Dynamic graph neural network with adaptive edge attributes for air quality prediction: A case study in China.

Air quality prediction is a typical Spatiotemporal modeling problem, which always uses different components to handle spatial and temporal dependencies in complex systems separately. Previous models based on time series analysis and recurrent neural network (RNN) methods have only modeled time series while ignoring spatial information. Previous graph convolution neural networks (GCNs) based methods usually require providing spatial correlation graph structure of observation sites in advance. The correlations among these sites and their strengths are usually calculated using prior information. However, due to the limitations of human cognition, limited prior information cannot reflect the real station-related structure or bring more effective information for accurate prediction. To this end, we propose a novel Dynamic Graph Neural Network with Adaptive Edge Attributes (DGN-AEA) on the message passing network, which generates the adaptive bidirected dynamic graph by learning the edge attributes as model parameters. Unlike prior information to establish edges, our method can obtain adaptive edge information through end-to-end training without any prior information. Thus reducing the complexity of the problem. Besides, the hidden structural information between the stations can be obtained as model by-products, which can help make some subsequent decision-making analyses. Experimental results show that our model received state-of-the-art performance than other baselines.

SF3B1 mutations in myelodysplastic syndromes: A potential therapeutic target for modulating the entire disease process.

Myelodysplastic syndromes (MDS) are clonal hematologic malignancies characterized by ineffective hematopoiesis and dysplasia of the myeloid cell lineage and are characterized by peripheral blood cytopenia and an increased risk of transformation to acute myeloid leukemia (AML). Approximately half of the patients with MDS have somatic mutations in the spliceosome gene. Splicing Factor 3B Subunit 1A (SF3B1), the most frequently occurring splicing factor mutation in MDS is significantly associated with the MDS-RS subtype. SF3B1 mutations are intimately involved in the MDS regulation of various pathophysiological processes, including impaired erythropoiesis, dysregulated iron metabolism homeostasis, hyperinflammatory features, and R-loop accumulation. In the fifth edition of the World Health Organization (WHO) classification criteria for MDS, MDS with SF3B1 mutations has been classified as an independent subtype, which plays a crucial role in identifying the disease phenotype, promoting tumor development, determining clinical features, and influencing tumor prognosis. Given that SF3B1 has demonstrated therapeutic vulnerability both in early MDS drivers and downstream events, therapy based on spliceosome-associated mutations is considered a novel strategy worth exploring in the future.

INAM-Based Image-Adaptive 3D LUTs for Underwater Image Enhancement.

To the best of our knowledge, applying adaptive three-dimensional lookup tables (3D LUTs) to underwater image enhancement is an unprecedented attempt. It can achieve excellent enhancement results compared to some other methods. However, in the image weight prediction process, the model uses the normalization method of Instance Normalization, which will significantly reduce the standard deviation of the features, thus degrading the performance of the network. To address this issue, we propose an Instance Normalization Adaptive Modulator (INAM) that amplifies the pixel bias by adaptively predicting modulation factors and introduce the INAM into the learning image-adaptive 3D LUTs for underwater image enhancement. The bias amplification strategy in INAM makes the edge information in the features more distinguishable. Therefore, the adaptive 3D LUTs with INAM can substantially improve the performance on underwater image enhancement. Extensive experiments are undertaken to demonstrate the effectiveness of the proposed method.

A dual-path dynamic directed graph convolutional network for air quality prediction.

Accurate air quality prediction can help cope with air pollution and improve the life quality. With the development of the deployments of low-cost air quality sensors, increasing data related to air quality has provided chances to find out more accurate prediction methods. Air quality is affected by many external factors such as the position, wind, meteorological information, and so on. Meanwhile, these factors are spatio-temporal dynamic and there are many dynamic contextual relationships between them. Many methods for air quality prediction do not consider these complex spatio-temporal correlations and dynamic contextual relationships. In this paper, we propose a dual-path dynamic directed graph convolutional network (DP-DDGCN) for air quality prediction. We first create a dual-path transposed dynamic directed graph according to static distance relationships of stations and the dynamic relationships generated by wind speed and directions. Then based on the dual-path dynamic directed graph, we can capture the dynamic spatial dependencies more comprehensively. After that we apply gated recurrent units (GRUs) and add the future meteorological features, to extract the complex temporal dependencies of historical air quality data. Using dual-path dynamic directed graph blocks and the GRUs, we finally construct a dynamic spatio-temporal gated recurrent block to capture the dynamic spatio-temporal contextual correlations. Based on real-world datasets, which record a large amount of PM2.5 concentration data, we compare the proposed model with the benchmark models. The experimental results show that our proposed model has the best performance in predicting the PM2.5 concentrations.

Discovery of cyclooxygenase-2 inhibitors from Kadsura coccinea by affinity ultrafiltration mass spectrometry and the anti-inflammatory activity.

The medicinal plant Kadsura coccinea distributing in South China, was widely used for reducing inflammation and relieving pain. Previous study in our laboratory had proved the significant therapeutic effects of K. coccinea extract on adjuvant arthritis rats. To explore the responsible components and possible mechanisms, an AUF-HPLC-Q-TOF/ MS method was employed for screening and characterizing COX-2 ligands from K. coccinea stems for the first time. Meanwhile, the molecular docking was performed to simulate the binding modes for ligands and COX-2, the cell-free enzyme activity assay was applied to verify the direct COX-2 inhibition of potential inhibitors, and the cell-based study on COX-2 expression was to evaluate the anti-inflammatory effect of (+)-Anwulignan. As a result, the potential COX-2 inhibitor (+)-Anwulignan significantly suppressing COX-2 expressions in LPS signaling pathways might be a good candidate for anti-inflammation and analgesia. In conclusion, AUF mass spectrometry combining the molecular docking and bioassays in vitro was an efficient approach for discovering enzyme inhibitors from traditional herbs.