196746 research outputs found
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Insights into the nature of ichthyotoxins from the <i>Chrysochromulina leadbeateri</i> blooms in Northern Norwegian fjords
No full textIn May–June 2019, the microalga Chrysochromulina leadbeateri caused a massive fish-killing event in several fjords in Northern Norway, resulting in the largest direct impact ever on aquaculture in northern Europe due to toxic algae. Motivated by the fact that no algal toxins have previously been described from C. leadbeateri, we set out to investigate the chemical nature and toxicity of secondary metabolites in extracts of two strains (UIO 393, UIO 394) isolated from the 2019 bloom, as well as one older strain (UIO 035) isolated during a bloom in Northern Norway in 1991. Initial LC–DAD–MS/MS-based molecular networking analysis of the crude MeOH extracts of the cultivated strains showed that their profiles of small organic molecules, including a large number of known lipids, were very similar, suggesting that the same class of toxin(s) were likely the causative agents of the two harmful algal bloom (HAB) events. Next, bioassay-guided fractionation using the RTgill-W1 cell line and metabolomics analysis pointed to a major compound affording [M + H]+ ions at m/z 1399.8333 as a possible toxin, corresponding to a compound with the formula C67H127ClO27. Moreover, our study unveiled a series of minor analogues exhibiting distinct patterns of chlorination and sulfation, together defining a new family of compounds, which we propose to name leadbeaterins. Remarkably, these suspected toxins were detected in situ in samples collected during the 2019 bloom close to Tromsø, thereby consistent with a role in fish kills. The elemental compositions of the putative C. leadbeateri ichthyotoxins strongly indicate them to be long linear polyhydroxylated polyketides, structurally similar to sterolysins reported from a number of dinoflagellates
Structural and tribological studies on the interaction of porcine gastric mucin with non- and cationic-modified β-lactoglobulins
No full textβ-lactoglobulin (BLG) is the major whey protein with negatively charges at neutral pH in aqueous media. Thus, the interaction with mucins, the major polyanionic component of mucus, is very weak due to the electrostatic repulsion between them. The present study postulated that cationization of BLG molecules may reverse the interaction characteristics between BLG and mucin from repulsive to associative. To this end, cationic-modified BLGs were prepared by grafting positively charged ethylenediamine (EDA) moieties into the negatively charged carboxyl groups on the aspartic and glutamic acid residues and compared with non-modified BLG upon mixing with porcine gastric mucin (PGM). To characterize the structural and conformational features of PGM, non/cationized BLGs, and their mixtures, various spectroscopic approaches, including zeta potential, dynamic light scattering (DLS), and circular dichroism (CD) spectroscopy were employed. Importantly, we have taken surface adsorption with optical waveguide lightmode spectroscopy (OWLS), and tribological properties with pin-on-disk tribometry at the sliding interface as the key approaches to determine the interaction nature between them as mixing PGM with polycations can lead to synergistic lubrication at the nonpolar substrate in neutral aqueous media as a result of an electrostatic association. All the spectroscopic studies and a substantial improvement in lubricity collectively supported a tenacious and associative interaction between PGM and cationized BLGs, but not between PGM and non-modified BLG. This study demonstrates a unique and successful approach to intensify the interaction between BLG and mucins, which is meaningful for a broad range of disciplines, including food science, macromolecular interactions, and biolubrication etc
Experimental Study of Electricity Generation from Solar Energy Using Organic Phase Change Materials and Thermoelectric Generator
No full textThe study investigates using edible oils (ostrich, mutton, beef, coconut) as natural phase change materials for solar energy absorption and storage. Exposed to 900 W/m2 direct radiation by a solar simulator, these materials harness captured energy at a specific depth to generate electricity through a thermoelectric device. The experimental results showed that coconut oil exhibits the highest thermal energy storage efficiency among others, measuring at 39%, while mutton tallow shows the lowest performance at 16.59%. Additionally, the performance of a system employing coconut oil as the best material, in combination with iron oxide nanoparticles and carbonized sawdust (CS) was experimentally evaluated at different mass fractions (0.3%, 0.6%, and 0.9%) to enhance thermal conductivity and sunlight absorption. The carbonized sawdust and its nanoparticles increased the thermal energy storage efficiency of the system by 62% and 53%, respectively. Moreover, the stored exergy by the phase change materials indicates that coconut oil and beef tallow had the highest and lowest exergy efficiencies of 6.3% and 3.3%, respectively. The combination of coconut oil with iron oxide nanoparticles and carbonized sawdust leads to 10% and 7.9% increased exergy efficiencies respectively
