A strategy for developing high-resolution DNA barcodes for species discrimination of wood specimens using the complete chloroplast genome of three Pterocarpus species.

MAIN CONCLUSION: A method for extraction of wood DNA and a strategy for designing high-resolution barcodes for wood were developed. Ycf1b was the prioritized barcode to resolve the Pterocarpus wood species studied. DNA barcoding, an effective tool for wood species identification, mainly focuses on universal barcodes and often lacks high resolution to differentiate species, especially for closely related taxa within the same genus. Therefore, more highly informative DNA barcodes need to be identified. This study is the first to report a strategy for developing specific DNA barcodes of wood tissues. The complete chloroplast genomes of leaf samples of three Pterocarpus species, i.e., P. indicus, P. santalinus, and P. tinctorius, were sequenced, and thereafter, the most variable DNA regions were identified on the scale of the complete chloroplast genomes. Finally, wood DNA was extracted from 30 wood specimens of the three Pterocarpus species, and DNA recovery rates of the selected regions were tested for applicability to verification on the wood specimens studied. The seven regions with the most variation (rpl32-ccsA, rpl20-clpP, trnC-rpoB, ycf1b, accD-ycf4, ycf1a, and psbK-accD) were identified from the chloroplast genome by quantifying nucleotide diversity (Pi > 0.02), which was remarkably higher than that of the plant universal barcodes (rbcL, matK, and trnH-psbA) and the previously reported barcodes (ndhF-rpl32 and trnL-F) used for phylogenetic analysis in Pterocarpus. After comprehensive evaluation of species discrimination ability and applicability, the ycf1b region performed well in terms of the recovery success rate (76.7%) and species identification (100%) for wood specimens of the three Pterocarpus species, and was identified as the preferred high-resolution chloroplast barcode for selected Pterocarpus species. It will offer technical support for curbing illegal timber harvesting activities and for conserving endangered and valuable wood species.

Machine learning approaches outperform distance- and tree-based methods for DNA barcoding of Pterocarpus wood.

MAIN CONCLUSION: Machine-learning approaches (MLAs) for DNA barcoding outperform distance- and tree-based methods on identification accuracy and cost-effectiveness to arrive at species-level identification of wood. DNA barcoding is a promising tool to combat illegal logging and associated trade, and the development of reliable and efficient analytical methods is essential for its extensive application in the trade of wood and in the forensics of natural materials more broadly. In this study, 120 DNA sequences of four barcodes (ITS2, matK, ndhF-rpl32, and rbcL) generated in our previous study and 85 downloaded from National Center for Biotechnology Information (NCBI) were collected to establish a reference data set for six commercial Pterocarpus woods. MLAs (BLOG, BP-neural network, SMO and J48) were compared with distance- (TaxonDNA) and tree-based (NJ tree) methods based on identification accuracy and cost-effectiveness across these six species, and also were applied to discriminate the CITES-listed species Pterocarpus santalinus from its anatomically similar species P. tinctorius for forensic identification. MLAs provided higher identification accuracy (30.8-100%) than distance- (15.1-97.4%) and tree-based methods (11.1-87.5%), with SMO performing the best among the machine learning classifiers. The two-locus combination ITS2 + matK when using SMO classifier exhibited the highest resolution (100%) with the fewest barcodes for discriminating the six Pterocarpus species. The CITES-listed species P. santalinus was discriminated successfully from P. tinctorius using MLAs with a single barcode, ndhF-rpl32. This study shows that MLAs provided higher identification accuracy and cost-effectiveness for forensic application over other analytical methods in DNA barcoding of Pterocarpus wood.

DNA barcoding of vouchered xylarium wood specimens of nine endangered Dalbergia species.

