Physico-chemical characterization and possible uses of sludge processed from an urban sewage treatment plant.

Nowadays, the challenge is to transform dehydrated sewage sludge resulting from wastewater treatment plants from waste into resource. Following this objective, the sludge was further dried and submitted to X-ray diffraction (XRD) and FTIR analysis. The sludge was first dried in ventilated and unventilated spaces at 50 ∘C and 100 ∘C, for 60 and 100 minutes (min) in each case. The final mass and evaporation degree of the sludge depends on the initial mass, ventilation type, drying time, and temperature. The ventilated drying space is preferred for temperature control, homogeneity, and higher evaporation degree. The influence of the drying process on the structure and behavior of the sewage sludge was emphasized through X-ray diffraction (XRD) and FTIR analysis. The XRD shows good structural properties of the sludge samples given by the reduction of the particle size through evaporation. According to FTIR, evaporation influences the depolymerization of the silicate network. The hydroxyl units and metallic ion modifiers can improve the sludge structure, but its intensity decreases through evaporation. With high content of solid substance, and good relation between the composition of the sludge and its structure and behavior, the dried sewage sludge can be used in: (i) agriculture, (ii) construction, (iii) the energy sector.

The Biochemistry and Effectiveness of Antioxidants in Food, Fruits, and Marine Algae.

It is more effective to maintain good health than to regain it after losing it. This work focuses on the biochemical defense mechanisms against free radicals and their role in building and maintaining antioxidant shields, aiming to show how to balance, as much as possible, the situations in which we are exposed to free radicals. To achieve this aim, foods, fruits, and marine algae with a high antioxidant content should constitute the basis of nutritional elements, since natural products are known to have significantly greater assimilation efficiency. This review also gives the perspective in which the use of antioxidants can extend the life of food products, by protecting them from damage caused by oxidation as well as their use as food additives.

Biological Activated Sludge from Wastewater Treatment Plant before and during the COVID-19 Pandemic.

The COVID-19 pandemic and the related measures brought a change in daily life that affected the characteristics of the municipal wastewater and further, of the biological activated sludge. The activated sludge process is the most widely used biological wastewater treatment process in developed areas. In this paper, we aim to show the situation of specific investigations concerning the variation of the physicochemical parameters and biological composition of the activated sludge from one conventional wastewater treatment plant from a metropolitan area. The investigations were carried out for three years: 2019, 2020 and 2021. The results showed the most representative taxa of microorganisms: Microtrix, Aspidisca cicada, Vorticella convallaria, Ciliata free of the unknown and Epistylis and Rotifers. Even if other microorganisms were found in the sludge flocs, their small presence did not influence in any way the quality of the activated sludge and of the wastewater treatment process. That is why we conclude that protozoa (especially Flagellates and Ciliates) and rotifers were the most important. Together with the values and variation of the physicochemical parameters, they indicated a good, healthy, and stable activated sludge, along with an efficient purifying treatment process, no matter the loading conditions.

Targeting Cell Death Mechanism Specifically in Triple Negative Breast Cancer Cell Lines.

Triple negative breast cancer (TNBC) is currently associated with a lack of treatment options. Arsenic derivatives have shown antitumoral activity both in vitro and in vivo; however, their mode of action is not completely understood. In this work we evaluate the response to arsenate of the double positive MCF-7 breast cancer cell line as well as of two different TNBC cell lines, Hs578T and MDA-MB-231. Multimodal experiments were conducted to this end, using functional assays and microarrays. Arsenate was found to induce cytoskeletal alteration, autophagy and apoptosis in TNBC cells, and moderate effects in MCF-7 cells. Gene expression analysis showed that the TNBC cell lines' response to arsenate was more prominent in the G2M checkpoint, autophagy and apoptosis compared to the Human Mammary Epithelial Cells (HMEC) and MCF-7 cell lines. We confirmed the downregulation of anti-apoptotic genes (MCL1, BCL2, TGFß1 and CCND1) by qRT-PCR, and on the protein level, for TGFß2, by ELISA. Insight into the mode of action of arsenate in TNBC cell lines it is provided, and we concluded that TNBC and non-TNBC cell lines reacted differently to arsenate treatment in this particular experimental setup. We suggest the future research of arsenate as a treatment strategy against TNBC.

