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We investigated the effects of seed burial depth and soil water content on seedling emergence and growth of Ulmus pumila var. sabulosa (sandy elm), an important native tree species distributed over the European-Asian steppe. Experimental sand burial depths in the soil were 0.5,1.0, 1.5, 2.0 and 2.5 cm, and soil water contents were 4%, 8%, 12% and 16% of field capacity. All two-way ANOVA (five sand burial depths and four soil water contents) results showed that seed burial depths, soil water content and their interactions significantly affected all the studied plant variables. Most of the times, seedling emergence conditions were greater at the lower sand burial depths (less than 1.0 cm) than at the higher (more than 1.0 cm) seed burial depths, and at the lower water content (less than 12%) than at the higher soil water content. However, high seed burial depths (more than 1.5 cm) or low soil water content (less than 12%) reduced seedling growth or change in the root/shoot biomass ratios. In conclusion, the most suitable range of sand burial was from 0.5 to 1.0 cm soil depth and soil water content was about 12%, respectively, for the processes of seedling emergence and growth. These findings indicate that seeds of the sandy elm should be kept at rather shallow soil depths, and water should be added up to 12% of soil capacity when conducting elm planting and management. Our findings could help to create a more appropriate sandy elm cultivation and understand sparse elm woodland recruitment failures in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Simple Summary: Complete resection of dysplastic and malignant tissue improves overall survival and delays cancer recurrence in oral cancer patients; however, intraoperative surgical margin assessment is limited to visual inspection and palpation, making it difficult to achieve total resection. There is currently no tool capable of providing real-time, accurate, and continuous margin-assessment guidance during oral cancer resection surgery. Multispectral autofluorescence lifetime imaging (maFLIM) is a label-free imaging modality that enables quantifying a plurality of metabolic and compositional autofluorescence biomarkers of oral dysplasia and cancer. We have developed and validated a machine-learning assisted computer aided detection (CAD) system for automated discrimination of dysplastic and cancerous from healthy oral tissue based on in vivo widefield maFLIM endoscopy data. This CAD system can be potentially embedded into maFLIM endoscopes to enable continuous in situ detection of positive margins during oral cancer resection surgery, thus facilitating maximal tumor resection and improving surgical outcomes for oral cancer patients. Multispectral autofluorescence lifetime imaging (maFLIM) can be used to clinically image a plurality of metabolic and biochemical autofluorescence biomarkers of oral epithelial dysplasia and cancer. This study tested the hypothesis that maFLIM-derived autofluorescence biomarkers can be used in machine-learning (ML) models to discriminate dysplastic and cancerous from healthy oral tissue. Clinical widefield maFLIM endoscopy imaging of cancerous and dysplastic oral lesions was performed at two clinical centers. Endoscopic maFLIM images from 34 patients acquired at one of the clinical centers were used to optimize ML models for automated discrimination of dysplastic and cancerous from healthy oral tissue. A computer-aided detection system was developed and applied to a set of endoscopic maFLIM images from 23 patients acquired at the other clinical center, and its performance was quantified in terms of the area under the receiver operating characteristic curve (ROC-AUC). Discrimination of dysplastic and cancerous from healthy oral tissue was achieved with an ROC-AUC of 0.81. This study demonstrates the capabilities of widefield maFLIM endoscopy to clinically image autofluorescence biomarkers that can be used in ML models to discriminate dysplastic and cancerous from healthy oral tissue. Widefield maFLIM endoscopy thus holds potential for automated in situ detection of oral dysplasia and cancer. [ABSTRACT FROM AUTHOR]