Search

In recent years, machine learning (ML) based parameterizations have become increasingly common in Earth System Models (ESM). Sub-grid scale physical processes that would be computationally too expensive, e.g., atmospheric chemistry and cloud microphysics, can be emulated by ML algorithms such as neural networks.Neural networks are trained first on simulations of the sub-grid scale process that is to be emulated. They are then used in so-called inference mode to make predictions during the ESM run, replacing the original parameterization. Training usually requires GPUs, while inference may be done on CPU architectures.At first, neural networks are evaluated offline, i.e., independently of the ESM on appropriate datasets. However, their performance can ultimately only be evaluated in an online setting, where the ML algorithm is coupled to the ESM, including nonlinear interactions.We want to shorten the time spent in neural network development and offline testing and move quickly to online evaluation of ML components in our ESM of choice, ICON (Icosahedral Nonhydrostatic Weather and Climate Model). Since ICON is written in Fortran, and modern ML algorithms are developed in the Python ecosystem, this requires efficient bridges between the two programming languages. The Fortran-Python bridge must be flexible to allow for iterative development of the neural network. Changes to the ESM codebase should be as few as possible, and the runtime overhead should not limit development.In our contribution we explore three strategies to call the neural network inference from within Fortran using (i) embedded Python code compiled in a dynamic library, (ii) pipes, and (iii) MPI using the ICON coupler YAC. We provide quantitative benchmarks for the proposed Fortran-Python bridges and assess their overall suitability in a qualitative way to derive best practices. The Fortran-Python bridge enables scientists and developers to evaluate ML components in an online setting, and can be extended to other parameterizations and ESMs.

Earthquake prediction relies on identification of distinctive patterns of precursory parameters that might precede a large earthquake. However, these patterns are typically not reliably observed in the field. Laboratory stick-slip experiments provide an analog of seismic cycle observed in nature in fully controlled conditions with the associated Acoustic Emission (AE) activity reproducing basic characteristics of seismicity preceding and following the large lab earthquake. Recent laboratory studies showed that the deployment of Machine Learning/Artificial Intelligence techniques has lead to new state of the art results in lab earthquake prediction on smooth faults while using simple statistical features derived from raw AE signals and AE-derived catalogs. However, not enough work has been done on explainability of earthquakes preparatory process on rough faults by leveraging deep learning techniques. In this work we attempt to mitigate this gap and analyze/grade a pool of explainable seismo-mechanical features through the eyes of neural networks.We used AE data from three laboratory stick-slip experiments performed in triaxial pressure vessel on Westerly Granite samples. Samples were first fractured at 75MPa confining pressure creating rough fault surfaces. The following stick-slip experiments were performed at constant displacement rate. The experimental procedure led to an extremely complex slip pattern composed of large and small slips of the whole surface, as well as the confined slips highlighted only with AE data and no externally measured slip. The AE catalog was used to extract temporal evolution of 16 seismo-mechanical and statistical features characterizing evolution of stress and damage in response to the axial stress change. The feature pool included clearly physically interpretable parameters such as AE rates, b-value, fractal dimension, AE localization, clustering and triggering properties, and features characterizing the variability of local stress field. We apply explainable AI techniques to identify what features are more important to forecast axial stress and stress drop. Our feature ranking and importance evaluation with the help of neural networks can serve as an indicator as to what research directions are more promising to take for further feature engineering efforts with an emphasis on explainability of earthquake phenomena.

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.

This research aims at developing and applying a machine learning based algorithm to detect geological structural features at the exposed Dead Sea shoreline. We focus on sinkhole, stream-channel and crack features that appear in different material at the coastlines, and post partly a threat for the local population and infrastructure. We use high resolution orthophotos and satellite images from the last years, as well as derived topographic models to reach an automatic identification and classification of these structures. The aim is to train a convolutional neural network that can identify these structures on recent datasets from satellite images in order to establish an automated detection of hazardous and active zones in the area. Furthermore, we use the algorithms to detect structures in the shallow waters of the lake.

