Vertical tanks are commonly used appliances for liquids, and its capacity is very important for quantitative liquid ratio and liquid trade. In order to measure the capacity of vertical tanks more conveniently, this paper proposes a vertical tank capacity measurement method based on Monte Carlo Method. The method arranges a plurality of sensor points on the inner surface of the tank, and then performs Monte Carlo tests by generating a large number of random sample points, and finally calculates the capacity by counting the sample points that meet the criterion. The criterion for whether a sample point is located in the tank, which is the core issue, is established with the coordinates of sensor points and the distance between different sensor points along the surface of the tank. The results show that the absolute error of the measurement results of the proposed method does not exceed ±0.0003[m3], and the absolute error of capacity per unit volume has a linear relationship with the height of the vertical tank, and has little effect with the radial size of the vertical tank.

Continuous glucose monitors (CGMs) are increasingly utilized as a way to provide healthcare to the over 10% of Americans that have diabetes. Fully insertable and optically transduced biosensors are poised to further improve CGMs by extending the device lifetime and reducing cost. However, optical modeling of light propagation in tissue is necessary to ascertain device performance.

The estimation of the cardiotoxicity of compounds is an important task for the drug discovery as well as for the risk assessment in ecological aspect. The experimental estimation of the above endpoint is complex and expensive. Hence, the theoretical computational methods are very attractive alternative of the direct experiment. A model for cardiac toxicity of 400 hERG blocker compounds (pIC50) is built up using the Monte Carlo method. Three different splits into the visible training set (in fact, the training set plus the calibration set) and invisible validation sets examined. The predictive potential is very good for all examined splits. The statistical characteristics for the external validation set are (i) the coefficient of determination r(2)=(0.90-0.93); and (ii) root-mean squared error s=(0.30-0.40).

Janus, patchy, stripe-like, or random arrangements of molecules within the ligand shell of nanoparticles affect many properties. Among all existing ligand shell morphology characterization methods, the one based on mass spectroscopy is arguably the simplest. Its greatest limitation is that the results are qualitative. Here, we use a tailor-made Monte Carlo type program that fits the whole MALDI spectrum and generates a 3D model of the ligand shell. Quantitative description of the ligand shell in terms of nearest neighbor distribution and characteristic length scale can be readily extracted by the model, and are compared with the results of other characterization methods. A parameter related to the intermolecular interaction is extracted when this method is combined with NMR. This approach could become the routine method to characterize the ligand shell morphology of many nanoparticles and we provide an open access program to facilitate its use.

In this study QSPR models were developed to predict the complexation of structurally diverse compounds with β-cyclodextrin based on SMILES notation optimal descriptors using Monte Carlo method. The predictive potential of the applied approach was tested with three random splits into the sub-training, calibration, test and validation sets and with different statistical methods. Obtained results demonstrate that Monte Carlo method based modeling is a very promising computational method in the QSPR studies for predicting the complexation of structurally diverse compounds with β-cyclodextrin. The SMILES attributes (structural features both local and global), defined as molecular fragments, which are promoters of the increase/decrease of molecular binding constants were identified. These structural features were correlated to the complexation process and their identification helped to improve the understanding for the complexation mechanisms of the host molecules.

A series of 107 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio) thymine (HEPT) with anti-HIV-1 activity as a non-nucleoside reverse transcriptase inhibitor (NNRTI) has been studied. Monte Carlo method has been used as a tool to build up the quantitative structure-activity relationships (QSAR) for anti-HIV-1 activity. The QSAR models were calculated with the representation of the molecular structure by simplified molecular input-line entry system and by the molecular graph. Three various splits into training and test set were examined. Statistical quality of all build models is very good. Best calculated model had following statistical parameters: for training set r(2) = 0.8818, q(2) = 0.8774 and r(2) = 0.9360, q(2) = 0.9243 for test set. Structural indicators (alerts) for increase and decrease of the IC50 are defined. Using defined structural alerts computer aided design of new potential anti-HIV-1 HEPT derivates is presented.

An important issue in statistical inference for semiparametric models is how to provide reliable and consistent variance estimation. Brown and Wang (2005. Standard errors and covariance matrices for smoothed rank estimators. Biometrika 92: , 732-746) proposed a variance estimation procedure based on an induced smoothing for non-smooth estimating functions. Herein a Monte Carlo version is developed that does not require any explicit form for the estimating function itself, as long as numerical evaluation can be carried out. A general convergence theory is established, showing that any one-step iteration leads to a consistent variance estimator and continuation of the iterations converges at an exponential rate. The method is demonstrated through the Buckley-James estimator and the weighted log-rank estimators for censored linear regression, and rank estimation for multiple event times data.

