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.
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.
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.
The aim of the study is to identify the candidate biomarkers of heat stress (HS) in the urine of lactating dairy goats through the application of proton Nuclear Magnetic Resonance (1H NMR)-based metabolomic analysis. Dairy does (n = 16) in mid-lactation were submitted to thermal neutral (TN; indoors; 15 to 20°C; 40 to 45% humidity) or HS (climatic chamber; 37°C day, 30°C night; 40% humidity) conditions according to a crossover design (2 periods of 21 days). Thermophysiological traits and lactational performances were recorded and milk composition analyzed during each period. Urine samples were collected at day 15 of each period for 1H NMR spectroscopy analysis. Principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) assessment with cross validation were used to identify the goat urinary metabolome from the Human Metabolome Data Base. HS increased rectal temperature (1.2°C), respiratory rate (3.5-fold) and water intake (74%), but decreased feed intake (35%) and body weight (5%) of the lactating does. No differences were detected in milk yield, but HS decreased the milk contents of fat (9%), protein (16%) and lactose (5%). Metabolomics allowed separating TN and HS urinary clusters by PLS-DA. Most discriminating metabolites were hippurate and other phenylalanine (Phe) derivative compounds, which increased in HS vs. TN does. The greater excretion of these gut-derived toxic compounds indicated that HS induced a harmful gastrointestinal microbiota overgrowth, which should have sequestered aromatic amino acids for their metabolism and decreased the synthesis of neurotransmitters and thyroid hormones, with a negative impact on milk yield and composition. In conclusion, HS markedly changed the thermophysiological traits and lactational performances of dairy goats, which were translated into their urinary metabolomic profile through the presence of gut-derived toxic compounds. Hippurate and other Phe-derivative compounds are suggested as urinary biomarkers to detect heat-stressed dairy animals in practice.
Sex estimation is used in forensic anthropology to assist the identification of individual remains. However, the estimation techniques tend to be unique and applicable only to a certain population. This paper analyzed sex estimation on living individual child below 19 years old using the length of 19 bones of left hand applied for three classification techniques, which were Discriminant Function Analysis (DFA), Support Vector Machine (SVM) and Artificial Neural Network (ANN) multilayer perceptron. These techniques were carried out on X-ray images of the left hand taken from an Asian population data set. All the 19 bones of the left hand were measured using Free Image software, and all the techniques were performed using MATLAB. The group of age "16-19" years old and "7-9" years old were the groups that could be used for sex estimation with as their average of accuracy percentage was above 80%. ANN model was the best classification technique with the highest average of accuracy percentage in the two groups of age compared to other classification techniques. The results show that each classification technique has the best accuracy percentage on each different group of age.
Common spatial pattern (CSP) is shown to be an effective pre-processing algorithm in order to discriminate different classes of motor-based EEG signals by obtaining suitable spatial filters. The performance of these filters can be improved by regularized CSP, in which available prior information is added in terms of regularization terms into the objective function of conventional CSP. Variety of prior information can be used in this way. In this paper, we used time correlation between different classes of EEG signal as the prior information, which is clarified similarity between different classes of signal for regularizing CSP. Furthermore, the proposed objective function can be easily extended to more than two-class problems. We used three different standard datasets to evaluate the performance of the proposed method. Correlation-based CSP (CCSP) outperformed original CSP as well as the existing regularized CSP, Principle Component Cnalysis (PCA) and Fisher Discriminate Analysis (FDA) in both two-class and multi-class scenarios. The simulation results showed that the proposed method outperformed conventional CSP by 6.9% in 2-class and 2.23% in multi-class problem in term of mean classification accuracy.
Wild stocks of endangered mrigal carp, Cirrhinus cirrhosus (Bloch 1795), continues to decline rapidly in the Indo-Ganges river basin. With an objective to evaluate its population status, landmark-based morphometric and meristic variations among three different stocks viz., hatchery (Jessore), baor (Gopalganj) and river (Faridpur) in Bangladesh were studied. Significant differences were observed in 10 of the 15 morphometric measurements viz., head length, standard length, fork length, length of base of spinous, pre-orbital length, eye length, post-orbital length, length of upper jaw, height of pelvic fin and barbel length, two of the 8 meristic counts viz., scales above the lateral line and pectoral fin rays and 10 of the 22 truss network measurements viz., 1 to 10, 2 to 3, 2 to 8, 2 to 9, 2 to 10, 3 to 4, 3 to 8, 4 to 5, 4 to 7 and 9 to 10 among the stocks. For morphometric and landmark measurements, the 1st discriminant function (DF) accounted for 58.1% and the 2nd DF accounted for 41.9% of the among-group variability. In discriminant space, the river stock was isolated from the other two stocks. On the other hand, baor and hatchery stocks formed a very compact cluster. A dendrogram based on the hierarchical cluster analysis using morphometric and truss distance data placed the hatchery and baor in one cluster and the river in another cluster and the distance between the river and hatchery populations was the highest. Morphological differences among stocks are expected, because of their geographical isolation and their origin from different ancestors. The baseline information derived from the present study would be useful for genetic studies and in the assessment of environmental impacts on C. cirrhosus populations in Bangladesh.
