Breast cancer is the most common cancer among women globally, and the number of young women diagnosed with this disease is gradually increasing over the years. Mammography is the current gold-standard technique although it is known to be less sensitive in detecting tumors in woman with dense breast tissue. Detecting an early-stage tumor in young women is very crucial for better survival chance and treatment. The thermography technique has the capability to provide an additional functional information on physiological changes to mammography by describing thermal and vascular properties of the tissues. Studies on breast thermography have been carried out to improve the accuracy level of the thermography technique in various perspectives. However, the limitation of gathering women affected by cancer in different age groups had necessitated this comprehensive study which is aimed to investigate the effect of different density levels on the surface temperature distribution profile of the breast models. These models, namely extremely dense (ED), heterogeneously dense (HD), scattered fibroglandular (SF), and predominantly fatty (PF), with embedded tumors were developed using the finite element method. A conventional Pennes' bioheat model was used to perform the numerical simulation on different case studies, and the results obtained were then compared using a hypothesis statistical analysis method to the reference breast model developed previously. The results obtained show that ED, SF, and PF breast models had significant mean differences in surface temperature profile with a p value <0.025, while HD breast model data pair agreed with the null hypothesis formulated due to the comparable tissue composition percentage to the reference model. The findings suggested that various breast density levels should be considered as a contributing factor to the surface thermal distribution profile alteration in both breast cancer detection and analysis when using the thermography technique.
Matched MeSH terms: Data Interpretation, Statistical
This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study.
Matched MeSH terms: Data Interpretation, Statistical*
The importance of instilling leadership skills in students has always been a main subject of discussion in Malaysia. Malaysian Secondary School Students Leadership Inventory (M3SLI) is an instrument which has been piloted tested in year 2013. The main purpose of this study is to examine and optimize the functioning of the rating scale categories in M3SLI by investigating the rating scale category counts, average and expected rating scale category measures, and steps calibrations. In detail, the study was aimed to (1) identify whether the five-point rating scale was functioning as intended and (2) review the effect of a rating scale category revision on the psychometric characteristics of M3SLI. The study was carried out on students aged between 13 to 18 years (2183 students) by stratified random sampling in 26 public schools in Sabah, Malaysia, with the results analysed using Winsteps. This study found that the rating scale of Personality and Values constructs needed to be modified while the scale for Leadership Skills was maintained. For future studies, other aspects of psychometric properties like differential item functioning (DIF) based on demographic variables such as gender, school locations and forms should be researched on prior to the use of the instrument.
Matched MeSH terms: Data Interpretation, Statistical
Multiple imputation method is a widely used method in missing data analysis. The method consists of a three-stage
process including imputation, analyzing and pooling. The number of imputations to be selected in the imputation step
in the first stage is important. Hence, this study aimed to examine the performance of multiple imputation method at
different numbers of imputations. Monotone missing data pattern was created in the study by deleting approximately 24%
of the observations from the continuous result variable with complete data. At the first stage of the multiple imputation
method, monotone regression imputation at different numbers of imputations (m=3, 5, 10 and 50) was performed. In the
second stage, parameter estimations and their standard errors were obtained by applying general linear model to each
of the complete data sets obtained. In the final stage, the obtained results were pooled and the effect of the numbers of
imputations on parameter estimations and their standard errors were evaluated on the basis of these results. In conclusion,
efficiency of parameter estimations at the number of imputation m=50 was determined as about 99%. Hence, at the
determined missing observation rate, increase was determined in efficiency and performance of the multiple imputation
method as the number of imputations increased.
Matched MeSH terms: Data Interpretation, Statistical
When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method.
Matched MeSH terms: Data Interpretation, Statistical*
Forecasting naturally occurring phenomena is a common problem in many domains of science, and this has been addressed and investigated by many scientists. The importance of time series prediction stems from the fact that it has wide range of applications, including control systems, engineering processes, environmental systems and economics. From the knowledge of some aspects of the previous behaviour of the system, the aim of the prediction process is to determine or predict its future behaviour. In this paper, we consider a novel application of a higher order polynomial neural network architecture called Dynamic Ridge Polynomial Neural Network that combines the properties of higher order and recurrent neural networks for the prediction of physical time series. In this study, four types of signals have been used, which are; The Lorenz attractor, mean value of the AE index, sunspot number, and heat wave temperature. The simulation results showed good improvements in terms of the signal to noise ratio in comparison to a number of higher order and feedforward neural networks in comparison to the benchmarked techniques.