Performance analysis of a novel hybrid device with floating breakwater and wave energy converter integrated
No full textAs a kind of clean energy, wave energy has attracted increasing attention in recent years. However, due to its high cost and low efficiency, wave energy has not been widely commercialized worldwide. According to previous research works, the combination of wave energy devices and floating breakwaters can reduce the cost of wave power generation devices efficiently. In this paper, a hybrid device with wave energy converter (WEC) and flexible porous floating breakwater integrated is proposed. The hydrodynamic performance of this device is studied by numerical simulation based on Reynolds-Averaged Navier–Stokes (RANS) method which has been validated through experimental results. Based on this, a series of numerical simulations are performed under different power take-off stiffness (KPTO) and different power take-off damping coefficient (CPTO) to calculate the motion and transmission coefficient of the device. By comparing the numerical results, the influence of KPTO and CPTO of the hybrid device on its wave attenuation performance and energy conversion efficiency are analyzed. The results indicate that under the incident waves of period 0.56 s and 0.89 s, the recommended value of KPTO is 3.0 N/m and 0.5 N/m respectively. While for incident waves of period 0.56 s, in order to achieve better wave attenuation performance and power generation efficiency, the recommended value of CPTO should be between 10.55 Ns/m and 14.23 Ns/m. On this basis, the influence of the size of breakwater on the wave attenuation performance and power generation efficiency of the hybrid device is also discussed. Moreover, through a cost analysis, it can be inferred that the utilization of the baseline model is more economically viable
Formal Verification of Railway Interlockings: a Compositional Approach Based on a Library of Pre-verified Components
No full textA railway interlocking system (RIS) is a safety critical system that allows to control the train traffic. Modern RIS rely on their software to guarantee the absence of dangerous situations leading to train collisions or derailments. For more than twenty years [5], researchers have worked on the development of formal method approaches to verify the absence of bugs in the RIS software and thereby improving the safety of the railway systems. A very popular formal verification approach is model checking. Practically, model checking of complex RIS remains hard due to the so-called state space explosion problem. Compositional verification can solve this issue by reducing a big network controlled by a RIS into a set of smaller sub-networks while still guaranteeing the safety of the composite. In this context, two different decomposition technique were proposed by the RobustRailS and the Louvain research groups. This article goes one step further and proposes a verification strategy based on the creation of a library made of typical re-usable pre-verified sub-networks (i.e., building blocks). During compositional verification, the goal is then to decompose the network into sub-networks that are in the library such that they do not need to be verified
Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generation
No full textShortcut learning is when a model – e.g. a cardiac disease classifier – exploits correlations between the target label and a spurious shortcut feature, e.g. a pacemaker, to predict the target label based on the shortcut rather than real discriminative features. This is common in medical imaging, where treatment and clinical annotations correlate with disease labels, making them easy shortcuts to predict disease. We propose a novel detection and quantification of the impact of potential shortcut features via a fast diffusion-based counterfactual image generation that can synthetically remove or add shortcuts. Via a novel self-optimized masking scheme we spatially limit the changes made with no extra inference step, encouraging the removal of spatially constrained shortcut features while ensuring that the shortcut-free counterfactuals preserve their remaining image features to a high degree. Using these, we assess how shortcut features influence model predictions. This is enabled by our second contribution: An efficient diffusion-based counterfactual explanation method with significant inference speed-up at comparable image quality as state-of-the-art. We confirm this on two large chest X-ray datasets, a skin lesion dataset, and CelebA. Our code is publicly available at https://fastdime.compute.dtu.dk
Identifying Nonalcoholic Fatty Liver Disease and Advanced Liver Fibrosis from MRI in UK Biobank
