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A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.

We present the first quantum field theory model of inflation that is renormalizable in the matter sector, with a super-Hubble inflaton mass and sub-Planckian field excursions, which is thus technically natural and consistent with a high-energy completion within a theory of quantum gravity. This is done in the framework of warm inflation, where we show, for the first time, that strong dissipation can fully sustain a slow-roll trajectory with slow-roll parameters larger than unity in a way that is both theoretically and observationally consistent. The inflaton field corresponds to the relative phase between two complex scalar fields that collectively break a U(1) gauge symmetry, and dissipates its energy into scalar degrees of freedom in the warm cosmic heat bath. A discrete interchange symmetry protects the inflaton mass from large thermal corrections. We further show that the dissipation coefficient decreases with temperature in certain parametric regimes, which prevents a large growth of thermal inflaton fluctuations. We find, in particular, a very good agreement with the Planck legacy data for a simple quadratic inflaton potential, predicting a low tensor-to-scalar ratio r≲10−5.

Background Model building is a crucial part of omics based biomedical research to transfer classifications and obtain insights into underlying mechanisms. Feature selection is often based on minimizing error between model predictions and given classification (maximizing accuracy). Human ratings/classifications, however, might be error prone, with discordance rates between experts of 5–15%. We therefore evaluate if a feature pre-filtering step might improve identification of features associated with true underlying groups. Methods Data was simulated for up to 100 samples and up to 10,000 features, 10% of which were associated with the ground truth comprising 2–10 normally distributed populations. Binary and semi-quantitative ratings with varying error probabilities were used as classification. For feature preselection standard cross-validation (V2) was compared to a novel heuristic (V1) applying univariate testing, multiplicity adjustment and cross-validation on switched dependent (classification) and independent (features) variables. Preselected features were used to train logistic regression/linear models (backward selection, AIC). Predictions were compared against the ground truth (ROC, multiclass-ROC). As use case, multiple feature selection/classification methods were benchmarked against the novel heuristic to identify prognostically different G-CIMP negative glioblastoma tumors from the TCGA-GBM 450 k methylation array data cohort, starting from a fuzzy umap based rough and erroneous separation. Results V1 yielded higher median AUC ranks for two true groups (ground truth), with smaller differences for true graduated differences (3–10 groups). Lower fractions of models were successfully fit with V1. Median AUCs for binary classification and two true groups were 0.91 (range: 0.54–1.00) for V1 (Benjamini-Hochberg) and 0.70 (0.28–1.00) for V2, 13% (n = 616) of V2 models showed AUCs

ObjectiveTo find new immune-related prognostic markers for non-small cell lung cancer (NSCLC).MethodsWe found GSE14814 is related to NSCLC in GEO database. The non-small cell lung cancer observation (NSCLC-OBS) group was evaluated for immunity and divided into high and low groups for differential gene screening according to the score of immune evaluation. A single factor COX regression analysis was performed to select the genes related to prognosis. A prognostic model was constructed by machine learning, and test whether the model has a test efficacy for prognosis. A chip-in-chip non-small cell lung cancer chemotherapy (NSCLC-ACT) sample was used as a validation dataset for the same validation and prognostic analysis of the model. The coexpression genes of hub genes were obtained by pearson analysis and gene enrichment, function enrichment and protein interaction analysis. The tumor samples of patients with different clinical stages were detected by immunohistochemistry and the expression difference of prognostic genes in tumor tissues of patients with different stages was compared.ResultsBy screening, we found that LYN, C3, COPG2IT1, HLA.DQA1, and TNFRSF17 is closely related to prognosis. After machine learning, we constructed the immune prognosis model from these 5 genes, and the model AUC values were greater than 0.9 at three time periods of 1, 3, and 5 years; the total survival period of the low-risk group was significantly better than that of the high-risk group. The results of prognosis analysis in ACT samples were consistent with OBS groups. The coexpression genes are mainly involved B cell receptor signaling pathway and are mainly enriched in apoptotic cell clearance. Prognostic key genes are highly correlated with PDCD1, PDCD1LG2, LAG3, and CTLA4 immune checkpoints. The immunohistochemical results showed that the expression of COPG2IT1 and HLA.DQA1 in stage III increased significantly and the expression of LYN, C3, and TNFRSF17 in stage III decreased significantly compared with that of stage I. The experimental results are consistent with the previous analysis.ConclusionLYN, C3, COPG2IT1, LA.DQA1, and NFRSF17 may be new immune markers to judge the prognosis of patients with non-small cell lung cancer.

An autonomous underwater vehicle (AUV) is an economical and safe tool that is well-suited for search, investigation, identification, and salvage operations on the sea floor. Path planning technology, which primarily includes modeling methods and path search algorithms, is an important technology for AUVs. In recent years, the AUV path planning technology has rapidly developed. Compared with land robots, AUVs must endure complex underwater environments and consider various factors, such as currents, water pressure, and topography. Challenges exist in terms of online obstacle avoidance, three-dimensional environment path planning, and the robustness of the algorithms. Adapting a complex environment and finding a suitable path planning method comprise the main problem that must be solved. In this paper, we summarize the principles, advantages, and disadvantages of modeling and path search technologies for AUVs. The most prominent feature of this paper is to summarize the improvement methods of various technical shortcomings and improve the original methods, such as dynamic obstacle avoidance, optimization path, coverage, and processing speed. In addition to summarizing the characteristics of each algorithm, this paper intuitively demonstrates the experimental environment, the real-time nature, the path planning range of the AUV, and so on. We also discuss the application scenarios of various modeling and path search technologies for AUVs. In addition, we discuss the challenges of AUVs and the direction of future research.

