The Nutri-Score front-of-pack label, which classifies the nutritional quality of products in one of 5 classes (A to E), is one of the main candidates for standardized front-of-pack labeling in the EU. The algorithm underpinning the Nutri-Score label is derived from the Food Standard Agency (FSA) nutrient profile model, originally a binary model developed to regulate the marketing of foods to children in the UK. This review describes the development and validation process of the Nutri-Score algorithm. While the Nutri-Score label is one of the most studied front-of-pack labels in the EU, its validity and applicability in the European context is still undetermined. For several European countries, content validity (i.e., ability to rank foods according to healthfulness) has been evaluated. Studies showed Nutri-Score's ability to classify foods across the board of the total food supply, but did not show the actual healthfulness of products within different classes. Convergent validity (i.e., ability to categorize products in a similar way as other systems such as dietary guidelines) was assessed with the French dietary guidelines; further adaptations of the Nutri-Score algorithm seem needed to ensure alignment with food-based dietary guidelines across the EU. Predictive validity (i.e., ability to predict disease risk when applied to population dietary data) could be re-assessed after adaptations are made to the algorithm. Currently, seven countries have implemented or aim to implement Nutri-Score. These countries appointed an international scientific committee to evaluate Nutri-Score, its underlying algorithm and its applicability in a European context. With this review, we hope to contribute to the scientific and political discussions with respect to nutrition labeling in the EU.
MULTIFILE
OBJECTIVE: To further test the validity and clinical usefulness of the steep ramp test (SRT) in estimating exercise tolerance in cancer survivors by external validation and extension of previously published prediction models for peak oxygen consumption (Vo2peak) and peak power output (Wpeak).DESIGN: Cross-sectional study.SETTING: Multicenter.PARTICIPANTS: Cancer survivors (N=283) in 2 randomized controlled exercise trials.INTERVENTIONS: Not applicable.MAIN OUTCOME MEASURES: Prediction model accuracy was assessed by intraclass correlation coefficients (ICCs) and limits of agreement (LOA). Multiple linear regression was used for model extension. Clinical performance was judged by the percentage of accurate endurance exercise prescriptions.RESULTS: ICCs of SRT-predicted Vo2peak and Wpeak with these values as obtained by the cardiopulmonary exercise test were .61 and .73, respectively, using the previously published prediction models. 95% LOA were ±705mL/min with a bias of 190mL/min for Vo2peak and ±59W with a bias of 5W for Wpeak. Modest improvements were obtained by adding body weight and sex to the regression equation for the prediction of Vo2peak (ICC, .73; 95% LOA, ±608mL/min) and by adding age, height, and sex for the prediction of Wpeak (ICC, .81; 95% LOA, ±48W). Accuracy of endurance exercise prescription improved from 57% accurate prescriptions to 68% accurate prescriptions with the new prediction model for Wpeak.CONCLUSIONS: Predictions of Vo2peak and Wpeak based on the SRT are adequate at the group level, but insufficiently accurate in individual patients. The multivariable prediction model for Wpeak can be used cautiously (eg, supplemented with a Borg score) to aid endurance exercise prescription.
Background: The aim of this study is to validate a newly developed nurses' self-efficacy sources inventory. We test the validity of a five-dimensional model of sources of self-efficacy, which we contrast with the traditional four-dimensional model based on Bandura's theoretical concepts. Methods: Confirmatory factor analysis was used in the development of the newly developed self-efficacy measure. Model fit was evaluated based upon commonly recommended goodness-of-fit indices, including the χ2 of the model fit, the Root Mean Square Error of approximation (RMSEA), the Tucker-Lewis Index (TLI), the Standardized Root Mean Square Residual (SRMR), and the Bayesian Information Criterion (BIC). Results: All 22 items of the newly developed five-factor sources of self-efficacy have high factor loadings (range .40-.80). Structural equation modeling showed that a five-factor model is favoured over the four-factor model. Conclusions and implications: Results of this study show that differentiation of the vicarious experience source into a peer- and expert based source reflects better how nursing students develop self-efficacy beliefs. This has implications for clinical learning environments: a better and differentiated use of self-efficacy sources can stimulate the professional development of nursing students.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The Water Framework Directive imposes challenges regarding the environmental risk of plastic pollution. The quantification, qualification, monitoring, and risk assessment of nanoplastics and small microplastic (<20 µm) is crucial. Environmental nano- and micro-plastics (NMPs) are highly diverse, accounting for this diversity poses a big challenge in developing a comprehensive understanding of NMPs detection, quantification, fate, and risks. Two major issues currently limit progress within this field: (a) validation and broadening the current analytical tools (b) uncertainty with respect to NMPs occurrence and behaviour at small scales (< 20 micron). Tracking NMPs in environmental systems is currently limited to micron size plastics due to the size detection limit of the available analytical techniques. There are currently no methods that can detect nanoplastics in real environmental systems. A major bottleneck is the incompatibility between commercially available NMPs and those generated from plastic fragments degradation in the environment. To track nanoplastics in environmental and biological systems, some research groups synthesized metal-doped nanoplastics, often limited to one polymer type and using high concentrations of surfactants, rendering these synthesized nanoplastics to not be representative of nanoplatics found in real environment. NanoManu proposes using Electrohydrodynamic Atomization to generate metal doped NMPs of different polymers types, sizes, and shapes, which will be representative of the real environmental nanoplastics. The synthesized nanoplastics will be used as model particles in environmental studies. The synthesized nanoplastics will be characterized and tested using different analytical methods, e.g., SEM-EDX, TEX, GCpyrMS, FFF, µFTIR and SP-ICP-MS. NanoManu is a first and critical step towards generating a comprehensive state-of-the-art analytical and environmental knowledge on the environmental fate and risks of nanoplastics. This knowledge impacts current risk assessment tools, efficient interventions to limit emissions and adequate regulations related to NMPs.
