Multiscale Modeling of Amyloid Fibrils Formed by Aggregating Peptides Derived from the Amyloidogenic Fragment of the A-Chain of Insulin

Computational prediction of molecular structures of amyloid fibrils remains an exceedingly challenging task. In this work, we propose a multi-scale modeling procedure for the structure prediction of amyloid fibrils formed by the association of ACC1-13 aggregation-prone peptides derived from the N-terminal region of insulin’s A-chain. First, a large number of protofilament models composed of five copies of interacting ACC1-13 peptides were predicted by application of CABS-dock coarse-grained (CG) docking simulations. Next, the models were reconstructed to all-atom (AA) representations and refined during molecular dynamics (MD) simulations in explicit solvent. The top-scored protofilament models, selected using symmetry criteria, were used for the assembly of long fibril structures. Finally, the amyloid fibril models resulting from the AA MD simulations were compared with atomic force microscopy (AFM) imaging experimental data. The obtained results indicate that the proposed multi-scale modeling procedure is capable of predicting protofilaments with high accuracy and may be applied for structure prediction and analysis of other amyloid fibrils.

DESAIN MOZAIK PADA BINGKAI JAJARAN GENJANG DENGAN MOTIF GEOMETRIS

This research is intended to obtain the steps of a parallelogram frame mosaic design with a Pinwheel tile pattern with geometric motifs. The design of the basic shape of the mosaic on the interior of a parallelogram which is then filled with several geometric motifs in the basic shape of the mosaic is the method used in this study. The results obtained from this study are the basic modeling procedure for the mosaic with a parallelogram frame. the first step, setting the second repetition (iteration) pinwheel tile. the second step, dividing the field on the frame into several basic shapes of mosaics. then for the procedure for filling the basic shape of the geometric patterned mosaic with the following steps. First, determine the geometric motifs that match the selected mosaic shapes. Secondly, fill the motif into each basic form. Thirdly, fill colour on the background.

Piecewise Modeling the Accumulated Daily Growth of COVID-19 Deaths: The Case of the State of São Paulo, Brazil

The pandemic scenery caused by the new coronavirus, called SARS-CoV-2, increased interest in statistical models capable of projecting the evolution of the number of cases (and associated deaths) due to COVID-19 in countries, states and/or cities. This interest is mainly due to the fact that the projections may help the government agencies in making decisions in relation to procedures of prevention of the disease. Since the growth of the number of cases (and deaths) of COVID-19, in general, has presented a heterogeneous evolution over time, it is important that the modeling procedure is capable of identifying periods with different growth rates and proposing an adequate model for each period. Here, we present a modeling procedure based on the fit of a piecewise growth model for the cumulative number of deaths. We opt to focus on the modeling of the cumulative number of deaths because, other than for the number of cases, these values do not depend on the number of diagnostic tests performed. In the proposed approach, the model is updated in the course of the pandemic, and whenever a “new” period of the pandemic is identified, it creates a new sub-dataset composed of the cumulative number of deaths registered from the change point and a new growth model is chosen for that period. Three growth models were fitted for each period: exponential, logistic and Gompertz models. The best model for the cumulative number of deaths recorded is the one with the smallest mean square error and the smallest Akaike information criterion (AIC) and Bayesian information criterion (BIC) values. This approach is illustrated in a case study, in which we model the number of deaths due to COVID-19 recorded in the State of São Paulo, Brazil. The results have shown that the fit of a piecewise model is very effective for explaining the different periods of the pandemic evolution.

Exemplar-based judgment or direct recall: On a problematic procedure for estimating parameters in exemplar models of quantitative judgment

Exemplar models are often used in research on multiple-cue judgments to describe the underlying process of participants’ responses. In these experiments, participants are repeatedly presented with the same exemplars (e.g., poisonous bugs) and instructed to memorize these exemplars and their corresponding criterion values (e.g., the toxicity of a bug). We propose that there are two possible outcomes when participants judge one of the already learned exemplars in some later block of the experiment. They either have memorized the exemplar and their respective criterion value and are thus able to recall the exact value, or they have not learned the exemplar and thus have to judge its criterion value, as if it was a new stimulus. We argue that psychologically, the judgments of participants in a multiple-cue judgment experiment are a mixture of these two qualitatively distinct cognitive processes: judgment and recall. However, the cognitive modeling procedure usually applied does not make any distinction between these processes and the data generated by them. We investigated potential effects of disregarding the distinction between these two processes on the parameter recovery and the model fit of one exemplar model. We present results of a simulation as well as the reanalysis of five experimental data sets showing that the current combination of experimental design and modeling procedure can bias parameter estimates, impair their validity, and negatively affect the fit and predictive performance of the model. We also present a latent-mixture extension of the original model as a possible solution to these issues.

An examination of extreme floods, effects on land-use change and seasonality in the Lower St. Johns River Basin, Florida using HSPF and statistical methods

As population growth and urbanization are steadily rising, the need for dependable flood estimation techniques is crucial. This study evaluates extreme flood events in select sub-basins of the Lower St. Johns River in Florida, USA. The study summarizes work of a recent thesis and combines that work with new research regarding the effect of urbanization on the natural hydrologic processes and flood magnitudes in the watershed. Additionally, the effects of varying seasonality into the hydrologic modeling procedure are also investigated. This research focuses on determining the 10-, 25-, 50-, and 100-year return frequency flood flows in Julington Creek, Ortega River, and Pablo Creek of the Lower St. Johns River Basin in Florida, USA. The major findings of this research indicate that by implementing a range of flood estimation methods one can better describe the inherent uncertainty with traditional estimates. Also, the research showed that varying seasonality in the hydrologic modeling procedure does not result in vast differences in the resulting flood estimates. However, various land-use scenarios may produce simulated flood flows of greater magnitude – especially when a more urbanized land-use scenario is modeled.

Acoustics on small scales

We present a modelling strategy based on the finite element method to describe flexible, piezoelectric structures surrounded by a compressible fluid, including viscosity. Non-conforming interfaces based on the Mortar method are used to couple the different physical domains. Finally, we present an application example of a piezoelectrically actuated MEMS structure to illustrate the modeling procedure and the impact of viscous effects.

A Fuzzy-Decomposition Grey Modeling Procedure for Management Decision Analysis

To cope with the increasingly fierce market competition environment, enterprises need to quickly respond to business issues and maintain business advantages, which require timely and correct decisions. In this context, the general mathematical modeling method may cause overfitting phenomenon when using small data sets, so it is difficult to ensure good analysis performance. Therefore, it is significant for enterprises to use limited samples to analyze and forecast. Over the past few decades, the grey model and its extensions have been shown to be effective tools for processing small data sets. To further enforce the effectiveness of data uncertainty processing, a fuzzy-decomposition modeling procedure for grey models is developed. Specifically, Latent Information (LI) function is employed to decompose the initial series into three subseries; next, the three subseries are used to build three grey models and create the estimated values of the three subseries; finally, the weighted average method is applying to combine the estimated values of the three subseries into a single final predicted value. After the actual test on the monthly demand data of the thin-film transistor liquid crystal display panels, the proposed fuzzy-decomposition modeling procedure can result in good prediction outcomes and is thus an appropriate decision analysis tool for managers.