Examining plant uptake and translocation of emerging contaminants using machine learning: Implications to food security.

When water and solutes enter the plant root through the epidermis, organic contaminants in solution either cross the root membranes and transport through the vascular pathways to the aerial tissues or accumulate in the plant roots. The accumulation of contaminants in plant roots and edible tissues is measured by root concentration factor (RCF) and fruit concentration factor (FCF). In this paper, 1) a neural network (NN) was applied to model RCF based on physicochemical properties of organic compounds, 2) correlation and significance of physicochemical properties were assessed using statistical analysis, 3) fuzzy logic was used to examine the simultaneous impacts of significant compound properties on RCF and FCF, 4) a clustering algorithm (k-means) was used to identify unique groups and discover hidden relationships within contaminants in various parts of the plants. The physicochemical cutoffs achieved by fuzzy logic for the RCF and the FCF were compared versus the cutoffs for compounds that crossed the plant root membranes and found their way into transpiration stream (measured by transpiration stream concentration factor, TSCF). The NN predicted the RCF with improved accuracy compared to mechanistic models. The analysis indicated that log Kow, molecular weight, and rotatable bonds are the most important properties for predicting the RCF. These significant compound properties are positively correlated with RCF while they are negatively correlated with TSCF. Comparing the relationships between compound properties in various plant tissues showed that compounds detected in the edible parts have physicochemical cutoffs that are more like the compounds crossing the plant root membranes (into xylem tissues) than the compounds accumulating in the plant roots, with clear relationships to food security. The cluster analysis placed the contaminants into three meaningful groups that were in agreement with the results of fuzzy logic.

A deeper look at plant uptake of environmental contaminants using intelligent approaches.

Uptake of contaminants from the groundwater is one pathway of interest, and efforts have been made to relate root exposure to transloation throughout the plant, termed the transpiration stream concentration factor (TSCF). This work utilized machine learning techniques and statistcal analysis to improve the understanding of plant uptake and translocation of emerging contaminants. Neural network (NN) was used to develop a reliable model for predicting TSCF using physicochemical properties of compounds. Fuzzy logic was as a technique to examine the simultaneous impact of properties on TSCF, and interactions between compound properties. The significant and effective compound properties were determined using stepwise and forward regression as two widely used statiscal techniques. Clustering was used for detecting the hidden structures in the plant uptake data set. The NN predicted the TSCF with improved accuracy compared to mechanistic models. We also delivered new insight to compound properteis and their importance in transmembrane migration. The sensitivity analysis indicated that log Kow, molecular weight, hydrogen bond donor, and rotatable bonds are the most important properties. The results of fuzzy logic demonstrated that the relationship between molecular weight and log Kow with TSCF are both bell-shape and sigmoidal. The employed clustering algorithms all discovered two major distinct clusters in the data set.

Ensemble statistical and subspace clustering model for analysis of autism spectrum disorder phenotypes.

Heterogeneity in Autism Spectrum Disorder (ASD) is complex including variability in behavioral phenotype as well as clinical, physiologic, and pathologic parameters. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) now diagnoses ASD using a 2-dimensional model based social communication deficits and fixated interests and repetitive behaviors. Sorting out heterogeneity is crucial for study of etiology, diagnosis, treatment and prognosis. In this paper, we present an ensemble model for analyzing ASD phenotypes using several machine learning techniques and a k-dimensional subspace clustering algorithm. Our ensemble also incorporates statistical methods at several stages of analysis. We apply this model to a sample of 208 probands drawn from the Simon Simplex Collection Missouri Site patients. The results provide useful evidence that is helpful in elucidating the phenotype complexity within ASD. Our model can be extended to other disorders that exhibit a diverse range of heterogeneity.