The capability of Deep Learning (DL) for operational wind speed retrieval from the measured Delay-Doppler Maps (DDMs) is recently characterized. It is shown that such techniques can lead to a significant improvement in the derived atmospheric data products. A global ocean dataset is developed processing the measurements of NASA Cyclone GNSS (CYGNSS). The model is based on convolutional layers for direct feature extraction from bistatic radar cross-section (BRCS) DDMs and fully connected layers for processing ancillary technical and higher-level input parameters. This model leads to an RMSE of 1.36 m/s and a significant improvement of 28% in comparison to the officially operational retrieval algorithm.From the theoretical knowledge, several error sources are known, the modeling and correction of which is not easy due to their highly nonlinear interaction with other and the dependent parameters. DL is potentially able to learn such trends and correct the associated biases. For instance, rain splash on the ocean surface and swell waves alter the surface roughness, and consequently, the GNSS scattering patterns, which appear as a considerable bias in GNSS-R wind products. The magnitude of such biases is nonlinearly dependent on several technical and environmental parameters including the reflection geometry, and ocean surface state. After a brief introduction to the known physical mechanisms, we discuss how a DL-based fusion with data on bias-causing parameters, can improve the wind speed predictions.

This presentation reports on support done under the aegis of Helmholtz AI for a wide range of machine learning based solutions for research questions related to Earth and Environmental sciences. We will give insight into typical problem statements from Earth observation and Earth system modeling that are good candidates for experimentation with ML methods and report on our accumulated experience tackling such challenges with individual support projects. We address these projects in an agile, iterative manner and during the definition phase, we direct special attention towards assembling practically meaningful demonstrators within a couple of months. A recent focus of our work lies on tackling software engineering concerns for building ML-ESM hybrids.Our implementation workflow covers stages from data exploration to model tuning. A project may often start with evaluating available data and deciding on basic feasibility, apparent limitations such as biases or a lack of labels, and splitting into training and test data. Setting up a data processing workflow to subselect and compile training data is often the next step, followed by setting up a model architecture. We have made good experience with automatic tooling to tune hyperparameters and test and optimize network architectures. In typical implementation projects, these stages may repeat many times to improve results and cover aspects such as errors due to confusing samples, incorporating domain model knowledge, testing alternative architectures and ML approaches, and dealing with memory limitations and performance optimization.Over the past two years, we have supported Helmholtz-based researchers from many subdisciplines on making the best use of ML methods along with these steps. Example projects include wind speed regression on GNSS-R data, emulation of atmospheric chemistry modeling, Earth System model parameterizations with ML, marine litter detection, and rogue waves prediction. The poster presentation will highlight selected best practices across these projects. We are happy to share our experience as it may prove useful to applications in wider Earth System modeling. If you are interested in discussing your challenge with us, please feel free to chat with us.

Research data currently face a huge increase of data objects with an increasing variety of types (data types, formats) and variety of workflows by which objects need to be managed across their lifecycle by data infrastructures. Researchers desire to shorten the workflows from data generation to analysis and publication, and the full workflow needs to become transparent to multiple stakeholders, including research administrators and funders. This poses challenges for research infrastructures and user-oriented data services in terms of not only making data and workflows findable, accessible, interoperable and reusable, but also doing so in a way that leverages machine support for better efficiency. One primary need to be addressed is that of findability, and achieving better findability has benefits for other aspects of data and workflow management. In this article, we describe how machine capabilities can be extended to make workflows more findable, in particular by leveraging the Digital Object Architecture, common object operations and machine learning techniques.

The extracellular matrix (ECM) of soft tissues in vivo has remarkable biological and structural properties. Thereby, the ECM provides mechanical stability while it still can be rearranged via cellular remodeling during tissue maturation or healing processes. However, modern synthetic alternatives fail to provide these key features among basic properties. Synthetic matrices are usually completely degraded or are inert regarding cellular remodeling. Based on a refined electrospinning process, a method is developed to generate synthetic scaffolds with highly porous fibrous structures and enhanced fiber‐to‐fiber distances. Since this approach allows for cell migration, matrix remodeling, and ECM synthesis, the scaffold provides an ideal platform for the generation of soft tissue equivalents. Using this matrix, an electrospun‐based multilayered skin equivalent composed of a stratified epidermis, a dermal compartment, and a subcutis is able to be generated without the use of animal matrix components. The extension of classical dense electrospun scaffolds with high porosities and motile fibers generates a fully synthetic and defined alternative to collagen‐gel‐based tissue models and is a promising system for the construction of tissue equivalents as in vitro models or in vivo implants.

