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Introduction to Smog Chamber and Its Application in Studying the Formation Mechanisms of Secondary Air Pollution
No full textLayer-specific cell counts in BigBrain – decomposing cortex-wide numbers based on cytoarchitectonics
No full textIntroduction: Cell counts of the cerebral cortex represent one of the most fundamental characteristics of brain organization, and serve as the basis for studying evolution, development and disease (e.g., [1, 2]). However, the total number of cells in the brain or cerebral cortex does not reflect the variation between layers and cortical areas, which is related to the functional heterogeneity of the human brain. Therefore, we investigated layer- specific cell counts in 94 cytoarchitectonic cortical areas in the anatomical BigBrain model [3].Methods: The study is based on cell counts and cortical thickness measurements in high- resolution 2D scans (1 μm in-plane resolution), most of which are already publicly available [4]. In total, 940 patches were analyzed across 94 areas of the Julich Brain Atlas [5] (Fig. 1), with 10 patches sampled per area, ensuring a >65% probability of region being present at each location. Using siibra-python [6], patches were assigned to Julich Brain cytoarchitectonic probabilistic maps by transforming their locations to MNI space [7]. These were chosen to be perpendicular to the cortical surface. Manual layer annotations were performed by anatomical experts and independently verified. Cells were segmented using a novel deep learning approach [8, 9] including correction for truncated cells (Fig1). Cell numbers were corrected for histological shrinkage [10]. To cross-validate data and capture intersubject variability among brains, patches from frontal pole area Fp1, motor area 4a, and visual area hOc1of ten Julich- atlas brains were analyzed using the same counting approach and compared to the BigBrain data. Results: Comprehensive data sets of 940 image patches were obtained including the 1 μm images with manual annotation of cortical layers, the cell counts, cell sizes, and cortical as well as layer thicknesses (Fig1, Fig 2). The analysis revealed a considerable regional variations in cell counts across layers and areas as illustrated in Fig. 2. E.g., areas of the insula showed up to 15% variance. After shrinkage correction and adjusting for truncated cells, the corrected total cell count of the human cortex has been estimated to be approximately 33.9 billion. Based on a neuron-to-glia ratio of 1:1.5 [1], this corresponds to 13.6 billion neurons and 20.3 billion glial cells. The average cortical thickness across all regions was 2667.15 μm. Quantitative measures of areas Fp1 of the BigBrain (Fig. 2) and of the other two areas were in the range of variation of the ten brains from Julich Brain Atlas. The data will be shared as part of a growing dataset collection [11] in accordance with the FAIR principles via the EBRAINS infrastructure.Discussion: This study has introduced a new, comprehensive dataset with detailed area- and layer specific cell counts of the human cerebral cortex, supplementing previous data at whole- cortex level [1]. It extended our knowledge on cytoarchitectonic differences, e.g., etween granular, dysgranular and granular areas and further quantified regional differences at laminar level. The considerable differences between areas within the insular cortex, as one example, confirms the hypothesis that macroanatomically defined regions do not adequately reflect the microstructural organization of the brain; they may lump together structurally and functionally different areas. Data on cortical thickness correspond to earlier histological and MR-based findings [2, 12, 13]. We will continuously supplement the data together with new releases of Julich Brain Atlas areas. Cell counts based on cytoarchitectonics may serve as reference for comparative and disease studies, and inform modeling and simulation, and AI, highlighting the value of high-resolution atlases for capturing details of microscopical brain organization.References:von Bartheld, C.S., et al., The search for true numbers of neurons and glial cells in the human brain: A review of 150 years of cell counting. J Comp Neurol, 2016. 