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Although motivation has been shown to have substantial influence on learning, the relative significance of Students’ motivational characteristics, compared to other school-related factors, for student learning and performance is still unclear. Furthermore, knowledge about the relative importance of different situational variables for predicting these motivational characteristics is crucial for educational decisions about how to enhance student motivation. This study examined (1) the relative importance of motivational characteristics derived from five different theories on motivation and epistemic beliefs, compared to almost 300 situational factors, for predicting student performance on the TIMSS 2011 achievement tests in science and mathematics, and (2) how student motivational characteristics can be predicted by the background variables in the TIMSS 2011 questionnaires and an additional questionnaire about motivation accompanying TIMSS in Sweden. Up to 52% of the variation in student performance could be predicted by models containing all background variables, and student motivational characteristics were among the most important variables in the model. Models that comprised only student motivational characteristics from several motivation theories predicted up to 27% of student performance on the achievement test, while models using only single motivational characteristics predicted, on average, 7%. Results emphasize teachers’ importance for student motivation. Five teacher features were consistently among the most important variables in predicting Students’ motivational characteristics. These five variables predicted as much of the variation in important student motivational characteristics as the remaining 300 situational variables together. Interaction between the learner and the learning environment: Effects on the learner’s affective experiences and learning outcomes

An important demand exists in the field of forensic analysis to objectively determine the post-mortem interval (PMI) when human skeletal remains are discovered. It is widely known that bones undergo different chemical and physical processes after death, mainly due to their interaction with the environment in which they are found, although it is not known exactly what these processes consist of. Multiple techniques have been used so far to follow up these and other post-mortem changes and thus establish the time elapsed since the individual’s death, but they present important drawbacks in terms of reliability and accuracy. The aim of this research was to propose an analytical methodology capable of determining the PMI by using non-destructive Raman spectroscopy measurements of human skeletal remains. The recorded Raman spectra provided valuable and potentially useful information from which a multivariate study was performed by means of orthogonal partial least squares regression (OPLSR) in order to correlate the PMI with the detected spectral modifications. A collection of 53 real human skeletal remains with known PMI (15 years ≤ PMI ≤ 87 years) was analysed and used for building and validating the OPLS model. The PMI of 10 out of 14 validation samples could be determined with an accuracy error of less than 30%, demonstrating the adequate predictive performance of the OPLS model even in spite of the large inter-individual variability it handled. This opens up the possibility of applying the OPLS model in combination with non-destructive techniques to the determination of the PMI of human skeletal remains that have been buried in conditions similar or equal to those of cemetery niches and in a geographic location with a Mediterranean climate, which is an important achievement for forensic medicine and anthropology.

Experimental solvation free energies are nowadays commonly included as target properties in the validation of condensed-phase force fields, sometimes even in their calibration. In a previous article [Kashefolghetaet al.,J. Chem. Theory. Comput., 2020,16, 7556-7580], we showed how the involved comparison between experimental and simulation results could be made more systematic by considering a full matrix of cross-solvation free energies. For a set ofNmolecules that are all in the liquid state under ambient conditions, such a matrix encompassesN×Nentries for considering each of theNmolecules either as solute (A) or as solvent (B). In the quoted study, a cross-solvation matrix of 25 × 25 experimental value was introduced, considering 25 small molecules representative for alkanes, chloroalkanes, ethers, ketones, esters, alcohols, amines, and amides. This experimental data was used to compare the relative accuracies of four popular condensed-phase force fields, namely GROMOS-2016H66, OPLS-AA, AMBER-GAFF, and CHARMM-CGenFF. In the present work, the comparison is extended to five additional force fields, namely GROMOS-54A7, GROMOS-ATB, OPLS-LBCC, AMBER-GAFF2, and OpenFF. Considering these nine force fields, the correlation coefficients between experimental values and simulation results range from 0.76 to 0.88, the root-mean-square errors (RMSEs) from 2.9 to 4.8 kJ mol−1, and average errors (AVEEs) from −1.5 to +1.0 kJ mol−1. In terms of RMSEs, GROMOS-2016H66 and OPLS-AA present the best accuracy (2.9 kJ mol−1), followed by OPLS-LBCC, AMBER-GAFF2, AMBER-GAFF, and OpenFF (3.3 to 3.6 kJ mol−1), and then by GROMOS-54A7, CHARM-CGenFF, and GROMOS-ATB (4.0 to 4.8 kJ mol−1). These differences are statistically significant but not very pronounced, and are distributed rather heterogeneously over the set of compounds within the different force fields., Physical Chemistry Chemical Physics, 23 (23), ISSN:1463-9084, ISSN:1463-9076