MAIN CONCLUSION: ITS2+ trnH - psbA was the best combination of DNA barcode to resolve the Dalbergia wood species studied. We demonstrate the feasibility of building a DNA barcode reference database using xylarium wood specimens. The increase in illegal logging and timber trade of CITES-listed tropical species necessitates the development of unambiguous identification methods at the species level. For these methods to be fully functional and deployable for law enforcement, they must work using wood or wood products. DNA barcoding of wood has been promoted as a promising tool for species identification; however, the main barrier to extensive application of DNA barcoding to wood is the lack of a comprehensive and reliable DNA reference library of barcodes from wood. In this study, xylarium wood specimens of nine Dalbergia species were selected from the Wood Collection of the Chinese Academy of Forestry and DNA was then extracted from them for further PCR amplification of eight potential DNA barcode sequences (ITS2, matK, trnL, trnH-psbA, trnV-trnM1, trnV-trnM2, trnC-petN, and trnS-trnG). The barcodes were tested singly and in combination for species-level discrimination ability by tree-based [neighbor-joining (NJ)] and distance-based (TaxonDNA) methods. We found that the discrimination ability of DNA barcodes in combination was higher than any single DNA marker among the Dalbergia species studied, with the best two-marker combination of ITS2+trnH-psbA analyzed with NJ trees performing the best (100% accuracy). These barcodes are relatively short regions (<350 bp) and amplification reactions were performed with high success (≥90%) using wood as the source material, a necessary factor to apply DNA barcoding to timber trade. The present results demonstrate the feasibility of using vouchered xylarium specimens to build DNA barcoding reference databases.

How to discriminate wood of CITES-listed tree species from their look-alikes: using an attention mechanism with the ResNet model on an enhanced macroscopic image dataset.

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.

Opening the black box: explainable deep-learning classification of wood microscopic image of endangered tree species.

BACKGROUND: Traditional method of wood species identification involves the use of hand lens by wood anatomists, which is a time-consuming method that usually identifies only at the genetic level. Computer vision method can achieve "species" level identification but cannot provide an explanation on what features are used for the identification. Thus, in this study, we used computer vision methods coupled with deep learning to reveal interspecific differences between closely related tree species. RESULT: A total of 850 images were collected from the cross and tangential sections of 15 wood species. These images were used to construct a deep-learning model to discriminate wood species, and a classification accuracy of 99.3% was obtained. The key features between species in machine identification were targeted by feature visualization methods, mainly the axial parenchyma arrangements and vessel in cross section and the wood ray in tangential section. Moreover, the degree of importance of the vessels of different tree species in the cross-section images was determined by the manual feature labeling method. The results showed that vessels play an important role in the identification of Dalbergia, Pterocarpus, Swartzia, Carapa, and Cedrela, but exhibited limited resolutions on discriminating Swietenia species. CONCLUSION: The research results provide a computer-assisted tool for identifying endangered tree species in laboratory scenarios, which can be used to combat illegal logging and related trade and contribute to the implementation of CITES convention and the conservation of global biodiversity.

Quantitative Assessment of Breast Tumor: Comparison of Four Methods of Positioning Region of Interest for Synthetic Relaxometry and Diffusion Measurement.

RATIONALE AND OBJECTIVES: To compare the differences in apparent diffusion coefficient (ADC) and synthetic magnetic resonance (MR) measurements of four region of interest (ROI) placement methods for breast tumor and to investigate their diagnostic performance. METHODS: 110 (70 malignant, 40 benign) newly diagnosed breast tumors were evaluated. The patients underwent 3.0 T MR examinations including diffusion-weighted imaging and synthetic MR. Two radiologists independently measured ADCs, T1 relaxation time (T1), T2 relaxation time (T2), and proton density (PD) using four ROI methods: round, square, freehand, and whole-tumor volume (WTV). The interclass correlation coefficient (ICC) was used to assess their measurement reliability. Diagnostic performance was evaluated using multivariate logistic regression analysis and the receiver operating characteristic (ROC) curves. RESULTS: The mean values of all ROI methods showed good or excellent interobserver reproducibility (0.79-0.99) and showed the best diagnostic performance compared to the minimum and maximum values. The square ROI exhibited superior performance in differentiating between benign from malignant breast lesions, followed by the freehand ROI. T2, PD, and ADC values were significantly lower in malignant breast lesions compared to benign ones for all ROI methods (p < 0.05). Multiparameters of T2 + ADC demonstrated the highest AUC values (0.82-0.95), surpassing the diagnostic efficacy of ADC or T2 alone (p < 0.05). CONCLUSION: ROI placement significantly influences ADC and synthetic MR values measured in breast tumors. Square ROI and mean values showed superior performance in differentiating benign and malignant breast lesions. The multiparameters of T2 + ADC surpassed the diagnostic efficacy of a single parameter.

