Pharmaceutical and personal care products-induced stress symptoms and detoxification mechanisms in cucumber plants.

Contamination of agricultural soils by pharmaceutical and personal care products (PPCPs) resulting from the application of treated wastewater, biosolids and animal wastes constitutes a potential environmental risk in many countries. To date a handful of studies have considered the phytotoxicity of individual PPCPs in crop plants, however, little is known about the effect of PPCPs as mixtures at environmentally relevant levels. This study investigated the uptake and transport, physiological responses and detoxification of a mixture of 17 PPCPs in cucumber seedlings. All PPCPs were detected at higher concentrations in roots compared to leaves, with root activity inhibited in a dose-dependent manner. At 5-50 μg/L, the mature leaves exhibited burnt edges as well as a reduction in photosynthesis pigments. Reactive oxygen species (ROS) production and lipid peroxidation increased with increasing PPCP concentrations; and their contents were greater in roots than in leaves for all PPCP treatments. Enzymes involved in various functions, including oxidative stress (superoxide dismutase and ascorbate peroxidase) and xenobiotic metabolism (peroxidase and glutathione S-transferase), were elevated to different levels depending on the PPCP concentration. Glutathione content gradually increased in leaves, while a maxima occurred at 0.5 μg L-1 PPCPs in roots, followed by a decrease thereafter. This study illustrated the complexity of phytotoxicity after exposure to PPCP mixtures, and provided insights into the molecular mechanisms likely responsible for the detoxification of PPCPs in higher plants

Formation of biologically active benzodiazepine metabolites in Arabidopsis thaliana cell cultures and vegetable plants under hydroponic conditions.

The use of recycled water for agricultural irrigation comes with the concern of exposure to crops by contaminants of emerging concerns (CECs). The concentration of CECs in plant tissues will depend on uptake, translocation and metabolism in plants. However, relatively little is known about plant metabolism of CECs, particularly under chronic exposure conditions. In this study, metabolism of the pharmaceutical diazepam was investigated in Arabidopsis thaliana cells and cucumber (Cucumis sativus) and radish (Raphanus sativus) seedlings grown in hydroponic solution following acute (7 d)/high concentration (1 mg L-1), and chronic (28 d)/low concentration (1 μg L-1) exposures. Liquid chromatography paired with mass spectrometry, 14C tracing, and enzyme extractions, were used to characterize the metabolic phases. The three major metabolites of diazepam - nordiazepam, temazepam and oxazepam - were detected as Phase I metabolites, with the longevity corresponding to that of human metabolism. Nordiazepam was the most prevalent metabolite at the end of the 5 d incubation in A. thaliana cells and 7 d, 28 d seedling cultivations. At the end of 7 d cultivation, non-extractable residues (Phase III) in radish and cucumber seedlings accounted for 14% and 33% of the added 14C-diazepam, respectively. By the end of 28 d incubation, the non-extractable radioactivity fraction further increased to 47% and 61%, indicating Phase III metabolism as an important destination for diazepam. Significant changes to glycosyltransferase activity were detected in both cucumber and radish seedlings exposed to diazepam. Findings of this study highlight the need to consider the formation of bioactive transformation intermediates and different phases of metabolism to achieve a comprehensive understanding of risks of CECs in agroecosystems

Acetaminophen detoxification in cucumber plants via induction of glutathione S-transferases.

Many pharmaceutical and personal care products (PPCPs) enter agroecosystems during reuse of treated wastewater and biosolids, presenting potential impacts on plant development. Here, acetaminophen, one of the most-used pharmaceuticals, was used to explore roles of glutathione (GSH) conjugation in its biotransformation in crop plants. Acetaminophen was taken up by plants, and conjugated quickly with GSH. After exposure to 5 mg L-1 acetaminophen for 144 h, GSH-acetaminophen conjugates were 15.2 ± 1.3 nmol g-1 and 1.2 ± 0.1 nmol g-1 in cucumber roots and leaves, respectively. Glutathione-acetaminophen was also observed in common bean, alfalfa, tomato, and wheat. Inhibition of cytochrome P450 decreased GSH conjugation. Moreover, the GSH conjugate was found to further convert to cysteine and N-acetylcysteine conjugates. Glutathione S-transferase activity was significantly elevated after exposure to acetaminophen, while levels of GSH decreased by 55.4% in roots after 48 h, followed by a gradual recovery thereafter. Enzymes involved in GSH synthesis, regeneration and transport were consistently induced to maintain the GSH homeostasis. Therefore, GST-mediated conjugation likely played a crucial role in minimizing phytotoxicity of acetaminophen and other PPCPs in plants

Intrusion of polyethylene glycol into solid-state nanopores

The intrusion of PEG aqueous solution into solid-state-nanopores upon mechanical pressure is experimentally investigated. By using hydrophobic nanoporous silica with a broad range of pore sizes, the characteristic size of PEG chains in water while penetrating nanopores is measured and analyzed, which increases with molecular weight and decreases with concentration of PEG. Its sensitivity to molecular weight is relatively limited due to nano-confinement. The inclusion of PEG as an intruding liquid imposes a rate effect on the intrusion pressure, and inhibits the extrusion from the nanopores