Sugars and Sweeteners: Structure, Properties and In Silico Modeling.

Several studies report the effects of excessive use of sugars and sweeteners in the diet. These include obesity, cardiac diseases, diabetes, and even lymphomas, leukemias, cancers of the bladder and brain, chronic fatigue syndrome, Parkinson's disease, Alzheimer's disease, multiple sclerosis, autism, and systemic lupus. On the other hand, each sugar and sweetener has a distinct metabolic assimilation process, and its chemical structure plays an important role in this process. Several scientific papers present the biological effects of the sugars and sweeteners in relation to their chemical structure. One important issue dealing with the sugars is the degree of similarity in their structures, focusing mostly on optical isomerism. Finding and developing new sugars and sweeteners with desired properties is an emerging research area, in which in silico approaches play an important role.

Multiple Linear Regressions by Maximizing the Likelihood under Assumption of Generalized Gauss-Laplace Distribution of the Error.

Multiple linear regression analysis is widely used to link an outcome with predictors for better understanding of the behaviour of the outcome of interest. Usually, under the assumption that the errors follow a normal distribution, the coefficients of the model are estimated by minimizing the sum of squared deviations. A new approach based on maximum likelihood estimation is proposed for finding the coefficients on linear models with two predictors without any constrictive assumptions on the distribution of the errors. The algorithm was developed, implemented, and tested as proof-of-concept using fourteen sets of compounds by investigating the link between activity/property (as outcome) and structural feature information incorporated by molecular descriptors (as predictors). The results on real data demonstrated that in all investigated cases the power of the error is significantly different by the convenient value of two when the Gauss-Laplace distribution was used to relax the constrictive assumption of the normal distribution of the error. Therefore, the Gauss-Laplace distribution of the error could not be rejected while the hypothesis that the power of the error from Gauss-Laplace distribution is normal distributed also failed to be rejected.

Inside of the Linear Relation between Dependent and Independent Variables.

Simple and multiple linear regression analyses are statistical methods used to investigate the link between activity/property of active compounds and the structural chemical features. One assumption of the linear regression is that the errors follow a normal distribution. This paper introduced a new approach to solving the simple linear regression in which no assumptions about the distribution of the errors are made. The proposed approach maximizes the probability of observing the event according to the random error. The use of the proposed approach is illustrated in ten classes of compounds with different activities or properties. The proposed method proved reliable and was showed to fit properly the observed data compared to the convenient approach of normal distribution of the errors.

Distribution on contingency of alignment of two literal sequences under constrains.

The case of ungapped alignment of two literal sequences under constrains is considered. The analysis lead to general formulas for probability mass function and cumulative distribution function for the general case of using an alphabet with a chosen number of letters (e.g. 4 for deoxyribonucleic acid sequences) in the expression of the literal sequences. Formulas for three statistics including mean, mode, and standard deviation were obtained. Distributions are depicted for three important particular cases: alignment on binary sequences, alignment of trinomial series (such as coming from generalized Kronecker delta), and alignment of genetic sequences (with four literals in the alphabet). A particular case when sequences contain each letter of the alphabet at least once in both sequences has also been analyzed and some statistics for this restricted case are given.

Modeling the antioxidant capacity of red wine from different production years and sources under censoring.

The health benefit of drinking wine, expressed as capacity to defend the human organism from the free radicals action and thus reducing the oxidative stress, has already been demonstrated, and the results had been published in scientific literature. The aim of our study was to develop and assess a model able to estimate the antioxidant capacity (AC) of several samples of Romanian wines and to evaluate the AC dependency on the vintage (defined as the year in which wine was produced) and grape variety under presence of censored data. A contingency of two grape varieties from two different vineyards in Romania and five production years, with some missing experimental data, was used to conduct the analysis. The analysis showed that the antioxidant capacity of the investigated wines is linearly dependent on the vintage. Furthermore, an iterative algorithm was developed and applied to obtain the coefficients of the model and to estimate the missing experimental value. The contribution of wine source to the antioxidant capacity proved equal to 11%.

Supervised evolution: research concerning the number of evolutions that occur under certain constraints.

