Diagnostic Plots Research Articles

Abstract CT243: Clinical trial simulation to inform dose selection of zilurgisertib, an ALK2 inhibitor, in patients with anemia due to myelofibrosis (MF)

Abstract Disease-associated anemia is reported in over one-third of patients at MF diagnosis and can be exacerbated by currently available MF therapies. Further, anemia is associated with poor prognosis in MF. Activation of activin receptor-like kinase (ALK)-2 (also known as ACVR1) may contribute to MF-associated anemia via up-regulated levels of hepcidin. Zilurgisertib (INCB000928) is a potent and selective ALK2 inhibitor that has demonstrated dose-dependent improvement in hemoglobin levels in a cancer-induced anemia mouse model, in which maximum hemoglobin increase was observed when zilurgisertib trough concentration covered pSMAD IC50 for at least half of the dosing interval. Zilurgisertib is being evaluated as monotherapy or in combination with ruxolitinib in a phase 1/2 dose-escalation and -expansion study in patients with anemia due to MF (NCT04455841). The objective of the current study is to use modeling and simulation, which integrate both preclinical and clinical data, to inform dose finding for the ongoing phase 1/2 trial in MF patients. Population (Pop) PK analyses were conducted using a total of 3089 zilurgisertib plasma concentrations from 161 healthy subjects in 3 completed phase 1 studies: a single ascending dose study (64 subjects, 10-500 mg), a multiple ascending dose study (59 subjects, 50-400 mg QD and 300 mg BID), and a drug-drug interaction study (38 subjects, 100 mg single dose of zilurgisertib alone). A nonlinear mixed-effects modeling approach in Monolix software was used for the analyses. Zilurgisertib PK were well-characterized by a 3-compartment model with nonlinear clearance and inter-occasion variability on absorption rate constant and bioavailability. The diagnostic plots and visual predictive check demonstrated that the model appropriately characterized the zilurgisertib plasma concentrations. Because zilurgisertib PK are similar between MF patients and healthy subjects, as suggested by available PK data in MF patients receiving doses up to 200 mg, the pop PK model developed with data from healthy subjects was used to simulate steady-state exposures in MF patients receiving 200 to 600 mg daily dose. Simulation results showed that daily doses of ≥600 mg would result in >90% of patients achieving exposure similar to the observed exposure associated with maximum efficacy in the mouse model. The conclusions from this clinical trial simulation support the decision to continue dose escalation in MF patients receiving zilurgisertib monotherapy. Citation Format: Yan-ou Yang, Hong Yang, Betty Lamothe, Matthew Stubbs, Amanda McBride, Francis Seguy, Kevin Rockich, Jay Getsy, Phillip Wang, Xiang Liu, Jeff Jackson, Ekaterine Asatiani, Xuejun Chen. Clinical trial simulation to inform dose selection of zilurgisertib, an ALK2 inhibitor, in patients with anemia due to myelofibrosis (MF) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr CT243.

Spatial Data Analytics-Assisted Subsurface Modeling: A Duvernay Case Study

Data analytics facilitate the examination of spatial data sets by using multiple techniques to find and understand patterns to guide decision making. However, standard data analysis tools assume that the data are independent and identically distributed, an assumption that spatial data sets usually do not fulfill. Furthermore, the usual methods neglect spatial continuity and the inherent data paucity that should be considered in the data analytics workflow. We present a new approach that combines data analytics, geostatistics, and optimization techniques to provide an end-to-end workflow to analyze two-dimensional (2D) data sets. The proposed workflow identifies outliers based on their spatial location or distribution, models geological trends using a Gaussian kernel, models the semivariogram, and performs sequential Gaussian simulation applying kriging or cokriging for cosimulation. Moreover, it provides metrics and diagnostic plots to evaluate the goodness of the results at each step. It is also semiautomatic because it leverages the user’s judgment for subsequent operations. For optimization, the workflow uses Bayesian optimization and evolutionary algorithms. We demonstrate the use of the workflow by analyzing 1,152 wells over the Duvernay Formation in Canada. The examples include the simulation of density-porosity as the secondary feature and the cosimulation of total organic content constrained by the former. The proposed workflow helps focus more on interpreting the results than the modeling parameters, reducing workforce time and subjective errors. Moreover, the spatial simulation includes multiple realizations to assess uncertainty and support decision making in data paucity scenarios. Overall, the proposed workflow is a valuable and complementary tool for evaluating uncertainty in mature geospatial data.

