166 resultados para automated external defibrillators


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Environmental sensors collect massive amounts of audio data. This thesis investigates computational methods to support human analysts in identifying faunal vocalisations from that audio. A series of experiments was conducted to trial the effectiveness of novel user interfaces. This research examines the rapid scanning of spectrograms, decision support tools for users, and cleaning methods for folksonomies. Together, these investigations demonstrate that providing computational support to human analysts increases their efficiency and accuracy; this allows bioacoustics projects to efficiently utilise their valuable human analysts.

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Although the external influence of scholars has usually been approximated by publication and citation count, the array of scholarly activities is far more extensive. Today, new technologies, in particular Internet search engines, allow more accurate measurement of scholars' influence on societal discourse. Hence, in this article, we analyse the relation between the internal and external influence of 723 top economists using the number of pages indexed by Google and Bing as a measure of external influence. We not only identify a small association between these scholars’ internal and external influence but also a correlation between internal influence, as captured by receipt of such major academic awards as the Nobel Prize and John Bates Clark Medal, and the external prominence of the top 100 researchers (JEL Code: A11, A13, Z18).

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This paper presents a new active learning query strategy for information extraction, called Domain Knowledge Informativeness (DKI). Active learning is often used to reduce the amount of annotation effort required to obtain training data for machine learning algorithms. A key component of an active learning approach is the query strategy, which is used to iteratively select samples for annotation. Knowledge resources have been used in information extraction as a means to derive additional features for sample representation. DKI is, however, the first query strategy that exploits such resources to inform sample selection. To evaluate the merits of DKI, in particular with respect to the reduction in annotation effort that the new query strategy allows to achieve, we conduct a comprehensive empirical comparison of active learning query strategies for information extraction within the clinical domain. The clinical domain was chosen for this work because of the availability of extensive structured knowledge resources which have often been exploited for feature generation. In addition, the clinical domain offers a compelling use case for active learning because of the necessary high costs and hurdles associated with obtaining annotations in this domain. Our experimental findings demonstrated that 1) amongst existing query strategies, the ones based on the classification model’s confidence are a better choice for clinical data as they perform equally well with a much lighter computational load, and 2) significant reductions in annotation effort are achievable by exploiting knowledge resources within active learning query strategies, with up to 14% less tokens and concepts to manually annotate than with state-of-the-art query strategies.

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We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles in brain MRI scans, providing an efficient approach to monitor degenerative disease in clinical studies and drug trials. First, we used a set of parameterized surfaces to represent the ventricles in four subjects' manually labeled brain MRI scans (atlases). We fluidly registered each atlas and mesh model to MRIs from 17 Alzheimer's disease (AD) patients and 13 age- and gender-matched healthy elderly control subjects, and 18 asymptomatic ApoE4-carriers and 18 age- and gender-matched non-carriers. We examined genotyped healthy subjects with the goal of detecting subtle effects of a gene that confers heightened risk for Alzheimer's disease. We averaged the meshes extracted for each 3D MR data set, and combined the automated segmentations with a radial mapping approach to localize ventricular shape differences in patients. Validation experiments comparing automated and expert manual segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease- and gene-related alterations improved, as the number of atlases, N, was increased from 1 to 9. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases. We formulated a statistical stopping criterion to determine the optimal number of atlases to use. Healthy ApoE4-carriers and those with AD showed local ventricular abnormalities. This high-throughput method for morphometric studies further motivates the combination of genetic and neuroimaging strategies in predicting AD progression and treatment response. © 2007 Elsevier Inc. All rights reserved.

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We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles, designed for monitoring degenerative disease effects in clinical neuroscience studies and drug trials. First we used a set of parameterized surfaces to represent the ventricles in a manually labeled set of 9 subjects' MRIs (atlases). We fluidly registered each of these atlases and mesh models to a set of MRIs from 12 Alzheimer's disease (AD) patients and 14 matched healthy elderly subjects, and we averaged the resulting meshes for each of these images. Validation experiments on expert segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease-related alterations monotonically improved as the number of atlases, N, was increased from 1 to 9. We then combined the segmentations with a radial mapping approach to localize ventricular shape differences in patients. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases, and we formulated a statistical stopping criterion to determine the optimal value of N. Anterior horn anomalies in Alzheimer's patients were only detected with the multi-atlas segmentation, which clearly outperformed the standard single-atlas approach.

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An automated method for extracting brain volumes from three commonly acquired three-dimensional (3D) MR images (proton density, T1 weighted, and T2-weighted) of the human head is described. The procedure is divided into four levels: preprocessing, segmentation, scalp removal, and postprocessing. A user-provided reference point is the sole operator-dependent input required. The method's parameters were first optimized and then fixed and applied to 30 repeat data sets from 15 normal older adult subjects to investigate its reproducibility. Percent differences between total brain volumes (TBVs) for the subjects' repeated data sets ranged from .5% to 2.2%. We conclude that the method is both robust and reproducible and has the potential for wide application.