No full textNon-alcoholic fatty liver disease (NAFLD) and its progressive form of non-alcoholic steatohepatitis (NASH) pose a major public health problem that affects more than 30% of the global population. Since NAFLD is asymptomatic in the early stages, sufferers often remain untreated until the onset of NASH, which can lead to fibrosis and eventually cirrhosis of the liver. This condition is traditionally diagnosed via liver biopsy, which is invasive and associated with significant risks for the patient and susceptibility to sampling errors. These limitations underscore the necessity for non-invasive tools to assess disease severity. We explore the potential of magnetic resonance imaging (MRI) sequences in the UK Biobank (UKBB) to classify individuals as having either a healthy liver, NAFLD, or progressive NAFLD-associated advanced fibrosis. For the classification inputs, we utilize proton density fat fraction (PDFF) and native spin-lattice relaxation time (T1) maps, as well as serum biomarker data for assessing the sub-cohorts. The best models achieve near-perfect performance on identifying healthy individuals and NAFLD with AUCs of 0.99 and 0.98 respectively, while individuals with advanced fibrosis are under-diagnosed with an AUC of 0.67 at best. While segmentation decreases model performance, when classifying on full images, we make use of non-liver-related features, which is sub-optimal if we want to detect liver-related imaging biomarkers
Advancing life cycle based chemical toxicity characterization through digitalization
No full textChemicals are vital to modern society, but their rapid production and use lead to hazardous emissions, affecting human and ecosystem health. Chemical toxicity characterization is an essential tool to help assess and mitigate these impacts but requires diverse chemical input data that are unavailable for most of the >350,000 globally registered chemicals and mixtures. Machine learning (ML) methods have achieved remarkable predictive performance across scientific fields and offer high potential to fill these data gaps across input parameters and chemicals. However, the systematic uptake of ML methods to address data gaps in chemical toxicity characterization remains limited due to challenges undermining confidence in their predictions. In particular, ML’s limited extrapolative capacity constrains reliable predictions to domains represented within the training data. This obscures which data gaps across various chemicals and input parameters can be effectively addressed by developing ML prediction methods. Further, integrating predictions from different ML models in chemical assessments requires quantifying uncertainty in predictions to account for input data quality variations. However, uncertainty quantification is challenging and not standard in ML practice. Therefore, practical examples of developing and integrating ML-based predictions with quantified uncertainty into chemical toxicity characterization are urgently needed to build trust in prediction-based chemical assessments.The work presented in this PhD thesis addresses these challenges by focusing on four research objectives: (1) Prioritize input parameters in characterizing human toxicity and ecotoxicity impacts for developing ML-based approaches based on their relevance for obtaining robust characterization results and their suitability for ML. (2) Analyze the chemical space covered by ML-based approaches trained with available measured data for relevant input parameters for characterizing human toxicity and ecotoxicity impacts relative to the global chemical market. (3) Identify and develop suitable ML-based approaches capable of quantifying data- and model-related uncertainty in predictions across diverse chemical structures. (4) Demonstrate the use of uncertain ML-based predictions to gain insights on chemical toxicity for the global chemical market and for designing safer and more sustainable chemical synthesis using illustrative case studies.Following an introductory chapter, chapter 2 presents a framework for prioritizing input data gaps in chemical toxicity characterization, prioritizing 13 out of its 38 input parameters for ML model development while flagging additional nine parameters with critical data gaps. Chapter 3 offers an assessment of the potential for ML to address the broader realm of >130,000 marketed chemicals for the prioritized parameters, finding that based on 1 to 10% of available data, ML can potentially predict 8 to 46% of marketed chemicals. This predictive potential was highly dependent on the chemical diversity represented in the available input parameter data. These results demonstrated that ML can significantly contribute to filling data gaps in chemical toxicity characterization. However, it left several crucial input parameters and more than 50% of marketed chemicals across prioritized parameters difficult to address. Chapter 2 and 3 highlighted that strategic efforts are needed to increase data availability focusing on data diversity and advanced modeling approaches to leverage alternative data sources and domain knowledge. Chapter 4 presents an approach for developing uncertainty-aware ML models that transparently communicate prediction reliability through fully quantified uncertainty intervals. It demonstrates that both conformal prediction (CP) and Bayesian neural networks (BNN) can provide robust estimates of prediction reliability by providing quantified uncertainty ranges that effectively address various aspects of data- and model-related uncertainty. Developing uncertainty-aware models caused no substantial loss of predictive performance compared to standard ML approaches without uncertainty quantification. Additionally, these models harnessed the full potential of available data, enabling robust predictions across a broader range of chemicals by accurately reflecting differences in prediction reliability. Chapter 