Troubleshooting Bayesian cognitive models.

Using Bayesian methods to apply computational models of cognitive processes, or Bayesian cognitive modeling, is an important new trend in psychological research. The rise of Bayesian cognitive modeling has been accelerated by the introduction of software that efficiently automates the Markov chain Monte Carlo sampling used for Bayesian model fitting-including the popular Stan and PyMC packages, which automate the dynamic Hamiltonian Monte Carlo and No-U-Turn Sampler (HMC/NUTS) algorithms that we spotlight here. Unfortunately, Bayesian cognitive models can struggle to pass the growing number of diagnostic checks required of Bayesian models. If any failures are left undetected, inferences about cognition based on the model's output may be biased or incorrect. As such, Bayesian cognitive models almost always require troubleshooting before being used for inference. Here, we present a deep treatment of the diagnostic checks and procedures that are critical for effective troubleshooting, but are often left underspecified by tutorial papers. After a conceptual introduction to Bayesian cognitive modeling and HMC/NUTS sampling, we outline the diagnostic metrics, procedures, and plots necessary to detect problems in model output with an emphasis on how these requirements have recently been changed and extended. Throughout, we explain how uncovering the exact nature of the problem is often the key to identifying solutions. We also demonstrate the troubleshooting process for an example hierarchical Bayesian model of reinforcement learning, including supplementary code. With this comprehensive guide to techniques for detecting, identifying, and overcoming problems in fitting Bayesian cognitive models, psychologists across subfields can more confidently build and use Bayesian cognitive models in their research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Implementation of non-linear mixed effects models defined by fractional differential equations.

Fractional differential equations (FDEs), i.e. differential equations with derivatives of non-integer order, can describe certain experimental datasets more accurately than classic models and have found application in pharmacokinetics (PKs), but wider applicability has been hindered by the lack of appropriate software. In the present work an extension of NONMEM software is introduced, as a FORTRAN subroutine, that allows the definition of nonlinear mixed effects (NLME) models with FDEs. The new subroutine can handle arbitrary user defined linear and nonlinear models with multiple equations, and multiple doses and can be integrated in NONMEM workflows seamlessly, working well with third party packages. The performance of the subroutine in parameter estimation exercises, with simple linear and nonlinear (Michaelis-Menten) fractional PK models has been evaluated by simulations and an application to a real clinical dataset of diazepam is presented. In the simulation study, model parameters were estimated for each of 100 simulated datasets for the two models. The relative mean bias (RMB) and relative root mean square error (RRMSE) were calculated in order to assess the bias and precision of the methodology. In all cases both RMB and RRMSE were below 20% showing high accuracy and precision for the estimates. For the diazepam application the fractional model that best described the drug kinetics was a one-compartment linear model which had similar performance, according to diagnostic plots and Visual Predictive Check, to a three-compartment classic model, but including four less parameters than the latter. To the best of our knowledge, it is the first attempt to use FDE systems in an NLME framework, so the approach could be of interest to other disciplines apart from PKs.

Statistical Analysis and Optimization of the Experimental Results on Performance of Green Aluminum-7075 Hybrid Composites

The present study assessed the potential of engaging response surface analysis in the experimental design, modeling, and optimization of the strength performance of aluminum-7075 green composite. The design of the experiment was carried out via the Box–Behnken method and the independent variables are rice husk ash (RHA) at 3–12 wt.%, glass powder (GP) at 2–10 wt.%, and stirring temperature (ST) at 600–800 °C. Responses examined are yield, ultimate tensile, flexural, and impact strengths, as well as microhardness and compressive strength. ANOVA analysis revealed that the input factors had consequential contributions to each response, eventually presenting regression models statistically fit to represent the experimental data, further affirmed by the diagnostic plots. The result of the optimization envisaged an optimal combination at 7.2% RHA, 6.2 GP, and 695 °C with a desirability of 0.910. A comparison between the predicted values for the responses and the values of the validation experiment revealed an error of <5% for each response. Consequently, the models are certified adequate for response predictions at 95% confidence, and the optimum combination is adequate for the design of the composite.

