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Cellular senescence is characterized by an irreversible cell cycle arrest and a pro‐inflammatory senescence‐associated secretory phenotype (SASP), which is a major contributor to aging and age‐related diseases. Clearance of senescent cells has been shown to improve brain function in mouse models of neurodegenerative diseases. However, it is still unknown whether senescent cell clearance alleviates cognitive dysfunction during the aging process. To investigate this, we first conducted single‐nuclei and single‐cell RNA‐seq in the hippocampus from young and aged mice. We observed an age‐dependent increase in p16Ink4a senescent cells, which was more pronounced in microglia and oligodendrocyte progenitor cells and characterized by a SASP. We then aged INK‐ATTAC mice, in which p16Ink4a‐positive senescent cells can be genetically eliminated upon treatment with the drug AP20187 and treated them either with AP20187 or with the senolytic cocktail Dasatinib and Quercetin. We observed that both strategies resulted in a decrease in p16Ink4a exclusively in the microglial population, resulting in reduced microglial activation and reduced expression of SASP factors. Importantly, both approaches significantly improved cognitive function in aged mice. Our data provide proof‐of‐concept for senolytic interventions' being a potential therapeutic avenue for alleviating age‐associated cognitive impairment. Senescence is a major contributor to aging and age‐related diseases. However, it is still unknown whether senolytics impact on cognitive function during the aging process. We found that both pharmacogenetic clearance of p16Ink4a senescent cells or treatment with senolytic cocktail Dasatinib and Quercetin, reduced senescent microglia in the hippocampus and improved cognitive function in aged mice.

In this paper, we explore the use of generative artificial intelligence (GenAI) for ship propeller design. While traditional forward machine learning models predict the performance of mechanical components based on given design parameters, GenAI models aim to generate designs that achieve specified performance targets. In particular, we employ conditional flow matching to establish a bidirectional mapping between design parameters and simulated noise that is conditioned on performance labels. This approach enables the generation of multiple valid designs corresponding to the same performance targets by sampling over the noise vector. To support model training, we generate data using a vortex lattice method for numerical simulation and analyze the trade-off between model accuracy and the amount of available data. We further propose data augmentation using pseudo-labels derived from less data-intensive forward surrogate models, which can often improve overall model performance. Finally, we present examples of distinct propeller geometries that exhibit nearly identical performance characteristics, illustrating the versatility and potential of GenAI in engineering design.

Convolutional neural networks revolutionized computer vision and natrual language processing. Their efficiency, as compared to fully connected neural networks, has its origin in the architecture, where convolutions reflect the translation invariance in space and time in pattern or speech recognition tasks. Recently, Cohen and Welling have put this in the broader perspective of invariance under symmetry groups, which leads to the concept of group equivaiant neural networks and more generally steerable neural networks. In this article, we review the architecture of such networks including equivariant layers and filter banks, activation with capsules and group pooling. We apply this formalism to the symmetric group, for which we work out a number of details on representations and capsules that are not found in the literature.

Generative learning generates high dimensional data based on low dimensional conditions, also called prompts. Therefore, generative learning algorithms are eligible for solving (Bayesian) inverse problems. In this article we compare a traditional Bayesian inverse approach based on a forward regression model and a prior sampled with the Markov Chain Monte Carlo method with three state of the art generative learning models, namely conditional Generative Adversarial Networks, Invertible Neural Networks and Conditional Flow Matching. We apply them to a problem of gas turbine combustor design where we map six independent design parameters to three performance labels. We propose several metrics for the evaluation of this inverse design approaches and measure the accuracy of the labels of the generated designs along with the diversity. We also study the performance as a function of the training dataset size. Our benchmark has a clear winner, as Conditional Flow Matching consistently outperforms all competing approaches.

The need to burn 100% H2 in high efficient gas turbines featuring low NOx combustion in premix mode require the complete redesign of the combustion system to ensure stable operation without any flashback. Since all engine frames featuring a power range from 4 MW up to 600 MW are affected, a huge design effort is expected. To reduce this effort, especially to transfer knowledge between the different engine classes, generative design methods using latest AI technology will provide promising potential. In this work, this challenge is approached utilizing the current advances in generative artificial intelligence. We train an Invertible Neural Network (INN) on an expandable database of geometrically parameterized combustor designs with simulated performance labels. Utilizing the INN in its inverse direction, multiple design proposals are generated which fulfill specified performance labels.

