Identification of light nonaqueous phase liquid groundwater contamination source based on empirical mode decomposition and deep learning.

The simulation optimization method was used to the identification of light nonaqueous phase liquid (LNAPL) groundwater contamination source (GCS) with the help of a hypothetical case in this study. When applying the simulation optimization method to identify GCS, it was a common technical means to establish surrogate model for the simulation model to participate in the iterative calculation to reduce the calculation load and calculation time. However, it was difficult for a single modeling method to establish surrogate model with high accuracy for the LNAPL contamination multiphase flow simulation model (MFSM). To give full play to advantages of single surrogate model and improve the accuracy of the surrogate model to the MFSM, a combination of deep belief neural network (DBNN) and long short-term memory (LSTM) neural network was used to establish artificial intelligence ensemble surrogate model (AIESM) for the MFSM. At the same time, to reduce the influence of noise in observed concentrations on the accuracy of the identification results, empirical mode decomposition (EMD) and wavelet analysis methods were used to denoise the observed concentrations, and their noise reduction effects were compared. The observed concentrations with better noise reduction effect and the observed concentrations without denoising were used to construct the objective function, and constraints of the optimization model were determined meanwhile. Then, the objective function and the constraints were integrated to build the optimization model to identify GCS and simulation model parameters. Applying the AIESM instead of the MFSM to embed in the optimization model and participate in the iterative calculation. Finally, the genetic algorithm (GA) was used to solve the optimization model to obtain the identification results of GCS and simulation model parameters. The results showed that compared with the single DBNN and LSTM surrogate models, AIESM obtained the highest accuracy and could replace the MFSM to participate in the iterative calculation, thereby reducing the calculation load and calculation time by more than 99%. Comparing with the wavelet analysis, EMD could reduce the noise in the concentrations more effectively, improved the accuracy of the approximated concentrations to the actual values, and increased the accuracy of the GCSs identification results by 1.45%.

Comparative analysis of groundwater contaminant sources identification based on simulation optimization and ensemble Kalman filter.

The location and release history of groundwater contaminant sources (GCSs) are usually unknown after groundwater contamination is detected, thereby greatly hindering the design of contamination remediation schemes and contamination risk assessments. Many previous studies have used prior information such as the observed contaminant concentrations (OCC) to obtain information of GCSs, and various methods have been proposed for identifying GCSs, including simulation optimization (S/O) and ensemble Kalman filter (EnKF) methods. For the first time, the present study compared the suitability of the S/O and EnKF methods for GCSs identification based on two case studies by specifically considering the calculation time and effectiveness of GCS identification. The results showed that EnKF could reduce the calculation time required by more than 62% compared with S/O. However, the time saved did not compensate for the poor accuracy of the GCSs identification results. When the simulated contaminant concentrations (SCC) were used for GCSs identification, the MRE of the identification results with the S/O and EnKF methods were 2.79% and 5.09% in case one, respectively, and were 4.75% and 6.72% in case two. When the OCC were used for GCSs identification, the MRE of the identification results with the S/O and EnKF methods were 27.77% and 110.74% in case one, respectively, and 27.53% and 60.61% in case two. The identification results obtained using the EnKF method were not credible and the superior performance of the S/O method was obvious, thereby indicating that the EnKF method is much less suitable for actual GCSs identification compared with the S/O method.

Groundwater contamination source identification using improved differential evolution Markov chain algorithm.

The groundwater contamination source identification (GCSI) can provide important bases for the design of pollution remediation plans. The Bayesian theory is commonly used in the GCSI problem. Usually, we use the Markov chain Monte Carlo (MCMC) method to realize the Bayesian framework. However, due to the ill-posed nature of the GCSI and the system model's complexity, the conventional MCMC algorithm is time-consuming and has low accuracy. In this study, we proposed an adaptive mutation differential evolution Markov chain (AM-DEMC) algorithm. In this algorithm, the Kent mapping chaotic sequence method, combined with differential evolution (DE) algorithm, was used to generate the initial population. In the iteration process, we introduced a hybrid mutation strategy to generate the candidate vectors. Moreover, we adaptively adjust the essential parameter F of the AM-DEMC algorithm according to the individual fitness value. For further improving the efficiency of solving the GCSI problem, the Kriging method was used to establish a surrogate model to avoid the enormous computational load associated with the numerical simulation model. Finally, a hypothetical groundwater contamination case was given to verify the effectiveness of the AM-DEMC algorithm. The results indicated that the proposed AM-DEMC algorithm successfully identified the contamination sources' characteristics and simulation model's parameters. It also exhibited stronger search-ability and higher accuracy than the MCMC and DE-MC algorithms.

Exploration of novel anti-angiogenic PEDF-derived peptides with improved activitives by inhibiting proliferation, suppressing migration, and inducing 67LR internalization.

Diabetic retinopathy (DR) remains high incidence and accounts for severe impact on vision in diabetics, but its mechanism is still poorly understood. Abnormal migration and proliferation of endothelial cells (ECs) drive neovascular retinopathies, which has an important role in promoting the occurrence and development of DR. In this study, we designed and synthesized a series of PEDF-derived peptides as angiogenesis inhibitors. Especially, compound G24 significantly inhibited the cell proliferation in VEGF-activated human umbilical vein endothelial cells (HUVECs) with IC50 values of 2.88 ± 0.19 µM. Further biological evaluation demonstrated that compound G24 exhibited strong inducing-effects on cell apoptosis and internalization of 67LR, and advanced inhibitory potency in cell migration and angiogenesis formed by HUVECs in vitro. In summary, the optimal compound G24 as a novel angiogenesis inhibitor showed the potentiality in the further research for the treatment for DR.

Design, synthesis, and biological evaluation of novel 4,4-difluoro-1-methyl-N, 6-diphenyl-5, 6-dihydro-4H-pyrimido [4, 5-b] [1, 2, 4] triazolo [4, 3-d] [1, 4] diazepin-8-amine derivatives as potential BRD4 inhibitors.

Bromodomain-containing protein 4 (BRD4) plays an extremely important physiological role in cancer, and the BRD4 inhibitors can effectively inhibit the proliferation of tumor cells. By taking BI-2536 (PLK1 and BRD4 inhibitor) as the lead compound, sixteen novel BRD4 inhibitors with the 4,4-difluoro-1-methyl-N,6-diphenyl-5,6-dihydro-4H-pyrimido[4,5-b] [1,2,4] triazolo[4,3-d] [1,4] diazepine-8-amine structure were designed and synthetized. Among the target compounds, compound 15h exhibited outstanding inhibition for BRD4-BD1 (IC50 value of 0.42 µM) in the BRD4-BD1 inhibitory activity assay. Additionally, cell growth inhibition assay demonstrated that compound 15h potently suppressed the proliferation of MV4-11 cells (IC50 value of 0.51 µM). Besides, compound 15h induced apoptosis and G0/G1 cycle arrest in MV4-11 leukemia cells effectively, and downregulated the expression of c-Myc in a dose-dependent manner. In summary, the optimal compound 15h is expected to become the clinical therapeutic drug for further research.

Identification of groundwater contamination sources and hydraulic parameters based on bayesian regularization deep neural network.

Simultaneous identification of various features of groundwater contamination sources and hydraulic parameters, such as hydraulic conductivities, can result in high-nonlinear inverse problem, which significantly hinders identification. A surrogate model was proposed to relieve computational burden caused by massive callings to simulation model in identification. However, shallow learning surrogate model may show limited fitting ability to high nonlinear problem. Thus, in this study, a simulation-optimization method based on Bayesian regularization deep neural network (BRDNN) surrogate model was proposed to efficiently solve high-nonlinear inverse problem. This method identified eight variables including locations and release intensities of two pollution sources and hydraulic conductivities of two partitions. Three hidden layers were employed in the BRDNN surrogate model, which profoundly improved the fitting capacity of nonlinear mapping relationship to the simulation model. Furthermore, Bayesian regularization was applied in the training process of neural network to solve overfitting problem. The results indicated that BRDNN was capable of establishing input-output interplay of high nonlinear inverse problem, which substantially reduced computational cost while ensuring a desirable level of accuracy. The utility of simulation-optimization on the basis of BRDNN surrogate model provided stable and reliable inversion results for groundwater contamination sources and hydraulic parameters.

Novel HLA-A2 restricted antigenic peptide derivatives with high affinity for the treatment of breast cancer expressing NY-ESO-1.

Tumor immunotherapy based on specific tumor antigen has become the focus for breast cancer, and research into cancer/testes antigens (CTA) is progressing. As an important member in the CTA, NY-ESO-1 plays a crucial role in the treatment and prognosis of breast cancer. In this study, we aimed to improve the binding ability to MHC by designing and synthesizing stable NY-ESO-1-derived peptides, based on NetMHC 4.0 webserver (http://www.cbs.dtu.dk/services/NetMHC/) and HLP webserver (http://crdd.osdd.net/raghava/hlp/pep_both.htm). Moreover, after modification of the lead compound, affinity of the peptides to human leukocyte antigen-A2 (HLA-A2) was determined by a flow cytometry and an inverted fluorescence microscope in T2 cells that show high expression of HLA-A2. The results demonstrated that the affinity of peptides II-4 and II-10 to HLA-A2 was significantly better when compared to others (II-Lead, II-1 ~ II-3, II-5 ~ II-9, II-11 ~ II-15). Further studies indicated that II-4 and II-10, especially II-4, significantly promoted the maturation of HLA-A2-positive human peripheral blood-derived dendritic cells (DCs) from morphology and surface markers, the activation of CD8 + T lymphocytes, and the type-specific killing effect on HLA-A2+/NY-ESO-1+ MDA-MB-231 cells. Molecular docking studies suggested a strong interaction between peptide II-4 and HLA-A2, thereby indicating that the II-4 is a promising candidate with antigenic potential in the field of immunotherapy that needs more studies.

