Control of neuronal ion channel function by glycogen synthase kinase-3: new prospective for an old kinase

Glycogen synthase kinase 3 (GSK-3) is an evolutionarily conserved multifaceted ubiquitous enzyme. In the central nervous system (CNS), GSK-3 acts through an intricate network of intracellular signaling pathways culminating in a highly divergent cascade of phosphorylations that control neuronal function during development and adulthood. Accumulated evidence indicates that altered levels of GSK-3 correlate with maladaptive plasticity of neuronal circuitries in psychiatric disorders, addictive behaviors, and neurodegenerative diseases, and pharmacological interventions known to limit GSK-3 can counteract some of these deficits. Thus, targeting the GSK-3 cascade for therapeutic interventions against this broad spectrum of brain diseases has raised a tremendous interest. Yet, the multitude of GSK-3 downstream effectors poses a substantial challenge in the development of selective and potent medications that could efficiently block or modulate the activity of this enzyme. Although the full range of GSK-3 molecular targets are far from resolved, exciting new evidence indicates that ion channels regulating excitability, neurotransmitter release, and synaptic transmission, which ultimately contribute to the mechanisms underling brain plasticity and higher level cognitive and emotional processing, are new promising targets of this enzyme. Here, we will revise this new emerging role of GSK-3 in controling the activity of voltage-gated Na(+), K(+), Ca(2+) channels and ligand-gated glutamate receptors with the goal of highlighting new relevant endpoints of the neuronal GSK-3 cascade that could provide a platform for a better understanding of the mechanisms underlying the dysfunction of this kinase in the CNS and serve as a guidance for medication development against the broad range of GSK-3-linked human diseases

Improved Methods for Fluorescence Microscopy Detection of Macromolecules at the Axon Initial Segment

The axonal initial segment (AIS) is the subcellular compartment required for initiation of the action potential in neurons. Scaffolding and regulatory proteins at the AIS cluster with ion channels ensuring the integrity of electrical signaling. Interference with the configuration of this protein network can lead to profound effects on neuronal polarity, excitability, cell-to-cell connectivity and brain circuit plasticity. As such, the ability to visualize AIS components with precision provides an invaluable opportunity for parsing out key molecular determinants of neuronal function. Fluorescence-based immunolabeling is a sensitive method for morphological and molecular characterization of fine structures in neurons. Yet, even when combined with confocal microscopy, detection of AIS elements with immunofluorescence has been limited by the loss of antigenicity caused by fixative materials. This technical barrier has posed significant limitations in detecting AIS components alone or in combination with other markers. Here, we designed improved protocols targeted to confocal immunofluorescence detection of the AIS marker fibroblast growth factor 14 (FGF14) in combination with the cytoskeletal-associated protein Ankyrin-G, the scaffolding protein βIV-spectrin, voltage-gated Na+ (Nav) channels (especially the Nav1.6 isoform) and critical cell type-specific neuronal markers such as parvalbumin, calbindin, and NeuN in the mouse brain. Notably, we demonstrate that intracardiac perfusion of animals with a commercially available solution containing 1% formaldehyde and 0.5% methanol, followed by brief fixation with cold acetone is an optimal and sensitive protocol for FGF14 and other AIS marker detection that guarantees excellent tissue integrity. With variations in the procedure, we also significantly improved the detection of Nav1.6, a Nav isoform known for its fixative-sensitivity. Overall, this study provides an ensemble of immunohistochemical recipes that permit excellent staining of otherwise invisible molecules within well-preserved tissue architecture. While improving the specific investigation of AIS physiology and cell biology, our thorough study can also serve as a roadmap for optimizing immunodetection of other fixative-sensitive proteins expanding the repertoire of enabling methods for brain studies

“Tuning aggregative versus non-aggregative lectin binding with glycosylated nanoparticles by the nature of the polymer ligand”

