Values Of Response Variables Research Articles

Development and Optimization of Gastro-Retentive Formulation of Hydralazine HCl

Hydralazine hydrochloride has a half-life of 2 to 4 hours with an oral bioavailability of 26-50%. Since hydralazine has a demethylating effect on various suppressor genes, it can be used in various types of cancer to support chemotherapy. The purpose of this study was to optimize and evaluate floating tablets of hydralazine hydrochloride designed to prolong the gastric residence time and to provide controlled release of the drug for 24 h. The floating tablets of hydralazine hydrochloride were prepared by the wet granulation method. Polymers of hydroxy propyl methyl cellulose (HPMC K100M), HPMC K15M, carbopol 940 and sodium bicarbonate were used as the release retarding agents. This study investigated utility of a 3-factor, 3-level Box-Behnken design and optimization process for floating tablet of Hydralazine with 5 replicates of center points. Amount of HPMC K4 (Hydroxy Propyl Methyl cellulose), amount of sodium bicarbonate were selected as the independent variables whereas total floating time (TFT), T90, % cumulative drug release at 24 hours, and T20, Q1 were selected as dependent variables. Non-Fickian diffusion release transport was confirmed as the release mechanism for the optimized formulation and the predicted values agreed well with the experimental values. Drug excipient compatibility studies were investigated by FTIR, DSC and XRD. The produced tablets exhibited good floating time and controlled drug release over a period of 24 h. The resultant data were critically analyzed to locate the composition of optimum formulations. All predicted values of response variables of optimized formulation demonstrated close agreement with the experimental data during optimization procedure.

An Improved Approach to Multivariate Linear Calibration [Mathematics Subject Classification 62P25

The article presents an approach to multivariate linear calibration based on the best linear predictor. The bias and mean squared error for the suggested predictor are derived in order to examine its properties. It has been examined that Bias/ and MSE/ are function of five invariant quantities. A simulation study is made for different values of response variables and sample sizes assuming different distributions for the explanatory variable. It is observed that the proposed estimator performs quite well. Some approximations to mean squared error have been suggested and the pivotal functions based on these approximations have been defined. Lower and upper tail probabilities have been calculated and it is examined that these are quite reasonable. These probabilities suggest that the relevant intervals have sensible confidence coefficient. Moreover, it is also shown that the multivariate classical and inverse estimators are special cases of the proposed estimator.

Preparation and Characterization of Microcapsules Encapsulating Octyl Methoxycinnamate by Complex Coacervation

The aim of this study was to prepare microcapsules entrapped a lipophilic ultraviolet absorber-octyl methoxycinnamate for its optimal application. A complex coacervation method was established. Through the appearance of encapsulated glass beads prepared with different wall materials, gelatin and sodium polyphosphate were selected as wall materials because of dense structure and less adhesion. The effects of three variables, core/(core+wall) ratio, pH at complex coacervation, and amount of cross-linking agent, on encapsulation efficiency were studied. Response surface methodology was employed to optimize the technology, and a polynomial regression model equation was generated. The optimization method enabled to predict the response variable value within the experimental range with good agreement between the predicted and experimental values. The microcapsules under the optimized conditions appeared spherical in shape with smooth nonporous surface and no tendency to aggregation. The photodegradation percentage of octyl methoxycinnamate after exposing to ultraviolet irradiation was determined and the transdermal permeation pattern of octyl methoxycinnamate and its photodegradation product from various formulations was estimated in vitro. From the results obtained, the microcapsules had achieved decreasing transdermal permeation of octyl methoxycinnamate and its photodegradation product, enhancing the accumulation of the sunscreen at the administration site, and then increasing the protection effect and safety. This study revealed the potential of sunscreen microcapsules as new skin drug delivery systems.

