Revealing the Formation Dynamics of Janus Polymer Particles: Insights from Experiments and Molecular Dynamics.

Seeded emulsion polymerization is one of the best-known methods for preparing polymer particles with a controlled size, composition, and shape. It first requires the preparation of seed particles, which are then swollen with additional monomer (the same as the one used for the seed or a different one), to either increase the seed's size or change its morphology. The use of surfactants plays a central role in guaranteeing the required colloidal stability and contributing to the final shape and structure of the particles by lowering the interfacial energy between the polymer of the seed and the added monomer. We here study the polymerization of methyl methacrylate in the presence of polystyrene seed particles at various surfactant concentrations in the presence and absence of a surfactant (sodium dodecyl sulfate). We first show experimentally that the morphology of the colloidal particles can be tuned from Janus to core-shell, depending on the presence or absence of surfactant on the seeds particles' surface. Furthermore, using classical molecular dynamics simulations, we investigate the mechanism and behavior of the surfactants during the first stages of the polymerization process. We use a newly developed approach based on contact statistical analysis to confirm the critical role played by the organization of surfactant molecules on the surface of the seed particles in dictating the final particle morphology.

Collection of Partition Coefficients in Hexadecyltrimethylammonium Bromide, Sodium Cholate, and Lithium Perfluorooctanesulfonate Micellar Solutions: Experimental Determination and Computational Predictions.

This study focuses on determining the partition coefficients (logP) of a diverse set of 63 molecules in three distinct micellar systems: hexadecyltrimethylammonium bromide (HTAB), sodium cholate (SC), and lithium perfluorooctanesulfonate (LPFOS). The experimental log p values were obtained through micellar electrokinetic chromatography (MEKC) experiments, conducted under controlled pH conditions. Then, Quantum Mechanics (QM) and machine learning approaches are proposed for the prediction of the partition coefficients in these three micellar systems. In the applied QM approach, the experimentally obtained partition coefficients were correlated with the calculated values for the case of the 15 solvent mixtures. Using Density Function Theory (DFT) with the B3LYP functional, we calculated the solvation free energies of 63 molecules in these 16 solvents. The combined data from the experimental partition coefficients in the three micellar formulations showed that the 1-propanol/water combination demonstrated the best agreement with the experimental partition coefficients for the SC and HTAB micelles. Moreover, we employed the SVM approach and k-means clustering based on the generation of the chemical descriptor space. The analysis revealed distinct partitioning patterns associated with specific characteristic features within each identified class. These results indicate the utility of the combined techniques when we want an efficient and quicker model for predicting partition coefficients in diverse micelles.

Developing an Intelligent Data Analysis Approach for Marine Sediments.

(1) Background: As the chemical and physicochemical properties of marine sediments are closely related to natural and anthropogenic events, it is a real challenge to use their specific assessment as an indicator of environmental pollution discharges. (2) Methods: It is addressed in this study that collection with intelligent data analysis methods, such as cluster analysis, principal component analysis, and source apportionment modeling, are applied for the assessment of the quality of marine sediment and for the identification of the contribution of pollution sources to the formation of the total concentration of polluting species. A study of sediment samples was carried out on 174 samples from three different areas along the coast of the Varna Gulf, Bulgaria. This was performed to determine the effects of pollution. As chemical descriptors, 34 indicators (toxic metals, polyaromatic hydrocarbons, polychlorinated biphenyls, nutrient components, humidity, and ignition loss) were used. The major goal of the present study was to assess the sediment quality in three different areas along the Gulf of Varna, Bulgaria by the source apportionment method. (3) Results: There is a general pattern for identifying three types of pollution sources in each area of the coastline with varying degrees of variation between zone A (industrially impacted zones), zone B (recreational areas), and zone C (anthropogenic and industrial wastes). (4) Conclusions: The quantitative apportionment procedure made it possible to determine the contribution of each identified pollution source for each zone in forming the total pollutant concentrations.

Multivariate Statistical Approach for Nephrines in Women with Obesity.

