L E Sargent

M.D R L Orozco¹

P Redwood¹

I Yong¹

Caldelari

F Sargent²

L E Macaskie¹

Towards an integrated system for bio-energy: Hydrogen production by Escherichia coli and use of palladium-coated waste cells for electricity generation in a fuel cell

Keywords: biohydrogen, Escherichia coli, fuel cell, palladium

Towards an integrated system for bio-energy: hydrogen production by and use of palladium-coated waste cells

for electricity generation in a fuel cell

Introduction

Hydrogen and fuel cell technologies offer a feasible alternative to fossil fuels that is consistent with environmental demands for low carbon energy and zero emissions. Hydrogen is currently produced mainly as a by-product from the petrochemical industry. This is unsustainable and, furthermore, commercial H 2 contains traces of CO, a fuel cell catalyst poison. Currently the life time of a proton exchange membrane fuel cell (PEMFC) is less than 4,000 h (~ 167 days) whereas that required for a commercial market launch is 40,000 h (5 years; [START_REF] Aki | Penetration of residential fuel cells and CO 2 mitigationcase studies in Japan my multi-objective models[END_REF]. Photovoltaically-driven water electrolysis produces clean H 2 but the requirement for solar energy, the large light capture area, and efficiency of photovoltaic materials are currently limiting factors [START_REF] Mackay | Sustainable Energy -without the hot air[END_REF]. Use of H 2 as a storable energy vector can overcome the problems of intermittency of renewable power systems.

Clean H 2 can also be made via microbial fermentation of organic wastes [START_REF] Davila-Vazquez | Fermentative biohydrogen production: trends and perspectives[END_REF][START_REF] Redwood | Integrating dark and light biohydrogen production strategies: towards the hydrogen economy[END_REF]. Biomass (sugary, starchy, and ligno-cellulosic wastes) is an abundant renewable resource and is, therefore, potentially capable of supporting a future sustainable H 2 economy. The proportion of the energy demand which could be satisfied in this way is difficult to estimate, but it was reported that diverting landfill-destined cellulosic waste into bioH 2 production in the Netherlands would produce enough H 2 to meet 9% of the domestic energy demand [START_REF] De Vrije | Dark hydrogen fermentations[END_REF]. Furthermore it has been predicted that bioconversion of organic waste to biogas could provide up to 7.5% of the UK's 2020 renewable energy commitment (Anon 2009). Escherichia coli, an example of a facultative anaerobe, converts sugar via the mixed acid fermentation to gaseous products (H 2 and CO 2 ) and soluble products including the organic acids (OAs) acetate, lactate, formate and small amounts of succinate, and also ethanol [START_REF] Mat-Jan | Mutants of Escherichia coli deficient in the fermentative lactate dehydrogenase[END_REF]. H 2 production in E. coli is mediated by the formate hydrogenlyase (FHL) complex which is located on the inner cell membrane. FHL catalyses the release of H 2 and CO 2 from formate in response to an increase in intracellular formate, which signals the reduction in extracellular pH caused by OA formation (Penfold etal. 2003;[START_REF] Redwood | Bio-hydrogen and biomass supported palladium catalyst for energy production and waste minimisation[END_REF]2009). The bioH 2 is sufficiently clean (after CO 2 removal) to be used directly in a PEMFC without further purification [START_REF] Macaskie | Applications of bacterial hydrogenases in waste decontamination, manufacture of novel bionanocatalysts and in sustainable energy[END_REF].

Formate is the sole precursor of H 2 in E. coli with a maximum yield 2 mol H 2 per mol glucose [START_REF] Mat-Jan | Mutants of Escherichia coli deficient in the fermentative lactate dehydrogenase[END_REF].

Although other organisms such as Enterobacter and Clostridium can produce, maximally, 4 mol H 2 /mol glucose via redox carrier-linked reversible hydrogenases these systems are inhibited even at a very low partial pressure of H 2 , whereas E. coli is unaffected due to the use of an irreversible formate-linked hydrogenase system (FHL).

The H 2 yield was improved further by utilising the produced OAs in a downstream photobioreactor to make a second H 2 stream via nitrogenase activity; effectively here the E. coli fermentation has a dual function in both H 2 and OA production [START_REF] Redwood | Bio-hydrogen and biomass supported palladium catalyst for energy production and waste minimisation[END_REF]2009).

Bio-hydrogen has additional potential due to its compatibility with fuel cells [START_REF] Macaskie | Applications of bacterial hydrogenases in waste decontamination, manufacture of novel bionanocatalysts and in sustainable energy[END_REF]. These utilise precious metal catalyst to split the H 2 for electricity production; hence the second target for process improvement is the fuel cell itself since robustness is a major limitation for commercial development [START_REF] Aki | Penetration of residential fuel cells and CO 2 mitigationcase studies in Japan my multi-objective models[END_REF]above). Previous work showed that spent yeast biomass, coated with platinum nanoparticles can function as the anode material in a proton exchange membrane fuel Cell (PEMFC) [START_REF] Dimitriadis | Pt-based electro-catalytic materials derived from biosorption processes and their exploitation in fuel cell technology[END_REF]) while a parallel study showed that D. desulfuricans could behave similarly when coated with either Pd(0) or Pt(0) nanoparticles fabricated via the hydrogenase-mediated reduction of Pd(II) or Pt(IV) [START_REF] Yong | From biomineralization to fuel cells: biomanufacture of Pt and Pd nanocrystals for fuel cell electrode catalyst[END_REF]). The PEMFC power output was comparable to commercial Pt(0) fuel cell catalyst under similar conditions [START_REF] Yong | From biomineralization to fuel cells: biomanufacture of Pt and Pd nanocrystals for fuel cell electrode catalyst[END_REF]). Cells of E. coli can fulfil a similar role but the power output using Bio-Pd E. coli was substantially lower than that obtained by using Bio-Pd D. desulfuricans (Yong et al. 2008). For economy and sustainability via re-use of the H 2 -producing biomass to make Bio-Pd/Pt and hence electricity ‗in process', the power output from a PEMFC made with Bio-Pd E. coli must be increased, but without detriment to the production of either bioH 2 in the ‗dark' fermentation or the organic acid co-products which provide the feedstock for the secondary-stage photofermentation to generate the second H 2 stream [START_REF] Redwood | Dissecting the roles of E. coli hydrogenases in biohydrogen production[END_REF][START_REF] Redwood | Integrating dark and light biohydrogen production strategies: towards the hydrogen economy[END_REF]). Hence, the dual objectives of this work were to compare hydrogen production and the ‗catalytic quality' of Bio-Pd made from cells of E. coli strain MC4100 and its mutant IC007 developed within this study. The downstream photofermentation described elsewhere [START_REF] Redwood | Integrating dark and light biohydrogen production strategies: towards the hydrogen economy[END_REF] utilises Rhodobacter sphaeroides to make additional H 2 via rapid separation and utilisation of the organic acid products of the E. coli fermentation.

Therefore, the opportunity was also taken to evaluate the potential to use Bio-Pd R. sphaeroides in a PEMFC.

Materials and Methods

Bacterial strains and hydrogen production. Desulfovibrio desulfuricans was as described previously [START_REF] Yong | From biomineralization to fuel cells: biomanufacture of Pt and Pd nanocrystals for fuel cell electrode catalyst[END_REF]. Rhodobacter sphaeroides OU001 was grown as described by [START_REF] Redwood | Bio-hydrogen and biomass supported palladium catalyst for energy production and waste minimisation[END_REF]. For comparison of E. coli strains, E. coli HD701 (H 2 overproducer; derived from the parental strain E. coli MC4100 via upregulation of formate hydrogenlyase: [START_REF] Penfold | Increased hydrogen production by Escherichia coli strain HD701 in comparison with the wild-type parent strain MC4100[END_REF] was also used. E. coli strain IC007 ( focA, ΔfocB, nirC, hycA, ΔldhA, ΔtatABC::Apra R ) was also derived from the parental strain MC4100. Construction of IC007 took advantage of the PCR-targeting approach of [START_REF] Datsenko | One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products[END_REF] and the ‗Keio Collection' of single, kanamycin resistance-marked E. coli deletion strains [START_REF] Baba | Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection[END_REF]. First, for deletion of focA and focB, the apramycin resistance cassette and flanking FLP recombinase recognition sequences encoded on plasmid pIJ773 [START_REF] Gust | PCR-targeted Streptomyces gene replacement identifies a protein domain needed for biosynthesis of the sesquiterpene soil odor geosmin[END_REF] were amplified using primers based on the FRT recognition sequences together with the focA or focB flanking sequences. In each case the PCR-amplified apramycin resistance cassette was transformed into electro-competent E. coli strain BW25113 harbouring plasmid pKD20 and double cross-overs were selected by plating the transformants onto LB plates containing apramycin. Disruption of the targeted gene was confirmed by PCR, and the marked deletions were subsequently transduced using P1 phage into MC4100 using the method of [START_REF] Miller | A Short Course in Bacterial Genetics: A Laboratory Manual and Handbook for Escherichia coli and Related Bacteria[END_REF] The apramycin resistance cassette was subsequently ‗flipped out' of the marked deletions using the method described by [START_REF] Datsenko | One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products[END_REF] to give strains IC00A and IC00B. Loss of the apramycin-resistance cassette was confirmed by PCR and apramycin sensitivity. Using identical methods, the focA and focB mutant alleles were then combined to give a double mutant strain IC001 (as MC4100, ΔfocA, ΔfocB), which was used as a base to build additional mutations upon. The nirC gene was deleted first, using identical methods, to yield IC002 (as MC4100, ΔfocA, ΔfocB, ΔnirC). Next, the ΔhycA::Kan R allele from E. coli strain JW2695 was moved by P1 transduction into IC002 and flipped out using pCP20 (Amp R ) to yield strain IC003 (as MC4100, ΔfocA, ΔfocB, ΔnirC, ΔhycA). Next, a ΔldhA::Cam R allele, which was prepared by PCR, was introduced into IC003 and flipped out resulting in IC005 (as MC4100, ΔfocA, ΔfocB, ΔnirC, ΔhycA, ΔldhA). Finally, a ΔtatABC::Apra R cassette was prepared by PCR and introduced into IC005 to yield IC007 (as MC4100, ΔfocA, ΔfocB, ΔnirC, ΔhycA, ΔldhA, ΔtatABC::Apra R ). The genotypic identity of strain IC007 is summarised in Table 1.