다중반응최적화를 위한 상호교호적 접근법

We study the problem of multiple response optimization (MRO) and focus on the selection of input levels which will produce desirable output quality. We propose an interactive multiple objective optimization approach to the input design. The earlier interactive methods utilized for MRO communicate with the decision maker only using the response variable values, in order to improve the current response values, thereby resulting in the corresponding design solution automatically. In their interaction steps of preference articulation, no account is taken of any active changes in design variable values. On the contrary, our approach permits the decision maker to change the design variable values in its interaction stage, which makes possible the consideration of the preference or economics of the design variable side. Using some typical value functions, we also demonstrate that our method converges reasonably well to the known optimal solutions.

Efficient computation of smoothing splines via adaptive basis sampling

Smoothing splines provide flexible nonparametric regression estimators. However, the high computational cost of smoothing splines for large datasets has hindered their wide application. In this article, we develop a new method, named adaptive basis sampling, for efficient computation of smoothing splines in super-large samples. Except for the univariate case where the Reinsch algorithm is applicable, a smoothing spline for a regression problem with sample size n can be expressed as a linear combination of n basis functions and its computational complexity is generally O(n3). We achieve a more scalable computation in the multivariate case by evaluating the smoothing spline using a smaller set of basis functions, obtained by an adaptive sampling scheme that uses values of the response variable. Our asymptotic analysis shows that smoothing splines computed via adaptive basis sampling converge to the true function at the same rate as full basis smoothing splines. Using simulation studies and a large-scale deep earth core-mantle boundary imaging study, we show that the proposed method outperforms a sampling method that does not use the values of response variables.

Preparation and characterization of plant based lipophilic colorants as water soluble nanoparticle

Response surface methodology (RSM) was used in this study to obtain empirically significant (p<0.05) models predicting the mean particle size, PDI of the prepared nanodispersions of alizarin (a plant based pigment), extracted from Rubia tinctorum L. roots, as a function of organic to aqueous phase addition rate, emulsifier concentration and polymer concentrations. The mentioned processing variables were then optimized to produce the nanodispersions with minimum particle size and maximum homogeneity (minimum PDI). The significant (p<0.05) correlation between the process parameters and physical characteristics of alizarin nanodispersions through second order polynomial models were obtained. It was demonstrated that mixing the organic phase containing alizarin and the aqueous phase containing Tween 20 as emulsifier in concentration of 3.5 ml and PVP as polymer with concentration of 240 mg with the rate of 0.45 ml/min would give the most desired alizarin nanodispersions with minimum particle size (45.15 nm) and PDI (0.1898). No significant differences observed between the experimental and predicted values of response variables, confirmed the adequacy of models.

Mapping by spatial predictors exploiting remotely sensed and ground data: A comparative design-based perspective

This study was designed to compare the performance – in terms of bias and accuracy – of four different parametric, semiparametric and nonparametric methods in spatially predicting a forest response variable using auxiliary information from remote sensing. The comparison was carried out in simulated and real populations where the value of response variable was known for each pixel of the study region. Sampling was simulated through a tessellation stratified design. Universal kriging and cokriging were considered among parametric methods based on the spatial autocorrelation of the forest response variable. Locally weighted regression and k-nearest neighbor predictors were considered among semiparametric and nonparametric methods based on the information from neighboring sites in the auxiliary variable space. The study was performed from a design-based perspective, taking the populations as fixed and replicating the sampling procedure with 1000 Monte Carlo simulation runs. On the basis of the empirical values of relative bias and relative root mean squared error it was concluded that universal kriging and cokriging were more suitable in the presence of strong spatial autocorrelation of the forest variable, while locally weighted regression and k-nearest neighbors were more suitable when the auxiliary variables were well correlated with the response variable. Results of the study advise that attention should be paid when mapping forest variables characterized by highly heterogeneous structures. The guidelines of this study can be adopted even for mapping environmental attributes beside forestry.

