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In the present paper, we introduce the Smart Energy System Control Laboratory (SESCL) as a fully-automated and user-oriented research infrastructure for controlling and operating smart energy systems in the context of a microgrid-under-test setting. SESCL’s high level of automation and capacity to fully function in a grid-decoupled way allow for the study and evaluation of yet-to-be-developed tools and algorithms for energy technologies and grid control strategies on the edge of system stability, but in a safe environment. In the context of various European Smart Grid Laboratories, the new concept and specifications of SESCL are outlined in depth. The key advantages of SESCL are highlighted as (i) the provisioning of a fully-automated busbar matrix to provide a very flexible and adjustable microgrid topology; (ii) the capability of load shedding or integration of grid participants, as well as changing the microgrid topology on demand; (iii) and the possibility to control and modify setpoints and operating parameters of grid participants during runtime. Inspired by real-world events in island grids, the islanding of a microgrid is utilized as a use case to illustrate the capabilities of the SESCL research infrastructure.

Touch-response experimentation in zebrafish helps researchers better understand the link between genetics, drug effects, and behaviors. However, commonly manually conducted experimentation cannot fulfill a high-throughput screening and often delivers low accuracy and lacks reproducibility. Thus, the main aim of this work is to establish a fully automated robot-assisted experimentation system with minimal human participation to conduct the touch-response experimentation with freely swimming zebrafish larvae. Our designed system is able to undertake the role of repeated touch-response experiments at predefined specific location of the larvae in different ages and under different conditions, with high accuracy, robustness, and repeatability, and can also get comparable experimental results. The errors of the detection methods are less than 3 pixels and the offset errors of the touching points are less than 5%. Designed for high-efficiency experimentation, this system will promisingly release a great amount of the burden for the biological operators from touch-response experiments and may also have potential applications in other organisms for touch-evoked response analysis.

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.

The opportunistic yeastCandida albicansis the most common cause of candidiasis. With only four classes of antifungal drugs on the market, resistance is becoming a problem in the treatment of fungal infections, especially in immunocompromised patients. The development of novel antifungal drugs with different modes of action is urgent. In 2016, we developed a groundbreaking new medium-throughput method to distinguish the effects of antibacterial agents. Using small-angle X-ray scattering for biological samples (BioSAXS), it is now possible to screen hundreds of new antibacterial compounds and select those with the highest probability for a novel mode of action. However, yeast (eukaryotic) cells are highly structured compared to bacteria and the action of an antifungal drug might leave most structures unchanged. In the pioneering work described here, we explored the possibility if BioSAXS can be used to measure the ultrastructural changes ofCandida albicansdirectly or indirectly induced by antifungal compounds. For this exploratory study, we used the well-characterized antifungal drug flucytosine. BioSAXS measurements were performed on the synchrotron P12 BioSAXS beamline, EMBL (DESY, Hamburg) on treated and untreated yeastC. albicans. BioSAXS curves were analysed using principal component analysis (PCA). The PCA showed that flucytosine-treated and untreated yeast were separated. Based on that success further measurements were performed on five antifungal peptides (1. Cecropin A-melittin hybrid [CA(1-7)M(2-9)], KWKLFKKIGAVLKVL; 2. Lasioglossin LL-III, VNWKKILGKIIKVVK; 3. Mastoparan M, INLKAIAALAKKLL; 4. Bmkn2, FIGAIARLLSKIFGKR; and 5. optP7, KRRVRWIIW). The ultrastructural changes ofC. albicansindicate that the peptides may have different modes of action compared to Flucytosine as well as to each other, except for the Cecropin A-melittin hybrid [CA(1-7)M(2-9)] and optP7, showing very similar effects onCandida albicans. This very first study demonstrates that BioSAXS shows great promise to be used for antifungal drug development. The use of such a tool like BioSAXS could accelerate and de-risk antifungal drug development, however, further experiments are necessary to establish this application.

