Multiple Linear Regression Procedure Research Articles

SUSTAINABILITY-ORIENTED INNOVATION AND CSR: A QUANTITATIVE RESEARCH IN BRAZILIAN MULTINATIONALS

Objective: The purpose of this study is to analyze the relationship between Sustainability-Oriented Innovation (SOI) and Corporate Social Responsibility (CSR) in Brazilian multinationals. Theoretical Framework: The theoretical framework provides relevant information on CSR and SOI, which forms the basis for the development of hypotheses. Method: To collect data, an e-survey was conducted with Brazilian multinationals operating in various industries and segments, resulting in 60 valid questionnaires. The data was analyzed through multivariate analysis, employing canonical correlation and multiple linear regression procedures. Results and Discussion: The results of the multivariate analysis empirically supported five hypotheses (h1, h2, h3, h4, and h5). However, the results did not support the hypothesis that predicted a positive correlation between SOI practices and CSR as a differentiation strategy. Research Implications: The practical and theoretical implications of this research are significant, particularly in shedding light on emerging market multinationals, with a specific focus on Brazilian companies. By specifically examining Brazilian multinationals, this research fills a crucial gap in the literature, offering insights into the unique challenges and opportunities faced by firms from an emerging market like Brazil. Originality/Value: The findings of this study provide valuable guidance for Brazilian multinational corporations seeking to enhance their CSR initiatives and SOI practices. By understanding the relationships between CSR, SOI, and other key variables, these companies can develop more effective strategies to navigate global markets while simultaneously addressing social and environmental concerns.

The Effects of Prenatal Diet on Calf Performance and Perspectives for Fetal Programming Studies: A Meta-Analytical Investigation

Simple SummaryPrenatal nutrition can reshape an animal’s developmental trajectory, with persistent long-term consequences for the progeny. In prenatal life, the effects induced by the intrauterine conditions can be expressed as changes in offspring growth and meat quality. Thus, there are diverse sources of variations that contribute to the variations reported for fetal-programming responses in beef cattle, making it difficult to interpret and apply the results. In this sense, finding a common factor among the studies with which to group them may offer an opportunity to quantify the fetal-programming effects holistically and to obtain more applicable responses. With the increasing number of publications, it is important to summarize the quantitative measurements available in the scientific literature. In the present study, data from 35 publications were used. We verified that there is a gap related to the effects of maternal nutrition to females, at the beginning of gestation, and in zebu and crossbred animals, indicating new perspectives for future fetal-programming studies. In summary, our findings highlight an association between prenatal energy and protein supply and its effects on the offspring birth weight, weaning weight, and the daily weight gain of pregnant beef cows during pregnancy.This meta-analysis aimed to identify knowledge gaps in the scientific literature on future fetal-programming studies and to investigate the factors that determine the performance of beef cows and their offspring. A dataset composed of 35 publications was used. The prenatal diet, body weight (BW), average daily gain (ADG) during pregnancy, and calf sex were elicited as possible modulators of the beef cows and their offspring performance. Then, the correlations between these variables and the outcomes of interest were investigated. A mixed multiple linear regression procedure was used to evaluate the relationships between the responses and all the possible explanatory variables. A knowledge gap was observed in studies focused on zebu animals, with respect to the offspring sex and the consequences of prenatal nutrition in early pregnancy. The absence of studies considering the possible effects promoted by the interactions between the different stressors’ sources during pregnancy was also detected. A regression analysis showed that prenatal diets with higher levels of protein improved the ADG of pregnant beef cows and that heavier cows give birth to heavier calves. Variations in the BW at weaning were related to the BW at birth and calf sex. Therefore, this research reinforces the importance of monitoring the prenatal nutrition of beef cows.

