A Concise Total Synthesis of (--)-Maoecrystal Z

The first total synthesis of (--)-maoecrystal Z is described. The key steps of the synthesis include a diastereoselective Ti^(III)-mediated reductive epoxide coupling reaction and a diastereoselective Sm^(II)-mediated reductive cascade cyclization reaction. These transformations enabled the preparation of (--)-maoecrystal Z in only 12 steps from (--)-γ-cyclogeraniol

A unified strategy for the synthesis of (−)-maoecrystal Z, (−)-trichorabdal A, and (−)-longikaurin E

Herein we describe in full our investigations that led to the completion of the first total syntheses of (−)-maoecrystal Z, (−)-trichorabdal A, and (−)-longikaurin E. The unified strategy employs a Ti^(III)-mediated reductive epoxide coupling to rapidly prepare a key spirolactone. Highly diastereoselective Sm^(II)-mediated reductive cyclizations and a Pd^(II)-mediated oxidative cyclization enable the construction of three architecturally distinct ent-kauranoid frameworks from this common intermediate

Charge Solitons in 1-D Arrays of Serially Coupled Josephson Junctions

We study a 1-D array of Josephson coupled superconducting grains with kinetic inductance which dominates over the Josephson inductance. In this limit the dynamics of excess Cooper pairs in the array is described in terms of charge solitons, created by polarization of the grains. We analyze the dynamics of these topological excitations, which are dual to the fluxons in a long Josephson junction, using the continuum sine-Gordon model. We find that their classical relativistic motion leads to saturation branches in the I-V characteristic of the array. We then discuss the semi-classical quantization of the charge soliton, and show that it is consistent with the large kinetic inductance of the array. We study the dynamics of a quantum charge soliton in a ring-shaped array biased by an external flux through its center. If the dephasing length of the quantum charge soliton is larger than the circumference of the array, quantum phenomena like persistent current and coherent current oscillations are expected. As the characteristic width of the charge soliton is of the order of 100 microns, it is a macroscopic quantum object. We discuss the dephasing mechanisms which can suppress the quantum behaviour of the charge soliton.Comment: 26 pages, LaTex, 7 Postscript figure

Disorder Dynamics in Battery Nanoparticles During Phase Transitions Revealed by Operando Single-Particle Diffraction

Structural and ion-ordering phase transitions limit the viability of sodium-ion intercalation materials in grid scale battery storage by reducing their lifetime. However, the combination of phenomena in nanoparticulate electrodes creates complex behavior that is difficult to investigate, especially on the single nanoparticle scale under operating conditions. In this work, operando single-particle x-ray diffraction (oSP-XRD) is used to observe single-particle rotation, interlayer spacing, and layer misorientation in a functional sodium-ion battery. oSP-XRD is applied to Na$_{2/3}$[Ni$_{1/3}$Mn$_{2/3}$]O$_{2}$, an archetypal P2-type sodium-ion positive electrode material with the notorious P2-O2 phase transition induced by sodium (de)intercalation. It is found that during sodium extraction, the misorientation of crystalline layers inside individual particles increases before the layers suddenly align just prior to the P2-O2 transition. The increase in the long-range order coincides with an additional voltage plateau signifying a phase transition prior to the P2-O2 transition. To explain the layer alignment, a model for the phase evolution is proposed that includes a transition from localized to correlated Jahn-Teller distortions. The model is anticipated to guide further characterization and engineering of sodium-ion intercalation materials with P2-O2 type transitions. oSP-XRD therefore opens a powerful avenue for revealing complex phase behavior in heterogeneous nanoparticulate systems.Comment: 23 pages, 4 main figures, 9 supplemental figure

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing