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Measuring correlation and entanglement between molecular orbitals on a trapped-ion quantum computer
Abstract Quantifying correlation and entanglement between molecular orbitals can elucidate the role of quantum effects in strongly correlated reaction processes. However, accurately storing the wavefunction for a classical computation of those quantities can be prohibitive. Here we use the Quantinuum H1-1 trapped-ion quantum computer to calculate von Neumann entropies which quantify the orbital correlation and entanglement in a strongly correlated molecular system relevant to lithium-ion batteries (vinylene carbonate interacting with an O2 molecule). As shown in previous works, fermionic superselection rules decrease correlations and reduce measurement overheads for constructing orbital reduced density matrices. Taking into account superselection rules we further reduce the number of measurements by finding commuting sets of Pauli operators. Using low overhead noise reduction techniques, we calculate von Neumann entropies in excellent agreement with noiseless benchmarks, indicating that correlations and entanglement between molecular orbitals can be accurately estimated from a quantum computation. Our results show that the one-orbital entanglement vanishes unless opposite-spin open shell configurations are present in the wavefunction.
Green PEGylated-Sily@ZnFe2O4 nanocomposites for amelioration of ROS and DNA damage in rat liver
Deep learning and digital twin integration for structural damage detection in ancient pagodas
Curcumin supplementation improves the clinical outcomes of patients with diabetes and atherosclerotic cardiovascular risk
Abstract Atherosclerotic cardiovascular diseases (ASCVD) significantly contribute to global mortality, especially in type 2 diabetes mellitus (T2DM), necessitating effective preventive strategies. Curcumin is proposed to lower blood pressure, glucose level, and improve lipid profiles as an adjunctive treatment. The study aimed to assess the safety and efficacy of Curcumin supplementation on clinical outcomes and ASCVD risk of T2DM patients. Seventy-two diabetic patients with an ASCVD risk score of ≥ 5% were randomly assigned to Curcumin group (500 mg Turmeric curcumin® thrice daily + conventional therapy) or Control group (conventional therapy only). Curcumin significantly reduced SBP and DBP (P ≤ 0.001 and P = 0.020, respectively) and improved ASCVD risk classification (P = 0.004). LDL-C (P = 0.024), TNF-α (P = 0.044), and MDA (P = 0.028) levels decreased, while HDL-C increased (P = 0.024) versus control. No significant differences were found between groups regarding HbA1c, FBG, TC or TG (P > 0.05). Mild adverse effects were reported, including nausea (13.9%), headache (11.1%), yellow stool (11.1%), and diarrhea (5.6%). It is concluded that Curcumin improves ASCVD risk classification, lowers SBP, DBP, LDL-C, TNF-alpha, and MDA, increases HDL-C, and is well tolerated with minor adverse effects, without impacting on BMI, HR, HbA1c, FBG, TC, or TG.
RETRACTED ARTICLE: PGRP-S promotes hepatocellular carcinoma progression via MAPK/ERK pathway by interaction with TTC1
Optimized summary-statistic-based single-cell eQTL meta-analysis
Abstract The identification of expression quantitative trait loci (eQTLs) holds great potential to improve the interpretation of disease-associated genetic variation. As many such disease-associated variants act in a context-, tissue- or even cell-type-specific manner, single-cell RNA-sequencing (scRNA-seq) data is uniquely suitable for identifying the specific cell type or context in which these genetic variants act. However, due to the limited sample sizes in single-cell studies, discovery of cell-type-specific eQTLs is now limited. To improve power to detect such eQTLs, large-scale joint analyses are needed. These are however, complicated by privacy constraints due to sharing of genotype data and the measurement and technical variety across different scRNA-seq datasets as a result of differences in mRNA capture efficiency, experimental protocols, and sequencing strategies. A solution to these issues is a federated weighted meta-analysis (WMA) approach in which summary statistics are integrated using dataset-specific weights. Here, we compare different strategies and provide best practice recommendations for eQTL WMA across scRNA-seq datasets.
