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The complex interactions between groundwater chemical environments and PFAS present challenges for data analysis and factor assessment of the spatial distribution and source attribution of PFAS in groundwater. This study employed spatial response analysis combining self-organizing maps (SOM), K-means clustering, Spearman correlation, positive matrix factorization (PMF) and risk quotient (RQ), to uncover the spatial characteristics, driving factors, sources, and human health risks of groundwater PFAS in the Pearl River Basin. The results indicated that the characteristics of PFAS in groundwater were classified into 16 neurons, which were further divided into 6 clusters (I-VI). This division was due to the contribution of industrial pollution (33.2 %) and domestic pollution (31.5 %) to the composition of PFAS in groundwater. In addition, the hydrochemical indicators such as pH, dissolved organic carbon (DOC), chloride (Cl-), and calcium ions (Ca2+) might also affect the distribution pattern of PFAS. The potential human health risk in the area was minimal, with cluster Ⅱ presenting the highest risk (RQ value 0.25) which is closely related to PFOA emissions from fluoropolymer industry. This study provides a theoretical basis and data support for applying of SOM to the visualization and control of PFAS contamination in groundwater.

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The river course is a transitional area connecting the source and receiving water bodies. The dissolved organic matter (DOM) in the river course is an important factor affecting the aquatic environment and ecological health. However, there are shortcomings in studying the differences and quantitative contributions of river DOM in different seasons and sources. In this study, ultraviolet-visible (UV-vis) and three-dimensional fluorescence spectra were used to characterize the optical properties, analyze the spatiotemporal changes, and establish the quantitative relationship between environmental factors and DOM in the inflow rivers of Baiyangdian Lake. The results showed that the relative DOM concentrations in summer and autumn were significantly higher than those in the other seasons (P < 0.001) and that the DOM source (SR < 1) was mainly exogenous. The fluorescence abundance of protein-like substances (C1 + C2 + C3) was the highest in spring, whereas that of humus C4 was the highest in autumn. Moreover, the inflow rivers exhibited strong autogenetic characteristics (BIX > 1) throughout the year. Self-organizing maps (SOM) indicated that the main driving factors of water quality were NO3--N in spring, autumn, and winter and DO, pH, and chemical oxygen demand (COD) in summer. Random forest analysis showed that the fluorescent components (C1-C4) were closely related to the migration and transformation of nitrogen, and pH and nitrogen were the main predictors of each component. The Mantel test and structural equation model (SEM) showed that temperature and NO3--N significantly influenced the DOM concentration, components, and molecular properties in different seasons. Moreover, the river source also affected the distribution mechanism of DOM in the water body. Our study comprehensively analyzed the response of DOM in inflow rivers in different seasons and water sources, providing a basis for further understanding the driving mechanisms of water quality.

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CONTEXT: Ubiquitin-like with PHD and RING finger domain containing protein 1 (UHRF1) is responsible for preserving the stability of genomic methylation through the recruitment of DNA methyltransferase 1 (DNMT1). However, the interaction between Developmental pluripotency associated 3 (DPPA3) and the pre-PHD-PHD (PPHD) domain of UHRF1 hinders the nuclear localization of UHRF1. This disruption has implications for potential cancer treatment strategies. Drugs that mimic the binding pattern between DPPA3 and PPHD could offer a promising approach to cancer treatment. Our study reveals that DPPA3 undergoes dissociation from the C-terminal through three different modes of helix unfolding. Furthermore, we have identified key residue pairs involved in this dissociation process and potential drug-targeting residues. These findings offer valuable insights into the dissociation mechanism of DPPA3 from PPHD and have the potential to inform the design of novel drugs targeting UHRF1 for cancer therapy. METHODS: To comprehend the dissociation process and binding patterns of PPHD-DPPA3, we employed enhanced sampling techniques, including steered molecular dynamics (SMD) and conventional molecular dynamics (cMD). Additionally, we utilized self-organizing maps (SOM) and time-resolved force distribution analysis (TRFDA) methodologies. The Gromacs software was used for performing molecular dynamics simulations, and the AMBER FF14SB force field was applied to the protein.

