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The pinewood nematode (PWN), Bursaphelenchus xylophilus, the causal agent of pine wilt disease (PWD), is a serious pest and pathogen of forest tree species, in particular among the genus Pinus. It was first reported from Japan in the beginning of the XXth century, where it became the major ecological catastrophe of pine forests, with losses reaching over 2 million m3/ year in the 1980s. It has since then spread to other Asian countries such as China, Taiwan and Korea, causing serious losses and economic damage. In 1999, the PWN was first detected in the European Union (EU), in Portugal, and immmediately prompted several government (national and EU) actions to assess the extent of the nematode’s presence, and to contain B. xylophilus and its insect vector (Monochamus galloprovincialis) to an area with a 30km radius in the Setúbal Peninsula, 20 km south of Lisbon. International wood trade, with its political as well as economic ramifications, has been seriously jeopardized. The origin of the population of PWN found in Portugal remains elusive. Several hypotheses may be considered regarding pathway analysis, basically from two general origins: North America or the Far East (Japan or China). World trade of wood products such as timber, wooden crates, palettes, etc… play an important role in the potential dissemination of the pinewood nematode. In fact, human activities involving the movement of wood products may be considered the single most important factor in spreading of the PWN. Despite the dedicated and concerted actions of government agencies, this disease continues to spread. Very recently (2006), in Portugal, forestry and phytosanitary authorities (DGRF and DGPC) have announced a new strategy for the control and ultimately the erradication of the nematode, under the coordination of the national program for the control of the pinewood nematode (PROLUNP). Research regarding the bioecology of the nematode and insect as well as new detection methods, e.g., involving real-time PCR, has progressed since 1999. International agreements (GATT, WTO) and sharing of scientific information is of paramount importance to effectively control the nematode and its vector, and thus protect our forest ecosystems and forest economy.
Pinewood nematode. --- Pinewood nematode --- Conifer wilt. --- Pine --- Control. --- Diseases and pests --- Aphelenchoides lignophilus --- Aphelenchoides xylophilus --- Bursaphelenchus lignicolus --- Bursaphelenchus lignophilus --- Bursaphelenchus xylophilus --- Laimaphelenchus lignophilus --- Pine wood nematode --- Bursaphelenchus --- Pine wilt disease --- Wilt disease of pine --- Wilt of conifers --- Conifers --- Nematode diseases of plants --- Wilt diseases --- Pines --- Pinus --- Pinaceae --- Forests and forestry. --- Plant diseases. --- Invertebrates. --- Plant Ecology. --- Forestry. --- Plant Pathology. --- Botany --- Plants --- Ecology --- Invertebrata --- Animals --- Communicable diseases in plants --- Crop diseases --- Crops --- Diseases of plants --- Microbial diseases in plants --- Pathological botany --- Pathology, Vegetable --- Phytopathology --- Plant pathology --- Vegetable pathology --- Agricultural pests --- Crop losses --- Diseased plants --- Phytopathogenic microorganisms --- Plant pathologists --- Plant quarantine --- Forest land --- Forest lands --- Forest planting --- Forest production --- Forest sciences --- Forestation --- Forested lands --- Forestland --- Forestlands --- Forestry --- Forestry industry --- Forestry sciences --- Land, Forest --- Lands, Forest --- Silviculture --- Sylviculture --- Woodlands --- Woods (Forests) --- Agriculture --- Natural resources --- Afforestation --- Arboriculture --- Logging --- Timber --- Tree crops --- Trees --- Pathology --- Diseases --- Wounds and injuries --- Phytoecology --- Vegetation ecology --- Plant pathology. --- Plant ecology. --- Floristic ecology
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Forest tree improvement has mainly been implemented to enhance the productivity of artificial forests. However, given the drastically changing global environment, improvement of various traits related to environmental adaptability is more essential than ever. This book focuses on genetic information, including trait heritability and the physiological mechanisms thereof, which facilitate tree improvement. Nineteen papers are included, reporting genetic approaches to improving various species, including conifers, broad-leaf trees, and bamboo. All of the papers in this book provide cutting-edge genetic information on tree genetics and suggest research directions for future tree improvement.
