A numerical renormalisation group method for the analysis of critical spreading activity in spiking neural networks

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The use of exergy analysis to benchmark the resource efficiency of municipal waste water treatment plants in Ireland

Exergy Analysis has been identified in the literature as a powerful tool to benchmark the resource efficiency of thermal systems. The exergy approach provides a rational basis for process optimisation, where, in theory, the processes with the greatest exergy destruction represent the greatest energy efficiency opportunities. Exergy analysis of a Waste Water Treatment Plant (WWTP) has been performed. In addition, two separate reference environments for WWTPs are defined based on plant location. Biological oxygen demand was identified as the most useful parameter when calculating the chemical exergy of organic matter in waste water. The results of this study indicate that organic matter is the principal contributor to chemical exergy values and that exergy analysis is a useful approach to identify inefficient processes within a WWTP

Opportunities for process control optimisation in Irish municipal wastewater treatment plants

As societies ever increasing reliance on electrical energy continues, the role of process optimisation becomes more and more prevalent. This paper presents an energy audit of a typical Irish wastewater treatment plant (P.E. 30,000 ) and attempts to investigate measures to increase the energy efficiencies within treatment plants across Ireland. Based on an in depth review of international energy efficient wastewater treatment plants, energy savings opportunities exist via the use of variable frequency drives to control pumps and blowers; the introduction of inter-basin dissolved oxygen control systems to provide the varying, relevant oxygen requirements to the aeration basin; and effective plant management using appropriate control strategies via accurate sensor feedback and real-time, online monitoring

Life cycle assessment of waste water treatment plants in Ireland

The European Water Act 91/271/EEC introduced a series of measures for the purpose of protecting the environment from the adverse effects of effluent discharge from Waste Water Treatment Plants (WWTP). There are environmental costs associated with attaining the required level of water quality set out in the act such as, emissions from energy production, ecotoxicity from sludge application to land. The goal of this study is to assess these costs. Life Cycle Assessment (LCA) has been the analytical tool used to evaluate the environmental loadings. The CML 2001 Life Cycle Impact Assessment (LCIA) methodology has been adopted and implemented using GaBi 6.0 LCA software. Two plants of varying size and location were chosen for the study. The study found that energy consumption and sludge application to land are the largest contributors to the environmental impact associated with waste water treatment

Benchmarking resource efficiency in wastewater treatment plants: developing best practices

Energy and water are inextricably linked global resources which are under stress; water is required to generate electricity, and energy is required to purify water. Wastewater treatment plants (WWTPs) are an integral part of the water resources chain. Individual plants operate continually and are subject to a number of pressures (e.g. population changes, varying influent due to storm water, more stringent requirements for WWTP managers to meet discharge limits etc.) making the implementation of resource efficiencies uniquely challenging. Implementing efficiencies in WWTPs requires robust benchmarking and key performance indicator (KPI) tools, in order to implement more effective control, and identify opportunities for improvement. In Ireland, and internationally, these challenges have long been recognised, therefore a great deal of attention is focused on developing benchmarking tools suitable for the wastewater sector. This study presents a unique benchmarking system that enables WWTP managers and engineers isolate where and how resources are used and identify potential resource consumption mitigation measures within WWTPs. A unique and critical element of this benchmarking system is a tool (KPIAdvisor) that enables stakeholders to easily (i) assess the current level and accuracy of data collection undertaken at a WWTP; (ii) decide whether opting into a benchmarking system would be feasible based on the level of data collection onsite; (iii) identify data sources which may require corrective action prior to the adoption of a benchmarking system. KPIAdvisor automatically informs the construction and customisation of a KPI calculation and reporting tool (KPICalc) in order to ensure its applicability in a wide variety of WWTPs. This feature ensures that KPICalc users will not be presented with modules which are irrelevant, and streamlines data entry, thus increasing the toolkit’s usability. As part of the resource benchmarking system, KPIAdvisor enables resource efficiencies to be identified with ease, owing to the automated customisation of the benchmarking system achieved from KPIAdvisor output

Life cycle assessment of wastewater treatment plants in Ireland

The Urban Wastewater Treatment Directive 91/271/EEC introduced a series of measures for the purpose of protecting the environment from the adverse effects of effluent discharge from wastewater treatment plants. There are environmental costs associated with attaining the required level of water quality set out in the directive such as greenhouse gas emissions due to energy production, and ecotoxicity from sludge application to land. The goal of this study is to assess the environmental costs in an Irish context, focusing specifically on the effects of variation in scale and discharge limitation. Life cycle assessment is the analytical tool used to evaluate the environmental impact. The life cycle impact assessment methodology developed by the Centre of Environmental Science, Leiden University (2010) has been adopted and implemented using GaBi 6.0 life cycle assessment software. Two plants of varying size and location were chosen for the study. The study found that energy consumption and sludge application to land are the largest contributors to the overall environmental impact associated with the treatment process at both plants. Economies of scale were observed in energy usage during secondary aeration

Integration of copy number and transcriptomics provides risk stratification in prostate cancer : a discovery and validation cohort study

