Results 61 to 70 of about 15,646 (263)

Derivation and characterization of retinal pigment epithelium from urine‐derived iPSCs

FEBS Open Bio, EarlyView.
Age‐related macular degeneration causes vision loss via RPE dysfunction and loss. Traditional iPSC therapies rely on invasive biopsies, limiting scalability. Here, we utilize urine‐derived stem cells as an accessible source to generate u‐iPSCs, successfully differentiated into pigmented RPE. This “Urine‐to‐Retina” platform provides a promising path for
Daniella Beiner, Hainan Zhu, Carol Christine Bosholm, Heuy‐Ching Wang, Tracy Criswell, Anthony Atala, Jian‐Xing Ma, Yuanyuan Zhang   +7 more
wiley   +1 more source

Quantum deep learning by sampling neural nets with a quantum annealer. [PDF]

Sci Rep, 2023
Higham CF, Bedford A.
europepmc   +1 more source

Differential Bayesian Neural Nets

CoRR, 2019
Neural Ordinary Differential Equations (N-ODEs) are a powerful building block for learning systems, which extend residual networks to a continuous-time dynamical system. We propose a Bayesian version of N-ODEs that enables well-calibrated quantification of prediction uncertainty, while maintaining the expressive power of their deterministic counterpart.
Andreas Look, Melih Kandemir
openaire   +2 more sources

Acute caffeine treatment protects the developing retina from ischemia‐induced cell death

FEBS Open Bio, EarlyView.
Caffeine reduces cell death in the developing retina under ischemia (OGD). This effect does not involve BDNF upregulation or antioxidant pathways (NRF2/VEGF). Neuroprotection occurs mainly through adenosine A2A receptor antagonism, decreasing glutamate release and excitotoxicity, highlighting caffeine's potential as an acute neuroprotective agent in ...
Amanda Alves Nascimento, Danniel Pereira‐Figueiredo, Gabriella Grossi de Lima Dias, Gabriel Ferreira dos Santos, Mariana Rodrigues Pereira, Rafael Brito, Karin C Calaza   +6 more
wiley   +1 more source

Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models. [PDF]

Int J Mol Sci, 2023
Yue T, Wang Y, Zhang L, Gu C, Xue H, Wang W, Lyu Q, Dun Y.   +7 more
europepmc   +1 more source

One size does not fit all: An in vitro evaluation of the effects of bezafibrate and medroxyprogesterone acetate on human SH‐SY5Y and U‐87 MG cancer cells

FEBS Open Bio, EarlyView.
Drugs previously repurposed to target blood cancers reduced neuroblastoma and glioblastoma cell growth and viability. However, their levels of anticancer activity were different and their clinical application may be problematic due to side effects at effective doses.
Abhishek Kharawatkar, Francesco Michelangeli, Farhat Latif Khanim, Christopher Martin Bunce, William Eustace Basil Johnson   +4 more
wiley   +1 more source

Directed evolution of enzymes at the crossroads of tradition and innovation

FEBS Open Bio, EarlyView.
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova, Andrej Mirossay, Erik Sedlak   +2 more
wiley   +1 more source

Effects of Biological Adhesion on the Hydrodynamic Characteristics of Different Panel Net Materials: A BP Neural Network Approach

Journal of Marine Science and Engineering
Biofouling is a serious problem in marine aquaculture facilities, exerting several negative effects on cage structures. In this study, different materials of nets were placed in the Fujian Sea area of China, and the main biological adhesion species were ...
Yongli Liu, Wei Liu, Lei Wang, Minghua Min, Lei Li, Liang Wang, Shuo Ma   +6 more
doaj   +1 more source

Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. [PDF]

Sci Rep, 2023
Sharma B, Chenthamarakshan V, Dhurandhar A, Pereira S, Hendler JA, Dordick JS, Das P.   +6 more
europepmc   +1 more source

Artificial neural net attractors [PDF]

Computers & Graphics, 1998
Abstract Aesthetically appealing patterns are produced by the dynamical behavior of artificial neural networks with randomly chosen connection strengths. These feed-forward networks have a single hidden layer of neurons and a single output, which is fed back to the input to produce a scalar time series that is always bounded and often chaotic. Sample
openaire   +1 more source