Hyperaccumulation is generally highly specific for a single element, for example nickel (Ni). The recently-discovered hyperaccumulator Glochidion cf. sericeum (Phyllanthaceae) from Malaysia is unusual in that it simultaneously accumulates nickel and cobalt (Co) with up to 1500 μg g-1 foliar of both elements. We set out to determine whether distribution and associated ligands for Ni and Co complexation differ in this species. We postulated that Co hyperaccumulation coincides with Ni hyperaccumulation operating on similar physiological pathways. However, the ostensibly lower tolerance for Co at the cellular level results in the exudation of Co on the leaf surface in the form of lesions. The formation of such lesions is akin to phytotoxicity responses described for manganese (Mn). Hence, in contrast to Ni, which is stored principally inside the foliar epidermal cells, the accumulation response to Co consists of an extracellular mechanism. The chemical speciation of Ni and Co, in terms of the coordinating ligands involved and principal oxidation state, is similar and associated with carboxylic acids (citrate for Ni and tartrate or malate for Co) and the hydrated metal ion. Some oxidation to Co3+, presumably on the surface of leaves after exudation, was observed.
In the title compound, C11H12O2S2, two independent but virtually superimposable mol-ecules, A and B, comprise the asymmetric unit. In each mol-ecule, the 1,3-di-thiane ring has a chair conformation with the 1,4-disposed C atoms being above and below the plane through the remaining four atoms. The substituted benzene ring occupies an equatorial position in each case and forms dihedral angles of 85.62 (9) (mol-ecule A) and 85.69 (8)° (mol-ecule B) with the least-squares plane through the 1,3-di-thiane ring. The difference between the mol-ecules rests in the conformation of the five-membered 1,3-dioxole ring which is an envelope in mol-ecule A (the methyl-ene C atom is the flap) and almost planar in mol-ecule B (r.m.s. deviation = 0.046 Å). In the crystal, mol-ecules of A self-associate into supra-molecular zigzag chains (generated by glide symmetry along the c axis) via methyl-ene C-H⋯π inter-actions. Mol-ecules of B form similar chains. The chains pack with no specific directional inter-molecular inter-actions between them.
In the title compound, C10H11BrS2, the 1,3-di-thiane ring has a chair conformation with the 1,4-disposed C atoms being above and below the remaining four atoms. The bromo-benzene ring occupies an equatorial position and forms a dihedral angle of 86.38 (12)° with the least-squares plane through the 1,3-di-thiane ring. Thus, to a first approximation the mol-ecule has mirror symmetry with the mirror containing the bromo-benzene ring and the 1,4-disposed C atoms of the 1,3-di-thiane ring. In the crystal, mol-ecules associate via weak methyl-ene-bromo-benzene C-H⋯π and π-π [Cg⋯Cg = 3.7770 (14) Å for centrosymmetrically related bromo-benzene rings] inter-actions, forming supra-molecular layers parallel to [10-1]; these stack with no specific inter-molecular inter-actions between them.
This study analyzed the volatile organic compounds (VOCs) of three mango varieties (Harumanis, Tong Dam and Susu) for the discrimination of authentic Harumanis from other mangoes. The VOCs of these mangoes were extracted and analysed nondestructively using Head Space-Solid Phase Micro Extraction (HS-SPME) coupled to Gas Chromatography-Mass Spectrometry (GC-MS). Prior to the analytical method, two simple sensory analyses were carried out to assess the ability of the consumers to differentiate between the Harumanis and Tong Dam mangoes as well as their preferences towards these mangoes. On the other hand, chemometrics techniques, such as principal components analysis (PCA), hierarchical clustering analysis (HCA), and discriminant analysis (DA), were used to visualise grouping tendencies of the volatile compounds detected. These techniques were successful in identifying the grouping tendencies of the mango samples according to the presence of their respective volatile compounds, thus enabling the identification of the groups of substances responsible for the discrimination between the authentic and unauthentic Harumanis mangoes. In addition, three ocimene compounds, namely beta-ocimene, trans beta-ocimene, and allo-ocimene, can be considered as chemical markers of the Harumanis mango, as these compounds exist in all Harumanis mango, regardless the different sources of the mangoes obtained.
