Storying and Deaf Children

Peer reviewe

The environment and host haloes of the brightest z~6 Lyman-break galaxies

By studying the large-scale structure of the bright high-redshift Lyman-break galaxy (LBG) population it is possible to gain an insight into the role of environment in galaxy formation physics in the early Universe. We measure the clustering of a sample of bright (-22.7<M_UV<-21.125) LBGs at z~6 and use a halo occupation distribution (HOD) model to measure their typical halo masses. We find that the clustering amplitude and corresponding HOD fits suggests that these sources are highly biased (b~8) objects in the densest regions of the high-redshift Universe. Coupled with the observed rapid evolution of the number density of these objects, our results suggest that the shape of high luminosity end of the luminosity function is related to feedback processes or dust obscuration in the early Universe - as opposed to a scenario where these sources are predominantly rare instances of the much more numerous M_UV ~ -19 population of galaxies caught in a particularly vigorous period of star formation. There is a slight tension between the number densities and clustering measurements, which we interpret this as a signal that a refinement of the model halo bias relation at high redshifts or the incorporation of quasi-linear effects may be needed for future attempts at modelling the clustering and number counts. Finally, the difference in number density between the fields (UltraVISTA has a surface density ~1.8 times greater than UDS) is shown to be consistent with the cosmic variance implied by the clustering measurements.Comment: 19 pages, 8 figures, accepted MNRAS 23rd March 201

Long term minimum tillage investigations, Stubble management, Deep ripping

Direct drilling Long term minimum tillage investigations (1) Continuous cropping – 77A16, 77A18, 77MT15, 77WH17, 77WH13, 78M25. (2) Rotational cropping – 77A43, 77E52, 77M35, 77M56, 77MT51, 77WH8. Stubble management – 79M7, 79WH6, 82M34, 84M1, 82LG4, 82LG46 (82KD1). Deep ripping - 82M35 in Minimum Tillage Rotation section also contains a deep ripping treatment. 77WH17, 80A44, 80NO46, 81M45, 81NO3, 81NO4, 82GE37, 82GE38, 82M30, 82M46, 82M60, 82ME38, 82N32, 82WH49, 84E24, (84C42, 84C43, 84C44, 84C45, 84C46) Eradu Sandplain – ECRS, 84E23, 84E24, 84JE43, 84JE44, 84LG37, 84M38, 84NO58, 84WH2, 84WH3, 84WH39. Additional deep ripping research is included in summaries by W. Bowden, D. Tennant, J. Hamblin, J. Wilson

A class of quadratic deformations of Lie superalgebras

We study certain Z_2-graded, finite-dimensional polynomial algebras of degree 2 which are a special class of deformations of Lie superalgebras, which we call quadratic Lie superalgebras. Starting from the formal definition, we discuss the generalised Jacobi relations in the context of the Koszul property, and give a proof of the PBW basis theorem. We give several concrete examples of quadratic Lie superalgebras for low dimensional cases, and discuss aspects of their structure constants for the `type I' class. We derive the equivalent of the Kac module construction for typical and atypical modules, and a related direct construction of irreducible modules due to Gould. We investigate in detail one specific case, the quadratic generalisation gl_2(n/1) of the Lie superalgebra sl(n/1). We formulate the general atypicality conditions at level 1, and present an analysis of zero-and one-step atypical modules for a certain family of Kac modules.Comment: 26pp, LaTeX. Original title: "Finite dimensional quadratic Lie superalgebras"; abstract re-worded; text clarified; 3 references added; rearrangement of minor appendices into text; new subsection 4.

