Amino Acid and Carbohydrate Metabolism Are Coordinated to Maintain Energetic Balance during Drought in Sugarcane.

The ability to expand crop plantations without irrigation is a major goal to increase agriculture sustainability. To achieve this end, we need to understand the mechanisms that govern plant growth responses under drought conditions. In this study, we combined physiological, transcriptomic, and genomic data to provide a comprehensive picture of drought and recovery responses in the leaves and roots of sugarcane. Transcriptomic profiling using oligoarrays and RNA-seq identified 2898 (out of 21,902) and 46,062 (out of 373,869) transcripts as differentially expressed, respectively. Co-expression analysis revealed modules enriched in photosynthesis, small molecule metabolism, alpha-amino acid metabolism, trehalose biosynthesis, serine family amino acid metabolism, and carbohydrate transport. Together, our findings reveal that carbohydrate metabolism is coordinated with the degradation of amino acids to provide carbon skeletons to the tricarboxylic acid cycle. This coordination may help to maintain energetic balance during drought stress adaptation, facilitating recovery after the stress is alleviated. Our results shed light on candidate regulatory elements and pave the way to biotechnology strategies towards the development of drought-tolerant sugarcane plants.

Detection of pneumothorax on ultrasound using artificial intelligence.

BACKGROUND: Ultrasound (US) for the detection of pneumothorax shows excellent sensitivity in the hands of skilled providers. Artificial intelligence may facilitate the movement of US for pneumothorax into the prehospital setting. The large amount of training data required for conventional neural network methodologies has limited their use in US so far. METHODS: A limited training database was supplied by Defense Advanced Research Projects Agency of 30 patients, 15 cases with pneumothorax and 15 cases without. There were two US videos per patient, of which we were allowed to choose one to train on, so that a limited set of 30 videos were used. Images were annotated for ribs and pleural interface. The software performed anatomic reconstruction to identify the region of interest bounding the pleura. Three neural networks were created to analyze images on a pixel-by-pixel fashion with direct voting determining the outcome. Independent verification and validation was performed on a data set gathered by the Department of Defense. RESULTS: Anatomic reconstruction with the identification of ribs and pleura was able to be accomplished on all images. On independent verification and validation against the Department of Defense testing data, our program concurred with the SME 80% of the time and achieved a 86% sensitivity (18/21) for pneumothorax and a 75% specificity for the absence of pneumothorax (18/24). Some of the mistakes by our artificial intelligence can be explained by chest wall motion, hepatization of the underlying lung, or being equivocal cases. CONCLUSION: Using learning with limited labeling techniques, pneumothorax was identified on US with an accuracy of 80%. Several potential improvements are controlling for chest wall motion and the use of longer videos. LEVEL OF EVIDENCE: Diagnostic Tests; Level III.

Interactive, in-browser cinematic volume rendering of medical images.

The diversity and utility of cinematic volume rendering (CVR) for medical image visualization have grown rapidly in recent years. At the same time, volume rendering on augmented and virtual reality systems is attracting greater interest with the advance of the WebXR standard. This paper introduces CVR extensions to the open-source visualization toolkit (vtk.js) that supports WebXR. This paper also summarizes two studies that were conducted to evaluate the speed and quality of various CVR techniques on a variety of medical data. This work is intended to provide the first open-source solution for CVR that can be used for in-browser rendering as well as for WebXR research and applications. This paper aims to help medical imaging researchers and developers make more informed decision when selecting CVR algorithms for their applications. Our software and this paper also provide a foundation for new research and product development at the intersection of medical imaging, web visualization, XR, and CVR.

Single-cell RNA sequencing of developing maize ears facilitates functional analysis and trait candidate gene discovery.

Crop productivity depends on activity of meristems that produce optimized plant architectures, including that of the maize ear. A comprehensive understanding of development requires insight into the full diversity of cell types and developmental domains and the gene networks required to specify them. Until now, these were identified primarily by morphology and insights from classical genetics, which are limited by genetic redundancy and pleiotropy. Here, we investigated the transcriptional profiles of 12,525 single cells from developing maize ears. The resulting developmental atlas provides a single-cell RNA sequencing (scRNA-seq) map of an inflorescence. We validated our results by mRNA in situ hybridization and by fluorescence-activated cell sorting (FACS) RNA-seq, and we show how these data may facilitate genetic studies by predicting genetic redundancy, integrating transcriptional networks, and identifying candidate genes associated with crop yield traits.