Displaying publications 81 - 100 of 311 in total

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  1. Fulltext Developing an index based on GIS and statistical analysis for family support of substance abuser in Terengganu
    Juahir, H., Fazillah, A., Kamarudin, M.K.A., Toriman, E., Mohamad, N., Fairuz, A., et al.
    Malaysian Journal of Applied Sciences, 2018;2(2):45-54.
    MyJurnal
    Family support has a strong impact on individuals and there is no exception in substance abuse
    recovery process. Family support manages to play a positive role in substance abuse problems. The
    present study deals with the developing model of family support substance abuser with the
    combination method of Geographic Information System (GIS) and statistical models. The data used
    for this study was collected from seven districts in Terengganu with a constant number of
    respondents. 35 respondents for each district were involved in this study. It was then processed using
    factor analysis (FA) to develop index of family support. By using the developed indices, GIS tool was
    used to plot the distribution map of family support indices according to each form of family support.
    The result indicated that the highest index for all form of family support abuser was located in Besut
    district. High level of family support is essential as an effort for rehabilitation process of substance
    abusers.
    Matched MeSH terms: Models, Statistical
  2. Fulltext Early warning and response system (EWARS) for dengue outbreaks: Recent advancements towards widespread applications in critical settings
    Hussain-Alkhateeb L, Kroeger A, Olliaro P, Rocklöv J, Sewe MO, Tejeda G, et al.
    PLoS One, 2018;13(5):e0196811.
    PMID: 29727447 DOI: 10.1371/journal.pone.0196811
    BACKGROUND: Dengue outbreaks are increasing in frequency over space and time, affecting people's health and burdening resource-constrained health systems. The ability to detect early emerging outbreaks is key to mounting an effective response. The early warning and response system (EWARS) is a toolkit that provides countries with early-warning systems for efficient and cost-effective local responses. EWARS uses outbreak and alarm indicators to derive prediction models that can be used prospectively to predict a forthcoming dengue outbreak at district level.

    METHODS: We report on the development of the EWARS tool, based on users' recommendations into a convenient, user-friendly and reliable software aided by a user's workbook and its field testing in 30 health districts in Brazil, Malaysia and Mexico.

    FINDINGS: 34 Health officers from the 30 study districts who had used the original EWARS for 7 to 10 months responded to a questionnaire with mainly open-ended questions. Qualitative content analysis showed that participants were generally satisfied with the tool but preferred open-access vs. commercial software. EWARS users also stated that the geographical unit should be the district, while access to meteorological information should be improved. These recommendations were incorporated into the second-generation EWARS-R, using the free R software, combined with recent surveillance data and resulted in higher sensitivities and positive predictive values of alarm signals compared to the first-generation EWARS. Currently the use of satellite data for meteorological information is being tested and a dashboard is being developed to increase user-friendliness of the tool. The inclusion of other Aedes borne viral diseases is under discussion.

    CONCLUSION: EWARS is a pragmatic and useful tool for detecting imminent dengue outbreaks to trigger early response activities.

    Matched MeSH terms: Models, Statistical
  3. Fulltext Improving forecasting accuracy for stock market data using EMD-HW bagging
    Awajan AM, Ismail MT, Al Wadi S
    PLoS One, 2018;13(7):e0199582.
    PMID: 30016323 DOI: 10.1371/journal.pone.0199582
    Many researchers documented that the stock market data are nonstationary and nonlinear time series data. In this study, we use EMD-HW bagging method for nonstationary and nonlinear time series forecasting. The EMD-HW bagging method is based on the empirical mode decomposition (EMD), the moving block bootstrap and the Holt-Winter. The stock market time series of six countries are used to compare EMD-HW bagging method. This comparison is based on five forecasting error measurements. The comparison shows that the forecasting results of EMD-HW bagging are more accurate than the forecasting results of the fourteen selected methods.
    Matched MeSH terms: Models, Statistical*
  4. Fulltext Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation
    Illias HA, Zhao Liang W
    PLoS One, 2018;13(1):e0191366.
    PMID: 29370230 DOI: 10.1371/journal.pone.0191366
    Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.
    Matched MeSH terms: Models, Statistical
  5. Fulltext Optimization of physical conditions for the production of thermostable T1 lipase in Pichia guilliermondii strain SO using response surface methodology
    Abu ML, Nooh HM, Oslan SN, Salleh AB
    BMC Biotechnol, 2017 Nov 10;17(1):78.
    PMID: 29126403 DOI: 10.1186/s12896-017-0397-7
    BACKGROUND: Pichia guilliermondii was found capable of expressing the recombinant thermostable lipase without methanol under the control of methanol dependent alcohol oxidase 1 promoter (AOXp 1). In this study, statistical approaches were employed for the screening and optimisation of physical conditions for T1 lipase production in P. guilliermondii.

