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Fulltext Response surface optimisation and antioxidant characterisation of high antioxidant soft jelly prepared from Baccaurea angulata fruit juice and Trigona sp. honey using central composite design

Mat Alewi, N. A., Ibrahim, M., Md Isa, M. L., Abdull Rasad, M. S. B., Abdul Rafa, A. A., Anuar, M. N. N.

International Food Research Journal, 2020;27(3):454-464.
MyJurnal

The optimum combination of Baccaurea angulata fruit juice (X1: 15 - 85 ratio) and Trigona sp. honey (TH) (X2: 15 - 85 ratio) in developing a high antioxidant soft jelly was investigated based
on the antioxidant capacity (Y1), phenolic (Y2), and flavonoid (Y3) content. Response surface
methodology (RSM), via central composite design (CCD), was used to produce optimal combination effects of the two independent variables (B. angulata fruit juice and TH) for highest
recovery of antioxidant capacity (AC), total phenolic content (TPC), and total flavonoid content
(TFC). The polynomial models generated were satisfactory. The lack-of-fit test were higher
than p > 0.05 for all three analyses, signifying the suitability of the models in accurately predicting the variations. Predicted values of the analysis agreed with those of the experimental values.
An optimum combination of B. angulata fruit juice and TH was developed (ratio 40:40). The
sample also exhibited significant FRAP and DPPH radical scavenging activities. Several
polyphenols were identified for the samples through UHPLC-MS/MS. In conclusion, B. angulata and Trigona sp. honey have high potentials to be used in fortifying the soft jelly samples,
making them prospective food supplements due to their nutritional and health benefits.

Matched MeSH terms: Models, Statistical
Fulltext Transverse cerebellar diameter as a sonographic parameter for estimating gestational age: an experience from Yemeni population

Muneera A. S. Yahya, Husni A. Al- Goshae, Hameed M. Aklan, Maha Abdul-aziz, Abdullah A. Al-Mikhlafy

Malaysian Journal of Medicine and Health Sciences, 2020;16(107):23-27.
MyJurnal

Introduction: Estimation of gestational age (GA) is clinically crucial for managing pregnancy and assessing the foetal anatomy, growth and development. Transverse cerebellar diameter (TCD) has been reported as an accurate tool for dating the pregnancy. This study aimed to determine the accuracy of foetal TCD for dating the pregnancy and to con- struct a reference chart for GA of Yemeni foetuses. Methods: We conducted this prospective cross-sectional study among 400 Yemeni pregnant women between 18 and 40 weeks of gestation provided that they were with known last menstrual period and singleton normal pregnancies. Sonographic TCDs were measured for each foetus. The mean TCD was measured for gestational weeks separately, and a polynomial regression model was then used to predict the GA by TCD. Results: There was a robust correlation between GA and TCD (r = 0.995, p

Matched MeSH terms: Models, Statistical
Fulltext Economic-statistical design of synthetic np chart with estimated process parameter

Lee MH, Khoo MBC, Chew X, Then PHH

PLoS One, 2020;15(4):e0230994.
PMID: 32267874 DOI: 10.1371/journal.pone.0230994

The economic-statistical design of the synthetic np chart with estimated process parameter is presented in this study. The effect of process parameter estimation on the expected cost of the synthetic np chart is investigated with the imposed statistical constraints. The minimum number of preliminary subgroups is determined where an almost similar expected cost to the known process parameter case is desired for the given cost model parameters. However, the available number of preliminary subgroups in practice is usually limited, especially when the number of preliminary subgroups is large. Consequently, the optimal chart parameters of the synthetic np chart are computed by considering the practical number of preliminary subgroups in which the cost function is minimized. This leads to a lower expected cost compared to that of adopting the optimal chart parameter corresponding to the known process parameter case.

Matched MeSH terms: Models, Statistical
Fulltext Modelling excess mortality among breast cancer patients in the North East Region of Peninsular Malaysia, 2007-2011: a population-based study

Hanis TM, Yaacob NM, Hairon SM, Abdullah S, Nordin N, Abdullah NH, et al.

