Bio-surfactants-based lipid architectures as nanomedicine platforms

The use of nanocarriers for drug delivery and imaging purposes have highly increased in the last decades. Both hard and soft matter-based formulations can provide selective and efficient treatment in several administration routes. Indeed, the biocompatibility and the biodegradability of the formulations represent a key requirement in order to translate the in vitro studies into in vivo investigations. Therefore, lipids are a safe choice as building blocks to formulate a large variety of liquid crystalline architectures in water. Vesicles, hexosomes and cubosomes have been adopted as nanomedicine platforms providing excellent biological performances. However, several drawbacks may impact the application of these carriers: the poor stability in the physiological environment and the biodegradability of the stabilizing agent required to sterically stabilized the nanoparticles (NPs) are few examples. Given the importance these materials have acquired nowadays in the nanomedicine field, this thesis is devoted to investigating on the factors that can enhance the physico-chemical and biological performances of these nanoparticles for systemic and topical administration. Most of the formulations presented in this thesis were prepared using monoolein as building block, given its biocompatibility and lower cytotoxicity in comparison with other surfactants. However, the potential application of cell-derived nanoparticles known as nanoerythrosomes for medical imaging was also explored. Therefore, the thesis evaluated different approaches: (i) evaluation of the effect of various stabilizers (modified poloxamers, hemicellulose and polyphosphoesters) on monoolein-based cubosomes features, in order to formulate nanoparticles suitable for systemic administration. This investigation was focused on the physico-chemical (bulk and surface) characterization of the empty carriers and of those loaded with antioxidants or fluorophores suitable for in vitro imaging. Bioassays (viability and uptake experiments) were conducted in order to evaluate the biological performance of the differently stabilized cubosomes. (ii) the effect of permeation enhancers and edge activators on monoolein-based vesicles and hexosomes for topical administration. In vitro permeation tests were performed to show the efficacy of these carriers into overcoming the stratum corneum, the first layer of the skin, to deliver antioxidants. (iii) the potential role of nanoparticles derived from red blood cells, nanoerythrosomes, as personal medicine for application in optical imaging. Cross-linking and Click Chemistry were employed to decorate the surface of the nanoparticles and their emission properties in a physiological buffer were evaluate

Sparse Stabilization and Control of Alignment Models

From a mathematical point of view self-organization can be described as patterns to which certain dynamical systems modeling social dynamics tend spontaneously to be attracted. In this paper we explore situations beyond self-organization, in particular how to externally control such dynamical systems in order to eventually enforce pattern formation also in those situations where this wished phenomenon does not result from spontaneous convergence. Our focus is on dynamical systems of Cucker-Smale type, modeling consensus emergence, and we question the existence of stabilization and optimal control strategies which require the minimal amount of external intervention for nevertheless inducing consensus in a group of interacting agents. We provide a variational criterion to explicitly design feedback controls that are componentwise sparse, i.e. with at most one nonzero component at every instant of time. Controls sharing this sparsity feature are very realistic and convenient for practical issues. Moreover, the maximally sparse ones are instantaneously optimal in terms of the decay rate of a suitably designed Lyapunov functional, measuring the distance from consensus. As a consequence we provide a mathematical justification to the general principle according to which "sparse is better" in the sense that a policy maker, who is not allowed to predict future developments, should always consider more favorable to intervene with stronger action on the fewest possible instantaneous optimal leaders rather than trying to control more agents with minor strength in order to achieve group consensus. We then establish local and global sparse controllability properties to consensus and, finally, we analyze the sparsity of solutions of the finite time optimal control problem where the minimization criterion is a combination of the distance from consensus and of the l1-norm of the control.Comment: 33 pages, 5 figure

Finite Sample Identification of Wide Shallow Neural Networks with Biases

Artificial neural networks are functions depending on a finite number of parameters typically encoded as weights and biases. The identification of the parameters of the network from finite samples of input-output pairs is often referred to as the \emph{teacher-student model}, and this model has represented a popular framework for understanding training and generalization. Even if the problem is NP-complete in the worst case, a rapidly growing literature -- after adding suitable distributional assumptions -- has established finite sample identification of two-layer networks with a number of neurons $m=\mathcal O(D)$, $D$ being the input dimension. For the range $D<m<D^2$ the problem becomes harder, and truly little is known for networks parametrized by biases as well. This paper fills the gap by providing constructive methods and theoretical guarantees of finite sample identification for such wider shallow networks with biases. Our approach is based on a two-step pipeline: first, we recover the direction of the weights, by exploiting second order information; next, we identify the signs by suitable algebraic evaluations, and we recover the biases by empirical risk minimization via gradient descent. Numerical results demonstrate the effectiveness of our approach

Spatially inhomogeneous evolutionary games

We introduce and study a mean-field model for a system of spatially distributed players interacting through an evolutionary game driven by a replicator dynamics. Strategies evolve by a replicator dynamics influenced by the position and the interaction between different players and return a feedback on the velocity field guiding their motion. One of the main novelties of our approach concerns the description of the whole system, which can be represent-dimensional state space (pairs (x, σ) of position and distribution of strategies). We provide a Lagrangian and a Eulerian description of the evolution, and we prove their equivalence, together with existence, uniqueness, and stability of the solution. As a byproduct of the stability result, we also obtain convergence of the finite agents model to our mean-field formulation, when the number N of the players goes to infinity, and the initial discrete distribution of positions and strategies converge. To this aim we develop some basic functional analytic tools to deal with interaction dynamics and continuity equations in Banach spaces that could be of independent interest. © 2021 The Authors. Communications on Pure and Applied Mathematics published by Wiley Periodicals LLC

I principi epistemologici della botanica di Guy de La Brosse

This paper investigates some core aspects of Guy de La Brosse’s (1586-1641) botanical work. In the first section, the focus is on the epistemological principles of La Brosse’s botany by analyzing the first and second book of the treatise De la nature, vertu et utilité des plantes (1628). In the second section, the author discusses the role of Paracelsus’s chemistry in La Brosse’s work, with a particular attention to the third book of the De la nature. The final section deals with La Brosse’s interest in the visualization of plants. Here, the author provides the transcription and first Italian translation of a short manuscript related to Abraham Bosse’s engravings for La Brosse’s unfinished book Icones posthumae

Nitrogen budget and statistical entropy analysis of the Tiber River catchment, a highly anthropized environment

Modern farming causes a decline in the recycling of the soil's inorganic matter due to losses by leaching, runoff, or infiltration into the groundwater. The Soil System Budget approach was applied to evaluate the net N budget at the catchment and sub-catchment levels of the Tiber River (central Italy) in order to establish the causes for different N budgets among the sub-catchments. Statistical Entropy Analysis (SEA) was used to evaluate the N efficiency of the Tiber River and its sub-catchments, providing information on the dispersion of different N forms in the environment. The total N inputs exceeded the total outputs, showing a low N retention (15.8%) at the catchment level, although some sub-catchments showed higher N retention values. The Utilized Agricultural Area was important in the determination of the N balance, as it was linked to zoo- and agricultural activities, although the Random Forest analysis showed that the importance ranking changed with the land use. The low N retention of the Tiber catchment was due to the soil characteristics (Cambisols and Leptosols), loads from atmospheric deposition, biological fixation, and the livestock industry. The SEA simulations showed a reduction of the N released into the atmosphere and groundwater compartments from 34% to 6% through a reduction of the N loads by 50%