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Moisture content is a very important parameter for municipal solid waste (MSW) treatment technology selection and design. However, the moisture content of MSW collected from different urban areas is influenced by its physical composition in these areas. The aim of this study was to analyze the key components of MSW for predicting moisture content in different functional areas via the development of an artificial neural network (ANN) model. The dataset used in this study was collected in Shanghai from 2007 to 2019. Considering the influence of functional areas, the model obtained the performance with MAE of 2.67, RMSE of 3.29, and R2 of 0.83, and an eight-fold cross validation showed acceptable results. The inter-quartile range (IQR) and isolation forest were compared to detect and remove outliers. In descending order, the moisture content was ranked as commercial/residential > office > cleaning areas. Based on a parameter exclusion method, kitchen, rubber, and plastic wastes show the greatest influence on moisture content in residential and commercial areas. In cleaning and office areas, paper, wood and bamboo waste products were the most important components. The determination of key components in different functional areas is of benefit for reducing the workload of moisture content estimation.

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.

DNA barcoding technology has emerged as one of the most promising tools available to identify timber at the species level, contributing to the monitoring of the timber trade and the conservation of forestry sources. This paper reviews the progress, challenges, and existing problems in the development of DNA barcoding for wood identification in the last ten years. There is a focus on the optimization of DNA extraction methods for processed or ancient wood, the strategy of screening high-resolution DNA barcodes suitable for wood identification, the development of a wood DNA reference database especially for priority taxa, and the comparison and comprehensive application of sequence analytical methods to achieve accurate identification. In addition to DNA barcoding, the feasibility of other genetic methods for wood identification is also discussed. Furthermore, future research orientation and strategy of wood DNA barcoding are presented. We argue that wood DNA barcoding integrated with other methodologies including wood anatomy can offer an effective approach and a new perspective to promote legal logging for timber trade custody and global biodiversity conservation.

The isolation of wood DNA is a crucial step in the process of genetic identification of wood tissues and the current wood DNA extraction method is a limiting factor. For some valuable wood samples sent for forensic identification, the size of allowable sample is limited. Additionally, the identification process is so lengthy that it often cannot meet the needs of law enforcement. This study describes an optimized protocol that minimizes the sample size and duration of DNA extraction without decreasing the yield of DNA. Experiments on sample mass per extraction, sample lysis time and DNA precipitation time were carried out by a series of gradient tests. The amounts of DNA extracted were evaluated by the copy numbers of target DNA barcodes (rbcL and matK) from droplet digital PCR (ddPCR). It is the first to apply ddPCR technology to quantify and evaluate accurately DNA extracted from wood. The results indicated that 300 mg is an optimal sample mass when keeping the volume of DNA lysis buffer constant, which reduces the sample usage by 40%. Five hours is the optimal sample lysis time. Extending the duration of DNA precipitation does not significantly increase the amplicon yield from wood specimens of Pterocarpus erinaceus. The protocol developed in this study shortens the period of DNA extraction from wood tissues by approximately 58%. The amplicon yields obtained using the optimized method in this study indicate good extraction efficiency, and the wood samples sent for certification were identified as Pterocarpus erinaceus using the barcode combination matK+ndhF-rpl32+ITS2. This method will be suitable for the broad applicability of DNA identification and conservation of global wood resources.

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.

Made available in DSpace on 2019-10-04T12:13:41Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-05-01 National Natural Science Foundation of China National High-level Talent for Special Support Program of China China Scholarship Council 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-rp132, 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-rp132. 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. Chinese Acad Forestry, Chinese Res Inst Wood Ind, Dept Wood Anat & Utilizat, Beijing 100091, Peoples R China Chinese Acad Forestry, Wood Collect WOODPEDIA, Beijing 100091, Peoples R China US Forest Serv, Forest Prod Lab, Ctr Wood Anat Res, USDA, Madison, WI 53726 USA Univ Wisconsin, Dept Bot, Madison, WI 53706 USA Purdue Univ, Dept Forestry & Natl Resources, W Lafayette, IN 47907 USA Univ Estadual Paulista, Ciencias Biol Bot, Botucatu, SP, Brazil Univ Estadual Paulista, Ciencias Biol Bot, Botucatu, SP, Brazil National Natural Science Foundation of China: 31600451 National High-level Talent for Special Support Program of China: W02020331 China Scholarship Council: 2017-3109

Pterocarpus santalinus, listed in CITES Appendix II, is an endangered timber species as a result of illegal harvesting due to its high value and commercial demand. The growing demand for P. santalinus and timbers with the morphologically similar Pterocarpus tinctorius has resulted in confusion as well as identification problems. Therefore, it is of vital importance to explore reliable ways to accurately discriminate between P. santalinus and P. tinctorius. In this study, the method of direct analysis in real time and fourier transform ion cyclotron resonance mass spectrometry (DART-FTICR-MS), combined with multivariate statistical analysis, was used to extract chemical information from xylarium wood specimens and to explore the feasibility of distinguishing these two species. Significant differences were observed in their DART-FTICR-MS spectra. Orthogonal partial least square-discriminant analysis (OPLS-DA) showed the highest prediction, with an accuracy of 100%. These findings demonstrate the feasibility of authenticating wood types using DART-FTICR-MS coupled with multivariate statistical analysis.

