Inverse Prediction Research Articles

Reversible Watermarking using Histogram Shifting and Pixel Differences with Inverse S-scan order and Local Prediction

Reversible Watermarking using Histogram Shifting and Pixel Differences with Inverse S-scan order and Local Prediction

Predicting channel bed topography in hydraulic falls

We consider inverse methods for predicting the channel bed topography in experiments of hydraulic falls. Nonlinear solutions and weakly nonlinear approximations from Euler-based models are compared to experimental observations. Accurate predictions are obtained for the maximum height of the topography and its constant horizontal level far downstream using the nonlinear method. The weakly nonlinear approximation is shown only to be a good predictor of the maximum height of the topography. The error in the inverse predictions is examined and discussed.

Fuzzy Time Series Prediction Model and Application based on Fuzzy Inverse

This paper presents a forecast model of fuzzy time series prediction to study the prediction of enrollment. It takes the percentage of enrollment changes as the domain, constructs the inverse fuzzy number and predicts the enrollment of Qiong Zhou University from 2005 to 2013. Compared with the existing models, the mean square error and prediction error of the ameliorated model are smaller and the precision is higher. The authors get the better method to solve the prediction problem based on the inverse fuzzy prediction model.

Percolation in suspensions of hard nanoparticles: From spheres to needles

We investigate geometric percolation and scaling relations in suspensions of nanorods, covering the entire range of aspect ratios from spheres to extremely slender needles. A new version of connectedness percolation theory is introduced and tested against specialised Monte Carlo simulations. The theory accurately predicts percolation thresholds for aspect ratios of rod length to width as low as 10. The percolation threshold for rod-like particles of aspect ratios below 1000 deviates significantly from the inverse aspect ratio scaling prediction, thought to be valid in the limit of infinitely slender rods and often used as a rule of thumb for nanofibres in composite materials. Hence, most fibres that are currently used as fillers in composite materials cannot be regarded as practically infinitely slender for the purposes of percolation theory. Comparing percolation thresholds of hard rods and new benchmark results for ideal rods, we find that i) for large aspect ratios, they differ by a factor that is inversely proportional to the connectivity distance between the hard cores, and ii) they approach the slender rod limit differently.

Pre-stack Simultaneous AVO Inversion and Lithology Prediction of Shale Gas Reservoir

Pre-stack Simultaneous AVO Inversion and Lithology Prediction of Shale Gas Reservoir

Estrogen Receptor Expression in 21-Gene Recurrence Score Predicts Increased Late Recurrence for Estrogen-Positive/HER2-Negative Breast Cancer.

To identify the individual genes or gene modules that lead to the OncoptypeDx 21-gene recurrence score's reduced performance after 5 years and thereby identify indices of residual risk that may guide selection of patients for extended adjuvant therapy. We conducted a retrospective assessment of the relationship between (i) the individual genes and gene modules of the Recurrence Score and (ii) early (0-5 years) and late (5-10 years) recurrence rates in 1,125 postmenopausal patients with primary estrogen receptor-positive breast cancer treated with anastrozole or tamoxifen in the Arimidex, Tamoxifen, Alone or Combined (ATAC) randomized clinical trial. In the HER2-negative population (n = 1,009), estimates of recurrence risk were similar between years 0-5 and 5-10 for proliferation and invasion modules but markedly different for the estrogen module and genes within it (all split at the median): for low estrogen module, annual recurrence rates were similar across the two time windows (2.06% vs. 2.46%, respectively); for high estrogen module, annual rates were 1.14% versus 2.72%, respectively (P interaction = 0.004). Estrogen receptor transcript levels showed inverse prediction across the time windows: HR, 0.88 (0.73-1.07) and 1.19 (0.99-1.43), respectively (P interaction = 0.03). Similar time-, module-, and estrogen-dependent relationships were seen for distant recurrence. Patients with tumors with high estrogen receptor transcript levels benefit most from 5 years' endocrine therapy but show increased recurrence rates after 5 years and may benefit from extended therapy. Improved prognostic profiles may be created by considering period of treatment and follow-up time.

