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Proteins are essential components of all living cells and so the study of their in situ expression, proteomics, has wide reaching applications. Peptide identification in proteomics typically relies on matching high resolution tandem mass spectra to a protein database but can also be performed de novo. While artificial spectra have been successfully incorporated into database search pipelines to increase peptide identification rates, little work has been done to investigate the utility of artificial spectra in the context of de novo peptide identification. Here, we perform a critical analysis of the use of artificial data for the training and evaluation of de novo peptide identification algorithms. First, we classify the different fragment ion types present in real spectra and then estimate the number of spurious matches using random peptides. We then categorise the different types of noise present in real spectra. Finally, we transfer this knowledge to artificial data and test the performance of a state-of-the-art de novo peptide identification algorithm trained using artificial spectra with and without relevant noise addition. Noise supplementation increased artificial training data performance from 30% to 77% of real training data peptide recall. While real data performance was not fully replicated, this work provides the first steps towards an artificial spectrum framework for the training and evaluation of de novo peptide identification algorithms. Further enhanced artificial spectra may allow for more in depth analysis of de novo algorithms as well as alleviating the reliance on database searches for training data.

Data centres, though a necessary part of modern society, are being stigmatised for consuming vast amounts of electricity for their operational and cooling needs. Due to Ireland’s reliance on fossil fuels to meet the increased energy demand of data centres, the data centres are contributing significantly to Ireland’s total carbon emissions. As much of this energy is expelled from data centres as waste heat energy, the potential for recycling some of this wasted heat energy was explored using environmentally friendly systems from recent publications. The recovered waste heat energy was applied in a vertical farming system, and the benefits of this waste heat to the vertical farm were analysed and quantified in two scenarios. Using conservative estimates, it was predicted that each vertical farm could be between 5–23% the size of the data centre and produce enough food to feed between 14–61 adults their daily calorie needs, and between 13–58 people their daily fresh produce requirements, depending on the scenario applied. For a more accurate prediction, each vertical farm would have to be assessed on a case-by-case basis, as there is no current research in this area. However, there was not enough data available on Irish data centres to perform these calculations.

Proteomics aims to characterise system-wide protein expression and typically relies on mass-spectrometry and peptide fragmentation, followed by a database search for protein identification. It has wide ranging applications from clinical to environmental settings and virtually impacts on every area of biology. In that context, de novo peptide sequencing is becoming increasingly popular. Historically its performance lagged behind database search methods but with the integration of machine learning, this field of research is gaining momentum. To enable de novo peptide sequencing to realise its full potential, it is critical to explore the mass spectrometry data underpinning peptide identification. In this research we investigate the characteristics of tandem mass spectra using 8 published datasets. We then evaluate two state of the art de novo peptide sequencing algorithms, Novor and DeepNovo, with a particular focus on their performance with regard to missing fragmentation cleavage sites and noise. DeepNovo was found to perform better than Novor overall. However, Novor recalled more correct amino acids when 6 or more cleavage sites were missing. Furthermore, less than 11% of each algorithms’ correct peptide predictions emanate from data with more than one missing cleavage site, highlighting the issues missing cleavages pose. We further investigate how the algorithms manage to correctly identify peptides with many of these missing fragmentation cleavages. We show how noise negatively impacts the performance of both algorithms, when high intensity peaks are considered. Finally, we provide recommendations regarding further algorithms’ improvements and offer potential avenues to overcome current inherent data limitations.

