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Compared to cell therapy, where cells are injected into a defect region, the treatment of heart infarction with cells seeded in a vascularized scaffold bears advantages, such as an immediate nutrient supply or a controllable and persistent localization of cells. For this purpose, decellularized native tissues are a preferable choice as they provide an in vivo-like microenvironment. However, the quality of such scaffolds strongly depends on the decellularization process. Therefore, two protocols based on sodium dodecyl sulfate or sodium deoxycholate were tailored and optimized for the decellularization of a porcine heart. The obtained scaffolds were tested for their applicability to generate vascularized cardiac patches. Decellularization with sodium dodecyl sulfate was found to be more suitable and resulted in scaffolds with a low amount of DNA, a highly preserved extracellular matrix composition, and structure shown by GAG quantification and immunohistochemistry. After seeding human endothelial cells into the vasculature, a coagulation assay demonstrated the functionality of the endothelial cells to minimize the clotting of blood. Human-induced pluripotent-stem-cell-derived cardiomyocytes in co-culture with fibroblasts and mesenchymal stem cells transferred the scaffold into a vascularized cardiac patch spontaneously contracting with a frequency of 25.61 ± 5.99 beats/min for over 16 weeks. The customized decellularization protocol based on sodium dodecyl sulfate renders a step towards a preclinical evaluation of the scaffolds. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms can be used in Earth system models (ESMs) to emulate sub-grid-scale processes. Due to the statistical nature of ML algorithms and the high complexity of ESMs, these hybrid ML ESMs require careful validation. Simulation stability needs to be monitored in fully coupled simulations, and the plausibility of results needs to be evaluated in suitable experiments. We present the coupling of SuperdropNet, a machine learning model for emulating warm-rain processes in cloud microphysics, with ICON (Icosahedral Nonhydrostatic) model v2.6.5. SuperdropNet is trained on computationally expensive droplet-based simulations and can serve as an inexpensive proxy within weather prediction models. SuperdropNet emulates the collision–coalescence of rain and cloud droplets in a warm-rain scenario and replaces the collision–coalescence process in the two-moment cloud microphysics scheme. We address the technical challenge of integrating SuperdropNet, developed in Python and PyTorch, into ICON, written in Fortran, by implementing three different coupling strategies: embedded Python via the C foreign function interface (CFFI), pipes, and coupling of program components via Yet Another Coupler (YAC). We validate the emulator in the warm-bubble scenario and find that SuperdropNet runs stably within the experiment. By comparing experiment outcomes of the two-moment bulk scheme with SuperdropNet, we find that the results are physically consistent and discuss differences that are observed in several diagnostic variables. In addition, we provide a quantitative and qualitative computational benchmark for three different coupling strategies – embedded Python, coupler YAC, and pipes – and find that embedded Python is a useful software tool for validating hybrid ML ESMs. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms can be used in Earth System models (ESMs) to emulate sub-grid-scale processes. Due to the statistical nature of ML algorithms and the high complexity of ESMs, these hybrid ML-ESMs require careful validation. Simulation stability needs to be monitored in fully coupled simulations, and the plausibility of results needs to be evaluated in suitable experiments. We present the coupling of SuperdropNet, a machine learning model for emulating warm rain processes in cloud microphysics, into ICON 2.6.5. SuperdropNet is trained on computationally expensive droplet based simulations and can serve as an inexpensive proxy within weather prediction models. SuperdropNet emulates the collision-coalescence of rain and cloud droplets in a warm rain scenario and replaces the collision-coalescence process in the two-moment cloud microphysics scheme. We address the technical challenge of integrating SuperdropNet, developed in Python and PyTorch, into ICON, written in Fortran, by implementing three different coupling strategies: embedded Python via the C Foreign Function Interface, pipes, and coupling of program components via YetAnotherCoupler (YAC). We validate the emulator in the warm bubble scenario and find that SuperdropNet runs stable within the experiment. In comparing experiment outcomes from the bulk moment scheme and SuperdropNet, we find that the results are physically consistent, and discuss differences that are observed for several diagnostic variables. In addition, we provide a quantitative and qualitative computational benchmark for three different coupling strategies— embedded Python, coupler YAC, and pipes—and find that embedded Python is a useful software tool for validating hybrid ML-ESMs. [ABSTRACT FROM AUTHOR]

