Results 261 to 270 of about 116,877 (316)

Hyperosmotic stress‐induced redistribution of pre‐mRNA cleavage factor I subunits is associated with shifts in alternative polyadenylation

FEBS Open Bio, EarlyView.
Hyperosmotic stress triggers the relocation of the CFIm complex from the nucleus to the cytoplasm. This shift creates a nuclear ‘stoichiometric bottleneck’, limiting CFIm availability for mRNA processing. Consequently, specific mRNAs like NUDT21 and DICER1 undergo targeted 3′UTR shortening, demonstrating how spatial protein dynamics drive rapid ...
Hitomi Soumiya, Masatake Osawa, Hidefumi Fukumitsu   +2 more
wiley   +1 more source

Loss of AMBRA1 activates MAPK and angiogenesis signaling pathways in melanoma cells

FEBS Open Bio, EarlyView.
Loss of AMBRA1 in melanoma cells activates multiple oncogenic pathways associated with tumor progression. Transcriptomic and protein network analyses revealed that AMBRA1 depletion enhances MAPK/ERK signaling, angiogenesis, TGF‐β/EMT signaling, and Wnt/axon guidance pathways.
Milad Ibrahim, Marco Corazzari, Iman Osman, Jane Armstrong, Noel Carter   +4 more
wiley   +1 more source

Effects of IGFBP4 deficiency on human preadipocyte proliferation and differentiation through the IGF1R/AKT pathway

FEBS Open Bio, EarlyView.
IGFBP4 knockdown (KD) impairs preadipocyte proliferation and is associated with IGF1R protein downregulation and attenuated AKT phosphorylation. The mechanisms by which IGFBP4 KD influences the IGF1R/AKT signaling pathway involve newly synthesized proteins and lysosomal degradation pathways. Created in BioRender.
Yujia Guo, Sandy Richter, Christoph Bach, Wieland Kiess, Anna Kirstein, Diana Le Duc, Antje Garten   +6 more
wiley   +1 more source

Pathways and pitfalls: a qualitative study of student experiences in biomedical science education

FEBS Open Bio, EarlyView.
Biomedical science students from underrepresented backgrounds face barriers including financial strain, disrupted laboratory access and cultural exclusion. Peer networks provide vital support when institutional systems are difficult to navigate. To create inclusive learning environments and achieve academic success, educators should blend active, hands‐
Olivia J. Russell, Cassy Ross, Panagiota S. Filippou, Bruno Silvester Lopes, Dominic Gilchrist, Paul Dean, Kamar Ameen‐Ali, Tora K. Smulders‐Srinivasan, Ahmad A. Khundakar   +8 more
wiley   +1 more source

Parallel Genetic Programming

, 1996
A parallel implementation of Genetic Programming using PVM is described. Two different topologies for parallel implementation of GP are examined. Both of them are based on the island model for evolutionary algorithms. It is shown that considerable speedup of the GP execution can be achieved and that the parallel versions of the algorithm are very ...
Dimitris C. Dracopoulos, Duncan Self
openaire   +2 more sources

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Genetic programming

IEEE Intelligent Systems, 2000
Wolfgang Banzhaf, J R Koza
exaly   +2 more sources

Genetic programming with genetic regulatory networks

Proceedings of the 15th annual conference on Genetic and evolutionary computation, 2013
Evolutionary Algorithms (EA) approach differently from nature the genotype - phenotype relationship, and this view is a recurrent issue among researchers. Recently, some researchers have started exploring computationally the new comprehension of the multitude of regulatory mechanisms that are fundamental in both processes of inheritance and of ...
Rui L. Lopes, Ernesto Costa
openaire   +1 more source

Semantic genetic programming

Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2015
Semantic genetic programming is a recent, rapidly growing trend in Genetic Programming (GP) that aims at opening the 'black box' of the evaluation function and make explicit use of more information on program behavior in the search. In the most common scenario of evaluating a GP program on a set of input-output examples (fitness cases), the semantic ...
Alberto Moraglio, Krzysztof Krawiec
openaire   +1 more source

Enzyme genetic programming

Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), 2002
Programming is a process of optimization; taking a specification, which tells us what we want, and transforming it into an implementation, a program, which causes the target system to do exactly what we want. Conventionally, this optimization is achieved through manual design.
Michael A. Lones, Andy M. Tyrrell
openaire   +1 more source

Parametric Genetic Programming

2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems (SCIS-ISIS), 2020
In conventional Genetic Programming for regression problems, the whole population evolves to adapt to the training data. However, individuals cannot adjust themselves to provided data, which results in slow convergence. On the other hand, gradient-based methods such as gradient descent quickly converge toward the solution by using local gradient ...
Adam Kotaro Pindur, Takahiro Horiba, Hitoshi Iba   +2 more
openaire   +1 more source