Positioning Social Work Researchers for Engaged Scholarship to Promote Public Impact

The concept of engaged scholarship has garnered significant attention across numerous scientific disciplines. Engaged scholarship can be conceptualized as both a method centered on cocreating and applying new knowledge and a movement focused on prioritizing community identification of needs and social problem-solving strategies. In an effort to position social work researchers for engaged scholarship to promote public impact, we provide an overview of the following engaged-scholarship mechanisms: (a) community-based participatory research, (b) participatory action research, (c) practice-based research networks, (d) translational research, (e) transdisciplinary scientific collaborations, (f) systemic evaluation, and (g) developmental evaluation. We address the contextual factors that may influence the extent to which social work researchers can successfully pursue engaged scholarship and conclude by explicating a plausible relationship between engaged scholarship and public impact scholarship. Specifically, we apply the diffusion of innovations model and community dissonance theory to conceptually position engaged scholarship as a vehicle for promoting and optimizing public impact scholarship

Wirkungen des Ökologischen Landbaus auf Bodenerosion durch Wasser

Soil erosion is still one of the major problems in relation to soil protection and it is necessary to have tools for assessment of soil losses. Changes of the farm structure like building a biogas power plant or changing the cropping system may affect soil losses. Before implementing those changes knowledge is necessary. The implemen-tation and application of an adequate tool is shown on the research farm Scheyern. The results of this model were compared with measured values for soil losses to demonstrate the applicability. Beside this attention is invited to conditions of ecological farming which have an impact on soil erosion

Telomere length measurement by a novel monochrome multiplex quantitative PCR method

The current quantitative polymerase chain reaction (QPCR) assay of telomere length measures telomere (T) signals in experimental DNA samples in one set of reaction wells, and single copy gene (S) signals in separate wells, in comparison to a reference DNA, to yield relative T/S ratios that are proportional to average telomere length. Multiplexing this assay is desirable, because variation in the amount of DNA pipetted would no longer contribute to variation in T/S, since T and S would be collected within each reaction, from the same input DNA. Multiplexing also increases throughput and lowers costs, since half as many reactions are needed. Here, we present the first multiplexed QPCR method for telomere length measurement. Remarkably, a single fluorescent DNA-intercalating dye is sufficient in this system, because T signals can be collected in early cycles, before S signals rise above baseline, and S signals can be collected at a temperature that fully melts the telomere product, sending its signal to baseline. The correlation of T/S ratios with Terminal Restriction Fragment (TRF) lengths measured by Southern blot was stronger with this monochrome multiplex QPCR method (R2 = 0.844) than with our original singleplex method (R2 = 0.677). Multiplex T/S results from independent runs on different days were highly reproducible (R2 = 0.91)

Learning under Distributed Weak Supervision

The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research

Electromagnetic Compatibility Testing of Implantable Neurostimulators Exposed to Metal Detectors

This paper presents results of electromagnetic compatibility (EMC) testing of three implantable neurostimulators exposed to the magnetic fields emitted from several walk-through and hand-held metal detectors. The motivation behind this testing comes from numerous adverse event reports involving active implantable medical devices (AIMDs) and security systems that have been received by the Food and Drug Administration (FDA). EMC testing was performed using three neurostimulators exposed to the emissions from 12 walk-through metal detectors (WTMDs) and 32 hand-held metal detectors (HHMDs). Emission measurements were performed on all HHMDs and WTMDs and summary data is presented. Results from the EMC testing indicate possible electromagnetic interference (EMI) between one of the neurostimulators and one WTMD and indicate that EMI between the three neurostimulators and HHMDs is unlikely. The results suggest that worst case situations for EMC testing are hard to predict and testing all major medical device modes and setting parameters are necessary to understand and characterize the EMC of AIMDs

Estimating Categorical Counterfactuals via Deep Twin Networks

Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. To perform counterfactual inference, one requires knowledge of the underlying causal mechanisms. However, causal mechanisms cannot be uniquely determined from observations and interventions alone. This raises the question of how to choose the causal mechanisms so that resulting counterfactual inference is trustworthy in a given domain. This question has been addressed in causal models with binary variables, but the case of categorical variables remains unanswered. We address this challenge by introducing for causal models with categorical variables the notion of counterfactual ordering, a principle that posits desirable properties causal mechanisms should posses, and prove that it is equivalent to specific functional constraints on the causal mechanisms. To learn causal mechanisms satisfying these constraints, and perform counterfactual inference with them, we introduce deep twin networks. These are deep neural networks that, when trained, are capable of twin network counterfactual inference -- an alternative to the abduction, action, & prediction method. We empirically test our approach on diverse real-world and semi-synthetic data from medicine, epidemiology, and finance, reporting accurate estimation of counterfactual probabilities while demonstrating the issues that arise with counterfactual reasoning when counterfactual ordering is not enforced