    RESULT: The screening of six physical conditions by Plackett-Burman Design has identified pH, inoculum size and incubation time as exerting significant effects on lipase production. These three conditions were further optimised using, Box-Behnken Design of Response Surface Methodology, which predicted an optimum medium comprising pH 6, 24 h incubation time and 2% inoculum size. T1 lipase activity of 2.0 U/mL was produced with a biomass of OD600 23.0.

    CONCLUSION: The process of using RSM for optimisation yielded a 3-fold increase of T1 lipase over medium before optimisation. Therefore, this result has proven that T1 lipase can be produced at a higher yield in P. guilliermondii.

    Matched MeSH terms: Models, Statistical
  6. Modified-BRISQUE as no reference image quality assessment for structural MR images
    Chow LS, Rajagopal H
    Magn Reson Imaging, 2017 11;43:74-87.
    PMID: 28716679 DOI: 10.1016/j.mri.2017.07.016
    An effective and practical Image Quality Assessment (IQA) model is needed to assess the image quality produced from any new hardware or software in MRI. A highly competitive No Reference - IQA (NR - IQA) model called Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) initially designed for natural images were modified to evaluate structural MR images. The BRISQUE model measures the image quality by using the locally normalized luminance coefficients, which were used to calculate the image features. The modified-BRISQUE model trained a new regression model using MR image features and Difference Mean Opinion Score (DMOS) from 775 MR images. Two types of benchmarks: objective and subjective assessments were used as performance evaluators for both original and modified-BRISQUE models. There was a high correlation between the modified-BRISQUE with both benchmarks, and they were higher than those for the original BRISQUE. There was a significant percentage improvement in their correlation values. The modified-BRISQUE was statistically better than the original BRISQUE. The modified-BRISQUE model can accurately measure the image quality of MR images. It is a practical NR-IQA model for MR images without using reference images.
    Matched MeSH terms: Models, Statistical
  7. A comparison of bivariate, multivariate random-effects, and Poisson correlated gamma-frailty models to meta-analyze individual patient data of ordinal scale diagnostic tests
    Simoneau G, Levis B, Cuijpers P, Ioannidis JPA, Patten SB, Shrier I, et al.
    Biom J, 2017 Nov;59(6):1317-1338.
    PMID: 28692782 DOI: 10.1002/bimj.201600184
    Individual patient data (IPD) meta-analyses are increasingly common in the literature. In the context of estimating the diagnostic accuracy of ordinal or semi-continuous scale tests, sensitivity and specificity are often reported for a given threshold or a small set of thresholds, and a meta-analysis is conducted via a bivariate approach to account for their correlation. When IPD are available, sensitivity and specificity can be pooled for every possible threshold. Our objective was to compare the bivariate approach, which can be applied separately at every threshold, to two multivariate methods: the ordinal multivariate random-effects model and the Poisson correlated gamma-frailty model. Our comparison was empirical, using IPD from 13 studies that evaluated the diagnostic accuracy of the 9-item Patient Health Questionnaire depression screening tool, and included simulations. The empirical comparison showed that the implementation of the two multivariate methods is more laborious in terms of computational time and sensitivity to user-supplied values compared to the bivariate approach. Simulations showed that ignoring the within-study correlation of sensitivity and specificity across thresholds did not worsen inferences with the bivariate approach compared to the Poisson model. The ordinal approach was not suitable for simulations because the model was highly sensitive to user-supplied starting values. We tentatively recommend the bivariate approach rather than more complex multivariate methods for IPD diagnostic accuracy meta-analyses of ordinal scale tests, although the limited type of diagnostic data considered in the simulation study restricts the generalization of our findings.
    Matched MeSH terms: Models, Statistical*
  8. Do the physical and environment PETTLEP elements predict sport imagery ability?
    Anuar N, Williams SE, Cumming J
    Eur J Sport Sci, 2017 Nov;17(10):1319-1327.
    PMID: 28950801 DOI: 10.1080/17461391.2017.1377290
