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

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

Simultaneous determination of the UV-filters benzyl salicylate, phenyl salicylate, octyl salicylate, homosalate, 3-(4-methylbenzylidene) camphor and 3-benzylidene camphor in human placental tissue by LC–MS/MS. Assessment of their in vitro endocrine activity

UV-filters are widely used in many personal care products and cosmetics. Recent studies indicate that some organic UV-filters can accumulate in biota and act as endocrine disruptors, but there are few studies on the occurrence and fate of these compounds in humans. In the present work, a new liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to assess the presence of six UV-filters in current use (benzyl salicylate, phenyl salicylate, octyl salicylate, homosalate, 3-(4-methylbenzylidene) camphor, and 3-benzylidene camphor) in human placental tissue is proposed. Bisphenol A-d16 was used as surrogate for the determination of benzyl salicylate, phenyl salicylate, octyl salicylate and homosalate in negative mode and benzophenone-d10, was used as surrogate for the determination of 3-(4-methylbenzylidene) camphor and 3-benzylidene camphor in positive mode. The found limits of detection ranged from 0.4 to 0.6ngg(-1) and the limits of quantification ranged from 1.3 to 2.0ngg(-1), while variability was under 13.7%. Recovery rates for spiked samples ranged from 97% to 104%. Moreover, the interactions of these compounds with the human estrogen receptor alpha (hERα) and androgen receptor (hAR), using two in vitro bioassays based on reporter gene expression and cell proliferation assessment, were also investigated. All tested compounds, except benzyl salicylate and octyl salicylate, showed estrogenic activity in the E-Screen bioassay whereas only homosalate and 3-(4-methylbenzylidene) camphor were potent hAR antagonists