Faculty Bibliography

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disorder most commonly associated with repetitive traumatic brain injury (TBI) and characterized by the presence of neurofibrillary tangles of tau protein, known as a tauopathy. Currently, the diagnosis of CTE can only be definitively established postmortem. However, a new positron emission tomography (PET) ligand, [18F]T807/AV1451, may provide the antemortem detection of tau aggregates, and thus various tauopathies, including CTE. Our goal was to examine [18F]T807/AV1451 retention in athletes with neuropsychiatric symptoms associated with a history of multiple concussions. Here we report a 39-year-old retired National Football League player who suffered 22 concussions and manifested progressive neuropsychiatric symptoms. Emotional lability and irritability were the chief complaints. Serial neuropsychological exams revealed a decline in executive functioning, processing speed and fine motor skills. Naming was below average but other cognitive functions were preserved. Structural analysis of longitudinally acquired magenetic resonance imaging scans revealed cortical thinning in the left frontal and lateral temporal areas, as well as volume loss in the basal ganglia. PET with [18F]florbetapir was negative for amyloidosis. The [18F]T807/AV1451 PET showed multifocal areas of retention at the cortical gray matter-white matter junction, a distribution considered pathognomonic for CTE. [18F]T807/AV1451 standard uptake value (SUV) analysis showed increased uptake (SUVr⩾1.1) in bilateral cingulate, occipital, and orbitofrontal cortices, and several temporal areas. Although definitive identification of the neuropathological underpinnings basis for [18F]T807/AV1451 retention requires postmortem correlation, our data suggest that [18F]T807/AV1451 tauopathy imaging may be a promising tool to detect and diagnose CTE-related tauopathy in living subjects.

AIMS/OBJECTIVE:Normalization of hypercholesterolaemia, inflammation, hyperglycaemia, and obesity are main desired targets to prevent cardiovascular clinical events. Here we present a novel regulator of cholesterol metabolism, which simultaneously impacts on glucose intolerance and inflammation. METHODS AND RESULTS/RESULTS:Mice deficient for oxygen sensor HIF-prolyl hydroxylase 1 (PHD1) were backcrossed onto an atherogenic low-density lipoprotein receptor (LDLR) knockout background and atherosclerosis was studied upon 8 weeks of western-type diet. PHD1-/-LDLR-/- mice presented a sharp reduction in VLDL and LDL plasma cholesterol levels. In line, atherosclerotic plaque development, as measured by plaque area, necrotic core expansion and plaque stage was hampered in PHD1-/-LDLR-/- mice. Mechanistically, cholesterol-lowering in PHD1 deficient mice was a result of enhanced cholesterol excretion from blood to intestines and ultimately faeces. Additionally, flow cytometry of whole blood of these mice revealed significantly reduced counts of leucocytes and particularly of Ly6Chigh pro-inflammatory monocytes. In addition, when studying PHD1-/- in diet-induced obesity (14 weeks high-fat diet) mice were less glucose intolerant when compared with WT littermate controls. CONCLUSION/CONCLUSIONS:Overall, PHD1 knockout mice display a metabolic phenotype that generally is deemed protective for cardiovascular disease. Future studies should focus on the efficacy, safety, and gender-specific effects of PHD1 inhibition in humans, and unravel the molecular actors responsible for PHD1-driven, likely intestinal, and regulation of cholesterol metabolism.

Models of cancer progression provide insights on the order of accumulation of genetic alterations during cancer development. Algorithms to infer such models from the currently available mutational profiles collected from different cancer patients (cross-sectional data) have been defined in the literature since late the 90s. These algorithms differ in the way they extract a graphical model of the events modelling the progression, e.g., somatic mutations or copy-number alterations. TRONCO is an R package for TRanslational ONcology which provides a series of functions to assist the user in the analysis of cross-sectional genomic data and, in particular, it implements algorithms that aim to model cancer progression by means of the notion of selective advantage. These algorithms are proved to outperform the current state-of-the-art in the inference of cancer progression models. TRONCO also provides functionalities to load input cross-sectional data, set up the execution of the algorithms, assess the statistical confidence in the results, and visualize the models. Availability. Freely available at http://www.bioconductor.org/ under GPL license; project hosted at http://bimib.disco.unimib.it/ and https://github.com/BIMIB-DISCo/TRONCO.

