Lattice ellipsoidal statistical BGK model for thermal non-equilibrium flows

A thermal lattice Boltzmann model is constructed on the basis of the ellipsoidal statistical Bhatnagar-Gross-Krook (ES-BGK) collision operator via the Hermite moment representation. The resulting lattice ES-BGK model uses a single distribution function and features an adjustable Prandtl number. Numerical simulations show that using a moderate discrete velocity set, this model can accurately recover steady and transient solutions of the ES-BGK equation in the slip-flow and early transition regimes in the small Mach number limit that is typical of microscale problems of practical interest. In the transition regime in particular, comparisons with numerical solutions of the ES-BGK model, direct Monte Carlo and low-variance deviational Monte Carlo simulations show good accuracy for values of the Knudsen number up to approximately 0:5. On the other hand, highly non-equilibrium phenomena characterized by high Mach numbers, such as viscous heating and force-driven Poiseuille flow for large values of the driving force, are more difficult to capture quantitatively in the transition regime using discretizations that have been chosen with computational efficiency in mind such as the one used here, although improved accuracy is observed as the number of discrete velocities is increased

Network Algebraization and Port Relationship for Power-Electronic-Dominated Power Systems

Different from the quasi-static network in the traditional power system, the dynamic network in the power-electronic-dominated power system should be considered due to rapid response of converters' controls. In this paper, a nonlinear differential-algebraic model framework is established with algebraic equations for dynamic electrical networks and differential equations for the (source) nodes, by generalizing the Kron reduction. The internal and terminal voltages of source nodes including converters are chosen as ports of nodes and networks. Correspondingly, the impact of dynamic network becomes clear, namely, it serves as a voltage divider and generates the terminal voltage based on the internal voltage of the sources instantaneously, even when the dynamics of inductance are included. With this simplest model, the roles of both nodes and the network become apparent.Simulations verify the proposed model framework in the modified 9-bus system.Comment: 4 pages, 6 figure

Multiscale lattice Boltzmann approach to modeling gas flows

For multiscale gas flows, kinetic-continuum hybrid method is usually used to balance the computational accuracy and efficiency. However, the kinetic-continuum coupling is not straightforward since the coupled methods are based on different theoretical frameworks. In particular, it is not easy to recover the non-equilibrium information required by the kinetic method which is lost by the continuum model at the coupling interface. Therefore, we present a multiscale lattice Boltzmann (LB) method which deploys high-order LB models in highly rarefied flow regions and low-order ones in less rarefied regions. Since this multiscale approach is based on the same theoretical framework, the coupling precess becomes simple. The non-equilibrium information will not be lost at the interface as low-order LB models can also retain this information. The simulation results confirm that the present method can achieve model accuracy with reduced computational cost

Establishment and application of a TaqMan-based multiplex real-time PCR for simultaneous detection of three porcine diarrhea viruses

IntroductionPorcine viral diarrhea is a common clinical disease, which results in high mortality and economic losses in the pig industry. Porcine epidemic diarrhea virus (PEDV), porcine rotavirus (PoRV), and porcine deltacoronavirus (PDCoV) are important diarrhea viruses in pig herds. The similarities of their clinical symptoms and pathological changes make it difficult to distinguish these three viruses clinically. Therefore, there is a need for a highly sensitive and specific method to simultaneously detect and differentiate these viruses.MethodsA multiplex real-time PCR assay using TaqMan probes was developed to simultaneously detect PEDV, PoRV, and PDCoV. To assess the efficacy of the established assay, 30 clinical samples with diarrhea symptoms were used to compare the results obtained from the multiplex real-time PCR assay with those obtained from commercial singleplex real-time PCR kit. Importantly, a total of 4,800 diarrhea samples were tested and analyzed to validate the utility of the assay.ResultsThis multiplex real-time PCR assay showed high sensitivity, specificity, and excellent repeatability with a detection limit of 1 × 102 copies/μL. Comparing the results of the commercial singleplex real-time PCR kit and the multiplex real-time PCR method for detecting PEDV, PoRV, and PDCoV, there was complete agreement between the two approaches. Clinical data revealed single infection rates of 6.56% for PEDV, 21.69% for PoRV, and 6.65% for PDCoV. The co-infection rates were 11.83% for PEDV + PoRV, 0.29% for PEDV + PDCoV, 5.71% for PoRV + PDCoV, and 1.29% for PEDV + PDCoV + PoRV, respectively.DiscussionThe multiplex real-time PCR method established in this study is a valuable diagnostic tool for simultaneously differentiating PEDV, PoRV, and PDCoV. This method is expected to significantly contribute to prevent and control the spread of infectious diseases, as well as aid in conducting epidemiological investigations

