Displaying publications 41 - 60 of 311 in total

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  1. Improvement of production of citric acid from oil palm empty fruit bunches: optimization of media by statistical experimental designs
    Bari MN, Alam MZ, Muyibi SA, Jamal P, Abdullah-Al-Mamun
    Bioresour Technol, 2009 Jun;100(12):3113-20.
    PMID: 19231166 DOI: 10.1016/j.biortech.2009.01.005
    A sequential optimization based on statistical design and one-factor-at-a-time (OFAT) method was employed to optimize the media constituents for the improvement of citric acid production from oil palm empty fruit bunches (EFB) through solid state bioconversion using Aspergillus niger IBO-103MNB. The results obtained from the Plackett-Burman design indicated that the co-substrate (sucrose), stimulator (methanol) and minerals (Zn, Cu, Mn and Mg) were found to be the major factors for further optimization. Based on the OFAT method, the selected medium constituents and inoculum concentration were optimized by the central composite design (CCD) under the response surface methodology (RSM). The statistical analysis showed that the optimum media containing 6.4% (w/w) of sucrose, 9% (v/w) of minerals and 15.5% (v/w) of inoculum gave the maximum production of citric acid (337.94 g/kg of dry EFB). The analysis showed that sucrose (p<0.0011) and mineral solution (p<0.0061) were more significant compared to inoculum concentration (p<0.0127) for the citric acid production.
    Matched MeSH terms: Models, Statistical
  2. Optimization of microporous palm shell activated carbon production for flue gas desulphurization: experimental and statistical studies
    Sumathi S, Bhatia S, Lee KT, Mohamed AR
    Bioresour Technol, 2009 Feb;100(4):1614-21.
    PMID: 18952414 DOI: 10.1016/j.biortech.2008.09.020
    Optimizing the production of microporous activated carbon from waste palm shell was done by applying experimental design methodology. The product, palm shell activated carbon was tested for removal of SO2 gas from flue gas. The activated carbon production was mathematically described as a function of parameters such as flow rate, activation time and activation temperature of carbonization. These parameters were modeled using response surface methodology. The experiments were carried out as a central composite design consisting of 32 experiments. Quadratic models were developed for surface area, total pore volume, and microporosity in term of micropore fraction. The models were used to obtain the optimum process condition for the production of microporous palm shell activated carbon useful for SO2 removal. The optimized palm shell activated carbon with surface area of 973 m(2)/g, total pore volume of 0.78 cc/g and micropore fraction of 70.5% showed an excellent agreement with the amount predicted by the statistical analysis. Palm shell activated carbon with higher surface area and microporosity fraction showed good adsorption affinity for SO2 removal.
    Matched MeSH terms: Models, Statistical*
  3. Process optimization and performance evaluation on sequential ionic liquid dissolution-solid acid saccharification of sago waste
    Lee KM, Ngoh GC, Chua AS
    Bioresour Technol, 2013 Feb;130:1-7.
    PMID: 23280179 DOI: 10.1016/j.biortech.2012.11.124
    The production of reducing sugars from sago waste via sequential ionic liquid dissolution-solid acid saccharification was optimized in this study. Ionic liquid dissolution of sago waste with 1-butyl-3-methylimidazolium chloride ([BMIM]Cl) was conducted prior to the solid acid saccharification with Amberlyst 15 (A15). The effect of time, temperature and substrate loading during dissolution reaction; and the effect of time, temperature and catalyst loading during saccharification reaction were examined by applying central composite design (CCD) separately. Both dissolution and saccharification reactions were respectively modeled into quadratic polynomial equations with good predictive accuracies. A high reducing sugars yield of 98.3% was obtained under the optimized conditions, i.e. dissolution at 1.75h, 160°C, 1.5% substrate loading, and saccharification at 0.5h, 130°C, 4% catalyst loading. From comparison studies of different saccharification schemes, the sequential ionic liquid dissolution-solid acid saccharification has proven to be a potential method in reducing sugars production from the lignocellulosic biomass.