Research & information: general --- early selection --- stomatal characteristics --- water stress --- water relations --- specific leaf area --- Eucalyptus clones --- LTR-retrotransposon --- Ty3-gypsy --- Ty1-copia --- IRAP --- molecular markers --- bamboo --- Phyllostachys --- genetic diversity --- populations structure --- AMOVA --- central-marginal hypothesis --- cline --- Pinaceae --- trailing edge population --- Sakhalin fir --- sub-boreal forest --- gibberellin --- male strobilus induction --- transcriptome --- conifer --- Cryptomeria japonica --- linkage map --- male sterility --- marker-assisted selection --- C. fortunei --- differentially expressed genes --- phenylpropanoid metabolism --- candidate genes --- Camellia oleifera --- leaf senescence --- transcriptome analysis --- senescence-associated genes --- physiological characterization --- cpDNA --- next generation sequencing --- northern limit --- nucleotide diversity --- phylogeny --- In/Del --- SNP --- SSR --- Chinese fir --- heartwood --- secondary metabolites --- widely targeted metabolomics --- flavonoids --- amplicon sequencing --- AmpliSeq --- genomic selection --- Japanese cedar (Cryptomeria japonica) --- multiplexed SNP genotyping --- spatial autocorrelation error --- pine wood disease --- resistance to pine wood nematode --- inoculation test --- multisite --- cumulative temperature --- Pinus thunbergii --- Thujopsis dolabrata --- EST-SSR markers --- varieties --- population structure --- pine wilt disease --- Bursaphelenchus xylophilus --- genotype by environment interaction --- Japanese black pine --- variance component --- local adaptation --- silviculture --- seed zone --- tree improvement program --- breeding --- genotype × environment interaction --- mast seeding --- seed production --- thinning --- forest tree breeding --- high-throughput phenotyping --- epigenetics --- genotyping --- genomic prediction models --- quantitative trait locus --- breeding cycle --- Cryptomeria japonica var. sinensis --- demographic history --- RAD-seq --- ancient tree --- conservation --- infrared thermography --- chlorophyll fluorescence --- cumulative drought stress --- genetic conservation --- genetic management --- pine wood nematode --- pine wood nematode-Pinus thunbergii resistant trees --- early selection --- stomatal characteristics --- water stress --- water relations --- specific leaf area --- Eucalyptus clones --- LTR-retrotransposon --- Ty3-gypsy --- Ty1-copia --- IRAP --- molecular markers --- bamboo --- Phyllostachys --- genetic diversity --- populations structure --- AMOVA --- central-marginal hypothesis --- cline --- Pinaceae --- trailing edge population --- Sakhalin fir --- sub-boreal forest --- gibberellin --- male strobilus induction --- transcriptome --- conifer --- Cryptomeria japonica --- linkage map --- male sterility --- marker-assisted selection --- C. fortunei --- differentially expressed genes --- phenylpropanoid metabolism --- candidate genes --- Camellia oleifera --- leaf senescence --- transcriptome analysis --- senescence-associated genes --- physiological characterization --- cpDNA --- next generation sequencing --- northern limit --- nucleotide diversity --- phylogeny --- In/Del --- SNP --- SSR --- Chinese fir --- heartwood --- secondary metabolites --- widely targeted metabolomics --- flavonoids --- amplicon sequencing --- AmpliSeq --- genomic selection --- Japanese cedar (Cryptomeria japonica) --- multiplexed SNP genotyping --- spatial autocorrelation error --- pine wood disease --- resistance to pine wood nematode --- inoculation test --- multisite --- cumulative temperature --- Pinus thunbergii --- Thujopsis dolabrata --- EST-SSR markers --- varieties --- population structure --- pine wilt disease --- Bursaphelenchus xylophilus --- genotype by environment interaction --- Japanese black pine --- variance component --- local adaptation --- silviculture --- seed zone --- tree improvement program --- breeding --- genotype × environment interaction --- mast seeding --- seed production --- thinning --- forest tree breeding --- high-throughput phenotyping --- epigenetics --- genotyping --- genomic prediction models --- quantitative trait locus --- breeding cycle --- Cryptomeria japonica var. sinensis --- demographic history --- RAD-seq --- ancient tree --- conservation --- infrared thermography --- chlorophyll fluorescence --- cumulative drought stress --- genetic conservation --- genetic management --- pine wood nematode --- pine wood nematode-Pinus thunbergii resistant trees
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The reprint focuses on artificial intelligence-based learning approaches and their applications in remote sensing fields. The explosive development of machine learning, deep learning approaches and its wide applications in signal processing have been witnessed in remote sensing. The new developments in remote sensing have led to a high resolution monitoring of ground on a global scale, giving a huge amount of ground observation data. Thus, artificial intelligence-based deep learning approaches and its applied signal processing are required for remote sensing. These approaches can be universal or specific tools of artificial intelligence, including well known neural networks, regression methods, decision trees, etc. It is worth compiling the various cutting-edge techniques and reporting on their promising applications.