SU‐C‐BRD‐05: Non‐Invasive in Vivo Biodosimetry in Radiotherapy Patients Using Electron Paramagnetic Resonance (EPR) Spectroscopy

Purpose:Medical intervention following a major, unplanned radiation event can elevate the human whole body exposure LD50 from 3 to 7 Gy. On a large scale, intervention cannot be achieved effectively without accurate and efficient triage. Current methods of retrospective biodosimetry are restricted in capability and applicability; published human data is limited. We aim to further develop, validate, and optimize an automated field‐deployable in vivo electron paramagnetic resonance (EPR) instrument that can fill this need.Methods:Ionizing radiation creates highly‐stable, carbonate‐based free radicals within tooth enamel. Using a process similar to nuclear magnetic resonance, EPR directly measures the presence of radiation‐induced free radicals. We performed baseline EPR measurements on one of the upper central incisors of total body irradiation (TBI) and head and neck (H&N) radiotherapy patients before their first treatment. Additional measurements were performed between subsequent fractions to examine the EPR response with increasing radiation dose. Independent dosimetry measurements were performed with optically‐stimulated luminescent dosimeters (OSLDs) and diodes to more accurately establish the relationship between EPR signal and delivered radiation dose.Results:36 EPR measurements were performed over the course of four months on two TBI and four H & N radiotherapy patients. We observe a linear increase in EPR signal with increasing dose across the entirety of the tested range. A linear least squares‐weighted fit of delivered dose versus measured signal amplitude yields an adjusted R‐square of 0.966. The standard error of inverse prediction (SEIP) is 1.77 Gy. For doses up to 7 Gy, the range most relevant to triage, we calculate an SEIP of 1.29 Gy.Conclusion:EPR spectroscopy provides a promising method of retrospective, non‐invasive, in vivo biodosimetry. Our preliminary data show an excellent correlation between predicted signal amplitude and delivered dose. With further development, a robust means of predicting delivered radiation dose from EPR measurements is expected.This project was funded by the Biomedical Advanced Research and Development Authority (BARDA) within the U.S. Department of Health and Human Services subcontracted through the Geisel School of Medicine at Dartmouth and by the Dartmouth Physically‐Based Biodosimetry Center for Medical Countermeasures Against Radiation (Dart‐Dose CMCR) Pilot Program.

Kinematic earthquake source inversion and tsunami runup prediction with regional geophysical data

AbstractRapid near‐source earthquake source modeling relying only on strong motion data is limited by instrumental offsets and magnitude saturation, adversely affecting subsequent tsunami prediction. Seismogeodetic displacement and velocity waveforms estimated from an optimal combination of high‐rate GPS and strong motion data overcome these limitations. Supplementing land‐based data with offshore wave measurements by seafloor pressure sensors and GPS‐equipped buoys can further improve the image of the earthquake source and prediction of tsunami extent, inundation, and runup. We present a kinematic source model obtained from a retrospective real‐time analysis of a heterogeneous data set for the 2011 Mw9.0 Tohoku‐Oki, Japan, earthquake. Our model is consistent with conceptual models of subduction zones, exhibiting depth dependent behavior that is quantified through frequency domain analysis of slip rate functions. The stress drop distribution is found to be significantly more correlated with aftershock locations and mechanism types when off‐shore data are included. The kinematic model parameters are then used as initial conditions in a fully nonlinear tsunami propagation analysis. Notably, we include the horizontal advection of steeply sloping bathymetric features. Comparison with post‐event on‐land survey measurements demonstrates that the tsunami's inundation and runup are predicted with considerable accuracy, only limited in scale by the resolution of available topography and bathymetry. We conclude that it is possible to produce credible and rapid, kinematic source models and tsunami predictions within minutes of earthquake onset time for near‐source coastal regions most susceptible to loss of life and damage to critical infrastructure, regardless of earthquake magnitude.

Prediction of porosity and thermal diffusivity in a porous fin using differential evolution algorithm

In this paper, simultaneous inverse prediction of two parameters such as the porosity and thermal diffusivity of the fluid in a porous fin is done for satisfying a given temperature distribution. Only three temperature measurements are assumed to be available on the surface of the fin and prediction of the parameters is accomplished by using the differential evolution (DE)-based optimization technique. It is shown that the present problem is inherently ill-posed in terms of the retrieval of the value of fluid thermal diffusivity for which many possible solutions exist, which is expected to adapt the fin under different conditions. In the present work, two numerical examples provide engineering insight into the problem of designing porous fins using good thermal conductors like aluminum and copper along with the working of DE. Finally, the efficacy of DE for the present problem is also shown by comparing its performance with few other optimization methods.