Glycan–lectin interactions drive a diverse range of biological signaling and recognition processes. The display of glycans in multivalent format enables their intrinsically weak binding affinity to lectins to be overcome by the cluster glycoside effect, which results in a non-linear increase in binding affinity. As many lectins have multiple binding sites, upon interaction with glycosylated nanomaterials either aggregation or surface binding without aggregation can occur. Depending on the application area, either one of these responses are desirable (or undesirable) but methods to tune the aggregation state, independently from the overall extent/affinity of binding are currently missing. Herein, we use gold nanoparticles decorated with galactose-terminated polymer ligands, obtained by photo-initiated RAFT polymerization to ensure high end-group fidelity, to show the dramatic impact on agglutination behaviour due to the chemistry of the polymer linker. Poly(N-hydroxyethyl acrylamide) (PHEA)-coated gold nanoparticles, a polymer widely used as a non-ionic stabilizer, showed preference for aggregation with lectins compared to poly(N-(2-hydroxypropyl)methacrylamide) (PHPMA)-coated nanoparticles which retained colloidal stability, across a wide range of polymer lengths and particle core sizes. Using biolayer interferometry, it was observed that both coatings gave rise to similar binding affinity and hence provided conclusive evidence that aggregation rate alone cannot be used to measure affinity between nanoparticle systems with different stabilizing linkers. This is significant, as turbidimetry is widely used to demonstrate glycomaterial activity, although this work shows the most aggregating may not be the most avid, when comparing different polymer backbones/coating. Overall, our findings underline the potential of PHPMA as the coating of choice for applications where aggregation upon lectin binding would be problematic, such as in vivo imaging or drug delivery

Short-term load forecasting of non-residential building with hybrid LSTM and ARX model

LAUREA MAGISTRALENell’attuale contesto industriale in rapida evoluzione, la predizione efficace ed efficiente del carico elettrico di industrie ed edifici specialmente nella fase di avvio è un problema critico. La mancanza di dati all’inizio dell’utilizzo rende difficile poter prendere decisioni affidabili. Per questo motivo, da oltre 60 anni i ricercatori investono il loro tempo nel trovare buone soluzioni. Pertanto, in questa tesi viene proposto un nuovo approccio a due stadi per la predizione del consumo elettrico a breve termine (STLF), utilizzando una piccola quantità di dati (e.g. 2 settimane) al fine di predirne giorno per giorno il carico della settimana successiva. L’analisi numerica si basa su dati reali raccolti da una fabbrica italiana nel Bergamasco. Il modello di predizione finale usa dapprima un rete neurale basata su Long-Short-Term-Memory (LSTM) e poi un modello Autoregressivo con Input Esogeno (ARX). Il primo viene utilizzato con lo scopo di predire la dinamica non lineare, mentre il secondo viene utilizzato per la predizione delle rimanenti dinamiche. Il modello proposto è integrato con dati meteorologici e con un input fittizio (FI). In più vengono proposti dei modelli per la predizione dei dati meteorologici utilizzando soluzioni basate su reti neurali e approcci intuitivi. Una revisione della letteratura per conoscere lo stato dell’arte è eseguita prima dell’analisi numerica considerando precedenti lavori che già trattano e analizzano questi dati. Per determinare la capacità predittiva del modello viene utilizzato il coefficiente MAPE, mentre per determinare la capacità di fitting dei dati viene usato il coefficiente R2. Il MAPE e l’R2 del nuovo modello sono paragonati a quelli ottenuti con un modello ARX come proposto in un precedente lavoro e con lo stesso modello LSTM. I risultati dimostrano che il metodo proposto possiede maggiori capacità di predizione rispetto agli altri modelli. Ciò suggerisce che nuovi studi dovrebbero essere condotti basandosi su questa soluzione per sfruttare meglio tutte le caratteristiche e migliorare ulteriormente la capacità predittiva. Un importante vantaggio di questa soluzione è la possibilità di applicare precedenti risultati di ricerca per calcolare errori di accuratezza garantiti del residuo derivante del modello lineare Autoregressivo.In the current fast-changing industrial environment, the effective and efficient prediction of electricity demand of factories and buildings is a critical issue especially in the early stage. In fact, the lack of data at the beginning of the system’s life makes it difficult to take reliable decisions. This is why in the past 60 years researchers tried to invest their time in looking for a good solution. In this thesis a new prediction approach for Short-Load-Term-Forecasting (STLF) using a small amount of data (e.g. 2 weeks) is developed to predict one-day-ahead load of the following week. The numerical validation is based on real data collected by an Italian company located in Bergamo. The final two-stage prediction model employs Long-Short-Term-Memory (LSTM) based Neural Network and an Autoregressive with Exogenous Input (ARX) linear model. The former catches the non-linear complex dynamics and the latter captures the remaining linear dynamics. The proposed model is integrated with weather data and Fictitious Input (FI). Neural Network and intuitive Naive approaches for weather data are developed and the model with the overall best prediction capabilities is considered. A literature review is performed to understand the state-of-the-art prior to the quantitative studies of the proposed approach considering previous thesis on this specific dataset. Furthermore, two coefficients are used for comparative analysis. MAPE coefficient determines forecasting performances and R2 coefficient is employed for fitting performances. The new model’s MAPE and R2 are compared to state-of-the-art ARX and with the single LSTM model results. The proposed method demonstrates superior one-day-ahead forecasting performances with respect to the other models. This conclusion suggests the proposed model should be further developed to better exploit all the characteristics and furtherly improve the forecast. One of the most relevant advantages is the possibility to exploit previous research to compute guaranteed error bounds on the linear residual prediction