BMC Public Health, 2019 Dec 30;19(1):1754.
PMID: 31888561 DOI: 10.1186/s12889-019-8113-2

BACKGROUND: Measurement of breast cancer burden and identification of its influencing factors help in the development of public health policy and strategy against the disease. This study aimed to examine the variability of the excess mortality of female breast cancer patients in the North East Region of Peninsular Malaysia.
METHODS: This retrospective cohort study was conducted using breast cancer data from the Kelantan Cancer Registry between 2007 and 2011, and Kelantan general population mortality data. The breast cancer cases were followed up for 5 years until 2016. Out of 598 cases, 549 cases met the study criteria and were included in the analysis. Modelling of excess mortality was conducted using Poisson regression.
RESULTS: Excess mortality of breast cancer varied according to age group (50 years old and below vs above 50 years old, Adj. EHR: 1.47; 95% CI: 1.31, 4.09; P = 0.004), ethnicity (Malay vs non-Malay, Adj. EHR: 2.31; 95% CI: 1.11, 1.96; P = 0.008), and stage (stage III and IV vs. stage I and II, Adj. EHR: 5.75; 95% CI: 4.24, 7.81; P

Matched MeSH terms: Models, Statistical
A hybrid mathematical programming model for optimal project portfolio selection using fuzzy inference system and analytic hierarchy process

Tavana M, Khosrojerdi G, Mina H, Rahman A

Eval Program Plann, 2019 12;77:101703.
PMID: 31442587 DOI: 10.1016/j.evalprogplan.2019.101703

The primary goal in project portfolio management is to select and manage the optimal set of projects that contribute the maximum in business value. However, selecting Information Technology (IT) projects is a difficult task due to the complexities and uncertainties inherent in the strategic-operational nature of the process, and the existence of both quantitative and qualitative criteria. We propose a two-stage process to select an optimal project portfolio with the aim of maximizing project benefits and minimizing project risks. We construct a two-stage hybrid mathematical programming model by integrating Fuzzy Analytic Hierarchy Process (FAHP) with Fuzzy Inference System (FIS). This hybrid framework provides the ability to consider both the quantitative and qualitative criteria while considering budget constraints and project risks. We also present a real-world case study in the cybersecurity industry to exhibit the applicability and demonstrate the efficacy of our proposed method.

Matched MeSH terms: Models, Statistical*
An ensemble model for forecasting infectious diseases in India

Shashvat K, Basu R, Bhondekar PA, Kaur A

Trop Biomed, 2019 Dec 01;36(4):822-832.
PMID: 33597454

Time series modelling and forecasting plays an important role in various domains. The objective of this paper is to construct a simple average ensemble method to forecast the number of cases for infectious diseases like dengue and typhoid and compare it by applying models for forecasting. In this paper we have also evaluated the correlation between the number of typhoid and dengue cases with the ecological variables. The monthly data of dengue and typhoid cases from 2014 to 2017 were taken from integrated diseases surveillance programme, Government of India. This data was analysed by three models namely support vector regression, neural network and linear regression. The proposed simple average ensemble model was constructed by ensemble of three applied regression models i.e. SVR, NN and LR. We combine the regression models based upon the error metrics such as Mean Square Error, Root Mean Square Error and Mean Absolute Error. It was found that proposed ensemble method performed better in terms of forecast measures. The finding demonstrates that the proposed model outperforms as compared to already available applied models on the basis of forecast accuracy.

Matched MeSH terms: Models, Statistical
Parceling Cannot Reduce Factor Indeterminacy in Factor Analysis: A Research Note

Rigdon EE, Becker JM, Sarstedt M

Psychometrika, 2019 09;84(3):772-780.
PMID: 31292860 DOI: 10.1007/s11336-019-09677-2

Parceling-using composites of observed variables as indicators for a common factor-strengthens loadings, but reduces the number of indicators. Factor indeterminacy is reduced when there are many observed variables per factor, and when loadings and factor correlations are strong. It is proven that parceling cannot reduce factor indeterminacy. In special cases where the ratio of loading to residual variance is the same for all items included in each parcel, factor indeterminacy is unaffected by parceling. Otherwise, parceling worsens factor indeterminacy. While factor indeterminacy does not affect the parameter estimates, standard errors, or fit indices associated with a factor model, it does create uncertainty, which endangers valid inference.