Oregano is one of the most used culinary herb and it is often adult­erated with cheaper plants. In this study, GC–MS was used for identification and quan­tification of metabolites from 104 samples of oregano (Origanum vul­gare and O. onites) adulterated with olive (Olea europaea), venetian sumac (Cotinus coggy­gria) and myrtle (Myrtus communis) leaves, at five different concentration levels. The metabolomics profiles obtained after the two-step derivatization, involving methoxyamination and silanization, were subjected to multivariate data analysis to reveal markers of adulteration and to build the reg­ression models on the basis of the oregano-to-adulterants mixing ratio. Ortho­gonal partial least squares enabled detection of oregano adulterations with olive, Venetian sumac and myrtle leaves. Sorbitol levels distinguished oregano samples adulterated with olive leaves, while shikimic and quinic acids were recognized as discrimination factor for adulteration of oregano with venetian sumac. Fructose and quinic acid levels correlated with oregano adulteration with myrtle. Orthogonal partial least squares discriminant analysis enabled dis­crimination of O. vulgare and O. onites samples, where catechollactate was found to be discriminating metabolite. Оригано је једна од најчешће коришћених кулинарских биљака и често се криво- твори јефтинијим биљкама. У овој студији, гаснa хроматографијa–масенa спектро- метријa коришћена је за идентификацију и квантификацију метаболита из 104 узорка оригана (Origanum vulgare и O. onites) кривотвореног маслином (Olea europea), венеци- јанским сумаком (Cotinus coggygria) и миртом (Myrtus communis), у пет различитих концентрација. Метаболомички профили добијени након двостепене дериватизације, која укључује метоксиаминовање и силанизацију, подвргнути су мултиваријантној ана- лизи података како би се открили маркери кривотворења и направили регресиони модели на основу односа мешања оригана и биљака за кривотворење. Ортогонална дели- мична анализа најмањих квадрата је омогућила детекцију кривотворења оригана лишћем маслине, венецијанског сумака и мирте. Садржај сорбитола разликовао је узорке оригана кривотворених лишћем маслине, док су шикиминска и кининска кисе- лина препознате као фактор разликовања за кривотворење оригана венецијанским сумаком. Садржај фруктозе и кининске киселине у корелацији су са кривотворењем оригана миртом. Ортогонална делимична анализа најмањих квадрата – дискриминантна анализа је омогућила разликовање узорака O. vulgare и O. onites, при чему је одређено да је катехоллактат метаболит који разликује ове две биљне врсте. Part of the theme issue honoring Professor Emeritus Slobodan Milosavljevićs 80th birthday.

BACKGROUND The aberrant activation of the PI3K/mTOR signaling circuitry is one of the most frequently dysregulated signaling events in head and neck squamous cell carcinoma (HNSCC). Here, we conducted a single-arm, open-label phase IIa clinical trial in individuals with oral premalignant lesions (OPLs) to explore the potential of metformin to target PI3K/mTOR signaling for HNSCC prevention. METHODS Individuals with OPLs, but who were otherwise healthy and without diabetes, underwent pretreatment and posttreatment clinical exam and biopsy. Participants received metformin for 12 weeks (week 1, 500 mg; week 2, 1000 mg; weeks 3–12, 2000 mg daily). Pretreatment and posttreatment biopsies, saliva, and blood were obtained for biomarker analysis, including IHC assessment of mTOR signaling and exome sequencing. RESULTS Twenty-three participants were evaluable for response. The clinical response rate (defined as a ≥50% reduction in lesion size) was 17%. Although lower than the proposed threshold for favorable clinical response, the histological response rate (improvement in histological grade) was 60%, including 17% complete responses and 43% partial responses. Logistic regression analysis revealed that when compared with never smokers, current and former smokers had statistically significantly increased histological responses (P = 0.016). Remarkably, a significant correlation existed between decreased mTOR activity (pS6 IHC staining) in the basal epithelial layers of OPLs and the histological (P = 0.04) and clinical (P = 0.01) responses. CONCLUSION To our knowledge this is the first phase II trial of metformin in individuals with OPLs, providing evidence that metformin administration results in encouraging histological responses and mTOR pathway modulation, thus supporting its further investigation as a chemopreventive agent. TRIAL REGISTRATION NCT02581137 FUNDING NIH contract HHSN261201200031I, grants R01DE026644 and R01DE026870

Bitterness is a common aversive flavor attribute of foods associated with low consumer acceptance. Untargeted LC–MS flavoromic profiling was utilized to identify endogenous compounds that influence the generation of the bitter compound 9,12,13-trihydroxy-trans-10-octadecenoic acid (pinellic acid) during bread making. A diverse sample set of wheat germplasm was chemically profiled. The corresponding pinellic acid concentrations after dough formation were modeled by orthogonal partial least squares (OPLS) with good fit (R2Y = 0.8) and predictive ability (Q2 = 0.6). The most predictive feature (negatively correlated), postulated to interfere with the biosynthetic pathway, was identified as schaftoside, an apigenin di-C-glycoside. Recombination experiments involving the addition of schaftoside to flour prior to breadmaking resulted in a 26% decrease in pinellic acid formation and significantly lower perceived bitterness intensity in whole wheat bread. This work provides novel understanding of bitter generation pathways in wheat products and new strategies to improve flavor profiles and consumer acceptability.

This research provides an accurate description of the origin for fruit spirits. In total, 63 samples of various kinds of fruit spirits (especially from apples, pears, plums, apricots and mirabelle) were analysed using headspace-solid phase microextraction and gas chromatography with flame-ionization detector. Obtained volatile profiles were treated and analysed by multivariate regression with a reduction of dimensionality-orthogonal projections to latent structure for the classification of fruit spirits according to their fruit of origin. Basic result of statistical analysis was the differentiation of spirits to groups with respect to fruit kind. Tested kinds of fruit spirits were strictly separated from each other. The selection was achieved with a specificity of 1.000 and a sensitivity of 1.000 for each kind of spirit. The statistical model was verified by an external validation. Hierarchical cluster analysis (calculation of distances by Ward’s method) showed a similarity of volatile profiles of pome fruit spirits (apple and pear brandies) and stone fruit spirits (especially mirabelle and plum brandies). Tento výzkum poskytuje přesný popis původu ovocných destilátů. Celkem bylo analyzováno 63 vzorků různých druhů ovocných destilátů (zejména z jablek, hrušek, švestek, meruněk a mirabel) pomocí metod mikroextrakce tuhou fází z parního prostoru a plynové chromatografie s plamenovým ionizačním detektorem. Získané profily těkavých látek byly zpracovány a analyzovány metodou ortogonální projekce do latentních struktur s cílem klasifikace ovocných destilátů, přičemž základním výsledkem statistické analýzy bylo rozlišení destilátů na skupiny s ohledem na ovocné druhy. Testované druhy ovocných destilátů byly od sebe odděleny, klasifikace byla dosažena se stoprocentní specificitou a senzitivitou pro každý druh destilátu. Statistický model byl ověřen externí validací. Hierarchická shluková analýza (výpočet vzdáleností Wardovou metodou) ukázala podobnost těkavých profilů destilátů z jádřincových plodů (pálenky z jablek a hrušek) a destilátů z peckovin (zejména pálenky ze švestek a mirabelek).

