National Supercomputing Center of Tianjin

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.

We address a specific problem of the Newman-Janis algorithm: how to determine the general form of the complex transformation for the Schwarzschild metric and ensure that the resulting axisymmetric metric satisfies the zero-scalar-curvature condition . In this context, the zero-scalar-curvature condition acts as a constraint. Owing to this condition, we refer to the class of black holes as the "Newman-Janis class of Schwarzschild black holes" to emphasize the potential of the Newman-Janis algorithm as a classification tool for axisymmetric black holes. The general complex transformation we derive not only generates the Kerr, Taub-NUT, and Kerr-Taub-NUT black holes under specific choices of parameters but also suggests the existence of additional axisymmetric black holes. Our findings create an alternative avenue using the Newman-Janis algorithm to construct new axisymmetric black holes.

Few-shot knowledge graph reasoning (FS-KGR) try to infer missing facts in a knowledge graphs using limited data (such as only 3/5 samples).Existing strategies have shown good performance by mining more supervised information for few-shot learning through meta-learning and self-supervised learning. However, the problem of insufficient samples has not been fundamentally solved. In this paper, we propose a novel algorithm based on adversarial learning for Enhancing Negative samples in few-shot scenarios of FS-KGR, termed FS-KEN. Specifically, we are the first to use GAN to conduct data augmentation on FS-KGR scenario. FS-KEN uses policy gradient GANs for negative sample augmentation, solving the gradient back-propagation issue in traditional GANs. The generator aims to produce high-quality negative entities. while the objective of the discriminator is to distinguish between generated entities and real entities. Comprehensive experiments conducted on two few-shot knowledge graph completion datasets reveal that FS-KEN surpasses other baseline models, achieving state-of-the-art results.

Patients with juvenile idiopathic arthritis (JIA) may develop adult rheumatic diseases later in life, and prolonged or recurrent disease activity is often associated with substantial disability; therefore, it is important to identify patients with JIA at high risk of developing adult rheumatic diseases and provide specialized attention and preventive care to them. To elucidate the full extent of the genetic association of JIA with adult rheumatic diseases, to improve treatment strategies and patient outcomes for patients at high risk of developing long-term rheumatic diseases. In this genetic association study of 4 disease genome-wide association study (GWAS) cohorts from 2013 to 2024 (JIA, rheumatoid arthritis [RA], systemic lupus erythematosus [SLE], and systemic sclerosis [SSc]), patients in the JIA cohort were recruited from the US, Australia, and Norway (with a UK cohort included in the meta-analyzed cohort), while patients in the other 3 cohorts were recruited from US and Western European countries. All analyses were conducted between September 2023 and April 2024. Genetic associations. Genetic correlations and shared genomic loci between JIA and adult rheumatic diseases. Genetic correlation analyses and cross-trait meta-analysis were conducted on the JIA cohort and the summary statistics of the GWASs from adult rheumatic diseases (RA, SLE, and SSc). Mendelian randomization analyses were also conducted. This study included 33 207 patients across the 4 cohorts, with 4550 patients in the meta-analyzed JIA cohort (JIA cohort: 1485 patients with arthritis onset before 16 years; 1017 female [68.5%]; 10 352 controls; UK cohort: 3305 patients with JIA; 9196 controls), 143 61 patients in the RA cohort, 5201 patients in the SLE cohort; and 9095 patients in the SSc cohort. After the GWAS result of the JIA cohort was meta-analyzed with the UK JIA cohort, there was a total of 4550 JIA cases and 18 446 controls. The analysis revealed a significant global correlation between JIA and adult rheumatic diseases, with 84 regions harboring signals associated with multiple diseases. Cross-trait analyses uncovered novel disease loci and 20 loci associated with JIA and adult diseases. Mendelian randomization analysis revealed the significant association of 11 proteins with rheumatic disorders. Both shared, organ-specific, and disease-specific critical cell types were highlighted. In this genetic association study, there was significant genetic overlap between JIA and adult rheumatic diseases. These findings may help to refine JIA classification, risk stratification, and therapeutic strategy of repurposing adult disease drugs for pediatric patients with similar mechanisms.

Abstract DNA-based data storage is a promising solution to the challenges of large-scale data storage. However, the low throughput of the mainstream inkjet-based DNA synthesis method has hindered its widespread adoption. In contrast, high-throughput electrochemical synthesis provides higher throughput but with more nucleotide insertion, deletion, and substitution errors. Here, we propose an innovative coding scheme with high error correction capabilities, called DNA StairLoop. This coding scheme features a staircase interleaver and allows for component codes such as the convolutional code and the ow-density Parity-check code, allowing for flexible adaptation of the coding scheme. Both the row and the column decoders are soft input and soft output, enabling further improvement in data recovery accuracy through iterative decoding. The staircase interleaver facilitates extensive parallel decoding capabilities while effectively preserving parallelism across a multitude of nodes. In the in vitro experiment, DNA StairLoop successfully recovered the raw information in a staircase block with a nucleotide error rate of more than 8%. The simulations revealed that the DNA StairLoop can correct 10% nucleotide errors. Moreover, in parallel computing processing, the decoding time of our code continues to decrease dramatically.