This work deals with uncertainty quantification for a generic input distribution to some resource-intensive simulation, e.g., requiring the solution of a partial differential equation. While efficient numerical methods exist to compute integrals for high-dimensional Gaussian and other separable distributions based on sparse grids (SG), input data arising in practice often does not fall into this class. We therefore employ transport maps to transform complex distributions to multivatiate standard normals. In generative learning, a number of neural network architectures have been introduced that accomplish this task approximately. Examples are affine coupling flows (ACF) and ordinary differential equation-based networks such as conditional flow matching (CFM). To compute the expectation of a quantity of interest, we numerically integrate the composition of the inverse of the learned transport map with the simulation code output. As this map is integrated over a multivariate Gaussian distribution, SG techniques can be applied. Viewing the images of the SG quadrature nodes as learned quadrature nodes for a given complex distribution motivates our title. We demonstrate our method for monomials of total degrees for which the unmapped SG rules are exact. We also apply our approach to the stationary diffusion equation with coefficients modeled by exponentiated Lévy random fields, using a Karhunen-Loève-like modal expansions with 9 and 25 modes. In a series of numerical experiments, we investigate errors due to learning accuracy, quadrature, statistical estimation, truncation of the modal series of the input random field, and training data size for three normalizing flows (ACF, conditional Flow Matching and Optimal transport Flow Matching) We discuss the mathematical assumptions on which our approach is based and demonstrate its shortcomings when these are violated.

Studies of military casualties and their influence on political will and combat effectiveness are common. Studies of civilian casualties are rare. This book addresses this gap in the literature. As an introduction to the full range of methods for recording and estimating casualties, the set of essays will be a foundation for the nascent field of counting civilian casualties. Collaboration between Carnegie Mellon University and the University of Pittsburgh brought together for first time researchers from a diverse set of disciplines to discuss various methodologies for recording and estimating civilian casualties. The chapters reflect the unique exchange of views among top specialists, many of whom had not previously engaged each other in debate. The book is unusual in its geographical coverage. The chapters include cases from Latin America, South America, Europe, Africa, the Middle East, and Asia. While some of the studies focus on a single country, the majority of them are comparative in nature.

Friedreich ataxia (FA) is an autosomal recessive disease with a complex neurological phenotype, but the most common cause of death is heart failure. This study presents a systematic analysis of 15 fixed and 13 frozen archival autopsy tissues of FA hearts and 10 normal controls (8 frozen) by measurement of cardiomyocyte hypertrophy; tissue frataxin assay; X-ray fluorescence (XRF) of iron (Fe) and zinc (Zn) in polyethylene glycol-embedded samples of left and right ventricular walls (LVW, RVW) and ventricular septum (VS); metal quantification in bulk digests by inductively-coupled plasma optical emission spectrometry (ICP-OES); Fe histochemistry; and immunohistochemistry and immunofluorescence of cytosolic and mitochondrial ferritins and of the inflammatory markers CD68 and hepcidin. FA cardiomyocytes were significantly larger than normal and surrounded by fibrotic endomysium. Frataxin in LVW was reduced to less than 15 ng/g wet weight (normal 235.4 ± 75.1 ng/g). All sections displayed characteristic Fe-reactive inclusions in cardiomyocytes, and XRF confirmed significant regional Fe accumulation in LVW and VS. In contrast, ICP-OES analysis of bulk extracts revealed normal total Fe levels in LVW, RVW, and VS. Cardiac Zn remained normal by XRF and assay of bulk digests. Cytosolic and mitochondrial ferritins exhibited extensive co-localization in cardiomyocytes, representing translational and transcriptional responses to Fe, respectively. Fe accumulation progressed from a few small granules to coarse aggregates in phagocytized cardiomyocytes. All cases met the \"Dallas criteria\" of myocarditis. Inflammatory cells contained CD68 and cytosolic ferritin, and most also expressed the Fe-regulatory hormone hepcidin. Inflammation is an important factor in the pathogenesis of FA cardiomyopathy but may be more evident in advanced stages of the disease. Hepcidin-induced failure of Fe export from macrophages is a likely contributory cause of damage to the heart in FA. Frataxin replacement and anti-inflammatory agents are potential therapies in FA cardiomyopathy.

Sport occupies a key position in the cultural profile of a nation. This study forms a comparative guide to sport across Europe, in terms of its relative political and social status, and how it has contributed to national achievement.