Browse Articles
Discover research articles across all indexed journals
miR-542-3p targets TTF1 to regulate proliferation, invasion, and migration of lung adenocarcinoma via the MAPK signaling pathway
Correction to “Tuning the Directional Solubility of Ionic Liquids through Multicomponent Ions for Low-Temperature Desalination”
Study the removal of copper ions from wastewater using an array of horizontal rough vibrating zinc discs
Abstract The removal of copper ions from synthetic wastewater was investigated using cementation on arrays of oscillating smooth and rough (corrugated) zinc discs under different conditions initial copper ions concentration, vibration intensity (frequency, amplitude), spacing between zinc discs, surface roughness (peak to valley height) and temperature. The results showed that the mass transfer coefficient increased significantly with increasing vibration intensity, initial copper ion concentration, disc spacing, degree of surface roughness, and temperature. Rough (corrugated) discs exhibited significantly higher mass transfer rates than smooth discs, emphasizing the critical role of surface roughness in enhancing diffusion-controlled cementation. The effect of temperature was found to fit Arrhenius equation with an activation energy of 2.58 $$\text{k}\text{c}\text{a}\text{l}/\text{m}\text{o}\text{l}\text{e}$$ which confirms the diffusion-controlled nature of the reaction. The cementation rates were expressed in terms of the mass transfer coefficient. Dimensional analysis was performed to correlate the mass transfer data, resulting in two separate correlations for the smooth and corrugated arrays. These correlations provide a reliable basis for the design and scale-up of vibratory cementation reactors from bench to industrial scale. The present data fit the correlations Sh = 0.0134 Re 1.06 Sc 0.33 (S/d c ) 0.46 for the array of smooth zinc discs, Sh = 0.0711 Re 0.96 Sc 0.33 (S/d c ) −0.49 (P/d c ) 0.49 for array of rough zinc discs under the conditions: 3926.54 < Re < 70,115.28, 1397.2 < Sc < 1538.2, 0.14 < S/d c < 0.36, and 0.014 < P/ d c < 0.036 for rough discs.
Stereoreversed C–O Activation Unlocks All-Carbon Tetrasubstituted <i>Z</i> -Alkene Synthesis
Reduced-order modeling of nonlinear multiscale industrial systems via sparse regression in latent representations
Abstract Data-driven modeling of nonlinear industrial processes is often complicated by heterogeneous temporal dynamics, measurement noise, and fixed-rate data acquisition. Under such conditions, direct regression on raw time-series data may become sensitive to sampling imbalance and fast transient behavior, leading to degraded predictive performance. This work proposes a structured reduced-order modeling framework for constructing compact and numerically stable predictive surrogates. The approach integrates adaptive resampling to redistribute temporal information, spline-based smoothing for stable derivative estimation, delay embedding to incorporate short-term temporal structure, kernel-based dimensionality reduction to extract dominant patterns, and sparse regression in a latent coordinate space to obtain parsimonious dynamical models. Within this framework, sparsity is used primarily to control model complexity in the reduced representation rather than to recover explicit governing equations. The method is evaluated on two benchmark reactor systems and a real grinding–classification process using chronological train/test splits and multi-step rollout prediction. The results indicate that the proposed approach can improve predictive robustness and numerical stability compared with direct sparse regression and its partial variants, particularly in the presence of multiscale temporal behavior and moderate measurement noise. The framework provides a practical strategy for predictive reduced-order modeling under realistic industrial data constraints. Its design emphasizes stability and compactness of the learned dynamics, while acknowledging that the resulting models are defined in a latent representation and are not intended as exact reconstructions of physical governing equations.
