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Additional file 1. Figures. S1–S14, and Tables S1, S2.

Additional file 1: Figure S1. Schematic representation of the production of autologous mICSI-derived blastocysts. Figure S2. WGBS data of Sus scrofa oocytes (Oc) and ICSI- (IB) and mICSI-derived blastocysts (MB) analysed by the 100-CpG window method and visualized by SeqMonk. Figure S3. Comparative analysis of WGBS data sets from Sus scrofa oocytes (Oc), ICSI- (IB) and mICSI-derived blastocysts (MB). Figure S4. DNA methylation status in each Sus scrofa chromosome. Figure S5. Longitudinal comparison of DMRs to capture differences in the DNA methylation reprogramming process as a result of mtDNA supplementation. Figure S6. Methylation status of imprinted genes (A) KCNQ1, (B) GNAS and (C) MEST in Sus scrofa oocytes (Oc) and blastocysts (IB and MB). Figure S7. PCA of Sus scrofa ICSI- (red) and mICSI- (green) derived blastocyst RNAseq data. Figure S8. Volcano plots displaying differential gene expression between Sus scrofa ICSI- and mICSI-derived blastocysts. Figure S9. Expression of (A) 52 DEGs between ICSI- and mICSI-derived blastocysts (Table 2); (B) genes catalysing cytosine methylation and demethylation; and (C) genes involved in embryonic genome activation presented by heatmap. Figure S10. Expression of genes of interest in Sus scrofa ICSI- and mICSI-derived blastocysts presented by box plots.

Additional file 2: Figure S1. Mutation rates of the CRC patients. This plot shows number of mutations in each CRC sample. Figure S2. Identify best-fitted distribution to discover significant genes. This plot shows our comparison of different distribution techniques to fit the number of mutations in the genes and identify significantly mutated genes. Figure S3. Identify best-fitted distribution to discover significant gene-motifs. This plot shows our comparison of different distribution techniques to fit the number of mutations in the gene-motifs and identify significantly mutated gene-motifs. Figure S4. An overview of gene-motifs concept. We first identify 382 significantly mutated coding genes in colorectal cancers (candidate genes). We then used Fisher exact test to identify those motifs that significantly mutated within candidate genes. Figure S5. Selected 3131 features in two most significant PCAs before scaling. This plot shows two principal components (PCs) that demonstrates the potential discrimination that can be obtained from our identified features. Figure S6. Illustration of patients in two first PCAs of features. Distribution of CRC samples through 3131 gene-motif features by PCA analysis. Figure S7. Correlation between our identified signatures and Alexandrov's signatures in each CRC subtype separately. Figure S8. Mutational load of protein coding genes in each subtype separately. Each bar chart shows fraction of samples with mutation in a gene. Figure S9. Mutational load of long non-coding RNA genes in each subtype separately. Each bar chart shows fraction of samples with mutation in a lncRNA. Figure S10. Mutation rates in coding and lncRNA genes in each subtype. Red color indicates average number of mutations in lncRNA genes and green color indicates average number of mutations in coding genes. Figure S11. Consequence type analysis. Figure shows fraction of mutations in different consequence types for each subtype. Figure S12. Mutation rate in transcripts in genes TTN, PCDHA2, BRAF, APC. Figure shows mutational rate in different transcripts of genes TTN, PCDHA2, BRAF, and APC across the CRC subtypes identified in this study. Figure S13. Analysis of age distribution of CRC samples in the identified subtypes. Figure S14. Evaluation plot for deciphering 3-mer mutational signatures in the CRC samples. We used the CANCERSIGN tool [57] to identify mutational signatures in CRC samples. The evaluation plot of deciphering 3-mer mutational signatures become optimized for seven signatures.

