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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).

Background: Little is known about outcomes of adjuvant-treated melanoma patients beyond the clinical trial setting. Since 2019, adjuvant-treated melanoma patients have been registered in the DMTR, a population-based registry to monitor the quality and safety of melanoma care in the Netherlands. This study aims to describe treatment patterns, relapse, and toxicity rates of adjuvant-treated melanoma patients beyond the clinical trial setting. Methods: Analyses were performed on adjuvant-treated melanoma patients included in the DMTR. Descriptive statistics were used to analyse patient-, and treatment characteristics. A baseline registration completeness analysis was performed, and an analysis on trial eligibility in clinical practice patients. Recurrence-free survival (RFS) at 12-months was estimated with the Kaplan–Meier method. Results: A total of 641 patients were treated with adjuvant anti-PD-1 therapy. RFS at 12-months was 70.6% (95% CI, 66.9–74.6) with a median follow-up of 12.8 months. Sex, stage of disease and Breslow thickness were associated with a higher hazard for RFS. Eighteen per cent of the anti-PD-1-treated patients developed grade ≥3 toxicity. Sixty-one per cent of patients prematurely discontinued anti-PD-1 therapy. Conclusion: Adjuvant anti-PD-1 treatment of resected stage III/IV melanoma in daily practice showed slightly higher toxicity rates and more frequent premature discontinuation but similar RFS rates compared to trials.

Postapproval trials and patient registries have their pros and cons in the generation of postapproval data. No direct comparison between clinical outcomes of these data sources currently exists for advanced melanoma patients. We aimed to investigate whether a patient registry can complement or even replace postapproval trials. Postapproval single-arm clinical trial data from the Medicines Evaluation Board and real-world data from the Dutch Melanoma Treatment Registry were used. The study population consisted of advanced melanoma patients with brain metastases treated with targeted therapies (BRAF- or BRAF-MEK inhibitors) in the first line. A Cox hazard regression model and a propensity score matching (PSM) model were used to compare the two patient populations. Compared to patients treated in postapproval trials (n = 467), real-world patients (n = 602) had significantly higher age, higher ECOG performance status, more often ≥3 organ involvement and more symptomatic brain metastases. Lactate dehydrogenase levels were similar between both groups. The unadjusted median overall survival (mOS) in postapproval clinical trial patients was 8.7 (95% CI, 8.1-10.4) months compared to 7.2 (95% CI, 6.5-7.7) months (P < 0.01) in real-world patients. With the Cox hazard regression model, survival was adjusted for prognostic factors, which led to a statistically insignificant difference in mOS for trial and real-world patients of 8.7 (95% CI, 7.9-10.4) months compared to 7.3 (95% CI, 6.3-7.9) months, respectively. The PSM model resulted in 310 matched patients with similar survival (P = 0.9). Clinical outcomes of both data sources were similar. Registries could be a complementary data source to postapproval clinical trials to establish information on clinical outcomes in specific subpopulations.

Background: Anti-PD-1 antibodies and BRAF/MEK inhibitors are the two main groups of systemic therapy in the treatment of BRAFV600-mutant advanced melanoma. Until now, data are inconclusive on which therapy to use as first-line treatment. The aim of this study was to use propensity score matching to compare first-line anti-PD-1 monotherapy vs. BRAF/MEK inhibitors in advanced BRAFV600-mutant melanoma patients. Methods: We selected patients diagnosed between 2014 and 2017 with advanced melanoma and a known BRAFV600-mutation treated with first-line BRAF/MEK inhibitors or anti-PD-1 antibodies, registered in the Dutch Melanoma Treatment Registry. Patients were matched based on their propensity scores using the nearest neighbour and the optimal matching method. Results: Between 2014 and 2017, a total of 330 and 254 advanced melanoma patients received BRAF/MEK inhibitors and anti-PD-1 monotherapy as first-line systemic therapy. In the matched cohort, patients receiving anti-PD-1 antibodies as a first-line treatment had a higher median and 2-year overall survival compared to patients treated with first-line BRAF/MEK inhibitors, 42.3 months (95% CI: 37.3-NE) vs. 19.8 months (95% CI: 16.7–24.3) and 85.4% (95% CI: 58.1–73.6) vs. 41.7% (95% CI: 34.2–51.0). Conclusions: Our data suggest that in the matched BRAFV600-mutant advanced melanoma patients, anti-PD-1 monotherapy is the preferred first-line treatment in patients with relatively favourable patient and tumour characteristics.