Study of Logging Curve Processing Methods in Nanpu 5th Structure Seismic Inversion

Logging curve processing can effectively eliminate the systematic error of the data and It is the critical initial basic work to improve the precision in reservoir prediction and new hydrocarbon accumulation description while getting a new knowledge of reservoir sand body, Combining with the practical situation of Nanpu structure, the paper optimize logging curve processing methods based on fitting the trend surface. Development of Nanpu 5th tectonic lithology, core porosity, curve correlation coefficient, inversion prediction sandstone and effective sandstone accuracy is obviously increased after applying the technology of well logging curve processing on inversion analysis of East No.1 section the Nanpu 5th structure so as to prove the rationality of the application of trend surface treatment methods of this area, to reduce blindness, and to provides highly consistent logging data to improve development precision.

Predicting growth conditions from internal metabolic fluxes in an in-silico model of E. coli.

A widely studied problem in systems biology is to predict bacterial phenotype from growth conditions, using mechanistic models such as flux balance analysis (FBA). However, the inverse prediction of growth conditions from phenotype is rarely considered. Here we develop a computational framework to carry out this inverse prediction on a computational model of bacterial metabolism. We use FBA to calculate bacterial phenotypes from growth conditions in E. coli, and then we assess how accurately we can predict the original growth conditions from the phenotypes. Prediction is carried out via regularized multinomial regression. Our analysis provides several important physiological and statistical insights. First, we show that by analyzing metabolic end products we can consistently predict growth conditions. Second, prediction is reliable even in the presence of small amounts of impurities. Third, flux through a relatively small number of reactions per growth source (∼10) is sufficient for accurate prediction. Fourth, combining the predictions from two separate models, one trained only on carbon sources and one only on nitrogen sources, performs better than models trained to perform joint prediction. Finally, that separate predictions perform better than a more sophisticated joint prediction scheme suggests that carbon and nitrogen utilization pathways, despite jointly affecting cellular growth, may be fairly decoupled in terms of their dependence on specific assortments of molecular precursors.

Predicting time to threshold for initiating antiretroviral treatment to evaluate cost of treatment as prevention of human immunodeficiency virus.

The goal of this paper is to predict the additional amount of antiretroviral treatment that would be required to implement a policy of treating all HIV-infected people at time of detection of infection rather than at the time that their CD4 T-lymphocyte counts are observed to be below a threshold-the current standard of care. We describe a sampling-based inverse prediction method for predicting time from HIV infection to attainment of the CD4 threshold and apply it to a set of treatment-naive HIV-infected subjects in a village in Botswana who participated in a household survey that collected cross-sectional CD4 counts. The inferential target of interest is the population-level mean time to reaching CD4-based treatment threshold in this group of subjects. To address the challenges arising from the fact that these subject's dates of HIV infection are unknown, we make use of data from an auxiliary cohort study of subjects enrolled shortly after HIV infection in which CD4 counts were measured over time. We use a multiple imputation framework to combine across the different sources of data, and discuss how the methods compensate for the length-biased sampling inherent in cross-sectional screening procedures, such as household surveys. We comment on how the results bear upon analyses of costs of implementation of treatment-for-prevention use of antiretroviral drugs in HIV prevention interventions.

Human structural proteome-wide characterization of Cyclosporine A targets.

Off-target interactions of a popular immunosuppressant Cyclosporine A (CSA) with several proteins besides its molecular target, cyclophilin A, are implicated in the activation of signaling pathways that lead to numerous side effects of this drug. Using structural human proteome and a novel algorithm for inverse ligand binding prediction, ILbind, we determined a comprehensive set of 100+ putative partners of CSA. We empirically show that predictive quality of ILbind is better compared with other available predictors for this compound. We linked the putative target proteins, which include many new partners of CSA, with cellular functions, canonical pathways and toxicities that are typical for patients who take this drug. We used complementary approaches (molecular docking, molecular dynamics, surface plasmon resonance binding analysis and enzymatic assays) to validate and characterize three novel CSA targets: calpain 2, caspase 3 and p38 MAP kinase 14. The three targets are involved in the apoptotic pathways, are interconnected and are implicated in nephrotoxicity.

