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The interaction of water with synthetically prepared goethite (α-FeOOH) and amorphous hyd-rated ferric oxide surfaces was studied using i.r. absorption and water vapor adsorption measurements. I.R. results show that the last traces of physically adsorbed water are removed from the amorphous material by outgassing at 25°C. In contrast, goethite retains approximately a monolayer of physically adsorbed water with similar outgassing. This monolayer of water on goethite, which is presumably hydrogen-bonded at least in part with structural hydroxyls, is readily exchangeable with D2O.
Integral entropies of adsorption were evaluated from water vapor adsorption isotherms at 15, 25 and 35°C and compared with values for mobile and immobile layers calculated through application of statistical mechanics (McCafferty and Zettlemoyer, 1970). Entropy values for both the first physically adsorbed monolayer of water on the amorphous material and the second monolayer on goethite were about the same as or greater than those calculated for an immobile layer, indicating strong hydrogen bonding of water by both surfaces. The larger deviation between the entropy values for goethite and those calculated for the immobile layer may be associated with changes in the structure of the first as well as the second physically adsorbed water layers. Surface areas, calculated using the BET method, were 320 and 32 m2 g-1 for the amorphous material and goethite respectively. Since the unit surface activity is probably about the same for the two materials, it follows that as the amorphous material crystallizes to form goethite, there would be a reduction in total surface activity in proportion to the reduction in surface area.
The nature of freshly-precipitated and aged hydrated ferric oxides prepared by the addition of ferric chloride to KOH was investigated by the use of scanning and transmission electron microscopy, X-ray diffraction, i.r. absorption, and pH 3·0 ammonium oxalate extraction. The results show the fresh material to be essentially non-crystalline hydrated ferric oxide, which when aged at 60°C C and high pH rapidly crystallizes as goethite, without any indication of coexisting hematite. The various methods were evaluated as indices of crystallinity for aging materials. The acid ammonium oxalate method was shown to extract selectively only the non-crystalline portion of such mixtures. The use of X-ray diffraction analysis for estimating aging stage requires elimination of the preferred orientation of the goethite crystals. While both the oxalate and X-ray methods can detect as little as 2 per cent crystallinity, the oxalate method is probably superior for quantitative determinations as it depends directly on an inherent difference in the solubility of the crystalline and non-crystalline materials, rather than on a technique dependent intensity measurement. The use of the intensity of the O-H bending vibrations of the infrared absorption spectra can also potentially detect as little as 2 per cent crystallinity, but the procedure is probably less useful for quantitative determinations than the oxalate or X-ray methods because of the problem of evaluating the area under the peaks.
End members and species defined with permissible ranges of composition are presented for the true micas, the brittle micas and the interlayer-cation-deficient micas. The determination of the crystallochemical formula for different available chemical data is outlined, and a system of modifiers and suffixes is given to allow the expression of unusual chemical substitutions or polytypic stacking arrangements. Tables of mica synonyms, varieties, ill-defined materials and a list of names formerly or erroneously used for micas are presented. The Mica Subcommittee was appointed by the Commission on New Minerals and Mineral Names (“Commission”) of the International Mineralogical Association (IMA). The definitions and recommendations presented were approved by the Commission.
Since the initial publication of A Compendium of Strategies to Prevent Healthcare-Associated Infections in Acute Care Hospitals in 2008, the prevention of healthcare-associated infections (HAIs) has continued to be a national priority. Progress in healthcare epidemiology, infection prevention, antimicrobial stewardship, and implementation science research has led to improvements in our understanding of effective strategies for HAI prevention. Despite these advances, HAIs continue to affect ∼1 of every 31 hospitalized patients,1 leading to substantial morbidity, mortality, and excess healthcare expenditures,1 and persistent gaps remain between what is recommended and what is practiced.
