Results 71 to 80 of about 232,386 (277)
Remote Sensing, 2016 Remote sensing image clustering is a challenging task considering its intrinsic complexity. Recently, by combining the spectral and spatial information of the remote sensing data, the clustering performance can be dramatically enhanced, termed as ...
Ailong Ma, Yanfei Zhong, Liangpei Zhang
doaj +1 more source
Sensors, 2022 Hyperspectral imaging can simultaneously acquire spectral and spatial information of the samples and is, therefore, widely applied in the non-destructive detection of grain quality.
Zhen Kang +5 more
doaj +1 more source
FEBS Letters, EarlyView.We reconstituted Synechocystis glycogen synthesis in vitro from purified enzymes and showed that two GlgA isoenzymes produce glycogen with different architectures: GlgA1 yields denser, highly branched glycogen, whereas GlgA2 synthesizes longer, less‐branched chains.
Kenric Lee +3 more
wiley +1 more source
Structural biology of ferritin nanocages
FEBS Letters, EarlyView.Ferritin is a conserved iron‐storage protein that sequesters iron as a ferric mineral core within a nanocage, protecting cells from oxidative damage and maintaining iron homeostasis. This review discusses ferritin biology, structure, and function, and highlights recent cryo‐EM studies revealing mechanisms of ferritinophagy, cellular iron uptake, and ...
Eloise Mastrangelo, Flavio Di Pisa
wiley +1 more source
Making Laplacians commute [PDF]
, 2013 In this paper, we construct multimodal spectral geometry by finding a pair of closest commuting operators (CCO) to a given pair of Laplacians. The CCOs are jointly diagonalizable and hence have the same eigenbasis.
Bronstein, Michael M. +2 more
core
Tensor Spectral Clustering for Partitioning Higher-order Network Structures
, 2015 Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of ...
Benson, Austin R. +2 more
core +1 more source
Molecular Oncology, EarlyView.AZD9291 has shown promise in targeted cancer therapy but is limited by resistance. In this study, we employed metabolic labeling and LC–MS/MS to profile time‐resolved nascent protein perturbations, allowing dynamic tracking of drug‐responsive proteins. We demonstrated that increased NNMT expression is associated with drug resistance, highlighting NNMT ...
Zhanwu Hou +5 more
wiley +1 more source
Large Scale Spectral Clustering Using Approximate Commute Time Embedding
, 2012 Spectral clustering is a novel clustering method which can detect complex shapes of data clusters. However, it requires the eigen decomposition of the graph Laplacian matrix, which is proportion to $O(n^3)$ and thus is not suitable for large scale ...
C. Fowlkes +10 more
core +1 more source
An Improvement of Spectral Clustering via Message Passing and Density Sensitive Similarity
IEEE Access, 2019 Spectral clustering transforms the data clustering problem into a graph-partitioning problem and classifies data points by finding the optimal sub-graphs. Traditional spectral clustering algorithms use Gaussian kernel function to construct the similarity
Lijuan Wang, Shifei Ding, Hongjie Jia
doaj +1 more source
PARALLEL SPATIOTEMPORAL SPECTRAL CLUSTERING WITH MASSIVE TRAJECTORY DATA [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017 Massive trajectory data contains wealth useful information and knowledge. Spectral clustering, which has been shown to be effective in finding clusters, becomes an important clustering approaches in the trajectory data mining.
Y. Z. Gu +5 more
doaj +1 more source