A model-based meta analysis study of sodium glucose co-transporter-2 inhibitors.

Type 2 diabetes mellitus (T2DM) agent sodium-glucose co-transporter 2 (SGLT2) inhibitors show special benefits in reducing body weight and heart failure risks. To accelerate clinical development for novel SGLT2 inhibitors, a quantitative relationship among pharmacokinetics, pharmacodynamics, and disease end points (PK/PD/end points) in healthy subjects and patients with T2DM was developed. PK/PD/end point data in published clinical studies for three globally marketed SGLT2 inhibitors (dapagliflozin, canagliflozin, and empagliflozin) were collected according to pre-set criteria. Overall, 80 papers with 880 PK, 27 PD, 848 fasting plasma glucose (FPG), and 1219 hemoglobin A1c (HbA1c) data were collected. A two-compartmental model with Hill's equation was utilized to capture PK/PD profiles. A novel translational biomarker, the change of urine glucose excretion (UGE) from baseline normalized by FPG (ΔUGEc ) was identified to bridge healthy subjects and patients with T2DM with different disease statuses. ΔUGEc was found to have a similar maximum increase with different half-maximal effective concentration values of 56.6, 2310, and 841 mg/mL·h for dapagliflozin, canagliflozin, and empagliflozin respectively. ΔUGEc will change FPG based on linear function. HbA1c profiles were captured by indirect response model. Additional placebo effect was also considered for both end points. The PK/ΔUGEc /FPG/HbA1c relationship was validated internally using diagnostic plots and visual assessment and further validated externally using the fourth globally approved same-in-class drug (ertugliflozin). This validated quantitative PK/PD/end point relationship offers novel insight into long-term efficacy prediction for SGLT2 inhibitors. The novelty identified ΔUGEc could make the comparison of different SGLT2 inhibitors' efficacy characteristics easier, and achieve early prediction from healthy subjects to patients.

Optimization of a New Antioxidant Formulation Using a Simplex Lattice Mixture Design of Apium graveolens L., Coriandrum sativum L., and Petroselinum crispum M. Grown in Northern Morocco.

A statistical Simplex Lattice Mixture design was applied to develop a new formulation based on a combination of three plants grown in northern Morocco: Apium graveolens L., Coriandrum sativum L., and Petroselinum crispum M. We examined the extraction yield, total polyphenol content (TPC), 2'2-diphenyl-l-picrylhydrazyl (DPPH) radical scavenging activity, and total antioxidant capacity (TAC). The results of this screening study showed that C. sativum L. had the highest content of DPPH (53.22%) and TAC (37.46 ± 0.29 mg Eq AA/g DW) compared to the other two plants, while P. crispum M. showed the highest TPC (18.52 ± 0.32 mg Eq GA/g DW). Furthermore, the ANOVA analysis of the mixture design showed that all three responses (DPPH, TAC, and TPC) were statistically significant, with determination coefficients of 97%, 93%, and 91%, respectively, and fit the cubic model. Moreover, the diagnostic plots showed good correlation between the experimental and predicted values. Therefore, the best combination obtained under optimal conditions (P1 = 0.611, P2 = 0.289, P3 = 0.100) was characterized by DPPH, TAC, and TPC of 56.21%, 72.74 mg Eq AA/g DW, and 21.98 mg Eq GA/g DW, respectively. The results of this study reinforce the view of stimulating the effect of plant combinations to achieve better antioxidant activities, thus providing a better formulation using designs of mixtures for the food industry and in cosmetic and pharmaceutical applications. Moreover, our findings support the traditional use of the Apiaceae plant species in managing many disorders cited in the Moroccan pharmacopeia.

