Essays on social networks, altruism and information diffusion

This dissertation consists of three standalone articles that contribute to the economics literature concerning technology adoption, information diffusion, and network economics in one way or another, using a couple of primary data sources from Ethiopia. The first empirical paper identifies the main behavioral factors affecting the adoption of brand new (radical) and upgraded (incremental) bioenergy innovations in Ethiopia. The results highlight the importance of targeting different instruments to increase the adoption rate of the two types of innovations. The second and the third empirical papers of this thesis, use primary data collected from 3,693 high school students in Ethiopia, and shed light on how we should select informants to effectively and equitably disseminate new information, mainly concerning environmental issues. There are different well-recognized standard centrality measures that are used to select informants. These standard centrality measures, however, are based on the network topology---shaped only by the number of connections---and fail to incorporate the intrinsic motivations of the informants. This thesis introduces an augmented centrality measure (ACM) by modifying the eigenvector centrality measure through weighting the adjacency matrix with the altruism levels of connected nodes. The results from the two papers suggest that targeting informants based on network position and behavioral attributes ensures more effective and equitable (gender perspective) transmission of information in social networks than selecting informants on network centrality measures alone. Notably, when the information is concerned with environmental issues.

Synthesis and characterization of novel selective NKCC1 inhibitors for the treatment of down syndrome and brain disorders characterized by depolarizing GABAergic transmission.

Proper GABAergic transmission through Cl-permeable GABAA receptors is fundamental for physiological brain development and function. Indeed, defective GABAergic signaling – due to a high NKCC1/KCC2 expression ratio – has been implicated in several neurodevelopmental disorders (e.g., Down syndrome, DS, Autism spectrum disorders, ASD). Interestingly, NKCC1 inhibition by the FDA-approved diuretic drug bumetanide reverts cognitive deficits in the TS65Dn mouse models of DS and core symptoms in other models of brain disorders. However, the required chronic treatment with bumetanide is burdened by its diuretic side effects caused by the antagonization of the kidney Cl importer NKCC2. This may lead to hypokalemia, while jeopardizing drug compliance. Crucially, these issues would be solved by selective NKCC1 inhibitors, thus devoid of the diuretic effect of bumetanide. To this aim, starting from bumetanide’s structure, we applied a ligand-based computational approach to design new molecular entities that we tested in vitro for their capacity to selectively block NKCC1. Extensive synthetic efforts and structure-activity relationships analyses allowed us to improve in vitro potency and overall drug-like properties of the initially identified chemical hits. As a result, we identified a new highly potent NKCC1 inhibitor (ARN23746) that displayed excellent solubility, metabolic stability, and no significant effect on NKCC2 in vitro. Moreover, this novel and selective NKCC1 inhibitor was able to rescue cognitive deficits in DS mice and social/repetitive behaviors in ASD mice, with no diuretic effect and no overt toxicity upon chronic treatment in adult animals. Thus, ARN23746 a selective NKCC1 inhibitor devoid of the diuretic effect – represents a suitable and solid therapeutic strategy for the treatment of Down syndrome and all the brain neurological disorders characterized by depolarizing GABAergic transmission.