Matched MeSH terms: Models, Statistical
A hybrid data envelopment analysis and game theory model for performance measurement in healthcare

Zare H, Tavana M, Mardani A, Masoudian S, Kamali Saraji M

Health Care Manag Sci, 2019 Sep;22(3):475-488.
PMID: 30225622 DOI: 10.1007/s10729-018-9456-4

Performance measurement plays an important role in the successful design and reform of regional healthcare management systems. In this study, we propose a hybrid data envelopment analysis (DEA) and game theory model for measuring the performance and productivity in the healthcare centers. The input and output variables associated with the efficiency of the healthcare centers are identified by reviewing the relevant literature, and then used in conjunction with the internal organizational data. The selected indicators and collected data are then weighted and prioritized with the help of experts in the field. A case study is presented to demonstrate the applicability and efficacy of the proposed model. The results reveal useful information and insights on the efficiency levels of the regional healthcare centers in the case study.

Matched MeSH terms: Models, Statistical*
Developing a dengue prediction model based on climate in Tawau, Malaysia

Jayaraj VJ, Avoi R, Gopalakrishnan N, Raja DB, Umasa Y

Acta Trop, 2019 Sep;197:105055.
PMID: 31185224 DOI: 10.1016/j.actatropica.2019.105055

Dengue is fast becoming the most urgent health issue in Malaysia, recording close to a 10-fold increase in cases over the last decade. With much uncertainty hovering over the recently introduced tetravalent vaccine and no effective antiviral drugs, vector control remains the most important strategy in combating dengue. This study analyses the relationship between weather predictors including its lagged terms, and dengue incidence in the District of Tawau over a period of 12 years, from 2006 to 2017. A forecasting model purposed to predict future outbreaks in Tawau was then developed using this data. Monthly dengue incidence data, mean temperature, maximum temperature, minimum temperature, mean relative humidity and mean rainfall over a period of 12 years from 2006 to 2017 in Tawau were retrieved from Tawau District Health Office and the Malaysian Meteorological Department. Cross-correlation analysis between weather predictors, lagged terms of weather predictors and dengue incidences established statistically significant cross-correlation between lagged periods of weather predictors-namely maximum temperature, mean relative humidity and mean rainfall with dengue incidence at time lags of 4-6 months. These variables were then employed into 3 different methods: a multivariate Poisson regression model, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and a SARIMA with external regressors for selection. Three models were selected but the SARIMA with external regressors model utilising maximum temperature at a lag of 6 months (p-value:0.001), minimum temperature at a lag of 4 months (p-value:0.01), mean relative humidity at a lag of 2 months (p-value:0.001), and mean rainfall at a lag of 6 months (p-value:0.001) produced an AIC of 841.94, and a log-likelihood score of -413.97 establishing it as the best fitting model of the methodologies utilised. In validating the models, they were utilised to develop forecasts with the model selected with the highest accuracy of predictions being the SARIMA model predicting 1 month in advance (MAE: 7.032, MSE: 83.977). This study establishes the effect of weather on the intensity and magnitude of dengue incidence as has been previously studied. A prediction model remains a novel method of evidence-based forecasting in Tawau, Sabah. The model developed in this study, demonstrated an ability to forecast potential dengue outbreaks 1 to 4 months in advance. These findings are not dissimilar to what has been previously studied in many different countries- with temperature and humidity consistently being established as powerful predictors of dengue incidence magnitude. When used in prognostication, it can enhance- decision making and allow judicious use of resources in public health setting. Nevertheless, the model remains a work in progress- requiring larger and more diverse data.

Matched MeSH terms: Models, Statistical
Fulltext Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer

Walsh N, Zhang H, Hyland PL, Yang Q, Mocci E, Zhang M, et al.

J Natl Cancer Inst, 2019 Jun 01;111(6):557-567.
PMID: 30541042 DOI: 10.1093/jnci/djy155

BACKGROUND: Genome-wide association studies (GWAS) identify associations of individual single-nucleotide polymorphisms (SNPs) with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants is a powerful tool to identify networks of susceptibility genes.
METHODS: We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided.
RESULTS: We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P ≤ 1.3 × 10-5), the strongest associations were detected in five pathways and gene sets, including maturity-onset diabetes of the young, regulation of beta-cell development, role of epidermal growth factor (EGF) receptor transactivation by G protein-coupled receptors in cardiac hypertrophy pathways, and the Nikolsky breast cancer chr17q11-q21 amplicon and Pujana ATM Pearson correlation coefficient (PCC) network gene sets. We identified and validated rs876493 and three correlating SNPs (PGAP3) and rs3124737 (CASP7) from the Pujana ATM PCC gene set as eQTLs in two normal derived pancreas tissue datasets.
CONCLUSION: Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified.

