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

Variational-hemivariational inequalities in nonlinear elasticity. The coercive case

summary:Existence of a solution of the problem of nonlinear elasticity with non-classical boundary conditions, when the relationship between the corresponding dual quantities is given in terms of a nonmonotone and generally multivalued relation. The mathematical formulation leads to a problem of non-smooth and nonconvex optimization, and in its weak form to hemivariational inequalities and to the determination of the so called substationary points of the given potential

Gender Variation and Expression of Monoecy in Juniperus phoenicea (L.) (Cupressaceae)

Variation of gender expression and cone production is described quantitatively for Juniperus phoenicea L. populations in southern Spain and Morocco. The species is monoecious, but most populations showed a dichotomy of gender expression at flowering, with predominantly "male" and predominantly "female" plants and few "monoecious" individuals, a functionally subdioecious breeding system. The proportion of female plants in the Spanish populations ranged from 31% (R. B. Donana) to 40% (Cda. Sabinas, 1988) and did not exceed 10% in Morocco. Most plants with femaleness values < .40 failed to set full-sized seed cones or produced very small crops. Individual plants showed a significant constancy of gender expression in consecutive years. Most inconsistencies in sexual behavior involved transitions between the male and female expressions and their respective "inconstant" conditions. Between-year variations in seed-bearing cone production largely reflected changes in female flowering gender of the individual plants; years with large crop production were characterized by increases in average female gender expression for a given gender category and, as a result, a greater percentage of the population producing female cones. Plants differing in gender expression showed no significant differences in size. Male plants always produced fewer than 10 female cones per crop, and inconstant males rarely exceeded 200 female cones; female plants usually had crop sizes above 100 cones, except in the seasons of cone crop failure. Individual plants also differed in annual shoot growth, but these differences were unrelated to both gender expression and cone production in the previous season. Differences among populations accounted for 52% of total variance in female cone size, while the effect of the individual plant accounted for 26%; only 22% was attributable to within-plant variation. A nested model with gender category as the main effect and plant as a nested effect accounted for 88% of total variation in five cone characteristics, but gender effect accounted for ≤ 2%Peer reviewe