Current implantable electrodes facilitate only a low cellular infiltration impairing the long-term integration into the host's tissue. To accomplish a seamless electronic-tissue interface, conductive three-dimensional (3D) scaffolds were generated by carbonization of electro-spun fiber meshes. When introducing NaCl particles as porogens, tailored tissue-like electrodes were generated. Characterization of the porous 3D fiber electrodes demonstrated improved material and electrical characteristics compared to standard carbon fiber meshes or flat gold surfaces. The feasibility of the porous 3D electrodes was assessed by cell culture experiments, confirming the migration of cells into the electrode and the formation of contracting cardiomyocyte clusters. Finally, a complex cardiac co-culture system proved the integration of the tissue into the 3D electrode in long-term culture of 7 weeks. These results strengthen the development of tissue-like 3D scaffolds as alternative to two-dimensional (2D) electrodes. [ABSTRACT FROM AUTHOR]

The foreign body reaction to neuronal electrode implants limits potential applications as well as the therapeutic period. Developments in the basic electrode design might improve the tissue compatibility and thereby reduce the foreign body reaction. In this work, the approach of embedding 3D carbon nanofiber electrodes in extracellular matrix (ECM) synthesized by human fibroblasts for a compatible connection to neuronal cells was investigated. Porous electrode material was manufactured by solution coelectrospinning of polyacrylonitrile and polyamide as a fibrous porogen. Moreover, NaCl represented an additional particulate porogen. To achieve the required conductivity for an electrical interface, meshes were carbonized. Through the application of two different porogens, the electrodes' flexibility and porosity was improved. Human dermal fibroblasts were cultured on the electrode surface for ECM generation and removed afterwards. Scanning electron microscopy imaging revealed a nano fibrous ECM network covering the carbon fibers. The collagen amount of the ECM coating was quantified by hydroxyproline-assays. The modification with the natural protein coating on the electrode functionality resulted in a minor increase of the electrical capacity, which slightly improved the already outstanding electrical interface properties. Increased cell numbers of SH-SY5Y cell line on ECM-modified electrodes demonstrated an improved cell adhesion. During cell differentiation, the natural ECM enhanced the formation of neurites regarding length and branching. The conducted experiments indicated the prevention of direct cell-electrode contacts by the modification, which might help to shield temporary the electrode from immunological cells to reduce the foreign body reaction and improve the electrodes' tissue integration. [ABSTRACT FROM AUTHOR]

Pacemaker systems are an essential tool for the treatment of cardiovascular diseases. However, the immune system’s natural response to a foreign body results in the encapsulation of a pacemaker electrode and an impaired energy efficiency by increasing the excitation threshold. The integration of the electrode into the tissue is affected by implant properties such as size, mechanical flexibility, shape, and dimensionality. Three-dimensional, tissue-like electrode scaffolds render an alternative to currently used planar metal electrodes. Based on a modified electrospinning process and a high temperature treatment, a conductive, porous fiber scaffold was fabricated. The electrical and immunological properties of this 3D electrode were compared to 2D TiN electrodes. An increased surface of the fiber electrode compared to the planar 2D electrode, showed an enhanced electrical performance. Moreover, the migration of cells into the 3D construct was observed and a lower inflammatory response was induced. After early and late in vivo host response evaluation subcutaneously, the 3D fiber scaffold showed no adverse foreign body response. By embedding the 3D fiber scaffold in human cardiomyocytes, a tissue-electrode hybrid was generated that facilitates a high regenerative capacity and a low risk of fibrosis. This hybrid was implanted onto a spontaneously beating, tissue-engineered human cardiac patch to investigate if a seamless electronic-tissue interface is generated. The fusion of this hybrid electrode with a cardiac patch resulted in a mechanical stable and electrical excitable unit. Thereby, the feasibility of a seamless tissue-electrode interface was proven. [ABSTRACT FROM AUTHOR]

The current handling of data in earth observation, modelling and prediction measures gives cause for critical consideration, since we all too often carelessly ignore data uncertainty. We think that Earth scientists are generally aware of the importance of linking data to quantitative uncertainty measures. But we also think that uncertainty quantification of Earth observation data too often fails at very early stages. We claim that data acquisition without uncertainty quantification is not sustainable and machine learning and computational modelling cannot unfold their potential when analysing complex natural systems like the Earth. Current approaches such as stochastic perturbation of parameters or initial conditions cannot quantify uncertainty or bias arising from the choice of model, limiting scientific progress. We need incentives stimulating the honest treatment of uncertainty starting during data acquisition, continuing through analysis methodology and prediction results. Computational modellers and machine learning experts have a critical role, since they enjoy high esteem from stakeholders and their methodologies and their results critically depend on data uncertainty. If both want to advance their uncertainty assessment of models and predictions of complex systems like the Earth, they have a common problem to solve. Together, computational modellers and machine learners could develop new strategies for bias identification and uncertainty quantification offering a more all‐embracing uncertainty quantification than any known methodology. But since it starts for computational modellers and machine learners with data and their uncertainty, the fundamental first step in such a development would be leveraging shareholder esteem to insistently advocate for reduction of ignorance when it comes to uncertainty quantification of data. [ABSTRACT FROM AUTHOR]

