978 research outputs found
To shift or not to shift:Quotation and attraction in DGS
There are two main competing views about the nature of sign language role shift within formal semantics today: Quer (2005) and Schlenker (2017a,b), following now standard analyses of indexical shift in spoken languages, analyze it as a soĀcalled āmonstrous operatorā, while K. Davidson (2015) and Maier (2017), following more traditional and cognitive approaches, analyze it as form of quotation. Examples of role shift in which some indexicals are shifted and some unshifted pose a prima facie problem for both approaches. We show that the quotational approach can deal with these examples in terms of unquotation and a pragmatic principle of āattractionā. We present a systematic empirical investigation of the predictions of the quotation/attraction approach in DGS (German Sign Language). Results for the second person pronoun, IX2, fully support the attraction hypothesis, while results for IX1 and HERE are inconclusive
Challenge Results Are Not Reproducible
While clinical trials are the state-of-the-art methods to assess the effect
of new medication in a comparative manner, benchmarking in the field of medical
image analysis is performed by so-called challenges. Recently, comprehensive
analysis of multiple biomedical image analysis challenges revealed large
discrepancies between the impact of challenges and quality control of the
design and reporting standard. This work aims to follow up on these results and
attempts to address the specific question of the reproducibility of the
participants methods. In an effort to determine whether alternative
interpretations of the method description may change the challenge ranking, we
reproduced the algorithms submitted to the 2019 Robust Medical Image
Segmentation Challenge (ROBUST-MIS). The leaderboard differed substantially
between the original challenge and reimplementation, indicating that challenge
rankings may not be sufficiently reproducible.Comment: Accepted at BVM 202
Validation of a German version of the Ethical Leadership at Work questionnaire (ELW) by Kalshoven et al. (2011)
Steinmann B, NĆ¼bold A, Maier GW. Validation of a German version of the Ethical Leadership at Work questionnaire (ELW) by Kalshoven et al. (2011). Frontiers in Psychology. 2016;7: 446.The present study evaluates the psychometric properties of a German version of the Ethical Leadership at Work questionnaire (ELW-D), and further embeds the construct of ethical leadership within its nomological network. Confirmatory factor analyses (CFAs) based on the total sample of N = 363 employees support the assumed seven-factor structure of the German translation. Within a sub-sample of N = 133, the ELW-D shows positive correlations with related leadership behaviors (transformational leadership, contingent reward, and servant leadership), and negative correlations with destructive ones (passive leadership, autocratic leadership, and abusive supervision), approving convergent validity of the scale. Comparisons of correlated correlation coefficients reveal restrictions of its discriminant validity. In support of the criterion-related validity (N = 100), the ELW-D relates to work-related attitudes (e.g., job satisfaction, satisfaction with the leader, trust in the leader) and follower behaviors (e.g., extra effort, organizational citizenship behavior) in the way expected. Besides, ELW-D-dimensions show incremental validity over and above the Ethical Leadership Scale, emphasizing the added value of this questionnaire
Deployment of Image Analysis Algorithms under Prevalence Shifts
Domain gaps are among the most relevant roadblocks in the clinical
translation of machine learning (ML)-based solutions for medical image
analysis. While current research focuses on new training paradigms and network
architectures, little attention is given to the specific effect of prevalence
shifts on an algorithm deployed in practice. Such discrepancies between class
frequencies in the data used for a method's development/validation and that in
its deployment environment(s) are of great importance, for example in the
context of artificial intelligence (AI) democratization, as disease prevalences
may vary widely across time and location. Our contribution is twofold. First,
we empirically demonstrate the potentially severe consequences of missing
prevalence handling by analyzing (i) the extent of miscalibration, (ii) the
deviation of the decision threshold from the optimum, and (iii) the ability of
validation metrics to reflect neural network performance on the deployment
population as a function of the discrepancy between development and deployment
prevalence. Second, we propose a workflow for prevalence-aware image
classification that uses estimated deployment prevalences to adjust a trained
classifier to a new environment, without requiring additional annotated
deployment data. Comprehensive experiments based on a diverse set of 30 medical
classification tasks showcase the benefit of the proposed workflow in
generating better classifier decisions and more reliable performance estimates
compared to current practice
Ten years of image analysis and machine learning competitions in dementia
Machine learning methods exploiting multi-parametric biomarkers, especially
based on neuroimaging, have huge potential to improve early diagnosis of
dementia and to predict which individuals are at-risk of developing dementia.
To benchmark algorithms in the field of machine learning and neuroimaging in
dementia and assess their potential for use in clinical practice and clinical
trials, seven grand challenges have been organized in the last decade.
The seven grand challenges addressed questions related to screening, clinical
status estimation, prediction and monitoring in (pre-clinical) dementia. There
was little overlap in clinical questions, tasks and performance metrics.
Whereas this aids providing insight on a broad range of questions, it also
limits the validation of results across challenges. The validation process
itself was mostly comparable between challenges, using similar methods for
ensuring objective comparison, uncertainty estimation and statistical testing.
