Displaying publications 41 - 60 of 104 in total

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  1. Fulltext Evaluation of Two New Indices of Blood Pressure Variability Using Postural Change in Older Fallers
    Goh CH, Ng SC, Kamaruzzaman SB, Chin AV, Poi PJ, Chee KH, et al.
    Medicine (Baltimore), 2016 May;95(19):e3614.
    PMID: 27175670 DOI: 10.1097/MD.0000000000003614
    To evaluate the utility of blood pressure variability (BPV) calculated using previously published and newly introduced indices using the variables falls and age as comparators.While postural hypotension has long been considered a risk factor for falls, there is currently no documented evidence on the relationship between BPV and falls.A case-controlled study involving 25 fallers and 25 nonfallers was conducted. Systolic (SBPV) and diastolic blood pressure variability (DBPV) were assessed using 5 indices: standard deviation (SD), standard deviation of most stable continuous 120 beats (staSD), average real variability (ARV), root mean square of real variability (RMSRV), and standard deviation of real variability (SDRV). Continuous beat-to-beat blood pressure was recorded during 10 minutes' supine rest and 3 minutes' standing.Standing SBPV was significantly higher than supine SBPV using 4 indices in both groups. The standing-to-supine-BPV ratio (SSR) was then computed for each subject (staSD, ARV, RMSRV, and SDRV). Standing-to-supine ratio for SBPV was significantly higher among fallers compared to nonfallers using RMSRV and SDRV (P = 0.034 and P = 0.025). Using linear discriminant analysis (LDA), 3 indices (ARV, RMSRV, and SDRV) of SSR SBPV provided accuracies of 61.6%, 61.2%, and 60.0% for the prediction of falls which is comparable with timed-up and go (TUG), 64.4%.This study suggests that SSR SBPV using RMSRV and SDRV is a potential predictor for falls among older patients, and deserves further evaluation in larger prospective studies.
    Matched MeSH terms: Discriminant Analysis
  2. Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications
    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
  3. Characterization of spatial and temporal variability in hydrochemistry of Johor Straits, Malaysia
    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
  4. Comparison of prediction model using spatial discriminant analysis for marine water quality index in mangrove estuarine zones
    Samsudin MS, Azid A, Khalit SI, Sani MSA, Lananan F
    Mar Pollut Bull, 2019 Apr;141:472-481.
    PMID: 30955758 DOI: 10.1016/j.marpolbul.2019.02.045
    The prediction models of MWQI in mangrove and estuarine zones were constructed. The 2011-2015 data employed in this study entailed 13 parameters from six monitoring stations in West Malaysia. Spatial discriminant analysis (SDA) had recommended seven significant parameters to develop the MWQI which were DO, TSS, O&G, PO4, Cd, Cr and Zn. These selected parameters were then used to develop prediction models for the MWQI using artificial neural network (ANN) and multiple linear regressions (MLR). The SDA-ANN model had higher R2 value for training (0.9044) and validation (0.7113) results than SDA-MLR model and was chosen as the best model in mangrove estuarine zone. The SDA-ANN model had also demonstrated lower RMSE (5.224) than the SDA-MLR (12.7755). In summary, this work suggested that ANN was an effective tool to compute the MWQ in mangrove estuarine zone and a powerful alternative prediction model as compared to the other modelling methods.
    Matched MeSH terms: Discriminant Analysis
  5. Fulltext Cross-cultural adaptation of modified dental pain screening questionnaire (M-DePaQ) in a primary dental care clinic in Kuala Lumpur
    Kia ACL, Dalia Abdullah, Seong JS, Chiang SC, Pau A
    Malaysian Journal of Public Health Medicine, 2017;17(1):38-45.
    A validated screening tool for patient triage based on the pain symptoms, could potentially optimize the resources and expertise available in dental pain management. The aim of this study was to translate and validate the Modified Dental Pain Questionnaire (M-DePaQ) for use in categorizing patients with pain into three groups of common dental conditions. Forward Malay and Chinese translation was performed, followed by backward English translation. The translation was reviewed by an expert panel and pre-tested on patients who are native speakers.Consecutive patients aged 18 years and older experiencing pain and attending the primary dental care clinic completed the questionnaires. Four calibrated dentists made clinical diagnoses independent of the questionnaire responses. For data analysis, the cases were split randomly into Random Sample 1 (RS1) and Random Sample 2 (RS2). Discriminant analysis was performed on RS1 to develop a model for classifying dental pain cases into three groups. The model was applied to cases in RS2, and a cross-validated accuracy rate was obtained. Criterion validity was assessed using measures such as sensitivity, specificity, positive predictive value, and kappa. Of the 234 questionnaires distributed, 216 (92.3%) were returned. Classification rates were recorded at 73.8% for RS1, 75.0% for RS2, and 71.1% for all cases. The sensitivity values were 0.72, 0.39, and 0.43 for Groups 1, 2, and 3, respectively. The corresponding specificity values were 0.42, 0.87, and 0.94. The discriminant validity of the adapted questionnaire was satisfactory, but the criterion validity could not be established because of biases incorporated in the study.
