Biology subjects

Calderini, M.

Publications and source records attributed to Calderini, M..

2 recordsLinked to original sources

bioRxiv2024

Accurate non-invasive quantification of astaxanthin content using hyperspectral images and machine learning

Commercial cultivation of the microalgae Haematococcus pluvialis to produce natural astaxanthin has gained significant traction due to the high antioxidant capacity of this pigment and its application in foods, feed, cosmetics and nutraceuticals. However, monitoring of astaxanthin content in cultures remains challenging and relies on invasive, time consuming and expensive approaches. In this study, we employed reflectance hyperspectral imaging (HSI) of H. pluvialis suspensions within the visible spectrum, combined with a 1-dimensional convolutional neural network (CNN) to predict the astaxanthin content (g mg-1) as quantified by high-performance liquid chromatography (HPLC). This approach had low average prediction error (5.9%) across a gradient of astaxanthin contents and was only unreliable at very low contents (<0.6 g mg-1). In addition, our machine learning model outperformed single or dual wavelength linear regression models even when the spectral data was obtained with a spectrophotometer coupled with an integrating sphere. Overall, this study proposes the use of HSI in combination with a CNN for precise non-invasive quantification of astaxanthin in cell suspensions.

bioinformatics↗

bioRxiv2019

Learning long temporal sequences in spiking networks by multiplexing neural oscillations

Many cognitive and behavioral tasks - such as interval timing, spatial navigation, motor control and speech - require the execution of precisely-timed sequences of neural activation that cannot be fully explained by a succession of external stimuli. We show how repeatable and reliable patterns of spatiotemporal activity can be generated in chaotic and noisy spiking recurrent neural networks. We propose a general solution for networks to autonomously produce rich patterns of activity by providing a multi-periodic oscillatory signal as input. We show that the model accurately learns a variety of tasks, including speech generation, motor control and spatial navigation. Further, the model performs temporal rescaling of natural spoken words and exhibits sequential neural activity commonly found in experimental data involving temporal processing. In the context of spatial navigation, the model learns and replays compressed sequences of place cells and captures features of neural activity such as the emergence of ripples and theta phase precession. Together, our findings suggest that combining oscillatory neuronal inputs with different frequencies provides a key mechanism to generate precisely timed sequences of activity in recurrent circuits of the brain.

neuroscience↗