The influence of organic and conventional fertilisation and crop protection practices, preceding crop, harvest year and weather conditions on yield and quality of potato (Solanum tuberosum) in a long-term management trial

The effects of organic versus conventional crop management practices (fertilisation, crop protection) and preceding crop on potato tuber yield (total, marketable, tuber size grade distribution) and quality (proportion of diseased, green and damaged tubers, tuber macro-nutrient concentrations) parameters were investigated over six years (2004–2009) as part of a long-term factorial field trial in North East England. Inter-year variability (the effects of weather and preceding crop) was observed to have a profound effect on yields and quality parameters, and this variability was greater in organic fertility systems. Total and marketable yields were significantly reduced by the use of both organic crop protection and fertility management. However, the yield gap between organic and conventional fertilisation regimes was greater and more variable than that between crop protection practices. This appears to be attributable mainly to lower and less predictable nitrogen supply in organically fertilised crops. Increased incidence of late blight in organic crop protection systems only occurred when conventional fertilisation was applied. In organically fertilised crops yield was significantly higher following grass/red clover leys than winter wheat, but there was no pre-crop effect in conventionally fertilised crops. The results highlight that nitrogen supply from organic fertilisers rather than inefficient pest and disease control may be the major limiting factor for yields in organic potato production systems

The UKC3 regional coupled environmental prediction system

This paper describes an updated configuration of the regional coupled research system, termed UKC3, developed and evaluated under the UK Environmental Prediction collaboration. This represents a further step towards a vision of simulating the numerous interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land using more integrated regional coupled prediction systems at kilometre-scale resolution. The UKC3 coupled system incorporates models of the atmosphere (Met Office Unified Model), land surface with river routing (JULES), shelf-sea ocean (NEMO) and ocean surface waves (WAVEWATCH III®), coupled together using OASIS3-MCT libraries. The major update introduced since the UKC2 configuration is an explicit representation of wave–ocean feedbacks through introduction of wave-to-ocean coupling. Ocean model results demonstrate that wave coupling, in particular representing the wave-modified surface drag, has a small but positive improvement on the agreement between simulated sea surface temperatures and in situ observations, relative to simulations without wave feedbacks. Other incremental developments to the coupled modelling capability introduced since the UKC2 configuration are also detailed. Coupled regional prediction systems are of interest for applications across a range of timescales, from hours to decades ahead. The first results from four simulation experiments, each of the order of 1 month in duration, are analysed and discussed in the context of characterizing the potential benefits of coupled prediction on forecast skill. Results across atmosphere, ocean and wave components are shown to be stable over time periods of weeks. The coupled approach shows notable improvements in surface temperature, wave state (in near-coastal regions) and wind speed over the sea, whereas the prediction quality of other quantities shows no significant improvement or degradation relative to the equivalent uncoupled control simulations

Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis.

Multiple sclerosis is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability. Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals, and systematic attempts to identify linkage in multiplex families have confirmed that variation within the major histocompatibility complex (MHC) exerts the greatest individual effect on risk. Modestly powered genome-wide association studies (GWAS) have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects have a key role in disease susceptibility. Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the HLA-DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the class I region. Immunologically relevant genes are significantly overrepresented among those mapping close to the identified loci and particularly implicate T-helper-cell differentiation in the pathogenesis of multiple sclerosis

A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio

Intended Consequences Statement in Conservation Science and Practice

As the biodiversity crisis accelerates, the stakes are higher for threatened plants and animals. Rebuilding the health of our planet will require addressing underlying threats at many scales, including habitat loss and climate change. Conservation interventions such as habitat protection, management, restoration, predator control, trans location, genetic rescue, and biological control have the potential to help threatened or endangered species avert extinction. These existing, well-tested methods can be complemented and augmented by more frequent and faster adoption of new technologies, such as powerful new genetic tools. In addition, synthetic biology might offer solutions to currently intractable conservation problems. We believe that conservation needs to be bold and clear-eyed in this moment of great urgency

Frequent Missense and Insertion/Deletion Polymorphisms in the Ovine Shadoo Gene Parallel Species-Specific Variation in PrP

