A water quality prediction model based on signal decomposition and ensemble deep learning techniques.

Accurate water quality predictions are critical for water resource protection, and dissolved oxygen (DO) reflects overall river water quality and ecosystem health. This study proposes a hybrid model based on the fusion of signal decomposition and deep learning for predicting river water quality. Initially, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to split the internal series of DO into numerous internal mode functions (IMFs). Subsequently, we employed multi-scale fuzzy entropy (MFE) to compute the entropy values for each IMF component. Time-varying filtered empirical mode decomposition (TVFEMD) is used to further extract features in high-frequency subsequences after linearly aggregating the high-frequency sequences. Finally, support vector machine (SVM) and long short-term memory (LSTM) neural networks are used to predict low- and high-frequency subsequences. Moreover, by comparing it with single models, models based on 'single layer decomposition-prediction-ensemble' and combination models using different methods, the feasibility of the proposed model in predicting water quality data for the Xinlian section of Fuhe River and the Chucha section of Ganjiang River was verified. As a result, the combined prediction approach developed in this work has improved generalizability and prediction accuracy, and it may be used to forecast water quality in complicated waters.

Urchin-like fluorinated covalent organic frameworks decorated fabric for effective self-cleaning and versatile oil/water separation.

Oil contamination and industrial organic pollutants emission have been a serious problem affecting the ecological and residential environment. Membrane-based separation shows great application prospect due to its low-cost, environmental-friendly and easy operation. Therefore, the development of efficient oil-water separation membranes is highly desirable. Herein, a fabric filter with superwettability was prepared by coating urchin-like fluorinated covalent organic frameworks (COFs) on fabric, which was well utilized in filtering immiscible oil-water mixture and surfactant-stabilized water-in-oil emulsion driven only by gravity for the first time. The as-prepared COF fabric filter (defined as fabric@u-FCOF) possessed many outstanding properties, including superhydrophobicity with the water contact angle of approximately 151.6°, satisfactory resistance for alkaline, acidic and saline environments, as well as superior mechanical durability under harsh conditions. Because of the super-micropore of fabric@u-FCOF and the nanopore in the COF coating, the obtained fabric@u-FCOF exhibited excellent performances in terms of separation efficiency and permeability, in which the oil flux was up to 16964 L·m-1·h-2 and separation efficiency for the mixed o-dichlorobenzene/water was higher than 99.4%. In addition, the fabric@u-FCOF also showed excellent self-cleaning performance due to the micro/nano hierarchical structure of its surface. These excellent properties make it an ideal candidate for applications of oil/water separation and water purification.

Super-hydrophilic sulfonate-modified covalent organic framework nanosheets for efficient separation and enrichment of glycopeptides.

Highly efficient extraction of glycopeptides prior to mass spectrometry detection is extremely crucial for glycoproteomic research, especially in disease biomarker research. Reported here is the first time by applying two-dimensional (2D) covalent organic framework (COFs) nanosheets for highly efficient enrichment of glycopeptides. Particularly, by incorporating hydrophilic monomers through a bottom-up strategy, the 2D COF nanosheets (denoted as NUS-9) displayed an ultra-high graft density of sulfonic groups and super-hydrophilicity. In addition, because of the large surface area, low steric hindrance, high chemical stability, and abundant accessibility sites of 2D COF nanosheets, NUS-9 exhibited remarkable efficiency for glycopeptide enrichment, involving excellent detection sensitivity (0.01 fmol µL-1), outstanding enrichment capability, and good enrichment selectivity (1:1500, horseradish peroxidase (HRP) tryptic digest to bovine serum albumin (BSA) tryptic digest), and recovery (92.2 ± 2.0%). Moreover, the NUS-9 was able to unambiguously detect 631 endogenous glycopeptides from human saliva, demonstrating an unparalleled high efficiency in glycopeptide enrichment. Gene ontology analyses of proteins from human saliva enriched by NUS-9 demonstrated its potential for comprehensive glycoproteome analysis.

Facile synthesis of Cu2+-immobilized magnetic covalent organic frameworks for highly efficient enrichment and sensitive determination of five phthalate monoesters from mouse plasma with HPLC-MS/MS.

