The University of Melbourne, established in 1853, is one of Australia’s oldest and leading research-intensive universities, located in Melbourne, Victoria. It is dedicated to benefiting society through transformative education and research, fostering a culture of curiosity and creativity, and producing highly skilled professionals with a global impact. NEC Labs America collaborates with the University of Melbourne on secure federated optimization and scalable deep learning systems. Our work improves AI training in constrained and distributed environments. Please read about our latest news and collaborative publications with the University of Melbourne.

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Spatial Signatures for Predicting Immunotherapy Outcomes Using Multi-Omics in Non-Small Cell Lung Cancer

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Non-small cell lung cancer (NSCLC) shows variable responses to immunotherapy, highlighting the need for biomarkers to guide patient selection. We applied a spatial multi-omics approach to 234 advanced NSCLC patients treated with programmed death 1-based immunotherapy across three cohorts to identify biomarkers associated with outcome. Spatial proteomics (n?=?67) and spatial compartment-based transcriptomics (n?=?131) enabled profiling of the tumor immune microenvironment (TIME). Using spatial proteomics, we identified a resistance cell-type signature including proliferating tumor cells, granulocytes, vessels (hazard ratio (HR)?=?3.8, P?=?0.004), and a response signature, including M1/M2 macrophages and CD4 T cells (HR?=?0.4, P?=?0.019). We then generated a cell-to-gene resistance signature using spatial transcriptomics, which was predictive of poor outcomes (HR?=?5.3, 2.2, 1.7 across Yale, University of Queensland and University of Athens cohorts), while a cell-to-gene response signature predicted favorable outcomes (HR?=?0.22, 0.38 and 0.56, respectively). This framework enables robust TIME modeling and identifies biomarkers to support precision immunotherapy in NSCLC.

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Multi-parameter distributed fiber sensing with higherorder optical and acoustic modes

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We propose a novel multi-parameter sensing technique based on a Brillouin optical time domain reflectometry in the elliptical-core few-mode fiber, using higher-order optical and acoustic modes. Multiple Brillouin peaks are observed for the backscattering of both the LP01 mode and LP11 mode. We characterize the temperature and strain coefficients for various optical–acoustic mode pairs. By selecting the proper combination of modes pairs, the performance of multi-parameter sensing can be optimized. Distributed sensing of temperature and strain is demonstrated over a 0.5-km elliptical-core few-mode fiber, with the discriminative uncertainty of 0.28°C and 5.81 ?? for temperature and strain, respectively.

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Distributed Temperature and Strain Sensing Using Brillouin Optical Time Domain Reflectometry Over a Few Mode Elliptical Core Optical Fiber

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We propose a single-ended Brillouin-based sensor in elliptical-core few-mode optical fiber for multi-parameter measurement using spontaneous Brillouin scattering. Distributed sensing of temperature and strain is demonstrated over 0.5 km elliptical-core few-mode fiber.

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