Persistent organic pollutants (POPs) - QSPR classification models by means of Machine learning strategies.

Persistent Organic pollutants (POPs) are toxic chemicals with a shallow degradation rate and global negative impact. Their physicochemical is combined with the complex effects of long-term POPs accumulation in the environment and transport function through the food chain. That is why POPs have been linked to adverse effects on human health and animals.

Unveiling the Effect of Low pH on the SARS-CoV-2 Main Protease by Molecular Dynamics Simulations.

(1) Background: Main Protease (Mpro) is an attractive therapeutic target that acts in the replication and transcription of the SARS-CoV-2 coronavirus. Mpro is rich in residues exposed to protonation/deprotonation changes which could affect its enzymatic function. This work aimed to explore the effect of the protonation/deprotonation states of Mpro at different pHs using computational techniques.

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).

Combinatorial K-Means Clustering as a Machine Learning Tool Applied to Diabetes Mellitus Type 2.

A new original procedure based on k-means clustering is designed to find the most appropriate clinical variables able to efficiently separate into groups similar patients diagnosed with diabetes mellitus type 2 (DMT2) and underlying diseases (arterial hypertonia (AH), ischemic heart disease (CHD), diabetic polyneuropathy (DPNP), and diabetic microangiopathy (DMA)). Clustering is a machine learning tool for discovering structures in datasets. Clustering has been proven to be efficient for pattern recognition based on clinical records.

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.

Vibrational Analysis of Manganese(II) Oxalates Hydrates: An In Silico Statistical Approach.

The experimental and computational vibrational study for three different manganese(II) oxalates hydrates was explored. The elucidation of IR and Raman spectra were discussed based on their structural singularity; in the same way, they establish some interesting relations between them in the field of computational and statistical approaches. The density functional theory (DFT) computational approach was conducted for accurate prediction and interpretation of the intermolecular effects based on experimental and calculated IR and Raman spectra in the solid-state data in combination with multivariate statistical technique.

Searching for Solvents with an Increased Carbon Dioxide Solubility Using Multivariate Statistics.

Ionic liquids (ILs) are used in various fields of chemistry. One of them is CO capture, a process that is quite well described. The solubility of CO in ILs can be used as a model to investigate gas absorption processes.

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 bacteria.

Assessment of ecotoxicity and total volatile organic compound (TVOC) emissions from food and children's toy products.

The development of new methods for identifying a broad spectrum of analytes, as well as highly selective tools to provide the most accurate information regarding the processes and relationships in the world, has been an area of interest for researchers for many years. The information obtained with these tools provides valuable data to complement existing knowledge but, above all, to identify and determine previously unknown hazards. Recently, attention has been paid to the migration of xenobiotics from the surfaces of various everyday objects and the resulting impacts on human health.

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.

Impact of inorganic ions and pH variations on toxicity and endocrine potential of selected environmentally relevant pharmaceuticals.

Assessment of the impact of pharmaceutical residues on living organisms is a very complex subject. Apart from taking into account the toxicity of individual compounds, environmental factors should also be taken into account. In this paper, attempts were made to assess the impact of coexisting inorganic ions and changes in pH on the toxicity of ten selected pharmaceuticals.

Simultaneous grouping and ranking with combination of SOM and TOPSIS for selection of preferable analytical procedure for furan determination in food.

Novel methodology for grouping and ranking with application of self-organizing maps and multicriteria decision analysis is presented. The dataset consists of 22 objects that are analytical procedures applied to furan determination in food samples. They are described by 10 variables, referred to their analytical performance, environmental and economic aspects.

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, logK and logK were modelled for 43 rather nonpolar solvents and 69 polar ones.

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.

Iron oxide nanoparticles - In vivo/in vitro biomedical applications and in silico studies.

The review presents a broad overview of the biomedical applications of surface functionalized iron oxide nanoparticles (IONPs) as magnetic resonance imaging (MRI) agents for sensitive and precise diagnosis tool and synergistic combination with other imaging modalities. Then, the recent progress in therapeutic applications, such as hyperthermia is discussed and the available toxicity data of magnetic nanoparticles concerning in vitro and in vivo biomedical applications are addressed. This review also presents the available computer models using molecular dynamics (MD), Monte Carlo (MC) and density functional theory (DFT), as a basis for a complete understanding of the behaviour and morphology of functionalized IONPs, for improving NPs surface design and expanding the potential applications in nanomedicine.

Modeling of pharmaceuticals mixtures toxicity with deviation ratio and best-fit functions models.