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%.

Reliable wood identification and proof of the provenance of trees is the first step for combating illegal logging. DNA barcoding belongs to the promising tools in this regard, for which reliable methods and reference libraries are needed. Machine learning approaches (MLAs) are tailored to the necessities of DNA barcoding, which are based on mathematical multivaried analysis. In the present study, eightDalbergiatimber species were investigated in terms of their DNA sequences focusing on four barcodes (ITS2,matK,trnH-psbA andtrnL) by means of the MLAs BLOG and WEKA for wood species identification. The data material downloaded from NCBI (288 sequences) and taken from a previous study of the authors (153 DNA sequences) was taken as dataset for calibration. The MLAs’ effectivity was verified through identification of non-vouchered wood specimens. The results indicate that the SMO classifier as part of the WEKA approach performed the best (98%~100%) for discriminating the eightDalbergiatimber species. Moreover, the two-locus combination ITS2+trnH-psbA showed the highest success rate. Furthermore, the non-vouchered wood specimens were successfully identified by means of ITS2+trnH-psbA with the SMO classifier. The MLAs are successful in combi- nation with DNA barcode reference libraries for the identification of endangeredDalbergiatimber species.

Illegal logging and associated trade aggravate the over-exploitation of Swietenia species, of which S. macrophylla King, S. mahagoni (L.) Jacq, and S. humilis Zucc. have been listed in Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Appendix Ⅱ. Implementation of CITES necessitates the development of efficient forensic tools to identify wood species accurately, and ideally ones readily deployable in wood anatomy laboratories across the world. Herein, a method using quantitative wood anatomy data in combination with machine learning models to discriminate between three Swietenia species is presented, in addition to a second model focusing only on the two historically more important species S. mahagoni and S. macrophylla. The intra- and inter-specific variations in nine quantitative wood anatomical characters were measured and calculated based on 278 wood specimens, and four machine learning classifiers&mdash, Decision Tree C5.0, Na&iuml, ve Bayes (NB), Support Vector Machine (SVM), and Artificial Neural Network (ANN)&mdash, were used to discriminate between the species. Among these species, S. macrophylla exhibited the largest intraspecific variation, and all three species showed at least partly overlapping values for all nine characters. SVM performed the best of all the classifiers, with an overall accuracy of 91.4% and a per-species correct identification rate of 66.7%, 95.0%, and 80.0% for S. humilis, S. macrophylla, and S. mahagoni, respectively. The two-species model discriminated between S. macrophylla and S. mahagoni with accuracies of over 90.0% using SVM. These accuracies are lower than perfect forensic certainty but nonetheless demonstrate that quantitative wood anatomy data in combination with machine learning models can be applied as an efficient tool to discriminate anatomically between similar species in the wood anatomy laboratory. It is probable that a range of previously anatomically inseparable species may become identifiable by incorporating in-depth analysis of quantitative characters and appropriate statistical classifiers.

The East Indian sandalwood, Santalum album L., is known for its fragrant heartwood and extractive oils. The increasing demand for the valuable sandalwood products has led to illegal and excessive logging, and there are other Santalum species in the commercial market as substitutes (adulterants). Improved tools for the identification of Santalum species are needed to control this situation. In this study, four chloroplast DNA (cpDNA) regions were screened (matK, psbA-trnH, trnK and trnL) on 35 vouchered specimens covering five Santalum species, i.e. Santalum acuminatum, S. album, Santalum lanceolatum, Santalum murrayanum and Santalum spicatum. The goal of this study was to test the species discrimination ability by means of both the TaxonDNA and neighbor-joining (NJ) methods. The results indicate that the combination of psbA-trnH+trnK gave the best performance for discrimination (100%) of the studied Santalum species concerning the discrimination ability and recovery rate. Meanwhile, six unvouchered wood specimens were retrieved and accurately identified at the species level based on the recommended DNA barcodes. The DNA barcode method is now ready for application in the monitoring of the trade of this valuable resource, and provides an effective approach for wood species identification and product authentication.