The approach of this paper is to predict the sand mass distribution in an urban stormwater holding pond at the Stormwater Management And Road Tunnel (SMART) Control Centre, Malaysia, using simulated depth average floodwater velocity diverted into the holding during storm events. Discriminant analysis (DA) was applied to derive the classification function to spatially distinguish areas of relatively high and low sand mass compositions based on the simulated water velocity variations at corresponding locations of gravimetrically measured sand mass composition of surface sediment samples. Three inflow parameter values, 16, 40 and 80 m(3) s(-1), representing diverted floodwater discharge for three storm event conditions were fixed as input parameters of the hydrodynamic model. The sand (grain size > 0.063 mm) mass composition of the surface sediment measured at 29 sampling locations ranges from 3.7 to 45.5%. The sampling locations of the surface sediment were spatially clustered into two groups based on the sand mass composition. The sand mass composition of group 1 is relatively lower (3.69 to 12.20%) compared to group 2 (16.90 to 45.55%). Two Fisher's linear discriminant functions, F 1 and F 2, were generated to predict areas; both consist of relatively higher and lower sand mass compositions based on the relationship between the simulated flow velocity and the measured surface sand composition at corresponding sampling locations. F 1 = -9.405 + 4232.119 × A - 1795.805 × B + 281.224 × C, and F 2 = -2.842 + 2725.137 × A - 1307.688 × B + 231.353 × C. A, B and C represent the simulated flow velocity generated by inflow parameter values of 16, 40 and 80 m(3) s(-1), respectively. The model correctly predicts 88.9 and 100.0% of sampling locations consisting of relatively high and low sand mass percentages, respectively, with the cross-validated classification showing that, overall, 82.8% are correctly classified. The model predicts that 31.4% of the model domain areas consist of high-sand mass composition areas and the remaining 68.6% comprise low-sand mass composition areas.
To assess the cross-sectional construct validity of the Malay-translated and cross-culturally adapted FACT-H&N (v 4.0) for discriminative use in a sample of Malaysian oral cancer patients. A cross-sectional study of adults newly diagnosed with oral cancer. HRQOL data were collected using the FACT-H&N (v 4.0), a global question and a supplementary set of eight questions ('MAQ') obtained earlier in pilot work. Of the 76 participants (61.8% female; 23.7% younger than 50), most (96.1%) had oral squamous cell carcinoma; two-thirds were in Stages III or IV. At baseline, patients' mean FACT summary (FACT-G, FACT-H&N, FACT-H&N TOI, and FHNSI) and subscale (pwb, swb, ewb, fwb, and hnsc) scores were towards the higher end of the range. Equal proportions (36.8%) rated their overall HRQOL as 'good' or 'average'; fewer than one-quarter rated it as 'poor', and only two as 'very good'. All six FACT summary and most subscales had moderate-to-good internal consistency. For all summary scales, those with 'very poor/poor' self-rated HRQOL differed significantly from the 'good/very good' group. All FACT summary scales correlated strongly (r>0.75). Summary scales showed convergent validity (r>0.90) but little discriminant validity. The discriminant validity of the FHNSI improved with the addition of the MAQ. The FACT-H&N summary scales and most subscales demonstrated acceptable cross-sectional construct validity, reliability and discriminative ability, and thus appear appropriate for further use among Malaysian oral cancer patients.
We examined the geographical variation of the skull size and shape of the lesser mouse deer (Tragulus javanicus) from Laos, Thailand, Peninsular Malaysia, Sumatra, Java, Borneo, Langkawi and some Islands of Tenasserim in Myanmar. Although the influence of the climatic condition on skull size was not confirmed in the mainland populations, the skull became rostro-caudally longer in the populations of Tenasserim and Sumatra because of island isolation effect. The skull size was classified into the following three clusters of localities from the matrix of Q-mode correlation coefficients: 1) Langkawi and Tenasserim, 2) Laos and Thailand, 3) Sumatra and Borneo. The skulls in the population of Java belong to the cluster of Langkawi and Tenasserim in male, however were morphologically similar to those in the cluster of Borneo and Sumatra. The canonical discriminant analysis pointed out that the Laos and Tenasserim populations were separated from the other ones and that the populations of Sumatra, Java and Borneo were intermingled each other.