In coupled global circulation models, chemical interaction between atmospheric trace gases is modelled through dedicated atmospheric chemistry submodels. As these components tend to be computationally expensive, one is often faced with the situation to either run the models with chemistry in relatively coarse resolution, or to ignore atmospheric chemistry altogether. Here an alternative approach is presented in order to overcome the high computational costs while attaining comparable quality of results. A fully connected neural network is used to make predictions of chemical tendencies. As input data of the neural network serve chemical mixing ratios, temperature, pressure, the ozone column and the solar zenith angle, all resulting from the global numerical atmosphere-chemistry model EMAC. The time period considered is 3 month, divided in time steps of consecutive 11 hours. In total, 181 time steps are analysed, from which the first 128 are used as training data, the following 26 as validation data and the last 27 are kept for final testing. The EMAC model produces results of 110 chemicals at a horizontal grid of 160x320 and 90 vertical levels. In our preliminary approach, only 6 of these chemicals - which correspond to the chemicals describing the Chapman mechanism and the nitrogen oxides - are predicted and the analysis area is restricted to the stratosphere. Further, chemicals that are zero at 95% or more of the data points have been deleted from the input data. The results of the neural network represent the spatial patterns of the climate model data very well and are in the same order of magnitude. Spatial correlations depend on the chemical and the vertical level, but are in general >0.95 at levels where the considered variable is present. However, errors are increasing during the validation period, which is probably due to trends in the analysed data. This work presents a proof of concept that neural networks are able to predict atmospheric chemistry tendencies. Left for future work is a detailed hyperparameter tuning in order to optimize the model and the extension to longer time periods to overcome modelling problems due to seasonal trends in the data.

The Global Navigation Satellite System Reflectometry (GNSS-R) is a novel remote sensing technique exploiting GNSS signals after reflection off the Earth's surface. The capability of spaceborne GNSS-R to monitor ocean state and the surface wind is recently well demonstrated, which offers an unprecedented sampling rate and much robustness during rainfall. The Cyclone GNSS (CyGNSS) is the first spaceborne mission fully dedicated to GNSS-R, launched in December 2016.Thanks to the low development costs of the GNSS-R satellite missions as well as the capability of tracking multiple reflected signals from numerous GNSS transmitters, the GNSS-R datasets are much bigger compared to those from conventional remote sensing techniques. The CyGNSS provides a high number of unique samples in the order of a few millions monthly. Deep learning can therefore be implemented in GNSS-R even more efficiently than other remote sensing domains. With the upcoming GNSS-R CubeSats, the data volume is expected to increase in the near future and GNSS-R “Big data” can be a future challenge. Deep learning methods are additionally able to correct the potential effects, both technical and geophysical, dictated by data empirically when the mechanisms are not well described by the theoretical knowledge. This poses the question if GNSS-R should embrace deep learning and can benefit from this modern data scientific method like other Earth Observation domains.The receivers onboard CyGNSS cross-correlate the reflected signals received at a nadir antenna to a locally generated replica. The cross-correlation power at a range of the signal delay and Doppler frequency shift is the observational output of the receivers being called delay-Doppler Maps (DDMs). The mapped power is inversely proportional to the ocean roughness and consequently surface winds.Few recent studies innovatively show some merits of machine learning techniques for the derivations of ocean winds from the DDMs. However, the capability of machine learning techniques, especially deep learning for an operational data derivation needs to be better characterized. Normally, the operational retrieval algorithms are developed based on an existing dataset and are supposed to operate on the upcoming measurements. Therefore, machine learning-based models are supposed to generalize well on the unseen data in future periods. Herein, we aim at the characterization of deep learning capabilities for these GNSS-R operational purposes.In this interdisciplinary study, we present a deep learning algorithm processing the CyGNSS measurements to derive wind speed data. The model is supposed to meet an acceptable level of generalization on the upcoming unseen data, and alternatively can be used as an operational processing algorithm. We propose a deep model based on convolutional and fully connected layers processing the DDMs besides ancillary input features. The model leads to the so-far best quality of global wind speed estimates using GNSS-R measurements with a general root mean square error of 1.3 m/s over unseen data in a time span different from that of the training data.

