Primary mediastinal (thymic) B-cell lymphoma is a high-grade non- Hodgkin's lymphoma with unique features. By using comparative genomic hybridization and interphase cytogenetics, 26 tumors were analyzed to identify genomic imbalances. Gains of chromosomal material were much more frequent than losses (110 v 10) and involved chromosomes 9p, 12q, and Xq (31% to 50%). Interestingly, gain of Xq coincided with gain of 9p. Distinct high-level amplifications were found in four subregions. In 2 cases, amplifications of proto-oncogene REL were shown by filter hybridization, indicating a possible pathogenic role of this gene. The characteristic pattern of chromosomal imbalances distinct from other B- cell lymphomas suggests a specific pathway of genetic changes associated with this lymphoma.

technology demonstrates outstanding power conversion efficiencies (PCEs), exceeding 25%. [3] Despite numerous favorable optoelectronic properties of perovskite semiconductors, four key challenges remain and delay the successful commercialization of perovskite solar cells (PSCs): 1) the long-term stability, 2) the toxicity of the contained lead, 3) upscaling to large-areas, and 4) unlocking cost-effective, reliable large-scale production (high throughput and high yield). [2,4] Traditional efforts in material science and device engineering in the field are based on countless trialand-error experiments. However, these approaches for material discovery, process development, characterization, full device evaluation, and stability testing are often complicated, expensive, laborious, and time-consuming given the large experimental parameter space. [5] These drawbacks motivate the implementation of autonomous experimentation methods and data-driven techniques like machine learning (ML). [6,7] In an increasing number of research fields, ML methods are employed to identify yet undiscovered correlations and to provide insights into fundamental working principles. Besides pattern extraction, ML can be utilized to make classifications or predictions and to uncover new insights into the studied data. For this reason, ML algorithms are successfully adopted to an increasing number of applications in materials science, [8][9][10][11] encompassing,

Holistic deciphering of spatially-resolved delivery and biokinetics of nanoparticles (NPs) in the lung, along with the mobility of tissue-resident macrophages (TRMs) and their role in regulating NP cellular fate, remains unclear. Multimodal imaging and deep learning were applied to elucidate the longitudinal inter- and intra-acinar deposition features and regional dosimetry of NPs. The initial NP distribution patterns depended significantly on the pulmonary delivery routes and were most uniform for aerosol inhalation. Artificial intelligence-driven 3D airway segmentation enabled direct determination of bronchial and acinar NP dose. Longitudinal imaging uncovered an intra-acinar NP kinetics profile independent of delivery route. Contrary to the traditional notion of passive diffusion, this study reveals that long-term NP lung retention is facilitated by intra-acinar NP transport mediated by phagocytosis and patrolling of TRMs. Overall, this study elucidates the complexities of NP-lung delivery features and TRM immunity on the fate of biopersistent NPs.