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Histopathology images capture tissue morphology, while spatial transcriptomics (ST) provides spatially resolved gene expression, offering complementary molecular insights. However, acquiring ST data is costly and time-consuming, limiting its practical use. To address this, we propose HAGE (Hierarchical Alignment Gene-Enhanced), a framework that enhances pathology representation learning by predicting gene expression directly from histological images and integrating molecular context into the pathology model. HAGE leverages gene-type embeddings, which encode relationships among genes, guiding the model in learning biologically meaningful expression patterns. To further improve alignment between histology and gene expression, we introduce a hierarchical clustering strategy that groups image patches based on molecular and visual similarity, capturing both local and global dependencies. HAGE consistently outperforms existing methods across six datasets. In particular, on the HER2+ breast cancer cohort, it significantly improves the Pearson correlation coefficient by 8.0% and achieves substantial reductions in mean squared error and mean absolute error by 18.1% and 38.0%, respectively. Beyond gene expression prediction, HAGE improves downstream tasks, such as patch-level cancer classification and whole-slide image diagnostics, demonstrating its broader applicability. To the best of our knowledge, HAGE is the first framework to integrate gene co-expression as prior knowledge into a pathology image encoder via a cross-attention mechanism, enabling more biologically informed and accurate pathology representations. https://github.com/uta-smile/gene_expression. 

Presentation attacks such as using a contact lens with a printed pattern or printouts of an iris can be utilized to bypass a biometric security system. The first international iris liveness competition was launched in 2013 in order to assess the performance of presentation attack detection (PAD) algorithms, with a second competition in 2015. This paper presents results of the third competition, LivDet-Iris 2017. Three software-based approaches to Presentation Attack Detection were submitted. Four datasets of live and spoof images were tested with an additional cross-sensor test. New datasets and novel situations of data have resulted in this competition being of a higher difficulty than previous competitions. Anonymous received the best results with a rate of rejected live samples of 3.36% and rate of accepted spoof samples of 14.71%. The results show that even with advances, printed iris attacks as well as patterned contacts lenses are still difficult for software-based systems to detect. Printed iris images were easier to be differentiated from live images in comparison to patterned contact lenses as was also seen in previous competitions.