A Spatiotemporal Analysis of the McKean Complex on the Northern Plains

Characterizing hunter-gatherer mobility has been problematic in archaeological research (Anthony 1990). For pre-contact cultures on the Northern Plains there is no documentation of the human decisions involved in movement processes. This thesis examines the known information available regarding the McKean Complex on the Northern Plains. Using radiocarbon ages and known site locations, Kriging analysis was used to create a predictive model to examine spread of this archaeological complex, directions of movement, and origins. This thesis re-examines existing theories regarding origin and migration with regards to this model. The geographic distribution of projectile point styles, floral remains and faunal remains are also examined. This research provides a comprehensive database of stratified sites with McKean components as well as a comprehensive database of McKean radiocarbon ages associated with McKean projectile points. This study offers new information regarding subsistence and expansion of the complex, providing a preliminary model towards re-examining the McKean Complex. The model will benefit from future research with regards to the McKean Complex as more radiocarbon ages taken from McKean sites can only help improve the current model and help provide a greater understanding of this Complex on the Northern Plains

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

Alien Registration- Bouffard, Yvonne (Biddeford, York County)

https://digitalmaine.com/alien_docs/2131/thumbnail.jp