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ABSTRACT IMPACT: Despite its importance in systemic diseases such as diabetes, the eye is notably difficult to examine for non-specialists; this study introduces a fully automated approach for eye disease screening, coupling a deep learning algorithm with a robotically-aligned optical coherence tomography system to improve eye care in non-ophthalmology settings. OBJECTIVES/GOALS: This study aims to develop and test a deep learning (DL) method to classify images acquired from a robotically-aligned optical coherence tomography (OCT) system as normal vs. abnormal. The long-term goal of our study is to integrate artificial intelligence and robotic eye imaging to fully automate eye disease screening in diverse clinical settings. METHODS/STUDY POPULATION: Between August and October 2020, patients seen at the Duke Eye Center and healthy volunteers (age ≥18) were imaged with a custom, robotically-aligned OCT (RAOCT) system following routine eye exam. Using transfer learning, we adapted a preexisting convolutional neural network to train a DL algorithm to classify OCT images as normal vs. abnormal. The model was trained and validated on two publicly available OCT datasets and two of our own RAOCT volumes. For external testing, the top-performing model based on validation was applied to a representative averaged B-scan from each of the remaining RAOCT volumes. The model’s performance was evaluated against a reference standard of clinical diagnoses by retina specialists. Saliency maps were created to visualize the areas contributing most to the model predictions. RESULTS/ANTICIPATED RESULTS: The training and validation datasets included 87,697 OCT images, of which 59,743 were abnormal. The top-performing DL model had a training accuracy of 96% and a validation accuracy of 99%. For external testing, 43 eyes of 27 subjects were imaged with the robotically-aligned OCT system. Compared to clinical diagnoses, the model correctly labeled 18 out of 22 normal averaged B-scans and 18 out of 21 abnormal averaged B-scans. Overall, in the testing set, the model had an AUC for the detection of pathology of 0.92, an accuracy of 84%, a sensitivity of 86%, and a specificity of 82%. For the correctly predicted scans, saliency maps identified the areas contributing most to the DL algorithm’s predictions, which matched the regions of greatest clinical importance. DISCUSSION/SIGNIFICANCE OF FINDINGS: This is the first study to develop and apply a DL model to images acquired from a self-aligning OCT system, demonstrating the potential of integrating DL and robotic eye imaging to automate eye disease screening. We are working to translate this technology for use in emergency departments and primary care, where it will have the greatest impact.
Major progress has recently been made regarding the biostratigraphy, lithostratigraphy and isotope chemostratigraphy of the lower Cambrian successions in South Australia, in particular of the Arrowie Basin, which has facilitated robust global stratigraphic correlations. However, lack of faunal and sedimentological data from the lower Cambrian Normanville Group in the eastern Stansbury Basin, South Australia – particularly the transition from the Fork Tree Limestone to the Heatherdale Shale – has prevented resolution of the age range, lithofacies, depositional environments and regional correlation of this succession. Here we present detailed sedimentologic, biostratigraphic and chemostratigraphic data through this transition in the eastern Stansbury Basin. Three lithofacies are identified that indicate a deepening depositional environment ranging from inner-mid-shelf (Lithofacies A and B) to outer shelf (Lithofacies C). New δ13C chemostratigraphic data capture global positive excursion III within the lower Heatherdale Shale. Recovered bradoriid Sinskolutella cuspidata supports an upper Stage 2 (Micrina etheridgei Zone). The combined geochemistry and palaeontology data reveal that the lower Heatherdale Shale is older than previously appreciated. This integrated study improves regional chronostratigraphic resolution and interbasinal correlation, and better constrains the depositional setting of this important lower Cambrian package from the eastern Stansbury Basin, South Australia.
A framework to critically consider the ecological sustainability messaging in children’s literature is presented to authors, illustrators and editors, as well as teachers, parents and students/children. We have applied this framework to three books from the Children’s Book Council of Australia (CBCA) 2015 Notables list using critical discourse analysis (CDA). Findings suggest that there are themes and images in these award-winning texts that do not support ecological sustainability and we argue that children’s literature should be judged with criteria including ecological sustainability. Our hope is that ecological sustainability principles and practices lead to changes in social discourse through intergenerational storytelling.
