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The working environment for tubing in oil and gas fields is becoming more and more serious due to the exploration of unconventional oil and gas resources, leading to the increasing need for a protective internal coating to be used in tubing. Therefore, a new mica–graphene/epoxy composite coating with different graphene contents (0.0, 0.2, 0.5, 0.7, and 1.0 wt.%) was prepared to improve the tubing resistance to a corrosive medium, an autoclave was used to simulate the working environment, and an electrochemical workstation assisted by three-electrodes was used to study the electrochemical characteristics of the coating. The results showed that the addition of a certain amount of graphene into the mica/epoxy coating significantly improved the corrosion resistance of the composite coating, and when the graphene content increased, the corrosion resistance of the mica/epoxy coating first increased and then decreased when the corrosion current density of a 35 wt.% 800# mica/epoxy coating with a 0.7 wt.% graphene content was the lowest (7.11 × 10−13 A·cm−2), the corrosion potential was the highest (292 mV), the polarization resistance was the largest (3.463 × 109 Ω·cm2), and the corrosion resistance was improved by 89.3% compared to the coating without graphene. Furthermore, the adhesion of the coating with 0.7 wt.% graphene was also the largest (8.81 MPa, increased by 3.4%) and had the smallest diffusion coefficient (1.566 × 107 cm2·s−1, decreased by 76.1%), and the thermal stability improved by 18.6%. Finally, the corrosion resistance mechanism of the composite coating with different graphene contents at different soaking times was revealed based on the electrochemistry and morphology characteristics other than water absorption and contact angle.

Saponins extracted from Panax notoginseng are neuroprotective, but the mechanisms underlying this effect remain unclear. In the present study, we established a rat model of thoracic（T10） spinal cord transection, and injected Panax notoginseng saponins（100 mg/kg） or saline 30 minutes after injury. Locomotor functions were assessed using the Basso, Beattie, and Bresnahan（BBB） scale from 1 to 30 days after injury, and immunohistochemistry was carried out in the ventral horn of the spinal cord at 1 and 7 days to determine expression of nerve growth factor（NGF） and brain-derived neurotrophic factor（BDNF）. Our results show that at 7–30 days post injury, the BBB score was higher in rats treated with Panax notoginseng saponins than in those that received saline. Furthermore, at 7 days, more NGF- and BDNF-immunoreactive neurons were observed in the ventral horn of the spinal cord of rats that had received Panax notoginseng saponins than in those that received saline. These results indicate that Panax notoginseng saponins caused an upregulation of NGF and BDNF in rats with spinal cord transection, and improved hindlimb motor function.

Metabolomics is emerging as a powerful tool for studying metabolic processes, identifying crucial biomarkers responsible for metabolic characteristics and revealing metabolic mechanisms, which construct the content of discovery metabolomics. The crucial biomarkers can be used to reprogram a metabolome, leading to an aimed metabolic strategy to cope with alteration of internal and external environments, naming reprogramming metabo- Iomics here. The striking feature on the similarity of the basic metabolic pathways and components among vastly different species makes the reprogramming metabolomics possible when the engineered metabolites play biological roles in cellular activity as a substrate of enzymes and a regulator to other molecules including proteins. The reprogramming metabolomics approach can be used to clarify metabolic mechanisms of responding to changed internal and external environmental factors and to estab- lish a framework to develop targeted tools for dealing with the changes such as controlling and/or preventing infec- tion with pathogens and enhancing host immunity against pathogens. This review introduces the current state and trends of discovery metabolomics and reprogramming metabolomics and highlights the importance of repro- gramming metabolomics.