As Electric Vehicle (EV) demand increases, so does the demand for efficient Smart Charging (SC) applications. How- ever, SC is only acceptable if the EV user’s mobility requirements and risk preferences are fulfilled, i.e. their respective EV has enough charge to make their planned journey. To fulfill these requirements and risk preferences, the SC application must consider the predicted parking duration at a given location and the uncertainty associated with this prediction. However, certain regions of uncertainty are more critical than others for user- centric SC applications, and therefore, such uncertainty must be explicitly quantified. Therefore, the present paper presents multiple approaches to customize the uncertainty quantification of parking duration predictions specifically for EV user-centric SC applications. We decompose parking duration prediction errors into a critical component which results in undercharging, and a non-critical component. Furthermore, we derive quantile- based security levels that can minimize the probability of a critical error given a user’s risk preferences. We evaluate our customized uncertainty quantification with four different proba- bilistic prediction models on an openly available semi-synthetic mobility data set and a data set consisting of real EV trips. We show that our customized uncertainty quantification can regulate critical errors, even in challenging real-world data with high fluctuation and uncertainty

BackgroundCerebral organoids simulate the structure and function of the developing human brainin vitro, offering a large potential for personalized therapeutic strategies. The enormous growth of this research area over the past decade with its capability for clinical translation makes a non-invasive, automated analysis pipeline of organoids highly desirable.PurposeThis work presents the first application of magnetic resonance imaging (MRI) for the non-invasive quantification and quality assessment of cerebral organoids using an automated analysis tool. Three specific objectives are addressed, namely organoid segmentation to investigate organoid development over time, global cysticity classification, and local cyst segmentation.MethodsNine wildtype cerebral organoids were imaged over nine weeks using high-field 9.4T MRI including a 3D T2*-w and 2D diffusion tensor imaging (DTI) sequence. This dataset was used to train a deep learning-based 3D U-Net for organoid and local cyst segmentation. For global cysticity classification, we developed a new metric,compactness, to separate low- and high-quality organoids.ResultsThe 3D U-Net achieved a Dice score of 0.92±0.06 (mean ± SD) for organoid segmentation in the T2*-w sequence. For global cysticity classification,compactnessseparated low- and high-quality organoids with high accuracy (ROC AUC 0.98). DTI showed that low-quality organoids have a significantly higher diffusion than high-quality organoids (p < .001). For local cyst segmentation in T2*-w, the 3D U-Net achieved a Dice score of 0.63±0.15 (mean ± SD).ConclusionWe present a novel non-invasive approach to monitor and analyze cerebral organoids over time using high-field MRI and state-of-the-art tools for automated image analysis, offering a comparative pipeline for personalized medicine. We show that organoid growth can be monitored reliably over time and low- and high-quality organoids can be separated with high accuracy. Local cyst segmentation is feasible but could be further improved in the future.

Digitalisation trends of Industry 4.0 and Internet of Things led to an unprecedented growth of manufacturing data. This opens new horizons for data-driven methods, such as Machine Learning (ML), in monitoring of manufacturing processes. In this work, we propose ML pipelines for quality monitoring in Resistance Spot Welding. Previous approaches mostly focused on estimating quality of welding based on data collected from laboratory or experimental settings. Then, they mostly treated welding operations as independent events while welding is a continuous process with a systematic dynamics and production cycles caused by maintenance. Besides, model interpretation based on engineering know-how, which is an important and common practice in manufacturing industry, has mostly been ignored. In this work, we address these three issues by developing a novel feature-engineering based ML approach. Our method was developed on top of real production data. It allows to analyse sequences of welding instances collected from running manufacturing lines. By capturing dependencies across sequences of welding instances, our method allows to predict quality of upcoming welding operations before they happen. Furthermore, in our work we strive to combine the view of engineering and data science by discussing characteristics of welding data that have been little discussed in the literature, by designing sophisticated feature engineering strategies with support of domain knowledge, and by interpreting the results of ML analysis intensively to provide insights for engineering. We developed 12 ML pipelines in two dimensions: settings of feature engineering and ML methods, where we considered 4 feature settings and 3 ML methods (linear regression, multi-layer perception and support vector regression). We extensively evaluated our ML pipelines on data from two running industrial production lines of 27 welding machines with promising results.

Many biological oscillators share two properties: they are subject to stochastic fluctuations (noise) and they must reliably adjust their period to changing environmental conditions (entrainment). While noise seems to distort the ability of single oscillators to entrain, in populations of oscillators noise allows entrainment for a wider range of input amplitudes and periods. Here, we investigate, how this effect depends on the noise intensity and the number of oscillators in the population. We have found that, if a population consists of a sufficient number of oscillators, increasing noise intensity leads to faster entrainment after a phase change of the input signal (jet lag) and increases sensitivity to low-amplitude input signals.SIGNIFICANCELive is characterized by rhythms, such as daily changes in activity or the heartbeat. These rhythms are reflected in molecular oscillations generated at the level of individual cells. These oscillations are inherently noisy, but still cells reliably synchronize to external signals and provide reliable timing for other biological processes. Here, we show how noise can be beneficial to cell populations in terms of synchronization to external signals. Specifically, noise can increase the sensitivity to weak external signals and speed up adjustment to jet-lag-like perturbations.

