauss7

Meta-analyses of bipolar disorder (BD) genome-wide association studies (GWAS) have identified several genome-wide significant signals in European-ancestry samples, but so far account for little of the inherited risk. We performed a meta-analysis of 750,000 high-quality genetic markers on a combined sample of 14,000 subjects of European and Asian-ancestry (phase I). The most significant findings were further tested in an extended sample of 17,700 cases and controls (phase II). The results suggest novel association findings near the genes TRANK1 (LBA1), LMAN2L and PTGFR. In phase I, the most significant single nucleotide polymorphism (SNP), rs9834970 near TRANK1, was significant at the P=2.4 × 10(-11) level, with no heterogeneity. Supportive evidence for prior association findings near ANK3 and a locus on chromosome 3p21.1 was also observed. The phase II results were similar, although the heterogeneity test became significant for several SNPs. On the basis of these results and other established risk loci, we used the method developed by Park et al. to estimate the number, and the effect size distribution, of BD risk loci that could still be found by GWAS methods. We estimate that >63,000 case-control samples would be needed to identify the 105BD risk loci discoverable by GWAS, and that these will together explain <6% of the inherited risk. These results support previous GWAS findings and identify three new candidate genes for BD. Further studies are needed to replicate these findings and may potentially lead to identification of functional variants. Sample size will remain a limiting factor in the discovery of common alleles associated with BD.

Prognostic modelling is important in clinical practice and epidemiology for patient management and research. Electronic health records (EHR) provide large quantities of data for such models, but conventional epidemiological approaches require significant researcher time to implement. Expert selection of variables, fine-tuning of variable transformations and interactions, and imputing missing values are time-consuming and could bias subsequent analysis, particularly given that missingness in EHR is both high, and may carry meaning. Using a cohort of 80,000 patients from the CALIBER programme, we compared traditional modelling and machine-learning approaches in EHR. First, we used Cox models and random survival forests with and without imputation on 27 expert-selected, preprocessed variables to predict all-cause mortality. We then used Cox models, random forests and elastic net regression on an extended dataset with 586 variables to build prognostic models and identify novel prognostic factors without prior expert input. We observed that data-driven models used on an extended dataset can outperform conventional models for prognosis, without data preprocessing or imputing missing values. An elastic net Cox regression based with 586 unimputed variables with continuous values discretised achieved a C-index of 0.801 (bootstrapped 95% CI 0.799 to 0.802), compared to 0.793 (0.791 to 0.794) for a traditional Cox model comprising 27 expert-selected variables with imputation for missing values. We also found that data-driven models allow identification of novel prognostic variables; that the absence of values for particular variables carries meaning, and can have significant implications for prognosis; and that variables often have a nonlinear association with mortality, which discretised Cox models and random forests can elucidate. This demonstrates that machine-learning approaches applied to raw EHR data can be used to build models for use in research and clinical practice, and identify novel predictive variables and their effects to inform future research.

Air was sampled from the porous firn layer at the NEEM site in Northern Greenland. We use an ensemble of ten reference tracers of known atmospheric history to characterise the transport properties of the site. By analysing uncertainties in both data and the reference gas atmospheric histories, we can objectively assign weights to each of the gases used for the depth-diffusivity reconstruction. We define an objective root mean square criterion that is minimised in the model tuning procedure. Each tracer constrains the firn profile differently through its unique atmospheric history and free air diffusivity, making our multiple-tracer characterisation method a clear improvement over the commonly used single-tracer tuning. Six firn air transport models are tuned to the NEEM site; all models successfully reproduce the data within a 1σ Gaussian distribution. A comparison between two replicate boreholes drilled 64 m apart shows differences in measured mixing ratio profiles that exceed the experimental error. We find evidence that diffusivity does not vanish completely in the lock-in zone, as is commonly assumed. The ice age- gas age difference (Δage) at the firn-ice transition is calculated to be 182+3−9 yr. We further present the first intercomparison study of firn air models, where we introduce diagnostic scenarios designed to probe specific aspects of the model physics. Our results show that there are major differences in the way the models handle advective transport. Furthermore, diffusive fractionation of isotopes in the firn is poorly constrained by the models, which has consequences for attempts to reconstruct the isotopic composition of trace gases back in time using firn air and ice core records.

