Results 61 to 70 of about 1,569,447 (281)

Mapping the evolution of mitochondrial complex I through structural variation

FEBS Letters, EarlyView.
Respiratory complex I (CI) is crucial for bioenergetic metabolism in many prokaryotes and eukaryotes. It is composed of a conserved set of core subunits and additional accessory subunits that vary depending on the organism. Here, we categorize CI subunits from available structures to map the evolution of CI across eukaryotes. Respiratory complex I (CI)
Dong‐Woo Shin, Tingting Chen, James A. Letts   +2 more
wiley   +1 more source

TCS-FEEL: Topology-Optimized Federated Edge Learning with Client Selection

Sensors
Federated learning (FL) enables distributed model training across sensor-equipped edge devices while preserving data privacy. However, its performance is often hindered by statistical heterogeneity among clients and system heterogeneity in dynamic ...
Hui Chen, He Li
doaj   +1 more source

Adaptive resource optimization mechanism for blockchain sharding in digital twin edge network

Tongxin xuebao, 2023
To address the security and privacy issues for data sharing in digital twin edge networks, a new distributed and secure data sharing mechanism was developed by leveraging blockchain sharding.Considering dynamic and time varying features in digital twin ...
Li JIANG, Shengli XIE, Hui TIAN
doaj   +2 more sources

An upstream open reading frame regulates expression of the mitochondrial protein Slm35 and mitophagy flux

FEBS Letters, EarlyView.
This study reveals how the mitochondrial protein Slm35 is regulated in Saccharomyces cerevisiae. The authors identify stress‐responsive DNA elements and two upstream open reading frames (uORFs) in the 5′ untranslated region of SLM35. One uORF restricts translation, and its mutation increases Slm35 protein levels and mitophagy.
Hernán Romo‐Casanueva, Ariann E. Mendoza‐Martínez, P. Abril Medina‐Flores, Carlos Campero‐Basaldua, Alexander DeLuna, Soledad Funes   +5 more
wiley   +1 more source

Heterogeneity-aware device selection for efficient federated edge learning

International Journal of Intelligent Networks
Federated learning (FL) combined with mobile edge computing (FEEL) provides an end-to-edge synergetic learning approach to allow end devices to participate in machine learning model training parallelly while ensuring user privacy is maintained.
Yiran Shi, Jieyan Nie, Xingwei Li, Hui Li   +3 more
doaj   +1 more source

Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services

Applied Sciences, 2018
Smart cities aim at integrating various IoT (Internet of Things) technologies by providing many opportunities for the development, governance, and management of user services.
Hind Bangui, Said Rakrak, Said Raghay, Barbora Buhnova   +3 more
doaj   +1 more source

Dynamic Data Sample Selection and Scheduling in Edge Federated Learning

IEEE Open Journal of the Communications Society, 2023
Federated Learning (FL) is a state-of-the-art paradigm used in Edge Computing (EC). It enables distributed learning to train on cross-device data, achieving efficient performance, and ensuring data privacy. In the era of Big Data, the Internet of Things (
Mohamed Adel Serhani, Haftay Gebreslasie Abreha, Asadullah Tariq, Mohammad Hayajneh, Yang Xu, Kadhim Hayawi   +5 more
doaj   +1 more source

Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks [PDF]

, 2014
Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity.
Bender, Steven C., Godoy, Sebastian E., Hayat, Majeed M., Jang, Woo-Yong, Krishna, Sanjay, Paskaleva, Biliana S.   +5 more
core   +1 more source

Design of Non-Binary Quasi-Cyclic LDPC Codes by ACE Optimization

, 2013
An algorithm for constructing Tanner graphs of non-binary irregular quasi-cyclic LDPC codes is introduced. It employs a new method for selection of edge labels allowing control over the code's non-binary ACE spectrum and resulting in low error-floor. The
Bazarsky, Alex, Litsyn, Simon, Presman, Noam   +2 more
core   +1 more source

Cell wall target fragment discovery using a low‐cost, minimal fragment library

FEBS Letters, EarlyView.
LoCoFrag100 is a fragment library made up of 100 different compounds. Similarity between the fragments is minimized and 10 different fragments are mixed into a single cocktail, which is soaked to protein crystals. These crystals are analysed by X‐ray crystallography, revealing the binding modes of the bound fragment ligands.
Kaizhou Yan, Mathew Stanley, Olawale Raimi, Andrew T. Ferenbach, Helge C Dorfmueller, Daan M. F. van Aalten   +5 more
wiley   +1 more source