Search

•Novel insights into antibiotic resistance mechanisms: the study provides new understanding of the metabolic alterations in E. coliunder the influence of ofloxacin, highlighting a complex interplay between metabolic pathways and antibiotic resistance.•Comprehensive multi-omics approach: utilization of integrated transcriptomics, proteomics, and acetylomics analyses to dissect E. coli’s response to ofloxacin pressure, offering a holistic view of the changes in bacterial metabolism and potential resistance strategies.•Identification of key metabolic pathways: the research identifies significant changes in critical metabolic pathways, including carbohydrate metabolism and the tricarboxylic acid cycle, under antibiotic stress.•Role of protein acetylation in resistance: the study reveals a link between altered protein acetylation patterns and antibiotic resistance, suggesting a novel mechanism by which E. coliadapts to antimicrobial agents.•Implications for antimicrobial therapy: the findings provide valuable insights into the development of new therapeutic strategies to combat antibiotic resistance in E. coli, emphasizing the importance of targeting metabolic pathways.

This article delves into the potential opportunities and challenges that generative AI introduces to software security analysis based on its fundamentals and applications. In addition, a simple case is used to demonstrate the validity of our outlook.

As a hot research topic in remote sensing, effectively integrating the advantageous features of multispectral and panchromatic images is the main challenge for fusing these two remote sensing images. This article proposes a multiscale frequency fusion network based on ConvGRU. To address the underutilization of texture features, we extract multiscale bandpass and low-pass sub-bands representing texture and content features through Contourlet decomposition. Multiscale bandpass sub-bands contain more comprehensive and concentrated texture details. Then, by proposing a multiscale frequency feature extractor based on ConvGRU, we effectively integrate and enhance sub-bands of different scales and frequencies, fully utilizing the characteristics of multispectral and panchromatic images and scale transmission. With these enhanced sub-band features, we obtain more comprehensive scale-enhanced texture features. Simultaneously, content features are also preserved as dual-source image features. Moreover, to reduce redundancy between fused features and make more efficient use of the obtained enhanced features, we designed an Inver-band integrator (IBI) module. It can fuse enhanced features at different scales, improve the complementarity between features, and thus achieve effective fusion. Experimental results demonstrate the effectiveness and robustness of our model on multiple datasets. Our codes are available at https://github.com/Xidian-AIGroup190726/GMFnet.

In recent years, small object detection has remained challenging in remote sensing tasks. First, small objects inherently have fewer pixels, making them susceptible to interference from prominently featured larger objects during feature extraction. Second, existing detection methods solely based on the intersection over union (IOU) loss are disadvantageous for small object detection and fail to leverage the rich prior information in remote sensing images. Based on these observations, we propose a significant feature elimination and sample assessment network for small object detection called SESA-Net, based on the Facet derivative model. SESA-Net introduces prior information to the network through the directional derivatives characteristic of remote sensing images. The overall network comprises the adaptive derivative mask (ADM) module and sample importance assessment (SIA) strategy. The ADM module eliminates significant responses from shallow large objects, directing the network’s focus toward the features of shallow small objects. The SIA strategy addresses the limitations of the IOU loss function using high-quality positive samples generated by ADM to provide an evaluation strategy for different positive samples of small objects. This enables the network to focus more on high-quality positive samples, thereby improving the accuracy of small object detection. The effectiveness of the proposed algorithm has been validated on multiple datasets. Our code is available at https://github.com/Xidian-AIGroup190726/RS-objectdetection-SESANet.

Recently, remote sensing technology has developed faster and faster, and obtaining high-quality panchromatic (PAN) and multispectral (MS) images has become more accessible. The complementarity between them provides new opportunities in multisource remote sensing image classification. However, solving the problem of the semantic gap between multisource high-level features and, at the same time, utilizing the complementary properties between them to reduce intersource information redundancy is still a challenge. This article constructs an $I^{3}$ RL-Net for the multisource remote sensing image classification task. Specifically, we design a cross-source interactive enhanced fusion module (CIEF-Module). For multilevel multisource features, by strengthening the dependencies of intrasource features and conducting intersource enhanced fusion, intrasource correlation features are refined, and the problem of the intersource semantic gap can be effectively alleviated. During the cross-source interaction process, we design a complementary representation supervised learning strategy (CRSL-Strategy). According to the similarities and differences of multisource features, it can adaptively promote complementary feature learning, thus generating a nonredundant multisource representation. The method has been verified to be effective on multiple RS datasets. The code is open source at: https://github.com/Xidian-AIGroup190726/Ping-Pie-I3RL-Net.git.

