274 research outputs found
SENSITIVITY TO SCOPE: EVIDENCE FROM A CVM STUDY OF WETLANDS
Wetlands valuation is a situation in which CVM studies might be expected to fail scope tests. This paper reports results from a split-sample CVM study of Wisconsin wetlands. The survey employed a multiple-bounded, polychotomous-choice format, and compared WTP distributions using the method of convolutions. The survey demonstrated sensitivity to scope.Resource /Energy Economics and Policy,
Interlacing in atomic resolution scanning transmission electron microscopy
Fast frame-rates are desirable in scanning transmission electron microscopy
for a number of reasons: controlling electron beam dose, capturing in-situ
events or reducing the appearance of scan distortions. Whilst several
strategies exist for increasing frame-rates, many impact image quality or
require investment in advanced scan hardware. Here we present an interlaced
imaging approach to achieve minimal loss of image quality with faster
frame-rates that can be implemented on many existing scan controllers. We
further demonstrate that our interlacing approach provides the best possible
strain precision for a given electron dose compared with other contemporary
approaches
How Fast is Your Detector? The Effect of Temporal Response on Image Quality
With increasing interest in high-speed imaging should come an increased
interest in the response times of our scanning transmission electron microscope
(STEM) detectors. Previous works have previously highlighted and contrasted
performance of various detectors for quantitative compositional or structural
studies, but here we shift the focus to detector temporal response, and the
effect this has on captured images. The rise and decay times of eight
detectors' single electron response are reported, as well as measurements of
their flatness, roundness, smoothness, and ellipticity. We develop and apply a
methodology for incorporating the temporal detector response into simulations,
showing that a loss of resolution is apparent in both the images and their
Fourier transforms. We conclude that the solid-state detector outperforms the
photomultiplier-tube (PMT) based detectors in all areas bar a slightly less
elliptical central hole and is likely the best detector to use for the majority
of applications. However, using tools introduced here we encourage users to
effectively evaluate what detector is most suitable for their experimental
needs
Real-world post-deployment performance of a novel machine learning-based digital health technology for skin lesion assessment and suggestions for post-market surveillance
This is the final version. Available on open access from Frontiers Media via the DOI in this recordData availability statement:
The datasets presented in this article are not readily available because the data included in this manuscript have been collected as part of the routine post-market surveillance programme for DERM, conducted by Skin Analytics, London. Requests to access the datasets should be directed to DK, [email protected] and DM, [email protected]: Deep Ensemble for Recognition of Malignancy (DERM) is an artificial intelligence as a medical device (AIaMD) tool for skin lesion assessment. METHODS: We report prospective real-world performance from its deployment within skin cancer pathways at two National Health Service hospitals (UK) between July 2021 and October 2022. RESULTS: A total of 14,500 cases were seen, including patients 18-100 years old with Fitzpatrick skin types I-VI represented. Based on 8,571 lesions assessed by DERM with confirmed outcomes, versions A and B demonstrated very high sensitivity for detecting melanoma (95.0-100.0%) or malignancy (96.0-100.0%). Benign lesion specificity was 40.7-49.4% (DERM-vA) and 70.1-73.4% (DERM-vB). DERM identified 15.0-31.0% of cases as eligible for discharge. DISCUSSION: We show DERM performance in-line with sensitivity targets and pre-marketing authorisation research, and it reduced the caseload for hospital specialists in two pathways. Based on our experience we offer suggestions on key elements of post-market surveillance for AIaMDs
Response: Commentary: Real-world post-deployment performance of a novel machine learning-based digital health technology for skin lesion assessment and suggestions for post-market surveillance
This is the final version. Available on open access from Frontiers Media via the DOI in this recordSkin Analytic
Lifeworld Inc. : and what to do about it
Can we detect changes in the way that the world turns up as they turn up? This paper makes such an attempt. The first part of the paper argues that a wide-ranging change is occurring in the ontological preconditions of Euro-American cultures, based in reworking what and how an event is produced. Driven by the security – entertainment complex, the aim is to mass produce phenomenological encounter: Lifeworld Inc as I call it. Swimming in a sea of data, such an aim requires the construction of just enough authenticity over and over again. In the second part of the paper, I go on to argue that this new world requires a different kind of social science, one that is experimental in its orientation—just as Lifeworld Inc is—but with a mission to provoke awareness in untoward ways in order to produce new means of association. Only thus, or so I argue, can social science add to the world we are now beginning to live in
Preliminary Assessment of the Efficacy of a T-Cell–Based Influenza Vaccine, MVA-NP+M1, in Humans
A single vaccination with MVA-NP+M1 boosts T-cell responses to conserved influenza antigens in humans. Protection against influenza disease and virus shedding was demonstrated in an influenza virus challenge study
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