This cross-sectional study compared serum lipoprotein (a) [Lp(a)] concentrations in type 1 and type 2 diabetic subjects and examined the determinants of Lp(a) concentrations in both types of diabetes. Serum Lp(a) was measured in 26 type 1 and 107 type 2 diabetic patients and 126 non-diabetic controls. HbA(1c), fasting lipids and urinary albumin were also assayed. Lp(a) concentrations were higher in both type 1 and type 2 diabetic patients compared with controls (P<0.0001 and P<0.0001, respectively), and were higher in type 1 than type 2 diabetic patients (P<0.05). Waist-hip ratio (WHR) was an independent determinant of Lp(a) concentrations in both type 1 and type 2 diabetes.
This paper investigates the use of a neural-network-based intelligent learning system for the prediction of drug release profiles. An experimental study in transdermal iontophoresis (TI) is employed to evaluate the applicability of a particular neural network (NN) model, i.e. the Gaussian mixture model (GMM), in modeling and predicting drug release profiles. A number of tests are systematically designed using the face-centered central composite design (CCD) approach to examine the effects of various process variables simultaneously during the iontophoresis process. The GMM is then applied to model and predict the drug release profiles based on the data samples collected from the experiments. The GMM results are compared with those from multiple regression models. In addition, the bootstrap method is used to assess the reliability of the network predictions by estimating confidence intervals associated with the results. The results demonstrate that the combination of the face-centered CCD and GMM can be employed as a useful intelligent tool for the prediction of time-series profiles in pharmaceutical and biomedical experiments.
In the initial clinical examination of a child with ambiguous genitalia an accurate measurement of the corporeal length is needed. Most often the corporeal length is measured with a ruler from the symphysis pubis to the tip of the glans of a stretched penis. More recently, ultrasound has been successfully used to measure corporeal length. This study aimed to (i) establish normal values for corporeal length in normal male newborns using ultrasound measurement, (ii) compare these measurements to stretched corporeal measurements, (iii) compare the corporeal length of newborns of different races, and (iv) determine the relationship between corporeal length and birth weight, birth length and head circumference. This was a prospective study of 141 newborns. Ultrasound imaging was done in an oblique parasagittal plane such that the corpus could be included in a single image and measured, Stretched corporeal length was measured with the penis stretched alongside a wooden spatula and the length from the pubic bone to the tip of the glans was marked on the spatula and measured. By ultrasound measurement the mean corporeal length of the normal newborn was 3.18 +/- 0.56cm. There was no significant difference in the mean corporeal length when determined by ultrasound and by stretched corporeal measurement. There was no significant difference in the mean corporeal length of the different races when the length was determined by either ultrasound or stretched corporeal measurement. There was a positive correlation between ultrasound length and birth weight and birth length. However, there was no correlation between ultrasound length and head circumference. There was no significant correlation between the stretched length and either birth weight, birth length or head circumference.
Kajian garis pangkal pengimejan resonans magnet kefungsian (fMRI) telah dijalankan ke atas 2 orang subjek lelaki sihat (kidal dan tidak kidal) masing-masing berumur 22 dan 25 tahun. Imbasan fMRI dijalankan menggunakan sistem pengimejan resonans magnet (MRI) 1.5 T di Jabatan Radiologi, Hospital Universiti Kebangsaan Malaysia. Kajian ini menggunakan gerakanjari tangan kanan dan kiri untuk merangsang aktiviti neuron di dalam korteks serebrum. Paradigma 5 kitar aktifIrehat digunakan dengan setiap kitar mengandungi satu blok aktif dan satu blok rehat yang masing-masing mengandungi 10 siri pengukuran. Imej fMRI dianalisis menggunakan pekej perisian MatLab dan pemetaan statistik berparameter 2 (sPM2). Proses pendaftaran jasad tegar menggunakan penjelmaan afin 6 parameter dilakukan ke atas kesemua imej kefungsian berwajaran T2*. Keputusan menunjukkan bahawa pergerakan subjek adalah minimum sama ada dalam arah translasi (< 1 mm) atau putaran (< 1 ). Kesemua imej dinormalkan melalui proses peledingan tak linear menggunakan penjelmaan afin 12 parameter dan didapati sepadan dengan pencontoh yang telahpun mematuhi ruang anatomi piawai. Walau bagaimanapun, bentuk, resolusi dan kontras imej kefungsian telah berubah sedikit berbanding dengan imej asal. Pelicinan imej menggunakan kernel Gaussian isotropik 6 mm menyebabkan data imej lebih bersifat parametrik dengan kehilangan yang ketara dalam resolusi dan kontras. Pengasingan struktur yang dilakukan ke atas imej berwajaran T1 mengklaskan tisu otak kepadajirim kelabu, jirim putih dan bendalir serebrospina. Pasca pemprosesan ruang bagi imej kefungsian dan struktur menjadikan data imej bersifat parametrik dengan taburan jenis Gaussian dan sedia untuk dianalisis menggunakan model linear am dan teori medan rawak Gaussian.
Kajian garis pangkal pengimejan resonans magnet kefungsian (fMRI) telah dijalankan ke atas 2 orang subjek lelaki sihat dominan tangan kanan dan kiri. Kajian ini menggunakan gerakan jari tangan kanan dan kiri untuk merangsang aktiviti neuron di dalam korteks serebrum. Subjek diarahkan supaya menekan jari-jari pada ibu jari secara bergilir-gilir semasa imbasan fMRI dilakukan. Paradigma 5 kitar aktif-rehat digunakan dengan setiap kitar mengandungi satu blok aktif dan satu blok rehat dengan 10 siri pengukuran untuk setiap blok. Seratus isipadu imej fMRI bagi setiap subjek dianalisis menggunakan pekej perisian MatLab dan SPM2. Model linear am (GLM) digunakan untuk menganggar secara statistik parameter yang mencirikan model rangsangan hemodinamik bagi gerakan jari. Kesimpulan mengenai pengaktifan otak yang diperhatikan dijana secara statistik berasaskan teori medan rawak (RFT) Gaussian. Keputusan menunjukkan bahawa rantau otak yang aktif akibat gerakan jari adalah pada girus presentral merangkumi kawasan motor primer. Pengaktifan otak adalah secara kontralateral terhadap gerakan jari tangan kanan dan kiri. Keamatan isyarat keadaan aktif didapati lebih tinggi secara bererti (p < 0.001) daripada keamatan isyarat keadaan rehat. Bilangan voksel yang aktif didapati lebih tinggi pada hemisfera otak yang mengawal gerakan jari bagi tangan yang tidak dominan untuk kedua-dua subjek. Keputusan ini menyokong fakta bahawa kawasan pengaktifan motor pada hemisfera otak semasa gerakan jari tangan yang tidak dominan mengalami rangsangan hemodinamik yang lebih tinggi dan kawasan pengaktifan yang lebih luas berbanding dengan kawasan pengaktifan pada hemisfera otak yang mengawal gerakan jari bagi tangan yang dominan.
This paper analyzes the asymmetric long memory volatility dependency of the interday prices of Composite Index (CI) at Bursa Malaysia by using GARCH family models. The GARCH type models are used with the assumption that the innovations series follow either one of the following distributions: Gaussian, Student -t and skewed Student -t. The stock returns' long memory dependency is determined using the Hurst parameter. The long memory and asymmetric volatility are modelled by fractionally integrated GARCH models. It is found that the asymmetric and long memory GARCH models with skewed student-t distribution give better predictive ability on the volatility of the Kuala Lumpur Composite Index (KLCI).
Skin colour is an important visual cue for face detection, face recogmtlon, hand segmentation for gesture analysis and filtering of objectionable images. In this paper, the adaptive skin color detection model is proposed, based on two bivariate normal distribution models of the skin chromatic subspace, and on image segmentation using an automatic and adaptive multi-thresholding technique. Experimental results on images presenting a wide range of variations in lighting condition and background demonstrate the efficiency of the proposed skin-segmentation algorithm.
An analysis of the power spectral density of ultra-wideband (UWB) signals is presented in order to evaluate the effects of cumulative interference from multiple UWB devices on victim narrowband systems in their overlay bands like WiFi (i.e. IEEE802.11a) and 3rdG systems (Universal mobile telecommunications system/wideband code division multiple access). In this paper, the performances are studied through the bit-error-rate as a function of signal-to-noise ratio as well as signal-to-interference power ratio using computer simulation and exploiting the realistic channel model (i.e. modified Saleh-Valenzuela model). Several modifications of a generic Gaussian pulse waveform with lengths in the order of nanoseconds were used to generate UWB spectra. Different kinds of pulse modulation (i.e. antipodal and orthogonal) schemes were also taken into account.
