The Jiangmen Underground Neutrino Observatory (JUNO) collaboration has completed the construction of the 20,000-ton liquid scintillator detector and the associated muon veto detector system. To meet the physics objectives, the materials used in the detector must exhibit low radioactive contamination. The single-event rate in the fiducial volume (R < 17.2 m) of the scintillator is required to be approximately 7 Hz for energies above 0.7 MeV, resulting in an accidental coincidence background of about 1 event per day for reactor neutrino physics analyses. Since the beginning of the construction phase, we have screened the natural radioactivity content of thousands of materials, to select those that meet the design background budget. The radioactive impurity concentrations of the materials ultimately used in the JUNO detector are summarized in this paper. The construction of the entire detector and the subsequent filling of the liquid scintillator were completed in August 2025. From the initial data, the total count rate of natural radioactivity within the detector's fiducial volume has met the requirements and is sufficient to support the reactor antineutrino analysis.

Low-dose computed tomography plays a crucial role in reducing radiation exposure in clinical imaging, however, the resultant noise significantly impacts image quality and diagnostic precision. Recent transformer-based models have demonstrated strong denoising capabilities but are often constrained by high computational complexity. To overcome these limitations, we propose AMFA-Net, an adaptive multi-order feature aggregation network that provides a lightweight architecture for enhancing highresolution feature representation in low-dose CT imaging. AMFA-Net effectively integrates local and global contexts within high-resolution feature maps while learning discriminative representations through multi-order context aggregation. We introduce an agent-based self-attention crossshaped window transformer block that efficiently captures global context in high-resolution feature maps, which is subsequently fused with backbone features to preserve critical structural information. Our approach employs multiorder gated aggregation to adaptively guide the network in capturing expressive interactions that may be overlooked in fused features, thereby producing robust representations for denoised image reconstruction. Experiments on two challenging public datasets with 25% and 10% full-dose CT image quality demonstrate that our method surpasses state-of-the-art approaches in denoising performance with low computational cost, highlighting its potential for realtime medical applications.

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20-kton liquid scintillator-based, low-radioactivity, multi-purpose neutrino detector located 693 meters (1800 m.w.e.) underground in the Guangdong province, China. To detect scintillation light produced in the target, the detector is equipped with 17,612 20-inch photomultipliers (PMTs), forming the Large PMT system (LPMT). In addition, 25,600 3-inch photomultipliers (the Small Photomultiplier System or SPMT) are deployed in the gaps between the LPMTs. This paper presents the design and performance of the underwater front-end electronics developed for the SPMT system. It details the individual electronics boards and their key components, the inter-board interfaces, the system-level design, and the firmware architecture that supports data acquisition and control. It also outlines mechanical and thermal integration, board validation procedures, and system performance metrics. The readout chain includes digitization of 128 PMT channels per unit, synchronized time-stamping, charge measurement, event packaging, and bandwidth management. Comprehensive validation confirms the system's readiness to meet JUNO's stringent physics goals. The underwater electronics achieve noise levels as low as 0.04 photoelectrons with minimal crosstalk (below 0.4

High Energy Physics (HEP) experiments like BESIII produce petabyte-scale data. Extracting physics results requires complex workflows (simulation, reconstruction, statistical analysis, etc.) that traditionally take experts months or years. Current manual methods are labor-intensive, prone to bias, and limit large-scale systematic scans. As data grows, this paradigm slows discovery. Large Language Models (LLMs) offer a solution. Their natural language understanding and code generation capabilities allow them to interpret scientific tasks and integrate with HEP tools (e.g., ROOT, BOSS) to act as an "AI partner" for autonomous analysis. We present Dr.Sai, an LLM-powered multi-agent system that translates natural language into rigorous physics workflows. As validation, Dr.Sai performed large-scale re-measurements of ten J/psi decay branching fractions - without manual coding. It successfully navigated the real BESIII computing environment and produced results matching established benchmarks. The article details Dr.Sai's architecture, the validation results, and performance evaluation. This work provides a blueprint for autonomous discovery, with relevance to other data-intensive fields like astronomy and genomics.

