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Environmental DNA (eDNA) surveying has potential to become a powerful tool for sustainable parasite control. As trematode parasites require an intermediate snail host that is often aquatic or amphibious to fulfil their lifecycle, water-based eDNA analyses can be used to screen habitats for the presence of snail hosts and identify trematode infection risk areas. The aim of this study was to identify climatic and environmental factors associated with the detection of Galba truncatula eDNA. Fourteen potential G. truncatula habitats on two farms were surveyed over a 9-month period, with eDNA detected using a filter capture, extraction and PCR protocol with data analysed using a generalized estimation equation. The probability of detecting G. truncatula eDNA increased in habitats where snails were visually detected, as temperature increased, and as water pH decreased (P < 0.05). Rainfall was positively associated with eDNA detection in watercourse habitats on farm A, but negatively associated with eDNA detection in watercourse habitats on farm B (P < 0.001), which may be explained by differences in watercourse gradient. This study is the first to identify factors associated with trematode intermediate snail host eDNA detection. These factors should be considered in standardized protocols to evaluate the results of future eDNA surveys.
Salt marshes have been lost or degraded as the intensity of human impacts to coastal landscapes has increased due to agriculture, transportation, urban and industrial development, and climate change. Because salt marshes have limited distribution and embody a variety of ecological functions that are important to humans (see ecosystem services, Chapter 15), many societies have recognized the need to preserve remaining marshes, restore those that have been degraded, and create new marshes in areas where they have been lost. An emerging and critical threat to tidal marshes across the globe is increasing rates of sea level rise and other aspects of climate change, which complicates but also heightens the urgency for restoration. By restoration we mean re-establishing natural conditions and the processes needed to support their functions, especially self-maintenance (see Box 17.1). Typically, salt marshes are self-maintaining, with salt tolerant plants, mineral sediments, and tidal flooding interacting to maintain elevation and ecological functions under dynamic conditions (Chapters 4, 7, 8).
People with CHD are at increased risk for executive functioning deficits. Meta-analyses of these measures in CHD patients compared to healthy controls have not been reported.
To examine differences in executive functions in individuals with CHD compared to healthy controls.
We performed a systematic review of publications from 1 January, 1986 to 15 June, 2020 indexed in PubMed, CINAHL, EMBASE, PsycInfo, Web of Science, and the Cochrane Library.
Inclusion criteria were (1) studies containing at least one executive function measure; (2) participants were over the age of three.
Data extraction and quality assessment were performed independently by two authors. We used a shifting unit-of-analysis approach and pooled data using a random effects model.
The search yielded 61,217 results. Twenty-eight studies met criteria. A total of 7789 people with CHD were compared with 8187 healthy controls. We found the following standardised mean differences: −0.628 (−0.726, −0.531) for cognitive flexibility and set shifting, −0.469 (−0.606, −0.333) for inhibition, −0.369 (−0.466, −0.273) for working memory, −0.334 (−0.546, −0.121) for planning/problem solving, −0.361 (−0.576, −0.147) for summary measures, and −0.444 (−0.614, −0.274) for reporter-based measures (p < 0.001).
Our analysis consisted of cross-sectional and observational studies. We could not quantify the effect of collinearity.
Individuals with CHD appear to have at least moderate deficits in executive functions. Given the growing population of people with CHD, more attention should be devoted to identifying executive dysfunction in this vulnerable group.
The objective of this chapter is to introduce the University of Kentucky IR4TD Lean Systems Program (LSP) and the concept of “True Lean,” as well as to discuss what we have observed to be critical challenges (derailers) to the successful implementation of Toyota Production System-(TPS)-based principles within non-Toyota organizations. This learning stems from experience teaching, coaching, and facilitating lean implementation activities in a wide range of industries over the past twenty-five years. Participants in the LSP Lean Certification program have been sent by over 175 companies representing industries from healthcare, steel, glass, ceramics, textiles, automotive, railroads, aerospace, commercial aviation, fast food restaurants, and food processing manufacturers as well as government, education, and NGOs. This chapter shares data collected from our staff and clients in an effort to help understand the current condition of lean in industry today and the major challenges confronting successful lean implementations.
