We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure no-reply@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
The First Large Absorption Survey in H i (FLASH) is a large-area radio survey for neutral hydrogen in and around galaxies in the intermediate redshift range 0.4 < z < 1.0, using the 21-cm H i absorption line as a probe of cold neutral gas. The survey uses the ASKAP radio telescope and will cover 24,000 deg2 of sky over the next five years. FLASH breaks new ground in two ways – it is the first large H i absorption survey to be carried out without any optical preselection of targets, and we use an automated Bayesian line-finding tool to search through large datasets and assign a statistical significance to potential line detections. Two Pilot Surveys, covering around 3000 deg2 of sky, were carried out in 2019-22 to test and verify the strategy for the full FLASH survey. The processed data products from these Pilot Surveys (spectral-line cubes, continuum images, and catalogues) are public and available online. In this paper, we describe the FLASH spectral-line and continuum data products and discuss the quality of the H i spectra and the completeness of our automated line search. Finally, we present a set of 30 new H i absorption lines that were robustly detected in the Pilot Surveys, almost doubling the number of known H i absorption systems at 0.4 < z < 1. The detected lines span a wide range in H i optical depth, including three lines with a peak optical depth τ > 1, and appear to be a mixture of intervening and associated systems. Interestingly, around two-thirds of the lines found in this untargeted sample are detected against sources with a peaked-spectrum radio continuum, which are only a minor (5-20%) fraction of the overall radio-source population. The detection rate for H i absorption lines in the Pilot Surveys (0.3 to 0.5 lines per 40 deg2 ASKAP field) is a factor of two below the expected value. One possible reason for this is the presence of a range of spectral-line artefacts in the Pilot Survey data that have now been mitigated and are not expected to recur in the full FLASH survey. A future paper in this series will discuss the host galaxies of the H i absorption systems identified here.
We present results of frequency tripling experiments performed at the Hilase facility on a cryogenically gas cooled multi-slab ytterbium-doped yttrium aluminum garnet laser system, Bivoj/DiPOLE. The laser produces high-energy ns pulses at 10 Hz repetition rate, which are frequency doubled using a type-I phase-matched lithium triborate (LBO) crystal and consequently frequency summed using a type-II phase-matched LBO crystal. We demonstrated a stable frequency conversion to 343 nm at 50 J energy and 10 Hz repetition rate with conversion efficiency of 53%.
Energy literacy can empower individuals to make informed decisions about energy use. However, the level of public interest in learning about energy-related topics remains uncertain, and there is a dearth of research exploring energy literacy-related knowledge gaps. This mixed-methods study aimed to address those issues. A survey of 3,843 citizens from four European countries revealed that most citizens have only a moderate interest in learning about energy. Age, gender, educational level, income level, living situation and environmental attitudes appear to have a significant effect on individuals’ interests. The study identified key knowledge demand areas regarding saving energy and reducing costs, becoming self-sufficient in energy production and cooperating with others for more efficient energy use. The findings indicate that engagement with energy-related topics could be improved by considering affective factors such as individual interest. The study also reveals a need for greater interdisciplinarity in energy research.
Housing is a critical part of every state’s infrastructure. However, in most advanced economies the state no longer builds very much of it, leaving it instead to private housebuilders. Because of their control over the supply of land, and the barriers to entry into the housebuilding industry, private housebuilders have potentially major structural power over the state. At the same time, private housebuilders are also tied to their land, and face other barriers to exit, thus limiting their ability to relocate capital elsewhere. Drawing on a range of secondary data sources, including earnings calls transcripts, annual reports and government policy documents, this paper demonstrates how the three largest volume housebuilders in England leveraged their structural power to shape the mortgage market support schemes that were introduced in the aftermath of the Global Financial Crisis. These schemes have since underpinned their exceptional levels of profitability. We conclude, though, that far from being an absolute resource, this structural power was only enabled by the prevailing neoliberal, home-owning Anglo-liberal ‘growth model’ in which these housebuilders were embedded.
Northern Arizona University, Flagstaff, Arizona, USA, recently installed a MIni CArbon DAting System (MICADAS) with a gas interface system (GIS) for determining the 14C content of CO2 gas released by the acid dissolution of biogenic carbonates. We compare 48 paired graphite, GIS, and direct carbonate 14C determinations of individual mollusk shells and echinoid tests. GIS sample sizes ranged between 0.5 and 1.5 mg and span 0.1 to 45.1 ka BP (n = 42). A reduced major axis regression shows a strong relationship between GIS and graphite percent Modern Carbon (pMC) values (m = 1.011; 95% CI [0.997–1.023], R2 = 0.999) that is superior to the relationship between the direct carbonate and graphite values (m = 0.978; 95% CI [0.959-0.999], R2 = 0.997). Sixty percent of GIS pMC values are within ±0.5 pMC of their graphite counterparts, compared to 26% of direct carbonate pMC values. The precision of GIS analyses is approximately ±70 14C yrs to 6.5 ka BP and decreases to approximately ±130 14C yrs at 12.5 ka BP. This precision is on par with direct carbonate and is approximately five times larger than for graphite. Six Plio-Pleistocene mollusk and echinoid samples yield finite ages when analyzed as direct carbonate but yield non-finite ages when analyzed as graphite or as GIS. Our results show that GIS 14C dating of biogenic carbonates is preferable to direct carbonate 14C dating and is an efficient alternative to standard graphite 14C dating when the precision of graphite 14C dating is not required.