Generated synthetic time series aim to be both realistic by mirroring the characteristics of real-world time series and useful by including characteristics that are useful for subsequent applications, such as forecasting and missing value imputation. To generate such realistic and useful time series, we require generation methods capable of controlling the non-stationarity and periodicities of the generated time series. However, existing approaches do not consider such explicit control. Therefore, in the present paper, we present a novel approach to control non-stationarity and periodicities with calendar and statistical information when generating time series. We first define the requirements for methods to generate time series with non-stationarity and periodicities, which we show are not fulfilled by existing generation methods. Second, we formally describe the novel approach for controlling non-stationarity and periodicities in generated time series. Thirdly, we introduce an exemplary implementation of this approach using a conditional Invertible Neural Network (cINN). We evaluate this cINN empirically in experiments with real-world data sets and compare it to state-of-the-art time series generation methods. Our experiments show that the evaluated cINN can generate time series with controlled periodicities and non-stationarity, and it also generally outperforms the selected benchmarks.

Smart charging of Electric Vehicles (EVs) reduces operating costs, allows more sustainable battery usage, and promotes the rise of electric mobility. In addition, bidirectional charging and improved connectivity enables efficient power grid support. Today, however, uncoordinated charging, e.g. governed by users' habits, is still the norm. Thus, the impact of upcoming smart charging applications is mostly unexplored. We aim to estimate the expenses inherent with smart charging, e.g. battery aging costs, and give suggestions for further research. Using typical on-board sensor data we concisely model and validate an EV battery. We then integrate the battery model into a realistic smart charging use case and compare it with measurements of real EV charging. The results show that i) the temperature dependence of battery aging requires precise thermal models for charging power greater than 7 kW, ii) disregarding battery aging underestimates EVs' operating costs by approx. 30%, and iii) the profitability of Vehicle-to-Grid (V2G) services based on bidirectional power flow, e.g. energy arbitrage, depends on battery aging costs and the electricity price spread., Comment: Revised and Resubmitted to IEEE Transaction on Smart Grid

Zusammenfassung Durch das Aufkommen von kostengünstigen und mobilen Kameras ist es möglich, einfach und flächendeckend ungeordnete Bilddaten in großem Umfang zu erheben. Dadurch können Veränderungen von Gebieten bzw. Objekten aufgezeichnet werden. Die Abbildung von objektbezogenen Veränderungen in aufgezeichneten Bildaufnahmen ist jedoch eine Herausforderung, da diese von externen oder szenischen Veränderungen überlagert werden können. Insbesondere der Umgang mit stark variierenden Blickwinkeln ist eine aktuelle Forschungsfrage. In dieser Arbeit wird ein allgemeines Vorgehen zur Repräsentation von Objektveränderungen vorgestellt. Es wird ein Datensatz eingeführt, um den Ansatz auf Realdaten zu evaluieren. In diesem Zusammenhang werden unterschiedliche Konfigurationen getestet und im Nachgang Empfehlungen für eine effektive Parametrierung herausgearbeitet. Im Anschluss wird als Anwendung eine Klassifikation von Objektzuständen vorgestellt.

Being non-invasive, cheap and widely available, the 12-lead electrocardiogram (ECG) is a standard method to assess cardiac function. Still, its reliable interpretation requires specialized knowledge and experience, rendering a second opinion valuable. We evaluated the performance of machine learning based classification of 11,705 healthy and bundle branch block 12-lead ECGs from 3 open databases. For each lead of the ECG signal, a representative QRS-complex template was extracted automatically. Principal component analysis (PCA) was applied to the concatenated, normalized and rescaled QRS signals to reduce their dimensionality. Multilayer perceptron and support-vector machine classifiers were trained using the principal components of weighted and non-weighted QRS template signals as input data. Classifiers achieved F1 scores between 0.92 and 0.96 on the test set for different input configurations. Anomaly based weighting slightly improved the performance of the classifiers. Neither class-wise PCA for feature extraction nor adding information on sex, gender and electrical heart axis to the input data yielded considerable improvement of the F1 scores. The achieved classification accuracy is similar to deep learning classifier performances and should generalize robustly to other ECG datasets. Our results suggest that this simple and well interpretable approach based on morphological signal characteristics is suitable for automatically and non-invasively identifying bundle branch block pathologies in clinical or smart electronics contexts.