Ecological indicators for abandoned mines, Phase 1: Review of the literature

Mine waters have been identified as a significant issue in the majority of Environment Agency draft River Basin Management Plans. They are one of the largest drivers for chemical pollution in the draft Impact Assessment for the Water Framework Directive (WFD), with significant failures of environmental quality standards (EQS) for metals (particularly Cd, Pb, Zn, Cu, Fe) in many rivers linked to abandoned mines. Existing EQS may be overprotective of aquatic life which may have adapted over centuries of exposure. This study forms part of a larger project to investigate the ecological impact of metals in rivers, to develop water quality targets (alternative objectives for the WFD) for aquatic ecosystems impacted by long-term mining pollution. The report reviews literature on EQS failures, metal effects on aquatic biota and effects of water chemistry, and uses this information to consider further work. A preliminary assessment of water quality and biology data for 87 sites across Gwynedd and Ceredigion (Wales) shows that existing Environment Agency water quality and biology data could be used to establish statistical relations between chemical variables and metrics of ecological quality. Visual representation and preliminary statistical analyses show that invertebrate diversity declines with increasing zinc concentration. However, the situation is more complex because the effects of other metals are not readily apparent. Furthermore, pH and aluminium also affect streamwater invertebrates, making it difficult to tease out toxicity due to individual mine-derived metals. The most characteristic feature of the plant communities of metal-impacted systems is a reduction in diversity, compared to that found in comparable unimpacted streams. Some species thrive in the presence of heavy metals, presumably because they are able to develop metal tolerance, whilst others consistently disappear. Effects are, however, confounded by water chemistry, particularly pH. Tolerant species are spread across a number of divisions of photosynthetic organisms, though green algae, diatoms and blue-green algae are usually most abundant, often thriving in the absence of competition and/or grazing. Current UK monitoring techniques focus on community composition and, whilst these provide a sampling and analytical framework for studies of metal impacts, the metrics are not sensitive to these impacts. There is scope for developing new metrics, based on community-level analyses and for looking at morphological variations common in some taxa at elevated metal concentrations. On the whole, community-based metrics are recommended, as these are easier to relate to ecological status definitions. With respect to invertebrates and fish, metals affect individuals, population and communities but sensitivity varies among species, life stages, sexes, trophic groups and with body condition. Acclimation or adaptation may cause varying sensitivity even within species. Ecosystem-scale effects, for example on ecological function, are poorly understood. Effects vary between metals such as cadmium, copper, lead, chromium, zinc and nickel in order of decreasing toxicity. Aluminium is important in acidified headwaters. Biological effects depend on speciation, toxicity, availability, mixtures, complexation and exposure conditions, for example discharge (flow). Current water quality monitoring is unlikely to detect short-term episodic increases in metal concentrations or evaluate the bioavailability of elevated metal concentrations in sediments. These factors create uncertainty in detecting ecological impairment in metal-impacted ecosystems. Moreover, most widely used biological indicators for UK freshwaters were developed for other pressures and none distinguishes metal impacts from other causes of impairment. Key ecological needs for better regulation and management of metals in rivers include: i) models relating metal data to ecological data that better represent influences on metal toxicity; ii) biodiagnostic indices to reflect metal effects; iii) better methods to identify metal acclimation or adaptation among sensitive taxa; iv) better investigative procedures to isolate metal effects from other pressures. Laboratory data on the effects of water chemistry on cationic metal toxicity and bioaccumulation show that a number of chemical parameters, particularly pH, dissolved organic carbon (DOC) and major cations (Na, Mg, K, Ca) exert a major influence on the toxicity and/or bioaccumulation of cationic metals. The biotic ligand model (BLM) provides a conceptual framework for understanding these water chemistry effects as a combination of the influence of chemical speciation, and metal uptake by organisms in competition with H+ and other cations. In some cases where the BLM cannot describe effects, empirical bioavailable models have been successfully used. Laboratory data on the effects of metal mixtures across different water chemistries are sparse, with implications for transferring understanding to mining-impacted sites in the field where mixture effects are likely. The available field data, although relatively sparse, indicate that water chemistry influences metal effects on aquatic ecosystems. This occurs through complexation reactions, notably involving dissolved organic matter and metals such as Al, Cu and Pb. Secondly, because bioaccumulation and toxicity are partly governed by complexation reactions, competition effects among metals, and between metals and H+, give rise to dependences upon water chemistry. There is evidence that combinations of metals are active in the field; the main study conducted so far demonstrated the combined effects of Al and Zn, and suggested, less certainly, that Cu and H+ can also contribute. Chemical speciation is essential to interpret and predict observed effects in the field. Speciation results need to be combined with a model that relates free ion concentrations to toxic effect. Understanding the toxic effects of heavy metals derived from abandoned mines requires the simultaneous consideration of the acidity-related components Al and H+. There are a number of reasons why organisms in waters affected by abandoned mines may experience different levels of metal toxicity than in the laboratory. This could lead to discrepancies between actual field behaviour and that predicted by EQS derived from laboratory experiments, as would be applied within the WFD. The main factors to consider are adaptation/acclimation, water chemistry, and the effects of combinations of metals. Secondary effects are metals in food, metals supplied by sediments, and variability in stream flows. Two of the most prominent factors, namely adaptation/ acclimation and bioavailability, could justify changes in EQS or the adoption of an alternative measure of toxic effects in the field. Given that abandoned mines are widespread in England and Wales, and the high cost of their remediation to meet proposed WFD EQS criteria, further research into the question is clearly justified. Although ecological communities of mine-affected streamwaters might be over-protected by proposed WFD EQS, there are some conditions under which metals emanating from abandoned mines definitely exert toxic effects on biota. The main issue is therefore the reliable identification of chemical conditions that are unacceptable and comparison of those conditions with those predicted by WFD EQS. If significant differences can convincingly be demonstrated, the argument could be made for alternative standards for waters affected by abandoned mines. Therefore in our view, the immediate research priority is to improve the quantification of metal effects under field circumstances. Demonstration of dose-response relationships, based on metal mixtures and their chemical speciation, and the use of better biological tools to detect and diagnose community-level impairment, would provide the necessary scientific information

Tillage investigations.