Advanced Driver Assistance Systems (ADAS) have introduced several benefits in the vehicular industry, and their proliferation presents potential opportunities to decrease road accidents. The reasons are mainly attributed to the enhanced perception of the driving environment and reduced human errors. However, as environmental and infrastructural conditions influence the performance of ADAS, the estimation of accident reductions varies across geographical regions. This study presents an interdisciplinary methodology that integrates the literature on advanced driving technologies and road safety to quantify the expected impact of ADAS on accident reduction across combinations of road types, lighting, and weather conditions. The paper investigates the safety effectiveness of ADAS and the distribution of frequency and severity of road accidents across 18 driving contexts and eight accident types. Using road safety reports from the United Kingdom (UK), it is found that a high concentration of accidents (77%) occurs within a small subset of contextual conditions (4 out of 18) and that the most severe accidents happen in dark conditions on rural roads or motorways. The results of the safety effectiveness analysis show that a full deployment of the six most common ADAS would reduce the road accident frequency in the UK by 23.8%, representing an annual decrease of 18,925 accidents. The results also show that the most frequent accident contexts, urban-clear-daylight and rural-clear-daylight, can be reduced by 29%, avoiding 7,020 and 3,472 accidents, respectively. Automatic Emergency Braking (AEB) is the most impactful technology, reducing three out of the four most frequent accident categories – intersection (by 28%), rear-end (by 27.7%), and pedestrian accidents (by 28.4%). This study helps prioritise resources in ADAS research and development focusing on the most relevant contexts to reduce the frequency and severity of road accidents. Furthermore, the identified contextual accident hotspots can assist road safety stakeholders in risk mitigation programs.

Background In contrast to immune checkpoint inhibitors, the use of antibodies as agonists of immune costimulatory receptors as cancer therapeutics has largely failed. We sought to address this problem using a new class of modular synthetic drugs, termed tumor-targeted immune cell agonists (TICAs), based on constrained bicyclic peptides (Bicycles).Methods Phage libraries displaying Bicycles were panned for binders against tumor necrosis factor (TNF) superfamily receptors CD137 and OX40, and tumor antigens EphA2, Nectin-4 and programmed death ligand 1. The CD137 and OX40 Bicycles were chemically conjugated to tumor antigen Bicycles with different linkers and stoichiometric ratios of binders to obtain a library of low molecular weight TICAs (MW

A telematics device is a vehicle instrument that comes preinstalled by the vehicle manufacturer or can be added later. The device records information about driving behavior, including speed, acceleration, and turning force. When connected to vehicle computers, the device can also provide additional information regarding the mechanical usage and condition of the vehicle. All of this information can be transmitted to a central database via mobile networks. The information provided has led to new services such as Usage Based Insurance (UBI). A range of consultants, industry commentators and academics have produced an abundance of projections on how telematics information will allow the introduction of services from personalized insurance, bespoke entertainment and advertise and vehicle energy optimization, particularly for Electric Vehicles (EVs). In this paper we examine these potential services against a backdrop of nascent regulatory limitations and against the technical capacity of the devices. Using a case study approach, we examine three applications that can use telematics information. We find that the expectations of service providers will be significantly tempered by regulatory and technical hurdles. In our discussion we detail these limitations and suggest a more realistic rollout of ancillary services.

To manage greenhouse gas emissions, directives on renewable energy usage have been developed by the European Commission with the objective to reduce overall emissions by 40% by 2030 which presents a significant potential for renewable energy sources. At the same time, it is a challenge for these energy technologies which can only be solved by integrated solutions. Carbon capture and storage combined with geothermal energy could serve as a novel approach to reduce CO2 emissions and at the same time facilitate some of the negative impacts associated with fossil fuel-based power plants. This study focuses on the technical and economic feasibility of combining these technologies based on a published model, data and market research. In the European Union, Germany is the most energy intensive country, and it also has an untapped potential for geothermal energy in the northern as well as the western regions. The CO2 plume geothermal system using supercritical carbon dioxide as the working fluid can be utilized in natural high porosity (10–20%) and permeability (2.5 × 10−14–8.4 × 10−16 m2) reservoirs with temperatures as low as 65.8 °C. The feasibility of the project was assessed based on market conditions and policy support in Germany as well as the geologic background of sandstone reservoirs near industrialized areas (Dortmund, Frankfurt) and the possibility of carbon capture integration and CO2 injection. The levelized cost of electricity for a base case results in € 0.060/kWh. Optimal system type was assessed in a system optimization model. The project has a potential to supply 6600/12000 households with clean energy (electricity/heat) and sequester carbon dioxide at the same time. A trading scheme for carbon dioxide further expands potential opportunities.

