RESUMO
As phenomics data volume and dimensionality increase due to advancements in sensor technology, there is an urgent need to develop and implement scalable data processing pipelines. Current phenomics data processing pipelines lack modularity, extensibility, and processing distribution across sensor modalities and phenotyping platforms. To address these challenges, we developed PhytoOracle (PO), a suite of modular, scalable pipelines for processing large volumes of field phenomics RGB, thermal, PSII chlorophyll fluorescence 2D images, and 3D point clouds. PhytoOracle aims to (i) improve data processing efficiency; (ii) provide an extensible, reproducible computing framework; and (iii) enable data fusion of multi-modal phenomics data. PhytoOracle integrates open-source distributed computing frameworks for parallel processing on high-performance computing, cloud, and local computing environments. Each pipeline component is available as a standalone container, providing transferability, extensibility, and reproducibility. The PO pipeline extracts and associates individual plant traits across sensor modalities and collection time points, representing a unique multi-system approach to addressing the genotype-phenotype gap. To date, PO supports lettuce and sorghum phenotypic trait extraction, with a goal of widening the range of supported species in the future. At the maximum number of cores tested in this study (1,024 cores), PO processing times were: 235 minutes for 9,270 RGB images (140.7 GB), 235 minutes for 9,270 thermal images (5.4 GB), and 13 minutes for 39,678 PSII images (86.2 GB). These processing times represent end-to-end processing, from raw data to fully processed numerical phenotypic trait data. Repeatability values of 0.39-0.95 (bounding area), 0.81-0.95 (axis-aligned bounding volume), 0.79-0.94 (oriented bounding volume), 0.83-0.95 (plant height), and 0.81-0.95 (number of points) were observed in Field Scanalyzer data. We also show the ability of PO to process drone data with a repeatability of 0.55-0.95 (bounding area).
RESUMO
PURPOSE OF REVIEW: The treatment of cancer-induced bone pain (CIBP) has been proven ineffective and relies heavily on opioids, the target of highly visible criticism for their negative side effects. Alternative therapeutic agents are needed and the last few years have brought promising results, detailed in this review. RECENT FINDINGS: Cysteine/glutamate antiporter system, xc, cannabinoids, kappa opioids, and a ceramide axis have all been shown to have potential as novel therapeutic targets without the negative effects of opioids. SUMMARY: Review of the most recent and promising studies involving CIBP, specifically within murine models. Cancer pain has been reported by 30-50% of all cancer patients and even more in late stages, however the standard of care is not effective to treat CIBP. The complicated and chronic nature of this type of pain response renders over the counter analgesics and opioids largely ineffective as well as difficult to use due to unwanted side effects. Preclinical studies have been standardized and replicated while novel treatments have been explored utilizing various alternative receptor pathways: cysteine/glutamate antiporter system, xc, cannabinoid type 1 receptor, kappa opioids, and a ceramide axis sphingosine-1-phosphate/sphingosine-1-phosphate receptor 1.