524(18): p. 3865-3895.von Economo, C.F., et al., Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen. 1925: J. Springer.Amunts, K., et al., BigBrain: an ultrahigh-resolution 3D human brain model. Science, 340(6139): p. 1472-5.Schiffer, C., Lepage, C., Omidyeganeh, M., Mohlberg, H., Brandstetter, A., Bludau, S., Heuer, K., Toussaint, P.-J., Wenzel, S., Dickscheid, T., Evans, A. C., & Amunts, K. , Selected 1 micron scans of BigBrain histological sections (v1.0) 2022. Amunts, K., et al., Julich-Brain: A 3D probabilistic atlas of the human brain's cytoarchitecture. Science, 2020. 369(6506): p. 988-992. Dickscheid, T., Gui, X., Simsek, A. N., Koehnen, L., Marcenko, V., Schiffer, C., Bludau, S., & Amunts, K., siibra-python (Zenodo). https://zenodo.org/records/14184565. Lebenberg, J., et al., A framework based on sulcal constraints to align preterm, infant and adult human brain images acquired in vivo and post mortem. Brain Struct Funct, 223(9): p. 4153-4168. Ma, J., et al., The multimodality cell segmentation challenge: toward universal solutions. Nat Methods, 2024. 21(6): p. 1103-1113. Upschulte, E., et al., Contour proposal networks for biomedical instance segmentation. Med Image Anal, 2022. 77: p. 102371. Amunts, K., et al., Gender-specific left-right asymmetries in human visual cortex. J Neurosci, 2007. 27(6): p. 1356-64. Dickscheid, T., Bludau, S., Paquola, C., Schiffer, C., Upschulte, E., & Amunts, K., Layerspecific distributions of segmented cells in different cytoarchitectonic regions of BigBrain iso cortex. EBRAINS project: https://search.kg.ebrains.eu/instances/f06a2fd1-a9ca-42a3-b754-adaa025adb10. Frangou, S., et al., Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years. Hum Brain Mapp, 2022. 43(1): p. 431-451. Wagstyl, K., et al., BigBrain 3D atlas of cortical layers: Cortical and laminar thickness gradients diverge in sensory and motor cortices. PLoS Biol, 2020. 18(4): p. e3000678. </ol
Going 3D with AI: Full 3D Reconstructions of Cytoarchitectonic Maps in BigBrain
No full textThe BigBrain dataset represents the first ultrahigh-resolution 3D model of the human brain at 20 µm isotropic resolution, reconstructed from 7,404 histological sections of a human post-mortem brain. This unique dataset provides the basis for cytoarchitectonic mapping at a level of anatomical detail that bridges microscopic cellular organization with macroscale brain imaging. Traditionally, cytoarchitectonic areas have been delineated on individual histological sections, resulting in 2D maps that are difficult to integrate into 3D brain reference spaces.To address this, we applied the AtLaSUi tool to reconstruct delineated BigBrain areas in full 3D. This workflow transforms manual 2D annotations into volumetric, topologically consistent maps that preserve the fine-grained borders of cortical regions. The resulting 3D maps enable spatially continuous visualization of cortical areas and facilitate direct comparison with structural and functional neuroimaging data.The reconstructed areas are part of the Julich-Brain Atlas, a continuously expanding cytoarchitectonic atlas of the human brain. All maps are openly available through the EBRAINS research infrastructure and can be explored, accessed, and programmatically queried via the siibra tool suite. By making these maps accessible in a standardized 3D reference space, we contribute to the integration of microstructural data with multimodal neuroimaging and to the advancement of open, reproducible neuroscience
Growth, structure, and superconductivity - ferromagnetism interplay in YBa2Cu3O7-x/SrRuO3 thin films and heterostructures
No full text
Partitioning Cryogenic Integrated Electronics for Scalable Spin Qubit Operation
No full textA systematic investigation of currently implemented cryogenic electronics for controlling and reading out spin qubits using complementary metal-oxide semiconductor (CMOS) technology is done. Scalability is the primary focus in developing these circuits, as enabling universal quantum computing requires increasing the number of qubits by several orders of magnitude. Low power dissipation is a main quality benchmark for cryogenic circuits that is being optimized. Next to that, the possibility of connecting multiple control signals to each qubit is a challenge for large qubit numbers. Depending on the placement of the electronics, different power budgets and connectivity options are available. These characteristics are used to discuss medium and long-term use-cases for existing designs and what future concepts and developments might be possible or necessary for full scalability on the example of spin qubit operation electronics
