Improving plant drought tolerance and growth under water limitation through combinatorial engineering of signalling networks

Agriculture is by far the biggest water consumer on our planet, accounting for 70 per cent of all freshwater withdrawals. Climate change and a growing world population increase pressure on agriculture to use water more efficiently ('more crop per drop'). Water-use efficiency (WUE) and drought tolerance of crops are complex traits that are determined by many physiological processes whose interplay is not well understood. Here, we describe a combinatorial engineering approach to optimize signalling networks involved in the control of stress tolerance. Screening a large population of combinatorially transformed plant lines, we identified a combination of calcium-dependent protein kinase genes that confers enhanced drought stress tolerance and improved growth under water-limiting conditions. Targeted introduction of this gene combination into plants increased plant survival under drought and enhanced growth under water-limited conditions. Our work provides an efficient strategy for engineering complex signalling networks to improve plant performance under adverse environmental conditions, which does not depend on prior understanding of network function

Developing serious games for cultural heritage: a state-of-the-art review

Although the widespread use of gaming for leisure purposes has been well documented, the use of games to support cultural heritage purposes, such as historical teaching and learning, or for enhancing museum visits, has been less well considered. The state-of-the-art in serious game technology is identical to that of the state-of-the-art in entertainment games technology. As a result, the field of serious heritage games concerns itself with recent advances in computer games, real-time computer graphics, virtual and augmented reality and artificial intelligence. On the other hand, the main strengths of serious gaming applications may be generalised as being in the areas of communication, visual expression of information, collaboration mechanisms, interactivity and entertainment. In this report, we will focus on the state-of-the-art with respect to the theories, methods and technologies used in serious heritage games. We provide an overview of existing literature of relevance to the domain, discuss the strengths and weaknesses of the described methods and point out unsolved problems and challenges. In addition, several case studies illustrating the application of methods and technologies used in cultural heritage are presented

The Polycomb repressive complex 2 deposits H3K27me3 and represses transposable elements in a broad range of eukaryotes

The mobility of transposable elements (TEs) contributes to evolution of genomes. Their uncontrolled activity causes genomic instability; therefore, expression of TEs is silenced by host genomes. TEs are marked with DNA and H3K9 methylation, which are associated with silencing in flowering plants, animals, and fungi. However, in distantly related groups of eukaryotes, TEs are marked by H3K27me3 deposited by the Polycomb repressive complex 2 (PRC2), an epigenetic mark associated with gene silencing in flowering plants and animals. The direct silencing of TEs by PRC2 has so far only been shown in one species of ciliates. To test if PRC2 silences TEs in a broader range of eukaryotes, we generated mutants with reduced PRC2 activity and analyzed the role of PRC2 in extant species along the lineage of Archaeplastida and in the diatom P. tricornutum. In this diatom and the red alga C. merolae, a greater proportion of TEs than genes were repressed by PRC2, whereas a greater proportion of genes than TEs were repressed by PRC2 in bryophytes. In flowering plants, TEs contained potential cis-elements recognized by transcription factors and associated with neighbor genes as transcriptional units repressed by PRC2. Thus, silencing of TEs by PRC2 is observed not only in Archaeplastida but also in diatoms and ciliates, suggesting that PRC2 deposited H3K27me3 to silence TEs in the last common ancestor of eukaryotes. We hypothesize that during the evolution of Archaeplastida, TE fragments marked with H3K27me3 were selected to shape transcriptional regulation, controlling networks of genes regulated by PRC2

Analysis of the Effects of MARME Treatment on Respiratory Flow Using the Lattice-Boltzmann Method

Transverse maxillary deficiency is a common pathological condition. Patients suffering from this pathology often have narrowed airways compared to healthy humans. To cure such an anatomic defective position, a new method, the Miniscrew-Assisted Rapid Maxillary Expansion (MARME), has been developed. In previous studies, the effects of this treatment on respiration have been analyzed by examining the volume of a nasal cavity and the corresponding nasopharynx before and after treatment. In this study the fluid mechanical effects of MARME treatment on the respiratory flow and on the breathing capability are analyzed numerically. The realistic three-dimensional geometries of the nasal cavity employed for the simulation are reconstructed from Computer Tomography images. The flow within these geometries is simulated using a thermal Lattice-Boltzmann method. The results confirm that the respiratory resistance and the average wall-shear stress decrease after the MARME treatment. The heating capability, however, deteriorates

