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

Serum potassium and adverse outcomes across the range of kidney function: a CKD Prognosis Consortium meta-analysis.

Aims: Both hypo- and hyperkalaemia can have immediate deleterious physiological effects, and less is known about long-term risks. The objective was to determine the risks of all-cause mortality, cardiovascular mortality, and end-stage renal disease associated with potassium levels across the range of kidney function and evaluate for consistency across cohorts in a global consortium. Methods and results: We performed an individual-level data meta-analysis of 27 international cohorts [10 general population, 7 high cardiovascular risk, and 10 chronic kidney disease (CKD)] in the CKD Prognosis Consortium. We used Cox regression followed by random-effects meta-analysis to assess the relationship between baseline potassium and adverse outcomes, adjusted for demographic and clinical characteristics, overall and across strata of estimated glomerular filtration rate (eGFR) and albuminuria. We included 1 217 986 participants followed up for a mean of 6.9 years. The average age was 55 ± 16 years, average eGFR was 83 ± 23 mL/min/1.73 m2, and 17% had moderate- to-severe increased albuminuria levels. The mean baseline potassium was 4.2 ± 0.4 mmol/L. The risk of serum potassium of >5.5 mmol/L was related to lower eGFR and higher albuminuria. The risk relationship between potassium levels and adverse outcomes was U-shaped, with the lowest risk at serum potassium of 4-4.5 mmol/L. Compared with a reference of 4.2 mmol/L, the adjusted hazard ratio for all-cause mortality was 1.22 [95% confidence interval (CI) 1.15-1.29] at 5.5 mmol/L and 1.49 (95% CI 1.26-1.76) at 3.0 mmol/L. Risks were similar by eGFR, albuminuria, renin-angiotensin-aldosterone system inhibitor use, and across cohorts. Conclusions: Outpatient potassium levels both above and below the normal range are consistently associated with adverse outcomes, with similar risk relationships across eGFR and albuminuria

The Intersection of Interfacial Forces and Electrochemical Reactions

We review recent developments in experimental techniques that simultaneously combine measurements of the interaction forces or energies between two extended surfaces immersed in electrolyte solutions—primarily aqueous—with simultaneous monitoring of their (electro)chemical reactions and controlling the electrochemical surface potential of at least one of the surfaces. Combination of these complementary techniques allows for simultaneous real time monitoring of angstrom level changes in surface thickness and roughness, surface–surface interaction energies, and charge and mass transferred via electrochemical reactions, dissolution, and adsorption, and/or charging of electric double layers. These techniques employ the surface forces apparatus (SFA) combined with various “electrochemical attachments” for in situ measurements of various physical and (electro)chemical properties (e.g., cyclic voltammetry), optical imaging, and electric potentials and currents generated naturally during an interaction, as well as when electric fields (potential differences) are applied between the surfaces and/or solution—in some cases allowing for the chemical reaction equation to be unambiguously determined. We discuss how the physical interactions between two different surfaces when brought close to each other (<10 nm) can affect their chemistry, and suggest further extensions of these techniques to biological systems and simultaneous in situ spectroscopic measurements for chemical analysis

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

Economic Geography and Human Rights

This is a working paper.This paper investigates the geo-political and international economic aspects of human rights performance using a pooled cross-section time-series data set. We start with simple descriptive accounts of the recent geographic history of human rights performance. We then test for basic economic effects of income and then apply tools from the spatial economics literature to examine the degree to which clusters of relative human rights performance exist. Using spatial weighting models we analyse the spatial impact of proximity and human rights performance of neghbours on overall levels of human rights performance. Unlike previous studies, our approach treats this spatial impact as partly endogenous: one country’s human rights performance may affect its neighbours through a variety of potential geographical spillover mechanisms. The spatial weighting models take into account size and distance of neighbours in order to compare each country’s human rights performance with what would be predicted by regression on a weighted average of its neighbours’ performance. The findings sugest that there are (a) geographical clusters of human rights performance and (b) size and proximity effects for human rights performance, both of which have significant implications for the promotion and protection of human rights