Leopold Eidlitz: Becoming an American architect

Leopold Eidlitz (1823-1908) was born in Prague and trained in Vienna as a land manager, a position in which he would have worked for the Austrian government as a building inspector or designer of small, rural structures. He came to the United States seeking work as an architect in 1843. Arriving alone, he quickly settled into American society, and within three years moved from a job with Richard Upjohn, the English-born designer of Trinity Church, Wall Street, into his own practice. He subsequently married into an old New England family and began a career in which he worked with the most prominent members of the New York City and State political and architectural communities Although Eidlitz\u27s architectural ideas were progressive, they were not unique for their time. He held that a building\u27s massing should emerge from its plan, that materials should be used in a rational manner, and that ornament should be used to enhance structure, materials, and function. For these reasons, some have considered him an organicist or proto-functionalist. However, his philosophical and architectural concerns were more complex. Eidlitz approved of the emerging convergence of engineering and architecture, but he also believed in the socially redemptive role for art advanced by German Idealist philosophers. He considered architecture to be an art and was certain that science would assure its progress by eliminating the arbitrariness associated with indefinable and unsupportable notions of taste. In this way, art would be reconciled with technology and assure its progress. Emulation of or rupture with the past would not be necessary for architecture because beautiful forms would be valued for the knowledge they imparted rather than the precedent they conveyed

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Lung Cancer Recurrence Risk Prediction through Integrated Deep Learning Evaluation

Background: Prognostic risk factors for completely resected stage IA non-small-cell lung cancers (NSCLCs) have advanced minimally over recent decades. Although several biomarkers have been found to be associated with cancer recurrence, their added value to TNM staging and tumor grade are unclear. Methods: Features of preoperative low-dose CT image and histologic findings of hematoxylin- and eosin-stained tissue sections of resected lung tumor specimens were extracted from 182 stage IA NSCLC patients in the National Lung Screening Trial. These features were combined to predict the risk of tumor recurrence or progression through integrated deep learning evaluation (IDLE). Added values of IDLE to TNM staging and tumor grade in progression risk prediction and risk stratification were evaluated. Results: The 5-year AUC of IDLE was 0.817 ± 0.037 as compared to the AUC = 0.561 ± 0.042 and 0.573 ± 0.044 from the TNM stage and tumor grade, respectively. The IDLE score was significantly associated with cancer recurrence (p < 0.0001) even after adjusting for TNM staging and tumor grade. Synergy between chest CT image markers and histological markers was the driving force of the deep learning algorithm to produce a stronger prognostic predictor. Conclusions: Integrating markers from preoperative CT images and pathologist’s readings of resected lung specimens through deep learning can improve risk stratification of stage 1A NSCLC patients over TNM staging and tumor grade alone. Our study suggests that combining markers from nonoverlapping platforms can increase the cancer risk prediction accuracy.Other UBCNon UBCReviewedFacultyResearche