In this paper the tensions between environmental policy, which commits to limiting and reducing greenhouse gas (GHG) emissions, and agricultural policy which seeks to increase agricultural production and agriculture’s contribution to Ireland economic recovery are explored. Results from a partial equilibrium model of the Irish agricultural sector which is capable of simulating the impact of policy change on agricultural activity levels and associated GHG emissions are used to investigate this dilemma. Ireland, as part of the EU Effort Sharing Agreement, has committed to reducing its GHG emissions by 20 percent by 2020 and in the event of a successor to the Kyoto Protocol by 30 percent below the 2005 level of emissions. In Ireland emissions from agriculture account, in a European context, for a very large share of total GHG emissions. Any reduction in Irish national emissions will likely require a reduction in the emissions from agriculture. In this policy context the Irish Government has adopted an ambitious growth strategy for the Irish agricultural sector, known as Food Harvest 2020. The Food Harvest strategy does not explicitly address how such dynamic growth in agricultural production can be achieved while simultaneously reducing GHG emissions from agriculture. This tension between Irish environmental and agricultural policies is likely to be replicated at the European and global levels given the significant contribution of agricultural production to anthropogenic climate change and the role of agriculture in addressing emergent food security concerns.

The application of artificial fertiliser continues to be a vital component of the production system on the bulk of Irish farms, accounting for approximately nine percent of total costs on dairy and cattle farms (Hennessy et al. 2011). However, the average application of artificial nitrogen fertiliser per hectare of grassland has been in decline recently. This reduction in use is likely due to a number of factors including better on-farm grassland management, as well as better management and utilisation of organic manures, the introduction of the Rural Environmental Protection Scheme, the Nitrates Directive, and more recently higher fertiliser prices. Changes in the level of artificial nitrogen usage are likely to have significant implications for agricultural productivity and the environment, both in terms of nitrate emissions and greenhouse gas emissions. Therefore, a better understanding of the factors affecting fertiliser demand, as well as the relationship between fertiliser use and agricultural production levels is required. In this study an unbalanced panel dataset was constructed using data for the period 2000 to 2010 from the Irish National Farm Survey (NFS) and used to estimate two fixed effects models. The first model estimated the elasticity of demand for artificial nitrogen fertiliser applied on grassland. A second fixed effects model was developed to estimate the relationship between stocking rate and the level of artificial nitrogen applied on grassland.

Using Ireland as a case study, the overall aim of this paper is to determine if decoupled payments affect farmers’ behaviour. Using a dynamic, multi product, partial equilibrium model of the EU agricultural sector, this paper first compares levels of production that would be expected if decoupled payments had no impact on farmers’ activity with actual observed outcomes. Second this paper compares cereal and cattle farmers’ profitability prior to decoupling with that observed after the introduction of decoupled payments. The analysis presented here would suggest that decoupled payments do still maintain a significant effect on agricultural activity with farmers using this new form of support to partly subsidise unprofitable farm production.

To date within Europe, a regulatory approach has been favoured when trying to curtail emissions from agriculture, the Nitrates Directive being a recent example. Economic theory indicates that market based solutions such as tradable emissions permits are the least cost means of achieving desired reductions in emissions. This paper compares the impact on farm incomes of a regulatory approach to emissions abatement with an emissions trading approach. A farm-level linear programming model for the Irish agriculture sector is constructed. A 20 percent reduction in Greenhouse Gas (GHG) emissions is introduced and the impact on farm incomes is measured. The linear programming model is then used to determine each farmer’s shadow value for an emissions permit. These shadow values are then weighted to estimate supply and demand curves and used to simulate a market for emissions permits and the farm incomes are re-estimated. Finally, the implications for farm incomes of both abatement strategies are compared with a scenario where no constraint is placed on GHG emissions.

There is increasing interest in biomass crops as an alternative farm enterprise. However, given the relatively low uptake of these crops in Ireland, there is limited information concerning the risk associated with their production and its potential impact on returns. The uncertainty surrounding risky variables such as the costs of production, yield level, price per tonne and opportunity cost of land make it difficult to accurately calculate the returns to biomass crops. Their lengthy production lifespan may only serve to heighten the level of risk that affects key variables. A stochastic budgeting model is used to calculate the returns from willow and miscanthus. The opportunity cost of land is accounted for through the inclusion of the foregone returns from selected conventional agricultural activities. The potential for bioremediation to boost returns is also examined. The NPV of various biomass investment options are simulated to ascertain the full distribution of possible returns. The results of these simulations are then compared using their respective CDF’s and the investments are ranked using Stochastic Efficiency with Respect to a Function (SERF).

