Results 91 to 100 of about 1,144,226 (279)

Domain adaptation hyperspectral image fusion based on spatial-spectral domain separation

Geo-spatial Information Science
Hyperspectral datasets captured by airborne are one of the important data sources in the field of hyperspectral image fusion. However, the limited number of data samples often hinders the optimal performance of deep learning-based methods on these ...
Yucheng Sun, Yong Ma, Yuan Yao, Xiaoguang Mei, Jun Huang, Jiayi Ma   +5 more
doaj   +1 more source

A methionine‐lined active site governs carbocation stabilization and product specificity in a bacterial terpene synthase

FEBS Letters, EarlyView.
This study reveals a unique active site enriched in methionine residues and demonstrates that these residues play a critical role by stabilizing carbocation intermediates through novel sulfur–cation interactions. Structure‐guided mutagenesis further revealed variants with significantly altered product profiles, enhancing pseudopterosin formation. These
Marion Ringel, Carl P. O. Helmer, Shani Zev, Ronja Driller, Emily Buhr, Markus Reinbold, Renana Schwartz, Gabriel Foley, Mikael Boden, Daniel Garbe, Gerhard Schenk, Dan Thomas Major, Bernhard Loll, Thomas Brück   +13 more
wiley   +1 more source

Valosin‐containing protein counteracts ATP‐driven dissolution of FUS condensates through its ATPase activity in vitro

FEBS Letters, EarlyView.
Biomolecular condensates formed by fused in sarcoma (FUS) are dissolved by high ATP concentrations yet persist in cells. Using a reconstituted system, we demonstrate that valosin‐containing protein (VCP), an AAA+ ATPase, counteracts ATP‐driven dissolution of FUS condensates through its D2 ATPase activity.
Hitomi Kimura, Shin‐ichi Tate, Kyota Yasuda   +2 more
wiley   +1 more source

Predicting domain adaptivity

Proceedings of the 20th ACM international conference on Multimedia, 2012
Over the years, the academic researchers have contributed various visual concept classifiers. Nevertheless, given a new dataset, most researchers still prefer to develop large number of classifiers from scratch despite expensive labeling efforts and limited computing resources.
YAO, Ting, NGO, Chong-wah, ZHU, Shiai
openaire   +3 more sources

Linking neurogenesis, oligodendrogenesis, and myelination defects to neurodevelopmental disruption in primary mitochondrial disorders

FEBS Letters, EarlyView.
Mitochondrial remodeling shapes neural and glial lineage progression by matching metabolic supply with demand. Elevated OXPHOS supports differentiation and myelin formation, while myelin compaction lowers mitochondrial dependence, revealing mitochondria as key drivers of developmental energy adaptation.
Sahitya Ranjan Biswas, Porter L. Tomsick, Alicia M. Pickrell, Paul D. Morton   +3 more
wiley   +1 more source

Domain Adaptation of Time Series Classification

IEEE Access
In the era of big data and the rapid development of sensor technology, the time series classification problem has become an important research direction in the field of data mining.
Xinli Wang, Rencheng Sun
doaj   +1 more source

Organizing the interface—Plasma membrane architecture and receptor dynamics in virus‐cell interactions

FEBS Letters, EarlyView.
Plasma membranes contain dynamic nanoscale domains that organize lipids and receptors. Because viruses operate at similar scales, this architecture shapes early infection steps, including attachment, receptor engagement, and entry. Using influenza A virus and HIV‐1 as examples, we highlight how receptor nanoclusters, multivalent glycan interactions ...
Jan Schlegel, Christian Sieben
wiley   +1 more source

AAA+ protein unfoldases—the Moirai of the proteome

FEBS Letters, EarlyView.
AAA+ unfoldases are essential molecular motors that power protein degradation and disaggregation. This review integrates recent cryo‐electron microscopy (cryo‐EM) structures and single‐molecule biophysical data to reconcile competing models of substrate translocation.
Stavros Azinas, Marta Carroni
wiley   +1 more source

Simplified Neural Unsupervised Domain Adaptation

, 2019
Unsupervised domain adaptation (UDA) is the task of modifying a statistical model trained on labeled data from a source domain to achieve better performance on data from a target domain, with access to only unlabeled data in the target domain.
Miller, Timothy A
core  

Style Adaptation for Domain-Adaptive Semantic Segmentation

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Unsupervised Domain Adaptation (UDA) refers to the method that utilizes annotated source domain data and unlabeled target domain data to train a model capable of generalizing to the target domain data. Domain discrepancy leads to a significant decrease in the performance of general network models trained on the source domain data when applied to the ...
Li, Ting, Chao, Jianshu, An, Deyu
openaire   +2 more sources