Structural genomics of the barley Mla powdery mildew resistance complex

Powdery mildew of barley, caused by Blumeria ( Erysiphe) graminis f. sp. hordei, is a model system for investigating the mechanism of gene-for-gene interaction between large-genome cereals and obligate-fungal pathogens. We used AFLP markers to saturate the Mla region in a high-resolution mapping population. These tightly linked genetic markers were used to develop a physical contig of YAC and BAC clones spanning the Mla cluster. A lowpass and BAC-end sequencing strategies revealed three distinct NBS-LRR resistance-gene homologue (RGH) families in the contig. Genetic and physical mapping delimited the RGH families to a 240-kb interval. Recombination suppression was found within the RGH.;The 261,265 by complete sequence of two overlap BACs spanning the Mla locus revealed thirty-two protein-encoding and two tRNA ser genes. The protein-encoding genes are organized as three gene-rich islands separated by two 40-kb complexes of nested transposable elements and a gene-poor region. Sixteen of these genes are plant-defense related; 12 of these 16 are associated with defense against powdery mildew disease, but function in different signaling pathways. The clustering of those plant defense-related genes is similar to that in the mammalian major histocompatibility complex (MHC). Evolutionary analysis indicated that the present Mla region was developed over 7 million years through several duplication and inversion events in addition to nested TE insertion.;A heterochromatic knob-like sequence, KL1HS1, is present in this barley Mla complex. KL1HS1 is hypermethylated and recombination in the region is suppressed. KL1HS1 combines the structural characteristics of the two cloned knobs from Arabidopsis. This knob-like sequence is comprised of a gene-poor core with diverse tandem repeats, a genedense island resulting from a 4Q-kb tandem duplication, and a nested transposable element complex. In contrast to other knobs, KL1HS is gene-rich and transcriptionally active. A positional shift was observed and this shifting resulted from a bidirectional expansion of KL1HS1. Extensive arrays of various tandem duplications in the knob are postulated to arise from a DNA replication slippage mechanism in combination with a hairpin structure of the knob-associated repeats

Empirical Study of Deep Learning for Text Classification in Legal Document Review

Predictive coding has been widely used in legal matters to find relevant or privileged documents in large sets of electronically stored information. It saves the time and cost significantly. Logistic Regression (LR) and Support Vector Machines (SVM) are two popular machine learning algorithms used in predictive coding. Recently, deep learning received a lot of attentions in many industries. This paper reports our preliminary studies in using deep learning in legal document review. Specifically, we conducted experiments to compare deep learning results with results obtained using a SVM algorithm on the four datasets of real legal matters. Our results showed that CNN performed better with larger volume of training dataset and should be a fit method in the text classification in legal industry.Comment: 2018 IEEE International Conference on Big Data (Big Data

Characteristics of NOM Released to Water from Different Forest and Agricultural Soils

The characteristics of natural organic matter (NOM) released to water from a soil environment were investigated based on the release potential and the quality indexes of SUVA, fluorescence EEM and molecular weight distribution using eight forest and agricultural soils collected from a representative river catchment (Kani River catchment in Gifu, Japan). The content of organic matter (OM) and its release potential to water differed obviously with type of soil origin, in the following order: vegetable field (VF) < paddy field (PF) < broadleaf forest (BF) < coniferous forest (CF) and VF < PF < CF < BF, respectively. For the released NOM, SUVA under different pH conditions decreased in the following order: neutral condition [3.6-24.0 m-1/(mg/L)] > basic condition [3.5-8.0 m-1/(mg/L)] > acidic condition [2.5-3.0 m-1/(mg/L)]. Humic acids (P1), fulvic acids (P2) and protein-like substances (P3) were the main components of the NOM released under neutral condition. The proportion of P1 and P2 released from the forest soils was lower than that from the agricultural soils. Marked differences in UV-absorbing constituents of NOM between forest and agricultural soils were found in the peak with a molecular weight of about 9800 g/mol identified as PSS (polystyrene sulfonate)

Coronavirus accessory protein ORF3 biology and its contribution to viral behavior and pathogenesis

Coronavirus porcine epidemic diarrhea virus (PEDV) is classified in the genu

Robot Motion Planning Method Based on Incremental High-Dimensional Mixture Probabilistic Model

The sampling-based motion planner is the mainstream method to solve the motion planning problem in high-dimensional space. In the process of exploring robot configuration space, this type of algorithm needs to perform collision query on a large number of samples, which greatly limits their planning efficiency. Therefore, this paper uses machine learning methods to establish a probabilistic model of the obstacle region in configuration space by learning a large number of labeled samples. Based on this, the high-dimensional samples' rapid collision query is realized. The influence of number of Gaussian components on the fitting accuracy is analyzed in detail, and a self-adaptive model training method based on Greedy expectation-maximization (EM) algorithm is proposed. At the same time, this method has the capability of online updating and can eliminate model fitting errors due to environmental changes. Finally, the model is combined with a variety of sampling-based motion planners and is validated in multiple sets of simulations and real world experiments. The results show that, compared with traditional methods, the proposed method has significantly improved the planning efficiency

Neuroprotective effects of etanercept on diabetic retinopathy via regulation of the TNF-α/NF-κB signaling pathway

Purpose: To study the influence of etanercept on diabetic retinopathy in rats via tumor necrosis factor alpha (TNF-α)/nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway. Methods: Thirty-six Sprague-Dawley (SD) rats were randomly divided into normal, model and etanercept groups. The expression of Caspase-3 was determined by immunohistochemistry, while the relative protein and mRNA expression levels of TNF-α and NF-κB were determined by Western blotting and quantitative polymerase chain reaction, respectively. Besides, the contents of TNF-α and interleukin-1 beta (IL-1β) were evaluated using enzyme-linked immunosorbent assay (ELISA), while cell apoptosis was assessed by terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL). Results: Immunohistochemical studies showed that the mean optical density of tissues positive for caspase-3 in both the model and etanercept groups were significantly higher than in the normal group (p < 0.05), while the mean optical density in the etanercept group was significantly lower than that in the model group (p < 0.05). The protein expression levels of TNF-α and NF-κB in the etanercept group were significantly lower than those in the model group (p < 0.05). Furthermore, mRNA expressions of TNF-α and NF-κB declined in the etanercept group (p < 0.05); in addition, TNF-α, and IL-1β levels in the etanercept group were lower than in the model group (p < 0.05). Cell apoptosis in the etanercept group was also lower than in the model group. Conclusion: Etanercept suppresses TNF-α/NF-κB signaling pathway thereby repressing inflammation and cell apoptosis in diabetic retinopathy rats. Therefore, etenercept’s neuroprotective effect may potentially be useful in developing a suitable therapy for diabetic neuropathy