Measurement of the Depth of Lesions on Proximal Surfaces with SWIR Multispectral Transillumination and Reflectance Imaging

The aim of this study was to compare the diagnostic performance of dual short-wavelength infrared (SWIR) transillumination and reflectance multispectral imaging devices for imaging interproximal lesions with radiography using extracted teeth that had been imaged with micro-computed tomography (microCT). Thirty-six extracted teeth with 67 lesions on the proximal surfaces were imaged using a newly fabricated SWIR multispectral proximal transillumination and reflectance imaging device along with an existing SWIR multispectral occlusal transillumination and reflectance device. The ability of SWIR imaging and radiography to detect lesions and accurately assess lesion dimensions were compared using microCT as a standard. Occlusal and proximal transillumination and occlusal reflectance performed best for imaging interproximal lesions while proximal reflectance was not useful for imaging interproximal lesions from tooth buccal and lingual surfaces. There was high correlation of the lesion dimensions measured in occlusal and proximal transillumination images compared to microCT and moderate correlation in occlusal reflectance images. The correlation between the lesion depth measured in radiographs and the lesion depth measured with microCT was not significant. This study demonstrates that SWIR occlusal and proximal transillumination and SWIR occlusal reflectance images are useful for imaging interproximal lesions and they provide more accurate measurements of lesion severity

3D Rotation and Translation for Hyperbolic Knowledge Graph Embedding

The main objective of Knowledge Graph (KG) embeddings is to learn low-dimensional representations of entities and relations, enabling the prediction of missing facts. A significant challenge in achieving better KG embeddings lies in capturing relation patterns, including symmetry, antisymmetry, inversion, commutative composition, non-commutative composition, hierarchy, and multiplicity. This study introduces a novel model called 3H-TH (3D Rotation and Translation in Hyperbolic space) that captures these relation patterns simultaneously. In contrast, previous attempts have not achieved satisfactory performance across all the mentioned properties at the same time. The experimental results demonstrate that the new model outperforms existing state-of-the-art models in terms of accuracy, hierarchy property, and other relation patterns in low-dimensional space, meanwhile performing similarly in high-dimensional space.Comment: 19 pages, EACL2024 mai

Mechanism underlying Müller cell pyroptosis and its role in the development of proliferative vitreoretinopathy

Objectives: To explore the mechanism underlying Müller Cell Pyroptosis (MCP) and its role in the development of Proliferative Vitreoretinopathy (PVR). Method: The expression of pyroptosis-related factors, namely, cysteinyl aspartate-specific proteinase (caspase-1), interleukin (IL)-1β, IL-18, and Gasdermin D (GSDMD), was detected by quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) and western blotting at the mRNA and protein levels, respectively, in retinal tissues. Müller and spontaneously Arising Retinal Pigment Epithelia (ARPE)-19 primary cells with GSDMD overexpression or knockdown were cultivated. Western blotting was used to detect the levels of the following pyroptosis-related factors in retinal tissues: caspase-1, IL-1β, IL-18, and GSDMD. Through Cell Adhesion (CA) experiments, the changes in ARPE-19 CA in each group were observed. The migration and invasion of ARPE-19 cells were measured using the Transwell assay. The proliferation of ARPE-19 cells was measured with a Cell Counting Kit 8 (CCK-8) assay. Finally, the expression of the cytokines IL-1β and IL-18 in the ARPE-19 cell culture medium was detected using the Enzyme-Linked Immunosorbent Assay (ELISA). Results: Compared with the surrounding normal tissues, the expression of caspase-1, IL-1β, IL-18, and GSDMD at the protein and mRNA levels in the retinal proliferative membrane samples of the patients decreased significantly (p < 0.05). MCP significantly enhanced ARPE-19 CA, migration and invasion, proliferation, and cytokine expression (p < 0.05). Conclusions: MCP can promote the development of PVR lesions

HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks

Heterogeneous graph neural networks (GNNs) have been successful in handling heterogeneous graphs. In existing heterogeneous GNNs, meta-path plays an essential role. However, recent work pointed out that simple homogeneous graph model without meta-path can also achieve comparable results, which calls into question the necessity of meta-path. In this paper, we first present the intrinsic difference about meta-path-based and meta-path-free models, i.e., how to select neighbors for node aggregation. Then, we propose a novel framework to utilize the rich type semantic information in heterogeneous graphs comprehensively, namely HAGNN (Hybrid Aggregation for Heterogeneous GNNs). The core of HAGNN is to leverage the meta-path neighbors and the directly connected neighbors simultaneously for node aggregations. HAGNN divides the overall aggregation process into two phases: meta-path-based intra-type aggregation and meta-path-free inter-type aggregation. During the intra-type aggregation phase, we propose a new data structure called fused meta-path graph and perform structural semantic aware aggregation on it. Finally, we combine the embeddings generated by each phase. Compared with existing heterogeneous GNN models, HAGNN can take full advantage of the heterogeneity in heterogeneous graphs. Extensive experimental results on node classification, node clustering, and link prediction tasks show that HAGNN outperforms the existing modes, demonstrating the effectiveness of HAGNN

