Large variability of intake of lick-block supplement among individual animals under different forage types in group-fed cattle

Novel in-paddock technologies, such as electronic feeders (EF), allow measuring individual supplement intake and feeding behaviour of molasses and mineral blocks offered as free-choice to group-fed cattle. EF might also help to capture temporal changes in supplement intake as a result of forage type and grazing management (Titgemeyer et al., 2004). We studied the variability in lick-block supplement intake of individual animals as they grazed paddocks with different forage quantity and qualit

Disproportionate CH4 sink strength from an endemic, sub-alpine Australian soil microbial community

Soil-to-atmosphere methane (CH4) fluxes are dependent on opposing microbial processes of production and consumption. Here we use a soil–vegetation gradient in an Australian sub-alpine ecosystem to examine links between composition of soil microbial communities, and the fluxes of greenhouse gases they regulate. For each soil/vegetation type (forest, grassland, and bog), we meas-ured carbon dioxide (CO2) and CH4 fluxes and their production/consumption at 5 cm intervals to a depth of 30 cm. All soils were sources of CO2, ranging from 49 to 93 mg CO2 m −2 h −1. Forest soils were strong net sinks for CH4, at rates of up to −413 µg CH4 m −2 h −1. Grassland soils varied, with some soils acting as sources and some as sinks, but overall averaged −97 µg CH4 m −2 h −1. Bog soils were net sources of CH4 (+340 µg CH4 m −2 h −1). Methanotrophs were dominated by USCα in forest and grassland soils, and Candidatus Methylomirabilis in the bog soils. Methylocystis were also de-tected at relatively low abundance in all soils. Our study suggests that there is a disproportionately large contribution of these ecosystems to the global soil CH4 sink, which highlights our dependence on soil ecosystem services in remote locations driven by unique populations of soil microbes. It is paramount to explore and understand these remote, hard-to-reach ecosystems to better understand biogeochemical cycles that underpin global sustainability

Microbes as engines of ecosystem function : When does community structure enhance predictions of ecosystem processes?

FUNDING This work was supported by NSF grant DEB-1221215 to DN, as well as grants supporting the generation of our datasets as acknowledged in their original publications and in Supplementary Table S1. ACKNOWLEDGMENT We thank the USGS Powell Center ‘Next Generation Microbes’ working group, anonymous reviews, Brett Melbourne, and Alan Townsend for valuable feedback on this project.Peer reviewedPublisher PD

SMARTfarm Learning Hub: Next generation technologies for agricultural education: Final report 2018

In 2015-2016 there were 282,000 people employed in agriculture in Australia (Australian Bureau of Agricultural and Resource Economics and Sciences [ABARES], 2017). Despite the recognition that the modern agricultural industry is complex and demanding, it still has one of the lowest proportion of workers with post-secondary qualifications across the economy (Senate Standing Committees on Education, Employment and Workplace Relations, 2012), with approximately 7.8 per cent of the agricultural workforce with tertiary qualifications compared with 25 per cent for the broader population (Pratley, 2012). Pratley and Botwright Acuna (2015) have also reported that there is already a skills shortage in the industry, with an estimated four jobs available for every tertiary agricultural graduate in Australia

Finding Diagnostically Useful Patterns in Quantitative Phenotypic Data.

Trio-based whole-exome sequence (WES) data have established confident genetic diagnoses in ∼40% of previously undiagnosed individuals recruited to the Deciphering Developmental Disorders (DDD) study. Here we aim to use the breadth of phenotypic information recorded in DDD to augment diagnosis and disease variant discovery in probands. Median Euclidean distances (mEuD) were employed as a simple measure of similarity of quantitative phenotypic data within sets of ≥10 individuals with plausibly causative de novo mutations (DNM) in 28 different developmental disorder genes. 13/28 (46.4%) showed significant similarity for growth or developmental milestone metrics, 10/28 (35.7%) showed similarity in HPO term usage, and 12/28 (43%) showed no phenotypic similarity. Pairwise comparisons of individuals with high-impact inherited variants to the 32 individuals with causative DNM in ANKRD11 using only growth z-scores highlighted 5 likely causative inherited variants and two unrecognized DNM resulting in an 18% diagnostic uplift for this gene. Using an independent approach, naive Bayes classification of growth and developmental data produced reasonably discriminative models for the 24 DNM genes with sufficiently complete data. An unsupervised naive Bayes classification of 6,993 probands with WES data and sufficient phenotypic information defined 23 in silico syndromes (ISSs) and was used to test a "phenotype first" approach to the discovery of causative genotypes using WES variants strictly filtered on allele frequency, mutation consequence, and evidence of constraint in humans. This highlighted heterozygous de novo nonsynonymous variants in SPTBN2 as causative in three DDD probands