Various types of synthetic polyesters have been developed as biomaterials for tissue engineering. These materials commonly possess biodegradability, biocompatibility, and formability, which are preferable properties for bone regeneration. The major challenge of using synthetic polyesters is the result of low cell affinity due to their hydrophobic nature, which hinders efficient cell seeding and active cell dynamics. To improve wettability, plasma treatment is widely used in industry. Here, we performed surface activation with oxygen plasma to hydrophobic copolymers, poly(L-lactide-co-trimethylene carbonate), which were shaped in 2D films and 3D microporous scaffolds , and then we evaluated the resulting surface properties and the cellular responses of rat bone marrow stem cells (rBMSC) to the material. Using scanning electron microscopy and Fourier-transform infrared spectroscopy, we demonstrated that short-term plasma treatment increased nanotopographical surface roughness and wettability with minimal change in surface chemistry. On treated surfaces, initial cell adhesion and elongation were significantly promoted, and seeding efficiency was improved. In an osteoinductive environment, rBMSC on plasma-treated scaffolds exhibited accelerated osteogenic differentiation with osteogenic markers including RUNX2, osterix, bone sialoprotein, and osteocalcin upregulated, and a greater amount of collagen matrix and mineral deposition were found. This study shows the utility of plasma surface activation for polymeric scaffolds in bone tissue engineering. publishedVersion

The AIM project (Artificial Intelligence Innovation for Earth System Analytics and Modelling), part of the recently funded Helmholtz AI platform, will research and apply innovative methods from the areas of artificial intelligence and machine learning to Earth system analytics and modelling. As part of the AIM project, DKRZ (German Climate Computing Centre) hosts a team of technical consultants who will support users in reaping the benefits of the new methods through collaborative work on scientific cases, providing, among other things, counselling, technical services and support, trainings. The presentation gives an overview on the scope and services of the consultants team and some first scientific cases worked on.

The AIM project (Artificial Intelligence Innovation for Earth System Analytics and Modelling), part of the recently funded Helmholtz AI platform, will research and apply innovative methods from the areas of artificial intelligence and machine learning to Earth system analytics and modelling. As part of the AIM project, DKRZ (German Climate Computing Centre) hosts a team of technical consultants who will support users in reaping the benefits of the new methods through collaborative work on scientific cases, providing, among other things, counselling, technical services and support, trainings. The presentation gives an overview on the scope and services of the consultants team.

This contribution highlights the Python xarray technique in context of a climate specific application (typical formats are NetCDF, GRIB and HDF).We will see how to use in-file metadata and why they are so powerful for data analysis, in particular by looking at community specific problems, e.g. one can select purely on coordinate variable names. ECAS, the ENES Climate Analytics Service available at Deutsches Klimarechenzentrum (DKRZ), will help by enabling faster access to the high-volume simulation data output from climate modeling experiments. In this respect, we can also make use of “dask” which was developed for parallel computing and can smoothly work with xarray. This is extremely useful when we want to exploit fully the advantages of our supercomputer.Our fully integrated service offers an interface via Jupyter notebooks (ecaslab.dkrz.de). We provide an analysis environment without the need of costly transfers, accessing CF standardized data files and all accessible via the ESGF portal on our nodes (esgf-data.dkrz.de). We can analyse the data of e.g. CMIP5, CMIP6, Grand Ensemble and observation data. ECAS was developed in the frame of European Open Source Cloud (EOSC) hub.