Robot Learning for Manipulation of Deformable Linear Objects

Deformable Object Manipulation (DOM) is a challenging problem in robotics. Until recently there has been limited research on the subject, with most robotic manipulation methods being developed for rigid objects. Part of the challenge in DOM is that non-rigid objects require solutions capable of generalizing to changes in shape and mechanical properties. Recently, Machine Learning (ML) has been proven successful in other fields where generalization is important such as computer vision, thus encouraging the application of ML to robotics as well. Notably, Reinforcement Learning (RL) has shown promise in finding control policies for manipulation of rigid objects. However, RL requires large amounts of data that are better satisfied in simulation while deformable objects are inherently more difficult to model and simulate. This thesis presents ReForm, a simulation sandbox for robotic manipulation of Deformable Linear Objects (DLOs) such as cables, ropes, and wires. DLO manipulation is an interesting problem for a variety of applications throughout manufacturing, agriculture, and medicine. Currently, this sandbox includes six shape control tasks, which are classified as explicit when a precise shape is to be achieved, or implicit when the deformation is just a consequence of a more abstract goal, e.g. wrapping a DLO around another object. The proposed simulation environments aim to facilitate comparison and reproducibility of robot learning research. To that end, an RL algorithm is tested on each simulated task providing initial benchmarking results. ReForm is one of three concurrent frameworks to first support DOM problems. This thesis also addresses the problem of DLO state representation for an explicit shape control problem. Moreover, the effects of elastoplastic properties on the RL reward definition are investigated. From a control perspective, DLOs with these properties are particularly challenging to manipulate due to their nonlinear behavior, acting elastic up to a yield point after which they become permanently deformed. A low-dimensional representation from discrete differential geometry is proposed, offering more descriptive shape information than a simple point-cloud while avoiding the need for curve fitting. Empirical results show that this representation leads to a better goal description in the presence of elastoplasticity, preventing the RL algorithm from converging to local minima which correspond to incorrect shapes of the DLO

