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The assessment of emerging data-intelligence technologies for modeling Mg+2 and SO4-2 surface water quality

Jamei M, Ahmadianfar I, Karbasi M, Jawad AH, Farooque AA, Yaseen ZM

J Environ Manage, 2021 Dec 15;300:113774.
PMID: 34560461 DOI: 10.1016/j.jenvman.2021.113774

The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health. The main goal of this research is to establish two novel wavelet-complementary intelligence paradigms so-called wavelet least square support vector machine coupled with improved simulated annealing (W-LSSVM-ISA) and the wavelet extended Kalman filter integrated with artificial neural network (W-EKF- ANN) for accurate forecasting of the monthly), magnesium (Mg+2), and sulfate (SO4-2) indices at Maroon River, in Southwest of Iran. The monthly River flow (Q), electrical conductivity (EC), Mg+2, and SO4-2 data recorded at Tange-Takab station for the period 1980-2016. Some preprocessing procedures consisting of specifying the number of lag times and decomposition of the existing original signals into multi-resolution sub-series using three mother wavelets were performed to develop predictive models. In addition, the best subset regression analysis was designed to separately assess the best selective combinations for Mg+2 and SO4-2. The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg+2 and SO4-2, respectively.

Matched MeSH terms: Least-Squares Analysis
Fulltext The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis

Abbasi GA, Tiew LY, Tang J, Goh YN, Thurasamy R

PLoS One, 2021;16(3):e0247582.
PMID: 33684120 DOI: 10.1371/journal.pone.0247582

In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, particularly in emerging markets like Malaysia. The purpose of the study is to examine whether the application of deep learning-based dual-stage Partial Least Square-Structural Equation Modelling (PLS-SEM) & Artificial Neural Network (ANN) analysis enable better in-depth research results as compared to single-step PLS-SEM approach and to excavate factors which can predict behavioural intention to adopt cryptocurrency. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model were extended with the inclusion of trust and personnel innovativeness. The model was further validated by introducing a new path model compared to the original UTAUT2 model and the moderating role of personal innovativeness between performance expectancy and price value, with a sample of 314 respondents. Contrary to previous technology adoption studies that used PLS-SEM & ANN as single-stage analysis, this study further enhanced the analysis by applying a deep learning-based dual-stage PLS-SEM and ANN method. The application of deep learning-based dual-stage PLS-SEM & ANN analysis is a novel methodological approach, detecting both linear and non-linear associations among constructs. At the same time, it is regarded as a superior statistical approach as compared to traditional hybrid shallow SEM & ANN single-stage analysis. Also, sensitivity analysis provides normalised importance using multi-layer perceptron with the feed-forward-back-propagation algorithm. Furthermore, the deep learning-based dual-stage PLS-SEM & ANN revealed that trust proved to be the strongest predictor in driving user intention. The introduction of this new methodology and the theoretical contribution opens the vistas of the extant body of knowledge in technology-adoption related literature. This study also provides theoretical, practical and methodological contributions.

Matched MeSH terms: Least-Squares Analysis
The Relationship Between Spirituality Dimensions and Death Anxiety among Iranian Veterans: Partial Least Squares Structural Equation Modeling Approach

Pahlevan Sharif S, Sharif Nia H, Lehto RH, Moradbeigi M, Naghavi N, Goudarzian AH, et al.

J Relig Health, 2021 Apr;60(2):999-1014.
PMID: 31646425 DOI: 10.1007/s10943-019-00931-6

The purpose of the present study was to examine the relationship among spiritual intelligence, spiritual well-being and death anxiety among Iranian veterans. In this predictive correlational study, 211 veterans completed King and DeCicco's Spiritual Intelligence Scale, Paloutzian and Ellison's Spiritual Well-being Scale and Templer's Death Anxiety Scale-Extended. After confirming the reliability of the constructs using intra-class correlation coefficient, partial least squares structural equation modeling method was utilized to assess the impact of spiritual well-being and spiritual intelligence on death anxiety. This study found a significant positive relationship between spiritual intelligence and death anxiety after controlling for the effects of age, education level and disability. However, there was a significant negative relationship between spiritual well-being and death anxiety among Iranian veterans. Negative relationships were found between spiritual well-being and death anxiety among Iranian veterans. However, spiritual intelligence had a positive impact on death anxiety.