The analytical methods for the determination of the amine solvent properties do not provide input data for real-time process control and optimization and are labor-intensive, time-consuming, and impractical for studies of dynamic changes in a process. In this study, the potential of nondestructive determination of amine concentration, CO2 loading, and water content in CO2 absorption solvent in the gas processing unit was investigated through Fourier transform near-infrared (FT-NIR) spectroscopy that has the ability to readily carry out multicomponent analysis in association with multivariate analysis methods. The FT-NIR spectra for the solvent were captured and interpreted by using suitable spectra wavenumber regions through multivariate statistical techniques such as partial least square (PLS). The calibration model developed for amine determination had the highest coefficient of determination (R2) of 0.9955 and RMSECV of 0.75%. CO2 calibration model achieved R2 of 0.9902 with RMSECV of 0.25% whereas the water calibration model had R2 of 0.9915 with RMSECV of 1.02%. The statistical evaluation of the validation samples also confirmed that the difference between the actual value and the predicted value from the calibration model was not significantly different and acceptable. Therefore, the amine, CO2, and water models have given a satisfactory result for the concentration determination using the FT-NIR technique. The results of this study indicated that FT-NIR spectroscopy with chemometrics and multivariate technique can be used for the CO2 solvent monitoring to replace the time-consuming and labor-intensive conventional methods.
Sustainable and alternative energy sources of biofuel and solar power panel have been revolutionizing the lives and economy of many countries. However, these changes mainly occur in the urban areas and the rural population section has long been ignored by policy makers and government in the provision of energy. It is only recently that solar and biofuel are finally making in road to provide cheap and clean energy sources to rural population. As a result, literatures on consumer behavior of rural population towards sustainable energy sources are still very scarce. The present research aims to fulfill this gap by developing a conceptual model to investigate the adoption of solar power and biofuel energy resources in the cross-cultural setting of Malaysia and Pakistan. The data was collected from the rural areas of Pakistan and Malaysia. The two-stage data analysis method of partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) have been applied to satisfy both linear and non-linear regression assumptions, respectively. The results show that consumer in rural areas of Pakistan are willing and possess intention to adopt both biofuel and solar power for commercial and domestic use. Additionally, the results confirm that branding, economic, and altruistic factors are important in yielding intention to use towards biofuel and solar power panel in Pakistan which are validated by the results obtained in Malaysia. Other factors such as climate change awareness, retailer services quality, and ease of use are also important. The results offer wide-ranging theoretical and managerial implications.
Perceived usefulness and perceived ease of use constitute important belief factors when technology adoption decisions are made within a non-mandatory setting. This paper investigated the role played by psychological ownership in shaping teachers' beliefs about using a cloud-based virtual learning environment (VLE). Psychological ownership is increasingly becoming a relevant phenomenon in technology adoption research, where people can feel psychologically attached to a particular technology. The study proposed that such phenomenon can also occur when using a VLE, and a hypothesised model with six constructs was tested with 629 Malaysian teachers from 21 schools. Results from structural equation modelling-partial least squares analysis found teachers' experiences with the VLE significantly influenced psychological ownership, which in turn significantly predicted perceived usefulness and perceived ease of use of the VLE. Overall, the model possesses predictive relevance for the outcome predictors as indicated by Stone-Geisser's Q2, and accounted for 61.6% of variance in perceived usefulness and 62.0% of variance in perceived ease of use. This study provides insights into the motivation behind teachers' beliefs which are shaped by their experiences with the VLE. Implications for theory and practice were discussed based on the insights of the study.
This paper's novel focus is predicting the leaf nitrogen content of rice during growing and maturing. A multispectral image processing-based prediction model of the Radial Basis Function Neural Network (RBFNN) model was proposed. Moreover, this paper depicted three primary points as the following: First, collect images of rice leaves (RL) from a controlled condition experimental laboratory and new shoot leaves in different stages in the visible light spectrum, and apply digital image processing technology to extract the color characteristics of RL and the morphological characteristics of the new shoot leaves. Secondly, the RBFNN model, the General Regression Model (GRL), and the General Regression Method (GRM) model were constructed based on the extracted image feature parameters and the nitrogen content of rice leaves. Third, the RBFNN is optimized by and Partial Least-Squares Regression (RBFNN-PLSR) model. Finally, the validation results show that the nitrogen content prediction models at growing and mature stages that the mean absolute error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) of the RFBNN model during the rice-growing stage and the mature stage are 0.6418 (%), 0.5399 (%), 0.0652 (%), and 0.3540 (%), 0.1566 (%), 0.0214 (%) respectively, the predicted value of the model fits well with the actual value. Finally, the model may be used to give the best foundation for achieving exact fertilization control by continuously monitoring the nitrogen nutrition status of rice. In addition, at the growing stage, the RBFNN model shows better results compared to both GRL and GRM, in which MAE is reduced by 0.2233% and 0.2785%, respectively.
