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Photoinduced dynamics of CO on Ru(0001): Understanding experiments by simulations with all degrees of freedom
Real-time pump–probe experiments are powerful tools for monitoring chemical reactions but often need parallel theoretical modeling to disentangle different contributions. Monitoring x-ray spectra of photoinduced dynamics of CO on Ru(0001) provided a strong indication for a transient “precursor state” of unidentified nature to various subsequent outcomes. So far, the precise nature of the postulated precursor has also remained elusive in state-of-the-art ab initio molecular dynamics models, including single-moving CO molecules. In the present work, we have constructed a density functional theory-based machine learning interatomic potential energy surface that is valid for all ionic degrees of freedom of the system, comprising many molecules at various coverages and moving surface atoms. Our Langevin dynamics with electronic friction based on the new potential energy surface identified the precursor state as dynamically trapped molecules around 6 Å from the surface that arise from adsorbate–adsorbate interactions. We have compared our results to experimental observations and calculated the dependence of reaction probabilities on pump laser fluence and initial surface coverage.
Multi-target drug discovery for rheumatoid arthritis: a comprehensive computational approach using bioactive compounds
Immune infiltration related PRDX4 facilitates the malignant features and drug resistance of breast cancer
Author Correction: Daidzein alleviates skin fibrosis by suppressing TGF-β1 signaling pathway via targeting PKM2
Genome-based reclassification of Micromonospora veneta Kaewkla et al. 2022 as a later heterotypic synonym of Micromonospora coerulea Jensen 1932 (Approved lists 1980)
Location allocation approach based on influence of points of dispensing
Linking gas production to microbial fuel cell output: a novel approach to assess soybean processing and selenium bioavailability
Abstract This study considered the effects of soybean processing methods (raw, roasted, microwaved) and selenium (Se) supplementation (nano-Se, sodium selenite) on in vitro rumen fermentation kinetics and microbial fuel cell (MFC) performance. Soybeans were thermally processed, and gas production (GP) and MFC voltage were measured over 96–120 h. Chemical analysis revealed microwave processing increased crude protein (39.20% vs. 37.35% raw) and reduced fiber content, enhancing digestibility. Gas production kinetics showed microwaved soybeans yielded the highest cumulative GP (312.75 mL/g DM at 96 h), surpassing roasted and raw treatments, likely due to structural modifications improving microbial accessibility. Nano-Se supplementation further amplified GP (320.04 mL/g DM at 96 h) and MFC voltage (3502.60 mV at 120 h), outperforming inorganic Se, attributed to enhanced microbial activity and antioxidant capacity. MFC voltage correlated strongly with GP (r = 0.95–0.99), validating MFCs as a dual-metric tool for assessing fermentation efficiency. Microwave processing generated the highest voltage (3241.30 mV), reflecting efficient electron transfer from disrupted fibrous structures. Nano-Se accelerated microbial kinetics, demonstrating superior bioavailability. Results highlight that thermal processing, particularly microwaving, optimizes nutrient utilization, while nano-Se enhances rumen microbial functions. The integration of GP and MFC metrics provides novel insights into feed degradability and microbial energetics, offering strategies to improve ruminant productivity and reduce environmental impacts. This study underscores the potential of combining advanced processing techniques and selenium supplementation to refine feed formulations and advance sustainable livestock practices.
Comparison of post ablation left atrial volume index versus left atrial reverse remodeling for prognostic events in persistent atrial fibrillation
Spatiotemporal dynamics of benzylisoquinoline alkaloid gene expression and co-expression networks during Papaver Somniferum developmental stages
Design and optimization of imageable microspheres for locoregional cancer therapy
Thermodynamically consistent modeling of granular soils using physics-informed neural networks
Abstract In recent years, data-driven approaches have gained considerable momentum in the scientific and engineering communities, owing to their capacity to extract complex patterns from high-dimensional data. Despite their potential, these approaches often require extensive high-quality datasets, may exhibit limited extrapolation capability beyond the training domain, and lack a rigorous foundation grounded in physical and thermodynamic principles. To overcome these limitations, physics-informed neural networks have been introduced, embedding governing equations directly into the learning process. Building upon this paradigm, this study presents a novel thermodynamically consistent constitutive model for granular soils, developed within the framework of geotechnically- and physics-informed neural networks (GINN). The model integrates physical laws with data-driven learning via a composite loss function. These include: (i) strictly non-negative material dissipation rate to ensure thermodynamic admissibility, (ii) an admissible range for the predicted stress state, and (iii) bounds on the predicted void ratio. The material dissipation rate is calculated using the total work input and a free energy potential expressed in terms of stress invariants. The model is validated against monotonic drained triaxial test data for specimens prepared with diverse initial void ratios and stress states. The model accurately simulates both the shear strength and dilative response of granular soil samples. Its predictive performance is further benchmarked against two widely adopted constitutive models from the literature, demonstrating comparable accuracy while maintaining consistency with thermodynamic laws.
