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

Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

Revealing latent structure in data is an active field of research, having brought exciting new models such as variational autoencoders and generative adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery. However, a major challenge is the lack of suitable benchmarks for an objective and quantitative evaluation of learned representations. To address this issue we introduce Morpho-MNIST. We extend the popular MNIST dataset by adding a morphometric analysis enabling quantitative comparison of different models, identification of the roles of latent variables, and characterisation of sample diversity. We further propose a set of quantifiable perturbations to assess the performance of unsupervised and supervised methods on challenging tasks such as outlier detection and domain adaptation

Spin-sensitive Bleaching and Spin-Relaxation in QW's

Spin-sensitive saturation of absorption of infrared radiation has been investigated in p-type GaAs QWs. It is shown that the absorption saturation of circularly polarized radiation is mostly controlled by the spin relaxation time of the holes. The saturation behavior has been investigated for different QW widths and in dependence on the temperature with the result that the saturation intensity substantially decreases with narrowing of QWs. Spin relaxation times were experimentally obtained by making use of calculated (linear) absorption coefficients for inter-subband transitions

Developing and validating a new scale to measure the acceptability of health apps among adolescents

Background The acceptability of health interventions is centrally important to achieving their desired health outcomes. The construct of acceptability of mobile health interventions among adolescents is neither well-defined nor consistently operationalized. Objectives Building on the theoretical framework of acceptability, these two studies developed and assessed the reliability and validity of a new scale to measure the acceptability of mobile health applications (“apps”) among adolescents. Methods We followed a structured scale development process including exploratory factor analyses (EFAs), confirmatory factor analyses (CFAs), and employed structural equation modeling (SEM) to assess the relationship between the scale and app usage. Adolescent participants used the Fooducate healthy eating app and completed the acceptability scale at baseline and one-week follow-up. Results EFA (n = 182) determined that the acceptability of health apps was a multidimensional construct with six latent factors: affective attitude, burden, ethicality, intervention coherence, perceived effectiveness, and self-efficacy. CFA (n = 161) from the second sample affirmed the six-factor structure and the unidimensional structures for each of the six subscales. However, CFA did not confirm the higher-order latent factor model suggesting that the six subscales reflect unique aspects of acceptability. SEM indicated that two of the subscales—ethicality and self-efficacy—were predictive of health app usage at one-week follow-up. Conclusions These results highlight the importance of ethicality and self-efficacy for health app acceptability. Future research testing and adapting this new acceptability scale will enhance measurement tools in the fields of mobile health and adolescent health

Spin relaxation times of 2D holes from spin sensitive bleaching of inter-subband absorption

We present spin relaxation times of 2D holes obtained by means of spin sensitive bleaching of the absorption of infrared radiation in p-type GaAs/AlGaAs quantum wells (QWs). It is shown that the saturation of inter-subband absorption of circularly polarized radiation is mainly controlled by the spin relaxation time of the holes. The saturation behavior has been determined for different QW widths and in a wide temperature range with the result that the saturation intensity substantially decreases with narrowing of the QWs. Spin relaxation times are derived from the measured saturation intensities by making use of calculated (linear) absorption coefficients for direct inter-subband transitions. It is shown that spin relaxation is due to the D'yakonov-Perel' mechanism governed by hole-hole scattering. The problem of selection rules is addressed.Comment: 14 pages, 5 figure

Using advanced quantitative methods to study the prevention of social problems

Social work has embraced prevention as one of its grand challenges-recognising the need to understand risk and protective factors for social problems that, if addressed, may prevent social disadvantage and mental health problems from occurring. To best study prevention, social workers must become fluent in understanding and using advanced methodologies that illuminate developmental processes, depict individual and subgroup differences, and rule out potential confounds so as to shed light on important risk and protective factors. The purpose of this article is to provide a simple introduction to four advanced methods: latent growth curves (LGM), mediation models, latent class/profile models and propensity score models. Latent growth curve models are helpful for understanding changes in the developmental course of a risk factor over time. Mediation models are useful tools for understanding how risk and protective factors may affect outcomes. Latent class and latent profile models allow researchers to understand how combinations of risk factors may be linked to youth outcomes. Propensity score models allow researchers to reduce the effects of selection bias on their estimates of the relationships between risk factors and outcomes. We discuss the research questions appropriate for each type of model, the type of data required, and the strengths and weaknesses of each approach. We also include suggestions for further reading