Microservice systems are inherently complex and prone to failures, which can significantly impact user experience. Existing diagnostic approaches based on single-modal data such as logs, metrics, or traces cannot comprehensively capture failure patterns. For those multimodal data-based failure diagnosis methods, the dominant modality can overshadow others, hindering low-yield modalities from fully leveraging their characteristics. This paper proposes Medicine, a modal-independent microservice failure diagnosis framework based on multimodal adaptive optimization. It encodes different modalities separately to retain their unique features and employs adaptive optimization to adjust the learning pace between modalities, thereby enhancing overall diagnostic performance. Experimental results demonstrate that Medicine outperforms existing single-modal and multimodal diagnostic approaches on three public datasets, with F1-score improving by 15.72% to 70.84%. Even in cases where individual modal data is missing or of lower quality, Medicine maintains high diagnostic accuracy.

Solving fluid–structure interaction (FSI) problems using traditional methods poses significant challenges in the field of numerical simulation. The multiphysics coupling library precise code interaction coupling environment (preCICE), renowned for its robust multiphysics coupling capabilities, offers a promising solution for FSI problems. It supports various open/closed source software and commercial computational fluid dynamics solvers in a black box manner. However, preCICE currently mainly supports coupling schemes of mesh-based methods as well as few meshless methods. This paper proposes a critical grid interpolation (CGI) method as an intermediate medium to enable the particle method to interface with preCICE. The CGI method leverages particles and critical meshes to interpolate displacement and force, thereby allowing the pure Lagrangian smoothed particle hydrodynamics (SPH) method to solve FSI problems. The critical mesh serves as a coupling interface between particles and meshes, theoretically resolving the mesh mismatch issue inherent in particle methods when connecting to preCICE. To validate the performance of the proposed CGI method, we conducted experiments where the fluid and the structure were discretized using SPH and the finite element method, respectively. The results demonstrate the effectiveness of the CGI method in solving FSI problems. Additionally, we have made our source code for the SPH fluid adapter open-source and available on (https://github.com/terrylongsifan/AdapterSPH), facilitating further development of preCICE compatibility with more meshless methods. This study significantly advances the application of SPH in FSI problems by integrating it with preCICE, thereby expanding the versatility and applicability of multiphysics simulations.

Recently, controlling air pollution has become increasingly significant due to its impact on our health and daily lives. To prevent and control pollution, it is crucial to trace its source. Many researches have been developed for tracing the source of pollution. However, traditional methods using large-scale simulations need a large number of computation resources and time-consuming. In addition, traditional traceability algorithms do not consider topographic factors, which can cause a certain amount of errors. To resolve above problems, an interactive visual analytics system for pollutant traceability is proposed. In our method, instead of three-dimensional field data, only two-dimensional grid data is enough to track pollution sources in real time. Furthermore, our method can further improve precision through considering topographic factors, which are usually ignored by existing methods. Finally, the possible pollution sources are also identified in our method. This is achieved through analysis of changes in pollutant concentration and the distribution of man-made emission sources. In order to verify the effectiveness of this method, we propose a series of application examples to comprehensively analyze the sources of pollutants.

Thermal conductivity and power factor are key factors in evaluating heat transfer performance and designing thermoelectric conversion devices. To search for materials with ultralow thermal conductivity and a high power factor, we proposed a set of universal statistical interaction descriptors (SIDs) and developed accurate machine learning models for the prediction of thermoelectric properties. For lattice thermal conductivity prediction, the SID-based model achieved the state-of-the-art results with an average absolute error of 1.76W m-1 K-1. The well-performing models predicted that hypervalent triiodides XI3 (X = Rb, Cs) have ultralow thermal conductivities and high power factors. Combining first-principles calculations, the self-consistent phonon theory, and the Boltzmann transport equation, we obtained the anharmonic lattice thermal conductivities of 0.10 and 0.13W m-1 K-1 for CsI3 and RbI3 in the c-axis direction at 300K, respectively. Further studies show that the ultralow thermal conductivity of XI3 arises from the competition of vibrations between alkali metal atoms and halogen atoms. In addition, at 700K, the thermoelectric figure of merit ZT values of CsI3 and RbI3 are 4.10 and 1.52, respectively, at the optimal hole doping level, which indicates hypervalent triiodides are potential high performance thermoelectric materials.