Quantitative Prediction Method Research Articles

Significance of Reductive Metabolism in Human Intestine and Quantitative Prediction of Intestinal First-Pass Metabolism by Cytosolic Reductive Enzymes

The number of new drug candidates that are cleared via non-cytochrome P450 (P450) enzymes has increased. However, unlike oxidation by P450, the roles of reductive enzymes are less understood. The metabolism in intestine is especially not well known. The purposes of this study were to investigate the significance of reductive metabolism in human intestine, and to establish a quantitative prediction method of intestinal first-pass metabolism by cytosolic reductive enzymes, using haloperidol, mebendazole, and ziprasidone. First, we estimated the metabolic activities for these compounds in intestine and liver using subcellular fractions. Metabolic activities were detected in human intestinal cytosol (HIC) for all three compounds, and the intrinsic clearance values were higher than those in human liver cytosol for haloperidol and mebendazole. These metabolic activities in HIC were NADPH- and/or NADH-dependent. Furthermore, the metabolic activities for all three compounds in HIC were largely inhibited by menadione, which has been used as a carbonyl reductase (CBR)-selective chemical inhibitor. Therefore, considering subcellular location, cofactor requirement, and chemical inhibition, these compounds might be metabolized by CBRs in human intestine. Subsequently, we tried to quantitatively predict intestinal availability (F(g)) for these compounds using human intestinal S9 (HIS9). Our prediction model using apparent permeability of parallel artificial membrane permeability assay and metabolic activities in HIS9 could predict F(g) in humans for the three compounds well. In conclusion, CBRs might have higher metabolic activities in human intestine than in human liver. Furthermore, our prediction method of human F(g) using HIS9 is applicable to substrates of cytosolic reductive enzymes.

Quantitative Prediction Method for Shrinkage Porosity Considering Molten Metal Supply by Pressure in Squeeze Casting

It is important to predict shrinkage porosity quantitatively in squeeze casting process by CAE. A new calculation method has been developed considering molten metal supply by pressure in squeeze casting. Using this method, the size of shrinkage porosity in an aluminum suspension part can be simulated quantitatively. The predicted results were compared with the X-ray inspected results of actual castings, and the size of shrinkage porosity has been investigated by changing squeezed pressure. As the results, the predicted size of shrinkage porosities was found to agree well with the sizes of shrinkage porosities of the actual castings.

Study of Quantitative Numerical Prediction of Cavitation Erosion in Cavitating Flow

Cavitation erosion is a material damage phenomenon caused by the repeated application of impulsive pressure on a material surface induced by bubble collapse, and the establishment of a method by which to numerically predict cavitation erosion is desired. In the present study, a numerical quantitative prediction method of cavitation erosion in a cavitating flow is proposed. In the present method, a one-way coupled analysis of a cavitating flow field based on a gas-liquid two-phase Navier–Stokes equation (Eulerian) and bubbles in the cavitating flow by bubble dynamics (Lagrangian) is used to treat temporally and spatially different scale phenomena, such as the macroscopic phenomenon of a cavitating flow and the microscopic phenomenon of bubble collapse. Impulsive pressures acting on a material surface are evaluated based on the bubble collapse position, time, and intensity, and the erosion rate is quantitatively predicted using an existing material-dependent relationship between the impulsive energy (square of the impulsive force) and the maximum erosion rate. The erosion rate on a NACA0015 hydrofoil surface in an unsteady transient cavitating flow is predicted by the proposed method. The distribution of the predicted erosion rate corresponds qualitatively to the distribution of an experimental surface roughness increment of the same hydrofoil. Furthermore, the predicted erosion rate considering the bubble nuclei distribution is found to be of the same order of magnitude as the actual erosion rate, which indicates that considering bubble nuclei distribution is important for the prediction of cavitation erosion and that the present prediction method is valid to some degree.

Prediction of mining subsidence under thin bedrocks and thick unconsolidated layers based on field measurement and artificial neural networks

The deformation characteristics of subsidence and movement induced by mining under thin bedrocks and thick unconsolidated layers are researched using field measurement and the prediction method of artificial neural networks (ANN). Firstly, the occurrence characteristics of thin bedrock and thick unconsolidated layers were analyzed in a research coal field. Based on the measured data, the characteristics of ground movement show that the surface subsidence deformation of mining under thin bedrock is more intensive than that of mining under normal thickness bedrock. Such is evident through the settlement time concentrating, the maximum surface subsidence being greater than the thickness of coal seam, the distribution of ground movement and deformation being concentrated, the range extension being wide, the active period being intensive and concentrated, the surface damage being very serious, and the crack development being significant. A quantitative prediction method is made on mining subsidence under thin bedrocks and thick unconsolidated layers by means of ANN. The improved neural network was used for modeling and predicting the mining subsidence. The ANN output can reflect the change trend of ground movement and deformation. The forecasting results are in good agreement with the real observation results.