Matched MeSH terms: Models, Statistical
External validation of a modified trauma and injury severity score model in major trauma injury

Chen WS, Tan JH, Mohamad Y, Imran R

Injury, 2019 May;50(5):1118-1124.
PMID: 30591225 DOI: 10.1016/j.injury.2018.12.031

BACKGROUND: The establishment of an accurate prognostic model in major trauma patients is important mainly because this group of patients will benefit the most. Clinical prediction models must be validated internally and externally on a regular basis to ensure the prediction is accurate and current. This study aims to externally validate two prediction models, the Trauma and Injury Severity Score model developed using the Major Trauma Outcome Study in North America (MTOS-TRISS model), and the NTrD-TRISS model, which is a refined MTOS-TRISS model with coefficients derived from the Malaysian National Trauma Database (NTrD), by regarding mortality as the outcome measurement.
METHOD: This retrospective study included patients with major trauma injuries reported to a trauma centre of Hospital Sultanah Aminah over a 6-year period from 2011 and 2017. Model validation was examined using the measures of discrimination and calibration. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC) and 95% confidence interval (CI). The Hosmer-Lemeshow (H-L) goodness-of-fit test was used to examine calibration capabilities. The predictive validity of both MTOS-TRISS and NTrD-TRISS models were further evaluated by incorporating parameters such as the New Injury Severity Scale and the Injury Severity Score.
RESULTS: Total patients of 3788 (3434 blunt and 354 penetrating injuries) with average age of 37 years (standard deviation of 16 years) were included in this study. All MTOS-TRISS and NTrD-TRISS models examined in this study showed adequate discriminative ability with AUCs ranged from 0.86 to 0.89 for patients with blunt trauma mechanism and 0.89 to 0.99 for patients with penetrating trauma mechanism. The H-L goodness-of-fit test indicated the NTrD-TRISS model calibrated as good as the MTOS-TRISS model for patients with blunt trauma mechanism.
CONCLUSION: For patients with blunt trauma mechanism, both the MTOS-TRISS and NTrD-TRISS models showed good discrimination and calibration performances. Discrimination performance for the NTrD-TRISS model was revealed to be as good as the MTOS-TRISS model specifically for patients with penetrating trauma mechanism. Overall, this validation study has ascertained the discrimination and calibration performances of the NTrD-TRISS model to be as good as the MTOS-TRISS model particularly for patients with blunt trauma mechanism.

Matched MeSH terms: Models, Statistical
Evaluation of model-based methods in estimating respiratory mechanics in the presence of variable patient effort

Redmond DP, Chiew YS, Major V, Chase JG

Comput Methods Programs Biomed, 2019 Apr;171:67-79.
PMID: 27697371 DOI: 10.1016/j.cmpb.2016.09.011

Monitoring of respiratory mechanics is required for guiding patient-specific mechanical ventilation settings in critical care. Many models of respiratory mechanics perform poorly in the presence of variable patient effort. Typical modelling approaches either attempt to mitigate the effect of the patient effort on the airway pressure waveforms, or attempt to capture the size and shape of the patient effort. This work analyses a range of methods to identify respiratory mechanics in volume controlled ventilation modes when there is patient effort. The models are compared using 4 Datasets, each with a sample of 30 breaths before, and 2-3 minutes after sedation has been administered. The sedation will reduce patient efforts, but the underlying pulmonary mechanical properties are unlikely to change during this short time. Model identified parameters from breathing cycles with patient effort are compared to breathing cycles that do not have patient effort. All models have advantages and disadvantages, so model selection may be specific to the respiratory mechanics application. However, in general, the combined method of iterative interpolative pressure reconstruction, and stacking multiple consecutive breaths together has the best performance over the Dataset. The variability of identified elastance when there is patient effort is the lowest with this method, and there is little systematic offset in identified mechanics when sedation is administered.

Matched MeSH terms: Models, Statistical*
Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models

Azareh A, Rahmati O, Rafiei-Sardooi E, Sankey JB, Lee S, Shahabi H, et al.