Catecholamines are physiological regulators of carbohydrate and lipid metabolism during stress, but their chronic influence on metabolic changes in obese patients is still not clarified. The present study aimed to establish the associations between the catecholamine metabolites and metabolic syndrome (MS) components in obese women as well as to reveal the possible hidden subgroups of patients through hierarchical cluster analysis and principal component analysis. The 24-h urine excretion of metanephrine and normetanephrine was investigated in 150 obese women (54 non diabetic without MS, 70 non-diabetic with MS and 26 with type 2 diabetes). The interrelations between carbohydrate disturbances, metabolic syndrome components and stress response hormones were studied. Exploratory data analysis was used to determine different patterns of similarities among the patients. Normetanephrine concentrations were significantly increased in postmenopausal patients and in women with morbid obesity, type 2 diabetes, and hypertension but not with prediabetes. Both metanephrine and normetanephrine levels were positively associated with glucose concentrations one hour after glucose load irrespectively of the insulin levels. The exploratory data analysis showed different risk subgroups among the investigated obese women. The development of predictive tools that include not only traditional metabolic risk factors, but also markers of stress response systems might help for specific risk estimation in obesity patients.

Tracing the movement of persistent organic pollutants at a high-mountain sampling site by chemometric assessment.

The main objective of the present study was to determine and differentiate the concentration levels, to define the probable sources of persistent organic pollutants (POPs) pollution in the atmospheric air and their seasonal variations in Bulgaria, on the high mountain peak Moussala, Rila Mountain. The study was based on the obtained results from the passive monitoring of POPs in 2014-2017. During this period, the measurements of POPs were performed with passive samplers, advanced instrumental methods analytically determined the concentrations of PAHs, and the analysis of the obtained data was performed by the multivariate statistical analysis (cluster, factor and time-series analysis). It is shown that the POPs species could be correctly classified according to their chemical nature into several patterns of similarity and their concentration profile depends on the annual season.

Fuzzy partitioning of clinical data for DMT2 patients.

The present study represents an original approach to data interpretation of clinical data for patients with diagnosis diabetes mellitus type 2 (DMT2) using fuzzy clustering as a tool for intelligent data analysis. Fuzzy clustering is often used in classification and interpretation of medical data (including in medical diagnosis studies) but in this study it is applied with a different goal: to separate a group of 100 patients with DMT2 from a control group of healthy volunteers and, further, to reveal three different patterns of similarity between the patients. Each pattern is described by specific descriptors (variables), which ensure pattern interpretation by appearance of underling disease to DMT2.

Calculating the Partition Coefficients of Organic Solvents in Octanol/Water and Octanol/Air.

Partition coefficients define how a solute is distributed between two immiscible phases at equilibrium. The experimental estimation of partition coefficients in a complex system can be an expensive, difficult, and time-consuming process. Here a computational strategy to predict the distributions of a set of solutes in two relevant phase equilibria is presented. The octanol/water and octanol/air partition coefficients are predicted for a group of polar solvents using density functional theory (DFT) calculations in combination with a solvation model based on density (SMD) and are in excellent agreement with experimental data. Thus, the use of quantum-chemical calculations to predict partition coefficients from free energies should be a valuable alternative for unknown solvents. The obtained results indicate that the SMD continuum model in conjunction with any of the three DFT functionals (B3LYP, M06-2X, and M11) agrees with the observed experimental values. The highest correlation to experimental data for the octanol/water partition coefficients was reached by the M11 functional; for the octanol/air partition coefficient, the M06-2X functional yielded the best performance. To the best of our knowledge, this is the first computational approach for the prediction of octanol/air partition coefficients by DFT calculations, which has remarkable accuracy and precision.

Chemometric Assessment of Soil Pollution and Pollution Source Apportionment for an Industrially Impacted Region around a Non-Ferrous Metal Smelter in Bulgaria.

