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Spatial transcriptomics (ST) provides essential spatial context by mapping gene expression within tissue, enabling detailed study of cellular heterogeneity and tissue organization. However, aligning ST data with histology images poses challenges due to inherent spatial distortions and modality-specific variations. Existing methods largely rely on direct alignment, which often fails to capture complex cross-modal relationships. To address these limitations, we propose a novel framework that aligns gene and image features using a ranking-based alignment loss, preserving relative similarity across modalities and enabling robust multi-scale alignment. To further enhance the alignment's stability, we employ self-supervised knowledge distillation with a teacher-student network architecture, effectively mitigating disruptions from high dimensionality, sparsity, and noise in gene expression data. Extensive experiments on gene expression prediction and survival analysis demonstrate our framework's effectiveness, showing improved alignment and predictive performance over existing methods and establishing a robust tool for gene-guided image representation learning in digital pathology., Comment: 17 pages, 8 figures

Glioblastoma is the most common primary adult brain tumor, with a grim prognosis - median survival of 12-18 months following treatment, and 4 months otherwise. Glioblastoma is widely infiltrative in the cerebral hemispheres and well-defined by heterogeneous molecular and micro-environmental histopathologic profiles, which pose a major obstacle in treatment. Correctly diagnosing these tumors and assessing their heterogeneity is crucial for choosing the precise treatment and potentially enhancing patient survival rates. In the gold-standard histopathology-based approach to tumor diagnosis, detecting various morpho-pathological features of distinct histology throughout digitized tissue sections is crucial. Such "features" include the presence of cellular tumor, geographic necrosis, pseudopalisading necrosis, areas abundant in microvascular proliferation, infiltration into the cortex, wide extension in subcortical white matter, leptomeningeal infiltration, regions dense with macrophages, and the presence of perivascular or scattered lymphocytes. With these features in mind and building upon the main aim of the BraTS Cluster of Challenges https://www.synapse.org/brats2024, the goal of the BraTS-Path challenge is to provide a systematically prepared comprehensive dataset and a benchmarking environment to develop and fairly compare deep-learning models capable of identifying tumor sub-regions of distinct histologic profile. These models aim to further our understanding of the disease and assist in the diagnosis and grading of conditions in a consistent manner.

The most sensitive direct method to establish the absolute neutrino mass is observation of the endpoint of the tritium beta-decay spectrum. Cyclotron Radiation Emission Spectroscopy (CRES) is a precision spectrographic technique that can probe much of the unexplored neutrino mass range with $\mathcal{O}({\rm eV})$ resolution. A lower bound of $m(\nu_e) \gtrsim 9(0.1)\, {\rm meV}$ is set by observations of neutrino oscillations, while the KATRIN Experiment - the current-generation tritium beta-decay experiment that is based on Magnetic Adiabatic Collimation with an Electrostatic (MAC-E) filter - will achieve a sensitivity of $m(\nu_e) \lesssim 0.2\,{\rm eV}$. The CRES technique aims to avoid the difficulties in scaling up a MAC-E filter-based experiment to achieve a lower mass sensitivity. In this paper we review the current status of the CRES technique and describe Project 8, a phased absolute neutrino mass experiment that has the potential to reach sensitivities down to $m(\nu_e) \lesssim 40\,{\rm meV}$ using an atomic tritium source., Comment: 19 pages, 8 figures, to be published in Journal of Physics G

This is a review of the theoretical background, experimental techniques, and phenomenology of what is called the "Glauber Model" in relativistic heavy ion physics. This model is used to calculate "geometric" quantities, which are typically expressed as impact parameter (b), number of participating nucleons (N_part) and number of binary nucleon-nucleon collisions (N_coll). A brief history of the original Glauber model is presented, with emphasis on its development into the purely classical, geometric picture that is used for present-day data analyses. Distinctions are made between the "optical limit" and Monte Carlo approaches, which are often used interchangably but have some essential differences in particular contexts. The methods used by the four RHIC experiments are compared and contrasted, although the end results are reassuringly similar for the various geometric observables. Finally, several important RHIC measurements are highlighted that rely on geometric quantities, estimated from Glauber calculations, to draw insight from experimental observables. The status and future of Glauber modeling in the next generation of heavy ion physics studies is briefly discussed., Comment: 43 pages, 26 figures, submitted to Annual Review of Nuclear and Particle Science