Matched MeSH terms: Data Interpretation, Statistical
To segment an image using the random walks algorithm; users are often required to initialize the approximate locations of the objects and background in the image. Due to its segmenting model that is mainly reflected by the relationship among the neighborhood pixels and its boundary conditions, random walks algorithm has made itself sensitive to the inputs of the seeds. Instead of considering the relationship between the neighborhood pixels solely, an attempt has been made to modify the weighting function that accounts for the intensity changes between the neighborhood nodes. Local affiliation within the defined neighborhood region of the two nodes is taken into consideration by incorporating an extra penalty term into the weighting function. Besides that, to better segment images, particularly medical images with texture features, GLCM variance is incorporated into the weighting function through kernel density estimation (KDE). The probability density of each pixel belonging to the initialized seeds is estimated and integrated into the weighting function. To test the performance of the proposed weighting model, several medical images that mainly made up of 174-brain tumor images are experimented. These experiments establish that the proposed method produces better segmentation results than the original random walks.
Matched MeSH terms: Data Interpretation, Statistical*
Well-defined image can assist user to identify region of interest during segmentation. However, complex medical image is usually characterized by poor tissue contrast and low background luminance. The contrast improvement can lift image visual quality, but the fundamental contrast enhancement methods often overlook the sudden jump problem. In this work, the proposed bihistogram Bezier curve contrast enhancement introduces the concept of "adequate contrast enhancement" to overcome sudden jump problem in knee magnetic resonance image. Since every image produces its own intensity distribution, the adequate contrast enhancement checks on the image's maximum intensity distortion and uses intensity discrepancy reduction to generate Bezier transform curve. The proposed method improves tissue contrast and preserves pertinent knee features without compromising natural image appearance. Besides, statistical results from Fisher's Least Significant Difference test and the Duncan test have consistently indicated that the proposed method outperforms fundamental contrast enhancement methods to exalt image visual quality. As the study is limited to relatively small image database, future works will include a larger dataset with osteoarthritic images to assess the clinical effectiveness of the proposed method to facilitate the image inspection.
Matched MeSH terms: Data Interpretation, Statistical
INTRODUCTION: There is a lack of study comparing the attitudes toward epilepsy between the teachers and general population, teachers and students, using a similar quantitative scale.
METHODS: This study was performed in one primary and one secondary school in Kuala Lumpur, Malaysia, using the Public Attitudes Toward Epilepsy (PATE) scale.
RESULTS: A total of 186 teachers aged 39.6±10.4 years completed the questionnaire. The mean scores in both personal and general domains of PATE scale were significantly better in the teachers, comparing to the scores in the secondary and college students reported in previous study (Lim et al., 2013; p<0.001 and <0.05, respectively). The mean scores in personal domain was significantly better in the teachers, comparing to the general population reported by Lim et al. (2012; p<0.001). This hold true when comparing teachers with general population with tertiary education, suggesting that the better attitude is specific to the job, rather than tertiary education generally. Subanalysis showed that the attitudes of teachers were significantly better than the general population and the students related to employment and social life, but were equally negative on issues directly related to education, such as placing children with epilepsy in regular classes.
CONCLUSION: Teachers had more positive attitudes toward epilepsy as compared with the general population with tertiary education. Attitude to epilepsy may differ specific to types of work.
Matched MeSH terms: Data Interpretation, Statistical
Epidemiological studies have shown significant ethnic differences in coronary heart disease death rates with South Asians showing significantly greater coronary heart disease mortality than other groups.
Matched MeSH terms: Data Interpretation, Statistical
Fourier transform infrared spectroscopy (FTIR) combined with multivariate calibration of partial least square (PLS) was developed and optimized for the analysis of Nigella seed oil (NSO) in binary and ternary mixtures with corn oil (CO) and soybean oil (SO). Based on PLS modeling performed, quantitative analysis of NSO in binary mixtures with CO carried out using the second derivative FTIR spectra at combined frequencies of 2977-3028, 1666-1739, and 740-1446 cm(-1) revealed the highest value of coefficient of determination (R (2), 0.9984) and the lowest value of root mean square error of calibration (RMSEC, 1.34% v/v). NSO in binary mixtures with SO is successfully determined at the combined frequencies of 2985-3024 and 752-1755 cm(-1) using the first derivative FTIR spectra with R (2) and RMSEC values of 0.9970 and 0.47% v/v, respectively. Meanwhile, the second derivative FTIR spectra at the combined frequencies of 2977-3028 cm(-1), 1666-1739 cm(-1), and 740-1446 cm(-1) were selected for quantitative analysis of NSO in ternary mixture with CO and SO with R (2) and RMSEC values of 0.9993 and 0.86% v/v, respectively. The results showed that FTIR spectrophotometry is an accurate technique for the quantitative analysis of NSO in binary and ternary mixtures with CO and SO.
Matched MeSH terms: Data Interpretation, Statistical
By using a linear operator, we obtain some new results for a normalized analytic function f defined by means of the Hadamard product of Hurwitz zeta function. A class related to this function will be introduced and the properties will be discussed.
Matched MeSH terms: Data Interpretation, Statistical
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: Data Interpretation, Statistical
The Bland-Altman method is the most popular method used to assess the agreement of medical instruments. The main concern about this method is the presence of proportional bias. The slope of the regression line fitted to the Bland-Altman plot should be tested to exclude proportional bias. The aim of this study was to determine whether the overestimation of bias in the Bland-Altman analysis is still present even when the proportional bias has been excluded.
Matched MeSH terms: Data Interpretation, Statistical
The aim of this study is to investigate whether IL-6, IL-10 and TGF-β are able to confer resistance to apoptosis in nasopharyngeal carcinoma cells by upregulating the expression of survivin.
Matched MeSH terms: Data Interpretation, Statistical
Toxoplasma gondii is a parasite that generates latent cysts in the brain; reactivation of these cysts may lead to fatal toxoplasmic encephalitis, for which treatment remains unsuccessful. We assessed spiramycin pharmacokinetics coadministered with metronidazole, the eradication of brain cysts and the in vitro reactivation. Male BALB/c mice were fed 1,000 tachyzoites orally to develop chronic toxoplasmosis. Four weeks later, infected mice underwent different treatments: (i) infected untreated mice (n = 9), which received vehicle only; (ii) a spiramycin-only group (n = 9), 400 mg/kg daily for 7 days; (iii) a metronidazole-only group (n = 9), 500 mg/kg daily for 7 days; and (iv) a combination group (n = 9), which received both spiramycin (400 mg/kg) and metronidazole (500 mg/kg) daily for 7 days. An uninfected control group (n = 10) was administered vehicle only. After treatment, the brain cysts were counted, brain homogenates were cultured in confluent Vero cells, and cysts and tachyzoites were counted after 1 week. Separately, pharmacokinetic profiles (plasma and brain) were assessed after a single dose of spiramycin (400 mg/kg), metronidazole (500 mg/kg), or both. Metronidazole treatment increased the brain spiramycin area under the concentration-time curve from 0 h to ∞ (AUC(0-∞)) by 67% without affecting its plasma disposition. Metronidazole plasma and brain AUC(0-∞) values were reduced 9 and 62%, respectively, after spiramycin coadministration. Enhanced spiramycin brain exposure after coadministration reduced brain cysts 15-fold (79 ± 23 for the combination treatment versus 1,198 ± 153 for the untreated control group [P < 0.05]) and 10-fold versus the spiramycin-only group (768 ± 125). Metronidazole alone showed no effect (1,028 ± 149). Tachyzoites were absent in the brain. Spiramycin reduced in vitro reactivation. Metronidazole increased spiramycin brain penetration, causing a significant reduction of T. gondii brain cysts, with potential clinical translatability for chronic toxoplasmosis treatment.
Matched MeSH terms: Data Interpretation, Statistical
Clostridium perfringens (CP) and sulphite reducing clostridia (SRC) densities in the Selangor River, Bernam River and Tengi River Canal were examined between April 2007 and January 2008. Water samples were taken from two or three locations along each river, using either depth-integration or grab sampling methods. The downstream sampling site of the Selangor River, Rantau Panjang, reported the highest arithmetic mean of CP and SRC densities (583.45 and 8,120.08 cfu/100 ml, respectively). Both CP and SRC densities in the Selangor River increased further downstream, but the reverse was true in the Bernam River. The SRC densities in these rivers were significantly different from each other (p < 0.05) when comparing upstream and downstream results, but CP densities were not significantly different (p > 0.05). SRC densities were significantly correlated (p < 0.05) in different locations along the Selangor River and the Bernam River. The CP densities did not show such pattern (p > 0.05). River discharge had no significant correlation with SRC or CP densities by study site (p > 0.05). Since the Selangor River has a denser human population along its banks, this study confirms CP as a suitable indicator of human fecal contamination. However, tracing CP distribution along the river is more difficult than SRC. To our knowledge, this is the first study of CP and SRC densities from Malaysian rivers. CP densities found in this study were within the range of general water bodies reported from other countries.
Matched MeSH terms: Data Interpretation, Statistical
Correlated ordinal data are common in many areas of research. The data may arise from longitudinal studies in biology, medical, or clinical fields. The prominent characteristic of these data is that the within-subject observations are correlated, whilst between-subject observations are independent. Many methods have been proposed to analyze correlated ordinal data. One way to evaluate the performance of a proposed model or the performance of small or moderate size data sets is through simulation studies. It is thus important to provide a tool for generating correlated ordinal data to be used in simulation studies. In this paper, we describe a macro program on how to generate correlated ordinal data based on R language and SAS IML.
Matched MeSH terms: Data Interpretation, Statistical*
Parkinson disease (PD) affects the lives of both the individuals and their family members. This study aims at investigating for clinical as well as socio-demographic factors associated with the perception of burden among the caregivers of individuals with PD in Malaysia.
Matched MeSH terms: Data Interpretation, Statistical