Results 61 to 70 of about 580,652 (288)

Loss of proton‐sensing TDAG8 increases tumor progression in mouse models of colon cancer

open access: yesMolecular Oncology, EarlyView.
Loss of the pH‐sensing receptor TDAG8 accelerates colorectal cancer progression in mice. Animals lacking TDAG8 expression had increased tumor growth, DNA damage, and recruitment of tumor‐associated immune cells, including macrophages, neutrophils, and monocytes.
Ermanno Malagola   +11 more
wiley   +1 more source

Mirror Descent and Exponentiated Gradient Algorithms Using Trace-Form Entropies

open access: yesEntropy
This paper introduces a broad class of Mirror Descent (MD) and Generalized Exponentiated Gradient (GEG) algorithms derived from trace-form entropies defined via deformed logarithms. Leveraging these generalized entropies yields MD and GEG algorithms with
Andrzej Cichocki   +3 more
doaj   +1 more source

Application of Denaturing Gradient Gel ElectrophoresisApplication of Denaturing Gradient Gel Electrophoresis(DGGE) Methods on Parent-Offspring Relationship of the Coral Pocillopora damicornis [PDF]

open access: yes, 2008
DGGE (Denaturing Gradient Gel Electrophoresis) is the most powerful methods for mutation detection currentlyavailable. In DGGE, DNA fragments of the same length but with different sequences can be separated.
Permata W, Diah, Hirose, M, Hidaka , M
core  

Exon 7 splicing of ERα predicts poor prognosis and increases phenotypic heterogeneity in luminal a subtype breast cancer

open access: yesFEBS Open Bio, EarlyView.
ERα splice variant ERα∆7 lacks the C‐terminus, and its expression may change phenotypes of breast cancers. Our results showed that ERα∆7 is found in the luminal A subtype, and elevated ERα∆7 levels are linked to improved cell survival with lower proliferation and migration.
Long Wai Tsui   +10 more
wiley   +1 more source

Natural gradient algorithm for cyclostationary sources

open access: yes, 2020
A new approach to blind source separation of cyclostationary sources is introduced which incorporates a cyclic pre-whitening operation within the learning rule, and thereby provides a new member of the family of natural gradient algorithms. The technique
Jafari, MG, Chambers, JA, Mandic, DP
core   +2 more sources

Improving Flat Maxima with Natural Gradient for Better Adversarial Transferability

open access: yesBig Data and Cognitive Computing
Deep neural networks are vulnerable and susceptible to adversarial examples, which can induce erroneous predictions by injecting imperceptible perturbations.
Yunfei Long, Huosheng Xu
doaj   +1 more source

Stochastic search using the natural gradient [PDF]

open access: yesProceedings of the 26th Annual International Conference on Machine Learning, 2009
To optimize unknown 'fitness' functions, we present Natural Evolution Strategies, a novel algorithm that constitutes a principled alternative to standard stochastic search methods. It maintains a multinormal distribution on the set of solution candidates.
Yi Sun 0003   +3 more
openaire   +1 more source

Screening and epitope characterization of Nidogen‐2‐specific nanobodies

open access: yesFEBS Open Bio, EarlyView.
Camel immunization and phage display were employed to generate high‐affinity VHH nanobodies against Nidogen‐2. After library construction, biopanning, ELISA screening, sequencing, and recombinant expression, selected nanobodies were purified and characterized, leading to the preliminary exploration of a nanobody‐based sandwich ELISA for specific ...
Jianchuan Wen   +9 more
wiley   +1 more source

Ancient Living Organisms Escaping from, or Imprisoned in, the Vents?

open access: yesLife, 2017
We have recently criticised the natural pH gradient hypothesis which purports to explain how the difference in pH between fluid issuing from ancient alkali vents and the more acidic Hadean ocean could have driven molecular machines that catalyse ...
J. Baz Jackson
doaj   +1 more source

Natural gradient descent with momentum

open access: yesCoRR
We consider the problem of approximating a function by an element of a nonlinear manifold which admits a differentiable parametrization, typical examples being neural networks with differentiable activation functions or tensor networks. Natural gradient descent (NGD) for the optimization of a loss function can be seen as a preconditioned gradient ...
Anthony Nouy, Agustín Somacal
openaire   +2 more sources

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