It is known that evolution may lead to a new species while adaptation may lead to a new variety. In this manuscript, we present an analysis of the number of evolutions (defined by improvement of the score associated with an objective function of a genetic algorithm) in an experiment supervised by a genetic algorithm, experiment conducted on octan-1-ol/H20 partition coefficient of polychlorinated biphenyls. The numbers of evolutions resulted from 9 implemented evolution strategies were investigated. Evolutions arisen from the first 20 000 generations coming from 46 independent runs were recorded. A distribution analysis has been conducted for each evolution strategy. Without exception, the Weibull distribution fits well with the number of evolutions at a significance level of 5% for any evolution strategy. Furthermore, the Weibull distribution could not be rejected when different merged samples were investigated.

Molecular design and QSARs/QSPRs with molecular descriptors family.

The aim of the present paper is to present the methodology of the molecular descriptors family (MDF) as an integrative tool in molecular modeling and its abilities as a multivariate QSAR/QSPR modeling tool. An algorithm for extracting useful information from the topological and geometrical representation of chemical compounds was developed and integrated to calculate MDF members. The MDF methodology was implemented and the software is available online (http://l.academicdirect.org/Chemistry/SARs/MDF_SARs/). This integrative tool was developed in order to maximize performance, functionality, efficiency and portability. The MDF methodology is able to provide reliable and valid multiple linear regression models. Furthermore, in many cases, the MDF models were better than the published results in the literature in terms of correlation coefficients (statistically significant Steiger's Z test at a significance level of 5%) and/or in terms of values of information criteria and Kubinyi function. The MDF methodology developed and implemented as a platform for investigating and characterizing quantitative relationships between the chemical structure and the activity/property of active compounds was used on more than 50 study cases. In almost all cases, the methodology allowed obtaining of QSAR/QSPR models improved in explanatory power of structure-activity and structure-property relationships. The algorithms applied in the computation of geometric and topological descriptors (useful in modeling physicochemical or biological properties of molecules) and those used in searching for reliable and valid multiple linear regression models certain enrich the pool of low-cost low-time drug design tools.

The effect of leverage and/or influential on structure-activity relationships.

In the spirit of reporting valid and reliable Quantitative Structure-Activity Relationship (QSAR) models, the aim of our research was to assess how the leverage (analysis with Hat matrix, h(i)) and the influential (analysis with Cook's distance, D(i)) of QSAR models may reflect the models reliability and their characteristics. The datasets included in this research were collected from previously published papers. Seven datasets which accomplished the imposed inclusion criteria were analyzed. Three models were obtained for each dataset (full-model, h(i)-model and D(i)-model) and several statistical validation criteria were applied to the models. In 5 out of 7 sets the correlation coefficient increased when compounds with either h(i) or D(i) higher than the threshold were removed. Withdrawn compounds varied from 2 to 4 for h(i)-models and from 1 to 13 for D(i)-models. Validation statistics showed that D(i)-models possess systematically better agreement than both full-models and h(i)-models. Removal of influential compounds from training set significantly improves the model and is recommended to be conducted in the process of quantitative structure-activity relationships developing. Cook's distance approach should be combined with hat matrix analysis in order to identify the compounds candidates for removal.

Poisson parameters of antimicrobial activity: a quantitative structure-activity approach.

A contingency of observed antimicrobial activities measured for several compounds vs. a series of bacteria was analyzed. A factor analysis revealed the existence of a certain probability distribution function of the antimicrobial activity. A quantitative structure-activity relationship analysis for the overall antimicrobial ability was conducted using the population statistics associated with identified probability distribution function. The antimicrobial activity proved to follow the Poisson distribution if just one factor varies (such as chemical compound or bacteria). The Poisson parameter estimating antimicrobial effect, giving both mean and variance of the antimicrobial activity, was used to develop structure-activity models describing the effect of compounds on bacteria and fungi species. Two approaches were employed to obtain the models, and for every approach, a model was selected, further investigated and found to be statistically significant. The best predictive model for antimicrobial effect on bacteria and fungi species was identified using graphical representation of observed vs. calculated values as well as several predictive power parameters.