Euclid preparation

The European Space Agency's Euclid mission will provide high-quality imaging for about 1.5 billion galaxies. A software pipeline to automatically process and analyse such a huge amount of data in real time is being developed by the Science Ground Segment of the Euclid Consortium; this pipeline will include a model-fitting algorithm, which will provide photometric and morphological estimates of paramount importance for the core science goals of the mission and for legacy science. The Euclid Morphology Challenge is a comparative investigation of the performance of five model-fitting software packages on simulated Euclid data, aimed at providing the baseline to identify the best-suited algorithm to be implemented in the pipeline. In this paper we describe the simulated dataset, and we discuss the photometry results. A companion paper is focussed on the structural and morphological estimates. We created mock Euclid images simulating five fields of view of 0.48 deg2 each in the IE band of the VIS instrument, containing a total of about one and a half million galaxies (of which 350 000 have a nominal signal-to-noise ratio above 5), each with three realisations of galaxy profiles (single and double Sérsic, and 'realistic' profiles obtained with a neural network); for one of the fields in the double Sérsic realisation, we also simulated images for the three near-infrared YE, JE, and HE bands of the NISP-P instrument, and five Rubin/LSST optical complementary bands (u, g, r, i, and z), which together form a typical dataset for an Euclid observation. The images were simulated at the expected Euclid Wide Survey depths. To analyse the results, we created diagnostic plots and defined metrics to take into account the completeness of the provided catalogues, as well as the median biases, dispersions, and outlier fractions of their measured flux distributions. Five model-fitting software packages (DeepLeGATo, Galapagos-2, Morfometryka, ProFit, and SourceXtractor++) were compared, all typically providing good results. Of the differences among them, some were at least partly due to the distinct strategies adopted to perform the measurements. In the best-case scenario, the median bias of the measured fluxes in the analytical profile realisations is below 1% at a signal-to-noise ratio above 5 in IE, and above 10 in all the other bands; the dispersion of the distribution is typically comparable to the theoretically expected one, with a small fraction of catastrophic outliers. However, we can expect that real observations will prove to be more demanding, since the results were found to be less accurate for the most realistic realisation. We conclude that existing model-fitting software can provide accurate photometric measurements on Euclid datasets. The results of the challenge are fully available and reproducible through an online plotting tool.

Biochar yield prediction using response surface methodology: effect of fixed carbon and pyrolysis operating conditions

Generating value from wastes via pyrolysis has been increasingly researched in recent times. Biochar is a versatile pyrolysis product with yields based on many process parameters, including feedstock type and particle size, and operating conditions such as pyrolysis reactor, heating rate, residence time, and reaction temperature. The heterogeneous nature of waste biomass creates challenges in controlling the pyrolysis’ product selectivity. Intensive and time-consuming experimental studies are often required to determine product distribution for the pyrolysis of each unique feedstock. Alternatively, prediction models that learn from a wide range of existing experimental data may provide insight into potential yields for different biomass sources. Several advanced models exist in the literature which can predict the yield of biochar and subsequent products based on operating temperature. However, these models do not consider the combined effect of biomass characteristics and operating conditions on biochar yield, which is considered a decisive factor for biochar formation. As such, the objective of this study is to develop a prediction model based on the biomass’ fixed carbon content (14–22%), reaction temperature (350–750 °C), and heating rate (5–10 °C/min) using the response surface methodology. Biomasses, date stones, spent coffee grounds, and cow manure have been used to design a Box-Behnken experiment based on the three factors for the biochar yield response. An empirical equation is developed based on a statistically significant quadratic model to produce optimized biochar yield with high prediction accuracy. The study discussed the 3D response and diagnostic plots and conducted validation experiments to confirm the applicability of the developed model. The biochar yields are significantly affected by the fixed carbon content of the feedstock and the reaction temperature, and the experimental validation confirms the accuracy of biochar yield quantification. The model can be easily applied for further process flow modeling of biomass pyrolysis, only relying on proximate feed analysis, operating temperature, and heating rate.