Learning Shape Control of Elastoplastic Deformable Linear Objects

Deformable object manipulation tasks have long been regarded as challenging robotic problems. However, until recently very little work has been done on the subject, with most robotic manipulation methods being developed for rigid objects. Deformable objects are more difficult to model and simulate, which has limited the use of model-free Reinforcement Learning (RL) strategies, due to their need for large amounts of data that can only be satisfied in simulation. This paper proposes a new shape control task for Deformable Linear Objects (DLOs). More notably, we present the first study on the effects of elastoplastic properties on this type of problem. Objects with elastoplasticity such as metal wires, are found in various applications and are challenging to manipulate due to their nonlinear behavior. We first highlight the challenges of solving such a manipulation task from an RL perspective, particularly in defining the reward. Then, based on concepts from differential geometry, we propose an intrinsic shape representation using discrete curvature and torsion. Finally, we show through an empirical study that in order to successfully solve the proposed task using Deep Deterministic Policy Gradient (DDPG), the reward needs to include intrinsic information about the shape of the DLO

Airbnb Online Experience an analysis of the digital space

With Covid-19 we have had a further increase in peer-to-peer exchanges, which have also taken on a digital nature in terms of the content of the exchange. For this very reason, the following research aims to verify, whether there is a relationship between the physical location of digital experiences and the residence of the users of that experience, through the help of a correspondence analysis. From the perspective of a Mixed Methods design, following the correspondence analysis, a content analysis and digital ethnography of the reviews of 16 experiences on Airbnb's portal was arranged, in order to go and understand some of the elements of similarity and dissimilarity among the spatial aggregation groups, identifying what elements direct the choice of a digital experience

Elastin-Hyaluronan Bioconjugate as Bioactive Component in Electrospun Scaffolds

Hyaluronic acid or hyaluronan (HA) and elastin‐inspired peptides (EL) have been widely recognized as bioinspired materials useful in biomedical applications. The aim of the present work is the production of electrospun scaffolds as wound dressing materials which would benefit from synergic action of the bioactivity of elastin peptides and the regenerative properties of hyaluronic acid. Taking advantage of thiol‐ene chemistry, a bioactive elastin peptide was successfully conjugated to methacrylated hyaluronic acid (MAHA) and electrospun together with poly‐d,l‐lactide (PDLLA). To the best of our knowledge, limited reports on peptide‐conjugated hyaluronic acid were described in literature, and none of these was employed for the production of electrospun scaffolds. The conformational studies carried out by Circular Dichroism (CD) on the bioconjugated compound confirmed the preservation of secondary structure of the peptide after conjugation while Scanning Electron Microscopy (SEM) revealed the supramolecular structure of the electrospun scaffolds. Overall, the study demonstrates that the bioconjugation of hyaluronic acid with the elastin peptide improved the electrospinning processability with improved characteristics in terms of morphology of the final scaffolds