• CyGNSSnet is a deep learning model predicting wind speed from CyGNSS data. • Inspired by image processing, convolutional layers extract features from DDMs. • The model yields a significant improvement to the current operational algorithm. • CyGNSSnet outperforms existing methods in terms of RMSE on the test data. • The study shows great potential for deep learning in GNSS remote sensing. GNSS Reflectometry (GNSS-R) is a novel remote sensing technique for the monitoring of geophysical parameters using reflected GNSS signals from the Earth's surface. Ocean wind speed monitoring is the main objective of the recently launched Cyclone GNSS (CyGNSS), a GNSS-R constellation of eight microsatellites, launched in late 2016. In this study, the capability of deep learning, especially, for an operational wind speed data derivation from the measured Delay-Doppler Maps (DDMs) is characterized. CyGNSSnet is based on convolutional layers for the feature extraction from bistatic radar cross section (BRCS) DDMs, along with fully connected layers for processing ancillary technical and higher-level input parameters. The best architecture is determined on a validation set and is evaluated over a completely blind dataset from a different time span than that of the training data to validate the generality of the model for operational usage. After a data quality control, CyGNSSnet results in an RMSE of 1.36 m/s leading to a significant improvement by 28% in comparison to the officially operational retrieval algorithm. The RMSE is the lowest among those seen in the literature for any conventional or machine learning-based algorithm. The benefits of the convolutional layers, the advantages and weaknesses of the model are discussed. CyGNSSnet offers efficient processing of GNSS-R measurements for high-quality global ocean winds. [ABSTRACT FROM AUTHOR]

The development of novel fibrous biomaterials and further processing of medical devices is still challenging. For instance, titanium(IV) oxide is a well-established biocompatible material, and the synthesis of TiOx particles and coatings via the sol-gel process has frequently been published. However, synthesis protocols of sol-gel-derived TiOx fibers are hardly known. In this publication, the authors present a synthesis and fabrication of purely sol-gel-derived TiOx fiber fleeces starting from the liquid sol-gel precursor titanium ethylate (TEOT). Here, the α-hydroxy-carboxylic acid lactic acid (LA) was used as a chelating ligand to reduce the reactivity towards hydrolysis of TEOT enabling a spinnable sol. The resulting fibers were processed into a non-woven fleece, characterized with FTIR, 13C-MAS-NMR, XRD, and screened with regard to their stability in physiological solution. They revealed an unexpected dependency between the LA content and the dissolution behavior. Finally, in vitro cell culture experiments proved their potential suitability as an open-mesh structured scaffold material, even for challenging applications such as therapeutic medicinal products (ATMPs). [ABSTRACT FROM AUTHOR]

Recently, Machine learning (ML) has been widely utilized for laboratory earthquake (labquake) prediction using various types of data. This study pioneers in time to failure (TTF) prediction based on ML using acoustic emission (AE) records from three laboratory stick-slip experiments performed on Westerly granite samples with naturally fractured rough faults, more similar to the heterogeneous fault structures in the nature. 47 catalog-driven seismo-mechanical and statistical features are extracted introducing some new features based on focal mechanism. A regression voting ensemble of Long-Short Term Memory (LSTM) networks predicts TTF with a coefficient of determination (R 2) of 70% on the test dataset. Feature importance analysis revealed that AE rate, correlation integral, event proximity, and focal mechanism-based features are the most important features for TTF prediction. Results reveal that the network uses all information among the features for prediction, including general trends in high correlated features as well as fine details about local variations and fault evolution involved in low correlated features. Therefore, some highly correlated and physically meaningful features may be considered less important for TTF prediction due to their correlation with other important features. Our study provides a ground for applying catalog-driven to constrain TTF of complex heterogeneous rough faults, which is capable to be developed for real application. • Catalog-driven features are informative for preparatory phase characterization. • ML can predict TTF using physics-informed features in natural rough faults. • Feature importance analysis reveals most important features for TTF prediction. • AE rate, correlation integral, proximity and focal mechanism features are important. [ABSTRACT FROM AUTHOR]