Expectation Maximization (EM) algorithm has experienced a significant increase in terms of usage in many fields of study. In this paper, the performance of the said algorithm in finding the Maximum Likelihood for the Gaussian Mixed Models (GMM), a probabilistic model normally used in fraud detection and recognizing a person’s voice in speech recognition field, is shown and discussed. At the end of the paper, some suggestions for future research works will also be given.
This paper introduces an approach to perform segmentation of regions in computed tomography (CT) images that exhibit intra-region intensity variations and at the same time have similar intensity distributions with surrounding/adjacent regions. In this work, we adapt a feature computed from wavelet transform called wavelet energy to represent the region information. The wavelet energy is embedded into a level set model to formulate the segmentation model called wavelet energy-guided level set-based active contour (WELSAC). The WELSAC model is evaluated using several synthetic and CT images focusing on tumour cases, which contain regions demonstrating the characteristics of intra-region intensity variations and having high similarity in intensity distributions with the adjacent regions. The obtained results show that the proposed WELSAC model is able to segment regions of interest in close correspondence with the manual delineation provided by the medical experts and to provide a solution for tumour detection.
The range-gated imaging systems are reliable underwater imaging system with the capability to minimize backscattering effect from turbid media. The tail-gating technique has been developed to fine tune the signal to backscattering ratio and hence improve the gated image quality. However, the tail-gating technique has limited image quality enhancement in high turbidity levels. In this paper, we developed a numerical model of range-gated underwater imaging system for near target in turbid medium. The simulation results matched the experimental work favorably. Further investigation using this numerical model shows that the multiple scattering components of the backscattering noise dominate for propagation length larger than 4.2 Attenuation Length (AL). This has limited the enhancement of tail-gating technique in high turbidity conditions.
Diabetic retinopathy (DR) is a sight threatening complication due to diabetes mellitus that affects the retina. At present, the classification of DR is based on the International Clinical Diabetic Retinopathy Disease Severity. In this paper, FAZ enlargement with DR progression is investigated to enable a new and an effective grading protocol DR severity in an observational clinical study. The performance of a computerised DR monitoring and grading system that digitally analyses colour fundus image to measure the enlargement of FAZ and grade DR is evaluated. The range of FAZ area is optimised to accurately determine DR severity stage and progression stages using a Gaussian Bayes classifier. The system achieves high accuracies of above 96%, sensitivities higher than 88% and specificities higher than 96%, in grading of DR severity. In particular, high sensitivity (100%), specificity (>98%) and accuracy (99%) values are obtained for No DR (normal) and Severe NPDR/PDR stages. The system performance indicates that the DR system is suitable for early detection of DR and for effective treatment of severe cases.
In this paper, four types of circular probability distribution were used to evaluate which circular probability distribution gives the best fitting for southwesterly Malaysian wind direction data, namely circular uniform distribution, von Mises distribution, wrapped-normal distribution and wrapped-Cauchy distribution. The four locations chosen were Alor Setar, Langkawi, Melaka and Senai. Two performance indicators or goodness of fit tests which are mean circular distance and chord length were used to test which distribution give the best fitting.
By using a pair of tweezers to generate the intense optical vortices within the PANDA ring resonator, the required molecules (drug volumes) can be trapped and moved dynamically within the molecular bus networks, in which the required diagnosis or drug delivery targets can be performed within the network. The advantage of the proposed system is that the proposed diagnostic method can perform within the tiny system (thin film device or circuit), which can be available for a human embedded device for diagnostic use. The channel spacing of the trapped volumes (molecules) within the bus molecular networks can be provided.