    Matched MeSH terms: Models, Statistical
  4. Kinetic behaviour of free lipase and mica-based immobilized lipase catalyzing the synthesis of sugar esters
    Zaidan UH, Abdul Rahman MB, Othman SS, Basri M, Abdulmalek E, Rahman RN, et al.
    Biosci Biotechnol Biochem, 2011;75(8):1446-50.
    PMID: 21821960
    The utilization of natural mica as a biocatalyst support in kinetic investigations is first described in this study. The formation of lactose caprate from lactose sugar and capric acid, using free lipase (free-CRL) and lipase immobilized on nanoporous mica (NER-CRL) as a biocatalyst, was evaluated through a kinetic study. The apparent kinetic parameters, K(m) and V(max), were determined by means of the Michaelis-Menten kinetic model. The Ping-Pong Bi-Bi mechanism with single substrate inhibition was adopted as it best explains the experimental findings. The kinetic results show lower K(m) values with NER-CRL than with free-CRL, indicating the higher affinity of NER-CRL towards both substrates at the maximum reaction velocity (V(max,app)>V(max)). The kinetic parameters deduced from this model were used to simulate reaction rate data which were in close agreement with the experimental values.
    Matched MeSH terms: Models, Statistical
  5. A Weibull statistics-based lignocellulose saccharification model and a built-in parameter accurately predict lignocellulose hydrolysis performance
    Wang M, Han L, Liu S, Zhao X, Yang J, Loh SK, et al.
    Biotechnol J, 2015 Sep;10(9):1424-33.
    PMID: 26121186 DOI: 10.1002/biot.201400723
    Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed.
    Matched MeSH terms: Models, Statistical*
  6. Nutrient improvement using statistical optimization for growth of Schizophyllum commune, and its antifungal activity against wood degrading fungi of rubberwood
    Teoh YP, Don MM, Ujang S
    Biotechnol Prog, 2012 Jan-Feb;28(1):232-41.
    PMID: 21990033 DOI: 10.1002/btpr.714
    Two statistical tools, Plackett-Burman design (PBD) and Box-Behnken design (BBD) were used to optimize the mycelia growth of Schizophyllum commune with different nutrient components. Results showed that 32.92 g/L of biomass were produced using a medium consisting of 18.74 g/L yeast extract, 38.65 g/L glucose, and 0.59 g/L MgSO(4).7H(2)O. The experimental data fitted well with the model predicted values within 0.09 to 0.77% error. The biomass was also tested for antifungal activity against wood degrading fungi of rubberwood. Results showed that the minimum inhibitory concentration (MIC) values for antifungal activity range from 0.16 to 5.00 μg/μL. The GC-MS analysis indicated that this fungus produced several compounds, such as glycerin, 2(3H)-furanone, 5-heptyldihydro-, 4H-pyran-4-one, 2,3-dihydro-3,5-dihydroxy-6-methyl-, and triacetin.
    Matched MeSH terms: Models, Statistical
  7. Prediction of breast cancer risk based on common genetic variants in women of East Asian ancestry
    Wen W, Shu XO, Guo X, Cai Q, Long J, Bolla MK, et al.
    Breast Cancer Res, 2016 12 08;18(1):124.
    PMID: 27931260
    BACKGROUND: Approximately 100 common breast cancer susceptibility alleles have been identified in genome-wide association studies (GWAS). The utility of these variants in breast cancer risk prediction models has not been evaluated adequately in women of Asian ancestry.

    METHODS: We evaluated 88 breast cancer risk variants that were identified previously by GWAS in 11,760 cases and 11,612 controls of Asian ancestry. SNPs confirmed to be associated with breast cancer risk in Asian women were used to construct a polygenic risk score (PRS). The relative and absolute risks of breast cancer by the PRS percentiles were estimated based on the PRS distribution, and were used to stratify women into different levels of breast cancer risk.