JS1-4 - Evolutional Drug Development with the Use of Microdosing and PET Imaging

ABSTRACT Microdose clinical trials can be utilized for the purpose to gain pharmacokinetic information on a tentative drug candidate in human using labelled compound(s) and AMS or imaging technology using PET, or non-labelled compound(s) and LC/MS/MS, and to select drug candidate by ensuring adequate properties in early stage. In 2008, the NEDO (New Energy and Industrial Technology Development Organization) project entitled ‘Establishment of Evolutional Drug development with the Use of Microdosing Clinical Trial’ was adopted. In this project, the quantitative prediction method on drug absorption, distribution, metabolism and excretion (ADME) using modeling and simulation will be applied to human to validate this methodology. That is, based on the in vitro data on metabolism, transport and binding with animal and human tissues, the drug concentration–time profiles in the plasma and target tissues such as the brain, tumor, liver and kidney will be predicted and the validity of the predictions will be investigated by using clinical studies under its microdose and therapeutic dose conditions. PET is a promising approach to determine the functional change of transporters associated with genetic polymorphisms or drug–drug interactions. Labeled PET probes have been developed for specific transporters in this NEDO project. In addition, in 2010, the Ministry of Education, Culture, Sports, Science and Technology (MEXT) has launched its second program, the ‘J-AMP; Japan Advanced Molecular Imaging Program’, using the two major centers (RIKEN, NIRS). In this project, research is undertaken focusing on the cancer and dementia fields to contribute to the development of new drugs and biomarkers as diagnostic tools. Here, in this presentation, I will share with you our recent progress in the use of the analysis of plasma clearance of drugs and PET imaging to evaluate the transporter function in vivo.

QSAR Modeling and Design of Cationic Antimicrobial Peptides Based on Structural Properties of Amino Acids

Drug resistance to existing antibiotics poses alarming threats to global public health, which inspires heightened interests in searching for new antibiotics, including antimicrobial peptides (AMPs). Accurate prediction of antibacterial activities of AMPs may expedite novel AMP design and reduce the costs and efforts involved in laboratory screening. In the present study, a novel quantitative prediction method of AMP was established by quantitative structure-activity relationship (QSAR) modeling based on the physicochemical properties of amino acids. The indices of these physicochemical properties were used to define AMP. The structural variables were optimized by stepwise regression (STR). Three series of AMPs from the QSAR model were constructed by multiple linear regressions (MLR). These QSAR models showed good performance in reliability and predictability. The normalized regression coefficients of the QSAR model and the contribution of amino acids at each position of AMP may determine the suitableness of a particular residue at any given position. QSAR models constructed by STR-MLR should prove to be useful tools in peptide design with respect to the calculation, explanation, good and reliable performance, and definition of physiochemical properties.

Polymeric Gas Separation Membranes

This short Perspective conveys to the general reader of Macromolecules basic approaches of materials science of polymeric membranes for gas and vapor separation. The relations between the polymer structure and transport properties of rubbery and glassy membrane materials are considered. On the basis of acquired information, several methods for quantitative prediction of permeability were developed, and their comparative analysis is given in the Perspective. The past decade was marked by the appearance of a number of novel interesting membrane materials, which will be briefly described in the text. In conclusion, novel approaches for achieving highly permeable and permselective materials (e.g., mixed matrix membranes) will be considered as well as several relevant but not solved so far problems of membrane gas separation.

Financial Distress Prediction of Chinese-Listed Companies Based on PCA and WNNs

The operating status of an enterprise is disclosed periodically in a financial statement. Financial distress prediction is important for business bankruptcy prevention, and various quantitative prediction methods based on financial ratios have been proposed. This paper presents a financial distress prediction model based on wavelet neural networks (WNNs). The transfer functions of the neurons in WNNs are wavelet base functions which are determined by dilation and translation factors. Back propagation algorithm was used to train the WNNs. Principal component analysis (PCA) method was used to reduce the dimension of the inputs of the WNNs. Multiple discriminate analysis (MDA), Logit, Probit, and WNNs were employed to a dataset selected from Chinese-listed companies. The results demonstrate that the proposed WNNs-based model performs well in comparison with the other three models.

Electron–TO-phonon interaction in polar crystals

In the present work, the interaction between electrons and long-wavelength transverse optical (TO) vibrations in a polar insulator is considered. The crystal model employed for the theoretical analysis includes classical potentials and electronic polarizabilities. A significant enhancement of the strength of the electron–TO-phonon interaction in ferroelectrics has been found. A microscopic justification of this effect is given. A bridge that relates the interaction of electrons with the polar long-wavelength TO modes of lattice vibrations to the long-range dipole–dipole interaction is established. As an application of our analysis, a novel method for quantitative predictions of the electron–TO–phonon interaction constants in polar crystals via the data of experimental studies is suggested.

