Search Results (17,135)

Postmenopausal osteoporosis is a major global health concern, particularly affecting aging women, and necessitates innovative treatment options. Herbal medicine, with its multi-compound, multi-target characteristics, offers a promising approach for complex diseases. In this study, we applied multiscale network and random walk-based analyses to identify candidate herbs and their active ingredients for postmenopausal osteoporosis, focusing on their underlying mechanisms. A dataset of medicinal herbs, their active ingredients, and protein targets was compiled, and diffusion profiles were calculated to assess the propagation effects. Through correlation analysis, we prioritized herbs based on their relevance to osteoporosis, identifying the top candidates like Benincasae Semen, Glehniae Radix, Corydalis Tuber, and Houttuyniae Herba. Gene Set Enrichment Analysis (GSEA) revealed that the 49 core protein targets of these herbs were significantly associated with pathways related to inflammation, osteoclast differentiation, and estrogen metabolism. Notably, compounds such as falcarindiol from Glehniae Radix and tetrahydrocoptisine from Corydalis Tuber—previously unstudied for osteoporosis—were predicted to interact with inflammation-related proteins, including IL6, IL1B, and TNF, affecting key biological processes like apoptosis and cell proliferation. This study advances the understanding of herbal therapies for osteoporosis and offers a framework for discovering novel therapeutic agents. Full article

Abstract: This study focuses on the characterization and re-engineering of glucose transport in β-galactosidase (BglD) to enhance its catalytic efficiency. Computational prediction methods were employed to identify key residues constituting access tunnels for lactose and glucose, revealing distinct pockets for both substrates. In silico simulated saturation mutagenesis of residues T215 and T473 led to the identification of eight mutant variants exhibiting potential enhancements in glucose transport. Site-directed mutagenesis at T215 and T473 resulted in mutants with consistently enhanced specific activities, turnover rates, and catalytic efficiencies. These mutants also demonstrated improved galactooligosaccharide (GOS) synthesis, yielding an 8.1–10.6% enhancement over wild-type BglD yield. Structural analysis revealed that the mutants exhibited transformed configurations and localizations of glucose conduits, facilitating expedited glucose release. This study’s findings suggest that the re-engineered mutants offer promising avenues for enhancing BglD’s catalytic efficiency and glucose translocation, thereby improving GOS synthesis. By-product (glucose) re-tunneling is a viable approach for enzyme tunnel engineering and holds significant promise for the molecular evolution of enzymes. Full article

It is imperative to comprehend the mechanisms that underlie drug toxicity in order to enhance the efficacy and safety of novel therapeutic agents. The capacity to identify molecular pathways that contribute to drug-induced toxicity has been significantly enhanced by recent developments in omics technologies, such as transcriptomics, proteomics, and metabolomics. This has enabled the early identification of potential adverse effects. These insights are further enhanced by computational tools, including quantitative structure–activity relationship (QSAR) analyses and machine learning models, which accurately predict toxicity endpoints. Additionally, technologies such as physiologically based pharmacokinetic (PBPK) modeling and micro-physiological systems (MPS) provide more precise preclinical-to-clinical translation, thereby improving drug safety assessments. This review emphasizes the synergy between sophisticated screening technologies, in silico modeling, and omics data, emphasizing their roles in reducing late-stage drug development failures. Challenges persist in the integration of a variety of data types and the interpretation of intricate biological interactions, despite the progress that has been made. The development of standardized methodologies that further enhance predictive toxicology is contingent upon the ongoing collaboration between researchers, clinicians, and regulatory bodies. This collaboration ensures the development of therapeutic pharmaceuticals that are more effective and safer. Full article

Due to model errors caused by local variations in cloud precipitation processes, there are still significant uncertainties in current predictions and simulations of short-duration heavy rainfall. To tackle this problem, the effects of warming on cloud-precipitation efficiency was analyzed utilizing a weather research and forecasting (WRF) model. The analysis focused on a rainstorm corridor event that took place in July 2020. Rainstorm events from 4–6 July formed a narrow rain belt with precipitation exceeded 300 mm in the middle and lower reaches of the Yangtze River. Temperature sensitivity tests revealed that warming intensified the potential temperature gradient between north and south, leading to stronger upward motion on the front. It also strengthened the southwest wind, which resulted in more pronounced precipitation peaks. Warming led to a stronger accumulation and release of convective instability energy. Convective available potential energy (CAPE) and convective inhibition (CIN) both increased correspondingly with the temperature. The precipitation efficiency increased sequentially with 2 °C warming to 27.4%, 31.2%, and 33.1%. Warming can affect the cloud precipitation efficiency by both promoting and suppressing convective activity, which may be one of the reasons for the enhancement of extreme precipitation under global warming. The diagnostic relationship between upward moisture flux and lower atmospheric stability during precipitation evolution was also revealed. Full article