Agriculture in Ireland accounts for a higher proportion of greenhouse gas (GHG) emissions than in any other EU member state. Furthermore as part of the EU’s commitment to reduce emissions by 20 percent by 2020, Ireland is one of the few countries who will have to cuts its 2005 GHG emissions level by the full 20 percent. Given the magnitude of the cut in national emissions that is required and the size of agriculture’s contribution to Ireland’s total emissions, the agriculture sector has been identified by some parties as a sector that could make a significant contribution to achieving the national target. In order to evaluate the impact on Irish farmers of reducing GHG emissions it is necessary to first estimate the marginal cost of emissions abatement. This paper uses Irish farm-level data to construct a linear programming model which in turn is used to estimate the marginal abatement cost curve for GHG emissions on Irish farms and this is aggregated to estimate a marginal cost curve for the agriculture sector. The impact of an emissions tax in achieving targeted levels of GHG emissions will be measured under a baseline scenario of no policy change.

Research into Greenhouse Gas (GHG) emissions from Irish agriculture has focused on two main themes (i) projecting future emission levels and (ii) devising abatement strategies at the farm level such as changes in animal diet, better waste management and or changes in farm management practices. These abatement strategies will have costs associated with them some of which, such as capital investment or reducing livestock numbers, may be substantial. However economic theory indicates that market based solutions such as tradable emissions permits (TEP’s) are the least cost means of achieving desired reductions in emissions. To date within Europe a regulatory approach has been favoured when trying to curtail emissions from agriculture, the Nitrates Directive being a recent example. This paper seeks to compare the impact on farm incomes of a regulatory approach to emissions abatement with a TEP’s approach. In order to do this data from the Irish National Farm Survey is used to construct a farm-level Linear Programming (LP) model for each farmer within the dataset. Firstly a baseline scenario with no constraint on emissions is run. We then enforce a 20 percent reduction in emissions and the impact on farm incomes is measured. The LP model is then used to determine each farmers shadow value for a TEP. These shadow values are then weighted up to estimate the supply and demand and used to simulate a market for TEP’s and the farm income is re-estimated. Finally the implications for farm incomes of both abatement strategies are compared with the baseline scenario.

This paper explores the implications of an expansion of the EU milk quota. The paper begins by examining the relative competitiveness of dairy farming in the EU. FADN data is used to derive production cost measures and partial productivity indicators for selected Member States. The results show that Irish dairy farmers have relatively low cash costs of production and that output per hectare and per labour unit is low, suggesting that Ireland may be well placed to expand production if the milk quota regime were reformed. The subsequent sections of the paper address the implications of an expansion of the EU milk quota. Results of an analysis conducted using the FAPRI-Ireland partial equilibrium model are summarised while the results and methods of a farm level analysis are discussed in more detail. The FAPRI-Ireland farm level model integrates econometric and linear programming modelling to simulate the farmer behaviour. Results suggest that the majority of Irish farmers would benefit from milk quota expansion, although the extent to which they would benefit largely depends on the availability and price of traded quota in their local quota market.

The agronomic characteristics of willow and miscanthus make these crops highly susceptible to risk. This is particularly true in a country such as Ireland which has limited experience in the production of these crops. Issues such as soil and climate suitability have as yet to be resolved. The lengthy production lifespan of energy crops only serve to heighten the level of risk that affects key variables. The uncertainty surrounding the risk variables involved in producing willow and miscanthus, such as the annual yield level and the energy price, make it difficult to accurately calculate the returns of such a project. The returns from willow and miscanthus are compared with those of conventional agricultural enterprises using Stochastic Efficiency with Respect to a Function (SERF). A risk premium is calculated which farmers would need to be compensated with in order for them to be indifferent between their current enterprise and switching to biomass crop production. With the exception of spring barley, a risk premium is required if farmers are to be indifferent between their current enterprise and willow or miscanthus. The value of the risk premium required to entice farmers to switch to miscanthus production is significantly less than that required for willow. This suggests that a greater level of risk is associated with willow than with miscanthus.