Stocks of E. coli strains, maintained at -80 °C in 75% glycerol, were plated on nutrient agar (Oxoid) and incubated overnight at 30 ºC. For experiments, colonies were picked for pre-culture into nutrient broth (Oxoid, 5 mL), with sodium formate (0.5% w/v) pH 7, and shaken (6 h, 30 °C). Cells were inoculated (10 µl) in two 2 l Erlenmeyer flasks containing 1 l of the same medium and shaken for 16 h (30 °C). Cell pellets, obtained by centrifugation, were washed twice in 100 ml phosphate buffered saline (PBS: 1.43 g Na 2 HPO 4 0.2 g KH 2 PO 4, 0.8 g NaCl, 0.2 g KCl/l, pH 7.0) and resuspended in 20 ml PBS to produce a concentrated inoculum for fermentation containing ~ 40-46 g dry weight/L (OD 600nm of 1 corresponded to a concentration of 0.48 g dry weight/l (a previously determined conversion factor).

Fermentations (two conditions, each in duplicate) were performed consisting of a 5 l fermenter (Fermac 200series, Electrolab UK) with pH, temperature and agitation control, a -scrubber‖ column containing 2 M NaOH, a H 2 collector cylinder full of water and a N 2 gas supply. At least 99.5% of CO 2 was trapped in the scrubber solution as verified by GC [START_REF] Redwood | Bio-hydrogen and biomass supported palladium catalyst for energy production and waste minimisation[END_REF]) and H 2 was collected and quantified by water displacement in a graduated collector cylinder. All tubes and connectors were previously sterilized with 70% ethanol.

The fermentation vessels contained 3 l fermentation medium (de-ionized water; in each 3 litre batch: 42.60 g Na 2 SO 4 , 10.46 g K 2 HPO 4 , 0.2 g KH 2 PO 4 , 0.2 g (NH 4 ) 2 SO 4 , pH 5.8). All solutions were prepared using deionized water and analytical grade reagents. The following supplements were aseptically added to the fermenter just before inoculation: 1 M MgSO 4 .7H 2 O (6 ml), 2 M glucose (30 ml; 20 mM final concentration), trace elements solution (9 ml) and polyethylene glycol antifoam (PEG; 0.5 ml). The system was purged with O 2 -free N 2 (OFN), (through a 0.2 µm filter) for at least 30 min before inoculation. pH was maintained throughout the fermentation at pH 5.5-5.7 by dosing 2 M NaOH and 2 M H 2 SO 4 via an Electrolab Fermac260 pH controller, at 30 °C with stirring (300 rpm). To start each experiment the OFN was stopped, N 2 tubes were depressurized and valves to the gas collector cylinder were opened to allow the system to equilibrate for at least 45 min.

Sampling and analysis

For glucose, organic acids and ethanol analysis, samples were periodically withdrawn from the culture, filtered (0.2 µm supor-membrane syringe filter) and stored at -20ºC before analysis. Glucose determination was by the colorimetric dinitrosalicylic acid assay [START_REF] Chaplin | Monosaccharides. In Carbohydrate analysis: a Practical Approach[END_REF]). Organic acids were measured by an anion HPLC (Dionex 600-series) as described previously [START_REF] Redwood | A two-stage, two-organism process for biohydrogen from glucose[END_REF]. Ethanol was analysed using a Cecil Adept HPLC system equipped with Resex-RCM column (Phenomenex); RI detector; temperature 75 °C; eluate H 2 O; flow rate 0.5 ml/min; 30 min experiment time. Bacterial growth was monitored via the OD 600 values (OD 600 :1 ≡ 0.482 g DW/l). H 2 production was monitored and measured by the displacement of water from graduated cylinders with time-lapse photography for continuous monitoring. CO 2 was removed into 2 M NaOH scrubber solution with universal indicator pH 9-13. H 2 in collectors was confirmed using a H 2 sensor detector.

Previous work (Sauter et al. 1992 and DW Penfold, unpublished work) showed that CO 2 and H 2 are the only gases evolved under these conditions. The H 2 yield was calculated in relation to a theoretical maximum of 2 mol H 2 /mol glucose (Clark 1989); 90% of this yield was achieved previously under similar conditions [START_REF] Redwood | Bio-hydrogen and biomass supported palladium catalyst for energy production and waste minimisation[END_REF].

Preparation of fuel cell electrode materials.

For experiments to test the utility of ‗palladised' cells in a fuel cell D. desulfuricans (reference organism: [START_REF] Yong | From biomineralization to fuel cells: biomanufacture of Pt and Pd nanocrystals for fuel cell electrode catalyst[END_REF]) and E. coli strains (this study) were grown as described by [START_REF] Yong | Bioreduction and bio-crystallization of palladium by Desulfovibrio desulfuricans NCIMB 8307[END_REF] and [START_REF] Penfold | Inactivation of the Escherichia coli K-12 twin-arginine translocation system promotes increased hydrogen production[END_REF], respectively. Cells were palladised as described previously (5% Pd by mass; [START_REF] Yong | From biomineralization to fuel cells: biomanufacture of Pt and Pd nanocrystals for fuel cell electrode catalyst[END_REF], washed in acetone dried and ground, and then transferred into 10 ml alumina ceramic crucibles for sintering (i.e. carbonising) in a programmable furnace. The temperature was increased from room temperature to 700 o C within 4 h and held at 700 o C for a further 4 h before cooling to room temperature in the furnace.

Commercial submicron Pd powder (C-Pd) (Sigma-Aldrich, Germany), and ‗Bio-Pds' (20 mg of each, as metal) were mixed separately with pure activated carbon powder (80 mg; BDH Chemicals Ltd, UK). Nafion perfluorinated ion-exchange resin (0.2 ml; 10 % (w/v) in water, Sigma-Aldrich) and water (1 ml) were added to each sample containing 20% Pd and 80% C. The sample was mixed well, manually applied evenly onto 16 cm 2 Teflon-treated carbon paper (Fuel Cell Scientific, USA), and dried at room temperature. The laboratory-made electrodes were tested for electricity production versus the commercial Pd powder in a small proton exchange membrane (PEM) fuel cell as described previously [START_REF] Yong | From biomineralization to fuel cells: biomanufacture of Pt and Pd nanocrystals for fuel cell electrode catalyst[END_REF]) and the maximum power output (mW) was recorded.

Results and discussion

Hydrogen and organic acid production by E. coli strains MC4100 and IC007 H 2 production (ml), biomass (g), glucose consumption (mmol), organic acid (OA) and ethanol production (mmol) were measured and quantified; reproducibility was usually within 10% between separate experiments.

Hydrogen overproduction by E. coli strain HD701 (via derepression of the H 2 -producing formate hydrogen lyase complex [START_REF] Penfold | Increased hydrogen production by Escherichia coli strain HD701 in comparison with the wild-type parent strain MC4100[END_REF]) and by strains deficient in uptake hydrogenases [START_REF] Penfold | Inactivation of the Escherichia coli K-12 twin-arginine translocation system promotes increased hydrogen production[END_REF][START_REF] Redwood | Dissecting the roles of E. coli hydrogenases in biohydrogen production[END_REF] were described previously and production of OA by the latter was also quantified [START_REF] Redwood | A two-stage, two-organism process for biohydrogen from glucose[END_REF]. A combination of the hycA and tat mutations did not increase hydrogen production over that obtained with one mutation alone [START_REF] Penfold | Inactivation of the Escherichia coli K-12 twin-arginine translocation system promotes increased hydrogen production[END_REF]. Hence, in this work, further metabolic engineering of E. coli was performed. A new strain (IC007) was prepared that combined mutations additional to those in the hycA and tatABC genes previously studied (Table 1). Strain IC007 was rendered devoid its formate transporters (FocA and FocB), and the FocA/B homolog NirC, in an attempt to impair formate secretion during fermentation and utilise early and sustained formate hydrogenlyase (FHL) activity. In addition, strain IC007 lacks an active ldhA gene, which encodes an NADH-dependent lactate dehydrogenase that is normally active in the later stages of fermentative growth. The rationale in removing LdhA was to prevent the metabolism of pyruvate to lactate and, instead, shunt additional pyruvate towards formate and hence boost H 2 production.

The fermentation kinetics of strains MC4100 and IC007 are compared in Figure 1 and summarised in the fermentation balances shown in Table 2. During a 24 h fermentation, both strains grew very slightly, limited by the low concentration of added N source; note that prior to the incubation the cells had been grown aerobically to high density, with formate as a FHL inducer [START_REF] Redwood | Dissecting the roles of E. coli hydrogenases in biohydrogen production[END_REF]. The parent strain MC4100 consumed all of the initial added glucose within 24h, whereas the mutant strain IC007 consumed only 70% of the added glucose over 24h. Correspondingly, the MC4100 parent strain evolved H 2 more rapidly than strain IC007 (Fig. 1 a,b), the rate peaking at ~ 4.2 mmol H 2 /h during the initial 10h and gradually decreasing within 24h. The overall yields of H 2 produced were 41% and 37% for strains MC4100 and IC007, respectively, and not significantly different on the basis of the two experiments reported (Table 2). 2) showed that lactate was the main organic product of the parent strain MC4100 but the lactate yield was negligible in strain IC007 due to the ldhA mutation. However, the result of this was not an increase in H 2 production but in non-lactate by-products, especially pyruvate, succinate and ethanol. Despite the loss of lactate production, the overall yield of H 2 in strain IC007 was not significantly different from the parent strain MC4100, which can be attributed to the accumulation of alternative byproducts (succinate) and H 2 precursors (pyruvate and formate). The inability of strain IC007 to reassimilate all of the formate initially secreted into the reaction medium may be attributed to the lack of dedicated formate transport machinery in this strain and partially accounts for the reduced H 2 yield observed. Clearly, lactate dehydrogenase is a major route for pyruvate dissimilation and recycling of NADH during fermentation. The loss of ldhA leads to the accumulation and ultimate secretion of pyruvate; the shortfall in NADH recycling via removal of lactate production is compensated by an increased production of ethanol and succinate.