Image processing techniques are widely used within automotive series production, including production of electronic control units (ECUs). Deep learning approaches have made rapid advances during the last years, but are not prominent in those industrial settings yet. One major obstacle is the lack of suitable training data. We adapt the recently developed method of domain randomization to our use case of 3D pose estimation of ECU housings. We create purely synthetic data with high visual diversity to train artificial neural networks (ANNs). This enables ANNs to estimate the 3D pose of a real sample part with high accuracy from a single low-resolution RGB image in a production-like setting. Requirements regarding measurement hardware are very low. Our entire setup is fully automated and can be transferred to related industrial use cases.

Object detectors are central to autonomous driving and are widely used in driver assistance systems. Object detectors are trained on a finite amount of data within a specific domain, hampering detection performance when applying object detectors to samples from other domains during inference, an effect known as domain gap. Domain gap is a concern for data-driven applications, evoking repetitive retraining of networks when the applications unfold into other domains. With object detectors that have been trained on day images only, a domain gap can be observed in object detection by night. Training object detectors on night images is critical because of the enormous effort required to generate an adequate amount of diversely labeled data, and existing data sets often tend to overfit specific domain characteristics. For the first time, this work proposes adapting domains by online image-to-image translation to expand an object detector's domain of operation. The domain gap is decreased without additional labeling effort and without having to retrain the object detector while unfolding into the target domain. The approach follows the concept of domain adaptation, shifting the target domain samples into the domain knownto the object detector (source domain). Firstly, the UNIT network is trained for domain adaptation and subsequently cast into an online domain adaptation module, which narrows down the domain gap. Domain adaptation capabilities are evaluated qualitatively by displaying translated samples and visualizing the domain shift through the 2D tSNE algorithm. We quantitatively benchmark the domain adaptation's influence on a state-of-the-art object detector, and on a retrained object detector, for mean average precision, mean recall, and the resulting F1-score. Our approach achieves an F1 score improvement of $5.27 \%$ within object detection by night when applying online domain adaptation. The evaluation is executed on the BDD100K benchmark data set.

Behavioral analysis of moving animals relies on a faithful recording and track analysis to extract relevant parameters of movement. To study group behavior and social interactions, often simultaneous analyses of individuals are required. To detect social interactions, for example to identify the leader of a group as opposed to followers, one needs an error-free segmentation of individual tracks throughout time. While automated tracking algorithms exist that are quick and easy to use, inevitable errors will occur during tracking. To solve this problem, we introduce a robust algorithm called epiTracker for segmentation and tracking of multiple animals in two-dimensional (2D) videos along with an easy-to-use correction method that allows one to obtain error-free segmentation. We have implemented two graphical user interfaces to allow user-friendly control of the functions. Using six labeled 2D datasets, the effort to obtain accurate labels is quantified and compared to alternative available software solutions. Both the labeled datasets and the software are publicly available.

Communication is crucial for organismic interactions, from bacteria, to fungi, to humans. Humans may use the visual sense to monitor the environment before starting acoustic interactions. In comparison, fungi lack a visual system, instead, hyphae use a cell-to-cell dialogue based on secreted signaling molecules to orchestrate cell fusion and establish hyphal networks. Hyphae alternate roles as signal-sender and signal-receiver, as can be visualized via the putative signaling protein, Soft, which is recruited in an oscillatory manner to the respective cytoplasmic membrane of interacting hyphae. Here, we show that signal oscillations already occur in single hyphae of Arthrobotrys flagrans in the absence of a potential fusion partner. They occurred in the same phase as growth oscillations. Once two fusion partners came into each other’s vicinity, their oscillation frequencies slowed down (entrainment phase) and transit into anti-phasic synchronization of the two cells’ oscillations with frequencies of 130 +/-20 sec. Single-cell oscillations, transient entrainment, and anti-phasic oscillations were reproduced by a mathematical model where nearby hyphae can absorb and secrete a limited molecular signaling component into a shared extra-cellular space. We show that intracellular Ca2+ concentrations oscillate in two approaching hyphae, and depletion of Ca2+ in the surrounding affected vesicle-driven extension of the hyphal tip, abolished single-cell molecular oscillations and the anti-phasic synchronization of two hyphae. Our results suggest that single hyphae engage in a “monologue” that may be used for exploration of the environment and can dynamically shift their extra-cellular signaling systems into a “dialogue” to initiate hyphal fusion.Significance statementCommunication at the cellular level often relies on chemical signal exchange. One prominent example is the fusion of fungal hyphae to form complex hyphal networks. As opposed to mating-type dependent cell fusion, cell-fusion events described here occur in genetically identical cells. Relying only on one chemical signaling channel raises the question of how communication is initiated. We discovered that individual hyphae constantly perform signal oscillations, comparable to a cellular “monologue” until they meet another hypha with which they then coordinate signal oscillations in a cell-to-cell dialogue. We also show that signal oscillations are mechanistically interlinked with calcium-dependent growth oscillations. Although the signaling molecule(s) has not been identified yet, it is highly likely linked to the hyphal growth machinery.