Groundwater contaminant source characterization with simulation model parameter estimation utilizing a heuristic search strategy based on the stochastic-simulation statistic method.

In this study, a heuristic search strategy based on stochastic-simulation statistic (S-S) approach was developed for groundwater contaminant source characterization (GCSC) with simulation model parameter estimation. First, single kernel extreme learning machine (KELM) was built as surrogate system of the numerical simulation model to reduce huge computational load while evaluating the likelihood. However, compared with single KELM, multi-kernel extreme learning machine (MK-ELM) is more flexible for large amounts of data. To improve the approximation accuracy of the surrogate system to numerical simulation model, the MK-ELM surrogate system was first developed. Then, a heuristic search iterative process was first designed for GCSC with simulation model parameter estimation. The self-adaptive sampling method was proved to be more efficient than one-time sampling. Based on this idea, a self-adaptive feedback correction step was inserted into the heuristic search iterative process to ameliorate the training samples of the surrogate system in the posterior region, which further improved accuracy of simultaneous identification results. Finally, the identification results were obtained when the iteration terminated. The proposed approaches were tested in a hypothetical case study. It was shown that the heuristic search strategy can be used to assist in groundwater contaminant source characterization with simulation model parameter estimation.

Parallel heuristic search strategy based on a Bayesian approach for simultaneous recognition of contaminant sources and aquifer parameters at DNAPL-contaminated sites.

In this study, we develop a parallel heuristic search strategy based on a Bayesian approach for simultaneously recognizing groundwater contaminant sources and aquifer parameters (unknown variables) at sites contaminated with dense non-aqueous phase liquids (DNAPLs). The parallel search strategy is time-consuming because thousands of simulation models must run in order to calculate the likelihood. Various stand-alone surrogate systems for the simulation models have been established, but they also have unavoidable limitations. Thus, we develop an optimal combined surrogate system by combining Gaussian process, kernel extreme learning machine, and support vector regression methods using a differential evolution algorithm with a variable mutation rate based on the rand-to-best/1/bin strategy, thereby improving the approximation accuracy of the surrogate system to the simulation model and significantly decreasing the high computational cost. Utilizing the optimal combined surrogate system reduced the CPU time by more than 400 times. In the iterative parallel heuristic search process, each round of iteration involves determining the candidate points and state transitions. The Monte Carlo approach is used widely for selecting candidate point, but this approach does not readily converge to the posterior distribution for unknown variables when the probability density function types are complex with weak search ergodicity. In order to improve the search ergodicity, we develop a particle swarm optimization algorithm with a non-linear decreasing inertia weight and Metropolis criterion, which is more suitable for unknown variables with complex probability density functions. The recognition results are obtained simultaneously when the iterative process terminates. We assess our proposed approaches based on a hypothetical case study at a three-dimensional site contaminated with DNAPLs. The results demonstrate that the parallel heuristic search strategy is helpful for the simultaneous recognition of DNAPL contaminant sources in groundwater and aquifer parameters.

Groundwater contamination sources identification based on kernel extreme learning machine and its effect due to wavelet denoising technique.

Measurements of contaminant concentrations inevitably contain noise because of accidental and systematic errors. However, groundwater contamination sources identification (GCSI) is highly dependent on the data measurements, which directly affect the accuracy of the identification results. Thus, in the present study, the wavelet hierarchical threshold denoising method was employed to denoise concentration measurements and the denoised measurements were then used for GCSI. A 0-1 mixed-integer nonlinear programming optimization model (0-1 MINLP) based on a kernel extreme learning machine (KELM) was applied to identify the location and release history of a contamination source. The results showed the following. (1) The wavelet hierarchical threshold denoising method was not very effective when applied to concentration measurements observed every 2 months (the number of measurements is small and relatively discrete) compared with those obtained every 2 days (the number of measurements is large and relatively continuous). (2) When the concentration measurements containing noise were employed for GCSI, the identifications results were further from the true values when the measurements contained more noise. The approximation of the identification results to the true values improved when the denoised concentration measurements were employed for GCSI. (3) The 0-1 MINLP based on the surrogate KELM model could simultaneously identify the location and release history of contamination sources, as well reducing the computational load and decreasing the calculation time by 96.5% when solving the 0-1 MINLP.

Optimal design of groundwater pollution monitoring network based on the SVR surrogate model under uncertainty.