    The present study aimed to examine whether physical and environment elements of PETTLEP imagery relate to the ability to image five types of sport imagery (i.e. skill, strategy, goal, affect and mastery). Two hundred and ninety participants (152 males, 148 females; Mage = 20.24 years, SD = 4.36) from various sports completed the Sport Imagery Ability Questionnaire (SIAQ), and a set of items designed specifically for the study to assess how frequently participants incorporate physical (e.g. 'I make small movements or gestures during the imagery') and environment (e.g. 'I image in the real training/competition environment') elements of PETTLEP imagery. Structural equation modelling tested a hypothesised model in which imagery priming (i.e. the best fitting physical and environment elements) significantly and positively predicted imagery ability of the different imagery types (skill, β = 0.38; strategy, β = 0.23; goal, β = 0.21; affect, β = 0.25; mastery, β = 0.22). The model was a good fit to the data: χ2(174) = 263.87, p 
    Matched MeSH terms: Models, Statistical
  9. Renewable energy, carbon emissions, and economic growth in 24 Asian countries: evidence from panel cointegration analysis
    Lu WC
    Environ Sci Pollut Res Int, 2017 Nov;24(33):26006-26015.
    PMID: 28942473 DOI: 10.1007/s11356-017-0259-9
    This article aims to investigate the relationship among renewable energy consumption, carbon dioxide (CO2) emissions, and GDP using panel data for 24 Asian countries between 1990 and 2012. Panel cross-sectional dependence tests and unit root test, which considers cross-sectional dependence across countries, are used to ensure that the empirical results are correct. Using the panel cointegration model, the vector error correction model, and the Granger causality test, this paper finds that a long-run equilibrium exists among renewable energy consumption, carbon emission, and GDP. CO2 emissions have a positive effect on renewable energy consumption in the Philippines, Pakistan, China, Iraq, Yemen, and Saudi Arabia. A 1% increase in GDP will increase renewable energy by 0.64%. Renewable energy is significantly determined by GDP in India, Sri Lanka, the Philippines, Thailand, Turkey, Malaysia, Jordan, United Arab Emirates, Saudi Arabia, and Mongolia. A unidirectional causality runs from GDP to CO2 emissions, and two bidirectional causal relationships were found between CO2 emissions and renewable energy consumption and between renewable energy consumption and GDP. The findings can assist governments in curbing pollution from air pollutants, execute energy conservation policy, and reduce unnecessary wastage of energy.
    Matched MeSH terms: Models, Statistical*
  10. Fulltext Implications for Drug Characterization in Glucose Tolerance Tests Without Insulin: Simulation Study of Power and Predictions Using Model-Based Analysis
    Sheikh Ghadzi SM, Karlsson MO, Kjellsson MC
    CPT Pharmacometrics Syst Pharmacol, 2017 10;6(10):686-694.
    PMID: 28575547 DOI: 10.1002/psp4.12214
    In antihyperglycemic drug development, drug effects are usually characterized using glucose provocations. Analyzing provocation data using pharmacometrics has shown powerful, enabling small studies. In preclinical drug development, high power is attractive due to the experiment sizes; however, insulin is not always available, which potentially impacts power and predictive performance. This simulation study was performed to investigate the implications of performing model-based drug characterization without insulin. The integrated glucose-insulin model was used to simulate and re-estimated oral glucose tolerance tests using a crossover design of placebo and study compound. Drug effects were implemented on seven different mechanisms of action (MOA); one by one or in two-drug combinations. This study showed that exclusion of insulin may severely reduce the power to distinguish the correct from competing drug effect, and to detect a primary or secondary drug effect, however, it did not affect the predictive performance of the model.
    Matched MeSH terms: Models, Statistical*
  11. The present and future disease burden of hepatitis C virus infections with today's treatment paradigm: Volume 4
    Chan HLY, Chen CJ, Omede O, Al Qamish J, Al Naamani K, Bane A, et al.
    J Viral Hepat, 2017 10;24 Suppl 2:25-43.
    PMID: 29105283 DOI: 10.1111/jvh.12760
    Factors influencing the morbidity and mortality associated with viremic hepatitis C virus (HCV) infection change over time and place, making it difficult to compare reported estimates. Models were developed for 17 countries (Bahrain, Bulgaria, Cameroon, Colombia, Croatia, Dominican Republic, Ethiopia, Ghana, Hong Kong, Jordan, Kazakhstan, Malaysia, Morocco, Nigeria, Qatar and Taiwan) to quantify and characterize the viremic population as well as forecast the changes in the infected population and the corresponding disease burden from 2015 to 2030. Model inputs were agreed upon through expert consensus, and a standardized methodology was followed to allow for comparison across countries. The viremic prevalence is expected to remain constant or decline in all but four countries (Ethiopia, Ghana, Jordan and Oman); however, HCV-related morbidity and mortality will increase in all countries except Qatar and Taiwan. In Qatar, the high-treatment rate will contribute to a reduction in total cases and HCV-related morbidity by 2030. In the remaining countries, however, the current treatment paradigm will be insufficient to achieve large reductions in HCV-related morbidity and mortality.
