Flow-to-fracture transition and pattern formation in a discontinuous shear thickening fluid

Recent theoretical and experimental work suggests a frictionless-frictional transition with increasing inter-particle pressure explains the extreme solid-like response of discontinuous shear thickening suspensions. However, analysis of macroscopic discontinuous shear thickening flow in geometries other than the standard rheometry tools remain scarce. Here we use a Hele-Shaw cell geometry to visualise gas-driven invasion patterns in discontinuous shear thickening cornstarch suspensions. We plot quantitative results from pattern analysis in a volume fraction-pressure phase diagram and explain them in context of rheological measurements. We observe three distinct pattern morphologies: viscous fingering, dendritic fracturing, and system-wide fracturing, which correspond to the same packing fraction ranges as weak shear thickening, discontinuous shear thickening, and shear-jammed regimes

Identification of a Carboxyl group at the Active Site of Barley MaIt Phosphatase

The dependence of the maximal velocity, Vm with pH for barley malt phosphatase has been studied from pH 4.0 to 5.4. The data show that an ionizable group in the enzyme-substrate complex with a pK2 of 4-4.6 is important for enzymic activity. These results suggest strongly the involvement of a carboxyl group of a glutamic or aspartic acid residue at the active site of this enzyme

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

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