Displaying publications 61 - 80 of 107 in total

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  1. Abdullah P, Abdullah SMS, Jaafar O, Mahmud M, Khalik WMAWM
    Mar Pollut Bull, 2015 Dec 15;101(1):378-385.
    PMID: 26476861 DOI: 10.1016/j.marpolbul.2015.10.014
    Characterization of hydrochemistry changes in Johor Straits within 5 years of monitoring works was successfully carried out. Water quality data sets (27 stations and 19 parameters) collected in this area were interpreted subject to multivariate statistical analysis. Cluster analysis grouped all the stations into four clusters ((Dlink/Dmax) × 100<90) and two clusters ((Dlink/Dmax) × 100<80) for site and period similarities. Principal component analysis rendered six significant components (eigenvalue>1) that explained 82.6% of the total variance of the data set. Classification matrix of discriminant analysis assigned 88.9-92.6% and 83.3-100% correctness in spatial and temporal variability, respectively. Times series analysis then confirmed that only four parameters were not significant over time change. Therefore, it is imperative that the environmental impact of reclamation and dredging works, municipal or industrial discharge, marine aquaculture and shipping activities in this area be effectively controlled and managed.
    Matched MeSH terms: Discriminant Analysis
  2. Kudva MV, Zawawi M, Rafee N, Ismail O, Muda JR
    Med J Malaysia, 1989 Sep;44(3):236-42.
    PMID: 2626138
    The objective of the study was to determine whether discriminant analysis of characteristics of dyspepsia can differentiate peptic ulcer from non-ulcer dyspepsia in a Malaysian population. Two hundred and twenty six patients with dyspepsia were interviewed using a standard history questionnaire before undergoing upper gastrointestinal endoscopy. Forty seven patients had peptic ulcer while 149 others were classified as having non-ulcer dyspepsia. Stepwise logistic regression analysis was done on 25 variables. The study showed that only five of these variables could differentiate peptic ulcer from non-ulcer dyspepsia, namely, nocturnal pain, pain before meals or when hungry, absence of nausea, age and sex. A scoring system was devised based on these discriminant symptoms. At a sensitivity of 51%, the specificity for peptic ulcer was 83%, but only prospective studies will determine if this scoring system is of actual clinical value.
    Matched MeSH terms: Discriminant Analysis
  3. Mediani A, Abas F, Maulidiani M, Abu Bakar Sajak A, Khatib A, Tan CP, et al.
    J Physiol Biochem, 2018 May 15.
    PMID: 29766441 DOI: 10.1007/s13105-018-0631-3
    Diabetes mellitus (DM) is a chronic disease that can affect metabolism of glucose and other metabolites. In this study, the normal- and obese-diabetic rats were compared to understand the diabetes disorders of type 1 and 2 diabetes mellitus. This was done by evaluating their urine metabolites using proton nuclear magnetic resonance (1H NMR)-based metabolomics and comparing with controls at different time points, considering the induction periods of obesity and diabetes. The biochemical parameters of the serum were also investigated. The obese-diabetic model was developed by feeding the rats a high-fat diet and inducing diabetic conditions with a low dose of streptozotocin (STZ) (25 mg/kg bw). However, the normal rats were induced by a high dose of STZ (55 mg/kg bw). A partial least squares discriminant analysis (PLS-DA) model showed the biomarkers of both DM types compared to control. The synthesis and degradation of ketone bodies, tricarboxylic (TCA) cycles, and amino acid pathways were the ones most involved in the variation with the highest impact. The diabetic groups also exhibited a noticeable increase in the plasma glucose level and lipid profile disorders compared to the control. There was also an increase in the plasma cholesterol and low-density lipoprotein (LDL) levels and a decline in the high-density lipoprotein (HDL) of diabetic rats. The normal-diabetic rats exhibited the highest effect of all parameters compared to the obese-diabetic rats in the advancement of the DM period. This finding can build a platform to understand the metabolic and biochemical complications of both types of DM and can generate ideas for finding targeted drugs.
    Matched MeSH terms: Discriminant Analysis
  4. Ang KH
    Sains Malaysiana, 2018;47:471-479.
