Results 111 to 120 of about 152,595 (295)

Nanoscale Mapping of the Subcellular Glycosylation Landscape

open access: yesAdvanced Science, EarlyView.
Using multiplexed super‐resolution imaging with fluorophore‐labeled lectins, this study reports intracellular glycosylation at the nanoscale across organelles and synaptic specializations. Extending glycan analysis beyond the cell surface, Glyco‐STORM reveals distinct glycosylation nanodomains in the ER, Golgi, lysosomes, and synaptic sites.
Helene Gregoria Schroeter   +4 more
wiley   +1 more source

Cluster-Centered Visualization Techniques for Fuzzy Clustering Results to Judge Single Clusters

open access: yesApplied Sciences
Fuzzy clustering, as a powerful method for pattern recognition and data analysis, often produces complex results that require careful examination of individual clusters.
Kai Vahldiek, Frank Klawonn
doaj   +1 more source

Fuzzy clustering: insights and a new approach [PDF]

open access: yes, 2004
Fuzzy clustering extends crisp clustering in the sense that objects can belong to various clusters with different membership degrees at the same time, whereas crisp or deterministic clustering assigns each object to a unique cluster.
Klawonn, Frank
core  

CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions

open access: yesAdvanced Science, EarlyView.
CellPolaris decodes how transcription factors guide cell fate by building gene regulatory networks from transcriptomic data using transfer learning. It generates tissue‐ and cell‐type‐specific networks, identifies master regulators in cell state transitions, and simulates TF perturbations in developmental processes.
Guihai Feng   +27 more
wiley   +1 more source

Bilateral Weighted Fuzzy C-Means Clustering

open access: yesIranian Journal of Electrical and Electronic Engineering, 2012
Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise.
A. H. Hadjahmadi   +2 more
doaj  

Ferroptosis‐Mediated Hippocampal Neuronal Loss Post‐mTBI: Chromatin Accessibility Profiling and Single‐Nucleus Transcriptomics

open access: yesAdvanced Science, EarlyView.
Hippocampal single ‐nucleus transcriptomes and chromatin accessibility after mild traumatic brain injury reveal dentate granule neuron vulnerability driven by ferroptosis. The c‐Jun–Tmsb4x–Slc2a2 axis modulates lipid peroxidation and iron dysregulation.
Manrui Li   +13 more
wiley   +1 more source

Unpaired Learning‐Enabled Nanotube Identification from AFM Images

open access: yesAdvanced Science, EarlyView.
Identifying nanotubes on rough substrates is notoriously challenging for conventional image analysis. This work presents an unpaired deep learning approach that automatically extracts nanotube networks from atomic force microscopy images, even on complex polymeric surfaces used in roll‐to‐roll printing.
Soyoung Na   +10 more
wiley   +1 more source

A Fuzzy Clustering Approach to Identify Pedestrians' Traffic Behavior Patterns. [PDF]

open access: yesJ Res Health Sci, 2023
Saeipour P   +3 more
europepmc   +1 more source

Atomistic Understanding of 2D Monatomic Phase‐Change Material for Non‐Volatile Optical Applications

open access: yesAdvanced Science, EarlyView.
Antimony is a promising monatomic phase‐change material. Scaling down the film thickness is necessary to prolong the amorphous‐state lifetime, but it alters the optical properties. The combined computational and experimental study shows that, as thickness decreases, the extinction coefficient and optical contrast are reduced in the near‐infrared ...
Hanyi Zhang   +10 more
wiley   +1 more source

HIDF: Integrating Tree‐Structured scRNA‐seq Heterogeneity for Hierarchical Deconvolution of Spatial Transcriptomics

open access: yesAdvanced Science, EarlyView.
The prevailing neglect of cellular hierarchies in current spatial transcriptomics deconvolution often obscures cellular heterogeneity and impedes the identification of fine‐grained subtypes. To address this issue, HIDF employs a cluster‐tree and dual regularization to systematically model cellular hierarchical structures.
Zhiyi Zou   +5 more
wiley   +1 more source

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