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

ACADEMIC PROFESSION AND IDEOLOGY OF "SLOW SCHOLARSHIP"

The article touches upon the concept of slow scholarship that has been widely spread among academic professionals abroad due to the higher education reform oriented to ideas of new managerialism. The call for slow scholarship is a reaction that faculty shows against transformation of their time budgets and weakening of professional freedom. We present a brief review of key writings on slow scholarship and discuss how these ideas can be adopted to the local context. We also reveal that research of academic profession in Russia pays relatively little attention to the issue of working time budgets and time use, while it is of a major importance to understand the changes that take place at universities and other academic institutions

Carborane-Containing Folic Acid bis-Amides: Synthesis and In Vitro Evaluation of Novel Promising Agents for Boron Delivery to Tumour Cells

The design of highly selective low-toxic, low-molecular weight agents for boron delivery to tumour cells is of decisive importance for the development of boron neutron capture therapy (BNCT), a modern efficient combined method for cancer treatment. In this work, we developed a simple method for the preparation of new closo- and nido-carborane-containing folic acid bis-amides containing 18–20 boron atoms per molecule. Folic acid derivatives containing nido-carborane residues were characterised by high water solubility, low cytotoxicity, and demonstrated a good ability to deliver boron to tumour cells in in vitro experiments (up to 7.0 µg B/106 cells in the case of U87 MG human glioblastoma cells). The results obtained demonstrate the high potential of folic acid–nido-carborane conjugates as boron delivery agents to tumour cells for application in BNCT

Diversity and Distribution of Helminths in Wild Ruminants of the Russian Arctic: Reindeer (<i>Rangifer tarandus</i>), Muskoxen (<i>Ovibos moschatus</i>), and Snow Sheep (<i>Ovis nivicola</i>)

The Russian Arctic supports wild sympatric ruminants and their data-deficient helminths. In this study, we: (1) collected fecal samples of wild and semiwild reindeer (Rangifer tarandus), muskoxen (Ovibos moschatus), and snow sheep (Ovis nivicola) across Palearctic North territories: Arkhangelsk Oblast (including Novaya Zemlya archipelago), Karelia and Sakha Republics, Kola, Yamal, Taimyr, and Chukotka Peninsulas, Bering, Svalbard, and Wrangel Islands; (2) conducted a coprological survey (noninvasive life-time method preferable for protected animals) to obtain eggs and larvae of helminths inhabiting digestive, respiratory, nervous, and muscular systems; (3) identified helminths according to their morphology and DNA sequences; (4) estimated parasite load per host; (5) analyzed our findings. Varestrongylus eleguneniensis (in reindeer) was reported for the Palearctic for the first time, while Orthostrongylus sp. was reported both for R. tarandus and for the Palearctic for the first time. Capillarid-type eggs were reported for snow sheep for the first time. The question of the role of wild Arctic ruminants as vectors for rotifers was raised

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