    In recent years, Malaysia has experienced quite a few number of chronic air pollution problems and it has become a
    major contributor to the deterioration of human health and ecosystems. This study aimed to assess the air quality data
    and identify the pattern of air pollution sources using chemometric analysis through hierarchical cluster analysis (HCA),
    discriminant analysis (DA), principal component analysis (PCA) and multiple linear regression analysis (MLR). The air
    quality data from January 2016 until December 2016 was obtained from the Department of Environment Malaysia. Air
    quality data from eight sampling stations in Selangor include the selected variables of nitrogen dioxide (NO2
    ), ozone (O3
    ),
    sulfur dioxide (SO2
    ), carbon monoxide (CO) and particulate matter (PM10). The HCA resulted in three clusters, namely low
    pollution source (LPS), moderate pollution source (MPS) and slightly high pollution source (SHPS). Meanwhile, DA resulted
    in two and four variables for the forward stepwise mode and the backward stepwise mode, respectively. Through PCA,
    it was identified that the main pollutants of LPS, MPS and SHPS came from industrial and vehicle emissions, agricultural
    systems, residential factors and natural emission sources. Among the three models yielded from the MLR analysis, it was
    found that SHPS is the most suitable model to be used for the prediction of Air Pollution Index. This study concluded that
    a clearer review and practical design of air quality monitoring network would be beneficial for better management of
    air pollution. The study also suggested that chemometric techniques have the ability to show significant information on
    spatial variability for large and complex air quality data.
    Matched MeSH terms: Discriminant Analysis
  5. Sharin SN, Sani MSA, Jaafar MA, Yuswan MH, Kassim NK, Manaf YN, et al.
    Food Chem, 2021 Jun 01;346:128654.
    PMID: 33461823 DOI: 10.1016/j.foodchem.2020.128654
    Identification of honey origin based on specific chemical markers is important for honey authentication. This study is aimed to differentiate Malaysian stingless bee honey from different entomological origins (Heterotrigona bakeri, Geniotrigona thoracica and Tetrigona binghami) based on physicochemical properties (pH, moisture content, ash, total soluble solid and electrical conductivity) and volatile compound profiles. The discrimination pattern of 75 honey samples was observed using Principal Component Analysis (PCA), Hierarchical Clustering Analysis (HCA), Partial Least Square-Discriminant Analysis (PLS-DA), and Support Vector Machine (SVM). The profiles of H. bakeri and G. thoracica honey were close to each other, but clearly separated from T. binghami honey, consistent with their phylogenetic relationship. T. binghami honey is marked by significantly higher electrical conductivity, moisture and ash content, and high abundance of 2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde, 2,6,6-trimethyl-1-cyclohexene-1-acetaldehyde and ethyl 2-(5-methyl-5-vinyltetrahydrofuran-2-yl)propan-2-yl carbonate. Copaene was proposed as chemical marker for G. thoracica honey. The potential of different parameters that aid in honey authentication was highlighted.
    Matched MeSH terms: Discriminant Analysis
  6. Mohd Khairuddin I, Sidek SN, P P Abdul Majeed A, Mohd Razman MA, Ahmad Puzi A, Md Yusof H
    PeerJ Comput Sci, 2021;7:e379.
    PMID: 33817026 DOI: 10.7717/peerj-cs.379
    Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject's intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects' biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them.
    Matched MeSH terms: Discriminant Analysis
  7. Nik Mohd Fakhruddin NNI, Shahar S, Ismail IS, Ahmad Azam A, Rajab NF
    Nutrients, 2020 Sep 23;12(10).
    PMID: 32977370 DOI: 10.3390/nu12102900
