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We introduce a new software package for determining linkages between datasets without common identifiers. We apply these methods to three datasets commonly used in academic research on syndicated lending: Refinitiv LPC DealScan, the Shared National Credit Database, and S&P Global Market Intelligence Compustat. We benchmark the results of our match using results from the literature and previously matched files that are publicly available. We find that the company level matching is enhanced by careful cleaning of the data and considering hierarchical relationships. For loan level matching, a tailored approach based on a good understanding of the data can be better in certain dimensions than a more pure machine learning approach. The R package for the company level match can be found on Github at https://github.com/seunglee98/fedmatch. [ABSTRACT FROM AUTHOR]

Elizabethkingia miricola ( E. miricola ) is a gram-negative non-fermentative bacterium which is rarely encountered. It is usually misidentified or considered as a contaminant in routine microbiology laboratories due to the limitations in conventional biochemical techniques. However, with the advent of the matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), the identification of non-fermenters has become easy and this has led to enhanced understanding of the clinical significance of these uncommonly isolated microorganisms. The genus Elizabethkingia has only two species E. meningoseptica and E. miricola . Both of these organisms are known to be multi-drug resistant and therefore, their accurate identification and antimicrobial susceptibility testing are necessary prior to the initiation of appropriate therapy. In the world literature till date, only 3 cases of sepsis caused by E. miricola have been reported. We present the first case of E. miricola association with urinary tract infection., Competing Interests: Conflict-of-interest statement: None.