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The microbial mediated production of nitrous oxide (N2O) and its reduction to dinitrogen (N2) via denitrification represents a loss of nitrogen (N) from fertilised agro-ecosystems to the atmosphere. Although denitrification has received great interest by biogeochemists in the last decades, the magnitude of N2lossesand related N2:N2O ratios from soils still are largely unknown due to methodical constraints. We present a novel 15N tracer approach, based on a previous developed tracer method to study denitrification in pure bacterial cultures which was modified for the use on soil incubations in a completely automated laboratory set up. The method uses a background air in the incubation vessels that is replaced with a helium-oxygen gas mixture with a 50-fold reduced N2 background (2 % v/v). This method allows for a direct and sensitive quantification of the N2 and N2O emissions from the soil with isotope-ratio mass spectrometry after 15N labelling of denitrification N substrates and minimises the sensitivity to the intrusion of atmospheric N2 at the same time. The incubation set up was used to determine the influence of different soil moisture levels on N2 and N2O emissions from a sub-tropical pasture soil in Queensland/Australia. The soil was labelled with an equivalent of 50 μg-N per gram dry soil by broadcast application of KNO3solution (4 at.% 15N) and incubated for 3 days at 80% and 100% water filled pore space (WFPS), respectively. The headspace of the incubation vessel was sampled automatically over 12hrs each day and 3 samples (0, 6, and 12 hrs after incubation start) of headspace gas analysed for N2 and N2O with an isotope-ratio mass spectrometer (DELTA V Plus, Thermo Fisher Scientific, Bremen, Germany(. In addition, the soil was analysed for 15N NO3- and NH4+ using the 15N diffusion method, which enabled us to obtain a complete N balance. The method proved to be highly sensitive for N2 and N2O emissions detecting N2O emissions ranging from 20 to 627 μN kg-1soil-1hr-1and N2 emissions ranging from 4.2 to 43 μN kg-1soil-1hr-1for the different treatments. The main end-product of denitrification was N2O for both water contents with N2 accounting for 9% and 13% of the total denitrification losses at 80% and 100%WFPS, respectively. Between 95-100% of the added 15N fertiliser could be recovered. Gross nitrification over the 3 days amounted to 8.6 μN g-1 soil-1 and 4.7 μN g-1 soil-1, denitrification to 4.1 μN g-1 soil-1 and 11.8 μN g-1 soil-1at 80% and 100%WFPS, respectively. The results confirm that the tested method allows for a direct and highly sensitive detection of N2 and N2O fluxes from soils and hence offers a sensitive tool to study denitrification and N turnover in terrestrial agro-ecosystems.

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In this paper, we detail the development of two stakeholder relationships scales. The scales measure major project managers' perceived competence in developing (establishing and maintaining) high quality, effective relationships with stakeholders who are internal and external to their organization. Our sample consists of 373 major project managers from a sub-set of the Australian defense industry. Both the internal stakeholder relationships scale and the external stakeholder relationships scale demonstrated validity and reliability. This research has implications for the interpersonal work relationships literature and the stakeholder management literature. We recommend that researchers test these scales with multiple samples, across different project types and project industries in the future. The stakeholder relationship scales should be versatile enough to be applied to project management generally but are perhaps best suited to major project environments.

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External morphology is commonly used to identify bats as well as to investigate flight and foraging behavior, typically relying on simple length and area measures or ratios. However, geometric morphometrics is increasingly used in the biological sciences to analyse variation in shape and discriminate among species and populations. Here we compare the ability of traditional versus geometric morphometric methods in discriminating between closely related bat species – in this case European horseshoe bats (Rhinolophidae, Chiroptera) – based on morphology of the wing, body and tail. In addition to comparing morphometric methods, we used geometric morphometrics to detect interspecies differences as shape changes. Geometric morphometrics yielded improved species discrimination relative to traditional methods. The predicted shape for the variation along the between group principal components revealed that the largest differences between species lay in the extent to which the wing reaches in the direction of the head. This strong trend in interspecific shape variation is associated with size, which we interpret as an evolutionary allometry pattern.

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Modern intramedullary nails, which are utilised for the treatment of bone fractures, need to be designed to fit the anatomy of the patient population. Traditional and recent semi-automated approaches for quantifying the anatomical fit between bones and nail designs suffer from various drawbacks. This thesis proposed an automated comprehensive nail design validation method. The developed software tool was utilised to quantify the anatomical fit of four commercial nail designs. Furthermore, the thesis demonstrated the existence of a bone-nail specific nail entry point. The developed method is of great benefit for the implant manufacturing industry as a nail design validation tool.