Learning normal appearance for fetal anomaly screening: application to the unsupervised detection of Hypoplastic Left Heart Syndrome

Congenital heart disease is considered as one the most common groups of congenital malformations which affects 6 − 11 per 1000 newborns. In this work, an automated framework for detection of cardiac anomalies during ultrasound screening is proposed and evaluated on the example of Hypoplastic Left Heart Syndrome (HLHS), a sub-category of congenital heart disease. We propose an unsupervised approach that learns healthy anatomy exclusively from clinically confirmed normal control patients. We evaluate a number of known anomaly detection frameworks together with a new model architecture based on the α-GAN network and find evidence that the proposed model performs significantly better than the state-of-the-art in image-based anomaly detection, yielding average 0.81 AUC and a better robustness towards initialisation compared to previous works

Tracing the fate of microplastic carbon in the aquatic food web by compound-specific isotope analysis

Increasing abundance of microplastics (MP) in marine and freshwaters is currently one of the greatest environmental concerns. Since plastics are fairly resistant to chemical decomposition, breakdown and reutilization of MP carbon complexes requires microbial activity. Currently, only a few microbial isolates have been shown to degrade MPs, and direct measurements of the fate of the MP carbon are still lacking. We used compound-specific isotope analysis to track the fate of fully labelled 13C-polyethylene (PE) MP carbon across the aquatic microbial-animal interface. Isotopic values of respired CO2 and membrane lipids showed that MP carbon was partly mineralized and partly used for cell growth. Microbial mineralization and assimilation of PE-MP carbon was most active when inoculated microbes were obtained from highly humic waters, which contain recalcitrant substrate sources. Mixotrophic algae (Cryptomonas sp.) and herbivorous zooplankton (Daphnia magna) used microbial mediated PE-MP carbon in their cell membrane fatty acids. Moreover, heteronanoflagellates and mixotrophic algae sequestered MP carbon for synthesizing essential ω-6 and ω-3 polyunsaturated fatty acids. Thus, this study demonstrates that aquatic micro-organisms can produce, biochemically upgrade, and trophically transfer nutritionally important biomolecules from PE-MP.Increasing abundance of microplastics (MP) in marine and freshwaters is currently one of the greatest environmental concerns. Since plastics are fairly resistant to chemical decomposition, breakdown and reutilization of MP carbon complexes requires microbial activity. Currently, only a few microbial isolates have been shown to degrade MPs, and direct measurements of the fate of the MP carbon are still lacking. We used compound-specific isotope analysis to track the fate of fully labelled C-13-polyethylene (PE) MP carbon across the aquatic microbial-animal interface. Isotopic values of respired CO2 and membrane lipids showed that MP carbon was partly mineralized and partly used for cell growth. Microbial mineralization and assimilation of PE-MP carbon was most active when inoculated microbes were obtained from highly humic waters, which contain recalcitrant substrate sources. Mixotrophic algae (Cryptomonas sp.) and herbivorous zooplankton (Daphnia magna) used microbial mediated PE-MP carbon in their cell membrane fatty acids. Moreover, heteronanoflagellates and mixotrophic algae sequestered MP carbon for synthesizing essential omega-6 and omega-3 polyunsaturated fatty acids. Thus, this study demonstrates that aquatic micro-organisms can produce, biochemically upgrade, and trophically transfer nutritionally important biomolecules from PE-MP.Peer reviewe