Anisotropic Interfacial Force Field for Interfaces of Water with Hexagonal Boron Nitride

This study introduces an anisotropic interfacial potential that provides an accurate description of the van der Waals (vdW) interactions between water and hexagonal boron nitride (h-BN) at their interface. Benchmarked against the strongly constrained and appropriately normed (SCAN) functional, the developed force field demonstrates remarkable consistency with reference data sets, including binding energy curves and sliding potential energy surfaces for various configurations involving a water molecule adsorbed atop the h-BN surface. These findings highlight the significant improvement achieved by the developed force field in empirically describing the anisotropic vdW interactions of the water/h-BN heterointerfaces. Utilizing this anisotropic force field, molecular dynamics simulations demonstrate that atomically-flat pristine h-BN exhibits inherent hydrophobicity. However, when atomic-step surface roughness is introduced, the wettability of h-BN undergoes a significant change, leading to a hydrophilic nature. The calculated water contact angle (WCA) for the roughened h-BN surface is approximately 64{\deg}, which closely aligns with experimental WCA values ranging from 52{\deg} to 67{\deg}. These findings indicate the high probability of the presence of atomic steps on the surfaces of experimental h-BN samples, emphasizing the need for further experimental verification. The development of the anisotropic interfacial force field for accurately describing interactions at the water/h-BN heterointerfaces is a significant advancement in accurately simulating the wettability of two-dimensional (2D) materials, offering a reliable tool for studying the dynamic and transport properties of water at these interfaces, with implications for materials science and nanotechnology.Comment: 22 pages, 5 figure

Effectiveness of early rhythm control in improving clinical outcomes in patients with atrial fibrillation:a systematic review and meta-analysis

BackgroundCurrent guidelines recommend rhythm control for improving symptoms and quality of life in symptomatic patients with atrial fibrillation (AF). However, the long-term prognostic outcomes of rhythm control compared with rate control are still inconclusive. In this meta-analysis, we aimed to assess the effects of early rhythm control compared with rate control on clinical outcomes in newly diagnosed AF patients.MethodsWe systematically searched the PubMed and Embase databases up to August 2022 for randomized and observational studies reporting the associations of early rhythm control (defined as within 12 months of AF diagnosis) with effectiveness outcomes. The primary outcome was a composite of death, stroke, admission to hospital for heart failure (HF), or acute coronary syndrome (ACS). Hazard ratios (HRs) and 95% confidence intervals (CIs) from each study were pooled using a random-effects model, complemented with an inverse variance heterogeneity or quality effects model.ResultsA total of 8 studies involving 447,202 AF patients were included, and 23.5% of participants underwent an early rhythm-control therapy. In the pooled analysis using the random-effects model, compared with rate control, the early rhythm-control strategy was significantly associated with reductions in the primary composite outcome (HR = 0.88, 95% CI: 0.86-0.89) and secondary outcomes, including stroke or systemic embolism (HR = 0.78, 95% CI: 0.71-0.85), ischemic stroke (HR = 0.81, 95% CI: 0.69-0.94), cardiovascular death (HR = 0.83, 95% CI: 0.70-0.99), HF hospitalization (HR = 0.90, 95% CI: 0.88-0.92), and ACS (HR = 0.86, 95% CI: 0.76-0.98). Reanalyses using the inverse variance heterogeneity or quality effects model yielded similar results.ConclusionsOur current meta-analysis suggested that early initiation of rhythm control treatment was associated with improved adverse effectiveness outcomes in patients who had been diagnosed with AF within 1 year.RegistrationThe study protocol was registered to PROSPERO (CRD42021295405)

Label-free immunoassay for porcine circovirus type 2 based on excessively tilted fiber grating modified with staphylococcal protein A

