‘McDonald’s Is Good for My Social Life’. Developing Health Promotion Together with Adolescent Girls from Disadvantaged Neighbourhoods in Amsterdam

There is limited knowledge about key factors that enable adolescent girls with a low socioeconomic position (SEP) to adopt a healthy lifestyle. This paper aims to better understand the comp

Pharmacogenomics of Interferon-ß Therapy in Multiple Sclerosis: Baseline IFN Signature Determines Pharmacological Differences between Patients

Multiple sclerosis (MS) is a heterogeneous disease. In order to understand the partial responsiveness to IFNbeta in Relapsing Remitting MS (RRMS) we studied the pharmacological effects of IFNbeta therapy. Large scale gene expression profiling was performed on peripheral blood of 16 RRMS patients at baseline and one month after the start of IFNbeta therapy. Differential gene expression was analyzed by Significance Analysis of Microarrays. Subsequent expression analyses on specific genes were performed after three and six months of treatment. Peripheral blood mononuclear cells (PBMC) were isolated and stimulated in vitro with IFNbeta. Genes of interest were measured and validated by quantitative realtime PCR. An independent group of 30 RRMS patients was used for validation. Pharmacogenomics revealed a marked variation in the pharmacological response to IFNbeta between patients. A total of 126 genes were upregulated in a subset of patients whereas in other patients these genes were downregulated or unchanged after one month of IFNbeta therapy. Most interestingly, we observed that the extent of the pharmacological response correlates negatively with the baseline expression of a specific set of 15 IFN response genes (R = -0.7208; p = 0.0016). The negative correlation was maintained after three (R = -0.7363; p = 0.0027) and six (R = -0.8154; p = 0.0004) months of treatment, as determined by gene expression levels of the most significant correlating gene. Similar results were obtained in an independent group of patients (n = 30; R = -0.4719; p = 0.0085). Moreover, the ex vivo results could be confirmed by in vitro stimulation of purified PBMCs at baseline with IFNbeta indicating that differential responsiveness to IFNbeta is an intrinsic feature of peripheral blood cells at baseline. These data imply that the expression levels of IFN response genes in the peripheral blood of MS patients prior to treatment could serve a role as biomarker for the differential clinical response to IFNbet

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

The ATLAS project - VIII. Modelling the formation and evolution of fast and slow rotator early-type galaxies within ΛCDM

The definitive version can be found at: http://onlinelibrary.wiley.com/ Copyright Royal Astronomical SocietyWe propose a simple model for the origin of fast and slow rotator early-type galaxies (ETG) within the hierarchical Λcold dark matter (ΛCDM) scenario, that is based on the assumption that the mass fraction of stellar discs in ETGs is a proxy for the specific angular momentum expressed via λ. Within our model we reproduce the fraction of fast and slow rotators as a function of magnitude in the ATLAS survey, assuming that fast-rotating ETGs have at least 10 per cent of their total stellar mass in a disc component. In agreement with ATLAS observations we find that slow rotators are predominantly galaxies with M > 10M contributing ~20 per cent to the overall ETG population. We show in detail that the growth histories of fast and slow rotators are different, supporting the classification of ETGs into these two categories. Slow rotators accrete between ~50 and 90 per cent of their stellar mass from satellites and their most massive progenitors have on average up to three major mergers during their evolution. Fast rotators in contrast accrete less than 50 per cent and have on average less than one major merger in their past. We find that the underlying physical reason for the different growth histories is the slowing down and ultimately complete shut-down of gas cooling in massive galaxies. Once cooling and associated star formation in disc stop, galaxies grow via infall from satellites. Frequent minor mergers thereby destroy existing stellar discs via violent relaxation and also tend to lower the specific angular momentum of the main stellar body, lowering λ into the slow rotator regime. On average, the last gas-rich major merger interaction in slow rotators happens at z > 1.5, followed by a series of minor mergers. These results support the idea that kinematically decoupled cores (KDC) form during gas-rich major mergers at high z followed by minor mergers, which build-up the outer layers of the remnant, and make remnants that are initially too flat compared to observations become rounder. Fast rotators are less likely to form such KDCs due to the fact that they have on average less than one major merger in their past. Fast rotators in our model have different formation paths. The majority, 78 per cent, has bulge-to-total stellar mass ratios (B/T) > 0.5 and managed to grow stellar discs due to continued gas cooling or bulges due to frequent minor mergers. The remaining 22 per cent live in high-density environments and consist of low B/T galaxies with gas fractions below 15 per cent, that have exhausted their cold gas reservoir and have no hot halo from which gas can cool. These fast rotators most likely resemble the flattened disc-like fast rotators in the ATLAS survey. Our results predict that ETGs can change their state from fast to slow rotator and vice versa, while the former is taking place predominantly at low z (z 10M) fast rotators being more than one order of magnitude more frequent at z~ 2.Peer reviewe