Precision in Defining Diagnostic Plots Optimizes Well-Test Results

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 206108, “Diagnostic Plots of Pressure-Transient, Rate-Transient, and Diagnostic Fracture Injection/Falloff Tests,” by David Craig, SPE, Occidental, and Thomas Blasingame, SPE, Texas A&M University. The paper has not been peer reviewed. _ All transient-test interpretation methods use diagnostic plots for the identification of wellbore or fracture storage distortion, flow regimes, and other parameters. The associated diagnostic plots are not interchangeable between such tests. The objective of this work is to clearly define the appropriate diagnostic plots for each type of transient test. Introduction In the complete paper, the authors review constant-rate drawdown solutions for a few specific cases of interest and the plotting function used to match buildup data to the drawdown solutions. They extend their review to the cases of buildup or falloff analysis following short flow periods and illustrate the differences between analysis of buildup or falloffs following short and long flow periods. After covering pressure-transient testing, they discuss rate-transient diagnostic plots and the ambiguity observed during analysis of multifractured horizontal well diagnostic plots and the uncertainty of analyst straight lines. Finally, they discuss and demonstrate the correct plotting functions and flow-regime interpretation for diagnostic fracture injection and falloff tests. Multiple examples from the literature are included as part of the discussion. Pressure-Transient Diagnostic Plots Pressure-transient-testing diagnostic plots evolved from the solutions to constant-rate drawdown problems. The authors’ intent is to review infinite-acting solutions for radial flow with wellbore storage and skin and flow through an infinite-conductivity fracture. On a log-log plot, the characteristic slopes, detailed in the complete paper, are unit slope, ½ slope, and zero slope. Many authors have proposed plotting functions for overlaying the observed pressure response from transient tests other than a constant-rate drawdown on the drawdown solutions. In many cases, the slopes identifying storage distortion and flow regimes from drawdown solutions will match and can be used to identify storage and flow regimes from pressure-buildup tests. A situation in which pressure-buildup or falloff test plotting functions fail to match the drawdown solution is when the flow period before shut-in is short. Short-flow transient tests have been reviewed by multiple authors. In lower-permeability reservoirs, pressure-transient tests with short flow periods are very common, and, in unconventional reservoirs, short-flow transient tests are the most-common transient test. Diagnostic plots constructed based on the characteristic shapes of the dimensionless pressure and the dimensionless pressure derivative with respect to the natural logarithm of dimensionless time for constant-rate drawdown solutions cannot be applied in the same manner for short-flow transient tests. With short-flow transient tests, the observed derivative of pressure difference with respect to the natural logarithm of time matches the negative product of dimensionless time and the second derivative of dimensionless pressure with respect to dimensionless time. Additionally, the commonly known impulse derivative, which is defined as the product of time and the derivative of pressure difference with respect to the natural logarithm of time, will match the negative product of dimensionless time squared and the second derivative of dimensionless pressure with respect to dimensionless time.

Coaptation Length as Predictor of Recurrent Mitral Regurgitation After Surgical Repair for Degenerative Mitral Valve Disease: Meta-Analysis of Reconstructed Time-to-Event Data

Coaptation Length as Predictor of Recurrent Mitral Regurgitation After Surgical Repair for Degenerative Mitral Valve Disease: Meta-Analysis of Reconstructed Time-to-Event Data

Modeling of Lemon Production: A Stepwise Regression Approach

Lemon, which is both tropical and subtropical fruit, was formerly used for only entertaining the guests, but today, as a result of its many different uses; it is highly popular with both consumers and producers. Bangladesh is home to extensive lemon groves due to the fruit's high nutritional value, favorable growing conditions, and widespread demand on both domestic and international markets. This study examined the combined impact of farm specific factor and climatic variable on lemon production in Bangladesh. Here the stepwise regression technique with various diagnostic plots in R studio was used to assess the suitable multiple linear regression model and an effective autoregressive integrated moving average (ARIMA) model-based time series analysis was produced for future projection of lemon. The study found a positive link between lemon output and price but a negative correlation with climate variables. Annually, one substantial drop in the lemon cultivars as a result of climatic stress is initiated due to the most significant aspects of which are temperatures and rainfall. Result found, with each millimeter of increased AMR, output diminished by 0.0006419 times on average. The production drops by 0.3564144 times on average for every unit increase in annual mean temperature (AMT). Price increases for lemons may be traced back to covid-19, when production was increased in response to increasing demand about the health benefits of lemons. Also, the investigation found ARIMA (1,1,1) model with drift to the best for future projections of lemon production.

Embedding R inside the PhysPK Bio-simulation Software for Pharmacokinetics Population Analysis