To achieve carbon neutrality in China’s fossil energy sector, saline aquifer CO₂ geological storage has become a critical strategy. As research into carbon reduction and storage potential evaluation advances across various geological scales, the need arises for consolidating key CO₂ storage cases and establishing a standardized classification system and evaluation methodology. This paper provides a comprehensive review of notable CO₂ storage projects in saline aquifers, covering aspects such as project overviews, structural and reservoir characteristics, caprock integrity, and seismic monitoring protocols. Drawing on insights from mineral and oil and gas exploration, as well as international methods, this paper outlines the stages and potential levels of saline aquifer storage in China. It proposes an evaluation framework with formulas and reference values for key coefficients. The study includes successful global projects, such as Sleipner and Snøhvit in Norway, In Salah in Algeria, and Shenhua in China’s Ordos Basin, which provide valuable insights for long-term carbon capture and storage (CCS). By examining geological characteristics, injection, and monitoring protocols in these projects, this paper analyzes how geological features impact CO₂ storage outcomes. For example, the Sleipner project’s success is linked to its straightforward structure, favorable reservoir properties, and stable caprock, while Snøhvit illustrates diverse structural suitability, and In Salah demonstrates the influence of fractures on storage efficacy. CO₂ storage activities are segmented into four stages—survey, investigation, exploration, and injection—and are further categorized by storage potential: geological, technical, techno-economic, and engineering capacities. This study also presents evaluation levels (prediction, control, technically recoverable, and engineering) that support effective reservoir selection, potential classification, and calculations considering factors like reservoir stability and sealing efficacy. Depending on application needs, volumetric or mechanistic methods are recommended, with precise determination of geological, displacement, and cost coefficients. For China, a dynamic evaluation mechanism characterized by multi-scale, tiered approaches and increasing precision over time is essential for robust storage potential assessment. The levels and methods outlined here serve as a scientific foundation for regional and stage-based comparisons, guiding engineering approvals and underground space management. To align with practical engineering demands, ongoing innovation through laboratory experiments, simulations, and field practice is crucial, supporting continual refinement of formulas and key parameter determinations. Full article

Objective: The aim of this study was to develop quantitative structure–pharmacokinetics relationship (QSPKR) models for a group of xanthine derivatives with proven pharmacological activity and to investigate its applicability for the prediction of the pharmacokinetics of these compounds. Methods: The SYBYL-X, KowWin, and MarvinSketch programs were employed to generate a total of fourteen descriptor variables for a series of new compounds: 7- and 7,8-substituted theophylline derivatives (GR-1–GR-8) and three well-known methylxanthines. Pharmacokinetic profiles of all compounds were determined after intravenous administration of studied compounds to cannulated male rats. Pharmacokinetic parameters were calculated using noncompartmental analysis. Results: Multiple linear regression revealed that logD was the main determinant of the variability in V_ss, λ_z, and CL of the studied compounds. Moreover, λ_z and CL depended on LUMO and HEFO, while for V_z COAR was the only explanatory variable. The developed QSPKR models accounted for most of the variation in V_ss, λ_z, CL, and fraction unbound (f_u) (R² ranged from 0.68 to 0.91). Cross-validation confirmed the predictive ability of the models (Q² = 0.60, 0.71, 0.34, and 0.32 for V_ss, λ_z, CL, and f_u, respectively). Conclusions: The multivariate QSPKR models developed in this study adequately predicted the overall pharmacokinetic behavior of xanthine derivatives in rats. Full article