A Comparison of Variability in Two Cassin’s Auklet &lt;i&gt;(Ptychoramphus aleuticus)&lt;/i&gt; Colonies Using Spline-based Nonparametric Models

While variability in the reproductive performance of a population over time is a familiar and useful concept to ecologists, it can be difficult to capture mathematically. Commonly used ecological variability statistics, such as the standard deviation of the logarithm and coefficient of variation, discard the time-ordering of observations and consider only the unordered response variable values. We used a relatively new methodology, the cubic regression spline (a flexible curve fitted to a scatterplot of data), both to illustrate trends in reproductive performance over time and to explore the utility of the cubic regression spline roughness penalty (J) as a statistic for measuring variability while retaining time-ordering information. We concluded that although J measures variability in a mathematical sense, it can be inappropriate in a population ecology context because of sensitivity to small-scale fluctuations. To illustrate our methodology, we used the CRS approach in an analysis of historical data from two Cassin’s Auklet colonies located on Frederick and Triangle Islands in coastal BC, developing a model for the annual mean nestling growth rate on each island over seven contiguous years. Model selection indicated a complex (nonlinear) trend in growth rate on both islands. We report higher variability in the resident bird population of Triangle Island than Frederick Island, based on a comparison of the fitted curves, and the values of the coefficient of variation and population variability summary statistics.

Effects of 90-day feeding of transgenic Bt rice TT51 on the reproductive system in male rats

Rice is a staple food crop; however, the threat of pests leads to a serious decline in its output and quality. The CryAb/CryAc gene, encodes a synthetic fusion Bacillus thuringiensis (Bt) crystal protein, was introduced into rice MingHui63 to produce insect-resistant rice TT51. This study was undertaken to investigate potential unintended effects of TT51 on the reproductive system in male rats. Male rats were treated with diets containing 60% of either TT51 or MingHui63 by weight, nutritionally balanced to an AIN93G diet, for 90days. An additional negative control group of rats were fed with a rice-based AIN93G diet. Body weights, food intake, hematology, serum chemistry, serum hormone levels, sperm parameters and relative organ/body weights were measured, and gross as well as microscopic pathology were examined. No diet-related significant differences in the values of response variables were observed between rats that were fed with diet containing transgenic TT51, MingHui63 and the control in this 90-day feeding study. In addition, necropsy and histopathology examination indicated no treatment-related changes. The results from the present study indicated that TT51 does not appear to exert any effect on the reproductive system in male rats compared with MingHui63 or the control.

Modeling and optimization of reductive degradation of chloramphenicol in aqueous solution by zero-valent bimetallic nanoparticles

The present study aims to investigate the individual and combined effects of temperature, pH, zero-valent bimetallic nanoparticles (ZVBMNPs) dose, and chloramphenicol (CP) concentration on the reductive degradation of CP using ZVBMNPs in aqueous medium. Iron-silver ZVBMNPs were synthesized. Batch experimental data were generated using a four-factor statistical experimental design. CP reduction by ZVBMNPs was optimized using the response surface modeling (RSM) and artificial neural network-genetic algorithm (ANN-GA) approaches. The RSM and ANN methodologies were also compared for their predictive and generalization abilities using the same training and validation data set. Reductive by-products of CP were identified using liquid chromatography-mass spectrometry technique. The optimized process variables (RSM and ANN-GA approaches) yielded CP reduction capacity of 57.37 and 57.10 mg g(-1), respectively, as compared to the experimental value of 54.0 mg g(-1) with un-optimized variables. The ANN-GA and RSM methodologies yielded comparable results and helped to achieve a higher reduction (>6%) of CP by the ZVBMNPs as compared to the experimental value. The root mean squared error, relative standard error of prediction and correlation coefficient between the measured and model-predicted values of response variable were 1.34, 3.79, and 0.964 for RSM and 0.03, 0.07, and 0.999 for ANN models for the training and 1.39, 3.47, and 0.996 for RSM and 1.25, 3.11, and 0.990 for ANN models for the validation set. Predictive and generalization abilities of both the RSM and ANN models were comparable. The synthesized ZVBMNPs may be used for an efficient reductive removal of CP from the water.