The present study deals with assessment of ecotoxicological parameters of 9 drugs (diclofenac (sodium salt), oxytetracycline hydrochloride, fluoxetine hydrochloride, chloramphenicol, ketoprofen, progesterone, estrone, androstenedione and gemfibrozil), present in the environmental compartments at specific concentration levels, and their mutual combinations by couples against Microtox® and XenoScreen YES/YAS® bioassays. As the quantitative assessment of ecotoxicity of drug mixtures is an complex and sophisticated topic in the present study we have used two major approaches to gain specific information on the mutual impact of two separate drugs present in a mixture. The first approach is well documented in many toxicological studies and follows the procedure for assessing three types of models, namely concentration addition (CA), independent action (IA) and simple interaction (SI) by calculation of a model deviation ratio (MDR) for each one of the experiments carried out.

Hasse diagram as a green analytical metrics tool: ranking of methods for benzo[a]pyrene determination in sediments.

This study presents an application of the Hasse diagram technique (HDT) as the assessment tool to select the most appropriate analytical procedures according to their greenness or the best analytical performance. The dataset consists of analytical procedures for benzo[a]pyrene determination in sediment samples, which were described by 11 variables concerning their greenness and analytical performance. Two analyses with the HDT were performed-the first one with metrological variables and the second one with "green" variables as input data.

Urban air quality assessment using monitoring data of fractionized aerosol samples, chemometrics and meteorological conditions.

The present article deals with assessment of urban air by using monitoring data for 10 different aerosol fractions (0.015-16 μm) collected at a typical urban site in City of Thessaloniki, Greece. The data set was subject to multivariate statistical analysis (cluster analysis and principal components analysis) and, additionally, to HYSPLIT back trajectory modeling in order to assess in a better way the impact of the weather conditions on the pollution sources identified.

Multivariate statistical comparison of analytical procedures for benzene and phenol determination with respect to their environmental impact.

The study describes the possibility of application of self-organizing maps technique to assess the greenness of analytical methodologies. The metrological and "environmental impact" parameters of procedures for benzene and phenol determination in water samples were sets of input data for chemometric analysis. Totally 47 objects and 8 variables formed the data used for analysis.

Novel approach to ecotoxicological risk assessment of sediments cores around the shipwreck by the use of self-organizing maps.

Marine and coastal pollution plays an increasingly important role due to recent severe accidents which drew attention to the consequences of oil spills causing widespread devastation of marine ecosystems. All these problems cannot be solved without conducting environmental studies in the area of possible oil spill and performing chemometric evaluation of the data obtained looking for similar patterns among pollutants and optimize environmental monitoring during eventual spills and possible remediation actions - what is the aim of the work presented. Following the chemical and ecotoxicological studies self-organising maps technique has been applied as a competitive learning algorithm based on unsupervised learning process.

Assessment of the water quality of Kłodnica River catchment using self-organizing maps.

Risk assessment of industrial areas heavily polluted due to anthropogenic actions is of increasing concern worldwide. So is the case of Polish Silesia region where mostly heavy industry like smelters, mining, chemical industries as well as heat and electricity production facilities are being located. Such situation raises numerous questions about environmental state of local water bodies with special attention paid to the Kłodnica Catchment which is receiving waste waters from numerous industrial plants.

Hasse diagram technique as a tool for water quality assessment.

The management of the quality large water catchments is a complex problem which requires intelligent data analysis on various levels - analytical, spatial, and temporal. Recently, a successful approach is developed combining advanced multivariate data treatment approaches like self-organizing maps of Kohonen (SOM) and Hasse diagram technique (HDT). In the first step of the environmetric analysis the monitoring data were subject to pre-processing using SOMs to reduce the number of objects and/or water quality parameters.

Relationship between heavy metal distribution in sediment samples and their ecotoxicity by the use of the Hasse diagram technique.

Many studies assessing the quality of sediments and their pollution impact use monitoring data consisting predominantly of chemical indicators. Recently, ecotoxicity estimates have been used as very important parameters of the ecological state of sediment samples. Thus, a more complete sediment risk assessment is achieved and more reliable information on the sediment pollution history is extracted.

Statistical modeling of air pollution.

The present communication deals with the application of several chemometric methods (principal components analysis, source apportioning on absolute principal components scores, chemical mass balance, self-organizing maps) to various aerosol data collections from different regions in Europe. It is shown that different latent factors explaining over 75 % of the total variance are responsible for the data structure and could be reliable identified and interpreted. Further, the contribution of each identified source to the formation of the particle total mass and chemical compounds total concentration is calculated.

Classification and modeling of different fractions of aerosol monitoring data.

The present study deals with the application of self-organizing maps (SOM) of Kohonen and four-way Tucker method for the classification and modeling, respectively, of aerosol monitoring data sets from two sampling points (Arnoldstein and Unterloibach) located close to the border, between Austria and Slovenia. The goal of the chemometric data treatment was to find some specific patterns in the classification maps and in the four-way model of complexity [2422] for 5 different aerosol fractions collected in 4 different seasons of the year. The results indicated a distinct separation of the ultrafine particles (PM 0.