Sustainable energy systems are characterised by an increased integration of renewable energy sources, which magnifies the fluctuations in energy supply. Methods to to cope with these magnified fluctuations, such as load shifting, typically require accurate short-term load forecasts. Although numerous machine learning models have been developed to improve short-term load forecasting (STLF), these models often require large amounts of training data. Unfortunately, such data is usually not available, for example, due to new users or privacy concerns. Therefore, obtaining accurate short-term load forecasts with little data is a major challenge. The present paper thus proposes the latent space-based forecast enhancer (LSFE), a method which combines transfer learning and data augmentation to enhance STLF when training data is limited. The LSFE first trains a generative model on source data similar to the target data before using the latent space data representation of the target data to generate seed noise. Finally, we use this seed noise to generate synthetic data, which we combine with real data to enhance STLF. We evaluate the LSFE on real-world electricity data by examining the influence of its components, analysing its influence on obtained forecasts, and comparing its performance to benchmark models. We show that the Latent Space-based Forecast Enhancer is generally capable of improving the forecast accuracy and thus helps to successfully meet the challenge of limited available training data.

SummaryReliable deep learning segmentation for microfluidic live-cell imaging requires comprehensive ground truth data. ObiWan-Microbi is a microservice platform combining the strength of state-of-the-art technologies into a unique integrated workflow for data management and efficient ground truth generation for instance segmentation, empowering collaborative semi-automated image annotation in the cloud.Availability and ImplementationObiWan-Microbi is open-source and available under the MIT license at https://github.com/hip-satomi/ObiWan-Microbi, along documentation and usage examples.Contactk.noeh@fz-juelich.deSupplementary informationSupplementary data are available online.

Downhill skiing technique represents the complex coordinative movement patterns needed to control skiing motion. While scientific understanding of skiing technique is still incomplete, not least due to challenges in objectively measuring it, practitioners such as ski instructors have developed sophisticated and comprehensive descriptions of skiing technique. The current paper describes a 3-step proof-of-concept study introducing a technology platform for quantifying skiing technique that utilizes the practitioners’ expert knowledge. The approach utilizes an inertial measurement unit system (Xsens™) and presents a motion analysis algorithm based on the Principal Movement (PM) concept. In step 1, certified ski instructors skied specified technique elements according to technique variations described in ski instruction curricula. The obtained data was used to establish a PM-coordinate system for skiing movements. In step 2, the techniques parallel and carving turns were compared. Step 3 presents a case study where the technique analysis methodology is applied to advise an individual skier on potential technique improvements. All objectives of the study were met, proving the suitability of the proposed technology for scientific and applied technique evaluations of downhill skiing. The underlying conceptual approach - utilizing expert knowledge and skills to generate tailored variability in motion data (step 1) that then dominate the orientation of the PMs, which, in turn, can serve as measures for technique elements of interest - could be applied in many other sports or for other applications in human movement analyses.

Here we report two antimicrobial peptides (AMPs), HG2 and HG4 identified from a rumen microbiome metagenomic dataset, with activity against multidrug-resistant (MDR) bacteria, especially methicillin-resistant Staphylococcus aureus (MRSA) strains, a major hospital and community-acquired pathogen. We employed the classifier model design to analyse, visualise, and interpret AMP activities. This approach allowed in silico discrimination of promising lead AMP candidates for experimental evaluation. The lead AMPs, HG2 and HG4, are fast-acting and show anti-biofilm and anti-inflammatory activities in vitro and demonstrated little toxicity to human primary cell lines. The peptides were effective in vivo within a Galleria mellonella model of MRSA USA300 infection. In terms of mechanism of action, HG2 and HG4 appear to interact with the cytoplasmic membrane of target cells and may inhibit other cellular processes, whilst preferentially binding to bacterial lipids over human cell lipids. Therefore, these AMPs may offer additional therapeutic templates for MDR bacterial infections.