    Matched MeSH terms: Models, Statistical*
  12. Fulltext Optimization of thermostable organic solvent-tolerant lipase production by thermotolerant Rhizopus sp. using solid-state fermentation of palm kernel cake
    Riyadi FA, Alam MZ, Salleh MN, Salleh HM
    3 Biotech, 2017 Oct;7(5):300.
    PMID: 28884067 DOI: 10.1007/s13205-017-0932-1
    This study enhanced the production of thermostable organic solvent-tolerant (TS-OST) lipase by locally isolated thermotolerant Rhizopus sp. strain using solid-state fermentation (SSF) of palm kernel cake (PKC). The optimum conditions were achieved using a series of statistical approaches. The cultivation parameters, which include fermentation time, moisture content, temperature, pH, inoculum size, various carbon and nitrogen sources, as well as other supplements, were initially screened by the definitive screening design, and one-factor-at-a-time using PKC as the basal medium. Three significant factors (olive oil concentration, pH, and inoculum size) were further optimized using face-centred central composite design. The results indicated a successful and significant improvement of lipase activity by almost two-fold compared to the initial screening production. The findings showed that the optimal conditions were 2% (v/w) inoculum size, 2% (v/w) olive oil, 0.6% (w/w) peptone, 2% (v/w) ethanol, 70% moisture content at initial pH 10.0 and 45 °C within 72 h of fermentation. Process optimization resulted in maximum lipase activity of 58.63 U/gram dry solids (gds). The analysis of variance showed that the statistical model was significant (p value <0.0001) and reliable with a high value of R2 (0.98) and adjusted R2 (0.96). This indicates a better correlation between the actual and predicted responses of lipase production. By considering this study, the low-cost PKC through SSF appears to be promising in the utilization of agro-industrial waste for TS-OST lipase production. This is because satisfactory enzyme activity could be attained that promises industrial applications.
    Matched MeSH terms: Models, Statistical
  13. Fulltext First integrative trend analysis for a great ape species in Borneo
    Santika T, Ancrenaz M, Wilson KA, Spehar S, Abram N, Banes GL, et al.
    Sci Rep, 2017 07 07;7(1):4839.
    PMID: 28687788 DOI: 10.1038/s41598-017-04435-9
    For many threatened species the rate and drivers of population decline are difficult to assess accurately: species' surveys are typically restricted to small geographic areas, are conducted over short time periods, and employ a wide range of survey protocols. We addressed methodological challenges for assessing change in the abundance of an endangered species. We applied novel methods for integrating field and interview survey data for the critically endangered Bornean orangutan (Pongo pygmaeus), allowing a deeper understanding of the species' persistence through time. Our analysis revealed that Bornean orangutan populations have declined at a rate of 25% over the last 10 years. Survival rates of the species are lowest in areas with intermediate rainfall, where complex interrelations between soil fertility, agricultural productivity, and human settlement patterns influence persistence. These areas also have highest threats from human-wildlife conflict. Survival rates are further positively associated with forest extent, but are lower in areas where surrounding forest has been recently converted to industrial agriculture. Our study highlights the urgency of determining specific management interventions needed in different locations to counter the trend of decline and its associated drivers.
    Matched MeSH terms: Models, Statistical
  14. Fulltext Global mortality variations in patients with heart failure: results from the International Congestive Heart Failure (INTER-CHF) prospective cohort study
    Dokainish H, Teo K, Zhu J, Roy A, AlHabib KF, ElSayed A, et al.
    Lancet Glob Health, 2017 07;5(7):e665-e672.
    PMID: 28476564 DOI: 10.1016/S2214-109X(17)30196-1
    BACKGROUND: Most data on mortality and prognostic factors in patients with heart failure come from North America and Europe, with little information from other regions. Here, in the International Congestive Heart Failure (INTER-CHF) study, we aimed to measure mortality at 1 year in patients with heart failure in Africa, China, India, the Middle East, southeast Asia and South America; we also explored demographic, clinical, and socioeconomic variables associated with mortality.