The transition mode of canard rotor/wing (CRW) aircraft is complex and important. During the transition flight, components that generate lift are transferred from one to another, in addition, the redundancy of control system may induce control conflict even plane crash if they don't cooperate well. It is significant to investigate the trim strategy, redundant control, and flight dynamics characteristics of CRW during transition flight. First, aerodynamic forces and moments were calculated by combining the blade element theory, computational fluid dynamics (CFD) and engineering estimation, and the motion equations of CRW in transition mode are established. Next, by analyzing the principle of transition flight, a trim strategy is proposed, and the trim results are credible and reasonable. Then, a control model for solving redundant control is proposed, which can realize simple and effective control during the transition process. Finally, by analyzing the eigenvalues, it is found that the stability of most modes grows with the increase of forwarding flying speed in the transition process, whereas the variation of minority modes is complicated. The results demonstrate the complexity of dynamic characteristics of CRW in transition mode. The trim strategy and control model and the analysis of the dynamic characteristics in the paper can be used for the subsequent control system design and overall optimization design.

X-ray crystallography and cryo-electron microscopy (cryo-EM) are complementary techniques for structure determination. Crystallography usually reveals more detailed information, while cryo-EM is an extremely useful technique for studying large-sized macromolecules. As the gap between the resolution of crystallography and cryo-EM data narrows, the cryo-EM map of a macromolecule could serve as an initial model to solve the phase problem of crystal diffraction for high-resolution structure determination. FSEARCH is a procedure to utilize the low-resolution molecular shape for crystallographic phasing. The IPCAS (Iterative Protein Crystal structure Automatic Solution) pipeline is an automatic direct-methods-aided dual-space iterative phasing and model-building procedure. When only an electron-density map is available as the starting point, IPCAS is capable of generating a completed model from the phases of the input map automatically, without the requirement of an initial model. In this study, a hybrid method integrating X-ray crystallography with cryo-EM to help with structure determination is presented. With a cryo-EM map as the starting point, the workflow of the method involves three steps. (1) Cryo-EM map replacement: FSEARCH is utilized to find the correct translation and orientation of the cryo-EM map in the crystallographic unit cell and generates the initial low-resolution map. (2) Phase extension: the phases calculated from the correctly placed cryo-EM map are extended to high-resolution X-ray data by non-crystallographic symmetry averaging with phenix.resolve. (3) Model building: IPCAS is used to generate an initial model using the phase-extended map and perform model completion by iteration. Four cases (the lowest cryo-EM map resolution being 6.9 Å) have been tested for the general applicability of the hybrid method, and almost complete models have been generated for all test cases with reasonable Rwork/Rfree. The hybrid method therefore provides an automated tool for X-ray structure determination using a cryo-EM map as the starting point.

Recent developments in cryogenic electron microscopy (cryo‐EM) have enabled structural studies of large macromolecular complexes at resolutions previously only attainable using macromolecular crystallography. Although a number of methods can already assist in de novo building of models into high‐resolution cryo‐EM maps, automated and reliable map interpretation remains a challenge. Presented here is a systematic study of the accuracy of models built into cryo‐EM maps using ARP/wARP. It is demonstrated that the local resolution is a good indicator of map interpretability, and for the majority of the test cases ARP/wARP correctly builds 90% of main‐chain fragments in regions where the local resolution is 4.0 Å or better. It is also demonstrated that the coordinate accuracy for models built into cryo‐EM maps is comparable to that of X‐ray crystallographic models at similar local cryo‐EM and crystallographic resolutions. The model accuracy also correlates with the refined atomic displacement parameters. [ABSTRACT FROM AUTHOR]

Background. Model building is a crucial part of omics based biomedical research to transfer classifications and obtain insights into underlying mechanisms. Feature selection is often based on minimizing error between model predictions and given classification (maximizing accuracy). Human ratings/classifications, however, might be error prone, with discordance rates between experts of 5-15%. We therefore evaluate if a feature pre-filtering step might improve identification of features associated with true underlying groups. Methods. Data was simulated for up to 100 samples and up to 10,000 features, 10% of which were associated with the ground truth comprising 2-10 normally distributed populations. Binary and semi-quantitative ratings with varying error probabilities were used as classification. For feature preselection standard cross-validation (V2) was compared to a novel heuristic (V1) applying univariate testing, multiplicity adjustment and cross-validation on switched dependent (classification) and independent (features) variables. Preselected features were used to train logistic regression/linear models (backward selection, AIC). Predictions were compared against the ground truth (ROC, multiclass-ROC). As use case, multiple feature selection/classification methods were benchmarked against the novel heuristic to identify prognostically different G-CIMP negative glioblastoma tumors from the TCGA-GBM 450 k methylation array data cohort, starting from a fuzzy umap based rough and erroneous separation. Results. V1 yielded higher median AUC ranks for two true groups (ground truth), with smaller differences for true graduated differences (3-10 groups). Lower fractions of models were successfully fit with V1. Median AUCs for binary classification and two true groups were 0.91 (range: 0.54-1.00) for V1 (Benjamini-Hochberg) and 0.70 (0.28-1.00) for V2, 13% (nD616) of V2 models showed AUCs