In a typical workplace, organizational policies and their compliance requirements set the stage upon which the behavioral patterns of individual agents evolve. The agents"™ personal utilities, access to information, and strategic deceptions shape the signaling systems of an intricate information-asymmetric game, thus mystifying assessment and management of organizational risks, which are primarily due to unintentional insider threats. Compliance games, as discussed here, model a rudimentary version of this signaling game between a sender (employee) and a receiver (organization). The analysis of these games"™ equilibria as well as their dynamics in repeated game settings illuminate the effectiveness or risks of an organizational policy. These questions are explored via a repeated and agent-based simulation of compliance signaling games, leading to the following: (1) a simple but broadly applicable model for interactions between sender agents (employees) and receiver agents (principals in the organization), (2) an investigation of how the game theoretic approach yields the plausible dynamics of compliance, and (3) design of experiments to estimate parameters of the systems: evolutionary learning rates of agents, the efficacy of auditing using a trembling hand strategy, effects of non-stationary and multiple principal agents, and ultimately, the robustness of the system under perturbation of various related parameters (costs, penalties, benefits, etc.). The paper concludes with a number of empirical studies, illustrating a battery of compliance games under varying environments designed to investigate agent based learning, system control, and optimization. The studies indicate how agents through limited interactions described by behavior traces may learn and optimize responses to a stationary defense, expose sensitive parameters and emergent properties and indicate the possibility of controlling interventions which actuate game parameters. We believe that the work is of practical importance"”for example, in constraining the vulnerability surfaces arising from compliance games.

We present a game theoretic framework that models strategic interactions among humans and things that are assumed to be interconnected by a social-technological network, as in an internet of humans and things (IOHT). Often a pair of agents in the network interacts in order for an informed sender-agent to signal an uninformed receiver-agent to take an action that benefits each of the players; the benefits to the pair of agents are modeled by two separate utility functions, both depending on the sender"™s private information, the signal exchanged, and the receiver"™s revealed (and also possibly unrevealed) action. In general, the two agents"™ utilities may not be aligned and may encourage deceptive behavior. For example, a sender, aware of his/her own private "state of ignorance", may seek useful information from a receiver who owns powerful computational resources to search a large corpus of webpages; the sender does so by sending a signal to the receiver in the form of a keyword. Obvious examples of deceptiveness here range from attempts to hide one"™s intentions to auctioning the keywords on an ad exchange through real-time bidding. A rather troublesome situation occurs when deceptions are employed to breach the security of the system, thus making the entire social-technological network unreliable. Earlier, we proposed a signaling-game-theoretic framework to alleviate this problem. This paper further enhances that framework by reconfiguring signals to possess more complex structures (epistatic signals to represent attack and defense options over a given set of vulnerabilities). We explore two augmentations to the original evolutionary signaling game by first enhancing mutation bias toward strategies performing well in previous populations and second allowing the parameters of the utility functions to depend on population preferences giving rise to a minority game with epistatic signaling. The resulting game systems are empirically studied through extensive computer simulation.

We envision a game theoretic model of an organization so as to devise new mechanisms to improve compliance and reduce various insider threats - be it intentional or unintentional, while paying proportional attention to various intertwined issues: namely in the form of deception, privacy, trust, global utilities and stability. For this purpose, we primarily rely on a realistic formulation of classical information-asymmetric signaling games, in a repeated form, while allowing the agents to dynamically vary their strategic choices as their utilities get (mis)aligned. To better understand the multifaceted security concerns in existing and emerging multi-agent interactions within an organization, we map, model and analyze various challenging scenarios of threats: namely, those by design or those by negligence. We also describe a bridge to the future by investigating the extendability of the proposed mechanisms in a specific embodiment, where available meta-data is mined to model behavioral propensities of the agents. Simulation and empirical analysis indicate promising results for this approach to deliver new mechanisms and control regimes.