Expression of the Antimicrobial Peptide Piscidin 1 and Neuropeptides in Fish Gill and Skin: A Potential Participation in Neuro-Immune Interaction

Antimicrobial peptides (AMPs) are found widespread in nature and possess antimicrobial and immunomodulatory activities. Due to their multifunctional properties, these peptides are a focus of growing body of interest and have been characterized in several fish species. Due to their similarities in amino-acid composition and amphipathic design, it has been suggested that neuropeptides may be directly involved in the innate immune response against pathogen intruders. In this review, we report the molecular characterization of the fish-specific AMP piscidin1, the production of an antibody raised against this peptide and the immunohistochemical identification of this peptide and enkephalins in the neuroepithelial cells (NECs) in the gill of several teleost fish species living in different habitats. In spite of the abundant literature on Piscidin1, the biological role of this peptide in fish visceral organs remains poorly explored, as well as the role of the neuropeptides in neuroimmune interaction in fish. The NECs, by their role as sensors of hypoxia changes in the external environments, in combination with their endocrine nature and secretion of immunomodulatory substances would influence various types of immune cells that contain piscidin, such as mast cells and eosinophils, both showing interaction with the nervous system. The discovery of piscidins in the gill and skin, their diversity and their role in the regulation of immune response will lead to better selection of these immunomodulatory molecules as drug targets to retain antimicrobial barrier function and for aquaculture therapy in the future.Expression of the Antimicrobial Peptide Piscidin 1 and Neuropeptides in Fish Gill and Skin: A Potential Participation in Neuro-Immune InteractionpublishedVersio

Localization of Acetylcholine, Alpha 7-NAChR and the Antimicrobial Peptide Piscidin 1 in the Macrophages of Fish Gut: Evidence for a Cholinergic System, Diverse Macrophage Populations and Polarization of Immune Responses

20 pages, 9 figures, 2 tables.-- Data Availability Statement: Not applicableThe recognition and elimination of invading pathogens are vital for host survival. Macrophages play a central role in host protection and cells functionally reminiscent of vertebrate macrophages are present in all multicellular organisms. A pattern responsible for bacterial recognition found on the surface of macrophages is CD14. These cells possess a repertoire of antimicrobial molecules stored in their granules and lysosomes. Polarization states observed in mammalian macrophages termed M1 and M2 also likely exist in fish macrophages. Markers for macrophage subtypes are slowly but definitively emerging in fish species. In the present study cell markers such as CD14, acetylcholine, alpha 7 acetylcholine nicotinic receptor (nAChR) subtype, the inducible nitric oxidase synthase (iNOS), and the antimicrobial peptide piscidin 1 are reported for the first time in the intestinal macrophages of both catfish Heteropneustes fossilis (Bloch, 1794) and the African bonytongue Heterotis niloticus (Cuvier, 1829) along the anterior and the posterior axis and the concentric muscle layers. Many antimicrobial effector responses of vertebrate macrophages including respiratory burst and NO induction are similar across the diverse animal taxa. Antibodies against calbindin coupled with ones to VAChT and tubulin revealed the localization of myenteric and submucosal plexuses, which are made up of enteric neurons, glial cells, and nerves near macrophages. Current studies allow for the elucidation of multiple roles of macrophages in disease models providing an insight into their in vivo function in fishPeer reviewe

Optimal energy management and performance evaluation of an Integrated Mobility System: the "Life for Silver Coast" case study

Nowdays, Climate Change and Global Warming are very relevant issues and Humankind relies on Renewable Energy Sources (RESs) for mitigating environmental impacts. RESs exploitation implies the adoption of a Distributed Energy Generation (DEG), implemented through local electrical grids called Microgrids (MGs). The intent of harvesting as much as energy possible, dealing with the RESs unpredictable nature, makes researchers develop suitable ICT systems (Energy Management Systems or EMSs). Smart Grids (SGs) are systems composed of many MGs, thanks to which a whole urban area can perform an efficient energy management. Energy Communities, made up of companies, research centres and Universities strive to design and realize SGs, in a sustainable development vision. In this context, the sustainable mobility system realized in the "LIFE for Silver Coast" European Project is a very good test bench for EMSs synthesis. In fact, Electric Vehicles (EVs) and charging stations will be integrated in the Project Area and managed through proprietary EMSs. In addition, the achieved knowhow can be used by the Energy Community to develop Smart Grids, not only in the same area. In this thesis, the Evolutionary Fuzzy System (EFS) paradigm is applied for the synthesis of an EMS. In particular, a double-step optimization Hierarchical Genetic Algorithm (HGA) procedure is implemented for reducing the computational cost. The resulting Fuzzy Inference System- Genetic Algorithm (FIS-GA) is tested for the onboard optimal energy management of the LIFE "Valentino" Class e-boat, with the purpose of implementing the same EMS in a residential MG. In addition, an application based on Life Quality indicators related to mobility systems is presented

