Exploring transcriptional signalling mediated by OsWRKY13, a potential regulator of multiple physiological processes in rice

BACKGROUND Rice transcription regulator OsWRKY13 influences the functioning of more than 500 genes in multiple signalling pathways, with roles in disease resistance, redox homeostasis, abiotic stress responses, and development. RESULTS To determine the putative transcriptional regulation mechanism of OsWRKY13, the putative cis-acting elements of OsWRKY13-influenced genes were analyzed using the whole genome expression profiling of OsWRKY13-activated plants generated with the Affymetrix GeneChip Rice Genome Array. At least 39 transcription factor genes were influenced by OsWRKY13, and 30 of them were downregulated. The promoters of OsWRKY13-upregulated genes were overrepresented with W-boxes for WRKY protein binding, whereas the promoters of OsWRKY13-downregulated genes were enriched with cis-elements putatively for binding of MYB and AP2/EREBP types of transcription factors. Consistent with the distinctive distribution of these cis-elements in up- and downregulated genes, nine WRKY genes were influenced by OsWRKY13 and the promoters of five of them were bound by OsWRKY13 in vitro; all seven differentially expressed AP2/EREBP genes and six of the seven differentially expressed MYB genes were suppressed by in OsWRKY13-activated plants. A subset of OsWRKY13-influenced WRKY genes were involved in host-pathogen interactions. CONCLUSION These results suggest that OsWRKY13-mediated signalling pathways are partitioned by different transcription factors. WRKY proteins may play important roles in the monitoring of OsWRKY13-upregulated genes and genes involved in pathogen-induced defence responses, whereas MYB and AP2/EREBP proteins may contribute most to the control of OsWRKY13-downregulated genes.This work was supported by grants from the National Program of High Technology Development of China, the National Program on the Development of Basic Research in China, and the National Natural Science Foundation of China

Oximetry with the NMR signals of hemoglobin Val E11 and Tyr C7

The NMR visibility of the signals from erythrocyte hemoglobin (Hb) presents an opportunity to assess the vascular PO2 (partial pressure of oxygen) in vivo to gather insight into the regulation of O2 transport, especially in contracting muscle tissue. Some concerns, however, have arisen about the validity of using the Val E11 signal as an indicator of PO2, since its intensity depends on tertiary structural changes, in contrast to the quaternary structure changes associated with relaxed (R) and tense (T) transition during O2 binding. We have examined the Val E11 and Tyr C7 signal intensity as a function of Hb saturation by developing an oximetry system, which permits the comparative analysis of the NMR and spectrophotometric measurements. The spectrophotometric assay defines the Hb saturation level at a given PO2 and yields standard oxygen-binding curves. Under defined PO2 and Hb saturation values, the NMR measurements have determined that the Val E11 signal, as well as the Tyr C7 signal, tracks closely Hb saturation and can therefore serve as a vascular oxygen biomarker

NRPA: Neural Recommendation with Personalized Attention

Existing review-based recommendation methods usually use the same model to learn the representations of all users/items from reviews posted by users towards items. However, different users have different preference and different items have different characteristics. Thus, the same word or similar reviews may have different informativeness for different users and items. In this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews. We use a review encoder to learn representations of reviews from words, and a user/item encoder to learn representations of users or items from reviews. We propose a personalized attention model, and apply it to both review and user/item encoders to select different important words and reviews for different users/items. Experiments on five datasets validate our approach can effectively improve the performance of neural recommendation.Comment: 4 pages, 4 figure

Evaluating and Optimizing IP Lookup on Many core Processors

International audienceIn recent years, there has been a growing interest in multi/many core processors as a target architecture for high performance software router. Because of its key position in routers, hardware IP lookup implementation has been intensively studied with TCAM and FPGA based architecture. However, increasing interest in software implementation has also been observed. In this paper, we evaluate the performance of software only IP lookup on a many core chip, the TILEPro64 processor. For this purpose we have implemented two widely used IP lookup algorithms, DIR-24-8-BASIC and Tree Bitmap. We evaluate the performance of these two algorithms over the TILEPro64 processor with both synthetic and real-world traces. After a detailed analysis, we propose a hybrid scheme which provides high lookup speed and low worst case update overhead. Our work shows how to exploit the architectural features of TILEPro64 to improve the performance, including many optimization in both single-core and parallelism aspects. Experiment results show by using only 18 cores, we can achieve a lookup throughput of 60Mpps (almost 40Gbps) with low power consumption, which demonstrates great performance potentials in many core processor

Solving High-dimensional Parametric Elliptic Equation Using Tensor Neural Network

In this paper, we introduce a tensor neural network based machine learning method for solving the elliptic partial differential equations with random coefficients in a bounded physical domain. With the help of tensor product structure, we can transform the high-dimensional integrations of tensor neural network functions to one-dimensional integrations which can be computed with the classical quadrature schemes with high accuracy. The complexity of its calculation can be reduced from the exponential scale to a polynomial scale. The corresponding machine learning method is designed for solving high-dimensional parametric elliptic equations. Some numerical examples are provided to validate the accuracy and efficiency of the proposed algorithms.Comment: 22 pages, 25 figures. arXiv admin note: substantial text overlap with arXiv:2311.0273

Position-Aware Subgraph Neural Networks with Data-Efficient Learning

Data-efficient learning on graphs (GEL) is essential in real-world applications. Existing GEL methods focus on learning useful representations for nodes, edges, or entire graphs with ``small'' labeled data. But the problem of data-efficient learning for subgraph prediction has not been explored. The challenges of this problem lie in the following aspects: 1) It is crucial for subgraphs to learn positional features to acquire structural information in the base graph in which they exist. Although the existing subgraph neural network method is capable of learning disentangled position encodings, the overall computational complexity is very high. 2) Prevailing graph augmentation methods for GEL, including rule-based, sample-based, adaptive, and automated methods, are not suitable for augmenting subgraphs because a subgraph contains fewer nodes but richer information such as position, neighbor, and structure. Subgraph augmentation is more susceptible to undesirable perturbations. 3) Only a small number of nodes in the base graph are contained in subgraphs, which leads to a potential ``bias'' problem that the subgraph representation learning is dominated by these ``hot'' nodes. By contrast, the remaining nodes fail to be fully learned, which reduces the generalization ability of subgraph representation learning. In this paper, we aim to address the challenges above and propose a Position-Aware Data-Efficient Learning framework for subgraph neural networks called PADEL. Specifically, we propose a novel node position encoding method that is anchor-free, and design a new generative subgraph augmentation method based on a diffused variational subgraph autoencoder, and we propose exploratory and exploitable views for subgraph contrastive learning. Extensive experiment results on three real-world datasets show the superiority of our proposed method over state-of-the-art baselines.Comment: 9 pages, 7 figures, accepted by WSDM 2