Analysis of OA production (Fig 1 c,d and Table

Similar observations regarding different ldh mutants were made by [START_REF] Mat-Jan | Mutants of Escherichia coli deficient in the fermentative lactate dehydrogenase[END_REF] and [START_REF] Zelic | Process strategies to enhance pyruvate production with recombinant Escherichia coli: From repetitive fed-batch to in situ product recovery with fully integrated electrodialysis[END_REF] in ldh mutants. However, we also observed that when strain IC700 was allowed to ferment for a further 10 24 h, the pyruvate was reassimilated, which is in accordance with the observations of [START_REF] Sode | Construction and characterisation of fermentative lactate dehydrogenase E. coli mutant and its potential for bacterial hydrogen production[END_REF] where, likewise, the reaction was followed to completion.

We conclude that eliminating lactate production did not benefit the H 2 yield in this study as the additional intermediate either accumulated or gave enhanced production of alternative products, particularly succinate and ethanol. Eliminating succinate production would be difficult due to the key role of succinate-producing enzymes in central metabolism. Further, succinate is potentially useful in the downstream photobioreactor and hence could be re-used ‗in process'. Jointly upregulating the activity of pyruvate formate lyase and FHL could hypothetically channel more pyruvate intermediate towards H 2 production, an approach to be addressed in future work. Work is currently in progress to reduce the production of ethanol since this product cannot be transferred easily into the downstream photobioreactor but this metabolic engineering will have implications in the ability of the cells to recycle NADH and such mutants may be highly impaired or non-viable.

Electricity production by palladised cells of E. coli in a fuel cell.

Cells of the E. coli strains MC4100 and IC007 (this study), together with strain HD701 (for which hydrogen over production was as described previously [START_REF] Penfold | Increased hydrogen production by Escherichia coli strain HD701 in comparison with the wild-type parent strain MC4100[END_REF] but Bio-Pd of this strain has not been tested), R. sphaeroides [START_REF] Redwood | Bio-hydrogen and biomass supported palladium catalyst for energy production and waste minimisation[END_REF]) and D. desulfuricans (positive control) were coated with palladium nanoparticles as described previously [START_REF] Yong | From biomineralization to fuel cells: biomanufacture of Pt and Pd nanocrystals for fuel cell electrode catalyst[END_REF]) and tested for their ability to produce electricity as anodes in a PEM fuel cell. Sintering of palladised biomass was essential to obtain catalytic activity (P. Yong, unpublished) since biomass is predominantly water and sintering carbonises the biomass to make it electrically conductive. Bio-Pd was tested at a loading of 5% by mass since this was shown to be comparable to commercial Pd(0) in chemical catalysis (hydrogenation: Creamer et al. 2007). At this Pd(0) loading (Table 3) Bio-Pd D. desulfuricans produced power comparably to commercial finely-ground Pd(0) while Bio-Pd MC4100 and also Bio-Pd HD701 produced little power (~18-28 mW). However Bio-Pd IC007 produced ~ 56 mW, which was ~ three-fold higher than the parent strain (~18 mW). A more detailed comparison is reported elsewhere [START_REF] Yong | Biorefining of precious metals from wastes-an answer to manufacturing of cheap nanocatalysts for fuel cells and power generation via an integrated biorefinery[END_REF].

Bio-Pd R. sphaeroides also produced little power (~ 20 mW) but, in contrast to D. desulfuricans [START_REF] Mikheenko | Bioaccumulation of palladium by Desulfovibrio fructosovorans and hydrogenase deficient strains[END_REF]) and E. coli [START_REF] Deplanche | Involvement of hydrogenases in the formation of highly catalytic Pd(0) nanoparticles by bioreduction of Pd(II) using Escherichia coli strains Microbiology in press[END_REF] the enzymatic mechanism underlying Pd(0) nanoparticle formation has not been eludicidated. This would repay study in order to develop similar possibilities for strain improvement using molecular engineering since R. sphaeroides is also produced as a waste from biohydrogen production (see Introduction). Representative FC data are shown, since the power output is critically dependent on the exact preparation of the electrode but in 3 replicate experiments the Bio-Pd(0) of the mutant outperformed that of the parent strain consistently as shown.

From these results, we conclude that the catalytic activity of the palladium in a fuel cell relates to the enzymatic composition and localisation in the strain which produced it. A similar conclusion with respect to chemical catalysis was drawn [START_REF] Rousset | Use of bacterium strains for the preparation of metallic biocatalysts, in particular for the preparation of palladium biocatalysts[END_REF]) using a mutant of D. fructosovorans which lacked its periplasmic hydrogenases; here, the Pd(0) relocated to the site of the remaining cytoplasmic hydrogenase [START_REF] Mikheenko | Bioaccumulation of palladium by Desulfovibrio fructosovorans and hydrogenase deficient strains[END_REF] and was catalytically more active in the reduction of Cr(VI) [START_REF] Rousset | Use of bacterium strains for the preparation of metallic biocatalysts, in particular for the preparation of palladium biocatalysts[END_REF]). In the present case, the key difference between strains HD701 and IC007 is that the Tat transport system is missing, and therefore there are no functional periplasm-facing hydrogenases. Therefore, strain IC007, like the D. fructosovorans mutant (above) may be location-modified with respect to making Pd-nanoparticles. However no obvious differences were observed in initial comparisons of the Bio-Pd produced by strains MC4100 and IC007 [START_REF] Yong | Biorefining of precious metals from wastes-an answer to manufacturing of cheap nanocatalysts for fuel cells and power generation via an integrated biorefinery[END_REF]) and a high resolution TEM study is clearly warranted. Taken together, the two studies using D. fructosovorans and E. coli indicate that ‗relocating' the Pd-nanoparticles may be beneficial to the ‗quality' of the catalyst produced. Potential pleiotropic effects of the mutations introduced into strain IC007 were not investigated but warrant further investigation. The extent to which the ‗biochemical' component of the Bio-Pd moderates the catalytic activity of its Pd(0) ‗partner' after sintering of the material is a subject of current investigation.

Conclusion

In terms of H 2 production, strain IC007 offers little benefit over 24 h compared to its parent strain MC4100, although its ability to produce more succinate and pyruvate could be beneficial as secondary products. However, in terms of an integrated energy process, where biomass is re-used ‗in process', the mutant strain offers more potential to make a better fuel cell although this still falls short (by ~ 50%) of the use of D. desulfuricans as a support for ‗Bio-Pd' for this purpose. The extent to which further molecular engineering could improve the rates and yields of hydrogen production without compromising fuel cell catalyst performance is the subject of ongoing studies. Native metallised biomass was inactive in a PEMFC. Samples (all 5% Pd w/w) were, therefore, sintered before use to carbonise the biomass, as described in Materials and Methods. E. coli strain HD701 and R. sphaeroides were tested at 5% loading of Pd(0) only; a comparison of Bio-Pd of the D.desulfuricans, E. coli MC4100 and ICOO7 strains at 25% Pd(0) loading is reported elsewhere [START_REF] Yong | Biorefining of precious metals from wastes-an answer to manufacturing of cheap nanocatalysts for fuel cells and power generation via an integrated biorefinery[END_REF] 20

Legends to Figures

Figure 1 .

 1 Figure 1. Hydrogen production (a,b) and organic acid production (c,d) from E. coli MC4100 (a,c) and IC007

  Fig.1

Table 1

 1 Genotypic identity of strain E. coli IC007 Strain IC007 was made from its parent MC4100 as described in Materials and Methods. It combines the ability to overproduce hydrogen via deletion of the FHL regulator FhlA and the Tat system with impaired ability to transport formate. It does not make lactate, potentially increasing the metabolic flux into the other fermentation products.

	Deletion	Affected gene product (resultant phenotype)

Table 2 .

 2 Fermentation balance of E. coli strains MC4100 and IC007 Propionate and butyrate were not detected. Lac: lactate; Pyr: pyruvate; Form: formate; Succ: succinate; Ac: acetate; EtOH: ethanol ** The yield of CO 2 was estimated as described by[START_REF] Redwood | Bio-hydrogen and biomass supported palladium catalyst for energy production and waste minimisation[END_REF], equation 2. *** Carbon balance was calculated as[START_REF] Redwood | Bio-hydrogen and biomass supported palladium catalyst for energy production and waste minimisation[END_REF], but including pyruvate (3 carbon equivalents).

	Strain Repeat	Time (h)	Lac	Product yields (mol/mol glucose)* Pyr Form Succ Ac EtOH	H 2	CO 2 **	Growth (g/mol glucose)	Carbon balance ***
	MC4100	Expt 1 22.75 0.892 Expt 2 22.75 0.789	0 0	0.048 0.024 0.196 0.326 0.2 0.354	0.405 0.438	0.788 0.849	0.51 0.54	3.99 4.49	99% 93%
	IC007	Expt 1 23.5 0.008 0.518 0.079 0.492 0.227 Expt 2 23.5 0.007 0.527 0.069 0.479 0.222	0.704 0.685	0.735 0.76	0.36 0.36	4.41 2.64	102% 99%

*

Table 3 .

 3 Maximum power output (P max , mW) from a PEM fuel cell using anodes made from commercial precious metals and Bio-Pd.

	Preparation	P max (mW)	Reference
	Commercial Pt(0)	170	Yong et al. 2007
	Bio-Pt D. desulfuricans	170	Yong et al. 2007
	Commercial Pd(0)	100	This study
	Bio-Pd D. desulfuricans	108	This study
	Bio-Pd E. coli HD701	28	This study
	Bio-Pd E. coli MC4100	18	This study
	Bio-Pd E. coli IC007	56	This study
	Bio-Pd R. sphaeroides OU001	19.5	Redwood 2007

Acknowledgements

The financial support of the BBSRC (Grant Nos BB/C516195/2 and BB/E003788/1), the EPSRC (Grant Nos EP/D05768X/1 and EP/E034888/1), the Royal Society (Industrial Fellowship to LEM), Advantage West Midlands (Grant ref. POC46) and the Government of Mexico (studentship no. 203186) to RLO is acknowledged, with thanks.


