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Waning antibody responses after COVID-19 vaccination combined with the emergence of the SARS-CoV-2 Omicron lineage led to reduced vaccine effectiveness. As a countermeasure, bivalent mRNA-based booster vaccines encoding the ancestral spike protein in combination with that of Omicron BA.1 or BA.5 were introduced. Since then, different BA.2-descendent lineages have become dominant, such as XBB.1.5, JN.1, or EG.5.1. Here, we report post-hoc analyses of data from the SWITCH-ON study, assessing how different COVID-19 priming regimens affect the immunogenicity of bivalent booster vaccinations and breakthrough infections (NCT05471440). BA.1 and BA.5 bivalent vaccines boosted neutralizing antibodies and T-cells up to 3 months after boost; however, cross-neutralization of XBB.1.5 was poor. Interestingly, different combinations of prime-boost regimens induced divergent responses: participants primed with Ad26.COV2.S developed lower binding antibody levels after bivalent boost while neutralization and T-cell responses were similar to mRNA-based primed participants. In contrast, the breadth of neutralization was higher in mRNA-primed and bivalent BA.5 boosted participants. Combined, our data further support the current use of monovalent vaccines based on circulating strains when vaccinating risk groups, as recently recommended by the WHO. We emphasize the importance of the continuous assessment of immune responses targeting circulating variants to guide future COVID-19 vaccination policies.

Attacks on Federated Learning (FL) can severely reduce the quality of the generated models and limit the usefulness of this emerging learning paradigm that enables on-premise decentralized learning. However, existing untargeted attacks are not practical for many scenarios as they assume that i) the attacker knows every update of benign clients, or ii) the attacker has a large dataset to locally train updates imitating benign parties. In this paper, we propose a data-free untargeted attack (DFA) that synthesizes malicious data to craft adversarial models without eavesdropping on the transmission of benign clients at all or requiring a large quantity of task-specific training data. We design two variants of DFA, namely DFA-R and DFA-G, which differ in how they trade off stealthiness and effectiveness. Specifically, DFA-R iteratively optimizes a malicious data layer to minimize the prediction confidence of all outputs of the global model, whereas DFA-G interactively trains a malicious data generator network by steering the output of the global model toward a particular class. Experimental results on Fashion-MNIST, Cifar-10, and SVHN show that DFA, despite requiring fewer assumptions than existing attacks, achieves similar or even higher attack success rate than state-of-the-art untargeted attacks against various state-of-the-art defense mechanisms. Concretely, they can evade all considered defense mechanisms in at least 50% of the cases for CIFAR-10 and often reduce the accuracy by more than a factor of 2. Consequently, we design REFD, a defense specifically crafted to protect against data-free attacks. REFD leverages a reference dataset to detect updates that are biased or have a low confidence. It greatly improves upon existing defenses by filtering out the malicious updates and achieves high global model accuracy., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Data-Intensive Systems

Catalyzed by the popularity of blockchain technology, there has recently been a renewed interest in the design, implementation and evaluation of decentralized systems. Most of these systems are intended to be deployed at scale and in heterogeneous environments with real users and unpredictable workloads. Nevertheless, most research in this field evaluates such systems in controlled environments that poorly reflect the complex conditions of real-world environments. In this work, we argue that deployment is crucial to understanding decentralized mechanisms in a real-world environment and an enabler to building more robust and sustainable systems. We highlight the merits of deployment by comparing this approach with other experimental setups and show how our lab applied a deployment-first methodology. We then outline how we use Tribler, our peer-to-peer file-sharing application, to deploy and monitor decentralized mechanisms at scale. We illustrate the application of our methodology by describing a deployment trial in experimental tokenomics. Finally, we summarize four lessons learned from multiple deployment trials where we applied our methodology., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Data-Intensive Systems

