Browse Articles

Frontispiece: Liquid Metal Nanobiohybrids for High‐Performance Solar‐Driven Methanogenesis via Multi‐Interface Engineering

Effects of essential tremor on longevity and mortality rates in families

Essential Tremor (ET) is a common movement disorder characterized by action tremors, primarily affecting the hands and head. lthough previous studies have suggested potential links between ET and aging-related diseases, its relationship with longevity remains unclear, with conflicting evidence in the literature. To investigate this association, we analyzed data from 1,493 individuals across 145 families, encompassing both ET-positive (ET+) and ET-negative (ET−) participants. Using comprehensive statistical methods, including survival function estimation and regression modeling, we examined the potential influence of ET on lifespan. The median age of our participants was 67 years (IQR 54–77). Among deceased individuals, those with ET had a higher median age at death (80 years, IQR 70–86) compared to their ET− counterparts (70 years, IQR 59–77). Living ET+ participants also demonstrated slightly higher median ages (63 years, IQR 53–74) than living ET− individuals (60 years, IQR 49–71). Survival analysis revealed a significantly prolonged lifespan for ET+ individuals compared to ET− individuals (log-rank p =  1.11 ×  10 ⁻23). Furthermore, hazard ratio (HR) calculations indicated a reduced risk of mortality for the ET+ group (HR =  0.44, CI95% =  0.37–0.52), particularly among males. These findings suggest that ET may be associated with increased longevity, though the underlying biological mechanisms remain unclear. Further research is essential to elucidate the processes contributing to this relationship and to explore its implications for understanding aging and neurodegenerative disorders.

Assessment of the long RR intervals using convolutional neural networks in single-lead long-term Holter electrocardiogram recordings

Tracking and analyzing the spatio-temporal changes of rice planting structure in Poyang Lake using multi-model fusion method with sentinel-2 multi temporal data

Accurate and efficient extraction of rice planting structures, coupled with comprehensive analysis of their spatiotemporal dynamics and driving factors, is crucial for rice yield estimation and optimized water resource management in the Poyang Lake region. However, traditional approaches face significant limitations: single machine learning models often yield insufficient classification accuracy, while existing fusion models typically involve complex processing workflows and exhibit low computational efficiency. To address these challenges, this study developed an efficient and simplified fusion model based on a scoring strategy to determine rice planting structures from 2018 to 2023, followed by an in-depth analysis of their spatiotemporal patterns and underlying drivers. The evaluation results demonstrated that four individual classification models of K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Decision Tree (GBDT) achieved Overall Accuracy of 85.29%–90.07%, Kappa coefficients of 0.786–0.855, User Accuracy of 80.51%–93.02%, and Mapping Accuracy of 80.87%–92.63%. The proposed scoring-based fusion model significantly enhanced these metrics, improving Overall Accuracy by 3.36%–9.16%, Kappa coefficient by 5.15%–14.38%, User Accuracy by 0.37%–11.13%, and Mapping Accuracy by 0.48%–10.71%. Spatiotemporal analysis revealed distinct trends in rice cultivation patterns: single-cropping rice and regenerated rice showed consistent expansion, both in planting area and proportion, with a spatial tendency towards flat regions. Conversely, double-cropping rice exhibited a gradual decline, with its cultivation areas contracting towards the central lake region. These shifts were primarily driven by socioeconomic factors, particularly rural labor migration and rising fertilizer prices, which have incentivized farmers to adopt less labor-intensive and lower-input cultivation systems, such as single-cropping and regenerated rice. The findings offer a novel methodological framework for precise extraction of crop planting structures, and a scientific foundation for local governments to develop targeted water resource management strategies.

Validation of body composition parameters extracted via deep learning-based segmentation from routine computed tomographies

Abstract Sarcopenia and body composition metrics are strongly associated with patient outcomes. In this study, we developed and validated a flexible, open-access pipeline integrating available deep learning-based segmentation models with pre- and postprocessing steps to extract body composition measures from routine computed tomography (CT) scans. In 337 surgical oncology patients, total skeletal muscle tissue (SMtotal), psoas muscle tissue (SMpsoas), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were quantified both manually and using the pipeline. Automated and manual measurements showed strong correlations (SMpsoas: r = 0.776, VAT: r = 0.993, SAT: r = 0.984; all P < 0.001). Measurement discrepancies primarily resulted from segmentation errors, anatomical anomalies or image irregularities. SMpsoas measurements showed substantial variability depending on slice selection, whereas SMtotal, averaged across all L3 levels, provided greater measurement stability. Overall, SMtotal performed comparably to SMpsoas in predicting overall survival (OS). In summary, body composition measures derived from the pipeline strongly correlated with manual measurements and were prognostic for OS. The increased stability of SMtotal across vertebral levels suggests it may serve as a more reliable alternative to psoas-based assessments. Future studies should address the identified areas of improvement to enhance the accuracy of automated segmentation models.