International audience; Orthogonal Projection to Latent Structures (OPLS) is a preprocessing method that was presented as an improvement of the PLS algorithm it was issued from. Nevertheless, according to the bibliography its added value is questionnable both for prediction and interpretation. To contribute to a better understanding, we investigated the relationship between OPLS and the Net Analyte Signal (NAS). For four numerical applications, the matrix obtained after the OPLS deflation tended towards a matrix of rank 1 when the number of removed dimensions increased. Therefore, the row-vectors of this matrix are collinear to the NAS, and so the usual one-latent-variable PLS1 regression following the OPLS preprocessing can be replaced by almost any regression method. Moreover, the interpretation relies on a vector of rank one issued from the deflated matrix, which does not bring more than the regular PLS regression vector.

We synthesized Schiff base and its complexes derivatives of chitosan (CS) in order to develop antibiotic compounds based on functionalized-chitosan against gram-positive and gram-negative bacteria. IR, UV-Vis, AFM, SEM, Melting point, X-ray diffraction (XRD), elemental analysis, and 1H NMR techniques were employed to characterize the chemical structures and properties of these compounds. XRD, UV-Vis, and 1H NMR techniques confirmed the formation of Schiff base and its functionalized-chitosan to metals. Subsequently, our antibacterial studies revealed that antibacterial activities of [Zn(Schiff base)(CS)] against S. aureus bacteria increased compared to those of their compounds. In addition, hemolysis test of CS-Schiff base-Cu(II) demonstrated better hemolytic activity than vitamin C, CS-Schiff base, CS-Schiff base-Zn(II), and CS-Schiff base-Ni(II). In a computational strategy, we carried out the optimization of compounds with molecular mechanics (MM+), Semi-emprical (AM1), Abinitio (STO-3G), AMBER, BIO+(CHARMM), and OPLS. Frontier orbital density distributions (HOMO and LUMO), and the optimized computational UV of the compounds were assessed. The optimized computational UV-Vis was similar to the experimental UV-Vis. We applied the docking methods to predict the DNA binding affinity, Staphylococcus aureus enoyl-acyl carrier protein reductase (ENRs), and Staphylococcus aureus enoyl-acyl carrier protein reductase (saFabI). Ultimately, the obtained data herein suggested that Schiff base is more selective toward ENRs and saFabI compared to chitosan, its complexes, and metronidazole.

The increasing concern on the harmful effects caused by mineral oil-based lubricants towards the environment has given impetus to the evolution of green-lubricants. Vegetable oils are highly biodegradable, renewable, and possesses good lubricating property. In the present study Pongamia pinnata, non-edible vegetable oil, also known as Karanja Oil (KO) was used as the base oil for a lubricant. The preliminary properties, such as fatty acid profile and viscosity, which has a vital role in governing the performance of lubricants were evaluated experimentally as per international standards. The shear viscosity of KO which constitutes 8 major fatty acids were predicted using non-equilibrium molecular dynamics (NEMD) and periodic perturbation (PP) method using Optimised Potentials for Liquid Simulations (OPLS) and Generalized Amber Force Field (GAFF). The shear viscosities were evaluated at temperatures ranging from 313K to 373 K and pressure P = 0.1 MPa. The experimental and simulation data of KO shear viscosity are in line with each other using OPLS. The kinematic viscosities were calculated using the shear viscosities and densities obtained from simulation. The variation between experimental and simulation data is less while using OPLS, while GAFF force fields resulted in higher deviations.

Two methodologies were developed to monitor the biodiesel content of mafurra in mixtures with diesel using hydrogen nuclear magnetic resonance (1H NMR) Spectroscopy combined with the multivariate regression by orthogonal projections to latent structure (OPLS) and partial least squares (PLS). The efficiency of these methodologies was analyzed based on the figures of merit and the fit of the models through the correlation of the measured and predicted values of the calibration and prediction sets. The results of the figures of merit in the OPLS model were better than in the PLS model. A high correlation between the measured and predicted values was evident in the OPLS model, with a correlation coefficient (R2) greater than 0.99, demonstrating a better fit of the OPLS model in relation to the PLS model which presented a correlation coefficient (R2) less than 0.98. The OPLS model is more robust and has good predictive capacity than the PLS model because it obtained a higher Q 2 value. The excellent results of the application of 1H NMR spectroscopy combined with multivariate regression by OPLS suggest that this analytical methodology is ideal, feasible, efficient and suitable for use by inspection agencies to control the quality of this fuel.

Background Wide QRS‐T angles and inflammatory activity are markers of future cardiovascular events including sudden cardiac death (SCD). The association between wide QRS‐T angles and inflammatory activation is however not fully understood. Methods 1,094 study participants of both sexes, 50–64 years old, were included from a randomly selected population‐based cohort as a part of the Swedish CArdioPulmonary bioImage Study (SCAPIS) pilot study. Serum samples were analyzed for markers of inflammation, cardiac wall stress/injury, and the metabolic syndrome. Wide QRS‐T angles were defined using Frank vectorcardiography. Variables were analyzed through unsupervised principal component analysis (PCA) as well as Orthogonal Projections to Latent Structures (OPLS) modeling. In addition, a subset of study participants was analyzed in a post hoc matched group design. Results Wide QRS‐T angles correlated positively with markers of inflammation, cardiac wall stress/injury, the metabolic syndrome, and male sex in both PCA and OPLS models. In the matched post hoc analysis, participants with wide QRS‐T angles had significantly higher counts of white blood cells (WBC) and neutrophils in comparison with matched controls. WBC as well as the number of neutrophils, monocytes, basophils, eosinophils and levels of C‐reactive protein, IL‐1, IL‐4, IL‐6, TNF‐α, and NT‐pro‐BNP were also significantly higher in comparison with healthy controls. Conclusions Markers of inflammatory activation and cardiac injury/wall stress were significantly higher in the presence of wide QRS‐T angles. These results corroborate an association between abnormal electrophysiological function and inflammatory activation and may have implications for the prediction of SCD.

