The steady improvement in the performance of computing systems seen for many decades is levelling off as the miniaturization of semiconducting technology approaches the atomic limit, facing severe physical and technological issues. Neuromorphic compu...
We present a biologically motivated model for visual self-localization which extracts a spatial representation of the environment directly from high dimensional image data by employing a single unsupervised learning rule. The resulting representation...
The RAW264.7 cell model was employed to screen immunomodulatory selenium-containing peptides from selenium-enriched rice protein hydrolysates (SPHs). Moreover, the selenium-containing peptides of high-activity protein hydrolysates were purified by Se...
Unbiased estimates of neuron numbers within substantia nigra are crucial for experimental Parkinson's disease models and gene-function studies. Unbiased stereological counting techniques with optical fractionation are successfully implemented, but ar...
Obesity is increasingly prevalent and associated with increased risk of developing type 2 diabetes, cardiovascular diseases, and cancer. Magnetic resonance imaging (MRI) is an accurate method for determination of body fat volume and distribution. How...
Modern computational approaches and machine learning techniques accelerate the invention of new drugs. Generative models can discover novel molecular structures within hours, while conventional drug discovery pipelines require months of work. In this...
BACKGROUND: Quantitative traits or continuous outcomes related to complex diseases can provide more information and therefore more accurate analysis for identifying gene-gene and gene- environment interactions associated with complex diseases. Multif...
PURPOSE: We sought to assess whether machine learning-based classification approaches can improve the classification of pancreatic tumor models relative to more simplistic analysis methods, using T relaxation, CEST, and DCE MRI.
The capacity to learn abstract concepts such as 'sameness' and 'difference' is considered a higher-order cognitive function, typically thought to be dependent on top-down neocortical processing. It is therefore surprising that honey bees apparantly h...
This study develops a reliable radial basis function neural networks (RBFNNs) to estimate freshness for tilapia fillets stored under non-isothermal conditions by using optimal wavelengths from hyperspectral imaging (HSI). The results show that, for t...
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