Quality-by-design approach for development of aqueous headspace microextraction GC-MS method for targeted metabolomics of small aldehydes in plasma of cardiovascular patients

Lipid peroxidation products, such as short chain aldehydes, are powerful biomarkers of oxidative stress, due to the advantage of long lifetime compared to other metabolites of the lipidome. This work proposes an advanced combined derivatization/solvent-less extraction procedure from plasma followed by rapid Gas Chromatography with Mass Spectrometric detection (GC-MS). A new sample pretreatment protocol is presented which is based on a combination of aldehyde derivatization with methoxyamine under fully aqueous-based conditions of diluted plasma samples followed by headspace solid-phase microextraction (HS-SPME) which is faster compared to methods in the literature serving the same purpose. Being the smallest oximation reagent, methoxyamine derivatization does not require a silylation step of hydroxyl groups as customary and made it possible to have the shortest run times for this series of aldehydes by GC-MS. A Response Surface Methodology (RSM) is employed to optimize the HS-SPME of the aldehyde methoximes to provide insights into the Design Space (DS) of HS-SPME of aldehydes of variable chain lengths and unsaturation. The workflow includes a Quality by Design (QbD) approach for optimization of sample microextraction and derivatization methodology under fully aqueous conditions, in contrast to all reported non-aqueous tedious and long extraction methods in the literature followed by development of a rapid GC-MS assay. The optimal sample preparation obtained from the RSM, and multiple linear regression procedure involved addition of 15 mg methoxyamine (CH3ONH2) and 160 mg Na2SO4 to 0.5 mL plasma diluted to 1 mL with water in an extraction vial followed by HS-SPME using Polydimethylsiloxane/Divinylbenzene fiber at 750 rpm and 77 °C for 15 min. The developed HS-SPME-GC-MS method was validated according to FDA guidelines in SIM mode and applied for targeted determination of lipid peroxidation aldehyde metabolites in plasma of 24 cardiovascular patients vs 20 healthy controls. The run time of the GCMS method was less than 15 min and the LOQ of the 10 targeted aldehydes were 0.5 nM for decanal, 5 nM for hexanal, heptanal, octanal, citronellal and citral, 7 nM for malondialdehyde, 35 nM for 4- hydroxynonenal, 105 nM for 4- hydroxyhexenal and 500 nM for glyoxal. Hexanal, Malondialdehyde and Hydroxynonenal concentrations were significantly higher in patients (p-value<0.05) in the targeted study, while citral was significantly lower as obtained from the untargeted study. Reporting an aldehydic profile signature -whether predictive or diagnostic-for cardiovascular patients would support proper medical intervention at the initiation or progression phases of the disease when expanded on larger number of subjects.

Prediction Equations for Spirometry in Adults from Northern India

Most of the Indian studies on prediction equations for spirometry in adults are several decades old and may have lost their utility as these were carried out with equipment and standardisation protocols that have since changed. Their validity is further questionable as the lung health of the population is likely to have changed over time. To develop prediction equations for spirometry in adults of north Indian origin using the 2005 American Thoracic Society/European Respiratory Society (ATS/ERS) recommendations on standardisation. Normal healthy non-smoker subjects, both males and females, aged 18 years and above underwent spirometry using a non-heated Fleisch Pneumotach spirometer calibrated daily. The dataset was randomly divided into training (70%) and test (30%) sets and the former was used to develop the equations. These were validated on the test data set. Prediction equations were developed separately for males and females for forced vital capacity (FVC), forced expiratory volume in first second (FEV1), FEV1/FVC ratio, and instantaneous expiratory flow rates using multiple linear regression procedure with different transformations of dependent and/or independent variables to achieve the best-fitting models for the data. The equations were compared with the previous ones developed in the same population in the 1960s. In all, 685 (489 males, 196 females) subjects performed spirometry that was technically acceptable and repeatable. All the spirometry parameters were significantly higher among males except the FEV1/FVC ratio that was significantly higher in females. Overall, age had a negative relationship with the spirometry parameters while height was positively correlated with each, except for the FEV1/FVC ratio that was related only to age. Weight was included in the models for FVC, forced expiratory flow (FEF75) and FEV1/FVC ratio in males, but its contribution was very small. Standard errors of estimate were provided to enable calculation of the lower limits of normal and standardised residuals for these parameters. The equations were found to be valid on the test dataset, and therefore, may be extended to general population. Comparison with the 1960s equations revealed lack of good agreement, and substantially higher predicted FVC with the current equations, especially in the forty-years-plus age group, in both males and females. Even in the age group upto 40 years, the level of agreement was clinically not acceptable. Validated prediction equations have been developed for spirometry variables in adults of north Indian origin using the current ATS/ERS spirometry standardisation recommendations. The equations suggest an improvement in the lung health of the population over time in the middle-aged and the elderly. These equations should address a long-felt unmet need and enable a more appropriate evaluation of spirometry data in different chest diseases in Indian subjects.