Circularly Polarized Luminescence from Silicon QDs in the Near-Infrared with Chiral Ligands
Spatiotemporal attention-based dance motion recognition and intelligent correction system using 3D motion capture
Leveraging Mechanistic Insights into Stereoretentive ROMP for Precision Synthesis of Poly( <i>p</i> -phenylene vinylene)s
GDT-SwinKid: A hybrid model for precise renal lesion analysis
Detecting and delineating renal lesions accurately remains a significant clinical problem due to the variety of kidney pathology and subtle differences in CT image interpretation. In this paper, we present the design of a next-generation hybrid model called GDT-SwinKid (Gamma Distribution-based Swin Transformer for Renal Lesions), which integrates the hierarchical feature attention mechanisms of Swin Transforms with a modified U-Net decoder and employs advanced statistical modeling (specifically through an adaptive Gamma distribution). The design of GDT-SwinKid allows for both precise extraction of fine details regarding kidney lesions, as well as achieving overall contextual awareness using cross-attention and Gamma-modulated feature refinement to address the drawbacks of existing approaches. Through extensive validation utilizing a large set of clinical datasets, GDT-SwinKid achieved better performance through segmentation and classification, obtaining Dice coefficients as high as 0.95, with AUC values approaching 0.99, when compared to leading transformers and convolutional models. An absolute improvement of 5–9% in Dice coefficient compared to conventional U-Net and Swin Transformer baselines, and an increase in AUC-ROC values approaching 0.99, outperforming existing hybrid and transformer-based methods on the same CT kidney dataset. The inclusion of explainable attention maps and deep supervision provides increased trust and accountability while enabling the rapid and robust integration of GDT-SwinKid into diagnostic pipelines for kidney imaging. GDT-SwinKid combines statistical sensitivity, hierarchical attention and clinical transparency to provide a new standard for automated kidney lesion analysis and to increase the reliability and use of newly developed AI techniques in renal imaging.
Evaluation of toxic metal contamination and source allocation in agricultural soils of Chhattisgarh, India: multivariate and artificial network approaches
Long-term adherence to adapted physical activity in patients with chronic inflammatory arthritis: Insights from a longitudinal observational study
Adapted physical activity is recommended as a non-pharmacological strategy for the management of chronic inflammatory arthritis. However, the long-term adherence of patients to physical activity programs remains a major challenge in clinical practice. The present study aimed to assess adherence to World Health Organization (WHO) physical activity recommendations and to identify factors associated with long-term adherence in patients with chronic inflammatory arthritis (CIA). This longitudinal observational study included patients with spondyloarthritis, psoriatic arthritis, or rheumatoid arthritis who were referred to a specialized center for adapted physical activity between 2019 and 2021. Baseline demographic, clinical, and treatment-related variables were collected during the initial medical consultation. In August 2023, participants were contacted by telephone to assess their level of physical activity using a standardized questionnaire. Adherence was defined as meeting the WHO recommendations of at least 150 minutes of moderate-intensity physical activity or 75 minutes of vigorous physical activity per week. Among the 78 patients initially included, 61 responded to the follow-up assessment. Of these, 41 participants (67.2%) met the WHO physical activity recommendations. In univariate analyses, no clinical or treatment-related variables were significantly associated with long-term adherence. Male sex was the only factor significantly associated with adherence, with men showing a higher likelihood of meeting the WHO recommendations after adjustment for underlying pathology (OR 7.85, 95% CI 1.60–38.42). In this real-world cohort of patients with chronic inflammatory arthritis, approximately two-thirds of respondents maintained a level of physical activity consistent with WHO recommendations more than one year after the initial consultation. Clinical disease characteristics were not associated with adherence, while sex appeared to influence long-term engagement in physical activity. These findings highlight the need for further studies to better understand determinants of long-term adherence to adapted physical activity in this population.
The Developmental Emergence of Tonic and Phasic REM Sleep in Rats
REM sleep is composed of two substates—phasic and tonic—that differ in their behavioral, sensory, and electrophysiological features. Although these substates are well characterized in adults, their developmental trajectory remains unclear. Here, we examined the development of tonic and phasic REM in rats of either sex from Postnatal Day (P)12–P24, spanning a period of rapid corticothalamic development. We recorded local field potentials and single units from the primary motor cortex (M1), together with high-speed video and electromyographic recordings of the nuchal muscle. Periods of behavioral quiescence along with high delta power indicated NREM sleep, whereas periods of sustained muscle atonia and low delta power indicated REM sleep. At P16, M1 theta oscillations first appeared, and the delay to the first twitch increased, revealing the start of a distinct twitch-free portion of REM sleep. Motivated by this, we divided REM sleep into phasic and tonic periods, with and without twitching, respectively. Spiking activity and gamma power were consistently higher during phasic REM. At P20, phasic REM also showed faster theta oscillations than tonic REM. At P24, tonic REM was accompanied by a distinct alpha oscillation. These results show that the features distinguishing the two REM substates appear sequentially across development, revealing a progressive differentiation of REM sleep into tonic and phasic periods, a developmental refinement that may support increasingly complex forms of sleep-dependent plasticity.