Background: Bivalent mRNA-based COVID-19 vaccines encoding the ancestral and omicron spike (S) protein were developed as a countermeasure against antigenically distinct SARS-CoV-2 variants. We aimed to assess the (variant-specific) immunogenicity and reactogenicity of mRNA-based bivalent omicron (BA.1) vaccines in individuals who were primed with adenovirus-based or mRNA-based vaccines encoding the ancestral spike protein. Methods: We analysed results of the direct boost group of the SWITCH ON study, an open-label, multicentre, randomised controlled trial. Health-care workers from four academic hospitals in the Netherlands aged 18–65 years who had completed a primary COVID-19 vaccination regimen and received one booster of an mRNA-based vaccine, given no later than 3 months previously, were eligible. Participants were randomly assigned (1:1) using computer software in block sizes of 16 and 24 to receive an omicron BA.1 bivalent booster straight away (direct boost group) or a bivalent omicron BA.5 booster, postponed for 90 days (postponed boost group), stratified by priming regimen. The BNT162b2 OMI BA.1 boost was given to participants younger than 45 years, and the mRNA-1273.214 boost was given to participants 45 years or older, as per Dutch guidelines. The direct boost group, whose results are presented here, were divided into four subgroups for analysis: (1) Ad26.COV2.S (Johnson & Johnson) prime and BNT162b2 OMI BA.1 (BioNTech–Pfizer) boost (Ad/P), (2) mRNA-based prime and BNT162b2 OMI BA.1 boost (mRNA/P), (3) Ad26.COV2.S prime and mRNA-1273.214 (Moderna) boost (Ad/M), and (4) mRNA-based prime and mRNA-1273.214 boost (mRNA/M). The primary outcome was fold change in S protein S1 subunit-specific IgG antibodies before and 28 days after booster vaccination. The primary outcome and safety were assessed in all participants except those who withdrew, had a SARS-CoV-2 breakthrough infection, or had a missing blood sample at day 0 or day 28. This tria

Federated learning (FL) enables collaborative learning between parties, called clients, without sharing the original and potentially sensitive data. To ensure fast convergence in the presence of such heterogeneous clients, it is imperative to timely select clients who can effectively contribute to learning. A realistic but overlooked case of heterogeneous clients are Mavericks, who monopolize the possession of certain data types, e.g., children hospitals possess most of the data on pediatric cardiology. In this paper, we address the importance and tackle the challenges of Mavericks by exploring two types of client selection strategies. First, we show theoretically and through simulations that the common contribution-based approach, Shapley Value, underestimates the contribution of Mavericks and is hence not effective as a measure to select clients. Then, we propose FedEMD, an adaptive strategy with competitive overhead based on the Wasserstein distance, supported by a proven convergence bound. As FedEMD adapts the selection probability such that Mavericks are preferably selected when the model benefits from improvement on rare classes, it consistently ensures the fast convergence in the presence of different types of Mavericks. Compared to existing strategies, including Shapley Value-based ones, FedEMD improves the convergence speed of neural network classifiers with FedAvg aggregation by 26.9% and its performance is consistent across various levels of heterogeneity., Data-Intensive Systems

OBJECTIVES: A high body mass index (BMI) is associated with an unfavorable disease course in COVID-19, but not among those who require admission to the ICU. This has not been examined across different age groups. We examined whether age modifies the association between BMI and mortality among critically ill COVID-19 patients.DESIGN: An observational cohort study.SETTING: A nationwide registry analysis of critically ill patients with COVID-19 registered in the National Intensive Care Evaluation registry.PATIENTS: We included 15,701 critically ill patients with COVID-19 (10,768 males [68.6%] with median [interquartile range] age 64 yr [55-71 yr]), of whom 1,402 (8.9%) patients were less than 45 years.INTERVENTIONS: None.MEASUREMENTS AND MAIN RESULTS: In the total sample and after adjustment for age, gender, Acute Physiology and Chronic Health Evaluation IV, mechanical ventilation, and use of vasoactive drugs, we found that a BMI greater than or equal to 30 kg/m2 does not affect hospital mortality (adjusted odds ratio [ORadj] = 0.98; 95% CI, 0.90-1.06; p = 0.62). For patients less than 45 years old, but not for those greater than or equal to 45 years old, a BMI greater than or equal to 30 kg/m2 was associated with a lower hospital mortality (ORadj = 0.59; 95% CI, 0.36-0.96; p = 0.03).CONCLUSIONS: A higher BMI may be favorably associated with a lower mortality among those less than 45 years old. This is in line with the so-called "obesity paradox" that was established for other groups of critically ill patients in broad age ranges. Further research is needed to understand this favorable association in young critically ill patients with COVID-19.

