Fuzzy argumentation for trust

In an open Multi-Agent System, the goals of agents acting on behalf of their owners often conflict with each other. Therefore, a personal agent protecting the interest of a single user cannot always rely on them. Consequently, such a personal agent needs to be able to reason about trusting (information or services provided by) other agents. Existing algorithms that perform such reasoning mainly focus on the immediate utility of a trusting decision, but do not provide an explanation of their actions to the user. This may hinder the acceptance of agent-based technologies in sensitive applications where users need to rely on their personal agents. Against this background, we propose a new approach to trust based on argumentation that aims to expose the rationale behind such trusting decisions. Our solution features a separation of opponent modeling and decision making. It uses possibilistic logic to model behavior of opponents, and we propose an extension of the argumentation framework by Amgoud and Prade to use the fuzzy rules within these models for well-supported decisions

Bounded Decentralised Coordination over Multiple Objectives

We propose the bounded multi-objective max-sum algorithm (B-MOMS), the first decentralised coordination algorithm for multi-objective optimisation problems. B-MOMS extends the max-sum message-passing algorithm for decentralised coordination to compute bounded approximate solutions to multi-objective decentralised constraint optimisation problems (MO-DCOPs). Specifically, we prove the optimality of B-MOMS in acyclic constraint graphs, and derive problem dependent bounds on its approximation ratio when these graphs contain cycles. Furthermore, we empirically evaluate its performance on a multi-objective extension of the canonical graph colouring problem. In so doing, we demonstrate that, for the settings we consider, the approximation ratio never exceeds 2, and is typically less than 1.5 for less-constrained graphs. Moreover, the runtime required by B-MOMS on the problem instances we considered never exceeds 30 minutes, even for maximally constrained graphs with $100$ agents. Thus, B-MOMS brings the problem of multi-objective optimisation well within the boundaries of the limited capabilities of embedded agents

Active Learning of Gaussian Processes for Spatial Functions in Mobile Sensor Networks

This paper proposes a spatial function modeling approach using mobile sensor networks, which potentially can be used for environmental surveillance applications. The mobile sensor nodes are able to sample the point observations of an 2D spatial function. On the one hand, they will use the observations to generate a predictive model of the spatial function. On the other hand, they will make collective motion decisions to move into the regions where high uncertainties of the predictive model exist. In the end, an accurate predictive model is obtained in the sensor network and all the mobile sensor nodes are distributed in the environment with an optimized pattern. Gaussian process regression is selected as the modeling technique in the proposed approach. The hyperparameters of Gaussian process model are learned online to improve the accuracy of the predictive model. The collective motion control of mobile sensor nodes is based on a locational optimization algorithm, which utilizes an information entropy of the predicted Gaussian process to explore the environment and reduce the uncertainty of predictive model. Simulation results are provided to show the performance of the proposed approach. © 2011 IFAC

Stratified Management for Bacterial Infections in Late Preterm and Term Neonates:Current Strategies and Future Opportunities Toward Precision Medicine

Bacterial infections remain a major cause of morbidity and mortality in the neonatal period. Therefore, many neonates, including late preterm and term neonates, are exposed to antibiotics in the first weeks of life. Data on the importance of inter-individual differences and disease signatures are accumulating. Differences that may potentially influence treatment requirement and success rate. However, currently, many neonates are treated following a “one size fits all” approach, based on general protocols and standard antibiotic treatment regimens. Precision medicine has emerged in the last years and is perceived as a new, holistic, way of stratifying patients based on large-scale data including patient characteristics and disease specific features. Specific to sepsis, differences in disease susceptibility, disease severity, immune response and pharmacokinetics and -dynamics can be used for the development of treatment algorithms helping clinicians decide when and how to treat a specific patient or a specific subpopulation. In this review, we highlight the current and future developments that could allow transition to a more precise manner of antibiotic treatment in late preterm and term neonates, and propose a research agenda toward precision medicine for neonatal bacterial infections.</p

WS1.3 Respiratory microbiota dynamics in newborns with cystic fibrosis and healthy controls: A longitudinal study

IEEEMost malware are introduced into a computer system by applications that communicate with the outside world. These applications (called portals) are key components for system security. This paper presents an efficient anti-malware framework un

Trends in paediatric inpatient antibiotic therapy in a secondary care setting

There is growing attention for antimicrobial stewardship in paediatrics. Currently, little is known about secondary care antibiotic practice. We analysed trends in time with respect to inpatient antibiotic use in a secondary paediatric care setting. Total inpatient antibiotic consumption per year (2010–2015) and antibiotic prescriptions for urinary tract infection (UTI) and lower respiratory tract infection (LRTI) were analysed. Variables were total, antibiotic-specific, and intravenous days of therapy (DOT/100PD) and for UT

Oral antibiotics for neonatal infections: a systematic review and meta-analysis

Background: Worldwide many neonates suffer from bacterial infections. Adequate treatment is important but is associated with prolonged hospitalization for intravenous administration. In older children, oral switch therapy has been proven effective and safe for several indications and is now standard care. Objectives: To evaluate the currently available evidence on pharmacokinetics, safety and efficacy of oral antibiotics and oral switch therapy in neonates (0–28 days old). Methods: We performed systematic searches in Medline, Embase.com, Cochrane, Google Scholar and Web of Science. Studies were eligible if they described the use of oral antibiotics in neonates (0–28 days old), including antibiotic switch studies and pharmacological studies. Results: Thirty-one studies met the inclusion criteria. Compared with parenteral administration, oral antibiotics generally reach their maximum concentration later and have a lower bioavailability, but in the majority of cases adequate serum levels for bacterial killing are reached. Furthermore, studies on efficacy of oral antibiotics showed equal relapse rates (