Finding new molecules to prevent the growth of antimicrobial resistance is a hot topic for scientists worldwide. It has been reported that some raw bioproducts containing Monascus polyketides have antimicrobial activities, but extensive studies on this effect have not been conducted. In this context, our studies aimed to evaluate the antimicrobial properties of six raw bioproducts containing three classes of microbial polyketides biosynthesized by three Monascus strains through solid-state biosynthesis. As a methodology, we performed in silico predictions using programs such as PyMOL v3.0.4 and employed ESI-MS techniques to provide evidence of the presence of the six studied compounds in our bioproducts. The results obtained in silico were validated through in vitro studies using the Kirby-Bauer diffusion method on bacteria and fungi. The test performed in silico showed that Monascorubramine has the highest affinity for both Gram-positive and Gram-negative bacteria, followed by yellow polyketides such as Ankaflavin and Monascin. The estimated pharmacokinetic parameters indicated high gastrointestinal absorption and the potential to cross the blood-brain barrier for all studied compounds. However, the compounds also inhibit most enzymes involved in drug metabolism, presenting some level of toxicity. The best in vitro results were obtained for S. aureus, with an extract containing yellow Monascus polyketides. Predictions made for E. coli were validated in vitro for P. aeruginosa, S. enterica, and S. marcescens, as well as for fungi. Significant antibacterial properties were observed during this study for C. albicans, S. aureus, and fungal dermatophytes for crude bioproducts containing Monascus polyketides. In conclusion, the antimicrobial properties of Monascus polyketides were validated both in silico and in vitro. However, due to their potential toxicity, these bioproducts would be safer to use as topical formulations. Full article

Schizophrenia is a complex neuropsychiatric disorder characterized by disruptions in brain connectivity and cognitive functioning. Continuous monitoring of neural activity is essential, as it allows for the detection of subtle changes in brain connectivity patterns, which could provide early warnings of cognitive decline or symptom exacerbation, ultimately facilitating timely therapeutic interventions. This paper proposes a novel approach for detecting schizophrenia-related abnormalities using deep learning (DL) techniques applied to electroencephalogram (EEG) data. Using an openly available EEG dataset on schizophrenia, the focus is on preprocessed event-related potentials (ERPs) from key electrode sites and applied transfer entropy (TE) analysis to quantify the directional flow of information between brain regions. TE matrices were generated to capture neural connectivity patterns, which were then used as input for a hybrid DL model, combining convolutional neural networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The model achieved a performant accuracy of 99.94% in classifying schizophrenia-related abnormalities, demonstrating its potential for real-time mental health monitoring. The generated TE matrices revealed significant differences in connectivity between the two groups, particularly in frontal and central brain regions, which are critical for cognitive processing. These findings were further validated by correlating the results with EEG data obtained from the Muse 2 headband, emphasizing the potential for portable, non-invasive monitoring of schizophrenia in real-world settings. The final model, integrated into the NeuroPredict platform, offers a scalable solution for continuous mental health monitoring. By incorporating EEG data, heart rate, sleep patterns, and environmental metrics, NeuroPredict facilitates early detection and personalized interventions for schizophrenia patients. Full article

(1) Background: donor-derived circulating free DNA (dd-cfDNA), an innovative biomarker with great potential for the early identification and prevention of graft damage. (2) Methods: Samples were collected prospectively and the study was performed retrospectively to analyze dd-cfDNA plasma levels in 30 kidney transplant patients during their post-transplant follow-up (15 days, 3, 6, and 9 months), to determine if the result could be of interest in the identification of possible adverse events, especially rejection. The aim was to verify whether the data on sensitivity, specificity, NPV, and PPV compare with reference values and creatinine values. (3) Results: We observed levels of dd cfDNA > 1% in six of nine patients with active rejection (ABMR or TCMR) and elevated values (>0.5%) in two other patients in this rejection group. Our results show low values of sensitivity = 50%, specificity = 61.11%, rejection NPV = 64.71%, and rejection PPV = 46.13% of the technique compared to reference values previously published. With respect to creatinine, only for TCRM, we observed better results for dd-cfDNA in these parameters than in creatinine. Also, our data suggest that dd-cfDNA could help to differentiate those patients with dnDSAs that are going to through rejection better than creatinine, specially at 15 d post transplant. In this study, this appears to have no positive predictive value for borderline rejection (BR) or TCMR IA. (4) Conclusions: plasma levels of dd-cfDNA could be considered an additional or alternative biomarker for graft rejection monitoring in early post-kidney transplant up to several months before its clinical presentation, especially for patients with suspected TCMR or ABMR. Full article