We propose, in this paper, a framework for time series and nonlinear system modeling, called the basis function matrix-based flexible coefficient autoregressive (BFM-FCAR) model. It has very flexible nonlinear structure. We show that many famous nonlinear time series models can be derived under this framework by choosing the proper basis function matrices. Some probabilistic properties (the conditions of geometrical ergodicity) of the BFM-FCAR model are investigated. Taking advantage of the model structure, we present an efficient parameter estimation algorithm for the proposed framework by using the variable projection method. Finally, we show how new models are generated from the proposed framework. [ABSTRACT FROM AUTHOR]

Visualization designs typically need to be evaluated with user studies, because their suitability for a particular task is hard to predict. What the field of visualization is currently lacking are theories and models that can be used to explain why certain designs work and others do not. This paper outlines a general framework for modeling visualization processes that can serve as the first step towards such a theory. It surveys related research in mathematical and computational psychology and argues for the use of dynamic Bayesian networks to describe these time-dependent, probabilistic processes. It is discussed how these models could be used to aid in design evaluation. The development of concrete models will be a long process. Thus, the paper outlines a research program sketching how to develop prototypes and their extensions from existing models, controlled experiments, and observational studies. [ABSTRACT FROM AUTHOR]

In the United States, rape culture is a prevalent phenomenon that has contributed to desensitization to scenes in popular media that might have been considered unacceptable in the past. This paper explores the proposal of a model (Rape Culture and Violence Legitimization Model, RCVL) to understand the factors that prompt a society's focus and often acceptance of rape culture. A component of RCVL is then applied to the Disney Movie, Maleficent, to explore one facet of the proposed model. Future applications are discussed to continue societal change directed toward these important issues both inside and outside the social work classroom. [ABSTRACT FROM AUTHOR]

Biological macromolecules have complex three‐dimensional shapes that are experimentally examined using X‐ray crystallography and electron cryo‐microscopy. Interpreting the data that these methods yield involves building 3D atomic models. With almost every data set, some portion of the time put into creating these models must be spent manually modifying the model in order to make it consistent with the data; this is difficult and time‐consuming, in part because the data are 'blurry' in three dimensions. This paper describes the design and assessment of CootVR (available at http://hamishtodd1.github.io/cvr), a prototype computer program for performing this task in virtual reality, allowing structural biologists to build molecular models into cryo‐EM and crystallographic data using their hands. CootVR was timed against Coot for a very specific model‐building task, and was found to give an order‐of‐magnitude speedup for this task. A from‐scratch model build using CootVR was also attempted; from this experience it is concluded that currently CootVR does not give a speedup over Coot overall. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Iris validation is a Python package created to represent comprehensive per‐residue validation metrics for entire protein chains in a compact, readable and interactive view. These metrics can either be calculated by Iris, or by a third‐party program such as MolProbity. We show that those parts of a protein model requiring attention may generate ripples across the metrics on the diagram, immediately catching the modeler's attention. Iris can run as a standalone tool, or be plugged into existing structural biology software to display per‐chain model quality at a glance, with a particular emphasis on evaluating incremental changes resulting from the iterative nature of model building and refinement. Finally, the integration of Iris into the CCP4i2 graphical user interface is provided as a showcase of its pluggable design. [ABSTRACT FROM AUTHOR]

Scientific understanding as a subject of inquiry has become widely discussed in philosophy of science and is often addressed through case studies from history of science. Even though these historical reconstructions engage with details of scientific practice, they usually provide only limited information about the gradual formation of understanding in ongoing processes of model and theory construction. Based on a qualitative ethnographic study of an ecological research project, this article shifts attention from understanding in the context of historical case studies to evidence of current case studies. By taking de Regt's (Understanding scientific understanding. Oxford University Press, New York, 2017) contextual theory of scientific understanding into the field, it confirms core tenets of the contextual theory (e.g. the crucial role of visualization and visualizability) suggesting a normative character with respect to scientific activities. However, the case study also shows the limitations of de Regt's latest version of this theory as an attempt to explain the development of understanding in current practice. This article provides a model representing the emergence of scientific understanding that exposes main features of scientific understanding such as its gradual formation, its relation to skills and imagination, and its capacity for knowledge selectivity. The ethnographic evidence presented here supports the claim that something unique can be learned by looking into ongoing research practices that can't be gained by studying historical case studies. [ABSTRACT FROM AUTHOR]