Ecosystem modeling, a pillar of the systems ecology paradigm (SEP), addresses questions such as, how much carbon and nitrogen are cycled within ecological sites, landscapes, or indeed the earth system? Or how are human activities modifying these flows? Modeling, when coupled with field and laboratory studies, represents the essence of the SEP in that they embody accumulated knowledge and generate hypotheses to test understanding of ecosystem processes and behavior. Initially, ecosystem models were primarily used to improve our understanding about how biophysical aspects of ecosystems operate. However, current ecosystem models are widely used to make accurate predictions about how large-scale phenomena such as climate change and management practices impact ecosystem dynamics and assess potential effects of these changes on economic activity and policy making. In sum, ecosystem models embedded in the SEP remain our best mechanism to integrate diverse types of knowledge regarding how the earth system functions and to make quantitative predictions that can be confronted with observations of reality. Modeling efforts discussed are the Century ecosystem model, DayCent ecosystem model, Grassland Ecosystem Model ELM, food web models, Savanna model, agent-based and coupled systems modeling, and Bayesian modeling.
The Old Copper Complex (OCC) refers to the production of heavy copper-tool technology by Archaic Native American societies in the Lake Superior region. To better define the timing of the OCC, we evaluated 53 (eight new and 45 published) radiocarbon (14C) dates associated with copper artifacts and mines. We compared these dates to six lake sediment-based chronologies of copper mining and annealing in the Michigan Copper District. 14C dates grouped by archaeological context show that cremation remains, and wood and cordage embedded in copper artifacts have ages that overlap with the timing of high lead (Pb) concentrations in lake sediment. In contrast, dates in stratigraphic association and from mines are younger than those from embedded and cremation materials, suggesting that the former groups reflect the timing of processes that occurred post-abandonment. The comparatively young dates obtained from copper mines therefore likely reflect abandonment and infill of the mines rather than active use. Excluding three anomalously young samples, the ages of embedded organic material associated with 15 OCC copper artifacts range from 8500 to 3580 cal BP, confirming that the OCC is among the oldest known metalworking societies in the world.
Perceived discrimination is associated with worse mental health. Few studies have assessed whether perceived discrimination (i) is associated with the risk of psychotic disorders and (ii) contributes to an increased risk among minority ethnic groups relative to the ethnic majority.
We used data from the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions Work Package 2, a population-based case−control study of incident psychotic disorders in 17 catchment sites across six countries. We calculated odds ratios (OR) and 95% confidence intervals (95% CI) for the associations between perceived discrimination and psychosis using mixed-effects logistic regression models. We used stratified and mediation analyses to explore differences for minority ethnic groups.
Reporting any perceived experience of major discrimination (e.g. unfair treatment by police, not getting hired) was higher in cases than controls (41.8% v. 34.2%). Pervasive experiences of discrimination (≥3 types) were also higher in cases than controls (11.3% v. 5.5%). In fully adjusted models, the odds of psychosis were 1.20 (95% CI 0.91–1.59) for any discrimination and 1.79 (95% CI 1.19–1.59) for pervasive discrimination compared with no discrimination. In stratified analyses, the magnitude of association for pervasive experiences of discrimination appeared stronger for minority ethnic groups (OR = 1.73, 95% CI 1.12–2.68) than the ethnic majority (OR = 1.42, 95% CI 0.65–3.10). In exploratory mediation analysis, pervasive discrimination minimally explained excess risk among minority ethnic groups (5.1%).
Pervasive experiences of discrimination are associated with slightly increased odds of psychotic disorders and may minimally help explain excess risk for minority ethnic groups.