Matched MeSH terms: Least-Squares Analysis
Fulltext The Influence of Person-Environment Fit on the Turnover Intention of Nurses in Jordan: The Moderating Effect of Psychological Empowerment

Amarneh S, Raza A, Matloob S, Alharbi RK, Abbasi MA

Nurs Res Pract, 2021;2021:6688603.
PMID: 33815841 DOI: 10.1155/2021/6688603

There is an acute shortage of nurses worldwide, including in Jordan. The nursing shortage is considered to be a crucial and complex challenge across healthcare systems and has stretched to a warning threshold. High turnover among nurses in Jordan is an enduring problem and is believed to be the foremost cause of the nurse shortage. The purpose of this study was to investigate the multidimensional impact of the person-environment (P-E) fit on the job satisfaction (JS) and turnover intention (TI) of registered nurses. The moderating effect of psychological empowerment (PE) on the relationship between JS and TI was also investigated. Based on a quantitative research design, data were collected purposively from 383 registered nurses working at private Jordanian hospitals through self-administered structured questionnaires. Statistical Package for Social Sciences (SPSS) 25 and Smart Partial Least Squares (PLS) 3.2.8 were used to analyze the statistical data. The results showed that there is a significant relationship between person-job fit (P-J fit), person-supervisor fit (P-S fit), and JS. However, this study found an insignificant relationship between person-organization fit (P-O fit) and JS. Moreover, PE was also significantly moderate between JS and TI of nurses. This study offers an important policy intervention that helps healthcare organizations to understand the enduring issue of nurse turnover. Additionally, policy recommendations to mitigate nurse turnover in Jordan are outlined.

Matched MeSH terms: Least-Squares Analysis
The Influence of Organizational External Factors on Construction Risk Management among Nigerian Construction Companies

Adeleke AQ, Bahaudin AY, Kamaruddeen AM, Bamgbade JA, Salimon MG, Khan MWA, et al.

Saf Health Work, 2018 Mar;9(1):115-124.
PMID: 30363069 DOI: 10.1016/j.shaw.2017.05.004

Background: Substantial empirical research has shown conflicting results regarding the influence of organizational external factors on construction risk management, suggesting the necessity to introduce a moderator into the study. The present research confirmed whether rules and regulations matter on the relationships between organizational external factors and construction risk management.
Methods: Based on discouragement and organizational control theory, this research examined the effects of organizational external factors and rules and regulations on construction risk management among 238 employees operating in construction companies in Abuja and Lagos, Nigeria. A personally administered questionnaire was used to acquire the data. The data were analyzed using partial least squares structural equation modeling.
Results: A significant positive relationship between organizational external factors and construction risk management was asserted. This study also found a significant positive relationship between rules and regulations and construction risk management. As anticipated, rules and regulations were found to moderate the relationship between organizational external factors and construction risk management, with a significant positive result. Similarly, a significant interaction effect was also found between rules and regulations and organizational external factors. Implications of the research from a Nigerian point of view have also been discussed.
Conclusion: Political, economy, and technology factors helped the construction companies to reduce the chance of risk occurrence during the construction activities. Rules and regulations also helped to lessen the rate of accidents involving construction workers as well as the duration of the projects. Similarly, the influence of the organizational external factors with rules and regulations on construction risk management has proven that most of the construction companies that implement the aforementioned factors have the chance to deliver their projects within the stipulated time, cost, and qualities, which can be used as a yardstick to measure a good project.

