Interaction Coefficients Research Articles

Aromatic vs alicyclic: Hydrophobicity of the ionic liquid on protein stability and fibril formation

Ionic liquids (ILs) are environment-friendly solvents with potential applications in biochemical reactions and as pharmaceutical ingredients. They are used to stabilize proteins and act as anti-aggregation agents in which the hydrophobic effect of ILs is suggested to have a primary role. To understand the differences in the effect of aromatic and alicyclic cations of ILs on thermal stability and fibrillation propensity of proteins, the effect of two alicyclic (butyl-1-methylpyrrolidinium and butyl-1-methylpiperidinium) and two aromatic (butyl-4-methylpyridinium and butyl-3-methylimidazolium) ILs on lysozyme was investigated by spectroscopy, densitometry, and molecular dynamics simulation. The results suggest that all the ILs switch the fibrillation pathway from nucleation-independent to nucleation-dependent mechanism at ≥200 mM. The fibrillation is slowed down by 28–35-fold in 500 mM of ILs, exceptionally pyrrolidinium-IL delays the fibrillation by ∼70-fold. All the ILs destabilize the protein during thermal denaturation. Though in all cases the unfolding is enthalpy-driven, pyridinium-IL at higher concentrations exhibits an entropy-driven unfolding effect. The volumetric analysis shows a higher thermal expansion coefficient for Lyz in pyridinium-IL which might be due to the increased internal cavity volume of the protein upon binding of the IL. These results corroborate with the computational studies that the preferential interaction coefficient of aromatic-ILs is more, particularly at a higher concentration (200 mM). Four preferable binding sites for ILs on the protein could be identified that includes the amyloidogenic regions, β-hairpin and the hydrophobic cluster 5. Also, the ILs are found to preferably interact with the protein through their alicyclic/aromatic ring and alkyl sidechain. These results suggest that the binding of ILs to the amyloidogenic region inhibits the fibril formation which has a similar effect for both alicyclic and aromatic ILs whereas aromatic-ILs show a larger destabilization effect than alicyclic-ILs at higher concentrations.

An empiric on geopolitical risk and the tourism–economic growth nexus

Purpose This study empirically examines the moderating role of geopolitical risk on the tourism–economic growth nexus by applying a recent geopolitical risk indicator developed by Caldara and Iacoviello (2022) in a cross-country panel data growth model context for a sample of 24 countries.Design/methodology/approach A Dummy Variable Least Squares panel data model, nonparametric covariance matrix estimator and SYS-GMM estimation techniques are employed for the analysis. The authors capture the GPR moderating effect by disaggregating the cross-country sample according to low versus high country GPR score and through a GPR interaction coefficient. Several controls are included in the models such as gross fixed capital formation and—consistent with Barro (1990)—government consumption. Trade openness is used to account for the export-led growth effect. In line with neoclassical growth theory (e.g. Barro, 1991), the authors also include the real interest rate, to account for policy makers' commitment to macroeconomic stability, financial depth, as a proxy for financial development, population growth and the level of secondary school education. The authors also control for unobserved country-specific and time-invariant effects.Findings The research finds that the interaction term of geopolitical risk significantly contributes to the predictive ability of the regression and provides empirical evidence that confirms that only in low geopolitical risk countries international tourism positively and significantly contributes to economic growth. Important theoretical and policy implications flow from these findings.Originality/value The study not only contributes to advancing academic knowledge on the tourism–growth nexus, it also has impact beyond academia. Many countries have in the past pursued and many continue to pursue, tourism specialization and/or tourism-led growth strategies based on the theoretically well-established and empirically validated positive link between inbound tourism and economic growth. The findings alert policy makers in such countries to the significant moderating role that geopolitical risk plays in affecting the above-mentioned relationship and to the importance of prioritizing geopolitical stability as a policy precursor for the successful implementation of such strategies.

