Grouping complex systems: a weighted network comparative analysis

In this study, the authors compare two inter-municipal commuting networks (MCN) pertaining to the Italian islands of Sardinia and Sicily, by approaching their characterization through a weighted network analysis. They develop on the results obtained for the MCN of Sardinia (De Montis et al. 2007) and attempt to use network analysis as a mean of detection of similarities or dissimilarities between the systems at hand

Modeling commuting systems through a complex network analysis: a study of the Italian islands of Sardinia and Sicily

This study analyzes the inter-municipal commuting systems of the Italian islands of Sardinia and Sicily, employing weighted network analysis technique. Based on the results obtained for the Sardinian commuting network, the network analysis is used to identify similarities and dissimilarities between the two systems

Dynamic Max-Consensus and Size Estimation of Anonymous Multi-Agent Networks

In this paper we propose a novel consensus protocol for discrete-time multi-agent systems (MAS), which solves the dynamic consensus problem on the max value, i.e., the dynamic max-consensus problem. In the dynamic max-consensus problem to each agent is fed a an exogenous reference signal, the objective of each agent is to estimate the instantaneous and time-varying value of the maximum among the signals fed to the network, by exploiting only local and anonymous interactions among the agents. The absolute and relative tracking error of the proposed distributed control protocol is theoretically characterized and is shown to be bounded and by tuning its parameters it is possible to trade-off convergence time for steady-state error. The dynamic Max-consensus algorithm is then applied to solve the distributed size estimation problem in a dynamic setting where the size of the network is time-varying during the execution of the estimation algorithm. Numerical simulations are provided to corroborate the theoretical analysis

Stability of Nonexpansive Monotone Systems and Application to Recurrent Neural Networks

This letter shows that trajectories of continuous-time monotone systems (in the sense of Kamke-Muller) converge to equilibrium points if their vector field is continuously differentiable and if they are nonexpansive w.r.t. a diagonally weighted infinity norm. Differently from the current literature trend, the system is not required to be contractive but merely nonexpansive, thus allowing for multiple equilibrium points. Easy-to-check conditions on the vector field to verify that the system is both monotone and nonexpansive are provided. This is done by showing that nonexpansiveness is implied by subhomogeneity of the system, a generalization of the translation invariance property. We apply the results in the context of RNNs, thus providing sufficient conditions for convergence of the state trajectories of nonexpansive monotone neural networks that are not contractive

Distributed Estimation of the Laplacian Spectrum via Wave Equation and Distributed Optimization

This paper presents a distributed algorithm to estimate all distinct eigenvalues of the Laplacian matrix encoding the unknown topology of a multi-agent system. The agents interact according to the discrete-time wave equation so that their state trajectory persistently oscillates with modes that depend on the eigenvalues of the Laplacian matrix. In this way, the problem of distributed estimation of the eigenvalues of the Laplacian is recast into that of estimating the modes of evolution of the state-trajectory of a linear dynamical system. Unlike previous literature, this paper formulates a distributed optimization problem where, by considering its own state trajectory, each agent estimates all distinct eigenvalues of the Laplacian matrix. The main advantages of the proposed algorithm are the ability of each agent to estimate also eigenvalues corresponding to modes unobservable from its own state trajectory, a much greater numerical stability, and therefore improved scalability to large networks wit h respect to competing approaches, as evidenced by the numerical comparisons

Ontologies for Quantified Self: a Semantic Approach

The spreading of devices and applications that allow people to collect personal information opens new opportunities for user modeling (UM). In this new scenario UM together with personal informatics (PI) can offer a new way for self-monitoring that can provide the users with a sophisticated mirror of their behavior, attitudes and habits and their consequences on their life, on the environment and on contexts in which they live in. These new forms of self-reflection and self-knowledge can trigger and motivate the behavior change. In this paper we describe the first step in this direction, focusing on opportunities offered by semantic web ontologies for data integration and reasoning over data for recommendation purposes