Study data are deposited in NCBI GEO (unique identifier number GSE70770).Background : Understanding the heterogeneous genotypes and phenotypes of prostate cancer is fundamental to improving the way we treat this disease. As yet, there are no validated descriptions of prostate cancer subgroups derived from integrated genomics linked with clinical outcome. Methods : In a study of 482 tumour, benign and germline samples from 259 men with primary prostate cancer, we used integrative analysis of copy number alterations (CNA) and array transcriptomics to identify genomic loci that affect expression levels of mRNA in an expression quantitative trait loci (eQTL) approach, to stratify patients into subgroups that we then associated with future clinical behaviour, and compared with either CNA or transcriptomics alone. Findings : We identified five separate patient subgroups with distinct genomic alterations and expression profiles based on 100 discriminating genes in our separate discovery and validation sets of 125 and 103 men. These subgroups were able to consistently predict biochemical relapse (p = 0.0017 and p = 0.016 respectively) and were further validated in a third cohort with long-term follow-up (p = 0.027). We show the relative contributions of gene expression and copy number data on phenotype, and demonstrate the improved power gained from integrative analyses. We confirm alterations in six genes previously associated with prostate cancer ( MAP3K7, MELK, RCBTB2, ELAC2, TPD52, ZBTB4), and also identify 94 genes not previously linked to prostate cancer progression that would not have been detected using either transcript or copy number data alone. We confirm a number of previously published molecular changes associated with high risk disease, including MYC amplification, and NKX3-1, RB1 and PTEN deletions, as well as over-expression of PCA3 and AMACR, and loss of MSMB in tumour tissue. A subset of the 100 genes outperforms established clinical predictors of poor prognosis (PSA, Gleason score), as well as previously published gene signatures (p = 0.0001). We further show how our molecular profiles can be used for the early detection of aggressive cases in a clinical setting, and inform treatment decisions. Interpretation : For the first time in prostate cancer this study demonstrates the importance of integrated genomic analyses incorporating both benign and tumour tissue data in identifying molecular alterations leading to the generation of robust gene sets that are predictive of clinical outcome in independent patient cohorts.Publisher PDFPeer reviewe

Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study.

BACKGROUND: Understanding the heterogeneous genotypes and phenotypes of prostate cancer is fundamental to improving the way we treat this disease. As yet, there are no validated descriptions of prostate cancer subgroups derived from integrated genomics linked with clinical outcome. METHODS: In a study of 482 tumour, benign and germline samples from 259 men with primary prostate cancer, we used integrative analysis of copy number alterations (CNA) and array transcriptomics to identify genomic loci that affect expression levels of mRNA in an expression quantitative trait loci (eQTL) approach, to stratify patients into subgroups that we then associated with future clinical behaviour, and compared with either CNA or transcriptomics alone. FINDINGS: We identified five separate patient subgroups with distinct genomic alterations and expression profiles based on 100 discriminating genes in our separate discovery and validation sets of 125 and 103 men. These subgroups were able to consistently predict biochemical relapse (p = 0.0017 and p = 0.016 respectively) and were further validated in a third cohort with long-term follow-up (p = 0.027). We show the relative contributions of gene expression and copy number data on phenotype, and demonstrate the improved power gained from integrative analyses. We confirm alterations in six genes previously associated with prostate cancer (MAP3K7, MELK, RCBTB2, ELAC2, TPD52, ZBTB4), and also identify 94 genes not previously linked to prostate cancer progression that would not have been detected using either transcript or copy number data alone. We confirm a number of previously published molecular changes associated with high risk disease, including MYC amplification, and NKX3-1, RB1 and PTEN deletions, as well as over-expression of PCA3 and AMACR, and loss of MSMB in tumour tissue. A subset of the 100 genes outperforms established clinical predictors of poor prognosis (PSA, Gleason score), as well as previously published gene signatures (p = 0.0001). We further show how our molecular profiles can be used for the early detection of aggressive cases in a clinical setting, and inform treatment decisions. INTERPRETATION: For the first time in prostate cancer this study demonstrates the importance of integrated genomic analyses incorporating both benign and tumour tissue data in identifying molecular alterations leading to the generation of robust gene sets that are predictive of clinical outcome in independent patient cohorts.Cambridge work was funded by a CRUK programme grant awarded to DEN; Swedish work and tissue collections were funded by grants from the Linne Centre for Breast and Prostate Cancer (CRISP, grant 70867901), Karolinska Institutet, the Swedish Research Council (K2010-70X-20430-04-3), and the Swedish Cancer Society (11-0287).This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.ebiom.2015.07.01

The role of panel studies in research on economic behavior

The analytic and monetary costs and benefits of panel surveys are assessed in light of experiences from the Panel Study of Income Dynamics, an 18-year panel survey on the economic status and behavior of the U.S. population. The analytic benefits of panel are formidable, ranging from description of gross change to various analytic advantages of continous and discrete time modelling. Analytic costs such as the conditioning of responses in subsequent participation or nonresponse bias are possible in panel surveys, but their effects can be minimized with proper data collection procedures and analytic adjustments. Surprisingly, the monetary costs of panel surveys are less than the costs of comparable repeated cross-sectional surveys.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26660/1/0000204.pd