Fermi Surface and Band Renormalization in (Sr,K)Fe2_2As2_2 Superconductor from Angle-Resolved Photoemission Spectroscopy

High resolution angle-resolved photoemission measurements have been carried out on (Sr,K)Fe$_2$As$_2$ superconductor (Tc=21 K). Three hole-like Fermi surface sheets are clearly resolved for the first time around the Gamma point. The overall electronic structure shows significant difference from the band structure calculations. Qualitative agreement between the measured and calculated band structure is realized by assuming a chemical potential shift of -0.2 eV. The obvious band renormalization suggests the importance of electron correlation in understanding the electronic structure of the Fe-based compounds.Comment: 4 pages, 4 figure

Improving protein order-disorder classification using charge-hydropathy plots

BACKGROUND: The earliest whole protein order/disorder predictor (Uversky et al., Proteins, 41: 415-427 (2000)), herein called the charge-hydropathy (C-H) plot, was originally developed using the Kyte-Doolittle (1982) hydropathy scale (Kyte & Doolittle., J. Mol. Biol, 157: 105-132(1982)). Here the goal is to determine whether the performance of the C-H plot in separating structured and disordered proteins can be improved by using an alternative hydropathy scale. RESULTS: Using the performance of the CH-plot as the metric, we compared 19 alternative hydropathy scales, with the finding that the Guy (1985) hydropathy scale (Guy, Biophys. J, 47:61-70(1985)) was the best of the tested hydropathy scales for separating large collections structured proteins and intrinsically disordered proteins (IDPs) on the C-H plot. Next, we developed a new scale, named IDP-Hydropathy, which further improves the discrimination between structured proteins and IDPs. Applying the C-H plot to a dataset containing 109 IDPs and 563 non-homologous fully structured proteins, the Kyte-Doolittle (1982) hydropathy scale, the Guy (1985) hydropathy scale, and the IDP-Hydropathy scale gave balanced two-state classification accuracies of 79%, 84%, and 90%, respectively, indicating a very substantial overall improvement is obtained by using different hydropathy scales. A correlation study shows that IDP-Hydropathy is strongly correlated with other hydropathy scales, thus suggesting that IDP-Hydropathy probably has only minor contributions from amino acid properties other than hydropathy. CONCLUSION: We suggest that IDP-Hydropathy would likely be the best scale to use for any type of algorithm developed to predict protein disorder

A deep learning model for drug screening and evaluation in bladder cancer organoids

Three-dimensional cell tissue culture, which produces biological structures termed organoids, has rapidly promoted the progress of biological research, including basic research, drug discovery, and regenerative medicine. However, due to the lack of algorithms and software, analysis of organoid growth is labor intensive and time-consuming. Currently it requires individual measurements using software such as ImageJ, leading to low screening efficiency when used for a high throughput screen. To solve this problem, we developed a bladder cancer organoid culture system, generated microscopic images, and developed a novel automatic image segmentation model, AU2Net (Attention and Cross U2Net). Using a dataset of two hundred images from growing organoids (day1 to day 7) and organoids with or without drug treatment, our model applies deep learning technology for image segmentation. To further improve the accuracy of model prediction, a variety of methods are integrated to improve the model’s specificity, including adding Grouping Cross Merge (GCM) modules at the model’s jump joints to strengthen the model’s feature information. After feature information acquisition, a residual attentional gate (RAG) is added to suppress unnecessary feature propagation and improve the precision of organoids segmentation by establishing rich context-dependent models for local features. Experimental results show that each optimization scheme can significantly improve model performance. The sensitivity, specificity, and F1-Score of the ACU2Net model reached 94.81%, 88.50%, and 91.54% respectively, which exceed those of U-Net, Attention U-Net, and other available network models. Together, this novel ACU2Net model can provide more accurate segmentation results from organoid images and can improve the efficiency of drug screening evaluation using organoids