Practice and Experience using High Performance Computing and Quantum Computing to Speed-up Data Science Methods in Scientific Applications

High-Performance Computing (HPC) can quickly process scientific data and perform complex calculations at extremely high speeds. A vast increase in HPC use across scientific communities is observed, especially in using parallel data science methods to speed-up scientific applications. HPC enables scaling up machine and deep learning algorithms that inherently solve optimization problems. More recently, the field of quantum machine learning evolved as another HPC related approach to speed-up data science methods. This paper will address primarily traditional HPC and partly the new quantum machine learning aspects, whereby the latter specifically focus on our experiences on using quantum annealing at the Juelich Supercomputing Centre (JSC). Quantum annealing is particularly effective for solving optimization problems like those that are inherent in machine learning methods. We contrast these new experiences with our lessons learned of using many parallel data science methods with a high number of Graphical Processing Units (GPUs). That includes modular supercomputers such as JUWELS, the fastest European supercomputer at the time of writing. Apart from practice and experience with HPC co-design applications, technical challenges and solutions are discussed, such as using interactive access via JupyterLab on typical batch-oriented HPC systems or enabling distributed training tools for deep learning on our HPC systems

Visualisierung auf Großbildschirmen: Herausforderung eines neuen Ausgabegeräts

Entwicklungen in der Displaytechnologie haben in den vergangenen Jahren eine Vielzahl hochauflösender Bildschirme hervorgebracht. Der Forschungsverbund ,,Information at your finger tips – interaktive Visualisierung für Gigapixel Displays“ hat sich mit den Herausforderungen beschäftigt, die diese Technologie für viele Bereiche der Informatik in sich birgt. Hierbei wurden sowohl neue Grafiksysteme untersucht als auch Interaktionsmethoden und Darstellungsformen sowie deren Anwendung in Visualisierung und Kunst

Computational Meshing for CFD Simulations

In CFD modelling, small cells or elements are created to fill the volume to simulate the flow in. They constitute a mesh where each cell represents a discrete space that represents the flow locally. Mathematical equations that represent the flow physics are then applied to each cell of the mesh. Generating a high quality mesh is extremely important to obtain reliable solutions and to guarantee numerical stability. This chapter begins with a basic introduction to a typical workflow and guidelines for generating high quality meshes, and concludes with some more advanced topics, i.e., how to generate meshes in parallel, a discussion on mesh quality, and examples on the application of lattice-Boltzmann methods to simulate flow without any turbulence modelling on highly-resolved meshes

Enabling Hyperparameter-Tuning of AI Models for Healthcare using the CoE RAISE Unique AI Framework for HPC

The European Center of Excellence in Exascale Computing "Research on AI- and Simulation-Based Engineering at Exascale" (CoE RAISE) is a project funded by the European Commission. One of its central goals is to develop a Unique AI Framework (UAIF) that simplifies the development of AI models on cutting-edge supercomputers. However, those supercomputers’ High-Performance Computing (HPC) environments require the knowledge of many low-level modules that all need to work together in different software versions (e.g., TensorFlow, Python, NCCL, PyTorch) and various concrete supercomputer hardware deployments (e.g., JUWELS, JURECA, DEEP, JUPITER and other EuroHPC Joint Undertaking HPC resources). This paper will describe our analyzed complex challenges for AI researchers using those environments and explain how to overcome them using the UAIF. In addition, it will show the benefits of using the UAIF hypertuning capability to make AI models better (i.e., better parameters) and faster by using HPC. Also, to demonstrate that the UAIF approach is indeed simple, we describe the adoption of selected UAIF building blocks by healthcare applications. The examples include AI models for the Acute Respiratory Distress Syndrome (ARDS). Finally, we highlight other AI models of use cases that co-designed the UAIF