Different yellowing degrees and the industrial utilization of flue-cured tobacco leaves

Yellowing is a key stage in the curing of flue-cured tobacco (Nicotiana tobacum L.) as much of the chemical transformation occurs during this period. This study examined the effect of different yellowing degrees on the value of flue-cured tobacco leaves at the farm level for both processing and manufacturing. The study was conducted in the counties of Chuxiong, Dali, and Yuxi in Yunnan, China over two years. Yellowing treatments have been designed to have either a mild or a regular yellowing degree. Yield, value, appearance, suction property, smoking characteristics, and physical resistance to further processing were investigated to evaluate the effect of degree of yellowing on the industrial utilization of flue-cured tobacco leaves. The regular yellowing degree enhanced yield, value, and appearance compared to the mild yellowing degree, regardless of cultivar or location; however, physical resistance to further processing and the suction property of the mild yellowing degree treatment were better than with the regular yellowing degree regardless of cultivar or location. Furthermore, although the regular yellowing degree recorded higher smoking characteristic scores than the mild yellowing degree immediately after flue-curing, the scores of mild yellowing degree leaves could be further augmented by increasing intensity in the re-drying stage. The smoking characteristic score in the regular yellowing degree can only be increased by low intensity re-drying, and significantly decreased by mild and high intensity re-drying. Therefore, in terms of industrial utilization, mild yellowing is the better choice for flue-curing tobacco. This study also suggested that the current regular yellowing stage in Yunnan should be shortened to meet the demands of the traditional tobacco industry

Early detection of pine wilt disease tree candidates using time-series of spectral signatures

Pine wilt disease (PWD), caused by pine wood nematode (PWN), poses a tremendous threat to global pine forests because it can result in rapid and widespread infestations within months, leading to large-scale tree mortality. Therefore, the implementation of preventive measures relies on early detection of PWD. Unmanned aerial vehicle (UAV)-based hyperspectral images (HSI) can detect tree-level changes and are thus an effective tool for forest change detection. However, previous studies mainly used single-date UAV-based HSI data, which could not monitor the temporal changes of disease distribution and determine the optimal detection period. To achieve these purposes, multi-temporal data is required. In this study, Pinus koraiensis stands were surveyed in the field from May to October during an outbreak of PWD. Concurrently, multi-temporal UAV-based red, green, and blue bands (RGB) and HSI data were also obtained. During the survey, 59 trees were confirmed to be infested with PWD, and 59 non-infested trees were used as control. Spectral features of each tree crown, such as spectral reflectance, first and second-order spectral derivatives, and vegetation indices (VIs), were analyzed to identify those useful for early monitoring of PWD. The Random Forest (RF) classification algorithm was used to examine the separability between the two groups of trees (control and infested trees). The results showed that: (1) the responses of the tree crown spectral features to PWD infestation could be detected before symptoms were noticeable in RGB data and field surveys; (2) the spectral derivatives were the most discriminable variables, followed by spectral reflectance and VIs; (3) based on the HSI data from July to October, the two groups of trees were successfully separated using the RF classifier, with an overall classification accuracy of 0.75–0.95. Our results illustrate the potential of UAV-based HSI for PWD early monitoring

The complex hexaploid oil‐Camellia genome traces back its phylogenomic history and multi‐omics analysis of Camellia oil biosynthesis

Summary: Oil‐Camellia (Camellia oleifera), belonging to the Theaceae family Camellia, is an important woody edible oil tree species. The Camellia oil in its mature seed kernels, mainly consists of more than 90% unsaturated fatty acids, tea polyphenols, flavonoids, squalene and other active substances, which is one of the best quality edible vegetable oils in the world. However, genetic research and molecular breeding on oil‐Camellia are challenging due to its complex genetic background. Here, we successfully report a chromosome‐scale genome assembly for a hexaploid oil‐Camellia cultivar Changlin40. This assembly contains 8.80 Gb genomic sequences with scaffold N50 of 180.0 Mb and 45 pseudochromosomes comprising 15 homologous groups with three members each, which contain 135 868 genes with an average length of 3936 bp. Referring to the diploid genome, intragenomic and intergenomic comparisons of synteny indicate homologous chromosomal similarity and changes. Moreover, comparative and evolutionary analyses reveal three rounds of whole‐genome duplication (WGD) events, as well as the possible diversification of hexaploid Changlin40 with diploid occurred approximately 9.06 million years ago (MYA). Furthermore, through the combination of genomics, transcriptomics and metabolomics approaches, a complex regulatory network was constructed and allows to identify potential key structural genes (SAD, FAD2 and FAD3) and transcription factors (AP2 and C2H2) that regulate the metabolism of Camellia oil, especially for unsaturated fatty acids biosynthesis. Overall, the genomic resource generated from this study has great potential to accelerate the research for the molecular biology and genetic improvement of hexaploid oil‐Camellia, as well as to understand polyploid genome evolution

Vardiational Bayesian Hybrid Multi-Bernoulli and CPHD Filters for Superpositional Sensors

This paper addresses the problem of multi-target tracking with superpositional sensors, while the covariance matrices of measurement noise are not known. The proposed method is based on the hybrid multi-Bernoulli cardinalized probability hypothesis density (HMB-CPHD) filter, which has been developed for superpositional sensors-based multi-target tracking with known measurement noises. Specifically, we firstly propose the Gaussian mixture (GM) implementation of the HMB-CPHD filter, and then the covariance matrices of measurement noises are augmented into the target state vector, resulting in the Gaussian and inverse Wishart mixture (GIWM) representation of the augmented state. Then the variational Bayesian (VB) method is exploited to approximate the posterior distribution so that it maintains the same form as the prior distribution. A remarkable feature of the proposed method is that it can jointly perform multi-target tracking and measurement noise covariance estimation. The performance of the proposed algorithm is demonstrated via simulations

Polycyclic aromatic hydrocarbons (PAHs) in indoor dusts of Guizhou, southwest of China: status, sources and potential human health risk.

Polycyclic aromatic hydrocarbons (PAHs) were analyzed for 136 indoor dust samples collected from Guizhou province, southwest of China. The ∑18PAHs concentrations ranged from 2.18 μg•g-1 to 14.20 μg•g-1 with the mean value of 6.78 μg•g-1. The highest Σ18PAHs concentration was found in dust samples from orefields, followed by city, town and village. Moreover, the mean concentration of Σ18PAHs in indoor dust was at least 10% higher than that of outdoors. The 4-6 rings PAHs, contributing more than 70% of ∑18PAHs, were the dominant species. PAHs ratios, principal component analysis with multiple linear regression (PCA-MLR) and hierarchical clustering analysis (HCA) were applied to evaluate the possible sources. Two major origins of PAHs in indoor dust were identified as vehicle emissions and coal combustion. The mean incremental lifetime cancer risk (ILCR) due to human exposure to indoor dust PAHs in city, town, village and orefield of Guizhou province, China was 6.14×10-6, 5.00×10-6, 3.08×10-6, 6.02×10-6 for children and 5.92×10-6, 4.83×10-6, 2.97×10-6, 5.81×10-6 for adults, respectively