The present study deals with the assessment of pollution caused by a large industrial facility using multivariate statistical methods. The primary goal is to classify specific pollution sources and to apportion their involvement in the formation of the total concentration of the chemical parameters being monitored. This aim is accomplished by intelligent data analysis based on cluster analysis, principal component analysis and principal component regression analysis. Five latent factors are found to explain over 80% of the total variance of the system being conditionally named "organic", "non-ferrous smelter", "acidic", "secondary anthropogenic contribution" and "natural" factor. The apportionment models designate the contribution of the identified sources quantitatively and help in the interpretation of risk assessment and management actions. Since the study takes into account pollution uptake from soil to a cabbage plant, the data interpretation could help in introducing biomonitoring aspects of the assessment. The chemometric expertise helps in revealing hidden relationships between the objects and the variables involved to achieve a better understanding of specific pollution events in the soil of a severely industrially impacted region.

Modeling and MANOVA studies on toxicity and endocrine potential of packaging materials exposed to different extraction schemes.

The stability of the linings of packaging that is in contact with the goods stored has been of major concern during decades of the development of packaging materials. In this work, an attempt was undertaken to assess the applicability of using two bioassays (Microtox® and XenoScreen YES/YAS) in estimating the stability of packaging (cans, caps, multilayer material) and the impact of their degradation on the toxicity of some simulated media. The assessment of the impact of packaging storage conditions (temperature, disinfection, preservation, extracting and washing solvents) was planned and performed with i) regression modeling of the experimental effects on the ecotoxicity readings, ii) ANOVA and MANOVA estimation of the experimental conditions as significant factors affecting the toxicity results and iii) FTIR analysis of the packages. It is shown that the effects of temperature and extraction solvents could be quantitatively assessed by the agreement between all methods applied. It can be stated that temperature and acidity as well as the alcohol content in the sensitive media have the greatest impact on the toxicity of the extract and thus on the stability of the internal lining and the extractability of xenobiotics.

Pre-selection and assessment of green organic solvents by clustering chemometric tools.

The study presents the result of the application of chemometric tools for selection of physicochemical parameters of solvents for predicting missing variables - bioconcentration factors, water-octanol and octanol-air partitioning constants. EPI Suite software was successfully applied to predict missing values for solvents commonly considered as "green". Values for logBCF, logKOW and logKOA were modelled for 43 rather nonpolar solvents and 69 polar ones. Application of multivariate statistics was also proved to be useful in the assessment of the obtained modelling results. The presented approach can be one of the first steps and support tools in the assessment of chemicals in terms of their greenness.

Chemometric Assessment and Best-Fit Function Modelling of the Toxic Potential of Selected Food Packaging Extracts.

Food packaging materials constitute an ever more threatening environmental pollutant. This study examined options to specifically assess the ecotoxicity of packaged wastes, such as cans, subjected to various experimental treatments (in terms of extraction media, time of exposure, and temperature) that imitate several basic conditions of the process of food production. The extracts were studied for their ecotoxicity with bioluminescent Vibrio fischeri bacteria. The first objective of this study was to find patterns of similarity between different experimental conditions; we used multivariate statistical methods, such as hierarchical cluster analysis, to interpret the impact of experimental conditions on the ecotoxicity signals of the package extracts. Our second objective was to apply best-fit function modelling for additional data interpretation, taking into account, that ecotoxicity for various temperature conditions is time- and temperature dependent. We mathematically confirmed that chemometric data treatment allows for better understanding how different experimental conditions imitating the real use of food packaging. We also demonstrate that the level of ecotoxicity depends on different extraction media, time of exposure, and temperature regime.

Ionization and Conformational Equilibria of Citric Acid: Delocalized Proton Binding in Solution.

