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Currently, researchers have not yet found the antioxidant activity and structure of Piper nigrum L. polysaccharides (PNLP) and their modified derivatives. PNLP possesses advantages such as simple and stable structure, ease of separation and purification, high carbohydrate content, and excellent antioxidant activity, yet it remains largely undeveloped. This study aims to investigate the antioxidant activity and fundamental structure of PNLP and its three derivatives, and to provide a theoretical explanation for the relationship between them. Three derivatized polysaccharides, namely acetylated polysaccharides prepared from PNLP (Ac-PNLP), carboxymethylated polysaccharides prepared from PNLP (CM-PNLP) and phosphorylated polysaccharides prepared from PNLP (P-PNLP), which had degree of substitution (DS) of 0.41, 0.48, 0.11, and carbohydrate contents of 37.19%, 53.97%, and 36.83%, respectively. P-PNLP was shown to have good scavenging ability in DPPH radical scavenging and hydroxyl radical scavenging assays, and it was hypothesized that the antioxidant activity of the chemically modified PNLP was increased, which may be attributed to the introduction of the new functional groups and its altered structure-activity relationship resulted. This experiment provides insights into the structure and activity of PNLP and its derivatives, offering explanations for the relationship between the two. It establishes a theoretical foundation for the application of PNLP in the food and pharmaceutical industries.

Rainfall-induced instability of loess slopes presents significant threats to infrastructure and ecological systems. Vegetation serves as an effective measure to enhance slope stability through mechanical reinforcement by roots and hydrological regulation of soil moisture. The influence of vegetation root system characteristics, including root tensile strength and rooting depth, on the stability of loess slopes subjected to rainfall infiltration is investigated using a finite element model developed in COMSOL®, which couples seepage and mechanical behavior. Rainfall infiltration, pore water pressure evolution, and progressive slope failure are simulated to analyze the stability response. Varying levels of additional cohesion provided by roots and different rooting depths are systematically evaluated. The results indicate that stronger root systems and deeper rooting depths significantly enhance slope stability by increasing the factor of safety, delaying plastic zone development, and reducing displacement. The reinforcement effect becomes more pronounced on steeper slopes, while its marginal contribution diminishes with increasing root depth beyond a certain threshold. These findings provide insights into the role of vegetation in mitigating rainfall-induced slope failures and provide practical guidance for the selection and application of vegetation in ecological slope stabilization projects.

Background Non-invasive diagnostic methods, including medical imaging techniques and blood biomarkers such as alpha-fetoprotein (AFP), have been crucial in detecting hepatocellular carcinoma (HCC). However, imaging techniques are only effective for tumor size larger than 2 cm. AFP measurement remains unsatisfactory due to high rate of misdiagnosis and underdiagnosis. Therefore, new reliable biomarkers and better non-invasive diagnostic approach are necessary for HCC identification. Methods The differentially expressed genes were identified using multiple public RNA-seq data of liver tissues from healthy individuals and HCC patients including peritumoral and tumor tissues. The hub genes for HCC diagnosis were identified combining pathway enrichment analysis and protein–protein interaction network analysis. The performance of hub genes for non-invasive HCC diagnosis was analyzed in plasma of healthy individuals, HBV infected patients, and HCC patients based on exosomal RNA-seq data. A multi-layer perceptron (MLP) model based on exosomal hub genes was developed for non-invasive HCC diagnosis. Results Through differential gene expression and pathway enrichment analysis on multiple public RNA-seq datasets, we first identified 30 dysregulated genes in HCC tissues. Protein-protein interaction analysis further narrowed down this list to 10 key genes: BRCA2 , CDK1 , MCM4 , PLK1 , DNA2 , BLM , PCNA , POLD1 , BRCA1 and FEN1 . By further evaluation using additional public HCC tissue datasets, POLD1 and MCM4 were excluded from consideration as potential biomarkers due to their suboptimal performance. Notably, CDK1 , FEN1 , and PCNA gene were found to be significantly elevated in the plasma exosomes of HCC patients compared to non-HCC individuals, including those with HBV-infected hepatitis and healthy controls. The MLP model, based on three biomarkers, showed an area under the curve (AUC) of 0.85 and 0.84 in training and test dataset respectively, after adjusting for the covariates sex and age. Conclusion We identified three key genes, CDK1 , FEN1 , and PCNA , as exosomal biomarkers for non-invasive diagnosis of HCC. The MLP model utilizing three biomarkers showed good differentiation between non-HCC individuals and HCC patients, which exhibits promising potential as a non-invasive diagnostic tool for detecting HCC. Additional validation with a larger sample size is essential to thoroughly assess the reliability of the biomarkers and the model’s performance.