Correction: Spatial-temporal evolution of the allometric relationship between urban economic and health resources in the Yangtze River Delta urban agglomeration

Advantages of imperfect dice rolls over coin flips for random number generation

Face exploration, emotion recognition, and emotional enhancement of memory in relapsing-remitting multiple sclerosis

Background Recognizing familiar faces and identifying emotions through facial expressions are essential for social functioning. This study aimed to examine whether people with relapsing-remitting multiple sclerosis (PwMS) differ from healthy control individuals (HC) in their performance on different tasks related to facial emotion processing. Methods In a cross-sectional controlled study, 30 PwMS and 35 HC completed a baseline neuropsychological evaluation and experimental tasks assessing visual exploration of facial stimuli through eye tracking, facial emotion recognition, and facial memory recognition. The facial stimuli displayed either a neutral expression or an emotion (happiness, fear, or disgust). Results PwMS and HC performed comparably in facial emotion recognition. In facial memory recognition, HC were significantly more accurate in recognizing previously seen fearful faces compared to neutral faces (Wilcoxon test, Z = -2.26, P = 0.024), demonstrating emotional enhancement of memory. In contrast, PwMS did not exhibit a memory advantage for fearful faces over neutral faces (P > 0.05). Groups also differed in the eye-tracking task. In all but one condition (disgust), PwMS showed a significantly greater tendency to explore the eye area rather than the mouth area compared to HC. Conclusions Changes in visual exploration and a lack of emotional enhancement of memory are observed in PwMS, who otherwise demonstrate intact facial emotion recognition. These results suggest altered emotion-cognition interactions in PwMS. Early detection of subtle changes and targeted interventions may help prevent future debilitating impairments in social functioning.

Constructing formal models of cryptographic protocols from Alice&Bob style specifications via LLM

Predicting mechanical properties of CFRP composites using data-driven models with comparative analysis

Carbon fiber reinforced polymer (CFRP) composites are increasingly utilized for their lightweight and superior mechanical properties. This study uses machine learning models to predict the mechanical properties of CFRP composites based on the volume fraction of carbon nanotubes (CNTs), interlayer volume fraction, glass transition temperature, and manufacturing pressure. Sixty-two samples covering nine different types of CFRPs were designed, manufactured, and experimentally tested. Three machine learning models, namely ridge regression, random forest, and support vector regression, were trained on the data and compared. The results demonstrated a high prediction accuracy for the flexural strength (R2 =  0.966), flexural modulus (R2 =  0.871), and the mode-II energy release rate (R2 =  0.903). The study highlights the effectiveness of data-driven models in predicting key mechanical properties of CFRP composites, potentially reducing the need for extensive experimental testing and facilitating more efficient material design.

Single-cell transcriptome integrated with genome-wide association study reveals heterogeneity of carotid and femoral plaques and its association with plaque stability

Single-cell transcriptomics reveals immunosuppressive microenvironment and highlights tumor-promoting macrophage cells in Glioblastoma

Glioblastoma (GBM) is the most prevalent and aggressive primary brain malignancy in adults. Nevertheless, the cellular heterogeneity and complexity within the GBM microenvironment (TME) are still not fully understood, posing a significant obstacle in the advancement of more efficient immunotherapies for GBM. In this study, we conducted an integrated analysis of 48 tumor fragments from 24 GBM patients at the single-cell level, uncovering substantial molecular diversity within immune infiltrates. We characterized molecular signatures for five distinct tumor-associated macrophages (TAMs) subtypes. Notably, the TAM_MRC1 subtype displayed a pronounced M2 polarization signature. Additionally, we identified a subtype of natural killer (NK) cells, designated CD56dim_DNAJB1. This subtype is characterized by an exhausted phenotype, evidenced by an elevated stress signature and enrichment in the PD-L1/PD-1 checkpoint pathway. Our findings also highlight significant cell-cell interactions among malignant glioma cells, TAM, and NK cells within the TME. Overall, this research sheds light on the functional heterogeneity of glioma and immune cells in the TME, providing potential targets for therapeutic intervention in this immunologically cold cancer.