77A16, red/brown sandy loam - York gum. 77WH17, yellow clayey-sand (Wongan loamy sand). 77M13, red sandy clay loam (Salmon Gum, Gimlet). 77Mt15, gravelly loamy sand/sandy loam - forest soil. 77A43 brown loamy sand/sandy loam - jam country. 77WH88, grey loamy sand over gravel at 50 cm - Elphin soil series – Mallee. 77M56, red sandy clay loam - salmon gum, Gimlet. 82M35, Loamy sand/sandy loam – Mallee. 77E52, fine white sand over gravel at 40 cm. 85SG28, grey-brown calcareous Earth - Kumarl – SGRS. 84M064, heavy land management systems - medium rainfall area. 84M063, heavy land management systems - ne wheat belt - continuous wheat additional heavy land long term direct drill trials, 84KA28. Minimum tillage, direct drilling, modified combine investigations. 87E35, White Grey Fleming Sandplain over gravel varying from 20 40 cm. 87E36, Fleming Sandplain Alongside 87E35. 87E37, Fleming Sandplain near E35, 36. 87M76, 87M77 Depth of cultivation with modified combine, and deep ripping. 87WH52 Wongan Loamy Sand - Depth of cultivation during Seeding, and Deep Ripping. 87Na81, 87Na 84 cultivate, direct drill, and speed of seeding. 86SG27, direct drilling, reduced tillage and conventional at two times of seeding with and without flexi coil. 87WH45, direct drill, scarifying, disc ploughing, chisel ploughing at two depths and deep ripping. 79WH6, grey and yellow loamy sand with gravel. 79M7, yellow loamy sand - Mallee scrub 82M34, salmon gum/gimlet clay loam. 84M1, stubble effect on heavy land - salmon gum soil – MRS. 85Ba32, deep ripping yellow sand/yellow loamy sand – BARS. 886LG67, 886LG68, DD scarifying and depth of ripping 2GE37, 82GE38, yellow sandplain - Naraling lupin/wheat rotation (2 blocks). 82M60, Semi Wodgil - MRS old lease block. 87M5, species and cultivar response to deep ripping on acidic yellow sandplain. 87M78, ripping, two times of seeding, three wheat varieties - Mallee soil, MRS. 86Mo28 Cultivation and Gypsum on Hard Setting Clay Loam - A. Tonkin, Coomberdale. 87Mo1, 87Mo2, 87Mo3, deep ripping on sands in the Minyulo Brook catchment west of Moora - Farmers Brennan and Edgar. 87SG31, Circle Valley and over clay. Pasture. 77WH17, yellow clayey sand (Wongan loamy sand). 82WH49, times of ripping in a 2 pasture: 2 wheat rotation - Wongan loamy sand. 84WH39, two machines at two speeds of ripping - Wongan loamy sand. 85WH41, depth of ripping by shank spacing. 85WH62, depth of ripping by shank spacing - Wongan loamy sand. 86WH4, two times of seeding, with and without ripping, 7 rates of nitrogen, one rate N applied late. 86WH43, deep ripping response by wheat and barley varieties - Wongan loamy sand. 86WH66, time of ripping, pre and post seeding - Wongan loamy sand. 87WH55, ripping times pre and post seeding with two seeding rates. MISCELLANEOUS TRIALS. 86M79, fallowing and deep ripping with two times of seeding on sandy clay loam. 87M2, banding superphosphate at depths below the seed - Newland, yellow sandplain, Carrabin - Jarvis and Bolland. 81SG1, Kumarl soil - SGRS - crop/fallow rotation. 87SG32 and 87SG33, rate of seeding-wheat on Kumarl soil

Long term minimum tillage investigations, stubble management techniques, deep ripping and seeding machine comparisons.