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The Adriatic Sea plays a crucial role as both a significant fishing ground and a thriving trading market for small pelagic edible fish. Recognized for their nutritional value, these fish are esteemed for their high protein content and abundance of polyunsaturated omega-3 and omega-6 fatty acids, making them a sought-after and healthful food choice. Nevertheless, pelagic species can also serve as a reservoir for lipophilic organochlorine pollutants, posing potential risks to human health. In this study, we compared traditional classification methods traditional principal component analysis (PCA) and Ward's clustering with an advanced self-organizing map (SOM) algorithm in determining distribution patterns of 24 organochlorines and 19 fatty acids in sardine and anchovy samples taken from the eastern Adriatic. The outcomes reveal the strengths and weaknesses of the three approaches (PCA, Ward's clustering, and SOM). However, it is evident that SOM has proven to be the most effective in offering detailed information and data visualization. Although sardines and anchovies exhibit similar distribution patterns for p,p'-DDE, PCB-28, PCB-138, PCB-153, PCB-118, and PCB-170, they differ in the concentrations of fatty acids such as stearic, palmitic, myristic, oleic, docosapentaenoic, and docosahexaenoic acid. Our findings supply valuable insights for environmental authorities and fish consumers concerning the potential risks associated with organochlorines in these two types of fish.

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The current richness of sequence data needs efficient methodologies to display and analyze the complexity of the information in a compact and readable manner. Traditionally, phylogenetic trees and sequence similarity networks have been used to display and analyze sequences of protein families. These methods aim to shed light on key computational biology problems such as sequence classification and functional inference. Here, we present a new methodology, AlignScape, based on self-organizing maps. AlignScape is applied to three large families of proteins: the kinases and GPCRs from human, and bacterial T6SS proteins. AlignScape provides a map of the similarity landscape and a tree representation of multiple sequence alignments These representations are useful to display, cluster, and classify sequences as well as identify functional trends. The efficient GPU implementation of AlignScape allows the analysis of large MSAs in a few minutes. Furthermore, we show how the AlignScape analysis of proteins belonging to the T6SS complex can be used to predict coevolving partners.

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The objective of this article is to develop a methodology for selecting the appropriate number of clusters to group and identify human postures using neural networks with unsupervised self-organizing maps. Although unsupervised clustering algorithms have proven effective in recognizing human postures, many works are limited to testing which data are correctly or incorrectly recognized. They often neglect the task of selecting the appropriate number of groups (where the number of clusters corresponds to the number of output neurons, i.e., the number of postures) using clustering quality assessments. The use of quality scores to determine the number of clusters frees the expert to make subjective decisions about the number of postures, enabling the use of unsupervised learning. Due to high dimensionality and data variability, expert decisions (referred to as data labeling) can be difficult and time-consuming. In our case, there is no manual labeling step. We introduce a new clustering quality score: the discriminant score (DS). We describe the process of selecting the most suitable number of postures using human activity records captured by RGB-D cameras. Comparative studies on the usefulness of popular clustering quality scores-such as the silhouette coefficient, Dunn index, Calinski-Harabasz index, Davies-Bouldin index, and DS-for posture classification tasks are presented, along with graphical illustrations of the results produced by DS. The findings show that DS offers good quality in posture recognition, effectively following postural transitions and similarities.

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The aim of this study is to automatically analyze, characterize and classify physical performance and body composition data of a cohort of Mexican community-dwelling older adults. Self-organizing maps (SOM) were used to identify similar profiles in 562 older adults living in Mexico City that participated in this study. Data regarding demographics, geriatric syndromes, comorbidities, physical performance, and body composition were obtained. The sample was divided by sex, and the multidimensional analysis included age, gait speed over height, grip strength over body mass index, one-legged stance, lean appendicular mass percentage, and fat percentage. Using the SOM neural network, seven profile types for older men and women were identified. This analysis provided maps depicting a set of clusters qualitatively characterizing groups of older adults that share similar profiles of body composition and physical performance. The SOM neural network proved to be a useful tool for analyzing multidimensional health care data and facilitating its interpretability. It provided a visual representation of the non-linear relationship between physical performance and body composition variables, as well as the identification of seven characteristic profiles in this cohort.

