Results 81 to 90 of about 502,069 (324)
Diversity and complexity in neural organoids
FEBS Letters, EarlyView.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
Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source
Frontiers in Applied Mathematics and StatisticsTemporal text data, such as news articles or Twitter feeds, often comprises a mixture of long-lasting trends and transient topics. Effective topic modeling strategies should detect both types and clearly locate them in time.
Lara Kassab +6 more
doaj +1 more source
Hierarchical Sparse Coding of Objects in Deep Convolutional Neural Networks
Frontiers in Computational Neuroscience, 2020 Recently, deep convolutional neural networks (DCNNs) have attained human-level performances on challenging object recognition tasks owing to their complex internal representation.
Xingyu Liu, Zonglei Zhen, Jia Liu
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, 2014 Cointegration analysis is used to estimate the long-run equilibrium relations between several time series. The coefficients of these long-run equilibrium relations are the cointegrating vectors. In this paper, we provide a sparse estimator of the cointegrating vectors.
Ines Wilms, Christophe Croux
openaire +2 more sources
Investigating transcription factor dynamics in health and disease using FRAP
FEBS Letters, EarlyView.FRAP analysis of GFP‐tagged transcription factors reveals how molecular mobility and target engagement change in response to drug treatment. By combining live‐cell imaging, quantitative model fitting, and statistical analysis, this approach uncovers transcription factor dynamics linked to disease mechanisms, providing a powerful framework for ...
Kannan Govindaraj +3 more
wiley +1 more source
Semantic Mapping for Lexical Sparseness Reduction in Parsing [PDF]
, 2013 Bilexical information is known to be helpful in parse disambiguation, but the benefit is limited because of lexical sparseness. An approach us- ing word classes can reduce sparseness and po- tentially leads to more accurate parsing.
van Noord, Gertjan +2 more
core +4 more sources
Crossings, 2015 Samuel Beckett's best-known work begins with the following stage direction: A country road. A tree. Evening.
Joseph Brooker
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Hippocampal remapping is constrained by sparseness rather than capacity. [PDF]
PLoS Computational Biology, 2014 Grid cells in the medial entorhinal cortex encode space with firing fields that are arranged on the nodes of spatial hexagonal lattices. Potential candidates to read out the space information of this grid code and to combine it with other sensory cues ...
Axel Kammerer, Christian Leibold
doaj +1 more source
Sparse Image Reconstruction using Sparse Priors [PDF]
2006 International Conference on Image Processing, 2006 Sparse image reconstruction is of interest in the fields of radioastronomy and molecular imaging. The observation is assumed to be a linear transformation of the image, and corrupted by additive white Gaussian noise. We study the usage of sparse priors in the empirical Bayes framework: it permits the selection of the hyperparameters of the prior in a ...
Michael Ting +2 more
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