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20-kiloton liquid scintillator neutrino detector, located 650 meters (1800 m.w.e.) underground in Jiangmen, Guangdong, China. JUNO is primarily designed for reactor neutrino measurements and has been taking data since 2025. With the largest mass of its kind and an excellent energy resolution, JUNO is a leading observatory for high-precision measurements of MeV neutrinos. The standard global trigger system serves as the primary trigger for JUNO. We present a newly developed multi-messenger trigger system that extends the capabilities of the global trigger by providing a lower energy threshold and an independent monitoring capability. During the 2025 operation, it achieved an effective energy threshold of approximately 110 +/- 10 keV, providing a lower threshold configuration suitable for low-energy event analysis. The system shows the potential to further reduce the threshold to well below 100 keV. Based on the multi-messenger trigger system, an astrophysical monitor has been developed to receive and process external alerts from other messengers, such as gravitational-wave observations. A Transient Neutrino Burst Monitor is integrated to detect short-time-scale neutrino burst events and enables real-time monitoring of transient astrophysical phenomena. The system is sensitive to neutrino bursts from core-collapse supernovae within a distance of about 250 kpc.

[This corrects the article DOI: 10.34133/research.1018.].

Scientific data generated from the China Spallation Neutron Source face significant challenges in semantic standardization, cross-source alignment and integration, and dataset construction for AI tasks, making it difficult to directly support artificial intelligence model training and data-driven scientific discovery. Therefore, how to construct high-quality AI4S-oriented datasets from complex neutron science data has become a key challenge for advancing the intelligent transformation of neutron science research. To address these challenges, built upon the existing data governance system of neutron sources, this study proposes a five-layer technical framework for high-quality dataset construction, which specifically includes: (1) a standard specification layer, which unifies metadata standards and data formats to achieve logical alignment of multi-source heterogeneous data; (2) a data management layer, which establishes a multidimensional data storage and association infrastructure to support efficient retrieval and access of large-scale data; (3) a quality enhancement layer, which leverages artificial intelligence techniques to automatically complete and logically validate scientific metadata, thereby improving data quality; (4) a data fusion layer, which advances from both data flow integration and large-model-based intelligent agents to explore alignment between experimental data and simulation and textual data; and (5) a task adaptation layer, which utilizes large language models for intelligent annotation to construct end-to-end high-quality datasets tailored to specific AI tasks. Based on this framework, exploratory validations are conducted in multiple scientific scenarios: (1) in inelastic neutron scattering studies, an experiment-centered dataset integrating experimental data with first-principles calculations and particle transport simulations is constructed, effectively alleviating the issue of limited experimental data; (2) in shale pore structure research, multimodal fused data are constructed by integrating small-angle neutron scattering experimental data with literature data, enabling high-precision prediction of pore structure parameters; (3) in neutron diffraction structure analysis, an experiment-simulation-literature collaborative data agent is developed to automate refinement processes and dataset generation, forming AI-Ready datasets for spectral analysis and structure prediction tasks.Finally, this study presents perspectives on high-quality dataset construction for neutron sources. First, it is necessary to strengthen fine-grained control of metadata quality at the source and institutional levels. Second, continuous integration of advanced data and artificial intelligence technologies should be pursued to explore more efficient and intelligent construction approaches. Third, it is essential to proactively investigate next-generation data governance systems with self-evolving capabilities, enabling long-term sustainable development and continuously self-reinforcing data infrastructure.

We present the HepAI Nougat (HaiNougat), a model derived from the Visual Transformer model Nougat, specifically fine-tuned for the intricate parsing requirements of high-energy physics texts, aimed to address the challenge of parsing complex formulas and tables in high-energy physics documents, which contain 20% more formulas per page and 27% longer formulas than typical academic documents. HaiNougat demonstrates superior performance in accurately converting PDF-formatted academic documents into Mathpix Markdown, a lightweight markup language, particularly for documents dense with complex mathematical formulas. The effectiveness of HaiNougat has been validated on a specialized dataset of high-energy physics documents and a diverse dataset of general scientific papers. All code associated with HaiNougat is openly available at https://github.com/ai4hep/hai-nougat. Furthermore, HaiNougat has been successfully deployed into the HepAI platform, with a demo accessible via https://ai.ihep.ac.cn/m/hai-nougat.

Over 25,600 3-inch photomultiplier tubes (PMTs) have been instrumented for the central detector of the Jiangmen Underground Neutrino Observatory. Each PMT is equipped with a high-voltage divider and a frontend cable with waterproof sealing. Groups of sixteen PMTs are connected to the underwater frontend readout electronics via specialized multi-channel waterproof connectors. This paper outlines the design and mass production processes for the high-voltage divider, the cable and connector, as well as the waterproof potting of the PMT bases. The results of the acceptance tests of all the integrated PMTs are also presented.