BACKGROUND: The cellular prion protein PrP(C) is encoded by the Prnp gene. This protein is expressed in the central nervous system (CNS) and serves as a precursor to the misfolded PrP(Sc) isoform in prion diseases. The prototype prion disease is scrapie in sheep, and whereas Prnp exhibits common missense polymorphisms for V136A, R154H and Q171R in ovine populations, genetic variation in mouse Prnp is limited. Recently the CNS glycoprotein Shadoo (Sho) has been shown to resemble PrP(C) both in a central hydrophobic domain and in activity in a toxicity assay performed in cerebellar neurons. Sho protein levels are reduced in prion infections in rodents. Prompted by these properties of the Sho protein we investigated the extent of natural variation in SPRN. PRINCIPAL FINDINGS: Paralleling the case for ovine versus human and murine PRNP, we failed to detect significant coding polymorphisms that alter the mature Sho protein in a sample of neurologically normal humans, or in diverse strains of mice. However, ovine SPRN exhibited 4 missense mutations and expansion/contraction in a series of 5 tandem Ala/Gly-containing repeats R1-R5 encoding Sho's hydrophobic domain. A Val71Ala polymorphism and polymorphic expansion of wt 67(Ala)(3)Gly70 to 67(Ala)(5)Gly72 reached frequencies of 20%, with other alleles including Delta67-70 and a 67(Ala)(6)Gly73 expansion. Sheep V71, A71, Delta67-70 and 67(Ala)(6)Gly73 SPRN alleles encoded proteins with similar stability and posttranslational processing in transfected neuroblastoma cells. SIGNIFICANCE: Frequent coding polymorphisms are a hallmark of the sheep PRNP gene and our data indicate a similar situation applies to ovine SPRN. Whether a common selection pressure balances diversity at both loci remains to be established

Design and implementation of a generalized laboratory data model

<p>Abstract</p> <p>Background</p> <p>Investigators in the biological sciences continue to exploit laboratory automation methods and have dramatically increased the rates at which they can generate data. In many environments, the methods themselves also evolve in a rapid and fluid manner. These observations point to the importance of robust information management systems in the modern laboratory. Designing and implementing such systems is non-trivial and it appears that in many cases a database project ultimately proves unserviceable.</p> <p>Results</p> <p>We describe a general modeling framework for laboratory data and its implementation as an information management system. The model utilizes several abstraction techniques, focusing especially on the concepts of inheritance and meta-data. Traditional approaches commingle event-oriented data with regular entity data in <it>ad hoc </it>ways. Instead, we define distinct regular entity and event schemas, but fully integrate these via a standardized interface. The design allows straightforward definition of a "processing pipeline" as a sequence of events, obviating the need for separate workflow management systems. A layer above the event-oriented schema integrates events into a workflow by defining "processing directives", which act as automated project managers of items in the system. Directives can be added or modified in an almost trivial fashion, i.e., without the need for schema modification or re-certification of applications. Association between regular entities and events is managed via simple "many-to-many" relationships. We describe the programming interface, as well as techniques for handling input/output, process control, and state transitions.</p> <p>Conclusion</p> <p>The implementation described here has served as the Washington University Genome Sequencing Center's primary information system for several years. It handles all transactions underlying a throughput rate of about 9 million sequencing reactions of various kinds per month and has handily weathered a number of major pipeline reconfigurations. The basic data model can be readily adapted to other high-volume processing environments.</p

Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration.

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles

Meta-analysis of Genome-Wide Association Studies for Extraversion: Findings from the Genetics of Personality Consortium

Extraversion is a relatively stable and heritable personality trait associated with numerous psychosocial, lifestyle and health outcomes. Despite its substantial heritability, no genetic variants have been detected in previous genome-wide association (GWA) studies, which may be due to relatively small sample sizes of those studies. Here, we report on a large meta-analysis of GWA studies for extraversion in 63,030 subjects in 29 cohorts. Extraversion item data from multiple personality inventories were harmonized across inventories and cohorts. No genome-wide significant associations were found at the single nucleotide polymorphism (SNP) level but there was one significant hit at the gene level for a long non-coding RNA site (LOC101928162). Genome-wide complex trait analysis in two large cohorts showed that the additive variance explained by common SNPs was not significantly different from zero, but polygenic risk scores, weighted using linkage information, significantly predicted extraversion scores in an independent cohort. These results show that extraversion is a highly polygenic personality trait, with an architecture possibly different from other complex human traits, including other personality traits. Future studies are required to further determine which genetic variants, by what modes of gene action, constitute the heritable nature of extraversion

Meta-analysis of genome-wide association studies for extraversion:Findings from the Genetics of Personality Consortium

Extraversion is a relatively stable and heritable personality trait associated with numerous psychosocial, lifestyle and health outcomes. Despite its substantial heritability, no genetic variants have been detected in previous genome-wide association (GWA) studies, which may be due to relatively small sample sizes of those studies. Here, we report on a large meta-analysis of GWA studies for extraversion in 63,030 subjects in 29 cohorts. Extraversion item data from multiple personality inventories were harmonized across inventories and cohorts. No genome-wide significant associations were found at the single nucleotide polymorphism (SNP) level but there was one significant hit at the gene level for a long non-coding RNA site (LOC101928162). Genome-wide complex trait analysis in two large cohorts showed that the additive variance explained by common SNPs was not significantly different from zero, but polygenic risk scores, weighted using linkage information, significantly predicted extraversion scores in an independent cohort. These results show that extraversion is a highly polygenic personality trait, with an architecture possibly different from other complex human traits, including other personality traits. Future studies are required to further determine which genetic variants, by what modes of gene action, constitute the heritable nature of extraversion