The microspeciation of citric acid is studied by analyzing NMR titration data. When the site binding (SB) model, which assumes fully localized proton binding to the carboxylic groups, is used to obtain microscopic energy parameters (dissociation constants, pair and triplet interaction energies between charged carboxylate groups), contradictory results are obtained. The resulting macroscopic constants are in very good agreement with the values reported in the literature using potentiometry. However, the found pair interaction energy between the terminal carboxylates and the triplet interaction energy are physically meaningless. To solve this apparent contradiction, we consider the possibility of delocalized proton binding, so that the proton can be exchanged at high velocity in the NMR time scale through short, strong, low-barrier (SSLB) hydrogen bonds. With this aim, ab initio MP2 calculations using the SMD polarizable continuum model for the solvent were performed and the fully roto-microspeciation elucidated. First, fully localized proton binding was assumed, and the resulting microstate probabilities are in reasonable agreement with those reported in previous works that use selective blocking of the carboxylic groups. They are, however, in clear disagreement with the microstate probabilities derived from the NMR titration data, which predict, within a very narrow confidence interval, a unique microspecies for the symmetric di-ionized form. Moreover, counterintuitively, the interaction between terminal charged groups is much larger than that between central and terminal groups. As a consequence, we have explored the possibility of delocalized proton binding by calculating the energy of intermediate proton positions between two carbolxylic groups. The results reveal that the exchange of the proton through the hydrogen bonds is in some cases produced without energetic barrier. This effect is specially relevant in the di-ionized form, with all the most stable conformations forming a SSLB, which together would constitute the only microstate detected by NMR. An alternative reaction scheme for the ionization process, based on proton delocalization, is proposed.

Insight into electric field-induced rupture mechanism of water-in-toluene emulsion films from a model system.

This paper presents a model, which we have designed to get insight into the development of electro-induced instability of a thin toluene emulsion film in contact with the saline aqueous phase. Molecular dynamics (MD) simulations demonstrate the role of charge accumulation in the toluene-film rupture induced by a DC electric field. Two ensembles-NVT and NPT-are used to determine the critical value of the external field at which the film ruptures, the charge distribution and capacitance of the thin film, number densities, and the film structure. The rupture mechanism as seen from this model is the following: in both NVT and NPT ensembles, condenser plates, where the charge density is maximal, are situated at the very border between the bulk aqueous (water) phase and the mixed layer. No ion penetration is observed within the toluene core, thus leaving all the distribution of charges within the mixed zone and the bulk phase that could be attributed to the formation of hydration shells. When the critical electric field is reached within a certain time after the field application, electric discharge occurs indicating the beginning of the rupturing process. The MD simulations indicate that the NPT ensemble predicts a value of the critical field that is closer to the experimental finding.

Application of chemometric techniques in studies of toxicity of selected commercially available products for infants and children.

The goal of the present study is to assess the impact of the experimental conditions for extraction procedures (time of extraction, thermal treatment and type of extraction media) as applied to several baby and infant products checked for their possible ecotoxicological response when tested by various ecotoxicity tests (Microtox®, Ostracodtoxkit F™ and Xenoscreen YES/YAS™). The systems under consideration are multidimensional by nature and, therefore, the appropriate assessment approach was intelligent data analysis (chemometrics). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were selected as reliable data mining methods for the interpretation of the ecotoxicity data. We show that the different experimental conditions have a significant impact on the ecotoxicity levels observed, especially those measured by Microtox® and Ostracodtoxkit F™ tests. The time of contact proves to be a very significant factor for all extraction media and ecotoxicity test procedures. The present study is a pioneering effort to offer a specific expert approach for analysing links between the type of test measurement methodology and imposed experimental conditions to mimic real-life circumstances in the use of baby and infant products.

In silico screening of potential agonists of a glucagon-like peptide-1 receptor among female sex hormone derivatives.

