信息工程最新文献

Typhoid fever is an important health issue in developing countries, and the pathogenicity of Salmonella enterica serovar Typhi (S. ser. Typhi) depends on the presence of different virulence factors. Therefore, this study aimed to investigate the spread of virulence genes among S. Typhi isolates from patients with typhoid fever in Baghdad, Iraq. Sixty S. Typhi isolates were collected from several hospitals in Baghdad and identified using VITEK-II and confirmed by polymerase chain reaction (PCR) to detect the 16S rRNA gene. After testing their susceptibility to different antimicrobials (via the disk diffusion method), we found the highest resistance rates (100 %) were to ampicillin, piperacillin, cefotaxime, and ceftriaxone. The highest sensitivity rates (100 %) were to ertapenem, imipenem, meropenem, and sulfamethoxazole/trimethoprim. The presence of genes encoding for virulence in S. Typhi isolates was tested by conventional PCR. The results showed that out of 60 isolates, 59 (98.3 %), 59 (98.3 %), 58 (96.7 %), and 60 (100 %) were positive for viaB, staA, cdtB, and orfL genes, respectively. The sequencing of PCR products (viaB, staA, cdtB, and orfL genes) was carried out at the Macrogen Company (Seoul, Korea). The sequences were compared with nucleotide sequences in the BLAST GenBank database, and data obtained from the sequencing of these virulence genes were submitted to GenBank under different accession numbers. A phylogenetic analysis of the 16S rRNA gene sequence found a high similarity between local sequences and the closely related sequences of genes in GenBank. The presence of the viaB, staA, cdtB, and orfL virulence genes in nearly all of the isolates under examination suggests that they play an important role in the pathogenicity of local isolates.

This article introduces an open-source software stack designed for autonomous 1:10 scale model vehicles. Initially developed for the Bosch Future Mobility Challenge (BFMC) student competition, this versatile software stack is applicable to a variety of autonomous driving competitions. The stack comprises perception, planning, and control modules, each essential for precise and reliable scene understanding in complex environments such as a miniature smart city in the context of BFMC. Given the limited computing power of model vehicles and the necessity for low-latency real-time applications, the stack is implemented in C++, employs YOLO Version 5 s for environmental perception, and leverages the state-of-the-art Robot Operating System (ROS) for inter-process communication. We believe that this article and the accompanying open-source software will be a valuable resource for future teams participating in autonomous driving student competitions. Our work can serve as a foundational tool for novice teams and a reference for more experienced participants. The code and data are publicly available on GitHub.

This study aims to investigate the potential impact of inhibitors targeting the papain-like protease (PLpro) of SARS-CoV-2 on viral replication and the host immune response. A mathematical model was developed to simulate the interaction among susceptible cells, infected cells, PLpro, and immune cells, incorporating data on PLpro inhibition. Through numerical simulations using MATLAB, the model parameters were estimated based on available statistical data. The results indicate that strategically positioned inhibitors could impede the virus’s access to host cellular machinery, thereby enhancing the immune response and gradually reducing susceptible and infected cells over time. The dynamics of the viral enzyme PLpro showed reduced activity with the introduction of the inhibitor, leading to a decline in viral replication. Moreover, the immune cell population exhibited functional recovery as the inhibitor suppressed PLpro activity. These findings suggest that inhibitors targeting PLpro may serve as therapeutic interventions against SARS-CoV-2 by inhibiting viral replication and bolstering the immune response.

Background

External knowledge representations play an essential role in knowledge-based visual question and answering to better understand complex scenarios in the open world. Recent entity-relationship embedding approaches are deficient in representing some complex relations, resulting in a lack of topic-related knowledge and redundancy in topic-irrelevant information.

Methods

To this end, we propose MKEAH: Multimodal Knowledge Extraction and Accumulation on Hyperplanes. To ensure that the lengths of the feature vectors projected onto the hyperplane compare equally and to filter out sufficient topic-irrelevant information, two losses are proposed to learn the triplet representations from the complementary views: range loss and orthogonal loss. To interpret the capability of extracting topic-related knowledge, we present the Topic Similarity (TS) between topic and entity-relations.

Results

Experimental results demonstrate the effectiveness of hyperplane embedding for knowledge representation in knowledge-based visual question answering. Our model outperformed state-of-the-art methods by 2.12% and 3.24% on two challenging knowledge-request datasets: OK-VQA and KRVQA, respectively.

Conclusions

The obvious advantages of our model in TS show that using hyperplane embedding to represent multimodal knowledge can improve its ability to extract topic-related knowledge.

The hands and face are the most important parts for expressing sign language morphemes in sign language videos. However, we find that existing Continuous Sign Language Recognition (CSLR) methods lack the mining of hand and face information in visual backbones or use expensive and time-consuming external extractors to explore this information. In addition, the signs have different lengths, whereas previous CSLR methods typically use a fixed-length window to segment the video to capture sequential features and then perform global temporal modeling, which disturbs the perception of complete signs. In this study, we propose a Multi-Scale Context-Aware network (MSCA-Net) to solve the aforementioned problems. Our MSCA-Net contains two main modules: (1) Multi-Scale Motion Attention (MSMA), which uses the differences among frames to perceive information of the hands and face in multiple spatial scales, replacing the heavy feature extractors; and (2) Multi-Scale Temporal Modeling (MSTM), which explores crucial temporal information in the sign language video from different temporal scales. We conduct extensive experiments using three widely used sign language datasets, i.e., RWTH-PHOENIX-Weather-2014, RWTH-PHOENIX-Weather-2014T, and CSL-Daily. The proposed MSCA-Net achieve state-of-the-art performance, demonstrating the effectiveness of our approach.

Digital watermarking technology plays an essential role in the work of anti-counterfeiting and traceability. However, image watermarking algorithms are weak against hybrid attacks, especially geometric at-tacks, such as cropping attacks, rotation attacks, etc. We propose a robust blind image watermarking algorithm that combines stable interest points and deep learning networks to improve the robustness of the watermarking algorithm further. First, to extract more sparse and stable interest points, we use the Superpoint algorithm for generation and design two steps to perform the screening procedure. We first keep the points with the highest possibility in a given region to ensure the sparsity of the points and then filter the robust interest points by hybrid attacks to ensure high stability. The message is embedded in sub-blocks centered on stable interest points using a deep learning-based framework. Different kinds of attacks and simulated noise are added to the adversarial training to guarantee the robustness of embedded blocks. We use the ConvNext network for watermark extraction and determine the division threshold based on the decoded values of the unembedded sub-blocks. Through extensive experimental results, we demonstrate that our proposed algorithm can improve the accuracy of the network in extracting information while ensuring high invisibility between the embedded image and the original cover image. Comparison with previous SOTA work reveals that our algorithm can achieve better visual and numerical results on hybrid and geometric attacks.