A Large-Sample Confidence Interval for the Inverse Prediction of Quantile Differences in Logistic Regression for Two Independent Tests

ABSTRACTLogistic regression is a nonlinear method used for modeling a dichotomous (i.e., binary) response variable as a function of covariates. Such models have wide applicability and have proved especially useful in the health sciences where the question of effective dose (ED) or lethal dose (LD) is a central issue. For example, in toxicology studies, LD50 traditionally denotes the dose at which 50% of treated individuals perish. More generally, is the dose level corresponding to an average given percentage, 100p (i.e., the 100th quantile) of individuals responding to a treatment. In our field of flight test, logistic regression has a similar wide applicability. It is common to record a response as {hit, miss} or {success, failure} and to count the number of response successes at each level of input; the response is thus a quantal variable. In the example we develop here, the interest is in an application of blip-scan radar where the response is Y = {detect, no detect} and the covariate is range from target. In particular, we are interested in obtaining a confidence interval for the difference in range between two same-percentile values, one from each of two independent flights. The difference may be due to a different radar equipment configuration on each of the two flights and engineers are interested in quantifying the size of this difference in the detection performance. We approach the problem analytically and derive a symmetric confidence interval approximation for the average difference that is straightforward to compute and does not require simulation. Our results are based on the large-sample properties of maximum likelihood estimates and extend a result in nonlinear modeling given by Schwenke and Milliken (1991). The confidence interval so constructed is shown to give good probability coverage. Monte Carlo simulation is used to evaluate the procedure.

More than the sum of the parts: forest climate response from joint species distribution models.

The perceived threat of climate change is often evaluated from species distribution models that are fitted to many species independently and then added together. This approach ignores the fact that species are jointly distributed and limit one another. Species respond to the same underlying climatic variables, and the abundance of any one species can be constrained by competition; a large increase in one is inevitably linked to declines of others. Omitting this basic relationship explains why responses modeled independently do not agree with the species richness or basal areas of actual forests. We introduce a joint species distribution modeling approach (JSDM), which is unique in three ways, and apply it to forests of eastern North America. First, it accommodates the joint distribution of species. Second, this joint distribution includes both abundance and presence-absence data. We solve the common issue of large numbers of zeros in abundance data by accommodating zeros in both stem counts and basal area data, i.e., a new approach to zero inflation. Finally, inverse prediction can be applied to the joint distribution of predictions to integrate the role of climate risks across all species and identify geographic areas where communities will change most (in terms of changes in abundance) with climate change. Application to forests in the eastern United States shows that climate can have greatest impact in the Northeast, due to temperature, and in the Upper Midwest, due to temperature and precipitation. Thus, these are the regions experiencing the fastest warming and are also identified as most responsive at this scale.

An evaluation of sampling methods used to produce insect growth models for postmortem interval estimation.

Many authors produced carrion insect development data for predicting the age of an insect from a corpse. Under some circumstances, this age value is a minimum postmortem interval. There are no standard protocols for such experiments, and the literature includes a variety of sampling methods. To our knowledge, there has been no investigation of how the choice of sampling method can be expected to influence the performance of the resulting predictive model. We calculated 95% inverse prediction confidence limits for growth curves of the forensically important carrion flies Chrysomya megacephala and Sarconesia chlorogaster (Calliphoridae) at a constant temperature. Confidence limits constructed on data for entire age cohorts were considered to be the most realistic and were used to judge the effect of various subsampling schemes from the literature. Random subsamples yielded predictive models very similar to those of the complete data. Because taking genuinely random subsamples would require a great deal of effort, we imagine that it would be worthwhile only if the larval measurement technique were especially slow and/or expensive. However, although some authors claimed to use random samples, their published methods suggest otherwise. Subsampling the largest larvae produced a predictive model that performed poorly, with confidence intervals about an estimate of age being unjustifiably narrow and unlikely to contain the true age. We believe these results indicate that most forensic insect development studies should involve the measurement of entire age cohorts rather than subsamples of one or more cohorts.

