Editorial: Cure or curse? Compliance in digital healthcare

The edition features first-rate articles by specialists in the field of healthcare and data security. Apart from that we will face some classical compliance topics and last but not least CEJ Founder Michele DeStefanos new book Legal Upheaval will be introduced and reviewed

Physiological constrains on Sverdrup's Critical-Depth-Hypothesis: the influences of dark respiration and sinking

Discussions on the controls initiating the onset of the phytoplankton spring bloom in particular in the North Atlantic have since Sverdrup been dominated by the role of physical and biological drivers. Undoubtedly, these drivers play an important role in phytoplankton dynamics and thus the onset of the spring bloom. However, they neglect the cells ability to modify vital rates in response to changes in the external environment. In this study, we use a non-hydrostatic convection model coupled to an Individual-Based-Model to simulate changes phytoplankton cells during the transition from winter conditions as driven by convective mixing, and the onset of thermal stratification resulting in the spring bloom. The comparison between a simulation using a standard fixed rate approach in line with the original Sverdrup hypothesis and a simulation parameterized to include variable respiration and sinking rates showed that the latter approach was able to capture the observed phytoplankton concentration during deep convective mixing, the timing and magnitude of the spring bloom as well as simulating realistic physiological rates. In contrast, the model employing fixed rate parameterizations could only replicate field observations when employing unrealistic parameter values. These results highlight the necessity to consider not only the physical and biological external controls determining phytoplankton dynamics but also the cells ability to modify critical physiological rates in response to external constraints. Understanding these adaptive qualities will be of increasing importance in the future as species assemblages and physical controls change with changing climate

Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphones

The emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. In this work, deep convolutional neural networks (DCNN) applied to smartphone inertial sensor data were shown to better distinguish healthy from MS participant ambulation, compared to standard Support Vector Machine (SVM) feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework utilised the ambulatory information learned on Human Activity Recognition (HAR) tasks collected from similar smartphone-based sensor data. A lack of transparency of "black-box" deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus persons with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions

Loop-Mediated Isothermal Amplification: An Advanced Method for the Detection of Giardia

This chapter provides a reliable and quick method for detection of Giardia duodenalis (which causes a dangerous diarrheal disease), prevention of further spreading, identification of the source of contamination, and eventually minimize health risk and economic damage normally caused by an outbreak. The loop-mediated isothermal amplification (LAMP) method is based on the enrichment of parasite-specific nucleotide sequences, similar to PCR, but it is significantly faster and less susceptible to interference. Here, we give an overview of how we developed this method, and using the example of G. duodenalis as a water-associated pathogen, we present an optimized examination scheme for its detection in water. For this purpose, we have analyzed data from extensive electronic libraries PubMed®/MEDLINE®, filtered out relevant articles with a keyword search, and summarized them. The number of publications on LAMP method has generally increased steadily since its first report in 2000. LAMP, used for detection of Giardia, especially surpasses all other methods due to the high specificity, sensitivity, robustness, and cost effectiveness. The ever-increasing number of publications on application of LAMP is similar to the development of PCR in the 1990s of the last century. Certainly, the method will be further developed in future, but it already offers many advantages over other methods for effective detection of G. duodenalis infections and will therefore certainly gain in popularity

Mechanisms for Correcting Judicial Errors in Germany

The article presents the status quo of the law of retrial in Germany and gives an overview of the law and practice of the latter in favour of the convicted and to the disadvantage of the defendant. Particularly, the formal and material prerequisites for a successful petition to retry th