Results 221 to 230 of about 25,482,812 (306)

Reconstruction and analysis of the gene network regulating apoptosis in hepatocellular carcinoma based on scRNA-seq data and the ANDSystem knowledge base. [PDF]

Vavilovskii Zhurnal Genet Selektsii
Adamovskaya AV, Yatsyk IV, Kleshchev MA, Demenkov PS, Ivanisenko TV, Ivanisenko VA.   +5 more
europepmc   +1 more source

Automated Alignment Powered by Computer Vision Streamlines the Two‐Photon Polymerization‐Based Micro 3D Printing of Multiscale and Multimaterial Structures

Advanced Intelligent Discovery, EarlyView.
Two‐photon polymerization enables high‐resolution microfabrication, but performing alignment when printing multiple structures is difficult. Here, we present a fast, robust, and open‐source protocol for automated alignment on Nanoscribe systems. Achieving ≈0.4 μm accuracy in under 5 s, our protocol reduces time and error in multimaterial printing. This
Daniel Maher, Valentina Mendoza Martinez, Marcin Piotr Piekarczyk, Stefan Sützl, Ada‐Ioana Bunea   +4 more
wiley   +1 more source

The effect of dimeric bisbenzimidazoles on the activity of DNA repair enzymes TDP1, TDP2, PARP1 and PARP2. [PDF]

Vavilovskii Zhurnal Genet Selektsii
Dyrkheeva NS, Chernyshova IA, Arutyunyan AF, Zakharenko AL, Kutuzov MM, Naumenko KN, Venzel AS, Ivanisenko VA, Deyev SM, Zhuze AL, Lavrik OI.   +10 more
europepmc   +1 more source

Retraction: Eya1 controls cell polarity, spindle orientation, cell fate and Notch signaling in distal embryonic lung epithelium. Development doi: 10.1242/dev.058479

, 2017
Ahmed El-Hashash, Gianluca Turcatel, Denise Al Alam, Sue Buckley, Hiroshi Tokumitsu, Savério Bellusci, David Warburton   +6 more
openalex   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

FlyDEGdb knowledge base on differentially expressed genes of Drosophila melanogaster, a model object in biomedicine. [PDF]

Vavilovskii Zhurnal Genet Selektsii
Podkolodnaya OA, Deryuzhenko MA, Tverdokhleb NN, Zolotareva KA, Makovka YV, Podkolodny NL, Suslov VV, Chadaeva IV, Fedoseeva LA, Seryapina AA, Oshchepkov DY, Bogomolov AG, Kondratyuk EY, Redina OE, Markel AL, Gruntenko NE, Ponomarenko MP.   +16 more
europepmc   +1 more source

Cross‐Modal Characterization of Thin‐Film MoS2 Using Generative Models

Advanced Intelligent Systems, EarlyView.
Cross‐modal learning is evaluated using atomic force microscopy (AFM), Raman spectroscopy, and photoluminescence spectroscopy (PL) through unsupervised learning, regression, and autoencoder models. Autoencoder models are used to generate spectroscopy data from the microscopy images.
Isaiah A. Moses, Chen Chen, Joan M. Redwing, Wesley F. Reinhart   +3 more
wiley   +1 more source

<b>Os destaques das pesquisas sobre interpretação de língua de sinais no contexto acadêmico da interpretação comunitária</b><br>DOI: 10.5007/2175-7968.2010v2n26p13

, 2010
Melanie Metzger
openalex   +2 more sources

gnSPADE: Incorporating Gene Network Structures Enhances Reference‐Free Deconvolution in Spatial Transcriptomics

Advanced Intelligent Systems, EarlyView.
gnSPADE integrates gene‐network structures into a probabilistic topic modeling framework to achieve reference‐free cell‐type deconvolution in spatial transcriptomics. By embedding gene connectivity within the generative process, gnSPADE enhances biological interpretability and accuracy across simulated and real datasets, revealing spatial organization ...
Aoqi Xie, Yuehua Cui
wiley   +1 more source

Identification of proteins regulating phenotype-associated genes of M2 macrophages: a bioinformatic analysis. [PDF]

Vavilovskii Zhurnal Genet Selektsii
Antropova EA, Yatsyk IV, Demenkov PS, Ivanisenko TV, Ivanisenko VA.   +4 more
europepmc   +1 more source