Genetic analysis of baculovirus resistance in lepidopteran model insect Bombyx mori L.

In order to clarify the resistant mechanism of BmNPV in silkworm, and from negative to prove agricultural pest inheritance of virus resistance, in this study, we used the highly resistant strain NB and susceptible strain 306 as the material through the method of classical genetics experiment, and proved that the baculovirus resistance in silkworm is controlled by a pair of autosomal dominant major gene. At the same time, we used random amplification of polymorphic DNA (RAPD) random primers to screen a molecular marker which are in high linkage with the resistant trait. Validity of the molecular marker was proved in BC1, F2 populations, which further demonstrated that the baculovirus resistance in silkworm is controlled by a pair of autosomal dominant major gene. This can provide an effective research basis for the emergence of baculovirus resistance in pest and its resistant mechanism.Key words: Bombyx mori, molecular markers, genetic analysis, biological control, baculovirus resistance

Light Field Salient Object Detection: A Review and Benchmark

Salient object detection (SOD) is a long-standing research topic in computer vision and has drawn an increasing amount of research interest in the past decade. This paper provides the first comprehensive review and benchmark for light field SOD, which has long been lacking in the saliency community. Firstly, we introduce preliminary knowledge on light fields, including theory and data forms, and then review existing studies on light field SOD, covering ten traditional models, seven deep learning-based models, one comparative study, and one brief review. Existing datasets for light field SOD are also summarized with detailed information and statistical analyses. Secondly, we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets, from which insightful discussions and analyses, including a comparison between light field SOD and RGB-D SOD models, are achieved. Besides, due to the inconsistency of datasets in their current forms, we further generate complete data and supplement focal stacks, depth maps and multi-view images for the inconsistent datasets, making them consistent and unified. Our supplemental data makes a universal benchmark possible. Lastly, because light field SOD is quite a special problem attributed to its diverse data representations and high dependency on acquisition hardware, making it differ greatly from other saliency detection tasks, we provide nine hints into the challenges and future directions, and outline several open issues. We hope our review and benchmarking could help advance research in this field. All the materials including collected models, datasets, benchmarking results, and supplemented light field datasets will be publicly available on our project site https://github.com/kerenfu/LFSOD-Survey

High-speed rail and tourism expansion in China: a spatial spillover effect perspective

Tourism exerts a great effect on the modern economy and relies largely on the flow of people facilitated by high-quality transportation infrastructure. Applying a spatial econometric method, this paper investigates the effect of high-speed rail (HSR) on tourism expansion in China from the view of the spatial spillover effect. Based on a 276 Chinese cities’ panel dataset over 2005–2019, a positive role of HSR in tourism expansion is observed. Compared with cities unconnected to the HSR network, cities accessible by HSR experienced a 22% increase in tourism revenue and a 38% rise in tourist arrivals. In addition, the connection of a city to the HSR network also exerts a great spatial spillover role in the increase of tourism revenue and arrivals in peripheral cities which are not directly connected by HSR. The research findings offer important insights on the relationship between transportation infrastructure and tourism with significant policy implications regarding tourism development. First published online 6 November 202

Management of Hepatic Encephalopathy by Traditional Chinese Medicine

In spite of the impressive progress in the investigation of hepatic encephalopathy (HE), the complex mechanisms underlying the onset and deterioration of HE are still not fully understood. Currently, none of the existing theories provide conclusive explanations on the symptoms that link liver dysfunction to nervous system disorders and clinical manifestations. This paper summarized the diagnostic and therapeutic approaches used for HE in modern medicine and traditional Chinese medicine and provided future perspective in HE therapies from the viewpoint of holistic and personalized Chinese medicine