M Kopec-Medrek

M Widuchowska

A Kotulska

E Zycinska-Dębska

E J Kucharz

Magdalena Kopec

SERUM CYSTATIN C LEVEL IN PATIENTS WITH RHEUMATOID ARTHRITIS AFTER SINGLE INFUSION OF INFLIXIMAB

Sirs,

Rheumatoid arthritis (RA) is a chronic inflammatory disease that often leads to impairment of the kidney function. Cystatin C might be used as marker of early renal dysfunction, moreover it correlates better with GFR than serum creatinin level [START_REF] Knight | Factors influencing serum cystatin C levels other than renal function and the impact on renal function measurement[END_REF].

Mannge et al [START_REF] Mangge | Cystatin C, an early indicator for incipiens renal diseases in rheumatoid arthritis[END_REF] reported that in a group of 56 patients with RA in 60% of patients serum cystatin C level was increased while only in 3 patient's serum creatinine level was increased.

The present study was designed to evaluate the effect of infliximab, anti-TNF-α antibody therapy on serum cystatin C level and its association with changes in creatinine, GFR, inflammatory indices (ESR, CRP) and DAS 28 in patients with RA with initially normal creatinine level.

Ten RF positive, postmenopausal, no smoking, without diabetes mellitus and hypertension women with RA aged 50.8 ± 5.96 yrs were investigated. Patients met the 1987 American College of Rheumatology (ACR) criteria for RA. Duration of RA 7.08 ± 1.2 yrs. All patients were treated with infliximab (3mg/kg of body mass). Patients were also given prednisone in an unchanged. dose of 7.5±1.16 mg/day and methotrexate in a dose of 15 mg/week. Some authors indicate [START_REF] Risch | Effects of glucocorticoid immunosuppression on serum cystatin C concentrations in renal transplant patients[END_REF] that change of the steroids dose might influence the serum cystatin C level. Blood samples were obtained at 8:00 AM after overning fasting, before and one day after the first infusion of infliximab. Serum cystatin C was assayed with ELISA (Human Cystatin C ELISA, Bio Vendor Laboratory Medicine, Inc.) before and after single infusion of infliximab. ESR, CRP, serum creatinine concentration (before and after treatment) were assayed with routine methods. DAS 28 was calculated with commonly used formula, glomerular filtration rate (GFR) was estimated by the Cockroft-Gault (CG) formula. The characteristic of the patients (ESR, CRP, DAS28, serum creatinine level and GFR) before and after the infusion of infliximab are shown in Table 1. The results were expressed as mean ± standard deviation (SD). Differences were tested for significance using the Student's t-test or the Wilcoxon test where appropriate. Correlations were assessed using Spearman's rank correlation.

The results are shown in Table 1. After one dose of infliximab there was a significant increase in mean cystatin C level. Creatinine level and GFR did not change significantly.

There was a significant decrease in the disease activity (ESR, CRP, DAS 28). There was no statistical correlation between the evaluated parameters.

In the currently published literature the opinions on influence of inflammatory state on serum cystatin level are controversial. Karstila et al [START_REF] Karstila | Measurement of the Kidney Function in patients with Rheumatoid Arthritis: Plasma Cystatin C versus Cr-EDTA Clearance[END_REF] did not find any influence of inflammatory parameters (CRP, ESR) on cystatin C levels. Knight et al. [START_REF] Knight | Factors influencing serum cystatin C levels other than renal function and the impact on renal function measurement[END_REF] shown that higher serum CRP levels are associated with higher serum cystatin C levels.

In contrast Bokarewa et al. [START_REF] Bokarewa | Cystatin C binds serum amyloid A, downregulating its cytokinegenerating properties[END_REF] proved that in patients with RA there is relationship between serum cystatin C level and serum amyloid A level (SAA), an acute phase reactant. Cystatin C is thought to bind SAA and therefore its serum level might be decreased in patients with active RA.

Probably the increase in serum cystatin C level in patients with RA after one dose of infliximab in this study is caused by the decrease in serum SAA as a results of decrease of the inflammatory process [START_REF] Bokarewa | Cystatin C binds serum amyloid A, downregulating its cytokinegenerating properties[END_REF] and is not dependent on the renal function. However there was no statistically significant correlation between serum cystatin C level and the acute phase reactants. Further investigations are required before firm conclusions can be drawn regarding the influence of infliximab on serum cystatin C level.

Disclosure of Interest: None declared.

Table 1 .

 1 Characteristics of the patients with rheumatoid arthritis before and after single infusion of infliximab (mean ± standard deviation (SD)).Statistical significance of the difference from the corresponding group *p<0.05

	Investigated	ESR [mm/hr]	CRP [mg/l]	DAS 28	Serum	GFR	Serum cystatin C
	group				[mg/dl] creatinine	[ml/min/1.73m 2 ] (Cockroft-Gault)	[ng/ml]
	Before
	infliximab	40.2±29.75	35.5±32.58	6.24±1.35	0.78±0.13	97.7±27.56	356.86±362.21
	After
	infliximab	38.5±29.56* 30.46±28.85*	4.27±0.79*	0.79±0.16	98.33±25.81	406.70±369.61*


Dr Tiago André Marques

Len Thomas

Stephen W Martin

David K Mellinger

Susan Jarvis

Ronald P Morrissey

Carroll-Anne Ciminello

Nancy Dimarzio

Tiago André

Steve W Martin

Carol-Anne Ciminello

Stephen W Martin

Carroll-Anne Ciminello

Stephen W Martin

Carol-Anne Ciminello

Spatially explicit capture-recapture methods to estimate minke whale density from data collected at bottom-mounted hydrophones

Keywords: Bayesian statistics, density, minke whale, passive acoustic monitoring, spatially explicit capture recapture, WinBUGS minke whale, passive acoustic monitoring, proximity detector, secr, spatially explicit capture recapture, OpenBUGS

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The estimation of animal density and abundance is a fundamental requirement for effective management and conservation decisions. However, this is particularly challenging for many cetacean species, which typically occur over very large areas, at low densities, and spend a large proportion of their time submersed. All this makes them especially challenging to survey using standard visual methods. These include distance sampling methods, namely shipboard and aerial surveys in which line transects or cue counting approaches are used (see [START_REF] Buckland | Introduction to distance sampling -Estimating abundance of biological populations[END_REF] for details), as well as capture-recapture methods (e.g. [START_REF] Evans | Monitoring cetaceans in european waters[END_REF] based on photo-ID or DNA. While working well under certain circumstances, all of these methods have several shortcomings. Low encounter rates create problems in analysis and low precision in the estimates, and surveys are restricted to good weather and daylight conditions. This makes them cost ineffective for many scenarios.

In recent years, acoustic data has been proposed as having information about density [START_REF] Mellinger | An overview of fixed passive acoustic observation methods for cetaceans[END_REF]. Common sense alone suggests that the amount of animalproduced sound (however it is measured) might act as an index of animal abundance.

The challenge is to find ways to convert that amount of sound to animal density.

Using sound to detect and localize animals from towed hydrophone arrays has been successfully implemented for sperm whales (e.g. [START_REF] Barlow | Estimates of sperm whale abundance in the northeastern temperate Pacific from a combined acoustic and visual survey[END_REF]. However, this approach does not really differ from an analysis perspective from conventional line transect distance sampling. On the other hand, [START_REF] Marques | Estimating cetacean population density using fixed passive acoustic sensors: an example with Blainville's beaked whales[END_REF] presented the first example in which data from fixed hydrophones was used to estimate cetacean density, using an approach akin to cue counting. Unlike for conventional cue counting, the detection function was estimated using a regression based approach using a sample of data for which the animal locations and vocalizations were known from acoustic dive tags.

If one has an array of fixed hydrophones, sounds detected at multiple hydrophones can be seen as capture-recapture data. Each sound can be assigned a capture history, say a 1 for each hydrophone where it was detected and a 0 on hydrophones where it was missed. This assumes we can tell when the same sound is received at multiple hy-drophones, say from timing and/or frequency information, just as we assume we can tell when the same individual is sighted in a photo ID study. Standard capture-recapture analyses could be undertaken, but there are several reasons to focus on the use of spatially explicit capture-recapture (SECR, [START_REF] Borchers | Spatially explicit maximum likelihood methods for capture-recapture studies[END_REF]; Borchers (this volume)) methods for estimating density. Firstly, SECR methods explicitly model the dependence of capture probability on distance, thereby reducing the un-modelled heterogeneity that usually hinders capture-recapture analysis. For acoustic data we may expect distance of the sound source to be a major component of capture probability.

Secondly, SECR methods estimate density and abundance over an explicitly-defined area, as opposed to traditional methods where the area sampled is not clearly defined and hence converting abundance to density is problematic. SECR has received considerable attention recently, both from classical likelihood (e.g. [START_REF] Efford | chap Density estimation by spatially explicit capture-recapture: likelihood-based methods[END_REF][START_REF] Borchers | Spatially explicit maximum likelihood methods for capture-recapture studies[END_REF][START_REF] Efford | Population density estimated from locations of individuals on a passive detector array[END_REF] and Bayesian (e.g. [START_REF] Royle | A hierarchical model for spatial capture-recapture data[END_REF][START_REF] Royle | Analysis of capture-recapture models with individual covariates using data augmentation[END_REF]Royle et al, 2009a) perspectives. In particular [START_REF] Dawson | Bird population density estimated from acoustic signals[END_REF] have applied likelihood-based methods to acoustic data, estimating bird density from a set of 4 microphones.

In this paper, we compare Bayesian and likelihood based approaches to SECR, using as motivation the estimation of density of sounds produced by common minke whales (Balaenoptera acutorostrata) off the coast of Kauai, Hawaii. Minke whales are one of the most abundant baleen whale species worldwide, but they are also one of the smallest and can be very difficult to detect using standard visual survey methods.