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Background: Recently there have been efforts to derive safe, efficient processes to rule out acute coronary syndrome (ACS) in emergency department (ED) chest pain patients. We aimed to prospectively validate an ACS assessment pathway (the 2-Hour Accelerated Diagnostic Protocol to Assess Patients with Chest Pain Symptoms Using Contemporary Troponins as the Only Biomarker (ADAPT) pathway) under pragmatic ED working conditions. Methods: This prospective cohort study included patients with atraumatic chest pain in whom ACS was suspected but who did not have clear evidence of ischaemia on ECG. Thrombolysis in myocardial infarction (TIMI) score and troponin (TnI Ultra) were measured at ED presentation, 2 h later and according to current national recommendations. The primary outcome of interest was the occurrence of major adverse cardiac events (MACE) including prevalent myocardial infarction (MI) at 30 days in the group who had a TIMI score of 0 and had presentation and 2-h TnI assays <99th percentile. Results: Eight hundred and forty patients were studied of whom 177 (21%) had a TIMI score of 0. There were no MI, MACE or revascularization in the per protocol and intention-to-treat 2-h troponin groups (0%, 95% confidence interval (CI) 0% to 4.5% and 0%, 95% CI 0% to 3.8%, respectively). The negative predictive value (NPV) was 100% (95% CI 95.5% to 100%) and 100% (95% CI 96.2% to 100%), respectively. Conclusions: A 2-h accelerated rule-out process for ED chest pain patients using electrocardiography, a TIMI score of 0 and a contemporary sensitive troponin assay accurately identifies a group at very low risk of 30-day MI or MACE.

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The mechanical environment around the healing of broken bone is very important as it determines the way the fracture will heal. Over the past decade there has been great clinical interest in improving bone healing by altering the mechanical environment through the fixation stability around the lesion. One constraint of preclinical animal research in this area is the lack of experimental control over the local mechanical environment within a large segmental defect as well as osteotomies as they heal. In this paper we report on the design and use of an external fixator to study the healing of large segmental bone defects or osteotomies. This device not only allows for controlled axial stiffness on the bone lesion as it heals, but it also enables the change of stiffness during the healing process in vivo. The conducted experiments have shown that the fixators were able to maintain a 5 mm femoral defect gap in rats in vivo during unrestricted cage activity for at least 8 weeks. Likewise, we observed no distortion or infections, including pin infections during the entire healing period. These results demonstrate that our newly developed external fixator was able to achieve reproducible and standardized stabilization, and the alteration of the mechanical environment of in vivo rat large bone defects and various size osteotomies. This confirms that the external fixation device is well suited for preclinical research investigations using a rat model in the field of bone regeneration and repair.

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In this paper we explore how small and medium-sized enterprises (SMEs) engage in external knowledge sourcing, a form of inbound open innovation. We draw upon a sample of 1,411 SMEs and empirically conceptualize a typology of strategic types of external knowledge sourcing, namely minimal, supply-chain, technology-oriented, application-oriented, and full-scope sourcing. Each strategy reflects the nature of external interactions and is linked to a distinct mixture of four internal practices for managing innovation. Both full-scope and application-oriented sourcing offer performance benefits and are associated with a stronger focus on managing innovation. However, they differ in their managerial focus on strategic and operational aspects.

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Objectives To externally evaluate the accuracy of the new Vancouver Chest Pain Rule and to assess the diagnostic accuracy using either sensitive or highly sensitive troponin assays. Methods Prospectively collected data from 2 emergency departments (EDs) in Australia and New Zealand were analysed. Based on the new Vancouver Chest Pain Rule, low-risk patients were identified using electrocardiogram results, cardiac history, nitrate use, age, pain characteristics and troponin results at 2 hours after presentation. The primary outcome was 30-day diagnosis of acute coronary syndrome (ACS), including acute myocardial infarction, and unstable angina. Sensitivity, specificity, positive predictive values and negative predictive values were calculated to assess the accuracy of the new Vancouver Chest Pain Rule using either sensitive or highly sensitive troponin assay results. Results Of the 1635 patients, 20.4% had an ACS diagnosis at 30 days. Using the highly sensitive troponin assay, 212 (13.0%) patients were eligible for early discharge with 3 patients (1.4%) diagnosed with ACS. Sensitivity was 99.1% (95% CI 97.4-99.7), specificity was 16.1 (95% CI 14.2-18.2), positive predictive values was 23.3 (95% CI 21.1-25.5) and negative predictive values was 98.6 (95% CI 95.9-99.5). The diagnostic accuracy of the rule was similar using the sensitive troponin assay. Conclusions The new Vancouver Chest Pain Rule should be used for the identification of low risk patients presenting to EDs with symptoms of possible ACS, and will reduce the proportion of patients requiring lengthy assessment; however we recommend further outpatient investigation for coronary artery disease in patients identified as low risk.

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Debates on gene patents have necessitated the analysis of patents that disclose and reference human sequences. In this study, we built an automated classifier that assigns sequences to one of nine predefined categories according to their functional roles in patent claims by applying natural language processing and supervised learning techniques. To improve its correctness, we experimented with various feature mappings, resulting in the maximal accuracy of 79%.