We report a 40-year history of SF6 atmospheric mole fractions measured at the Advanced Global Atmospheric Gases Experiment (AGAGE) monitoring sites, combined with archived air samples, to determine emission estimates from 1978 to 2018. Previously we reported a global emission rate of 7.3±0.6 Gg yr−1 in 2008 and over the past decade emissions have continued to increase by about 24 % to 9.04±0.35 Gg yr−1 in 2018. We show that changing patterns in SF6 consumption from developed (Kyoto Protocol Annex-1) to developing countries (non-Annex-1) and the rapid global expansion of the electric power industry, mainly in Asia, have increased the demand for SF6-insulated switchgear, circuit breakers, and transformers. The large bank of SF6 sequestered in this electrical equipment provides a substantial source of emissions from maintenance, replacement, and continuous leakage. Other emissive sources of SF6 occur from the magnesium, aluminium, and electronics industries as well as more minor industrial applications. More recently, reported emissions, including those from electrical equipment and metal industries, primarily in the Annex-1 countries, have declined steadily through substitution of alternative blanketing gases and technological improvements in less emissive equipment and more efficient industrial practices. Nevertheless, there are still demands for SF6 in Annex-1 countries due to economic growth, as well as continuing emissions from older equipment and additional emissions from newly installed SF6-insulated electrical equipment, although at low emission rates. In addition, in the non-Annex-1 countries, SF6 emissions have increased due to an expansion in the growth of the electrical power, metal, and electronics industries to support their continuing development. There is an annual difference of 2.5–5 Gg yr−1 (1990–2018) between our modelled top-down emissions and the UNFCCC-reported bottom-up emissions (United Nations Framework Convention on Climate Change), which we attempt to reconcile through analysis of the potential contribution of emissions from the various industrial applications which use SF6. We also investigate regional emissions in East Asia (China, S. Korea) and western Europe and their respective contributions to the global atmospheric SF6 inventory. On an average annual basis, our estimated emissions from the whole of China are approximately 10 times greater than emissions from western Europe. In 2018, our modelled Chinese and western European emissions accounted for ∼36 % and 3.1 %, respectively, of our global SF6 emissions estimate.

IN READING quotations of yours from a recent publication of the University of California on the essentials of orchard cultivation and irrigation, I am reminded of my own experience in ranching a quarter of a century ago, when, an a young man whose health had failed from too strenuous work in a city office, I came to...

Boron (B) isotopes are widely used in the Earth sciences to trace processes ranging from slab recycling in the mantle to changes in ocean pH and atmospheric CO2. Boron isotope analysis is increasingly achieved by multi‐collector inductively coupled plasma mass spectrometry, which requires separation of B from the sample matrix. Traditional column chromatography methods for this separation have a well‐established track record but are time consuming and prone to contamination from airborne blank. Here, we present an extensive array of tests that establish a novel method for B purification using a batch method. We discuss the key controls and limitations on sample loading, matrix removal and B elution including sample volume, ionic strength, buffer to acid ratio and elution volume, all of which may also help optimize column‐based methods. We find consistent, low procedural blanks of 10 ± 16 pg and excellent reproducibility: 10 ng NIST RM 8301 foram [8301f] yields 14.58 ± 0.11‰ 2SD n = 15; 2.5 ng 8301f yields 14.60 ± 0.19‰ 2SD, n = 31; and overall long term 2SD on n = 218 samples pooling different sample sizes yields 14.62 ± 0.21‰ 2SD. This method also offers significant advantages in throughput, allowing the processing of 24 samples in ∼5 hr. This boron batch method thus provides a fast, reproducible, low‐blank method for purification of boron for high precision isotopic analyses. Key Points Optimized method for B purification for δ11B analysis by multi‐collector inductively coupled plasma mass spectrometry Allows faster sample processing (24 samples in ∼5 hr) Minimizes procedural blanks (typically <20 pg)

Ruminants microbial consortium is responsible for ruminal fermentation, a process which converts fibrous feeds unsuitable for human consumption into desirable dairy and meat products, begins to establish soon after birth. However, it undergoes a significant transition when digestion shifts from the lower intestine to ruminal fermentation. We hypothesised that delaying the transition from a high milk diet to an exclusively solid food diet (weaning) would lessen the severity of changes in the gastrointestinal microbiome during this transition. β-diversity of ruminal and faecal microbiota shifted rapidly in early-weaned calves (6 weeks), whereas, a more gradual shift was observed in late-weaned calves (8 weeks) up to weaning. Bacteroidetes and Firmicutes were the most abundant ruminal phyla in pre- and post-weaned calves, respectively. Yet, the relative abundance of these phyla remained stable in faeces (P ≥ 0.391). Inferred gene families assigned to KEGG pathways revealed an increase in ruminal carbohydrate metabolism (P ≤ 0.009) at 9, compared to 5 weeks. Conversely, carbohydrate metabolism in faeces declined (P ≤ 0.002) following a change in weaning status (i.e., the shift from pre- to post-weaning). Our results indicate weaning later facilitates a more gradual shift in microbiota and could potentially explain the negative effects of early-weaning associated with feeding a high-plane of pre-weaning nutrition.