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Human cerebral organoids model tumor infiltration and migration supported by astrocytes in an autologous setting
No full textSummaryEfforts to achieve precise and efficient tumor targeting of highly malignant brain tumors are constrained by the dearth of appropriate models to study the effects and potential side effects of radiation, chemotherapy, and immunotherapy on the most complex human organ, the brain. We established a cerebral organoid model of brain tumorigenesis in an autologous setting by overexpressing c-MYC as one of the most common oncogenes in brain tumors. GFP+/c-MYChighcells were isolated from tumor organoids and used in two different culture approaches: assembloids comprising of a normal cerebral organoid with a GFP+/c-MYChightumor sphere and co-culture of cerebral organoid slices at air-liquid interface with GFP+/c-MYChighcells. GFP+/c-MYChighcells used in both approaches exhibited tumor-like properties, including overexpression of the c-MYC oncogene, high proliferative and invasive potential, and an immature phenotype as evidenced by increased expression of Ki-67, VIM, and CD133. Organoids and organoid slices served as suitable scaffolds for infiltrating tumor-like cells. Using our highly reproducible and powerful model system that allows long-term culture, we demonstrated that the migratory and infiltrative potential of tumor-like cells is shaped by the environment in which glia cells provide support to tumor-like cells
Revealing patterns in major depressive disorder with machine learning and networks
No full textMajor depressive disorder (MDD) is a multifaceted condition that affects millions of people worldwide and is a leading cause of disability. There is an urgent need for an automated and objective method to detect MDD due to the limitations of traditional diagnostic approaches. In this paper, we propose a methodology based on machine and deep learning to classify patients with MDD and identify altered functional connectivity patterns from EEG data. We compare several connectivity metrics and machine learning algorithms. Complex network measures are used to identify structural brain abnormalities in MDD. Using Spearman correlation for network construction and the SVM classifier, we verify that it is possible to identify MDD patients with high accuracy, exceeding literature results. The SHAP (SHAPley Additive Explanations) summary plot highlights the importance of C4-F8 connections and also reveals dysfunction in certain brain areas and hyperconnectivity in others. Despite the lower performance of the complex network measures for the classification problem, assortativity was found to be a promising biomarker. Our findings suggest that understanding and diagnosing MDD may be aided by the use of machine learning methods and complex networks
Radiation measurements in the stratosphere by the balloon experiment ASTRABAX
No full textStratospheric balloons are a platform of choice for meteorology, climate and atmospheric research, offering some advantages over sounding rockets and satellites. The Aschaffenburg Stratospheric Balloon Experiment (ASTRABAX)
carries out experiments on material treatments and life sciences under the extreme radiation exposures at high altitude. The pioneer ASTRABAX flight took place mid of October 2024 in Germany. Measurements examined the UV spectral region by a miniature spectrometer and captured secondary cosmic rays employing a Geiger counter. Additional experiments assessed irradiation effects on polydopamine X-ray mirror coatings. The platform also carried samples of biological cells to evaluate effects of exposure to low-dose irradiation of high-energy particles, gamma rays and UV radiation simultaneously. Biological experiments under such realistic conditions are relevant for genetic research on Earth, for human space flights as well as for the understanding of astrobiological processes
Polydopamine: a bio-inspired polymer for X-ray mirror coatings and other technical applications
No full textAlthough the organic molecule dopamine (3,4-dihydroxyphenethylamine) is commonly known as one of the “hormones of happiness”, thin polymer films of polydopamine (PDA) also have interesting technical properties. PDA is a very strong glue that sticks on almost everything, even under water. In nature, PDA is found in the byssal thread cuticles of mussels. When produced by dip-coating, the self-organizing PDA layers grow in a reproducible thickness of single or multiple molecule monolayers of a few nanometres thickness only. Here we present an optimized preparation regime as derived from polymerization analysis through absorption spectroscopy. One application is the use of thin PDA overcoatings to increase the soft X-ray reflectivity of astronomical X-ray mirrors. Furthermore, we give an outlook to other technical applications for this interesting material, presenting this bio-inspired organic polymer as an innovative technical solution for the future, with applications such as PDA-based super-capacitors and its promising role in enhancing separator materials for batteries
Novel X-ray optics developed within the AHEAD2020 project
No full textWe present a summary of our contribution to the EU Horizon 2020 project AHEAD2020, with emphasis on the X-ray optics work package. The Czech Technical University – together with other collaborating institutes - studied innovative Lobster Eye (LE) and Kirkpatrick-Baez (KB) X-ray modules, based on the Multi Foil Optics technology (MFO). In addition, two major events were organized, namely a students workshop in December 2023 and a summer school in May 2024. The KB optics represents a promising and cost effective alternative to the currently used Wolter I telescopes. The LE X-ray optics, based on the Schmidt design, has a wide field of view (FOV) with a short focal length, making it suitable for CubeSat application. Thereby the 2D LE optics consist of two orthogonal sub-modules of flat smooth reflecting foils, each sub-modules focuses in one direction. The advantage of such optics is that it preserves the angular resolution throughout the FOV even for off-axis points, as demonstrated by simulations and measurements. There was a collaboration with Aschaffenburg University in design, development, and testing of the double LE module HORUS, comparing different reflecting coatings
Fabrication and analysis of through-glass vias for glass-based electronic packaging using an ultrashort pulsed laser
No full textFractographic Analysis and Fatigue Behavior of Additively Manufactured Ni‐Superalloy Components with Post Processing Heat Treatment and Hot Isostatic Pressing
No full textA report is made on a study of mechanical properties and fractographic characteristics of laser powder bed fusion (PBF‐LB/M)‐built Inconel 718, performing heat treatments and hot‐isostatic pressing. For this, tensile components are heat‐treated by different processes, as namely stress relief (SR), SR and double aging (SR + DA), and hot‐isostatic pressing are conducted. For the mechanical testing, the ultimate tensile strength (UTS) as well as the fatigue behavior are evaluated, examining differences in maximum load behavior, elongation, and the different regimes of fatigue. As changes in material structure can be observed, the sole SR leads to a diminished UTS, while the combination of SR + DA develops an UTS of
R
m
= 1277 MPa. Within the fatigue behavior, the HIP shows a very balanced material structure with an increased high cycle and very high cycle regime, as the texture gets homogenized during the heat treatment. The metallographic analysis can quantify the material changes, as the density and the hardness are improved by virtue of the heat treatments. Furthermore, the fractographic analysis shows the differences in fracture behavior, arising due to the microstructural changes, as crack initiation points, crack propagation, and forced fractures can be categorized by scanning electron microscopy
Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks
No full textCut-in maneuvers in high-speed traffic pose critical challenges that can lead to abrupt braking and collisions, necessitating safe and efficient lane change strategies. We propose a Dynamic Bayesian Network (DBN) framework to integrate lateral evidence with safety assessment models, thereby predicting lane changes and ensuring safe cut-in maneuvers effectively. Our proposed framework comprises three key probabilistic hypotheses (lateral evidence, lateral safety, and longitudinal safety) that facilitate the decision-making process through dynamic data processing and assessments of vehicle positions, lateral velocities, relative distance, and Time-to-Collision (TTC) computations. The DBN model's performance compared with other conventional approaches demonstrates superior performance in crash reduction, especially in critical high-speed scenarios, while maintaining a competitive performance in low-speed scenarios. This paves the way for robust, scalable, and efficient safety validation in automated driving systems