The Adult Attachment Interview (AAI) is a widely used measure in developmental science that assesses adults’ current states of mind regarding early attachment-related experiences with their primary caregivers. The standard system for coding the AAI recommends classifying individuals categorically as having an autonomous, dismissing, preoccupied, or unresolved attachment state of mind. However, previous factor and taxometric analyses suggest that: (a) adults’ attachment states of mind are captured by two weakly correlated factors reflecting adults’ dismissing and preoccupied states of mind and (b) individual differences on these factors are continuously rather than categorically distributed. The current study revisited these suggestions about the latent structure of AAI scales by leveraging individual participant data from 40 studies (N = 3,218), with a particular focus on the controversial observation from prior factor analytic work that indicators of preoccupied states of mind and indicators of unresolved states of mind about loss and trauma loaded on a common factor. Confirmatory factor analyses indicated that: (a) a 2-factor model with weakly correlated dismissing and preoccupied factors and (b) a 3-factor model that further distinguished unresolved from preoccupied states of mind were both compatible with the data. The preoccupied and unresolved factors in the 3-factor model were highly correlated. Taxometric analyses suggested that individual differences in dismissing, preoccupied, and unresolved states of mind were more consistent with a continuous than a categorical model. The importance of additional tests of predictive validity of the various models is emphasized.
Vitamin D deficiency (serum 25-hydroxyvitamin D (25(OH)D) concentration <50 nmol/l) is recognised as a public health problem globally. The present study details the prevalence and predictors of vitamin D deficiency in a nationally representative sample (n 3250) of Australian Aboriginal and Torres Strait Islander adults aged ≥18 years. We used data from the 2012–2013 Australian Aboriginal and Torres Strait Islander Health Survey (AATSIHS). Serum 25(OH)D concentrations were measured by liquid chromatography-tandem MS. Survey-weighted logistic regression models were used to determine the independent predictors of vitamin D deficiency. Approximately 27 % of adult AATSIHS participants were vitamin D deficient. Vitamin D deficiency was more prevalent in remote areas (39 %) than in non-remote areas (23 %). Independent predictors of vitamin D deficiency included assessment during winter (men, adjusted OR (aOR) 5·7; 95 % CI 2·2, 14·6; women, aOR 2·2; 95 % CI 1·3, 3·8) and spring (men, aOR 3·3; 95 % CI 1·4, 7·5; women, aOR 2·6; 95 % CI 1·5, 4·5) compared with summer, and obesity (men, aOR 2·6; 95 % CI 1·2, 5·4; women, aOR 4·3; 95 % CI 2·8, 6·8) compared with healthy weight. Statistically significant associations were evident for current smokers (men only, aOR 2·0; 95 % CI 1·2, 3·4), remote-dwelling women (aOR 2·0; 95 % CI 1·4, 2·9) and university-educated women (aOR 2·4; 95 % CI 1·2, 4·8). Given the high prevalence of vitamin D deficiency in this population, strategies to maintain adequate vitamin D status through safe sun exposure and dietary approaches are needed.
Small carpenter bees (Ceratina calcarata Robertson) (Hymenoptera: Apidae) build their nests in both sunny and shady sites, so maternal decisions about nest sites influence the thermal environment experienced by juveniles throughout development. A previous study demonstrated that when larvae and pupae were raised in the laboratory at room temperature, those from sunny nests developed more slowly than those from shady nests. This suggested that bees developing in sunny nests slowed their metabolism or that bees developing in shady nests increased their metabolism. To test this hypothesis, we performed a field experiment in which bees nested in full sun, full shade, or semi-shade. We brought larvae and pupae into the laboratory to be raised to adulthood at room temperature and measured their metabolic rates (VCO2) at 10 °C, 25 °C, and 40 °C. As expected, bees had higher VCO2 at higher test temperatures, but significant interaction also occurred between test temperature and field treatment, such that bees from sunny nests exhibited higher metabolic rates at 40 °C. Because small carpenter bees frequently nest in full sun, adaptation to high nest temperatures may involve activation of thermal protection mechanisms at the cost of slower development.