The widespread impact of the coronavirus disease 2019 (COVID-19) pandemic on HAI outcomes2 in acute-care hospitals has further highlighted the essential role of infection prevention programs and the critical importance of prioritizing efforts that can be sustained even in the face of resource requirements from COVID-19 and future infectious diseases crises.3
The Compendium: 2022 Updates document provides acute-care hospitals with up-to-date, practical expert guidance to assist in prioritizing and implementing HAI prevention efforts. It is the product of a highly collaborative effort led by the Society for Healthcare Epidemiology of America (SHEA), the Infectious Disease Society of America (IDSA), the Association for Professionals in Infection Control and Epidemiology (APIC), the American Hospital Association (AHA), and The Joint Commission, with major contributions from representatives of organizations and societies with content expertise, including the Centers for Disease Control and Prevention (CDC), the Pediatric Infectious Disease Society (PIDS), the Society for Critical Care Medicine (SCCM), the Society for Hospital Medicine (SHM), the Surgical Infection Society (SIS), and others.
Edited by
Andreas Rasche, Copenhagen Business School,Mette Morsing, Principles for Responsible Management Education (PRME), UN GlobalCompact, United Nations,Jeremy Moon, Copenhagen Business School,Arno Kourula, Amsterdam Business School, University of Amsterdam
The ability of business to meaningfully engage with those groups and individuals who it affects, and is affected by – its stakeholders – is a critical component in humanity’s pursuit of net zero, sustainable development for all. This chapter explores and unpacks stakeholder approaches to corporate sustainability and responsible business.
It starts off by framing the big picture of why a stakeholder approach – one that accounts for the interdependencies between business, society and nature – is increasingly pivotal during the uncertain and ambiguous times of the 2020s. Following on, the chapter introduces the core features of a stakeholder approach to corporate sustainability. The third section looks at different stakeholder models. It illustrates stakeholder model development in business settings over time, as well as the influence of the cultural context, such as the one found in the Nordic countries, to foster a stakeholder mindset in business.
The fourth and final section considers stakeholder theory in the age of sustainability, with particular coverage given to the implications of new Information Communication Technologies (ICT) for successful stakeholder engagement strategies.
Weaved in throughout this chapter, and parallel to the online case study, are a number of examples designed to illustrate how a stakeholder approach manifests in the ‘real world’.
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.
Quizalofop-resistant rice allows for over-the-top applications of quizalofop, a herbicide that inhibits acetyl-coenzyme A carboxylase. However, previous reports have indicated that quizalofop applied postemergence may cause significant injury to quizalofop-resistant rice. Therefore, field experiments were conducted to evaluate the response of quizalofop-resistant rice cultivars to quizalofop applications across different planting dates. Under controlled conditions, the effects of soil moisture content, air temperature, and light intensity on quizalofop-resistant rice sensitivity to quizalofop were investigated. In the planting date experiment, injury of more than 11 percentage points was observed on early-planted rice compared with late-planted rice at the 5-leaf stage, with higher injury observed under saturated soil conditions. However, quizalofop applications at the labeled rate caused ≤16% reduction in yield regardless of planting environment. Quizalofop-resistant cultivars exhibited more injury by at least 25 percentage points when soil was maintained at 90% or 100% of field capacity because rice cultivars ‘PVL01’, ‘PVL02’, and ‘RTv7231 MA’ exhibited ≥42%, 30%, and ≥54% injury, respectively, compared with ≤10%, ≤5%, and ≤22% injury, respectively, at 40% or 50% of field capacity, pooled over rating timing. Greater injury ranging from 18% to 31% was observed on quizalofop-resistant rice grown under low light intensity (600 µmol m−2s−1) compared with 5% to 14% injury under high light intensity (1,150 µmol m−2s−1). The injury persisted from 7 to 28 d after 5-leaf stage application (DAFT), averaged over quizalofop-resistant cultivars and air temperatures (20/15 C and 30/25 C day/night, respectively). At 7 DAFT, greater injury (by 5 to 21 percentage points) was observed on quizalofop-resistant cultivars; PVL01, PVL02, and RTv7231 MA exhibited 33%, 9%, and 58% injury, respectively, under 20/15 C temperature conditions compared with 13%, 4%, and 37% injury, respectively, under 30/25 C day/night conditions averaged over light intensities. Overall, quizalofop is likely to cause a greater risk for injury to quizalofop-resistant rice if it is applied under cool, cloudy, and moist soil conditions.