Development of a simple, highly selective, and sensitive analytical method for phthalate monoesters (mPAEs) remains a challenge due to the complexity of biological samples. To address this issue, Cu2+ immobilized magnetic covalent organic frameworks (Fe3O4@TtDt@Cu2+ composites) with core-shell structures were prepared to enhance the enrichment efficiency of mPAEs by a facile approach synthesis of COFs shells with inherent bifunctional groups on Fe3O4 NPs and further Cu2+ immobilization. The composites exhibit high specific surface area (348.1 m2 g-1), outstanding saturation magnetization (34.94 emu g-1), ordered mesoporous structure, Cu2+ immobilization, and excellent thermal stability. Accordingly, a magnetic solid-phase extraction (MSPE) pretreatment technique based on Cu2+ immobilized COF composites combined with high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) was established, and key parameters including the adsorbent amount, adsorption time, elution solvent, etc. were examined in detail. The developed analytical method showed wide linear ranges (10-8000 ng L-1), low limit of detections (LODs, 2-10 ng L-1), and good correlation coefficients (R2 ≥ 0.9904) for the five mPAEs. Furthermore, the analytical method was also successfully applied to the highly sensitive detection of metabolite mPAEs in mouse plasma samples, indicating the promising application of the Fe3O4@TtDt@Cu2+ composites as a quick and efficient adsorbent in the sample pretreatment.

Influence of Deep Invasive Tumor Thrombus on the Surgical Complexity and Prognosis of Patients With Non-Metastatic Renal Cell Carcinoma Combined With Venous Tumor Thrombus.

BACKGROUND: To evaluate the impact of deep invasive tumor thrombus (DITT) on the surgical complexity and prognosis of patients with renal cell carcinoma with venous tumor thrombus. METHODS: We retrospectively reviewed clinical data of 138 patients with non-metastatic renal cell carcinoma combined with venous tumor thrombus, who underwent surgical treatment in Peking University Third Hospital from January 2015 to June 2020. Patients were divided into the DITT group (84 patients) and non-invasive tumor thrombus (NITT) group (54 patients). Chi-square, t-test and Mann-Whitney U test were used for categorical and continuous variables, respectively. Kaplan-Meier plots were performed to evaluate the influence of DITT. Univariable and multivariable Cox regressions were conducted to determine independent prognostic factors and then assembled to make a nomogram to predict the survival. The performance of the nomogram was evaluated by Harrell's consistency index (C-index) and calibration plot. RESULTS: Deep invasive tumor thrombus significantly increased the difficulty of surgery for patients with renal cell carcinoma with venous tumor thrombus, which is mainly reflected in longer operation time (p < 0.001), more surgical bleeding (p  < 0.001), a higher proportion of perioperative blood transfusion (p  = 0.006), a higher proportion of open surgery (p = 0.001), a longer postoperative hospital stay (p = 0.003), and a higher proportion of postoperative complications (p = 0.001). DITT (hazard ratio [HR] = 2.781, p = 0.040) was one of the independent risk factors for worse prognosis. Multivariate analysis showed that sarcoma-like differentiation (p = 0.040), tumor thrombus invasion (p = 0.040), low hemoglobin (p = 0.003), and pathological type (p < 0.001) were independent prognostic factors. The nomogram, combining all these predictors, showed powerful prognostic ability with a C-index of 78.8% (CI: 71.2%-86.4%). The predicted risk closely matches the observed recurrence probability. CONCLUSION: Deep invasive tumor thrombus significantly increased the difficulty of surgeries for patients of renal cell carcinoma with venous tumor thrombus, and may lead to poor prognosis.

Syndiospecific Coordination Polymerization of Si-H-Containing Styrenes Catalyzed by Scandium Complex and Synthesis of Styrene-Based Triblock Copolymers.

The controlled syndiospecific polymerization of three Si-H-containing styrenes, that is, 4-(methylhydrosilyl)styrene (FSt-1), 4-(dimethylhydrosilyl)styrene (FSt-2) and 4-(diisopropylhydrosilyl)styrene (FSt-3), is realized in the presence of (C5 Me4 SiMe3 )Sc (CH2 C6 H5 )2 (THF)/[Ph3 C][B(C6 F5 )4 ]. Then a series of FSt-b-styrene-b-FSt triblock copolymers (FSt-St-FSt) are synthesized facilely via a sequential monomer feeding process (FSt-2, styrene, and FSt-2, respectively) during the polymerization. The syndiotactic polystyrene (sPS) block in the middle endows the copolymer with high melting point above 250 °C, whereas the Si-H groups on monomer FSt-2 introduce functional pendants to the end-blocks (with Si-H content of 31-63 mol%). Finally by a mild and high-effective hydrosilylation reaction, novel polar-group functionalized sPS is obtained.