Dear Editor, Fungal infection involves the invasion of tissues by one or more species of fungi, and this is a serious problem in both medical settings and agriculture [Brown et al., 2012]. Thus, the development of efficient strategies for management of these infections is urgently needed [Rodriguez-Martfn et al., 2010]. Recently, a number of antifungal proteins have been reported, including AFP from Aspergillus giganteus, PAF from Penicillium chrysogenum, NAF from Penicillium nalviogense, and AnAFP from Aspergillus niger [Marx et al., 2008; Geisen 2000]. These proteins have demonstrated antifungal activity against opportunistic plant and animal pathogens, such as Fusanum sp., Botrytis sp., and Asper- gillus sp. [Meyer, 2008]. We have previously characterized an antifungal protein （PcPAF） from the culture supernatant of the fungal strain Penicillium citrinum W1, which was isolated from sediment obtained from the Southwest Indian Ocean. PcPAF is thermostable and displays antifungal activity against various pathogenic fungi, including Trichoderma viride, Fusarium oxysporum, Alternaria Iongipes, and Pae- cilomyces variotii [Wen et al., 2014]. Therefore, large-scale production of PcPAF would enable the further drug development of this compound.

Low-extremity ischemic disease is a common complication in diabetic patients, leading to reduced quality of life and potential amputation. This study investigated the therapeutic effect of spinal cord stimulation (SCS) on patients with diabetic foot disease and a rat model of diabetic foot injury. SCS was applied to patients with diabetic foot disease, with clinical assessments performed before and after therapy. Blood levels of NGF, BDNF, and NT-3 were determined by ELISA. A rat model of diabetic foot injury was established to validate NT-3’s role in SCS therapy. SCS therapy improved the condition of patients with diabetic ischemic foot disease and promoted wound healing in the rat model. NT-3 levels significantly increased after SCS therapy in both patients and rats. Recombinant NT-3 administration improved wound healing and re-vascularization in the rat model, while NT-3 neutralization abrogated SCS’s therapeutic effect. SCS improves the condition of patients with diabetic ischemic foot disease by inducing NT-3 production. Both SCS and NT-3 supplementation show therapeutic potential for ameliorating diabetic foot disease.

The working environment for tubing in oil and gas fields is becoming more and more serious due to the exploration of unconventional oil and gas resources, leading to the increasing need for a protective internal coating to be used in tubing. Therefore, a new mica-graphene/epoxy composite coating with different graphene contents (0.0, 0.2, 0.5, 0.7, and 1.0 wt.%) was prepared to improve the tubing resistance to a corrosive medium, an autoclave was used to simulate the working environment, and an electrochemical workstation assisted by three-electrodes was used to study the electrochemical characteristics of the coating. The results showed that the addition of a certain amount of graphene into the mica/epoxy coating significantly improved the corrosion resistance of the composite coating, and when the graphene content increased, the corrosion resistance of the mica/epoxy coating first increased and then decreased when the corrosion current density of a 35 wt.% 800 mica/epoxy coating with a 0.7 wt.% graphene content was the lowest (7.11 × 10 A·cm ), the corrosion potential was the highest (292 mV), the polarization resistance was the largest (3.463 × 10 Ω·cm ), and the corrosion resistance was improved by 89.3% compared to the coating without graphene. Furthermore, the adhesion of the coating with 0.7 wt.% graphene was also the largest (8.81 MPa, increased by 3.4%) and had the smallest diffusion coefficient (1.566 × 10 cm ·s , decreased by 76.1%), and the thermal stability improved by 18.6%. Finally, the corrosion resistance mechanism of the composite coating with different graphene contents at different soaking times was revealed based on the electrochemistry and morphology characteristics other than water absorption and contact angle.

With the success of Diffusion Models for image generation, the technologies also have revolutionized the aesthetic Quick Response (QR) code generation. Despite significant improvements in visual attractiveness for the beautified codes, their scannabilities are usually sacrificed and thus hinder their practical uses in real-world scenarios. To address this issue, we propose a novel training-free Diffusion-based QR Code generator (DiffQRCoder) to effectively craft both scannable and visually pleasing QR codes. The proposed approach introduces Scanning-Robust Perceptual Guidance (SRPG), a new diffusion guidance for Diffusion Models to guarantee the generated aesthetic codes to obey the ground-truth QR codes while maintaining their attractiveness during the denoising process. Additionally, we present another post-processing technique, Scanning Robust Manifold Projected Gradient Descent (SR-MPGD), to further enhance their scanning robustness through iterative latent space optimization. With extensive experiments, the results demonstrate that our approach not only outperforms other compared methods in Scanning Success Rate (SSR) with better or comparable CLIP aesthetic score (CLIP-aes.) but also significantly improves the SSR of the ControlNet-only approach from 60% to 99%. The subjective evaluation indicates that our approach achieves promising visual attractiveness to users as well. Finally, even with different scanning angles and the most rigorous error tolerance settings, our approach robustly achieves over 95% SSR, demonstrating its capability for real-world applications. Our project page is available at https://jwliao1209.github.io/DiffQRCoder.

