Browse Articles
Discover research articles across all indexed journals
Light-Driven Deaminative Formal Nucleophilic Substitution Via C(sp <sup>3</sup> )–N Bond Cleavage: Nucleophilic Transamination of Alkyl Amines
Reply to Rana and Singh: Critical thresholds and critical actions for evolvable AI
Development of type-specific amplicon schemes for whole-genome sequencing of seasonal human coronaviruses from clinical samples
Site-Selective Trimodular Polymerization to Amino Acid-Based Poly(ether amide)s with Divergent Properties
Synergistic mix design strategy for carbon black–modified metakaolin geopolymer composites toward structural energy-storage applications
Coupling High-Throughput Density Functional Theory, Automated Experimentation, and Adaptive Experimental Design To Achieve Selective Rare-Earth Element Separations
Comparison of NASM Corrective Exercise Alone Versus with Kinesio Taping on Navicular Drop and Lower Limb Function in Female Adolescents with Flexible Flatfoot: A Randomized Clinical Trial
Bioorthogonal Regulation of Negatively Charged Protein Side Chains in Living Systems
Nitrogen enrichment and Ulva green tide dynamics on Jeju Island: insights from biomass, physiological traits, and stable isotopes signatures
Spin-Vibronic Coupling in Indenoindenodibenzothiophene Diradicaloids
Automation of 3D liver spheroid generation and acetaminophen dose–response on the MO:BOT enhances assay robustness and precision
Abstract Human 3D in vitro models are increasingly adopted in preclinical drug discovery, as they better recapitulate tissue-like architecture, cell-cell interactions, and organ-specific functions compared with conventional 2D cultures. In parallel, initiatives such as the FDA Modernization Act 2.0 and the proposed 3.0 encourage the use of human-relevant in vitro systems as New Approach Methodologies (NAMs) alongside animal models. However, most 3D cell-based workflows rely on manual protocols that are difficult to standardize, producing variable spheroid morphology and fragmented culture steps that impair assay robustness and cross-study comparability. To address these challenges, we present the MO:BOT, a modular benchtop platform designed to standardize and automate complex 3D cell-based workflows. The MO:BOT integrates critical steps of 3D cell culture, including accurate cell seeding, precise medium exchange, on-deck image-based quality control, scalable compound dosing, and downstream assays. Here, we describe a proof-of-concept automated protocol to generate HepG2 liver spheroids and perform acetaminophen (APAP) drug-response testing. Compared with a manual workflow, the MO:BOT not only reduces hands-on time but also minimizes well-to-well variability in liver spheroid size and improves overall spheroid viability. We demonstrate that standardized, well-tuned pipetting routines can maintain the integrity of delicate 3D structures. An MO:BOT-automated APAP dose-exposure experiment with seven concentrations yields a clear sigmoidal cell-toxicity profile with matching changes in viability, LDH release, and ALT activity, demonstrating that improved spheroid uniformity enhances the sensitivity and robustness of downstream assays. The MO:BOT advances the standardization, scalability, and reproducibility of 3D in vitro models, supporting their broader adoption in preclinical research.