Making words work: Using financial text as a predictor of financial events

We develop a methodology for automatically analyzing text to aid in discriminating firms that encounter catastrophic financial events. The dictionaries we create from Management Discussion and Analysis Sections (MD&A) of 10-Ks discriminate fraudulent from non-fraudulent firms 75% of the time and bankrupt from nonbankrupt firms 80% of the time. Our results compare favorably with quantitative prediction methods. We further test for complementarities by merging quantitative data with text data. We achieve our best prediction results for both bankruptcy (83.87%) and fraud (81.97%) with the combined data, showing that that the text of the MD&A complements the quantitative financial information.

Quantitative prediction of channel sand bodies based on seismic peak attributes in the frequency domain and its application

The boundary identification and quantitative thickness prediction of channel sand bodies are always difficult in seismic exploration. We present a new method for boundary identification and quantitative thickness prediction of channel sand bodies based on seismic peak attributes in the frequency domain. Using seismic forward modeling of a typical thin channel sand body, a new seismic attribute — the ratio of peak frequency to amplitude was constructed. Theoretical study demonstrated that seismic peak frequency is sensitive to the thickness of the channel sand bodies, while the amplitude attribute is sensitive to the strata lithology. The ratio of the two attributes can highlight the boundaries of the channel sand body. Moreover, the thickness of the thin channel sand bodies can be determined using the relationship between seismic peak frequency and thin layer thickness. Practical applications have demonstrated that the seismic peak frequency attribute can depict the horizontal distribution characteristics of channels very well. The ratio of peak frequency to amplitude attribute can improve the identification ability of channel sand body boundaries. Quantitative prediction and boundary identification of channel sand bodies with seismic peak attributes in the frequency domain are feasible.

S0803-2-2 流れ加速型腐食に対する減肉率予測モデルの検討(原子力発電システムおよび要素技術(2))

Flow Accelerated Corrosion (FAC) requires considerable attention in plant piping management, for its potential of catastrophic pipe rupture of main piping systems. For rational management dealing with FAC, besides present wall thickness measurement, quantitative prediction method is willing to be introduced for pipe residual lifetime evaluation, in parallel. In this study, FAC prediction model was investigated by re-constructing Sanchez-Caldera's method with single-oxide film based mechanism. The model's applicability was verified by adapting FAC experiment data.

MirWIP: microRNA target prediction based on microRNA-containing ribonucleoprotein–enriched transcripts

Target prediction for animal microRNAs (miRNAs) has been hindered by the small number of verified targets available to evaluate the accuracy of predicted miRNA-target interactions. Recently, a dataset of 3,404 miRNA-associated mRNA transcripts was identified by immunoprecipitation of the RNA-induced silencing complex components AIN-1 and AIN-2. Our analysis of this AIN-IP dataset revealed enrichment for defining characteristics of functional miRNA-target interactions, including structural accessibility of target sequences, total free energy of miRNA-target hybridization and topology of base-pairing to the 5' seed region of the miRNA. We used these enriched characteristics as the basis for a quantitative miRNA target prediction method, miRNA targets by weighting immunoprecipitation-enriched parameters (mirWIP), which optimizes sensitivity to verified miRNA-target interactions and specificity to the AIN-IP dataset. MirWIP can be used to capture all known conserved miRNA-mRNA target relationships in Caenorhabditis elegans at a lower false-positive rate than can the current standard methods.

Micromechanics Method to Evaluate Fatigue Life of Ni-Based Superalloys during Morphological Evolution

A quantitative life prediction method has been proposed to evaluate fatigue life during morphological evolution of precipitates in Ni-based superalloys. The method is essentially based on Eshelby’s equivalent inclusion theory and Mori-Tanaka’s mean field method. The shape stability and life prediction are discussed when the external stress and matrix plastic strain are applied. The calculated results show that the fatigue life is closely related with microstructures evolution of precipitates. The magnitude and sign of the external stress and matrix plastic strain have an important effect on fatigue life of Ni-based superalloys during the morphological evolution of precipitates.

Damage tolerance investigations on rails

The paper summarises damage tolerance investigations on railway rails which the authors have carried out in the context of the German–French joint project NOVUM (novel methods for quantitative prediction of rail performance at increased service loads) [Girardi L, Heyder R, Dider L, Boulanger D. IDR2-NOVUM promises lower rail maintenance costs. Railway Gazette Int 2005;(July):439–41 [1]]. The investigations include the determination of the crack driving force as a function of the various loading components a rail is subjected to and the simulation of residual lifetime. Features such as the local load input at the rail–wheel interface, dynamic effects and statistical aspects of crack resistance are explicitly taken into account.

