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Salamon, J.

Publications and source records attributed to Salamon, J..

2 recordsLinked to original sources

Spatial transcriptomics identifies distinct domains regulating yield-related traits of the wheat ear

Cereal inflorescences are complex, highly ordered structures composed of grain-producing florets that form within specialised branches called spikelets. The spikelets of wheat are arranged in two alternating rows along a central rachis, in a pattern determined during early reproductive development. While several genes that control spikelet development have been identified, the molecular processes that regulate their morphology and the formation of supporting structures, such as meristems and the rachis, remain poorly understood. Here, we used spatial transcriptomics to investigate the dynamic transcriptional landscape of a wheat inflorescence during spikelet development. We identified two spatially distinct regions that regulate spikelet architecture, including a primordium region characterised by RAMOSA2 activity, and a boundary region that expresses ALOG1 and known regulators of bract suppression. Developmental assays indicate that spikelets differentiate from meristematic regions, which is accompanied by formation of central vascular regions of the rachis and inflorescence base that express genes controlling spikelet number. The combined spatial transcriptome and genetic data reveal key regulators of spikelet development, including target genes for improving spikelet number and yield.

plant biology↗

BirdVox: Machine listening for bird migration monitoring

The steady decline of avian populations worldwide urgently calls for a cyber-physical system to monitor bird migration at the continental scale. Compared to other sources of information (radar and crowdsourced observations), bioacoustic sensor networks combine low latency with a high taxonomic specificity. However, the scarcity of flight calls in bioacoustic monitoring scenes (below 0.1% of total recording time) requires the automation of audio content analysis. In this article, we address the problem of scaling up the detection and classification of flight calls to a full-season dataset: 6672 hours across nine sensors, yielding around 480 million neural network predictions. Our proposed pipeline, BirdVox, combines multiple machine learning modules to produce per-species flight call counts. We evaluate BirdVox on an annotated subset of the full season (296 hours) and discuss the main sources of estimation error which are inherent to a real-world deployment: mechanical sensor failures, sensitivity to background noise, misdetection, and taxonomic confusion. After developing dedicated solutions to mitigate these sources of error, we demonstrate the usability of BirdVox by reporting a species-specific temporal estimate of flight call activity for the Swainsons Thrush (Catharus ustulatus).

ecology↗