Optimization of Extrusion Process for Producing High Antioxidant Instant Amaranth (&amp;lt;i&amp;gt;Amaranthus hypochondriacus&amp;lt;/i&amp;gt; L.) Flour Using Response Surface Methodology

The objective of this research was to determine the best combination of extrusion process variables for the production of a high antioxidant extruded amaranth flour (EAF) suitable to elaborate a nutraceutical beverage. Extrusion operation conditions were obtained from a factorial combination of process variables: Extrusion temperature (ET, 70℃ - 130?℃) and screw speed (SS, 100 - 220 rpm). Response surface methodology was employed as optimization technique; both the numeric and graphical methods were applied to obtain maximum values for response variables [Antioxidant capacity (AoxC) and water solubility index (WSI)]. The best combination of extrusion process variables was: Extrusion tem- perature (ET) = 130℃/Screw speed (SS) = 124 rpm. The raw amaranth flour (RAF) and optimized extruded amaranth flour (EAF) had an antioxidant activity of 3475 and 3903 μmol Trolox equivalents/100 g sample (dw), respectively. A 200 mL portion of the beverage prepared with 22 g of optimized EAF contained 3.16 g proteins, 1.09 g lipids, 17.39 g carbohydrates and 92 kcal. This portion covers 25.3% and 16.9% of the daily protein requirements for children 1-3 and 4 - 8 years old, respectively. A 200 mL portion of the beverage from optimized EAF contributes with 15.5% - 25.5% of the recommended daily intake for antioxidants, respectively. The nutraceutical beverage was evaluated with an average acceptability of 8.4 (level of satisfaction between “I like it” and “I like it extremely”) and could be used for health promotion and disease prevention as an alternative to beverages with low nutritional/nutraceutical value.

Stochastic approximation algorithm for industrial process optimisation

Stochastic approximation algorithms are alternative linear search methods for optimising control systems where the functional relationship between the response variable and the controllable factors in a process and its analytical model remain unknown. These algorithms have no criteria for selecting succession measurements ensuring convergence, meaning that, when implemented in practice, they may diverge with consequent waste of resources. The objective of this research was to determine industrial processes' optimum operating conditions by using a modified stochastic approximation algorithm, where its succession measurements were validated by obtaining response variable values for each iteration through simulation. The algorithm is presented in nine stages; its first six describe which are process independent and dependent variables, the type of experimental design selected, the experiments assigned and developed and the second order models obtained. The last three stages describe how the algorithm was developed, and the optimal values of the independent variables obtained. The algorithm was validated in 3 industrial processes which it was shown to be efficient for determining independent variables' optimum operating conditions (temperature and time): the first three iterations were obtained at 66°C in 3 hours 42 minutes for process 1, unlike processes 2 and 3 where the first iteration was obtained at 66°C in 6 hours 06 minutes and 80°C in 5 hours 06 minutes, respectively.

Comparison of broiler performance and carcass yields when fed transgenic maize grain containing event DP-Ø9814Ø-6 and processed fractions from transgenic soybeans containing event DP-356Ø43-5

The performance of broilers fed diets containing maize grain from event DP-Ø9814Ø-6 (98140; gat4621 and zm-hra genes) and processed fractions (meal, hulls, and oil) from soybeans containing event DP-356Ø43-5 (356043; gat4601 and gm-hra genes) was evaluated in a 42-d feeding study. Diets were produced with nontransgenic maize grain and soybean fractions from controls with comparable genetic backgrounds to 98140 and 356043 (control), 98140 maize and 356043 soybean (98140 + 356043), or 3 commercially available nontransgenic maize and soybean combinations. Ross 708 broilers (n = 120/group; 50% male, 50% female) were fed diets in 3 phases: starter (d 0 to 21), grower (d 22 to 35), and finisher (d 36 to 42). Starter diets contained (on average) 63% maize and 28% soybean meal, grower diets 66% maize and 26% soybean meal, and finisher diets 72% maize and 21% soybean meal; soybean hulls and oils were held constant at 1.0 and 0.5%, respectively, across all diets in all phases. Weight gain, feed intake, and mortality-adjusted feed efficiency were calculated for d 0 to 42. Standard organ and carcass yield data were collected on d 42. Data were analyzed using a mixed model ANOVA with differences between control and 98140 + 356043 group means considered significant at P < 0.05. Reference group data were used only to estimate experimental variability and to generate tolerance intervals. No significant differences were observed in weight gain, mortality, mortality-adjusted feed efficiency, organ yields, or carcass yields between broilers consuming diets produced with 98140 + 356043 and those consuming diets produced with control maize and soybean fractions. All values of response variables evaluated in the control and 98140 + 356043 groups fell within calculated tolerance intervals. Based on these results, it was concluded that the combination of genetically modified 98140 maize and 356043 soybean fractions was nutritionally equivalent to nontransgenic maize and soybean controls with comparable genetic backgrounds.