Current methods for the intraoperative determination of breast cancer margins commonly suffer from the insufficient accuracy, specificity and/or low speed of analysis, increasing the time and cost of operation as well the risk of cancer recurrence. The purpose of this study is to develop a method for the rapid and accurate determination of breast cancer margins using direct molecular profiling by mass spectrometry (MS). Direct molecular fingerprinting of tiny pieces of breast tissue (approximately 1 &times, 1 &times, 1 mm) is performed using a home-built tissue spray ionization source installed on a Maxis Impact quadrupole time-of-flight mass spectrometer (qTOF MS) (Bruker Daltonics, Hamburg, Germany). Statistical analysis of MS data from 50 samples of both normal and cancer tissue (from 25 patients) was performed using orthogonal projections onto latent structures discriminant analysis (OPLS-DA). Additionally, the results of OPLS classification of new 19 pieces of two tissue samples were compared with the results of histological analysis performed on the same tissues samples. The average time of analysis for one sample was about 5 min. Positive and negative ionization modes are used to provide complementary information and to find out the most informative method for a breast tissue classification. The analysis provides information on 11 lipid classes. OPLS-DA models are created for the classification of normal and cancer tissue based on the various datasets: All mass spectrometric peaks over 300 counts, peaks with a statistically significant difference of intensity determined by the Mann&ndash, Whitney U-test (p &lt, 0.05), peaks identified as lipids, both identified and significantly different peaks. The highest values of Q2 have models built on all MS peaks and on significantly different peaks. While such models are useful for classification itself, they are of less value for building explanatory mechanisms of pathophysiology and providing a pathway analysis. Models based on identified peaks are preferable from this point of view. Results obtained by OPLS-DA classification of the tissue spray MS data of a new sample set (n = 19) revealed 100% sensitivity and specificity when compared to histological analysis, the &ldquo, gold&rdquo, standard for tissue classification. &ldquo, All peaks&rdquo, and &ldquo, significantly different peaks&rdquo, datasets in the positive ion mode were ideal for breast cancer tissue classification. Our results indicate the potential of tissue spray mass spectrometry for rapid, accurate and intraoperative diagnostics of breast cancer tissue as a means to reduce surgical intervention.

Most livestock metabolomic studies involve relatively small, homogenous populations of animals. However, livestock farming systems are non-homogenous, and large and more diverse datasets are required to ensure that biomarkers are robust. The aims of this study were therefore to (1) investigate the feasibility of using a large and diverse dataset for untargeted proton nuclear magnetic resonance (1H NMR) serum metabolomic profiling, and (2) investigate the impact of fixed effects (farm of origin, parity and stage of lactation) on the serum metabolome of early-lactation dairy cows. First, we used multiple linear regression to correct a large spectral dataset (707 cows from 13 farms) for fixed effects prior to multivariate statistical analysis with principal component analysis (PCA). Results showed that farm of origin accounted for up to 57% of overall spectral variation, and nearly 80% of variation for some individual metabolite concentrations. Parity and week of lactation had much smaller effects on both the spectra as a whole and individual metabolites (&lt, 3% and &lt, 20%, respectively). In order to assess the effect of fixed effects on prediction accuracy and biomarker discovery, we used orthogonal partial least squares (OPLS) regression to quantify the relationship between NMR spectra and concentrations of the current gold standard serum biomarker of energy balance, &beta, hydroxybutyrate (BHBA). Models constructed using data from multiple farms provided reasonably robust predictions of serum BHBA concentration (0.05 &le, RMSE &le, 0.18). Fixed effects influenced the results biomarker discovery, however, these impacts could be controlled using the proposed method of linear regression spectral correction.

Objective The aim of the study was to evaluate the association of individual and combined single‐nucleotide polymorphisms in brain‐derived neurotrophic factor (BDNF), dopamine transporter (DAT), and catechol‐O‐methyltransferase (COMT) genes with the occurrence of motor levodopa‐induced complications (MLIC) in Parkinson's disease (PD). Materials and Methods We studied 76 patients with PD (MLIC occurred in 56.6%) and 60 controls. Allelic discrimination of rs6265 BDNF (Val66Met), rs397595 DAT (SLC6A3), and rs4680 COMT (Val158Met) genes were genotyped. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated using multinominal logistic regression. Orthogonal partial least squares (OPLS) analysis and OPLS discriminant analysis (OPLS‐DA) were used to analyze qualitative genetic data. Results The risk of PD in subjects with the AG BDNF genotype was increased sixfold (OR = 6.12, 95% CI = 2.88–13.02, p 1). There were no differences in distributions of BDNF, DAT and COMT genotypes between PD groups with and without MLIC, while OPLS model showed that genotype combination of AG BDNF, AG DAT, and GG COMT was correlated with MLIC and genotypes combination of GG BDNF, AA DAT, and AA COMT with lack of MLIC in PD patients (VIP > 1). Conclusions Our results confirmed the association of rs6265 BDNF (Val66Met) with the risk of PD and suggest a synergic effect of rs6265 BDNF (Val66Met), rs397595 DAT (SLC6A3), and rs4680 COMT (Val158Met) polymorphisms on the occurrence of MLIC., The aim of the study was to evaluate the association of individual and combined single‐nucleotide polymorphisms in brain‐derived neurotrophic factor (BDNF), dopamine transporter (DAT), and catechol‐O‐methyltransferase (COMT) genes with the occurrence of motor levodopa‐induced complications (MLIC) in Parkinson's disease (PD). Seventy‐six patients with PD and 60 controls were included. Allelic discrimination of rs6265 BDNF (Val66Met), rs397595 DAT (SLC6A3), and rs4680 COMT (Val158Met) genes were genotyped. The risk of PD in subjects with the AG BDNF genotype was increased sixfold, and AG BDNF and AG DAT genotypes were correlated with PD in orthogonal partial least squares discriminant analysis (OPLS‐DA). OPLS model showed that genotype combination AG BDNF, AG DAT, and GG COMT was correlated with MLIC and genotypes combination GG BDNF, AA DAT, and AA COMT with lack of MLIC in PD patients.