Although commonly sighted in high latitude waters, they are rarely seen in tropical and sub-tropical areas, despite being heard there during winter and spring. This is particularly true around the Hawaiian Islands, where extensive aerial and shipboard surveys (e.g. [START_REF] Mobley | Results of aerial surveys of marine mammals in the major Hawaiian Islands (1993-98)[END_REF][START_REF] Rankin | A visual sighting and acoustic detections of minke whales, Balaenoptera acutorostrata (Cetacea: Balaenopteridae), in nearshore Hawaiian waters[END_REF] have produced only a handful of sightings, but the characteristic "boing" sound attributed to minke whales [START_REF] Barlow | Estimates of sperm whale abundance in the northeastern temperate Pacific from a combined acoustic and visual survey[END_REF] can be detected readily in the right season (e.g. [START_REF] Rankin | A visual sighting and acoustic detections of minke whales, Balaenoptera acutorostrata (Cetacea: Balaenopteridae), in nearshore Hawaiian waters[END_REF].

Therefore, methods based on acoustic rather than visual detections might prove more effective at estimating their abundance.

The data used here come from a set of 16 bottom-mounted hydrophones that are part of the U.S. Navy's Pacific Missile Range Facility (PMRF), an instrumented testing range located along the western shore of Kauai. The hydrophones are part of the Barking Sands Underwater Range Expansion (BSURE), which extends northwest of the island, covering approximately 2,300km 2 and having water depths to 4,500m throughout most of its area. While the hydrophones were designed for tracking underwater objects such as submarines and torpedos, they are capable of detecting minke whale boing vocalizations, and are therefore well suited to study this cryptic cetacean.

The paper is structured as follows. The next section describes the Bayesian and likelihood based approaches to SECR. These are then applied to the case study data in section 3. Section 4 presents a simulation study evaluating performance of the two approaches in a simple scenario that mimics the case study. Lastly, a discussion section gives the main conclusions and suggests potential avenues for future investigation.

It is not our intention in this paper to provide a definitive estimate of minke whale (sound) density in the study area; instead we focus on establishing the utility of the SECR methodology and comparing approaches to analysis. This is (to our knowledge) the first time that: (1) both a Bayesian and likelihood SECR implementation are directly compared, (2) a Bayesian SECR model has been applied to acoustic data, here using proximity detectors (see below for details) and (3) SECR is proposed to estimate density of cetaceans.

SECR models and inference

In this section, we outline the models and estimation approaches. A non-technical introduction to SECR models is given by Borchers (this volume); more details of the likelihood-based methods are in [START_REF] Borchers | Spatially explicit maximum likelihood methods for capture-recapture studies[END_REF] and [START_REF] Efford | Population density estimated from locations of individuals on a passive detector array[END_REF]; details of a similar Bayesian method (with different types of detectors) is in [START_REF] Royle | A hierarchical model for spatial capture-recapture data[END_REF] and Royle et al (2009a).

As initially conceived, SECR models consider that animals' home range centres are at unobserved locations X = (X 1 , X 2 , ..., X N ), where X i represents the position of animal i (i.e., its cartesian coordinates in 2-dimensional space). Inference is focused on estimating N , the number of home range centres in a given area A (e.g. [START_REF] Borchers | Spatially explicit maximum likelihood methods for capture-recapture studies[END_REF][START_REF] Royle | A hierarchical model for spatial capture-recapture data[END_REF], as well as density, D = N/A. In the current scenario, the equivalent to the home range centres are the actual sound source locations.

Hence the focus of our estimate using SECR is the number and density of sound sources within a given area A and time period T . The estimation of the actual animal abundance requires dividing the estimated sound source abundance N by T and sound production rate, and we do not deal with this here.

The Bayesian and likelihood approaches have several differences (details below), so we deal with them separately in the sections below. However, they both use the same model for sound detection, as follows. Consider an array of K hydrophones, each with known location. A sound produced at location X is detected at hydrophone k (k = 1, . . . , K) with probability p k (X; θ) where θ is a vector of detection parameters.

Hydrophones operate independently, so that the probability a sound is detected on at

least one hydrophone is p . (X; θ) = 1 -K k=1 (1 -p k (X; θ)).
Detection probability is assumed to be a non-increasing function of horizontal distance d X,k between sound source at X and hydrophone k: p k (X; θ) = g(d X,k ; θ). There are many candidate models for the distance detection function g; here we use three, all of which have a long history in the classical distance sampling literature:

1. half-normal g(d; θ) = g 0 exp(-d 2 /(2σ 2 )) with θ = (g 0 , σ) 2. hazard rate g(d; θ) = g 0 (1 -exp(-(d/σ) -z )) with θ = (g 0 , σ, z) 3. negative exponential g(d; θ) = g 0 exp(-d/σ)) with θ = (g 0 , σ)
Detectors such as this, where an object (in this case a sound) can be "captured" on more than one detector and where the detector can capture many objects in one "trapping session", are termed "proximity detectors" by [START_REF] Efford | chap Density estimation by spatially explicit capture-recapture: likelihood-based methods[END_REF]. Other detectors (not relevant to passive acoustics) are described in that paper. For simplicity in what follows, we consider only one "trapping session", although generalization to multiple sessions (with potentially varying animal densities and/or detection parameters) is simple.

Assume n sounds in the period of interest were detected on one or more hydrophones. Let ω ik represent the detection of the ith sound at the kth hydrophone, such that ω ik = 1 if the sound was detected, 0 otherwise. ω i is the capture history of the ith sound, and ω is all the recorded capture histories.

Likelihood-based methods

For simplicity, we assume that sound source locations are distributed in space according to a homogeneous Poisson process with intensity D. Extension to an inhomogeneous process is conceptually straightforward, and is given by [START_REF] Borchers | Spatially explicit maximum likelihood methods for capture-recapture studies[END_REF].

The joint likelihood for D and the detection parameters θ given the capture histories ω can be written

L(D, θ|n, ω) = P r(n|D, θ)P r(ω|n, θ) (1)
where P r(n|D, θ) is the marginal distribution of the number of sound sources detected, n, and P r(ω|n, θ) is the conditional distribution of the capture histories given n. (In the inhomogeneous Poisson case the latter distribution will also depend on the spatial intensity parameters.)

Given the assumption that sound source locations follow a Poisson process, then n is the outcome of a thinned Poisson process, which has a Poisson distribution with parameter Da(θ), where a(θ) = X∈A p . (X; θ)dX has an intuitive interpretation as the "effective sample area" (see Borchers, this volume, for details). The area A needs to be large enough that no detections can occur from outside of it; in practice [START_REF] Efford | secr -Spatially Explicit Capture-Recapture in R, version 1.2.10[END_REF] suggests it is sufficient to define A as a rectangle with limits formed by buffering the hydrophones at a distance w such that g(w) < 0.01. The first term in ( 1) is thus

P r(n|D, θ) = n! -1 Da(θ) n exp (-Da(θ)) . ( 2
)
Assuming independence between detections, the second term in (1) can be written

P r(ω|n, θ) = n n 1 , . . . , n C a(θ) -n X∈A n i=1 P r (ω i |X, θ) dX (3)
where the first part is the multinomial coefficient (with n 1 , . . . , n C representing the frequency of each of the C unique capture histories), the second part (a(θ) -n ) is there because we condition on the number of observed capture histories, and the remainder is the probability of obtaining capture history ω i given sound source location X, integrated over all possible locations. Since hydrophones operate independently,

P r (ω i |X, θ) = K k=1 p k (X; θ) ω ik (1 -p k (X; θ)) (1-ω ik ) . ( 4
)
Note that, because the sound source locations are not known, they are integrated out of the likelihood. In practice, to reduce computational effort during maximization, the likelihood is evaluated over a discrete grid of points, and the integrations become sums [START_REF] Efford | Population density estimated from locations of individuals on a passive detector array[END_REF]. The choice of the grid size is a compromise between computational efficiency and no influence on the results.

One approach to estimation of D, which we call the "full likelihood" approach, is joint maximization of the parameters D and θ in (1). Variances on parameters can be estimated from the inverse of the information matrix and profile likelihoods can be used to obtain confidence intervals.

An alternative (e.g. [START_REF] Borchers | Spatially explicit maximum likelihood methods for capture-recapture studies[END_REF] when D is homogeneous is to maximize the conditional likelihood

L (θ|n, ω) ∝ a(θ) -n X∈A n i=1 P r (ω i |X, θ) dX (5)
to obtain estimates of θ and hence â = a( θ). From this, D can be estimated using All of the above inference can be carried out using the secr package [START_REF] Efford | secr -Spatially Explicit Capture-Recapture in R, version 1.2.10[END_REF] in R (R Development Core Team, 2009).

Bayesian methods

There are several differences between the model described in the previous section and that used here. Firstly, the Bayesian model is parameterised in terms of abundance N within an area A, rather than density D, and this N is assumed to be a fixed quantity.

This leads to a binomial likelihood for n given N , rather than the Poisson likelihood for n given D in the previous section.. Secondly, data augmentation is used to deal with the un-observed capture histories and sound source locations. Thirdly, being Bayesian, prior distributions are used on all unknown parameters, although since uniform priors with widely spaced limits are used, the posterior and likelihood surfaces will have the same shape within the truncation bounds.

Let ω be the capture histories of the N sound sources in the study area A. n of these are observed, and therefore have one or more non-zero elements; the remaining (N -n) contain only zeros. The equivalent of (3), conditioning on the sound source locations, is then

P r( ω|N, X, θ) = N n0, . . . , n C N i=1 P r ( ω i |X i , θ) . ( 6
)
Inference is based on the joint posterior distribution P r(N, X, θ|ω) ∝ P r(N, X, θ)P r( ω|ω, N )L(N, X, θ| ω)

= P r(N )P r(X|N )P r(θ)L(N, X, θ| ω) (7)
where a discrete uniform prior distribution is used for P r(N ), with lower bound 0 and an (arbitrarily high) upper bound M , and uniform prior distributions are used for P r(X|N ) and P r(θ). Note that P r( ω|ω, N ) = 1, which is why it disappears from the second line, and L(N, X, θ| ω) has the same form as (6) except that ω is the fixed variable.