77 A 16, red/brown sandy loam. 77 WH 17, yellow clayey-sand (Wongan loamy sand). 77 M 13, red sandy clay loam (salmon gum, gimlet). 78 M 25, Yellow acid loamy sand (Wodgil). 77 Mt 15 Gravelly loamy sand/sandy loam - forest soil 77 E 18, fine white sand over fine sandy clay. 77 A 43, Brown loamy sand/sandy loam - Jam country.77 WH 88, Grey loamy sand over gravel at 50 cm - Elphin soil series – Mallee. 77 M 56, Red sandy clay loam - Salmon Gum, Gimlet. 82 M 35, Loamy sand/sandy loam – Mallee. 77 E 52, Fine white sand over gravel at 40 cm. 85 SG 28, Grey-brown calcareous earth - Kumarl – SGRS. 77 Mt 51, Gravelly sandy loam - forest soil. 79 WH 6, Grey and yellow loamy sand with gravel. 79 M 7, Yellow loamy sand - Mallee scrub. 82 M 34, Salmon Gum/Gimlet clay loam. 84 M 1, Stubble effect on heavy land - Salmon Gum soil – MRS. 82 M 35, deep ripping. 82 GE 37, 82 GE 38, Yellow sandplain - Naraling - Lupin/Wheat Rotation. 84 C 72, Eradu Sandplain – ECRS. 85 C 84, Eradu Sandplain ECRS. 77 WH 17, Yellow clayey sand (Wongan loamy sand). 84 WH 2, Species response to ripping - Wongan loamy sand - residual effects. 84 WH 3 - ripping and re-compaction of ripped soil - Wongan loamy sand. 85 WH 36, 85 WH 37, 85 WH 38, 85 WH 39, deep ripping four soil types – WHRS. 85 WH 40, deep ripping and scarifying comparisons with direct drill on four soil types. 85 WH 41, depth of ripping by shank spacing. 82 Mo30, yellow loamy sand - Dalwallinu - Taywood farms. 79 MO 19, deep ripping in a pasture/wheat rotation - heavy land - Nixon, Kalannie. 81 M 53, yellow loamy sand - Mallee/Wodgil – MRS. 82 Me 38, Residual effect of deep ripping in pasture/wheat/lupin rotation, yellow loamy sand – Koorda. 85 Me 57, 85 Me 58, 85 Me 59, 85 Me 60, 85 Me 61, 85 Me 62, ripping responses on paddocks with different cropping histories Crosthwaite, Holleton. 80 NO 46, yellow loamy sand - Tamma - R. Reid, Yorkrakine. 83 NO 69, yellow loamy sand - Tamma - Reid, Yorkrakine. 82 NO 48, 82 NO 49, yellow loamy sand - Tamma – Yorkrakine. 84 Ba 32, deep ripping yellow sand/yellow loamy sand – BARS. 85 Ba 33, deep ripping white sand/pale yellow sand – BARS. 85 Ba 4la, deep ripping and scarifying comparisons with direct drill on different soil types. 85 Ba 4lb, deep ripping and nutrition - deep very poor white sand – BARS. 84 E 24, deep white sand over gravel – EDRS. 85 E 29, cultivation depths and direct drilling machines - deep white sand – EDRS. 85 E 30, deep ripping and scarifying on Caitup soil – EDRS. 85 E 31, deep ripping and scarifying comparison with direct drill on four soil types. 85 TS 39, yellow sand/loamy sand, R and D Nottle, West Three Springs. Deep ripping pasture – Denbarker. 85 WH 42, yellow loamy sand – WHRS. 85 WH 43, yellow loamy sand – WHRS. 85 WH 44, yellow loamy sand – WHRS

The Stripe 82 1-2 GHz Very Large Array Snapshot Survey: Multiwavelength Counterparts

We have combined spectrosopic and photometric data from the Sloan Digital Sky Survey (SDSS) with $1.4$ GHz radio observations, conducted as part of the Stripe 82 $1-2$ GHz Snapshot Survey using the Karl G. Jansky Very Large Array (VLA), which covers $\sim100$ sq degrees, to a flux limit of 88 $\mu$Jy rms. Cross-matching the $11\,768$ radio source components with optical data via visual inspection results in a final sample of $4\,795$ cross-matched objects, of which $1\,996$ have spectroscopic redshifts and $2\,799$ objects have photometric redshifts. Three previously undiscovered Giant Radio Galaxies (GRGs) were found during the cross-matching process, which would have been missed using automated techniques. For the objects with spectroscopy we separate radio-loud Active Galactic Nuclei (AGN) and star-forming galaxies (SFGs) using three diagnostics and then further divide our radio-loud AGN into the HERG and LERG populations. A control matched sample of HERGs and LERGs, matched on stellar mass, redshift and radio luminosity, reveals that the host galaxies of LERGs are redder and more concentrated than HERGs. By combining with near-infrared data, we demonstrate that LERGs also follow a tight $K-z$ relationship. These results imply the LERG population are hosted by population of massive, passively evolving early-type galaxies. We go on to show that HERGs, LERGs, QSOs and star-forming galaxies in our sample all reside in different regions of a WISE colour-colour diagram. This cross-matched sample bridges the gap between previous `wide but shallow' and `deep but narrow' samples and will be useful for a number of future investigations.Comment: 17 pages, 19 figures. Resubmitted to MNRAS after the initial comment