New South Wales Vegetation classification and Assessment: Part 3, plant communities of the NSW Brigalow Belt South, Nandewar and west New England Bioregions and update of NSW Western Plains and South-western Slopes plant communities, Version 3 of the NSWVCA database

This fourth paper in the NSW Vegetation Classification and Assessment series covers the Brigalow Belt South-/1(BBS) and Nandewar (NAN) Bioregions and the western half of the New England Bioregion (NET), an area of 9.3 million hectares being 11.6% of NSW. It completes the NSWVCA coverage for the Border Rivers-Gwydir and Namoi CMA areas and records plant communities in the Central West and Hunter–Central Rivers CMA areas. In total, 585 plant communities are now classified in the NSWVCA covering 11.5 of the 18 Bioregions in NSW (78% of the State). Of these 226 communities are in the NSW Western Plains and 416 are in the NSW Western Slopes. 315 plant communities are classified in the BBS, NAN and west-NET Bioregions including 267 new descriptions since Version 2 was published in 2008. Descriptions of the 315 communities are provided in a 919 page report on the DVD accompanying this paper along with updated reports on other inland NSW bioregions and nine Catchment Management Authority areas fully or partly classified in the NSWVCA to date. A read-only version of Version 3 of the NSWVCA database is on the DVD for use on personal computers. A feature of the BBS and NAN Bioregions is the array of ironbark and bloodwood Eucalyptusdominated shrubby woodlands on sandstone and acid volcanic substrates extending from Dubbo to Queensland. This includes iconic natural areas such as Warrumbungle and Mount Kaputar National Parks and the 500,000 ha Pilliga Scrub forests. Large expanses of basalt-derived soils support grassy box woodland and native grasslands including those on the Liverpool Plains; near Moree; and around Inverell, most of which are cleared and threatened. Wetlands occur on sodic soils near Yetman and in large clay gilgais in the Pilliga region. Sedgelands are rare but occupy impeded creeks. Aeolian lunettes occur at Narran Lake and near Gilgandra. Areas of deep sand contain Allocasuarina, eucalypt mallee and Melaleuca uncinata heath. Tall grassy or ferny open forests occur on mountain ranges above 1000m elevation in the New England Bioregion and on the Liverpool Range while grassy box woodlands occupy lower elevations with lower rainfall and higher temperatures. The vegetation classification and assessment is based on over 100 published and unpublished vegetation surveys and map unit descriptions, expert advice, extra plot sampling and data analysis and over 25 000 km of road traverse with field checking at 805 sites. Key sources of data included floristic analyses produced in western regional forest assessments in the BBS and NAN Bioregions, floristic analyses in over 60 surveys of conservation reserves and analysis of plot data in the western NET Bioregion and covering parts of the Namoi and Border Rivers- Gwydir CMA areas. Approximately 60% of the woody native vegetation in the study area has been cleared resulting in large areas of “derived” native grasslands. As of June 2010, 7% of the area was in 136 protected areas and 127 of the 315 plant communities were assessed to be adequately protected in reserves. Using the NSWVCA database threat criteria, 15 plant communities were assessed as being Critically Endangered, 59 Endangered, 60 Vulnerable, 99 Near Threatened and 82 Least Concern. 61 of these communities are assessed as part of NSW or Commonwealth-listed Threatened Ecological Communities. Current threats include expanding dryland and irrigated cropping on alluvial plains, floodplains and gently undulating topography at lower elevations; over-grazing of steep hills; altered water tables and flooding regimes; localized mining; and the spread of exotic species, notably Coolatai Grass (Hyparrhenia hirta)

Implementing telephone triage in general practice: a process evaluation of a cluster randomised controlled trial

Background: Telephone triage represents one strategy to manage demand for face-to-face GP appointments in primary care. However, limited evidence exists of the challenges GP practices face in implementing telephone triage. We conducted a qualitative process evaluation alongside a UK-based cluster randomised trial (ESTEEM) which compared the impact of GP-led and nurse-led telephone triage with usual care on primary care workload, cost, patient experience, and safety for patients requesting a same-day GP consultation. The aim of the process study was to provide insights into the observed effects of the ESTEEM trial from the perspectives of staff and patients, and to specify the circumstances under which triage is likely to be successfully implemented. Here we report perspectives of staff. Methods: The intervention comprised implementation of either GP-led or nurse-led telephone triage for a period of 2-3 months. A qualitative evaluation was conducted using staff interviews recruited from eight general practices (4 GP triage, 4 Nurse triage) in the UK, implementing triage as part of the ESTEEM trial. Qualitative interviews were undertaken with 44 staff members in GP triage and nurse triage practices (16 GPs, 8 nurses, 7 practice managers, 13 administrative staff). Results: Staff reported diverse experiences and perceptions regarding the implementation of telephone triage, its effects on workload, and on the benefits of triage. Such diversity were explained by the different ways triage was organised, the staffing models used to support triage, how the introduction of triage was communicated across practice staff, and by how staff roles were reconfigured as a result of implementing triage. Conclusion: The findings from the process evaluation offer insight into the range of ways GP practices participating in ESTEEM implemented telephone triage, and the circumstances under which telephone triage can be successfully implemented beyond the context of a clinical trial. Staff experiences and perceptions of telephone triage are shaped by the way practices communicate with staff, prepare for and sustain the changes required to implement triage effectively, as well as by existing practice culture, and staff and patient behaviour arising in response to the changes made. Trial registration: Current Controlled Trials ISRCTN20687662. Registered 28 May 2009

