High genetic abundance of Rpi-blb2/Mi-1.2/Cami gene family in Solanaceae

Relative genomic positions of genes among potato (upper), pepper (middle) and tomato (lower) along chromosome 6. (DOCX 282 kb

Preparation and Application of Starch/Polyvinyl Alcohol/Citric Acid Ternary Blend Antimicrobial Functional Food Packaging Films

Ternary blend films were prepared with different ratios of starch/polyvinyl alcohol (PVA)/citric acid. The films were characterized by field emission scanning electron microscopy (FE-SEM), thermogravimetric analysis, as well as Fourier transform infrared (FTIR) analysis. The influence of different ratios of starch/polyvinyl alcohol (PVA)/citric acid and different drying times on the performance properties, transparency, tensile strength (TS), water vapor permeability (WVP), water solubility (WS), color difference (ΔE), and antimicrobial activity of the ternary blends films were investigated. The starch/polyvinyl alcohol/citric acid (S/P/C1:1:0, S/P/C3:1:0.08, and S/P/C3:3:0.08) films were all highly transparent. The S/P/C3:3:0.08 had a 54.31 times water-holding capacity of its own weight and its mechanical tensile strength was 46.45 MPa. In addition, its surface had good uniformity and compactness. The S/P/C3:1:0.08 and S/P/C3:3:0.08 showed strong antimicrobial activity to Listeria monocytogenes and Escherichia coli, which were the food-borne pathogenic bacteria used. The freshness test results of fresh figs showed that all of the blends prevented the formation of condensed water on the surface of the film, and the S/P/C3:1:0.08 and S/P/C3:3:0.08 prevented the deterioration of figs during storage. The films can be used as an active food packaging system due to their strong antibacterial effect

Classification of Tree Species in Different Seasons and Regions Based on Leaf Hyperspectral Images

This paper aims to establish a tree species identification model suitable for different seasons and regions based on leaf hyperspectral images, and to mine a more effective hyperspectral identification algorithm. Firstly, the reflectance spectra of leaves in different seasons and regions were analyzed. Then, to solve the problem that 0-element in sparse random (SR) coding matrices affects the classification performance of error-correcting output codes (ECOC), two versions of supervision-mechanism-based ECOC algorithms, namely SM-ECOC-V1 and SM-ECOC-V2, were proposed in this paper. In addition, the performance of the proposed algorithms was compared with that of six traditional algorithms based on all bands and feature bands. The experiment results show that seasonal and regional changes have an effect on the reflectance spectra of leaves, especially in the near-infrared region of 760–1000 nm. When the spectral information of different seasons and different regions is added into the identification model, tree species can be effectively classified. SM-ECOC-V2 achieves the best classification performance based on both all bands and feature bands. Furthermore, both SM-ECOC-V1 and SM-ECOC-V2 outperform the ECOC method under SR coding strategy, indicating the proposed methods can effectively avoid the influence of 0-element in SR coding matrix on classification performance

Classification of Tree Species in Different Seasons and Regions Based on Leaf Hyperspectral Images

This paper aims to establish a tree species identification model suitable for different seasons and regions based on leaf hyperspectral images, and to mine a more effective hyperspectral identification algorithm. Firstly, the reflectance spectra of leaves in different seasons and regions were analyzed. Then, to solve the problem that 0-element in sparse random (SR) coding matrices affects the classification performance of error-correcting output codes (ECOC), two versions of supervision-mechanism-based ECOC algorithms, namely SM-ECOC-V1 and SM-ECOC-V2, were proposed in this paper. In addition, the performance of the proposed algorithms was compared with that of six traditional algorithms based on all bands and feature bands. The experiment results show that seasonal and regional changes have an effect on the reflectance spectra of leaves, especially in the near-infrared region of 760–1000 nm. When the spectral information of different seasons and different regions is added into the identification model, tree species can be effectively classified. SM-ECOC-V2 achieves the best classification performance based on both all bands and feature bands. Furthermore, both SM-ECOC-V1 and SM-ECOC-V2 outperform the ECOC method under SR coding strategy, indicating the proposed methods can effectively avoid the influence of 0-element in SR coding matrix on classification performance