A unit root test based on the modified least squares (MLS) estimator for first-order autoregressive process is proposed and compared with unit root tests based on the ordinary least squares (OLS), the weighted symmetric (WS) and the modified weighted symmetric (MWS) estimators. The percentiles of the null distributions of the unit root test are also reported. The empirical probabilities of type I error and powers of the unit root tests were estimated via Monte Carlo simulation. The simulation results showed that all unit root tests can control the probability of type I error for all situations. The empirical power of the test is higher than the other unit root tests, and Apart from that, the and tests also provide the highest empirical power. As an illustration, the monthly series of U.S. nominal interest rates on three-month treasury bills is analyzed.
Understanding the main driving factors of oasis river nutrients in arid areas is important to identify the sources of water pollution and protect water resources. Twenty-seven sub-watersheds were selected in the lower oasis irrigated agricultural reaches of the Kaidu River watershed in arid Northwest China, divided into the site, riparian, and catchment buffer zones. Data on four sets of explanatory variables (topographic, soil, meteorological elements, and land use types) were collected. The relationships between explanatory variables and response variables (total phosphorus, TP and total nitrogen, TN) were analyzed by redundancy analysis (RDA). Partial least squares structural equation modeling (PLS-SEM) was used to quantify the relationship between explanatory as well as response variables and fit the path relationship among factors. The results showed that there were significant differences in the TP and TN concentrations at each sampling point. The catchment buffer exhibited the best explanatory power of the relationship between explanatory and response variables based on PLS-SEM. The effects of various land use types, meteorological elements (ME), soil, and topography in the catchment buffer were responsible for 54.3% of TP changes and for 68.5% of TN changes. Land use types, ME and soil were the main factors driving TP and TN changes, accounting for 95.56% and 94.84% of the total effects, respectively. The study provides a reference for river nutrients management in arid oases with irrigated agriculture and a scientific and targeted basis to mitigate water pollution and eutrophication of rivers in arid lands.
Amphetamine-type stimulants (ATS) are a group of chiral amine drugs which are commonly abused for their sympathomimetic and stimulant properties. ATS are extensively metabolised by hepatic cytochrome P450 enzymes. As metabolism of ATS has been shown to be highly stereospecific, stereoselective analytical methods are essential for the quantitative determination of ATS concentrations for both in vivo and in vitro studies of ATS metabolism. This paper describes a new stereoselective method for the simultaneous determination of amphetamine (AM), methamphetamine (MA), 3,4-methylenedioxymethamphetamine (MDMA), 3,4-methylenedioxyamphetamine (MDA), 4-hydroxy-3-methoxymethamphetamine (HMMA), 4-hydroxy-3-methoxyamphetamine (HMA), 3,4-hydroxymethamphetamine (HHMA) and 3,4-hydroxyamphetamine (HHA) in human urine samples validated according to the United States Food and Drug Administration guidelines. In this method, analytes are simultaneously extracted and derivatized with R-(-)-α-methoxy-α-(trifluoromethyl)phenylacetyl chloride (R-MTPCl) as the chiral derivatization reagent. Following this, the analytes were subjected to a second derivatization with N-methyl-N-trimethylsilyltrifluoroacetamide (MSTFA) which targets the hydroxyl groups present in HMMA, HMA, HHMA and HHA. The derivatized analytes were separated and quantified using gas chromatography-mass spectrometry (GC-MS). The method was evaluated according to the established guidelines for specificity, linearity, precision, accuracy, recovery and stability using a five-day protocol. Intra-day precision ranged from 0.89 to 11.23% RSD whereas inter-day precision was between 1.03 and 12.95% RSD. Accuracy values for the analytes ranged from -5.29% to 13.75%. Limits of quantitation were 10 μg/L for AM, MA, MDMA, HMA and HMMA and 2μg/L for MDA, HMA and HHA. Recoveries and stability values were also within accepted values. The method was applied to authentic ATS-positive samples.