In predictive microbiology, both primary and secondary models are widely used to estimate microbial growth, often applied through two-step or one-step modelling approaches. This study focused on developing a tool to predict the growth of Pseudomonas spp., a prominent bacterial genus in food spoilage, by applying machine learning regression models, including Support Vector Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR). The key environmental factors—temperature, water activity, and pH—served as predictor variables to model the growth of Pseudomonas spp. in culture media. To assess model performance, these machine learning approaches were compared with traditional models, namely the Gompertz, Logistic, Baranyi, and Huang models, using statistical indicators such as the adjusted coefficient of determination (R²_adj) and root mean square error (RMSE). Machine learning models provided superior accuracy over traditional approaches, with R²_adj values from 0.834 to 0.959 and RMSE values between 0.005 and 0.010, showcasing their ability to handle complex growth patterns more effectively. GPR emerged as the most accurate model for both training and testing datasets. In external validation, additional statistical indices (bias factor, B_f: 0.998 to 1.047; accuracy factor, A_f: 1.100 to 1.167) further supported GPR as a reliable alternative for microbial growth prediction. This machine learning-driven approach bypasses the need for the secondary modelling step required in traditional methods, highlighting its potential as a robust tool in predictive microbiology. Full article

We developed a tree ring width chronology from 1797 to 2020 (224 years) for the northwestern foothills of the Greater Khingan Mountains (GKMs) in northeastern China using 51 tree ring sample cores from 24 Pinus sylvestris var. mongolica (PSM). Pearson’s correlation analysis was used to analyze the relationship between tree ring width and regional climate factors. The standardized chronology was positively associated with the minimum temperature (T_min) in the previous May (r = 0.721, p < 0.01), indicating that this parameter was the main climatic factor limiting PSM growth in the region. We established a secure reconstruction equation for the May T_min from 1797 to 2020. There were 31 warm and 43 cold years in the 224-year reconstructed temperature series, accounting for 13.8% and 19.2% of the total years, respectively. Warm periods were observed in 1820–1829, 1877–1898, 1947–1958, and 1991–2020, whereas cold periods occurred in 1820, 1829–1870, 1899–1927, 1934–1947, and 1960–1988. The observed temperature sequence was highly consistent with the reconstructed sequence from the tree rings, which verified the reliability of the reconstructed results. The spatial correlation analysis indicated that the reconstructed temperature sequence accurately represented the temperature changes in the northwestern foothills of the GKM and surrounding areas. Multi-window spectral analysis and wavelet analysis revealed significant periodic fluctuations from 2 to 6 years, 21.2 years, 48.5 years, and 102.2 years. These periodic variations may be related to the El Niño–Southern Oscillation (ENSO), the Atlantic Multi-Year Intergenerational Oscillation (AMO), the Pacific Decadal Oscillation (PDO), and solar activity. This study expands the existing climate records in the region and provides valuable data support for understanding climate change patterns in the GKM and the scientific predictions of future climate changes. Full article

We utilized remote sensing and ground cover data to predict soil organic carbon (SOC) content across a vast geographic region. Employing a combination of machine learning and deep learning techniques, we developed a novel data fusion approach that integrated Digital Elevation Model (DEM) data, MODIS satellite imagery, WOSIS soil profile data, and CHELSA environmental data. This combined dataset, named GeoBlendMDWC, was specifically designed for SOC prediction. The primary aim of this research is to develop and evaluate a novel optimization algorithm for accurate SOC prediction by leveraging multi-source environmental data. Specifically, this study aims to (1) create an integrated dataset combining remote sensing and ground data for comprehensive SOC analysis, (2) develop a new optimization technique that enhances both machine learning and deep learning model performance, and (3) evaluate the algorithm’s efficiency and accuracy against established optimization methods like Jaya and GridSearchCV. This study focused on India, Australia, and South Africa, countries known for their significant agricultural activities. We introduced a novel optimization technique for both machine learning and deep neural networks, comparing its performance to established methods like the Jaya optimization technique and GridSearchCV. The models evaluated included XGBoost Regression, LightGBM, Gradient Boosting Regression (GBR), Random Forest Regression, Decision Tree Regression, and a Multilayer Perceptron (MLP) model. Our research demonstrated that the proposed optimization algorithm consistently outperformed existing methods in terms of execution time and performance. It achieved results comparable to GridSearchCV, reaching an R² of 90.16 , which was a significant improvement over the base XGBoost model’s R² of 79.08. In deep learning optimization, it significantly outperformed the Jaya algorithm, achieving an R² of 61.34 compared to Jaya’s 30.04. Moreover, it was 20–30 times faster than GridSearchCV. Given its speed and accuracy, this algorithm can be applied to real-time data processing in remote sensing satellites. This advanced methodology will greatly benefit the agriculture and farming sectors by providing precise SOC predictions. Full article