Optimization of Spray for Drying Morinda citrifolia L. Fruit Extract

Hot water extract of Morinda citrifolia L. fruit was spray dried using K-carrageenan (1 wt%). Spray drying was carried out according to D-optimal design and independent variables selected were temperature and Mcore/Mwall. Spray drying process was optimized by using Response Surface Methodology (RSM) for four different responses such as moisture content, DPPH scavenging activity, Total Phenolic Content (TPC) and Total Flavonoid (TF). Effects of temperature and ratio of core to wall material found to be significant on all responses. Applying desirability function method, optimal spray drying condition for K-carrageenan as binding material were found to be 1:1.5 (Mcore/Mwall) at 90°C. Experimental value of response variables match well with the predicted values. The nanoparticles obtained in this study represent an interesting food additive for incorporation into functional foods due to presence of antioxidants.

Experimental Design and Optimization of Ultrasound Treatment of Food Products

Mathematical modeling for food product development or unit operations modification to produce a food is increasing and adopting some statistical techniques, such as response surface methodology (RSM), to solve problems where several independent variables (or factors) influence the response variable value. Technical problems, data analysis and modeling, experimental design and independent variables’ choice make food scientist and developers harder engagement. Variable selection and optimization of outcome of desired product is challenging task. For this purpose, scientist should aim at determining optimum levels of the main ingredients/compounds in order to obtain suitable responses from desired properties (like aromas, color, physicochemical, rheological and sensory parameters). Therefore, by using statistical techniques, ingredients and their variation range can be tested with a minimum number of experiments while reducing energy, time and cost of testing. This procedure is crucial when laboratory testing of power ultrasound processing of food product should be scaled-up. Scientist that deals with this area of novel non-thermal food processing knows the problem of scaling up, and the procedure is mostly not linear when comparing results from small volumes to large ones. Therefore, experimental planning and design would lead to energy, time and consumables saving, and to be environmentally friendly.

Advancing Synthetic Ecology: A Database System to Facilitate Complex Ecological Meta‐Analyses

Advancing Synthetic Ecology: A Database System to Facilitate Complex Ecological Meta-Analyses

Optimization of supercritical fluid extraction of bioactive compounds from grape (Vitis labrusca B.) peel by using response surface methodology

article i nfo Supercritical fluid extraction (SFE) was applied for the extraction of valuable compounds from grape (Vitis labrusca B.) peel. Extraction was carried out according to an orthogonal array design (OAD) and independent variables selected were temperature, pressure and modifier concentration. SFE process was optimized by using response surface methodology (RSM) for the extract yield, total phenols, antioxidants and total anthocyanins from grape peel. Effects of extraction temperature and pressure were found to be significant on all responses. Optimal SFE conditions were identified as 45-46 °C temperature, 160-165 kg cm �2 pressure and 6-7% ethanol as modifier for maximum extract yield (12.31%), total phenols (2.156 mg GAE/100 mL), antioxidants (1.628 mg/mL) and total anthocyanins (1.176 mg/mL). Experimental values for response variables at these optimal conditions match well with the predicted values. Grape peel extracts obtained by SFE showed more than 93% DPPH radical scavenging activities. Industrial relevance: This study describes the response surface optimization of supercritical fluid extraction (SFE) process for the enhanced recovery of total phenols, antioxidant and anthocyanins from grape peel. SFE uses CO2 as supercritical fluid which is environment friendly solvent; allows extraction at lower temperature and the extracts obtained possess higher quality and safety. Industrially, it may be used as a promising technique for the extraction of bioactive compounds from plant materials.