    RESULTS: We confirmed significant associations with breast cancer risk for SNPs in 44 of the 78 previously reported loci at P 

    Matched MeSH terms: Models, Statistical
  8. Fulltext Statins and risk of treated incident diabetes in a primary care population
    Zaharan NL, Williams D, Bennett K
    Br J Clin Pharmacol, 2013 Apr;75(4):1118-24.
    PMID: 22845189 DOI: 10.1111/j.1365-2125.2012.04403.x
    (i) To examine the incidence of new onset treated diabetes in patients treated with different types of statins and (ii) the relationship between the duration and dose of statins and the subsequent development of new onset treated diabetes.
    Matched MeSH terms: Models, Statistical*
  9. Fulltext Implications for Drug Characterization in Glucose Tolerance Tests Without Insulin: Simulation Study of Power and Predictions Using Model-Based Analysis
    Sheikh Ghadzi SM, Karlsson MO, Kjellsson MC
    CPT Pharmacometrics Syst Pharmacol, 2017 10;6(10):686-694.
    PMID: 28575547 DOI: 10.1002/psp4.12214
    In antihyperglycemic drug development, drug effects are usually characterized using glucose provocations. Analyzing provocation data using pharmacometrics has shown powerful, enabling small studies. In preclinical drug development, high power is attractive due to the experiment sizes; however, insulin is not always available, which potentially impacts power and predictive performance. This simulation study was performed to investigate the implications of performing model-based drug characterization without insulin. The integrated glucose-insulin model was used to simulate and re-estimated oral glucose tolerance tests using a crossover design of placebo and study compound. Drug effects were implemented on seven different mechanisms of action (MOA); one by one or in two-drug combinations. This study showed that exclusion of insulin may severely reduce the power to distinguish the correct from competing drug effect, and to detect a primary or secondary drug effect, however, it did not affect the predictive performance of the model.
    Matched MeSH terms: Models, Statistical*
  10. Comparison of the Newtonian and relativistic predicted trajectories for a low-speed periodically delta-kicked system
    Lan BL
    Chaos, 2006 Sep;16(3):033107.
    PMID: 17014212
    The dynamics of a periodically delta-kicked Hamiltonian system moving at low speed (i.e., at speed much less than the speed of light) is studied numerically. In particular, the trajectory of the system predicted by Newtonian mechanics is compared with the trajectory predicted by special relativistic mechanics for the same parameters and initial conditions. We find that the Newtonian trajectory, although close to the relativistic trajectory for some time, eventually disagrees completely with the relativistic trajectory, regardless of the nature (chaotic, nonchaotic) of each trajectory. However, the agreement breaks down very fast if either the Newtonian or relativistic trajectory is chaotic, but very much slower if both the Newtonian and relativistic trajectories are nonchaotic. In the former chaotic case, the difference between the Newtonian and relativistic values for both position and momentum grows, on average, exponentially. In the latter nonchaotic case, the difference grows much slower, for example, linearly on average.
    Matched MeSH terms: Models, Statistical
  11. Comparative studies of RSM Box-Behnken and ANN-Anfis fuzzy statistical analysis for seawater biodegradability using TiO2 photocatalyst
    Mohammed N, Palaniandy P, Shaik F, Mewada H, Balakrishnan D
    Chemosphere, 2023 Feb;314:137665.