A framework is described for the efficient and realistic simulation of X-ray diffraction scans to train machine- or deep-learning models like convolutional neural networks for the automatic phase-identification task in multiphase compounds., Within the domain of analyzing powder X-ray diffraction (XRD) scans, manual examination of the recorded data is still the most popular method, but it requires some expertise and is time consuming. The usual workflow for the phase-identification task involves software for searching databases of known compounds and matching lists of d spacings and related intensities to the measured data. Most automated approaches apply some iterative procedure for the search/match process but fail to be generally reliable yet without the manual validation step of an expert. Recent advances in the field of machine and deep learning have led to the development of algorithms for use with diffraction patterns and are producing promising results in some applications. A limitation, however, is that thousands of training samples are required for the model to achieve a reliable performance and not enough measured samples are available. Accordingly, a framework for the efficient generation of thousands of synthetic XRD scans is presented which considers typical effects in realistic measurements and thus simulates realistic patterns for the training of machine- or deep-learning models. The generated data set can be applied to any machine- or deep-learning structure as training data so that the models learn to analyze measured XRD data based on synthetic diffraction patterns. Consequently, we train a convolutional neural network with the simulated diffraction patterns for application with iron ores or cements compounds and prove robustness against varying unit-cell parameters, preferred orientation and crystallite size in synthetic, as well as measured, XRD scans.

Dispersed negatively charged silica nanoparticles segregate inside microfluidic water‐in‐oil (W/O) droplets that are coated with a positively charged lipid shell. We report a methodology for the quantitative analysis of this self‐assembly process. By using real‐time fluorescence microscopy and automated analysis of the recorded images, kinetic data are obtained that characterize the electrostatically‐driven self‐assembly. We demonstrate that the segregation rates can be controlled by the installment of functional moieties on the nanoparticle's surface, such as nucleic acid and protein molecules. We anticipate that our method enables the quantitative and systematic investigation of the segregation of (bio)functionalized nanoparticles in microfluidic droplets. This could lead to complex supramolecular architectures on the inner surface of micrometer‐sized hollow spheres, which might be used, for example, as cell containers for applications in the life sciences., Studying segregation: The segregation behavior of functionalized silica nanoparticles in charged microfluidic droplets was quantitatively analyzed using a home‐made microfluidic device with automated data processing. Such self‐assembly systems can be exploited for life science applications.

Parametric quantile regression is a useful tool for obtaining probabilistic energy forecasts. Nonetheless, traditional quantile regressions may be complicated to obtain using complex data mining techniques (e.g., artificial neural networks), since they are trained using a non-differentiable cost function. This article presents a method that uses a new nearest neighbors quantile filter to obtain quantile regressions independently of the data mining technique utilized and without the non-differentiable cost function. This method is subsequently validated using the dataset from the 2014 Global Energy Forecasting Competition. The results show that the method presented here is able to solve the competition’s task with a similar accuracy to the competition’s winner and in a similar timeframe, but requiring a much less powerful computer. This property may be relevant in an online forecasting service for which the fast computation of probabilistic forecasts using less powerful machines is required.

The uncontrolled manifold (UCM) approach has been widely used in recent studies to examine variability in daily tasks; however, it has not yet been used to study running or the effects of expertise. Therefore, the aim of this study was to analyse the synergy structure stabilizing the centre of mass (CoM) trajectory in experts compared to novices during running at two different speeds using a subject-specific 3D model. A total of 25 healthy young adults (13 experts, 12 novices) participated in the study. All subjects ran at 10 and 15 km h

The purpose of the present proof-of-concept study was to use large-areaThree healthy individuals were examined roughly weekly over a total period of six weeks by large-areaTotal investigation times of 10 minutes maximum per participant were used to generate mosaic images with an average size of 3.61 mmThe results of this proof-of-concept study have demonstrated the feasibility of using

In the electricity grid, constantly balancing the supply and demand is critical for the network’s stability and any expected deviations require balancing efforts. This balancing becomes more challenging in future energy systems characterised by a high proportion of renewable generation due to the increased volatility of these renewables. In order to know when any balancing efforts are required, it is essential to predict the so-called net load, the difference between forecast energy demand and renewable supply. Although various forecasting approaches exist for both the individual components of the net load and the net load itself, it is unclear if it is more beneficial to aggregate several specialised forecasts to obtain the net load or to aggregate the input data to forecast the net load with one approach directly. Therefore, the present paper compares three net load forecasting approaches that exploit different levels of aggregation. We compare an aggregated strategy that directly forecasts the net load, a partially aggregated strategy that forecasts demand and supply separately, and a disaggregated strategy that forecasts demand and supply from each generator separately. We evaluate the forecast performance of all strategies with a simple and a complex forecasting model, both for deterministic and probabilistic forecasts, using one year of data from a simulated realistic future energy system characterised by a high share of renewable energy sources. We find that the partially aggregated strategy performs best, suggesting that a balance between specifically tailored forecasting models and aggregation is advantageous.

