Results 41 to 50 of about 16,533 (261)

Physics-informed neural networks for diffraction tomography

Advanced Photonics, 2022
We propose a physics-informed neural network as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmholtz equation as a physical loss, we can predict the scattered field accurately.
Amirhossein Saba, Carlo Gigli, Ahmed B. Ayoub, Demetri Psaltis   +3 more
openaire   +2 more sources

Increased Risk of Sarcomas in Children With Congenital Anomalies: Findings From the Genetic Overlap Between Anomalies and Cancer in Kids (GOBACK) Registry Linkage Study

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Pediatric sarcomas are a heterogeneous group of tumors that contribute disproportionately to cancer mortality in children. Although congenital anomalies are among the strongest known risk factors for childhood cancer, the risk of specific sarcoma subtypes among affected individuals has not yet been thoroughly evaluated. Procedure We
Russ Wolters, Ji Yun Tark, Tiffany M. Chambers, Tania A. Desrosiers, Michael E. Scheurer, Charles Shumate, Wendy N. Nembhard, Mahsa M. Yazdy, Eirini Nestoridi, Amanda E. Janitz, Jean Paul Tanner, Russell S. Kirby, Jason L. Salemi, Yao Yu, Chad D. Huff, Sharon E. Plon, Philip J. Lupo, Jeremy M. Schraw   +17 more
wiley   +1 more source

A Physics-Informed Neural Network Approach for Nearfield Acoustic Holography

Sensors, 2021
In this manuscript, we describe a novel methodology for nearfield acoustic holography (NAH). The proposed technique is based on convolutional neural networks, with autoencoder architecture, to reconstruct the pressure and velocity fields on the surface ...
Marco Olivieri, Mirco Pezzoli, Fabio Antonacci, Augusto Sarti   +3 more
doaj   +1 more source

Preconditioning for Physics-Informed Neural Networks

CoRR
Physics-informed neural networks (PINNs) have shown promise in solving various partial differential equations (PDEs). However, training pathologies have negatively affected the convergence and prediction accuracy of PINNs, which further limits their practical applications.
Songming Liu, Chang Su, Jiachen Yao, Zhongkai Hao, Hang Su 0006, Youjia Wu, Jun Zhu 0001   +6 more
openaire   +2 more sources

Microbiome−host proteostasis crosstalk—An emerging perspective on mechanisms and interventions toward healthy longevity

FEBS Letters, EarlyView.
Proteostasis and the gut microbiota play a key role in shaping host physiology. Microbiota‐derived metabolites, vitamins, and RNA modulate host proteostasis. Findings from model systems, including C. elegans, indicate microbes can either stabilize or disrupt host proteostasis.
Abhishek Anil Dubey, Maria Ermolaeva
wiley   +1 more source

Phosphoinositides and inositol phosphates as molecular glues

FEBS Letters, EarlyView.
Inositol phosphates (IPs) and phosphoinositides (PIPs) regulate diverse eukaryotic processes. Beyond recruiting signaling proteins or acting as structural cofactors, recent studies suggest they mediate protein–protein interactions as natural molecular glues.
Aleshia Seaton‐Terry, Alexander J. Cutright, Mong Na Claire Loi, Jessica Martin, Joseph W. Gilligan, Jakub N. Kubina, Michael J. Arambula, Susanna Tanada Palmu, Daniella Zhou, Raymond D. Blind   +9 more
wiley   +1 more source

FastLRNR and Sparse Physics Informed Backpropagation

Results in Applied Mathematics
We introduce Sparse Physics Informed Backpropagation (SPInProp), a new class of methods for accelerating backpropagation for a specialized neural network architecture called Low Rank Neural Representation (LRNR).
Woojin Cho, Kookjin Lee, Noseong Park, Donsub Rim, Gerrit Welper   +4 more
doaj   +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

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

Physics‐Informed Neural Network for Magnetization Distribution Estimation

IET Electric Power Applications
Accurately estimating the magnetization distribution in permanent magnets is critical for optimising their performance in various applications, such as electric motors, generators and magnetic sensors, where precise magnetic field control is essential. A
Zhi Gong, Zuqi Tang, Abdelkader Benabou
doaj   +1 more source