Implants elicit an immunological response after implantation that results in the worst case in a complete implant rejection. This biomaterial-induced inflammation is modulated by macrophages and can be influenced by nanotopographical surface structures such as titania nanotubes or fractal titanium nitride (TiN) surfaces. However, their specific impact on a distinct macrophage phenotype has not been identified. By using two different levels of nanostructures and smooth samples as controls, the influence of tubular TiO2 and fractal TiN nanostructures on primary human macrophages with M1 or M2-phenotype was investigated. Therefore, nanotopographical coatings were either, directly generated by physical vapor deposition (PVD) or by electrochemical anodization of titanium PVD coatings. The cellular response of macrophages was quantitatively assessed to demonstrate a difference in biocompatibility of nanotubes in respect to human M1 and M2-macrophages. Depending on the tube diameter of the nanotubular surfaces, low cell numbers and impaired cellular activity, was detected for M2-macrophages, whereas the impact of nanotubes on M1-polarized macrophages was negligible. Importantly, we could confirm this phenotypic response on the fractal TiN surfaces. The results indicate that the investigated topographies specifically impact the macrophage M2-subtype that modulates the formation of the fibrotic capsule and the long-term response to an implant. [ABSTRACT FROM AUTHOR]

The World Climate Research Programme (WCRP)'s Working Group on Climate Modelling (WGCM) Infrastructure Panel (WIP) was formed in 2014 in response to the explosive growth in size and complexity of Coupled Model Intercomparison Projects (CMIPs) between CMIP3 (2005-2006) and CMIP5 (2011-2012). This article presents the WIP recommendations for the global data infrastructure needed to support CMIP design, future growth, and evolution. Developed in close coordination with those who build and run the existing infrastructure (the Earth System Grid Federation; ESGF), the recommendations are based on several principles beginning with the need to separate requirements, implementation, and operations. Other important principles include the consideration of the diversity of community needs around data - a data ecosystem - the importance of provenance, the need for automation, and the obligation to measure costs and benefits. This paper concentrates on requirements, recognizing the diversity of communities involved (modelers, analysts, software developers, and downstream users). Such requirements include the need for scientific reproducibility and accountability alongside the need to record and track data usage. One key element is to generate a dataset-centric rather than system-centric focus, with an aim to making the infrastructure less prone to systemic failure. With these overarching principles and requirements, the WIP has produced a set of position papers, which are summarized in the latter pages of this document. They provide specifications for managing and delivering model output, including strategies for replication and versioning, licensing, data quality assurance, citation, long-term archiving, and dataset tracking. They also describe a new and more formal approach for specifying what data, and associated metadata, should be saved, which enables future data volumes to be estimated, particularly for well-defined projects such as CMIP6. The paper concludes with a future facing consideration of the global data infrastructure evolution that follows from the blurring of boundaries between climate and weather, and the changing nature of published scientific results in the digital age. [ABSTRACT FROM AUTHOR]

The World Climate Research Programme (WCRP)'s Working Group on Climate Modeling (WGCM) Infrastructure Panel (WIP) was formed in 2014 in response to the explosive growth in size and complexity of Coupled Model Intercomparison Projects (CMIPs) between CMIP3 (2005-06) and CMIP5 (2011-12). This article presents the WIP recommendations for the global data infrastructure needed to support CMIP design, future growth and evolution. Developed in close coordination with those who build and run the existing infrastructure (the Earth System Grid Federation), the recommendations are based on several principles beginning with the need to separate requirements, implementation, and operations. Other important principles include the consideration of data as a commodity in an ecosystem of users, the importance of provenance, the need for automation, and the obligation to measure costs and benefits. This paper concentrates on requirements, recognising the diversity of communities involved (modelers, analysts, software developers, and downstream users). Such requirements include the need for scientific reproducibility and accountability alongside the need to record and track data usage for the purpose of assigning credit. One key element is to generate a dataset-centric rather than system-centric focus, with an aim to making the infrastructure less prone to systemic failure. With these overarching principles and requirements, the WIP has produced a set of position papers, which are summarized here. They provide specifications for managing and delivering model output, including strategies for replication and versioning, licensing, data quality assurance, citation, long-term archival, and dataset tracking. They also describe a new and more formal approach for specifying what data, and associated metadata, should be saved, which enables future data volumes to be estimated. The paper concludes with a future-facing consideration of the global data infrastructure evolution that follows from the blurring of boundaries between climate and weather, and the changing nature of published scientific results in the digital age. [ABSTRACT FROM AUTHOR]