Panax ginseng is processed to diversify efficacy. Four processed ginsengs containing white ginseng (WG), tae-geuk ginseng (TG), red ginseng (RG), and black ginseng (BG) were analyzed using nuclear magnetic resonance (NMR) spectroscopy for screening overall primary metabolites. There were significant differences in the sugar content among these four processed ginseng products. WG had a high sucrose content, TG had a high maltose content, and BG had high fructose and glucose content. In the multivariate analyses of NMR spectra, the PCA score plot showed significant discrimination between the four processed ginsengs. For effective clustering, orthogonal partial least squares discriminant analyses (OPLS-DA) with a 1:1 comparison were conducted and all OPLS models were validated using the permutation test, the root mean square error of estimation (RMSEE), and the root mean square error of prediction (RMSEP). All OPLS-DA score plots showed clear separations of processed ginseng products, and sugars such as sucrose and fructose mainly contributed to these separations.

The development of high-throughput methods for the estimation of physicochemical and biological properties of drug candidates is highly desired in the pharmaceutical landscape. Affinity to plasma protein is one of the most important biological properties, which should be taken under concern during the design and assessment of future potential medicines. The main goal of this study was to develop a quantitative retention-activity relationship model, with rationalized in vivo and in silico approach to predict the affinity to human serum albumin (HSA), which is one of the most important plasma proteins. To achieve this goal, a set of 27 chemically diverse drugs with known affinity to HSA were analyzed by micellar electrokinetic chromatography (MEKC). The proposed model for HSA affinity assessment was based on retention in hexadecyltrimethylmonium bromide (CTAB) pseudostationary phase and chemically advanced template search (CATS) pharmacophore descriptors. The comparison of various regression methods, namely multiple linear regression (MLR), partial least squares regression (PLS), orthogonal partial least squares (OPLS), and support vector machine (SVM) were performed to develop a model with highest predictability. The obtained models are suitable for the prediction of drug affinity to human serum albumin using retention factor determined by MEKC and CATS descriptors, and only slightly differ in terms of coefficients of determination, Q2 value calculated using leave-one-out cross-validation technique and root-mean-squared error of cross-validation (RMSECV) as well as root-mean-square error in prediction (RMSEP) obtained by external validation.

Our recently developed optimized potentials for liquid simulations-virtual site ionic liquid (OPLS-VSIL) force field has been shown to provide accurate bulk phase properties and local ion-ion interactions for a wide variety of imidazolium-based ionic liquids. The force field features a virtual site that offloads negative charge to inside the plane of the ring with careful attention given to hydrogen bonding interactions. In this study, the Diels-Alder reaction between cyclopentadiene and methyl acrylate was computationally investigated in the ionic liquid 1-butyl-3-methylimidazolium hexafluorophosphate, [BMIM][PF6], as a basis for the validation of the OPLS-VSIL to properly reproduce a reaction medium environment. Mixed ab initio quantum mechanics and molecular mechanics (QM/MM) calculations coupled to free energy perturbation and Monte Carlo sampling (FEP/MC) that utilized M06-2X/6-31G(d) and OPLS-VSIL gave activation free energy barriers of 14.9 and 16.0 kcal/mol for the endo-cis and exo-cis Diels-Alder reaction pathways, respectively (exptl. &Delta, H&Dagger, of 14.6 kcal/mol). The endo selectivity trend was correctly predicted with a calculated 73% endo preference. The rate and selectivity enhancements present in the endo conformation were found to arise from preferential hydrogen bonding with the exposed C4 ring hydrogen on the BMIM cation. Weaker electronic stabilization of the exo transition state was predicted. For comparison, our earlier &plusmn, 0.8 charge-scaled OPLS-2009IL force field also yielded a &Delta, G&Dagger, of 14.9 kcal/mol for the favorable endo reaction pathway but did not adequately capture the highly organized solvent interactions present between the cation and Diels-Alder transition state.

Background: Open partial laryngeal surgery (OPLS) represents a wide array of procedures that can be fitted to treat different types of laryngeal cancer (LC). We would like to present our 30-years’ institutional experience, to analyze survival outcomes and to critically discuss prognostic factors. Methods: We reviewed all cases of OPLS performed at our Institution from 1982 to 2016 for LC. Survival analysis by Kaplan–Meier estimate was performed and prognostic variables by multivariate analysis were identified. Results: Mean follow-up time was 68.3 months, 30-day mortality 0.2%, subsequent functional total laryngectomy (TL) was 1.01%. Over 80% of cases were stage I to II. We had 25 local, 62 regional and eight distant recurrences. Local control was 94.9%, overall survival (OS) was 83.4% and disease-specific survival (DSS) was 87.7%. The two major risk factors significantly associated with the risk of death were cT and cN stage. CONCLUSIONS: We have confirmed that OPLS represents an oncologically sound option in the treatment of LC despite the emergence of non-surgical strategies and new transoral mininvasive techniques. Our results highlight that accurate staging, correct selection of the patient and a strong surgical expertise are of paramount importance in this type of surgery.