Since the outbreak of SARS-CoV-2, also known as COVID-19, conflicting theories have circulated on the influence of angiotensin-converting enzyme inhibitors (ACEi) and angiotensin II receptor blockers (ARB) on incidence and clinical course of COVID-19, but data are scarce. The COvid MEdicaTion (COMET) study is an observational, multinational study that focused on the clinical course of COVID-19 (i.e. hospital mortality and intensive care unit [ICU] admission), and included COVID-19 patients who were registered at the emergency department or admitted to clinical wards of 63 participating hospitals. Pharmacists, clinical pharmacologists or treating physicians collected data on medication prescribed prior to admission. The association between the medication and composite clinical endpoint, including mortality and ICU admission, was analysed by multivariable logistic regression models to adjust for potential confounders. A total of 4870 patients were enrolled. ACEi were used by 847 (17.4%) patients and ARB by 761 (15.6%) patients. No significant association was seen with ACEi and the composite endpoint (adjusted odds ratio [OR] 0.94; 95% confidence interval [CI] 0.79 to 1.12), mortality (OR 1.03; 95%CI 0.84 to 1.27) or ICU admission (OR 0.96; 95%CI 0.78 to 1.19) after adjustment for covariates. Similarly, no association was observed between ARB and the composite endpoint (OR 1.09; 95%CI 0.90 to 1.30), mortality (OR 1.12; OR 0.90 to 1.39) or ICU admission (OR 1.21; 95%CI 0.98 to 1.49). In conclusion, we found no evidence of a harmful or beneficial effect of ACEi or ARB use prior to hospital admission on ICU admission or hospital mortality.

Postapproval trials and patient registries have their pros and cons in the generation of postapproval data. No direct comparison between clinical outcomes of these data sources currently exists for advanced melanoma patients. We aimed to investigate whether a patient registry can complement or even replace postapproval trials. Postapproval single-arm clinical trial data from the Medicines Evaluation Board and real-world data from the Dutch Melanoma Treatment Registry were used. The study population consisted of advanced melanoma patients with brain metastases treated with targeted therapies (BRAF- or BRAF-MEK inhibitors) in the first line. A Cox hazard regression model and a propensity score matching (PSM) model were used to compare the two patient populations. Compared to patients treated in postapproval trials (n = 467), real-world patients (n = 602) had significantly higher age, higher ECOG performance status, more often ≥3 organ involvement and more symptomatic brain metastases. Lactate dehydrogenase levels were similar between both groups. The unadjusted median overall survival (mOS) in postapproval clinical trial patients was 8.7 (95% CI, 8.1-10.4) months compared to 7.2 (95% CI, 6.5-7.7) months (P < 0.01) in real-world patients. With the Cox hazard regression model, survival was adjusted for prognostic factors, which led to a statistically insignificant difference in mOS for trial and real-world patients of 8.7 (95% CI, 7.9-10.4) months compared to 7.3 (95% CI, 6.3-7.9) months, respectively. The PSM model resulted in 310 matched patients with similar survival (P = 0.9). Clinical outcomes of both data sources were similar. Registries could be a compleme

Routing based on greedy network embeddings enables efficient and privacypreserving routing in overlays where connectivity is restricted to mutually trusted nodes. In previous works, we proposed security enhancements to the embedding and routing procedures to protect against denial-of-service attacks by malicious overlay participants. In this work, we propose an improved timeout scheme to reduce the stabilization overhead of secure tree maintenance in response to node failures and malicious behavior. Furthermore, we present an attack-resistant packet replication scheme that leverages alternative paths discovered during routing, Distributed Systems