    Food intake biomarkers (FIBs) can reflect the intake of specific foods or dietary patterns (DP). DP for successful aging (SA) has been widely studied. However, the relationship between SA and DP characterized by FIBs still needs further exploration as the candidate markers are scarce. Thus, 1H-nuclear magnetic resonance (1H-NMR)-based urine metabolomics profiling was conducted to identify potential metabolites which can act as specific markers representing DP for SA. Urine sample of nine subjects from each three aging groups, SA, usual aging (UA), and mild cognitive impairment (MCI), were analyzed using the 1H-NMR metabolomic approach. Principal components analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were applied. The association between SA urinary metabolites and its DP was assessed using the Pearson's correlation analysis. The urine of SA subjects was characterized by the greater excretion of citrate, taurine, hypotaurine, serotonin, and melatonin as compared to UA and MCI. These urinary metabolites were associated with alteration in "taurine and hypotaurine metabolism" and "tryptophan metabolism" in SA elderly. Urinary serotonin (r = 0.48, p < 0.05) and melatonin (r = 0.47, p < 0.05) were associated with oat intake. These findings demonstrate that a metabolomic approach may be useful for correlating DP with SA urinary metabolites and for further understanding of SA development.
    Matched MeSH terms: Discriminant Analysis
  8. Norasikin Ab Azis, Mohd Saleh Ahmad Kamal, Zurain Radjeni, Ahmed Mediani, Renu Agarwal
    MyJurnal
    Introduction: This study examined the association of losartan induced changes in urinary
    metabolomic profile with the changes in blood pressure (BP) and renin-angiotensinaldosterone system (RAAS) in spontaneously hypertensive rats (SHR). Methods: Male SHR
    were administered with either 0.5 mL of distilled water (control group, n=6) or 10 mg.kg-1 of
    losartan (group 2, n=6) daily by oral gavage for 4 weeks. Body weight, BP, food and water
    intake were measured weekly. At week 4, urine was collected for urinary electrolyte analysis
    and metabolite profiling, after which the animals were euthanised by decapitation and blood
    was collected for analysis of components of RAAS and electrolyte concentrations. Urine
    metabolite profile of SHR was determined using proton nuclear magnetic resonance (
    1H-NMR)
    spectrometry combined with multivariate data analysis. Results: At week 4, losartan-treated
    SHR had significantly lower BP than non-treated SHR. There were no differences in water
    and food intake, body weight, serum and urinary electrolyte concentrations or in their urinary
    excretions between the two groups. No differences were evident in the components of RAAS
    except that the angiotensinogen level was significantly higher in losartan-treated SHR
    compared to non-treated SHR. Orthogonal partial least squares discriminant analysis (OPLSDA) showed clear separation of urinary metabolites between control and losartan-treated
    SHR. Losartan-treated SHR group was separated from the control group by changes in the
    intermediates involved in glycine, serine and threonine metabolism. Conclusion:
    Antihypertensive effect of losartan in SHR seems to be associated with changes in urinary
    metabolite profile, particularly involving the metabolism of glycine, serine and threonine.
    Matched MeSH terms: Discriminant Analysis
  9. Chong JA, Syed Mohamed AMF, Marizan Nor M, Pau A
    J Forensic Sci, 2020 Nov;65(6):2000-2007.
    PMID: 32692413 DOI: 10.1111/1556-4029.14507
    Although there is clinical applicability of the palatal rugae as an identification tool in forensic odontology, controversy exists whether the palatal rugae patterns are stable or variable. The greater the genetic component, the higher the probability that palatal rugae patterns are stable. The aim of this study was to compare the palatal rugae morphology between full siblings and the proportion of variability due to genetic component. This cross-sectional study was conducted on digital models of 162 siblings aged 15-30 years old. The palatal rugae patterns were assessed with Thomas and Kotze (1983) classification using Geomagic Studio software (3D Systems, Rock Hill, SC). The palatal rugae morphology between siblings showed significantly similar characteristics for total number of left rugae (p = 0.001), left primary rugae (p = 0.017), secondary rugae for right (p = 0.024) and left sides (p = 0.001), right straight rugae (p = 0.010), and right convergent rugae (p = 0.005) accounting for at least 6.25%-12.8% of the variability due to heredity. Despite the similarities found, the palatal rugae patterns showed significant differences between siblings of at least 46.9% (p = 0.001). Zero heritability was found in 9 of the 14 rugae patterns. Meanwhile, total number of rugae, primary, backward, and convergent rugae showed moderate heritability (h2  > 0.3) and total number of secondary rugae showed high heritability (h2  > 0.6). In conclusion, despite the individuality characteristics, an appreciable hereditary component is observed with significant similarities found between sibling pairs and the palatal rugae patterns were both environmentally and genetically influenced.