Predictivity approach for quantitative structure-property models. Application for blood-brain barrier permeation of diverse drug-like compounds.

The goal of the present research was to present a predictivity statistical approach applied on structure-based prediction models. The approach was applied to the domain of blood-brain barrier (BBB) permeation of diverse drug-like compounds. For this purpose, 15 statistical parameters and associated 95% confidence intervals computed on a 2 × 2 contingency table were defined as measures of predictivity for binary quantitative structure-property models. The predictivity approach was applied on a set of compounds comprised of 437 diverse molecules, 122 with measured BBB permeability and 315 classified as active or inactive. A training set of 81 compounds (~2/3 of 122 compounds assigned randomly) was used to identify the model and a test set of 41 compounds was used as the internal validation set. The molecular descriptor family on vertices cutting was the computation tool used to generate and calculate structural descriptors for all compounds. The identified model was assessed using the predictivity approach and compared to one model previously reported. The best-identified classification model proved to have an accuracy of 69% in the training set (95%CI [58.53-78.37]) and of 73% in the test set (95%CI [58.32-84.77]). The predictive accuracy obtained on the external set proved to be of 73% (95%CI [67.58-77.39]). The classification model proved to have better abilities in the classification of inactive compounds (specificity of ~74% [59.20-85.15]) compared to abilities in the classification of active compounds (sensitivity of ~64% [48.47-77.70]) in the training and external sets. The overall accuracy of the previously reported model seems not to be statistically significantly better compared to the identified model (~81% [71.45-87.80] in the training set, ~93% [78.12-98.17] in the test set and ~79% [70.19-86.58] in the external set). In conclusion, our predictivity approach allowed us to characterize the model obtained on the investigated set of compounds as well as compare it with a previously reported model. According to the obtained results, the reported model should be chosen if a correct classification of inactive compounds is desired and the previously reported model should be chosen if a correct classification of active compounds is most wanted.

Binding affinity of triphenyl acrylonitriles to estrogen receptors: quantitative structure-activity relationships.

AIM: The quantitative structure-activity relationship approach was applied to understand the relative binding affinity of triphenyl acrylonitriles to estrogen receptors. MATERIAL AND METHODS: A sample of previously studied triphenyl acrylonitriles was divided into training (18 compounds) and test sets (7 compounds) using a stratified random approach. The molecular descriptor family on vertices cutting (MDFV) approach was used in order to translate the structural information into descriptors. The relationship between binding activity and structural descriptors was identified using the multiple linear regression procedure. RESULTS: An optimal three-parameter equation with a determination coefficient of 0.9580 and a cross-validation leave-one-out parameter of 0.9408 was identified. The optimal model was assessed on a test set and a determination coefficient of 0.9004 was obtained. The MDFV model proved not to be significantly different from the previously reported model in terms of goodness-of-fit. In terms of information criteria (Akaike's, Bayesian, Amemiya, and Hannan-Quinn) and Kubinyi function, the MDFV model proved to perform better than the previously reported model. CONCLUSION: The optimal MDFV model was able to explain approximately 96% of the total variance in the estrogenic binding relative affinity of triphenyl acrylonitriles and to have estimation and prediction abilities. Although there were no significant differences in terms of goodness-of-fit, the MDFV model proved to exhibit better information parameters compared to the previously reported model using the same number of molecular descriptors.

A study of genetic algorithm evolution on the lipophilicity of polychlorinated biphenyls.

The search for multivariate linear regression (MLR) in quantitative structure-property relationships (QSPR) is a hard problem, due to the dimension of the entire search space. A genetic algorithm (GA) was developed and assessed, to select proper descriptors for predicting the octan-1-ol/H2O partition coefficient of polychlorinated biphenyls. The GA was implemented as a Windows based FreePascal application with MySQL connectivity for fetching the data. An outcome study based on 30 runs was done keeping all parameters constant: sample size, 8; number of variables in the MLR, 2; adaptation-imposed requirements; maximum number of generations, 1000; selection strategy, proportional; probability of mutation, 0.05; number of genes implied in mutation, 2; optimization parameter, r(2); optimization score, minimum in sample; and optimization objective, maximum. The results revealed that the number of evolutions followed the Poisson distribution with the sample size as parameter. The average of the determination coefficient is higher than 98% of the determination coefficient obtained through complete search, and follows the Gaussian distribution. The correlation coefficients obtained by the best performing GA-MLR models proved not to be statistically different from the correlation coefficient of the QSPR model obtained by complete search.