Discovery of 21 New Changing-look AGNs in Northern Sky

The rare case of changing-look (CL) AGNs, with the appearance or disappearance of broad Balmer emission lines within a few years, challenges our understanding of the AGN unified model. We present a sample of 21 new CL AGNs at $0.08<z<0.58$, which doubles the number of such objects known to date. These new CL AGNs were discovered by several ways, from (1) repeat spectra in the SDSS, (2) repeat spectra in the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) and SDSS, and (3) photometric variability and new spectroscopic observations. We use the photometric data from surveys, including the SDSS imaging survey, the Pan-STARRS1, the DESI Legacy imaging survey, the Wide-field Infrared Survey Explorer (WISE), the Catalina Real-time Transient Survey, and the Palomar Transient Factory. The estimated upper limits of transition timescale of the CL AGNs in this sample spans from 0.9 to 13 years in the rest frame. The continuum flux in the optical and mid-infrared becomes brighter when the CL AGNs turn on, or vice versa. Variations of more than 0.2 mag in $W1$ band were detected in 15 CL AGNs during the transition. The optical and mid-infrared variability is not consistent with the scenario of variable obscuration in 10 CL AGNs at more than $3\sigma$ confidence level. We confirm a bluer-when-brighter trend in the optical. However, the mid-infrared WISE colors $W1-W2$ become redder when the objects become brighter in the $W1$ band, possibly due to a stronger hot dust contribution in the $W2$ band when the AGN activity becomes stronger. The physical mechanism of type transition is important for understanding the evolution of AGNs.Comment: Accepted for publication in Ap

Collaborative multiple change detection methods for monitoring the spatio-temporal dynamics of mangroves in Beibu Gulf, China

Mangrove ecosystems are one of the most diverse and productive marine ecosystems around the world, although losses of global mangrove area have been occurring over the past decades. Therefore, tracking spatio-temporal changes and assessing the current state are essential for mangroves conservation. To solve the issues of inaccurate detection results of single algorithms and those limited to historical change detection, this study proposes the detect–monitor–predict (DMP) framework of mangroves for detecting time-series historical changes, monitoring abrupt near-real-time events, and predicting future trends in Beibu Gulf, China, through the synergetic use of multiple detection change algorithms. This study further developed a method for extracting mangroves using multi-source inter-annual time-series spectral indices images, and evaluated the performance of twenty-one spectral indices for capturing expansion events of mangroves. Finally, this study reveals the spatio-temporal dynamics of mangroves in Beibu Gulf from 1986 to 2021. In this study, we found that our method could extract mangrove growth regions from 1986 to 2021, and achieved 0.887 overall accuracy, which proved that this method is able to rapidly extract large-scale mangroves without field-based samples. We confirmed that the normalized difference vegetation index and tasseled cap angle outperform other spectral indexes in capturing mangrove expansion changes, while enhanced vegetation index and soil-adjusted vegetation index capture the change events with a time delay. This study revealed that mangrove changes displayed historical changes in the hierarchical gradient from land to sea with an average annual expansion of 239.822 ha in the Beibu Gulf during 1986–2021, detected slight improvements and deteriorations of some contemporary mangroves, and predicted 72.778% of mangroves with good growth conditions in the future

Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images

Mangrove-forest classification by using deep learning algorithms has attracted increasing attention but remains challenging. The current studies on the transfer classification of mangrove communities between different regions and different sensors are especially still unclear. To fill the research gap, this study developed a new deep-learning algorithm (encoder–decoder with mixed depth-wise convolution and cascade upsampling, MCCUNet) by modifying the encoder and decoder sections of the DeepLabV3+ algorithm and presented three transfer-learning strategies, namely frozen transfer learning (F-TL), fine-tuned transfer learning (Ft-TL), and sensor-and-phase transfer learning (SaP-TL), to classify mangrove communities by using the MCCUNet algorithm and high-resolution UAV multispectral images. This study combined the deep-learning algorithms with recursive feature elimination and principal component analysis (RFE–PCA), using a high-dimensional dataset to map and classify mangrove communities, and evaluated their classification performance. The results of this study showed the following: (1) The MCCUNet algorithm outperformed the original DeepLabV3+ algorithm for classifying mangrove communities, achieving the highest overall classification accuracy (OA), i.e., 97.24%, in all scenarios. (2) The RFE–PCA dimension reduction improved the classification performance of deep-learning algorithms. The OA of mangrove species from using the MCCUNet algorithm was improved by 7.27% after adding dimension-reduced texture features and vegetation indices. (3) The Ft-TL strategy enabled the algorithm to achieve better classification accuracy and stability than the F-TL strategy. The highest improvement in the F1–score of Spartina alterniflora was 19.56%, using the MCCUNet algorithm with the Ft-TL strategy. (4) The SaP-TL strategy produced better transfer-learning classifications of mangrove communities between images of different phases and sensors. The highest improvement in the F1–score of Aegiceras corniculatum was 19.85%, using the MCCUNet algorithm with the SaP-TL strategy. (5) All three transfer-learning strategies achieved high accuracy in classifying mangrove communities, with the mean F1–score of 84.37~95.25%