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Computer vision-based wood identification has been successfully applied to recognize tree species using digital images of wood sections or surfaces. However, this image-to-species approach can only recognize a limited number of species due to two main reasons: 1) the lack of a good reference database requiring high-quality standardized images from multiple individuals of hundreds or even thousands of traded timber species, and 2) species not included in the reference database cannot be identified without expert knowledge. Another bottleneck is that the feature extraction process used by these species recognition approaches is a black box, thereby creating a discrepancy between machine learning features and wood anatomical features. This discrepancy prevents wood anatomists from understanding how these machine-learning algorithms work. Here, we survey currently existing methods used in feature extraction, classification, and deep learning methods applied in wood identification along with their pitfalls and opportunities. As an example of how the field could move forward, we launch the idea of building an image-to-features-to-species identification approach based on microscopic wood images as well as text files comprising wood anatomical descriptions. If we can manage machine learning-based algorithms to recognize the main wood anatomical traits that experts use to identify species in a (semi-)automated way, this would boost wood identification in two ways: (1) extensive reference databases for each species would become less crucial as the databases are ordered at the trait level, (2) timber identification would become more feasible for species that have not yet been included in the reference database as long as wood anatomical descriptions are available. [ABSTRACT FROM AUTHOR]

Distraction osteogenesis (DO) is an efficient strategy that is employed for the treatment of large bone defects in craniomaxillofacial surgery. Despite its utility, however, DO is associated with a prolonged consolidation phase and a high complication rate that hinder its more widespread utilization. Panax notoginseng saponin (PNS) is a traditional Chinese medicine that is frequently administered for the treatment of a range of conditions. Herein, we explored the ability of PNS treatment to influence osteogenic differentiation using both rabbit bone marrow mesenchymal cells (BMSCs) and a model of mandibular DO. BMSC proliferation was assessed via CCK-8 assay, while osteogenic differentiation was monitored through ALP and alizarin red S staining. A PCR approach was used to evaluate the expression of genes associated with osteogenesis (ALP, Runx2, and OCN) and genes linked to the TGF pathway (T β R-II, SMAD2, SMAD3, and PPM1A). For in vivo experiments, treated BMSCs were locally injected into the DO gap, with PNS being injected into treated rabbits every other day throughout the experimental period. The quality of the regenerative process was assessed via scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), X-ray imaging, and hematoxylin and eosin (H&E) staining. These analyses revealed that PNS was able to promote BMSC osteogenesis and mandibular generation, driving the upregulation of osteogenesis-related genes at the mRNA levels through the modulation of the TGF- β 1/Smad pathway. Consistently, the overexpression or silencing of T β R-II in PNS-treated BMSCs was sufficient to modulate their osteogenic potential. Analyses of in vivo mandibular DO outcomes revealed significantly augmented new bone growth in the PNS-treated group relative to control animals, with maximal osteogenesis in the group overexpressing rabbit T β R-II. Together, these results highlight the PNS as a promising and cost-effective therapeutic tool with the potential to enhance bone regeneration in clinical contexts through the modulation of the TGF- β 1/Smad pathway., Competing Interests: All authors declare that they have no conflicts of interest., (Copyright © 2021 Di Liu et al.)