Lightning, the prevailing solution to Bitcoin's scalability issue, uses onion routing to hide senders and recipients of payments. Yet, the path between the sender and the recipient along which payments are routed is selected such that it is short, cost efficient, and fast. The low degree of randomness in the path selection entails that anonymity sets are small. However, quantifying the anonymity provided by Lightning is challenging due to the existence of multiple implementations that differ with regard to the path selection algorithm and exist in parallel within the network. In this paper, we propose a general method allowing a local internal attacker to determine sender and recipient anonymity sets. Based on an in-depth code review of three Lightning implementations, we analyze how an adversary can predict the sender and the recipient of a multi-hop transaction. Our simulations indicate that only one adversarial node on a payment path uniquely identifies at least one of sender and recipient for around 70% of the transactions observed by the adversary. Moreover, multiple colluding attackers can almost always identify sender and receiver uniquely., Distributed Systems

OBJECTIVE: To systematically collect clinical data from patients with a proven COVID-19 infection in the Netherlands.DESIGN: Data from 2579 patients with COVID-19 admitted to 10 Dutch centers in the period February to July 2020 are described. The clinical data are based on the WHO COVID case record form (CRF) and supplemented with patient characteristics of which recently an association disease severity has been reported.METHODS: Survival analyses were performed as primary statistical analysis. These Kaplan-Meier curves for time to (early) death (3 weeks) have been determined for pre-morbid patient characteristics and clinical, radiological and laboratory data at hospital admission.RESULTS: Total in-hospital mortality after 3 weeks was 22.2% (95% CI: 20.7% - 23.9%), hospital mortality within 21 days was significantly higher for elderly patients (> 70 years; 35, 0% (95% CI: 32.4% - 37.8%) and patients who died during the 21 days and were admitted to the intensive care (36.5% (95% CI: 32.1% - 41.3%)). Apart from that, in this Dutch population we also see a risk of early death in patients with co-morbidities (such as chronic neurological, nephrological and cardiac disorders and hypertension), and in patients with more home medication and / or with increased urea and creatinine levels.CONCLUSION: Early death due to a COVID-19 infection in the Netherlands appears to be associated with demographic variables (e.g. age), comorbidity (e.g. cardiovascular disease) but also disease char-acteristics at admission.

OBJECTIVE: To systematically collect clinical data from patients with a proven COVID-19 infection in the Netherlands.DESIGN: Data from 2579 patients with COVID-19 admitted to 10 Dutch centers in the period February to July 2020 are described. The clinical data are based on the WHO COVID case record form (CRF) and supplemented with patient characteristics of which recently an association disease severity has been reported.METHODS: Survival analyses were performed as primary statistical analysis. These Kaplan-Meier curves for time to (early) death (3 weeks) have been determined for pre-morbid patient characteristics and clinical, radiological and laboratory data at hospital admission.RESULTS: Total in-hospital mortality after 3 weeks was 22.2% (95% CI: 20.7% - 23.9%), hospital mortality within 21 days was significantly higher for elderly patients (> 70 years; 35, 0% (95% CI: 32.4% - 37.8%) and patients who died during the 21 days and were admitted to the intensive care (36.5% (95% CI: 32.1% - 41.3%)). Apart from that, in this Dutch population we also see a risk of early death in patients with co-morbidities (such as chronic neurological, nephrological and cardiac disorders and hypertension), and in patients with more home medication and / or with increased urea and creatinine levels.CONCLUSION: Early death due to a COVID-19 infection in the Netherlands appears to be associated with demographic variables (e.g. age), comorbidity (e.g. cardiovascular disease) but also disease char-acteristics at admission.

