MeWRKY IIas, subfamily genes of WRKY transcription factors from cassava, play an important role in disease resistance

Cassava (Manihot esculenta Crantz) is an important tropical crop for food, fodder, and energy. Cassava bacterial blight (CBB) caused by Xanthomonas axonopodis pv. manihotis (Xam) occurs in all cassava growing regions and threatens global cassava production. WRKY transcription factor family plays the essential roles during plant growth, development, and abiotic or biotic stress. Particularly, previous studies have revealed the important role of the group IIa WRKY genes in plant disease resistance. However, a comprehensive analysis of group IIa subfamily in cassava is still missing. Here, we identified 102 WRKY members, which were classified into three groups, I, II, and III. Transient expression showed that six MeWRKY IIas were localized in the nucleus. MeWRKY IIas transcripts accumulated significantly in response to SA, JA, and Xam. Overexpression of MeWRKY27 and MeWRKY33 in Arabidopsis enhanced its resistance to Pst DC3000. In contrast, silencing of MeWRKY27 and MeWRKY33 in cassava enhanced its susceptibility to Xam. Co-expression network analysis showed that different downstream genes are regulated by different MeWRKY IIa members. The functional analysis of downstream genes will provide clues for clarifying molecular mechanism of cassava disease resistance. Collectively, our results suggest that MeWRKY IIas are regulated by SA, JA signaling, and coordinate response to Xam infection

Monitoring the Severity of Pantana phyllostachysae Chao Infestation in Moso Bamboo Forests Based on UAV Multi-Spectral Remote Sensing Feature Selection

In recent years, the rapid development of unmanned aerial vehicle (UAV) remote sensing technology has provided a new means to efficiently monitor forest resources and effectively prevent and control pests and diseases. This study aims to develop a detection model to study the damage caused to Moso bamboo forests by Pantana phyllostachysae Chao (PPC), a major leaf-eating pest, at 5 cm resolution. Damage sensitive features were extracted from multispectral images acquired by UAVs and used to train detection models based on support vector machines (SVM), random forests (RF), and extreme gradient boosting tree (XGBoost) machine learning algorithms. The overall detection accuracy (OA) and Kappa coefficient of SVM, RF, and XGBoost were 81.95%, 0.733, 85.71%, 0.805, and 86.47%, 0.811, respectively. Meanwhile, the detection accuracies of SVM, RF, and XGBoost were 78.26%, 76.19%, and 80.95% for healthy, 75.00%, 83.87%, and 79.17% for mild damage, 83.33%, 86.49%, and 85.00% for moderate damage, and 82.5%, 90.91%, and 93.75% for severe damage Moso bamboo, respectively. Overall, XGBoost exhibited the best detection performance, followed by RF and SVM. Thus, the study findings provide a technical reference for the regional monitoring and control of PPC in Moso bamboo

Identification of Candidate Genes for the Plateau Adaptation of a Tibetan Amphipod, Gammarus lacustris, Through Integration of Genome and Transcriptome Sequencing

The amphipod Gammarus lacustris has been distributing in the Tibetan region with well-known uplifts of the Tibetan plateau. It is hence considered as a good model for investigating stress adaptations of the plateau. Here, we sequenced the whole-genome and full-length transcriptome of G. lacustris, and compared the transcriptome results with its counterpart Gammarus pisinnus from a nearby plain. Our main goal was to provide a genomic resource for investigation of genetic mechanisms, by which G. lacustris adapted to living on the plateau. The final draft genome assembly of G. lacustris was 5.07 gigabases (Gb), and it contained 443,304 scaffolds (>2 kb) with an N50 of 2,578 bp. A total of 8,858 unigenes were predicted in the full-length transcriptome of G. lacustris, with an average gene length of 1,811 bp. Compared with the G. pisinnus transcriptome, 2,672 differentially expressed genes (DEGs) were up-regulated and 2,881 DEGs were down-regulated in the G. lacustris transcriptome. Along with these critical DEGs, several enriched metabolic pathways, such as oxidative phosphorylation, ribosome, cell energy homeostasis, glycolysis and gluconeogenesis, were predicted to play essential roles in the plateau adaptation. In summary, the present study provides a genomic basis for understanding the plateau adaption of G. lacustris, which lays a fundamental basis for further biological and ecological studies on other resident aquatic species in the Tibetan plateau

Bayesian Nonparametric Modeling of Categorical Data for Information Fusion and Causal Inference

This paper presents a nonparametric regression model of categorical time series in the setting of conditional tensor factorization and Bayes network. The underlying algorithms are developed to provide a flexible and parsimonious representation for fusion of correlated information from heterogeneous sources, which can be used to improve the performance of prediction tasks and infer the causal relationship between key variables. The proposed method is first illustrated by numerical simulation and then validated with two real-world datasets: (1) experimental data, collected from a swirl-stabilized lean-premixed laboratory-scale combustor, for detection of thermoacoustic instabilities and (2) publicly available economics data for causal inference-making

Putting Sector Strategies to the Test

Examining committee: Colleen Chrisnger, faculty advisorThis paper undertakes a comparative analysis of earnings and employment growth in clustered and non-clustered industries in Washington State for the years 2003 through 2009. Further investigation compares cluster effects on different regional levels within counties and Workforce Development Areas. Location Quotients (LQs) were the principal measure of assessing the agglomeration of industry clusters. To evaluate the relationships between LQ and wage and employment growth, four regression models were used. Results indicate that private firms within an industry cluster have a significantly lower employment growth rate, on average, than firms not in a cluster. It is noteworthy that the research detects no significant relationship between wage growth and whether firms are located in an industry cluster