Ocean exploration is crucial for utilizing its extensive resources. Images captured by underwater robots suffer from issues such as color distortion and reduced contrast. To address the issue, an innovative enhancement algorithm is proposed, which integrates Transformer and Convolutional Neural Network (CNN) in a parallel fusion manner. Firstly, a novel transformer model is introduced to capture local features, employing peak-signal-to-noise ratio (PSNR) attention and linear operations. Subsequently, to extract global features, both temporal and frequency domain features are incorporated to construct the convolutional neural network. Finally, the image’s high and low frequency information are utilized to fuse different features. To demonstrate the algorithm’s effectiveness, underwater images with various levels of color distortion are selected for both qualitative and quantitative analyses. The experimental results demonstrate that our approach outperforms other mainstream methods, achieving superior PSNR and structural similarity index measure (SSIM) metrics and yielding a detection performance improvement of over ten percent.

Ocean exploration is crucial for utilizing its extensive resources. Images captured by underwater robots suffer from issues such as color distortion and reduced contrast. To address the issue, we propose an innovative enhancement algorithm that integrates Transformer and Convolutional Neural Network (CNN) in a parallel fusion manner. Firstly, a novel transformer model is intro-duced to capture local features, employing peak-signal-to-noise ratio (PSNR) attention and linear operations. Subsequently, to extract global features, both temporal and frequency domain features are incorporated to construct convolutional neural network. Finally, the Fourier’s high and low-frequency information of the original image are utilized to fuse different features. To demon-strate the algorithm's effectiveness, underwater images with various levels of color distortion are selected for both qualitative and quantitative analyses. The experimental results demonstrate that our approach surpasses other mainstream methods, achieving superior PSNR and structural sim-ilarity index measure (SSIM) metrics and leading to a detection performance improvement of over ten percent.

Trichlorfon (TCF), a widely used organophosphorus pesticide for crop protection, poses severe dermal toxicity risks as it readily penetrates the epidermal barrier and metabolizes into the more toxic dichlorvos (DDVP), triggering neurotoxicity, oxidative stress and inflammatory damage in exposed individuals. As a promising stoichiometric bioscavenger for pesticide detoxification, natural acetylcholinesterase (AChE) is still plagued by bottlenecks in thermal stability and transdermal delivery efficiency. To address these issues, this study employed a previously engineered high-activity and thermostable AChE variant (CpA-M5), evaluated its detoxification efficacy and mechanisms both in vitro and in vivo, and further developed a CpA-M5-loaded dissolvable microneedle system (CpA-M5-MN) for targeted dermal detoxification. In vitro assays demonstrated that CpA-M5 effectively restored endogenous AChE activity, scavenged ROS, reduced inflammation and alleviated apoptosis in TCF-exposed cells. Enzyme kinetics and molecular interaction analyses confirmed irreversible binding between CpA-M5 and TCF/DDVP, with DDVP showing higher binding affinity (K_D = 9.23 × 10⁻⁶ M, Ki = 15.56 μM) and complex stability (K_a = 2450.191 M⁻¹, ΔG = −39.37 ± 1.48 kcal/mol). In vivo, the CpA-M5-loaded microneedle system achieved targeted transdermal delivery, effectively overcoming the limitations of low permeability and rapid degradation of free enzymes. Pathological and metabolomic analyses demonstrated that CpA-M5-MN reduced skin residual TCF/DDVP levels, restored local oxidative-inflammatory homeostasis, and regulated glutathione and amino acid metabolic pathways, thereby achieving synergistic detoxification and metabolic repair effects. Collectively, this work presents a promising targeted detoxification platform for pesticide-induced dermal injury, and provides new insights into the clinical translation of engineered enzyme-based bioscavengers.

The 100 GHz-class ultrafast photonic integrated circuit (PIC) positions itself as a promising technology in the post-Moore era, when the bandwidth limit of metallic interconnections constrains current electronic integrated circuits. Nevertheless, the lack of an effective on-chip, CMOS-compatible laser source challenges the ongoing development of PIC. Germanium straintronics facilitate bandgap transformation from indirect to direct, thereby enabling effective band-to-band radiative recombination. Some parameters, such as nanowire diameters or crystalline orientation and strain direction, have a profound effect on the bandgap transformation of Ge nanowires. In this review, we will discuss changes in the fundamental physical properties of Ge nanowires under strain, including mechanical, electronic, optical, and thermal properties. Subsequently, we summarize common methods for strain engineering, as well as novel approaches that have emerged in recent years. Some notable application cases reported in the last few decades will be discussed in detail. This review may fill knowledge gaps and provide a solid background for forthcoming investigations of on-chip strained Ge lasers.