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The arrival of the Fifth Generation (5G) entails a significant evolution in the context of mobile communication networks. This new technology will bring heterogeneous scenarios with new types of services and an increasingly high number of users and nodes. The efficient management of such complex networks has become an important challenge. To address this problem, automatic and efficient algorithms must be developed to facilitate operators' management and optimization of their networks. These algorithms must be able to cope with a very high number of heterogeneous data and different types of scenarios. In this paper, a novel framework for a cellular network behavioral analysis and monitoring is presented. This framework is based on a combination of unsupervised and supervised machine learning techniques. The proposed system can analyze the behavior of cells and monitor them, searching for behavior changes over time. The information extracted by the framework can be used to improve subsequent management and optimization functions.

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Fatty acids (FAs) composition, 24 persistent organic pollutants (POPs), and 16 trace elements were examined in small pelagic fish (sardine, anchovy, round sardinella, chub and horse mackerels) caught by a fishing fleet for more than three years in the eastern Mediterranean Sea. Five Unmix source profiles associated with both sources, such as overlapping diet, including low-niche marine organisms and inputs from the surrounding environmental compartments were resolved. Inorganic compounds were notably more abundant in fish tissue than organochlorine xenobiotics. Comparison with the values of toxicological parameters revealed that the examined fish species are safe for human consumption, while the content of FAs emphasized the studied species as a valuable source of nutrients. A significant linear correlation was not observed between the 18 FAs and lipophilic organochlorines. Based on the obtained database, future assessments of the quality of edible fish species and the aquatic environment of the eastern Mediterranean Sea, which is known as an important fishing ground, could be significantly improved.

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An accurate estimation of exposure is essential for road collision rate estimation, which is key when evaluating the impact of road safety measures. The quasi-induced exposure method was developed to estimate relative exposure for different driver groups based on its main hypothesis: the not-at-fault drivers involved in two-vehicle collisions are taken as a random sample of driver populations. Liability assignment is thus crucial in this method to identify not-at-fault drivers, but often no liability labels are given in collision records, so unsupervised analysis tools are required. To date, most researchers consider only driver and speed offences in liability assignment, but an open question is if more information could be added. To this end, in this paper, the visual clustering technique of self-organizing maps (SOM) has been applied to better understand the multivariate structure in the data, to find out the most important variables for driver liability, analyzing their influence, and to identify relevant liability patterns. The results show that alcohol/drug use could be influential on liability and further analysis is required for disability and sudden illness. More information has been used, given that a larger proportion of the data was considered. SOM thus appears as a promising tool for liability assessment.

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Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review, we present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.

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This study investigated the effects of various pyrolysis temperatures and extraction salinities on the fluorescence features of DOM from Ulva prolifera-derived biochar under aseptic conditions using fluorescence excitation-emission matrix (EEM) spectroscopy with parallel factor (PARAFAC) analysis and self-organizing maps (SOM). Four humic-like substances and one protein-like substance were identified by the PARAFAC model. The contents and compositions of PARAFAC components depended more on the pyrolysis temperature than on the extraction salinity. A high pyrolysis temperature could enhance the release of humic-like DOM from biochar. Coupling PARAFAC and SOM facilitates the visualization and interpretation of the relationship between the pyrolysis temperature and the fluorescence properties of DOM. These results are valuable for understanding the effects and processes of macroalgal biochar in the possible environmental and industrial applications.