Sensory attributes of four different palm sugars were related to gas chromatography/mass spectrometry (GC/MS) analysis using partial least squares regression (PLS). The sweet caramel and burnt-like sensory attributes were strongly associated with 2-furfural and 2-furan methanol volatile compounds. The sensory scores for roasty and nutty were also associated with the GC/MS ratings for roasty and nutty-like aroma by its highest scores obtained from 2-ethyl-5-methyl pyrazine, 2,5-dimethyl pyrazine and 2,3-dimethyl pyrazine volatile compounds along the PC1 dimension. PLS analysis did not show correlation for the character impact compound furaneol, 2-ethyl-3,5-dimethyl pyrazine (EDMP) and 2,3-diethyl-5-methyl pyrazine (DEMP), which are perceived to be responsible for the sweet caramel-like and roasty/nutty attributes of palm sugars, respectively. This lack of relationship could partially be explained by covariance among the sensory ratings for the samples.
A linear quantitative structure activity relationship (QSAR) model is presented for predicting human immunodeficiency virus-1 (HIV-1) reverse transcriptase enzyme inhibition. The 2D QSAR and 3D-QSAR models were developed by stepwise multiple linear regression, partial least square (PLS) regression and k-nearest neighbor-molecular field analysis, PLS regression, respectively using a database consisting of 33 recently discovered benzoxazinones. The primary findings of this study is that the number of hydrogen atoms, number of (-NH2) group connected with solitary single bond alters the inhibition of HIV-1 reverse transcriptase. Further, presence of electrostatic, hydrophobic and steric field descriptors significantly affects the ability of benzoxazinone derivatives to inhibit HIV-1 reverse transcriptase. The selected descriptors could serve as a primer for the design of novel and potent antagonists of HIV-1 reverse transcriptase.
Objectives: The present study aimed to establish significant and validated quantitative structure-activity relationship (QSAR) models for neuraminidase inhibitors and correlate their physicochemical, steric, and electrostatic properties with their anti-influenza activity.
Materials and Methods: We have developed and validated 2D and 3D QSAR models by using multiple linear regression, partial least square regression, and k-nearest neighbor-molecular field analysis methods.
Results: 2D QSAR models had q2: 0.950 and pred_r2: 0.877 and 3D QSAR models had q2: 0.899 and pred_r2: 0.957. These results showed that the models werere predictive.
Conclusion: Parameters such as hydrogen count and hydrophilicity were involved in 2D QSAR models. The 3D QSAR study revealed that steric and hydrophobic descriptors were negatively contributed to neuraminidase inhibitory activity. The results of this study could be used as platform for design of better anti-influenza drugs.
The objective of this study is to investigate the demand for quality vegetables in Malaysia. This study estimates quality elasticities from the difference between expenditure and quantity elasticities in order to show the demand for quality vegetables in Malaysia. By using the Household Expenditure Survey 2004/2005, expenditure and quantity Engel equations are estimated via two stage least square. The positive estimated quality elasticities (except root and tuberous vegetable) show that Malaysian consumers tend to increase their demand for quality vegetables in response to their incomes rise. To be more specific, urban consumers are expected to demand more of higher quality vegetables (except root and tuberous vegetable) than rural consumers.
Using the Malaysian Household Expenditure Survey 2004/2005 data, this study investigated Malaysian consumers’ preference for beef quantity, quality, and lean beef. Demand and price models that incorporated consumer socio-economic variables were estimated via two-stage least squares (2SLS). This study showed that Malaysian consumers tend to demand for more quantity rather than quality of beef products. Malaysian consumers are also more responsive to price changes rather than fat reduction in beef products. It is more profitable for beef market players to increase their production as Malaysian consumers are expected to consume increasing amounts of beef products.