Regioselective modifications of natural polysaccharides

Polysaccharides are polymeric carbohydrates, usually formed of repeating units (either mono-, or higher oligosaccharides) joined together by glycosidic bonds. Some of these macromolecules are characterized by high natural availability (starch, cellulose, glycogen and chitin among others) and they have also a great biological importance, since they can be a source of energy for animal species. Moreover, they are structural elements of cellular walls and identification sites of cellular surfaces. An important class of polysaccharides is that of glycosaminoglycans animal sourced biomacromolecules that play a pivotal role in several biological processes. CS is included into the family of sulfated GAGs and is involved in the treatment of osteoarthritis and osteoarthrosis. From the structural point of view it is composed of a disaccharide repeating unit containing GlcA and GalNAc linked together through β-(1-->3) and β-(1-->4) glycosidic bonds, and displaying different sulfation patterns after in vivo polymerization. Indeed, depending on the position of sulfate groups, different disaccharide subunits could be described. Nonetheless, the low abundance of raw material, the labourious downstream purification and the growing application of this polysaccharide as a drug, led to development of a non-animal derived CS with a well-defined sulfation pattern, starting from Escherichia coli O5:K4:H4 sourced unsulfated chondroitin, through the optimization of a suitable sequenece of regioselective steps for its structural modification. This was based on the selective protection of O-4,6-GalNAc diol with a cyclic group (beznylidene), followed by acylation of O-2,3-GlcA diol on the polysaccharide backbone. By conducting benzylidenation and acetylation reactions one- or two pots, CSs with different sulfation patterns were obtained. In particular, sulfate groups randomly distributed either at position O-4 or at position O-6 of GalNAc units (CS-A,C) were obtained through the two-pots strategy, whereas the presence of additional sulfate groups was found at position O-3 of GlcA units when the protection reactions were conducted in one-pot fashion. This difference was ascribed to the formation of interglycosidic acetals during the insertion of benzylidene ring on O-4,6-GalNAc diol. These unusual acetals were rather acid-labile and could be not conserved after reaction work-up, thus, at the end of the semi-synthetic strategy, a chondroitin polysaccharide bearing sulfate groups exclusively on GalNAc units was afforded. Differently, stabilization in alkaline environment of the labile interglycosidic acetals by the two-pots strategy and their following oxidative cleavage allowed the semi-synthesis of CS species possessing sulfate groups not only on GalNAc units but also at position O-3 of some GlcA ones. It is worth noting that the detailed understanding of the factors influencing finely tailored chemical modifications on microbial sourced chondroitin is rather valuable because it allows the preparation of biologically relevant CSs from non-animal sources and with different, but highly controlled sulfation patterns. Indeed, CS-A,C is employed for several biomedical applications, as well as CSs possessing GlcA units decorated at O-3 position with sulfate groups are interesting for their neurite outgrowth promotion in the central nervous system. To GAGs family belongs also fCS. It is a glycosaminoglycan extracted from sea cucumbers (Echinodermata) and composed of a chondroitin sulfate backbone, substituted at position O-3 of GlcA units with heavily sulfated L-fucose side branches. fCS shows several biological properties, above all anticoagulant and antithrombotic activities that are tied to the branches of sulfated fucose on CS backbone. As heparin, fCS exerts these two activities by a serpin-dependent mechanism, in which thrombin inhibition is mediated by AT and HC-II. Importantly, and in contrast to heparin, fCS inhibits Xase factor and furthermore the Xa itself, through a serpin-independent mechanism too. These peculiar properties position fCS to potentially substitute heparin as anticoagulant and antithrombotic agent; indeed, fCS is currently under investigation in clinical trials as a new antithrombotic drug. In order to overcome the serious downsides of using animal-sourced polysaccharides for therapeutic purposes, such as ethical problems, contamination risks and discrepancies in composition, a regioselective modification of a chondroitin polysaccharide, obtained by fed-batch fermentation of E. coli O5:K4:H4, was developed, with the final aim to produce a safer and highly controllable fCS-based drug candidate. Derivatization started by esterification (either methylation or n-dodecylation) of carboxylic acid of GlcA subunits, to make chondroitin more soluble in aprotic solvents, then O-4,6 diol of GalNAc was protected by introduction of a benzylidene ring. The obtained derivatives were used as polysaccharide acceptors for glycosylation reactions, by coupling with suitable per-O-benzylated fucosyl donors under several conditions, trying to achieve a regiochemical