Concerns about a lack of reproducibility of statistically significant results have recently been raised in many fields, and it has been argued that this lack comes at substantial economic costs. We here report the results from prediction markets set up to quantify the reproducibility of 44 studies published in prominent psychology journals and replicated in the Reproducibility Project: Psychology. The prediction markets predict the outcomes of the replications well and outperform a survey of market participants’ individual forecasts. This shows that prediction markets are a promising tool for assessing the reproducibility of published scientific results. The prediction markets also allow us to estimate probabilities for the hypotheses being true at different testing stages, which provides valuable information regarding the temporal dynamics of scientific discovery. We find that the hypotheses being tested in psychology typically have low prior probabilities of being true (median, 9%) and that a “statistically significant” finding needs to be confirmed in a well-powered replication to have a high probability of being true. We argue that prediction markets could be used to obtain speedy information about reproducibility at low cost and could potentially even be used to determine which studies to replicate to optimally allocate limited resources into replications.

Wireless communication using electromagnetic wave carrying orbital angular momentum (OAM) has attracted increasing interest in recent years and its potential to increase channel capacity has been explored widely. In this paper, we compare the technique of using uniform linear array consist of circular traveling-wave OAM antennas for multiplexing with the conventional multiple-in-multiple-out (MIMO) communication method and numerical results show that the OAM based MIMO system can increase channel capacity while communication distance is long enough. An equivalent model is proposed to illustrate that the OAM multiplexing system is equivalent to a conventional MIMO system with a larger element spacing, which means OAM waves could decrease the spatial correlation of MIMO channel. In addition, the effects of some system parameters, such as OAM state interval and element spacing, on the capacity advantage of OAM based MIMO are also investigated. Our results reveal that OAM waves are complementary with MIMO method. OAM waves multiplexing is suitable for long-distance line-of-sight (LoS) communications or communications in open area where the multi-path effect is weak and can be used in massive MIMO systems as well.

Metabolism vulnerability of cisplatin resistance in BCa cells remains to be discovered, which we applied integrated multi-omics analysis to elucidate the metabolism related regulation mechanism in bladder cancer (BCa) microenvironment. Integrated multi-omics analysis of metabolomics and proteomics revealed that MAT2A regulated methionine metabolism contributes to cisplatin resistance in BCa cells. We further validated MAT2A and cancer stem cell markers were up-regulated and circARHGAP10 was down-regulated through the regulation of MAT2A protein stability in cisplatin resistant BCa cells. circARHGAP10 formed a complex with MAT2A and TRIM25 to accelerate the degradation of MAT2A through ubiquitin-proteasome pathway. Knockdown of MAT2A through overexpression of circARHGAP10 and restriction of methionine up-take was sufficient to overcome cisplatin resistance in vivo in immuno-deficiency model but not in immuno-competent model. Tumor-infiltrating CD8 + T cells characterized an exhausted phenotype in tumors with low methionine. High expression of SLC7A6 in BCa negatively correlated with expression of CD8. Synergistic inhibition of MAT2A and SLC7A6 could overcome cisplatin resistance in immuno-competent model in vivo. Cisplatin resistant BCa cells rely on methionine for survival and stem cell renewal. circARHGAP10/TRIM25/MAT2A regulation pathway plays an important role in cisplatin resistant BCa cells while circARHGAP10 and SLC7A6 should be evaluated as one of the therapeutic target of cisplatin resistant BCa.

Circular RNAs (circRNAs) drive several cellular processes including proliferation, survival, and differentiation. Here, we identified a circRNA hsa_circ_0007813, whose expression was upregulated in bladder cancer. High hsa_circ_0007813 expression was associated with larger tumor size, higher primary tumor T stage, and higher pathologic grade. Survival analysis showed that patients with high hsa_circ_0007813 expression levels had a poorer prognosis. Based on these findings from clinical tissue samples and cell lines, we assumed that hsa_circ_0007813 functioned a vital role in bladder cancer progression. Next, functional experiments revealed that knockdown of hsa_circ_0007813 inhibited proliferation, migration, and invasiveness of bladder cancer cells both in vitro and in vivo. Through extensive bioinformatic prediction and RNA pull-down assays, we identified hsa-miR-361-3p as a competing endogenous RNA of hsa_circ_0007813. Further bioinformatic studies narrowed targets to 35 possible downstream genes. We then found that knockdown of hsa_circ_0007813 led to altered cell autophagy, bringing our attention to IGF2R, one of the possible downstream genes. IGF2R was also known as cation-independent mannose-6-phosphate receptor (CI-M6PR), was discovered to participate in both autophagy and tumor biology. Regarding autophagy has a dominant role in the survival of tumor cells overcoming cellular stress and correlates with tumor progression, investigations were made to prove that hsa_circ_0007813 could regulate IGF2R expression via hsa-miR-361-3p sponging. The potential of hsa_circ_0007813 in regulating IGF2R expression explained its influence on cell behavior and clinical outcomes. Collectively, our data could offer new insight into the biology of circRNA in bladder cancer.

