Simple and accurate expressions for radial distribution functions of hard disk and hard sphere fluids

Analytical expressions for radial distribution function (RDF) are of critical importance for various applications, such as development of the perturbation theories for equilibrium properties. Theoretically, RDF expressions for odd-dimensional fluids can be obtained by solving the Percus-Yevick integral equations. But for even-dimensional cases, such as the hard disk (2D) fluid, analytical expressions are infeasible. The only 2D RDF is a heuristic expression proposed by Yuste et al. (J. Chem. Phys. 99, 2020,1993), which approximates the 2D RDF with an interpolation of the RDFs for hard-rod (1D) and hard-sphere (3D) fluids and provides acceptable estimations for an intermediate and low density range. In this work, we employ a simple and empirical expression for the 2D RDF and the 3D RDF based on the approach proposed by Trokhymchuk et al. for the 3D RDF (J. Chem. Phys., 123, 024501, 2005). The parameters are determined in such a way that the final RDF expressions are thermodynamically consistent, namely the pressure constraint and the isothermal compressibility constraint are both satisfied. The new RDFs for the 2D and 3D hard spheres are highly accurate for the entire density range up to the first-order phase transition points. The predictions of the first coordination numbers are consistent with simulation results for 3D fluid. Finally, by using the 2D RDF with a primitive second-order perturbation theory, the pressure-volume-temperature relation and vapor-liquid equilibrium are calculated for the 2D Lennard-Jones fluid. Comparisons with the simulation data show promising results.Comment: 15 pages, 11 Figure

Tourism Flows Prediction based on an Improved Grey GM(1,1) Model

AbstractThis study analyzes the factors affecting the tourist flow. These factors include tourism resources, traffic conditions and so on. In recent years, the grey forecasting model has achieved good prediction accuracy with limited data and has been widely used in various research fields. However, the grey forecasting model still have some potential problems that need to be improved, such as applicate range and prediction accuracy. It is found that original data and background value are main factors affecting the accuracy of the proposed model's application. To solve these problems, this study develops a optimization model for the GM(1,1) model problem which includes optimization of initial and background values. In order to reduce errors caused by back-ground values, the “new information prior using” principle is followed, and a liner function is dopted in the construe of background. Numerical examples verified that the simulation and prediction accuracy of the short-term forcasts is significantly increased. As a result, the newly improved model yields a high prediction capability

Distance-based novelty detection model for identifying individuals at risk of developing Alzheimer's disease

Introduction: Novelty detection (ND, also known as one-class classification) is a machine learning technique used to identify patterns that are typical of the majority class and can discriminate deviations as novelties. In the context of Alzheimer's disease (AD), ND could be employed to detect abnormal or atypical behavior that may indicate early signs of cognitive decline or the presence of the disease. To date, few research studies have used ND to discriminate the risk of developing AD and mild cognitive impairment (MCI) from healthy controls (HC). Methods: In this work, two distinct cohorts with highly heterogeneous data, derived from the Australian Imaging Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing project and the Fujian Medical University Union Hospital (FMUUH) China, were employed. An innovative framework with built-in easily interpretable ND models constructed solely on HC data was introduced along with proposing a strategy of distance to boundary (DtB) to detect MCI and AD. Subsequently, a web-based graphical user interface (GUI) that incorporates the proposed framework was developed for non-technical stakeholders. Results: Our experimental results indicate that the best overall performance of detecting AD individuals in AIBL and FMUUH datasets was obtained by using the Mixture of Gaussian-based ND algorithm applied to single modality, with an AUC of 0.8757 and 0.9443, a sensitivity of 96.79% and 89.09%, and a specificity of 89.63% and 90.92%, respectively. Discussion: The GUI offers an interactive platform to aid stakeholders in making diagnoses of MCI and AD, enabling streamlined decision-making processes. More importantly, the proposed DtB strategy could visually and quantitatively identify individuals at risk of developing AD

Identification and Functional Evaluation of miR-4633-5p as a Biomarker and Tumor Suppressor in Metastatic Melanoma

Background/Aims: Sinonasal mucosal melanoma (SMM) is a rare but extremely aggressive disease. Interestingly, however, as lethal as SMM, a few patients could survive for over 5 years without metastasis. However, biomarkers for metastatic SMM are lacking. Methods: Laser-capture microdissection combined with microRNA microarray and RT-qPCR was performed in formalin-fixed paraffin-embedded tissue samples from SMM patients whose follow-up studies were carried out in parallel. In vitro cell proliferation and invasion assays, gelatin zymography, western blot analysis and RT-qPCR were performed in melanoma cell lines. Results: In the discovery stage, miR-4633-5p expressed differentially in sinonasal mucosal melanoma patients with short and long disease-specific survival. Subsequent large-sample validation revealed that expression of miR-4633-5p was lower in metastatic SMM than in non-metastatic patients (P< 0.001). Moreover, miR-4633-5plow was able to identify metastatic SMM with specificity of 100% (5/5) and sensitivity of 87.5% (21/24). Multivariate analysis further pinpointed miR-4633-5p as an independent marker for metastasis (relative risk: 54.22, P< 0.001). In vitro, overexpression of miR-4633-5p suppressed the growth and invasiveness of melanoma cells through inhibiting activation of Akt pathway and secretion of MMP2, while knockdown of miR-4633-5p reversed the inhibitory effects. Conclusion: Our findings underpin miR-4633-5p as a predictive biomarker in metastatic SMM and a pivotal tumor suppressor that negatively regulates the invasive growth of melanoma cells. Quantitative detection of miR-4633-5p can diagnostically predict the risk of metastasis in SMM patients, which, in turn, may lead to more personalized treatment with better prognosis

Microbial-environmental interactions reveal the evaluation of fermentation time on the nutrient properties of soybean meal

Microbial fermentation techniques are often used to improve their quality, where the keys are fermentation strains and fermentation time. This study studied the interaction between microbiota and environmental (or nutritional) factors and microbiota at different fermentation times to determine the most appropriate time, using lactic acid bacteria as fermentation strains. It can be concluded that fermentation improved the nutritional value of soybean meals. In the early stages of fermentation, debris in soybean meal highly proliferated and destabilized the microbial community, while pH and nutritional conditions played an important role in helping its stabilization. In addition, we must pay attention to the interspecific interactions of microorganisms, which makes it easy to understand how the microbial community maintains community stability. A 4-day fermentation of soybean meal with Lactobacillus is recommended