Results 121 to 130 of about 5,397,718 (323)

Tau acetylation at K331 has limited impact on tau pathology in vivo

FEBS Letters, EarlyView.
We mapped tau post‐translational modifications in humanized MAPT knock‐in mice and in amyloid‐bearing double knock‐in mice. Acetylation within the repeat domain, particularly around K331, showed modest increases under amyloid pathology. To test functional relevance, we generated MAPTK331Q knock‐in mice.
Shoko Hashimoto, Yukio Matsuba, Mika Takahashi, Takaomi C. Saido   +3 more
wiley   +1 more source

Active Learning for Data Streams under Concept Drift and concept evolution. [PDF]

, 2016
Data streams classification is an important problem however, poses many challenges. Since the length of the data is theoretically infinite, it is impractical to store and process all the historical data.
Bouchachia, Abdelhamid, Mohamad, S., Sayed-Mouchaweh, M.   +2 more
core  

Calpain small subunit homodimerization is robust and calcium‐independent

FEBS Letters, EarlyView.
Calpains dimerize via penta‐EF‐hand (PEF) domains. Using single‐molecule force spectroscopy, we measured the strength and kinetics of PEF–PEF homodimer binding. The interaction is robust, shows a transient conformational step before dissociation, and remains largely insensitive to Ca2+.
Nesha May O. Andoy, Trina Dykstra‐MacPherson, Mathias A. Bell, Peter L. Davies, Ruby May A. Sullan   +4 more
wiley   +1 more source

Dynamic domain analysis for predicting concept drift in engineering AI-enabled software

Journal of Data and Information Science
This research addresses the challenge of concept drift in AI-enabled software, particularly within autonomous vehicle systems where concept drift in object recognition (like pedestrian detection) can lead to misclassifications and safety risks.
Shahzad Murtuza, Barzamini Hamed, Wilson Joseph, Alhoori Hamed, Rahimi Mona   +4 more
doaj   +1 more source

Counterfactual Explanations of Concept Drift

, 2020
The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time; as a consequence machine learning models may become inaccurate and need adjustment. While there do exist methods to detect concept drift or to adjust models in the presence of observed drift, the question of explaining ...
Hinder, Fabian, Hammer, Barbara
openaire   +2 more sources

Structural insights into an engineered feruloyl esterase with improved MHET degrading properties

FEBS Letters, EarlyView.
A feruloyl esterase was engineered to mimic key features of MHETase, enhancing the degradation of PET oligomers. Structural and computational analysis reveal how a point mutation stabilizes the active site and reshapes the binding cleft, expading substrate scope.
Panagiota Karampa, Konstantinos Makryniotis, Theofani‐Iosifina Sousani, Evangelos Topakas, Vangelis Daskalakis, Maria Dimarogona   +5 more
wiley   +1 more source

Concept Drift Detection Based on Typicality and Eccentricity

IEEE Access
Many applications and fields produce a vast quantity of time-relevant or continuously changing data which may represent new phenomena. This data stream behavior is known as Concept Drift. The need to efficiently and accurately process online data streams
Yuri Thomas P. Nunes, Luiz Affonso Guedes   +1 more
doaj   +1 more source

Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system

FEBS Letters, EarlyView.
Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley   +1 more source

DRIFTNET-EnVACK: Adaptive Drift Detection in Cloud Data Streams With Ensemble Variational Auto-Encoder Featuring Contextual Network

IEEE Access
The adoption of cloud computing has been increasingly common across several industries in recent years and offers unparalleled flexibility and scalability in managing computational resources.
Tajwar Mehmood, Seemab Latif, Rabia Latif, Hammad Majeed, Asad Waqar Malik   +4 more
doaj   +1 more source

Tiny Machine Learning for Concept Drift

IEEE Transactions on Neural Networks and Learning Systems
Tiny Machine Learning (TML) is a new research area whose goal is to design machine and deep learning techniques able to operate in Embedded Systems and IoT units, hence satisfying the severe technological constraints on memory, computation, and energy characterizing these pervasive devices.
Simone Disabato, Manuel Roveri
openaire   +4 more sources