Molecular- and pollen-based vegetation analysis in lake sediments from central Scandinavia

Plant and animal biodiversity can be studied by obtaining DNA directly from the environment. This new approach in combination with the use of generic barcoding primers (metabarcoding) has been suggested as complementary or alternative to traditional biodiversity monitoring in ancient soil sediments. However, the extent to which metabarcoding truly reflects plant composition remains unclear, as does its power to identify species with no pollen or macrofossil evidence. Here, we compared pollen-based and metabarcoding approaches to explore the Holocene plant composition around two lakes in central Scandinavia. At one site, we also compared barcoding results with those obtained in earlier studies with species-specific primers. The pollen analyses revealed a larger number of taxa (46), of which the majority (78%) was not identified by metabarcoding. The metabarcoding identified 14 taxa (MTUs), but allowed identification to a lower taxonomical level. The combined analyses identified 52 taxa. The barcoding primers may favour amplification of certain taxa, as they did not detect taxa previously identified with species-specific primers. Taphonomy and selectiveness of the primers are likely the major factors influencing these results. We conclude that metabarcoding from lake sediments provides a complementary, but not an alternative, tool to pollen analysis for investigating past flora. In the absence of other fossil evidence, metabarcoding gives a local and important signal from the vegetation, but the resulting assemblages show limited capacity to detect all taxa, regardless of their abundance around the lake. We suggest that metabarcoding is followed by pollen analysis and the use of species-specific primers to provide the most comprehensive signal from the environment. © 2013 Blackwell Publishing Ltd.

Spatial Allocation of Material Flow Analysis: A Geographic Information System Application of Material Flow Analysis in Ireland

This study concerns the spatial allocation of material flows, with emphasis on construction material in the Irish housing sector. It addresses some of the key issues concerning anthropogenic impact on the environment through spatial temporal visualisation of the flow of materials, wastes and emissions at different spatial levels. This is presented in the form of a spatial model, Spatial Allocation of Material Flow Analysis (SAMFA), which enables the simulation of construction material flows and associated energy use. SAMFA parallels the Island Limits project (EPA funded under 2004-SD-MS-22-M2), which aimed to create a material flow analysis of the Irish economy classified by industrial sector. SAMFA further develops this by attempting to establish the material flows at the subnational geographical scale that could be used in the development of local authority (LA) sustainability strategies and spatial planning frameworks by highlighting the cumulative environmental impacts of the development of the built environment. By drawing on the idea of planning support systems, SAMFA also aims to provide a cross-disciplinary, integrative medium for involving stakeholders in strategies for a sustainable built environment and, as such, would help illustrate the sustainability consequences of alternative The pilot run of the model in Kildare has shown that the model can be successfully calibrated and applied to develop alternative material flows and energy-use scenarios at the ED level. This has been demonstrated through the development of an integrated and a business-as-usual scenario, with the former integrating a range of potential material efficiency and energysaving policy options and the latter replicating conditions that best describe the current trend. Their comparison shows that the former is better than the latter in terms of both material and energy use. This report also identifies a number of potential areas of future research and areas of broader application. This includes improving the accuracy of the SAMFA model (e.g. by establishing actual life expectancy of buildings in the Irish context through field surveys) and the extension of the model to other Irish counties. This would establish SAMFA as a valuable predicting and monitoring tool that is capable of integrating national and local spatial planning objectives with actual environmental impacts. Furthermore, should the model prove successful at this level, it then has the potential to transfer the modelling approach to other areas of the built environment, such as commercial development and other key contributors of greenhouse emissions. The ultimate aim is to develop a meta-model for predicting the consequences of consumption patterns at the local scale. This therefore offers the possibility of creating critical links between socio technical systems with the most important challenge of all the limitations of the biophysical environment.