Mutation Linked to Autosomal Dominant Nocturnal Frontal Lobe Epilepsy Reduces Low-Sensitivity α4β2, and Increases α5α4β2, Nicotinic Receptor Surface Expression

A number of mutations in α4β2-containing (α4β2*) nicotinic acetylcholine (ACh) receptors (nAChRs) are linked to autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE), including one in the β2 subunit called β2V287L. Two α4β2* subtypes with different subunit stoichiometries and ACh sensitivities co-exist in the brain, a high-sensitivity subtype with (α4)2(β2)3 subunit stoichiometry and a low-sensitivity subtype with (α4)3(β2)2 stoichiometry. The α5 nicotinic subunit also co-assembles with α4β2 to form a high-sensitivity α5α4β2 nAChR. Previous studies suggest that the β2V287L mutation suppresses low-sensitivity α4β2* nAChR expression in a knock-in mouse model and also that α5 co-expression improves the surface expression of ADNFLE mutant nAChRs in a cell line. To test these hypotheses further, we expressed mutant and wild-type (WT) nAChRs in oocytes and mammalian cell lines, and measured the effects of the β2V287L mutation on surface receptor expression and the ACh response using electrophysiology, a voltage-sensitive fluorescent dye, and superecliptic pHluorin (SEP). The β2V287L mutation reduced the EC50 values of high- and low-sensitivity α4β2 nAChRs expressed in Xenopus oocytes for ACh by a similar factor and suppressed low-sensitivity α4β2 expression. In contrast, it did not affect the EC50 of α5α4β2 nAChRs for ACh. Measurements of the ACh responses of WT and mutant nAChRs expressed in mammalian cell lines using a voltage-sensitive fluorescent dye and whole-cell patch-clamping confirm the oocyte data. They also show that, despite reducing the maximum response, β2V287L increased the α4β2 response to a sub-saturating ACh concentration (1 μM). Finally, imaging SEP-tagged α5, α4, β2, and β2V287L subunits showed that β2V287L reduced total α4β2 nAChR surface expression, increased the number of β2 subunits per α4β2 receptor, and increased surface α5α4β2 nAChR expression. Thus, the β2V287L mutation alters the subunit composition and sensitivity of α4β2 nAChRs, and increases α5α4β2 surface expression

The FIRST Bright Quasar Survey. II. 60 Nights and 1200 Spectra Later

We have used the VLA FIRST survey and the APM catalog of the POSS-I plates as the basis for constructing a new radio-selected sample of optically bright quasars. This is the first radio-selected sample that is competitive in size with current optically selected quasar surveys. Using only two basic criteria, radio-optical positional coincidence and optical morphology, quasars and BL Lacs can be identified with 60% selection efficiency; the efficiency increases to 70% for objects fainter than magnitude 17. We show that a more sophisticated selection scheme can predict with better than 85% reliability which candidates will turn out to be quasars. This paper presents the second installment of the FIRST Bright Quasar Survey with a catalog of 636 quasars distributed over 2682 square degrees. The quasar sample is characterized and all spectra are displayed. The FBQS detects both radio-loud and radio-quiet quasars out to a redshift z>3. We find a large population of objects of intermediate radio-loudness; there is no evidence in our sample for a bimodal distribution of radio characteristics. The sample includes ~29 broad absorption line quasars, both high and low ionization, and a number of new objects with remarkable optical spectra.Comment: 41 pages plus 39 gifs which contain all quasar spectra. Accepted for publication in the Astrophysical Journal Supplement Serie

The Canada-France High-z Quasar Survey: nine new quasars and the luminosity function at redshift 6