Matched MeSH terms: Least-Squares Analysis
Fulltext The Influence of Neurocognitive Impairment, Depression, and Alcohol Use Disorders on Health-Related Quality of Life among Incarcerated, HIV-Infected, Opioid Dependent Malaysian Men: A Moderated Mediation Analysis

Shrestha R, Weikum D, Copenhaver M, Altice FL

AIDS Behav, 2017 Apr;21(4):1070-1081.
PMID: 27544515 DOI: 10.1007/s10461-016-1526-3

Prior research has widely recognized neurocognitive impairment (NCI), depression, and alcohol use disorders (AUDs) as important negative predictors of health-related quality of life (HRQoL) among people living with HIV (PLWH). No studies to date, however, have explored how these neuropsychological factors operate together and affect HRQoL. Incarcerated male PLWH (N = 301) meeting criteria for opioid dependence were recruited from Malaysia's largest prison. Standardized scales for NCI, depression, alcohol use disorders (AUDs) and HRQoL were used to conduct a moderated mediation model to explore the extent to which depression mediated the relationship between NCI, HRQoL, and AUDs using an ordinary least squares regression-based path analytic framework. Results showed that increasing levels of NCI (B = -0.1773, p

Matched MeSH terms: Least-Squares Analysis
Fulltext The Changes in Endogenous Metabolites in Hyperlipidemic Rats Treated with Herbal Mixture Containing Lemon, Apple Cider, Garlic, Ginger, and Honey

Abu Bakar Sajak A, Azlan A, Abas F, Hamzah H

Nutrients, 2021 Oct 12;13(10).
PMID: 34684574 DOI: 10.3390/nu13103573

An herbal mixture composed of lemon, apple cider, garlic, ginger and honey as a polyphenol-rich mixture (PRM) has been reported to contain hypolipidemic activity on human subjects and hyperlipidemic rats. However, the therapeutic effects of PRM on metabolites are not clearly understood. Therefore, this study aimed to provide new information on the causal impact of PRM on the endogenous metabolites, pathways and serum biochemistry. Serum samples of hyperlipidemic rats treated with PRM were subjected to biochemistry (lipid and liver profile) and hydroxymethylglutaryl-CoA enzyme reductase (HMG-CoA reductase) analyses. In contrast, the urine samples were subjected to urine metabolomics using 1H NMR. The serum biochemistry revealed that PRM at 500 mg/kg (PRM-H) managed to lower the total cholesterol level and low-density lipoprotein (LDL-C) (p < 0.05) and reduce the HMG-CoA reductase activity. The pathway analysis from urine metabolomics reveals that PRM-H altered 17 pathways, with the TCA cycle having the highest impact (0.26). Results also showed the relationship between the serum biochemistry of LDL-C and HMG-CoA reductase and urine metabolites (trimethylamine-N-oxide, dimethylglycine, allantoin and succinate). The study's findings demonstrated the potential of PRM at 500 mg/kg as an anti-hyperlipidemic by altering the TCA cycle, inhibiting HMG-CoA reductase and lowering the LDL-C in high cholesterol rats.

Matched MeSH terms: Least-Squares Analysis
Fulltext Test for the presence of autocorrelation in the modified Gompertz model used in fitting of Burkholderia sp. strain Neni-11 growth on acrylamide

Mohd. Yunus Shukor

Bioremediation Science and Technology Research, 2016;4(2):25-27.
MyJurnal

The growth of microorganism on substrates, whether toxic or not usually exhibits sigmoidal
pattern. This sigmoidal growth pattern can be modelled using primary models such as Logistic,
modified Gompertz, Richards, Schnute, Baranyi-Roberts, Von Bertalanffy, Buchanan threephase
and Huang. Previously, the modified Gompertz model was chosen to model the growth of
Burkholderia sp. strain Neni-11 on acrylamide, which shows a sigmoidal curve. The modified
Gompertz model relies on the ordinary least squares method, which in turn relies heavily on
several important assumptions, which include that the data does not show autocorrelation. In this
work we perform statistical diagnosis test to test for the presence of autocorrelation using the
Durbin-Watson test and found that the model was adequate and robust as no autocorrelation of
the data was found.