Regression Equations for Spirometry in Children Aged 6 to 17 Years in Delhi Region

Most of the studies carried out in India to develop regression equations for spirometry in children are now several years-to-decades old and had used equipment and measurement protocols that have since changed. Prediction equations using the current standardisation protocols for spirometry are not available. The lung health of the population may have changed too. To develop regression equations for spirometry for children aged 6 to 17 years of north Indian origin in Delhi region. School children of north Indian origin, as determined by mother tongue and parentage, aged 6 to 17 years were screened by a health questionnaire and physical examination and those found "normal" underwent spirometry according to the standardised procedure recommended by the American Thoracic Society/European Respiratory Society (ATS/ERS) task force in 2005. Pearson's correlation analysis was carried out to identify the predictor variables for spirometric parameters. Prediction equations were developed using the multiple linear regression procedure. The independent variables were entered in sequence of height, age and weight. R2, adjusted R2 and R2 change, standard errors of the estimate (SEE), and estimates of regression coefficients were obtained and the goodness of fit was examined. Data was obtained in 365 boys and 305 girls. Forced vital capacity (FVC), forced expiratory volume in one second (FEV1) peak expiratory flow rate (PEFR), forced expiratory flow rate at 50% and 75% exhalation of vial capacity (F50 and F75) and mean forced expiratory flow rate over the middle 50% of the vital capacity (F25-75) showed moderate to strong correlations with age, height and weight in both boys and girls. In both genders, the equations explained very high variability of FVC, FEV1 and PEFR as shown by the R2 values. The explained variability for flow rates was lesser, with that for F75 being the least. Regression equations for spirometry variables for children of north Indian origin in Delhi region have been developed. These represent the first such effort from India after the publication of the ATS/ERS task force 2005 guidelines on standardisation of spirometry.

Quantitative Structure-activity Relationship Studies and Nonlinear Optical Properties of 2-Phenylbenzofuran Derivatives: A Density Functional Theory Study

Density Functional Theory calculations, in the ground state of 2-Phenylbenzofuran, were carried out using the GGA-PBE, PBV86 and meta-GGA-TPSS hybrid functionals with 6-31G (d, p) as a basis set. First, theoretical calculations were performed using these functionals to obtain the stable conformer of the molecule. In addition, Mulliken population natural population and natural bond orbital analyses were calculated. The molecular electrostatic potential, band gap energies, global, local chemical reactivity descriptors and non-linear optical (NLO) properties were studied. Additionally, the NLO properties of 2-Phenylbenzofuran and those of its derivatives were investigated by GGA-PBE/6-31G (d,p) level of theory. The first-order hyperpolarizability value of all 2-Phenylbenzofuran derivatives was found within the range extending from 4.00 × 10-30 to 43.57 × 10-30 (esu). It indicated that they possess remarkable NLO properties. In addition, a multiple linear regression procedure was used to envisage the relationships between molecular descriptors and the activity of 2-Phenylbenzofuran derivatives; the quantitative structure-activity relationship (QSAR) studies were performed on them using quantum descriptors. The QSAR was applied to determine a correlation between the various physico-chemical parameters of the studied compounds and their biological activities. The statistical quality of the QSAR models was assessed using statistical parameters, i.e. R2, R2adj and R2cv.