Ionization occurs in the upper atmospheres of hot Jupiters and in the interiors of gas giant planets, leading to magnetohydrodynamic (MHD) effects that couple the momentum and the magnetic field, thereby significantly altering the dynamics. In regions of moderate temperatures, the gas is only partially ionized, which also leads to interactions with neutral molecules. To explore the turbulent dynamics of these regions, we utilize partially ionized magnetohydrodynamics (PIMHD), a two-fluid model – one neutral and one ionized – coupled by a collision term proportional to the difference in velocities. Motivated by planetary settings where rotation constrains the large-scale motions to be mostly two-dimensional, we perform a suite of simulations to examine the parameter space of two-dimensional PIMHD turbulence and pay particular attention to collisions and their role in the dynamics, dissipation and energy exchange between the two species. We arrive at, and numerically confirm, an expression for the energy loss due to collisions in both the weakly and strongly collisional limits, and show that, in the latter limit, the neutral fluid couples to the ions and behaves as an MHD fluid. Finally, we discuss some implications of our findings to current understanding of gas giant planet atmospheres.
Ultraviolet C (UV-C) light reduces contamination on high-touch clinical surfaces. We assessed the efficacy of 2 UV-C devices at eradicating important clinical pathogens in hyperbaric chambers. Both devices were similarly efficacious against MRSA but differed significantly against C. difficile. Additionally, direct UV-C exposure was more efficacious against both species than indirect exposure.
Registry-based trials have emerged as a potentially cost-saving study methodology. Early estimates of cost savings, however, conflated the benefits associated with registry utilisation and those associated with other aspects of pragmatic trial designs, which might not all be as broadly applicable. In this study, we sought to build a practical tool that investigators could use across disciplines to estimate the ranges of potential cost differences associated with implementing registry-based trials versus standard clinical trials.
We built simulation Markov models to compare unique costs associated with data acquisition, cleaning, and linkage under a registry-based trial design versus a standard clinical trial. We conducted one-way, two-way, and probabilistic sensitivity analyses, varying study characteristics over broad ranges, to determine thresholds at which investigators might optimally select each trial design.
Registry-based trials were more cost effective than standard clinical trials 98.6% of the time. Data-related cost savings ranged from $4300 to $600,000 with variation in study characteristics. Cost differences were most reactive to the number of patients in a study, the number of data elements per patient available in a registry, and the speed with which research coordinators could manually abstract data. Registry incorporation resulted in cost savings when as few as 3768 independent data elements were available and when manual data abstraction took as little as 3.4 seconds per data field.
Registries offer important resources for investigators. When available, their broad incorporation may help the scientific community reduce the costs of clinical investigation. We offer here a practical tool for investigators to assess potential costs savings.
Two common approaches to identify subgroups of patients with bipolar disorder are clustering methodology (mixture analysis) based on the age of onset, and a birth cohort analysis. This study investigates if a birth cohort effect will influence the results of clustering on the age of onset, using a large, international database.
The database includes 4037 patients with a diagnosis of bipolar I disorder, previously collected at 36 collection sites in 23 countries. Generalized estimating equations (GEE) were used to adjust the data for country median age, and in some models, birth cohort. Model-based clustering (mixture analysis) was then performed on the age of onset data using the residuals. Clinical variables in subgroups were compared.
There was a strong birth cohort effect. Without adjusting for the birth cohort, three subgroups were found by clustering. After adjusting for the birth cohort or when considering only those born after 1959, two subgroups were found. With results of either two or three subgroups, the youngest subgroup was more likely to have a family history of mood disorders and a first episode with depressed polarity. However, without adjusting for birth cohort (three subgroups), family history and polarity of the first episode could not be distinguished between the middle and oldest subgroups.
These results using international data confirm prior findings using single country data, that there are subgroups of bipolar I disorder based on the age of onset, and that there is a birth cohort effect. Including the birth cohort adjustment altered the number and characteristics of subgroups detected when clustering by age of onset. Further investigation is needed to determine if combining both approaches will identify subgroups that are more useful for research.