Model-based inquiry, inquiry-based learning, and phenomenon are all popular terms in K-12 science education right now. Science education in our public education system is rapidly changing due to the implementation of the Next Generation Science Standards (NGSS). These standards ask teachers to move away from direct instruction to having students develop their understanding of the natural world through guided-learning activities. Under NGSS, students are expected to develop this understanding through one of the main scientific practices, model building, which requires a complex, real-world phenomenon to drive the learning experience. Phenomena work best in the classroom when they apply to students' lives and pique their interest. Finding such phenomena can be hard - especially finding ones that have not already been thoroughly explained on the internet. A great way to find a complex, realworld phenomenon that will interest students is to partner with a local research lab to bring part of their research project into the classroom. This article lays out a process for bringing a local research project into the classroom and designing NGSS-aligned curricula around this project to create a more authentic learning experience for high school students. [ABSTRACT FROM AUTHOR]

Objective: To find new immune-related prognostic markers for non-small cell lung cancer (NSCLC). Methods: We found GSE14814 is related to NSCLC in GEO database. The non-small cell lung cancer observation (NSCLC-OBS) group was evaluated for immunity and divided into high and low groups for differential gene screening according to the score of immune evaluation. A single factor COX regression analysis was performed to select the genes related to prognosis. A prognostic model was constructed by machine learning, and test whether the model has a test efficacy for prognosis. A chip-in-chip non-small cell lung cancer chemotherapy (NSCLC-ACT) sample was used as a validation dataset for the same validation and prognostic analysis of the model. The coexpression genes of hub genes were obtained by pearson analysis and gene enrichment, function enrichment and protein interaction analysis. The tumor samples of patients with different clinical stages were detected by immunohistochemistry and the expression difference of prognostic genes in tumor tissues of patients with different stages was compared. Results: By screening, we found that LYN, C3, COPG2IT1, HLA.DQA1, and TNFRSF17 is closely related to prognosis. After machine learning, we constructed the immune prognosis model from these 5 genes, and the model AUC values were greater than 0.9 at three time periods of 1, 3, and 5 years; the total survival period of the low-risk group was significantly better than that of the high-risk group. The results of prognosis analysis in ACT samples were consistent with OBS groups. The coexpression genes are mainly involved B cell receptor signaling pathway and are mainly enriched in apoptotic cell clearance. Prognostic key genes are highly correlated with PDCD1, PDCD1LG2, LAG3, and CTLA4 immune checkpoints. The immunohistochemical results showed that the expression of COPG2IT1 and HLA.DQA1 in stage III increased significantly and the expression of LYN, C3, and TNFRSF17 in stage III decreased significantly compared with that of stage I. The experimental results are consistent with the previous analysis. Conclusion: LYN, C3, COPG2IT1, LA.DQA1, and NFRSF17 may be new immune markers to judge the prognosis of patients with non-small cell lung cancer. [ABSTRACT FROM AUTHOR]

The training effect is not only affected by many environmental disturbance factors, but also related to various factors such as the athlete itself. In this paper, the author analyze the regression prediction model of competitive sports based on SVM and artificial intelligence. Traditional statistical modeling simply compares existing data between players and compares them between data. Moreover, it is unable to formulate corresponding tactical strategies according to the situation of the opponent, and targeted training to strengthen the level of individual sports skills.By com-paring the effects of several kernel functions on the SVM modeling side, it is found that the RBF kernel function can make the SVM's prediction performance the best when dealing with the speed prediction problem. The experimental results show that this parameter optimization method can significantly improve the performance of the SVM regression machine. The prediction model based on support vector machine can effectively improve the prediction direction. Using artificial intelligence and image capture technology in sports can effectively improve the statistical efficiency and prediction effect of competition. [ABSTRACT FROM AUTHOR]

Aiming at that the traditional shock absorbers with adjustable throttle area are always designed based on servo solenoid valve, which shows many problems, such as complex structure, high design precision requirement and difficult to be controlled, a new type of damping adjustable shock absorber based on on-off solenoid valves was proposed. In the first, the structural design of the target shock absorb­er was conducted, and its working principle for achieving different damping states was analyzed. On this basis, the establishment process of the damping characteristics model of the target shock absorber was given,and the influence of main structural parameters on damping charac­teristics of the shock absorber was analyzed by simulations. The results indicate that the designed shock absorber can achieve four damping modes and eight damping states by just changing the on-off statuses of two solenoid valves, which makes it have simple structure and high re­liability.Meanwhile, the simulation results shown that the parameters of the four check valves and two on-off solenoid valves have significant influences on the damping characteristics of the shock absorber. The research results will lay an important theoretical foundation for the char­acteristics analysis and the determination of the main structural parameters of the target shock absorber [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For the last two decades, researchers have worked independently to automate protein model building, and four widely used software pipelines have been developed for this purpose: ARP/wARP, Buccaneer, Phenix AutoBuild and SHELXE. Here, the usefulness of combining these pipelines to improve the built protein structures by running them in pairwise combinations is examined. The results show that integrating these pipelines can lead to significant improvements in structure completeness and Rfree. In particular, running Phenix AutoBuild after Buccaneer improved structure completeness for 29% and 75% of the data sets that were examined at the original resolution and at a simulated lower resolution, respectively, compared with running Phenix AutoBuild on its own. In contrast, Phenix AutoBuild alone produced better structure completeness than the two pipelines combined for only 7% and 3% of these data sets. [ABSTRACT FROM AUTHOR]