ALMAGAL
No full textContext. A large fraction of stars form in clusters containing high-mass stars, which subsequently influences the local and galaxy-wide environment.Aims. Fundamental questions about the physics responsible for fragmenting molecular parsec-scale clumps into cores of a few thousand astronomical units (au) are still open, that only a statistically significant investigation with ALMA is able to address; for instance: the identification of the dominant agents that determine the core demographics, mass, and spatial distribution as a function of the physical properties of the hosting clumps, their evolutionary stage and the different Galactic environments in which they reside. The extent to which fragmentation is driven by clumps dynamics or mass transport in filaments also remains elusive.Methods. With the ALMAGAL project, we observed the 1.38 mm continuum and lines toward more than 1000 dense clumps in our Galaxy, with M ≥ 500 M⊙, Σ ≥ 0.1 g cm−2 and d ≤ 7.5 kiloparsec (kpc). Two different combinations of ALMA Compact Array (ACA) and 12-m array setups were used to deliver a minimum resolution of ∼1000 au over the entire sample distance range. The sample covers all evolutionary stages from infrared dark clouds (IRDCs) to H II regions from the tip of the Galactic bar to the outskirts of the Galaxy. With a continuum sensitivity of 0.1 mJy, ALMAGAL enables a complete study of the clump-to-core fragmentation process down to M ∼ 0.3 M⊙ across the Galaxy. The spectral setup includes several molecular lines to trace the multiscale physics and dynamics of gas, notably , , , , , , and , among others.Results. We present an initial overview of the observations and the early science product and results produced in the ALMAGAL Consortium, with a first characterization of the morphological properties of the continuum emission detected above 5σ in our fields. We used “perimeter-versus-area” and convex hull-versus-area metrics to classify the different morphologies. We find that more extended and morphologically complex (significantly departing from circular or generally convex) shapes are found toward clumps that are relatively more evolved and have higher surface densities.Conclusions. ALMAGAL is poised to serve as a game-changer for a number of specific issues in star formation: clump-to-core fragmentation processes, demographics of cores, core and clump gas chemistry and dynamics, infall and outflow dynamics, and disk detections. Many of these issues will be covered in the first generation of papers that closely follow on the present publication
Comparative analysis of the flow in a realistic human airway
No full textAccurate simulations of the flow in the human airway are essential for advancing diagnostic methods. Many existing computational studies rely on simplified geometries or turbulence models, limiting their simulation’s ability to resolve flow features such as shear-layer instabilities or secondary vortices. In this study, direct numerical simulations were performed for inspiratory flow through a detailed airway model that covers the nasal mask region to the sixth bronchial bifurcation. Simulations were conducted at two physiologically relevant REYNOLDS numbers with respect to the pharyngeal diameter, i.e., at Re_p = 400 (resting) and Re_p = 1200 (elevated breathing). A lattice-Boltzmann method was employed to directly simulate the flow, i.e., no turbulence model was used. The flow field was examined across four anatomical regions: (1) the nasal cavity, (2) the naso- and oropharynx, (3) the laryngopharynx and larynx, and (4) the trachea and carinal bifurcation. The total pressure loss increased from 9.76 Pa at Re_p = 400 to 41.93 Pa at Re_p = 1200. The nasal cavity accounted for the majority of this loss for both vortices in the nasopharyngeal bend and turbulent shear layers in the glottis jet enhanced the local pressure losses. In contrast, the carinal REYNOLDS numbers, though its relative contribution decreased from 81.3% at Re_p = 400 to 73.4% at Re_p = 1200. At Re_p = 1200, secondary bifurcation mitigated upstream unsteadiness and stabilized the flow. A key outcome is the spatial correlation between the pressure loss and the onset of flow instabilities across the four regions. This yields a novel perspective on how the flow resistance and vortex dynamics vary with geometric changes and flow rate