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 so­called ‘monstrous operator’, 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 ‘attraction’. 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, Nübold 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 PPARγ for this phenotype switch into consideration, it remains elusive how AC activate PPARγ 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 μg/ml staurosporine. Necrotic cells (NC) were prepared by heating cells for 20 minutes to 65°C. PPARγ activation was followed by stably transducing RAW264.7 macrophages with a vector encoding the red fluorescent protein mRuby after PPARγ binding to 4 × PPRE sites downstream of the reporter gene sequence. This readout was established by treatment with the PPARγ agonist rosiglitazone (1 μM) 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 μM) and overexpression. Results: Assuming that the molecule responsible for PPARγ 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 PPARγ in macrophages, Jurkat T cells were incubated for 30 minutes with the 5-LO inhibitor MK-866 (1 μM) 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 PPARγ 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 PPARγ activation in macrophages. Therefore, it will be challenging to determine whether 5-LO activity in AC, generated from other cell types, correlates with PPARγ activation, contributing to an immune-suppressed phenotype in macrophages

Exodex Adam—A 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 “feel” 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 user’s workspace. Exodex Adam uses a front-facing interface, with robotic fingers coupled to two of the user’s 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 “whole-hand” 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 hand–arm teleoperation using adaptive model-mediated teleoperation. Finally, we share the insights gained during our development process and use case deployments