Review of automated time series forecasting pipelines e1475 (2022). doi:10.1002/widm.1475, Published by Wiley, Hoboken, NJ

The sinoatrial node (SAN) is a complex structure that spontaneously depolarizes rhythmically (“pacing”) and excites the surrounding non-automatic cardiac cells (“drive”) to initiate each heart beat. However, the mechanisms by which the SAN cells can activate the large and hyperpolarized surrounding cardiac tissue are incompletely understood. Experimental studies demonstrated the presence of an insulating border that separates the SAN from the hyperpolarizing influence of the surrounding myocardium, except at a discrete number of sinoatrial exit pathways (SEP). We propose a highly detailed 3D model of the human SAN, including 3D SEPs to study the requirements for successful electrical activation of the primary pacemaking structure of the human heart. A total of 788 simulations investigate the ability of the SAN to pace and drive with different heterogeneous characteristics of the nodal tissue (gradient and mosaic models) and myocyte orientation. A sigmoidal distribution of the tissue conductivity combined with a mosaic model of SAN and atrial cells in the SEP was able to drive the right atrium (RA). Additionally, we investigated the influence of the SEPs by varying their number, length and width. SEPs created a transition zone of transmembrane voltage (TMV) and ionic currents to enable successful pace and drive. Unsuccessful simulations showed a hyperpolarized TMV (−66 mV), which blocked the L-type channels and attenuated the sodium-calcium exchanger. The fiber direction influenced the SEPs that preferentially activated the crista terminalis (CT). The location of the leading pacemaker site (LPS) shifted towards the SEP-free areas. LPSs were located closer to the SEP-free areas (3.46±1.42 mm), where the hyperpolarizing influence of the CT was reduced, compared to a larger distance from the LPS to the areas where SEPs were located (7.17±0.98 mm). This study identified the geometrical and electrophysiological aspects of the 3D SAN-SEP-CT structure required for successful pace-and-drive in silico.SIGNIFICANCEThe human sinoatrial node (SAN) is the intrinsic natural pacemaker of the heart. Despite its remarkable robustness to failure, the electrophysiological properties, and mechanisms by which the SAN overcomes the source-sink mismatch towards the hyperpolarized surrounding cardiac tissue remains a mystery. The SAN is electrically isolated from the hyperpolarized cardiac tissue, except at a discrete number of sinoatrial exit pathways (SEP). Using in silico experiments, we explore the influence of the fiber orientation, the SEPs’ number, geometry and location on the activation of the SAN and the surrounding atrial tissue. We provide the mechanisms in a first 3D model of the human SAN-SEP structure that can successfully drive the working myocardium.

Background: The purpose of the present proof-of-concept study was to use large-area in vivo confocal laser scanning microscopy (CLSM) mosaics to determine the migration rates of nerve branching points in the human corneal subbasal nerve plexus (SNP). Methods: Three healthy individuals were examined roughly weekly over a total period of six weeks by large-area in vivo confocal microscopy of the central cornea. An in-house developed prototype system for guided eye movement with an acquisition time of 40 s was used to image and generate large-area mosaics of the SNP. Kobayashi-structures and nerve entry points (EPs) were used as fixed structures to enable precise mosaic registration over time. The migration rate of 10 prominent nerve fiber branching points per participant was tracked and quantified over the longitudinal period. Results: Total investigation times of 10 minutes maximum per participant were used to generate mosaic images with an average size of 3.61 mm2 (range: 3.18–4.42 mm2). Overall mean branching point migration rates of (46.4±14.3), (48.8±15.5), and (50.9±13.9) µm/week were found for the three participants with no statistically significant difference. Longitudinal analyses of nerve branching point migration over time revealed significant time-dependent changes in migration rate only in participant 3 between the last two measurements [(63.7±12.3) and (43.0±12.5) µm/week, P1 mm2). The ability to monitor dynamic changes in the SNP opens a window to future studies of corneal nerve health and regenerative capacity in a number of systemic and ocular diseases. Since corneal nerves are considered part of the peripheral nervous system, this technique could also offer an objective diagnostic tool and biomarker for disease- or treatment-induced neuropathic changes.

