Results 51 to 60 of about 18,341 (302)

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

FEBS 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, Lea Nikolić, Lorenzo Maraio, Thomas Goiran, Nathan Karpilovsky, Stefano Sellitto, Vangelis Bouris, Jiang‐An Yin, Ronald Melki, Edward A Fon, Adriano Aguzzi, Elena De Cecco   +11 more
wiley   +1 more source

Evidential Physics-Informed Neural Networks

CoRR
We present a novel class of Physics-Informed Neural Networks that is formulated based on the principles of Evidential Deep Learning, where the model incorporates uncertainty quantification by learning parameters of a higher-order distribution. The dependent and trainable variables of the PDE residual loss and data-fitting loss terms are recast as ...
Hai Siong Tan, Kuancheng Wang, Rafe McBeth   +2 more
openaire   +2 more sources

Directed evolution of enzymes at the crossroads of tradition and innovation

FEBS Open Bio, EarlyView.
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova, Andrej Mirossay, Erik Sedlak   +2 more
wiley   +1 more source

Solution of Schrödinger Equation for Quantum Systems via Physics-Informed Neural Networks [PDF]

, 2023
openThe numerous successes achieved by machine learning techniques in many technical areas have sparked interest in the scientific community for their application in science.
ZINESI, PAOLO
core  

Training Physics- Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction

, 2023
Physics-informed neural networks (PINN s) are a newly emerging research frontier in machine learning, which incorporate certain physical laws that govern a given data set, e.g., those described by partial differential equations (PDEs), into the training ...
Dia, H, Grzybowska, H, Wang, B, Shafiei, S, Qin, AK, Mihaita, A-S   +5 more
core   +1 more source

Structure–Function Decoupling of the Sensorimotor and Default Mode Networks in Black Americans With MS

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Multiple sclerosis (MS) exhibits racially disparate rates of disease progression. Black people with MS (B‐PwMS) experience a more severe disease course than non‐Hispanic White people with MS (NHW‐PwMS). Here we investigated structural and functional connectivity as well as structure–function decoupling in the ...
Emilio Cipriano, Giacomo Boffa, Maria Petracca, Marta Ponzano, Nicole Graziano, Claire Wigley, Claire Riley, Jonathan Howard, Pietro Bontempi, Sylvia Klineova, Fred Lublin, Matilde Inglese   +11 more
wiley   +1 more source

Transient Stability Analysis with Physics-Informed Neural Networks

, 2023
We explore the possibility to use physics-informed neural networks to drastically accelerate the solution of ordinary differential-algebraic equations that govern the power system dynamics.
Chatzivasileiadis, Spyros, Stiasny, Jochen, Misyris, Georgios S.   +2 more
core  

Physics-informed neural network for gravity field modeling around Didymos binary system [PDF]

, 2023
LAUREA MAGISTRALEAl fine di prevedere con precisione l'orbita di un veicolo spaziale in prossimità di un corpo celeste, possono essere necessarie rappresentazioni ad alta fedeltà del campo gravitazionale.
GALEAZZI, MARCO
core  

SPIKANs: separable physics-informed Kolmogorov–Arnold networks

Machine Learning: Science and Technology
Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs) in scientific computing.
Bruno Jacob, Amanda A Howard, Panos Stinis   +2 more
doaj   +1 more source

Online Continual Physics-Informed Learning for Quadrotor State Estimation Under Wind-Induced Disturbances

Aerospace
Accurate state estimation for quadrotors under wind-induced disturbances remains a critical challenge in dynamic outdoor environments. Existing model-based and data-driven approaches often struggle with real-time adaptation and catastrophic forgetting ...
Yanhui Liu, Shuopeng Wang, Junhua Shi, Lina Hao   +3 more
doaj   +1 more source