Matched MeSH terms: Least-Squares Analysis
Fulltext System identification of helical strakes suppressed vortexinduced vibration for flexible pipes

Ooi, Ching Sheng, Lim, Meng Hee, Lee, Kee Quen, Kang, Hooi Siang, Mohd Salman Leong

Pertanika Journal of Science & Technology, 2020;28(2):493-508.
MyJurnal

Previous studies have indicated that the pipe-surface-mounted helical strakes effectively reduce vortex-induced vibration (VIV) under a uniform flow application, particularly during the lock-in region. Since VIV experiments are time-consuming, observation is generated with an interval helical strakes parameter in pitch and height to lessen tedious procedures and repetitive post-processing analyses. The aforementioned result subset is insufficient for helical strakes design optimisation because the trade-off between the helical strakes dimension, lock-in region and flow velocity are non-trivial. Thus, a parametric model based on an improved recursive least squares (RLS) parameter estimation technique is proposed to define the statistical relationship between input, or strakes and pipe dimension, and output, or VIV amplitude ratio. As results suggested, revised RLS estimated VIV model demonstrated an optimal prediction with the highest coefficient of determination and lowest Integral Absolute Error. The feasibility of VIV parametric model was validated by embed into Genetic Algorithm (GA) as the fitness function to acquire a desirable helical strakes dimension with minimum VIV amplitude. The rapid generation of optimal helical strakes dimension which returned the highest VIV suppression implied a superior simulation method compared to the experimental outcome.

Matched MeSH terms: Least-Squares Analysis
Fulltext Strategic public procurement regulatory compliance model with mediating effect of ethical behavior

Sarawa DI, Mas'ud A

Heliyon, 2020 Jan;6(1):e03132.
PMID: 32042941 DOI: 10.1016/j.heliyon.2019.e03132

The paper proposes and validates the strategic public procurement regulatory compliance model with mediation effect of ethical behavior. It expands the socio-economic theory of regulatory compliance to explore the mediating effect of ethical behavior on the influence of professionalism, familiarity, enforcement, resistance to political pressure and compliance with public procurement regulation. A quantitative research design was deployed using 125 procurement officers as a sample group. The data from the sample was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results validated the hypotheses for the strategic public procurement regulatory compliance model with mediation effect of ethical behavior. The study not only confirmed the earlier findings on the direct effects of professionalism, familiarity, enforcement, resistance to political pressure and ethical behavior on compliance, but also established the mediating effect of ethical behavior on compliance on all the predictors except resistance to political pressure. The study implied that ethical behavior of public procurement officers should be a strategic point of concern by both policymakers and professional bodies. Theoretically, the studyexpands thesocio-economic theory of regulatory compliance within the context of procurement literature through mediation effects of ethical behavior via structural analysis.

Matched MeSH terms: Least-Squares Analysis
Stereoselective method development and validation for determination of concentrations of amphetamine-type stimulants and metabolites in human urine using a simultaneous extraction-chiral derivatization approach

Wan Raihana WA, Gan SH, Tan SC

J Chromatogr B Analyt Technol Biomed Life Sci, 2011 Jan 1;879(1):8-16.
PMID: 21147046 DOI: 10.1016/j.jchromb.2010.10.037

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.

Matched MeSH terms: Least-Squares Analysis
Sparse PLS-Based Method for Overlapping Metabolite Set Enrichment Analysis

Deng L, Ma L, Cheng KK, Xu X, Raftery D, Dong J

J Proteome Res, 2021 06 04;20(6):3204-3213.
PMID: 34002606 DOI: 10.1021/acs.jproteome.1c00064

Metabolite set enrichment analysis (MSEA) has gained increasing research interest for identification of perturbed metabolic pathways in metabolomics. The method incorporates predefined metabolic pathways information in the analysis where metabolite sets are typically assumed to be mutually exclusive to each other. However, metabolic pathways are known to contain common metabolites and intermediates. This situation, along with limitations in metabolite detection or coverage leads to overlapping, incomplete metabolite sets in pathway analysis. For overlapping metabolite sets, MSEA tends to result in high false positives due to improper weights allocated to the overlapping metabolites. Here, we proposed an extended partial least squares (PLS) model with a new sparse scheme for overlapping metabolite set enrichment analysis, named overlapping group PLS (ogPLS) analysis. The weight vector of the ogPLS model was decomposed into pathway-specific subvectors, and then a group lasso penalty was imposed on these subvectors to achieve a proper weight allocation for the overlapping metabolites. Two strategies were adopted in the proposed ogPLS model to identify the perturbed metabolic pathways. The first strategy involves debiasing regularization, which was used to reduce inequalities amongst the predefined metabolic pathways. The second strategy is stable selection, which was used to rank pathways while avoiding the nuisance problems of model parameter optimization. Both simulated and real-world metabolomic datasets were used to evaluate the proposed method and compare with two other MSEA methods including Global-test and the multiblock PLS (MB-PLS)-based pathway importance in projection (PIP) methods. Using a simulated dataset with known perturbed pathways, the average true discovery rate for the ogPLS method was found to be higher than the Global-test and the MB-PLS-based PIP methods. Analysis with a real-world metabolomics dataset also indicated that the developed method was less prone to select pathways with highly overlapped detected metabolite sets. Compared with the two other methods, the proposed method features higher accuracy, lower false-positive rate, and is more robust when applied to overlapping metabolite set analysis. The developed ogPLS method may serve as an alternative MSEA method to facilitate biological interpretation of metabolomics data for overlapping metabolite sets.