Exact probabilities and confidence limits for binomial samples: applied to the difference between two proportions.

An exact probabilities method is proposed for computing the confidence limits of medical binomial parameters obtained based on the 2x2 contingency table. The developed algorithm was described and assessed for the difference between two binomial proportions (a bidimensional parameter). The behavior of the proposed method was analyzed and compared to four previously defined methods: Wald and Wilson, with and without continuity corrections. The exact probabilities method proved to be monotonic in computing the confidence limits. The experimental errors of the exact probabilities method applied to the difference between two proportions has never exceeded the imposed significance level of 5%.

Meta-heuristics on quantitative structure-activity relationships: study on polychlorinated biphenyls.

A genetic algorithm was developed and assessed in order to select pairs of proper structural descriptors able to estimate and predict octanol-water partition coefficients of polychlorinated biphenyls (PCBs). The molecular descriptors family was calculated for a sample of 206 PCBs. The problem of searching for the proper descriptors in order to identify structure-activity relationships was translated in genetic terms. The following parameters were imposed in the genetic algorithm (GA) search: sample size - 12, number of variables in multivariate linear regression - 4, imposed adaptation requirements - 3 criteria, maximum number of generations - 50,000, selection strategy - tournament, probability of parent/child mutation - 0.05, number of genes implied in the mutation - 2, optimization parameter - determination coefficient, optimization score - minimum in the sample, and optimization objective - maximum. The highest determination coefficient was obtained in the generation 17,277. Twenty-one evolutions were studied until the optimum solution was obtained. The model identified by the implemented genetic algorithm proved not to be statistically different from the model identified through complete search (Z(Steiger) = 1.37, p = 0.0861). According to this GA model, the relationship between the structure of PCBs and octanol-water partition coefficients was of geometric and topological nature as previously revealed by the complete search. The genetic algorithm proved its ability to identify two pairs of molecular descriptors able to characterize the relationship between the structure of PCBs and the octanol-water partition coefficient.

Comparison of quantitative structure-activity relationship model performances on carboquinone derivatives.

Quantitative structure-activity relationship (qSAR) models are used to understand how the structure and activity of chemical compounds relate. In the present study, 37 carboquinone derivatives were evaluated and two different qSAR models were developed using members of the Molecular Descriptors Family (MDF) and the Molecular Descriptors Family on Vertices (MDFV). The usual parameters of regression models and the following estimators were defined and calculated in order to analyze the validity and to compare the models: Akaike's information criteria (three parameters), Schwarz (or Bayesian) information criterion, Amemiya prediction criterion, Hannan-Quinn criterion, Kubinyi function, Steiger's Z test, and Akaike's weights. The MDF and MDFV models proved to have the same estimation ability of the goodness-of-fit according to Steiger's Z test. The MDFV model proved to be the best model for the considered carboquinone derivatives according to the defined information and prediction criteria, Kubinyi function, and Akaike's weights.

Statistical assessment of solvent mixture models used for separation of biological active compounds.

Two mathematical models with seven and six parameters have been created for use as methods for identification of the optimum mobile phase in chromatographic separations. A series of chromatographic response functions were proposed and implemented in order to assess and validate the models. The assessment was performed on a set of androstane isomers. Pearson, Spearman, Kendall tau-a,b,c and Goodman-Kruskal correlation coefficients were used in order to identify and to quantify the link and its nature (quantitative, categorical, semi-quantitative, both quantitative and categorical) between experimental values and the values estimated by the mathematical models. The study revealed that the six parameter model is valid and reliable for five chromatographic response factors (retardation factor, retardation factor ordered ascending by the chromatographic peak, resolution of pairs of compound, resolution matrix of successive chromatographic peaks, and quality factor). Furthermore, the model could be used as an instrument in analysis of the quality of experimental data. The results obtained by applying the model with six parameters for deviations of rank sums suggest that the data of the experiment no. 8 are questionable.