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Publisher Correction: A fault-tolerant neutral-atom architecture for universal quantum computation
County-level surveillance for the American dog tick (Dermacentor variabilis) and Rickettsia species in Kentucky
The smart sensors improving the world’s biggest cities
Deep vein thrombosis incidence after sequential Low-molecular-weight heparin and Rivaroxaban versus no prophylaxis in posterior cruciate ligament avulsion fractures
Study on deformation characteristics and support technology of roadway in deep complex stress field
An interpretable machine learning model using routine clinical data for early recurrence prediction in hepatocellular carcinoma
Effect of heat generated root canal filling techniques on bond strength of NeoSealer Flo BC, AH Plus BC and BioRoot RCS to root dentin
Growth differentiation factor-15 improves long-term mortality risk prediction beyond the GRACE 2.0 score after acute coronary syndrome
Abstract This study examined whether Growth Differentiation Factor-15 (GDF-15) and echocardiographic measures of systolic left ventricular function improve intermediate- and long-term mortality risk prediction beyond the guideline-endorsed GRACE 2.0 score after Acute Coronary Syndrome (ACS). 751 ACS patients were included. GDF-15, left ventricular ejection fraction (LVEF), and global longitudinal strain (GLS) were added stepwise to GRACE 2.0 in Cox regression models. Discriminative performance was assessed using the C-index for all-cause mortality at 3 years and long-term up to a median follow-up of 6.4 years. Mean age was 64.4 years, and 77% were men. There were 40 deaths at 3 years and 104 deaths by end-of-study. GDF-15 outperformed GRACE 2.0 for 3-year mortality prediction (time-dependent AUC 0.82 [95% CI 0.75–0.89] vs. 0.76 [95% CI 0.67–0.84]; P = 0.001). Adding GDF-15 to GRACE 2.0 improved long-term prognostic accuracy, increasing the C-index from 0.74 (95% CI 0.69–0.79) to 0.76 (95% CI 0.70–0.81). LVEF and GLS improved the C-index in the order of 0.01 when added to GRACE 2.0. GDF-15 meaningfully improved discrimination of all-cause death, both at intermediate- and long-term follow-up, when added on top of GRACE 2.0 whereas LVEF and GLS both provided minor improvements.
Urban heat stress and adolescent health: impacts and adaptation strategies in Indonesia
Tribological performance of UV treated nanodiamond reinforced polyurethane nanocomposites through Taguchi and machine learning technique
Abstract The objective of the current work is to investigate the tribological properties of nanodiamond (ND) reinforced polyurethane (PU) composite and examine the impact of UV irradiation on these properties. The experiments were optimized using the Taguchi design and ANOVA, while machine learning (ML) techniques were applied to predict the tribological performance. The study uses the Taguchi design of experiments (DOE) with an L27 orthogonal array to assess the effects of sliding distance (500–1500 m), sliding speed (100–300 rpm), load (10–30 N), composition (pure PU, 0.2 wt% ND, 0.5 wt% ND), and UV irradiation time (0, 200, 400 h) on wear rate and coefficient of friction (COF). The results show that incorporating 0.5 wt% ND significantly enhances PU performance, reducing the wear rate to 0.018 × 10⁻³ g/m and achieving a COF of 0.253 under optimal conditions of 30 N load, 0 h of UV irradiation, and 300 rpm. ANOVA reveals that composition and UV irradiation time are the most influential factors, contributing 52.52% and 35.46% to wear rate, and 22.18% and 50.57% to COF, respectively. Machine learning models, including Support Vector Regression (SVR), linear regression, and XGBoost, were used for performance prediction, with XGBoost providing the highest accuracy (R²/MSE = 0.99/0.000003 for wear rate, 0.98/0.00023 for COF). The findings highlight the potential for developing enhanced polyurethane nanocomposites with improved wear resistance for applications in industries such as automotive and aerospace, under varying tribological and environmental conditions.
Particle collisions cast light on how matter forms from seemingly empty space
A high-performance training-free pipeline for robust random telegraph signal characterization via adaptive wavelet-based denoising and Bayesian digitization methods
Facile sol–gel fabrication of MnOx/Graphite nanostructured electrodes for sustainable produced water treatment
Green and chemical synthesis of PEGylated ginger gold nanoparticles for neuro-nanomedicine applications
Probabilities of two alleles being identity by state at unobserved loci predicted by observed loci in cattle populations
Abstract This study aimed to investigate the prediction accuracy of the probability that alleles at unobserved loci are identity-by-state (IBS) using genome-based measures based on observed single nucleotide polymorphisms (SNPs). We performed a simulation analysis assumed to represent a cattle population with simulated and real SNP genotypes. The genome-based measures were based on the inbreeding coefficients in an individual and the additive relationship coefficients between two individuals. Reference values were defined as the probability that the alleles at unobserved SNPs were IBS. Reference values were predicted using both pedigree-based and genome-based measures with tens of thousands of SNPs. Prediction accuracy was calculated as the correlation coefficient between reference and predicted values. Our results showed that the inbreeding and additive relationship coefficients based on SNP-by-SNP with an allele frequency fixed at 0.5 and the coefficients based on the homozygous-segment with homozygous by descent and with run of homozygosity > 4 Mbp long demonstrated consistent high prediction accuracy in both simulated and real cattle populations. Our results also showed that the correlation coefficients of these measures were higher than those of pedigree-based measures. Our results indicate that genome-based measures utilizing observed SNPs can offer a more accurate prediction of IBS relationships at unobserved loci than pedigree-based measures in cattle populations.
Local infrared stimulation modulates spontaneous cortical slow wave dynamics in anesthetized rats
Abstract Cortical slow waves are hallmark oscillations of deep sleep and certain anesthetic conditions, yet the neurobiological mechanisms controlling their dynamics remain incompletely understood. Here, we investigated the effects of local near-infrared (NIR) stimulation on slow-wave activity in ketamine/xylazine-anesthetized rats. Using a silicon-based multimodal optrode, we simultaneously delivered NIR light and recorded local field potentials (LFPs) and multi-unit activity (MUA) across cortical layers in the primary somatosensory (S1Tr) and parietal association (PtA) cortices. NIR stimulation induces local tissue heating, resulting in reproducible and reversible changes in the properties of slow waves. Specifically, up-state durations were shortened, down-states prolonged, and MUA amplitudes during up-states increased, with steeper slopes at state transitions, indicative of enhanced neuronal synchronization. LFP amplitude and spectral changes varied across cortical regions: PtA exhibited increased slow wave (0.5–2 Hz) and high delta (2–4 Hz) band activity, while S1Tr showed a trend toward reduction. Our findings demonstrate that local infrared stimulation can reliably modulate cortical slow-wave dynamics, likely through temperature-mediated changes in neuronal excitability. This approach provides a minimally invasive method for precise, local manipulation of cortical network activity and offers new insights into the biophysical regulation of slow oscillations.