Routing based on greedy network embeddings enables efficient and privacypreserving routing in overlays where connectivity is restricted to mutually trusted nodes. In previous works, we proposed security enhancements to the embedding and routing procedures to protect against denial-of-service attacks by malicious overlay participants. In this work, we propose an improved timeout scheme to reduce the stabilization overhead of secure tree maintenance in response to node failures and malicious behavior. Furthermore, we present an attack-resistant packet replication scheme that leverages alternative paths discovered during routing, Dataintensive Systems

Lightning, the prevailing solution to Bitcoin's scalability issue, uses onion routing to hide senders and recipients of payments. Yet, the path between the sender and the recipient along which payments are routed is selected such that it is short, cost efficient, and fast. The low degree of randomness in the path selection entails that anonymity sets are small. However, quantifying the anonymity provided by Lightning is challenging due to the existence of multiple implementations that differ with regard to the path selection algorithm and exist in parallel within the network. In this paper, we propose a general method allowing a local internal attacker to determine sender and recipient anonymity sets. Based on an in-depth code review of three Lightning implementations, we analyze how an adversary can predict the sender and the recipient of a multi-hop transaction. Our simulations indicate that only one adversarial node on a payment path uniquely identifies at least one of sender and recipient for around 70% of the transactions observed by the adversary. Moreover, multiple colluding attackers can almost always identify sender and receiver uniquely., Data-Intensive Systems

Background: During the COVID-19 pandemic, the scarcity of resources has necessitated triage of critical care for patients with the disease. In patients aged 65 years and older, triage decisions are regularly based on degree of frailty measured by the Clinical Frailty Scale (CFS). However, the CFS could also be useful in patients younger than 65 years. We aimed to examine the association between CFS score and hospital mortality and between CFS score and admission to intensive care in adult patients of all ages with COVID-19 across Europe. Methods: This analysis was part of the COVID Medication (COMET) study, an international, multicentre, retrospective observational cohort study in 63 hospitals in 11 countries in Europe. Eligible patients were aged 18 years and older, had been admitted to hospital, and either tested positive by PCR for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or were judged to have a high clinical likelihood of having SARS-CoV-2 infection by the local COVID-19 expert team. CFS was used to assess level of frailty: fit (CFS 1–3), mildly frail (CFS 4–5), or frail (CFS 6–9). The primary outcome was hospital mortality. The secondary outcome was admission to intensive care. Data were analysed using a multivariable binary logistic regression model adjusted for covariates (age, sex, number of drugs prescribed, and type of drug class as a proxy for comorbidities). Findings: Between March 30 and July 15, 2020, 2434 patients (median age 68 years [IQR 55–77]; 1480 [61%] men, 954 [30%] women) had CFS scores available and were included in the analyses. In the total sample and in patients aged 65 years and older, frail patients and mildly frail patients had a significantly higher risk of hospital mortality than fit patients (total sample: CFS 6–9 vs CFS 1–3 odds ratio [OR] 2·71 [95% CI 2·04–3·60], p<0·0001 and CFS 4–5 vs CFS 1–3 OR 1·54 [1·16–2·06], p=0·0030; age

Background: Little is known about outcomes of adjuvant-treated melanoma patients beyond the clinical trial setting. Since 2019, adjuvant-treated melanoma patients have been registered in the DMTR, a population-based registry to monitor the quality and safety of melanoma care in the Netherlands. This study aims to describe treatment patterns, relapse, and toxicity rates of adjuvant-treated melanoma patients beyond the clinical trial setting. Methods: Analyses were performed on adjuvant-treated melanoma patients included in the DMTR. Descriptive statistics were used to analyse patient-, and treatment characteristics. A baseline registration completeness analysis was performed, and an analysis on trial eligibility in clinical practice patients. Recurrence-free survival (RFS) at 12-months was estimated with the Kaplan–Meier method. Results: A total of 641 patients were treated with adjuvant anti-PD-1 therapy. RFS at 12-months was 70.6% (95% CI, 66.9–74.6) with a median follow-up of 12.8 months. Sex, stage of disease and Breslow thickness were associated with a higher hazard for RFS. Eighteen per cent of the anti-PD-1-treated patients developed grade ≥3 toxicity. Sixty-one per cent of patients prematurely discontinued anti-PD-1 therapy. Conclusion: Adjuvant anti-PD-1 treatment of resected stage III/IV melanoma in daily practice showed slightly higher toxicity rates and more frequent premature discontinuation but similar RFS rates compared to trials.