    METHODS: We enrolled consecutive patients with heart failure (3695 [66%] clinic outpatients, 2105 [34%] hospital in patients) from 108 centres in six geographical regions. We recorded baseline demographic and clinical characteristics and followed up patients at 6 months and 1 year from enrolment to record symptoms, medications, and outcomes. Time to death was studied with Cox proportional hazards models adjusted for demographic and clinical variables, medications, socioeconomic variables, and region. We used the explained risk statistic to calculate the relative contribution of each level of adjustment to the risk of death.

    FINDINGS: We enrolled 5823 patients within 1 year (with 98% follow-up). Overall mortality was 16·5%: highest in Africa (34%) and India (23%), intermediate in southeast Asia (15%), and lowest in China (7%), South America (9%), and the Middle East (9%). Regional differences persisted after multivariable adjustment. Independent predictors of mortality included cardiac variables (New York Heart Association Functional Class III or IV, previous admission for heart failure, and valve disease) and non-cardiac variables (body-mass index, chronic kidney disease, and chronic obstructive pulmonary disease). 46% of mortality risk was explained by multivariable modelling with these variables; however, the remainder was unexplained.

    INTERPRETATION: Marked regional differences in mortality in patients with heart failure persisted after multivariable adjustment for cardiac and non-cardiac factors. Therefore, variations in mortality between regions could be the result of health-care infrastructure, quality and access, or environmental and genetic factors. Further studies in large, global cohorts are needed.

    FUNDING: The study was supported by Novartis.