Spatio-Temporal-Attention-Based Vehicle Trajectory Prediction Considering Multi-Vehicle Interaction in Mixed Traffic Flow

As a link connecting the environmental perception system and the decision-making system, accurate obstacle trajectory prediction provides a reliable guarantee of correct decision-making by autonomous vehicles. Oriented toward a mixed human-driven and machine-driven traffic environment, a vehicle trajectory prediction algorithm based on an encoding–decoding framework composed of a multiple-attention mechanism is proposed. Firstly, a directed graph is used to describe vehicle–vehicle motion dependencies. Then, by calculating the repulsive force between vehicles using a priori edge information based on the artificial potential field theory, vehicle–vehicle interaction coefficients are extracted via a graph attention mechanism (GAT). Subsequently, after concatenating the vehicle–vehicle interaction feature with the encoded vehicle trajectory vectors, a spatio-temporal attention mechanism is applied to determine the coupling relationship of hidden vectors. Finally, the predicted trajectory is generated by a gated recurrent unit (GRU) decoder. The training and evaluation of the proposed model were conducted on the NGSIM public dataset. The test results demonstrated that compared with existing baseline models, our approach has fewer prediction errors and better robustness. In addition, introducing artificial potential fields into the attention mechanism causes the model to have better interpretability.

Prediction model of interfacial tension of H2/H2O and (N2 + H2)/H2O systems using the linear gradient theory in combination with PR-EOS

Interfacial tension (IFT) is an important physical parameter to study the behavior of gas–liquid two-phase in microstructures. In this paper, the IFT values for hydrogen-containing binary and ternary systems are investigated, and the IFT values are measured and predicted for the H2/H2O and (N2 + H2)/H2O systems in the temperature range from 278.6 K to 368.2 K. A simplified linear gradient theory (LGT) coupled with a modified PR-EoS is utilized to solve the densities and IFT values of the H2/H2O and (N2 + H2)/H2O systems over the temperature range. In the process, a new empirical correlation equation is also proposed to determine the interaction coefficients of the ternary systems. The IFT values of the above binary/ternary systems decrease with increasing temperature over the whole temperature range, and the comparison with the experimental values shows that the predictive model exhibits predictive instability near the lowest and highest temperatures, and the overall average deviation of the predicted values from the experimental values is 1.02 %, which is in good agreement. The applicability of the above prediction models in pressure environments has also been briefly explained during the process of the study. The results of this study present for the first time the prediction model of interfacial tension for hydrogen-containing binary and ternary systems, which provides a theoretical basis for the study of gas–liquid two-phase behavior of hydrogen-containing systems.

The efficiency of using integrated control agents to manage the Dubas bug Ommatissus binotatus lybicus De Berg (Homoptera: Tropiducidae) in Karbala Governorate

This study aims to rate the efficiency of using chemical pesticides, bio-fungicides, and the interaction between them against the nymphal and adult stages of the Dubas insect (Ommatissus binotatus lybicus De Berg). Anyhow, the laboratory and field results of the pesticides significantly affected the treated phases. The field results of the treatments were significantly increased after around two weeks, as for the average percentage of death in the treated nymphs. It reached 89.29-75.03-72.5-71, respectively. The results of the interaction treatments between fungal biocides (Beauveria bassiana, Paecilomyces sp, Beauveria bassiana, Metarhizium anisopliae, Metarhizium anisopliae, and Paecilomyces sp) were significantly increased after two weeks of treatment, as the average percentage of death of the treated nymphs reached 63.95 -77.19 -63.5, respectively. The laboratory results of the treatments were significant for the pesticides used after nine days of treatment, as the average percentage of death of the adults reached 86.01 - 39.25 -32.16 -36.81, respectively. Moreover, the interaction coefficients between the fungal biocides mentioned in the paragraph above were significant after nine days of treatment. The average percentage of treated adults' deaths reached 38.13 -39.64 37.57 - 86.02 to treat the interaction between the chemical pesticide Actara and B. bassiana. The chemical pesticide Actara excelled in the field experiment, as it gave the highest percentage of mortality when used at concentrations of 1-1.5 g/L after two weeks of treatment. The average percentage of nymphs killed was 89.29.
 As for the laboratory experiment results, the chemical pesticide Actara was also superior, as it gave the highest mortality rate for adult females. The average mortality rate for adult females reached 86.01 after nine days of treatment compared to the rest of the treatments using other pesticides.

