Results 101 to 110 of about 71,961 (266)

Nutrient/TOR signaling controls adipose mitochondrial transcription factor A (TFAM) to regulate organismal growth in Drosophila

FEBS Letters, EarlyView.
Animals must match their growth rate to available nutrients. We show that in Drosophila larvae, the nutrient‐sensing TOR kinase controls growth by regulating levels of TFAM, a key regulator of mitochondrial function, in the adipose tissue. When nutrients are abundant, high TOR activity suppresses TFAM, lowering mitochondrial bioenergetic activity and ...
Shrivani Sriskanthadevan‐Pirahas, Shahoon Khan, Abhishek Sharma, Byoungchun Lee, Savraj S. Grewal   +4 more
wiley   +1 more source

A generative modeling approach to reconstructing 21 cm tomographic data

Machine Learning: Science and Technology
Analyses of the cosmic 21 cm signal are hampered by astrophysical foregrounds that are far stronger than the signal itself. These foregrounds, typically confined to a wedge-shaped region in Fourier space, often necessitate the removal of a vast majority ...
Nashwan Sabti, Ram Purandhar Reddy Sudha, Julian B Muñoz, Siddharth Mishra-Sharma, Taewook Youn   +4 more
doaj   +1 more source

MSTabVAE: Multi-Step Latent Conditional Variational Autoencoder for Imbalanced Tabular Data Synthesis

IEEE Access
Recent advances in artificial intelligence have expanded its applications in the financial domain, particularly in fraud detection, a critical task for preventing losses for both customers and institutions.
Minji Kang, Hyeryung Jang
doaj   +1 more source

Engineering peptides into antibodies—opportunities and strategies for therapeutic innovation

FEBS Letters, EarlyView.
Peptides and antibodies occupy complementary therapeutic niches. Peptides recognize difficult targets in a compact format, while antibodies add specificity, long half‐life, and effector functions. This review examines strategies that merge both modalities—peptide grafting into loops, terminal and Fc fusions, and bioconjugation—highlighting how ...
Jinling Wang, Ruslana Romaniuk, Hristo L. Svilenov   +2 more
wiley   +1 more source

Tensor tree learns hidden relational structures in data to construct generative models

Machine Learning: Science and Technology
Based on the tensor tree network with the Born machine framework, we propose a general method for constructing a generative model by expressing the target distribution function as the amplitude of the quantum wave function represented by a tensor tree ...
Kenji Harada, Tsuyoshi Okubo, Naoki Kawashima   +2 more
doaj   +1 more source

Entanglement as a Resource for Correlation: A Quantum Circuit Born Machine for System-Wide Flight-Delay Scenario Generation from Real BTS Data

Quantum Reports
Network delays are correlated, so treating hub airports as independent can severely understate simultaneous delay events. This study formulates system-wide flight-delay scenario generation with a pure-state quantum circuit Born machine (QCBM), in which ...
Mariwan Ahmed, Ismael Abdulrahman
doaj   +1 more source

Liver organoids: modelling complexity in homeostasis and disease

FEBS Letters, EarlyView.
Studying liver in vitro has been challenging because simple 2D cell cultures fail to capture liver's cellular and architectural complexity. To bridge this gap, scientists increasingly use organoids, 3D liver models which better mimic liver composition and function. This review examines recent advances in liver organoid complexity and realism, discusses
Anna M. Dowbaj, Meritxell Huch
wiley   +1 more source

Plastic tensor networks for interpretable generative modeling

Machine Learning: Science and Technology
A structural optimization scheme for a single-layer nonnegative adaptive tensor tree (NATT) that models a target probability distribution is proposed as an alternative paradigm for generative modeling.
Katsuya O Akamatsu, Kenji Harada, Tsuyoshi Okubo, Naoki Kawashima   +3 more
doaj   +1 more source

The role of generative AI in enhancing predictive modeling for cost-effectiveness analysis in healthcare

Digital Engineering
Healthcare economic evaluation increasingly relies on predictive modeling to inform resource allocation decisions. Traditional cost-effectiveness analysis (CEA) methodologies face significant challenges when processing complex, heterogeneous healthcare ...
Aanuoluwapo Clement David-Olawade, Ayodele Osunmakinde, Folasayo I. Ayoola, Eghosasere Egbon, David B. Olawade   +4 more
doaj   +1 more source

Generative Mesh Modeling

, 2005
Die generative Modellierung ist ein alternativer Ansatz zur Beschreibung von dreidimensionaler Form. Zugrunde liegt die Idee, ein Modell nicht wie üblich durch eine Ansammlung geometrischer Primitive (Dreiecke, Punkte, NURBS-Patches) zu beschreiben, sondern durch Funktionen.
openaire   +3 more sources