Scientific data analysis experiments and applications require software capable of handling domain-specific and data-intensive workflows. The increasing volume of scientific data is further exacerbating these data management and analytics challenges, pushing the community towards the definition of novel programming environments for dealing efficiently with complex experiments, while abstracting from the underlying computing infrastructure. ECASLab provides a user-friendly data analytics environment to support scientists in their daily research activities, in particular in the climate change domain, by integrating analysis tools with scientific datasets (e.g., from the ESGF data archive) and computing resources (i.e., Cloud and HPC-based). It combines the features of the ENES Climate Analytics Service (ECAS) and the JupyterHub service, with a wide set of scientific libraries from the Python landscape for data manipulation, analysis and visualization. ECASLab is being set up in the frame of the European Open Science Cloud (EOSC) platform - in the EU H2020 EOSC-Hub project - by CMCC (https://ecaslab.cmcc.it/) and DKRZ (https://ecaslab.dkrz.de/), which host two major instances of the environment. ECAS, which lies at the heart of ECASLab, enables scientists to perform data analysis experiments on large volumes of multi-dimensional data by providing a workflow-oriented, PID-supported, server-side and distributed computing approach. ECAS consists of multiple components, centered around the Ophidia High Performance Data Analytics framework, which has been integrated with data access and sharing services (e.g., EUDAT B2DROP/B2SHARE, Onedata), along with the EGI federated cloud infrastructure. The integration with JupyterHub provides a convenient interface for scientists to access the ECAS features for the development and execution of experiments, as well as for sharing results (and the experiment/workflow definition itself). ECAS parallel data analytics capabilities can be easily exploited in Jupyter Notebooks (by means of PyOphidia, the Ophidia Python bindings) together with well-known Python modules for processing and for plotting the results on charts and maps (e.g., Dask, Xarray, NumPy, Matplotlib, etc.). ECAS is also one of the compute services made available to climate scientists by the EU H2020 IS-ENES3 project. Hence, this integrated environment represents a complete software stack for the design and run of interactive experiments as well as complex and data-intensive workflows. One class of such large-scale workflows, efficiently implemented through the environment resources, refers to multi-model data analysis in the context of both CMIP5 and CMIP6 (i.e., precipitation trend analysis orchestrated in parallel over multiple CMIP-based datasets).

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.

The FAIR principles have been widely cited, endorsed and adopted by a broad range of stakeholders since their publication in 2016. By intention, the 15 FAIR guiding principles do not dictate specific technological implementations, but provide guidance for improving Findability, Accessibility, Interoperability and Reusability of digital resources. This has likely contributed to the broad adoption of the FAIR principles, because individual stakeholder communities can implement their own FAIR solutions. However, it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible implementations. Thus, while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways, for true interoperability we need to support convergence in implementation choices that are widely accessible and (re)-usable. We introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible, robust, widespread and consistent FAIR implementations. Any self-identified stakeholder community may either choose to reuse solutions from existing implementations, or when they spot a gap, accept the challenge to create the needed solution, which, ideally, can be used again by other communities in the future. Here, we provide interpretations and implementation considerations (choices and challenges) for each FAIR principle.

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.

The European Network for Earth System Modelling (ENES) Climate Analytics Service (ECAS) is a new service from the EOSC-hub project. It offers a Virtual Research Environment (VRE) to scientific users, combining a Python (Jupyter) work environment with support services for data access, computing and data sharing. ECAS is motivated by providing users with remote access to extensive computing and storage resources beyond what they may have access to locally, reducing the need to conduct costly data transfer, and helping to realize the vision of FAIR data management. ECAS aims at providing a paradigm shift for the ENES community and beyond with a strong focus on data intensive analysis, provenance management, and server-side approaches as opposed to the current ones mostly client-based, sequential and with limited or missing end-to-end analytics workflow and provenance capabilities. Furthermore, the integrated data analytics service enables basic data provenance tracking by establishing a graph of persistent identifiers (PIDs) through the whole chain, and thereby improving reusability, traceability, and reproducibility. ECAS targets multiple user groups, including researchers in lack of local computing and storage resources, researchers with interest in the high-volume climate data pools, and use within education and training scenarios.