    Study site: Multination
    Matched MeSH terms: Models, Statistical*
  15. Multi-objective evolutionary polynomial regression-based prediction of energy consumption probing
    Bonakdari H, Ebtehaj I, Akhbari A
    Water Sci Technol, 2017 Jun;75(12):2791-2799.
    PMID: 28659519 DOI: 10.2166/wst.2017.158
    Electrocoagulation (EC) is employed to investigate the energy consumption (EnC) of synthetic wastewater. In order to find the best process conditions, the influence of various parameters including initial pH, initial dye concentration, applied voltage, initial electrolyte concentration, and treatment time are investigated in this study. EnC is considered the main criterion of process evaluation in investigating the effect of the independent variables on the EC process and determining the optimum condition. Evolutionary polynomial regression is combined with a multi-objective genetic algorithm (EPR-MOGA) to present a new, simple and accurate equation for estimating EnC to overcome existing method weaknesses. To survey the influence of the effective variables, six different input combinations are considered. According to the results, EPR-MOGA Model 1 is the most accurate compared to other models, as it has the lowest error indices in predicting EnC (MARE = 0.35, RMSE = 2.33, SI = 0.23 and R2 = 0.98). A comparison of EPR-MOGA with reduced quadratic multiple regression methods in terms of feasibility confirms that EPR-MOGA is an effective alternative method. Moreover, the partial derivative sensitivity analysis method is employed to analyze the EnC variation trend according to input variables.
    Matched MeSH terms: Models, Statistical*
  16. Fulltext Flexible model-based clustering of mixed binary and continuous data: application to genetic regulation and cancer
    Zainul Abidin FN, Westhead DR
    Nucleic Acids Res, 2017 04 20;45(7):e53.
    PMID: 27994031 DOI: 10.1093/nar/gkw1270
    Clustering is used widely in 'omics' studies and is often tackled with standard methods, e.g. hierarchical clustering. However, the increasing need for integration of multiple data sets leads to a requirement for clustering methods applicable to mixed data types, where the straightforward application of standard methods is not necessarily the best approach. A particularly common problem involves clustering entities characterized by a mixture of binary data (e.g. presence/absence of mutations, binding, motifs and epigenetic marks) and continuous data (e.g. gene expression, protein abundance, metabolite levels). Here, we present a generic method based on a probabilistic model for clustering this type of data, and illustrate its application to genetic regulation and the clustering of cancer samples. We show that the resulting clusters lead to useful hypotheses: in the case of genetic regulation these concern regulation of groups of genes by specific sets of transcription factors and in the case of cancer samples combinations of gene mutations are related to patterns of gene expression. The clusters have potential mechanistic significance and in the latter case are significantly linked to survival. The method is available as a stand-alone software package (GNU General Public Licence) from http://github.com/BioToolsLeeds/FlexiCoClusteringPackage.git.
    Matched MeSH terms: Models, Statistical*
  17. Fulltext Infant body mass index peak and early childhood cardio-metabolic risk markers in a multi-ethnic Asian birth cohort
    Aris IM, Bernard JY, Chen LW, Tint MT, Pang WW, Lim WY, et al.
    Int J Epidemiol, 2017 04 01;46(2):513-525.
    PMID: 27649801 DOI: 10.1093/ije/dyw232
    Background: : Infant body mass index (BMI) peak has received much interest recently as a potential predictor of future obesity and metabolic risk. No studies, however, have examined infant BMI peak in Asian populations, in whom the risk of metabolic disease is higher.

    Methods: : We utilized data among 1020 infants from a mother-offspring cohort, who were Singapore citizens or permanent residents of Chinese, Malay or Indian ethnicity with homogeneous parental ethnic backgrounds, and did not receive chemotherapy, psychotropic drugs or have diabetes mellitus. Ethnicity was self-reported at recruitment and later confirmed using genotype analysis. Subject-specific BMI curves were fitted to infant BMI data using natural cubic splines with random coefficients to account for repeated measures in each child. We estimated characteristics of the child's BMI peak [age and magnitude at peak, average pre-peak velocity (aPPV)]. Systolic (SBP) and diastolic blood pressure (DBP), BMI, sum of skinfolds (SSF) and fat-mass index (FMI) were measured during a follow-up visit at age 48 months. Weighted multivariable linear regression was used to assess the predictors (maternal BMI, gestational weight gain, ethnicity, infant sex, gestational age, birthweight-for-gestational age and breastfeeding duration) of infant BMI peak and its associations with outcomes at 48 months. Comparisons between ethnicities were tested using Bonferroni post-hoc correction.