Gravel mulching is a widely employed strategy for water conservation in arid agricultural regions, with potential implications for soil carbon (C) sequestration and greenhouse gas emissions. However, soil respiration and CO₂-C emissions remain uncertain owing to less consideration of the influence of precipitation patterns and planting age. In this study, we investigated the soil respiration rate (R_soil) and cumulative CO₂-C emission (C_cum), both measured over a period of 72 h, along with soil properties and enzyme activities under different precipitation conditions based on gravel mulching with different planting ages. We analyzed the effects of planting ages on R_soil and C_cum and revealed the underlying mechanisms driving changes in environmental factors on R_soil and C_cum. The results demonstrated that the R_soil reached the maximum value at about 1 h, 0.5 h, and 0.25 h after rewetting in 1, 10, and 20 years of gravel mulching under the condition with 1 mm, 5 mm, and 10 mm of precipitation, respectively, whereas the R_soil exhibited its maximum at about 8 h after soil rewetting under precipitation of 30 mm. The C_cum induced by precipitation pulses tends to decrease with increasing years of gravel mulching. The C_cum was 0.0061 t ha⁻¹ in the 20-year gravel-mulched soil, representing a 53.79% reduction compared to the 1-year gravel-mulched soil. Soil organic matter (SOM), planting ages, and alkaline phosphatase (ALP) were the primary factors influencing the R_soil and C_cum in 0–20 cm, while SOM, planting ages, and soil porosity (AirP) were the key factors affecting the R_soil and C_cum in 20–40 cm. The R_soil and C_cum in the 0–20 cm soil were regulated by soil enzyme activities, while those of 20–40 cm soil were controlled by soil properties. This indicates that the decrease in R_soil and C_cum is caused by soil degradation, characterized by a decrease in SOM and ALP. This study offers a novel insight into the long-term environmental impact of gravel mulching measures in arid areas, which is helpful in providing a theoretical basis for dryland agricultural management. It is imperative to consider the duration of gravel mulching when predicting the potential for C sequestration in arid agricultural areas. Full article

Fall risk assessment is becoming an important concern, with the realization that falls, and more importantly fainting occurrences, in most cases require immediate medical attention and can pose huge health risks, as well as financial and social burdens. The development of an accurate inertial sensor-based fall risk assessment tool combined with machine learning algorithms could significantly advance healthcare. This research aims to investigate the development of a machine learning approach for falling and fainting detection, using wearable sensors with an emphasis on forward falls. In the current paper we address the problem of the lack of inertial time-series data to differentiate the forward fall event from normal activities, which are difficult to obtain from real subjects. To solve this problem, we proposed a forward dynamics method to generate necessary training data using the OpenSim software, version 4.5. To develop a model as close to the real world as possible, anthropometric data taken from the literature was used. The raw X and Y axes acceleration data was generated using OpenSim software, and ML fall prediction methods were trained. The machine learning (ML) accuracy was validated by testing with data acquired from six unique volunteers, considering the forward fall type. Full article

The plasmasphere within Earth’s magnetosphere plays a crucial role in space physics, with its electron density distribution being pivotal and strongly influenced by solar activity. Very Low Frequency (VLF) waves, including whistlers, provide valuable insights into this distribution, making the study of their propagation through the plasmasphere essential for predicting space weather impacts on various technologies. In this study, we evaluate the performance of different deep learning model sizes for lightning whistler detection using the YOLO (You Only Look Once) architecture. To achieve this, we transformed the entirety of raw data from the Arase (ERG) Satellite for August 2017 into 2736 images, which were then used to train the models. Our approach involves exposing the models to spectrogram diagrams—visual representations of the frequency content of signals—derived from the Arase Satellite’s WFC (WaveForm Capture) subsystem, with a focus on analyzing whistler-mode plasma waves. We experimented with various model sizes, adjusting epochs, and conducted performance analysis using a partial set of labeled data. The testing phase confirmed the effectiveness of the models, with YOLOv5n emerging as the optimal choice due to its compact size (3.7 MB) and impressive detection speed, making it suitable for resource-constrained applications. Despite challenges such as image quality and the detection of smaller whistlers, YOLOv5n demonstrated commendable accuracy in identifying scenarios with simple shapes, thereby contributing to a deeper understanding of whistlers’ impact on Earth’s magnetosphere and fulfilling the core objectives of this study. Full article