Limitations in rotational correction: 3D displacement analysis of midshaft clavicle fractures with titanium elastic nails

Analysis and prediction of carbon storage changes on the Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau, a crucial global carbon reservoir, plays an essential role in the carbon cycle. This study used the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to analyze land use and carbon storage changes from 2000 to 2020, and the Patch-generating Land Use Simulation (PLUS) model to predict land use trends and carbon storage for 2030 and 2040 under various scenarios, combining carbon density data. The impact of driving factors on carbon storage and spatial heterogeneity were assessed using the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. Results showed a fluctuating increase in carbon storage, mainly from grasslands and forests, with soil organic carbon as the largest pool. Positive factors included Digital Elevation Model (DEM), temperature, proximity to railways, roads, and Normalized Difference Vegetation Index (NDVI), while aridity was negative. Predictions suggest carbon storage will rise across all scenarios, with ecological protection showing the largest increase. This study comprehensively analyzes the impact of climate and land use changes on carbon storage in the Qinghai-Tibet Plateau, enhances understanding of the plateau’s ecosystem sustainability, and supports policy-making.

The relationship between regional homogeneity in resting-state functional magnetic resonance imaging and cognitive function in depressive disorders with migraine

Kernel composition in sorghum landraces revealed via analyses of genotype-by-environment interactions

Sorghum stands out among cereals due to its rich bioactive compound content and resilience to varying climates, addressing common issues such as protein, iron (Fe), and zinc (Zn) deficiencies in humans. This study aimed to determine the impact of the genotype, environment, and their interaction on the chemical and physical properties of sorghum grain across locations and seasons. A total of 361 sorghum landraces and four commercial checks were grown for two consecutive seasons from 2020 to 2021 at Melkassa (MK20 and MK21), Jimma (JM20 and JM21) and Miesso (MS20 and MS21). Using genotype main effects with genotype by environment interaction (GGE) ranking biplots, stable and high-performing genotypes were identified. MK21 emerged as an ideal environment for starch, while MS20 proved representative for protein content. For Fe content, environments MS21, MK20, and MK21 were representative, while MS20 and JM20 were discriminatory. MS21 was identified as the most representative for Zn content. These findings underline the diverse and specific performance of sorghum genotypes across various environmental conditions and traits. This study identified sorghum landraces with high and stable starch and protein content, as well as high and stable concentrations of Fe and Zn. Notably, genotypes like G358, G218, G221, G161, and G171 were noted for their high mean protein contents and stability. Genotypes such as G175, G248, G137, and G142, which demonstrated superior performance in Fe, and Zn content, are regarded as excellent candidates for further evaluation and incorporation into breeding programs, offering significant potential to enhance nutritional stability across diverse agroecological regions. Their consistent performance also highlights their potential to address micronutrient deficiencies, contributing to enhanced human nutrition and food security.

Genetic correlations and causal associations between BMI, HDL-C, and postoperative infections: a two-sample Mendelian randomization study

Rumor detection on social networks based on Temporal Tree Transformer

The rapid propagation of rumors on social media can give rise to various social issues, underscoring the necessity of swift and automated rumor detection. Existing studies typically identify rumors based on their textual or static propagation structural information, without considering the dynamic changes in the structure of rumor propagation over time. In this paper, we propose the Temporal Tree Transformer model, which simultaneously considers text, propagation structure, and temporal changes. By analyzing observing the growth of propagation tree structures in different time windows, we use Gated Recurrent Unit (GRU) to encode these trees to obtain better representations for the classification task. We evaluate our model’s performance using the PHEME dataset. In most existing studies, information leakage occurs when conversation threads from all events are randomly divided into training and test sets. We perform Leave-One-Event-Out (LOEO) cross-validation, which better reflects real-world scenarios. The experimental results show that our model achieves state-of-the-art accuracy 75.84% and Macro F1 score of 71.98%, respectively. These results demonstrate that extracting temporal features from propagation structures leads to improved model generalization.

Cerium oxide nanoparticles alleviate drought stress in apple seedlings by regulating ion homeostasis, antioxidant defense, gene expression, and phytohormone balance

In silico screening system based on a transcription factors regulatory network only using transcriptomic data

In this study, we developed a method to identify core transcription factors (TFs) involved in differentiation using only comprehensive gene analysis. The theory of in silico screening using TFs regulatory network analysis (ISNA) required the following requirements: (1) estimating promoter regions, (2) constructing TFs regulatory network (TRN) relationships using the nucleotide sequence information in the promoters and score matrices derived from TF consensus sequences, and (3) identifying candidate core TFs by determining dissociation constants (Kd values) within the relationships of TRN. ISNA demonstrated the ability to predict the core TFs involved in the endothelial-to-mesenchymal transition of human umbilical vein endothelial cell (HUVEC) and the differentiation of human embryonic stem cells into mesodermal cells. Using ISNA, we identified HMGA2 as a novel core TF in uterine epithelium. Notably, HMGA2 expression was predominantly detected in uterine epithelium, where it regulated cell proliferation in response to estrogen. These findings highlight ISNA’s potential to identify core TFs based on transcriptomic data.