Results 71 to 80 of about 244,425 (248)
Diversity and complexity in neural organoids
Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley +1 more source
In this work, considering the advantages of spectral conjugate gradient method and quasi-Newton method, a spectral three-term conjugate gradient method with random parameter is proposed.
Guoling Zhou, Yueting Yang, Mingyuan Cao
doaj +1 more source
Potential therapeutic targeting of BKCa channels in glioblastoma treatment
This review summarizes current insights into the role of BKCa and mitoBKCa channels in glioblastoma biology, their potential classification as oncochannels, and the emerging pharmacological strategies targeting these channels, emphasizing the translational challenges in developing BKCa‐directed therapies for glioblastoma treatment.
Kamila Maliszewska‐Olejniczak +4 more
wiley +1 more source
Convergence of Unregularized Online Learning Algorithms
In this paper we study the convergence of online gradient descent algorithms in reproducing kernel Hilbert spaces (RKHSs) without regularization. We establish a sufficient condition and a necessary condition for the convergence of excess generalization ...
Guo, Zheng-Chu, Lei, Yunwen, Shi, Lei
core +1 more source
LDAcoop: Integrating non‐linear population dynamics into the analysis of clonogenic growth in vitro
Limiting dilution assays (LDAs) quantify clonogenic growth by seeding serial dilutions of cells and scoring wells for colony formation. The fraction of negative wells is plotted against cells seeded and analyzed using the non‐linear modeling of LDAcoop.
Nikko Brix +13 more
wiley +1 more source
A Hybrid of DL and WYL Nonlinear Conjugate Gradient Methods
The conjugate gradient method is an efficient method for solving large-scale nonlinear optimization problems. In this paper, we propose a nonlinear conjugate gradient method which can be considered as a hybrid of DL and WYL conjugate gradient methods ...
Shengwei Yao, Bin Qin
doaj +1 more source
Accelerated Methods for $\alpha$-Weakly-Quasi-Convex Problems
Many problems encountered in training neural networks are non-convex. However, some of them satisfy conditions weaker than convexity, but which are still sufficient to guarantee the convergence of some first-order methods.
Gasnikov, Alexander, Guminov, Sergey
core
Single circulating tumor cells (sCTCs) from high‐grade serous ovarian cancer patients were enriched, imaged, and genomically profiled using WGA and NGS at different time points during treatment. sCTCs revealed enrichment of alterations in Chromosomes 2, 7, and 12 as well as persistent or emerging oncogenic CNAs, supporting sCTC identity.
Carolin Salmon +9 more
wiley +1 more source
Aptamers are used both therapeutically and as targeting agents in cancer treatment. We developed an aptamer‐targeted PLGA–TRAIL nanosystem that exhibited superior therapeutic efficacy in NOD/SCID breast cancer models. This nanosystem represents a novel biotechnological drug candidate for suppressing resistance development in breast cancer.
Gulen Melike Demirbolat +8 more
wiley +1 more source
The new spectral conjugate gradient method for large-scale unconstrained optimisation
The spectral conjugate gradient methods are very interesting and have been proved to be effective for strictly convex quadratic minimisation. In this paper, a new spectral conjugate gradient method is proposed to solve large-scale unconstrained ...
Li Wang +3 more
doaj +1 more source