The rapid progress in remote sensing technology has made it convenient for satellites to capture both multispectral (MS) and panchromatic (PAN) images. MS has more spectral information, and PAN has higher spatial resolution. How to exploit the complementarity between MS and PAN images, and effectively combine their respective advantageous features while alleviating mode differences, has become a crucial research task. This article designs a style separation and mode unification network (SSMU-Net) for MS and PAN image classification from a novel and effective perspective. The network can be divided into two stages: style separation and mode unification (MU). In the style separation stage, we use wavelet decomposition and techniques similar to generative adversarial networks to preliminarily separate the information of MS and PAN into different components. These components better preserve complete information from the original data and have their own advantages in style and content. Then, we propose a symmetrical triplet traction module to perform style traction on different components, making style features more unique and content features more unified, achieving feature separation and purification. In the MU stage, we design an encoder–decoder model to reduce the impact of mode differences. The experimental results from multiple datasets validate the effectiveness of our proposed method. Our overall accuracy (OA) improved by approximately 4% on the Shanghai and Beijing datasets, and it has exceeded 99.28% on the Hohhot and Vancouver datasets. Our code is available at: https://github.com/proudpie/SSMU-Net.

The roughness of the joint surface plays a significant role in evaluating the shear strength of rock. The waviness (first-order) and unevenness (second-order) of natural joints have different effects on the characterization of joint surface roughness. To accurately quantify the influence of the two-order asperity on the joint roughness coefficient (JRC) prediction of joint surface profile curve, the optimal sampling interval of the asperity was determined through the change of the Rp{R}_{{\rm{p}}}value of the joint surface profile curve. The separation of the two-order asperity of 48 joint surface profile curves was completed at the optimal sampling interval, and morphological parameters of the asperity such as iave{i}_{{\rm{ave}}}, Rmax{R}_{{\rm{\max }}}, and Rp{R}_{{\rm{p}}}were counted from three aspects: asperity angle of the profile curve, asperity degree, and the trace length. Based on the statistical results of the morphological parameters considering the two-order asperity, the new nonlinear prediction models were proposed. The results showed that the curve slope mutation point SI = 2 mm is the optimal separation distance of the two-order asperity of the joint surface profile curve. The refined separation method that considers the waviness and unevenness of morphological parameters can characterize the detailed morphological features of the joint surface in more dimensions. The support vector regression (SVR) and random forest (RF) models that take into account a two-order asperity separated results have higher accuracy than traditional models. The prediction accuracy has improved by 7–8% in SVR model compared with SVR(SO) and RF(SO). The SVR nonlinear model that considering separation of two-orders of joint surface roughness is more suitable for the prediction of JRC.

Supplemental Digital Content is available in the text.Raltitrexed has shown efficacy and safety in many tumor types; however, the clinical data on the treatment of hepatocellular carcinoma is rare. In this report, we aim to assess the efficacy and safety of raltitrexed plus oxaliplatin (OXA)-based transarterial chemoembolization (TACE) in patients with unresectable hepatocellular carcinoma (uHCC). Patients with uHCC were recruited from multi-centers in China and assigned randomly to raltitrexed+OXA-based (n=76), fluorouracil+OXA-based (n=76), and doxorubicin+OXA-based (n=75) TACE treatment. The primary end point was overall survival (OS). Tumor response was assessed using response evaluation criteria in solid tumors (RECIST), modified response evaluation criteria in solid tumors (mRECIST), and European Association for the Study of the Liver criteria (EASL). Safety and toxicity were evaluated using the National Cancer Institute Common Toxicity Criteria. The raltitrexed group showed a better disease control rate evaluated using RECIST (raltitrexed vs. fluorouracil vs. doxorubicin: 96.1 vs. 84.2 vs. 86.7%, P=0.05) and a better overall response rate on the basis of mRECIST (67.1 vs. 47.4 vs. 50.7%, P=0.03) and EASL (67.1 vs. 47.4 vs. 49.3%, P=0.02). The median OS and median progression-free survival (PFS) were higher in the raltitrexed group (median OS: 13.4 vs. 9.6 vs. 8.5 months; median PFS: 6.7 vs 4.9 vs 4.6 months). The most common toxicities included elevated aspartate aminotransferase (78.9 vs. 86.8 vs. 81.3%) and abdominal nonspecific pain (68.4 vs. 81.6 vs. 78.7%). No significant differences were found in the overall number of patients who experienced any toxicity. Raltitrexed plus OXA-based TACE suggested a safe and efficacious regimen in uHCC patients. The results warrant further clinical investigation.