Relationships between body weight and some linear morphometric traits in Nigerian Nsukka ecotype local hens

&#x0D; &#x0D; &#x0D; Body weight (BW) and linear body measurements of 120 Nigerian Nsukka ecotype local hens in terms of body length (BL), chest girth (CG), thigh circumference (TC), thigh length (TL), and shank length (SL) were taken and subjected to Pearson correlation analysis to obtain the relationship between these growth traits. Multiple Linear Stepwise Regression procedure was used to find the best linear combination of the linear body measurements that can best predict BW. The findings showed that linear body measurement traits of Nigerian heavy ecotype local hens were strongly (p &lt; 0.01) and positively correlated (0.5 &lt; r) except thigh length which recorded a moderate value (0.3 &lt; r &gt; 0.5). The regression equations of linear body measurement traits with BW were positive with coefficient of determination ranging from 60.5-74.6%. The stepwise analyses showed shank length as best single measurement with R2 = 0.605, while inclusion of our five predictors in an equation resulted in R2 = 0.746. Thus, the following equation was generated: BW = – 1485.70 + 82.06 SL + 58.34 BL + 38.70 TC + 8.39 CG + 3.85 TL. This showed that they accounted for 74.6% of the variations in the BW of Nigerian heavy ecotype local hens. The prediction equations generated from linear body measurement traits are positive and strong, therefore can accurately be employed to predict the relative BW of Nigerian heavy ecotype local hens during selection programs as well as in the market with the use of measuring tape.&#x0D; &#x0D; &#x0D;

Perceived family cohesion, social support, and quality of life in patients undergoing treatment for substance use disorders compared with patients with mental and physical disorders

PurposeSupport from family and other social network elements can be important in helping patients to cope with practical and emotional consequences of diseases. The aim of the study was to examine perception of family and social support and quality of life (QoL) in patients undergoing treatment for substance use disorders (SUDs). We compared them with patients in treatment for mental disorders (MDs) and physical disorders (PDs).MethodsWe used data from a national multicenter study that recruited patients (N = 518) from three treatment domains; SUD treatment units, MD treatment units, and PD treatment units (severe neurological conditions or cancer). Data on family cohesion, social support, and QoL were compared across patient groups. In addition, data on health variables was collected. We used a multiple linear regression procedure to examine how health and support variables were associated with QoL.ResultsFamily cohesion and social support in the SUD and MD groups were rated at similarly low levels, substantially lower than in the PD group. The SUD group exhibited a somewhat lower QoL than did the PD group, but their QoL was still in the near-to-normal range. In contrast, the MD group had markedly low QoL. When examining factors associated with QoL, we found that greater family cohesion and social support were positively associated with QoL. Mental distress was the strongest factor, and was negatively associated with QoL (beta − 0.15, 95% CI = − 0.17/− 0.14, p < 0.001).ConclusionService providers need to be aware of the weaker networks and less regulatory family and/or social support available to patients with SUDs. Providers should focus consistently on the social networks of patients and include patients’ families in treatment processes.

Study the Relationship between the University Student and Teacher using the Principal Component Analysis and Genetic Algorithms

Multivariate data analysis is one of the popular techniques, and among them is the Principal Component Analysis, or PCA, is a dimensionality-reduction method which is the process of converting a large number of related variables to a smaller number of unrelated factors, that still contains most of the information in the large set. Therefore, any phenomenon that consist of a large group of variables that are difficult to treat with in their initial form. The process of the interpreting these variables become complex process, so reducing these variables to a smaller is easier to deal with which is the aspiration of every researcher working in the field of principal component analysis. In this research, a multivariate data collection process was carried out which are relates to the nature of education and the relationship between the university student and the teacher, then studying and analyzing by Principal component analysis model, which is a technique used to summarize and condense data through the use of bonding software SPSS,2020. Thus, it will be illustrious that this research will fall into a concept Data Mining, and is also abbreviated, and then it is realized using genetic algorithms procedure, in latest version MATLAB 2019B, Application of Genetic Algorithms using simulation software with latest release MATLAB 2019, using the Multiple linear regression equation method. Multiple linear regression procedure to find the arrangement of independent variables within each factor of the factors obtained, by calculating the weight of the independent variable (Beta). Overall results were obtained for the eigenvalues of the stored correlation matrix, and the study required a Statistical analysis (PCA) method, and by reducing the number of the variables without losing much information about the original variables. The goal is to simplify their understanding. The disclosure of its structure and interpretation, in addition to reaching a set of conclusions that were discussed in detail, In addition to important recommendation.