Matched MeSH terms: Least-Squares Analysis
Fulltext Sorption of so2 and no by modified palm shell activated carbon: breakthrough curve model

S. Bhatia, K. T. Lee, A. R. Mohamed, Sumathi, S.

Pertanika Journal of Science & Technology, 2014;22(1):307-314.
MyJurnal

Simultaneous removal of SO2 and NO from simulated flue gas by cerium oxide supported over palm shell activated carbon (Ce/PSAC) was studied in a fixed bed adsorber. In this study, the adsorption breakthrough of SO2 and NO on Ce/PSAC at different reaction temperatures was manipulated to test their applicability to a model developed by Yoon and Nelson (1984) for breakthrough curves. Yoon and Nelson (1984) developed a relatively simple model addressing the adsorption and breakthrough of adsorbate vapour with respect to activated charcoal. This model was based on the assumption that the rate of decrease in the probability of adsorption for each adsorbate molecule is proportional to the probability of adsorbate adsorption and the probability of adsorbate breakthrough on the adsorbent. A regression analysis (least square method) has been used to give the model parameters of k and t1/2. The results showed that the agreement between the model and the experimental results is satisfactory. From the observation, it is concluded that the simple two-parameter model of Yoon and Nelson’s model can be applied for modelling the breakthrough curves of SO2 and NO gas adsorption over Ce/PSAC.

Matched MeSH terms: Least-Squares Analysis
Fulltext Smart technology for healthcare: Exploring the antecedents of adoption intention of healthcare wearable technology

Chau KY, Lam MHS, Cheung ML, Tso EKH, Flint SW, Broom DR, et al.

Health Psychol Res, 2019 Mar 11;7(1):8099.
PMID: 31583292 DOI: 10.4081/hpr.2019.8099

Technological advancement and personalized health information has led to an increase in people using and responding to wearable technology in the last decade. These changes are often perceived to be beneficial, providing greater information and insights about health for users, organizations and healthcare and government. However, to date, understanding the antecedents of its adoption is limited. Seeking to address this gap, this cross-sectional study examined what factors influence users' adoption intention of healthcare wearable technology. We used self-administrated online survey to explore adoption intentions of healthcare wearable devices in 171 adults residing in Hong Kong. We analyzed the data by Partial least squares - structural equation modelling (PLS-SEM). The results reveal that perceived convenience and perceived irreplaceability are key predictors of perceived usefulness, which in turn strengthens users' adoption intention. Additionally, the results also reveal that health belief is one of the key predictors of adoption intention. This paper contributes to the extant literature by providing understanding of how to strengthen users' intention to adopt healthcare wearable technology. This includes the strengthening of perceived convenience and perceived irreplaceability to enhance the perceived usefulness, incorporating the extensive communication in the area of healthcare messages, which is useful in strengthening consumers' adoption intention in healthcare wearable technology.

Matched MeSH terms: Least-Squares Analysis
Fulltext Simultaneous quantitative analysis of two functional food oils, extra virgin olive oil and virgin coconut oil using FTIR spectroscopy and multivariate calibration

Rohman, A., Che Man, Y.B.