Polyol ester (POE) is the main component of many refrigeration lubricants. In this work, the density and the viscosity of a typical, pure polyol ester, pentaerythritol tetrahexanoate (PEC6), were measured over 258.15 K to 373.15 K and predicted with molecular dynamics simulations. Nonequilibrium molecular dynamics (NEMD) was employed to compute the shear viscosities for different shear rates. The Eyring model was used to fit the shear viscosities and to extrapolate to the Newtonian viscosity. A protocol was proposed to perform a NEMD-Eyring fit for a low-error, reproducible and robust Newtonian viscosity calculation. Three force fields, i.e., OPLS, LOPLS, and DREIDING, were tested in terms of their density and viscosity prediction accuracy. OPLS gave the best density prediction (within ± 0.2 %), while LOPLS showed the best viscosity prediction accuracy (within - 4 % to 20 %). This work illustrates the value of molecular simulation in predicting lubricant properties and its potential in guiding the design of POE lubricants for desired properties.

[This corrects the article DOI: 10.3389/fnut.2022.998448.]., (Copyright © 2022 Gao, Hu, Xue, Li, Liu, Han, Tu and Zhao.)

New transferrable all-atom force field parameters for alkynes are presented, to be used as an extension of the OPLS-AA force field. The alkyne-specific intramolecular parameters, as well as the atomic partial charges, are assessed with ab initio calculations. The dispersive interactions are optimized to reproduce the liquid densities and enthalpies of vaporization of a training set of compounds over a wide range of state conditions, through extensive molecular dynamics simulations. The accuracy and transferability of the proposed force field is tested for the same compounds at different temperatures and for compounds outside the training set.

Ethnopharmacological relevance Syzygium polyanthum (Wight) Walp leaves are traditionally used to cure diabetes in many regions of Indonesia. Traditional use involves boiling the leaves until the water is reduced to half volume, and then the decoction is taken 1–2 times daily. Despite several studies reporting the antidiabetic activity of this plant, bioactive compounds have not been well identified. Aim of the study Indonesia is one of the countries with the highest diabetes cases, particularly type 2 diabetes mellitus (T2DM). Few people have access to modern medicinal treatment; thus, the role of antidiabetic traditional medicine has become increasingly important. This research aimed to identify α-glucosidase inhibitors from S. polyathum leaves using a metabolomics approach. When the active compounds of S. polyathum are properly identified, the quality of the herb can be more easily controlled. Materials and methods The dried leaves of S. polyanthum were extracted by a comprehensive extraction method using a solvent combination of n-hexane, acetone, and water in a gradient, resulting in a total of 42 fractions. All fractions were subjected to an in vitro α-glucosidase inhibition test and chemical profile analysis using Nuclear Magnetic Resonance (NMR) and high performance liquid chromatography (HPLC). Orthogonal projection least square (OPLS) analysis was used to correlate the two data to identify NMR signals, and HPLC chromatogram peaks correlated to the activity. 2D NMR and ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) analyses were also used to give more precise compound identification. The activity of the identified active compounds was confirmed by an in silico technique. Results and discussion The results of the α-glucosidase activity test showed that the most active fractions were obtained from solvents with medium polarity: Fractions 9 and 10 (F9 and F10), obtained from gradient acetone-water 4:1 and 3:2, respectively. The IC50 values of F9 and F10 were 24.8 and 31.8 μg/mL, respectively. NMR data showed that F9 had more intense and diverse signals in the aromatic region than F10. OPLS analysis results showed that some typical flavonoid signals abundant in F9 positively correlated with α-glucosidase activity. 2D NMR and UHPLC-HRMS analysis of F9 led to the conclusion that these signals could be attributed to myricetin-3-O-rhamnoside (myricitrin) and epigallocatechin-3-gallate (EGCG). In silico analysis confirmed these results, as myricitrin and EGCG had binding energies resembling acarbose as a positive control (-8.47, -8.19, and -10.13, respectively). Conclusions NMR and HPLC-metabolomics successfully identified myricitrin and EGCG as α-glucosidase inhibitors from S. polyanthum leaves, and docking analysis validated their inhibitory activity. The results of this study justified the traditional use of S. polyanthum as an antidiabetes herbal.

Rice-analogue (RA) made from sorghum (Sorghum bicolor L. Moench) is recently popular in Indonesia due to its potential as an alternative staple food besides rice. Sorghum has many phytochemi-cals with various functional properties including those correlating with anticancer activity. The RA produc-tion involves several steps; i.e. polishing, milling, mixing, extruding, and oven drying. This study used FTIR based metabolomics to identify whether these steps affect sorghum phytochemicals composition and its cytotoxicity against HCT-116 cell lines in-vitro. It was discovered that the cytotoxic of sorghum was rela-tively stable during the process. The RA final product (oven dried RA) showed higher activity (90.85%) as compared to other samples taken from previous steps (2000 ppm dose). The correlation between the FTIR profile and cytotoxic activity of RA was analyzed using one of the multivariate data analysis method namely orthogonal projection to the least square (OPLS). By comparing the OPLS data with FTIR data of compounds reported to be found in sorghum, it is shown that typical FTIR patterns for the phenolic com-pounds particularly ferulic acids, p-coumaric acid, and procyanidins, as well as phytosterols, were highly correlated with the RA’s cytotoxic activity. The signals were found to be dominant in the most active sample (oven dried RA). It can be concluded that sorghum phytochemicals responsible for its cytotoxic activity were not affected by the processing steps, therefore RA is potential to be promoted as an alterna-tive functional staple food in Indonesia.