WNN-Based Network Security Situation Quantitative Prediction Method and Its Optimization

The accurate and real-time prediction of network security situation is the premise and basis of preventing intrusions and attacks in a large-scale network. In order to predict the security situation more accurately, a quantitative prediction method of network security situation based on Wavelet Neural Network with Genetic Algorithm (GAWNN) is proposed. After analyzing the past and the current network security situation in detail, we build a network security situation prediction model based on wavelet neural network that is optimized by the improved genetic algorithm and then adopt GAWNN to predict the non-linear time series of network security situation. Simulation experiments prove that the proposed method has advantages over Wavelet Neural Network (WNN) method and Back Propagation Neural Network (BPNN) method with the same architecture in convergence speed, functional approximation and prediction accuracy. What is more, system security tendency and laws by which security analyzers and administrators can adjust security policies in near real-time are revealed from the prediction results as early as possible.

Financial distress early warning based on group decision making

Financial distress early warning is important for business bankruptcy prevention, and various quantitative prediction methods based on financial ratios have been proposed. However, little attention has been paid to the important role of experts’ experiential knowledge and non-financial information. From this point of view, the article puts forward a group decision-making approach based on experts’ knowledge and all kinds of financial or non-financial information to diagnose business financial distress. Based on the risk factors of enterprise financial distress, a qualitative attribute set and its scoring criteria are designed. A method integrating linguistic label and interval value is adopted for decision makers to express their preference on attributes, and a multi-expert negotiation mechanism is designed for weighting attributes. Diagnosis on business financial distress is made through the grey evaluation method, which also tries to find out the potential risks that may cause financial distress. Case study of a real world company is carried out to validate the proposed financial distress early warning method based on group decision making.

NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence

BackgroundBinding of peptides to Major Histocompatibility Complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC class I system (HLA-I) is extremely polymorphic. The number of registered HLA-I molecules has now surpassed 1500. Characterizing the specificity of each separately would be a major undertaking.Principal FindingsHere, we have drawn on a large database of known peptide-HLA-I interactions to develop a bioinformatics method, which takes both peptide and HLA sequence information into account, and generates quantitative predictions of the affinity of any peptide-HLA-I interaction. Prospective experimental validation of peptides predicted to bind to previously untested HLA-I molecules, cross-validation, and retrospective prediction of known HIV immune epitopes and endogenous presented peptides, all successfully validate this method. We further demonstrate that the method can be applied to perform a clustering analysis of MHC specificities and suggest using this clustering to select particularly informative novel MHC molecules for future biochemical and functional analysis.ConclusionsEncompassing all HLA molecules, this high-throughput computational method lends itself to epitope searches that are not only genome- and pathogen-wide, but also HLA-wide. Thus, it offers a truly global analysis of immune responses supporting rational development of vaccines and immunotherapy. It also promises to provide new basic insights into HLA structure-function relationships. The method is available at http://www.cbs.dtu.dk/services/NetMHCpan.

Quantitative Prediction Method for Shrinkage Porosity Considering Molten Metal Supply by Pressure in Squeeze Casting

It is important to predict shrinkage porosity quantitatively in squeeze casting process by CAE. A new calculation method has been developed considering molten metal supply by pressure in squeeze casting. Using this method, the size of shrinkage porosity in an aluminum suspension part can be simulated quantitatively. The predicted results were compared with the X-ray inspected results of actual castings, and the size of shrinkage porosity has been investigated by changing squeezed pressure. As the results, the predicted size of shrinkage porosities was found to agree well with the sizes of shrinkage porosities of the actual castings.

Prediction of howling for a sound system in an acoustical environment with both reverberant and direct sounds

Prediction of howling is a key technology for a howling suppression design for a sound system with a loudspeaker and microphone. A howling occurrence prediction method for a sound system in a reverberant room has already been presented [J. Ohga, J. Sakaguchi, ‘‘Prediction of howling of a sound system in a reverberant room,’’ W. C. Sabine Centennial Symposium (ASA, New York, 1994), 2aAAd4]. It is apparently useful for ordinary public address systems whose distances of loudspeakers from microphones are large. However, this result was not perfect because the direct sound component is not negligible in hands-free telephones or teleconference systems whose loudspeakers and microphones are set close to each other. This report gives a quantitative howling occurrence prediction method for a sound system in an acoustical environment with both reverberant and direct sounds. The following design parameters are obtained: (1) the increase of howling occurrence level from the power average value, (2) the level occurrence probability, and (3) the critical level chart given by an equation as a function of direct and reverberant sounds ratio. Prediction results for particular examples are compared with calculations of sound-field transfer functions. Results confirmed that it is practical.