The IntCal family of radiocarbon (14C) calibration curves is based on research spanning more than three decades. The IntCal group have collated the 14C and calendar age data (mostly derived from primary publications with other types of data and meta-data) and, since 2010, made them available for other sorts of analysis through an open-access database. This has ensured transparency in terms of the data used in the construction of the ratified calibration curves. As the IntCal database expands, work is underway to facilitate best practice for new data submissions, make more of the associated metadata available in a structured form, and help those wishing to process the data with programming languages such as R, Python, and MATLAB. The data and metadata are complex because of the range of different types of archives. A restructured interface, based on the “IntChron” open-access data model, includes tools which allow the data to be plotted and compared without the need for export. The intention is to include complementary information which can be used alongside the main 14C series to provide new insights into the global carbon cycle, as well as facilitating access to the data for other research applications. Overall, this work aims to streamline the generation of new calibration curves.
We report on frequency doubling of high-energy, high repetition rate ns pulses from a cryogenically gas cooled multi-slab ytterbium-doped yttrium aluminum garnet laser system, Bivoj/DiPOLE, using a type-I phase matched lithium triborate crystal. We achieved conversion to 515 nm with energy of 95 J at repetition rate of 10 Hz and conversion efficiency of 79%. High conversion efficiency was achieved due to successful depolarization compensation of the fundamental input beam.
Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.
Methods
We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.
Results
Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.
Conclusions
An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.
Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).
Methods
A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.
Results
In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.
Conclusions
Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.
Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.
Methods
This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.
Results
32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.
Conclusions
Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.
The first demonstration of laser action in ruby was made in 1960 by T. H. Maiman of Hughes Research Laboratories, USA. Many laboratories worldwide began the search for lasers using different materials, operating at different wavelengths. In the UK, academia, industry and the central laboratories took up the challenge from the earliest days to develop these systems for a broad range of applications. This historical review looks at the contribution the UK has made to the advancement of the technology, the development of systems and components and their exploitation over the last 60 years.
This study aimed to investigate general factors associated with prognosis regardless of the type of treatment received, for adults with depression in primary care.
Methods
We searched Medline, Embase, PsycINFO and Cochrane Central (inception to 12/01/2020) for RCTs that included the most commonly used comprehensive measure of depressive and anxiety disorder symptoms and diagnoses, in primary care depression RCTs (the Revised Clinical Interview Schedule: CIS-R). Two-stage random-effects meta-analyses were conducted.
Results
Twelve (n = 6024) of thirteen eligible studies (n = 6175) provided individual patient data. There was a 31% (95%CI: 25 to 37) difference in depressive symptoms at 3–4 months per standard deviation increase in baseline depressive symptoms. Four additional factors: the duration of anxiety; duration of depression; comorbid panic disorder; and a history of antidepressant treatment were also independently associated with poorer prognosis. There was evidence that the difference in prognosis when these factors were combined could be of clinical importance. Adding these variables improved the amount of variance explained in 3–4 month depressive symptoms from 16% using depressive symptom severity alone to 27%. Risk of bias (assessed with QUIPS) was low in all studies and quality (assessed with GRADE) was high. Sensitivity analyses did not alter our conclusions.
Conclusions
When adults seek treatment for depression clinicians should routinely assess for the duration of anxiety, duration of depression, comorbid panic disorder, and a history of antidepressant treatment alongside depressive symptom severity. This could provide clinicians and patients with useful and desired information to elucidate prognosis and aid the clinical management of depression.
The Rapid ASKAP Continuum Survey (RACS) is the first large-area survey to be conducted with the full 36-antenna Australian Square Kilometre Array Pathfinder (ASKAP) telescope. RACS will provide a shallow model of the ASKAP sky that will aid the calibration of future deep ASKAP surveys. RACS will cover the whole sky visible from the ASKAP site in Western Australia and will cover the full ASKAP band of 700–1800 MHz. The RACS images are generally deeper than the existing NRAO VLA Sky Survey and Sydney University Molonglo Sky Survey radio surveys and have better spatial resolution. All RACS survey products will be public, including radio images (with
$\sim$
15 arcsec resolution) and catalogues of about three million source components with spectral index and polarisation information. In this paper, we present a description of the RACS survey and the first data release of 903 images covering the sky south of declination
$+41^\circ$
made over a 288-MHz band centred at 887.5 MHz.
We report on the successful demonstration of a 150 J nanosecond pulsed cryogenic gas cooled, diode-pumped multi-slab Yb:YAG laser operating at 1 Hz. To the best of our knowledge, this is the highest energy ever recorded for a diode-pumped laser system.