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.
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.
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.
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.
Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.
The internet of reality or augmented reality has been considered a breakthrough and an outstanding critical mutation with an emphasis on data mining leading to dismantling of some of its assumptions among several of its stakeholders. In this work, we study the pillars of these technologies connected to web usage as the Internet of things (IoT) system's healthcare infrastructure. We used several data mining techniques to evaluate the online advertisement data set, which can be categorized as high dimensional with 1,553 attributes, and the imbalanced data set, which automatically simulates an IoT discrimination problem. The proposed methodology applies Fischer linear discrimination analysis (FLDA) and quadratic discrimination analysis (QDA) within random projection (RP) filters to compare our runtime and accuracy with support vector machine (SVM), K-nearest neighbor (KNN), and Multilayer perceptron (MLP) in IoT-based systems. Finally, the impact on number of projections was practically experimented, and the sensitivity of both FLDA and QDA with regard to precision and runtime was found to be challenging. The modeling results show not only improved accuracy, but also runtime improvements. When compared with SVM, KNN, and MLP in QDA and FLDA, runtime shortens by 20 times in our chosen data set simulated for a healthcare framework. The RP filtering in the preprocessing stage of the attribute selection, fulfilling the model's runtime, is a standpoint in the IoT industry. Index Terms: Data Mining, Random Projection, Fischer Linear Discriminant Analysis, Online Advertisement Dataset, Quadratic Discriminant Analysis, Feature Selection, Internet of Things.
Beef, buffalo, and pork adulteration in the food chain is an emerging and sensitive issue. Current molecular techniques to authenticate these species depend on polymerase chain reaction (PCR) assays involving long and single targets which break down under natural decomposition and/or processing treatments. This novel multiplex polymerase chain reaction-restriction fragment length polymorphism assay targeted two different gene sites for each of the bovine, buffalo, and porcine materials. This authentication ensured better security, first through a complementation approach because it is highly unlikely that both sites will be missing under compromised states, and second through molecular fingerprints. Mitochondrial cytochrome b and ND5 genes were targeted, and all targets (73, 90, 106, 120, 138, and 146 bp) were stable under extreme boiling and autoclaving treatments. Target specificity and authenticity were ensured through cross-amplification reaction and restriction digestion of PCR products with AluI, EciI, FatI, and CviKI-1 enzymes. A survey of Malaysian frankfurter products revealed rampant substitution of beef with buffalo but purity in porcine materials.
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.
Nitrate-nitrogen leaching from agricultural areas is a major cause for groundwater pollution. Polluted groundwater with high levels of nitrate is hazardous and cause adverse health effects. Human consumption of water with elevated levels of NO3-N has been linked to the infant disorder methemoglobinemia and also to non-Hodgkin's disease lymphoma in adults. This research aims to study the temporal patterns and source apportionment of nitrate-nitrogen leaching in a paddy soil at Ladang Merdeka Ismail Mulong in Kelantan, Malaysia. The complex data matrix (128 x 16) of nitrate-nitrogen parameters was subjected to multivariate analysis mainly Principal Component Analysis (PCA) and Discriminant Analysis (DA). PCA extracted four principal components from this data set which explained 86.4% of the total variance. The most important contributors were soil physical properties confirmed using Alyuda Forecaster software (R2 = 0.98). Discriminant analysis was used to evaluate the temporal variation in soil nitrate-nitrogen on leaching process. Discriminant analysis gave four parameters (hydraulic head, evapotranspiration, rainfall and temperature) contributing more than 98% correct assignments in temporal analysis. DA allowed reduction in dimensionality of the large data set which defines the four operating parameters most efficient and economical to be monitored for temporal variations. This knowledge is important so as to protect the precious groundwater from contamination with nitrate.
Sex determination is one of the main steps in the identification of human skeletal remains. It constitutes an initial step in personal identification from the skeletal remains. The aim of the present study was to provide the population-specific sex discriminating osteometric standards to aid human identification. The present study was conducted on 87 (174 sides) slices of crania using postmortem computed tomography in 45 males and 42 females, aged between 18 and 75 years. About 22 parameters of crania were measured using Osirix software 3-D Volume Rendering. Results showed that all parameters were significantly higher in males than in females except for orbital height of the left eye by independent t test (P<0.01). By discriminant analysis, the classification accuracy was 85.1%, and by regression, the classification accuracy ranged from 78.2% to 86.2%. In conclusion, cranium can be used to distinguish between males and females in the Malaysian population. The results of the present study can be used as a forensic tool for identification of unknown crania.