Introduction and Comparison of Novel Decentral Learning Schemes with Multiple Data Pools for Privacy-Preserving ECG Classification

Artificial intelligence and machine learning have led to prominent and spectacular innovations in various scenarios. Application in medicine, however, can be challenging due to privacy concerns and strict legal regulations. Methods that centralize knowledge instead of data could address this issue. In this work, 6 different decentralized machine learning algorithms are applied to 12-lead ECG classification and compared to conventional, centralized machine learning. The results show that state-of-the-art federated learning leads to reasonable losses of classification performance compared to a standard, central model (-0.054 AUROC) while providing a significantly higher level of privacy. A proposed weighted variant of federated learning (-0.049 AUROC) and an ensemble (-0.035 AUROC) outperformed the standard federated learning algorithm. Overall, considering multiple metrics, the novel batch-wise sequential learning scheme performed best (-0.036 AUROC to baseline). Although, the technical aspects of implementing them in a real-world application are to be carefully considered, the described algorithms constitute a way forward towards preserving-preserving AI in medicine

Sehen, was nicht verborgen blieben darf: Der Welt Erbe im Blick von Satelliten

Der Artikel zeigt beispielhaft einige Beiträge der satellitengestützten Fernerkundung zur Dokumentation von Stätten des Weltkulturerbes

Sehen, was nicht verborgen bleiben darf: Der Welt Erbe im Blick von Satelliten

Der Artikel zeigt beispielhaft einige Beiträge der satellitengestützten Fernerkundung zur Dokumentation von Stätten des Weltkulturerbes

Dynamic simulations on the mitochondrial fatty acid Beta-oxidation network

<p>Abstract</p> <p>Background</p> <p>The oxidation of fatty acids in mitochondria plays an important role in energy metabolism and genetic disorders of this pathway may cause metabolic diseases. Enzyme deficiencies can block the metabolism at defined reactions in the mitochondrion and lead to accumulation of specific substrates causing severe clinical manifestations. Ten of the disorders directly affecting mitochondrial fatty acid oxidation have been well-defined, implicating episodic hypoketotic hypoglycemia provoked by catabolic stress, multiple organ failure, muscle weakness, or hypertrophic cardiomyopathy. Additionally, syndromes of severe maternal illness (HELLP syndrome and AFLP) have been associated with pregnancies carrying a fetus affected by fatty acid oxidation deficiencies. However, little is known about fatty acids kinetics, especially during fasting or exercise when the demand for fatty acid oxidation is increased (catabolic stress).</p> <p>Results</p> <p>A computational kinetic network of 64 reactions with 91 compounds and 301 parameters was constructed to study dynamic properties of mitochondrial fatty acid β-oxidation. Various deficiencies of acyl-CoA dehydrogenase were simulated and verified with measured concentrations of indicative metabolites of screened newborns in Middle Europe and South Australia. The simulated accumulation of specific acyl-CoAs according to the investigated enzyme deficiencies are in agreement with experimental data and findings in literature. Investigation of the dynamic properties of the fatty acid β-oxidation reveals that the formation of acetyl-CoA – substrate for energy production – is highly impaired within the first hours of fasting corresponding to the rapid progress to coma within 1–2 hours. LCAD deficiency exhibits the highest accumulation of fatty acids along with marked increase of these substrates during catabolic stress and the lowest production rate of acetyl-CoA. These findings might confirm gestational loss to be the explanation that no human cases of LCAD deficiency have been described.</p> <p>Conclusion</p> <p>In summary, this work provides a detailed kinetic model of mitochondrial metabolism with specific focus on fatty acid β-oxidation to simulate and predict the dynamic response of that metabolic network in the context of human disease. Our findings offer insight into the disease process (e.g. rapid progress to coma) and might confirm new explanations (no human cases of LCAD deficiency), which can hardly be obtained from experimental data alone.</p

Atmospheric methane with SCIAMACHY: Operational Level 2 data analysis and verification