Knowledge on thermodynamic and transport properties of aqueous solutions of carbohydrates is of great interest for process and product design in the food, pharmaceutical, and biotechnological industries. Molecular simulation is a powerful tool to calculate these properties, but current classical force fields cannot provide accurate estimates for all properties of interest. The poor performance of the force fields is mainly observed for concentrated solutions, where solute-solute interactions are overestimated. In this study, we propose a method to refine force fields, such that solute-solute interactions are more accurately described. The OPLS force field combined with the SPC/Fw water model is used as a basis. We scale the nonbonded interaction parameters of sucrose, a disaccharide. The scaling factors are chosen in such a way that experimental thermodynamic and transport properties of aqueous solutions of sucrose are accurately reproduced. Using a scaling factor of 0.8 for Lennard-Jones energy parameters (ϵ) and a scaling factor of 0.95 for partial atomic charges (q), we find excellent agreement between experiments and computed liquid densities, thermodynamic factors, shear viscosities, self-diffusion coefficients, and Fick (mutual) diffusion coefficients. The transferability of these optimum scaling factors to other carbohydrates is verified by computing thermodynamic and transport properties of aqueous solutions of d-glucose, a monosaccharide. The good agreement between computed properties and experiments suggests that the scaled interaction parameters are transferable to other carbohydrates, especially for concentrated solutions.

In view of the widespread glyphosate herbicide (N-(phosphonomethyl) glycine) usage, the physical and chemical properties in aqueous solution have to be exploited to understand its role in environment. In this sense, molecular dynamics (MD) simulations can provide an atomistic detail in the description of such a system. Herein, partial atomic charges and dihedral angles were obtained quantum mechanically for glyphosate molecule. Parameters for MD simulation were implemented in the OPLS-AA force field to better understand the herbicide mechanism action. Results showed that atomic charges were consistent with the database of the force field. Additionally, potential energy curves for the dihedrals were consistent and could be used to run MD simulations. Therefore, the parameterisation reported for this molecule can be useful to explain studies involving its interaction with many enzymes and proteins such as 5-enolpyruvylshikimate 3-phosphate synthase enzyme (EPSP). Furthermore, considering these new data in OPLS-AA, numerous simulations can be proposed to unveil the effects of the glyphosate as an environment contaminant.

An atmospheric pressure laserspray ionization mass spectrometry (AP-LSI mini MS) has been developed and employed in the fast analysis of bacteria. Without using surfactants or any extracting methods, 21 foodborne bacteria from 12 genera were directly analyzed. Typical fingerprints of small molecules and lipids were detected and recognized in the mass spectra with high reproducibility. Furthermore, a supervised multivariate statistics method, orthogonal partial least squares (OPLS), was applied, and these bacteria could be differentiated at both genus and species levels. With improved performance in the future, AP-LSI mini MS could be a simple, effective, and fast approach for direct bacteria analysis on the field.

Background and objectives Postoperative morbidity after open partial laryngeal surgery (OPLS) may be serious, leading to a prolonged length of hospital stay and increasing costs. We sought to define the predictive factors of complications and to develop nomograms for patients eligible for OPLS based on clinical and surgical data. Methods We critically reviewed 535 patients with laryngeal carcinoma who underwent OPLS at our Institution from 1982 to 2007. We have identified patients affected by postoperative local, airway, dysphagia, bleeding, surgical site infection, dehiscence of pexy, emphysema, and laryngocutaneous fistula complications. We have analyzed them according to age, smoking, alcohol, tumor site, clinical T and N classification, type of OPLS and neck dissection, previous treatments. Prognostic factors were considered in a multivariate logistic regression model with backward stepwise elimination and selected to construct and design nomograms for overall and specific complications. The performance was assessed using the c-index, receiver operating characteristic, and calibration curves. Results Age, clinical T classification, type of OPLS, and alcohol were related to overall (35%) and airway complications. Nomograms were built for overall, dysphagia, and airway complications. Conclusions We have developed nomograms that can identify high-risk patients undergoing OPLS and that can help to prevent severe complications and to tailor surgical planning.

Although the protein backbone is the most fundamental part of the structure, the fine-tuning of side-chain conformations is important for protein function, for example, in protein-protein and protein-ligand interactions, and also in enzyme catalysis. While several benchmarks testing the performance of protein force fields for side chain properties have already been published, they often considered only a few force fields and were not tested against the same experimental observables; hence, they are not directly comparable. In this work, we explore the ability of twelve force fields, which are different flavors of AMBER, CHARMM, OPLS, or GROMOS, to reproduce average rotamer angles and rotamer populations obtained from extensive NMR studies of the 3 J and residual dipolar coupling constants for two small proteins: ubiquitin and GB3. Based on a total of 196 μs sampling time, our results reveal that all force fields identify the correct side chain angles, while the AMBER and CHARMM force fields clearly outperform the OPLS and GROMOS force fields in estimating rotamer populations. The three best force fields for representing the protein side chain dynamics are AMBER 14SB, AMBER 99SB*-ILDN, and CHARMM36. Furthermore, we observe that the side chain ensembles of buried amino acid residues are generally more accurately represented than those of the surface exposed residues.

