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48285 results in Computer Science

Page 260 of 2415

  • Pyrroloquinoline quinone influences intracellular alpha-synuclein aggregates

  • Elizabeth Mountford, Cynthia Mathew, Reena Ghildyal, Andrea Bugarcic
  • Journal:
    Experimental Results / Volume 4 / 2023
    Published online by Cambridge University Press:
    11 May 2023, e12
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  • Parkinson’s disease (PD) is an irreversible neurodegenerative disorder clinically manifesting in uncontrolled motor symptoms. There are two primary hallmark features of Parkinson’s disease—an irreversible loss of dopaminergic neurons of the substantia nigra pars compacta and formation of intracellular insoluble aggregates called Lewy bodies mostly composed of alpha-synuclein. Using a clinical improvements-first approach, we identified several clinical trials involving consumption of a specific diet or nutritional supplementation that improved motor and nonmotor functions. Here, we aimed to investigate if and how pyrroloquinoline quinone (PQQ) compound disrupts preformed alpha-synuclein deposits using SH-SY5Y cells, widely used Parkinson’s disease cellular model. SH-SY5Y neuroblastoma cells, incubated in presence of potassium chloride (KCl) to induce alpha-synuclein protein aggregation, were treated with PQQ for up to 48 hr. Resulting aggregates were examined and quantified using confocal microscopy. Overall, nutritional compound PQQ reduced the average number and overall size of intracellular cytoplasmic alpha-synuclein aggregates in a PD cellular model.

  • Reinforcement learning with modified exploration strategy for mobile robot path planning

  • Nesrine Khlif, Khraief Nahla, Belghith Safya
  • Journal:
    Robotica / Volume 41 / Issue 9 / September 2023
    Published online by Cambridge University Press:
    11 May 2023, pp. 2688-2702

  • Driven by the remarkable developments we have observed in recent years, path planning for mobile robots is a difficult part of robot navigation. Artificial intelligence applied to mobile robotics is also a distinct challenge; reinforcement learning (RL) is one of the most used algorithms in robotics. The exploration-exploitation dilemma is a motivating challenge for the performance of RL algorithms. The problem is balancing exploitation and exploration, as too much exploration leads to a decrease in cumulative reward, while too much exploitation locks the agent in a local optimum. This paper proposes a new path planning method for mobile robot based on Q-learning with an improved exploration strategy. In addition, a comparative study of Boltzmann distribution and $\epsilon$-greedy politics is presented. Through simulations, the better performance of the proposed method in terms of execution time, path length, and cost function is confirmed.

Page 260 of 2415