Molecular characterization and ligand binding specificity of the PDZ domain-containing protein GIPC3 from Schistosoma japonicum

BACKGROUND: Schistosomiasis is a serious global health problem that afflicts more than 230 million people in 77 countries. Long-term mass treatments with the only available drug, praziquantel, have caused growing concerns about drug resistance. PSD-95/Dlg/ZO-1 (PDZ) domain-containing proteins are recognized as potential targets for the next generation of drug development. However, the PDZ domain-containing protein family in parasites has largely been unexplored. METHODS: We present the molecular characteristics of a PDZ domain-containing protein, GIPC3, from Schistosoma japonicum (SjGIPC3) according to bioinformatics analysis and experimental approaches. The ligand binding specificity of the PDZ domain of SjGIPC3 was confirmed by screening an arbitrary peptide library in yeast two-hybrid (Y2H) assays. The native ligand candidates were predicted by Tailfit software based on the C-terminal binding specificity, and further validated by Y2H assays. RESULTS: SjGIPC3 is a single PDZ domain-containing protein comprised of 328 amino acid residues. Structural prediction revealed that a conserved PDZ domain was presented in the middle region of the protein. Phylogenetic analysis revealed that SjGIPC3 and other trematode orthologues clustered into a well-defined cluster but were distinguishable from those of other phyla. Transcriptional analysis by quantitative RT-PCR revealed that the SjGIPC3 gene was relatively highly expressed in the stages within the host, especially in male adult worms. By using Y2H assays to screen an arbitrary peptide library, we confirmed the C-terminal binding specificity of the SjGIPC3-PDZ domain, which could be deduced as a consensus sequence, -[SDEC]-[STIL]-[HSNQDE]-[VIL]*. Furthermore, six proteins were predicted to be native ligand candidates of SjGIPC3 based on the C-terminal binding properties and other biological information; four of these were confirmed to be potential ligands using the Y2H system. CONCLUSIONS: In this study, we first characterized a PDZ domain-containing protein GIPC3 in S. japonicum. The SjGIPC3-PDZ domain is able to bind both type I and II ligand C-terminal motifs. The identification of native ligand will help reveal the potential biological function of SjGIPC3. These data will facilitate the identification of novel drug targets against S. japonicum infections

SelFLoc: Selective Feature Fusion for Large-scale Point Cloud-based Place Recognition

Point cloud-based place recognition is crucial for mobile robots and autonomous vehicles, especially when the global positioning sensor is not accessible. LiDAR points are scattered on the surface of objects and buildings, which have strong shape priors along different axes. To enhance message passing along particular axes, Stacked Asymmetric Convolution Block (SACB) is designed, which is one of the main contributions in this paper. Comprehensive experiments demonstrate that asymmetric convolution and its corresponding strategies employed by SACB can contribute to the more effective representation of point cloud feature. On this basis, Selective Feature Fusion Block (SFFB), which is formed by stacking point- and channel-wise gating layers in a predefined sequence, is proposed to selectively boost salient local features in certain key regions, as well as to align the features before fusion phase. SACBs and SFFBs are combined to construct a robust and accurate architecture for point cloud-based place recognition, which is termed SelFLoc. Comparative experimental results show that SelFLoc achieves the state-of-the-art (SOTA) performance on the Oxford and other three in-house benchmarks with an improvement of 1.6 absolute percentages on mean average recall@1

Fly Ash Coated With Alumina Sol For Improving Strength And Thermal Insulation Of Mullite Porous Ceramics

The manufacturing of mullite porous ceramics with high strength and low thermal conductivity was achieved through foam gel-casting processes using fly ash coated with alumina sol layers. This research aimed to investigate the effect of alumina sol concentration on foaming slurry rheology, as well as the influence of alumina sol coating layers on the microstructure, phase compositions and properties of the resulting mullite porous ceramics. Increasing the alumina sol concentration from 5 to 20 wt% improved both the viscosity and thixotropy of the foaming slurries while enhanced the shear thinning behavior. Porous ceramics prepared with fly ash coated with alumina sol exhibited smaller pore size compared to the untreated fly ash porous ceramics. Moreover, the distribution of pores gradually became more homogenous in the porous ceramics with treated fly ash. Meanwhile, the weight-reduction, compressive strength and thermal insulation properties of the porous ceramics were improved significantly. The use of fly ash coated with alumina sol (with a concentration of 20 wt%) in the preparation of porous ceramics resulted in the formation of mullite whiskers within the pore walls. This created micron-size gaps between the whiskers, greatly enhancing the thermal insulation of the porous ceramics. Finally, the porous ceramics that were prepared using fly ash coated with alumina sol (with a concentration of 20 wt%) and sintered at a temperature of 1400 °C had a bulk density of 0.45 g/cm3, a compressive strength of 8 MPa, and a thermal conductivity of 0.15 W/m·k

M3^3CS: Multi-Target Masked Point Modeling with Learnable Codebook and Siamese Decoders

Masked point modeling has become a promising scheme of self-supervised pre-training for point clouds. Existing methods reconstruct either the original points or related features as the objective of pre-training. However, considering the diversity of downstream tasks, it is necessary for the model to have both low- and high-level representation modeling capabilities to capture geometric details and semantic contexts during pre-training. To this end, M$^3$CS is proposed to enable the model with the above abilities. Specifically, with masked point cloud as input, M$^3$CS introduces two decoders to predict masked representations and the original points simultaneously. While an extra decoder doubles parameters for the decoding process and may lead to overfitting, we propose siamese decoders to keep the amount of learnable parameters unchanged. Further, we propose an online codebook projecting continuous tokens into discrete ones before reconstructing masked points. In such way, we can enforce the decoder to take effect through the combinations of tokens rather than remembering each token. Comprehensive experiments show that M$^3$CS achieves superior performance at both classification and segmentation tasks, outperforming existing methods

Networked Time Series Prediction with Incomplete Data

A networked time series (NETS) is a family of time series on a given graph, one for each node. It has a wide range of applications from intelligent transportation, environment monitoring to smart grid management. An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph. Most existing methods require complete data for training. However, in real-world scenarios, it is not uncommon to have missing data due to sensor malfunction, incomplete sensing coverage, etc. In this paper, we study the problem of NETS prediction with incomplete data. We propose NETS-ImpGAN, a novel deep learning framework that can be trained on incomplete data with missing values in both history and future. Furthermore, we propose Graph Temporal Attention Networks, which incorporate the attention mechanism to capture both inter-time series and temporal correlations. We conduct extensive experiments on four real-world datasets under different missing patterns and missing rates. The experimental results show that NETS-ImpGAN outperforms existing methods, reducing the MAE by up to 25%