Left ventricular motion estimation is very important for diagnosing cardiac abnormality. One of the popular techniques, optical flow technique, promises useful results for motion quantification. However, optical flow technique often failed to provide smooth vector field due to the complexity of cardiac motion and the presence of speckle noise. This chapter proposed a new filtering technique, called quasi-Gaussian discrete cosine transform (QGDCT)-based filter, to enhance the optical flow field for myocardial motion estimation. Even though Gaussian filter and DCT concept have been implemented in other previous researches, this filter introduces a different approach of Gaussian filter model based on high frequency properties of cosine function. The QGDCT is a customized quasi discrete Gaussian filter in which its coefficients are derived from a selected two-dimensional DCT. This filter was implemented before and after the computation of optical flow to reduce the speckle noise and to improve the flow field smoothness, respectively. The algorithm was first validated on synthetic echocardiography image that simulates a contracting myocardium motion. Subsequently, this method was also implemented on clinical echocardiography images. To evaluate the performance of the technique, several quantitative measurements such as magnitude error, angular error, and standard error of measurement are computed and analyzed. The final motion estimation results were in good agreement with the physician manual interpretation.
Setting a question paper for test, quiz, and examination is one of the teachers’ tasks. The factors that are usually taken into consideration in carrying out this particular task are the level of difficulty of the questions and the level of the students’ ability. In addition, teachers will also have to consider the number of questions that have impact on the examination. This research describes a model-based test theory to study the confidence intervals for the projected number of items of a test, given the reliability of the test, the difficulty of the question, and the students’ ability. Using the simulated data, the confidence intervals of the projected number of items were examined. The probability coverage and the length of the confidence interval were also used to evaluate the confidence intervals. The results showed that the data with a normal distribution, the ratio variance components of 4:1:5 and reliability equal to 0.80 gave the best confidence interval for the projected number of items.
In many statistical analyses, data need to be approximately normal or normally distributed. The Kolmogorov-Smirnov test, Anderson-Darling test, Cramer-von Mises test, and Shapiro-Wilk test are four statistical tests that are widely used for checking normality. One of the factors that influence these tests is the sample size. Given any test of normality mentioned, this study determined the sample sizes at which the tests would indicate that the data is not normal. The performance of the tests was evaluated under various spectrums of non-normal distributions and different sample sizes. The results showed that the Shapiro-Wilk test is the best normality test because this test rejects the null hypothesis of normality test at the smallest sample size compared to the other tests, for all levels of skewness and kurtosis of these distributions.
This paper focuses on measuring risk due to extreme events going beyond the multivariate normal distribution of joint returns. The concept of tail dependence has been found useful as a tool to describe dependence between extreme data in finance. Specifically, we adopted a multivariate Copula-EGARCH approach in order to investigate the presence of conditional dependence between international financial markets. In addition, we proposed a mixed Clayton-Gumbel copula with estimators for measuring both, the upper and lower tail dependence. The results showed significant dependence for Singapore and Malaysia as well as for Singapore and US, while the dependence for Malaysia and US was relatively weak
This paper applies an expectation-maximization (EM) based Kalman smoother (KS) approach for single-trial event-related potential (ERP) estimation. Existing studies assume a Markov diffusion process for the dynamics of ERP parameters which is recursively estimated by optimal filtering approaches such as Kalman filter (KF). However, these studies only consider estimation of ERP state parameters while the model parameters are pre-specified using manual tuning, which is time-consuming for practical usage besides giving suboptimal estimates. We extend the KF approach by adding EM based maximum likelihood estimation of the model parameters to obtain more accurate ERP estimates automatically. We also introduce different model variants by allowing flexibility in the covariance structure of model noises. Optimal model selection is performed based on Akaike Information Criterion (AIC). The method is applied to estimation of chirp-evoked auditory brainstem responses (ABRs) for detection of wave V critical for assessment of hearing loss. Results shows that use of more complex covariances are better estimating inter-trial variability.
The recognition of microcalcifications and masses from digital mammographic images are important to aid the detection of breast cancer. In this paper, we applied morphological techniques to extract the embedded structures from the images for subsequent analysis. A mammographic phantom was created with embedded structures such as micronodules, nodules and fibrils. For the preprocessing techniques, intensity transformation of gray scale was applied to the image. The structures of the image were enhanced and segmented using dilation for a morphological operation with morphological closing. Next, low pass Gaussian filter was applied to the image to smooth and reduce noises. It was found that our method improved the detection of microcalcifications and masses with high Peak Signal To Noise Ratio (PSNR).