and stereochemical control of glycosidic bond formation. Fucosylated products were further modified, obtaining at the end of semi-synthetic route fCS polysaccharides bearing persulfated Fuc branches. In order to obtain different sulfation patterns on Fuc units, the semi-synthetic strategy was upgraded, with the synthesis of new suitably protected fucosyl donors, for achieving polysaccharides with a even higher control of regio- and stereoselectivity of Fuc branching and sulfation pattern on the chondroitin backbone. Moreover, modification on polysaccharide backbone afforded a different glycosyl acceptor, useful to further enlarge the library of the semi-synthesized fucosylated chondroitin sulfate and chondroitin sulfates (fC and fCS, respectively) polysaccharides for future detailed structure-activity relationship investigations. They were preliminarily assayed for anticoagulant activity, displaying an AT-dependent activity against factor Xa in the same range of low molecular mass fCS species obtained by partial depolymerization of natural polysaccharides. For HC-II mediated factor IIa activity, data were very close to heparin for fCSs with Fuc branches on the GlcA units, regardless of their sulfation pattern, whereas two of the three fCSs with Fuc branches on the GalNAc units, as well as unsulfated polysaccharides, displayed a much reduced anticoagulant activity. Among biological properties of fCS polysaccharides, it is worth noting that the inhibition of P- and L-selectin interaction with sialyl Lewis(x), is stronger than the heparin one. Interestingly, oligosaccharides prepared by depolymerization of fCS from Holoturia forskali still maintained a high affinity for P- and L-selectins, but displaying a lower adverse effects than native polysaccharide. In order to evaluate the same inhibition activity of depolymerized fucosylated chondroitin sulfate (dfCS) from natural sources, a semi-synthetic fCS polysaccharide was submitted to β-eliminative depolymerization to give a oligosaccharide to be tested for its interaction with P- and L- selectins by STD-NMR techniques, displaying a slightly minor affinity with respect to that obtained from the natural one. Chondroitin polysaccharide obtained from the fed-batch fermentation of E.coli O5:K4:H4 is, from a structural point of view, similar to the backbone of Colwellia psychrerythraea 34H capsular polysaccharide (CPS) displaying an unprecedented cryoprotectant function, and consisting of a tetrasaccharide repeating units composed of two aminosugars and two uronic acids, with one of the two latter bearing a L-threonine as substituent. In order to better understand the structure-cryoprotectant function relationship of this polysaccharide, microbial sourced chondroitin was coupled with L-threonine under several conditions, producing a semi-synthetic derivative that displayed a ice recrystallization inhibition much lower than the C. psychrerythraea CPS. A combined NMR-molecular dynamic study of its 3D structure showed a rather far arrangement between the two polysaccharides, thus demonstrating that threonine decoration of biomacroolecules is not a sufficient element for gaining ice ricrystallization inhibition in spite of several examples of Thr-rich (glycol)-proteins and polysaccharides with cryoprotectant activity in Nature. Another polysaccharide that was subjected to regioselective modifications is alginate, that consists of 1-->4-linked β-D-mannuronic acid (M) and its C-5 epimer α-L-guluronic acid (G) units. This natural copolymer is an important component of algae such kelp, and is also an exopolysaccharide of bacteria including Pseudomonas aeruginosa. Alginates are widely used in food, cosmetic and pharmaceutical industry. The sulfation of these polysaccharides exhibits compounds with carboxylic and sulfate groups close to each others as in heparin ones. Randomly sulfated alginates show anticoagulant activity, so regioselective modification of the polysaccharide backbone may help to understand the relationship between structure and properties in alginate sulfates. Indeed, a semi-synthetic sulfated alginate derivative (propylene glycol alginate sodium sulfate, PSS), has been employed as anti-cardiovascular disease drug in China, without control of degree of sulfation. Due to incomplete solubility and highly heterogeneous structure of natural alginic acids the strategy to obtain a regioselectively sulfated alginate polysaccharide was applied to β-D-polymannuronic acid, that is the simplest polysaccharide possessing the most homogeneous structure of all alginic acids. It was protected at O-2,3 diol by either application of an orthoester or benzylidene ring and in the latter case, the polymannuronic acid was derivatized at carboxylic function too in order to enhance its solubility in aprotic solvent. At the end of the semi-synthetic route compounds with different sulfation pattern were obtained, but with unclear and probably not complete regioselectivity. Therefore, further optimization on semi-synthetic strategy is needed for the production of regioselectively sulfated alginates and for the evaluation of their structure-activity relationships