In Ireland, truck transport by road dominates and will remain the main transportation mode of biomass. Cost efficiency and flexibility of forest transport can be typically improved by optimising routes. It is important to know every process and attributes within the workflow of roundwood transport. This study aimed to analyse characteristics of timber trucking in Ireland, and to estimate the least-cost route for the distribution of biomass with the use of geographic information systems (GIS). Firstly, a tracking system that recorded the truck’s movements and fuel consumption was installed. A total of 152 trips were recorded, routes were chosen by the truck driver. The recorded information was used to analyse the distances and times travelled loaded and unloaded per road class, breaks, loading and unloading times as well as fuel consumption. Secondly, the routes taken by the truck where compared with routes created using Network Analyst (NA), an extension of ArcGIS. Four scenarios based on route selection criteria were selected: shortest distance (S1), shorted time (S2), and prioritising high-class roads with shortest distance (S3) and time (S4). Results from the analysis of the tracking system data showed that driving both loaded and unloaded occupied on average 69% of the driver’s working shift; with an average time driving loaded of 49%. The travel distance per trip varied from 112 km and 197 km, with the truck driver using mostly national and regional roads. An average 2% of the total distance and 11% of the total time was spent driving on forest roads. In general, the truck’s speed recorded on the different road classes was on average 30% lower than the legal maximum speed. The average fuel consumption was 0.64 L/km. In terms of the route comparison, the driving directions from the truck routes coincided with 77% of the directions of the routes based on shortest driving time (S2 and S4). All the routes chosen by the driver had 22% longer distance than the routes in S1 (shortest distance). The routes selected based on shortest distance (S1 and S3) had the longest travelling time, approximately 19% more than the ones taken by the truck and 30% more than S2 and S4. The average running cost for the truck was 0.83 €/km. Choosing the shortest distance routes (S1 and S3) not only implies reducing travelling costs but also a reduction of CO2 emissions by 12% in comparison to routes in S2 and S4. However, when selecting the routes, travel time can be a much more crucial parameter to analyse rather than distance in terms of transportation costs. Choosing the routes generated in scenario S2 over S1 implied an increase in distance by 12% but a decrease in time of 30%. Less driving time translates into better driving conditions across higher classes or roads; less wear and tear of trucks; and lesser fuel used. It also complies with local authorities preferences of having timber trucks move on higher road types in order to minimise the expenses associated with road maintenance.

Willow is a fast growing, high yielding biomass crop that can help reduce reliance on fossil fuels. However, long establishment times to get to profitability and sustainable yield may deter interest in planting the crop. A number of different approaches were investigated to optimise and accelerate early growth. These approaches were water immersion, plastic application, altering stem orientation at planting, altering coppicing timings and applying growth hormone. Glasshouse and field trials were used to test the different approaches. In this work, planting material was soaked for a varying number of days and plastic was applied or not applied in field trials. In the planting orientation approach, stems were planted diagonally or vertically with half of the planting material above the ground level or horizontally below ground level. Additionally, willow crops were coppiced at different times throughout their first growing season and a growth hormone trial was also incorporated in this work. Water soaking, plastic application, coppicing during the growing season or hormone application did not improve early growth or yield. However, early growth and yield were increased by manipulating the planting orientation of willow stems. Planting orientation treatments in which part of the stem was left above the ground increased early growth and yield significantly compared to the control without requiring extra inputs at planting. The beneficial effects of coppicing can be achieved by manipulating the planting procedure so that the first year’s growth is not disregarded.