Prevalence and architecture of de novo mutations in developmental disorders.

The genomes of individuals with severe, undiagnosed developmental disorders are enriched in damaging de novo mutations (DNMs) in developmentally important genes. Here we have sequenced the exomes of 4,293 families containing individuals with developmental disorders, and meta-analysed these data with data from another 3,287 individuals with similar disorders. We show that the most important factors influencing the diagnostic yield of DNMs are the sex of the affected individual, the relatedness of their parents, whether close relatives are affected and the parental ages. We identified 94 genes enriched in damaging DNMs, including 14 that previously lacked compelling evidence of involvement in developmental disorders. We have also characterized the phenotypic diversity among these disorders. We estimate that 42% of our cohort carry pathogenic DNMs in coding sequences; approximately half of these DNMs disrupt gene function and the remainder result in altered protein function. We estimate that developmental disorders caused by DNMs have an average prevalence of 1 in 213 to 1 in 448 births, depending on parental age. Given current global demographics, this equates to almost 400,000 children born per year

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Distinct genetic architectures for syndromic and nonsyndromic congenital heart defects identified by exome sequencing.

Congenital heart defects (CHDs) have a neonatal incidence of 0.8-1% (refs. 1,2). Despite abundant examples of monogenic CHD in humans and mice, CHD has a low absolute sibling recurrence risk (∼2.7%), suggesting a considerable role for de novo mutations (DNMs) and/or incomplete penetrance. De novo protein-truncating variants (PTVs) have been shown to be enriched among the 10% of 'syndromic' patients with extra-cardiac manifestations. We exome sequenced 1,891 probands, including both syndromic CHD (S-CHD, n = 610) and nonsyndromic CHD (NS-CHD, n = 1,281). In S-CHD, we confirmed a significant enrichment of de novo PTVs but not inherited PTVs in known CHD-associated genes, consistent with recent findings. Conversely, in NS-CHD we observed significant enrichment of PTVs inherited from unaffected parents in CHD-associated genes. We identified three genome-wide significant S-CHD disorders caused by DNMs in CHD4, CDK13 and PRKD1. Our study finds evidence for distinct genetic architectures underlying the low sibling recurrence risk in S-CHD and NS-CHD

Large-scale discovery of novel genetic causes of developmental disorders

Despite three decades of successful, predominantly phenotype-driven discovery of the genetic causes of monogenic disorders1, up to half of children with severe developmental disorders of probable genetic origin remain without a genetic diagnosis. Particularly challenging are those disorders rare enough to have eluded recognition as a discrete clinical entity, those with highly variable clinical manifestations, and those that are difficult to distinguish from other, very similar, disorders. Here we demonstrate the power of using an unbiased genotype-driven approach2 to identify subsets of patients with similar disorders. By studying 1,133 children with severe, undiagnosed developmental disorders, and their parents, using a combination of exome sequencing3,4,5,6,7,8,9,10,11 and array-based detection of chromosomal rearrangements, we discovered 12 novel genes associated with developmental disorders. These newly implicated genes increase by 10% (from 28% to 31%) the proportion of children that could be diagnosed. Clustering of missense mutations in six of these newly implicated genes suggests that normal development is being perturbed by an activating or dominant-negative mechanism. Our findings demonstrate the value of adopting a comprehensive strategy, both genome-wide and nationwide, to elucidate the underlying causes of rare genetic disorders