Sci Total Environ, 2019 Mar 10;655:684-696.
PMID: 30476849 DOI: 10.1016/j.scitotenv.2018.11.235

Gully erosion susceptibility mapping is a fundamental tool for land-use planning aimed at mitigating land degradation. However, the capabilities of some state-of-the-art data-mining models for developing accurate maps of gully erosion susceptibility have not yet been fully investigated. This study assessed and compared the performance of two different types of data-mining models for accurately mapping gully erosion susceptibility at a regional scale in Chavar, Ilam, Iran. The two methods evaluated were: Certainty Factor (CF), a bivariate statistical model; and Maximum Entropy (ME), an advanced machine learning model. Several geographic and environmental factors that can contribute to gully erosion were considered as predictor variables of gully erosion susceptibility. Based on an existing differential GPS survey inventory of gully erosion, a total of 63 eroded gullies were spatially randomly split in a 70:30 ratio for use in model calibration and validation, respectively. Accuracy assessments completed with the receiver operating characteristic curve method showed that the ME-based regional gully susceptibility map has an area under the curve (AUC) value of 88.6% whereas the CF-based map has an AUC of 81.8%. According to jackknife tests that were used to investigate the relative importance of predictor variables, aspect, distance to river, lithology and land use are the most influential factors for the spatial distribution of gully erosion susceptibility in this region of Iran. The gully erosion susceptibility maps produced in this study could be useful tools for land managers and engineers tasked with road development, urbanization and other future development.

Matched MeSH terms: Models, Statistical
Spatio-temporal simulation and prediction of land-use change using conventional and machine learning models: a review

Aburas MM, Ahamad MSS, Omar NQ

Environ Monit Assess, 2019 Mar 05;191(4):205.
PMID: 30834982 DOI: 10.1007/s10661-019-7330-6

Spatio-temporal land-use change modeling, simulation, and prediction have become one of the critical issues in the last three decades due to uncertainty, structure, flexibility, accuracy, the ability for improvement, and the capability for integration of available models. Therefore, many types of models such as dynamic, statistical, and machine learning (ML) models have been used in the geographic information system (GIS) environment to fulfill the high-performance requirements of land-use modeling. This paper provides a literature review on models for modeling, simulating, and predicting land-use change to determine the best approach that can realistically simulate land-use changes. Therefore, the general characteristics of conventional and ML models for land-use change are described, and the different techniques used in the design of these models are classified. The strengths and weaknesses of the various dynamic, statistical, and ML models are determined according to the analysis and discussion of the characteristics of these models. The results of the review confirm that ML models are the most powerful models for simulating land-use change because they can include all driving forces of land-use change in the simulation process and simulate linear and non-linear phenomena, which dynamic models and statistical models are unable to do. However, ML models also have limitations. For instance, some ML models are complex, the simulation rules cannot be changed, and it is difficult to understand how ML models work in a system. However, this can be solved via the use of programming languages such as Python, which in turn improve the simulation capabilities of the ML models.

Matched MeSH terms: Models, Statistical
Optimization and modelling of enzymatic polymerization of ε-caprolactone to polycaprolactone using Candida Antartica Lipase B with response surface methodology and artificial neural network

Pakalapati H, Tariq MA, Arumugasamy SK

Enzyme Microb Technol, 2019 Mar;122:7-18.
PMID: 30638510 DOI: 10.1016/j.enzmictec.2018.12.001

Recently enzymatic catalysts have replaced organic and organometallic catalysts in the synthesis of bio-resorbable polymers. Enzymatic polymerization is considered as an alternative to conventional polymerization as they are less toxic, environmental friendly and can operate under mild conditions. In this research, the enzymatic ring-opening polymerization (e-ROP) of e-caprolactone (e-CL) using Candida Antartica Lipase B (CALB) as catalyst to produce the Polycaprolactone. Two modelling techniques namely response surface methodology (RSM) and artificial neural network (ANN) have been used in this work. RSM is used to optimize the parameters and to develop a model of the process. ANN is used to develop the model to predict the results obtained from the experiment. The parameters involved are time, reaction temperature, mixing speed and enzyme-solvent ratio. The experimental result is Polydispersity index (PDI) of the polymer. The experimental data obtained was adequately fitted into second-order polynomial models. Simulation was done using artificial neural network model developed with Mean absolute error (MAD) value of 1.65 in comparison with MAD value of 7.4 for RSM. The Regression value (R2) values of RSM and ANN were found to be 0.96 and 0.93 respectively. The predictive models were validated experimentally and were found to be in agreement with the experimental values.