COMPUTATIONAL SCREENING AND QSAR STUDY ON A SERIES THEOPHYLLINE DERIVATIVES AS ALDH1A1 INHIBITORS

In the present study, we explored a series of molecules with anticancer activity, so that qualitative and quantitative studies of the structure-activity relationship (SAR/QSAR) were performed on seventeen theophylline derivatives. These are inhibitors of ALDH1A1. The present study shows the importance of quantum chemical descriptors, constitutional descriptors and hydrophobicity to develop a better QSAR model, whose studied descriptors are LogP, MW, Pol, MR, S, V, HE, DM, EHOMO and ELUMO. A multiple linear regression (MLR) and artificial neural networks (ANN) procedure was used to design the relationships between molecular descriptors and the inhibition of ALDH1A1 by theophylline derivatives. The validation and good quality of the QSAR model are confirmed by a strong correlation between experimental and predicted activity.

Динамика ключевых точек лица как индикатор достоверности сообщаемой информации

This research describes a method for studying the authenticity/unauthenticity of the information re- ported by people in video images. It is based on automatic tracking of coordinates of key points of a speaker’s face using OpenFace software. When processing the data, the multiple linear regression procedure is used. It was found that the dynamics of neighboring key points in the obtained models has a multidirectional char- acter, indicating the presence of a superposition of several dynamic structures, corresponding to the characteristic complex changes in the face position and facial expressions of the sitter. Their isolation is realized by means of the principal component analysis. It is shown, that the first 11 principal components describe 99.7% of the variability of the initial data. The correlation analysis between the number of credibility/confidence statements on the set of time intervals and the principal component loadings, allows to differentiate the dynamic structures of the face, connected with the assessments of credibility of the reported information. Automated analysis of face dynamics optimizes the process of collecting empirical data on the sitter’s appearance and their semantic structuring, as well as expands the range of predictors of the assessments of the truthfulness of the messages received.

Efficiency assessment of e-learning compared to traditional education using Principal Component Analysis and verification of Genetic Algorithms

Multivariate data analysis is one of the common techniques that are used in the analysis of the main compounds that perform the process of converting a large number of related variables into a smaller number of unrelated compounds, In the case of the emergence of anomalous values, which can be detected in many ways, the adoption of the matrix of contrast and common contrast will lead to misleading results in the analysis of the principal compounds. Therefore, many of the phenomena that consist of a large group of variables that are difficult to deal with initially, and the process of interpreting these variables becomes a complex process, so reducing these variables to a lower setting is easier to deal with, and it is the aspiration of every researcher working in the field of main compounds analysis or factor analysis. Because of technological development and the ability to communicate by audio and video interaction at the same time, on this research, a multivariate data collection process was conducted, where an evaluation of the efficiency of e-learning was studied and analyzed by highlighting the process of analyzing real data using factor analysis by the Principal Component Analysis method. This is one of the techniques used to summarize and shorten the data and through the use of the SPSS: Statistical Packages for Social Sciences Program, Thus, it will be noted that the subject of the paper will flow into the concept of Data mining also, And then achieve it using genetic algorithms using the simulation program with its final version, which is MATLAB, also using the method of Multiple Linear Regression Procedure to find the arrangement of independent variables by calculating the weight of the independent variable. Total results were obtained for the eigenvalues of the stored correlation matrix or the rotating factor matrix, The study required conducting statistical analysis in the mentioned way and by reducing the number of variables without losing much information about the original variables and its aim is to simplify its understanding and reveal its structure and interpretation, The study required conducting statistical analysis in the mentioned way and by reducing the number of variables without losing much information about the original variables and its aim is to simplify its understanding and reveal its structure and interpretation. In addition to reaching a set of conclusions that were discussed in detail also the addition to the important recommendations.

