Geometric Inference with Microlens Arrays

This dissertation explores an alternative to traditional fiducial markers where geometric information is inferred from the observed position of 3D points seen in an image. We offer an alternative approach which enables geometric inference based on the relative orientation of markers in an image. We present markers fabricated from microlenses whose appearance changes depending on the marker\u27s orientation relative to the camera. First, we show how to manufacture and calibrate chromo-coding lenticular arrays to create a known relationship between the observed hue and orientation of the array. Second, we use 2 small chromo-coding lenticular arrays to estimate the pose of an object. Third, we use 3 large chromo-coding lenticular arrays to calibrate a camera with a single image. Finally, we create another type of fiducial marker from lenslet arrays that encode orientation with discrete black and white appearances. Collectively, these approaches oer new opportunities for pose estimation and camera calibration that are relevant for robotics, virtual reality, and augmented reality

Analytical performance of an immunoprofiling assay based on RNA models

As immuno-oncology drugs grow more popular in the treatment of cancer, better methods are needed to quantify the tumor immune cell component to determine which patients are most likely to benefit from treatment. Methods such as flow cytometry can accurately assess the composition of infiltrating immune cells; however, they show limited use in formalin-fixed, paraffin-embedded (FFPE) specimens. This article describes a novel hybrid-capture RNA sequencing assay, ImmunoPrism, that estimates the relative percentage abundance of eight immune cell types in FFPE solid tumors. Immune health expression models were generated using machine learning methods and used to uniquely identify each immune cell type using the most discriminatively expressed genes. The analytical performance of the assay was assessed using 101 libraries from 40 FFPE and 32 fresh-frozen samples. With defined samples, ImmunoPrism had a precision of ±2.72%, a total error of 2.75%, and a strong correlation (

Structure from shadow motion

In outdoor images, cast shadows define 3D constraints between the sun, the points casting a shadow, and the sur-faces onto which shadows are cast. This cast shadow struc-ture provides a powerful cue for 3D reconstruction, but re-quires that shadows be tracked over time, and this is dif-ficult as shadows have minimal texture. Thus, we develop a shadow tracking system that enforces geometric consis-tency for each track and then combines thousands of track-ing results to create a 3D model of scene geometry. We demonstrate reconstruction results on a variety of outdoor scenes, including some that show the 3D structure of oc-cluders never directly observed by the camera. 1

T Cell Subtype Profiling measures exhaustion and predicts anti-PD-1 response

Anti-PD-1 therapy can provide long, durable benefit to a fraction of patients. The on-label PD-L1 test, however, does not accurately predict response. To build a better biomarker, we created a method called T Cell Subtype Profiling (TCSP) that characterizes the abundance of T cell subtypes (TCSs) in FFPE specimens using five RNA models. These TCS RNA models are created using functional methods, and robustly discriminate between naïve, activated, exhausted, effector memory, and central memory TCSs, without the reliance on non-specific, classical markers. TCSP is analytically valid and corroborates associations between TCSs and clinical outcomes. Multianalyte biomarkers based on TCS estimates predicted response to anti-PD-1 therapy in three different cancers and outperformed the indicated PD-L1 test, as well as Tumor Mutational Burden. Given the utility of TCSP, we investigated the abundance of TCSs in TCGA cancers and created a portal to enable researchers to discover other TCSP-based biomarkers

Spent nuclear fuel management, characterisation, and dissolution behaviour: progress and achievement from SFC and DisCo

SFC is a work package in Eurad that investigates issues related to the properties of the spent nuclear fuel in the back-end of the nuclear fuel cycle. Decay heat, nuclide inventory, and fuel integrity (mechanical and otherwise), and not least the related uncertainties, are among the primary focal points of SFC. These have very significant importance for the safety and operational aspect of the back-end. One consequence is the operation economy of the back-end, where deeper understanding and quantification allow for significant optimization, meaning that significant parts of the costs can be reduced. In this paper, SFC is described, and examples of results are presented at about half-time of the work package, which will finish in 2024. The DisCo project started in 2017 and finished in November 2021 and was funded under the Horizon 2020 Euratom program. It investigated if the properties of modern fuel types, namely doped fuel, and MOX, cause any significant difference in the dissolution behavior of the fuel matrix compared with standard fuels. Spent nuclear fuel experiments were complemented with studies on model materials as well as the development of models describing the solid state, the dissolution process, and reactive transport in the near field. This research has improved the understanding of processes occurring at the interface between spent nuclear fuel and aqueous solution, such as redox reactions. Overall, the results show that from a long-term fuel matrix dissolution point of view, there is no significant difference between MOX fuel, Cr+Al-doped fuel, and standard fuels

Multidimensional biomarker predicts disease control in response to immunotherapy in recurrent or metastatic head and neck squamous-cell carcinoma.

PURPOSE: Anti-PD-1 therapy provides clinical benefit in 40-50% of patients with relapsed and/or metastatic head and neck squamous cell carcinoma (RM-HNSCC). Selection of anti- PD-1 therapy is typically based on patient PD-L1 immunohistochemistry (IHC) which has low specificity for predicting disease control. Therefore, there is a critical need for a clinical biomarker that will predict clinical benefit to anti-PD-1 treatment with high specificity. METHODS: Clinical treatment and outcomes data for 103 RM-HNSCC patients were paired with RNA-sequencing data from formalin-fixed patient samples. Using logistic regression methods, we developed a novel biomarker classifier based on expression patterns in the tumor immune microenvironment to predict disease control with monotherapy PD-1 inhibitors (pembrolizumab and nivolumab). The performance of the biomarker was internally validated using out-of-bag methods. RESULTS: The biomarker significantly predicted disease control (65% in predicted non-progressors vs. 17% in predicted progressors, p < 0.001) and was significantly correlated with overall survival (OS; p = 0.004). In addition, the biomarker outperformed PD-L1 IHC across numerous metrics including sensitivity (0.79 vs 0.64, respectively; p = 0.005) and specificity (0.70 vs 0.61, respectively; p = 0.009). CONCLUSION: This novel assay uses tumor immune microenvironment expression data to predict disease control and OS with high sensitivity and specificity in patients with RM-HNSCC treated with anti-PD-1 monotherapy