Introduction Global illegal trade in timbers is a major cause of the loss of tree species diversity. The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) has been developed to combat the illegal international timber trade. Its implementation relies on accurate wood identification techniques for field screening. However, meeting the demand for timber field screening at the species level using the traditional wood identification method depending on wood anatomy is complicated, time-consuming, and challenging for enforcement officials who did not major in wood science. Methods This study constructed a CITES-28 macroscopic image dataset, including 9,437 original images of 279 xylarium wood specimens from 14 CITES-listed commonly traded tree species and 14 look-alike species. We evaluated a suitable wood image preprocessing method and developed a highly effective computer vision classification model, SE-ResNet, on the enhanced image dataset. The model incorporated attention mechanism modules [squeeze-and-excitation networks (SENet)] into a convolutional neural network (ResNet) to identify 28 wood species. Results The results showed that the SE-ResNet model achieved a remarkable 99.65% accuracy. Additionally, image cropping and rotation were proven effective image preprocessing methods for data enhancement. This study also conducted real-world identification using images of new specimens from the timber market to test the model and achieved 82.3% accuracy. Conclusion This study presents a convolutional neural network model coupled with the SENet module to discriminate CITES-listed species with their look-alikes and investigates a standard guideline for enhancing wood transverse image data, providing a practical computer vision method tool to protect endangered tree species and highlighting its substantial potential for CITES implementation.

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Introduction This study addresses the urgent need for non-destructive identification of commercially valuable Dalbergia species, which are threatened by illegal logging. Effective identification methods are crucial for ecological conservation, biodiversity preservation, and the regulation of the timber trade. Methods We integrate Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging (HSI) with advanced machine learning techniques to enhance the precision and efficiency of wood species identification. Our methodology employs various modeling approaches, including Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN). These models analyze spectral data across Vis (383-982 nm), NIR (982-2386 nm), and full spectral ranges (383 nm to 2386 nm). We also assess the impact of preprocessing techniques such as Standard Normal Variate (SNV), Savitzky-Golay (SG) smoothing, normalization, and Multiplicative Scatter Correction (MSC) on model performance. Results With optimal preprocessing, both SVM and CNN models achieve 100% accuracy across NIR and full spectral ranges. The selection of an appropriate wavelength range is critical; utilizing the full spectrum captures a broader array of the wood's chemical and physical properties, significantly enhancing model accuracy and predictive power. Discussion These findings underscore the effectiveness of Vis/NIR HSI in wood species identification. They also highlight the importance of precise wavelength selection and preprocessing techniques to maximize both accuracy and cost-efficiency. This research contributes substantially to ecological conservation and the regulation of the timber trade by providing a reliable, non-destructive method for identifying threatened wood species.

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Image Quality Assessment (IQA) is essential for the accuracy of systems for automatic recognition of tree species for wood samples. In this study, a No-Reference IQA (NR-IQA), wood NR-IQA (WNR-IQA) metric was proposed to assess the quality of wood images. Support Vector Regression (SVR) was trained using Generalized Gaussian Distribution (GGD) and Asymmetric Generalized Gaussian Distribution (AGGD) features, which were measured for wood images. Meanwhile, the Mean Opinion Score (MOS) was obtained from the subjective evaluation. This was followed by a comparison between the proposed IQA metric, WNR-IQA, and three established NR-IQA metrics, namely Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), deepIQA, Deep Bilinear Convolutional Neural Networks (DB-CNN), and five Full Reference-IQA (FR-IQA) metrics known as MSSIM, SSIM, FSIM, IWSSIM, and GMSD. The proposed WNR-IQA metric, BRISQUE, deepIQA, DB-CNN, and FR-IQAs were then compared with MOS values to evaluate the performance of the automatic IQA metrics. As a result, the WNR-IQA metric exhibited a higher performance compared to BRISQUE, deepIQA, DB-CNN, and FR-IQA metrics. Highest quality images may not be routinely available due to logistic factors, such as dust, poor illumination, and hot environment present in the timber industry. Moreover, motion blur could occur due to the relative motion between the camera and the wood slice. Therefore, the advantage of WNR-IQA could be seen from its independency from a "perfect" reference image for the image quality evaluation.

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Cancer, a complex yet common disease, is caused by uncontrolled cell division and abnormal cell growth due to a variety of gene mutations. Seeking effective treatments for cancer is a major research focus, as the incidence of cancer is on the rise and drug resistance to existing anti-cancer drugs is major concern. Natural products have the potential to yield unique molecules and combinations of substances that may be effective against cancer with relatively low toxicity/better side effect profile compared to standard anticancer therapy. Drug discovery work with natural products has demonstrated that natural compounds display a wide range of biological activities correlating to anticancer effects. In this review, we discuss formononetin (C 16 H 12 O 4 ), which originates mainly from red clovers and the Chinese herb Astragalus membranaceus . The compound comes from a class of 7-hydroisoflavones with a substitution of methoxy group at position 4. Formononetin elicits antitumorigenic properties in vitro and in vivo by modulating numerous signaling pathways to induce cell apoptosis (by intrinsic pathway involving Bax, Bcl-2, and caspase-3 proteins) and cell cycle arrest (by regulating mediators like cyclin A, cyclin B1, and cyclin D1), suppress cell proliferation [by signal transducer and activator of transcription (STAT) activation, phosphatidylinositol 3-kinase/protein kinase-B (PI3K/AKT), and mitogen-activated protein kinase (MAPK) signaling pathway], and inhibit cell invasion [by regulating growth factors vascular endothelial growth factor (VEGF) and Fibroblast growth factor 2 (FGF2), and matrix metalloproteinase (MMP)-2 and MMP-9 proteins]. Co-treatment with other chemotherapy drugs such as bortezomib, LY2940002, U0126, sunitinib, epirubicin, doxorubicin, temozolomide, and metformin enhances the anticancer potential of both formononetin and the respective drugs through synergistic effect. Compiling the evidence thus far highlights the potential of formononetin to be a promising candidate for chemoprevention and chemotherapy.

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Background and aims Dalbergia nigra is one of the most valuable timber species of its genus, having been traded for over 300 years. Due to over-exploitation it is facing extinction and trade has been banned under CITES Appendix I since 1992. Current methods, primarily comparative wood anatomy, are inadequate for conclusive species identification. This study aims to find a set of anatomical characters that distinguish the wood of D. nigra from other commercially important species of Dalbergia from Latin America. Methods Qualitative and quantitative wood anatomy, principal components analysis and naïve Bayes classification were conducted on 43 specimens of Dalbergia, eight D. nigra and 35 from six other Latin American species. Key results Dalbergia cearensis and D. miscolobium can be distinguished from D. nigra on the basis of vessel frequency for the former, and ray frequency for the latter. Principal components analysis was unable to provide any further basis for separating the species. Naïve Bayes classification using the four characters: minimum vessel diameter; frequency of solitary vessels; mean ray width; and frequency of axially fused rays, classified all eight D. nigra correctly with no false negatives, but there was a false positive rate of 36.36 %. Conclusions Wood anatomy alone cannot distinguish D. nigra from all other commercially important Dalbergia species likely to be encountered by customs officials, but can be used to reduce the number of specimens that would need further study.

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