Vehicle Trajectory Prediction Based on Graph Convolutional Networks in Connected Vehicle Environment

Vehicle trajectory prediction is an important research basis for the decision making and path planning of the intelligent and connected vehicle. In the connected vehicle environment, vehicles share information and drive cooperatively, and the intelligent and connected vehicles are able to obtain more accurate and rich perception information, which provides a data basis for accurate prediction of vehicle trajectories. However, attaining accurate and effective vehicle trajectory predictions poses technical challenges due to insufficient extraction of vehicular spatial–temporal interaction features. In this paper, we propose a vehicle trajectory prediction model based on graph convolutional neural network (GCN) in a connected vehicle environment. Specifically, using the driving scene information obtained by the intelligent and connected vehicle, the spatial graph and temporal graph are constructed based on the spatial interaction coefficient (SIC) and self-attention mechanism, respectively. Then, the graph data are entered into the interaction extraction module, and the spatial interaction features and temporal interaction features are extracted separately using the graph convolutional networks, which are fused to obtain the spatial–temporal interaction information. Finally, the interaction features are learned based on the convolutional neural networks to output the future trajectory information of all vehicles in the scene by one forward operation rather than a step-by-step process. The ablation experiment results show that the method proposed in this study to model the spatiotemporal interaction among vehicles based on SIC and self-attention mechanism reduces the prediction error by 5% and 12%, respectively. The results from the model comparison experiment show that the proposed method engenders an 8% improvement in prediction accuracy over the state-of-the-art solution, providing technical and theoretical support for trajectory prediction research of intelligent and connected vehicles.

On the Fourier Analysis of the Einstein–Klein–Gordon System: Growth and Decay of the Fourier Constants

We consider the (1+3)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(1+3)$$\\end{document}-dimensional Einstein equations with negative cosmological constant coupled to a spherically symmetric, massless scalar field and study perturbations around the anti-de Sitter spacetime. We derive the resonant systems, pick out vanishing secular terms and discuss issues related to small divisors. Most importantly, we rigorously establish (sharp, in most of the cases) asymptotic behaviour for all the interaction coefficients. The latter is based on uniform estimates for the eigenfunctions associated to the linearized operator as well as on some oscillatory integrals.

A new heat transfer model based on non-equilibrium theory for annular flow condensation-part Ⅱ: Ternary zeotropic mixtures

In the past decades, attention has been paid to the flowcondensation heat transfer characteristics of zeotropic mixtures, but most of the research has been carried out on binary mixtures, with very few studies performed on multicomponent mixtures that contain ternary and more components. The condensing process of multicomponent mixtures is significantly more complex since there are interactions between the components except for mixture effects, and these mechanisms are still unclear. Therefore, existing prediction methods either being computationally complicated or fail to accurately describe the condensation characteristics of multicomponent zeotropic mixtures. Focused on this issue, a new heat transfer correlation for ternary zeotropic mixtures in annular flow condensation was proposed based on our previously developed heat transfer model for binary zeotropic mixtures. The ternary mixture is regarded as a combination of two binary mixtures by analyzing the relationship of the condensation rate difference between components and the concentration gradient, and binary interaction coefficients are introduced to estimate the relative contribution of the two independent concentration gradients in the condensing vapor of ternary zeotropic mixtures. The newly developed correlation is simple and agrees well with the condensation heat transfer coefficients of ternary zeotropic mixtures, with an average relative deviation (MRD) of -1.6 %, an average absolute relative deviation (MARD) of 13.9 %, and 90.2 % of the data points falling within ±30 % deviation band for a total of 307 annular flow data.