Injury to quizalofop-resistant rice was reported in some fields following postemergence applications of quizalofop. Glyphosate-resistant (GR) corn, cotton, and soybean, and imidazolinone-resistant rice are grown near quizalofop-resistant rice. Herbicide drift from glyphosate and imazethapyr and the resulting crop injury and potential yield loss is a cause of concern for producers. Field experiments conducted near Colt, and Keiser, AR, in 2021 evaluated whether low rates of glyphosate or imazethapyr interact with sequential quizalofop applications to exacerbate injury to quizalofop-resistant rice compared to quizalofop applications alone. Herbicide treatments consisted of a low rate of glyphosate (90 g ae ha−1) or imazethapyr (10.7 g ai ha−1) applied 10, 7, 4, and 0 d before the 2-leaf growth stage of rice, and glyphosate or imazethapyr, at the same rate and timings, followed by quizalofop at 120 g ai ha−1 applied to 2-leaf rice. All plots treated with quizalofop received a subsequent application of the same herbicide and rate at the 5-leaf rice stage. At 28 d after final treatment (DAFT), glyphosate followed by quizalofop the same day to 2-leaf rice caused 77% injury compared with 58% when glyphosate was applied alone, regardless of location. Glyphosate followed by quizalofop the same day reduced rough rice grain yield by 67% compared with 33% when glyphosate was applied alone to 2-leaf rice at the Colt location. Application of imazethapyr followed by quizalofop the same day to 2-leaf rice caused more injury (63% and 19% injury at the Colt and Keiser locations, respectively) than imazethapyr alone (42% and 7% injury at the Colt and Keiser locations, respectively) at 35 DAFT. Overall, glyphosate and imazethapyr followed by quizalofop applications worsened injury compared to glyphosate, imazethapyr, and quizalofop applications alone. As the interval between exposure to a low rate of glyphosate or imazethapyr and quizalofop decreases, the detrimental effect of herbicide on rice likewise increases.
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.
The impact of secondary fluorescence on the material compositions measured by X-ray analysis for layered semiconductor thin films is assessed using simulations performed by the DTSA-II and CalcZAF software tools. Three technologically important examples are investigated: AlxGa1−xN layers on either GaN or AlN substrates, InxAl1−xN on GaN, and Si-doped (SnxGa1−x)2O3 on Si. Trends in the differences caused by secondary fluorescence are explained in terms of the propensity of different elements to reabsorb either characteristic or bremsstrahlung X-rays and then to re-emit the characteristic X-rays used to determine composition of the layer under investigation. Under typical beam conditions (7–12 keV), the quantification of dopants/trace elements is found to be susceptible to secondary fluorescence and care must be taken to prevent erroneous results. The overall impact on major constituents is shown to be very small with a change of approximately 0.07 molar cation percent for Al0.3Ga0.7N/AlN layers and a maximum change of 0.08 at% in the Si content of (SnxGa1−x)2O3/Si layers. This provides confidence that previously reported wavelength-dispersive X-ray compositions are not compromised by secondary fluorescence.
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.
Retrospective self-report is typically used for diagnosing previous pediatric traumatic brain injury (TBI). A new semi-structured interview instrument (New Mexico Assessment of Pediatric TBI; NewMAP TBI) investigated test–retest reliability for TBI characteristics in both the TBI that qualified for study inclusion and for lifetime history of TBI.
Method:
One-hundred and eight-four mTBI (aged 8–18), 156 matched healthy controls (HC), and their parents completed the NewMAP TBI within 11 days (subacute; SA) and 4 months (early chronic; EC) of injury, with a subset returning at 1 year (late chronic; LC).
Results:
The test–retest reliability of common TBI characteristics [loss of consciousness (LOC), post-traumatic amnesia (PTA), retrograde amnesia, confusion/disorientation] and post-concussion symptoms (PCS) were examined across study visits. Aside from PTA, binary reporting (present/absent) for all TBI characteristics exhibited acceptable (≥0.60) test–retest reliability for both Qualifying and Remote TBIs across all three visits. In contrast, reliability for continuous data (exact duration) was generally unacceptable, with LOC and PCS meeting acceptable criteria at only half of the assessments. Transforming continuous self-report ratings into discrete categories based on injury severity resulted in acceptable reliability. Reliability was not strongly affected by the parent completing the NewMAP TBI.