A mapping technique was used in the present study to explore the biological and imaging characteristics of invasive breast cancer and normal breast tissues in Raman examination data and construct a diagnostic model for breast cancer. Raman examination data reflect the biochemical or molecular characteristics of the target tissues. A total of 45 specimens from patients with breast cancer who underwent surgery and 25 adjacent normal breast tissue specimens were included in the present study. Using the specimens, a total of 53 sets of mapping data and 2,597 pieces of Raman spectral data were obtained. The collected spectra were corrected and fitted, the Raman spectra were analyzed by robust statistical methods, and a diagnostic model was constructed using the k-Nearest Neighbor (KNN) method. The KNN classification method was applied to analyze the characteristics of the mapping test application. The percentage of outliers in the mapping data for malignant and normal breast tissues was 12.7 and 6.6%, respectively. The percentage of outlier data in the conventional single-point detection data for malignant and normal breast tissues was 24.5 and 26.0%, respectively. Analysis using a t-test identified a significant difference in the number of outliers between mapping and single-point detection for malignant (t=−6.169; P<0.001) and normal breast tissues (t=−8.873; P<0.001). Based on the mapping data, the accuracy, sensitivity and specificity for breast cancer detection by the diagnostic model constructed using the KNN method was 99.56, 96.6 and 98.48%, respectively. The positive and negative predictive value of this model was 99.56 and 89.04%, respectively. The data obtained by mapping technology demonstrated improved stability and contained less outliers compared with single-point detection. The diagnostic model constructed using the mapping data demonstrated excellent diagnostic performance and good correspondence with pathological results. The findings of the present study demonstrated the feasibility of the application of the diagnostic model for intraoperative real-time imaging for patients with breast cancer. This study provided the foundation of Raman spectroscopy-based diagnostic imaging at the molecular level. [ABSTRACT FROM AUTHOR]

Virtual characters are 3D geometric models of characters. They have a lot of applications in multimedia. In this paper, we propose a new physics-based deformation method and efficient character modelling framework for creation of detailed 3D virtual character models. Our proposed physics-based deformation method uses PDE surfaces. Here PDE is the abbreviation of Partial Differential Equation, and PDE surfaces are defined as sculpting force-driven shape representations of interpolation surfaces. Interpolation surfaces are obtained by interpolating key cross-section profile curves and the sculpting force-driven shape representation uses an analytical solution to a vector-valued partial differential equation involving sculpting forces to quickly obtain deformed shapes. Our proposed character modelling framework consists of global modeling and local modeling. The global modeling is also called model building, which is a process of creating a whole character model quickly with sketch-guided and template-based modeling techniques. The local modeling produces local details efficiently to improve the realism of the created character model with four shape manipulation techniques. The sketch-guided global modeling generates a character model from three different levels of sketched profile curves called primary, secondary and key cross-section curves in three orthographic views. The template-based global modeling obtains a new character model by deforming a template model to match the three different levels of profile curves. Four shape manipulation techniques for local modeling are investigated and integrated into the new modelling framework. They include: partial differential equation-based shape manipulation, generalized elliptic curve-driven shape manipulation, sketch assisted shape manipulation, and template-based shape manipulation. These new local modeling techniques have both global and local shape control functions and are efficient in local shape manipulation. The final character models are represented with a collection of surfaces, which are modeled with two types of geometric entities: generalized elliptic curves (GECs) and partial differential equation-based surfaces. Our experiments indicate that the proposed modeling approach can build detailed and realistic character models easily and quickly. [ABSTRACT FROM AUTHOR]

Manually identifying and correcting errors in protein models can be a slow process, but improvements in validation tools and automated model‐building software can contribute to reducing this burden. This article presents a new correctness score that is produced by combining multiple sources of information using a neural network. The residues in 639 automatically built models were marked as correct or incorrect by comparing them with the coordinates deposited in the PDB. A number of features were also calculated for each residue using Coot, including map‐to‐model correlation, density values, B factors, clashes, Ramachandran scores, rotamer scores and resolution. Two neural networks were created using these features as inputs: one to predict the correctness of main‐chain atoms and the other for side chains. The 639 structures were split into 511 that were used to train the neural networks and 128 that were used to test performance. The predicted correctness scores could correctly categorize 92.3% of the main‐chain atoms and 87.6% of the side chains. A Coot ML Correctness script was written to display the scores in a graphical user interface as well as for the automatic pruning of chains, residues and side chains with low scores. The automatic pruning function was added to the CCP4i2 Buccaneer automated model‐building pipeline, leading to significant improvements, especially for high‐resolution structures. [ABSTRACT FROM AUTHOR]

