Results 51 to 60 of about 946,507 (289)

Large‐scale bidirectional arrayed genetic screens identify OXR1 and EMC4 as modifiers of αSynuclein aggregation

open access: yesFEBS Open Bio, EarlyView.
Activation of the mitochondrial protein OXR1 increases pSyn129 αSynuclein aggregation by lowering ATP levels and altering mitochondrial membrane potential, particularly in response to MSA‐derived fibrils. In contrast, ablation of the ER protein EMC4 enhances autophagic flux and lysosomal clearance, broadly reducing α‐synuclein aggregates.
Sandesh Neupane   +11 more
wiley   +1 more source

A Q-Learning-Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi-Mobile Robot Systems

open access: yesComplexity, 2022
This paper proposes an adaptive formation tracking control algorithm optimized by Q-learning scheme for multiple mobile robots. In order to handle the model uncertainties and external disturbances, a desired linear extended state observer is designed to ...
Chen Zhang   +4 more
doaj   +1 more source

A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning [PDF]

open access: yes, 2011
In this tutorial paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalent rate constraint approach, the Lyapunov stability drift approach and the approximate Markov ...
Cui, Ying   +4 more
core   +2 more sources

Lessons Learned From a Delayed‐Start Trial of Modafinil for Freezing of Gait in Parkinson's Disease

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Freezing of gait (FOG) in people with Parkinson's disease (PwPD) is debilitating and has limited treatments. Modafinil modulates beta/gamma band activity in the pedunculopontine nucleus (PPN), like PPN deep brain stimulation. We therefore tested the hypothesis that Modafinil would improve FOG in PwPD.
Tuhin Virmani   +8 more
wiley   +1 more source

Dynamical stability and chaos in artificial neural network trajectories along training

open access: yesFrontiers in Complex Systems
The process of training an artificial neural network involves iteratively adapting its parameters so as to minimize the error of the network’s prediction, when confronted with a learning task.
Kaloyan Danovski   +2 more
doaj   +1 more source

Predicting Epileptogenic Tubers in Patients With Tuberous Sclerosis Complex Using a Fusion Model Integrating Lesion Network Mapping and Machine Learning

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Accurate localization of epileptogenic tubers (ETs) in patients with tuberous sclerosis complex (TSC) is essential but challenging, as these tubers lack distinct pathological or genetic markers to differentiate them from other cortical tubers.
Tinghong Liu   +11 more
wiley   +1 more source

Polynomial Iterative Learning Control (ILC) Tracking Control Design for Uncertain Repetitive Continuous-Time Linear Systems Applied to an Active Suspension of a Car Seat

open access: yesMathematics
This paper addresses the issue of polynomial iterative learning tracking control (Poly-ILC) for continuous-time linear systems (LTI) operating repetitively.
Selma Ben Attia   +4 more
doaj   +1 more source

Remote Monitoring in Myasthenia Gravis: Exploring Symptom Variability

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Myasthenia gravis (MG) is a rare, autoimmune disorder characterized by fluctuating muscle weakness and potential life‐threatening crises. While continuous specialized care is essential, access barriers often delay timely interventions. To address this, we developed MyaLink, a telemedical platform for MG patients.
Maike Stein   +13 more
wiley   +1 more source

A reinforcement learning theory for homeostatic regulation [PDF]

open access: yes, 2011
Reinforcement learning models address animal’s behavioral adaptation to its changing “external” environment, and are based on the assumption that Pavlovian, habitual and goal-directed responses seek to maximize reward acquisition.
Gutkin, B. S., Keramati, M.
core  

Learning Topology and Dynamics of Large Recurrent Neural Networks

open access: yes, 2014
Large-scale recurrent networks have drawn increasing attention recently because of their capabilities in modeling a large variety of real-world phenomena and physical mechanisms.
He, Yuejia, She, Yiyuan, Wu, Dapeng
core   +1 more source

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