    Matched MeSH terms: Discriminant Analysis
  10. Gopinath D, Kunnath Menon R, Chun Wie C, Banerjee M, Panda S, Mandal D, et al.
    J Oral Microbiol, 2020 Dec 09;13(1):1857998.
    PMID: 33391629 DOI: 10.1080/20002297.2020.1857998
    Objective: While some oral carcinomas appear to arise de novo, others develop within long-standing conditions of the oral cavity that have malignant potential, now known as oral potentially malignant disorders (OPMDs). The oral bacteriome associated with OPMD has been studied to a lesser extent than that associated with oral cancer. To characterize the association in detail we compared the bacteriome in whole mouth fluid (WMF) in patients with oral leukoplakia, oral cancer and healthy controls. Methods: WMF bacteriome from 20 leukoplakia patients, 31 patients with oral cancer and 23 healthy controls were profiled using the Illumina MiSeq platform. Sequencing reads were processed using DADA2, and taxonomical classification was performed using the phylogenetic placement method. Sparse Partial Least Squares Regression Discriminant Analysis model was used to identify bacterial taxa that best discriminate the studied groups. Results: We found considerable overlap between the WMF bacteriome of leukoplakia and oral cancer while a clearer separation between healthy controls and the former two disorders was observed. Specifically, the separation was attributed to 14 taxa belonging to the genera Megaspheara, unclassified enterobacteria, Prevotella, Porphyromonas, Rothia and Salmonella, Streptococcus, and Fusobacterium. The most discriminative bacterial genera between leukoplakia and oral cancer were Megasphaera, unclassified Enterobacteriae, Salmonella and Prevotella.Conclusion: Oral bacteria may play a role in the early stages of oral carcinogenesis as a dysbiotic bacteriome is associated with oral leukoplakia and this resembles that of oral cancer more than healthy controls. Our findings may have implications for developing oral cancer prevention strategies targeting early microbial drivers of oral carcinogenesis.
    Matched MeSH terms: Discriminant Analysis
  11. Al-Quraishi MS, Ishak AJ, Ahmad SA, Hasan MK, Al-Qurishi M, Ghapanchizadeh H, et al.
    Med Biol Eng Comput, 2017 May;55(5):747-758.
    PMID: 27484411 DOI: 10.1007/s11517-016-1551-4
    Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study.
    Matched MeSH terms: Discriminant Analysis
  12. Hossain MAM, Ali ME, Sultana S, Asing, Bonny SQ, Kader MA, et al.
    J Agric Food Chem, 2017 May 17;65(19):3975-3985.
    PMID: 28481513 DOI: 10.1021/acs.jafc.7b00730
    Cattle, buffalo, and porcine materials are widely adulterated, and their quantification might safeguard health, religious, economic, and social sanctity. Recently, conventional polymerase chain reaction (PCR) and PCR-restriction fragment length polymorphism (RFLP) assays have been documented but they are just suitable for identification, cannot quantify adulterations. We described here a quantitative tetraplex real-time PCR assay with TaqMan Probes to quantify contributions from cattle, buffalo, and porcine materials simultaneously. Amplicon-sizes were very short (106-, 90-, and 146-bp for cattle, buffalo, and porcine) because longer targets could be broken down, bringing serious ambiguity in molecular diagnostics. False negative detection was eliminated through an endogenous control (141-bp site of eukaryotic 18S rRNA). Analysis of 27 frankfurters and 27 meatballs reflected 84-115% target recovery at 0.1-10% adulterations. Finally, a test of 36 commercial products revealed 71% beef frankfurters, 100% meatballs, and 85% burgers contained buffalo adulteration, but no porcine was found in beef products.
    Matched MeSH terms: Discriminant Analysis
  13. Uncini A, Ippoliti L, Shahrizaila N, Sekiguchi Y, Kuwabara S
    Clin Neurophysiol, 2017 07;128(7):1176-1183.
    PMID: 28521265 DOI: 10.1016/j.clinph.2017.03.048
    OBJECTIVE: To optimize the electrodiagnosis of Guillain-Barré syndrome (GBS) subtypes at first study.

