Glycosaminoglycans (GAGs) are frequently associated with amyloid deposits in most amyloid diseases, and there is evidence to support their active role in amyloid fibril formation. The purpose of this study was to obtain structural insight into GAG-protein interactions and to better elucidate the molecular mechanism underlying the effect of GAGs on the amyloid aggregation process and on the related cytotoxicity. To this aim, using Fourier transform infrared and circular diochroism spectroscopy, electron microscopy and thioflavin fluorescence dye we examined the effect of heparin and other GAGs on the fibrillogenesis and cytotoxicity of aggregates formed by the amyloidogenic W7FW14 apomyoglobin mutant. Although this protein is unrelated to human disease, it is a suitable model for in vitro studies because it forms amyloid-like fibrils under physiological conditions of pH and temperature. Heparin strongly stimulated aggregation into amyloid fibrils, thereby abolishing the lag-phase normally detected following the kinetics of the process, and increasing the yield of fibrils. Moreover, the protein aggregates were harmless when assayed for cytotoxicity in vitro. Neutral or positive compounds did not affect the aggregation rate, and the early aggregates were highly cytotoxic. The surprising result that heparin induced amyloid fibril formation in wild-type apomyoglobin and in the partially folded intermediate state of the mutant, i.e., proteins that normally do not show any tendency to aggregate, suggested that the interaction of heparin with apomyoglobin is highly specific because of the presence, in protein turn regions, of consensus sequences consisting of alternating basic and non-basic residues that are capable of binding heparin molecules. Our data suggest that GAGs play a dual role in amyloidosis, namely, they promote beneficial fibril formation, but they also function as pathological chaperones by inducing amyloid aggregation.

Complex network theory (CNT) is gaining a lot of attention in the scientific community, due to its capability to model and interpret an impressive number of natural and anthropic phenomena. One of the most active CNT field concerns the evaluation of the centrality of vertices and edges in the network. Several metrics have been proposed, but all of them share a topological point of view, namely centrality descends from the local or global connectivity structure of the network. However, vertices can exhibit their own intrinsic relevance independent from topology; e.g., vertices representing strategic locations (e.g., hospitals, water and energy sources, etc.) or institutional roles (e.g., presidents, agencies, etc.). In these cases, the connectivity network structure and vertex intrinsic relevance mutually concur to define the centrality of vertices and edges.The purpose of this work is to embed the information about the intrinsic relevance of vertices into CNT tools to enhance the network analysis. We focus on the degree, closeness and betweenness metrics, being among the most used. Two examples, concerning a social (the historical Florence family's marriage network) and an infrastructure (a water supply system) network, demonstrate the effectiveness of the proposed relevance-embedding extension of the centrality metrics. IntroductionComplex network theory (CNT) is today one the most active research fields allowing to analyze and predict the behavior of a wide variety of complex systems. CNT is becoming an emerging paradigm to study an impressive number of networked systems, ranging from physics to social sciences, form biology to infrastructure engineering. In this line, the growing computational power and data availability are giving a crucial contribution to the rapid growth of CNT tools for the network classification (Cohen and Havlin, 2010; Newman, 2018) community detection (Girvan andNewman, 2002;Fortunato, 2010), vertex and edge centrality assessment (Newman, 2018), vulnerability (Boccaletti et al., 2006), spatial and temporal evolution (Barthélemy, 2018), etc..The evaluation of the centrality and the corresponding ranking have always been a key aspect in the network analysis. Freeman (1977) proposed the first comprehensive study of the topological

Nerve growth factor (NGF), an essential peptide for sensory neurons, seems to have opposite effects when administered peripherally or directly to the central nervous system. We investigated the effects of 7-days intrathecal (i.t.) infusion of NGF on neuronal and glial spinal markers relevant to neuropathic behavior induced by chronic constriction injury (CCI) of the sciatic nerve. Allodynic and hyperalgesic behaviors were investigated by Von Frey and thermal Plantar tests, respectively. NGF-treated animals showed reduced allodynia and thermal hyperalgesia, compared to control animals. We evaluated on lumbar spinal cord the expression of microglial (ED-1), astrocytic (GFAP and S-100beta), and C- and Adelta-fibers (SubP, IB-4 and Cb) markers. I.t. NGF treatment reduced reactive astrocytosis and the density of SubP, IB4 and Cb positive fibers in the dorsal horn of injured animals. Morphometric parameters of proximal sciatic nerve stump fibers and cells in DRG were also analyzed in CCI rats: myelin thickness was reduced and DRG neurons and satellite cells appeared hypertrophic. I.t. NGF treatment showed a beneficial effect in reversing these molecular and morphological alterations. Finally, we analyzed by immunohistochemistry the expression pattern of neurotrophin receptors TrkA, pTrkA, TrkB and p75(NTR). Substantial alterations in neurotrophin receptors expression were observed in the spinal cord of CCI and NGF-treated animals. Our results indicate that i.t. NGF administration reverses the neuro-glial morphomolecular changes occurring in neuropathic animals paralleled by alterations in neurotrophin receptors ratio, and suggest that NGF is effective in restoring homeostatic conditions in the spinal cord and maintaining analgesia in neuropathic pain.

The network connectivity structure of water distribution systems (WDSs) represents the domain where hydraulic processes occur, driving the emerging behavior of such systems, for example with respect to robustness and vulnerability. In complex network theory (CNT), a common way of classifying the network structure and connectivity is the association of the nodal degree distribution to specific probability distribution models, and during the last decades, researchers classified many real networks using the Poisson or Pareto distributions. In spite of the fact that degree‐based network classification could play a crucial role to assess WDS vulnerability, this task is not easy because the network structure of WDSs is strongly constrained by spatial characteristics of the environment where they are constructed. The consequence of these spatial constraints is that the nodal degree spans very small ranges in WDSs hindering a reliable classification by the standard approach based on the nodal degree distribution. This work investigates the classification of the network structure of 22 real WDSs, built in different environments, demonstrating that the Poisson distribution generally models the degree distributions very well. In order to overcome the problem of the reliable classification based on the standard nodal degree, we define the “neighborhood” degree, equal to the sum of the nodal degrees of the nearest topological neighbors (i.e., the adjacent nodes). This definition of “neighborhood” degree is consistent with the fact that the degree of a single node is not significant for analysis of WDSs.

Probes for the occurrence of endogenous D-aspartic acid (D-Asp) and N-methyl-D-aspartic acid (NMDA) in the neural complex and gonads of a protochordate, the ascidian Ciona intestinalis, have con¢rmed the presence of these two excitatory amino acids and their involvement in hormonal activity. A hormonal pathway similar to that which occurs in vertebrates has been discovered. In the cerebral ganglion D-Asp is synthesized from L-Asp by an aspartate racemase. Then, D-Asp is transferred through the blood stream into the neural gland where it gives rise to NMDA by means of an NMDA synthase. NMDA, in turn, passes from the neuronal gland into the gonads where it induces the synthesis and release of a gonadotropin-releasing hormone (GnRH). The GnRH in turn modulates the release and synthesis of testosterone and progesterone in the gonads, which are implicated in reproduction. ß