Sex / gender bias on the Face‐Name Associative Memory Exam in clinically‐normal older adults

AbstractBackgroundMany studies of memory in older adults report a sex difference, usually with female participants scoring slightly better than males. These sex differences may be partially due to the content of the tests, with some content being more memorable to males or females (https://doi.org/10.1093/ARCLIN/ACAC102). We investigated whether male and female older adults show differences on the Face‐Name Associative Memory Exam (FNAME‐12, designed to detect subtle Alzheimer’s disease‐related memory impairment). Specifically, do they show a bias towards remembering the stimuli that match their own sex?MethodParticipants (n = 432, see Table 1) in ‘Insight 46’, a sub‐study of the MRC National Survey of Health and Development (British 1946 birth cohort), completed FNAME‐12 (version A) at age ∼73 years. Stimuli comprise six male and six female faces (presented in alternating order), with names and occupations. Recall is tested four times after various delay intervals. Four total scores were calculated across all recall trials: female names; female occupations; male names; male occupations. We fitted multivariable regression models using Generalised Estimating Equations to investigate effects of item type (name/occupation), item gender (male/female) and participant sex (male/female), and interactions between these variables, controlling for age, education, socioeconomic position and prospectively‐collected childhood cognitive ability.ResultWomen outperformed men (coefficient averaged across the four conditions = 2.2 points [95% CIs 1.3, 3.1], p<0.001) (Fig1; Fig2). This difference was exaggerated for female stimuli (interaction coefficient = 2.0 [1.4, 2.6], p<0.001), and female stimuli occupations (3‐way interaction coefficient = 0.8 [‐0.0, 1.6] p = 0.050) i.e. women scored better than men even on male names, whereas men were particularly disadvantaged at recalling information associated with female stimuli, especially their occupations (Fig2).ConclusionWe found evidence of a gender bias on FNAME‐12 among male participants, where their recall was worse for female stimuli compared to male stimuli, which may partially explain their poorer performance on this test. Such biases should be considered when interpreting sex differences on memory tests. Further analyses before the conference will investigate longitudinal FNAME‐12 performance over ∼2.4 years, to explore whether this memory bias remains constant, or whether it may be a marker of memory decline (potentially reflecting increased reliance on the relatability of test content).

The triple-drug combination DBDx enhances the antitumor efficacy of PD-1 antibody associated with Treg modulation.

This study investigated the antitumor efficacy of programmed cell death protein-1 (PD-1) antibody and DBDx, a triple-drug combination of dipyridamole, bestatin, and dexamethasone, and their related immunomodulation. Mouse melanoma B16, mouse Lewis lung carcinoma, and mouse breast carcinoma 4T1 were used for evaluating the in vivo therapeutic efficacy of DBDx, PD-1 antibody, and their combination. The peripheral blood and tumor tissues of 4T1 tumor-bearing mice were collected to analyze regulatory T cells and measured using flow cytometry. The combination of PD-1 antibody and DBDx enhanced the therapeutic efficacy against B16 melanoma. The suppression of tumor growth by PD-1 antibody and DBDx was more significant than that by anti-PD-1 monotherapy. The tumor growth inhibition rates of PD-1 antibody, DBDx, and their combination were 54.0%, 72.4%, and 83.1%, respectively, suggesting a synergistic effect as determined by the coefficient of drug interaction. No significant changes were found in the body weights in all the above groups, indicating that the treated mice tolerated the applied drug doses. Similarly, enhanced therapeutic efficacy of the PD-1 antibody and DBDx combination was observed in murine Lewis lung carcinoma and 4T1 breast cancer models. In 4T1 breast cancer-bearing mice, the immunotherapy-related changes in lymphocytes in peripheral blood and tumor microenvironment were evaluated with flow cytometry. Compared with anti-PD-1 monotherapy, peripheral blood and tumor-infiltrating lymphocytes were found a lower ratio of regulatory T cell (Treg) subset cells and a higher ratio of CD8+/Treg cells. The combination of PD-1 antibody and DBDx could achieve enhanced therapeutic antitumor efficacy than anti-PD-1 monotherapy, suggesting potential for using the triple-drug combination DBDx in cancer immunotherapy.

Effect of government revenue on economic growth of sub-Saharan Africa: Does institutional quality matter?

