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Unlike other ubiquitin-like family members, UBL5 is structurally and functionally atypical, and a novel role in various biological processes and diseases has been discovered. UBL5 can stabilize the structure of the spliceosome, can promote post-transcriptional processing, and has been implicated in both DNA damage repair and protein unfolding reactions, as well as cellular mechanisms that are frequently exploited by viruses for their own proliferation during viral infections. In addition, UBL5 can inhibit viral infection by binding to the non-structural protein 3 of rice stripe virus and mediating its degradation. Therefore, UBL5 is an important link between viral infections and immunity, and its study will be beneficial for the prevention and treatment of viral infections in the future. However, a review of the current findings on the role of UBL5 in viral infection has not been undertaken. Therefore, in this review, we summarize the recent progress in understanding the functions of UBL5 and discuss its putative role in viral infections.

IntroductionVolatile organic compounds (VOCs) have attracted widespread attention due to their adverse effects on human health. Photocatalytic oxidation is an effective technology for degrading VOCs under ambient conditions.MethodsIn order to better understand the trends and development of global trends in photocatalytic oxidation of VOCs, the analysis of 2493 articles or reviews from the Science Citation Index Expanded (SCIE) in the Web of Science Core Collection, covering the period from 1998 to 2023, was conducted using CiteSpace and VOSviewer software.Results and DiscussionThe findings indicate significant growth in papers concerning photocatalytic oxidation of VOCs. China emerges as the most active country among the main drivers. Principal sources publishing relevant research are Applied Catalysis B-Environmental, Chemical Engineering Journal, Journal of Hazardous Materials, and Environmental Science and Technology. A relatively well-established theoretical framework has been developed for the study of photocatalytic oxidation of VOCs. In the field of VOCs photocatalytic oxidation, the focus is on the development and optimization of advanced photocatalysts with efficient charge separation, better adsorption performance, and a wider light response range. In addition, the in-depth study of the charge generation and transfer mechanisms within the photocatalysts, as well as the comprehensive understanding of the reaction kinetics and catalytic oxidation process, the optimization of the reaction conditions, and the improvement of the catalytic efficiency are at the forefront of the research in this field. This research system is advancing and becoming more refined, with its theoretical propositions, research findings, and methodologies increasingly employed and confirmed.

The control of microplastic pollution in the marine environment has become a growing public concern in recent years. To better grasp the trends and development of microplastic pollution control in the marine environment, the published literature in Science Citation Index Expanded (SCIE) database of Web of Science Core Collection from 2013 to 2022, up to a total of 2,357 articles or reviews was analyzed through CiteSpace and VOSviewer tools. The results show an exponential growth in the number of papers related to the control of microplastic pollution in the marine environment, with China, United States, India, and Australia providing the main drivers, while China being the most active country, with Science of the Total Environment , Marine Pollution Bulletin , Environmental Pollution and Chemosphere being the most important sources for publishing relevant research. A relatively complete theoretical framework has been developed for the control of marine microplastic pollution, focusing on the quantification, traceability and collectability of microplastics. However, few papers have focused on policy implications and technological innovations in this area. The research on marine microplastic pollution control has transitioned from traceability and hazard analysis of microplastics to the impact of economic activities and synthetic fibre on microplastic pollution. Microplastics in wastewater discharged from municipal wastewater treatment plants, human consumption, man-made fibers and synthetic polymers have become the frontier of research. The present study is of significance for better understanding and supporting further research on the control of microplastic pollution in the marine environment.

The removal of decabromodiphenyl ether (BDE 209), as a typical persistent organic pollutant (POP), is of worldwide concern. Mechanochemical (MC) processes are promising methods to degrade environmental pollutants, most of which use a single grinding reagent. The performance of MC processes with co-milling agents still needs to be further verified. In this study, an efficient MC treatment with combined utilization of lithium cobalt oxide (LiCoO2) and iron (Fe) as co-milling reagents for BDE 209 degradation was investigated. The synchronous action of LiCoO2 and Fe with a LiCoO2/Fe/Br molar ratio of 1.5:1.67:1 and a ball-to-powder ratio of 100:1 led to almost thorough-paced abatement and debromination of BDE 209 within 180 min using a ball milling rotation speed of 600 rpm. The reduction in particle sizes and the destruction of crystal structure in mixture powders with the increase in milling time induced the enhanced degradation of BDE 209, as characterized by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The X-ray photoelectron spectroscopy (XPS) characterization showed that the valence state of Co was converted from Co(III) to Co(II), and Fe(0) was changed to Fe(III) when treated with an MC process. This indicated that the reductive debromination of BDE 209 by Fe and the following oxidative degradation of debrominated products by LiCoO2 were integrated in a concerted way. It proved the removal of BDE 209 via an MC treatment. The full breakage of C-Br and C-O bonds in BDE 209 was confirmed by Fourier transform-infrared spectrometry (FT-IR) spectra, and a possible abatement pathway was also proposed based on the identified intermediate products using gas chromatography–mass spectrometry (GC-MS). These obtained results indicated that a combination of LiCoO2 and Fe as co-milling reagents is promising in the MC treatment of toxic halogenated pollutants like BDE 209.

