Results 241 to 250 of about 415,792 (296)

Diversity and geographical distribution of potential carbon monoxide oxidizers using molybdenum-containing enzymes in the ocean. [PDF]

mSphere
Imaura Y, Yamamoto K, Kamikawa R, Yoshida T.   +3 more
europepmc   +1 more source

Multidimensional Analysis of SARS-CoV-2 RNA in Nine Sites Located in Campania Region, Italy. [PDF]

Microorganisms
Lombardi A, Riccio P, Ragosta M, D'Emilio M, Bruzzese D, Imbrenda V, Borriello T, La Rosa G, Suffredini E, Torre I, Pennino F.   +10 more
europepmc   +1 more source

Design of Bayesian Clinical Trials With Clustered Data. [PDF]

Stat Med
Hagar L, Golchi S.
europepmc   +1 more source

Health risk assessment and geochemical characterisation of trace elements in street dust: a case study from a quarry-influenced urban area of Istanbul. [PDF]

Environ Geochem Health
Ulutaş K, Yıldız H, Emlek R, Tekin Ş, Yavuz E, Üçüncüoğlu D, Yücer E, Özkök E, Sevimoğlu O, Keskin A, Uyar S.   +10 more
europepmc   +1 more source

Benchmarking sketching methods on spatial transcriptomics data. [PDF]

Nucleic Acids Res
Gingerich IK, Goods BA, Frost HR.
europepmc   +1 more source

Clustering effects on the reactivity of alkoxy radicals: rate coefficients of ³(RO⋯OR) complexes accounting for multiple conformers.

Phys Chem Chem Phys
Zhao HF, Franzon L, Juttula S, Skog R, Myllys N, Kurtén T.   +5 more
europepmc   +1 more source

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Pixel sampling by clustering

Expert Systems with Applications, 2020
Abstract In this paper, we describe Pixel Sampling Clustering Technique (PSCT), a data-driven sampling procedure used to reduce pixel sets. We view the pixels in an image as a high redundancy 3D space. We also refer to this space as our color model.
Ariel E. Bayá, Mónica G. Larese
openaire   +2 more sources

Spectral Embedded Clustering: A Framework for In-Sample and Out-of-Sample Spectral Clustering

IEEE Transactions on Neural Networks, 2011
Spectral clustering (SC) methods have been successfully applied to many real-world applications. The success of these SC methods is largely based on the manifold assumption, namely, that two nearby data points in the high-density region of a low-dimensional data manifold have the same cluster label.
Feiping Nie 0001, Zinan Zeng, Ivor W. Tsang, Dong Xu 0001, Changshui Zhang   +4 more
openaire   +2 more sources