Conformational analysis and QSAR modeling of 14-membered macrolide analogues against mycobacterium tuberculosis

Electronic structures, effect of the substitution and structure physical-chemistry properties relationship for macrolide derivatives, have been studied by PM3 and ab initio methods. In the present work, the calculated values, namely net charges, bond lengths, MESP, dipole moments, electron-affinities, heats of formation, then, we treated the structural, physical and chemical relationships for a series of macrolide derivatives with inhibition activity against Mycobacterium tuberculosis. QSAR studies were done for these macrolide derivatives using a combination of various physicochemical descriptors. A multiple linear regression procedure was used to design the relationships between molecular descriptor and the activity of macrolide derivatives. Results validate the derived QSAR model.

The Factors Predicting Pre-Service Teachers’ Achievement in Teacher Training Classrooms

Purpose: This study aimed to predict the achievement of pre-service teachers majoring in education through some learner characteristics like gender, self-directed learning readiness dimensions, and course context (being in the flipped learning class and traditional teaching classes). Research Methods: This study employed a relational survey research design and involved 271 pre-service teachers who were selected according to the purposive sampling method. Data were collected by implementing an achievement test and Self-directed Learning Readiness Scale and analyzed through the Hierarchical Multiple Linear Regression procedure. Findings: The findings of the study showed that in the first model, gender and desire for learning predicted the achievement of pre-service teachers. The second model revealed that being in a flipped learning class predicted the academic achievement of pre-service teachers than being in any other classes taught according to traditional teaching principles. Implications for Research and Practice: It is suggested that pre-service teachers are provided with opportunities to apply what they have learned theoretically. Also, it may be suggested that instructors include various computer applications, software, games, and videos from YouTube to increase the pre-service teachers’ desire to learn. Lastly, for further studies, it may be suggested to include different variables in predicting the achievement of pre-service teachers.

DRUG LIKNESS FILTERS AND QSAR ANALYSIS OF CAMPHOR-BASED DIIMINES DERIVATIVES AS ANTIVIRAL AGENTS

In the present study, Quantitative structure–activity relationship (QSAR) study has been applied on twenty-five molecules of camphor-based symmetric diimines. A Multiple Linear Regression (MLR) procedure was used to correlate the relationships between molecular descriptors and the biological activity of camphor-based symmetric diimine derivatives. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based on the following descriptors: MW, HE, Pol, MR, MV, HBA, NRB, PSA, µ and Etotal, for the influenza virus reproduction inhibition to confirm the predictive power of the models. High correlation between experimental and predicted activities was observed, indicating good quality of the QSAR model.

Pyrazolo[1,5-a][1,3,5]triazin-2-thioxo-4-ones derivatives as thymidine phosphorylase inhibitors: Structure, drug-like calculations and quantitative structure-activity relationships (QSAR) modeling

After benchmarks on pyrazolo[1,5-a][1,3,5]triazin-2-thioxo-4-ones, we carried out B3LYP density functional theory computations on the structure and vibrational spectroscopy of a series of twenty-one of its derivatives exhibiting various extent of inhibitory activity against thymidine phosphorylase (TP). Then, we performed drug-like calculations, quantitative structure-activity relationship (QSAR) modeling and an evaluation of the physicochemical properties of this series. In order to design the relationships between molecular descriptors and TP inhibition by pyrazolo[1,5-a][1,3,5]triazin-2-thioxo-4-ones derivatives, we used a multiple linear regression (MLR) procedure. A QSAR model is predicted. A further external set of molecules was used for validation where a high correlation between experimental and predicted activity values is noticed.

DFT-based reactivity and combined QSAR, molecular docking of 1,2,4,5-Tetrazine derivatives as inhibitors of Pim-1 kinase

In the present work we have calculated several DFT reactivity descriptors for 1,2,4,5-Tetrazine at the B3LYP/6–311++G(d,p) level of theory in order to analyze its reactivity in vacuum and solvent phases. Whereas, the influence of the solvent was taken into account employing the PCM model. DFT-based descriptors such as (electronic chemical potential, electrophilicity, condensed Fukui function….) have been determined to predict the reactivity of 1,2,4,5-Tetrazine. A series of eighteen 1,2,4,5-Tetrazine derivatives was studied by using two computational techniques, namely, quantitative structure activity relationship (QSAR) and molecular docking. QSAR models of the antitumor activity of some 1,2,4,5-Tetrazine derivatives were established in gas and solvent phases which exhibited good statistical values for both cases. Whereas, multiple linear regression (MLR) procedure was used to obtain the best QSAR models and the leave-one-out (LOO) method to estimate the predictivity of our models. The most and the least active compounds were docked with the protein (3C4E) to confirm those obtained results from QSAR models and elucidate the binding mode between this type of compounds and corresponding protein.