Enhanced infection and transmission of the 2022–2024 Oropouche virus strain in the North American biting midge Culicoides sonorensis
Abstract Oropouche virus (OROV) is a vector-borne zoonotic virus that causes febrile illness in humans. Biting midges of the Culicoides genus are the primary vectors during human outbreaks. The 2022–2024 OROV outbreak has seen an increase in incidence, geographic expansion, and the emergence of previously undocumented symptoms. To better understand the basis of increased disease incidence, infection of the outbreak virus (OROV240023) was compared to a historical virus strain (rOROVBeAn19991) in Culicoides sonorensis, a midge species that has demonstrated historical competence. Higher levels of infection, dissemination, and transmission potential were observed in C. sonorensis infected with the outbreak strain compared to the historical strain, although infectious titers did not differ between the two viruses. OROV240023 was also detected in saliva at earlier time points than rOROVBeAn19991, indicating a shorter extrinsic incubation period of < 5 days compared to 7–14 days for rOROVBeAn19991. Taken together, our results demonstrate increased transmission potential of the outbreak strain in C. sonorensis midges, raising concern about the risk of spread within the United States following potential introduction. However, further studies are needed to evaluate the current strain in Culicoides species occurring within its outbreak range, including Culicoides paraensis, the confirmed South American vector of OROV.
Boron triggers grain boundary structural transformation in steel
Abstract Boron enhances the hardenability of low-alloyed steel and reduces embrittlement at low temperatures, at parts-per-million concentration levels. Its effectiveness arises from segregation to grain boundaries (GBs)-planar defects between crystals-yet atomic-scale evidence remains limited. We addressed this gap by synthesizing GBs with controllable geometry and orientation, enabling reproducible comparison with and without boron segregation. Differential phase-contrast imaging directly reveals boron at iron GBs, and in-situ TEM heating (20 °C to 800 °C) allows us to track the dynamic evolution of GB structures. We found that boron segregation induces local structural changes and triggers GB phase transformations, as corroborated by calculated GB defect phase diagrams spanning broad ranges of carbon and boron content. Our findings not only bridge a gap in understanding the interplay between GB structure and chemistry but also lay the groundwork for targeted design and passivation strategies in steel, potentially transforming its resistance to hydrogen embrittlement, corrosion, and mechanical failure.
Low phase angle indicates poor muscle strength and physical performance in patients with knee osteoarthritis awaiting total knee arthroplasty
Engineering of photo-inducible binary interaction tools for biomedical applications
Machine learning based shear strength prediction in reinforced concrete beams using Levy flight enhanced decision trees
Abstract Reinforced concrete (RC) T-beams are widely used in structural systems due to their efficient geometry and load-carrying capacity. However, accurately predicting their shear strength remains a challenge, particularly under complex loading scenarios. Conventional empirical approaches often struggle to adequately represent the complex and nonlinear relationships among structural design variables. In this study, a novel machine learning approach, termed Levy-DT, is introduced to enhance the prediction accuracy of shear strength in RC T-beams. The proposed method combines the structure of Decision Tree algorithm with Levy Flight, a stochastic optimization technique, to improve global search capabilities and avoid local minima. A comprehensive dataset comprising 195 experimentally tested T-beams is used to train and evaluate six different regression models, including optimized Decision Tree, Random Forest, AdaBoost, K-Nearest Neighbors, Ridge Regression, and the proposed Levy-DT. Model performance is assessed using multiple metrics such as R², RMSE, and MAE, with cross-validation employed for robustness. Systematic hyperparameter optimization is implemented for the baseline Decision Tree to ensure fair comparison. The results show that Levy-DT outperforms all other models, achieving the highest prediction accuracy with strong generalization. To further understand the model’s decision-making process, SHAP analysis is carried out, identifying axial force and reinforcement depth as key contributors to the shear strength estimation. This study highlights the potential of integrating optimization techniques with machine learning for reliable and interpretable structural predictions.