International Food Research Journal, 2011;18(4):1231-1235.
MyJurnal

Two functional food oils, namely extra virgin olive oil (EVOO) and virgin coconut oil (VCO) have been analyzed simultaneously using Fourier transform infrared (FTIR) spectroscopy. The performance of multivariate calibration of principle component regression (PCR) and partial least square regression (PLSR) was evaluated in order to give the best prediction model for such determination. FTIR spectra were treated with several treatments including mean centering (MC), derivatization, and standard normal variate (SNV) at the combined frequency regions of 3050 – 3000, 1660 – 1650, and 1200 – 900 cm-1. Based on its capability to give the highest values of coefficient of correlation (R) for the relationship between actual value of EVOO/VCO and FTIR predicted value together with the lowest values of root mean square error of calibration (RMSEC), PLSR with mean centered-first derivative spectra was chosen for simultaneous determination of EVOO and VCO. It can be concluded that FTIR spectroscopy combined with multivariate calibration of PLSR was successfully applied to simultaneously quantify EVOO and VCO with acceptable parameters.

Matched MeSH terms: Least-Squares Analysis
Fulltext Simultaneous hyperaccumulation of nickel and cobalt in the tree Glochidion cf. sericeum (Phyllanthaceae): elemental distribution and chemical speciation

van der Ent A, Mak R, de Jonge MD, Harris HH

Sci Rep, 2018 Jun 26;8(1):9683.
PMID: 29946061 DOI: 10.1038/s41598-018-26891-7

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.

Matched MeSH terms: Least-Squares Analysis
Screening of nanoemulsion components for asenapine maleate using validated RP-HPLC method

Kumbhar SA, Kokare CR, Shrivastava B, Gorain B

Ann Pharm Fr, 2020 May 06.
PMID: 32387177 DOI: 10.1016/j.pharma.2020.04.005

A novel, simple reversed-phase high-performance liquid chromatographic (RP-HPLC) analytical method was developed and validated for the quantitative determination of asenapine from various nanoemulsion components during pre-formulation screening. The developed method was validated according to ICH Q2 (R1) guidelines. The developed and validated method was precisely and accurately quantified asenapine in various oils, surfactants and co-surfactants. The separation of asenapine was carried out on Hypersil BDS C18, 250×4.6mm, 5μm particle size column using methanol: acetonitrile (90:10) as mobile phase with a flow rate of 1mL.min-1. Measurement at 270nm for the concentration range of 5 to 50μg.mL-1 of the analyte was found to be linear with the determination coefficient (r2) of 0.999 as calculated by the least square regression method. The validated method was sensitive with LOD of 10.0ng.mL-1 and LOQ of 30.0ng.mL-1. Further, the method was precise and accurate, where the intraday and interday precision values were ranged from 0.70-0.95 and 0.36-0.95, respectively with the corresponding accuracy were ranged from 98.80-100.63 and 98.36-100.63. This developed and validated RP-HPLC method for asenapine was applied in the quantitative determination and screening of various oils, surfactants, and co-surfactants during the development of the asenapine maleate nanoemulsion.

Matched MeSH terms: Least-Squares Analysis
Fulltext Salivary bacterial shifts in oral leukoplakia resemble the dysbiotic oral cancer bacteriome

Gopinath D, Kunnath Menon R, Chun Wie C, Banerjee M, Panda S, Mandal D, et al.

J Oral Microbiol, 2020 Dec 09;13(1):1857998.
PMID: 33391629 DOI: 10.1080/20002297.2020.1857998

Objective: While some oral carcinomas appear to arise de novo, others develop within long-standing conditions of the oral cavity that have malignant potential, now known as oral potentially malignant disorders (OPMDs). The oral bacteriome associated with OPMD has been studied to a lesser extent than that associated with oral cancer. To characterize the association in detail we compared the bacteriome in whole mouth fluid (WMF) in patients with oral leukoplakia, oral cancer and healthy controls. Methods: WMF bacteriome from 20 leukoplakia patients, 31 patients with oral cancer and 23 healthy controls were profiled using the Illumina MiSeq platform. Sequencing reads were processed using DADA2, and taxonomical classification was performed using the phylogenetic placement method. Sparse Partial Least Squares Regression Discriminant Analysis model was used to identify bacterial taxa that best discriminate the studied groups. Results: We found considerable overlap between the WMF bacteriome of leukoplakia and oral cancer while a clearer separation between healthy controls and the former two disorders was observed. Specifically, the separation was attributed to 14 taxa belonging to the genera Megaspheara, unclassified enterobacteria, Prevotella, Porphyromonas, Rothia and Salmonella, Streptococcus, and Fusobacterium. The most discriminative bacterial genera between leukoplakia and oral cancer were Megasphaera, unclassified Enterobacteriae, Salmonella and Prevotella.Conclusion: Oral bacteria may play a role in the early stages of oral carcinogenesis as a dysbiotic bacteriome is associated with oral leukoplakia and this resembles that of oral cancer more than healthy controls. Our findings may have implications for developing oral cancer prevention strategies targeting early microbial drivers of oral carcinogenesis.