Compact wideband bowtie dipole orthomode transducer

A compact wideband orthomode transducer (OMT) with an octave bandwidth is proposed for a 0.56-1.12 GHz receiver system in the five hundred meter aperture spherical radio telescope. The OMT operates in a cryostat at a temperature of 70 K and therefore it is critical to minimize its dimension while insuring good electrical properties. The complete OMT comprises 2 bowtie dipoles orthogonally arranged in a circular waveguide. Because of the innovative structure, competing modes, TM01 TE21, and TE01, have been effectively suppressed, and the bandwidth of the dominate mode TE11 has achieved to 2.08:1. The final optimized OMT has a length of 300 mm, shorter than a half of the popular quardruple-ridged waveguide OMT. Measurements at room temperature agree well with simulation results, with a reflection coefficient below -10 dB for both polarizations and cross-coupling levels of -30.5 dB over the whole required frequency range

Community prevalence of carbapenemase-producing Gram-negative bacteria

Purpose: To raise awareness of carbapenemase-producing organisms, identify “at-risk” patients when admitted in a medical healthcare facility, and to outline effective infection prevention and control measures in order to halt the entry and spread of these organisms. Methods: A total of 1043 un-duplicated urine specimens of healthy volunteers who had no travel history or history of hospitalization were screened. The carbapenemase genotype of each imipenem-resistant strain was determined. Molecular typing and homology analysis of the main carbapenemase-producing strains were used to reveal the mode of transmission of resistance genes. Through transfer joint experiments, the potential risk of spread of carbapenemase genes was assessed. Results: A total of 19 carbapenemase-producing strains from 1,043 non-duplicated healthy volunteers (1.82 %) were identified. The main carbapenemase-producing organism was E. coli (42.1 %, 8/19). The main carbapenemase genotype of E. coli was blaKPC-2 (7 strains). Results from multi-locus sequence typing (MLST) indicated that 7 E. coli isolates belonged to ST-10, ST-101, ST-131, ST-405, ST-410 and ST-1193 and ST-2562. Homologous cluster analysis revealed that the sequence types among the 7 E. coli were high in diversity. The blaKPC-2 gene was successfully transferred from these isolates to 10.22-14 via conjugation. All recipient cells showed marked decreases in carbapenem sensitivity to imipenem (p < 0.05)). The degrees of conjugation were 2.10±0.12 ×10-4, 1.96±0.14×10-4, 2.72±0.18 ×10-4, 3.15±0.20 × 10-4, 2.92±0.23 ×10-4, 3.50±0.20 ×10-4 and 4.12±0.24 ×10-4 in recipient cells of TC7.23-51, TC8.9-42, TC8.15-11, TC8.23-59-3, TC8.23-83, TC9.08-47 and TC10.13-15, respectively. Conclusion: The findings demonstrate the pattern and features of carbapenemase-insensitive E. coli. The blaKPC-2 was the main community-prevalent gene of carbapenem-resistant E. coli. In view of increasing incidence of resistance to multi-drug therapy, surveillance of insensitivity to antibiotics is vital, especially urinary system infection due to carbapenem-insensitive E. coli

Simple and Efficient Partial Graph Adversarial Attack: A New Perspective

As the study of graph neural networks becomes more intensive and comprehensive, their robustness and security have received great research interest. The existing global attack methods treat all nodes in the graph as their attack targets. Although existing methods have achieved excellent results, there is still considerable space for improvement. The key problem is that the current approaches rigidly follow the definition of global attacks. They ignore an important issue, i.e., different nodes have different robustness and are not equally resilient to attacks. From a global attacker's view, we should arrange the attack budget wisely, rather than wasting them on highly robust nodes. To this end, we propose a totally new method named partial graph attack (PGA), which selects the vulnerable nodes as attack targets. First, to select the vulnerable items, we propose a hierarchical target selection policy, which allows attackers to only focus on easy-to-attack nodes. Then, we propose a cost-effective anchor-picking policy to pick the most promising anchors for adding or removing edges, and a more aggressive iterative greedy-based attack method to perform more efficient attacks. Extensive experimental results demonstrate that PGA can achieve significant improvements in both attack effect and attack efficiency compared to other existing graph global attack methods

DIFER: Differentiable Automated Feature Engineering

Feature engineering, a crucial step of machine learning, aims to extract useful features from raw data to improve data quality. In recent years, great efforts have been devoted to Automated Feature Engineering (AutoFE) to replace expensive human labor. However, existing methods are computationally demanding due to treating AutoFE as a coarse-grained black-box optimization problem over a discrete space. In this work, we propose an efficient gradient-based method called DIFER to perform differentiable automated feature engineering in a continuous vector space. DIFER selects potential features based on evolutionary algorithm and leverages an encoder-predictor-decoder controller to optimize existing features. We map features into the continuous vector space via the encoder, optimize the embedding along the gradient direction induced by the predicted score, and recover better features from the optimized embedding by the decoder. Extensive experiments on classification and regression datasets demonstrate that DIFER can significantly improve the performance of various machine learning algorithms and outperform current state-of-the-art AutoFE methods in terms of both efficiency and performance.Comment: 8 pages, 5 figure