Diffusion Models have emerged as powerful generative models for high-quality image synthesis, with many subsequent image editing techniques based on them. However, the ease of text-based image editing introduces significant risks, such as malicious editing for scams or intellectual property infringement. Previous works have attempted to safeguard images from diffusion-based editing by adding imperceptible perturbations. These methods are costly and specifically target prevalent Latent Diffusion Models (LDMs), while Pixel-domain Diffusion Models (PDMs) remain largely unexplored and robust against such attacks. Our work addresses this gap by proposing a novel attack framework, AtkPDM. AtkPDM is mainly composed of a feature representation attacking loss that exploits vulnerabilities in denoising UNets and a latent optimization strategy to enhance the naturalness of adversarial images. Extensive experiments demonstrate the effectiveness of our approach in attacking dominant PDM-based editing methods (e.g., SDEdit) while maintaining reasonable fidelity and robustness against common defense methods. Additionally, our framework is extensible to LDMs, achieving comparable performance to existing approaches.

With the success of Diffusion Models for image generation, the technologies also have revolutionized the aesthetic Quick Response (QR) code generation. Despite significant improvements in visual attractiveness for the beautified codes, their scannabilities are usually sacrificed and thus hinder their practical uses in real-world scenarios. To address this issue, we propose a novel training-free Diffusion-based QR Code generator (DiffQRCoder) to effectively craft both scannable and visually pleasing QR codes. The proposed approach introduces Scanning-Robust Perceptual Guidance (SRPG), a new diffusion guidance for Diffusion Models to guarantee the generated aesthetic codes to obey the ground-truth QR codes while maintaining their attractiveness during the denoising process. Additionally, we present another post-processing technique, Scanning Robust Manifold Projected Gradient Descent (SR-MPGD), to further enhance their scanning robustness through iterative latent space optimization. With extensive experiments, the results demonstrate that our approach not only outperforms other compared methods in Scanning Success Rate (SSR) with better or comparable CLIP aesthetic score (CLIP-aes.) but also significantly improves the SSR of the ControlNet-only approach from 60% to 99%. The subjective evaluation indicates that our approach achieves promising visual attractiveness to users as well. Finally, even with different scanning angles and the most rigorous error tolerance settings, our approach robustly achieves over 95% SSR, demonstrating its capability for real-world applications. Our project page is available at https://jwliao1209.github.io/DiffQRCoder.

Diffusion Models have emerged as powerful generative models for high-quality image synthesis, with many subsequent image editing techniques based on them. However, the ease of text-based image editing introduces significant risks, such as malicious editing for scams or intellectual property infringement. Previous works have attempted to safeguard images from diffusion-based editing by adding imperceptible perturbations. These methods are costly and specifically target prevalent Latent Diffusion Models (LDMs), while Pixel-domain Diffusion Models (PDMs) remain largely unexplored and robust against such attacks. Our work addresses this gap by proposing a novel attacking framework with a feature representation attack loss that exploits vulnerabilities in denoising UNets and a latent optimization strategy to enhance the naturalness of protected images. Extensive experiments demonstrate the effectiveness of our approach in attacking dominant PDM-based editing methods (e.g., SDEdit) while maintaining reasonable protection fidelity and robustness against common defense methods. Additionally, our framework is extensible to LDMs, achieving comparable performance to existing approaches.