Pickled Egg Production: Effect of Brine Acetic Acid Concentration and Packing Conditions on Acidification Rate

U.S. federal regulations require that acidified foods must reach a pH of 4.6 or lower within 24 h of packaging or be kept refrigerated until then. Processes and formulations should be designed to satisfy this requirement, unless proper studies demonstrate the safety of other conditions. Our objective was to determine the effect of brine acetic acid concentration and packing conditions on the acidification rate of hard-boiled eggs. Eggs were acidified (60/40 egg-to-brine ratio) at various conditions of brine temperature, heat treatment to filled jars, and postpacking temperature: (i) 25 °C/none/25 °C (cold fill), (ii) 25 °C/none/2 °C (cold fill/refrigerated), (iii) 85 °C/none/25 °C (hot fill), and (iv) 25 °C/100 °C for 16 min/25 °C (water bath). Three brine concentrations were evaluated (7.5, 4.9, and 2.5% acetic acid) and egg pH values (whole, yolk, four points within egg) were measured from 4 to 144 h, with eggs equilibrating at pH 3.8, 4.0, and 4.3, respectively. Experiments were conducted in triplicate, and effects were considered significant when P < 0.05. Multiple linear regression analysis was conducted to evaluate the effect on pH values at the center of the yolk. Regression analysis showed that brine concentration of 2.5% decreased the acidification rate, while packing conditions of the hot fill trial increased it. Inverse prediction was used to determine the time for the center of the yolk and the total yolk to reach a pH value of 4.6. These results demonstrate the importance of conducting acidification studies with proper pH measurements to determine safe conditions to manufacture commercially stable pickled eggs.

On Inverse Prediction in Mixed Linear Models

Given training data, a model relating a multivariate response y to x, and y* from a mystery specimen, the objective is to infer what values x* might have given rise to y*. Two approaches are investigated and illustrated here. In one, inverse prediction, tenable values of x* are those at which y* does not test as an outlier. In the other, based on modeling x vs. y, the interval estimate of x* is a prediction interval on x at y*. These two are illustrated and compared graphically. Coverage probabilities of the two intervals are assessed by simulations.

In vivo EPR tooth dosimetry for triage after a radiation event involving large populations.

The management of radiation injuries following a catastrophic event where large numbers of people may have been exposed to life-threatening doses of ionizing radiation will rely critically on the availability and use of suitable biodosimetry methods. In vivo electron paramagnetic resonance (EPR) tooth dosimetry has a number of valuable and unique characteristics and capabilities that may help enable effective triage. We have produced a prototype of a deployable EPR tooth dosimeter and tested it in several in vitro and in vivo studies to characterize the performance and utility at the state of the art. This report focuses on recent advances in the technology, which strengthen the evidence that in vivo EPR tooth dosimetry can provide practical, accurate, and rapid measurements in the context of its intended use to help triage victims in the event of an improvised nuclear device. These advances provide evidence that the signal is stable, accurate to within 0.5 Gy, and can be successfully carried out in vivo. The stability over time of the radiation-induced EPR signal from whole teeth was measured to confirm its long-term stability and better characterize signal behavior in the hours following irradiation. Dosimetry measurements were taken for five pairs of natural human upper central incisors mounted within a simple anatomic mouth model that demonstrates the ability to achieve 0.5 Gy standard error of inverse dose prediction. An assessment of the use of intact upper incisors for dose estimation and screening was performed with volunteer subjects who have not been exposed to significant levels of ionizing radiation and patients who have undergone total body irradiation as part of bone marrow transplant procedures. Based on these and previous evaluations of the performance and use of the in vivo tooth dosimetry system, it is concluded that this system could be a very valuable resource to aid in the management of a massive radiological event.

COMBINING LONG MEMORY AND NONLINEAR MODEL OUTPUTS FOR INFLATION FORECAST

Long memory and nonlinearity have been proven as two models that are easily to be mistaken. In other words, nonlinearity is a strong candidate of spurious long memory by introducing a certain degree of fractional integration that lies in the region of long memory. Indeed, nonlinear process belongs to short memory with zero integration order. The idea of the forecast is to obtain the future condition with minimum error. Some researches argued that no matter what the model is, the important thing is we can generate a reliable forecast. Several tests have been proposed to solve the problem of distinguishing long memory and nonlinearity appears in a series. The power of the tests is somehow questionable in the sense that there is still a probability to obtain spurious result. To overcome this, model combination will be one of the solutions dealing with uncertainty in the model selection. In this case, it is assumed that both processes are candidates of best models with certain power to generate a good forecast. This research investigates the performance three model combination approaches to forecast the Indonesia inflation i.e., simple combination using balance weight as well as inverse Mean Prediction Error (MSPE) weight and Bayesian Model Averaging (BMA). These methods are capable to generate a reliable forecast in very short lead time. Combination using BMA outperforms the simple averaging for 1 ahead forecast, while MSPE performs best for long lead forecasts.