Comparative Transcriptomics of Multi-Stress Responses in Pachycladon cheesemanii and Arabidopsis thaliana.

The environment is seldom optimal for plant growth and changes in abiotic and biotic signals, including temperature, water availability, radiation and pests, induce plant responses to optimise survival. The New Zealand native plant species and close relative to Arabidopsis thaliana, Pachycladon cheesemanii, grows under environmental conditions that are unsustainable for many plant species. Here, we compare the responses of both species to different stressors (low temperature, salt and UV-B radiation) to help understand how P. cheesemanii can grow in such harsh environments. The stress transcriptomes were determined and comparative transcriptome and network analyses discovered similar and unique responses within species, and between the two plant species. A number of widely studied plant stress processes were highly conserved in A. thaliana and P. cheesemanii. However, in response to cold stress, Gene Ontology terms related to glycosinolate metabolism were only enriched in P. cheesemanii. Salt stress was associated with alteration of the cuticle and proline biosynthesis in A. thaliana and P. cheesemanii, respectively. Anthocyanin production may be a more important strategy to contribute to the UV-B radiation tolerance in P. cheesemanii. These results allowed us to define broad stress response pathways in A. thaliana and P. cheesemanii and suggested that regulation of glycosinolate, proline and anthocyanin metabolism are strategies that help mitigate environmental stress.

Collaborative-guided spectral abundance learning with bilinear mixing model for hyperspectral subpixel target detection.

Detecting subpixel targets is a considerably challenging issue in hyperspectral image processing and interpretation. Most of the existing hyperspectral subpixel target detection methods construct detectors based on the linear mixing model which regards a pixel as a linear combination of different spectral signatures. However, due to the multiple scattering, the linear mixing model cannot​ illustrate the multiple materials interactions that are nonlinear and widespread in real-world hyperspectral images, which could result in unsatisfactory performance in detecting subpixel targets. To alleviate this problem, this work presents a novel collaborative-guided spectral abundance learning model (denoted as CGSAL) for subpixel target detection based on the bilinear mixing model in hyperspectral images. The proposed CGSAL detects subpixel targets by learning a spectral abundance of the target signature in each pixel. In CGSAL, virtual endmembers and their abundance help to achieve good accuracy for modeling nonlinear scattering accounts for multiple materials interactions according to the bilinear mixing model. Besides, we impose a collaborative term to the spectral abundance learning model to emphasize the collaborative relationships between different endmembers, which contributes to accurate spectral abundance learning and further help to detect subpixel targets. Plentiful experiments and analyses are conducted on three real-world and one synthetic hyperspectral datasets to evaluate the effectiveness of the CGSAL in subpixel target detection. The experiment results demonstrate that the CGSAL achieves competitive performance in detecting subpixel targets and outperforms other state-of-the-art hyperspectral subpixel target detectors.

Weighted Feature Fusion of Convolutional Neural Network and Graph Attention Network for Hyperspectral Image Classification.

Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), such as Graph Attention Networks (GAT), are two classic neural network models, which are applied to the processing of grid data and graph data respectively. They have achieved outstanding performance in hyperspectral images (HSIs) classification field, which have attracted great interest. However, CNN has been facing the problem of small samples and GNN has to pay a huge computational cost, which restrict the performance of the two models. In this paper, we propose Weighted Feature Fusion of Convolutional Neural Network and Graph Attention Network (WFCG) for HSI classification, by using the characteristics of superpixel-based GAT and pixel-based CNN, which proved to be complementary. We first establish GAT with the help of superpixel-based encoder and decoder modules. Then we combined the attention mechanism to construct CNN. Finally, the features are weighted fusion with the characteristics of two neural network models. Rigorous experiments on three real-world HSI data sets show WFCG can fully explore the high-dimensional feature of HSI, and obtain competitive results compared to other state-of-the art methods.

Asymmetric Weighted Logistic Metric Learning for Hyperspectral Target Detection.

Traditional target detection methods assume that the background spectrum is subject to the Gaussian distribution, which may only perform well under certain conditions. In addition, traditional target detection methods suffer from the problem of the unbalanced number of target and background samples. To solve these problems, this study presents a novel target detection method based on asymmetric weighted logistic metric learning (AWLML). We first construct a logistic metric-learning approach as an objective function with a positive semidefinite constraint to learn the metric matrix from a set of labeled samples. Then, an asymmetric weighted strategy is provided to emphasize the unbalance between the number of target and background samples. Finally, an accelerated proximal gradient method is applied to identify the global minimum value. Extensive experiments on three challenging hyperspectral datasets demonstrate that the proposed AWLML algorithm improves the state-of-the-art target detection performance.

