Protein changes as robust signatures of fish chronic stress: a proteomics approach to fish welfare research

Background Aquaculture is a fast-growing industry and therefore welfare and environmental impact have become of utmost importance. Preventing stress associated to common aquaculture practices and optimizing the fish stress response by quantification of the stress level, are important steps towards the improvement of welfare standards. Stress is characterized by a cascade of physiological responses that, in-turn, induce further changes at the whole-animal level. These can either increase fitness or impair welfare. Nevertheless, monitorization of this dynamic process has, up until now, relied on indicators that are only a snapshot of the stress level experienced. Promising technological tools, such as proteomics, allow an unbiased approach for the discovery of potential biomarkers for stress monitoring. Within this scope, using Gilthead seabream (Sparus aurata) as a model, three chronic stress conditions, namely overcrowding, handling and hypoxia, were employed to evaluate the potential of the fish protein-based adaptations as reliable signatures of chronic stress, in contrast with the commonly used hormonal and metabolic indicators. Results A broad spectrum of biological variation regarding cortisol and glucose levels was observed, the values of which rose higher in net-handled fish. In this sense, a potential pattern of stressor-specificity was clear, as the level of response varied markedly between a persistent (crowding) and a repetitive stressor (handling). Gel-based proteomics analysis of the plasma proteome also revealed that net-handled fish had the highest number of differential proteins, compared to the other trials. Mass spectrometric analysis, followed by gene ontology enrichment and protein-protein interaction analyses, characterized those as humoral components of the innate immune system and key elements of the response to stimulus. Conclusions Overall, this study represents the first screening of more reliable signatures of physiological adaptation to chronic stress in fish, allowing the future development of novel biomarker models to monitor fish welfare.This study received Portuguese national funds from FCT - Foundation for Science and Technology through project UIDB/04326/2020 and project WELFISH (Refª 16–02-05-FMP-12, “Establishment of Welfare Biomarkers in farmed fish using a proteomics approach”) financed by Mar2020, in the framework of the program Portugal 2020. Cláudia Raposo de Magalhães acknowledges an FCT PhD scholarship, Refª SFRH/BD/138884/2018. Denise Schrama acknowledges an FCT PhD scholarship, Refª SFRH/BD/136319/2018.info:eu-repo/semantics/publishedVersio

Incremental Model Identification in Distributed Two-phase Reaction Systems

Transformation to variant and invariant states, called extents, is used to decouple the dynamic effects of reaction systems and serves as basis for incremental model identification, in which kinetic models are identified individually for each dynamic effect. This contribution introduces a novel transformation to extents for the incremental model identification of two-phase distributed reaction systems. Distributed reaction systems are discussed for two cases, namely, when measurements along the spatial coordinate are available and when there are not. In the second case, several measurements made under appropriate operating conditions are combined to overcome the lack of measurements along the spatial coordinate. This novel method is illustrated via the simulated example of a two-phase tubular reactor

Fish processing and digestion affect parvalbumins detectability in Gilthead Seabream and European seabass

Consumption of aquatic food, including fish, accounts for 17% of animal protein intake. However, fish consumption might also result in several side-effects such as sneezing, swelling and anaphylaxis in sensitized consumers. Fish allergy is an immune reaction to allergenic proteins in the fish muscle, for instance parvalbumin (PV), considered the major fish allergen. In this study, we characterize PV in two economically important fish species for southern European aquaculture, namely gilthead seabream and European seabass, to understand its stability during in vitro digestion and fish processing. This information is crucial for future studies on the allergenicity of processed fish products. PVs were extracted from fish muscles, identified by mass spectrometry (MS), and detected by sandwich enzyme-linked immunosorbent assay (ELISA) after simulated digestion and various food processing treatments. Secondary structures were determined by circular dichroism (CD) after purification by anion exchange and gel filtration chromatography. In both species, PVs presented as α-helical and β-sheet structures, at room temperature, were shown to unfold at boiling temperatures. In European seabass, PV detectability decreased during the simulated digestion and after 240 min (intestinal phase) no detection was observed, while steaming showed a decrease (p < 0.05) in PVs detectability in comparison to raw muscle samples, for both species. Additionally, freezing (−20 °C) for up to 12 months continued to reduce the detectability of PV in tested processing techniques. We concluded that PVs from both species are susceptible to digestion and processing techniques such as steaming and freezing. Our study obtained preliminary results for further research on the allergenic potential of PV after digestion and processing

Fast Estimation of Plant Steady State, with Application to Static RTO

Experimental assessment or prediction of plant steady state is important for many applications in the area of modeling and operation of continuous processes. For example, the iterative implementation of static real-time optimization requires reaching steady state for each successive operating point, which may be quite time-consuming. This paper presents an approach to speed up the estimation of plant steady state for imperfectly known dynamic systems that are characterized by (i) the presence of fast and slow states, with no effect of the slow states on the fast states, and (ii) the fact that the unknown part of the dynamics depends only on the fast states. The proposed approach takes advantage of measurement-based rate estimation, which consists in estimating rate signals without the knowledge or identification of rate models. Since one can use feedback control to speed up the convergence to steady state of the fast part of the plant, this rate estimation allows estimating the steady state of the slow part during transient operation. It is shown how this approach can be used to speed up the static real-time optimization of continuous processes. A simulated example illustrates its application to a continuous stirred-tank reactor