Following the approval of sustainable development goals at the global level, the link between fiscal policy, institutional quality, and economic growth has attracted special attention in economic literature. This study scrutinizes the effect of government revenue-institutional quality interaction on the economic growth of 43 Sub-Saharan Africa countries for the period of 2012-2022. Methodology-wise, the study employed the System Generalized Method of Moment (SGMM) to analyze the panel data gained from dependable data sources; the World Development Indicator and the Heritage Economic Freedom Index. The novelty of this study emanates from the estimation technique designated and the introduction of revenue-institutional quality into the economic growth model of SSA. The result of the study reveals that government revenue adversely affects economic growth while institutional quality positively enhances economic growth before interacting with each other. However, the interactive coefficient of government revenue and economic growth positively affected the real GDP growth rate of SSA countries over the study periods. Precisely, before interacting with institutional quality, a percentage change in government revenue, keeping all other things constant, leads to a 0.0866 percent decline in economic growth while it marks a 0.2329 percent upsurge in economic growth in the presence of institutional quality. The result of the study further shows that government revenue promotes the economic growth of the region when combined with institutional quality. On the other hand, foreign direct investment and openness to trade were the key sources of economic growth whereas the population growth rate adversely impacted economic growth in SSA countries. The policy implication of the study is that SSA needs to strengthen government revenue management. Further, the finding of the study implies that SSA countries need to improve institutional quality through promoting efficiency of the regulatory quality and the size of the SSA governments. In addition to this, the fast real GDP growth rate of SSA countries demands improved institutional quality indicators such as the rule of law and extended access to the open market.

Fiscal policy and economic growth in Sub-Saharan Africa: Do governance indicators matter?

This study investigated the linkage between fiscal policy-governance indicators interaction and economic growth in 36 Sub-Saharan Africa (SSA) countries from the periods of 2011-2021 inclusive. The study employed two-step system Generalized Method of Moment (GMM) estimation technique due to its practical relevance in panel data analysis. The data obtained from World Bank and World governance indicator was checked for unit root through the help of Im Pesaran Shin and Levin-Lin-Chu unit-root tests, and the result revealed that data was stationary and safe for further analysis. The result of the study also presented that direct economic effect of fiscal policy is negative and significant in SSA countries. However, the interaction of fiscal policy with governance indicators has positive and significant effect on economic growth. Accordingly, before interacting with governance indicators, a percentage change in fiscal policy leads to a 0.20 percent decline in economic growth of SSA countries. Contrary to this, the interactive coefficient of fiscal policy and government effectiveness (0.019) and interactive coefficient of fiscal policy and corruption control (0.0046) are found to be positive and significant. Further, the finding of the study revealed that fiscal policy-voice and accountability interaction coefficient (0.011) and interactive coefficient of fiscal policy-regulatory qualities (0.014) are positively and significantly affecting economic growth of SSA countries. The policy implication is that policy makers in SSA countries should encourage economic policies that improve government effectiveness, strong corruption control, clean public services and better regulatory qualities.

A semi-exact analytical study of the phase diagram of the two dimensional extended Holstein-Hubbard model

An extended Holstein-Hubbard model is studied in two dimensions analytically for both weak and strong correlations. A series of unitary transformations followed by an averaging with a fully-generalized phonon state is performed to obtain an effective electronic Hamiltonian. For weak correlation, the Hartree-Fock approximation is employed and for strong correlation, the electronic Hamiltonian is mapped on to an effective t−J model which is solved using the Gutzwiller approximation and the Zubarev technique. The variation of the effective Coulomb interaction strength is studied for different electron-electron and electron-phonon interaction coefficients and the SDW-CDW phase diagram is plotted using the Mott-Hubbard metallicity criteria. A metallic regime is shown to exist at the SDW-CDW cross-over region and the width of the metallic phase is found to be broader than the result obtained by numerical diagonalization method. The metallic phase is also found to be wider in two dimensions than in one dimension.

Development of a Multicomponent Adsorption Isotherm Equation and Its Validation by Modeling.