Neutron diffraction (ND) is an indispensable technique for determining atomic positions (especially light elements) and thus serves as a critical probe for revealing microscopic structures in materials science. However, traditional Rietveld refinement of ND data relies heavily on manual operation of specialized software, which is time-consuming, labor-intensive, and highly dependent on user expertise, severely hindering automated analysis. The automation of Rietveld refinement has long been a long-standing and challenging problem in crystallography. To address this challenge, this paper presents the Dr.Sai-Rongzai agent, an autonomous refinement assistant based on a large language model (LLM), a specialist knowledge base, and the GSAS-II refinement engine, achieving for the first time an intelligent refinement that integrates knowledge-driven decision-making. The agent accomplishes a fully automated workflow from natural language task parsing to autonomous decision-making, execution of refinement strategies, and report generation. Evaluation on five representative samples shows that the Rongzai agent achieves lower Rwp values than human specialists on three samples (2.88

BACKGROUND:Pathological scars, including hypertrophic scars and keloids, are fibrotic skin disorders marked by excessive collagen deposition and persistent inflammation, yet effective treatments remain limited. METHODS:We integrated single-cell RNA sequencing, bulk transcriptomic datasets, and artificial intelligence (AI)-driven machine learning and deep learning to identify molecular drivers of scarring, and subsequently validated our findings using both cellular and animal models. RESULTS:High-dimensional weighted gene co-expression network analysis (hdWGCNA) of scar-associated macrophage subsets revealed COPI coat complex alpha subunit (COPA) as a hub gene correlated with macrophage and fibroblast infiltration. AI-guided drug screening and molecular docking identified sisomicin, an aminoglycoside antibiotic, as a potential COPA-targeting agent. Functional assays in human hypertrophic scar-derived fibroblasts (HHFs) and TGF-β1-stimulated NFs showed that sisomicin inhibited proliferation, migration, and collagen synthesis, while increasing ROS and apoptosis. In bleomycin- and traction-induced mouse models, sisomicin reduced dermal thickening, collagen deposition, and COPA expression. CONCLUSION:This multi-omics and AI-based framework uncovers COPA as a key regulator of pathological scars and highlights sisomicin as a promising therapeutic candidate, offering a broadly applicable strategy for anti-fibrotic drug discovery.

HEPS is a fourth-generation synchrotron light source, and the experiments conducted at HEPS will transition to high-throughput, multi-modal, ultra-fast frequency, and cross-scale formats. The annual data flux generated by these experiments is anticipated to enter the 'Exa-scale' era. Given the substantial volume of high-throughput experimental data, a single computing node struggles to meet the computational demands for data analysis. Consequently, it is essential to develop a high-performance, robust, and user-friendly distributed parallel computing engine to enhance the performance of data processing software. Due to significant variations in data rates across different beamline stations, supporting heterogeneous resources (such as GPUs) in a flexible and fine-grained manner presents a challenge. To optimize computational efficiency and handle extensive datasets, we have developed a distributed parallel computing engine that can leverage scalable, heterogeneous computing resources to deliver HEPS' data analytical services across various scales. Experiment shows that our distributed computing engine significantly enhances the efficiency of HEPS' data processing.

Keloids and hypertrophic scars are characterized by pathologically excessive dermal fibrosis and aberrant wound healing. These pathological scars are characterized by continuous and histologically localized inflammation of the reticular dermis. Although several treatments have been used for hypertrophic scar and keloid, there is still no consensus and for the majority of patients, management is driven by individual clinical experience. Injection of intralesional botulinum toxin type A (BTX) was more effective in the treatment of hypertrophic scar and keloid than injection of placebo. However, little is known about the mechanism of BTX in the treatment of hypertrophic scar and keloid. We used mice wound healing model to track the response of fibrous repair. The histological changes of the fibrous repair were measured by H E, Masson and immunohistochemical staining. The quantitative analysis macrophages and angiogenesis were measured by immunohistochemical staining. The expression of Th1- and Th2-associated cytokines was measured by qPCR. We also measured oxoglutarate dehydrogenase (OGDH) and pyruvate dehydrogenase (PDH) activity, OGDH positive cells and mRNA used enzyme activity assay kits, immunohistochemical staining and qPCR, respectively. We discovered that BTX injection disrupted wound healing and fibrous repair, and reduced M1 macrophages infiltration and M2 macrophages polarization, while increased total macrophages number. BTX injection reduced Il1b, Il6 and TNF-alpha mRNA level, and induced Il13 and Il4 mRNA level. BTX injection reduced OGDH activity. BTX alter the pattern of immunometabolism that change the phenotype of macrophages, which inhibits keloids and hypertrophic scars. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