Rate effect of liquid infiltration into mesoporous materials

Rate effect of liquid infiltration in mesopores is associated with both liquid viscosity and the solid–liquid interfacial effect.</p

Elastomeric cellular structure enhanced by compressible liquid filler

Elastomeric cellular structures provide a promising solution for energy absorption. Their flexible and resilient nature is particularly relevant to protection of human bodies. Herein we develop an elastomeric cellular structure filled with nanoporous material functionalized (NMF) liquid. Due to the nanoscale infiltration in NMF liquid and its interaction with cell walls, the cellular structure has a much enhanced mechanical performance, in terms of loading capacity and energy absorption density. Moreover, it is validated that the structure is highly compressible and self-restoring. Its hyper-viscoelastic characteristics are elucidated

Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

Different aspects of a clinical sample can be revealed by multiple types of omics data. Integrated analysis of multi-omics data provides a comprehensive view of patients, which has the potential to facilitate more accurate clinical decision making. However, omics data are normally high dimensional with large number of molecular features and relatively small number of available samples with clinical labels. The "dimensionality curse" makes it challenging to train a machine learning model using high dimensional omics data like DNA methylation and gene expression profiles. Here we propose an end-to-end deep learning model called OmiVAE to extract low dimensional features and classify samples from multi-omics data. OmiVAE combines the basic structure of variational autoencoders with a classification network to achieve task-oriented feature extraction and multi-class classification. The training procedure of OmiVAE is comprised of an unsupervised phase without the classifier and a supervised phase with the classifier. During the unsupervised phase, a hierarchical cluster structure of samples can be automatically formed without the need for labels. And in the supervised phase, OmiVAE achieved an average classification accuracy of 97.49% after 10-fold cross-validation among 33 tumour types and normal samples, which shows better performance than other existing methods. The OmiVAE model learned from multi-omics data outperformed that using only one type of omics data, which indicates that the complementary information from different omics datatypes provides useful insights for biomedical tasks like cancer classification.Comment: 7 pages, 4 figure

Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records

The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis. However, due to imprecise descriptions, lack of gold standards and the demand for efficiency, annotating phenotypic abnormalities on millions of EHR narratives is still challenging. In this work, we propose a novel unsupervised deep learning framework to annotate the phenotypic abnormalities from EHRs via semantic latent representations. The proposed framework takes the advantage of Human Phenotype Ontology (HPO), which is a knowledge base of phenotypic abnormalities, to standardize the annotation results. Experiments have been conducted on 52,722 EHRs from MIMIC-III dataset. Quantitative and qualitative analysis have shown the proposed framework achieves state-of-the-art annotation performance and computational efficiency compared with other methods.Comment: Accepted by BIBM 2019 (Regular

Aggregate Model of District Heating Network for Integrated Energy Dispatch: A Physically Informed Data-Driven Approach

The district heating network (DHN) is essential in enhancing the operational flexibility of integrated energy systems (IES). Yet, it is hard to obtain an accurate and concise DHN model for the operation owing to complicated network features and imperfect measurement. Considering this, this paper proposes a physically informed data-driven aggregate model (AGM) for DHN, providing a concise description of the source-load relationship of DHN without exposing network details. First, we derive the analytical relationship between the state variables of the source and load nodes of DHN, offering a physical fundament for the AGM. Second, we propose a physics-informed estimator for AGM that is robust to low-quality measurement, in which the physical constraints associated with the parameter normalization and sparsity are embedded to improve the accuracy and robustness. Finally, we propose a physics-enhanced algorithm to solve the nonlinear estimator with non-closed constraints efficiently. Simulation results verify the effectiveness of the proposed method

Neurogenesis Potential Evaluation and Transcriptome Analysis of Fetal Hypothalamic Neural Stem/Progenitor Cells With Prenatal High Estradiol Exposure

High maternal estradiol is reported to induce metabolic disorders by modulating hypothalamic gene expression in offspring. Since neurogenesis plays a crucial role during hypothalamus development, we explored whether prenatal high estradiol exposure (HE) affects proliferation and differentiation of fetal hypothalamic neural stem/progenitor cells (NSC/NPCs) in mice and performed RNA sequencing to identify the critical genes involved. NSC/NPCs in HE mice presented attenuated cell proliferation but increased neuronal differentiation in vitro compared with control (NC) cells. Gene set enrichment analysis of mRNA profiles indicated that genes downregulated in HE NSC/NPCs were enriched in neurogenesis-related Gene Ontology (GO) terms, while genes upregulated in HE NSC/NPCs were enriched in response to estradiol. Protein-protein interaction analysis of genes with core enrichment in GO terms of neurogenesis and response to estradiol identified 10 Hub mRNAs, among which three were potentially correlated with six differentially expressed (DE) lncRNAs based on lncRNA profiling and co-expression analysis. These findings offer important insights into developmental modifications in hypothalamic NSC/NPCs and may provide new clues for further investigation on maternal environment programmed neural development disorders