Micro-fragmentation of four coral species towards the assembly of modular 3D structures for restoration
How climate change shapes global systemic risk transmission: A complex network approach
This study investigates the dynamic impact of climate change performance on extreme tail risk transmission across global financial markets. Based on the “Too Extreme to Fail” conceptual framework, we propose a cascading failure network model using QRNN-∆CoVaR and QRNN-∆CoES to quantify the domino effect of tail risk propagation. The model captures tail dependencies and reveals how variations in climate governance performance modulate the intensity and pathways of risk contagion. Our main analysis utilizes daily market data from 1998 to 2024, aligned with the Climate Change Performance Index data from 2007 through a matched time window approach. The findings demonstrate that climate-sensitive factors significantly amplify systemic vulnerabilities, whereas superior climate governance serves as a critical risk buffer during periods of extreme volatility. Empirical results reveal significant spatial and temporal heterogeneity in risk contribution, with certain regions exhibiting higher sensitivity and momentum during major financial crises. Backtesting results confirm that our proposed nonlinear framework provides superior accuracy in quantifying global systemic risks compared to traditional linear methods, offering a robust tool for climate-integrated financial stability monitoring.
Retraction Note: Mathematical analysis of isothermal study of reverse roll coating using Micropolar fluid
Evidence for mirror self-recognition in beluga whales (Delphinapterus leucas)
Tests of mirror self-recognition (MSR) have provided behavioral evidence of a high level of self-awareness in humans, chimpanzees, bonobos, orangutans, gorillas, bottlenose dolphins, Asian elephants, magpies, and to some extent in the cleaner wrasse. We conducted the standard mirror test with a social group of four beluga whales ( Delphinapterus leucas), one subadult and three adult females, housed together at the New York Aquarium of the Wildlife Conservation Society. We exposed the whales to a two-way plexiglass mirror and a transparent control surface during baseline and post-mirror sessions and recorded and analyzed their behavioral responses in the three conditions. Two of the four whales, the subadult and her mother, exhibited a rich suite of self-directed behaviors at the mirror and subsequent mark and control sham-mark tests were conducted with both whales. The adult female showed mark-directed behavior at the mirror and passed one of the initial mark tests in a series of tests given. The self-directed behaviors exhibited by both whales and mark directed behavior by the adult female provides evidence for the capacity of MSR in the beluga whale.
Enhancing amharic news classification through ontology-based feature fusion and logistic regression
Prediabetes, diabetes, and the risk of progression to diabetes among working population in Beijing-the Tongren HealthCare Study
Introduction Our retrospective cohort study (3–9 years) describes the progression and regression patterns among working adults aged 18–65 with normoglycemia, prediabetes, and diabetes, providing evidence for diabetes prevention and management strategies in this population. Research design and methods 15,765 subjects received baseline examinations between 2014 and 2018, with 14,623 (92.7%) completing follow-up, representing a follow-up period of 3–9 years for analysis. Chi-square tests were used to compare prediabetes and diabetes incidence, as well as rates of glycemic normalization among different age groups and genders in working populations. Cox proportional hazard regression models were used to estimate hazard ratios (HRs) and 95% confidence interval (CI). Results Among participants with baseline normoglycemia, 23.9% progressed to prediabetes, and 2.3% developed diabetes. Among prediabetic individuals, 28.0% progressed to diabetes, and 18.7% reverted to normoglycemia. Younger adults (18–40 years) exhibited significantly lower progression rates from normoglycemia to both prediabetes and diabetes compared to middle-aged adults (40–65 years) (prediabetes: 15.8% vs. 37.8%; diabetes: 1.2% vs. 4.2%; P < 0.001). Cox models revealed that young prediabetic individuals had a significantly higher risk of developing diabetes than middle-aged prediabetic individuals, showing a pronounced age-dependent risk pattern: prediabetic individuals aged 18–40 had an adjusted hazard ratio (HR) of 22.1 (95% CI: 14.9–32.7), compared to HR = 9.12 (7.45–11.2) in those aged 40–65. Conclusions Prediabetes among working-age adults, particularly in younger individuals (18–40 years), carries an exceptionally elevated risk of progression to diabetes. The observed sex disparities in progression among young adults highlight the need for age- and sex-specific prevention strategies.