ABSTRACT IMPACT: A machine learning approach using electronic health records can combine descriptive, population-level factors of pressure injury outcomes. OBJECTIVES/GOALS: Pressure injuries cause 60,000 deaths and cost $26 billion annually in the US, but prevention is laborious. We used clinical data to develop a machine learning algorithm for predicting pressure injury risk and prescribe the timing of intervention to help clinicians balance competing priorities. METHODS/STUDY POPULATION: We obtained 94,745 electronic health records with 7,000 predictors to calibrate a predictive algorithm of pressure injury risk. Machine learning was used to mine features predicting changes in pressure injury risk; random forests outperformed neural networks, boosting and bagging in feature selection. These features were fit to multilevel ordered logistic regression to create an algorithm that generated empirical Bayes estimates informing a decision-rule for follow-up based on individual risk trajectories over time. We used cross-validation to verify predictive validity, and constrained optimization to select a best-fit algorithm that reduced the time required to trigger patient follow-up. RESULTS/ANTICIPATED RESULTS: The algorithm significantly improved prediction of pressure injury risk (p<0.001) with an area under the ROC curve of 0.60 compared to the Braden Scale, a traditional clinician instrument of pressure injury risk. At a specificity of 0.50, the model achieved a sensitivity of 0.63 within 2.5 patient-days. Machine learning identified categorical increases in risk when patients were prescribed vasopressors (OR=16.4, p<0.001), beta-blockers (OR=4.8, p<0.001), erythropoietin stimulating agents (OR=3.0, p<0.001), or were ordered a urinalysis screen (OR=9.1, p<0.001), lipid panel (OR=5.7, p<0.001) or pre-albumin panel (OR=2.0, p<0.001). DISCUSSION/SIGNIFICANCE OF FINDINGS: This algorithm could help hospitals conserve resources within a critical period of patient vulnerability for pressure injury not reimbursed by Medicare. Savings generated by this approach could justify investment in machine learning to develop electronic warning systems for many iatrogenic injuries.
ABSTRACT IMPACT: Leverage community engagement to continue moving translational science and research forward. OBJECTIVES/GOALS: Engaging community in translational research improves innovation and speeds the movement of evidence into practice. Yet, it is unclear how community is engaged across the translational research spectrum or the degree of community-engagement used. We conducted a scoping review to fill this gap. METHODS/STUDY POPULATION: We used the PRISMA model search strategy with a range of databases (e.g., PubMed/Medline, Scopus) to identify articles published between January 2008 and November 2018 (n=167) and eliminated studies that did not use any level of community-engagement (n=102). Studies were coded for translational stage-corresponding to T0 (basic science), T1 (basic science to clinical research in humans; n=6), T2 (clinical efficacy and effectiveness research, n=45), T3 (dissemination and implementation research, n=95), and T4 (population health, n=21) as well as the degree of community engagement from least to most intensive (i.e., outreach, consultation, involvement, collaboration, shared leadership). RESULTS/ANTICIPATED RESULTS: The final number of eligible articles was 65. There was a relatively balanced distribution across levels of community engagement across articles (i.e., outreach, n=14; consultation, n=13; involvement, n=7; collaboration, n=15; shared leadership, n=16). Within these articles, the depth of community engagement varied with higher engagement typically occurring at later stages of translational research (T3 and T4), but more specifically in the dissemination and implementation science stage (T3). However, shared leadership, the most intensive form of engagement, was found in T2, T3, and T4 studies suggesting the value of community-engagement across the translational research spectrum. DISCUSSION/SIGNIFICANCE OF FINDINGS: A strong understanding of how various levels of community engagement are used in translational research, and the outcomes they produce, may to expedite the translation of knowledge into practice and enable practice-based needs to inform policy.
During the Randomized Assessment of Rapid Endovascular Treatment (EVT) of Ischemic Stroke (ESCAPE) trial, patient-level micro-costing data were collected. We report a cost-effectiveness analysis of EVT, using ESCAPE trial data and Markov simulation, from a universal, single-payer system using a societal perspective over a patient’s lifetime.