This paper presents an energy resolution study of the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3% at 1 MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components of the JUNO detector. Various factors affecting the detection of inverse beta decay signals have an impact on the energy resolution, extending beyond the statistical fluctuations of the detected number of photons, such as the properties of the liquid scintillator, performance of photomultiplier tubes, and the energy reconstruction algorithm. To account for these effects, a full JUNO simulation and reconstruction approach is employed. This enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy resolution. The results of this study reveal an energy resolution of 2.95% at 1 MeV. Furthermore, this study assesses the contribution of major effects to the overall energy resolution budget. This analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data collection. Moreover, it provides a guideline for comprehending the energy resolution characteristics of liquid scintillator-based detectors.

Low-dose computed tomography (LDCT) is widely used to reduce patient radiation exposure, but this reduction often comes at the cost of increased noise in the CT images. Although various deep learning-based methods have been developed for LDCT denoising, most struggle to balance local perception and global contextual capture, thus failing to highlight valuable expressions. This paper presents a multi-stage multi-order context aggregation learning framework designed for high-resolution feature map. The framework combines local perception with adaptive context aggregation to improve performance. Each stage employs the macro-architecture of a vision transformer and integrates edge-enhancement features. Initially, the input passes through feature embedding blocks, followed by the stacking of multiple multi-order context aggregation modules to enable efficient feature interaction. The context aggregation modules effectively generate more discriminative representations from features that incorporate edge information. Extensive experiments on two publicly available LDCT denoising datasets demonstrate that our method surpasses state-of-the-art models. Notably, our method strikes a better balance between network efficiency and denoising performance. The code will be made publicly available on https://code.ihep.ac.cn/lijf/MMCA.

Large-scale organic liquid scintillator detectors are highly efficient in the detection of MeV-scale electron antineutrinos. These signal events can be detected through inverse beta decay on protons, which produce a positron accompanied by a neutron. A noteworthy background for antineutrinos coming from nuclear power reactors and from the depths of the Earth (geoneutrinos) is generated by ( $$\alpha ,\,n$$ α , n ) reactions. In organic liquid scintillator detectors, $$\alpha $$ α particles emitted from intrinsic contaminants such as $$^{238}$$ 238 U, $$^{232}$$ 232 Th, and $$^{210}$$ 210 Pb/ $$^{210}$$ 210 Po, can be captured on $$^{13}$$ 13 C nuclei, followed by the emission of a MeV-scale neutron. Three distinct interaction mechanisms can produce prompt energy depositions preceding the delayed neutron capture, leading to a pair of events correlated in space and time within the detector. Thus, ( $$\alpha ,\,n$$ α , n ) reactions represent an indistinguishable background in liquid scintillator-based antineutrino detectors, where their expected rate and energy spectrum are typically evaluated via Monte Carlo simulations. This work presents results from the open-source SaG4n software, used to calculate the expected energy depositions from the neutron and any associated de-excitation products. Also simulated is a detailed detector response to these interactions, using a dedicated Geant4-based simulation software from the JUNO experiment. An expected measurable $$^{13}$$ 13 C $$(\alpha ,\,n)^{16}$$ ( α , n ) 16 O event rate and reconstructed prompt energy spectrum with associated uncertainties, are presented in the context of JUNO, however, the methods and results are applicable and relevant to other organic liquid scintillator neutrino detectors.