Glucagon-like peptide-1 (GLP-1) is an intestinal hormone that exerts its pleiotropic effects through a specific GLP-1 receptor (GLP-1R). The hormone-receptor complex might regulate glucose-dependent insulin secretion, and energy homeostasis; moreover, it could decrease inflammation and provide cardio- and neuroprotection. Additionally, the beneficial influence of GLP-1 on obesity in women might lead to improvement of their ovarian function. The links between metabolism and reproduction are tightly connected, and it is not surprising that different estrogen derivatives, estrogen-receptor modulator (SERM) and progestins used for gonadal and oncological disorders might influence carbohydrate and lipid metabolism. However, their possible influence on the GLP-1R has not been studied. The docking scores and top-ranked poses of raloxifene were much higher than those observed for other investigated SERMs and estradiol per se. Among different studied progestins, drospirenone showed slightly higher affinity to GLP-1R. Herein, the same data set of the drugs is evaluated by molecular dynamics (MD) simulations and compared with the obtained docking result. Notably, it is demonstrated that the used docking protocol and the applied MD calculations ranked the same ligand (raloxifene) as the best one. In the present study, raloxifene might exert an allosteric influence on GLP-1R signaling, which might contribute to potential beneficial effects on metabolism and weight regulation. However, further experimental and clinical studies are needed to reveal if the GLP-1R modulation has a real biological impact.Communicated by Ramaswamy H. Sarma.

Sequence-Based Prediction of Plant Allergenic Proteins: Machine Learning Classification Approach.

This Article proposes a novel chemometric approach to understanding and exploring the allergenic nature of food proteins. Using machine learning methods (supervised and unsupervised), this work aims to predict the allergenicity of plant proteins. The strategy is based on scoring descriptors and testing their classification performance. Partitioning was based on support vector machines (SVM), and a k-nearest neighbor (KNN) classifier was applied. A fivefold cross-validation approach was used to validate the KNN classifier in the variable selection step as well as the final classifier. To overcome the problem of food allergies, a robust and efficient method for protein classification is needed.

Machine Learning-Based Screening for Potential Singlet Fission Chromophores: The Challenge of Imbalanced Data Sets.

Excitation with one photon of a singlet fission (SF) material generates two triplet excitons, thus doubling the solar cell efficiency. Therefore, the SF molecules are regarded as new generation organic photovoltaics, but it is hard to identify them. Recently, it was demonstrated that molecules of low-to-intermediate diradical character (DRC) are potential SF chromophores. This prompts a low-cost strategy for finding new SF candidates by computational high-throughput workflows. We propose a machine learning aided screening for SF entrants based on their DRC. Our data set comprises 469 784 compounds extracted from the PubChem database, structurally rich but inherently imbalanced regarding DRC values. We developed well performing classification models that can retrieve potential SF chromophores. The latter (∼4%) were analyzed by K-means clustering to reveal qualitative structure-property relationships and to extract strategies for molecular design. The developed screening procedure and data set can be easily adapted for applications of diradicaloids in photonics and spintronics.

Computer-Aided Drug Design and Synthesis of Rhenium Clotrimazole Antimicrobial Agents.

In the context of the global health issue caused by the growing occurrence of antimicrobial resistance (AMR), the need for novel antimicrobial agents is becoming alarming. Inorganic and organometallic complexes represent a relatively untapped source of antibiotics. Here, we report a computer-aided drug design (CADD) based on a 'scaffold-hopping' approach for the synthesis and antibacterial evaluation of fac-Re(I) tricarbonyl complexes bearing clotrimazole (ctz) as a monodentate ligand. The prepared molecules were selected following a pre-screening in silico analysis according to modification of the 2,2'-bipyridine (bpy) ligand in the coordination sphere of the complexes. CADD pointed to chiral 4,5-pinene and 5,6-pinene bipyridine derivatives as the most promising candidates. The corresponding complexes were synthesized, tested toward methicillin-sensitive and -resistant S. aureus strains, and the obtained results evaluated with regard to their binding affinity with a homology model of the S. aureus MurG enzyme. Overall, the title species revealed very similar minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values as those of the reference compound used as the scaffold in our approach. The obtained docking scores advocate the viability of 'scaffold-hopping' for de novo design, a potential strategy for more cost- and time-efficient discovery of new antibiotics.

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action.

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.

A toolbox of machine learning software to support microbiome analysis.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.