In practice, the fact that the dimension of both ω and X depend on N raises computational issues. These are sidestepped by a further data augmentation, where (M-N) additional all-zero capture histories and sound source locations are added (see [START_REF] Royle | Analysis of multinomial models with unknown index using data augmentation[END_REF]

P r(z, X * , θ, ψ|ω, M ) ∝ P r(z, X * , θ, ψ)P r( ω * |ω, M )L(z, X * , θ, ψ| ω * ) = P r(X * )P r(θ)P r(ψ)P r(z|ψ)L(z, X * , θ| ω * ) (8)
where uniform prior distributions are used for P r(X * ) and P r(θ), a uniform (0,1) prior is used for P r(ψ), P r( ω * |ω, M ) = 1, and

L(z, X * , θ| ω * ) = M n0, . . . , n C M i=1 z i P r ω * i |X * i , θ . ( 9
)
The marginal posterior distributions of N , θ and X from (8) are then same as ( 7), but implementation is greatly simplified.

The most convenient route to fitting the Bayesian models to data is via short programs written in OpenBUGS [START_REF] Thomas | #draw a sound location x1[END_REF]. An example program is provided as an appendix. 3 Case study

Case study methods

The data come from six 10-minutes sample periods, taken from March and April 2006 and 2007 (Table 1). For this simple analysis, we collapsed data over sampling occasions and treated them as a single 1 hour period. Sounds were recorded at 16 bottom-mounted hydrophones in BSURE, spaced from 8km to 18km apart and arranged in two lines (Figure 1). A custom-developed detector and classifier (Mellinger et al., unpublished) was utilized to detect minke whale boing vocalizations on the multiple hydrophones.

The boing detector outputs included detailed timing and frequency content information. This information was utilized to make initial manual associations (i.e., determine whether detections at different hydrophones were of the same sound). The association outputs served as inputs to the SECR analysis.

Likelihood-based models were fit using the secr package (version 1.2.10) in R (version 2.9.2). As mentioned in section 2.1, it is necessary to define a buffer distance w for integration, and [START_REF] Efford | secr -Spatially Explicit Capture-Recapture in R, version 1.2.10[END_REF] advises setting it such that g(w) < 0.01. We took an iterative approach: fitting detection function models with increasing values for w and determining when values of log-likelihood, σ and D stabilized. We found that, for the half-normal, hazard rate, and negative exponential models, g(w) = 0.01 corresponded to w ≈ 80, 110, and150km respectively, and at these distances the log-likelihood and parameter estimates were stable to three significant figures. In our case this was good enough for model selection, because there were large differences among models; however had the models been closer, more accuracy and hence large buffer distances would have been required. Given the above buffer distances, we fit the models by maximizing the conditional likelihood (5) and selected the best model on the basis of minimum Akaike information criterion for small sample sizes (AICc). Minke whale boings are readily detectable on the bottom-mounted hydrophones, and it is highly plausible that all boings produced at zero distance are detected with certainty. Initial analyses, where g 0 was estimated, gave values of ĝ0 > 0.99. We therefore fixed this parameter at 1.0. After obtaining maximum likelihood estimates of the remaining detection function parameters, density was estimated using the Horvitz-Thompson-like formulation described in section 2.1, with conditional (fixed-N) variance estimate.

Bayesian models were fit in OpenBUGS version 3.0.3 using the Appendix code.

Since model selection is not straightforward in OpenBUGS (Deviance information criterion is not available for discrete nodes) only the half-normal detection function model was fit (the best fitting model using likelihood based methods), using a buffer of w = 80km and fixing g 0 = 1. The superpopulation size, M was chosen in a similar manner to the buffer: increasing values were tried until further changes had no affect on inference. A useful shortcut diagnostic was to check that the posterior upper 97.5th quantile for ψ was well away from its upper bound of 1. We found that a value of M ≈ 2 N worked well; this amounted to adding 350 artificial all-zero capture histories to the dataset (to give M =407). We set a uniform prior on σ with lower bound 0 and upper bound again large enough that it did not affect posterior estimates -for these data a value of 50 km was used (this value was obtained by trial and error, making sure the posterior results were not constrained by this choice). Starting values for the MCMC chain were set by choosing values for all quantities at random from their priors. Convergence was checked informally, by starting multiple chains from random start points, examining trace plots, and checking that resulting parameter estimates from different chains were indistinguishable except for Monte-Carlo error. Initial investigations showed that 3000 samples was a sufficient burn-in to ensure convergence to the target posterior distribution and that keeping 100,000 samples after that was sufficient for 3 significant figure accuracy in parameter estimates.

Case study results

There were 233 detections of 57 individual boing sounds in the test dataset (Table 1).

For the likelihood-based implementation, the half-normal detection function model was strongly favoured, with the next best model, the hazard rate, having a ∆AICc of > 13, and the negative exponential model trailing a distant third (Table 2). Estimated probability of detecting a sound for locations within A from the half-normal model is shown in Figure 1. The estimated detection functions (illustrated in Figure 2), and hence densities, were quite different among the three models, with the hazard rate giving a density estimate about 40% lower than the half-normal, with the negative exponential being about 50% lower again. Estimated density from the half normal model was 47.88 sounds per hour per 10,000km 2 (SE 10.60). This corresponds to 179 sounds per hour within the 80km buffer area used for that model (A = 37, 283 km 2 ).

The Bayesian implementation of the half-normal model gave very similar results to the likelihood-based implementation. The posterior mean estimate of ψ was 0.46 with 95% central posterior interval of 0.28-0.69. The upper value was well away from 1.0, providing reassurance that a large enough number of artificial zero capture histories had been used. 4 Simulation study

Simulation study methods

To check the performance of the different approaches under known conditions, we undertook a small simulation study. We simulated 100 replicate populations of N = 175 objects located at random within a rectangular study area defined by an 80km buffer around the hydrophones. This corresponds to a true density of D = 46.94 per 10,000km 2 . Hydrophones were located at the same positions as previously, and we simulated detection of each object at each hydrophone according to a half-normal detection function with σ = 20. The simulated data were analyzed in the same way as the real data, except for the following. For the likelihood-based analysis, we fit only a half-normal detection function. We were interested in comparing variance estimates from both the unconditional likelihood, and the conditional likelihood with assumed random and fixed N , so we recorded all three. For the Bayesian analysis, we used only 10,000 samples after burn-in, to save computer time. No thinning was used. Informal checks for convergence consisted in, for some of the simulated data sets, starting the Monte Carlo chains at different points and checking they converged on similar values.

Simulation study results

The simulated data comprised a mean of 187.23 detections (SD 30.57) of 49.78 detected objects (SD 6.23), slightly lower than the numbers in the case study (because a slightly lower D and σ was used).

Both likelihood and Bayesian methods gave very similar estimates on average, with negligible bias in estimates of both σ and D (Table 3). The standard deviation of the estimates (i.e., the actual standard deviation of the 100 replicate estimates of σ and D for each method) was also very similar between methods. The Bayesian method also did a good job of estimating the standard deviation: the mean of the estimated standard deviation on D was 9.29, while the actual standard deviation was 9.13. There was a suggestion that the full likelihood method slightly over-estimated the standard deviation: the mean estimate was 10.00 while the true value was 8.87.

This led to slightly high 95% confidence interval coverage for the full likelihood method (0.99); by contrast the coverage of the 95% posterior credibility interval was exactly at the expected 0.95. The standard deviations of the derived estimates of D from the conditional likelihood formulation were very similar, and closer to truth (9.27 for the binomial and 9.29 for the Poisson), and the corresponding confidence interval coverage was better.

Discussion

Although there is no ground truth with which to compare the case study estimates, the results appear reasonable given what is known about minke whale acoustic behaviour.

We expect certain detection at zero horizonal distance, and that was backed up by preliminary SECR analyses. The fitted half-normal detection function parameter estimate of 21.4 (likelihood-based) or 21.7 (Bayesian) is reasonable, corresponding to a detection probability of about 0.95 at 10km, 0.5 at 25km and 0.1 at 45km (Figure 2). Calls produced at constant source level and homogeneous propagation and background noise conditions will all be detectable to a certain distance, beyond which the received level falls below the threshold set for the detector and they are no longer detectable. The resulting "step" detection function would be best fit by the hazard rate model, with large (> 5) values of the z parameter. However, variation in source levels, propagation and noise all contribute to a "rounding off" of the detection shoulder, leading to an average detection function that is closer to the half-normal form (see, e.g. Burnham et al, 2004, figure 11.2). It is possible that more flexible models, such as finite mixtures or the semiparametric families used in conventional distance sampling, may provide a better fit. Future work on the case study will focus on applying more complex models of the detection process (such as time-varying detection) to a larger sample of data; parallel field work is also in progress that, if successful, will provide an estimate of animal call rate and potentially allow estimation of animal, rather than call, density.

We assume in this work that the manual association of sounds was done without error, i.e. that no detected sounds were incorrectly associated as having the same sound source, and that all detected sounds from a given source were identified and associated.

This seems a reasonable assumption given the amount of human effort put into this task. We envisage that applications of these methods in the future might be based on an automated association procedure. If one can characterize the association phase and an eventual association error process, it should be possible to include this directly in the estimation procedure. Therefore the precision in the estimates would include a component due to mis-association.

We also considered sound and hydrophone locations to exist in two-dimensional (horizontal) space, which is clearly a simplification for hydrophones located at around 4.5km depth and whales diving in the top few hundred meters of water. If the main determinant of detection probability is direct distance, rather than horizontal distance, then variation in whale and hydrophone depth represent un-modelled sources of heterogeneity. However, in our case, compared with other sources of variation (such as in source level and propagation conditions) this seems quite minor. In other cases (with deeper diving whales and shallower hydrophones, or smaller σ) it may be more important, in which case the methods could be extended to three dimensions, with additional assumptions about the depth distribution of sound sources being required.

Another simplification was that our analysis assumed a homogeneous density, even failing to account for the islands of Kauai and Niihau, both of which occur within the study area. This is acceptable given the preliminary nature of the test case analysis; however, in future work, we hope to explore the relationship between biologically relevant covariates such as depth and density. We will also need to account for the masking effect of islands on sound propagation.

Our simple simulation study showed that both likelihood and Bayesian methods yield unbiased estimates and standard deviations when their assumptions are met (or nearly met in the case of the likelihood method, which assumes random N when it was fixed in the simulations). Our estimates were based on likelihood modes in the former method and posterior means in the latter, but as with many analyses, this did not seem to generate a significant difference in estimates. Under small sample sizes, where likelihoods might be severely skewed, the posterior mode might be a better candidate than posterior mean in the Bayesian method.