These recommendations are the result of reflections by scientists and experts who are, or have been, involved in the preservation of high-energy physics data. The work has been done under the umbrella of the Data Lifecycle panel of the International Committee of Future Accelerators (ICFA), drawing on the expertise of a wide range of stakeholders. A key indicator of success in the data preservation efforts is the long-term usability of the data. Experience shows that achieving this requires providing a rich set of information in various forms, which can only be effectively collected and preserved during the period of active data use. The recommendations are intended to be actionable by the indicated actors and specific to the particle physics domain. They cover a wide range of actions, many of which are interdependent. These dependencies are indicated within the recommendations and can be used as a road map to guide implementation efforts. These recommendations are best accessed and viewed through the web application, see https://icfa-data-best-practices.app.cern.ch/

Diabetic wound healing presents a significant clinical challenge due to disrupted neuro-immune interactions. This study identifies the α7 nicotinic acetylcholine receptor (α7nAChR) as a key regulator of wound repair by linking cholinergic signaling to macrophage reprogramming. GEO analysis of diabetic foot ulcer (DFU) microenvironments revealed neuronal loss, M1 macrophage dominance, and chronic inflammation, all driven by impaired acetylcholine (ACh) secretion and α7nAChR inactivation. Mechanistically, taurine (TA) restored PC12 cell function under high glucose conditions by activating AMPK, alleviating oxidative and endoplasmic reticulum stress, and promoting ACh production. ACh activated macrophage α7nAChR, modulating M1/M2 polarization through JAK2/STAT3 activation and NF-κB suppression. To enhance TA bioavailability, ultrasound-responsive Ccr2-targeted TA nanoparticles (Ccr2@TA@LNP) were developed for site-specific delivery via Ccl2/Ccr2 chemotaxis. In diabetic neuropathy (DPN) mice, Ccr2@TA@LNP accelerated wound healing by increasing ACh levels, enhancing α7nAChR/CD206 expression, and reducing Ccl2-mediated inflammation. By integrating neuroprotection, macrophage reprogramming, and targeted nanotherapy, this study highlights TA as a multi-target agent that restores neuro-immune balance through the AMPK/α7nAChR/JAK2-STAT3 axis, offering a novel therapeutic strategy for diabetic wound treatment.

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment under construction in South China. This paper presents an updated estimate of JUNO's sensitivity to neutrino mass ordering using the reactor antineutrinos emitted from eight nuclear reactor cores in the Taishan and Yangjiang nuclear power plants. This measurement is planned by studying the fine interference pattern caused by quasi-vacuum oscillations in the oscillated antineutrino spectrum at a baseline of 52.5 km and is completely independent of the CP violating phase and neutrino mixing angle theta(23). The sensitivity is obtained through a joint analysis of JUNO and Taishan Antineutrino Observatory (TAO) detectors utilizing the best available knowledge to date about the location and overburden of the JUNO experimental site, local and global nuclear reactors, JUNO and TAO detector responses, expected event rates and spectra of signals and backgrounds, and systematic uncertainties of analysis inputs. We find that a 3 sigma median sensitivity to reject the wrong mass ordering hypothesis can be reached with an exposure of about 6.5 years x 26.6 GW thermal power.

Radiation-induced skin injury (RISI) is a common and debilitating complication of radiotherapy, characterized by persistent inflammation and delayed wound healing. Macrophages play a central role in this process; however, the molecular mechanisms governing their dysfunction under radiation stress remain poorly understood. To elucidate the role of triggering receptor expressed on myeloid cells 2 (TREM2) in macrophage regulation after irradiation, we combined single-cell RNA sequencing, in vivo mouse models, and in vitro macrophage assays. Conditional knockout mice (LysMCreTrem2flox/flox) were used to selectively delete Trem2 in macrophages. Radiation induced a distinct TREM2+ macrophage subset; however, despite elevated Trem2 mRNA, protein levels declined due to ADAM17-mediated shedding driven by radiation-induced reactive oxygen species (ROS) accumulation and NRF2 activation. Inhibition or small interfering RNA (siRNA)-mediated knockdown of ADAM17 restored TREM2 protein expression, reduced soluble TREM2 release, improved macrophage survival, and promoted anti-inflammatory M2 polarization. Conversely, Trem2 deficiency enhanced apoptosis, sustained inflammation, and delayed wound healing, whereas Trem2 overexpression or local adoptive transfer of TREM2+ macrophages accelerated tissue repair. Mechanistically, TREM2 conferred radioprotection through extracellular signal-regulated kinase (ERK) pathway activation, linking the ROS–NRF2–ADAM17 axis to TREM2–ERK signaling in macrophage survival and polarization. Collectively, these findings identify a novel regulatory cascade, ROS–NRF2–ADAM17–TREM2–ERK, that governs macrophage fate under irradiation. Targeting this pathway or supplementing TREM2+ macrophages may offer promising therapeutic strategies for mitigating RISI.