Organellar microcapture to extract nuclear and plastid DNA from recalcitrant wood specimens and trace evidence.

BACKGROUND: Illegal logging is a global crisis with significant environmental, economic, and social consequences. Efforts to combat it call for forensic methods to determine species identity, provenance, and individual identification of wood specimens throughout the forest products supply chain. DNA-based methodologies are the only tools with the potential to answer all three questions and the only ones that can be calibrated "non-destructively" by using leaves or other plant tissue and take advantage of publicly available DNA sequence databases. Despite the potential that DNA-based methods represent for wood forensics, low DNA yield from wood remains a limiting factor because, when compared to other plant tissues, wood has few living DNA-containing cells at functional maturity, it often has PCR-inhibiting extractives, and industrial processing of wood degrades DNA. To overcome these limitations, we developed a technique-organellar microcapture-to mechanically isolate intact nuclei and plastids from wood for subsequent DNA extraction, amplification, and sequencing. RESULTS: Here we demonstrate organellar microcapture wherein we remove individual nuclei from parenchyma cells in wood (fresh and aged) and leaves of Carya ovata and Tilia americana, amyloplasts from Carya wood, and chloroplasts from kale (Brassica sp.) leaf midribs. ITS (773 bp), ITS1 (350 bp), ITS2 (450 bp), and rbcL (620 bp) were amplified via polymerase chain reaction, sequenced, and heuristic searches against the NCBI database were used to confirm that recovered DNA corresponded to each taxon. CONCLUSION: Organellar microcapture, while too labor-intensive for routine extraction of many specimens, successfully recovered intact nuclei from wood samples collected more than sixty-five years ago, plastids from fresh sapwood and leaves, and presents great potential for DNA extraction from recalcitrant plant samples such as tissues rich in secondary metabolites, old specimens (archaeological, herbarium, and xylarium specimens), or trace evidence previously considered too small for analysis.

Anatomical adaptions of pits in two types of ray parenchyma cells in Populus tomentosa during the xylem differentiation.

Pits in ray parenchyma cells are important to understand the functional anatomy of the ray parenchyma network in the xylem but have been less studied. Herein, pits in two types of ray parenchyma cells, contact cells and isolation cells, across different developmental stages were qualitatively studied using 48-year-old Populus tomentosa trees. The timing of differentiation and death was determined by histochemical staining and polarized light microscopy. The dimension, shape and density of pits as well as cell wall thickness were measured using SEM and optical microscopy images of semi-thin radial sections and macerated ray parenchyma cells, and analyzed by multi-factor analyses of variance. Results showed that secondary wall thickening and lignification of contact cells begun near the cambium, contrarily those of isolation cells have started until the transition zone. But even in the sapwood, contact cell walls were still much thinner than isolation cell walls. Moreover, district anatomical adaptions of pits during the xylem differentiation were present between horizontal walls and tangential walls, between contact cells and isolation cells. Ray pits were simple to slightly bordered, whereas sieve-like pits were only shown on tangential walls of isolation cells. Pit density of horizontal walls was similar between contact cells and isolation cells, nevertheless greater pits were present on tangential walls, especially for isolation cells. In addition, pits of ray parenchyma cells in the heartwood were smaller and more bordered than those in the sapwood, particularly on the horizontal walls. Moreover, isolation cells had pits with the smaller dimensions, greater pits on the tangential walls, more bordered pits on horizontal walls, as well as longer and narrower cell morphology with much thicker cell walls than contact cells. To a certain extent, all these anatomical adaptations were developed to ensure distinct functions of the two types of ray parenchyma cells in the xylem and finally to support tree growth in demand.

High-Quality-Factor AlON Transparent Ceramics for 5 GHz Wi-Fi Aesthetically Decorative Antennas.