In New York City, a multi-disciplinary Mass Casualty Consultation team is proposed to support prioritization of patients for coordinated inter-facility transfer after a large-scale mass casualty event. This study examines factors that influence consultation team prioritization decisions.
As part of a multi-hospital functional exercise, 2 teams prioritized the same set of 69 patient profiles. Prioritization decisions were compared between teams. Agreement between teams was assessed based on patient profile demographics and injury severity. An investigator interviewed team leaders to determine reasons for discordant transfer decisions.
The 2 teams differed significantly in the total number of transfers recommended (49 vs 36; P = 0.003). However, there was substantial agreement when recommending transfer to burn centers, with 85.5% agreement and inter-rater reliability of 0.67 (confidence interval: 0.49–0.85). There was better agreement for patients with a higher acuity of injuries. Based on interviews, the most common reason for discordance was insider knowledge of the local community hospital and its capabilities.
A multi-disciplinary Mass Casualty Consultation team was able to rapidly prioritize patients for coordinated secondary transfer using limited clinical information. Training for consultation teams should emphasize guidelines for transfer based on existing services at sending and receiving hospitals, as knowledge of local community hospital capabilities influence physician decision-making.
The chapter summarises the main findings from the SDG chapters (1–17) combined with the results from a workshop in 2018 to answer the following questions: How is Agenda 2030 likely to interact with forests and people? What are the possible synergies, trade-offs between goals and targets? What are the contextual conditions that shape the interactions between SDGs and targets and subsequent impacts on forests and people? Two broad groups of SDGs emerge. One includes SDGs that primarily focus on institutional, governance and social conditions. Those contribute to an enabling environment for inclusive forest management and conservation with associated livelihood benefits. A second group of SDGs affect land use directly and thus are expected to impact forests. Progress in the first group of SDGs results in synergistic interactions and positive outcomes for forests and peoples. Among the second group of SDGs, the potential for trade-offs is high, with important repercussions for forest and people. Understanding the potential for these trade-offs is essential in order to avoid implementation pathways that favour a small subset of these SDGs at the expense of the others.
The introductory chapter introduces the Agenda 2030 and its 17 SDGs and briefly presents the process that led to its adoption. It discusses the nature of the SDGs, recognising the great variation in the nature, scope and function of the SDGs and related targets, and drawing attention to the interlinkages among the goals and targets. Forests provide ecosystem services that are crucial for human welfare and for reaching the SDGs. The chapter gives a brief overview of the world’s forests and forests’ contributions to the SDGs. Forests are only mentioned in two SDGs (SDG 6 and SDG 15). However, due to the interrelated nature of the SDGs and targets, the implementation of the SDG agenda will inevitably influence forests and forest-related livelihoods and the possibilities to achieve the forest specific targets. Understanding the potential impacts of SDGs on forests, forest-related livelihoods and forest-based options to generate progress towards achieving the SDGs, as well as the related tradeoffs and synergies, is crucial for efforts undertaken to reach these goals. It is especially important for reducing potential negative impacts and to leverage opportunities to create synergies that will ultimately determine whether comprehensive progress towards the SDGs will be accomplished.
This chapter summarises the lessons learnt in the book, based on a reflection process amongst the editors and a joint workshop with the lead authors. The key messages are that 1) forests are a crucial base for sustainable development, and need to be fully considered in all related decision making, 2) the SDGs will impact forests and the people dependent on them in many ways, with the exact impact being highly dependant on the respective ecological and socio-economic context, 3) the SDGs include partially conflicting visions for forests and people, corresponding to distinct values and interests, involving the necessity to consider trade-offs and set priorities when implementing them, 4) there are fundamental values and principles that may guide sustainable development related to forests and people regardless of the context, including basic human rights but also forest-specific aspects and principles for how existing trade-offs can be managed, 5) that there is the necessity to continuously learn from, and adapt, the process of implementing the SDGs. The chapter concludes by addressing the urgency of creative and forward-looking human engagement at the forest–people interface, to make sure that sustainable development can benefit both forests and people.