    Matched MeSH terms: Discriminant Analysis
  6. Fulltext Forensic analysis of accelerant on different fabrics using Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) and chemometrics techniques
    Farah Izza Jais, Sharifah Mastura, Naji Arafat Mahat, Dzulkiflee Ismail, Muhammad Naeim Mohamad Asri
    Malaysian Journal of Medicine and Health Sciences, 2020;16(2):112-117.
    MyJurnal
    Introduction: Accelerants and fabrics are commonly used to spread fire attributable to their highly flammable prop- erties. Hence, having the ability to discriminate the different types of accelerants on various types of fabrics after fire and/or arson using the non-destructive Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spec- troscopy coupled with chemometric techniques appears forensically relevant. Methods: Six types of fabrics viz. cotton, wool, silk, rayon, satin, and polyester, were burnt completely with RON95 and RON97 gasoline as well as diesel. Subsequently, the samples were analyzed by ATR-FTIR spectroscopy followed by Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for discriminating the different types of accelerants on such burned fabrics. Results: RON95 showed the fastest rate of burning on all fabric types. Results also revealed that while wool had the slowest burning rate for all the three different accelerants, polyester, cotton, and satin demon- strated the highest rate of burning in RON95, RON97, and diesel, respectively. FTIR spectra revealed the presence of alkane, alcohol, alkene, alkyne, aromatic, and amine compounds for all fabrics. The two dimensional PCA (PC1 versus PC2) demonstrated 71% of variance and it was improved by cross-validation through the three dimensional LDA technique with correct classification of 77.8%. Conclusion: ATR-FTIR spectroscopy coupled with chemometric techniques had enabled identification of the functional groups, as well as statistically supported discrimination of the different accelerants, a matter of relevance in forensic fire and arson investigations.
    Matched MeSH terms: Discriminant Analysis
  7. Fulltext UV-visible chemometrics approach for the determination of selected adulterants in claimed premixed coffee
    Muhammad Zul Fayyadh Azizo Rahman, Chong, Hui Wen, Lim, Vuanghao
    Malaysian Journal of Medicine and Health Sciences, 2018;14(102):1-6.
    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
  8. Fulltext Development of long-term talent in sports model based on anthropometric and motor fitness components using multivariate analysis
    Liyana Daud, Mohamad Razali Abdullah, Siti Musliha Mat-Rasid, Ahmad Bisyri Husin Musawi Maliki, Amr Alnaimat, Muhammad Rabani Hashim, et al.
    Malaysian Journal of Applied Sciences, 2019;4(1):38-47.
    MyJurnal
    The study attempts to use multivariate analysis to evaluate the profile of male player for developments of Long-Term Talent in Sports (LT-TiS) model based on anthropometric and motor fitness components. Data of anthropometric and motor fitness included power, flexibility, coordination and speed were obtained from 2019 respondents aged 7.32±0.52 year. Data interpretations were carried out using multivariate analysis of Principle Components Analysis (PCA) and Discriminant analysis (DA). The adequacy of sampling has been measured using Bartletts tests on sphericity and Kaiser-Meyer-Olkin (KMO) has been used, with this conformance of running the Principal Component Analysis (PCA). Then, Discriminant Analysis (DA) were used to validate the correctness of group classification by LT-TiS model. Then, Discriminant Analysis (DA) were used to validate the correctness of group classification by LT-TiS. As a result, two factors with eigenvalues greater than 1 were extracted which accounted for 55.00% of the variations present in the original variables was found. The two factors were used to obtain the factor score coefficients explained by 27.86% and 27.21% of the variations in player performance respectively. Factor 1 revealed high factor loading on motor fitness compared to factor 2 as it was significantly related to anthropometrics. A model was obtained using standardized coefficient of factor 1. Three clusters of performance were shaped in view by categorizing; LT−TiS≥65%, 40%≤LT−TiS
    Matched MeSH terms: Discriminant Analysis
  9. Sex estimation of Malaysian sub-adults using craniometrics: A computed tomography study
    Syed Mohd Hamdan SN, Rahmat RA, Abdul Razak F, Abd Kadir KA, Mohd Faizal Abdullah ER, Ibrahim N
    Leg Med (Tokyo), 2023 Sep;64:102275.
