Results 131 to 140 of about 559,125 (302)

RoundMi: A quantitative method to analyze mitochondrial morphology in mitotic cells

FEBS Open Bio, EarlyView.
RoundMi is a workflow for rapid analysis of mitochondrial morphology in mitotic cells. By combining adaptive preprocessing with automated segmentation and quantification, it enables accurate measurements from single focal plane images, reducing acquisition time and computational demands while remaining compatible with high‐throughput fixed and live ...
Elmira Parvindokht Bararpour, Christine Volmert, Valentina Piano   +2 more
wiley   +1 more source

An Improved K-Means Algorithm Based on Contour Similarity

Mathematics
The traditional k-means algorithm is widely used in large-scale data clustering because of its easy implementation and efficient process, but it also suffers from the disadvantages of local optimality and poor robustness.
Jing Zhao, Yanke Bao, Dongsheng Li, Xinguo Guan   +3 more
doaj   +1 more source

Retracted: Blockchain and K-Means Algorithm for Edge AI Computing. [PDF]

Comput Intell Neurosci, 2023
Intelligence And Neuroscience C.
europepmc   +1 more source

$Ibk$-means: An iterative batch $k$-means algorithm for big data clustering [PDF]

summary:Information technologies such as social media, mobile computing, and the realization of the industrial Internet of Things (IoT) produce huge amounts of data every day.
Alguliyev, Rasim, Aliyev, Mustafa, Aliguliyev, Ramiz, Bagirov, Adil M.   +3 more
core   +1 more source

Molecular characterization of covRS mutations in M1UK Streptococcus pyogenes

FEBS Open Bio, EarlyView.
Group A Streptococcus (GAS) acquires covRS mutations driving a hypervirulent bacterial state, frequently associated with invasive disease‐like necrotizing fasciitis. We demonstrate that the newly emerged M1UK GAS lineage can also acquire these mutations.
Jarrad Pritchard, Emma‐Jayne Proctor, Jiawa Wang, Nikolas P. Johnston, Andrew Hayes, Georgia McCorkell, Haibo Yu, Jai Tree, Mark R. Davies, Mark J. Walker, Ronald Sluyter, Stephan Brouwer, Martina L. Sanderson‐Smith   +12 more
wiley   +1 more source

A K-means-like algorithm for informetric data clustering

, 2015
The K-means algorithm is one of the most often used clustering techniques. However, when it comes to discovering clusters in informetric data sets that consist of non-increasingly ordered vectors of not necessarily conforming lengths, such a method ...
Anna Cena (13497691), Marek Gagolewski (13083165)   +1 more
core  

K-means cluster analysis.

, 2018
K-means clustering of differentially expressed genes changing over time (DEGots) from E6.5 to E17.5. The 5405 DEGots are clustered by K-means algorithm. n: number of genes in each cluster.
Joris A. van der Post (5775086), Gijs Afink (535618), Carrie Ris-Stalpers (433356), Souad Boussata (5775083), Aldo Jongejan (793996), Febilla Fernando (3501119)   +5 more
core   +1 more source

Transcripts enriched in codons that trigger P‐site tRNA‐mediated mRNA decay possess stable mRNA

FEBS Open Bio, EarlyView.
PTMD codons were first described by Mendel et al. as mediators of an mRNA decay pathway dependent on the human protein CNOT3, homologous to yeast Not5. Our findings confirm that PTMD codons destabilize transcripts; however, unlike in yeast, the human pathway specifically targets and slightly destabilizes primarily stable mRNAs.
Rodolfo Lopes Carneiro, Fernando Lucas Palhano   +1 more
wiley   +1 more source

Artificial Intelligence and Mental Well‐Being in Adult Education: Implications for Practice and Professional Responsibility

New Directions for Adult and Continuing Education, EarlyView.
ABSTRACT Mental well‐being is central to adult learner success, yet many adult education institutions lack capacity to provide timely and accessible support. This article examines how artificial intelligence (AI) can strengthen mental health–adjacent supports in adult and continuing higher education, with attention to professional practice and ...
Adam L. McClain, Thomas Wade
wiley   +1 more source

A New Clustering Algorithm Partition K-Means

, 2011
K-means is a classic algorithm of partition clustering, its speed is very fast, well, the clustering result is very sensitive to the initial cores. As a result, algorithm K-means does not always get the Global Optimization.
Chan Juan Yin, Yu Ping Xi, Min Chen
core   +1 more source