Transparent material has been widely used in product design and has seen a large increase in its use. In this paper, a kind of aesthetically decorative 5 GHz Wi-Fi dielectric resonator antenna (DRA) of aluminum oxynitride (AlON) transparent ceramic has been designed. High-quality-factor AlON transparent dielectric ceramics were fabricated by presintering at 1780 °C and further cold isostatic pressing (CIP) under a 200 MPa argon atmosphere. For a 9.0 mm thick specimen, the in-line light transmittance reached 83%. Optimum dielectric constant (εr = 9.32), quality factor (Qf = 47 960) and temperature coefficient (TCF = -51.7 ppm/°C) was achieved in the AlON transparent ceramic by cold isostatic pressing. As a result, the proposed aesthetically decorative DRA can achieve an impedance bandwidth of 32% (4.48-6.19 GHz), a high radiation efficiency of 85%, and a low cross-polarization discrimination (XPD) of -30 dB. To achieve a broad bandwidth, the proposed antenna was excited in its dominant TE111x mode and higher-order TE113x mode. The proposed antenna is thus an excellent candidate for an indoor decoration Wi-Fi antenna.

A computer-aided method for identifying the presence of softwood growth ring boundaries.

The objective of this study was to develop a computer-aided method to quantify the obvious degree of growth ring boundaries of softwood species, based on data analysis with some image processing technologies. For this purpose, a 5× magnified cross-section color micro-image of softwood was cropped into 20 sub-images, and then every image was binarized as a gray image according to an automatic threshold value. After that, the number of black pixels in the gray image was counted row by row and the number of black pixels was binarized to 0 or 100. Finally, a transition band from earlywood to latewood on the sub-image was identified. If everything goes as planned, the growth ring boundaries of the sub-image would be distinct. Otherwise would be indistinct or absent. If more than 50% sub-images are distinct, with the majority voting method, the growth ring boundaries of softwood would be distinct, otherwise would be indistinct or absent. The proposed method has been visualized as a growth-ring-boundary detecting system based on the .NET Framework. A sample of 100 micro-images (see S1 Fig via https://github.com/senly2019/Lin-Qizhao/) of softwood cross-sections were selected for evaluation purposes. In short, this detecting system computes the obvious degree of growth ring boundaries of softwood species by image processing involving image importing, image cropping, image reading, image grayscale, image binarization, data analysis. The results showed that the method used avoided mistakes made by the manual comparison method of identifying the presence of growth ring boundaries, and it has a high accuracy of 98%.

Application of de-lignified cellulose to enhance intracellular and extracellular lipid production from oleaginous yeast using acetic acid.

Two de-lignified cellulose of loofah sponge and sawdust were applied in two ways to enhance the lipid production from oleaginous yeast using acetic acid. When 30 g/L of acetic acid was used as a carbon source, direct addition of de-lignified loofah sponge or sawdust increased the extracellular lipid content to 33.94% and 53.25%, respectively. The latter reduced the energy input of lipid extraction process from 0.86 to 0.57 GJ per ton of biodiesel production. To relieve the inhibition caused by 40 g/L acetic acid, immobilization of oleaginous yeast on de-lignified sawdust increased the lipid concentration and yield from 3.83 g/L, 0.18 g/g C to 7.15 g/L, 0.20 g/g C, respectively. These improvements occurred due to the cell-immobilized sawdust which play an important role in the loading of cells and adsorption of acetic acid. Immobilized cultivation also increased the fatty acid proportion of C18:1, thereby improving biodiesel performance.

DNA Barcode Authentication and Library Development for the Wood of Six Commercial Pterocarpus Species: the Critical Role of Xylarium Specimens.

DNA barcoding has been proposed as a useful tool for forensic wood identification and development of a reliable DNA reference library is an essential first step. Xylaria (wood collections) are potentially enormous data repositories if DNA information could be extracted from wood specimens. In this study, 31 xylarium wood specimens and 8 leaf specimens of six important commercial species of Pterocarpus were selected to investigate the reliability of DNA barcodes for authentication at the species level and to determine the feasibility of building wood DNA barcode reference libraries from xylarium specimens. Four DNA barcodes (ITS2, matK, ndhF-rpl32 and rbcL) and their combination were tested to evaluate their discrimination ability for Pterocarpus species with both TaxonDNA and tree-based analytical methods. The results indicated that the combination barcode of matK + ndhF-rpl32 + ITS2 yielded the best discrimination for the Pterocarpus species studied. The mini-barcode ndhF-rpl32 (167-173 bps) performed well distinguishing P. santalinus from its wood anatomically inseparable species P. tinctorius. Results from this study verified not only the feasibility of building DNA barcode libraries using xylarium wood specimens, but the importance of using wood rather than leaves as the source tissue, when wood is the botanical material to be identified.