    PMID: 37229938 DOI: 10.1016/j.legalmed.2023.102275
    Sex estimation is crucial in biological profiling of skeletal human remains. Methods used for sex estimation in adults are less effective for sub-adults due to varied cranium patterns during the growth period. Hence, this study aimed to develop a sex estimation model for Malaysian sub-adults using craniometric measurements obtained through multi-slice computed tomography (MSCT). A total of 521 cranial MSCT dataset of sub-adult Malaysians (279 males, 242 females; 0-20 years old) were collected. Mimics software version 21.0 (Materialise, Leuven, Belgium) was used to construct three-dimensional (3D) models. A plane-to-plane (PTP) protocol was utilised to measure 14 selected craniometric parameters. Discriminant function analysis (DFA) and binary logistic regression (BLR) were used to statistically analyze the data. In this study, low level of sexual dimorphism was observed in cranium below 6 years old. The level was then increased with age. For sample validation data, the accuracy of DFA and BLR in estimating sex improved with age from 61.6% to 90.3%. All age groups except 0-2 and 3-6 showed high accuracy percentage (≥75%) when tested using DFA and BLR. DFA and BLR can be utilised to estimate sex for Malaysian sub-adult using MSCT craniometric measurements. However, BLR showed higher accuracy than DFA in sex estimation of sub-adults.
    Matched MeSH terms: Discriminant Analysis
  10. The Accuracy of Sex Estimation on Metatarsal Bones in a Northeastern Thai Population
    Phatsara M, Das S, Laowatthanaphong S, Tuamsuk P, Mahakkanukrauh P
    Clin Ter, 2016 May-Jun;167(3):72-6.
    PMID: 27424506 DOI: 10.7417/CT.2016.1929
    BACKGROUND: This study was carried out to evaluate the accuracy of sex estimation by discriminant analysis and stepwise discriminant analysis equations generated from metatarsal bones in a Thai population.
    MATERIAL AND METHODS: The testing samples utilized in this study consisted of 50 skeletons (25 males and 25 females) obtained from the Khon Kaen University Skeletal Collection, Department of Anatomy, Faculty of Medicine, Khon Kaen University. Seven measurements of metatarsal bones were measured in centimeters, using either a mini-osteometric board (MOB) or a sliding caliper. The values measured from the Khon Kaen Skeletal Collection were used to determine the accuracy and applicability of sex determination, as predicted by Y1-Y6 equations which were generated from a Chiang Mai Skeletal Collection.
    RESULTS: The percentage of sex determination accuracies predicted from the Y1-Y6 equations demonstrated accuracy rates of 80-95.6.
    CONCLUSIONS: The Chiang Mai sex determination equations, generated from metatarsal bones by discriminant analysis (Y1-Y3) and stepwise discriminant analysis (Y4-Y6), demonstrated high accuracy rates of prediction, suggesting that these equations may be useful for sex determination within the Thai population.
    KEYWORDS: Foot; Metatarsal bones; Sex determination; Thailand
    Matched MeSH terms: Discriminant Analysis
  11. Fulltext Anthropometric analysis of mandible: an important step for sex determination
    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
  12. Infrared-metabolomics approach in detecting changes in Andrographis paniculata metabolites due to different harvesting ages and times
    Yusof NA, Isha A, Ismail IS, Khatib A, Shaari K, Abas F, et al.
    J Sci Food Agric, 2015 Sep;95(12):2533-43.
    PMID: 25371390 DOI: 10.1002/jsfa.6987
    The metabolite changes in three germplasm accessions of Malaysia Andrographis paniculata (Burm. F.) Nees, viz. 11265 (H), 11341 (P) and 11248 (T), due to their different harvesting ages and times were successfully evaluated by attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy and translated through multivariate data analysis of principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). This present study revealed the feasibility of ATR-FTIR in detecting the trend changes of the major metabolites - andrographolide and neoandrographolide - functional groups in A. paniculata leaves of different accessions. The harvesting parameter was set at three different ages of 120, 150 and 180 days after transplanting (DAT) and at two different time sessions of morning (7:30-10:30 am) and evening (2:30-5.30 pm).