Similarity Metrics-Based Uncertainty Analysis of River Water Quality Models

Uncertainty analysis (UA) is essential to reinforce the decisions made by water resource engineers and managers. In this study, the stepwise multiple linear regression procedure assessed the relationship between water quality parameters and physical characteristics of 48 catchments in the southwestern basin of the Caspian Sea, Iran. The results of the modeling showed that the coefficient of determination ranged between 0.47 and 0.68 and indicated a positive relationship between the area (%) of agricultural lands and the sodium adsorption ratio (SAR), potassium (K) and total dissolved solids (TDS). A negative relationship was also found between bicarbonate (HCO3−) and the area (%) of the intermediate-density forest. In contrast to previous studies focusing on analyzing the uncertainty of the model parameters, we addressed the uncertainty of the model variables. The results of the GLUE-based uncertainty analysis (UA) performed on the model’s variables indicated that the measures of the R-factor for all models were between 0.13 and 0.98. The lowest R-factor was obtained for the HCO3− model (0.13) suggesting it performed well when predicting HCO3−. To increase the degree of objectivity in the GLUE-UA method, a set of similarity metrics, including Czekanowski, Motyka, Ruzicka, Cosine, Kumar-Hassebrook, Jaccard and Dice was applied to determine the degree of proximity and or similarity between the probability density functions of the measured and simulated water quality parameters. The measures of the similarity metrics for the HCO3− model were generally close to 1, indicating good performance and low uncertainty, while it showed higher uncertainty (between 0.2487 and 0.897) for the other three models (SAR, K, and TDS).

QSAR Studies of amino-pyrimidine derivatives as Mycobacterium tuberculosis Protein Kinase B inhibitors

Quantitative structure activity relationship (QSAR) analysis was applied to a series of amino-pyrimidine derivatives as PknB inhibitors using a combination of various physicochemical and quantum descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the chemotherapeutic activity of the amino-pyrimidine derivatives. Good agreement between experimental and predicted activity values, obtained in the validation procedure, indicated the good quality of the derived QSAR model. The statistically significant best QSAR model has a cross validated correlation coefficient R2CV= 0.973 and external predictive ability of prediction R2 = 0.778 was developed by MLR. The proposed model has good robustness and predictability when verified by internal and external validation.

In situ Determination of Nitrate and Hydrogen Sulfide in the Baltic Sea Using an Ultraviolet Spectrophotometer

Evaluating the health status of marine ecosystems becomes ever increasingly important especially against the backdrop of rising pressures from human activities. This is true especially for coastal seas such as the Baltic Sea that is surrounded by highly industrialized countries. Nutrients and pollutants such as nitrate and hydrogen sulfide, which have a major impact on ecosystem functioning, are two of several key environmental indicators for assessing the status of natural waters, and therefore of considerable interest. The frequency and the spatial coverage of the nitrate and hydrogen sulfide measurements are currently limited by the cost of the laboratory analysis and personnel. Optical in situ sensors can help to overcome this challenge by allowing reagentless and fast detection of dissolved chemical species. A chemical-free optical sensor has been used for direct and simultaneous measurements of both key parameters, and the results were compared with traditional methods. The data were collected during an observational program conducted in the Baltic Sea in February 2018. We used the OPUS UV spectral sensor, which was deployed for the first time in coastal waters, in combination with a deep-sea telemetry system to enable near-real time measurements during CTD profiling. Data processing was carried out using a multiple linear regression procedure. Measurements from both OPUS and on-board analysis were in good agreement. The results showed, that in situ UV-VIS spectrophotometry provides the capability to determine the concentration distributions of nitrate and hydrogen sulfide in the brackish waters of the Baltic Sea.