    PMID: 36581118 DOI: 10.1016/j.chemosphere.2022.137665
    In this approach, a batch reactor was employed to study the degradation of pollutants under natural sunlight using TiO2 as a photocatalyst. The effects of photocatalyst dosage, reaction time and pH were investigated by evaluating the percentage removal efficiencies of total organic carbon (TOC), chemical oxygen demand (COD), biological oxygen demand (BOD) and biodegradability (BOD/COD). Design Expert-Response Surface Methodology Box Behnken Design (BBD) and MATLAB Artificial Neural Network - Adaptive Neuro Fuzzy Inference system (ANN-ANFIS) methods were employed to perform the statistical modelling. The experimental values of maximum percentage removal efficiencies were found to be TOC = 82.4, COD = 85.9, BOD = 30.9% and biodegradability was 0.070. According to RSM-BBD and ANFIS analysis, the maximum percentage removal efficiencies were found to be TOC = 90.3, 82.4; COD = 85.4, 85.9; BOD = 28.9, 30.9% and the biodegradability = 0.074, 0.080 respectively at the pH 7.5, reaction time 300 min and photocatalyst dosage of 4 g L-1. The study reveals both models found to be well predicted as compared with experimental values. The values of R2 for RSM-BBD (0.920) and for ANFIS (0.990) models were almost close to 1. The ANFIS model was found to be marginally better than that of RSM-BBD.
    Matched MeSH terms: Models, Statistical*
  12. Forecasting asthma-related hospital admissions in London using negative binomial models
    Soyiri IN, Reidpath DD, Sarran C
    Chron Respir Dis, 2013 May;10(2):85-94.
    PMID: 23620439 DOI: 10.1177/1479972313482847
    Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005-2006), two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.
    Matched MeSH terms: Models, Statistical
  13. Periodontal treatment needs and workforce requirements: comparisons between the normative and sociodental approaches using different skill mix models
    Ab-Murat N, Sheiham A, Tsakos G, Watt R
    Community Dent Oral Epidemiol, 2015 Apr;43(2):106-15.
    PMID: 25178437 DOI: 10.1111/cdoe.12125
    Assessment of dental treatment needs has predominantly been based on the normative approach, despite its numerous limitations. The sociodental approach is a more rational method of needs assessment as it incorporates broader concepts of health and needs and behavioural propensity. This study compares estimates of periodontal dental treatment needs and workforce requirements for different skill mixes using normative and sociodental approaches among a sample of adults in Malaysia.
    Matched MeSH terms: Models, Statistical
  14. Fulltext Bayesian inference of the Weibull model based on interval-censored survival data
    Guure CB, Ibrahim NA, Adam MB
    Comput Math Methods Med, 2013;2013:849520.
    PMID: 23476718 DOI: 10.1155/2013/849520
    Interval-censored data consist of adjacent inspection times that surround an unknown failure time. We have in this paper reviewed the classical approach which is maximum likelihood in estimating the Weibull parameters with interval-censored data. We have also considered the Bayesian approach in estimating the Weibull parameters with interval-censored data under three loss functions. This study became necessary because of the limited discussion in the literature, if at all, with regard to estimating the Weibull parameters with interval-censored data using Bayesian. A simulation study is carried out to compare the performances of the methods. A real data application is also illustrated. It has been observed from the study that the Bayesian estimator is preferred to the classical maximum likelihood estimator for both the scale and shape parameters.
    Matched MeSH terms: Models, Statistical
  15. Fulltext An Efficient Optimization Method for Solving Unsupervised Data Classification Problems
    Shabanzadeh P, Yusof R
    Comput Math Methods Med, 2015;2015:802754.
    PMID: 26336509 DOI: 10.1155/2015/802754
    Unsupervised data classification (or clustering) analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO) algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.
    Matched MeSH terms: Models, Statistical
  16. Fulltext Fractional Diffusion Based Modelling and Prediction of Human Brain Response to External Stimuli
    Namazi H, Kulish VV
    Comput Math Methods Med, 2015;2015:148534.
    PMID: 26089955 DOI: 10.1155/2015/148534
    Human brain response is the result of the overall ability of the brain in analyzing different internal and external stimuli and thus making the proper decisions. During the last decades scientists have discovered more about this phenomenon and proposed some models based on computational, biological, or neuropsychological methods. Despite some advances in studies related to this area of the brain research, there were fewer efforts which have been done on the mathematical modeling of the human brain response to external stimuli. This research is devoted to the modeling and prediction of the human EEG signal, as an alert state of overall human brain activity monitoring, upon receiving external stimuli, based on fractional diffusion equations. The results of this modeling show very good agreement with the real human EEG signal and thus this model can be used for many types of applications such as prediction of seizure onset in patient with epilepsy.