Mean estimated standard deviation of D in the full likelihood method was high compared with the actual value (the standard deviation of the estimated D's), but this is understandable given that the method assumes population size in the study area is a Poisson random variable when it was actually fixed in the simulation. The two conditional likelihood variance estimators produced estimates of standard deviation that were smaller on average than the full likelihood estimates, and closer both to the actual standard deviation of the likelihood estimator and to the estimate from the Bayesian method. To understand these differences requires some discussion of the form of the conditional likelihood variance estimators. The fixed-N estimator has two components:

the first is design-derived and reflects the uncertainty in D arising from sampling only a proportion a of the study area A, assuming each animal is sampled independently;

the second is model-derived and reflects the additional uncertainty due to estimating the parameters of a. The random-N estimator also has two components, with the first arising from an assumption that the variance of n is equal to its mean (i.e., that animal locations are independent of one another), and the second component being the same as the second component of the fixed-N estimator. It turns out that the first component of the fixed-N estimator is just (1 -â/A) times the first component of the random-N estimator -this term can be thought of as a finite population correction factor that will cause the first component of the fixed-N estimator to go to zero when all of A is sampled. Hence, it is inevitable that the fixed-N estimator produces smaller estimates of variance than the random-N estimator, as we found. Because our simulations used a fixed N , all assumptions of the fixed-N estimator were met, and hence it was not surprising that it produced estimates of standard deviation in D that were close, on average, to the actual value. It was more surprising that the random-N estimator also performed well and this estimator deserves further investigation.

Given this initial investigation, there appears to be little difference between likelihood and Bayesian approaches. One major drawback of the Bayesian implementation in OpenBUGS is that there is no ready method of selecting among different candidate detection function models (or alternative models for spatial density distribution if a non-homogeneous distribution is assumed). In addition, the model formulation used here, with augmentation of the observed capture histories with a large number of artificial all-zero histories, results in rather long computation times (although still short when compared with the time required to collect and process the acoustic data). It is important to check that enough augmentation is used, that wide enough priors are set on detection parameters, and that the burn-in time and number of samples are sufficient to yield reliable estimates. By contrast, the likelihood-based methods are rather easier to implement, thanks to the secr R package. Model selection via AICc (or other criteria) is straightforward, convergence appeared reliable in the examples we used, and fitting was much faster than in OpenBUGS. For both methods one must check that an appropriately large buffer is used around the sample locations, and for the likelihood-based method one can also vary the number of grid points used in numerical integration. Our suspicion is that the Bayesian approach will make it easier to handle complex scenarios such as random effects in the detection process or mis-association of sounds -both are examples where data augmentation can potentially provide an elegant solution, enabling inference to proceed by integrating out the complicating fac-tors with relative ease. However, for simpler applications it appears at present that the likelihood-based approach is more convenient.

We checked the performance of the methods under ideal conditions, where the model assumptions were met and sample sizes were reasonably large. It would be useful to determine how well they perform under more challenging situations, such as alternative detection models, mis-association of calls, inhomogeneity in spatial density and small sample sizes. Previous simulation studies of other varieties of SECR methods have shown them to be reasonably robust to various challenges (e.g. [START_REF] Efford | chap Density estimation by spatially explicit capture-recapture: likelihood-based methods[END_REF].

For these methods to work, the optimal spacing between the acoustic sensors is a function of the scale of the detection process because the information about the detection process lies essentially in the "recaptures". Provided the acoustic data from multiple hydrophones can be seen as capture histories, the SECR approach becomes a natural one to estimate density. We predict in the future that statistical methods, sound processing, survey design and even hydrophone hardware might be developed and optimized with this goal in mind. The sound processing algorithms used here should be easily adapted to other scenarios. The hardware technology required to implement similar approaches still needs some development, and therefore the application of these methods outside a setting like a navy range is still not straightforward. The development of cheap and easily deployable sensors is desirable.

When a sound is detected at three or more hydrophones with appropriate geometry, then given precise information about arrival time and assumptions about sound propagation, it becomes possible to estimate the sound source location (in 2 dimensions; more detections are required for 3-d localization). Detection of echoes at a single hydrophone can also potentially be used to provide additional information about location. In the current study, we make no use of this information, but the SECR methods could be extended to utilize such information when available, potentially yielding more precise inferences. [START_REF] Dawson | Bird population density estimated from acoustic signals[END_REF] have shown how information about sound source distance that is contained in the relative received amplitude can be used to improve inference. Information on bearing from vector-sensing hydrophones could also potentially be used.

Passive acoustic methods have enormous potential to provide estimates of density and abundance in situations not readily amenable to surveys by other modalities. In many cases, however, we are limited by our knowledge of the vocal behaviour of the animals, e.g., call rates. Nevertheless, there is a great deal of research interest in the area and many ongoing studies aimed at increasing our knowledge. We anticipate that passive acoustic density estimation will be increasingly applied in future years.

the

  Horvitz-Thompson-like estimator D = n/â. While the estimates derived from both full and conditional likelihoods are equivalent, the Horvitz-Thompson-like formulation permits different variance estimators for D than the full likelihood.[START_REF] Borchers | Spatially explicit maximum likelihood methods for capture-recapture studies[END_REF] suggest two in their supplementary materials, one assuming fixed N in the study area, and the other random N . Both have design-based and model-based components (see Discussion), and can be expected to be more robust than the full likelihood estimator to departures from a Poisson animal distribution. (We note in passing that these two estimators are sometimes referred to as "binomial" and "Poisson", e.g., in[START_REF] Efford | secr -Spatially Explicit Capture-Recapture in R, version 1.2.10[END_REF], but we prefer to use the terms fixed-N and random-N as neither assumes animals follow binomial or Poisson distributions.) Confidence intervals can be obtained by assuming D follows a normal or log-normal distribution.

Fig. 1

 1 Fig. 1 Layout of BSURE case study hydrophones (crosses), solid contour lines showing probability of detecting a sound from that location with one or more hydrophones (denoted p . (X; θ) in the text) estimated from a likelihood-based analysis with the half-normal detection function model, and the dashed rectangle showing the 80km buffer used in that analyis.

  for the general framework, andRoyle et al (2009b) and[START_REF] Royle | A hierarchical model for spatial capture-recapture data[END_REF] for applications). Let M be the fixed size of a superpopulation of sound sources, with capture histories ω * and locations X

* . n of the capture histories are observed; the remaining (M -n) contain only zeros. Let z = (z 1 , . . . , z M ) be a vector of indicator variables, such that z i = 1 if sound source i is part of the population N , 0 otherwise. This means N is now a derived parameter in the model: N = M i=1 z i . Let the z i for each sound source follow a Bernoulli distribution with parameter ψ. Inference is then based on the joint posterior

Table 1

 1 Summary of case study data.

	Date	Start time # detections	# unique		Capture frequency
		(GMT)		boings (n) 1	2	3 4 5-9 10-14
	5 Mar 06	22:15:55	63	14	2	2	4 2 2	2
	13 Mar 06 23:05:28	75	12	1	3	0 0 6	2
	19 Apr 06	02:59:40	6	5	4	1	0 0 0	0
	19 Apr 06	04:29:40	12	10	8	2	0 0 0	0
	16 Apr 07	03:52:20	41	9	2	2	0 1 3	1
	21 Apr 07	02:49:43	36	7	1	0	1 1 3	1
	Total		233	57	18 10 5 4 14	6

Table 2

 2 Results from analysis of case study data. Values in brackets after estimates are standard errors. Density ( D) units are sounds per hour per 10,000km 2 . σ represents the scale parameter of the 3 models considered, z represents the shape parameter of the Hazard-Rate Estimated half-normal (solid line), hazard rate (dashed line) and negative exponential (dotted line) detection functions fit by maximum likelihood to the case study data.

	model.
	Model	∆AICc Param. estimates	D	CI( D)
	Likelihood-based method
	Half-normal	0	σ = 21.37(2.27)	47.88 (10.60) 40.23-72.10
	Hazard rate	13.11	σ = 27.36(3.84),	28.45 (7.48)	13.80-43.10
			z = 3.60(0.41)
	Negative exponential 46.71	σ = 32.56(11.87)	13.62 (8.13)	3.25-62.98
	Bayesian method
	Half-normal	-	σ = 21.72(2.50)	48.46 (10.26) 30.04-70.54

Table 3

 3 Summary of results from simulation study. For the mean estimated SD of the density estimate ( D) and corresponding confidence interval (CI) coverage, the three values represent respectively the full likelihood, binomial and Poisson based estimates. σ represents the scale parameter of the Half-Normal model.

	Statistic	Likelihood-based Bayesian
		method	method
	Mean σ (true value 20)	20.11	20.34
	SD σ	1.84	1.89
	Mean estimated SD σ	1.95	2.00
	95% CI coverage σ	0.96	0.96
	Mean D (true value 46.94)	47.28	48.00
	SD D	8.87	9.13
	Mean estimated SD D	10.00; 9.27; 9.29	9.29
	95% CI coverage D	0.99; 0.95; 0.94	0.95

Acknowledgements This research was undertaken as part of the DECAF project (Density Estimation for Cetaceans from passive Acoustic Fixed sensors), funded under the National Oceanographic Partnership Program jointly by the Joint Industry Programme and US National Marine Fisheries Service. We thank the other DECAF project members for their many contributions to this work. Murray Efford and Andy Royle provided prompt response to any issues that arose relating to implementation of the analysis. We are particularly grateful to David Borchers, who kindly stepped in to give the underlying EURING talk at short notice when TAM had to cancel his attendance at the last moment. Two anonymous reviewers and the session chairs have provided many helpful comments which improved the quality of this work.