Variant and Invariant States for Chemical Reaction Systems

Models of open non-isothermal heterogeneous reaction systems can be quite complex as they include information regarding the reactions, the inlet and outlet flows, the transfer of species between phases and the transfer of energy. This paper builds on the concept of reaction variants and invariants and proposes a linear transformation that allows viewing a complex nonlinear reaction system via decoupled dynamic variables, each one associated with a particular phenomenon such as a single chemical reaction, a specific mass transfer or heat transfer. Three aspects are discussed, namely, (i) the decoupling of reaction and transport phenomena in open non-isothermal both homogeneous and heterogeneous reactors, (ii) the decoupling of spatially distributed systems such as tubular reactors, and (iii) the applicability of the decoupling transformation towards the analysis of complex reaction systems, in particular with respect to the analysis of measured data in the absence of a kinetic model

Generalized Incremental Model Identification for Chemical Reaction Systems

Identification of kinetic models and estimation of kinetic parameters in chemical reaction systems can be done using Incremental Model Identification (IMI). By using IMI, it is possible to separate the effect of the different reactions and thus investigate each reaction individually. In contrast, with simultaneous approaches, it is necessary to work with a complete model that includes a rate candidate for each reaction, which might lead to a large number of possible model combinations. Hence, IMI allows faster computation of the identified models and estimated parameters [1]. There exist essentially two main approaches for IMI: extent-based IMI and rate-based IMI. In extent-based IMI, reaction rates are integrated to yield extents, and the parameters are estimated via least squares by fitting these simulated extents to experimental extents obtained from measured concentrations [2]; in rate-based IMI, the parameters are estimated via least squares by fitting simulated rates to experimental rates obtained by differentiation of measured concentrations [3]. This contribution proposes a generalized IMI method that offers much more flexibility in the use of measurements, particularly in the way the various measurements are weighted. The parameters are estimated via weighted least squares by comparing simulated and experimental extents. The peculiarity consists in comparing extent values not only at the measurement points but for all possible time intervals between measurement points. Then, it can be shown that both the extent-based and rate-based IMI can be reformulated as particular cases of this generalized method. For example, the extent-based method would correspond to positive and equal weights for all time intervals that start at time zero, while the rate-based method would correspond to positive and equal weights for all time intervals with a length of one sampling period. This reformulation allows the investigation of new approaches by testing compromises between different methods, which can potentially result in a better IMI method. With such a generalized method, it is also possible to test if there is an optimal weight distribution or, more generally, if there are important features in the weights to best perform model identification. The effect of the weight distribution on (i) the accuracy and precision of the parameters, and (ii) the model discrimination power can be investigated via different optimization methods, such as classic gradient-based algorithms or genetic algorithms. The different directions followed to find the best weight distribution are illustrated with simulated examples, and these results are compared to extent-based and rate-based IMI. [1] Bhatt et al., Chem. Eng. Sci., 2012, 83, 24-38 [2] Bhatt et al., Ind. & Eng. Chem. Res., 2011, 50, 12960-12974 [3] Brendel et al., Chem. Eng. Sci., 2006, 61, 5404-542

On the essence of parallel independence for the double-pushout and sesqui-pushout approaches

Parallel independence between transformation steps is a basic notion in the algebraic approaches to graph transformation, which is at the core of some static analysis techniques like Critical Pair Analysis. We propose a new categorical condition of parallel independence and show its equivalence with two other conditions proposed in the literature, for both left-linear and non-left-linear rules. Next we present some preliminary experimental results aimed at comparing the three conditions with respect to computational efficiency. To this aim, we implemented the three conditions, for left-linear rules only, in the Verigraph system, and used them to check parallel independence of pairs of overlapping redexes generated from some sample graph transformation systems over categories of typed graphs

Concept and Applications of Extents in Chemical Reaction Systems

Models of chemical reaction systems can be quite complex as they typically include information regarding the reactions, the various transfers of heat and mass, as well as the effects of the inlet and outlet flows. It is well known that a linear transformation involving the reaction stoichiometry allows artitioning the state space into a reaction invariant subspace and its complement. Alternative transformations have been proposed to partition the state space into various subspaces that are linked to the reactions, the heat and mass transfers, the inlets, and the initial conditions. This paper analyzes this partitioning of the state space, which helps isolate the effects of the various rate processes. The implications of this partitioning are discussed with respect to several modeling and estimation applications

Extent-based Model Identification of Surface Catalytic Reaction Systems

Identification of kinetic models and estimation of reaction and mass-transfer parameters is an important task for monitoring, control and optimization of industrial processes. A methodology called Extent-based Model Identification has been developed to separate the effects of reaction, mass transfer, and inlet and outlet flows for homogeneous and gas-liquid reaction systems. The decoupled effects, called extents, are used to decompose the model identification task incrementally into sub-problems of lower complexity, in which measured data are first transformed into extents and these extents are then modeled individually [1-3]. For the analysis of surface catalytic reaction systems, it is important to separate the coupled effects of transport phenomena and reactions. Therefore, the methodology of Extent-based Model Identification has been extended to gas-solid and gas-liquid-solid systems involving catalytic processes at the surface of a solid catalyst, described by Langmuir-Hinshelwood types of kinetic models. From measurements in the fluid and solid phases, the extent of each individual dynamic process is computed. A model is postulated for that process and the corresponding extent is simulated and compared with the computed extent. This procedure allows performing model identification and parameter estimation individually for each phenomenon and species (diffusion of substrates and products, adsorption of substrates, desorption of products and solid-phase reactions). [1] Bhatt et al., Ind. & Eng. Chem. Res., 2011, 50, 12960-12974 [2] Srinivasan et al., Chem. Eng. J., 2012, 208, 785-793 [3] Billeter et al., Anal. Chim. Acta, 2013, 767, 21-3