Adolescent exposure to micro/nanoplastics induces cognitive impairments in mice with neuronal morphological damage and multi-omic alterations

Polystyrene micro/nanoplastics (MPs/NPs) are globally recognized environmental concerns due to their widespread pollution and detrimental effects on physiological functions. However, the neurotoxic effects and underlying mechanisms of MPs/NPs on brain function in adolescents remain incompletely understood. This study investigated the effects of polystyrene MPs/NPs on neurobehavioral function in adolescent mice, utilizing multi-omic analysis and molecular biology assays to explore potential mechanisms. Following oral exposure of MPs (5 μm) or NPs (0.5 μm) at 0.5 mg/day for 4 weeks, NPs induced more severe cognitive impairment in mice than MPs, as assessed by the Morris water maze and Y-maze tests. This impairment might be associated with the neuron loss and neurogenesis inhibition caused by NPs, while dendritic spine loss mediated by MPs in the hippocampus. Furthermore, analysis of hippocampal transcriptome and Western blotting indicated the potential involvement of the PI3K/AKT pathway in NPs-induced neurotoxicity. Meanwhile, exposure to NPs induced more pronounced disruptions in the hippocampal metabolome and gut microbiota, and strong correlations were observed between changes in hippocampal metabolites and gut bacteria. This study elucidated the toxicity mechanism of MPs and NPs in inducing cognitive impairment in adolescent mice, providing insights into their toxicological impacts and potential strategies for intervention. © 2025 The Author

VisualCodeMOOC: A course platform for algorithms and data structures integrating a conversational agent for enhanced learning through dynamic visualizations

The abstract nature of algorithms and data structures poses challenges for students, and the integration of visualization into comprehensive learning systems remains underexplored. This article presents VisualCodeMOOC, incorporating VisualCodeChat, a conversational agent that enhances algorithm and data structure learning through dynamic visualizations and personalized feedback. The platform effectively addresses these challenges, improving student engagement and comprehension. With instructions structuring, novel response-based algorithm visualization, exercise design, VisualCodeMOOC provides a cohesive and supportive learning environment that promotes active learning. Evaluation results demonstrate its usability, responsiveness, and educational value, confirming its potential as a promising tool for advancing computer science education. © 2025 The Author

Enhancement of quantum coherence in solid-state qubits via interface engineering

Shallow nitrogen-vacancy (NV) centers in diamond are promising quantum sensors but suffer from noise-induced short coherence times due to bulk and surface impurities. We present interfacial engineering via oxygen termination and graphene patching, extending shallow NV coherence to over 1 ms, approaching the T1 limit. Raman spectroscopy and density-functional theory reveal surface termination-driven graphene charge transfer reduces spin noise by pairing surface electrons, supported by double electron-electron resonance spectroscopy showing fewer unpaired spins. Enhanced sensitivity enables detection of single weakly coupled 13C nuclear spins and external 11B spins from a hexagonal boron nitride (h-BN) layer, achieving nanoscale nuclear magnetic resonance. A protective h-BN top layer stabilizes the platform, ensuring robustness against harsh treatments and compatibility with target materials. This integrated approach advances practical quantum sensing by combining extended coherence, improved sensitivity, and device durability

FeVisQA: Free-Form Question Answering over Data Visualizations

Given a massive dataset, data visualization (DV) could efficiently express the insights and summaries behind the massive raw data by employing vivid visual representations. To create suitable DVs, users are required to get a comprehensive understanding of the raw data and then transfer their ideas into DVs by composing a suitable and accurate specification in some declarative visualization languages (DVLs, e.g., Vega-Lite). A specification is a JSON object defining the properties of the DVs, like the selected data, the transformations, the visual details, and so on. Due to its complicated grammar and details, DV has quite a steep learning curve, even for data analysts. In this paper, we propose a new task named FeVisQA, referring to Free-form Question Answering over data Visualizations. More specifically, given a raw dataset, a related DV (in the form of a specification), and a question, FeVisQA aims to predict a textual answer automatically. As a particular case of the general CodeQA (i.e., QA over general programming code like Python and Java) task, FeVisQA enables people to better comprehend data and its DVs by conducting logical reasoning when answering these questions. Since FeVisQA has not been studied in the literature, we first construct a benchmark dataset containing 152 datasets, 14,406 DVs, and 83,890 QA pairs. To tackle this new task, we design a novel neural network named FeVisQANet with advanced multi-modal encoder and adaptive decoder structures, and we also design a novel multi-step framework called VisQA for Multi-modal Large Language Models (MLLMs) based on Retrieval-augmented Generation (RAG) technology. Extensive experiments on our constructed datasets validate the rationale and effectiveness of this proposed FeVisQA task and the proposed model. While research on QA over text and table, machine reading comprehension, and CodeQA develops rapidly, prior works have yet to draw attention to question-answering over DVs. This study connects two important subareas, QA from the natural language process area and DV from the data engineering area. We hope this new dataset and model can serve as a helpful benchmark that would benefit the development of both fields