Using excessively tilted fiber grating (Ex-TFG) inscribed in standard single mode fiber, we developed a novel label-free immunoassay for specific detection of porcine circovirus type 2 (PCV2), which is a minim animal virus. Staphylococcal protein A (SPA) was used to modify the silanized fiber surface thus forming a SPA layer, which would greatly enhance the proportion of anti-PCV2 monoclonal antibody (MAb) bioactivity, thus improving the effectiveness of specific adsorption and binding events between anti-PCV2 MAbs and PCV2 antigens. Immunoassay experiments were carried out by monitoring the resonance wavelength shift of the proposed sensor under different PCV2 titer levels. Anti-PCV2 MAbs were thoroughly dissociated from the SPA layer by treatment with urea, and recombined to the SPA layer on the sensor surface for repeated immunoassay of PCV2. The specificity of the immunosensor was inspected by detecting porcine reproductive and respiratory syndrome virus (PRRSV) first, and PCV2 subsequently. The results showed a limit of detection (LOD) for the PCV2 immunosensor of ~9.371TCID50/mL, for a saturation value of ~4.801×103TCID50/mL, with good repeatability and excellent specificity

Modelling and Prediction of Random Delays in NCSs Using Double-Chain HMMs

This paper is concerned with the modelling and prediction of random delays in networked control systems. The stochastic distribution of the random delay in the current sampling period is assumed to be affected by the network state in the current sampling period as well as the random delay in the previous sampling period. Based on this assumption, the double-chain hidden Markov model (DCHMM) is proposed in this paper to model the delays. There are two Markov chains in this model. One is the hidden Markov chain which consists of the network states and the other is the observable Markov chain which consists of the delays. Moreover, the delays are also affected by the hidden network states, which constructs the DCHMM-based delay model. The initialization and optimization problems of the model parameters are solved by using the segmental K-mean clustering algorithm and the expectation maximization algorithm, respectively. Based on the model, the prediction of the controller-to-actuator (CA) delay in the current sampling period is obtained. The prediction can be used to design a controller to compensate the CA delay in the future research. Some comparative experiments are carried out to demonstrate the effectiveness and superiority of the proposed method

Detection of False Data Injection Attacks in Smart Grids Based on Expectation Maximization

The secure operation of smart grids is closely linked to state estimates that accurately reflect the physical characteristics of the grid. However, well-designed false data injection attacks (FDIAs) can manipulate the process of state estimation by injecting malicious data into the measurement data while bypassing the detection of the security system, ultimately causing the results of state estimation to deviate from secure values. Since FDIAs tampering with the measurement data of some buses will lead to error offset, this paper proposes an attack-detection algorithm based on statistical learning according to the different characteristic parameters of measurement error before and after tampering. In order to detect and classify false data from the measurement data, in this paper, we report the model establishment and estimation of error parameters for the tampered measurement data by combining the the k-means++ algorithm with the expectation maximization (EM) algorithm. At the same time, we located and recorded the bus that the attacker attempted to tamper with. In order to verify the feasibility of the algorithm proposed in this paper, the IEEE 5-bus standard test system and the IEEE 14-bus standard test system were used for simulation analysis. Numerical examples demonstrate that the combined use of the two algorithms can decrease the detection time to less than 0.011883 s and correctly locate the false data with a probability of more than 95%

Human FGF-21 Is a Substrate of Fibroblast Activation Protein.

FGF-21 is a key regulator of metabolism and potential drug candidate for the treatment of type II diabetes and other metabolic disorders. However, the half-life of active, circulating, human FGF-21 has recently been shown to be limited in mice and monkeys by a proteolytic cleavage between P171 and S172. Here, we show that fibroblast activation protein is the enzyme responsible for this proteolysis by demonstrating that purified FAP cleaves human FGF-21 at this site in vitro, and that an FAP-specific inhibitor, ARI-3099, blocks the activity in mouse, monkey and human plasma and prolongs the half-life of circulating human FGF-21 in mice. Mouse FGF-21, however, lacks the FAP cleavage site and is not cleaved by FAP. These findings indicate FAP may function in the regulation of metabolism and that FAP inhibitors may prove useful in the treatment of diabetes and metabolic disorders in humans, but pre-clinical proof of concept studies in rodents will be problematic