Time of flight mass spectrometry for quantitative data analysis in fast transient studies using a Temporal Analysis of Products (TAP) reactor

A Time of flight (ToF) mass spectrometer suitable in terms of sensitivity, detector response and time resolution, for application in fast transient Temporal Analysis of Products (TAP) kinetic catalyst characterization is reported. Technical difficulties associated with such application as well as the solutions implemented in terms of adaptations of the ToF apparatus are discussed. The performance of the ToF was validated and the full linearity of the specific detector over the full dynamic range was explored in order to ensure its applicability for the TAP application. The reported TAP-ToF setup is the first system that achieves the high level of sensitivity allowing monitoring of the full 0-200 AMU range simultaneously with sub-millisecond time resolution. In this new setup, the high sensitivity allows the use of low intensity pulses ensuring that transport through the reactor occurs in the Knudsen diffusion regime and that the data can, therefore, be fully analysed using the reported theoretical TAP models and data processing.

The use of Ecological and Carbon Footprint Analysis in regional policy making: application and insights using the REAP model

This paper builds on and extends previous research to contribute to ongoing discussion on the use of resource and carbon accounting tools in regional policy making. The Northern Visions project has produced the first evidence-based footpath setting out the actions that need to be taken to achieve the step changes in the Ecological and Carbon Footprint of Northern Ireland. A range of policies and strategies were evaluated using the Resources and Energy Analysis Programme. The analysis provided the first regional evidence base that current sustainable development policy commitments would not lead to the necessary reductions in either the Ecological Footprint or carbon dioxide emissions. Building on previous applications of Ecological Footprint analysis in regional policy making, the research has demonstrated that there is a valuable role for Ecological and Carbon Footprint Analysis in policy appraisal. The use of Ecological and Carbon Footprint Analysis in regional policy making has been evaluated and recommendations made on ongoing methodological development. The authors hope that the research can provide insights for the ongoing use Ecological and Carbon Footprint Analysis in regional policy making and help set out the priorities for research to support this important policy area

Export Cartels: Is it Legal to Target your Neighbour? Analysis in Light of Recent Case Law

Despite the growing sophistication of antitrust regimes around the world, export cartels benefit from special treatment: they are almost universally tolerated, if not encouraged in the countries of origin. Economists do not offer an unambiguous policy recommendation on how to deal with them in part due to the lack of empirical data. This article discusses arguments for and against export cartels and it identifies the existing gaps in the present regulatory framework. The theoretical part is followed by an analysis of the recent case law: a US cartel challenged with different outcomes in India and South Africa, as well as Chinese export cartels pursued in the USA. The Chinese cases are particularly topical as the conduct at stake, apart from being subject to private antitrust actions before US courts, was also challenged within the WTO dispute settlement framework, pointing out to the existing interface between trade and competition. While the recent developments prove that unaddressed issues tend not to vanish, the new South-North dimension has the potential of placing export cartels again on the international agenda. Pragmatic thinking suggests looking for the solution within the WTO framework.