Spectral-Spatial Weighted Kernel Manifold Embedded Distribution Alignment for Remote Sensing Image Classification.

Feature distortions of data are a typical problem in remote sensing image classification, especially in the area of transfer learning. In addition, many transfer learning-based methods only focus on spectral information and fail to utilize spatial information of remote sensing images. To tackle these problems, we propose spectral-spatial weighted kernel manifold embedded distribution alignment (SSWK-MEDA) for remote sensing image classification. The proposed method applies a novel spatial information filter to effectively use similarity between nearby sample pixels and avoid the influence of nonsample pixels. Then, a complex kernel combining spatial kernel and spectral kernel with different weights is constructed to adaptively balance the relative importance of spectral and spatial information of the remote sensing image. Finally, we utilize the geometric structure of features in manifold space to solve the problem of feature distortions of remote sensing data in transfer learning scenarios. SSWK-MEDA provides a novel approach for the combination of transfer learning and remote sensing image characteristics. Extensive experiments have demonstrated that the proposed method is more effective than several state-of-the-art methods.

Genome draft of the Arabidopsis relative Pachycladon cheesemanii reveals novel strategies to tolerate New Zealand's high ultraviolet B radiation environment.

BACKGROUND: Pachycladon cheesemanii is a close relative of Arabidopsis thaliana and is an allotetraploid perennial herb which is widespread in the South Island of New Zealand. It grows at altitudes of up to 1000 m where it is subject to relatively high levels of ultraviolet (UV)-B radiation. To gain first insights into how Pachycladon copes with UV-B stress, we sequenced its genome and compared the UV-B tolerance of two Pachycladon accessions with those of two A. thaliana accessions from different altitudes. RESULTS: A high-quality draft genome of P. cheesemanii was assembled with a high percentage of conserved single-copy plant orthologs. Synteny analysis with genomes from other species of the Brassicaceae family found a close phylogenetic relationship of P. cheesemanii with Boechera stricta from Brassicaceae lineage I. While UV-B radiation caused a greater growth reduction in the A. thaliana accessions than in the P. cheesemanii accessions, growth was not reduced in one P. cheesemanii accession. The homologues of A. thaliana UV-B radiation response genes were duplicated in P. cheesemanii, and an expression analysis of those genes indicated that the tolerance mechanism in P. cheesemanii appears to differ from that in A. thaliana. CONCLUSION: Although the P. cheesemanii genome shows close similarity with that of A. thaliana, it appears to have evolved novel strategies allowing the plant to tolerate relatively high UV-B radiation.

Genome-Wide Analysis of ROS Antioxidant Genes in Resurrection Species Suggest an Involvement of Distinct ROS Detoxification Systems during Desiccation.

Abiotic stress is one of the major threats to plant crop yield and productivity. When plants are exposed to stress, production of reactive oxygen species (ROS) increases, which could lead to extensive cellular damage and hence crop loss. During evolution, plants have acquired antioxidant defense systems which can not only detoxify ROS but also adjust ROS levels required for proper cell signaling. Ascorbate peroxidase (APX), glutathione peroxidase (GPX), catalase (CAT) and superoxide dismutase (SOD) are crucial enzymes involved in ROS detoxification. In this study, 40 putative APX, 28 GPX, 16 CAT, and 41 SOD genes were identified from genomes of the resurrection species Boea hygrometrica, Selaginella lepidophylla, Xerophyta viscosa, and Oropetium thomaeum, and the mesophile Selaginella moellendorffii. Phylogenetic analyses classified the APX, GPX, and SOD proteins into five clades each, and CAT proteins into three clades. Using co-expression network analysis, various regulatory modules were discovered, mainly involving glutathione, that likely work together to maintain ROS homeostasis upon desiccation stress in resurrection species. These regulatory modules also support the existence of species-specific ROS detoxification systems. The results suggest molecular pathways that regulate ROS in resurrection species and the role of APX, GPX, CAT and SOD genes in resurrection species during stress.

Species shift and multidrug resistance of Campylobacter from chicken and swine, China, 2008-14.