This work focuses on the use of the existing protein‐model‐building software Buccaneer to provide structural interpretation of electron cryo‐microscopy (cryo‐EM) maps. Originally developed for application to X‐ray crystallography, the necessary steps to optimise the usage of Buccaneer with cryo‐EM maps are shown. This approach has been applied to the data sets of 208 cryo‐EM maps with resolutions of better than 4 Å. The results obtained also show an evident improvement in the sequencing step when the initial reference map and model used for crystallographic cases are replaced by a cryo‐EM reference. All other necessary changes to settings in Buccaneer are implemented in the model‐building pipeline from within the CCP‐EM interface (as of version 1.4.0). [ABSTRACT FROM AUTHOR]

Model‐based testing (MBT) automates the design and generation of test cases from a model. This process includes model building, test selection criteria, test case generation, and test case execution stages. Current tools support this process at various levels of automation, most of them supporting three out of four stages. Among them is MBT4J, a platform that extends ModelJUnit with several techniques, offering a high level of automation for testing Java applications. In this study, the authors evaluate the efficacy of the MBT4J platform, in terms of the number of test cases generated, errors detected, and coverage metrics. A case study is conducted using two open‐source Java systems from public repositories, and 15 different configurations. MBT4J was able to automatically generate five models from the source code. It was also able to generate up to 2025 unique test cases for one system and up to 1044 for the other, resulting in 167 and 349 failed tests, respectively. Transition and transition pair coverage reached 100% for all models. Code coverage ranged between 72 and 84% for the one system and between 59 and 76% for the other. The study found that Greedy and Random were the most effective testers for finding errors. [ABSTRACT FROM AUTHOR]

The performance of automated protein model building usually decreases with resolution, mainly owing to the lower information content of the experimental data. This calls for a more elaborate use of the available structural information about macromolecules. Here, a new method is presented that uses structural homologues to improve the quality of protein models automatically constructed using ARP/wARP. The method uses local structural similarity between deposited models and the model being built, and results in longer main‐chain fragments that in turn can be more reliably docked to the protein sequence. The application of the homology‐based model extension method to the example of a CFA synthase at 2.7 Å resolution resulted in a more complete model with almost all of the residues correctly built and docked to the sequence. The method was also evaluated on 1493 molecular‐replacement solutions at a resolution of 4.0 Å and better that were submitted to the ARP/wARP web service for model building. A significant improvement in the completeness and sequence coverage of the built models has been observed. [ABSTRACT FROM AUTHOR]

Many applications depend on solving the satisfiability of formulæ involving propositional logic and first-order theories, a problem known as Satisfiability Modulo Theory. This article presents a new method for satisfiability modulo a combination of theories, named CDSAT, for Conflict-Driven SATisfiability. CDSAT also solves Satisfiability Modulo Assignment problems that may include assignments to first-order terms. A conflict-driven procedure assigns values to variables to build a model, and performs inferences on demand in order to solve conflicts between assignments and formulæ. CDSAT extends this paradigm to generic combinations of disjoint theories, each characterized by a collection of inference rules called theory module. CDSAT coordinates the theory modules in such a way that the conflict-driven reasoning happens in the union of the theories, not only in propositional logic. As there is no fixed hierarchy with propositional logic at the center and the other theories as satellites, CDSAT offers a flexible framework for model search. We prove the soundness, completeness, and termination of CDSAT, identifying sufficient requirements on theories and modules that ensure these properties. [ABSTRACT FROM AUTHOR]

China leads the world in apple production, contributing to over half of the global total. Annually, extensive pesticide use is employed in apple cultivation, but the uncertain quantities of pesticide deposition and indiscriminate application have resulted in significant pesticide loss and residue. This poses a considerable potential risk to human health. This paper focuses on ground-based plant protection and stratifies the canopy horizontally, using 3D point cloud reconstruction technology and tree deposition experiments to explore the relationship between canopy leaf area index and pesticide deposition. By calculating the droplet penetration coefficient based on the amount of solution deposited at various canopy layers, a functional model linking leaf area index and droplet penetration coefficient has been established, with a correlation coefficient (R2) of 0.92. Based on this model, and by integrating the canopy volume with the amount of solution deposited on the outermost layer of the canopy, the total solution deposition on the entire tree can be estimated. According to the deposition model, there is a negative correlation between the amount of solution deposited on the canopy and the leaf area index of the canopy. Moreover, the experiments have shown that when the spray vehicle travels at a speed of 1.5 m/s, the maximum amount of solution is deposited while still ensuring an effective deposition rate. The establishment of the deposition estimation model allows for the visualization of canopy solution deposition and can guide fruit farmers in determining the appropriate frequency of spraying based on the severity of pest and disease infestations. [Display omitted] • Accurately forecasting the deposition of solutions on the canopy of apple trees. • Development of a model for solution penetration coefficient. • Establishing standard parameters for the optimal voxel size of apple trees. • Determining the optimal driving speed for pesticide trucks. [ABSTRACT FROM AUTHOR]

Managers operate in highly different fields. Decision-making can be based on models reflecting in part these differences. The challenge is to connect the respective models without too great a disruption. A threefold procedural approach is proposed by chaining a scheme of modeling in a complex field to an operationalized model to statistical multivariate methods. Multivariate pattern-detecting methods offer the chance to evaluate patterns within the complex field partly. This step completes the cycle of research and improved models can be used in a further cycle.