We describe an ultra-wide-bandwidth, low-frequency receiver recently installed on the Parkes radio telescope. The receiver system provides continuous frequency coverage from 704 to 4032 MHz. For much of the band (
${\sim}60\%$
), the system temperature is approximately 22 K and the receiver system remains in a linear regime even in the presence of strong mobile phone transmissions. We discuss the scientific and technical aspects of the new receiver, including its astronomical objectives, as well as the feed, receiver, digitiser, and signal processor design. We describe the pipeline routines that form the archive-ready data products and how those data files can be accessed from the archives. The system performance is quantified, including the system noise and linearity, beam shape, antenna efficiency, polarisation calibration, and timing stability.
To describe pathogen distribution and rates for central-line–associated bloodstream infections (CLABSIs) from different acute-care locations during 2011–2017 to inform prevention efforts.
Methods:
CLABSI data from the Centers for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN) were analyzed. Percentages and pooled mean incidence density rates were calculated for a variety of pathogens and stratified by acute-care location groups (adult intensive care units [ICUs], pediatric ICUs [PICUs], adult wards, pediatric wards, and oncology wards).
Results:
From 2011 to 2017, 136,264 CLABSIs were reported to the NHSN by adult and pediatric acute-care locations; adult ICUs and wards reported the most CLABSIs: 59,461 (44%) and 40,763 (30%), respectively. In 2017, the most common pathogens were Candida spp/yeast in adult ICUs (27%) and Enterobacteriaceae in adult wards, pediatric wards, oncology wards, and PICUs (23%–31%). Most pathogen-specific CLABSI rates decreased over time, excepting Candida spp/yeast in adult ICUs and Enterobacteriaceae in oncology wards, which increased, and Staphylococcus aureus rates in pediatric locations, which did not change.
Conclusions:
The pathogens associated with CLABSIs differ across acute-care location groups. Learning how pathogen-targeted prevention efforts could augment current prevention strategies, such as strategies aimed at preventing Candida spp/yeast and Enterobacteriaceae CLABSIs, might further reduce national rates.
While extensive modelling - both physical and virtual - is imperative to develop right-first-time products, the parallel use of virtual and physical models gives rise to two interrelated issues: the lack of revision control for physical prototypes; and the need for designers to manually inspect, measure, and interpret modifications to either virtual or physical models, for subsequent update of the other. The Digital Twin paradigm addresses similar problems later in the product life-cycle, and while these digital twins, or the “twinning” process, have shown significant value, there is little work to date on their implementation in the earlier design stages. With large prospective benefits in increased product understanding, performance, and reduced design cycle time and cost, this paper explores the concept of using the Digital Twin in early design, including an introduction to digital twinning, examination of opportunities for and challenges of their implementation, a presentation of the structure of Early Stage Twins, and evaluation via two implementation cases.
In this paper we review the design and development of a 100 J, 10 Hz nanosecond pulsed laser, codenamed DiPOLE100X, being built at the Central Laser Facility (CLF). This 1 kW average power diode-pumped solid-state laser (DPSSL) is based on a master oscillator power amplifier (MOPA) design, which includes two cryogenic gas cooled amplifier stages based on DiPOLE multi-slab ceramic Yb:YAG amplifier technology developed at the CLF. The laser will produce pulses between 2 and 15 ns in duration with precise, arbitrarily selectable shapes, at pulse repetition rates up to 10 Hz, allowing real-time shape optimization for compression experiments. Once completed, the laser will be delivered to the European X-ray Free Electron Laser (XFEL) facility in Germany as a UK-funded contribution in kind, where it will be used to study extreme states of matter at the High Energy Density (HED) instrument.
Extinction is the complete loss of a species, but the accuracy of that status depends on the overall information about the species. Dracaena umbraculifera was described in 1797 from a cultivated plant attributed to Mauritius, but repeated surveys failed to relocate it and it was categorized as Extinct on the IUCN Red List. However, several individuals labelled as D. umbraculifera grow in botanical gardens, suggesting that the species’ IUCN status may be inaccurate. The goal of this study was to understand (1) where D. umbraculifera originated, (2) which species are its close relatives, (3) whether it is extinct, and (4) the identity of the botanical garden accessions and whether they have conservation value. We sequenced a cpDNA region of Dracaena from Mauritius, botanical garden accessions labelled as D. umbraculifera, and individuals confirmed to be D. umbraculifera based on morphology, one of which is a living plant in a private garden. We included GenBank accessions of Dracaena from Madagascar and other locations and reconstructed the phylogeny using Bayesian and parsimony approaches. Phylogenies indicated that D. umbraculifera is more closely related to Dracaena reflexa from Madagascar than to Mauritian Dracaena. As anecdotal information indicated that the living D. umbraculifera originated from Madagascar, we conducted field expeditions there and located five wild populations; the species’ IUCN status should therefore be Critically Endangered because < 50 wild individuals remain. Although the identity of many botanical garden samples remains unresolved, this study highlights the importance of living collections for facilitating new discoveries and the importance of documenting and conserving the flora of Madagascar.