Synthesis of an evolutionary Fuzzy multi-objective energy management system for an electric boat

Even though it is known that Renewable Energy Sources (RESs) are necessary to face Climate Change and pollution, technology is still in a developement phase, aiming at improving energy exploitation from RESs, as these type of sources suffer from low energy density and variability over time. Thus, proper ICT infrastructures equipped with a robust software, i.e., Energy Management System (EMS), are needed to ensure that Renewable Energy (RE) does not go to waste. Relatively small local electrical grids called Microgrids (MGs) represent the EMS ecosystem, since their main features are the proximity between generation and loads and the presence of Energy Storage Systems (ESSs) adopted to recover surplus energy. The Vehicle-to-Grid (V2G) paradigm helps to realize the Smart City, which in substance is an interconnection of MGs hosting electrical vehicles for an efficient energy management at a larger scale. In this context, e-boats have only recently been considered. Hence, in this work a Multi-Objective (MO) EMS is synthesized for an e-boat docked in a small Microgrid (PV generator and ESS) with the aim of maximizing the charging time of the e-boat ESS and spending as little as possible both for energy purchase and also in terms of ESS wear. A Fuzzy Inference System - Hierarchical Genetic Algorithm (FIS-HGA) is used to achieve the Pareto Front, with the HGA that is in charge of optimizing the FIS parameters. Results laid to a balanced trade-off between the two objectives, since the e-boat ESS is almost fully charged in a reasonable time and with a low cost, compatible with people transportation. Last but not least, the inference process of a FIS is easily interpretable, in the perspective of an Explainable AI

Nanogrids: A smart way to integrate public transportation electric vehicles into smart grids

The need for efficient integration of an Electric Vehicles (EVs) public transportation system into Smart Grids (SGs), has sparked the idea to equip them with Renewable Energy Systems (RESs), in order to reduce their impact on the SG. As a consequence, an EV can be seen as a Nanogrid (NG) whose energy flows are optimized by an Energy Management System (EMS). In this work, an EMS for an electric boat is synthesized by a Fuzzy Inference System-Hierarchical Genetic Algorithm (FIS-HGA). The electric boat follows cyclic routes day by day. Thus, single day training and test sets with a very short time step are chosen, with the aim of reducing the computational cost, without affecting accuracy. A convex optimization algorithm is applied for benchmark tests. Results show that the EMS successfully performs the EV energy flows optimization. It is remarkable that the EMS achieves good performances when tested on different days than the one it has been trained on, further reducing the computational cost

Classification and calibration techniques in predictive maintenance: A comparison between GMM and a custom one-class classifier

Modeling and predicting failures in the field of predictive maintenance is a challenging task. An important issue of an intelligent predictive maintenance system, exploited also for Condition Based Maintenance applications, is the failure probability estimation that can be found uncalibrated for most standard and custom classifiers grounded on Machine learning. In this paper are compared two classification techniques on a data set of faults collected in the real-world power grid that feeds the city of Rome, one based on a hybrid evolutionary-clustering technique, the other based on the well-known Gaussian Mixture Models setting. While the former adopts directly a custom-based weighted dissimilarity measure for facing unstructured and heterogeneous data, the latter needs a specific embedding technique step performed before the training procedure. Results show that both approaches reach good results with a different way of synthesizing a model of faults and with different structural complexities. Furthermore, besides the classification results, it is offered a comparison of the calibration status of the estimated probabilities of both classifiers, which can be a bottleneck for further applications and needs to be measured carefull

Mining m-grams by a granular computing approach for text classification

Text mining and text classification are gaining more and more importance in AI related research fields. Researchers are particularly focused on classification systems, based on structured data (such as sequences or graphs), facing the challenge of synthesizing interpretable models, exploiting gray-box approaches. In this paper, a novel gray-box text classifier is presented. Documents to be classified are split into their constituent words, or tokens. Groups of frequent m tokens (or m-grams) are suitably mined adopting the Granular Computing framework. By fastText algorithm, each token is encoded in a real-valued vector and a custom-based dissimilarity measure, grounded on the Edit family, is designed specifically to deal with m-grams. Through a clustering procedure the most representative m-grams, pertaining the corpus of documents, are extrapolated and arranged into a Symbolic Histogram representation. The latter allows embedding documents in a well-suited real-valued space in which a standard classifier, such as SVM, can safety operate. Along with the classification procedure, an Evolutionary Algorithm is in charge of performing features selection, which is able to select most relevant symbols – m-grams – for each class. This study shows how symbols can be fruitfully interpreted, allowing an interesting knowledge discovery procedure, in lights with the new requirements of modern explainable AI systems. The effectiveness of the proposed algorithm has been proved through a set of experiments on paper abstracts classification and SMS spam detection