    METHODS: The reference electrodiagnosis was obtained in 53 demyelinating and 45 axonal GBS patients on the basis of two serial studies and results of anti-ganglioside antibodies assay. We retrospectively employed sparse linear discriminant analysis (LDA), two existing electrodiagnostic criteria sets (Hadden et al., 1998; Rajabally et al., 2015) and one we propose that additionally evaluates duration of motor responses, sural sparing pattern and defines reversible conduction failure (RCF) in motor and sensory nerves at second study.

    RESULTS: At first study the misclassification error rates, compared to reference diagnoses, were: 15.3% for sparse LDA, 30% for our criteria, 45% for Rajabally's and 48% for Hadden's. Sparse LDA identified seven most powerful electrophysiological variables differentiating demyelinating and axonal subtypes and assigned to each patient the diagnostic probability of belonging to either subtype. At second study 46.6% of axonal GBS patients showed RCF in two motor and 8.8% in two sensory nerves.

    CONCLUSIONS: Based on a single study, sparse LDA showed the highest diagnostic accuracy. RCF is present in a considerable percentage of axonal patients.

    SIGNIFICANCE: Sparse LDA, a supervised statistical method of classification, should be introduced in the electrodiagnostic practice.

    Matched MeSH terms: Discriminant Analysis
  14. Zakaria SR, Saim N, Osman R, Abdul Haiyee Z, Juahir H
    Molecules, 2018 Sep 16;23(9).
    PMID: 30223605 DOI: 10.3390/molecules23092365
    This study analyzed the volatile organic compounds (VOCs) of three mango varieties (Harumanis, Tong Dam and Susu) for the discrimination of authentic Harumanis from other mangoes. The VOCs of these mangoes were extracted and analysed nondestructively using Head Space-Solid Phase Micro Extraction (HS-SPME) coupled to Gas Chromatography-Mass Spectrometry (GC-MS). Prior to the analytical method, two simple sensory analyses were carried out to assess the ability of the consumers to differentiate between the Harumanis and Tong Dam mangoes as well as their preferences towards these mangoes. On the other hand, chemometrics techniques, such as principal components analysis (PCA), hierarchical clustering analysis (HCA), and discriminant analysis (DA), were used to visualise grouping tendencies of the volatile compounds detected. These techniques were successful in identifying the grouping tendencies of the mango samples according to the presence of their respective volatile compounds, thus enabling the identification of the groups of substances responsible for the discrimination between the authentic and unauthentic Harumanis mangoes. In addition, three ocimene compounds, namely beta-ocimene, trans beta-ocimene, and allo-ocimene, can be considered as chemical markers of the Harumanis mango, as these compounds exist in all Harumanis mango, regardless the different sources of the mangoes obtained.
    Matched MeSH terms: Discriminant Analysis
  15. Alias A, Ibrahim A, Abu Bakar SN, Swarhib Shafie M, Das S, Abdullah N, et al.
    Clin Ter, 2018 11 6;169(5):e217-e223.
    PMID: 30393808 DOI: 10.7417/CT.2018.2082
    INTRODUCTION: The first step in the forensic identification is sex determination followed by age and stature estimation, as both are sex-dependent. The mandible is the largest, strongest and most durable bone in the face. Mandible is important for sex confirmation in absence of a complete pelvis and skull.

    AIM: The aim of the present study was to determine sex of human mandible from morphology, morphometric measurements as well as discriminant function analysis from the CT scan.

    MATERIALS AND METHODS: The present retrospective study comprised 79 subjects (48 males, 31 females), with age group between 18 and 74 years, and were obtained from the post mortem computed tomography data in the Hospital Kuala Lumpur. The parameters were divided into three morphologic and nine morphometric parameters, which were measured by using Osirix MD Software 3D Volume Rendering.