Abstract Background: PhysPK stands as a flexible and robust bio-simulation and modeling software designed for analysis of population pharmacokinetics (PK) and pharmacodynamics (PD) systems. PhysPK equips users with standard diagnostic plots for pre- and post-analysis to delineate PK and PD within population-based frameworks. Furthermore, PhysPK facilitates the establishment of mathematical models that elucidate the intricate interplay between exposure, safety, and efficacy. Methods: Enhancing simulation modeling capabilities necessitates seamless integration between commercial discrete-event PK and PD simulation tools and external software. This synergy can be amplified by incorporating open-source solutions, like R, which boasts a rich array of comprehensive packages tailored for diverse tasks, including data analysis (ggplot2), scientific computation (stats), application development (shiny), back-end web development (dplyr), and machine learning (CARAT). The integration of R within PhysPK holds the potential to efficiently interpret and analyze PK/PD output and routines using R packages. Results: This article presents a tutorial that highlights the incorporation of R code within PhysPK and the rendering of R scripts within the PhysPK monitor. The tutorial utilizes a two-compartment model for comparison against the analysis developed by Hosseini et al. in 2018 within the context of the gPKPDSim application and WinNonlin® software. The illustrative example that is provided and discussed demonstrate estimated and simulated plots, revealing negligible differences in the significance for CL and CLd (6.89 ± 0.2 and 45.5 ± 17.4 [reference], and 7.06 ± 0.32 and 49.04 ± 9.2 [PhysPK], respectively), as well as volumes V1 and V2 (49.15 ± 3.8 and 34.61 ± 5.2 [reference], and 48.8 ± 3.66, and 33.2 ± 3.95 [PhysPK], respectively). Conclusions: Our study underscores the potential of integrating open-source software, replete with an array of innovative packages, to elevate predictive capabilities and streamline analyses in PK methods. This integration ushers in new avenues for an advanced intelligent simulation modeling within the realm of PK, thus holding significant promise for the advancement of drug research and development.

A Two-Level Factorial Design for Screening Factors that Influence the Growth of Pseudomonas sp. Strain Dry135 on Acrylamide

Polyacrylamide is one of the most important sources of acrylamide in soil because it degrades into acrylamide over time. The breakdown of acrylamide by bacteria has experienced a steady but consistent increase in interest all over the world as a bioremediation technique. In this investigation, a previously obtained molybdenum-reducing bacterium with amide-degrading capabilities was found on critical parameters leading to optimum growth on acrylamide utilizing a two-level factorial design. The two-level factorial design was used in the screening of five independent parameters impacting the bacterium's growth on acrylamide. These variables include pH, temperature, incubation period, acrylamide concentration, and ammonium sulphate concentration. The two-factor factorial design was successful in identifying major contributing parameters in the growth of this bacterium on acrylamide, which were acrylamide concentration, pH, and incubation time (p<0.05), which can be further optimized using RSM in future research. ANOVA, Pareto's chart, pertubation's plot, and other diagnostic plots were used to analyze the significant contributing components or parameters. Diagnostic plots such as half-normal, Cook's distance, residual vs runs, leverage vs runs, Box-Cox, DFFITS, and DFBETAS all supported the two-level factorial result. The acrylamide range used in this study was well within the range reported to being tolerated by the majority of acrylamide-degrading bacteria. Incubation time is an expected finding because longer incubation time allows for higher growth, and incubation time ranging from two to five days for optimized growth has been documented in numerous acrylamide-degrading bacteria. Most acrylamide-degrading microorganisms grow well in near-neutral environments, and the results obtained in this investigation are consistent with published literature trends.

A Two-level Factorial Design for Screening Factors that Influence the Growth of Staphylococcus sp. strain Amr-15 on Acrylamide

Due to the fact that it breaks down into acrylamide over time, polyacrylamide is one of the most important sources of acrylamide in soil. As a strategy for bioremediation, the breakdown of acrylamide by the action of microbes has seen a gradual but consistent increase in attention all over the world. A previously isolated molybdenum-reducing bacterium with amide-degrading capability was further identified on significant parameters contributing to optimized growth on acrylamide using a two-level factorial design in this study. The two-level factorial design was adopted in the screening of five independent factors influencing the growth of the bacterium on acrylamide. These factors include pH, temperature, incubation time, acrylamide concentration and glucose concentration. The two-factor factorial design was successful in finding important contributing parameters in the growth of this bacterium on acrylamide, which were acrylamide concentration, pH and incubation time (p<0.05) that can be further optimized using RSM in future works. The important contributing factors or parameters were analysed using ANOVA, Pareto’s chart and perturbations plot and other diagnostic plots. The diagnostic plots such as half-normal, Cook’s distance, residual vs runs, leverage vs runs, Box-Cox, DFFITS, DFBETAS all supported the two-level factorial conclusion. This study was carried out using an acrylamide range well within the range reported to be tolerated by most acrylamide-degrading microorganisms. Incubation time is an expected result since longer incubation time allows more growth and incubation time ranging from two to five days for optimized growth has been reported in many acrylamide-degrading microorganisms. Most of the acrylamide-degrading microorganisms grow well in near-neutral conditions, of which the results obtained in this study conforms to the published literature trends.