2-Alkyl Furans Undergo Radiolytic Oxidative Protein Cross-Linking
Development and validation of novel prognostic hepatic-cardiac index and hemodynamic prediction models in pre-capillary pulmonary hypertension patients
Abstract Pulmonary arterial hypertension (PAH) is a life-threatening disorder marked by progressive elevation of pulmonary vascular resistance and ultimate right heart failure. Its diagnosis and risk stratification largely depend on invasive right heart catheterization (RHC). This study aimed to integrate routine laboratory and echocardiographic data to develop a novel multi-organ prognostic indicator and construct non-invasive hemodynamic estimation prediction models. This was a multi-center retrospective study enrolling a total of 44 routine laboratory and echocardiographic indices of pre-capillary pulmonary hypertension. Key prognostic variables were screened using Least absolute shrinkage and selection operator-Cox (LASSO-Cox) regression and random survival forest (RSF) analysis. The novel composite index was constructed based on correlation analysis with hemodynamic parameters. Prognostic performance was evaluated using Cox regression, Time-Dependent Receiver Operating Characteristic (time-dependent ROC) curve analysis, stratified analysis, and decision curve analysis (DCA). Linear prediction models for invasive hemodynamic parameters were established using variable selection and internal validation. A total of 179 patients with pre-capillary pulmonary hypertension were included. Using LASSO-Cox and random survival forest, 13 core prognostic variables were identified, including total bilirubin (TB) and B-type natriuretic peptide (BNP). The novel hepatic-cardiac index--TBI (log [total bilirubin × BNP]) was constructed and validated as an independent prognostic factor (HR=1.64, 95%CI: 1.287–2.09, P=0.0001). Predictive efficacy at 36 months was significantly higher for TBI (area under the curve, AUC = 0.777) than for BNP (AUC = 0.723, P = 0.021 with false discovery rate [FDR] adjustment). Stratified analysis confirmed that TBI remained prognostic in patients with BNP levels between 200 and 800 pg/mL (χ 2 =7.075, P = 0.008). Additionally, non-invasive linear prediction models for mean pulmonary arterial pressure(mPAP), pulmonary vascular resistance (PVR), and PVR×CO (cardiac output) were established with moderate accuracy (R 2 =0.575, 0.507, 0.523, respectively) Integration of laboratory and echocardiographic parameters enables improved non-invasive evaluation in pulmonary hypertension. The novel hepatic-cardiac index (TBI, log[TB×BNP]) serves as a robust and independent prognostic biomarker. Routine non-invasive indices can reliably estimate key hemodynamic parameters in clinical practice.
Synthesis of the Elusive Bulk Iodide Double-Perovskite Semiconductor, Cs <sub>2</sub> AgBiI <sub>6</sub> : Microcrystals and Photoconductive Films
Green technological innovation strengthens the association between public environmental concern and lower agricultural carbon emission intensity in China
Abstract Growing public environmental concern (PEC) has increasingly shaped the governance of agricultural sustainability, yet its role in mitigating agricultural carbon emissions (ACE) remains insufficiently understood. This study contributes to the literature by incorporating informal environmental regulation into the analysis of ACE and developing an integrated PEC–GTI–ACE analytical framework. Using panel data from 30 Chinese provinces over 2011–2022, we examine how PEC affects agricultural carbon emission intensity, as well as the moderating and nonlinear roles of green technological innovation (GTI), employing two-way fixed effects, moderation, and threshold regression models. The empirical results show that higher levels of public environmental concern significantly reduce agricultural carbon emission intensity, with a stable and robust coefficient of approximately − 0.060 across alternative specifications. GTI plays a reinforcing role in this process. The interaction between public concern and green innovation is significantly negative, with an estimated coefficient of − 0.014, indicating that advances in green innovation amplify the carbon mitigation effect of PEC. Further analysis reveals a nonlinear threshold effect in GTI, with a single threshold estimated at 0.337. Once this threshold is exceeded, the suppressive impact of PEC on ACE becomes markedly stronger. Subregional analyses indicate that the threshold effects observed in the eastern and central regions are broadly consistent with the national pattern, while the western region exhibits pronounced heterogeneity. By uncovering both the complementary and threshold-based mechanisms between public environmental awareness and green technological innovation, this study provides new evidence on how behavioral and technological factors jointly shape agricultural carbon reduction. Overall, the findings suggest that PEC and GTI act as complementary forces in mitigating ACE.
Chiral Quantum-Cutting
Hybrid deep learning–driven explainable AI framework for fault detection and classification in smart power grids
Small-scale photonic Kolmogorov-Arnold networks using standard telecom nonlinear modules
Abstract Photonic neural networks promise inference at the speed of light, yet most architectures combine linear optical meshes with electronic nonlinearities, reintroducing optical-electrical-optical bottlenecks. Kolmogorov-Arnold networks place trainable nonlinear functions on network edges, concentrating expressivity into a few structured modules. Each edge here is a single module built from a Mach-Zehnder interferometer, a semiconductor optical amplifier, and variable optical attenuators, giving a four-parameter transfer function set by gain saturation and interferometric mixing. A four-module network attains 94.3% accuracy (±3.9% s.d. over ten seeds) on nonlinear classification, and a seven-module network reaches R 2 = 0.986 ± 0.015 on six-input regression, remaining robust to 6-bit inputs and 14 dB signal-to-noise ratio. Here, we show that a fully differentiable physics model enables end-to-end optimization of these standard telecom modules, giving a practical route from simulation toward experimental demonstration of photonic Kolmogorov-Arnold networks.