Results 81 to 90 of about 11,928,939 (371)

The power of microRNA regulation—insights into immunity and metabolism

FEBS Letters, EarlyView.
MicroRNAs are emerging as crucial regulators at the intersection of metabolism and immunity. This review examines how miRNAs coordinate glucose and lipid metabolism while simultaneously modulating T‐cell development and immune responses. Moreover, it highlights how cutting‐edge artificial intelligence applications can identify miRNA biomarkers ...
Stefania Oliveto, Nicola Manfrini, Stefano Biffo   +2 more
wiley   +1 more source

miRNA‐29 regulates epidermal and mesenchymal functions in skin repair

FEBS Letters, EarlyView.
miRNA‐29 inhibits cell‐to‐cell and cell‐to‐matrix adhesion by silencing mRNA targets. Adhesion is controlled by complex interactions between many types of molecules coded by mRNAs. This is crucial for keeping together the layers of the skin and for regenerating the skin after wounding.
Lalitha Thiagarajan, Rosa Sanchez‐Alvarez, Chiho Kambara, Poojitha Rajasekar, Yuluang Wang, François Halloy, Jonathan Hall, Hans‐Jürgen Stark, Iris Martin, Petra Boukamp, Svitlana Kurinna   +10 more
wiley   +1 more source

Corrigendum: Do we need to reframe risk once again?

Jàmbá
No abstract available.
Ian Christoplos, John Mitchell
doaj   +1 more source

Physical education, muscle strengthening exercise, sport participation and their associations with screen time in adolescents

Frontiers in Public Health, 2023
Background/ObjectivePhysical activity (PA) has been suggested to reduce screen time. This study aimed to explore the associations of physical education (PE), muscle-strengthening exercise (MSE), and sport participation with screen time.MethodsA multi ...
Xiaoqing Hu, Clemens Drenowatz, Michael Duncan, Ran Bao, Ran Bao, Sitong Chen, Jinsheng He, Yan Tang   +7 more
doaj   +1 more source

Active learning enabled activity recognition [PDF]

Pervasive and Mobile Computing, 2016
© 2016 IEEE, 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom)
Abdullah Al Hafiz Khan, H M Sajjad Hossain, Nirmalya Roy   +2 more
openaire   +2 more sources

A cellular system to study responses to a collision between the transcription complex and a protein‐bound nick in the DNA template

FEBS Letters, EarlyView.
We present the cellular transcription‐coupled Flp‐nick system allowing the introduction of a Top1‐mimicking cleavage complex (Flpcc) at a Flp recognition target site within a controllable LacZ gene. LacZ transcription leads to the collision of RNA polymerase II (RNAPII) with Flpcc, and this causes RNAPII stalling, ubiquitination, and degradation.
Petra Herring, Morten Roedgaard, Camilla Myrup Holst, Helene Christensen, Birgitta R. Knudsen, Lotte Bjergbaek, Anni Hangaard Andersen   +6 more
wiley   +1 more source

Active Learning Based on Transfer Learning Techniques for Text Classification

IEEE Access, 2023
Text preprocessing is a common task in machine learning applications that involves hand-labeling sets. Although automatic and semi-automatic annotation of text data is a growing field, researchers need to develop models that use resources as efficiently ...
Daniela Onita
doaj   +1 more source

DeepAL: Deep Active Learning in Python [PDF]

arXiv, 2021
We present DeepAL, a Python library that implements several common strategies for active learning, with a particular emphasis on deep active learning. DeepAL provides a simple and unified framework based on PyTorch that allows users to easily load custom datasets, build custom data handlers, and design custom strategies without much modification of ...
arxiv  

Pengaruh Pembelajaran Active Learning Ttq terhadap Hasil Belajar Siswa pada Mata Pelajaran Pengetahuan Produk [PDF]

, 2017
The problem in this research is the low of learning outcomes and student attention on the subjects of product knowledge class XI PM in SMKN 3 Pontianak.
Sumarni, S. (Sumarni)
core  

Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide

npj Computational Materials, 2020
We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP).
G. Sivaraman, A. Krishnamoorthy, Matthias Baur, Christian Holm, M. Stan, Gábor Csányi, C. Benmore, Á. Vázquez-Mayagoitia   +7 more
semanticscholar   +1 more source