The increasing concern over climate change has led to a number of international agreements to control greenhouse gas emissions. Agriculture currently accounts for 28 percent of Ireland’s total greenhouse gas emission and therefore has a major role to play in Ireland achieving its emissions targets. To date research into reducing emissions from Irish agriculture has focused on devising abatement strategies at the farm level such as changes in animal feeding practices. Alternatively emissions could be controlled using market-based emissions abatement strategies such as emissions taxes or permit trading, which are in theory a least cost means of cutting emissions. This paper uses data from the Irish National Farm Survey to construct a farm-level Linear Programming model and to simulate a market for tradable emission permits. The impact on average gross margin of allowing farmers to reduce greenhouse gas emissions by trading permits is compared with a scenario where emissions are unconstrained and a scenario where a command and control approach is adopted to reduce emissions.

Ireland is currently importing 90 percent of its energy. The burning of domestically produced nonrenewable peat provides 4.9 percent of Ireland’s total primary energy supply while renewable biomass crops currently account for only 1 percent of the domestically produced energy supply. The Irish government have set a target of 30% of peat (approximately 0.9 million tonnes) used for electricity generation to be replaced by renewable energy crops. This would be equivalent to approximately 0.6 million tonnes of biomass crops or approximately 45,000 hectares of biomass. Direct payments and subsidies accounted for over 100 percent of average family farm income on beef and sheep farms in 2006. Therefore there appears to be significant potential for Irish farmers to replace conventional agricultural enterprises with biomass crops. A probit model was built to identify the socio-economic characteristics of farmers who may be willing to adopt energy crop production. The results from this were used in the construction of a linear programming model to determine the optimal enterprise for each farmer at varying energy prices.

The paper is a case study of how the Luxembourg Agreement of the Mid-Term Review of the Common Agricultural Policy may affect the structure of dairy and beef farming in the Republic of Ireland over the period 2002 to 2012. It describes the process used to assess some structural implications of a policy change. The data source for the paper is the Irish National Farm Survey. Prices of inputs and outputs following the policy change are obtained from a dynamic partial equilibrium model of the agricultural sector. Linear Programming is used to calculate the maximum profit on different farm types. Labour allocation on farms is estimated using a logit function. Exit from dairy production is also estimated. Some example results on dairy and beef farms are presented.

In this paper, a new routing algorithm based on a flooding method is introduced. Flooding techniques have been used previously, e.g. for broadcasting the routing table in the ARPAnet [1] and other special purpose networks [3][4][5]. However, sending data using flooding can often saturate the network [2] and it is usually regarded as an inefficient broadcast mechanism. Our approach is to flood a very short packet to explore an optimal route without relying on a pre-established routing table, and an efficient flood control algorithm to reduce the signalling traffic overhead. This is an inherently robust mechanism in the face of a network configuration change, achieves automatic load sharing across alternative routes and has potential to solve many contemporary routing problems. An earlier version of this mechanism was originally developed for virtual circuit establishment in the experimental Caroline ATM LAN [6][7] at Monash University.

