Engineering neuronal networks with nanomaterials: graphene shaping of synaptic activity

Graphene is a single atomic plane material consisting of sp2 -hybridized carbon atoms with a hexagonal structural organization and characterized by unique properties such as high electrical conductivity, mechanical flexibility and optical transparency. Due to their peculiar features, graphene and its derivates have attracted an increasing interest for biomedical applications including drug and gene delivery, imaging and diagnostic or tissue engineering. However, using graphene-based nanomaterials (GBNs) in modern medicine, in particular neurology, needs a greater and deeper understanding of the cell-nanomaterial interactions. In this framework we focus on studying the impact of GBNs on the neuronal network and their ability in shaping synaptic transmission. First, we exploited 3D elastomeric scaffolds enriched with graphene to better understand the effects of this nanomaterial on the neural activity when interfacing neurons and synapses in the third dimension. Our results, using imaging techniques, show the ability of graphene to modulate the neuronal network formation in a 3D environment which might be due to modulations in the excitatory/inhibitory ratio. Afterwards we investigated the interactions between graphene oxide flakes with small lateral size (s-GO) and isolated amygdala neurons and synapses. Thus, we developed and characterized an in vitro model of amygdala network using immunofluorescence and electrophysiological techniques. When we acutely applied s-GO to these cultures, the nanomaterial was capable to selectively alter the glutamatergic excitatory activity. This peculiar interaction may be taken into account for exploiting s-GO as a novel tool to target central nervous system (CNS) synapses

Quadratic Hedging and Optimization of Option Exercise Policies in Incomplete Markets and Discrete Time

This paper extends quadratic hedging from European to Bermudan options in discrete time when markets are incomplete and investigates its use for supporting exercise policy optimization. The key idea is to construct date specific approximate replicating portfolios. Hedging any given exercise policy can be done by solving a collection of stochastic dynamic programs. Optimizing the exercise policy based on the resulting martingale measure requires care. If this measure is risk neutral (RN), the value of an optimal such policy, which can be obtained by augmenting the hedging model with an exercise policy optimization step, is a no arbitrage one. Otherwise this approach must be refined by imposing time consistency on exercise policies, although the value of the resulting exercise policy may not be arbitrage free. Following the common pragmatic strategy of specifying quadratic hedging under an RN measure, e.g., one calibrated to market prices, avoids these issues. In particular, it provides a simple hedging policy with immediate practical applicability and is equivalent to exercise policy optimization under RN valuation, thus complementing it with a consistent hedging policy. A simple numerical example shows that this procedure generates effective hedging policies

Thin graphene oxide nanoflakes modulate glutamatergic synapses in the amygdala cultured circuits: exploiting synaptic approaches to anxiety disorders

Anxiety disorders (ADs) are nervous system maladies involving changes in the amygdala synaptic circuitry, such as an upregulation of excitatory neurotransmission at glutamatergic synapses. In the field of nanotechnology, thin graphene oxide flakes with nanoscale lateral size (s-GO) have shown outstanding promise for the manipulation of excitatory neuronal transmission with high temporal and spatial precision, thus they were considered as ideal candidates for modulating amygdalar glutamatergic transmission. Here, we validated an in vitro model of amygdala circuitry as a screening tool to target synapses, towards development of future ADs treatments. After one week in vitro, dissociated amygdalar neurons reconnected forming functional networks, whose development recapitulated that of the tissue of origin. When acutely applied to these cultures, s-GO flakes induced a selective modification of excitatory activity. This type of interaction between s-GO and amygdalar neurons may form the basis for the exploitation of alternative approaches in the treatment of ADs

Comparative Analysis of Incumbent and Emerging Liquefied Natural Gas Regasification Technologies

Energy plays a fundamental role in both manufacturing and services, and natural gas is quickly becoming a key energy source worldwide. Facilitating this emergence is the expanding network of ocean-going vessels that enable the matching of natural gas supply and demand on a global scale by transporting it in the form of liquefied natural gas (LNG) for eventual regasification at its destination. Until very recently only one type of technology has been available for transporting and regasifying LNG: Conventional LNG vessels and land based LNG regasification. It is now possible to transport and regasify LNG onboard special LNG vessels. Companies such as Excelerate Energy and Höegh LNG are currently developing LNG supply chains based on this new technology. Motivated by this recent development we engaged executives at Excelerate Energy to develop and apply to data an integrated analytic framework to compare these incumbent and emerging technologies. Our analysis brings to light basic principles delineating when to deploy each technology and how to configure the emerging technology. Some of our findings challenge conventional wisdom on the role to be played by the emerging technology; others provide answers to open questions faced by companies currently engaged in the commercial deployment of this technology. In addition, our integrated analytic framework has potential relevance for the evaluation of new technologies beyond this specific application

The Mitogenome Relationships and Phylogeography of Barn Swallows (Hirundo rustica)

The barn swallow (Hirundo rustica) poses a number of fascinating scientific questions, including the taxonomic status of postulated subspecies. Here, we obtained and assessed the sequence variation of 411 complete mitogenomes, mainly from the European H. r. rustica, but other subspecies as well. In almost every case, we observed subspecies-specific haplogroups, which we employed together with estimated radiation times to postulate a model for the geographical and temporal worldwide spread of the species. The female barn swallow carrying the Hirundo rustica ancestral mitogenome left Africa (or its vicinity) around 280 thousand years ago (kya), and her descendants expanded first into Eurasia and then, at least 51 kya, into the Americas, from where a relatively recent (<20 kya) back migration to Asia took place. The exception to the haplogroup subspecies specificity is represented by the sedentary Levantine H. r. transitiva that extensively shares haplogroup A with the migratory European H. r. rustica and, to a lesser extent, haplogroup B with the Egyptian H. r. savignii. Our data indicate that rustica and transitiva most likely derive from a sedentary Levantine population source that split at the end of the Younger Dryas (YD) (11.7 kya). Since then, however, transitiva received genetic inputs from and admixed with both the closely related rustica and the adjacent savignii. Demographic analyses confirm this species' strong link with climate fluctuations and human activities making it an excellent indicator for monitoring and assessing the impact of current global changes on wildlife

The Role of Price Spreads and Reoptimization in the Real Option Management of Commodity Storage Assets

The real option management of commodity storage assets is an important practical problem. Practition- ers approach the resulting stochastic optimization model using heuristic policies that rely on sequential reoptimization of linear programs. Used in conjunction with Monte Carlo simulation, these policies typ- ically yield near optimal lower bound estimates on the value of storage. This paper reveals that a simple one stage lookahead policy is optimal for a fast storage asset without frictions. Thus, in this (not entirely realistic) case the problem is easy and the reoptimization policies are unnecessary, albeit optimal. In contrast, this paper provides numerical and structural justification for the use of these policies in the general case. Further, the use of price spreads simplifies the estimation of near tight dual upper bounds on the value of storage. This approach relies on using the fast and frictionless asset optimal value func- tion to estimate dual upper bounds in the general case. Monte Carlo simulation and linear programming thus appear adequate for the near optimal valuation and management of commodity storage assets.</p