A Two-Level Factorial Design for Screening Factors that Influence the Growth of Bacillus sp. Strain UPM2021n isolated from a Mangrove Sediment on Acrylamide

Acrylamide; a neurotoxicant, is an emerging pollutant of global importance. As a strategy for bioremediation, the breakdown of acrylamide by the action of microbes has seen a gradual but consistent increase in attention all over the world. An acrylamide-degrading bacterium tentatively identified as Bacillus sp. strain UPM2021n was screened for significant parameters contributing to optimized growth on acrylamide using a two-level factorial design. The two-level factorial design was adopted in screening of five independent factors influencing the growth of the bacterium on acrylamide. These factors include pH, temperature, incubation time, acrylamide concentration and glucose concentration. A total of 32 experiments with three replications of the centre points were carried out. The two-level factorial design was successful in finding important contributing parameters in the growth of this bacterium on acrylamide, which were pH and incubation time (p<0.05) that can be further optimized using RSM in future works. The important contributing factors or parameters were analysed using ANOVA, Pareto’s chart and pertubation’s plot and other diagnostic plots. The diagnostic plots such as half-normal, Cook’s distance, residual vs runs, leverage vs runs, Box-Cox, DFFITS, DFBETAS all supported the two-level factorial conclusion with the exception of potentially two outliers that meant the experiment should either be repeated again using blocks or the potential outliers removed from analysis. This significant factors in this study are well within the range reported in many acrylamide-degrading microorganisms. The significant factors obtained in this study will be further processed using Response Surface Method (RSM).

A Two-Level Factorial Design for Screening Factors that Influence the Growth of E. cloacae strain UPM2021a on Acrylamide

The world has gradually but steadily paid more attention to the use of bacteria to break down acrylamide as a bioremediation technique. Using a two-level factorial design, a previously obtained molybdenum-reducing bacterium with the ability to degrade amides was further identified on important factors influencing its optimal development on acrylamide. In order to screen five distinct parameters impacting the development of the bacterium on acrylamide, a two-level factorial design was used. Three center point replications were used in a total of 32 tests. These variables include pH, temperature, the length of the incubation period, the concentrations of acrylamide and glucose. Acrylamide concentration, pH, and incubation time were found to be key factors in this bacterium's growth on acrylamide by the two-factor factorial design and were successfully adjusted using RSM in subsequent studies. Using ANOVA, Pareto's chart, pertubation's plot, and other diagnostic plots, the significant contributing components or parameters were analyzed. The two-level factorial conclusion was supported by diagnostic plots including half-normal, Cook's distance, residual vs runs, leverage vs runs, Box-Cox, DFFITS, and DFBETAS. The acrylamide range used in this investigation is well within the range that most acrylamide-degrading bacteria have been found to tolerate. Longer incubation times allow for higher growth, and many acrylamide-degrading microorganisms have been observed to have incubation times of two to five days for optimized growth. The majority of acrylamide-degrading microorganisms thrive at circumstances that are close to neutral, and the findings of this study are consistent with published literature trends in this regard.

A Two-Level Factorial Design for Screening Factors that Influence the Growth of Bacillus sp. Strain ZEID-14 on Acrylamide

Acrylamide is often used to strengthen soil structure and usually leachate from this application forms a major source of acrylamide pollution in the environment. The soil matrix means physicochemical methods of removal will be costly if not difficult. Bioremediation using acrylamide-degrading bacteria is an appealing technique. In this investigation, a previously isolated molybdenum-reducing bacterium with the ability to degrade amides was discovered based on critical characteristics contributing to optimal growth on acrylamide utilizing a two-level factorial design. Five independent parameters that influence the growth of the bacterium on acrylamide were evaluated using a two-level factorial design. Among these variables are pH, temperature, incubation period, acrylamide content, and glucose concentration. The two-factor factorial design was successful in identifying significant contributing parameters to the growth of this bacterium on acrylamide, namely acrylamide concentration, pH, and incubation time, which can be further otpimized using RSM in future studies. Using ANOVA, Pareto's chart, perturbation's plot, and other diagnostic plots, the significant contributing factors or parameters were examined. Half-normal, Cook's distance, residual vs runs, leverage versus runs, Box-Cox, DFFITS, and DFBETAS diagnostic plots all supported the two-level factorial result. This work was conducted using acrylamide concentrations well within the known tolerance range of most acrylamide-degrading bacteria. Incubation time is an expected consequence, as longer incubation time permits more growth. The majority of acrylamide-degrading microorganisms thrive under near-neutral circumstances, as indicated by the results of our investigation, which are consistent with previous literature trends.

