Experimental Proposal for Achieving Superadditive Communication Capacities with a Binary Quantum Alphabet

We demonstrate superadditivity in the communication capacity of a binary alphabet consisting of two nonorthogonal quantum states. For this scheme, collective decoding is performed two transmissions at a time. This improves upon the previous schemes of Sasaki et al. [Phys. Rev. A 58, 146 (1998)] where superadditivity was not achieved until a decoding of three or more transmissions at a time. This places superadditivity within the regime of a near-term laboratory demonstration. We propose an experimental test based upon an alphabet of low photon-number coherent states where the signal decoding is done with atomic state measurements on a single atom in a high-finesse optical cavity.Comment: 7 pages, 5 figure

Outcome after resection for perihilar cholangiocarcinoma in patients with primary sclerosing cholangitis: an international multicentre study

Item does not contain fulltextBACKGROUND: Resection for perihilar cholangiocarcinoma (pCCA) in primary sclerosing cholangitis (PSC) has been reported to lead to worse outcomes than resection for non-PSC pCCA. The aim of this study was to compare prognostic factors and outcomes after resection in patients with PSC-associated pCCA and non-PSC pCCA. METHODS: The international retrospective cohort comprised patients resected for pCCA from 21 centres (2000-2020). Patients operated with hepatobiliary resection, with pCCA verified by histology and with data on PSC status, were included. The primary outcome was overall survival. Secondary outcomes were disease-free survival and postoperative complications. RESULTS: Of 1128 pCCA patients, 34 (3.0%) had underlying PSC. Median overall survival after resection was 33 months for PSC patients and 29 months for non-PSC patients (p = .630). Complications (Clavien-Dindo grade ≥ 3) were more frequent in PSC pCCA (71% versus 44%, p = .003). The rate of posthepatectomy liver failure (21% versus 17%, p = .530) and 90-day mortality (12% versus 13%, p = 1.000) was similar for PSC and non-PSC patients. CONCLUSION: Median overall survival after resection for pCCA was similar in patients with underlying PSC and non-PSC patients. Complications were more frequent after resection for PSC-associated pCCA, with no difference in postoperative mortality

Generalisable long COVID subtypes: Findings from the NIH N3C and RECOVER programmes

Background: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. Methods: We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. Findings: We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. Interpretation: Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. Funding: NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz. © 2022 The AuthorsOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]