Evolving to the optoelectronic mouse for phycotoxin analysis in shellfish

Despite ethical and technical concerns, the in vivo method, or more commonly referred to mouse bioassay (MBA), is employed globally as a reference method for phycotoxin analysis in shellfish. This is particularly the case for paralytic shellfish poisoning (PSP) and emerging toxin monitoring. A high-performance liquid chromatography method (HPLC-FLD) has been developed for PSP toxin analysis, but due to difficulties and limitations in the method, this procedure has not been fully implemented as a replacement. Detection of the diarrhetic shellfish poisoning (DSP) toxins has moved towards LC-mass spectrometry (MS) analysis, whereas the analysis of the amnesic shellfish poisoning (ASP) toxin domoic acid is performed by HPLC. Although alternative methods of detection to the MBA have been described, each procedure is specific for a particular toxin and its analogues, with each group of toxins requiring separate analysis utilising different extraction procedures and analytical equipment. In addition, consideration towards the detection of unregulated and emerging toxins on the replacement of the MBA must be given. The ideal scenario for the monitoring of phycotoxins in shellfish and seafood would be to evolve to multiple toxin detection on a single bioanalytical sensing platform, i.e. 'an artificial mouse'. Immunologically based techniques and in particular surface plasmon resonance technology have been shown as a highly promising bioanalytical tool offering rapid, real-time detection requiring minimal quantities of toxin standards. A Biacore Q and a prototype multiplex SPR biosensor have been evaluated for their ability to be fit for purpose for the simultaneous detection of key regulated phycotoxin groups and the emerging toxin palytoxin. Deemed more applicable due to the separate flow channels, the prototype performance for domoic acid, okadaic acid, saxitoxin, and palytoxin calibration curves in shellfish achieved detection limits (IC20) of 4,000, 36, 144 and 46 μg/kg of mussel, respectively. A one-step extraction procedure demonstrated recoveries greater than 80 % for all toxins. For validation of the method at the 95 % confidence limit, the decision limits (CCα) determined from an extracted matrix curve were calculated to be 450, 36 and 24 μg/kg, and the detection capability (CCβ) as a screening method is ≤10 mg/kg, ≤160 μg/kg and ≤400 μg/kg for domoic acid, okadaic acid and saxitoxin, respectively.

Heterogeneous error-resilient scheme for spectral analysis in ultra-low power wearable electrocardiogram devices

Wearable devices performing advanced bio-signal analysis algorithms are aimed to foster a revolution in healthcare provision of chronic cardiac diseases. In this context, energy efficiency is of paramount importance, as long-term monitoring must be ensured while relying on a tiny power source. Operating at a scaled supply voltage, just above the threshold voltage, effectively helps in saving substantial energy, but it makes circuits, and especially memories, more prone to errors, threatening the correct execution of algorithms. The use of error detection and correction codes may help to protect the entire memory content, however it incurs in large area and energy overheads which may not be compatible with the tight energy budgets of wearable systems. To cope with this challenge, in this paper we propose to limit the overhead of traditional schemes by selectively detecting and correcting errors only in data highly impacting the end-to-end quality of service of ultra-low power wearable electrocardiogram (ECG) devices. This partition adopts the protection of either significant words or significant bits of each data element, according to the application characteristics (statistical properties of the data in the application buffers), and its impact in determining the output. The proposed heterogeneous error protection scheme in real ECG signals allows substantial energy savings (11% in wearable devices) compared to state-of-the-art approaches, like ECC, in which the whole memory is protected against errors. At the same time, it also results in negligible output quality degradation in the evaluated power spectrum analysis application of ECG signals.

The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia

Quantitative examination of prostate histology offers clues in the diagnostic classification of lesions and in the prediction of response to treatment and prognosis. To facilitate the collection of quantitative data, the development of machine vision systems is necessary. This study explored the use of imaging for identifying tissue abnormalities in prostate histology. Medium-power histological scenes were recorded from whole-mount radical prostatectomy sections at × 40 objective magnification and assessed by a pathologist as exhibiting stroma, normal tissue (nonneoplastic epithelial component), or prostatic carcinoma (PCa). A machine vision system was developed that divided the scenes into subregions of 100 × 100 pixels and subjected each to image-processing techniques. Analysis of morphological characteristics allowed the identification of normal tissue. Analysis of image texture demonstrated that Haralick feature 4 was the most suitable for discriminating stroma from PCa. Using these morphological and texture measurements, it was possible to define a classification scheme for each subregion. The machine vision system is designed to integrate these classification rules and generate digital maps of tissue composition from the classification of subregions; 79.3% of subregions were correctly classified. Established classification rates have demonstrated the validity of the methodology on small scenes; a logical extension was to apply the methodology to whole slide images via scanning technology. The machine vision system is capable of classifying these images. The machine vision system developed in this project facilitates the exploration of morphological and texture characteristics in quantifying tissue composition. It also illustrates the potential of quantitative methods to provide highly discriminatory information in the automated identification of prostatic lesions using computer vision.