2,646 research outputs found

    Ozone loss derived from balloon-borne tracer measurements and the SLIMCAT CTM

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    Balloon-borne measurements of CFC-11 (on flights of the DIRAC in situ gas chromatograph and the DESCARTES grab sampler), ClO and O3 were made during the 1999/2000 winter as part of the SOLVE-THESEO 2000 campaign. Here we present the CFC-11 data from nine flights and compare them first with data from other instruments which flew during the campaign and then with the vertical distributions calculated by the SLIMCAT 3-D CTM. We calculate ozone loss inside the Arctic vortex between late January and early March using the relation between CFC-11 and O3 measured on the flights, the peak ozone loss (1200 ppbv) occurs in the 440–470 K region in early March in reasonable agreement with other published empirical estimates. There is also a good agreement between ozone losses derived from three independent balloon tracer data sets used here. The magnitude and vertical distribution of the loss derived from the measurements is in good agreement with the loss calculated from SLIMCAT over Kiruna for the same days

    Emergency examination authorities in Queensland, Australia

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    Objective: In Queensland, where a person experiences a major disturbance in their mental capacity, and is at risk of serious harm to self and others, an emergency examination authority (EEA) authorises Queensland Police Service (QPS) and Queensland Ambulance Service (QAS) to detain and transport the person to an ED. In the ED, further detention for up to 12 h is authorised to allow the examination to be completed. Little published information describes these critical patient encounters. Methods: Queensland's Public Health Act (2005), amended in 2017, mandates the use of the approved EEA form. Data were extracted from a convenience sample of 942 EEAs including: (i) patient age, sex, address; (ii) free text descriptions by QPS and QAS officers of the person's behaviour and any serious risk of harm requiring urgent care; (iii) time examination period commenced; and (iv) outcome upon examination. Results: Of 942 EEA forms, 640 (68%) were retrieved at three ‘larger central’ hospitals and 302 (32%) at two ‘smaller regional’ hospitals in non-metropolitan Queensland. QPS initiated 342 (36%) and QAS 600 (64%) EEAs for 486 (52%) males, 453 (48%) females and two intersexes (<1%), aged from 9 to 85 years (median 29 years, 17% aged <18 years). EEAs commonly occurred on weekends (32%) and between 2300 and midnight (8%), characterised by ‘drug and/or alcohol issues’ (53%), ‘self-harm’ (40%), ‘patient aggression’ (25%) and multiple prior EEAs (23%). Although information was incomplete, most patients (78%, n = 419/534) required no inpatient admission. Conclusions: EEAs furnish unique records for evaluating the impacts of Queensland's novel legislative reforms

    Evidence and Ideology in Macroeconomics: The Case of Investment Cycles

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    The paper reports the principal findings of a long term research project on the description and explanation of business cycles. The research strongly confirmed the older view that business cycles have large systematic components that take the form of investment cycles. These quasi-periodic movements can be represented as low order, stochastic, dynamic processes with complex eigenvalues. Specifically, there is a fixed investment cycle of about 8 years and an inventory cycle of about 4 years. Maximum entropy spectral analysis was employed for the description of the cycles and continuous time econometrics for the explanatory models. The central explanatory mechanism is the second order accelerator, which incorporates adjustment costs both in relation to the capital stock and the rate of investment. By means of parametric resonance it was possible to show, both theoretically and empirically how cycles aggregate from the micro to the macro level. The same mathematical tool was also used to explain the international convergence of cycles. I argue that the theory of investment cycles was abandoned for ideological, not for evidential reasons. Methodological issues are also discussed