Background: Incisional hernias can be associated with pain or discomfort. Surgical repair especially mesh reinforcement, may likewise induce pain. The primary objective was to assess the incidence of pain after hernia repair in patients with and without pre-operative pain or discomfort. The secondary objectives were to determine the preferred mesh type, mesh location and surgical technique in minimizing postoperative pain or discomfort. Materials and methods: A registry-based prospective cohort study was performed, including patients undergoing incisional hernia repair between September 2011 and May 2019. Patients with a minimum follow-up of 3–6 months were included. The incidence of hernia related pain and discomfort was recorded perioperatively. Results: A total of 1312 patients were included. Pre-operatively, 1091 (83%) patients reported pain or discomfort. After hernia repair, 961 (73%) patients did not report pain or discomfort (mean follow-up = 11.1 months). Of the pre-operative asymptomatic patients (n = 221), 44 (20%, moderate or severe pain: n = 14, 32%) reported pain or discomfort after mean follow-up of 10.5 months. Of those patients initially reporting pain or discomfort (n = 1091), 307 (28%, moderate or severe pain: n = 80, 26%) still reported pain or discomfort after a mean follow-up of 11.3 months postoperatively. Conclusion: In symptomatic incisional hernia patients, hernia related complaints may be resolved in the majority of cases undergoing surgical repair. In asymptomatic incisional hernia patients, pain or discomfort may be induced in a considerable number of patients due to surgical repair and one should be aware if this postoperative complication.

This article outlines the major scientific objectives of the SHui project that seeks to optimize soil and water use in agricultural systems in the EU and China, by considering major current scientific challenges in this area. SHui (for Soil Hydrology research platform underpinning innovation to manage water scarcity in European and Chinese cropping systems) is large cooperative project that aims to provide significant advances through transdisciplinary research at multiple scales (plot, field, catchment and region). This paper explains our research platform of long-term experiments established at plot scale, approaches taken to integrate crop and hydrological models at field scale; coupled crop models and satellite-based observations at regional scales; decision support systems for specific farming situations; and the integration of these technologies to provide policy recommendations through socio-economic analysis of the impact of soil and water saving technologies. It also outlines the training of stakeholders to develop a basic common curriculum despite the subject being distributed across different disciplines and professions. As such, this article provides a review of major challenges for improving soil and water use in EU and China as well as information about the potential to access information made available by SHui, and to allow others to engage with the project.

Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of today's applications, e.g., keyboard prediction and object recognition. Its core principle is to learn from large amount of users data while preserving data privacy by design as collaborative users only need to share the machine learning models and keep data locally. The main challenge for such systems is to provide incentives to users to contribute high-quality models trained from their local data. In this paper, we aim to answer how well incentives recognize (in)accurate local models from honest and malicious users, and perceive their impacts on the model accuracy of federated learning systems. We first present a thorough survey on two contrasting perspectives: incentive mechanisms to measure the contribution of local models by honest users, and malicious users to deliberately degrade the overall model. We conduct simulation experiments to empirically demonstrate if existing contribution measurement schemes can disclose low-quality models from malicious users. Our results show there exists a clear tradeoff among measurement schemes in terms of the computational efficiency and effectiveness to distill the impact of malicious participants. We conclude this paper by discussing the research directions to design resilient contribution incentives., Distributed Systems