Freeze-dried, mucoadhesive system for vaginal delivery of the HIV microbicide, dapivirine: Optimisation by an artificial neural network

Dapivirine mucoadhesive gels and freeze-dried tablets were prepared using a 3 × 3 × 2 factorial design. An artificial neural network (ANN) with multi-layer perception was used to investigate the effect of hydroxypropyl-methylcellulose (HPMC): polyvinylpyrrolidone (PVP) ratio (X1), mucoadhesive concentration (X2) and delivery system (gel or freeze-dried mucoadhesive tablet, X3) on response variables; cumulative release of dapivirine at 24 h ( Q 24), mucoadhesive force ( F max) and zero-rate viscosity. Optimisation was performed by minimising the error between the experimental and predicted values of responses by ANN. The method was validated using check point analysis by preparing six formulations of gels and their corresponding freeze-dried tablets randomly selected from within the design space of contour plots. Experimental and predicted values of response variables were not significantly different ( p > 0.05, two-sided paired t-test). For gels, Q 24 values were higher than their corresponding freeze-dried tablets. F max values for freeze-dried tablets were significantly different (2–4 times greater, p > 0.05, two-sided paired t-test) compared to equivalent gels. Freeze-dried tablets having lower values for X1 and higher values for X2 components offered the best compromise between effective dapivirine release, mucoadhesion and viscosity such that increased vaginal residence time was likely to be achieved.

Nixtamalized Instant Flour from Corn (Zea mays L.) Meal: Optimization of Nixtamalization Conditions

ABSTRACTThe present investigation provides a new method for the nixtamalization process wherein corn endosperm fractions (corn meal) are treated in an alkaline solution that yields quality masa or instant masa flour like traditional nixtamalization process (alkaline cooking of corn with lime). The objective of this work was to determine the best combination of nixtamalization process variables for producing nixtamalized instant flour (NIF) from corn meal. Nixtamalization conditions were selected from factorial combinations of process variables including nixtamalization time (NT 8–22 min) and cooking temperature (CT 78–88°C). A central composite rotable experimental design was chosen. Lime concentration was 1% (10 g of Ca(OH)2/L of water) and ratio of corn meal to cooking medium was 1:4. At the end of each cooking, each treatment was steeped for 5 hr at room temperature (25°C). Nixtamalized corn meal was dried (55°C/12 hr) and milled to pass through 80 U.S. mesh to obtain NIF. Response surface methodology (RSM) was applied as an optimization technique over four response variables: masa firmness (MF), masa adhesiveness (MA), tortilla cutting force (CF), and tortilla tensile strength (TS). Predictive models for response variables were developed as a function of process variables. Conventional graphic methods were applied to obtain response variable values similar to the control (MASECA). Contour plots of each response variable applied superposition surface methodology to obtain a contour plot for observation and for selecting the best combination of nixtamalization time (NT 15 min) and cooking temperature (CT 83°C) for producing an optimized NIF from corn meal. Values of MF, MA, CF, and TS obtained from the predictive models were compared with those derived from experimental tests; a close agreement (coefficient of variance &lt; 10%) between both values was observed.

Stable Models and Causal Explanation in Evolutionary Biology

Models that fail to satisfy the Markov condition are unstable because changes in state variable values may cause changes in the values of background variables, and these changes in background lead to predictive error. Such error arises because non-Markovian models fail to track the causal relations generating the values of response variables. This has implications for discussions of the level of selection: under certain plausible conditoins most standard models of group selection will not satisfy the Markov condition when fit to data from real populations. These models neither correctly represent the causal structure generating nor correctly explain the phenomena of interest.