    Selenoprotein gene nomenclature

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    The human genome contains 25 genes coding for selenocysteine-containing proteins (selenoproteins). These proteins are involved in a variety of functions, most notably redox homeostasis. Selenoprotein enzymes with known functions are designated according to these functions: TXNRD1, TXNRD2, and TXNRD3 (thioredoxin reductases), GPX1, GPX2, GPX3, GPX4 and GPX6 (glutathione peroxidases), DIO1, DIO2, and DIO3 (iodothyronine deiodinases), MSRB1 (methionine-R-sulfoxide reductase 1) and SEPHS2 (selenophosphate synthetase 2). Selenoproteins without known functions have traditionally been denoted by SEL or SEP symbols. However, these symbols are sometimes ambiguous and conflict with the approved nomenclature for several other genes. Therefore, there is a need to implement a rational and coherent nomenclature system for selenoprotein-encoding genes. Our solution is to use the root symbol SELENO followed by a letter. This nomenclature applies to SELENOF (selenoprotein F, the 15 kDa selenoprotein, SEP15), SELENOH (selenoprotein H, SELH, C11orf31), SELENOI (selenoprotein I, SELI, EPT1), SELENOK (selenoprotein K, SELK), SELENOM (selenoprotein M, SELM), SELENON (selenoprotein N, SEPN1, SELN), SELENOO (selenoprotein O, SELO), SELENOP (selenoprotein P, SeP, SEPP1, SELP), SELENOS (selenoprotein S, SELS, SEPS1, VIMP), SELENOT (selenoprotein T, SELT), SELENOV (selenoprotein V, SELV) and SELENOW (selenoprotein W, SELW, SEPW1). This system, approved by the HUGO Gene Nomenclature Committee, also resolves conflicting, missing and ambiguous designations for selenoprotein genes and is applicable to selenoproteins across vertebrates

    Comparison of long-term mortality risk following normal exercise vs adenosine myocardial perfusion SPECT

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    A higher frequency of clinical events has been observed in patients undergoing pharmacological vs exercise myocardial perfusion single-photon emission computed tomography (SPECT). While this difference is attributed to greater age and co-morbidities, it is not known whether these tests also differ in prognostic ability among patients with similar clinical profiles. We assessed all-cause mortality rates in 6,069 patients, followed for 10.2 ± 1.7 years after undergoing exercise or adenosine SPECT. We employed propensity analysis to match exercise and adenosine subgroups by age, gender, symptoms, and coronary risk factors. Within our propensity-matched cohorts, adenosine patients had an annualized mortality rate event rates that was more than twice that of exercise patients (3.9% vs 1.6%, P &lt; .0001). Differences in mortality persisted among age groups, including those &lt;55 years old. In the exercise cohort, mortality was inversely related to exercise duration, with comparable mortality noted for patients exercising &lt;3 min and those undergoing adenosine testing. Among patients with normal stress SPECT tests, those undergoing adenosine testing manifest a mortality rate that is substantially higher than that observed among adequately exercising patients, but comparable to that observed among very poorly exercising patients. This elevated risk underscores an important challenge for managing patients undergoing pharmacological stress testing

    Measurement of the cosmic ray spectrum above 4×10184{\times}10^{18} eV using inclined events detected with the Pierre Auger Observatory

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    A measurement of the cosmic-ray spectrum for energies exceeding 4×10184{\times}10^{18} eV is presented, which is based on the analysis of showers with zenith angles greater than 6060^{\circ} detected with the Pierre Auger Observatory between 1 January 2004 and 31 December 2013. The measured spectrum confirms a flux suppression at the highest energies. Above 5.3×10185.3{\times}10^{18} eV, the "ankle", the flux can be described by a power law EγE^{-\gamma} with index γ=2.70±0.02(stat)±0.1(sys)\gamma=2.70 \pm 0.02 \,\text{(stat)} \pm 0.1\,\text{(sys)} followed by a smooth suppression region. For the energy (EsE_\text{s}) at which the spectral flux has fallen to one-half of its extrapolated value in the absence of suppression, we find Es=(5.12±0.25(stat)1.2+1.0(sys))×1019E_\text{s}=(5.12\pm0.25\,\text{(stat)}^{+1.0}_{-1.2}\,\text{(sys)}){\times}10^{19} eV.Comment: Replaced with published version. Added journal reference and DO
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