In general, winning algorithms performed rigorous data preprocessing and
combined a wide range of input features.
Despite high state-of-the-art performances, most of the methods evaluated by
the challenges are not clinically used. To increase impact, future challenges
could pay more attention to statistical analysis of which factors relate to
higher performance, to clinical questions beyond Alzheimer's disease, and to
using testing data beyond the Alzheimer's Disease Neuroimaging Initiative.
Grand challenges would be an ideal venue for assessing the generalizability of
algorithm performance to unseen data of other cohorts. Key for increasing
impact in this way are larger testing data sizes, which could be reached by
sharing algorithms rather than data to exploit data that cannot be shared.Comment: 12 pages, 4 table
5-Lipoxygenase contributes to PPAR [gamma] activation in macrophages in response to apoptotic cells
Background: One hallmark contributing to immune suppression during the late phase of sepsis is macrophage polarization to an anti-inflammatory phenotype upon contact with apoptotic cells (AC). Taking the important role of the nuclear receptor PPARĪ³ for this phenotype switch into consideration, it remains elusive how AC activate PPARĪ³ in macrophages. Therefore, we were interested to characterize the underlying principle.
Methods: Apoptosis was induced by treatment of Jurkat T cells for 3 hours with 0.5 Ī¼g/ml staurosporine. Necrotic cells (NC) were prepared by heating cells for 20 minutes to 65Ā°C. PPARĪ³ activation was followed by stably transducing RAW264.7 macrophages with a vector encoding the red fluorescent protein mRuby after PPARĪ³ binding to 4 Ć PPRE sites downstream of the reporter gene sequence. This readout was established by treatment with the PPARĪ³ agonist rosiglitazone (1 Ī¼M) and AC (5:1). Twenty-four hours after stimulation, mRuby expression was analysed by fluorescence microscopy. Lipid rafts of AC, NC, as well as living cells (LC) were enriched by sucrose gradient centrifugation. Fractions were analysed for lipid raft-associated marker proteins. Lipid rafts were incubated with transduced RAW264.7 macrophages as described above. 5-Lipoxygenase (5-LO) involvement was verified by pharmacological inhibition (MK-866, 1 Ī¼M) and overexpression.
Results: Assuming that the molecule responsible for PPARĪ³ activation in macrophages is localized in the cell membrane of AC, most probably associated to lipid rafts, we isolated lipid rafts from AC, NC and LC. Mass spectrometric analysis of lipid rafts of AC showed the expression of 5-LO, whereas lipid rafts of LC did not. Moreover, incubating macrophages with lipid rafts of AC induced mRuby expression. In contrast, lipid rafts of NC and LC did not. To verify the involvement of 5-LO in activating PPARĪ³ in macrophages, Jurkat T cells were incubated for 30 minutes with the 5-LO inhibitor MK-866 (1 Ī¼M) before apoptosis induction. In line with our hypothesis, these AC did not induce mRuby expression. Finally, although living Jurkat T cells overexpressing 5-LO did not activate PPARĪ³ in macrophages, mRuby expression was significantly increased when AC were generated from 5-LO overexpressing compared with wild-type Jurkat cells.
Conclusion: Our results suggest that induction of apoptosis activates 5-LO, localizing to lipid rafts, necessary for PPARĪ³ activation in macrophages. Therefore, it will be challenging to determine whether 5-LO activity in AC, generated from other cell types, correlates with PPARĪ³ activation, contributing to an immune-suppressed phenotype in macrophages
Exodex AdamāA Reconfigurable Dexterous Haptic User Interface for the Whole Hand
Applications for dexterous robot teleoperation and immersive virtual reality are growing. Haptic user input devices need to allow the user to intuitively command and seamlessly āfeelā the environment they work in, whether virtual or a remote site through an avatar. We introduce the DLR Exodex Adam, a reconfigurable, dexterous, whole-hand haptic input device. The device comprises multiple modular, three degrees of freedom (3-DOF) robotic fingers, whose placement on the device can be adjusted to optimize manipulability for different user hand sizes. Additionally, the device is mounted on a 7-DOF robot arm to increase the userās workspace. Exodex Adam uses a front-facing interface, with robotic fingers coupled to two of the userās fingertips, the thumb, and two points on the palm. Including the palm, as opposed to only the fingertips as is common in existing devices, enables accurate tracking of the whole hand without additional sensors such as a data glove or motion capture. By providing āwhole-handā interaction with omnidirectional force-feedback at the attachment points, we enable the user to experience the environment with the complete hand instead of only the fingertips, thus realizing deeper immersion. Interaction using Exodex Adam can range from palpation of objects and surfaces to manipulation using both power and precision grasps, all while receiving haptic feedback. This article details the concept and design of the Exodex Adam, as well as use cases where it is deployed with different command modalities. These include mixed-media interaction in a virtual environment, gesture-based telemanipulation, and robotic handāarm teleoperation using adaptive model-mediated teleoperation. Finally, we share the insights gained during our development process and use case deployments
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