Here, we report a novel polymeric nanoparticle prepared by the self-assembly of amphiphilic copolymers containing a fluorescent naphthalimide (NAPH) and a photochromic spiropyran (SP), which possesses reversibly photoswitchable dual-color fluorescence and controlled release properties. The amphiphilic copolymers were synthesized by incorporating NAPH and SP into methyl ether poly(ethylene glycol)-poly(β-amino esters) (MPEG-PAE) via quaternization. The nanoparticles would change between yellow and purple reversibly upon UV and visible light irradiation because of the photoisomerization between SP and merocyanine (MC). The corresponding fluorescence would be switched between green and orange-red reversibly upon blue light excitation through the fluorescence resonance energy transfer from the excited NAPH to the photoisomerized MC. Meanwhile, the prepared spherical nanoparticles could be swollen under UV irradiation as the hydrophobic SP isomerized to hydrophilic MC; the nanoparticles could also be swollen under acidic conditions because of the protonation of the amino groups of PAE. Upon UV light irradiation and acidic stimulation, the cargoes, hydrophobic Coumarin 102, encapsulated in the nanoparticles would be released. The prepared nanoparticles, which exhibit not only excellent reversible dual-color fluorescence properties but also prominent controlled release performance, will open up new possibilities for the combined application of fluorescence imaging and controlled release.
Very-long-chain (VLC) alkanes are the main wax compounds of tomato fruit and leaf. ECERIFERUM1 (CER1) and ECERIFERUM3 (CER3) are the two key genes involved in VLC alkane biosynthesis in Arabidopsis thaliana. However, the CER1 and CER3 homologous genes in tomato have not been investigated and their exact biological function remains unknown. We analyzed the wax profiles in tomato leaves and fruits at different growth stages, and characterized the CER1 and CER3 homologous genes. VLC alkanes were the predominant wax compounds both in the leaf and fruit at all developmental stages. We identified five CER1 homologs and two CER3 homologs in tomato, which were designated as SlCER1–1 to SlCER1–5 and SlCER3–1 and SlCER3–2 respectively. The genes exhibited tissue- and organ-dependent expression patterns and were induced by abiotic stresses. SlCER1–1 was localized to the endoplasmic reticulum (ER), which is also the main site of wax biosynthesis. Silencing the SlCER1–1 gene in tomato significantly reduced the amounts of n-Alkanes and branched alkanes, whereas its overexpression in Arabidopsis had the opposite effect. Under drought stress, both n-Alkanes and branched alkanes increased significantly in wild-type but not the SlCER1–1 RNAi tomato plants. Furthermore, SlCER1–1 silencing also increased the cuticular permeabilities of the leaves and fruits. In conclusion, SlCER1–1 is involved in wax alkane biosynthesis in tomato and plays an important role in the drought tolerance and fruit storability.
Without explicit description of map application themes, it is difficult for users to discover desired map resources from massive online Web Map Services (WMS). However, metadata-based map application theme extraction is a challenging multi-label text classification task due to limited training samples, mixed vocabularies, variable length and content arbitrariness of text fields. In this paper, we propose a novel multi-label text classification method, Text GCN-SW-KNN, based on geographic semantics and collaborative training to improve classification accuracy. The semi-supervised collaborative training adopts two base models, i.e. a modified Text Graph Convolutional Network (Text GCN) by utilizing Semantic Web, named Text GCN-SW, and widely-used Multi-Label K-Nearest Neighbor (ML-KNN). Text GCN-SW is improved from Text GCN by adjusting the adjacency matrix of the heterogeneous word document graph with the shortest semantic distances between themes and words in metadata text. The distances are calculated with the Semantic Web of Earth and Environmental Terminology (SWEET) and WordNet dictionaries. Experiments on both the WMS and layer metadata show that the proposed methods can achieve higher F1-score and accuracy than state-of-the-art baselines, and demonstrate better stability in repeating experiments and robustness to less training data. Text GCN-SW-KNN can be extended to other multi-label text classification scenario for better supporting metadata enhancement and geospatial resource discovery in Earth Science domain.
This paper treats aluminum surface treatment solution wastewater and adopts coagulation-chemical precipitation method (combination of CaO, PAC and PAM) to remove F -, PO 4 3− and SO 4 2− . The study explores an approach that can remove F -, PO 4 3− and SO 4 2− in the wastewater simultaneously. Through optimization and improvement of reagent dosage and reaction conditions, the treated wastewater reaches the national discharge standards. The experimental results show that the optimal conditions for removal of F -, PO 4 3− and SO 4 2− are: 1150 mg of CaO, 110 mg of PAC, 85 mg of PAM at pH 9 and a reaction temperature of 323 K for a reaction time of 30 min. After treatment, the concentration of F -is 4.30 mg/L, which is lower than discharge standards of fluoride-containing industrial wastewater (10 mg/L). The concentration of PO 4 3− is 0.90 mg/L, lower than the second grade of discharge standard of phosphate-containing industrial wastewater (1.0 mg/L). The concentration of SO 4 2− is 125mg/L, lower than the maximum of allowable concentration of sulfate in drinking water standards (250 mg/L).
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