Greedy embeddings on rooted spanning trees are the most promising solution to provide sufficiently scalable routing in dynamic networks with restricted topologies, for instance friend-to-friend overlays such as the Dark Freenet and payment channel networks such as Lightning. Yet, they are not deployed in practice, as electing a root and configuring addresses remains an unsolved problem in adverse environments. Indeed, faulty or malicious nodes might provide incorrect coordinates, prevent the network from stabilizing by simulating dynamics, or not start the assignment of coordinates in their subtree at all. All of the above attacks may result in an inability to route. To mitigate the above attacks, we design a novel embedding algorithm with an adapted distance metric that only relies on interconnections between benign subtrees for successful delivery. In other words, even if roots of (sub-)trees are malicious or faulty, the remaining nodes still receive coordinates and can communicate with nodes in their tree branch as well as other branches reachable via the neighborhood of their benign ancestors. Extensive simulations demonstrate that we thus facilitate efficient routing even when seemingly decisive parts of the network are under adversarial control., Distributed Systems

Payment channel networks like Bitcoin’s Lightning network are an auspicious approach for realizing high transaction throughput and almost-instant confirmations in blockchain networks. However, the ability to successfully conduct payments in such networks relies on the willingness of participants to lock collateral in the network. In Lightning, the key financial incentive to lock collateral are low fees for routing payments of other participants. While users can choose these fees, real-world data indicates that they mainly stick to default fees. By providing insights on beneficial choices for fees, we aim to incentivize users to lock more collateral and improve the effectiveness of the network. In this paper, we consider a node that given the network topology and the channel details establishes channels and chooses fees to maximize its financial gain. Our contributions are i) formalization of the optimization problem, ii) proving that the problem is NP-hard, and iii) designing and evaluating a greedy algorithm to approximate the optimal solution. In each step, our greedy algorithm establishes a channel that maximizes the increase to ’s total reward, which corresponds to maximizing the number of shortest paths passing through. Our simulation study leveraged real-world data sets to quantify the impact of our gain optimization and indicates that our strategy is at least a factor two better than other strategies., Coronavirus update: Authors whose travel is disrupted can arrange to give video presentations, Distributed Systems, Cyber Security

Current leader election algorithms fail in the presence of Sybil attacks, i.e., one malicious entity inserting many nodes, network dynamics, and restricted knowledge about the graph. However, social overlay networks, i.e., peer-to-peer networks with links corresponding to social relationships, face all of the above challenges. Social overlay networks naturally offer privacy, as they avoid connections with strangers, and furthermore prevent a Sybil attacker from controlling a large number of links in the graph. As recent ideas for scalable communication in such overlays rely heavily on attack resistant leader election, solving leader election for such overlays opens the door for decentralized, privacy-preserving, and secure communication at a large scale. In this work, we propose a novel leader election algorithm based on three-majority voting that utilizes timestamps and cryptographic signatures to detect leader faults in an attack resistant manner. We evaluate our algorithm with simulations on real-world as well as synthetic network topologies. Our results indicate that in networks whose degree sequence follows a power law, our leader election algorithm quickly achieves consensus for more than $80\%$ of all nodes. Furthermore, attackers are unlikely to become leaders as long as the number of connections they establish with honest nodes is low., Distributed Systems

Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of today's applications, e.g., keyboard prediction and object recognition. Its core principle is to learn from large amount of users data while preserving data privacy by design as collaborative users only need to share the machine learning models and keep data locally. The main challenge for such systems is to provide incentives to users to contribute high-quality models trained from their local data. In this paper, we aim to answer how well incentives recognize (in)accurate local models from honest and malicious users, and perceive their impacts on the model accuracy of federated learning systems. We first present a thorough survey on two contrasting perspectives: incentive mechanisms to measure the contribution of local models by honest users, and malicious users to deliberately degrade the overall model. We conduct simulation experiments to empirically demonstrate if existing contribution measurement schemes can disclose low-quality models from malicious users. Our results show there exists a clear tradeoff among measurement schemes in terms of the computational efficiency and effectiveness to distill the impact of malicious participants. We conclude this paper by discussing the research directions to design resilient contribution incentives., Data-Intensive Systems