Discovery of novel endocannabinoid level modulators by modification of old analgesic drugs

Fatty acid amide hydrolase (FAAH) is a serine hydrolase that catalyzes the deactivating hydrolysis of the fatty acid ethanolamide family of signaling lipids, which includes anandamide (AEA), an endogenous ligand for cannabinoid receptors. Endogenous FAAH substrates such as AEA serve key regulatory functions in the body and have been implicated in a variety of pathological conditions including pain, inflammation, sleep disorders, anxiety, depression, and vascular hypertension, and there has been an increasing interest in the development of inhibitors of this enzyme. Different structural classes of FAAH inhibitors have been reported including alpha-ketoheterocycles, (thio)hydantoins, piperidine/piperazine ureas, and carbamate derivatives. When tested, these compounds have been shown to be efficacious in models of inflammatory, visceral, and in some cases neuropathic pain without producing the central effects seen with directly acting cannabinoid receptor agonists. An intriguing aspect of FAAH inhibition is that some currently marketed nonsteroidal anti-inflammatory drugs (NSAIDs) have also been shown to be weak inhibitors of FAAH, but can be used as a template for the design of more potent compounds. However, structure–activity relationships of analogues of clinically used NSAIDs with respect to FAAH inhibition have been examined scarcely in the literature. These findings led us to design and synthesis of new series of FAAH inhibitors derivable from conjugation of heterocyclic structures with NSAIDs as profens, fenamates, and new their correlate molecules. In this keynote we report on the synthetic pathways to transform old analgesic drugs into FAAH inhibitors and SAR studies on the new inhibitor series

Synthesis and carbonic anhydrase I, II, IX and XII inhibitory activity of sulfamates incorporating piperazinyl-ureido moieties

A series of sulfamates were synthesized using as lead compound SLC-0111, a sulfonamide carbonic anhydrase (CA, EC 4.2.1.1) inhibitor in Phase I clinical trials. The new derivatives incorporated ureido moieties as spacers between the benzene sulfamate fragment which binds the zinc ion from the active site, and the tail of the inhibitor, but the urea moieties were part of a substituted piperazine ring system. The derivatives (and some of their phenol precursors) were tested for the inhibition of the cytosolic, hCA I and II (off target isoforms) and the trans-membrane, tumor-associated hCA IX and XII enzymes (anticancer drug targets). Generally hCA I was not effectively inhibited, whereas many low nanomolar inhibitors were evidenced against hCA II (KIs in the range of 1.0–94.4 nM), IX (KIs in the range of 0.91–36.9 nM), and XII (KIs in the range of 1.0–84.5 nM). The best substitution fragments at the piperazine ring included the following moieties: 3-methylphenyl, 2,3-dimethylphenyl, 4-methoxyphenyl, 6-arylpyrimidine-2-yl

Synthesis of Sulfonamides Incorporating Piperidinyl-Hydrazidoureido and Piperidinyl-Hydrazidothioureido Moieties and Their Carbonic Anhydrase I, II, IX and XII Inhibitory Activity

Here we report a small library of hydrazinocarbonyl-ureido and thioureido benzenesulfonamide derivatives, designed and synthesized as potent and selective human carbonic anhydrase inhibitors (hCAIs). The synthesized compounds were evaluated against isoforms hCA I, II, IX and XII using acetazolamide (AAZ) as standard inhibitor. Several urea and thiourea derivatives showed inhibitory activity at low nanomolar levels with selectivity against the cytosolic hCA II isoform, as well as the transmembrane, tumor-associated enzymes hCA IX and XII. The thiourea derivatives showed enhanced potency as compared to urea analogues. Additionally, eight compounds 5g, 5m, 5o, 5q, 6l, 6j, 6o and 6u were selected for docking analysis on isoform I, II, IX, XII to illustrate the potential interaction with the enzyme to better understand the activity against the different isoforms