    RESULTS: The Chi-square test showed that men were significantly association with square-shaped chin (92%), prominent muscle marking (85%) and everted gonial glare, whereas women had pointed chin (84%), less prominent muscle marking (90%) and inverted gonial glare (80%). All parameter measurements showed significantly greater values in males than in females by independent t-test (p< 0.01). By discriminant analysis, the classification accuracy was 78.5%, the sensitivity was 79.2% and the specificity was 77.4%. The discriminant function equation was formulated based on bigonial breath and condylar height, which were the best predictors.

    CONCLUSION: In conclusion, the mandible could be distinguished according to the sex. The results of the study can be used for identification of damaged and/or unknown mandible in the Malaysian population.

    Matched MeSH terms: Discriminant Analysis
  16. Muhammad Zul Fayyadh Azizo Rahman, Chong, Hui Wen, Lim, Vuanghao
    MyJurnal
    Adulterated premixed coffees have turned into an issue in Malaysia lately and have caught the eye of the authorities due to death reports linked to these products. The major cause of this issue is reported that these premixed coffees have passed food inspection test and eventually released to the market for public consumption. These coffees were claimed to be spiked with several sexual enhancers like sildenafil, tadalafil, and vardenafill, which are common drugs used to treat erectile dysfunction. Methods: Chemometrics approach using UV-Vis spectroscopy was developed to detect the selected sexual enhancer drugs found in commercial coffees by employing SIMCA-P software for the multivariate statistical analysis. Seven brands of coffee samples were purchased from local stores, and 30 sachets each were tested, hence totalling to 210 samples. Each sample was named H, J, G, W, N, T, and K, respectively. Results: Three multivariate models were generated, namely principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and partial least squares discriminant analysis (PLS-DA). OPLS-DA was selected as the best model for the overall results as it displayed minimal discriminate. Sildenafil, tadalafil, and vardenafil were detected in sample H, while vardenafil in brand J, and none in samples G, W, N, T, and K. Conclusion: OPLS-DA analysis showed discrimination for the sexual enhancer drugs in two brands of premixed coffee. The UV-Vis spectroscopy-based chemometrics method proved to be reliable and efficient in determining the selected drugs, as well as in saving time and cost.
    Matched MeSH terms: Discriminant Analysis
  17. Wang J, Chen T, Zhang W, Zhao Y, Yang S, Chen A
    Food Chem, 2020 May 30;313:126093.
    PMID: 31927205 DOI: 10.1016/j.foodchem.2019.126093
    Multivariate stable isotope analysis combined with chemometrics was used to investigate and discriminate rice samples from six rice producing provinces in China (Heilongjiang, Jilin, Jiangsu, Zhejiang, Hunan and Guizhou) and four other Asian rice producing countries (Thailand, Malaysia, Philippines, and Pakistan). The stable isotope characteristics were analyzed for rice of different species cultivated with varied farming methods at different altitudes and latitudes/longitudes. The index groups of δ13C, δ15N, δ18O, 207/206Pb and 208/207Pb were screened and established for the selected samples with different geographical features by means of principal component analysis (PCA) and discriminant analysis (DA), which would provide a sound technical solution for rice traceability and serve as a template for further research on the traceability of other agricultural products, especially plant-derived products.
    Matched MeSH terms: Discriminant Analysis
  18. Agbolade O, Nazri A, Yaakob R, Ghani AA, Cheah YK
    PLoS One, 2020;15(4):e0228402.
    PMID: 32271782 DOI: 10.1371/journal.pone.0228402
    BACKGROUND: The application of three-dimensional scan models offers a useful resource for studying craniofacial variation. The complex mathematical analysis for facial point acquisition in three-dimensional models has made many craniofacial assessments laborious.

    METHOD: This study investigates three-dimensional (3D) soft-tissue craniofacial variation, with relation to ethnicity, sex and age variables in British and Irish white Europeans. This utilizes a geometric morphometric approach on a subsampled dataset comprising 292 scans, taken from a Liverpool-York Head Model database. Shape variation and analysis of each variable are tested using 20 anchor anatomical landmarks and 480 sliding semi-landmarks.