Evaluation of a clinical decision rule to guide antibiotic prescription in children with suspected lower respiratory tract infection in The Netherlands

BACKGROUND: Optimising the use of antibiotics is a key component of antibiotic stewardship. Respiratory tract infections (RTIs) are the most common reason for antibiotic prescription in children, even though most of these infections in children under 5 years are viral. This study aims to safely reduce antibiotic prescriptions in children under 5 years with suspected lower RTI at the emergency department (ED), by implementing a clinical decision rule. METHODS AND FINDINGS: In a stepped-wedge cluster randomised trial, we included children aged 1-60 months presenting with fever and cough or dyspnoea to 8 EDs in The Netherlands. The EDs were of varying sizes, from diverse geographic and demographic regions, and of different hospital types (tertiary versus general). In the pre-intervention phase, children received usual care, according to the Dutch and NICE guidelines for febrile children. During the intervention phase, a validated clinical prediction model (Feverkidstool) including clinical characteristics and C-reactive protein (CRP) was implemented as a decision rule guiding antibiotic prescription. The intervention was that antibiotics were withheld in children with a low or intermediate predicted risk of bacterial pneumonia (≤10%, based on Feverkidstool). Co-primary outcomes were antibiotic prescription rate and strategy failure. Strategy failure was defined as secondary antibiotic prescriptions or hospitalisations, persistence of fever or oxygen dependency up to day 7, or complications. Hospitals were randomly allocated to 1 sequence of treatment each, using computer randomisation. The trial could not be blinded. We used multilevel logistic regression to estimate the effect of the intervention, clustered by hospital and adjusted for time period, age, sex, season, ill appearance, and fever duration; predicted risk was included in exploratory analysis. We included 999 children (61% male, median age 17 months [IQR 9 to 30]) between 1 January 2016 and 30 September 2018: 597 during the pre-intervention phase and 402 during the intervention phase. Most children (77%) were referred by a general practitioner, and half of children were hospitalised. Intention-to-treat analyses showed that overall antibiotic prescription was not reduced (30% to 25%, adjusted odds ratio [aOR] 1.07 [95% CI 0.57 to 2.01, p = 0.75]); strategy failure reduced from 23% to 16% (aOR 0.53 [95% CI 0.32 to 0.88, p = 0.01]). Exploratory analyses showed that the intervention influenced risk groups differently (p < 0.01), resulting in a reduction in antibiotic prescriptions in low/intermediate-risk children (17% to 6%; aOR 0.31 [95% CI 0.12 to 0.81, p = 0.02]) and a non-significant increase in the high-risk group (47% to 59%; aOR 2.28 [95% CI 0.84 to 6.17, p = 0.09]). Two complications occurred during the trial: 1 admission to the intensive care unit during follow-up and 1 pleural empyema at day 10 (both unrelated to the study intervention). Main limitations of the study were missing CRP values in the pre-intervention phase and a prolonged baseline period due to logistical issues, potentially affecting the power o

Oral and Intravenous Amoxicillin Dosing Recommendations in Neonates:A Pooled Population Pharmacokinetic Study

BACKGROUND: There is a lack of evidence on oral amoxicillin pharmacokinetics and exposure in neonates with possible serious bacterial infection (pSBI). We aimed to describe amoxicillin disposition following oral and intravenous administration and to provide dosing recommendations for preterm and term neonates treated for pSBI.METHODS: In this pooled-population pharmacokinetic study, 3 datasets were combined for nonlinear mixed-effects modeling. In order to evaluate amoxicillin exposure following oral and intravenous administration, pharmacokinetic profiles for different dosing regimens were simulated with the developed population pharmacokinetic model. A target of 50% time of the free fraction above the minimal inhibitory concentration (MIC) with an MICECOFF of 8 mg/L (to cover gram-negative bacteria such as Escherichia coli) was used.RESULTS: The cohort consisted of 261 (79 oral, 182 intravenous) neonates with a median (range) gestational age of 35.8 weeks (range, 24.9-42.4) and bodyweight of 2.6 kg (range, 0.5-5). A 1-compartment model with first-order absorption best described amoxicillin pharmacokinetics. Clearance (L/h/kg) in neonates born after 30 weeks' gestation increased with increasing postnatal age (PNA day 10, 1.25-fold; PNA day 20, 1.43-fold vs PNA day 3). Oral bioavailability was 87%. We found that a twice-daily regimen of 50 mg/kg/day is superior to a 3- or 4-times daily schedule in the first week of life for both oral and intravenous administration.CONCLUSIONS: This pooledpopulation pharmacokinetic description of intravenous and oral amoxicillin in neonates provides age-specific dosing recommendations. We conclude that neonates treated with oral amoxicillin in the first weeks of life reach adequate amoxicillin levels following a twice-daily dosing regimen. Oral amoxicillin therapy could therefore be an adequate, cost-effective, and more patient-friendly alternative for neonates worldwide.</p