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Abstract The testing and quality assurance of cryogenic superconducting detectors is a time- and labor-intensive process. As experiments deploy increasingly larger arrays of detectors, new methods are needed for performing this testing quickly. Here, we propose a process for flagging under-performing detector wafers before they are ever tested cryogenically. Detectors are imaged under an optical microscope, and computer vision techniques are used to analyze the images, searching for visual defects and other predictors of poor performance. Pipeline performance is verified via a suite of images with simulated defects, yielding a detection accuracy of 98.6%. Lastly, results from running the pipeline on prototype microwave kinetic inductance detectors from the planned SPT-3G+ experiment are presented. 

The Cornell Agricultural Systems Testbed and Demonstration site (CAST) for the Farm of the Future is a testbed and demonstration site for data-driven technologies and management practices where coordinated technology development, testing, demonstration, systematic integration of data, and exchanges of physical materials and ideas are shaping the Farm of the Future. CAST is a cluster of three farms in NY State that hosts data-driven research, extension, and education for crops and dairy production under the aegis of the Cornell Institute for Digital Agriculture. CAST advances climate-smart data-driven solutions for food systems, integrating commercially available and in-the-pipeline technologies and transformative practices. 

The Cornell Agricultural Systems Testbed and Demonstration site (CAST) for the Farm of the Future is a testbed and demonstration site for data-driven technologies and management practices where coordinated technology development, testing, demonstration, systematic integration of data, and exchanges of physical materials and ideas are shaping the Farm of the Future. CAST is a cluster of three farms in NY State that hosts data-driven research, extension, and education for crops and dairy production under the aegis of the Cornell Institute for Digital Agriculture. CAST advances climate-smart data-driven solutions for food systems, integrating commercially available and in-the-pipeline technologies and transformative practices. 

Sea turtles are one taxon of high conservation concern that encounter many pathogens, but their disease ecology is understudied, hindering our ability to predict impacts of disease on population viability. Fibropapillomatosis (FP) is a neoplastic tumor-forming disease that has been documented in all sea turtle species, with an especially high prevalence in green turtlesChelonia mydas.Here, we use Hawaiian green turtles (honu) as a study system to examine the roles of immunogenetic diversity and transcriptional modulation in sea turtle disease responses. Specifically, we quantified gene expression profiles associated with FP and characterized host diversity of major histocompatibility complex class I (MHCI) immune loci. We found 65 genes differentially expressed in blood between clinically healthy (n = 5) and FP-afflicted turtles (n = 5) with enriched biological processes of the innate immune system, aligned with expectations of reptilian immune systems and active disease resistance. Our results also suggest a role for disease tolerance in response to FP, as evidenced by enriched biological processes related to regulation of immune and metabolic homeostasis, increase in cellular detoxification, and increased tissue repair mechanisms. Honu (n = 89) had 23 unique MHCI alleles belonging to 3 distinct functional supertypes, but none were significantly associated with FP; this could be a result of intrinsic demographic properties of the population or reflect a lesser/differing role of the reptilian adaptive immune system. Our study advances the understanding of reptilian disease response and evolutionary mechanisms underlying immunogenetic diversity, both of which are important for promoting the adaptive potential of species vulnerable to extinction.