Technology: general issues --- History of engineering & technology --- Environmental science, engineering & technology --- pine wilt disease dataset --- GIS application visualization --- test-time augmentation --- object detection --- hard negative mining --- video synthetic aperture radar (SAR) --- moving target --- shadow detection --- deep learning --- false alarms --- missed detections --- synthetic aperture radar (SAR) --- on-board --- ship detection --- YOLOv5 --- lightweight detector --- remote sensing image --- spectral domain translation --- generative adversarial network --- paired translation --- synthetic aperture radar --- ship instance segmentation --- global context modeling --- boundary-aware box prediction --- land-use and land-cover --- built-up expansion --- probability modelling --- landscape fragmentation --- machine learning --- support vector machine --- frequency ratio --- fuzzy logic --- artificial intelligence --- remote sensing --- interferometric phase filtering --- sparse regularization (SR) --- deep learning (DL) --- neural convolutional network (CNN) --- semantic segmentation --- open data --- building extraction --- unet --- deeplab --- classifying-inversion method --- AIS --- atmospheric duct --- ship detection and classification --- rotated bounding box --- attention --- feature alignment --- weather nowcasting --- ResNeXt --- radar data --- spectral-spatial interaction network --- spectral-spatial attention --- pansharpening --- UAV visual navigation --- Siamese network --- multi-order feature --- MIoU --- imbalanced data classification --- data over-sampling --- graph convolutional network --- semi-supervised learning --- troposcatter --- tropospheric turbulence --- intercity co-channel interference --- concrete bridge --- visual inspection --- defect --- deep convolutional neural network --- transfer learning --- interpretation techniques --- weakly supervised semantic segmentation --- n/a
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Forest tree improvement has mainly been implemented to enhance the productivity of artificial forests. However, given the drastically changing global environment, improvement of various traits related to environmental adaptability is more essential than ever. This book focuses on genetic information, including trait heritability and the physiological mechanisms thereof, which facilitate tree improvement. Nineteen papers are included, reporting genetic approaches to improving various species, including conifers, broad-leaf trees, and bamboo. All of the papers in this book provide cutting-edge genetic information on tree genetics and suggest research directions for future tree improvement.
early selection --- stomatal characteristics --- water stress --- water relations --- specific leaf area --- Eucalyptus clones --- LTR-retrotransposon --- Ty3-gypsy --- Ty1-copia --- IRAP --- molecular markers --- bamboo --- Phyllostachys --- genetic diversity --- populations structure --- AMOVA --- central-marginal hypothesis --- cline --- Pinaceae --- trailing edge population --- Sakhalin fir --- sub-boreal forest --- gibberellin --- male strobilus induction --- transcriptome --- conifer --- Cryptomeria japonica --- linkage map --- male sterility --- marker-assisted selection --- C. fortunei --- differentially expressed genes --- phenylpropanoid metabolism --- candidate genes --- Camellia oleifera --- leaf senescence --- transcriptome analysis --- senescence-associated genes --- physiological characterization --- cpDNA --- next generation sequencing --- northern limit --- nucleotide diversity --- phylogeny --- In/Del --- SNP --- SSR --- Chinese fir --- heartwood --- secondary metabolites --- widely targeted metabolomics --- flavonoids --- amplicon sequencing --- AmpliSeq --- genomic selection --- Japanese cedar (Cryptomeria japonica) --- multiplexed SNP genotyping --- spatial autocorrelation error --- pine wood disease --- resistance to pine wood nematode --- inoculation test --- multisite --- cumulative temperature --- Pinus thunbergii --- Thujopsis dolabrata --- EST-SSR markers --- varieties --- population structure --- pine wilt disease --- Bursaphelenchus xylophilus --- genotype by environment interaction --- Japanese black pine --- variance component --- local adaptation --- silviculture --- seed zone --- tree improvement program --- breeding --- genotype × environment interaction --- mast seeding --- seed production --- thinning --- forest tree breeding --- high-throughput phenotyping --- epigenetics --- genotyping --- genomic prediction models --- quantitative trait locus --- breeding cycle --- Cryptomeria japonica var. sinensis --- demographic history --- RAD-seq --- ancient tree --- conservation --- infrared thermography --- chlorophyll fluorescence --- cumulative drought stress --- genetic conservation --- genetic management --- pine wood nematode --- pine wood nematode-Pinus thunbergii resistant trees --- n/a
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