In competitive markets, agribusiness firms have embarked on improving their service quality for building and maintaining a profitable relationship with their customers. However, such impact of service quality on business commitment has not been empirically investigated. To fill this gap, this study explores the relationship between service quality and commitment, using a case of supplier selection of fresh produce by hotel, restaurant, and catering (HORECA) sector in Malaysia. Using SERVQUAL as the main component of the conceptual framework, the relevant information was collected from 195 random HORECA operators and analyzed using partial least squares. The results indicate that service quality explains little of HORECA’s decision to stay with their current suppliers. While most service quality factors were insignificant, “responsiveness” in term of providing delivery service had a statistically significant positive impact on HORECA’s contractual arrangement with their current suppliers. These findings imply that quality service is being seen as a supplement; economic factors (e.g., prices and their stability, credit term) are likely to be the key drivers affecting buyer-seller relationships. If suppliers want to stay on course, they have to improve their service quality and focus more on delivery service. In addition, more research is needed in this relatively new area.
We developed a simple and sensitive method for the simultaneous detection of imatinib mesylate (IM) and its active metabolite, N-desmethyl imatinib (M1), in human serum samples. Separation was successfully achieved using an Agilent(®) ZORBAX Eclipse plus C(18) reversed phase column (50 mm × 2.1 mm, i.d.; 1.8 μm) under isocratic mobile phase conditions consisting of acetonitrile: 0.02 M potassium dihydrogen phosphate with 0.2% triethylamine at pH 3 (25:75, v/v) and ultra-violet detection was achieved at 235 nm. Extraction of the target compounds was completed using 100% cold acetonitrile. Good linearities (r(2)>0.99) for both IM and M1 were achieved for the concentration ranges of 50-1800 ng/mL and 50-360 ng/mL, respectively. The detection limits were 20 ng/mL and 10 ng/mL for M1 and IM, respectively. The intra- and inter-day precisions were less than 1% with percent recoveries of more than 90%. The method was successfully applied to calculate the pharmacokinetic parameters of chronic myeloid leukemia patients receiving imatinib. The method is suitable to be routinely applied for determination of IM and M1 in serum.
Paederia foetida L. (Rubiaceae) is a climber which is widely distributed in Asian countries including Malaysia. The plant is traditionally used to treat various diseases including diabetes. This study is to evaluate the enzymatic inhibition activity of Paederia foetida twigs extracts and to identify the metabolites responsible for the bioactivity by gas chromatography-mass spectrometry (GC-MS) metabolomics profiling. Three different twig extracts, namely, hexane (PFH), chloroform (PFC), and methanol (PFM), were submerged for their α-amylase and α-glucosidase inhibition potential in 5 replicates for each. Results obtained from the loading column scatter plot of orthogonal partial least square (OPLS) model revealed the presence of 12 bioactive compounds, namely, dl-α-tocopherol, n-hexadecanoic acid, 2-hexyl-1-decanol, stigmastanol, 2-nonadecanone, cholest-8(14)-en-3-ol, 4,4-dimethyl-, (3β,5α)-, stigmast-4-en-3-one, stigmasterol, 1-ethyl-1-tetradecyloxy-1-silacyclohexane, ɣ-sitosterol, stigmast-7-en-3-ol, (3β,5α,24S)-, and α-monostearin. In silico molecular docking was carried out using the crystal structure α-amylase (PDB ID: 4W93) and α-glucosidase (PDB ID: 3WY1). α-Amylase-n-hexadecanoic acid exhibited the lowest binding energy of -2.28 kcal/mol with two hydrogen bonds residue, namely, LYS178 and TYR174, along with hydrophobic interactions involving PRO140, TRP134, SER132, ASP135, and LYS172. The binding interactions of α-glucosidase-n-hexadecanoic acid complex ligand also showed the lowest binding energy among 5 major compounds with the energy value of -4.04 kcal/mol. The complex consists of one hydrogen bond interacting residue, ARG437, and hydrophobic interactions with ALA444, ASP141, GLN438, GLU432, GLY374, LEU373, LEU433, LYS352, PRO347, THR445, HIS348, and PRO351. The study provides informative data on the potential antidiabetic inhibitors identified in Paederia foetida twigs, indicating the plant has the therapeutic effect properties to manage diabetes.
The researchers examined the factor structure and model specifications of the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) with confirmatory tetrad analysis (CTA) using partial least squares-structural equation modeling (PLS-SEM) with a sample of adult clients (N = 298) receiving individual therapy at a university-based counseling research center. The CTA and PLS-SEM results identified the formative nature of the WHODAS 2.0 subscale scores, supporting an alternative measurement model of the WHODAS 2.0 scores as a second-order formative-formative model.