BackgroundMicroRNAs (miRNAs) are increasingly seen as important regulators of placental development and opportunistic biomarker targets. Given the difficulty in obtaining samples from early gestation and subsequent paucity of the same, investigation of the role of miRNAs in early gestation human placenta has been limited. To address this, we generated miRNA profiles using 96 placentas from presumed normal pregnancies, across early gestation, in combination with matched profiles from maternal plasma. Placenta samples range from 6–23 weeks’ gestation, a time period that includes placenta from the early, relatively low but physiological (6–10 weeks’ gestation) oxygen environment, and later, physiologically normal oxygen environment (11–23 weeks’ gestation).ResultsWe identified 637 miRNAs with expression in 86 samples (after removing poor quality samples), showing a clear gestational age gradient from 6–23 weeks’ gestation. We identified 374 differentially expressed (DE) miRNAs between placentas from 6–10 weeks’ versus 11–23 weeks’ gestation. We see a clear gestational age group bias in miRNA clusters C19MC, C14MC, miR-17∼92 and paralogs, regions that also include many DE miRNAs. Proportional change in expression of placenta-specific miRNA clusters was reflected in maternal plasma.ConclusionThe presumed introduction of oxygenated maternal blood into the placenta (between ∼10–12 weeks’ gestation) changes the miRNA profile of the chorionic villus, particularly in placenta-specific miRNA clusters. Data presented here comprise a clinically important reference set for studying early placenta development and may underpin the generation of minimally invasive methods for monitoring placental health.

The abundance of cell-free microRNA (miRNA) has been measured in many body fluids, including blood plasma, which has been proposed as a source with novel, minimally invasive biomarker potential for several diseases. Despite improvements in quantification methods for plasma miRNAs, there is no consensus on optimal reference miRNAs or to what extent haemolysis may affect plasma miRNA content. Here we propose a new method for the detection of haemolysis in miRNA high-throughput sequencing (HTS) data from libraries prepared using human plasma. To establish a miRNA haemolysis signature in plasma we first identified differentially expressed miRNAs between samples with known haemolysis status and selected miRNA with statistically significant higher abundance in our haemolysed group. Given there may be both technical and biological reasons for differential abundance of signature miRNA, and to ensure the method developed here was relevant outside of our specific context, that is women of reproductive age, we tested for significant differences between pregnant and non-pregnant groups. Here we report a novel 20 miRNA signature (miR-106b-3p, miR-140-3p, miR-142-5p, miR-532-5p, miR-17-5p, miR-19b-3p, miR-30c-5p, miR-324-5p, miR-192-5p, miR-660-5p, miR-186-5p, miR-425-5p, miR-25-3p, miR-363-3p, miR-183-5p, miR-451a, miR-182-5p, miR-191-5p, miR-194-5p, miR-20b-5p) that can be used to identify the presence of haemolysis, in silico, in high throughput miRNA sequencing data. Given the potential for haemolysis contamination, we recommend that assay for haemolysis detection become standard pre-analytical practice and provide here a simple method for haemolysis detection.

Refereed/Peer-reviewed The search for novel microRNA (miRNA) biomarkers in plasma is hampered by haemolysis, the lysis and subsequent release of red blood cell contents, including miRNAs, into surrounding fluid. The biomarker potential of miRNAs comes in part from their multicompartment origin and the long-lived nature of miRNA transcripts in plasma, giving researchers a functional window for tissues that are otherwise difficult or disadvantageous to sample. The inclusion of red-blood-cell-derived miRNA transcripts in downstream analysis introduces a source of error that is difficult to identify posthoc and may lead to spurious results. Where access to a physical specimen is not possible, our tool will provide an in silico approach to haemolysis prediction. We present DraculR, an interactive Shiny/R application that enables a user to upload miRNA expression data from a short-read sequencing of human plasma as a raw read counts table and interactively calculate a metric that indicates the degree of haemolysis contamination. The code, DraculR web tool and its tutorial are freely available as detailed herein.

This study examined the nature and accuracy of information available across online platforms for couples trying to conceive. A consumer simulation-based investigation of English websites and social media (Facebook, Twitter, Instagram) was undertaken using common search terms identified in a pilot study. Claims about fertility and pregnancy health were then extracted from the results and analysed thematically. The accuracy of each claim was assessed independently by six fertility and conception experts, rated on a scale of 1 (not factual) to 4 (highly factual), with scores collated to produce a median rating. Claims with a median score