5 applies this approach to predict human non-cancer toxicity points of departure (PODs) for >130,000 marketed chemicals, identifying chemical classes with high toxicity and significant prediction uncertainty. These results fill critical data gaps by providing predictions for many marketed chemicals with no prior estimates and guide future research to enhance predictions for chemical classes with high uncertainty, such as metals, inorganics, and macromolecules. Chapter 5 further explores practical challenges in applying digital tools to obtain robust toxicity characterization results through an illustrative case study aiming to identify safer building blocks for enzymatic amide bond synthesis. By propagating (semi)-quantitative input parameter uncertainties, the results revealed significant deviations between probabilistic best estimates and deterministic results, as well as large uncertainties that hindered reliable identification of toxicity impact differences among similar chemicals. This underscored the importance of quantifying uncertainty in input data predictions to obtain robust conclusions in comparative chemical assessments.This PhD project built the foundation for developing fit-for-purpose digital prediction tools for chemical toxicity characterization, offering a comprehensive view on critical gaps and strategies for addressing them. By prioritizing relevant input parameters and establishing the chemical target domain as a benchmark for the predictive capabilities required from ML-based approaches, the presented approaches guide future efforts for data curation and ML model development that can systematically enhance the availability and robustness of chemical toxicity characterization results. As an essential aspect, this project demonstrated the development of uncertainty-aware ML and its importance for effectively integrating predictions in chemical toxicity characterization to obtain robust conclusions from prediction-based chemical assessments. This approach also significantly improved data availability across globally marketed chemicals, as it allowed providing predictions with quantified uncertainty for highly diverse chemicals. Applying it to fill data gaps for other critical input parameters holds practical relevance for industry and academia, as it would significantly improve the availability and robustness of chemical risk and impact assessments, opening new possibilities for comparing alternatives across marketed chemicals and new chemical designs to create safer and more sustainable products. This PhD project thereby made a substantial contribution to the fields of chemical impact and risk assessment to support effective chemical management in minimizing chemical impacts on humans and ecosystems
Predator-induced defense decreases growth rate and photoprotective capacity in a nitrogen-limited dinoflagellate, <i>Alexandrium minutum</i>
No full textSome dinoflagellates produce toxic secondary metabolites that correlate with increased resistance to grazers. The allocation costs of toxin production have been repeatedly addressed, but with conflicting results. Few studies have considered the potential costs of this defense to the photosystem, even though defense toxins (e.g., karlotoxins and brevetoxins) are closely linked to the photoprotective process. Here, we used chemical cues from copepods to induce paralytic shellfish toxin (PST) production in resource-limited Alexandrium minutum and quantitatively determined the growth rate and potential trade-offs with the photosystem process. The results show that grazer-induced, more toxic A. minutum had larger cell volume, lower cell division rate, and lower pigment content under nitrogen-limited conditions than control cells. In addition, predator cues led to a lower relative abundance of photoprotective xanthophylls and a reduced de-epoxidation efficiency of the xanthophyll cycle under high light conditions, reducing the ability of the cells to resist photodamage. Decreased photoprotective capacity may reflect an overlooked defense cost of toxin production
Spatial estimation of unidirectional wave evolution based on ensemble data assimilation
No full textWith the limitation of the high sensitivity of nonlinear models to initial conditions, the accurate estimation of wave spatial evolution is difficult to perform at a long distance. At this stage, a helpful approach is to improve the accuracy and robustness of the model through data assimilation technique. A robust data assimilation framework is developed by coupling ensemble Kalman filtering (EnKF) with the nonlinear wave model. The spatial evolution is obtained by numerically integrating the viscous modified Nonlinear Schrödinger (MNLS) equation. The performance of the EnKF-MNLS coupled framework is tested using synthetic data and laboratory measurements. The synthetic data is generated by the MNLS simulation superposing the Gaussian noise. In the synthetic cases, the estimated wave envelopes agree well with the clean solution. The results of laboratory experiments indicate that the EnKF-MNLS framework can improve the accuracy of wave forecasts compared to noised MNLS simulations. This study aims to enhance the noise resistance of the nonlinear wave model in spatial evolution and improve the accuracy of the model forecast