Appendix: Example OpenBUGS code

This is the code used to run the application example in the Bayesian framework, a passive acoustic SECR with half-normal (HN) detection function. The user must input as data the following objects (object names in the code given inside brackets):

(1) the number of detected animals (n), (2) the boundaries of the region over which integration takes place (Xl, Xu and Y l, Y u), (3) the upper bound on the prior for sigma (maxSigma), ( 4) the number of added all 0's capture histories required for data augmentation (nzeroes), ( 5) the traps locations (traps, the trap x and y coordinates need to be respectively in columns 1 and 2), ( 6) the area over which abundance is estimated (Area) and ( 7) the capture histories (Y , a matrix in which position i, k is 1 if animal i was detected on trap k, and 0 otherwise). The random variables involved for which priors are required are (1) the inclusion probability (psi), ( 2


George Chen

Junwei Zhao

Observation of negative differential mobility and charge packet in polyethylene

Charge packet has been observed on many occasions but its physical mechanisms have never been properly understood. One of the models proposed by Lewis et al shows the presence of negative differential mobility with an electric field in semi crystalline polyethylene. In this letter we have observed the negative differential mobility using the transient space charge profile measured by the pulsed electroacoustic technique. By superimposing a short pulse voltage to a dc voltage, it is possible to obtain the velocity of holes at different applied fields. To our knowledge we have for the first time observed negative differential mobility in polyethylene. This observation provides crucial evidence to support Lewis's model and allows one to simulate charge packet and its behaviours.

Polymeric materials have been widely used as insulation in power engineering due to their excellent dielectric properties. With the advances in manufacturing and processing technology, the defects in these materials have been reduced significantly over the years. Accordingly, the operating electric field of the materials has been increased steadily to minimise the cost of power equipment and devices. On the other hand, electrical phenomena under high electric field become of significant importance as they are not only scientifically interesting but also related to the reliable operation of many devices. It has been recognized that the reliability issue of the insulation materials operating at high electric field is closely related to space charge formation and its dynamics.

The formation of space charge in dielectric materials at high electric field is a well-known phenomenon and has been a hot topic of scientific research in the last few years thanks to the significant developments in space charge mapping techniques. The presence of space charge will distort the applied electric field and may lead to a more severe electric field enhancement resulting in material degradation/ageing [START_REF] Chen | International symposium on High Voltage Engineering[END_REF]. On the other hand, space charge dynamics in the material have been used as an ageing marker to assess the status of the material [START_REF] Chen | [END_REF][3]. One of the observed and well-published phenomena in space charge research is charge packet and its dynamics [4][5][6][7][START_REF] Xia | Proc.8th ICPADM[END_REF][START_REF] Kon | [END_REF][10]. A charge packet can be loosely defined as a pulse of net charge that propagates across the material under the influence of electric field while maintaining its shape. It has been termed as an unexpected phenomenon and usually formed under high electric field and propagates through the material from one electrode to the other. In many cases, positive charge packet was reported although negative charge packet was also observed. Different mechanisms have been put forward to explain various features of charge packet. In recent paper by Lewis et al [11], they proposed the velocity-field characteristic for holes based on their charge transport model in polyethylene. The velocity of holes increases initially with the field and reaches its maximum at the threshold field E T . After E T the velocity of holes decreases with the increasing electric field as shown in Figure 1. When the applied field is lower than E T , any injected holes from the anode will modify the applied field. The embryo of charge packet is characterised by the field E A (rear) and E B (front) with E B >E A . The corresponding velocities are V A and V B . From Figure 1, it can be seen that V A <V B , meaning the charge packet will disperse during its propagation. However, when the applied field is above E T the velocities of rear and front charge packet change to V A ' and V B '. As V A '>V B ' the charge packet will increase. The growing packet reduces E A ' and increases E B ' until the corresponding velocity becomes the same, i.e. V A '=V B '=V P .

Figure 1 Velocity -field relationship for holes in polyethylene [11].

The characteristic described above is similar to part of the Gunn Effect [START_REF] Streetman | Chapter 10 in Solid State Electronic Device[END_REF] observed in semiconductors but the mechanisms are probably very different. For the Gunn Effect, it is believed that electrons can exist in a highmass low velocity state as well as their normal low-mass high velocity state in semiconductors. The normal states can be forced into the high-mass state by an electric field of sufficient strength. In this state electrons form clusters or domains which cross the region at a constant rate causing current to flow as a pulse. The detailed mechanism can be traced back to two conduction band energy levels, (normal lower valley) and L (satellite valley). In the lower valley, electrons exhibit a small effective mass and very high mobility while in the satellite valley electrons possess a large effective mass and very low mobility. The two valleys are separated by a small energy gap. Initially, most electrons reside near the bottom of the lower valley and they can readily be accelerated in a strong electric field to the energy in the order of the -L inter-valley separation. Electrons are then able to scatter into the satellite valley, resulting in a decrease in the average electron mobility. Above the threshold field E T , most electrons reside in the L valley. In polyethylene, the energy landscape for semiconductors may not apply. According to the model proposed by Lewis et al [11], hole transport in polyethylene can only occur via electron vacancies in or closely associated with the valence band and is consequently confined to polymer chains. Continuous movement of holes requires inter-chain hole transfer via tunnelling between closely adjacent polymer chains including chains in amorphous regions. Therefore, hole conduction process is more sensitive to morphology of polyethylene. When an electric field is applied to the material reorganisation of molecular chains in amorphous regions will take place due to induced mechanical stress. They have concluded that the above process would have adversely influence on hole tunnelling, consequently reducing hole mobility and encouraging hole trapping, leading to the proposed velocity-field diagram shown in Figure 1.

The above velocity and field characteristic has been used in simulation of charge packet behaviour in polyethylene [START_REF] Junfeng | Proceedings of 2008 International Symposium on Electrical Insulating Materials[END_REF]. However, there is no convincing experimental evidence so far to verify the model. In this letter, we report the velocity-field characteristic in polyethylene for holes based on transient space charge profiles. The pulsed electroacoustic (PEA) technique was utilised for pace charge measurement and the applied field ranges from 10 kVmm -1 to 70 kVmm -1 . Additive free low-density polyethylene (LDPE) films with a thickness of 100 µm were used in the present study. All the measurements were carried out at room temperature.

Different methods have been used to measure charge mobility in solid dielectrics such as time-flight method, transient space charge limited current method and surface potential decay method. Recently, Hozumi et al [START_REF] Hozumi | [END_REF] have proposed a new method to estimate mobility based on transient space charge measurement. By superimposing a pulse voltage to the applied dc voltage, it is possible to generate a charge packet that moves under the influence of the electric field. The schematic principle of the method is shown in Figure 2. When the external applied field is high enough charge injection takes place due to either Schottky injection or tunnelling. The injected charge will move under the influence of the electric field and may reach a qusiequilibrium after a period of time. In addition to the material itself, this time also depends on the applied field strength and temperature. An electric pulse is then applied and an extra charge packet will be initiated and move across the material. By subtracting the steady-state charge profile the small charge packet due to the pulse voltage becomes clearly visible and its movement can be used to estimate the velocity of the charge as shown in Figure 2. It has been reported [4] that the semicon electrode (polyethylene loaded with carbon black) injects more easily than the electrodes such as aluminium or gold. So in the present case the semicon was used as the anode and aluminium as the cathode to promote hole injection, and therefore, positive charge packet.

Figure 3 shows the transient charge distribution at an applied field of 20 kVmm -1 and subtracted plots. The magnitude of the pulse voltage is about 10 kV with a pulse width of 0.25s. The pulse magnitude is decreased when the applied voltage is increased while the pulse width remains the same. The packet charge is not obvious without the subtraction, but it can be clearly seen that the movement of charge packet goes towards the cathode after the subtraction as shown in Figure 3 (b). The velocity of holes depends on the applied field as shown in Figure 4 where the applied field is 50 kVmm -1 . It can be seen that the velocity of the charge packet is faster compared with that at 20 kVmm -1 . On the other hand, the injected negative charge seems to move very fast. A range of applied electric fields from 10 kVmm -1 to 70 kVmm -1 were investigated and the velocity obtained is illustrated in Figure 5. Each data point is the average of at least three measurements. When the applied field exceeds 70 kVmm -1 it takes a longer time to achieve the steady state. Therefore, the present results are limited to 70 kVmm -1 . However, it becomes very clear that the proposed characteristic of the velocity-field by Lewis et al [11] has been, to our knowledge, for the first time experimentally observed. The velocity and electric field relationship in Figure 5 deviates slightly from the one where the applied field is exceeding 60 kVmm -1 . Instead of decreasing continuously the velocity of holes increases. The shape is strikingly similar to the Gunn effect occurred in semiconductors. The Gunn Effect is responsible for the negative differential resistance observed in semiconductor materials. The above relationship may be directly related to the negative differential resistance observed in polyethylene reported many years ago [15][16] where the current and voltage characteristics of very thin polyethylene film with varying thickness from 10 to 50 nm were investigated. Negative differential resistance was observed and the onset of the negative differential resistance was found as low as 27 kVmm -1 . Crine [START_REF] Jean-Pierre | CEIDP 723[END_REF] has termed the negative differential resistance in polyethylene as one of the unexplained disturbing phenomena in the electrical properties of dielectric polymers. Based on the result obtained in this letter, the negative differential resistance observed in polyethylene can be attributed to the change in charge carrier mobility. As mentioned earlier, the charge packet has been observed under a wide range of conditions, the negative differential mobility obtained in the present work may be responsible for those observed in the electric field range below 80 kVmm -1 . It is believed that electron injection takes place as well especially when the positive charge packet moves close to the cathode. However, injected electrons seem to move very fast which may contribute to the formation of charge packet. On the other hand, the real charge packet always changes its shape during the propagation, i.e. diffused. This may be related to recombination of holes and electrons. The charge packet observed above 100 kVmm -1 may take place based on the different mechanisms such as a consequence of charge injection, ionisation and recombination. Currently, simulations using the velocity-field characteristic are underway to study charge packet behaviours in polyethylene. More importantly, the mobility versus electric field in aged polyethylene will also be investigated to unravel the relationship between ageing and charge mobility.

Figure 2

 2 Figure 2 Transient space charge profile and velocity estimation for holes.

Figure 3

 3 Figure 3 Transient space charge profiles at 20 kVmm -1 after pulse excitation: (a) before subtraction; (b) after subtraction.

Figure 4

 4 Figure 4 Transient space charge profiles subtracted from stable distribution at 50 kVmm -1 : (a) 3D plot; (b) contour plot.

Figure 5

 5 Figure 5 Dependence of hole velocity on the applied electric field.