Pathological and immunohistochemical study of lethal primary brain stem injuries

Abstract Background Many of the deaths that occur shortly after injury or in hospitals are caused by mild trauma. Slight morphological changes are often found in the brain stems of these patients during autopsy. The purpose of this study is to investigate the histopathological changes involved in primary brain stem injuries (PBSI) and their diagnostic significance. Methods A total of 65 patients who had died of PBSI and other conditions were randomly selected. They were divided into 2 groups, an injury group (25 cases) and a control group (20 cases). Slides of each patient’s midbrain, pons, and medulla oblongata were prepared and stained with HE, argentaffin, and immunohistochemical agents (GFAP, NF, amyloid-ß, MBP). Under low power (×100) and NF staining, the diameter of the thickest longitudinal axon was measured at its widest point. Ten such diameters were collected for each part of the brain (midbrain, pons, and medulla oblongata). Data were recorded and analyzed statistically. Results Brain stem contusions, astrocyte activity, edema, and pathological changes in the neurons were visibly different in the injury and control groups (P P  Conclusions These histopathological changes may prove beneficial to the pathological diagnosis of PBSI during autopsy. The measurement of axon diameters provides a referent quantitative index for the diagnosis of the specific causes of death involved in PBSI. Virtual Slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1345298818712204</p

A Query-Based Network for Rural Homestead Extraction from VHR Remote Sensing Images

It is very significant for rural planning to accurately count the number and area of rural homesteads by means of automation. The development of deep learning makes it possible to achieve this goal. At present, many effective works have been conducted to extract building objects from VHR images using semantic segmentation technology, but they do not extract instance objects and do not work for densely distributed and overlapping rural homesteads. Most of the existing mainstream instance segmentation frameworks are based on the top-down structure. The model is complex and requires a large number of manually set thresholds. In order to solve the above difficult problems, we designed a simple query-based instance segmentation framework, QueryFormer, which includes an encoder and a decoder. A multi-scale deformable attention mechanism is incorporated into the encoder, resulting in significant computational savings, while also achieving effective results. In the decoder, we designed multiple groups, and used a Many-to-One label assignment method to make the image feature region be queried faster. Experiments show that our method achieves better performance (52.8AP) than the other most advanced models (+0.8AP) in the task of extracting rural homesteads in dense regions. This study shows that query-based instance segmentation framework has strong application potential in remote sensing images

Land Cover Classification from Hyperspectral Images via Weighted Spatial–Spectral Joint Kernel Collaborative Representation Classifier

The continuous changes in Land Use and Land Cover (LULC) produce a significant impact on environmental factors. Highly accurate monitoring and updating of land cover information is essential for environmental protection, sustainable development, and land resource planning and management. Recently, Collaborative Representation (CR)-based methods have been widely used in land cover classification from Hyperspectral Images (HSIs). However, most CR methods consider the spatial information of HSI by taking the average or weighted average of spatial neighboring pixels of each pixel to improve the land cover classification performance, but do not take the spatial structure information for pixels into account. To address this problem, a novel Weighted Spatial–Spectral Joint CR Classification (WSSJCRC) method is proposed in this paper. WSSJCRC only performs spatial filtering on HSI through a weighted spatial filtering operator to alleviate the spectral shift caused by adjacency effect, but also utilizes the labeled training pixels to simultaneously represent each test pixel and its spatial neighborhood pixels to consider the spatial structure information of each test pixel to assist the classification of the test pixel. On this basis, the kernel version of WSSJCRC (i.e., WSSJKCRC) is also proposed, which projects the hyperspectral data into the kernel-induced high-dimensional feature space to enhance the separability of nonlinear samples. The experimental results on three real hyperspectral scenes show that the proposed WSSJKCRC method achieves the best land cover classification performance among all the compared methods. Specifically, the Overall Accuracy (OA), Average Accuracy (AA), and Kappa statistic (Kappa) of WSSJKCRC reach 96.21%, 96.20%, and 0.9555 for the Indian Pines scene, 97.02%, 96.64%, and 0.9605 for the Pavia University scene, and 95.55%, 97.97%, and 0.9504 for the Salinas scene, respectively. Moreover, the proposed WSSJKCRC method obtains the promising accuracy with OA over 95% on the three hyperspectral scenes under the situation of small-scale labeled samples, thus effectively reducing the labeling cost for HSI