Researchers have made significant efforts over the past few decades to understand adsorption by developing various simple adsorption isotherm models. However, though many contaminants usually occur as multicomponent mixtures in nature, multicomponent adsorption isotherms have received limited attention and remain an area of inadequate research. We have presented here in a new multicomponent adsorption isotherm model, named the Jeppu Amrutha Manipal Multicomponent (JAMM) isotherm, that can alleviate this problem. We first developed the JAMM multicomponent isotherm using our experimental data sets of arsenic and fluoride competitive adsorption on activated carbon. We then tested the JAMM multicomponent isotherm for a case study of cadmium and zinc competitive adsorption. Next, we further assessed the JAMM isotherm using another competitive adsorption case study of copper and chromium. Through extensive validation studies and error analysis, the JAMM isotherm was able to demonstrate its efficacy in predicting the adsorption behavior in several multicomponent adsorption systems accurately. The main advantage of JAMM isotherm over other multicomponent isotherms is that it utilizes and leverages the single-component adsorption parameters to simulate multicomponent isotherms. The proposed JAMM analytical isotherm model furthermore incorporates the interaction between the components, a mole fraction parameter, and a heterogeneity index, providing a more comprehensive modeling framework for multicomponent adsorption. The mole fraction term was introduced for the distribution of adsorption sites based on the relative number of molecules of each component. An additional term for interaction coefficient was introduced for the representation of interactions. During the validation of JAMM with three experimental case studies with negligible, small, and high competition systems of adsorbates, impressive predictions were exhibited, with the average normalized absolute percentage error as 6.05% and average R2 as 0.86, highlighting the model's robustness, versatility, and reliability. We propose that the new JAMM isotherm modeling framework might profoundly help in chemical engineering, environmental engineering, and materials science applications by providing a potent tool for analyzing and predicting multicomponent adsorption systems.

Evolutionary game analysis of behaviour strategy for UAV swarm in communication‐constrained environments

AbstractIn the process of unmanned aerial vehicles (UAV) cluster coordination, communication obstruction may cause situational misjudgement and increase the risk of decision‐making errors. Therefore, based on evolutionary game theory, this research aimed to study the behaviour decision‐making selection problem in the process of UAV swarm missions in limited communication networks. First, the evolution process of the UAV swarm confrontation game is analyzed and the decision‐making risks associated with the communication network of the UAV swarm during coordination, which are caused by the complex confrontation environment are identified. Second, the decision‐making risks and mechanisms of UAV swarm communication is discussed and an evolutionary game mathematical model for the cooperative strategy of a UAV swarm under communication constraints is established. Finally, the influence of various key parameters is simulated and analyzed, such as the initial strategy probability of both parties, amount of platform information data, information analysis and utilization ability, and information interaction coefficient, on the evolution of the UAV swarm strategy. This research provides a theoretical basis and feasible reference for the improvement of UAV mission decision response mechanisms and system model construction, which have important theoretical value and practical significance.

Criteria of Distribution Transitions in Dispersed Multiphase Systems Based on an Extended Lattice Model.

Dispersed multiphase systems are ubiquitous in biological systems, energy industries, and medical science. The distribution transition of the dispersed phase is critical to the properties and functions of the multiphase systems, among which the agglomeration, adsorption, and extraction processes are of most significance due to their impact on the colloidal stability, interface modification, and particle synthesis. To reveal fundamental correlations between the macroscopic particle distributions and the microscopic interactions, general thermodynamic models of the dispersed multiphase systems are needed. Here, based on Meyer's model, which is restricted to binary isotropic mixtures, we propose a novel extended lattice model that can be applied to multicomponent anisotropic mixtures with interfaces considered. For agglomeration, adsorption, and extraction processes, the approximate free energy differences between the initial distribution and the final distribution are obtained. Based on the minimum free energy principle, the above free energy differences are used to derive three criteria for the prediction of the preferable distribution of the system with given interparticle interaction potentials. While the quasi-uniform number density assumption is still required for all the previous lattice models, the long-range interactions neglected by previous lattice models are incorporated. The validity of our model and criteria is verified by many-body dissipative particle dynamics (mDPD) simulations. By tuning the interaction coefficients between mDPD particles, the simulated distribution transitions for all the agglomeration, adsorption, and extraction cases perfectly match the predictions from the three criteria. The good agreement between the theoretical predictions and the mDPD simulation results shows the great potential of our model for applications in various dispersed multiphase systems.

