Addiction beyond pharmacological effects: The role of environment complexity and bounded rationality

Several decision-making vulnerabilities have been identified as underlying causes for addictive behaviours, or the repeated execution of stereotyped actions despite their adverse consequences. These vulnerabilities are mostly associated with brain alterations caused by the consumption of substances of abuse. However, addiction can also happen in the absence of a pharmacological component, such as seen in pathological gambling and videogaming. We use a new reinforcement learning model to highlight a previously neglected vulnerability that we suggest interacts with those already identified, whilst playing a prominent role in non-pharmacological forms of addiction. Specifically, we show that a dual-learning system (i.e. combining model-based and model-free) can be vulnerable to highly rewarding, but suboptimal actions, that are followed by a complex ramification of stochastic adverse effects. This phenomenon is caused by the overload of the capabilities of an agent, as time and cognitive resources required for exploration, deliberation, situation recognition, and habit formation, all increase as a function of the depth and richness of detail of an environment. Furthermore, the cognitive overload can be aggravated due to alterations (e.g. caused by stress) in the bounded rationality, i.e. the limited amount of resources available for the model-based component, in turn increasing the agent’s chances to develop or maintain addictive behaviours. Our study demonstrates that, independent of drug consumption, addictive behaviours can arise in the interaction between the environmental complexity and the biologically finite resources available to explore and represent it

A Multilevel Computational Characterization of Endophenotypes in Addiction

Addiction is characterized by a profound intersubject (phenotypic) variability in the expression of addictive symptomatology and propensity to relapse following treatment. However, laboratory investigations have primarily focused on common neural substrates in addiction and have not yet been able to identify mechanisms that can account for the multifaceted phenotypic behaviors reported in the literature. To fill this knowledge gap theoretically, here we simulated phenotypic variations in addiction symptomology and responses to putative treatments, using both a neural model, based on cortico-striatal circuit dynamics, and an algorithmic model of reinforcement learning (RL). These simulations rely on the widely accepted assumption that both the ventral, model-based, goal-directed system and the dorsal, model-free, habitual system are vulnerable to extra-physiologic dopamine reinforcements triggered by addictive rewards. We found that endophenotypic differences in the balance between the two circuit or control systems resulted in an inverted-U shape in optimal choice behavior. Specifically, greater unbalance led to a higher likelihood of developing addiction and more severe drug-taking behaviors. Furthermore, endophenotypes with opposite asymmetrical biases among cortico-striatal circuits expressed similar addiction behaviors, but responded differently to simulated treatments, suggesting personalized treatment development could rely on endophenotypic rather than phenotypic differentiations. We propose our simulated results, confirmed across neural and algorithmic levels of analysis, inform on a fundamental and, to date, neglected quantitative method to characterize clinical heterogeneity in addiction

Creating disaggregated network services with eBPF: the Kubernetes network provider use case

The eBPF technology enables the creation of custom and highly efficient network services, running in the Linux kernel, tailored to the precise use case under consideration. However, the most prominent examples of such network services in eBPF follow a monolithic approach, in which all required code is created within the same program block. This makes the code hard to maintain, to extend, and difficult to reuse in other use cases. This paper leverages the Polycube framework to demonstrate that a disaggregated approach is feasible also with eBPF, with minimal overhead, introducing a larger degree of code reusability. This paper considers a complex network scenario, such as a complete network provider for Kubernetes, presenting the resulting architecture and a preliminary performance evaluation

BEAMS Lab at MIT: Status report

The Biological Engineering Accelerator Mass Spectrometry (BEAMS) Lab at the Massachusetts Institute of Technology is a facility dedicated to incorporating AMS into life sciences research. As such, it is focused exclusively on radiocarbon and tritium AMS and makes use of a particularly compact instrument of a size compatible with most laboratory space. Recent developments at the BEAMS Lab were aimed to improve different stages of the measurement process, such as the carbon sample injection interface, the simultaneous detection of tritium and hydrogen and finally, the overall operation of the system. Upgrades and results of those efforts are presented here.United States. National Institutes of Health (grant P30-ES02109)United States. National Institutes of Health (grant R42-CA084688)National Institutes of Health. National Center for Research Resources (grant UL1 RR 025005)GlaxoSmithKlin

Ion-Optics Calculations and Preliminary Precision Estimates of the Gas-Capable Ion Source for the 1-MV LLNL BioAMS Spectrometer

Ion-optics calculations were performed for a new ion source and injection beam line. This source, which can accept both solid and gaseous targets, will be installed onto the 1-MV BioAMS spectrometer at the Center for Accelerator Mass Spectrometry, located at Lawrence Livermore National Laboratory and will augment the current LLNL cesium-sputter solid sample ion source. The ion source and its associated injection beam line were designed to allow direct quantification of {sup 14}C/{sup 12}C and {sup 3}H/{sup 1}H isotope ratios from both solid and gaseous targets without the need for isotope switching. Once installed, this source will enable the direct linking of a nanoflow LC system to the spectrometer to provide for high-throughput LC-AMS quantitation from a continuous flow. Calculations show that, for small samples, the sensitivity of the gas-accepting ion source could be precision limited but zeptomole quantitation should be feasible