The therapeutic efficacy of a medical product after implantation depends strongly on the host-initiated fibrotic response (foreign body reaction). For novel biomaterials, it is of high relevance to understand this fibrotic process. As an alternative to in vivo studies, in vitro models mimic parts of the whole foreign body reaction. Aim of this study was to develop a wound model with key cells and matrix proteins in coculture. This approach combined blood components such as primary macrophages in a plasma-derived fibrin hydrogel, directly exposed to reference biomaterials (PTFE, glass, titanium). The soft tissue reaction is resembled by integrating fibroblasts in a collagen or a fibrin matrix. Those two experimental setups were conducted to show whether a long-term in vitro culture of 13 days is feasible. The response to reference biomaterials was assessed by multi-parametric analyses, comprising molecular profiling (cytokines, collagen I and s-actin) and tissue remodeling (cell adherence, histological structure, tissue deposition). Polytetrafluorethylene (PTFE) and titanium were tested as references to correlate the in vitro evaluation to previous in vivo studies. Most striking, both model setups evaluated references’ fibrotic characteristics as previously reported by in vivo studies. Statement of Significance We present a test platform applied for assessments on the foreign body reaction to biomaterials. This test system consists of blood components – macrophages and plasma-derived fibrin - as well as fibroblasts and collagen, generating a three-dimensional wound microenvironment. By this modular approach, we achieved a suitable test for long-term studies and overcame the limited short-term stability of whole blood tests. In contrast to previous models, macrophages’ viability is maintained during the extended culture period and excels the quality of the model. The potential to evaluate a foreign body reaction in vitro was demonstrated with defined reference materials. This model system might be of high potential as a screening platform to identify novel biomaterial candidates.

Cartilage degeneration is the major cause of chronic pain, lost mobility, and reduced quality of life for over estimated 150 million osteoarthritis sufferers worldwide. Despite intensive research, none of the available therapies can restore the hyaline cartilage surface beyond just fibrous repair. To overcome these limitations, numerous cell-based approaches for cartilage repair are being explored that aim to provide an appropriate microenvironment for chondrocyte maintenance and differentiation of multipotent mesenchymal stem cells (MSCs) toward the chondrogenic lineage. Articular cartilage is composed of highly organized collagen network that entails the tissue into four distinct zones and each zone into three different regions based on differences in matrix morphology and biochemistry. Current cartilage implants cannot establish the hierarchical tissue organization that seems critical for normal cartilage function. Therefore, in this study, a structured, multilayered collagen scaffold designed for the replacement of damaged cartilage is presented that allows repopulation by host cells and synthesis of a new natural matrix. By using the electrospinning method, the potential to engineer a scaffold consisting of two different collagen types is obtained. With the developed collagen scaffold, a five-layered biomaterial is created that has the potency to induce the differentiation of human bone marrow derived MSCs toward the chondrogenic lineage.

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.

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.

Several scientific communities relying on e-science infrastructures are in need of persistent identifiers for data and contextual information. In this article, we present a framework for persistent identification that fundamentally supports context information. It is installed as a number of low-level requirements and abstract data type descriptions, flexible enough to envelope context information while remaining compatible with existing definitions and infrastructures. The abstract data type definitions we draw from the requirements and exemplary use cases can act as an evaluation tool for existing implementations or as a blueprint for future persistent identification infrastructures. A prototypic implementation based on the Handle System is briefly introduced. We also lay the groundwork for establishing a graph of persistent entities that can act as a base layer for more sophisticated information schemas to preserve context information.