SCIAMACHY is a passive imaging spectrometer mounted on board ESA’s ENVISAT satellite to probe a large number of atmospheric trace gas species, such as methane, and their global distribution and evolution. Methane (CH4) is particularly interesting as it is one of the most abundant greenhouse gas in the Earth atmosphere. To analyze SCIAMACHY methane measurements, we used the DLR BIRRA (Beer InfraRed Retrieval Algorithm) to retrieve nadir methane concentrations from its infrared spectra in channel 6. By integrating the DLR BIRRA code into ESAs operational Level 2 processor, we expanded it to include atmospheric CH4 column measurements. We have therefore performed an extensive test and verification operation. Our tests are based on separate comparisons with existing space and ground-based obtained measurements of methane column density. We present here our strategy for quality check of this first version of a CH4 product. We will further discuss specific geographical areas we used to validate the products

An eHealth System for Pressure Ulcer Risk Assessment Based on Accelerometer and Pressure Data

Pressure ulcers are a common skin disease which is associated with pain, reduced autonomy, social isolation, and reduced quality of life. There are several systems for monitoring of pressure ulcer-related risk factors on the market, but up to now no satisfactory solution is available, especially for people with medium pressure ulcer risk. We present a novel pressure ulcer risk assessment and prevention system, which combines the advantages of accelerometer and pressure sensors for monitoring pressure ulcer risk factors. Sensors are used for detection of repositionings of the person lying on the mattress. Sensor data are sent to a tablet where they are analysed and presented graphically. The system was evaluated in a long-term test at the homes of people of the target group. Results indicate that the system is able to detect movements of persons while lying in bed. Weak correlation in between mobility and Braden pressure ulcer risk was found (correlation factor = 0.31). From our data, long-term trends could be visualized as well as 24 h mobility profiles. Such graphical illustrations might be helpful for caregivers in order to optimize care of people with medium to high pressure ulcer risk

KALIS – An eHealth system for biomedical risk analysis of drugs

Shoshi A, Müller U, Shoshi A, Ogultarhan V, Hofestädt R. KALIS – An eHealth system for biomedical risk analysis of drugs. In: Hayn D, Schreier G, eds. Health Informatics Meets eHealth: Digital Insight – Information-Driven Health &amp; Care. Proceedings of the 11th eHealth2017 Conference. Studies in Health Technology and Informatics. Vol 236. Amsterdam: IOS Press; 2017: 128-135.Background: In Germany, adverse drug reactions and events cause hospitalizations, which lead to numerous thousands of deaths and several million Euros in additional health costs annually. Objectives: Approximately one in two deaths could be avoided by an appropriate system for risk analysis of drugs. Methods: The integration and storage of several data sources from life sciences are an ongoing need to address various questions with respect to drug therapy. A software architecture for data integration was implemented in order to build up a new data warehouse named KALIS-DWH, which includes pharmacological, biomolecular and patient-related data. Results: Based on this comprehensive KALIS-DWH, an eHealth system named KALIS for biomedical risk analysis of drugs was implemented. The task-specific modules of KALIS offer efficient algorithms for analyzing medication and supporting decision-making in drug therapy. Conclusion: KALIS is meant to be a web-based information system for health professionals and researchers. KALIS provides comprehensive knowledge and modules for risk analysis of drugs, which can contribute to minimizing prescribing errors

The CO2Image mission: retrieval studies and performance analysis

The CO2Image satellite mission, led by the German Aerospace Center (DLR), aims to demonstrate the feasibility of quantifying carbon dioxide (CO2) and methane (CH4) emissions from medium-size point sources. Several DLR institutes are currently working on the reliminary design phase (Phase B) of the mission. Here we present a performance analysis based on the current instrument specifications. The Beer InfraRed Retrieval Algorithm (BIRRA), the line-by-line radiative transfer model Py4CAtS (Python for Computational ATmospheric Spectroscopy) and a COSIS (Carbon dioxide Sensing Imaging Spectrometer) instrument model are employed to infer CO2 and CH4 concentrations from synthetic COSIS spectra. We evaluate the instrument's performance and determine if it meets the intended requirements. The study assesses uncertainties in the retrieved concentrations as well as errors in point source emission estimates caused by instrument noise. First results suggest that the detection and quantification limits stated in the mission requirements document are justified. The analysis also demonstrates that retrieval errors tend to increase when the signal-to-noise ratio is low, complicating the distinction between emission sources and background concentrations. Furthermore, we discuss non-instrumental effects and demonstrate that the fit quality significantly improves if a low-level plume is scaled instead of a background reference profile that covers the atmosphere's full vertical extent. The analysis on heterogeneous scenes (high albedo contrast) reveals that the various instrument setups perform similarly for both molecules