Excessive total nitrogen (TN) in water bodies leads to eutrophication, algal blooms, and hypoxia, which pose significant risks to aquatic ecosystems and human health. Accurate real-time TN prediction is crucial for effective water quality management. This study presents an innovative approach that combines the distance correlation coefficient (DCC) for feature selection with a coupled Attention-Convolutional Neural Network-Bidirectional Long Short-Term Memory (At-CBiLSTM) model to predict TN concentrations in the Dongjiang River in China. A dataset of 28,922 time-series data points was collected from seven sampling sites along the Dongjiang River, spanning from November 2020 to February 2023. The DCC method identified conductivity, Permanganate Index (CODMn), and total phosphorus as the most significant predictors for TN levels. The At-CBiLSTM model, optimized with a time step of three, outperformed other models, including standalone Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), Convolutional Neural Network LSTM (CNN-LSTM), and Attention-LSTM variants, achieving excellent performance with the following metrics: mean absolute error (MAE) = 0.032, mean squared error (MSE) = 0.005, mean absolute percentage error (MAPE) = 0.218, and root mean squared error (RMSE) = 0.045. Importantly, increasing the number of input features beyond three variables led to a decline in model accuracy, underscoring the importance of DCC-driven feature selection. The results highlight that combining DCC with deep learning models, particularly At-CBiLSTM, effectively captures nonlinear temporal dependencies and improves prediction accuracy. This approach provides a solid foundation for real-time water quality monitoring and can inform targeted pollution control strategies in river ecosystems.

Rivers and the organisms living within them are highly vulnerable to hot thermal extremes. However, very little is known about river heatwaves, consecutive episodes of anomalously high temperature in rivers, and how they may evolve under climate change. Here we show that river heatwaves will become more intense and more persistent globally by the end of the 21 st century, with some tropical rivers reaching a persistent year-round heatwave state in the early 21 st century. Under the high-greenhouse-gas-emission scenario (Representative Concentration Pathway 8.5), the average intensity of river heatwaves will increase by ~4.2-fold, and the average duration by ~95-fold, relative to the baseline period (1976–2005). Nearly half of the world’s rivers are expected to experience a year-round heatwave state by the 2090 s. Global population exposure to river heatwaves will reach 16.8 billion person-weeks annually, with a disproportionately heavier burden on vulnerable low-income regions, such as the Congo River basin. Emerging persistent river heatwaves may push river ecosystems and aquatic organisms to their resilience limits, causing irreversible changes and widespread impacts. River heatwaves are becoming stronger and longer-lasting globally. Nearly half of the world’s rivers will reach a ‘permanent’ (year-round) heatwave state by the 2090 s under high greenhouse gas emissions scenario, and annual population exposure will reach 16.8 billion person-weeks.

Being able to replicate scientific findings is crucial for scientific progress 1 – 15 . We replicate 21 systematically selected experimental studies in the social sciences published in Nature and Science between 2010 and 2015 16 – 36 . The replications follow analysis plans reviewed by the original authors and pre-registered prior to the replications. The replications are high powered, with sample sizes on average about five times higher than in the original studies. We find a significant effect in the same direction as the original study for 13 (62%) studies, and the effect size of the replications is on average about 50% of the original effect size. Replicability varies between 12 (57%) and 14 (67%) studies for complementary replicability indicators. Consistent with these results, the estimated true-positive rate is 67% in a Bayesian analysis. The relative effect size of true positives is estimated to be 71%, suggesting that both false positives and inflated effect sizes of true positives contribute to imperfect reproducibility. Furthermore, we find that peer beliefs of replicability are strongly related to replicability, suggesting that the research community could predict which results would replicate and that failures to replicate were not the result of chance alone. Camerer et al. carried out replications of 21 Science and Nature social science experiments, successfully replicating 13 out of 21 (62%). Effect sizes of replications were about half of the size of the originals.