Ray traits affect secondary xylem development and wood properties. Pinus massonia and Cunninghamia lanceolata, commercially important timber species, were chosen to study the differences in wood ray traits of juvenile versus mature wood. Seven ray traits, i.e., percentage of rays, ray spacing, ray number, uniseriate ray height, fusiform ray height, ray parenchyma cell length and ray tracheid length, as well as eight wood axial tissue traits, were investigated quantitatively. Intraspecific variations in ray traits and axial tissue traits between juvenile wood and mature wood were displayed in violin plots. The results showed that anatomical differences between juvenile wood and mature wood were significant for both ray traits and axial tissue traits. Juvenile wood generally possessed the larger percentage of rays, higher ray spacing and ray number, smaller ray height and shorter ray cells than mature wood. A positive correlation was present between the ray parenchyma cell length and ray tracheid length. Negative correlations of the ray number and ray spacing with uniseriate ray height were found. Additionally, the axial tracheid cell wall thickness all had Pearson’s correlations with ray spacing, ray number and ray parenchyma cell length.

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.

Made available in DSpace on 2018-11-26T15:47:17Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-01-31 Fundamental Research Funds of CAF National Natural Science Foundation of China China Postdoctoral Science Foundation China Scholarship Council 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. Chinese Acad Forestry, Chinese Res Inst Wood Ind, Dept Wood Anat & Utilizat, Beijing 100091, Peoples R China Chinese Acad Forestry, Wood Collect WOODPEDIA, Beijing 100091, Peoples R China USDA Forest Serv, Ctr Wood Anat Res, Forest Prod Lab, Madison, WI 53726 USA Univ Wisconsin, Dept Bot, Madison, WI 53706 USA Purdue Univ, Dept Forestry & Nat Resources, W Lafayette, IN 47907 USA Univ Estadual Paulista, Ciencias Biol Bot, Botucatu, SP, Brazil Chinese Acad Trop Agr Sci, Rubber Res Inst, Haikou 571737, Hainan, Peoples R China Univ Estadual Paulista, Ciencias Biol Bot, Botucatu, SP, Brazil Fundamental Research Funds of CAF: CAFYBB2017MA013 Fundamental Research Funds of CAF: CAFYBB2017ZE003 National Natural Science Foundation of China: 31600451 China Postdoctoral Science Foundation: 2016M590152 China Scholarship Council: 2016-3035

Made available in DSpace on 2018-11-26T15:45:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-12-01 China Postdoctoral Science Foundation National Natural Science Foundation of China Fundamental Research Funds of Chinese Academy of Forestry China Scholarship Council U.S. State Department Interagency Agreement 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 (ae90%) 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. Chinese Acad Forestry, Chinese Res Inst Wood Ind, Dept Wood Anat & Utilizat, Beijing 100091, Peoples R China Chinese Acad Forestry, Wood Collect WOODPEDIA, Beijing 100091, Peoples R China US Forest Serv, Ctr Wood Anat Res, USDA, Forest Prod Lab, Madison, WI 53726 USA Univ Wisconsin, Dept Bot, Madison, WI 53706 USA Purdue Univ, Dept Forestry & Nat Resources, W Lafayette, IN 47907 USA Univ Estadual Paulista, Ciencias Biol Bot, Sao Paulo, Brazil Univ Estadual Paulista, Ciencias Biol Bot, Sao Paulo, Brazil China Postdoctoral Science Foundation: 2016M590152 National Natural Science Foundation of China: 31600451 Fundamental Research Funds of Chinese Academy of Forestry: CAFYBB2017ZE003 China Scholarship Council: 2016-3035 U.S. State Department Interagency Agreement: 19318814Y0010-140001-0001/P00001

This study explored an integrated proposal of using dewatered sludge to prepare media for Cryptococcus curvatus ATCC 20509 for accumulation of microbial lipids. A volatile fatty acids (VFAs)-containing solution produced by thermal alkaline pretreatment and anaerobic fermentation was used as the cultivation media. Combined conditioning processes including coagulation, acid separation, and struvite precipitation were innovatively set up to remove 98.1% of the ammonia nitrogen and 24.8%–52.3% of unavailable organics in the solution. The conditioned solution could be bio-converted to lipids at about 1.95 g/L with 39.7% lipid content, which was comparable to that achieved with waste activated sludge. The proportions of monounsaturated and polyunsaturated fatty acids were enhanced to 67.1%–74.4% by the integrated conditioning processes. Lipids produced from dewatered sludge were proposed to be favorable raw materials for biodiesel production.

Cloud computing technology is an emerging technology for various types of vast information to be processed in the data centers to overcome the serious shortage of resource such as space, power and cost, etc. Along with the development of cloud computing, security of cloud computing is becoming more and more critical. In this paper, we aim to propose a set of secure and efficient authenticated multi-party key agreement protocols based on the hierarchical identity-based cryptography (HIBC) for private cloud. For different scenarios, we design loop-level and cascade-level authentication protocols among users in a private cloud of hierarchical structure, respectively. The new multi-party protocols are supposed to be more efficient and secure than the other existing solutions.