    Matched MeSH terms: Discriminant Analysis
  13. Metabolomic analysis and biochemical changes in the urine and serum of streptozotocin-induced normal- and obese-diabetic rats
    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
  14. Fulltext Salivary bacterial shifts in oral leukoplakia resemble the dysbiotic oral cancer bacteriome
    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
  15. Application of FTIR-ATR spectroscopy coupled with multivariate analysis for rapid estimation of butter adulteration
    Fadzlillah NA, Rohman A, Ismail A, Mustafa S, Khatib A
    J Oleo Sci, 2013;62(8):555-62.
    PMID: 23985484
    In dairy product sector, butter is one of the potential sources of fat soluble vitamins, namely vitamin A, D, E, K; consequently, butter is taken into account as high valuable price from other dairy products. This fact has attracted unscrupulous market players to blind butter with other animal fats to gain economic profit. Animal fats like mutton fat (MF) are potential to be mixed with butter due to the similarity in terms of fatty acid composition. This study focused on the application of FTIR-ATR spectroscopy in conjunction with chemometrics for classification and quantification of MF as adulterant in butter. The FTIR spectral region of 3910-710 cm⁻¹ was used for classification between butter and butter blended with MF at various concentrations with the aid of discriminant analysis (DA). DA is able to classify butter and adulterated butter without any mistakenly grouped. For quantitative analysis, partial least square (PLS) regression was used to develop a calibration model at the frequency regions of 3910-710 cm⁻¹. The equation obtained for the relationship between actual value of MF and FTIR predicted values of MF in PLS calibration model was y = 0.998x + 1.033, with the values of coefficient of determination (R²) and root mean square error of calibration are 0.998 and 0.046% (v/v), respectively. The PLS calibration model was subsequently used for the prediction of independent samples containing butter in the binary mixtures with MF. Using 9 principal components, root mean square error of prediction (RMSEP) is 1.68% (v/v). The results showed that FTIR spectroscopy can be used for the classification and quantification of MF in butter formulation for verification purposes.
    Matched MeSH terms: Discriminant Analysis
  16. Fulltext The parasitoid, Verticia fasciventris causes morphological and behavioral changes in infected soldiers of the fungus-growing termite, Macrotermes carbonarius
    Neoh KB, Lee CY
    J Insect Sci, 2011;11:47.
    PMID: 21861651 DOI: 10.1673/031.011.4701
    The larval parasitoid Verticia fasciventris Malloch (Diptera: Calliphoridae) develops in the head of soldiers of the fungus-growing termite Macrotermes carbonarius (Hagen) (Isoptera: Termitidae). Morphological and behavioral changes in the host were evaluated and the termite castes and stages that were parasitized were identified. The larval emergence process is also described and possible mechanisms for the parasitoid fly's entry into the host body are discussed based on qualitative observations. Only a single larva per host was found. The mature larva pupated outside the host's body by exiting between the abdominal cerci. Parasitized soldiers possess a short and square-shaped head capsule, a pair of notably short mandibles, and a pair of 18-segmented antennae. Although parasitized soldiers were statistically less aggressive than healthy soldiers (P < 0.05), they expressed varying levels of aggression. Both minor and major soldiers can be parasitized and based on evidence from presoldiers, parasitization may begin during the precursor stages of soldiers. However, the stage at which parasitism first occurs has not been determined.
    Matched MeSH terms: Discriminant Analysis
  17. Fulltext Classification of Targets and Distractors Present in Visual Hemifields Using Time-Frequency Domain EEG Features
    Sweeti, Joshi D, Panigrahi BK, Anand S, Santhosh J
    J Healthc Eng, 2018;2018:9213707.
    PMID: 29808111 DOI: 10.1155/2018/9213707
    This paper presents a classification system to classify the cognitive load corresponding to targets and distractors present in opposite visual hemifields. The approach includes the study of EEG (electroencephalogram) signal features acquired in a spatial attention task. The process comprises of EEG feature selection based on the feature distribution, followed by the stepwise discriminant analysis- (SDA-) based channel selection. Repeated measure analysis of variance (rANOVA) is applied to test the statistical significance of the selected features. Classifiers are developed and compared using the selected features to classify the target and distractor present in visual hemifields. The results provide a maximum classification accuracy of 87.2% and 86.1% and an average classification accuracy of 76.5 ± 4% and 76.2 ± 5.3% over the thirteen subjects corresponding to the two task conditions. These correlates present a step towards building a feature-based neurofeedback system for visual attention.