Metallic implants have been correlated to local control failure for spinal sarcoma and chordoma patients due to the uncertainty of implant delineation from computed tomography (CT). Such uncertainty can compromise the proton Monte Carlo dose calculation (MCDC) accuracy. A component method is proposed to determine the dimension and volume of the implants from CT images.

By optimization of so-called correlation weights of attributes of simplified molecular input-line entry system (SMILES) quantitative structure - activity relationships (QSAR) for toxicity towards Pimephales promelas are established. A new SMILES attribute has been utilized in this work. This attribute is a molecular descriptor, which reflects (i) presence of different kinds of bonds (double, triple, and stereo chemical bonds); (ii) presence of nitrogen, oxygen, sulphur, and phosphorus atoms; and (iii) presence of fluorine, chlorine, bromine, and iodine atoms. The statistical characteristics of the best model are the following: n=226, r2=0.7630, RMSE=0.654 (training set); n=114, r2=0.7024, RMSE=0.766 (calibration set); n=226, r2=0.6292, RMSE=0.870 (validation set). A new criterion to select a preferable split into the training and validation sets are suggested and discussed.

Genome-wide data is used to stratify patients into classes for precision medicine using clustering algorithms. A common problem in this area is selection of the number of clusters (K). The Monti consensus clustering algorithm is a widely used method which uses stability selection to estimate K. However, the method has bias towards higher values of K and yields high numbers of false positives. As a solution, we developed Monte Carlo reference-based consensus clustering (M3C), which is based on this algorithm. M3C simulates null distributions of stability scores for a range of K values thus enabling a comparison with real data to remove bias and statistically test for the presence of structure. M3C corrects the inherent bias of consensus clustering as demonstrated on simulated and real expression data from The Cancer Genome Atlas (TCGA). For testing M3C, we developed clusterlab, a new method for simulating multivariate Gaussian clusters.

Biological membranes are complex assemblies of many different molecules of which analysis demands a variety of experimental and computational approaches. In this article, we explain challenges and advantages of atomistic Monte Carlo (MC) simulation of lipid membranes. We provide an introduction into the various move sets that are implemented in current MC methods for efficient conformational sampling of lipids and other molecules. In the second part, we demonstrate for a concrete example, how an atomistic local-move set can be implemented for MC simulations of phospholipid monomers and bilayer patches. We use our recently devised chain breakage/closure (CBC) local move set in the bond-/torsion angle space with the constant-bond-length approximation (CBLA) for the phospholipid dipalmitoylphosphatidylcholine (DPPC). We demonstrate rapid conformational equilibration for a single DPPC molecule, as assessed by calculation of molecular energies and entropies. We also show transition from a crystalline-like to a fluid DPPC bilayer by the CBC local-move MC method, as indicated by the electron density profile, head group orientation, area per lipid, and whole-lipid displacements. We discuss the potential of local-move MC methods in combination with molecular dynamics simulations, for example, for studying multi-component lipid membranes containing cholesterol.

Mesh-based Monte Carlo techniques for optical imaging allow for accurate modeling of light propagation in complex biological tissues. Recently, they have been developed within an efficient computational framework to be used as a forward model in optical tomography. However, commonly employed adaptive mesh discretization techniques have not yet been implemented for Monte Carlo based tomography. Herein, we propose a methodology to optimize the mesh discretization and analytically rescale the associated Jacobian based on the characteristics of the forward model. We demonstrate that this method maintains the accuracy of the forward model even in the case of temporal data sets while allowing for significant coarsening or refinement of the mesh.

The direct simulation Monte Carlo (DSMC) method, which is a probabilistic particle-based gas kinetic simulation approach, is employed in the present work to describe the physics of rarefied gas flow in super nanoporous materials (also known as mesoporous). The simulations are performed for different material porosities (0.5≤ϕ≤0.9), Knudsen numbers (0.05≤Kn≤1.0), and thermal boundary conditions (constant wall temperature and constant wall heat flux) at an inlet-to-outlet pressure ratio of 2. The present computational model captures the structure of heat and fluid flow in porous materials with various pore morphologies under rarefied gas flow regime and is applied to evaluate hydraulic tortuosity, permeability, and skin friction factor of gas (argon) flow in super nanoporous materials. The skin friction factors and permeabilities obtained from the present DSMC simulations are compared with the theoretical and numerical models available in the literature. The results show that the ratio of apparent to intrinsic permeability, hydraulic tortuosity, and skin friction factor increase with decreasing the material porosity. The hydraulic tortuosity and skin friction factor decrease with increasing the Knudsen number, leading to an increase in the apparent permeability. The results also show that the skin friction factor and apparent permeability increase with increasing the wall heat flux at a specific Knudsen number.

