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Machine learning driven benchtop Vis/NIR spectroscopy for online detection of hybrid citrus quality.
Food Res Int
February 2025
State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, Jiangsu Province, China; School of Food Science and Technology, Jiangnan University University, Wuxi, Jiangsu Province, China; Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, China. Electronic address:
The aim of this study was to explore application of visible and near-infrared (Vis/NIR) spectroscopy combined with machine learning models for SSC and TA prediction of hybrid citrus. The Vis/NIR spectra of samples including navel-region, equator-region and multi-region combination spectra in navel-region and equator-region were collected using a benchtop equipment. The performance of SSC and TA prediction models with different region spectra, including partial least squares (PLS), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM) and multilayer feedforward neural network (MFNN), was assessed.
View Article and Find Full Text PDFOngoing chromothripsis underpins osteosarcoma genome complexity and clonal evolution.
Cell
January 2025
European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton CB10 1SA, UK. Electronic address:
Osteosarcoma is the most common primary cancer of the bone, with a peak incidence in children and young adults. Using multi-region whole-genome sequencing, we find that chromothripsis is an ongoing mutational process, occurring subclonally in 74% of osteosarcomas. Chromothripsis generates highly unstable derivative chromosomes, the ongoing evolution of which drives the acquisition of oncogenic mutations, clonal diversification, and intra-tumor heterogeneity across diverse sarcomas and carcinomas.
View Article and Find Full Text PDFMapping the chromothripsis landscape in urothelial carcinoma unravels great intratumoral and intertumoral heterogeneity.
iScience
January 2025
Department of Biology, University of Copenhagen, 2100 Copenhagen, Denmark.
Chromothripsis, a hallmark of cancer, is characterized by extensive and localized DNA rearrangements involving one or a few chromosomes. However, its genome-wide frequency and characteristics in urothelial carcinoma (UC) remain largely unknown. Here, by analyzing single-regional and multi-regional whole-genome sequencing (WGS), we present the chromothripsis blueprint in 488 UC patients.
View Article and Find Full Text PDFProspective validation of ORACLE, a clonal expression biomarker associated with survival of patients with lung adenocarcinoma.
Nat Cancer
January 2025
Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
Human tumors are diverse in their natural history and response to treatment, which in part results from genetic and transcriptomic heterogeneity. In clinical practice, single-site needle biopsies are used to sample this diversity, but cancer biomarkers may be confounded by spatiogenomic heterogeneity within individual tumors. Here we investigate clonally expressed genes as a solution to the sampling bias problem by analyzing multiregion whole-exome and RNA sequencing data for 450 tumor regions from 184 patients with lung adenocarcinoma in the TRACERx study.
View Article and Find Full Text PDFA Semi-Supervised Multi-Region Segmentation Framework of Bladder Wall and Tumor with Wall-Enhanced Self-Supervised Pre-Training.
Bioengineering (Basel)
December 2024
School of Biomedical Engineering, Air Force Medical University, No. 169 Changle West Road, Xi'an 710032, China.
Bladder cancer is a prevalent and highly recurrent malignancy within the urinary tract. The accurate segmentation of the bladder wall and tumor in magnetic resonance imaging (MRI) is a crucial step in distinguishing between non-muscle-invasive and muscle-invasive types of bladder cancer, which plays a pivotal role in guiding clinical treatment decisions and influencing postoperative quality of life. The performance of data-driven methods is highly dependent on the quality of the annotations and datasets, however the amount of high-quality annotated data is very limited given the difficulty of professional radiologists to distinguish the mixed regions between the bladder wall and the tumor.
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