The classical nature of nuclear spin noise near clock transitions of Bi donors in silicon

Whether a quantum bath can be approximated as classical noise is a fundamental issue in central spin decoherence and also of practical importance in designing noise-resilient quantum control. Spin qubits based on bismuth donors in silicon have tunable interactions with nuclear spin baths and are first-order insensitive to magnetic noise at so-called clock-transitions (CTs). This system is therefore ideal for studying the quantum/classical nature of nuclear spin baths since the qubit-bath interaction strength determines the back-action on the baths and hence the adequacy of a classical noise model. We develop a Gaussian noise model with noise correlations determined by quantum calculations and compare the classical noise approximation to the full quantum bath theory. We experimentally test our model through dynamical decoupling sequence of up to 128 pulses, finding good agreement with simulations and measuring electron spin coherence times approaching one second - notably using natural silicon. Our theoretical and experimental study demonstrates that the noise from a nuclear spin bath is analogous to classical Gaussian noise if the back-action of the qubit on the bath is small compared to the internal bath dynamics, as is the case close to CTs. However, far from the CTs, the back-action of the central spin on the bath is such that the quantum model is required to accurately model spin decoherence.Comment: 5 pages, 3 figure

Uncovering many-body correlations in nanoscale nuclear spin baths by central spin decoherence

Many-body correlations can yield key insights into the nature of interacting systems; however, detecting them is often very challenging in many-particle physics, especially in nanoscale systems. Here, taking a phosphorus donor electron spin in a natural-abundance 29Si nuclear spin bath as our model system, we discover both theoretically and experimentally that many-body correlations in nanoscale nuclear spin baths produce identifiable signatures in the decoherence of the central spin under multiple-pulse dynamical decoupling control. We find that when the number of decoupling -pulses is odd, central spin decoherence is primarily driven by second-order nuclear spin correlations (pairwise flip-flop processes). In contrast, when the number of -pulses is even, fourth-order nuclear spin correlations (diagonal interaction renormalized pairwise flip-flop processes) are principally responsible for the central spin decoherence. Many-body correlations of different orders can thus be selectively detected by central spin decoherence under different dynamical decoupling controls, providing a useful approach to probing many-body processes in nanoscale nuclear spin baths

Computational Multiscale Modeling and Characterization of Piezoresistivity in Fuzzy Fiber Reinforced Polymer Composites

In this paper, the piezoresistive response (i.e. the change of resistance under the application of strain) of polymer composites reinforced by a novel material known as fuzzy fibers is characterized by using single tow piezoresistive fragmentation tests and modeled by using a 3D computational multiscale model based on the finite element analysis. In the characterization work, the fuzzy fiber tow is embedded in a dog-bone specimen infused by epoxy, with resistance and displacement measured simultaneously to obtain its piezoresistive response. An approximately linear and stable piezoresistive response is observed within the fuzzy fiber tow region yielding gauge factors on average of 0.14. Using a 3D multiscale mechanical–electrostatic coupled code and explicitly accounting for the local piezoresistive response of the anisotropic interphase region, the piezoresistive responses of the overall fuzzy fiber reinforced polymer composites are studied parametrically in an effort to provide qualitative guidance for the manufacture of fuzzy fiber reinforced polymer composites. It is observed from the model that the fuzzy fiber reinforced polymer composites with cylindrically orthotropic carbon nanotube interphase regions are dominated by the electrical tunneling effect between the nanotubes and can yield very large gauge factors while fuzzy fibers with randomly oriented carbon nanotubes in the interphase region yield smaller gauge factors as the material is electrically saturated by the carbon nanotubes. Finally, the modeling efforts provide plausible reasons for the observed small gauge factors in experiments in the form of a combination of high concentration randomly oriented carbon nanotube interphase regions separated by sparse nanotube regions along the fuzzy fiber length

On reducing mesh delay for peer-to-peer live streaming

Peer-to-peer (P2P) technology has emerged as a promising scalable solution for live streaming to large group. In this paper, we address the design of overlay which achieves low source-to-peer delay, is robust to user churn, accommodates of asymmetric and diverse uplink bandwidth, and continuously improves based on existing user pool. A natural choice is the use of mesh, where each peer is served by multiple parents. Since the peer delay in a mesh depends on its longest path through its parents, we study how to optimize such delay while meeting a certain streaming rate requirement. We first formulate the minimum delay mesh problem and show that it is NP-hard. Then we propose a centralized heuristic based on complete knowledge which serves as our benchmark and optimal solution for all the other schemes under comparison. Our heuristic makes use of the concept of power in network given by the ratio of throughput and delay. By maximizing the network power, our heuristic achieves very low delay. We then propose a simple distributed algorithm where peers select their parents based on the power concept. The algorithm makes continuous improvement on delay until some minimum delay is reached. Simulation results show that our distributed protocol performs close to the centralized one, and substantially outperforms traditional and state-of-the-art approaches

A DNA aptamer for binding and inhibition of DNA methyltransferase 1.