Association of liver fibrosis with cognitive decline in Parkinson’s disease

BackgroundCognitive decline is a common but variable non-motor manifestation of Parkinson’s disease. Chronic liver disease contributes to dementia, but its impact on cognitive performance in Parkinson’s disease is unknown. We assessed the effect of liver fibrosis on cognition in Parkinson’s disease. MethodsWe conducted a retrospective cohort study using data from the Parkinson’s Progression Markers Initiative. Our exposure was liver fibrosis at baseline, based on the validated Fibrosis-4 score. Our primary outcome was the Montreal Cognitive Assessment, and additional outcome measures were the Symbol Digit Modalities Test, the Benton Judgement of Line Orientation, the Letter-Number Sequencing Test, and the Modified Semantic Fluency Test. We used linear regression models to assess the relationship between liver fibrosis and scores on cognitive assessments at baseline and linear mixed models to evaluate the association between baseline Fibrosis-4 score with changes in each cognitive test over five years. Models were adjusted for demographics, comorbidities, and alcohol use. ResultsWe included 409 participants (mean age 61, 40 % women). There was no significant association between liver fibrosis and baseline performance on any of the cognitive assessments in adjusted models. However, over the subsequent five year period, liver fibrosis was associated with more rapid decline in scores on the Montreal Cognitive Assessment (interaction coefficient, −0.07; 95 % CI, −0.12, −0.02), the Symbol Digit Modalities Test, the Benton Judgement of Line Orientation, and the Modified Semantic Fluency Test. ConclusionIn people with Parkinson’s disease, the presence of comorbid liver fibrosis was associated with more rapid decline across multiple cognitive domains.

Wagner interaction coefficient between nitrogen and cobalt in liquid steel

Wagner interaction coefficient between nitrogen and cobalt in liquid steel

Tax competition among local governments: Evidence from the spillovers of location-based tax incentives in China

This paper exploits the tax incentives in China's Great Western Development (GWD) Program to identify tax competition among local governments. The reform reduces the statutory corporate income tax rate in GWD regions. Using this exogenous tax reduction as a quasi-natural experiment and adopting a difference-in-differences approach, we find that the reform in GWD regions leads to a 1.6328 percentage point decline in the effective corporate income tax rate and a 0.658 percentage point decline in the macro tax burden in non-GWD counties adjacent to the policy boundary. The results suggest that there are strategic interactions as well as a race to the bottom among counties in their tax setting. The results are stable across a series of robustness tests such as excluding other mechanisms of tax rate interaction and excluding the impact of other incentive policies in the GWD Program. Combined with the tax reduction effect in border GWD counties, we find that the tax interaction coefficient for the effective corporate income tax rate and macro tax burden between GWD and non-GWD counties along the border are 0.8718 and 0.3553, respectively. This reflects the intense tax competition among local governments in China. In addition, we demonstrate that tax competition mainly exists between geographically adjacent counties, as no such spillover effects are observed in non-GWD counties far from the policy boundary.

Influence of Brain Metastasis on Analgesia-Related Outcomes in Patients with Lung and Breast Cancers Treated with Naldemedine: A Propensity Score-Matched Analysis.

Naldemedine is structurally designed to prevent passage across the blood-brain barrier (BBB), resulting in the attenuation of opioid-induced constipation without interfering with the analgesic effects of opioids. However, the influence of brain metastasis (BM), as one indicator of BBB disruption, on the analgesic effects of opioids in patients treated with naldemedine remains unclear. To examine whether the analgesic effects of opioids following naldemedine treatment are lower in patients with BM than in those without BM, we surveyed inpatients with lung and breast cancers treated with naldemedine at Fujita Health University Hospital between April 2017 and March 2022. Changes in the numeric rating scale (NRS) scores, morphine milligram equivalents (MMEs), and the number of rescues were assessed as analgesia-related outcomes during the first 7 days of naldemedine treatment in patients with or without BM, matched by the propensity score. In total, 172 patients were enrolled. After propensity-score matching, 30 patients with BM and 60 patients without BM were included in the analysis. Changes in NRS scores, MMEs, and the number of rescues did not differ between patients with and without BM. In the linear mixed-effects model, the coefficient of interaction between patients with or without BM and the days for each outcome was not statistically significant. BM does not influence the analgesic effect of opioids in patients with lung and breast cancers treated with naldemedine. Naldemedine may be useful for treating BM.