Non-Destructive Detection of Moldy Walnuts Based on Hyperspectral Imaging Technology

Walnuts with their shells are a popular agricultural product in China. However, mildew from growth can sometimes be processed into foods. It is difficult to visually determine which walnuts have mildew without breaking the shells. A non-destructive method for detecting walnuts with mildew was studied by combining spectral data with image information. A total of 120 &ldquo;L&uuml;ling&rdquo; walnuts with shells were used for the mildew experiment. The characteristics of the spectral data from six surfaces of all samples were collected in the range of 370&ndash;1042 nm on days 0, 15, and 30. The spectrum was pretreated using SNV, and the feature bands were extracted using PCA and modeled using a support vector machine (SVM). The results show that the overall classification accuracy was 93%, with an of accuracy of 100% for INEN walnuts (normal internally and externally). The accuracy for IMEM walnuts (mildew internally and externally) reached 87.29%. There was an accuracy of 78.6% for IMEN walnuts (mildew internally and normal externally). The non-destructive detection of mildewed walnuts can be undertaken using hyperspectral imaging technology, which provides a new technique for exploring the mechanisms of walnuts with mildew

Long Noncoding RNA H19 in Digestive System Cancers: A Meta-Analysis of Its Association with Pathological Features

Long noncoding RNA (lncRNA) H19 has been reported to be upregulated in malignant digestive tumors, but its clinical relevance is not yet established. The meta-analysis was to investigate the association between H19 expression and pathological features of digestive system cancers. The databases of PubMed, EMBase, Web of Science, CNKI, and WanFang were searched for the related studies. A total of 478 patients from 6 studies were finally included. The meta-analysis showed that the patient group of high H19 expression had a higher risk of poorly differentiated grade, deep tumor invasion (T2 stage or more), lymph node metastasis, and advanced TNM stage than the group of low H19 expression, although there was no difference between them in terms of distant metastasis. Therefore, the high expression of lncRNA H19 might predict poor oncological outcomes of patients with digestive system cancers

Land Cover Classification from Hyperspectral Images via Weighted Spatial-Spectral Kernel Collaborative Representation with Tikhonov Regularization

Precise and timely classification of land cover types plays an important role in land resources planning and management. In this paper, nine kinds of land cover types in the acquired hyperspectral scene are classified based on the kernel collaborative representation method. To reduce the spectral shift caused by adjacency effect when mining the spatial-spectral features, a correlation coefficient-weighted spatial filtering operation is proposed in this paper. Additionally, by introducing this operation into the kernel collaborative representation method with Tikhonov regularization (KCRT) and discriminative KCRT (DKCRT) method, respectively, the weighted spatial-spectral KCRT (WSSKCRT) and weighted spatial-spectral DKCRT (WSSDKCRT) methods are constructed for land cover classification. Furthermore, aiming at the problem of difficulty of pixel labeling in hyperspectral images, this paper attempts to establish an effective land cover classification model in the case of small-size labeled samples. The proposed WSSKCRT and WSSDKCRT methods are compared with four methods, i.e., KCRT, DKCRT, KCRT with composite kernel (KCRT-CK), and joint DKCRT (JDKCRT). The experimental results show that the proposed WSSKCRT method achieves the best classification performance, and WSSKCRT and WSSDKCRT outperform KCRT-CK and JDKCRT, respectively, obtaining the OA over 94% with only 540 labeled training samples, which indicates that the proposed weighted spatial filtering operation can effectively alleviate the spectral shift caused by adjacency effect, and it can effectively classify land cover types under the situation of small-size labeled samples