Supervised deep learning approaches for automated diagnosis support require datasets annotated by experts. Intra-annotator variability of a single annotator and inter-annotator variability between annotators can affect the quality of the diagnosis support. As medical experts will always differ in annotation details, quantitative studies concerning the annotation quality are of particular interest. A consistent and noise-free annotation of large-scale datasets by, for example, dermatologists or pathologists is a current challenge. Hence, methods are needed to automatically inspect annotations in datasets. In this paper, we categorize annotation noise in image segmentation tasks, present methods to simulate annotation noise, and examine the impact on the segmentation quality. Two novel automated methods to identify intra-annotator and inter-annotator inconsistencies based on uncertainty-aware deep neural networks are proposed. We demonstrate the benefits of our automated inspection methods such as focused re-inspection of noisy annotations or the detection of generally different annotation styles using the biomedical ISIC 2017 Melanoma image segmentation dataset.

In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.

Frontiers in pharmacology 12, 769739 (2021). doi:10.3389/fphar.2021.769739, Antimicrobial peptides (AMPs) are a promising class of compounds being developed against multi-drug resistant bacteria. Hybridization has been reported to increase antimicrobial activity. Here, two proline-rich peptides (consP1: VRKPPYLPRPRPRPL-CONH2 and Bac5-v291: RWRRPIRRRPIRPPFWR-CONH2) were combined with two arginine-isoleucine-rich peptides (optP1: KIILRIRWR-CONH2 and optP7: KRRVRWIIW-CONH2). Proline-rich antimicrobial peptides (PrAMPs) are known to inhibit the bacterial ribosome, shown also for Bac5-v291, whereas it is hypothesized a “dirty drug” model for the arginine-isoleucine-rich peptides. That hypothesis was underpinned by transmission electron microscopy and biological small-angle X-ray scattering (BioSAXS). The strength of BioSAXS is the power to detect ultrastructural changes in millions of cells in a short time (seconds) in a high-throughput manner. This information can be used to classify antimicrobial compounds into groups according to the ultrastructural changes they inflict on bacteria and how the bacteria react towards that assault. Based on previous studies, this correlates very well with different modes of action. Due to the novelty of this approach direct identification of the target of the antimicrobial compound is not yet fully established, more research is needed. More research is needed to address this limitation. The hybrid peptides showed a stronger antimicrobial activity compared to the proline-rich peptides, except when compared to Bac5-v291 against E. coli. The increase in activity compared to the arginine-isoleucine-rich peptides was up to 6-fold, however, it was not a general increase but was dependent on the combination of peptides and bacteria. BioSAXS experiments revealed that proline-rich peptides and arginine-isoleucine-rich peptides induce very different ultrastructural changes in E. coli, whereas a hybrid peptide (hyP7B5GK) shows changes, different to both parental peptides and the untreated control. These different ultrastructural changes indicated that the mode of action of the parental peptides might be different from each other as well as from the hybrid peptide hyP7B5GK. All peptides showed very low haemolytic activity, some of them showed a 100-fold or larger therapeutic window, demonstrating the potential for further drug development., Published by Frontiers Media, Lausanne

According to WHO, currently 215 countries/areas/territories report a total of more than 176 million confirmed COVID-19 cases and 3.8 million deaths (June 18, 2021). SARS-CoV-2, the causative agent of COVID-19, does not impact only the respiratory system but also the various organs in the body. It can directly or indirectly affect the pulmonary system, cardiovascular system (including heart failure), renal system (including kidney failure), hepatic system (including liver failure), gastrointestinal system, nervous system, and/or various systems, leading to shock and multi-organ failure (Zaim et al., 2020). In consequence, comorbidity in these systems leads to a higher risk for a severe disease progression.

The morphometric assessment of the corneal subbasal nerve plexus (SNP) by confocal microscopy holds great potential as a sensitive biomarker for various ocular and systemic conditions and diseases. Automated wide-field montages (or large-area mosaic images) of the SNP provide an opportunity to overcome the limited field of view of the available imaging systems without the need for manual, subjective image selection for morphometric characterization. However, current wide-field montaging solutions usually calculate the mosaic image after the examination session, without a reliable means for the clinician to predict or estimate the resulting mosaic image quality during the examination. This contribution describes a novel approach for a real-time creation and visualization of a mosaic image of the SNP that facilitates an informed evaluation of the quality of the acquired image data immediately at the time of recording. In cases of insufficient data quality, the examination can be aborted and repeated immediately, while the patient is still at the microscope. Online mosaicking also offers the chance to identify an overlap of the imaged tissue region with previous SNP mosaic images, which can be particularly advantageous for follow-up examinations.

Medaka (Oryzias latipes) and zebrafish (Danio rerio) contribute substantially to our understanding of the genetic and molecular etiology of human cardiovascular diseases. In this context, the quantification of important cardiac functional parameters is fundamental. We have developed a framework that segments the ventricle of a medaka hatchling from image sequences and subsequently quantifies ventricular dimensions.