OBJECTIVES: The objective of this study was to investigate the prevalence and antimicrobial resistance of Campylobacter isolated from broiler chickens and swine during 2008-14. METHODS: Campylobacter isolates were collected from samples of intestinal content and excreta from broiler chickens and swine from slaughter houses as well as conventional farms in five Chinese provinces during 2008-14. The agar dilution method was used to determine the susceptibility of Campylobacter isolates to seven antimicrobial agents. The χ(2) test and Fisher's exact test were used to perform the statistical analysis. RESULTS: In total, 989 Campylobacter jejuni and 1991 Campylobacter coli were isolated from 10 535 samples. MIC results revealed a high prevalence of multidrug resistance among these Campylobacter isolates. In addition, we observed an apparent shift of the dominant species from C. jejuni to C. coli in chickens and this species shift coincided with an increased prevalence of macrolide-resistant C. coli. It is worth noting that almost 100% of the C. jejuni and C. coli isolates were resistant to fluoroquinolones. CONCLUSIONS: The high prevalence of fluoroquinolone and macrolide resistance in Campylobacter suggests that these two clinically important antibiotic classes may no longer be suitable for the treatment of human campylobacteriosis in China. Thus, enhanced surveillance and control efforts are needed to reduce antimicrobial resistance in this group of major foodborne pathogens.

Patatin-related phospholipase pPLAIIIδ influences auxin-responsive cell morphology and organ size in Arabidopsis and Brassica napus.

BACKGROUND: The members of the patatin-related phospholipase subfamily III (pPLAIIIs) have been implicated in the auxin response. However, it is not clear whether and how these genes affect plant and cell morphogenesis. Here, we studied the roles of the patatin-related phospholipase pPLAIIIδ in auxin-responsive cell morphology and organ size in Arabidopsis and Brassica napus. RESULTS: We show that overexpression of pPLAIIIδ inhibited longitudinal growth but promoted transverse growth in most organs of Arabidopsis and Brassica napus. Compared to wild-type plants, pPLAIIIδ-KO plants exhibited enhanced cell elongation in hypocotyls, and pPLAIIIδ-OE plants displayed broadened radial cell growth of hypocotyl and reduced leaf pavement cell polarity. For the hypocotyl phenotype in pPLAIIIδ mutants, which resembles the "triple response" to ethylene, we examined the expression of the ACS and ACO genes involved in ethylene biosynthesis and found that ACS4 and ACS5 were up-regulated by 2.5-fold on average in two OE lines compared with WT plants. The endogenous auxin distribution was disturbed in plants with altered pPLAIIIδ expression. pPLAIIIδ-OE and KO plants exhibited different sensitivities to indole-3-acetic acid-promoted hypocotyl elongation in both light and dark conditions. Gene expression analysis of auxin-induced genes in the dark showed that OE plants maintained a higher auxin response compared with WT and KO plants after treatment with 1 µM IAA for 12 h. Following treatment with 10 µM IAA for 30 min in the light, early auxin-induced genes were significantly up-regulated in two OE plant lines. CONCLUSIONS: These data suggest that the PLAIIIδ gene plays an important role in cell morphology and organ size through its involvement in the regulation of auxin distribution in plants.

Identification of heat responsive genes in Brassica napus siliques at the seed-filling stage through transcriptional profiling.

High temperature stress results in yield loss and alterations to seed composition during seed filling in oilseed rape (Brassica napus). However, the mechanism underlying this heat response is poorly understood. In this study, global transcription profiles of 20 d-old siliques of B. napus were analyzed after heat stress using a Brassica 95k EST microarray. The up-regulated genes included many HSF/HSP transcripts and other heat-related marker genes, such as ROF2, DREB2a, MBF1c and Hsa32, reflecting the conservation of key heat resistance factors among plants. Other up-regulated genes were preferentially expressed in heat-stressed silique walls or seeds, including some transcription factors and potential developmental regulators. In contrast, down-regulated genes differed between the silique wall and seeds and were largely tied to the biological functions of each tissue, such as glucosinolate metabolism in the silique wall and flavonoid synthesis in seeds. Additionally, a large proportion (one-third) of these differentially expressed genes had unknown functions. Based on these gene expression profiles, Arabidopsis mutants for eight heat-induced Brassica homologous genes were treated with different heat stress methods, and thermotolerance varied with each mutation, heat stress regimen and plant development stage. At least two of the eight mutants exhibited sensitivity to the heat treatments, suggesting the importance of the respective genes in responding to heat stress. In summary, this study elucidated the molecular bases of the heat responses in siliques during later reproductive stages and provides valuable information and gene resources for the genetic improvement of heat tolerance in oilseed rape breeding.