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1. Online Control for Adaptive Tapering of Medications

   https://doi.org/10.1109/CDC49753.2023.10384168  

   Gradu, Paula; Recht, Benjamin (December 2023, IEEE)

   We investigate adaptive protocols for the elimination or reduction of the use of medications or addictive substances. We formalize this problem as online optimization, minimizing the cumulative dose subject to constraints on individual well-being. We adapt a model of addiction from the psychology literature and show how it can be described by a class of linear time-invariant systems. For such systems, the optimal policy amounts to taking the smallest dose that maintains well-being. We derive a simple protocol based on integral control that requires no system identification, only needing approximate knowledge of the instantaneous dose response. This protocol is robust to model misspecification and is able to maintain an individual's well-being during the tapering process. Numerical experiments demonstrate that the adaptive protocol outperforms non-adaptive methods in terms of both maintenance of well-being and rate of dose reduction. 

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2. Multiscale topology in interactomic network: from transcriptome to antiaddiction drug repurposing

   https://doi.org/10.1093/bib/bbae054  

   Du, Hongyan; Wei, Guo-Wei; Hou, Tingjun (March 2024, Briefings in Bioinformatics)

   Abstract The escalating drug addiction crisis in the United States underscores the urgent need for innovative therapeutic strategies. This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment, bridging the gap between transcriptomic data analysis and drug discovery. We initiated our approach by conducting differential gene expression analysis on addiction-related transcriptomic data to identify key genes. We propose a novel topological differentiation to identify key genes from a protein–protein interaction network derived from DEGs. This method utilizes persistent Laplacians to accurately single out pivotal nodes within the network, conducting this analysis in a multiscale manner to ensure high reliability. Through rigorous literature validation, pathway analysis and data-availability scrutiny, we identified three pivotal molecular targets, mTOR, mGluR5 and NMDAR, for drug repurposing from DrugBank. We crafted machine learning models employing two natural language processing (NLP)-based embeddings and a traditional 2D fingerprint, which demonstrated robust predictive ability in gauging binding affinities of DrugBank compounds to selected targets. Furthermore, we elucidated the interactions of promising drugs with the targets and evaluated their drug-likeness. This study delineates a multi-faceted and comprehensive analytical framework, amalgamating bioinformatics, topological data analysis and machine learning, for drug repurposing in addiction treatment, setting the stage for subsequent experimental validation. The versatility of the methods we developed allows for applications across a range of diseases and transcriptomic datasets. 

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3. Adaptive Optimization for Stochastic Renewal Systems

   M. J. Neely (January 2024, arXiv:2401.07170v1)

   arXiv:2401.07170v1 (Ed.)

   This paper considers online optimization for a system that performs a sequence of back-to-back tasks. Each task can be processed in one of multiple processing modes that affect the duration of the task, the reward earned, and an additional vector of penalties (such as energy or cost). Let A[k] be a random matrix of parameters that specifies the duration, reward, and penalty vector under each processing option for task k. The goal is to observe A[k] at the start of each new task k and then choose a processing mode for the task so that, over time, time average reward is maximized subject to time average penalty constraints. This is a renewal optimization problem and is challenging because the probability distribution for the A[k] sequence is unknown. Prior work shows that any algorithm that comes within ϵ of optimality must have (1/ϵ^2) convergence time. The only known algorithm that can meet this bound operates without time average penalty constraints and uses a diminishing stepsize that cannot adapt when probabilities change. This paper develops a new algorithm that is adaptive and comes within O(ϵ) of optimality for any interval of  (1/ϵ^2) tasks over which probabilities are held fixed, regardless of probabilities before the start of the interval. 

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4. Evaluating Passive Myocardial Stiffness Using in vivo cine, cDTI, and Tagged MRI

   https://doi.org/10.1007/978-3-031-35302-4_54  

   Kolawole, Fikunwa O.; Wang, Vicky Y.; Freytag, Bianca; Loecher, Michael; Cork, Tyler E.; Nash, Martyn P.; Kuhl, Ellen; Ennis, Daniel B. (June 2023, Functional Imaging and Modeling of the Heart. FIMH 2023. Lecture Notes in Computer Science.)

   Bernard, O.; Clarysse, P.; Duchateau, N.; Ohayon, J.; Viallon, M (Ed.)

   Increased passive myocardial stiffness is implicated in the pathophysiology of many cardiac diseases, and its in vivo estimation can improve management of heart disease. MRI-driven computational constitutive modeling has been used extensively to evaluate passive myocardial stiffness. This approach requires subject-specific data that is best acquired with different MRI sequences: conventional cine (e.g. bSSFP), tagged MRI (or DENSE), and cardiac diffusion tensor imaging. However, due to the lack of comprehensive datasets and the challenge of incorporating multi-phase and single-phase disparate MRI data, no studies have combined in vivo cine bSSFP, tagged MRI, and cardiac diffusion tensor imaging to estimate passive myocardial stiffness. The objective of this work was to develop a personalized in silico left ventricular model to evaluate passive myocardial stiffness by integrating subject-specific geometric data derived from cine bSSFP, regional kinematics extracted from tagged MRI, and myocardial microstructure measured using in vivo cardiac diffusion tensor imaging. To demonstrate the feasibility of using a complete subject-specific imaging dataset for passive myocardial stiffness estimation, we calibrated a bulk stiffness parameter of a transversely isotropic exponential constitutive relation to match the local kinematic field extracted from tagged MRI. This work establishes a pipeline for developing subject-specific biomechanical ventricular models to probe passive myocardial mechanical behavior, using comprehensive cardiac imaging data from multiple in vivo MRI sequences. 

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5. Conditioned place preference for cocaine and methylphenidate in female mice from lines selectively bred for high voluntary wheel‐running behavior

   https://doi.org/10.1111/gbb.12700  

   Schmill, Margaret P.; Cadney, Marcell D.; Thompson, Zoe; Hiramatsu, Layla; Albuquerque, Ralph L.; McNamara, Monica P.; Castro, Alberto A.; Kay, Jarren C.; Buenaventura, Darrius G.; Ramirez, Jocelyn L.; et al (September 2020, Genes, Brain and Behavior)

   Abstract Behavioral addictions can come in many forms, including overeating, gambling and overexercising. All addictions share a common mechanism involving activation of the natural reward circuit and reinforcement learning, but the extent to which motivation for natural and drug rewards share similar neurogenetic mechanisms remains unknown. A unique mouse genetic model in which four replicate lines of female mice were selectively bred (>76 generations) for high voluntary wheel running (High Runner or HR lines) alongside four non‐selected control (C) lines were used to test the hypothesis that high motivation for exercise is associated with greater reward for cocaine (20 mg/kg) and methylphenidate (10 mg/kg) using the conditioned place preference (CPP) test. HR mice run ~three times as many revolutions/day as C mice, but the extent to which they have increased motivation for other rewards is unknown. Both HR and C mice displayed significant CPP for cocaine and methylphenidate, but with no statistical difference between linetypes for either drug. Taken together, results suggest that selective breeding for increased voluntary running has modified the reward circuit in the brain in a way that increases motivation for running without affecting cocaine or methylphenidate reward. 

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