Biology subjects

Atkinson, J. A.

Publications and source records attributed to Atkinson, J. A..

5 recordsLinked to original sources

bioRxiv2025

Gut stretch evokes rapid neural dynamics in vagal and hindbrain populations of larval zebrafish

Animals sense food quantity and quality to regulate feeding behaviors essential for survival. Enteroendocrine cells (EECs) in the gut epithelium detect luminal distension and nutrients, signaling this information to the brain via vagal sensory neurons. However, how mechanosensory and chemosensory signals are dynamically encoded by gut-brain circuits remains unclear, particularly during early development. Leveraging the transparency and genetic tractability of larval zebrafish, we developed a feeding assay offering liposome-based complex particles designed to release specific nutrients after consumption. By quantifying gut fluorescence after exposure to these particles in 9-day-old zebrafish, we demonstrate that EECs regulate nutrient-specific feeding soon after functional gut formation. To determine how these post-ingestive signals are encoded along the gut-brain circuitry, we developed a microgavage method enabling simultaneous stimulus delivery to the gut and volumetric two-photon calcium imaging. We found that repeated gut distension alone drove widespread activation and suppression in vagal and dorsal hindbrain neurons, emerging as a dominant visceral signal after two days of feeding onset. Although nutrient-evoked responses shared temporal features with distension, allyl isothiocyanate (AITC), an aversive compound found in wasabi, elicited neural dynamics with slower onset kinetics. These findings reveal that fast gut-to-brain communication emerges early in life, with mechanical distension and aversive chemical cues encoded through distinct temporal dynamics in developing interoceptive circuits. HighlightsO_LILarval zebrafish enteroendocrine cells regulate nutrient-specific feeding C_LIO_LILeft and right vagus similarly encode gut distension and nutrients C_LIO_LIGut distension drives rapid, widespread neural responses across the hindbrain C_LIO_LIEnteric stimuli evoke diverse fast neural dynamics in early development C_LI

neuroscience↗

bioRxiv2019

RootNav 2.0: Deep Learning for Automatic Navigation of Complex Plant Root Architectures

We present a new image analysis approach that provides fully-automatic extraction of complex root system architectures from a range of plant species in varied imaging setups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task Convolutional Neural Network architecture. The network has been designed to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. In addition, the network simultaneously locates seeds, and first and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. The proposed method is evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. The results are compared with semi-automatic analysis via the original RootNav tool, demonstrating comparable accuracy, with a 10-fold increase in speed. We then demonstrate the ability of the network to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. We transfer for a final time to images of Brassica napus from a hydroponic assay, and still demonstrate good accuracy despite many fewer training images. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools.

bioinformatics↗

bioRxiv2019

Identification of nitrogen-dependent QTL and underlying genes for root system architecture in hexaploid wheat

The root system architecture (RSA) of a crop has a profound effect on the uptake of nutrients and consequently the potential yield. However, little is known about the genetic basis of RSA and resource adaptive responses in wheat (Triticum aestivum L.). Here, a high-throughput germination paper plant phenotyping system was used to identify seedling traits in a wheat doubled haploid mapping population, Savannah x Rialto. Significant genotypic and nitrate-N treatment variation was found across the population for seedling traits with distinct trait grouping for root size-related traits and root distribution-related traits. Quantitative trait locus (QTL) analysis identified a total of 59 seedling trait QTLs. Across two nitrate treatments, 27 root QTLs were specific to the nitrate treatment. Transcriptomic analyses for one of the QTLs on chromosome 2D found under low nitrate conditions was pursued revealing gene enrichment in N-related biological processes and 17 candidate up-regulated genes with possible involvement in a root angle response. Together, these findings provide genetic insight into root system architecture and plant adaptive responses to nitrate and provide targets that could help improve N capture in wheat.

plant biology↗

bioRxiv2017

Deep Learning for Multi-task Plant Phenotyping

Plant phenotyping has continued to pose a challenge to computer vision for many years. There is a particular demand to accurately quantify images of crops, and the natural variability and structure of these plants presents unique difficulties. Recently, machine learning approaches have shown impressive results in many areas of computer vision, but these rely on large datasets that are at present not available for crops. We present a new dataset, called ACID, that provides hundreds of accurately annotated images of wheat spikes and spikelets, along with image level class annotation. We then present a deep learning approach capable of accurately localising wheat spikes and spikelets, despite the varied nature of this dataset. As well as locating features, our network offers near perfect counting accuracy for spikes (95.91%) and spikelets (99.66%). We also extend the network to perform simultaneous classification of images, demonstrating the power of multi-task deep architectures for plant phenotyping. We hope that our dataset will be useful to researchers in continued improvement of plant and crop phenotyping. With this in mind, alongside the dataset we will make all code and trained models available online.

plant biology↗

bioRxiv2017

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies

BackgroundGenetic analyses of plant root system development require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming).\n\nFindingsWe trained a Random Forest algorithm to infer architectural traits from automatically-extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify Quantitative Trait Loci that had previously been discovered using a semi-automated method.\n\nConclusionsWe have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput in large scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other area of plant phenotyping.

plant biology↗