Matched MeSH terms: Least-Squares Analysis
Fulltext Robust digit recognition with dynamic time warping and recursive least squares

Al-Haddad, S.A.R., Samad, S.A., Hussain, A., Ishak, K.A., Noor, A.O.A.

ASM Science Journal, 2008;2(1):75-81.
MyJurnal

Robustness is a key issue in speech recognition. A speech recognition algorithm for Malay digits from zero to nine and an algorithm for noise cancellation by using recursive least squares (RLS) is proposed in this article. This system consisted of speech processing inclusive of digit margin and recognition using zero crossing and energy calculations. Mel-frequency cepstral coefficient vectors were used to provide an estimate of the vocal tract filter. Meanwhile dynamic time warping was used to detect the nearest recorded voice with appropriate global constraint. The global constraint was used to set a valid search region because the variation of the speech rate of the speaker was considered to be limited in a reasonable range which meant that it could prune the unreasonable search space. The algorithm was tested on speech samples that were recorded as part of a Malay corpus. The results showed that the algorithm managed to recognize almost 80.5% of the Malay digits for all recorded words. The addition of a RLS noise canceller in the preprocessing stage increased the accuracy to 94.1%.

Matched MeSH terms: Least-Squares Analysis
Fulltext Robust Wavelength Selection Using Filter-Wrapper Method and Input Scaling on Near Infrared Spectral Data

Silalahi DD, Midi H, Arasan J, Mustafa MS, Caliman JP

Sensors (Basel), 2020 Sep 03;20(17).
PMID: 32899292 DOI: 10.3390/s20175001

The extraction of relevant wavelengths from a large dataset of Near Infrared Spectroscopy (NIRS) is a significant challenge in vibrational spectroscopy research. Nonetheless, this process allows the improvement in the chemical interpretability by emphasizing the chemical entities related to the chemical parameters of samples. With the complexity in the dataset, it may be possible that irrelevant wavelengths are still included in the multivariate calibration. This yields the computational process to become unnecessary complex and decreases the accuracy and robustness of the model. In multivariate analysis, Partial Least Square Regression (PLSR) is a method commonly used to build a predictive model from NIR spectral data. However, in the PLSR method and common commercial chemometrics software, there is no standard wavelength selection procedure applied to screen the irrelevant wavelengths. In this study, a new robust wavelength selection procedure called the modified VIP-MCUVE (mod-VIP-MCUVE) using Filter-Wrapper method and input scaling strategy is introduced. The proposed method combines the modified Variable Importance in Projection (VIP) and modified Monte Carlo Uninformative Variable Elimination (MCUVE) to calculate the scale matrix of the input variable. The modified VIP uses the orthogonal components of Partial Least Square (PLS) in investigating the informative variable in the model by applying the amount of variation both in X and y{SSX,SSY}, simultaneously. The modified MCUVE uses a robust reliability coefficient and a robust tolerance interval in the selection procedure. To evaluate the superiority of the proposed method, the classical VIP, MCUVE, and autoscaling procedure in classical PLSR were also included in the evaluation. Using artificial data with Monte Carlo simulation and NIR spectral data of oil palm (Elaeis guineensis Jacq.) fruit mesocarp, the study shows that the proposed method offers advantages to improve model interpretability, to be computationally extensive, and to produce better model accuracy.

Matched MeSH terms: Least-Squares Analysis

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