Surgical implantation of a biomaterial triggers foreign-body-induced fibrous encapsulation. Two major mechanisms of this complex physiological process are (I) chemotaxis of fibroblasts from surrounding tissue to the implant region, followed by (II) tissue remodeling. As an alternative to animal studies, we here propose a process-aligned \({in}\) \({vitro}\) test platform to investigate the material dependency of fibroblast chemotaxis and tissue remodeling mediated by material-resident macrophages. Embedded in a biomimetic three-dimensional collagen hydrogel, chemotaxis of fibroblasts in the direction of macrophage-material-conditioned cell culture supernatant was analyzed by live cell imaging. A combination of statistical analysis with a complementary parameterized random walk model allowed quantitative and qualitative characterization of the cellular walk process. We thereby identified an increasing macrophage-mediated chemotactic potential ranking of biomaterials from glass over polytetrafluorethylene to titanium. To address long-term effects of biomaterial-resident macrophages on fibroblasts in a three-dimensional microenvironment, we further studied tissue remodeling by applying macrophage-material-conditioned medium on fibrous \({in}\) \({vitro}\) tissue models. A high correlation of the \({in}\) \({vitro}\) tissue model to state of the art \({in}\) \({vivo}\) study data was found. Titanium exhibited a significantly lower tissue remodeling capacity compared to polytetrafluorethylene. With this approach, we identified a material dependency of both chemotaxis and tissue remodeling processes, strengthening knowledge on their specific contribution to the foreign body reaction.

On page 2191, the preparation of a multilayered electrospun scaffold that has the potency to induce chondrogenic differentiation of human mesenchymal stem cells in vitro is reported by Jenny W. Reboredo and co-workers. The novel biomaterial consists of collagen type I and II and is designed for the replacement of damaged cartilage that allows repopulation by host cells and synthesis of a new natural matrix.

Although there are improvements in acute care, ischemic heart disease is the major cause of death worldwide. As a treatment for heart failure or heart infarction, stem cell therapies emerged as a potential therapeutic option. First results have shown moderate improvements and have revealed several limitations, such as an insufficient retention, homing, and engraftment of the cells. These drawbacks result in a loss of cells shortly after implantation. To overcome these hurdles, a human cardiac patch is developed in this work, using a biological collagen-based vascularized scaffold (BioVaSc®). Endothelial cells are cultured in the pre-existing vascular structure to establish a physiological blood-tissue interface. A co-culture of fibroblasts, mesenchymal stem cells, and induced-pluripotent-stem-cell-derived cardiomyocytes is seeded on the vascularized scaffold. After two weeks, physiological cardiac functions and expression of cardiac-specific markers is detected. Moreover, physiological beating rates as well as responsiveness to drug treatment and electrical stimulation is observed. Bioreactor culture facilitates long-term culture up to four months. Due to its tissue characteristics, the patch constitutes a promising tool for drug development, in addition to its potential in clinical applications.

The interaction of cells with substrate surfaces is a key issue in tissue engineering. In a proof-of-concept study, the use of metallo-supramolecular polyelectrolytes (MEPE) as substrates for the adherent growth of eucaryotic cells is investigated. The chosen MEPE comprises iron(II), thus resulting in an overall positive charge of the surface, which is beneficial for cell adherence. Additionally, the metal ions can dissociate from the assembly thereby triggering cellular processes such as cell differentiation. As cell-based test system the well characterized cell line C2C12 is used. The investigated MEPE functions as noncytotoxic, biocompatible substrate. Additionally, the substrate positively affects the myogenic differentiation of the investigated cells. This proof-of-principle study suggests that MEPEs may be interesting as component for tissue engineering approaches.

Climate Knowledge Discovery Workshop; Hamburg, Germany, 30 March to 1 April 2011 Do complex networks combined with semantic Web technologies offer the next generation of solutions in climate science? To address this question, a first Climate Knowledge Discovery (CKD) Workshop, hosted by the German Climate Computing Center (Deutsches Klimarechenzentrum (DKRZ)), brought together climate and computer scientists from major American and European laboratories, data centers, and universities, as well as representatives from industry, the broader academic community, and the semantic Web communities. The participants, representing six countries, were concerned with large-scale Earth system modeling and computational data analysis. The motivation for the meeting was the growing problem that climate scientists generate data faster than it can be interpreted and the need to prepare for further exponential data increases. Current analysis approaches are focused primarily on traditional methods, which are best suited for large-scale phenomena and coarse-resolution data sets. The workshop focused on the open discussion of ideas and technologies to provide the next generation of solutions to cope with the increasing data volumes in climate science.