Results 151 to 160 of about 589,029 (277)

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Event detection in finance using hierarchical clustering algorithms on news and tweets. [PDF]

PeerJ Comput Sci, 2021
Carta S, Consoli S, Piras L, Podda AS, Reforgiato Recupero D.   +4 more
europepmc   +1 more source

Clustering Algorithms I: Sequential Algorithms

, 2009
Theodoridis, Sergios, Koutroumbas, Konstantinos   +1 more
openaire   +2 more sources

Complex Cryptographic and User‐Centric Physically Unclonable Functions Enabled by Strain‐Sensitive Nanocrystals via Selective Ligand Exchange

Advanced Functional Materials, EarlyView.
This study investigates electromechanical PUFs that improve on traditional electric PUFs. The electron transport materials are coated randomly through selective ligand exchange. It produces multiple keys and a key with motion dependent on percolation and strain, and approaches almost ideal inter‐ and intra‐hamming distances.
Seungshin Lim, Hanseok Seo, Taesung Park, Byung Ku Jung, Junhyeok Park, Young Kyun Choi, Dae Yang Oh, Soong Ju Oh   +7 more
wiley   +1 more source

Unsupervised clustering algorithms improve the reproducibility of dynamic contrast-enhanced magnetic resonance imaging pulmonary perfusion quantification in muco-obstructive lung diseases. [PDF]

Front Med (Lausanne), 2022
Konietzke M, Triphan SMF, Eichinger M, Bossert S, Heller H, Wege S, Eberhardt R, Puderbach MU, Kauczor HU, Heußel G, Heußel CP, Risse F, Wielpütz MO.   +12 more
europepmc   +1 more source

Clustering Algorithms II: Hierarchical Algorithms

, 2009
Theodoridis, Sergios, Koutroumbas, Konstantinos   +1 more
openaire   +2 more sources

Expanding Chemical Space of Nucleic Acid Nanoparticles for Tunable Antiviral‐Like Immunomodulatory Responses and Potent Adjuvant Activity

Advanced Functional Materials, EarlyView.
We introduce a nucleic acid nanoparticle (NANP) platform designed to be rrecognized by the human innate immune system in a regulated manner. By changing chemical composition while maintaining constant architectural parameters, we identify key determinants of immunorecognition enabling the rational design of NANPs with tunable immune activation profiles
Martin Panigaj, Hannah S. Newton, Jian Wang, Laxmi Pandey, Yelixza I. Avilla, Phong Nguyen, Morgan Chandler, Justin Halman, Yasmine Radwan, Stephanie Smith, Elijah Edmondson, Simone Difilippantonio, Chelsea Sanders, Bao Tran, Yongmei Zhao, Shaojun Xie, Edward Cedrone, Barry W. Neun, Meni Wanunu, Nikolay V. Dokholyan, Marina A. Dobrovolskaia, Kirill A. Afonin   +21 more
wiley   +1 more source

Testing network clustering algorithms with natural language processing. [PDF]

Front Artif Intell
Achitouv I, Chavalarias D, Gaume B.
europepmc   +1 more source

Peptide Sequencing With Single Acid Resolution Using a Sub‐Nanometer Diameter Pore

Advanced Functional Materials, EarlyView.
To sequence a single molecule of Aβ1−42–sodium dodecyl sulfate (SDS), the aggregate is forced through a sub‐nanopore 0.4 nm in diameter spanning a 4.0 nm thick membrane. The figure is a visual molecular dynamics (VMD) snapshot depicting the translocation of Aβ1−42–SDS through the pore; only the peptide, the SDS, the Na+ (yellow/green) and Cl− (cyan ...
Apurba Paul, Archith Rayabharam, Lisa Almonte, Eveline Rigo, Joshy Joseph, Ashutosh Kumar, Zhuxin Dong, Narayana Aluru, Gregory Timp   +8 more
wiley   +1 more source

Empirically calibrated simulations reveal the limits of phenotypic clustering algorithms for biodiversity assessment in data-scarce crops. [PDF]

PLoS One
Naino Jika AK.
europepmc   +1 more source