Loops in proteins are flexible regions connecting regular secondary structures. They are often involved in protein functions through interacting with other molecules. The irregularity and flexibility of loops make their structures difficult to determine experimentally and challenging to model computationally. Conformation sampling and energy evaluation are the two key components in loop modeling. We have developed a new method for loop conformation sampling and prediction based on a chain growth sequential Monte Carlo sampling strategy, called Distance-guided Sequential chain-Growth Monte Carlo (DISGRO). With an energy function designed specifically for loops, our method can efficiently generate high quality loop conformations with low energy that are enriched with near-native loop structures. The average minimum global backbone RMSD for 1,000 conformations of 12-residue loops is 1:53 A° , with a lowest energy RMSD of 2:99 A° , and an average ensembleRMSD of 5:23 A° . A novel geometric criterion is applied to speed up calculations. The computational cost of generating 1,000 conformations for each of the x loops in a benchmark dataset is only about 10 cpu minutes for 12-residue loops, compared to ca 180 cpu minutes using the FALCm method. Test results on benchmark datasets show that DISGRO performs comparably or better than previous successful methods, while requiring far less computing time. DISGRO is especially effective in modeling longer loops (10-17 residues).

Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex correlation structures. BNs have wide applications in many disciplines, including biology, social science, finance and biomedical science. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC)-based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the quality and diversity of sampled networks which were further improved by a third stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASP's potential in discovering novel biological relationships in integrative genomic studies.

A single gene may yield several isoforms with different functions through alternative splicing. Continuous efforts are devoted to developing machine-learning methods to predict isoform functions. However, existing methods do not consider the relevance of each feature to specific functions and ignore the noise caused by the irrelevant features. In this case, we hypothesize that constructing a feature selection framework to extract the function-relevant features might help improve the model accuracy in isoform function prediction.

A GATE Monte Carlo model of the Philips Vereos digital photon counting PET imaging system using silicon photo-multiplier detectors was proposed. It was evaluated against experimental data in accordance with NEMA guidelines. Comparisons were performed using listmode data in order to remain independent of image reconstruction algorithms. An original line of response-based method is proposed to estimate intrinsic spatial resolution without reconstruction. Four sets of experiments were performed: (1) count rates and scatter fraction, (2) energy and timing resolutions, (3) sensitivity, and (4) intrinsic spatial resolution. Experimental and simulated data were found to be in good agreement, with overall differences lower than 10% for activity concentrations used in most standard clinical applications. Illustrative image reconstructions were provided. In conclusion, the proposed Monte Carlo model was validated and can be used for numerous studies such as optimizing acquisition parameters or reconstruction algorithms.

In this study, we extended the replica exchange Monte Carlo (REMC) sampling method to protein-small molecule docking conformational prediction using RosettaLigand. In contrast to the traditional Monte Carlo (MC) and REMC sampling methods, these methods use multi-objective optimization Pareto front information to facilitate the selection of replicas for exchange.

Pancreatic ductal adenocarcinoma (PDAC) is the most aggressive form of pancreatic cancer. Its 5-year survival rate is only 3-5%. Perineural invasion (PNI) is a process of cancer cells invading the surrounding nerves and perineural spaces. It is considered to be associated with the poor prognosis of PDAC. About 90% of pancreatic cancer patients have PNI. The high incidence of PNI in pancreatic cancer limits radical resection and promotes local recurrence, which negatively affects life quality and survival time of the patients with pancreatic cancer.

The past decade has been marked with a proliferation of community detection algorithms that aim to organize nodes (e.g., individuals, brain regions, variables) into modular structures that indicate subgroups, clusters, or communities. Motivated by the emergence of big data across many fields of inquiry, these methodological developments have primarily focused on the detection of communities of nodes from matrices that are very large. However, it remains unknown if the algorithms can reliably detect communities in smaller graph sizes (i.e., 1000 nodes and fewer) which are commonly used in brain research. More importantly, these algorithms have predominantly been tested only on binary or sparse count matrices and it remains unclear the degree to which the algorithms can recover community structure for different types of matrices, such as the often used cross-correlation matrices representing functional connectivity across predefined brain regions. Of the publicly available approaches for weighted graphs that can detect communities in graph sizes of at least 1000, prior research has demonstrated that Newman's spectral approach (i.e., Leading Eigenvalue), Walktrap, Fast Modularity, the Louvain method (i.e., multilevel community method), Label Propagation, and Infomap all recover communities exceptionally well in certain circumstances. The purpose of the present Monte Carlo simulation study is to test these methods across a large number of conditions, including varied graph sizes and types of matrix (sparse count, correlation, and reflected Euclidean distance), to identify which algorithm is optimal for specific types of data matrices. The results indicate that when the data are in the form of sparse count networks (such as those seen in diffusion tensor imaging), Label Propagation and Walktrap surfaced as the most reliable methods for community detection. For dense, weighted networks such as correlation matrices capturing functional connectivity, Walktrap consistently outperformed the other approaches for recovering communities.