Under the Biofuels Obligation Scheme in Ireland, the biofuels penetration rate target for 2013 was set at 6% by volume from a previous 4% from 2010. In 2012 the fuel blend reached 3%, with approximately 70 million L of biodiesel and 56 million L of ethanol blended with diesel and gasoline, respectively. Up to and including April 2013, the current blend rate in Ireland for biodiesel was 2.3% and for bioethanol was 3.7% which equates to approximately 37.5 million L of biofuel for the first four months of 2013. The target of 10% by 2020 remains, which equates to approximately 420 million L yr−1. Achieving the biofuels target would require 345 ktoe by 2020 (14,400 TJ). Utilizing the indigenous biofuels in Ireland such as tallow, used cooking oil and oil seed rape leaves a shortfall of approximately 12,000 TJ or 350 million L (achieving only 17% of the 10% target) that must be either be imported or met by other renewables. Other solutions seem to suggest that microalgae (for biodiesel) and macroalgae (for bioethanol) could meet this shortfall for indigenous Irish production. This paper aims to review the characteristics of algae for biofuel production based on oil yields, cultivation, harvesting, processing and finally in terms of the European Union (EU) biofuels sustainability criteria, where, up to 2017, a 35% greenhouse gas (GHG) emissions reduction is required compared to fossil fuels. From 2017 onwards, a 50% GHG reduction is required for existing installations and from 2018, a 60% reduction for new installations is required.

Co-combustion performance trials of Meat and Bone Meal (MBM) and peat were conducted using a bubbling fluidized bed (BFB) reactor. In the combustion performance trials the effects of the co-combustion of MBM and peat on flue gas emissions, bed fluidization, ash agglomeration tendency in the bed and the composition and quality of the ash were studied. MBM was mixed with peat at 6 levels between 15% and 100%. Emissions were predominantly below regulatory limits. CO concentrations in the flue gas only exceeded the 100 mg/m3 limit upon combustion of pure MBM. SO2 emissions were found to be over the limit of 50 mg/m3, while in all trials NOx emissions were below the limit of 300 mg/m3. The HCl content of the flue gases was found to vary near the limit of 30 mg/m3. VOCs however were within their limits. The problem of bed agglomeration was avoided when the bed temperature was about 850 °C and only 20% MBM was co-combusted. This study indicates that a pilot scale BFB reactor can, under optimum conditions, be operated within emission limits when MBM is used as a co-fuel with peat. This can provide a basis for further scale-up development work in industrial scale BFB applications.

The Republic of Ireland, like many other countries is trying to diversify energy sources to counteract environmental, political and social concerns. Bioethanol from domestically grown agricultural crops is an indigenously produced alternative fuel that can potentially go towards meeting the goal of diversified energy supply. The Republic of Ireland’s distribution of existing soils and agricultural land-uses limit arable crop land to around 10% of total agricultural area. Demand for land to produce arable crops is expected to decrease, which could open the opportunity for bioethanol production. Bioethanol production plants are required to be of a sufficient scale in order to compete economically with other fuel sources, it is important therefore to determine if enough land exists around potential ethanol plant locations to meet the potential demands for feedstock. This study determines, through the use of a developed GIS based model, the potential quantities of feedstock that is available in the hinterlands of nine locations in the Republic of Ireland. The results indicate that three locations can meet all its feedstock demands using indigenously grown sugarbeet, while only one location can meet its demands using a combination of indigenous wheat and straw as the two locally sourced feedstocks.

MicroRNAs (miRNAs) are key regulators of gene expression and contribute to a variety of biological processes. Abnormal miRNA expression has been reported in various diseases including pathophysiology of breast cancer, where they regulate protumorigenic processes including vascular invasiveness, estrogen receptor status, chemotherapy resistance, invasion and metastasis. The miRBase sequence database, a public repository for newly discovered miRNAs, has grown rapidly with approximately >10,000 entries to date. Despite this rapid growth, many miRNAs have not yet been validated, and several others are yet to be identified. A lack of a full complement of miRNAs has imposed limitations on recognizing their important roles in cancer, including breast cancer. Using deep sequencing technology, we have identified 189 candidate novel microRNAs in human breast cancer cell lines with diverse tumorigenic potential. We further show that analysis of 500-nucleotide pri-microRNA secondary structure constitutes a reliable method to predict bona fide miRNAs as judged by experimental validation. Candidate novel breast cancer miRNAs with stem lengths of greater than 30 bp resulted in the generation of precursor and mature sequences in vivo. On the other hand, candidates with stem length less than 30 bp were less efficient in producing mature miRNA. This approach may be used to predict which candidate novel miRNA would qualify as bona fide miRNAs from deep sequencing data with approximately 90% accuracy.