Primary data collection alongside the ESCAPE trial provided a 3-month trial-specific, non-model, based cost per quality-adjusted life year (QALY). A Markov model utilizing ongoing lifetime costs and life expectancy from the literature was built to simulate the cost per QALY adopting a lifetime horizon. Health states were defined using the modified Rankin Scale (mRS) scores. Uncertainty was explored using scenario analysis and probabilistic sensitivity analysis.
The 3-month trial-based analysis resulted in a cost per QALY of $201,243 of EVT compared to the best standard of care. In the model-based analysis, using a societal perspective and a lifetime horizon, EVT dominated the standard of care; EVT was both more effective and less costly than the standard of care (−$91). When the time horizon was shortened to 1 year, EVT remains cost savings compared to standard of care (∼$15,376 per QALY gained with EVT). However, if the estimate of clinical effectiveness is 4% less than that demonstrated in ESCAPE, EVT is no longer cost savings compared to standard of care.
Results support the adoption of EVT as a treatment option for acute ischemic stroke, as the increase in costs associated with caring for EVT patients was recouped within the first year of stroke, and continued to provide cost savings over a patient’s lifetime.
In recent years, a variety of efforts have been made in political science to enable, encourage, or require scholars to be more open and explicit about the bases of their empirical claims and, in turn, make those claims more readily evaluable by others. While qualitative scholars have long taken an interest in making their research open, reflexive, and systematic, the recent push for overarching transparency norms and requirements has provoked serious concern within qualitative research communities and raised fundamental questions about the meaning, value, costs, and intellectual relevance of transparency for qualitative inquiry. In this Perspectives Reflection, we crystallize the central findings of a three-year deliberative process—the Qualitative Transparency Deliberations (QTD)—involving hundreds of political scientists in a broad discussion of these issues. Following an overview of the process and the key insights that emerged, we present summaries of the QTD Working Groups’ final reports. Drawing on a series of public, online conversations that unfolded at www.qualtd.net, the reports unpack transparency’s promise, practicalities, risks, and limitations in relation to different qualitative methodologies, forms of evidence, and research contexts. Taken as a whole, these reports—the full versions of which can be found in the Supplementary Materials—offer practical guidance to scholars designing and implementing qualitative research, and to editors, reviewers, and funders seeking to develop criteria of evaluation that are appropriate—as understood by relevant research communities—to the forms of inquiry being assessed. We dedicate this Reflection to the memory of our coauthor and QTD working group leader Kendra Koivu.1
Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction.
Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician–patient interaction.
Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback.
All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician–patient interaction.
The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician–patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.
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.
To describe epidemiologic and genomic characteristics of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak in a large skilled-nursing facility (SNF), and the strategies that controlled transmission.
Design, setting, and participants:
This cohort study was conducted during March 22–May 4, 2020, among all staff and residents at a 780-bed SNF in San Francisco, California.
Contact tracing and symptom screening guided targeted testing of staff and residents; respiratory specimens were also collected through serial point prevalence surveys (PPSs) in units with confirmed cases. Cases were confirmed by real-time reverse transcription–polymerase chain reaction testing for SARS-CoV-2, and whole-genome sequencing (WGS) was used to characterize viral isolate lineages and relatedness. Infection prevention and control (IPC) interventions included restricting from work any staff who had close contact with a confirmed case; restricting movement between units; implementing surgical face masking facility-wide; and the use of recommended PPE (ie, isolation gown, gloves, N95 respirator and eye protection) for clinical interactions in units with confirmed cases.