Conclusions:
Categorical reporting of TBI characteristics in children and adolescents can aid clinicians in retrospectively obtaining reliable estimates of TBI severity up to a year post-injury. However, test–retest reliability is strongly impacted by the initial data distribution, selected statistical methods, and potentially by patient difficulty in distinguishing among conceptually similar medical concepts (i.e., PTA vs. confusion).
Wavelength-dispersive X-ray (WDX) spectroscopy was used to measure silicon atom concentrations in the range 35–100 ppm [corresponding to (3–9) × 1018 cm−3] in doped AlxGa1–xN films using an electron probe microanalyser also equipped with a cathodoluminescence (CL) spectrometer. Doping with Si is the usual way to produce the n-type conducting layers that are critical in GaN- and AlxGa1–xN-based devices such as LEDs and laser diodes. Previously, we have shown excellent agreement for Mg dopant concentrations in p-GaN measured by WDX with values from the more widely used technique of secondary ion mass spectrometry (SIMS). However, a discrepancy between these methods has been reported when quantifying the n-type dopant, silicon. We identify the cause of discrepancy as inherent sample contamination and propose a way to correct this using a calibration relation. This new approach, using a method combining data derived from SIMS measurements on both GaN and AlxGa1–xN samples, provides the means to measure the Si content in these samples with account taken of variations in the ZAF corrections. This method presents a cost-effective and time-saving way to measure the Si doping and can also benefit from simultaneously measuring other signals, such as CL and electron channeling contrast imaging.
The Subglacial Antarctic Lakes Scientific Access (SALSA) Project accessed Mercer Subglacial Lake using environmentally clean hot-water drilling to examine interactions among ice, water, sediment, rock, microbes and carbon reservoirs within the lake water column and underlying sediments. A ~0.4 m diameter borehole was melted through 1087 m of ice and maintained over ~10 days, allowing observation of ice properties and collection of water and sediment with various tools. Over this period, SALSA collected: 60 L of lake water and 10 L of deep borehole water; microbes >0.2 μm in diameter from in situ filtration of ~100 L of lake water; 10 multicores 0.32–0.49 m long; 1.0 and 1.76 m long gravity cores; three conductivity–temperature–depth profiles of borehole and lake water; five discrete depth current meter measurements in the lake and images of ice, the lake water–ice interface and lake sediments. Temperature and conductivity data showed the hydrodynamic character of water mixing between the borehole and lake after entry. Models simulating melting of the ~6 m thick basal accreted ice layer imply that debris fall-out through the ~15 m water column to the lake sediments from borehole melting had little effect on the stratigraphy of surficial sediment cores.
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.
In this paper, we describe the system design and capabilities of the Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope at the conclusion of its construction project and commencement of science operations. ASKAP is one of the first radio telescopes to deploy phased array feed (PAF) technology on a large scale, giving it an instantaneous field of view that covers $31\,\textrm{deg}^{2}$ at $800\,\textrm{MHz}$. As a two-dimensional array of 36$\times$12 m antennas, with baselines ranging from 22 m to 6 km, ASKAP also has excellent snapshot imaging capability and 10 arcsec resolution. This, combined with 288 MHz of instantaneous bandwidth and a unique third axis of rotation on each antenna, gives ASKAP the capability to create high dynamic range images of large sky areas very quickly. It is an excellent telescope for surveys between 700 and $1800\,\textrm{MHz}$ and is expected to facilitate great advances in our understanding of galaxy formation, cosmology, and radio transients while opening new parameter space for discovery of the unknown.
Understanding place-based contributors to health requires geographically and culturally diverse study populations, but sharing location data is a significant challenge to multisite studies. Here, we describe a standardized and reproducible method to perform geospatial analyses for multisite studies. Using census tract-level information, we created software for geocoding and geospatial data linkage that was distributed to a consortium of birth cohorts located throughout the USA. Individual sites performed geospatial linkages and returned tract-level information for 8810 children to a central site for analyses. Our generalizable approach demonstrates the feasibility of geospatial analyses across study sites to promote collaborative translational research.