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Wastewater irrigation is a common livelihood practice in many parts of the developing world. With the continuous irrigation supply, groundwater systems in these regions perceive adverse impacts due to inadequate infrastructure to treat the wastewater. The current study area, Musi River irrigation system, is one such case study located in the peri-urban Hyderabad of South India. The Musi River water, which is used for irrigation, is composed of untreated and secondary treated wastewater from Hyderabad city. Kachiwani Singaram micro-watershed in the peri-urban Hyderabad is practicing wastewater irrigation for the last 40 years. The current quality of (untreated) wastewater used for irrigation is expected to have adverse impacts on the local aquifers, but detailed investigations are lacking. To elucidate the groundwater quality dynamics and seasonality of the wastewater irrigation impacts on the peri-urban agricultural system, we analyzed the groundwater quality on a monthly basis for one hydrological year in the wastewater and groundwater irrigated areas, which exist next to each other. The spatio-temporal variability of groundwater quality in the watershed was analyzed with respect to wastewater irrigation and seasonality using multivariate statistical analysis, multi-way modeling and self-organizing maps. This study indicates the significance of combining various statistical techniques for detailed evaluation of the groundwater processes in a wastewater irrigated agricultural system. The results suggest that concentrations of the major ionic substances increase after the monsoon season, especially in wastewater irrigated areas. Multi-way modeling identified the major polluted groundwaters to come from the wastewater irrigated parts of the watershed. Clusters of chemical variables identified by using self-organizing maps indicate that groundwater pollution is highly impacted by mineral interactions and long-term wastewater irrigation. The study recommends regular monitoring of water resources and development of sustainable management strategies to mitigate the aquifer pollution in wastewater irrigation systems.

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This paper investigates the relation of polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) in air samples with meteorological parameters (temperature, atmospheric pressure and relative humidity) using the Kohonen self-organizing map (SOM). Both gas- and particle-adsorbed phase of 20 PCB congeners and 7 OCPs including the three new ones (α-HCH, ß-HCH, and γ-HCH) listed in the Stockholm Convention were collected during a one-year period at urban locations in Zagreb (Croatia). Moving beyond existing studies, the SOM analysis showed that the meteorological characteristics of transient seasons such as spring had no influence on the dissimilarities in the behavior of PCBs and OCPs. Towards the identification of pollutant spatial patterns, the SOM did not isolate a clear phenomenon probably due to the absence of local pollution sources contributing to the elevated concentrations of these compounds. Overall, our results have shown that the SOM method, by recognizing significant differences among PCB and OCP seasonality, could be recommended in the analysis of pollutant distribution depending on temperature and atmospheric pressure.

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BACKGROUND AND OBJECTIVE: Pulmonary tuberculosis is a world emergency for the World Health Organization. Techniques and new diagnosis tools are important to battle this bacterial infection. There have been many advances in all those fields, but in developing countries such as Colombia, where the resources and infrastructure are limited, new fast and less expensive strategies are increasingly needed. Artificial neural networks are computational intelligence techniques that can be used in this kind of problems and offer additional support in the tuberculosis diagnosis process, providing a tool to medical staff to make decisions about management of subjects under suspicious of tuberculosis. MATERIALS AND METHODS: A database extracted from 105 subjects with precarious information of people under suspect of pulmonary tuberculosis was used in this study. Data extracted from sex, age, diabetes, homeless, AIDS status and a variable with clinical knowledge from the medical personnel were used. Models based on artificial neural networks were used, exploring supervised learning to detect the disease. Unsupervised learning was used to create three risk groups based on available information. RESULTS: Obtained results are comparable with traditional techniques for detection of tuberculosis, showing advantages such as fast and low implementation costs. Sensitivity of 97% and specificity of 71% where achieved. CONCLUSIONS: Used techniques allowed to obtain valuable information that can be useful for physicians who treat the disease in decision making processes, especially under limited infrastructure and data.