Chromatographic systems coupled with mass spectrometry detection are widely used in biological studies investigating how levels of biomolecules respond to different internal and external stimuli. Such changes are normally expected to be of low magnitude and therefore all experimental factors that can influence the analysis need to be understood and minimized. Run order effect is commonly observed and constitutes a major challenge in chromatography-mass spectrometry based profiling studies that needs to be addressed before the biological evaluation of measured data is made. So far there is no established consensus, metric or method that quickly estimates the size of this effect. In this paper we demonstrate how orthogonal projections to latent structures (OPLS®) can be used for objective quantification of the run order effect in profiling studies. The quantification metric is expressed as the amount of variation in the experimental data that is correlated to the run order. One of the primary advantages with this approach is that it provides a fast way of quantifying run-order effect for all detected features, not only internal standards. Results obtained from quantification of run order effect as provided by the OPLS can be used in the evaluation of data normalization, support the optimization of analytical protocols and identification of compounds highly influenced by instrumental drift. The application of OPLS for quantification of run order is demonstrated on experimental data from plasma profiling performed on three analytical platforms: GCMS metabolomics, LCMS metabolomics and LCMS lipidomics.

A new series of 2-alkylthio-N-(quinazolin-2-yl)benzenesulfonamide derivatives have been synthesized and evaluated in vitro for their antiproliferative activity by MTT assay against cancer cell lines HCT-116, MCF-7, and HeLa as well as the NCI-60 human tumor cell lines screen. In NCI screen, three compounds inhibited approximately 50% growth of RPMI-8226 and A549/ATCC cell lines. The mean of IC50 calculated in MTT assays for three tested cell lines was about 45 μM for four compounds. The QSAR allowed finding statistically significant OPLS models for HeLa cell line. Metabolic stability in vitro studies indicated favorable and unfavorable structural elements. The good metabolic stability, with t1/2 higher than 40 min, was observed for three derivatives, which together with their antiproliferative activity and good ADMET profile, makes them good leading structures for further research.

One-layer particle level set (OPLS) has been developed by using Lagrangian particles that are employed to correct both advection and re-initialization procedures of the level set function. In which, a level set function is utilized to smooth physical properties of the interface, while one-layer Lagrangian particles are used to track the interface directly. This method exhibits excellent mass conservation properties compared to the LS method. As a special aspect, the OPLS method enables management of merging and stretching of interface in an effective way. This capability is similar to the particle level set (PLS) method. However, the new approach of the OPLS method offers a more straightforward technique. This approach is validated with classical benchmark test cases, such as the long term advection of a circle, rotation of a slotted disk, single vertex in a box, merging and separating of circle. The results from the proposed method show good agreement with the numerical experiments published results and the OPLS method is verified to be highly reliable and accurate.

Acetylcholinesterase (AChE) is considered as one of the most favored drug targets for Alzheimer’s disease. The effects of different force fields (FFs) on ranking affinity of acetylcholinesterase inhibitors were obtained using the fast pulling of ligand (FPL) method in steered-molecular dynamics (SMD) simulations. GROMOS, AMBER, CHARMM, and OPLS-AA FFs were investigated in this work. The pulling work derived with GROMOS FF has the strongest correlation and smallest error compared with experimental binding affinity. Moreover, the CPU consumption in the calculations using GROMOS FF is the lowest, which could allow us to rank affinity of a large number of AChE ligands.

Molecular dynamics simulations for liquid acetone, the pure component and mixed with water, are performed to analyze the effect that the dielectric constant has on the solubility. The CGenFF and OPLS/AA force fields, together with two TraPPE_UA models, are used to simulate acetone, while the TIP4P/ϵ model is applied for water. The calculated dielectric constant of acetone models at room conditions follows a linear relation with their molecular dipole moment, allowing us to understand, in simulations, the miscibility problem of acetone in water. The experimental liquid and liquid–vapor properties as the pure component are reasonably well reproduced with those force fields. The TraPPE_UA04 and TraPPE_UA05 models predict dielectric constants for acetone in the liquid phase of 9 and 12, respectively. The experimental value is 21. The values obtained with the OPLS/AA and CGenFF models are 15 and 23, respectively. All of the models used, except CGenFF, give inhomogeneous mixtures at both low and moderate concen...

The effects of the configuration and temperature on the Young’s modulus of poly (methyl methacrylate) (PMMA) have been studied using molecular dynamics simulations. For the DREIDING force field und...

The binding free-energy profile of methanol to Candida antarctica lipase B (CALB) was calculated at infinite dilution and at a finite methanol concentration of 6.1 M using umbrella sampling molecular dynamics simulations with the OPLS all-atom force field. An additional validation of the results was performed by employing alchemical double decoupling simulations. The binding free-energy profiles have been used in a related research article to validate free-energy profiles obtained from direct counting simulations with the aim to use the kinetic information encoded in the latter. The data provided in this work will be useful to study concentration effects on binding, to test alternative free energy methods or to use the proposed simulation protocol for related systems.

Untargeted LC–MS flavoromic analysis was utilized to identify chemical compounds that impact consumer liking of whole wheat bread. Chemical fingerprints of thirteen whole wheat breads were modeled against consumer flavor liking scores by orthogonal partial least squares (OPLS) with good fit (R2Y = 0.98) and predictive ability (Q2 = 0.95). The four most predictive features (negatively correlated) were identified as 9S,12S,13S-trihydroxy-octadec-10E-enoic acid (pinellic acid), 9S,12S,13S-trihydroxy-octadeca-10E,15Z-dienoic acid, 8R*,9R*,10S*-trihydroxy-octadec-6Z-enoic acid, and 1-(octadeca-9Z,12Z-dienoyl)-sn-glycero-3-phosphocholine. Sensory validation studies including bitter threshold determination and recombination tests confirmed the contribution of these compounds to the perceived bitterness intensity of the bread samples and the overall negative impact on flavor liking. Lipoxygenase activity of the flour was reported to have a significant impact on the formation of the three bitter compounds (trihydroxy fatty acids) in the bread samples.