Matched MeSH terms: Models, Statistical
Appropriateness of deep vein thrombosis (DVT) prophylaxis use among medical inpatients: a DVT risk alert tool (DRAT) study

Diana Yap FS, Ng ZY, Wong CY, Muhamad Saifuzzaman MK, Yang LB

Med J Malaysia, 2019 02;74(1):45-50.
PMID: 30846662

INTRODUCTION: Increasing incidence of Venous Thromboembolism (VTE) has complicated treatment courses for hospitalised patients. Despite recommendation to support deep vein thrombosis (DVT) risk assessment and appropriate use of prophylaxis in medical inpatients, it is either neglected or prescribed unnecessarily by the clinicians. This study aimed to assess and compare the appropriateness of DVT prophylaxis prescribing between usual care versus a pharmacist-driven DVT Risk Alert Tool (DRAT) intervention among hospitalised medical patients.
METHODS: A prospective pre- and post-intervention study was conducted among medical inpatients in a Malaysian secondary care hospital. DVT and bleeding risks were stratified using validated Padua Risk Assessment Model (RAM) and International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) Bleeding Risk Assessment Model. Pharmacist-driven DRAT was developed and implemented post-interventional phase. DVT prophylaxis use was determined and its appropriateness was compared between pre and post study using multivariate logistic regression with IBM SPSS software version 21.0.
RESULTS: Overall, 286 patients (n=142 pre-intervention versus n=144 post-intervention) were conveniently recruited. The prevalence of DVT prophylaxis use was 10.8%. Appropriate thromboprophylaxis prescribing increased from 64.8% to 68.1% post-DRAT implementation. Of note, among high DVT risk patients, DRAT intervention was observed to be a significant predictor of appropriate thromboprophylaxis use (14.3% versus 31.3%; adjusted odds ratio=2.80; 95% CI 1.01 to 7.80; p<0.05).
CONCLUSION: The appropriateness of DVT prophylaxis use was suboptimal but doubled after implementation of DRAT intervention. Thus, an integrated risk stratification checklist is an effective approach for the improvement of rational DVT prophylaxis use.

Matched MeSH terms: Models, Statistical
Fulltext Predictive analysis across spatial scales links zoonotic malaria to deforestation

Brock PM, Fornace KM, Grigg MJ, Anstey NM, William T, Cox J, et al.

Proc Biol Sci, 2019 Jan 16;286(1894):20182351.
PMID: 30963872 DOI: 10.1098/rspb.2018.2351

The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria ( Plasmodium knowlesi) is associated with deforestation although mechanisms are unknown. Here, a novel application of a method for predicting disease occurrence that combines machine learning and statistics is used to identify the key spatial scales that define the relationship between zoonotic malaria cases and environmental change. Using data from satellite imagery, a case-control study, and a cross-sectional survey, predictive models of household-level occurrence of P. knowlesi were fitted with 16 variables summarized at 11 spatial scales simultaneously. The method identified a strong and well-defined peak of predictive influence of the proportion of cleared land within 1 km of households on P. knowlesi occurrence. Aspect (1 and 2 km), slope (0.5 km) and canopy regrowth (0.5 km) were important at small scales. By contrast, fragmentation of deforested areas influenced P. knowlesi occurrence probability most strongly at large scales (4 and 5 km). The identification of these spatial scales narrows the field of plausible mechanisms that connect land use change and P. knowlesi, allowing for the refinement of disease occurrence predictions and the design of spatially-targeted interventions.

Matched MeSH terms: Models, Statistical
Application of Box-Behnken design for ultrasound-assisted extraction and recycling preparative HPLC for isolation of anthraquinones from Cassia singueana

Jibril S, Basar N, Sirat HM, Wahab RA, Mahat NA, Nahar L, et al.

Phytochem Anal, 2019 Jan;30(1):101-109.
PMID: 30288828 DOI: 10.1002/pca.2795

INTRODUCTION: Cassia singueana Del. (Fabaceae) is a rare medicinal plant used in the traditional medicine preparations to treat various ailments. The root of C. singueana is a rich source of anthraquinones that possess anticancer, antibacterial and antifungal properties.
OBJECTIVE: The objective of this study was to develop an ultrasound-assisted extraction (UAE) method for achieving a high extraction yield of anthraquinones using the response surface methodology (RSM), Box-Behnken design (BBD), and a recycling preparative high-performance liquid chromatography (HPLC) protocol for isolation of anthraquinones from C. singueana.
METHODOLOGY: Optimisation of UAE was performed using the Box-Behnken experimental design. Recycling preparative HPLC was employed to isolate anthraquinones from the root extract of C. singueana.
RESULTS: The BBD was well-described by a quadratic polynomial model (R2 = 0.9751). The predicted optimal UAE conditions for a high extraction yield were obtained at: extraction time 25.00 min, temperature 50°C and solvent-sample ratio of 10 mL/g. Under the predicted conditions, the experimental value (1.65 ± 0.07%) closely agreed to the predicted yield (1.64%). The obtained crude extract of C. singueana root was subsequently purified to afford eight anthraquinones.
CONCLUSION: The extraction protocol described here is suitable for large-scale extraction of anthraquinones from plant extracts.