This study presents an exploratory analysis of Quick Service Restaurants (QSR) industry in the US during 2015 by using publicly available data and open-source R software. The data analysis framework and tools utilized here were developed primarily by Hadley Wickham and are included in his tidyverse package in R. This data analysis framework consists of data import, data wrangeling, data exploration (tranformation, visualization, and modeling), and communication of results (Wickham & Grolemund, 2016). These steps are illustrated by exploring the relationship between sales, customer satisfaction, and other characteristics of 65 QSR restaurants in the US. In order to facilitate reproduction and replication of this study, the dataset as well as the R code are included in this study.

The combination of cryo‐electron microscopy (cryo‐EM) and X‐ray crystallography reflects an important trend in structural biology. In a previously published study, a hybrid method for the determination of X‐ray structures using initial phases provided by the corresponding parts of cryo‐EM maps was presented. However, if the target structure of X‐ray crystallography is not identical but homologous to the corresponding molecular model of the cryo‐EM map, then the decrease in the accuracy of the starting phases makes the whole process more difficult. Here, a modified hybrid method is presented to handle such cases. The whole process includes three steps: cryo‐EM map replacement, phase extension by NCS averaging and dual‐space iterative model building. When the resolution gap between the cryo‐EM and X‐ray crystallographic data is large and the sequence identity is low, an intermediate stage of model building is necessary. Six test cases have been studied with sequence identity between the corresponding molecules in the cryo‐EM and X‐ray structures ranging from 34 to 52% and with sequence similarity ranging from 86 to 91%. This hybrid method consistently produced models with reasonable Rwork and Rfree values which agree well with the previously determined X‐ray structures for all test cases, thus indicating the general applicability of the method for X‐ray structure determination of homologues using cryo‐EM maps as a starting point. [ABSTRACT FROM AUTHOR]

FraGen is a real‐space structure determination program capable of building structure models of inorganic extended frameworks. Despite its high efficiency in model building, it does not have a graphical user interface (GUI), which makes the input of instructions and the interpretation of results rather difficult. In this work, a Python GUI for FraGen has been developed. Using FraGen‐GUI, users can easily prepare their instruction files for FraGen and visualize the corresponding output directly. More importantly, it can be used to remove duplicate models generated by FraGen, add bridging atoms that are missing in FraGen simulations and simulate XRD patterns for comparison with experimental results. [ABSTRACT FROM AUTHOR]

A comparison of four protein model‐building pipelines (ARP/wARP, Buccaneer, PHENIX AutoBuild and SHELXE) was performed using data sets from 202 experimentally phased cases, both with the data as observed and truncated to simulate lower resolutions. All pipelines were run using default parameters. Additionally, an ARP/wARP run was completed using models from Buccaneer. All pipelines achieved nearly complete protein structures and low Rwork/Rfree at resolutions between 1.2 and 1.9 Å, with PHENIX AutoBuild and ARP/wARP producing slightly lower R factors. At lower resolutions, Buccaneer leads to significantly more complete models. [ABSTRACT FROM AUTHOR]

From the mass textures' point of view, we present a comparative study of the S 3 flavor symmetry in the left–right symmetry model (LRSM) and the baryon minus lepton model (BLM) taking into account their predictions on the CKM mixing matrix. To do this, we recover the already studied quark mass matrix, that comes from some published papers, and under certain strong assumptions, one can show that there are predictive scenarios in the LRSM and BLM where the modified Fritzsch and nearest neighbor interaction (NNI) textures drive, respectively, the quark mixings. As the main result, the CKM mixing matrix is in good agreement with the last experimental data in the flavored BLM model. [ABSTRACT FROM AUTHOR]

The topmost polyamide (PA) layer of the thin‐film‐composite reverse osmosis (RO) membrane is the most important part in the membrane‐based RO technology. With the aid of molecular dynamics simulations, many PA layer‐related features in the RO process can be revealed. With many novel types of PA RO membranes out of trimesoyl chloride/m‐phenylenediamine monomers developed in the laboratory, a convenient model building tool for these PA layer systems is urgently needed to conduct the theoretical analysis. Here, we develop a new universal toolkit for constructing PA RO membranes, named as MembrFactory, which combines flexibility of force fields and membrane compositions. A key characteristic of our approach is the use of monomers as the starting state, and the final membrane model was obtained automatically by stepwise reaction between the functional groups on the monomers. The reliability of MembrFactory has been validated by constructing several common PA RO membranes. © 2019 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

In this study, we have aim to showsomeconnotation of fuzzy set type-2, and apply it in the numerical examples, by two sidesmaximum flow networkand transportation problems, when the path of networkarc between two nodes is fuzzy type-2 where the intersection point of twosets is fuzzy, As well as mentioned, A linear programming model wasbuilt for a numerical examples in the maximum flow and transportation problemswhere the indices of the optimal path from node start todestination is fuzzy. [ABSTRACT FROM AUTHOR]