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The combination of fluorescence excitation-emission matrices (EEM), parallel factor analysis (PARAFAC) and self-organizing maps (SOM) is shown to be a powerful tool in the follow up of dissolved organic matter (DOM) removal from landfill leachate by physical-chemical treatment consisting of coagulation, granular activated carbon (GAC) and ion exchange. Using PARAFAC, three DOM components were identified: C1 representing humic/fulvic-like compounds; C2 representing tryptophan-like compounds; and C3 representing humic-like compounds. Coagulation with ferric chloride (FeCl3) at a dose of 7 g/L reduced the maximum fluorescence of C1, C2 and C3 by 52%, 17% and 15% respectively, while polyaluminium chloride (PACl) reduced C1 only by 7% at the same dose. DOM removal during GAC and ion exchange treatment of raw and coagulated leachate exhibited different profiles. At less than 2 bed volumes (BV) of treatment, the humic components C1 and C3 were rapidly removed, whereas at BV ≥ 2 the tryptophan-like component C2 was preferentially removed. Overall, leachate treated with coagulation +10.6 BV GAC +10.6 BV ion exchange showed the highest removal of C1 (39% - FeCl3, 8% - PACl), C2 (74% - FeCl3, 68% - PACl) and no C3 removal; whereas only 52% C2 and no C1 and C3 removal was observed in raw leachate treated with 10.6 BV GAC + 10.6 BV ion exchange only. Analysis of PARAFAC-derived components with SOM revealed that coagulation, GAC and ion exchange can treat leachate at least 50% longer than only GAC and ion exchange before the fluorescence composition of leachate remains unchanged.

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We consider real-life smart parking systems where parking lot occupancy data are collected from field sensor devices and sent to backend servers for further processing and usage for applications. Our objective is to make these data useful to end users, such as parking managers, and, ultimately, to citizens. To this end, we concoct and validate an automated classification algorithm having two objectives: (1) outlier detection: to detect sensors with anomalous behavioral patterns, i.e., outliers; and (2) clustering: to group the parking sensors exhibiting similar patterns into distinct clusters. We first analyze the statistics of real parking data, obtaining suitable simulation models for parking traces. We then consider a simple classification algorithm based on the empirical complementary distribution function of occupancy times and show its limitations. Hence, we design a more sophisticated algorithm exploiting unsupervised learning techniques (self-organizing maps). These are tuned following a supervised approach using our trace generator and are compared against other clustering schemes, namely expectation maximization, k-means clustering and DBSCAN, considering six months of data from a real sensor deployment. Our approach is found to be superior in terms of classification accuracy, while also being capable of identifying all of the outliers in the dataset.

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The hazard of chemical compounds can be prioritized according to their PBT (persistence, bioaccumulation, toxicity) properties by using Self-Organizing Maps (SOM). The objective of the present study was to develop an Integrated Risk Index of Chemical Aquatic Pollution (IRICAP), useful to evaluate the risk associated to the exposure of chemical mixtures contained in river waters. Four Spanish river basins were considered as case-studies: Llobregat, Ebro, Jucar and Guadalquivir. A SOM-based hazard index (HI) was estimated for 205 organic compounds. IRICAP was calculated as the product of the HI by the concentration of each pollutant, and the results of all substances were aggregated. Finally, Pareto distribution was applied to the ranked lists of compounds in each site to prioritize those chemicals with the most significant incidence on the IRICAP. According to the HI outcomes, perfluoroalkyl substances, as well as specific illicit drugs and UV filters, were among the most hazardous compounds. Xylazine was identified as one of the chemicals with the highest contribution to the total IRICAP value in the different river basins, together with other pharmaceutical products such as loratadine and azaperol. These organic compounds should be proposed as target chemicals in the implementation of monitoring programs by regulatory organizations.

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Biological systems are highly organized and enormously coordinated maintaining greater complexity. The increment of secondary data generation and progress of modern mining techniques provided us an opportunity to discover hidden intra and inter relations among these non linear dataset. This will help in understanding the complex biological phenomenon with greater efficiency. In this paper we report comparative classification of Pyruvate Dehydrogenase protein sequences from bacterial sources based on 28 different physicochemical parameters (such as bulkiness, hydrophobicity, total positively and negatively charged residues, α helices, ß strand etc.) and 20 type amino acid compositions. Logistic, MLP (Multi Layer Perceptron), SMO (Sequential Minimal Optimization), RBFN (Radial Basis Function Network) and SL (simple logistic) methods were compared in this study. MLP was found to be the best method with maximum average accuracy of 88.20%. Same dataset was subjected for clustering using 2*2 grid of a two dimensional SOM (Self Organizing Maps). Clustering analysis revealed the proximity of the unannotated sequences with the Mycobacterium and Synechococcus genus.