A step forwards in immunomodulation and immunetolerance knowledge. HLA-G expression in co-cultures of peripheral blood mononuclear cells and stem cells after in vitro NGAL stimulation

NGAL (Neutrophil Gelatinase-associated Lipocalin ) is a protein of lipocalin superfamily. Recent literature focused on its biomarkers function in several pathological condition (acute and chronic kidney damage, autoimmune disease, malignancy). NGAL biological role is not well elucidated. Several are the demonstration of its bacteriostatic role. Recent papers have indeed highlight NGAL role in NFkB modulation. The aim of this study is to understand whether NGAL may exert a role in the activation (modulation) of T cell response through the regulation of HLA-G complex, a mediator of tolerance. From 8 healthy donors we obtained peripheral blood mononuclear cells (PBMCs) and we isolated by centrifugation on a Ficoll gradient. Cells were then treated with four concentrations of NGAL (40-320 ng/ml) with or without iron. We performed flow cytometry analysis and ELISA test. NGAL increased the HLA-G expression on CD4+ T cells, with an increasing corresponding to the dose. Iron effect is not of unique interpretation. NGAL adiction affects regulatory T cells increasing in vitro expansion of CD4+ CD25+ FoxP3+ cells. Neutralizing antibody against NGAL decreased HLA-G expression and reduced significantly CD4+ CD25+ FoxP3+ cells percentage. In conclusion, we provided in vitro evidence of NGAL involvement in cellular immunity. The potential role of NGAL as an immunomodulatory molecule has been evaluated: it has been shown that NGAL plays a pivotal role in the induction of immune tolerance up regulating HLA-G and T regulatory cells expression in healthy donors. As potential future scenario we highlight the in vivo role of NGAL in immunology and immunomodulation, and its possible relationship with immunosuppressive therapy efficacy, tolerance induction in transplant patients, and/or in other immunological disorders.

On the role of Computational Logic in Data Science: representing, learning, reasoning, and explaining knowledge

In this thesis we discuss in what ways computational logic (CL) and data science (DS) can jointly contribute to the management of knowledge within the scope of modern and future artificial intelligence (AI), and how technically-sound software technologies can be realised along the path. An agent-oriented mindset permeates the whole discussion, by stressing pivotal role of autonomous agents in exploiting both means to reach higher degrees of intelligence. Accordingly, the goals of this thesis are manifold. First, we elicit the analogies and differences among CL and DS, hence looking for possible synergies and complementarities along 4 major knowledge-related dimensions, namely representation, acquisition (a.k.a. learning), inference (a.k.a. reasoning), and explanation. In this regard, we propose a conceptual framework through which bridges these disciplines can be described and designed. We then survey the current state of the art of AI technologies, w.r.t. their capability to support bridging CL and DS in practice. After detecting lacks and opportunities, we propose the notion of logic ecosystem as the new conceptual, architectural, and technological solution supporting the incremental integration of symbolic and sub-symbolic AI. Finally, we discuss how our notion of logic ecosys- tem can be reified into actual software technology and extended towards many DS-related directions.

Innovation Diffusion and Biostimulant knowledge in Moroccan agriculture

Our study focused on Morocco investigating the dissemination of PBs amongst farmers belonging to the first pillar of the GMP, located in the Fès-Meknès region. As well as to assess how innovation adoption is influenced by the network of relationships that various farmers are involved in. We adopted an “ego network” approach to identify the primary stakeholders responsible for the diffusion of PBs. We collected data through “face-to-face” interviews with 80 farmers in April and May 2021. The data were processed with the aim of: 1) analysing the total number of main and specific topics discussed between egos and egos’ alters regarding the variation of some egos attributes; 2) analysing egos’ network characteristics using E-Net software, and 3) identifying the significant variables that influence farmers to access knowledge, use and reuse of PBs a Binary Logistic Regression (LR) was applied. The first result disclosed that the main PBs topics discussed were technical positioning, the need to use PBs, knowledge of PBs, and organic PBs. We noted that farmers have specific features: they have a high school diploma and a bachelor's degree; they are specialised in fruits and cereals farming, and they are managers and members of a professional organisation. The second result showed results of SNA: 1) PBs seem to become generally a common argument for farmers who have already exchanged fertiliser information with their alters; 2) we disclosed a moderate heterogeneity in the networks, farmers have access to information mainly from acquaintances and professionals, and 3) we revealed that networks have a relatively low density and alters are not tightly connected to each other. Farmers have a brokerage position in the networks controlling the flow of information about the PBs. LR revealed that both the farmers’ attributes and the networks’ characteristics influence growers to know, use and reuse PBs.