Polygenic insight identifies precision biomarkers decoding protein catabolism and autophagy pathways in obstructive sleep apnea
Current clinical profiles for Chinese hemodialysis patients
Learning behavior aware features across spaces for improved 3D human motion prediction
Aggregation potency and proinflammatory effects of SARS-CoV-2 proteins
Abstract Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, is primarily known as a respiratory disease. The continued study of the disease has shown that long-term COVID-19 symptoms include persisting effects of the virus on the brain when the infection is over, possibly even leading to neurodegeneration. However, the exact mechanisms of nervous system damage induced by SARS-CoV-2 are still unclear. In this study, we focused on two possibly shared pathways of SARS-CoV-2-induced neural dysfunction and neurodegeneration: protein aggregation, which is associated with impaired protein clearance, and inflammatory responses, which involve a hyper-active immune status. We observed distinct expression and distribution patterns of ten SARS-CoV-2 proteins in the two cell lines, meanwhile forming aggregation puncta and inducing pro-inflammatory responses. We found that the ER stress was induced and that the autophagy-lysosome pathway was inhibited upon viral protein expression. Boosting autophagy function attenuated protein aggregation, suggesting that modulation of autophagy might be a valid strategy for inhibiting cytotoxic effects of SARS-CoV- 2 proteins. Our study provides potential explanations of SARS-CoV-2-induced cell damage, based on shared cellular mechanisms and furthermore, suggests that modulation of proteostasis may serve as therapeutic strategies for preventing long-lasting SARS-CoV-2 cytotoxic effects.
A lightweight end to end traffic congestion detection framework using HRTNet on the Qinghai Tibet plateau
Individual stability of single-channel EEG measures over one year in healthy adults
Abstract The clinical applicability of electroencephalography (EEG) relies on the reliability and temporal stability of its measures. While the reliability of linear EEG measures is well established, the long-term stability of both linear and nonlinear measures at the individual level, as well as interindividual variability, remains underexplored. This study evaluated the one-year stability of EEG absolute band powers (theta, alpha, beta, and gamma) and nonlinear measures (Higuchi’s fractal dimension, Lempel–Ziv complexity, detrended fluctuation analysis, and in-phase Matrix Profile) across 12 monthly EEG recordings in nine healthy males aged 26–49. Intraclass correlation coefficients (ICCs) indicated excellent reliability across all measures, although beta power showed slightly reduced ICCs in temporal regions and gamma power demonstrated lower reliability in peripheral sites. At the individual level, nonlinear measures showed greater temporal stability than EEG band powers. Although a few individuals, particularly in band power measures, exhibited annual fluctuations comparable to or exceeding interindividual variability, most participants demonstrated consistent EEG profiles over time. These findings support the use of nonlinear EEG measures in longitudinal research and indicate their potential for developing personalized EEG-based neural biomarkers. They also highlight the importance of estimating expected individual variability when designing individualized monitoring approaches, as high reliability at the group level does not preclude substantial within-subject variability in some cases.
Photometric light curve analysis of three overcontact binary systems: ATO J255.8159+16.8821, CRTS J034336.4+264312, and NSVS 2669503
Abstract Multi-band photometric observations of three contact binaries (ATO J255.8159+16.8821, CRTS J034336.4+264312, and NSVS 2669503) were carried out using the 1.88 m telescope at the Kottamia Astronomical Observatory (KAO) in Egypt. New times of minima for all three systems have been calculated. In particular, for NSVS 2669503, we performed an $$O-C$$ analysis to investigate period variations. The results indicate a decreasing orbital period, with a rate of $$\textrm{d}P/\textrm{d}t \approx 1.85 \times 10^{-10}$$ days yr $$\phantom{0}^{-1}$$ . Analysis using the Wilson-Devinney (W-D) program revealed that all three systems are A-subtype contact binaries with mass ratios (q) of 0.47, 0.34, and 0.28, respectively. The results showed that ATO J255.8159+16.8821 exhibits the O’Connell effect, while the other two systems (CRTS J034336.4+264312 and NSVS 2669503) have a symmetric light curve. The fill-out factors of the systems were determined to be 0.204, 0.157, and 0.069, respectively, indicating all three systems are shallow contact systems. To understand their evolutionary status, mass-luminosity and mass-radius diagrams were plotted. These diagrams indicate that the primary components of the three systems are main sequence stars, whereas the less massive stars have evolved beyond the main sequence. The dynamical evolution of the systems is also discussed.