Zhuhai Fudan Innovation Research Institute

BackgroundAlthough interest in migraine has increased in recent years, important gaps remain in understanding and optimizing its management. These gaps are particularly pronounced in pediatric migraine, which continues to be understudied.Case reportThis case report demonstrates the efficacy and safety of transcutaneous auricular vagus nerve stimulation (taVNS) in an 8-year-old male patient with refractory chronic migraine with aura [two to three weekly episodes; visual analog scale (VAS): 5–9; duration of each episode was 24 h]. After discontinuing all prophylactic and abortive medications (except ibuprofen suspensions such as Motrin®), the patient underwent a 28-week taVNS protocol that involved the following phases: a 4-week acute intervention, a 4-week intermission period, a 12-week preventive intervention, and an 8-week follow-up. During the acute intervention phase, the patient’s headache duration decreased by 84.4%, and frequency was reduced to fewer than two episodes/week, with complete aura resolution. The preventive intervention yielded further improvement to fewer than 1 episode/week by week 8 (with a 37.5% reduction in medication use). At final follow-up, the patient maintained a medication-free status with only three mild episodes (VAS: 1–3; duration <30 min) in the last 4 weeks. No adverse events were observed.ConclusiontaVNS was effective and safe in the management of chronic migraine in the reported pediatric patient. These findings suggest the need for further evaluation of this non-pharmacological intervention in pediatric migraine.

Fine-grained traffic management and prediction are fundamental to key applications such as autonomous driving, lane change guidance, and traffic signal control. However, obtaining lane-level traffic data has become a critical bottleneck for data-driven models due to limitations in the types and number of sensors and issues with the accuracy of tracking algorithms. To address this, we propose the Fine-grained Road Traffic Inference (FRTI) task, which aims to generate more detailed lane-level traffic information using limited road data, providing a more energy-efficient and cost-effective solution for precise traffic management. This task is abstracted as the first scene of the spatio-temporal graph node generation problem. We designed a two-stage framework-RoadDiff-to solve the FRTI task. This framework leverages the Road-Lane Correlation Autoencoder-Decoder and the Lane Diffusion Module to fully utilize the limited spatio-temporal dependencies and distribution relationships of road data to accurately infer fine-grained lane traffic states. Based on existing research, we designed several baseline models with the potential to solve the FRTI task and conducted extensive experiments on six datasets representing different road conditions to validate the effectiveness of the RoadDiff model in addressing the FRTI task. The relevant datasets and code are available at https://github.com/ShuhaoLii/RoadDiff.

Children living in high-altitude plateau regions face unique health challenges due to chronic hypoxia, limited medical resources, and complex environmental factors. Traditional pediatric healthcare approaches often fall short in providing precise, timely, and personalized care in these areas. To address these limitations, this paper proposes an intelligent health assessment and disease early-warning framework tailored for plateau children. The system integrates large language models (LLMs) and Internet of Things (IoT) technologies to support multimodal data collection and real-time remote monitoring. By combining wearable sensor data, clinical records, medical imaging, and genetic information, the framework enables cloud-based analysis, intelligent reasoning, and individualized feedback. We detail the system architecture, core components, and data processing flow, and further explore the potential of AI-IoT fusion in transforming pediatric health management in under-resourced, high-altitude environments.

Civil aircraft use inertial navigation system(INS) to provide position information during taxiing to assist pilots in performing precise flight operations. However, due to the cumulative effect of internal error sources, the positioning error of the inertial navigation system increases over time. This paper presents a framework using a tailored transformers model architecture and large-scale dataset of over 700,000 source-target trajectory pairs across 225 airports. The model takes raw longitude and latitude data as input and outputs the reconstructed trajectory through a convolutional neural network feature extractor and multilayer encoder-decoder transformers based on attention mechanism and dimensionality reduction. Experiments demonstrate superior trajectory reconstruction accuracy compared to baseline methods, especially on long, granular sequences. This provides data and model foundation for leveraging deep learning to automatically and accurately correct INS positioning errors to ensure aviation safety during taxiing.

Affine frequency division multiplexing (AFDM) is a promising multicarrier technique utilizing chirp signals, and has been envisioned as an effective solution for high-mobility communication scenarios. In this paper, we develop a multiple-mode index modulation scheme tailored for AFDM, termed as MM-AFDM-IM, which aims to further improve the spectral and energy efficiencies of AFDM. Specifically, multiple constellation alphabets are selected for different chirp-based subcarriers (hereafter referred to as chirps). Aside from classical amplitude/phase modulation, additional information bits can be conveyed by the dynamic patterns of both constellation mode selection and chirp activation, without extra energy consumption. Furthermore, we discuss the mode selection strategy and derive an asymptotically tight upper bound on the bit error rate of the proposed scheme under maximum-likelihood detection. Simulation results are provided to demonstrate the superior performance of MM-AFDM-IM compared to conventional benchmark schemes.

Intrinsically Stretchable Polymer Semiconductor In article number 2300816, Zhihui Wang, Zhengran Yi, and co-workers propose a novel “regional conjugation” strategy to design an intrinsically stretchable high-performance polymer semiconductor. It enables excellent charge transport performance of fully stretchable transistors under large mechanical strain. This strategy provides a promising methodology for developing mechanically robust stretchable polymer thin film electronics.

[This corrects the article DOI: 10.1371/journal.pone.0301909.].

Asymmetric Pd-catalyzed three-component carboamination reactions of dienes to construct chiral cyclohexenylamines, which are of great importance in many fields of chemistry, have remained largely unexplored. Here, we demonstrate a highly enantio- and regioselective Pd/Ming-Phos-catalyzed carboamination reactions of 1,3-cyclohexadiene with readily available aryl iodides and anilines for facile access to diverse valuable chiral cyclohexenylamines. The process shows excellent functional group tolerance, easy scalability, and mild conditions. Moreover, mechanistic studies suggest that this reaction has a first-order dependence on the concentration of the palladium catalyst and aniline.