We present discovery imaging and spectroscopy for nine new z ~ 6 quasars found in the Canada-France High-z Quasar Survey (CFHQS) bringing the total number of CFHQS quasars to 19. By combining the CFHQS with the more luminous SDSS sample we are able to derive the quasar luminosity function from a sample of 40 quasars at redshifts 5.74 < z < 6.42. Our binned luminosity function shows a slightly lower normalisation and flatter slope than found in previous work. The binned data also suggest a break in the luminosity function at M_1450 approx -25. A double power law maximum likelihood fit to the data is consistent with the binned results. The luminosity function is strongly constrained (1 sigma uncertainty < 0.1 dex) over the range -27.5 < M_1450 < -24.7. The best-fit parameters are Phi(M_1450^*) = 1.14 x 10^-8 Mpc^-3 mag^-1, break magnitude M_1450^* = -25.13 and bright end slope beta = -2.81. However the covariance between beta and M_1450^* prevents strong constraints being placed on either parameter. For a break magnitude in the range -26 < M_1450^* < -24 we find -3.8 < beta < -2.3 at 95% confidence. We calculate the z = 6 quasar intergalactic ionizing flux and show it is between 20 and 100 times lower than that necessary for reionization. Finally, we use the luminosity function to predict how many higher redshift quasars may be discovered in future near-IR imaging surveys.Comment: 15 pages, 9 figures, AJ in pres

Spectroscopic Target Selection in the Sloan Digital Sky Survey: The Quasar Sample

We describe the algorithm for selecting quasar candidates for optical spectroscopy in the Sloan Digital Sky Survey. Quasar candidates are selected via their non-stellar colors in "ugriz" broad-band photometry, and by matching unresolved sources to the FIRST radio catalogs. The automated algorithm is sensitive to quasars at all redshifts lower than z=5.8. Extended sources are also targeted as low-redshift quasar candidates in order to investigate the evolution of Active Galactic Nuclei (AGN) at the faint end of the luminosity function. Nearly 95% of previously known quasars are recovered (based on 1540 quasars in 446 square degrees). The overall completeness, estimated from simulated quasars, is expected to be over 90%, whereas the overall efficiency (quasars:quasar candidates) is better than 65%. The selection algorithm targets ultraviolet excess quasars to i^*=19.1 and higher-redshift (z>3) quasars to i^*=20.2, yielding approximately 18 candidates per square degree. In addition to selecting ``normal'' quasars, the design of the algorithm makes it sensitive to atypical AGN such as Broad Absorption Line quasars and heavily reddened quasars.Comment: 62 pages, 15 figures (8 color), 8 tables. Accepted by AJ. For a version with higher quality color figures, see http://archive.stsci.edu/sdss/quasartarget/RichardsGT_qsotarget.preprint.p

Photometric Redshifts of Quasars

We demonstrate that the design of the Sloan Digital Sky Survey (SDSS) filter system and the quality of the SDSS imaging data are sufficient for determining accurate and precise photometric redshifts (``photo-z''s) of quasars. Using a sample of 2625 quasars, we show that photo-z determination is even possible for z<=2.2 despite the lack of a strong continuum break that robust photo-z techniques normally require. We find that, using our empirical method on our sample of objects known to be quasars, approximately 70% of the photometric redshifts are correct to within delta z = 0.2; the fraction of correct photometric redshifts is even better for z>3. The accuracy of quasar photometric redshifts does not appear to be dependent upon magnitude to nearly 21st magnitude in i'. Careful calibration of the color-redshift relation to 21st magnitude may allow for the discovery of on the order of 10^6 quasars candidates in addition to the 10^5 quasars that the SDSS will confirm spectroscopically. We discuss the efficient selection of quasar candidates from imaging data for use with the photometric redshift technique and the potential scientific uses of a large sample of quasar candidates with photometric redshifts.Comment: 29 pages, 8 figures, submitted to A

Supervised machine learning algorithms can classify open-text feedback of doctor performance with human-level accuracy

Background: Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor’s activity for the purposes of quality assurance, safety, and continuing professional development. Objective: The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors’ professional performance in the United Kingdom. Methods: We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians’ colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Results: Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to “popular” (recall=.97), “innovator” (recall=.98), and “respected” (recall=.87) codes and was lower for the “interpersonal” (recall=.80) and “professional” (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as “respected,” “professional,” and “interpersonal” related to higher doctor scores on the GMC-CQ compared with comments that were not classified (P.05). Conclusions: Machine learning algorithms can classify open-text feedback of doctor performance into multiple themes derived by human raters with high performance. Colleague open-text comments that signal respect, professionalism, and being interpersonal may be key indicators of doctor’s performance