Matched MeSH terms: Models, Statistical
Fulltext Land-use change alters the mechanisms assembling rainforest mammal communities in Borneo

Wearn OR, Carbone C, Rowcliffe JM, Pfeifer M, Bernard H, Ewers RM

J Anim Ecol, 2019 01;88(1):125-137.
PMID: 30178485 DOI: 10.1111/1365-2656.12903

The assembly of species communities at local scales is thought to be driven by environmental filtering, species interactions and spatial processes such as dispersal limitation. Little is known about how the relative balance of these drivers of community assembly changes along environmental gradients, especially man-made environmental gradients associated with land-use change. Using concurrent camera- and live-trapping, we investigated the local-scale assembly of mammal communities along a gradient of land-use intensity (old-growth forest, logged forest and oil palm plantations) in Borneo. We hypothesised that increasing land-use intensity would lead to an increasing dominance of environmental control over spatial processes in community assembly. Additionally, we hypothesised that competitive interactions among species might reduce in concert with declines in α-diversity (previously documented) along the land-use gradient. To test our first hypothesis, we partitioned community variance into the fractions explained by environmental and spatial variables. To test our second hypothesis, we used probabilistic models of expected species co-occurrence patterns, in particular focussing on the prevalence of spatial avoidance between species. Spatial avoidance might indicate competition, but might also be due to divergent habitat preferences. We found patterns that are consistent with a shift in the fundamental mechanics governing local community assembly. In support of our first hypothesis, the importance of spatial processes (dispersal limitation and fine-scale patterns of home-ranging) appeared to decrease from low to high intensity land-uses, whilst environmental control increased in importance (in particular due to fine-scale habitat structure). Support for our second hypothesis was weak: whilst we found that the prevalence of spatial avoidance decreased along the land-use gradient, in particular between congeneric species pairs most likely to be in competition, few instances of spatial avoidance were detected in any land-use, and most were likely due to divergent habitat preferences. The widespread changes in land-use occurring in the tropics might be altering not just the biodiversity found in landscapes, but also the fundamental mechanics governing the local assembly of communities. A better understanding of these mechanics, for a range of taxa, could underpin more effective conservation and management of threatened tropical landscapes.

Matched MeSH terms: Models, Statistical
Determination of biochemical oxygen demand and dissolved oxygen for semi-arid river environment: application of soft computing models

Tao H, Bobaker AM, Ramal MM, Yaseen ZM, Hossain MS, Shahid S

Environ Sci Pollut Res Int, 2019 Jan;26(1):923-937.
PMID: 30421367 DOI: 10.1007/s11356-018-3663-x

Surface and ground water resources are highly sensitive aquatic systems to contaminants due to their accessibility to multiple-point and non-point sources of pollutions. Determination of water quality variables using mathematical models instead of laboratory experiments can have venerable significance in term of the environmental prospective. In this research, application of a new developed hybrid response surface method (HRSM) which is a modified model of the existing response surface model (RSM) is proposed for the first time to predict biochemical oxygen demand (BOD) and dissolved oxygen (DO) in Euphrates River, Iraq. The model was constructed using various physical and chemical variables including water temperature (T), turbidity, power of hydrogen (pH), electrical conductivity (EC), alkalinity, calcium (Ca), chemical oxygen demand (COD), sulfate (SO4), total dissolved solids (TDS), and total suspended solids (TSS) as input attributes. The monthly water quality sampling data for the period 2004-2013 was considered for structuring the input-output pattern required for the development of the models. An advance analysis was conducted to comprehend the correlation between the predictors and predictand. The prediction performances of HRSM were compared with that of support vector regression (SVR) model which is one of the most predominate applied machine learning approaches of the state-of-the-art for water quality prediction. The results indicated a very optimistic modeling accuracy of the proposed HRSM model to predict BOD and DO. Furthermore, the results showed a robust alternative mathematical model for determining water quality particularly in a data scarce region like Iraq.

Matched MeSH terms: Models, Statistical*

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