National Supercomputing Center in Wuxi

Hemoglobin (Hb) concentration is a fundamental physiological marker widely used in the diagnosis of anemia and the assessment of cardiovascular health. Although invasive blood testing provides high accuracy, its reliance on laboratory infrastructure limits scalability and real-time applicability. Here, we present Hb-PPG, a four-wavelength photoplethysmography (PPG) dataset designed to support research on non-invasive hemoglobin assessment and cardiovascular monitoring. The dataset comprises 1008 PPG signal segments acquired at 660, 730, 850, and 940 nm from 252 adult subjects, alongside reference measurements of hemoglobin, fasting blood glucose, and brachial artery systolic and diastolic blood pressure. Hb-PPG enables systematic investigation of wavelength-dependent PPG signal characteristics and their relationships with hematological and hemodynamic parameters. By providing high-quality, multi-wavelength optical signals with clinically grounded reference data, this dataset facilitates the development, validation, and benchmarking of non-invasive approaches for hemoglobin estimation and related vascular health applications. The dataset is intended to support algorithm development, benchmarking, and methodological studies in non-invasive hemoglobin estimation, rather than direct clinical diagnosis.

Accumulating evidences have demonstrated that urolithin A (UroA) exerted a wide range of bioactivities, including antioxidant, anti-inflammatory, and mitochondrial function-enhancing effects, thereby highlighting its potential as a therapeutic agent for various diseases. Preclinical studies have shown that UroA induced mitophagy both in vitro and in vivo, preventing age-associated mitochondrial dysfunction and improving lifespan and muscle function as well as enhancing exercise capacity. However, its clinical application is limited by poor oral bioavailability and considerable interindividual variability in microbial conversion, as pharmacokinetic studies indicated low plasma exposure under standard administration. Moreover, approximately 10% of individuals are classified as urolithin nonproducers, independent of age, posing an additional challenge for clinical translation. To overcome those limitations, formulation strategies such as nanoparticles and liposomes have been developed, resulting in several-fold increases in systemic bioavailability compared with unformulated UroA. This review would provide a comprehensive overview of recent advances in the metabolism of UroA, current approaches to improve its bioavailability, safety evaluations, and elucidated the underlying mechanisms of its bioactivities. Furthermore, recent progresses in chemical and biotechnological synthesis strategies of UroA are also summarized. These insights will provide a scientific foundation for further utilization of UroA for human health.

<strong class="journal-contentHeaderColor">Abstract.</strong> As coupled Earth system models advance, it becomes increasingly feasible to attain higher spatial resolutions, thereby enabling more precise simulations and predictions of the evolution of the Earth system. Consequently, there is an urgent demand of highly-efficient optimization for extensive scientific programs on more power-efficient heterogeneous many-core systems. This study introduces a highly-efficient optimization approach tailored for kilometer-level resolution Earth System Models (ESMs) operating on heterogeneous many-core supercomputers. Leveraging scalable model configurations and innovative tripolar ocean/sea-ice grids that bolster spatial accuracy and computational efficiency, we initially establish a series of high resolutions (HRs) within a solitary component (either the atmosphere or ocean) while maintaining a fixed resolution for the other, resulting in notable enhancements in both model performance and efficacy. Furthermore, we have devised an OpenMP tool specifically optimized for the new Sunway supercomputer, facilitating automated code optimization. Our approach is designed to be non-intrusive, minimizing the need for manual code alterations while ensuring both performance gain and code consistency. We adopt a hybrid parallelization strategy combining Athread and OpenMP, achieving full parallel coverage for code segments with a runtime proportion exceeding 1 %. After optimization, the atmosphere, ocean, and sea-ice models achieve speedups of 4.43&times;, 1.86&times;, and 2.43&times;, respectively. Consequently, the overall simulation performance of the 5-km/3-km coupled model reaches 222 SDPD. This achievement renders multiple decadal scientific numerical simulations utilizing such HR coupled simulations feasible. Our work signifies a pivotal advancement in Earth system modeling, providing a robust framework for high-resolution climate simulations on more ubiquitous (next-generation) heterogeneous supercomputing platforms, such as GPUs, with minimal additional effort.

The powerful computing capabilities of supercomputers enable them to execute a wide variety of high performance computing (HPC) applications. However, when executing HPC applications, the low hardware utilization prevents the efficiency of supercomputers from being fully exploited. Performance analysis is an effective way to discover performance bottlenecks. Existing research focuses on the profiling or modeling of HPC application performance, lacking systematic automatic performance analysis and tuning methods. To address the above challenge, in this paper, we propose a composable suite for HPC applications that can achieve low-overhead performance profiling, fine-grained performance modeling and automatic performance tuning. Specifically, a low-overhead profiling method is first proposed to accurately characterize the performance of HPC applications while minimizing the interference of measurement. Then, fine-grained performance models are built to predict the performance of HPC applications based on the profiling sampling. Finally, according to the performance models, an automatic tuning method is designed to improve the performance of coupled HPC applications. The experimental results show that the overhead of our profiling method is less than 15% for the benchmarks and the real-world applications. The average relative error of our modeling method is less than 10%. The optimal layouts contribute to the total running time savings of 16.33% and the total computing fee savings of 31.44%. The effectiveness of our methods is superior to the baselines.

Abstract Magnetic tunnel junctions, as promising emerging spintronic devices, exhibit versatile capabilities in microwave detection and energy harvesting. Here, we modulate the rectification behavior of perpendicular magnetic anisotropy (PMA) based magnetic tunnel junction devices through adjusting the orientation of the external magnetic field, which allows the microwave detection performance to switch from a single-frequency response to a broadband one. The frequency bandwidth can reach up to 1.7 GHz and the maximum sensitivity can attain 270 mV/mW without a bias current. According to experimental analysis, this phenomenon originates from the different behavior of the free layer magnetic moment induced by the magnetic field. The frequency bandwidth dependence is qualitatively explicated by Landau-Lifshitz-Gilbert (LLG) equation. This key advancement effectively broadens the application prospects of magnetic tunnel junction-based microwave detectors.

Abstract We employed zonal wind data from Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics Doppler Interferometer, equatorial electrojet (EEJ) measurements from Jicamarca (12°S, 77°W), and global ionospheric total electron content (TEC) maps to investigate the effects of ultra‐fast Kelvin waves (UFKW) with zonal wavenumbers 2 and 3 propagating eastward (E2, E3) in the equatorial mesosphere on both ionospheric TEC and EEJ signatures, as well as their differences in ionospheric response characteristics compared to E1 waves. Periodic components in zonal wind, EEJ, and TEC are quantified through the least squares spectral fitting. Our findings reveal three distinct categories of UFKW events: Type 1 exhibits both TEC and EEJ responses, Type 2 shows TEC response without EEJ signature, and Type 3 displays neither TEC nor EEJ response. The finding reveals distinct seasonal dependencies in TEC responses: E2 and E3 waves exhibit significant seasonality, whereas E1 waves show negligible seasonal variation. Furthermore, E1 waves demonstrate higher ionospheric response occurrence rates compared to E2 and E3 waves. For E1 waves, shorter periods, larger amplitudes, and longer vertical wavelengths correlate strongly with enhanced ionospheric responsiveness. Conversely, amplitude exerts minimal influence on ionospheric responses for E2 and E3 waves. For E2 and E3 waves temporally coincident with E1 waves, E2 and E3 waves may elevate Type 2 event occurrence among concurrent E1 waves, while E1 waves tend to suppress ionospheric response capability in coincident E2 and E3 waves, increasing Type 3 event prevalence.