DNA methyltransferases (DNMTs) are enzymes responsible for establishing and maintaining DNA methylation in cells. DNMT inhibition is actively pursued in cancer treatment, dominantly through the formation of irreversible covalent complexes between small molecular compounds and DNMTs that suffers from low efficacy and high cytotoxicity, as well as no selectivity towards different DNMTs. Herein, we discover aptamers against the maintenance DNA methyltransferase, DNMT1, by coupling Asymmetrical Flow Field-Flow Fractionation (AF4) with Systematic Evolution of Ligands by EXponential enrichment (SELEX). One of the identified aptamers, Apt. #9, contains a stem-loop structure, and can displace the hemi-methylated DNA duplex, the native substrate of DNMT1, off the protein on sub-micromolar scale, leading for effective enzymatic inhibition. Apt. #9 shows no inhibition nor binding activity towards two de novo DNMTs, DNMT3A and DNMT3B. Intriguingly, it can enter cancer cells with over-expression of DNMT1, colocalize with DNMT1 inside the nuclei, and inhibit the activity of DNMT1 in cells. This study opens the possibility of exploring the aptameric DNMT inhibitors being a new cancer therapeutic approach, by modulating DNMT activity selectively through reversible interaction. The aptamers could also be valuable tools for study of the functions of DNMTs and the related epigenetic mechanisms

Effectiveness and safety of transcatheter aortic valve replacement in elderly people with severe aortic stenosis with different types of heart failure.

Impaired left ventricular function is an independent predictor of adverse clinical outcomes in patients with aortic stenosis. The aim of this study is to evaluate the short-term changes of echocardiographic parameters, New York Heart Association (NYHA) class and B-type natriuretic peptide (BNP) level and adverse events amongst patients with heart failure (HF) after transcatheter aortic valve replacement (TAVR) procedure. This was a retrospective cohort study conducted at affiliated Yantai Yuhuangding Hospital of Qingdao University between September 2017 and September 2022. TAVR cases were stratified into three groups [heart failure with reduced ejection fraction (HFrEF), heart failure with mildly reduced ejection fraction (HFmrEF), heart failure with preserved ejection fraction (HFpEF)] by left ventricular ejection fraction (LVEF). Baseline characteristics, changes in echocardiographic parameters (1 week and 1 month), BNP (1 month), and NYHA class (6 months) post-TAVR were compared across the three groups. Meanwhile, we observed the adverse events of the patients after TAVR. A total of 96 patients were included, of whom 15 (15.6%) had HFrEF, 15 (15.6%) had HFmrEF, and 66 (68.8%) had HFpEF. Compared to the HFpEF subgroup, patients in the HFrEF subgroup were younger (p < 0.05), and with a higher BNP (p < 0.05). The left ventricular end-diastolic dimension (LVEDD) in HFrEF group decreased significantly after TAVR. HFmrEF and HFrEF patients showed significant improvements in LVEF after TAVR. The pulmonary artery systolic pressure (PASP), aortic valve peak gradient (AVPG) and aortic valve peak gradient (V ) decreased significantly 1 month after TAVR in all three groups compared to the baseline (all p < 0.05). BNP significantly reduced in HFrEF group compared to HFpEF patients after TAVR (p < 0.05). The majority of patients experienced an improvement at least one NYHA class in all three groups 6 months post-TAVR. There is no significant increase in the risk of adverse events in the HFrEF group. Patients who underwent TAVR achieved significant improvements in BNP, NYHA class, LVEDD, LVEF, and PASP across the three HF classes, with a more rapid and pronounced improvement in the HFrEF and HFmrEF groups. Complication rates were low in the different HF groups. There is no significant increase in the risk of periprocedural complications in the HFrEF and HFmrEF groups. [Abstract copyright: © 2023. The Author(s).

The Effects of Indoor Environmental Exposures on Pediatric Asthma: A Discrete Event Simulation Model

Background: In the United States, asthma is the most common chronic disease of childhood across all socioeconomic classes and is the most frequent cause of hospitalization among children. Asthma exacerbations have been associated with exposure to residential indoor environmental stressors such as allergens and air pollutants as well as numerous additional factors. Simulation modeling is a valuable tool that can be used to evaluate interventions for complex multifactorial diseases such as asthma but in spite of its flexibility and applicability, modeling applications in either environmental exposures or asthma have been limited to date. Methods: We designed a discrete event simulation model to study the effect of environmental factors on asthma exacerbations in school-age children living in low-income multi-family housing. Model outcomes include asthma symptoms, medication use, hospitalizations, and emergency room visits. Environmental factors were linked to percent predicted forced expiratory volume in 1 second (FEV1%), which in turn was linked to risk equations for each outcome. Exposures affecting FEV1% included indoor and outdoor sources of $NO_2$ and $PM_{2.5}$, cockroach allergen, and dampness as a proxy for mold. Results: Model design parameters and equations are described in detail. We evaluated the model by simulating 50,000 children over 10 years and showed that pollutant concentrations and health outcome rates are comparable to values reported in the literature. In an application example, we simulated what would happen if the kitchen and bathroom exhaust fans were improved for the entire cohort, and showed reductions in pollutant concentrations and healthcare utilization rates. Conclusions: We describe the design and evaluation of a discrete event simulation model of pediatric asthma for children living in low-income multi-family housing. Our model simulates the effect of environmental factors (combustion pollutants and allergens), medication compliance, seasonality, and medical history on asthma outcomes (symptom-days, medication use, hospitalizations, and emergency room visits). The model can be used to evaluate building interventions and green building construction practices on pollutant concentrations, energy savings, and asthma healthcare utilization costs, and demonstrates the value of a simulation approach for studying complex diseases such as asthma