Engineering long-lived charge separation states boosts type-I ROS generation for efficient cancer therapy

Organic photosensitizers (PSs) with long-lived charge-separated states (CSs) are optimal for converting photonic energy into reactive oxygen species (ROS) by maximizing the interaction between excited electrons and holes in subsequent photoreactions. However, the substantial consumption of oxygen by the singlet oxygen species produced by these PSs can significantly impede their anticancer efficacy, because of the hypoxia nature of solid tumors. Herein, we present a rational strategy for the structural modification of the well-known Fukuzumi acridinium salt (9-mesityl-10-methylacridinium ion) with long-lived CSs, by incorporating a methyl-substituted diphenylamine group (named MTPAA). This modification significantly enhances type-I ROS generation. The “methyl effect” in MTPAA has distinguished merits of stabilized radical species through resonance, leading to an over 8-fold increase in type-I ROS generation compared to TPAA, which lacks the methyl group. Moreover, cellular experiments show that MTPAA with the “methyl effect” significantly enhances photodynamic therapy efficacy under hypoxic conditions. Our molecular design strategy offers a promising approach to creating high-performance type-I PSs and is anticipated to inspire broader exploration in other photosensitizer systems with long-lived CSs, serving as a versatile strategy for advancing type-I PS development. © 202

Machine learning assisted crystallographic reconstruction from atom probe tomographic images

Atom probe tomography (APT) is a powerful technique for three-dimensional (3D) atomic-scale imaging, enabling the accurate analysis on the compositional distribution at the nanoscale. How to accurately reconstruct crystallographic information from APT data, however, is still a great challenge due to the intrinsic nature of the APT technique. In this paper, we propose a novel approach that consists of the modified forward simulation process and the backward machine learning process to recover the tested crystal from APT data. The high-throughput forward simulations on Al single crystals of different orientations generate 10 000 original 3D images and data augmentation is implemented on the original images, resulting in 100 000 3D images. The big data allows the development of deep learning models and three deep learning algorithms of Convolutional Neural Network (CNN), Vision Transformer (ViT), and Variational Autoencoder (VAE) are used in the backward process. After training, the ViT model performs superior than the CNN and VAE models, which can recover the crystalline orientation outstandingly, as evaluated by the coefficient of determination R-2 and the Mean Percent Error (MPE), viz., R-2 = 0.93 and MPE = 0.43%, R-2 = 0.97 and MPE = 0.35%, and R-2 = 0.93 and MPE = 0.77% for the rotation angles phi, psi and theta, respectively, on the test dataset. The present work clearly demonstrates the capability of deep learning models in the recovery of the tested crystals from APT data, thereby paving the way for the further development of large artificial intelligent models of APT

FirePower: Towards a Foundation with Generalizable Knowledge for Architecture-Level Power Modeling

Power efficiency is a critical design objective in modern processor design. A high-fidelity architecture-level power modeling method is greatly needed by CPU architects for guiding early optimizations. However, traditional architecture-level power models can not meet the accuracy requirement, largely due to the discrepancy between the power model and actual design implementation. While some machine learning (ML)-based architecture-level power modeling methods have been proposed in recent years, the data-hungry ML model training process requires sufficient similar known designs, which are unrealistic in many development scenarios. This work proposes a new power modeling solution FirePower that targets few-shot learning scenario for new target architectures. FirePower proposes multiple new policies to utilize crossarchitecture knowledge. First, it develops power models at component level, and components are defined in a power-friendly manner. Second, it supports different generalization strategies for models of different components. Third, it formulates generalizable and architecture-specific design knowledge into two separate models. FirePower also supports the evaluation of the generalization quality. In our experiments, FirePower can achieve a low error percentage of 5.8% and a high correlation 𝑅 of 0.98 on average only using two configurations of target architecture. This is 8.8% lower in error percentage and 0.03 higher in 𝑅 compared with directly training McPAT-Calib baseline on configurations of target architecture