    Matched MeSH terms: Models, Statistical
  17. Fulltext A Unified Framework for Brain Segmentation in MR Images
    Yazdani S, Yusof R, Karimian A, Riazi AH, Bennamoun M
    Comput Math Methods Med, 2015;2015:829893.
    PMID: 26089978 DOI: 10.1155/2015/829893
    Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets.
    Matched MeSH terms: Models, Statistical
  18. Fulltext Round Randomized Learning Vector Quantization for Brain Tumor Imaging
    Sheikh Abdullah SN, Bohani FA, Nayef BH, Sahran S, Al Akash O, Iqbal Hussain R, et al.
    Comput Math Methods Med, 2016;2016:8603609.
    PMID: 27516807 DOI: 10.1155/2016/8603609
    Brain magnetic resonance imaging (MRI) classification into normal and abnormal is a critical and challenging task. Owing to that, several medical imaging classification techniques have been devised in which Learning Vector Quantization (LVQ) is amongst the potential. The main goal of this paper is to enhance the performance of LVQ technique in order to gain higher accuracy detection for brain tumor in MRIs. The classical way of selecting the winner code vector in LVQ is to measure the distance between the input vector and the codebook vectors using Euclidean distance function. In order to improve the winner selection technique, round off function is employed along with the Euclidean distance function. Moreover, in competitive learning classifiers, the fitting model is highly dependent on the class distribution. Therefore this paper proposed a multiresampling technique for which better class distribution can be achieved. This multiresampling is executed by using random selection via preclassification. The test data sample used are the brain tumor magnetic resonance images collected from Universiti Kebangsaan Malaysia Medical Center and UCI benchmark data sets. Comparative studies showed that the proposed methods with promising results are LVQ1, Multipass LVQ, Hierarchical LVQ, Multilayer Perceptron, and Radial Basis Function.
    Matched MeSH terms: Models, Statistical
  19. Fulltext Application of phase congruency for discriminating some lung diseases using chest radiograph
    Rijal OM, Ebrahimian H, Noor NM, Hussin A, Yunus A, Mahayiddin AA
    Comput Math Methods Med, 2015;2015:424970.
    PMID: 25918551 DOI: 10.1155/2015/424970
    A novel procedure using phase congruency is proposed for discriminating some lung disease using chest radiograph. Phase congruency provides information about transitions between adjacent pixels. Abrupt changes of phase congruency values between pixels may suggest a possible boundary or another feature that may be used for discrimination. This property of phase congruency may have potential for deciding between disease present and disease absent where the regions of infection on the images have no obvious shape, size, or configuration. Five texture measures calculated from phase congruency and Gabor were shown to be normally distributed. This gave good indicators of discrimination errors in the form of the probability of Type I Error (δ) and the probability of Type II Error (β). However, since 1 -  δ is the true positive fraction (TPF) and β is the false positive fraction (FPF), an ROC analysis was used to decide on the choice of texture measures. Given that features are normally distributed, for the discrimination between disease present and disease absent, energy, contrast, and homogeneity from phase congruency gave better results compared to those using Gabor. Similarly, for the more difficult problem of discriminating lobar pneumonia and lung cancer, entropy and homogeneity from phase congruency gave better results relative to Gabor.
    Matched MeSH terms: Models, Statistical
  20. Improved biliary detection and diagnosis through intelligent machine analysis
    Logeswaran R
    Comput Methods Programs Biomed, 2012 Sep;107(3):404-12.
    PMID: 21194781 DOI: 10.1016/j.cmpb.2010.12.002
    This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases.
    Matched MeSH terms: Models, Statistical
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