Of 725 staff and residents tested through targeted testing and serial PPSs, 21 (3%) were SARS-CoV-2 positive: 16 (76%) staff and 5 (24%) residents. Fifteen cases (71%) were linked to a single unit. Targeted testing identified 17 cases (81%), and PPSs identified 4 cases (19%). Most cases (71%) were identified before IPC interventions could be implemented. WGS was performed on SARS-CoV-2 isolates from 4 staff and 4 residents: 5 were of Santa Clara County lineage and the 3 others were distinct lineages.
Early implementation of targeted testing, serial PPSs, and multimodal IPC interventions limited SARS-CoV-2 transmission within the SNF.
This chapter comprises the following sections: names, taxonomy, subspecies and distribution, descriptive notes, habitat, movements and home range, activity patterns, feeding ecology, reproduction and growth, behavior, parasites and diseases, status in the wild, and status in captivity.
This is the first report on the association between trauma exposure and depression from the Advancing Understanding of RecOvery afteR traumA(AURORA) multisite longitudinal study of adverse post-traumatic neuropsychiatric sequelae (APNS) among participants seeking emergency department (ED) treatment in the aftermath of a traumatic life experience.
We focus on participants presenting at EDs after a motor vehicle collision (MVC), which characterizes most AURORA participants, and examine associations of participant socio-demographics and MVC characteristics with 8-week depression as mediated through peritraumatic symptoms and 2-week depression.
Eight-week depression prevalence was relatively high (27.8%) and associated with several MVC characteristics (being passenger v. driver; injuries to other people). Peritraumatic distress was associated with 2-week but not 8-week depression. Most of these associations held when controlling for peritraumatic symptoms and, to a lesser degree, depressive symptoms at 2-weeks post-trauma.
These observations, coupled with substantial variation in the relative strength of the mediating pathways across predictors, raises the possibility of diverse and potentially complex underlying biological and psychological processes that remain to be elucidated in more in-depth analyses of the rich and evolving AURORA database to find new targets for intervention and new tools for risk-based stratification following trauma exposure.
The perinatal period is a vulnerable time for the development of psychopathology, particularly mood and anxiety disorders. In the study of maternal anxiety, important questions remain regarding the association between maternal anxiety symptoms and subsequent child outcomes. This study examined the association between depressive and anxiety symptoms, namely social anxiety, panic, and agoraphobia disorder symptoms during the perinatal period and maternal perception of child behavior, specifically different facets of development and temperament. Participants (N = 104) were recruited during pregnancy from a community sample. Participants completed clinician-administered and self-report measures of depressive and anxiety symptoms during the third trimester of pregnancy and at 16 months postpartum; child behavior and temperament outcomes were assessed at 16 months postpartum. Child development areas included gross and fine motor skills, language and problem-solving abilities, and personal/social skills. Child temperament domains included surgency, negative affectivity, and effortful control. Hierarchical multiple regression analyses demonstrated that elevated prenatal social anxiety symptoms significantly predicted more negative maternal report of child behavior across most measured domains. Elevated prenatal social anxiety and panic symptoms predicted more negative maternal report of child effortful control. Depressive and agoraphobia symptoms were not significant predictors of child outcomes. Elevated anxiety symptoms appear to have a distinct association with maternal report of child development and temperament. Considering the relative influence of anxiety symptoms, particularly social anxiety, on maternal report of child behavior and temperament can help to identify potential difficulties early on in mother–child interactions as well as inform interventions for women and their families.
For many, classification is shrouded in mystery and questions such as ‘How do taxonomists find all those species?’ have led philosophers of science to discuss species concepts rather than how taxonomists actually discover natural entities. The same is true for monophyletic taxa in general: much is made of defining monophyletic taxa rather than discovering them. Ask a room full of systematists to define monophyly and there will probably be at least five different definitions (see Vanderlaan et al. 2013). Yet, every single one of those individuals will most likely be able to identify the same monophyletic taxon. All that said, it seems what systematists say they do is often not what they do (sensu Medawar  1968, epigraph above; see also Winsor 2001), discovering monophyly being a case in point.