De mentale gezondheid van meisjes in Nederland is tussen 2017 en 2021 sterk verslechterd. Dat blijkt uit het Health Behaviour in School-aged Children (HBSC)-rapport, met daarin de resultaten van 20 jaar onderzoek naar het welzijn en de gezondheid van jongeren in Nederland. Vandaag wordt het rapport tijdens het symposium ‘Jong in de 21ste eeuw’ in Utrecht uitgereikt aan Hare Majesteit Koningin Máxima.

Due to its high efficiency, routing based on greedy embeddings of rooted spanning trees is a promising approach for dynamic, large-scale networks with restricted topologies. Friend-to-friend (F2F) overlays, one key application of embedding-based routing, aim to prevent disclosure of their participants to malicious members by restricting exchange of messages to mutually trusted nodes. Since embeddings assign a unique integer vector to each node that encodes its position in a spanning tree of the overlay, attackers can infer network structure from knowledge about assigned vectors. As this information can be used to identify participants, an evaluation of the scale of leakage is needed. In this work, we analyze in detail which information malicious participants can infer from knowledge about assigned vectors. Also, we show that by monitoring packet trajectories, malicious participants cannot unambiguously infer links between nodes of unidentified participants. Using simulation, we find that the vector assignment procedure has a strong impact on the feasibility of inference. In F2F overlay networks, using vectors of randomly chosen numbers for routing decreases the mean number of discovered individuals by one order of magnitude compared to the popular approach of using child enumeration indexes as vector elements., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Data-Intensive Systems

Background: Recent reports suggest the limited efficacy of immune checkpoints inhibitors in advanced acral melanoma (AM). This study aims to investigate the clinical outcomes of immune checkpoint inhibitors in patients with stage III and IV AM and compare them to cutaneous melanoma (CM). Methods: We included patients with advanced AM and CM treated with first-line anti-programmed cell death (PD)-1 monotherapy or ipilimumab-nivolumab registered in the prospective nationwide Dutch Melanoma Treatment Registry. Objective response rates, progression-free survival (PFS) and overall survival (OS) were calculated. A Cox proportional hazard model was used to assess the prognostic factors with PFS and OS. Results: In total, 2058 patients (88 AM and 1970 CM) with advanced melanoma were included. First-line objective response rates were 34% for AM versus 54% for CM in the advanced anti-PD-1 cohort and 33% for AM versus 53% for CM in the advanced ipilimumab-nivolumab cohort. The Median PFS was significantly shorter for anti-PD-1 treated AM patients (3.1 months; 95%CI: 2.8–5.6) than patients with CM (10.1 months; 95%CI: 8.5–12.2) (P < 0.001). In patients with advanced melanoma, AM was significantly associated with a higher risk of progression (HRadj 1.63; 95%CI: 1.26–2.11; P < 0.001) and death (HRadj 1.54; 95%CI: 1.15–2.06; P = 0.004) than CM. Conclusions: This study shows lower effectiveness of anti-PD -1 monotherapy and ipilimumab-nivolumab in AM, with lower response rates, PFS and OS than CM. This group of patients should be prioritised in the development of alternative treatment strategies.

One-third of the Dutch dike system consists of peat dikes. Drought causes these dikes to crack and fail more easily. Visual dike inspections are therefore inefficient, especially considering the increas- ing climate changes of the future. Research has shown that a fiber optic sensor cable (FOS), used in distributed temperature sensing (DTS), can help measure soil thermal responses, but the question remains whether it is also suitable as a replacement for visual inspections of dikes. This study helps to answer this question by coupling a finite element method (FEM) model with measured DTS information collected at Flood Proof Holland (Delft).