    RESULTS: Significant ethnicity, sex, and age differences are observed for measurement covering major aspects of the craniofacial shape. The ethnicity shows subtle significant differences compared to sex and age; even though it presents the lowest classification accuracy. The magnitude of dimorphism in sex is revealed in the facial, nasal and crania measurement. Significant shape differences are also seen at each age group, with some distinct dimorphic features present in the age groups.

    CONCLUSIONS: The patterns of shape variation show that white British individuals have a more rounded head shape, whereas white Irish individuals have a narrower head shape. White British persons also demonstrate higher classification accuracy. Regarding sex patterns, males are relatively larger than females, especially in the mouth and nasal regions. Females presented with higher classification accuracy than males. The differences in the chin, mouth, nose, crania, and forehead emerge from different growth rates between the groups. Classification accuracy is best for children and senior adult age groups.

    Matched MeSH terms: Discriminant Analysis
  19. Nazri A, Agbolade O, Yaakob R, Ghani AA, Cheah YK
    BMC Bioinformatics, 2020 May 24;21(1):208.
    PMID: 32448182 DOI: 10.1186/s12859-020-3497-7
    BACKGROUND: Landmark-based approaches of two- or three-dimensional coordinates are the most widely used in geometric morphometrics (GM). As human face hosts the organs that act as the central interface for identification, more landmarks are needed to characterize biological shape variation. Because the use of few anatomical landmarks may not be sufficient for variability of some biological patterns and form, sliding semi-landmarks are required to quantify complex shape.

    RESULTS: This study investigates the effect of iterations in sliding semi-landmarks and their results on the predictive ability in GM analyses of soft-tissue in 3D human face. Principal Component Analysis (PCA) is used for feature selection and the gender are predicted using Linear Discriminant Analysis (LDA) to test the effect of each relaxation state. The results show that the classification accuracy is affected by the number of iterations but not in progressive pattern. Also, there is stability at 12 relaxation state with highest accuracy of 96.43% and an unchanging decline after the 12 relaxation state.

    CONCLUSIONS: The results indicate that there is a particular number of iteration or cycle where the sliding becomes optimally relaxed. This means the higher the number of iterations is not necessarily the higher the accuracy.

    Matched MeSH terms: Discriminant Analysis
  20. Acharya UR, Raghavendra U, Fujita H, Hagiwara Y, Koh JE, Jen Hong T, et al.
    Comput Biol Med, 2016 12 01;79:250-258.
    PMID: 27825038 DOI: 10.1016/j.compbiomed.2016.10.022
    Fatty liver disease (FLD) is reversible disease and can be treated, if it is identified at an early stage. However, if diagnosed at the later stage, it can progress to an advanced liver disease such as cirrhosis which may ultimately lead to death. Therefore, it is essential to detect it at an early stage before the disease progresses to an irreversible stage. Several non-invasive computer-aided techniques are proposed to assist in the early detection of FLD and cirrhosis using ultrasound images. In this work, we are proposing an algorithm to discriminate automatically the normal, FLD and cirrhosis ultrasound images using curvelet transform (CT) method. Higher order spectra (HOS) bispectrum, HOS phase, fuzzy, Kapoor, max, Renyi, Shannon, Vajda and Yager entropies are extracted from CT coefficients. These extracted features are subjected to locality sensitive discriminant analysis (LSDA) feature reduction method. Then these LSDA coefficients ranked based on F-value are fed to different classifiers to choose the best performing classifier using minimum number of features. Our proposed technique can characterize normal, FLD and cirrhosis using probabilistic neural network (PNN) classifier with an accuracy of 97.33%, specificity of 100.00% and sensitivity of 96.00% using only six features. In addition, these chosen features are used to develop a liver disease index (LDI) to differentiate the normal, FLD and cirrhosis classes using a single number. This can significantly help the radiologists to discriminate FLD and cirrhosis in their routine liver screening.
    Matched MeSH terms: Discriminant Analysis
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