The performance of automated model building in crystal structure determination usually decreases with the resolution of the experimental data, and may result in fragmented models and incorrect side‐chain assignment. Presented here are new methods for machine‐learning‐based docking of main‐chain fragments to the sequence and for their sequence‐independent connection using a dedicated library of protein fragments. The combined use of these new methods noticeably increases sequence coverage and reduces fragmentation of the protein models automatically built with ARP/wARP. [ABSTRACT FROM AUTHOR]

Extant interurban research in life expectancy greatly suffers from an underestimation of the role of place. Place is often conceptualized as a level of geography; this view ignores categorical differences between types of places. In addition, despite advances in theory and research that support their use, many important place-based factors remain under-utilized as control variables. We use multivariate analyses of life expectancy for the top and bottom quartiles of household income by sex in 148 US counties to compare the strengths of seventeen diverse variables. We find that cities' built, natural, and social environments play strong roles in life expectancy disparity among cities; many place-based variables consistently compare in strength to well-known control variables such as race, education, and behaviors. Furthermore, we find that place impacts men and women differently, even within the same income quartile. Indeed, some factors are associated with higher life expectancy in some demographic groups, and lower life expectancy in others. Researchers can protect against omitted variable bias when investigating public health outcomes by using a wider range of control variables. Researchers should also use better measures of place, and consider selecting specific cases to study. • A unique comparison of place-based and other well-known behavioral factors. • A critique of common assumptions for selecting cases and the unit of analysis. • Groups are impacted differently by their surroundings, even between genders in the same income bracket. [ABSTRACT FROM AUTHOR]

Three‐dimensional (3D) printing has been successfully applied to food processing, and widely appreciated by food science researchers. Recently, a lot of researches and review works have been done focusing on the ink's properties, printer design, and printing parameters. However, few articles specifically describe 3D models and slicing methods used in food printing. This work introduced the requirements for 3D printing models in some specific areas, discussed the critical parameter settings in the model slicing processes, and discussed the possible use of numerical techniques in the model building and printing of food 3D printing. The objective of this review is to increase researchers' focus on 3D printing models and slices in order to optimize the printing process and to provide some useful information for future research. [ABSTRACT FROM AUTHOR]

Solubility parameters are widely used in the polymer industry and are often applied in the high pressure field as well as they give the possibility of combining the effects of all operational parameters on solubility in a single term. We demonstrate a statistical methodology to apply solubility parameters in constructing a model to describe antisolvent fractionation based chiral resolution, which is a complex process including a chemical equilibrium, precipitation and extraction as well. The solubility parameter used in this article, is the Hansen parameter. The evaluation of experimental results of resolution and crystallization of ibuprofen with (R)-phenylethylamine based on diastereomeric salt formation by gas antisolvent fractionation method was carried out. Two sets of experiments were performed, one with methanol as organic solvent in an undesigned experiment and one with ethanol in a designed experiment. The utilization of D-optimal design in order to decrease the necessary number of experiments and to overcome the problem of constrained design space was demonstrated. Linear models including dependence of pressure, temperature and the solubility parameter were appropriate to describe the selectivity of the GASF optical resolution method in both sets of experiments. [ABSTRACT FROM AUTHOR]

N‐Glycosylation is one of the most common post‐translational modifications and is implicated in, for example, protein folding and interaction with ligands and receptors. N‐Glycosylation trees are complex structures of linked carbohydrate residues attached to asparagine residues. While carbohydrates are typically modeled in protein structures, they are often incomplete or have the wrong chemistry. Here, new tools are presented to automatically rebuild existing glycosylation trees, to extend them where possible, and to add new glycosylation trees if they are missing from the model. The method has been incorporated in the PDB‐REDO pipeline and has been applied to build or rebuild 16 452 carbohydrate residues in 11 651 glycosylation trees in 4498 structure models, and is also available from the PDB‐REDO web server. With better modeling of N‐glycosylation, the biological function of this important modification can be better and more easily understood. [ABSTRACT FROM AUTHOR]

From the five aspects of marine resource endowment, marine ecological environment, marine economic development, marine culture construction and marine system management, 29 specific indicators were selected to establish a marine ecological civilization evaluation index system for coastal cities. Through the construction of BP neural network model to measure the level of marine ecological civilization construction in 53 prefecture-level cities in China's coastal areas in 2016.According to the evaluation value, it is divided into five levels: high level, high level, medium level, low level and low level, and its spatial differentiation is analyzed. The results show that the construction of marine ecological civilization in China's coastal cities is generally moderate, but the differences between cities at various levels are obvious. The level of marine ecological civilization construction in Qingdao is the highest, and the level of marine ecological civilization construction in Tangshan is the lowest. According to the provincial distribution, Shanghai and Hainan have the highest construction level, Tianjin and Shandong provinces have higher levels, Guangdong and Fujian provinces have medium levels, and Hebei and Jiangsu provinces have the lowest construction level. The level of urban marine ecological civilization construction in the Yellow Sea is significantly higher than other sea areas. The endowment of marine resources and the quality of marine ecological environment are the basic factors affecting the construction of marine ecological civilization. Marine economic development is a key factor affecting the construction of marine ecological civilization. Marine culture and marine system are the factors that affect the level of marine ecological civilization construction. [ABSTRACT FROM AUTHOR]