Central Composite Design-based Optimization of Staphylococcus sp. strain Amr-15 Growth on Acrylamide as a Nitrogen Source

As an approach for bioremediation, the decomposition of acrylamide by microorganisms has received gradual but persistent worldwide interest. Prior to this study, a molybdenum-reducing bacteria had been identified and exhibited the ability to breakdown amides. Its key growth parameters on acrylamide were further investigated. A Central Composite Design (CCD) was employed to optimize the two previously identified key factors (incubation time and acrylamide concentration). For the examination of the significant factors or parameters, ANOVA, the perturbation plot, and numerous other diagnostic plots were employed. Using the "Numerical Optimisation" toolbox of Design Expert software, predicted ideal conditions were calculated. There were two ideal conditions investigated. The first was to determine the optimal growth under the employed range of variables, while the second was to forecast the optimal growth at the greatest acceptable acrylamide concentration of 1 g/L. The solution for the first predicted model predicted a maximum growth of 8.96 Log CFU/mL (95 percent confidence interval from 8.19 to 9.73), which was confirmed by experimental results with a growth of 9.88 Log CFU/mL (95 percent confidence interval from 9.79 to 9.97), which was close to the predicted values but significantly greater than the predicted values. The second numerical optimization for maximum growth with the highest acrylamide content. The solution had a predicted maximum growth of 7.81 Log CFU/mL (95 percent C.I. from 7.06 to 8.57) and was experimentally confirmed to have a growth of 8.74 Log CFU/mL (95 percent C.I. from 8.56 to 8.92), with the difference not being statistically significant (p0.05) indicating close agreement between predicted and experimental values. The findings of the RSM exercise demonstrated that growth on acrylamide may be optimized more efficiently with RSM than with OFAT, indicating that RSM is more useful in this regard than OFAT.

Response Surface Method for the Optimization of Pseudomonas sp. strain DrY135 Growth on Acrylamide as a Nitrogen Source

Acrylamide pollution and contamination can come from a variety of sources and is an emerging toxic pollutant that need remediation. A molybdenum-reducing bacteria that had been previously isolated and demonstrated the ability to degrade amides was further studied of its critical parameters contributing to optimum growth on acrylamide. The Box-Behnken design was utilized in optimizing the three previously identified significant components (pH, incubation time and acrylamide concentration). ANOVA, the pertubation's plot, and several other diagnostic plots were utilized in the analysis of the significant factors or parameters that contributed. The model was supported by the diagnostic plots including the half-normal, Cook's distance, leverage vs runs, residual vs runs, Box-Cox, DFFITS, and DFBETAS. Conditions that were predicted to be optimal were found and analyzed in order to find the optimal growth given the factors that were used and to determine the optimal concentration, which was 1 g/L. The solutions for finding the optimal growth predicted a growth maximum of 12.055 Log CFU/mL (95 percent confidence interval (C.I.) from 11.550 to 12.593), and verification using experimental results resulted in a growth of 12.908 Log CFU/mL (12.744 to 13.072) with the results being close to the predicted values but was significantly higher than the predicted data. The second numerical optimization was for predicting the highest acrylamide concentration tolerable for growth and the solution shows a predicted a growth maximum of 12.055 Log CFU/mL (95 percent confidence interval from 11.550 to 12.593). This solution was experimentally verified resulting in a growth of 12.195 Log CFU/mL (95 percent confidence interval from 11.806 to 12.584) with the actual results being in accordance with the predicted values. The results of the RSM exercise showed that growth on acrylamide may be optimized more effectively with RSM than with OFAT, which indicates that RSM is more useful than OFAT in this regard.