    Matched MeSH terms: Discriminant Analysis
  18. Assessment of arsenic and heavy metal contents in cockles (Anadara granosa) using multivariate statistical techniques
    Abbas Alkarkhi FM, Ismail N, Easa AM
    J Hazard Mater, 2008 Feb 11;150(3):783-9.
    PMID: 17590506
    Cockles (Anadara granosa) sample obtained from two rivers in the Penang State of Malaysia were analyzed for the content of arsenic (As) and heavy metals (Cr, Cd, Zn, Cu, Pb, and Hg) using a graphite flame atomic absorption spectrometer (GF-AAS) for Cr, Cd, Zn, Cu, Pb, As and cold vapor atomic absorption spectrometer (CV-AAS) for Hg. The two locations of interest with 20 sampling points of each location were Kuala Juru (Juru River) and Bukit Tambun (Jejawi River). Multivariate statistical techniques such as multivariate analysis of variance (MANOVA) and discriminant analysis (DA) were applied for analyzing the data. MANOVA showed a strong significant difference between the two rivers in term of As and heavy metals contents in cockles. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers. It provided an important data reduction as it used only two parameters (Zn and Cd) affording more than 72% correct assignations. Results indicated that the two rivers were different in terms of As and heavy metal contents in cockle, and the major difference was due to the contribution of Zn and Cd. A positive correlation was found between discriminate functions (DF) and Zn, Cd and Cr, whereas negative correlation was exhibited with other heavy metals. Therefore, DA allowed a reduction in the dimensionality of the data set, delineating a few indicator parameters responsible for large variations in heavy metals and arsenic content. Taking into account of these results, it can be suggested that a continuous monitoring of As and heavy metals in cockles be performed in these two rivers.
    Matched MeSH terms: Discriminant Analysis
  19. Statistical validation for the profiling of heroin by associating simulated postcut samples with the corresponding precut sample
    Chan KW, Tan GH, Wong RC
    J Forensic Sci, 2013 Jan;58 Suppl 1:S199-207.
    PMID: 23013257 DOI: 10.1111/j.1556-4029.2012.02285.x
    Statistical validation is crucial for the clustering of unknown samples. This study aims to demonstrate how statistical techniques can be optimized using simulated heroin samples containing a range of analyte concentrations that are similar to those of the case samples. Eight simulated heroin distribution links consisting of 64 postcut samples were prepared by mixing one of two mixtures of paracetamol-caffeine-dextromethorphan at different proportions with eight precut samples. Analyte contents and compositional variation of the prepared samples were investigated. A number of data pretreatments were evaluated by associating the postcut samples with the corresponding precut samples using principal component analysis and discriminant analysis. Subsequently, combinations of seven linkage methods and five distance measures were explored using hierarchical cluster analysis. In this study, Ward-Manhattan showed better distinctions between unrelated links and was able to cluster all related samples in very close distance under the known links on a dendogram. A similar discriminative outcome was also achieved by 90 unknown case samples when clustered via Ward-Manhattan.
    Matched MeSH terms: Discriminant Analysis
  20. Differentiation of Commercially Available Kratom by Purported Country of Origin using Inductively Coupled Plasma-Mass Spectrometry
    Braley C, Hondrogiannis EM
    J Forensic Sci, 2020 Mar;65(2):428-437.
    PMID: 31560807 DOI: 10.1111/1556-4029.14201
    Kratom is a plant material exhibiting both analgesic and stimulant effects and is also forensically relevant since it is abused as a "legal high." It is regulated in several countries but not scheduled in the United States at the federal level. This study used inductively coupled plasma-mass spectrometry (ICP-MS) to measure the concentrations of 13 elements in 19 kratom samples obtained from an online distributor selling kratom, from Borneo, Malaysia, Indonesia, Thailand, and Vietnam, for the purpose of using the elements to discriminate among purported country of origin, "suborigin," and strain. Analysis of variance revealed statistical differences in concentrations of elements from each group, while discriminant function analysis (using leave-one-out classification) successfully classified kratom samples by their purported country of origin (100%), "suborigin," (100%), and strain (86%). Our method illustrates the possibility of utilizing ICP-MS for determination of commercially available kratom samples by purported origin, "subororign," or by product line.
    Matched MeSH terms: Discriminant Analysis
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