    Results: : Of 1020 infants, 80.5% were followed up at the 48-month visit. Mean (SD) BMI, SSF and FMI at 48 months were 15.6 (1.8) kg/m 2 , 16.5 (5.3) mm and 3.8 (1.3) kg/m 2 , respectively. Mean (SD) age at peak BMI was 6.0 (1.6) months, with a magnitude of 17.2 (1.4) kg/m 2 and pre-peak velocity of 0.7 (0.3) kg/m 2 /month. Compared with Chinese infants, the peak occurred later in Malay {B [95% confidence interval (CI): 0.64 mo (0.36, 0.92)]} and Indian infants [1.11 mo (0.76, 1.46)] and was lower in magnitude in Indian infants [-0.45 kg/m 2 (-0.69, -0.20)]. Adjusting for maternal education, BMI, gestational weight gain, ethnicity, infant sex, gestational age, birthweight-for-gestational-age and breastfeeding duration, higher peak and aPPV were associated with greater BMI, SSF and FMI at 48 months. Age at peak was positively associated with BMI at 48 months [0.15 units (0.09, 0.22)], whereas peak magnitude was associated with SBP [0.17 units (0.05, 0.30)] and DBP at 48 months [0.10 units (0.01, 0.22)]. Older age and higher magnitude at peak were associated with increased risk of overweight at 48 months [Relative Risk (95% CI): 1.35 (1.12-1.62) for age; 1.89 (1.60-2.24) for magnitude]. The associations of BMI peak with BMI and SSF at 48 months were stronger in Malay and Indian children than in Chinese children.

    Conclusions: : Ethnic-specific differences in BMI peak characteristics, and associations of BMI peak with early childhood cardio-metabolic markers, suggest an important impact of early BMI development on later metabolic outcomes in Asian populations.

    Matched MeSH terms: Models, Statistical
  18. Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2s of ECG signals
    Sudarshan VK, Acharya UR, Oh SL, Adam M, Tan JH, Chua CK, et al.
    Comput Biol Med, 2017 04 01;83:48-58.
    PMID: 28231511 DOI: 10.1016/j.compbiomed.2017.01.019
    Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2s duration up to six levels to obtain the coefficients. From these DTCWT coefficients, statistical features are extracted and ranked using Bhattacharyya, entropy, minimum redundancy maximum relevance (mRMR), receiver-operating characteristics (ROC), Wilcoxon, t-test and reliefF methods. Ranked features are subjected to k-nearest neighbor (KNN) and decision tree (DT) classifiers for automated differentiation of CHF and normal ECG signals. We have achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity in the identification of CHF affected ECG signals using 45 features. The proposed method is able to detect CHF patients accurately using only 2s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments.
    Matched MeSH terms: Models, Statistical
  19. Factor structure of parent and teacher ratings of the ODD symptoms for Malaysian primary school children
    Gomez R
    Asian J Psychiatr, 2017 Feb;25:22-26.
    PMID: 28262156 DOI: 10.1016/j.ajp.2016.10.013
    This present study used confirmatory factor analysis (CFA) to examine the applicability of one-, two- three- and second order Oppositional Defiant Disorder (ODD) factor models, proposed in previous studies, in a group of Malaysian primary school children. These models were primarily based on parent reports. In the current study, parent and teacher ratings of the ODD symptoms were obtained for 934 children. For both groups of respondents, the findings showing some support for all models examined, with most support for a second order model with Burke et al. (2010) three factors (oppositional, antagonistic, and negative affect) as the primary factors. The diagnostic implications of the findings are discussed.
    Matched MeSH terms: Models, Statistical*
  20. Fulltext Prediction of Hexaconazole Concentration in the Top Most Layer of Oil Palm Plantation Soil Using Exploratory Data Analysis (EDA)
    Maznah Z, Halimah M, Shitan M, Kumar Karmokar P, Najwa S
    PLoS One, 2017;12(1):e0166203.
    PMID: 28060816 DOI: 10.1371/journal.pone.0166203
    Ganoderma boninense is a fungus that can affect oil palm trees and cause a serious disease called the basal stem root (BSR). This disease causes the death of more than 80% of oil palm trees midway through their economic life and hexaconazole is one of the particular fungicides that can control this fungus. Hexaconazole can be applied by the soil drenching method and it will be of interest to know the concentration of the residue in the soil after treatment with respect to time. Hence, a field study was conducted in order to determine the actual concentration of hexaconazole in soil. In the present paper, a new approach that can be used to predict the concentration of pesticides in the soil is proposed. The statistical analysis revealed that the Exploratory Data Analysis (EDA) techniques would be appropriate in this study. The EDA techniques were used to fit a robust resistant model and predict the concentration of the residue in the topmost layer of the soil.
    Matched MeSH terms: Models, Statistical
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