Serverless computing has grown rapidly for serving Large Language Model (LLM) inference due to its pay-as-you-go pricing, fine-grained GPU usage, and rapid scaling. However, our analysis reveals that current serverless can effectively serve general LLM but fail with Low-Rank Adaptation (LoRA) inference due to three key limitations: 1) massive parameter redundancy among functions where 99% of weights are unnecessarily duplicated, 2) costly artifact loading latency beyond LLM loading, and 3) magnified resource contention when serving multiple LoRA LLMs. These inefficiencies lead to massive GPU wastage, increased Time-To-First-Token (TTFT), and high monetary costs. We propose ServerlessLoRA, a novel serverless inference system designed for faster and cheaper LoRA LLM serving. ServerlessLoRA enables secure backbone LLM sharing across isolated LoRA functions to reduce redundancy. We design a pre-loading method that pre-loads comprehensive LoRA artifacts to minimize cold-start latency. Furthermore, ServerlessLoRA employs contention aware batching and offloading to mitigate GPU resource conflicts during bursty workloads. Experiment on industrial workloads demonstrates that ServerlessLoRA reduces TTFT by up to 86% and cuts monetary costs by up to 89% compared to state-of-the-art LLM inference solutions.

LLM-powered agents are emerging as a dominant paradigm for autonomous task solving. Unlike standard inference workloads, agents operate in a strictly serial \"LLM-tool\" loop, where the LLM must wait for external tool execution at every step. This execution model introduces severe latency bottlenecks. To address this problem, we propose PASTE, a Pattern-Aware Speculative Tool Execution method designed to hide tool latency through speculation. PASTE is based on the insight that although agent requests are semantically diverse, they exhibit stable application level control flows (recurring tool-call sequences) and predictable data dependencies (parameter passing between tools). By exploiting these properties, PASTE improves agent serving performance through speculative tool execution. Experimental results against state of the art baselines show that PASTE reduces average task completion time by 48.5% and improves tool execution throughput by 1.8x.

Recent advances in multimodal large language models unlock unprecedented opportunities for GUI automation. However, a fundamental challenge remains: how to efficiently acquire high-quality training data while maintaining annotation reliability? We introduce a self-evolving training pipeline powered by the Calibrated Step Reward System, which converts model-generated trajectories into reliable training signals through trajectory-level calibration, achieving >90% annotation accuracy with 10-100x lower cost. Leveraging this pipeline, we introduce Step-GUI, a family of models (4B/8B) that achieves state-of-the-art GUI performance (8B: 80.2% AndroidWorld, 48.5% OSWorld, 62.6% ScreenShot-Pro) while maintaining robust general capabilities. As GUI agent capabilities improve, practical deployment demands standardized interfaces across heterogeneous devices while protecting user privacy. To this end, we propose GUI-MCP, the first Model Context Protocol for GUI automation with hierarchical architecture that combines low-level atomic operations and high-level task delegation to local specialist models, enabling high-privacy execution where sensitive data stays on-device. Finally, to assess whether agents can handle authentic everyday usage, we introduce AndroidDaily, a benchmark grounded in real-world mobile usage patterns with 3146 static actions and 235 end-to-end tasks across high-frequency daily scenarios (8B: static 89.91%, end-to-end 52.50%). Our work advances the development of practical GUI agents and demonstrates strong potential for real-world deployment in everyday digital interactions.

Assembly of nanoparticles into macroscopic materials with mechanical robustness, green processability, and recastable ability is an important and challenging task in materials science and nanotechnology. As an emerging nanoparticle with superior properties, macroscopic materials assembled from carbon dots will inherit their properties and further offer collective properties; however, macroscopic materials assembled from carbon dots solely remain unexplored. Here we report macroscopic films assembled from carbon dots modified by ureido pyrimidinone. These films show tunable fluorescence inherited from carbon dots. More importantly, these films exhibit collective properties including self-healing, re-castability, and superior mechanical properties, with Young’s modulus over 490 MPa and breaking strength over 30 MPa. The macroscopic films maintain original mechanical properties after several cycles of recasting. Through scratch healing and welding experiments, these films show good self-healing properties under mild conditions. Moreover, the molecular dynamics simulation reveals that the interplay of interparticle and intraparticle hydrogen bonding controls mechanical properties of macroscopic films. Notably, these films are processed into diverse shapes by an eco-friendly hydrosetting method. The methodology and results in this work shed light on the exploration of functional macroscopic materials assembled from nanoparticles and will accelerate innovative developments of nanomaterials in practical applications. The assembly of nanoparticles into macroscopic materials forms the basis for various advanced material applications. Here, the authors develop macroscopic materials that are both recastable and mechanically robust, despite being composed purely of carbon dots.

Self-construal (orientations of independence and interdependence) is a fundamental concept that guides human behaviour, and it is linked to a large number of brain regions. However, understanding the connectivity of these regions and the critical principles underlying these self-functions are lacking. Because brain activity linked to self-related processes are intrinsic, the resting-state method has received substantial attention. Here, we focused on resting-state functional connectivity matrices based on brain asymmetry as indexed by the differential partition of the connectivity located in mirrored positions of the two hemispheres, hemispheric specialization measured using the intra-hemispheric (left or right) connectivity, brain communication via inter-hemispheric interactions, and global connectivity as the sum of the two intra-hemispheric connectivity. Combining machine learning techniques with hypothesis-driven network mapping approaches, we demonstrated that orientations of independence and interdependence were best predicted by the asymmetric matrix compared to brain communication, hemispheric specialization, and global connectivity matrices. The network results revealed that there were distinct asymmetric connections between the default mode network, the salience network and the executive control network which characterise independence and interdependence. These analyses shed light on the importance of brain asymmetry in understanding how complex self-functions are optimally represented in the brain networks.