The simulation-optimization method is widely used in the design of the groundwater pollution monitoring network (GPMN). The uncertainty of the simulation model will significantly affect the design results of GPMN. When the Monte Carlo method is used to consider the influence of model uncertainty on the optimization results, the simulation model needs to be invoked many times, which will cause a huge amount of calculation. To reduce the calculation load, the study proposed to use the support vector regression (SVR) method to construct the surrogate model to couple the simulation model and the optimization model in the optimal design of GPMN. The optimization goal is to maximize the accuracy of the spatial description of pollution plume in each monitoring period. The study also considered the dynamic changes in the migration and morphological of pollution plumes in the optimization of GPMN. Finally, the West Shechang coal gangue pile in Fushun of China was used as a case study to verify the effectiveness of the above method. The results demonstrate that the SVR surrogate model can fit the input-output relationship of the simulation model to a high degree with less computation. The optimized monitoring network can reveal essential and comprehensive information about pollution plumes. The study provides a stable and reliable method for the design of GPMN.

Multiobjective optimization of the groundwater exploitation layout in coastal areas based on multiple surrogate models.

Seawater intrusion is a common problem in coastal areas. The rational distribution of groundwater exploitation can minimize the scope of seawater intrusion and maximize groundwater exploitation. In this study, an optimization method for the groundwater exploitation layout in coastal areas was proposed. Based on the numerical simulation model of variable-density groundwater, a multiobjective groundwater management model was constructed with the objectives of maximizing groundwater exploitation and minimizing seawater intrusion. The optimization model was solved by nondominated sorted genetic algorithm-II (NSGA-II). To improve the computational efficiency of the optimization model, the surrogate models of the groundwater simulation model were built by using three different methods: kriging, support vector regression (SVR), and kernel extreme learning machines (KELM). Finally, the above methods were tested in Longkou City of China. The results show that the use of surrogate models can greatly reduce the computing time for solving seawater intrusion management problems. The surrogate model of the variable-density groundwater simulation model based on the SVR method has the best performance. The groundwater exploitation layout optimized by the above method is reasonable and can reflect the actual hydrogeological conditions in the study area. This study provides a reliable way to optimize the groundwater exploitation layout in coastal areas.

Altertoxins with Quorum Sensing Inhibitory Activities from The Marine-Derived Fungus Cladosporium sp. KFD33.

Five new perylenequinone derivatives, altertoxins VIII-XII (1-5), as well as one known compound cladosporol I (6), were isolated from the fermentation broth of the marine-derived fungus Cladosporium sp. KFD33 from a blood cockle from Haikou Bay, China. Their structures were determined based on spectroscopic methods and ECD spectra analysis along with quantum ECD calculations. Compounds 1-6 exhibited quorum sensing inhibitory activities against Chromobacterium violaceum CV026 with MIC values of 30, 30, 20, 30, 20 and 30 µg/well, respectively.

Quinazoline-Containing Indole Alkaloids from the Marine-Derived Fungus Aspergillus sp. HNMF114.

Seven new quinazoline-containing indole alkaloids (1-7) named aspertoryadins A-G, along with nine known ones (8-16), were isolated from the marine-derived fungus Aspergillus sp. HNMF114 from the bivalve mollusk Sanguinolaria chinensis. The structures of the new compounds were elucidated from spectroscopic data, X-ray diffraction analysis, ECD spectra analysis, and ECD calculations. Compound 1 bears an aminosulfonyl group in the structure, which is rarely encountered in natural products. Compounds 6, 7, and 13 exhibited quorum sensing inhibitory activity against Chromobacterium violaceum CV026 with MIC values of 32, 32, and 16 µg/well, respectively.

Indole-Diterpenoids with Protein Tyrosine Phosphatase Inhibitory Activities from the Marine-Derived Fungus Penicillium sp. KFD28.

Five new indole-terpenoids named penerpenes E-I (1-5), along with seven known ones (6-12), were isolated from the marine-derived fungus Penicillium sp. KFD28 from a bivalve mollusk, Meretrix lusoria. The structures of the new compounds were elucidated from spectroscopic data and ECD spectroscopic analyses. Compound 1 was assigned as an indole-diterpenoid with a unique 6/5/5/6/6/5/5 heptacyclic ring system. Compound 2 represents an indole-diterpenoid with a new carbon skeleton derived from paxilline by the loss of three carbons (C-23/24/25). Compound 3 contains an additional oxygen atom between C-21 and C-22 compared to paxilline to form an unusual 6/5/5/6/6/7 hexacyclic ring system bearing a 1,3-dioxepane ring, which is rarely encountered in natural products. Compounds 1, 2, 4, and 6 showed inhibitory activities against protein tyrosine phosphatase 1B (PTP1B) with IC50 values of 14, 27, 23, and 13 µM, respectively.

Design, synthesis and biological evaluation of novel 4,5-dihydro-[1,2,4]triazolo[4,3-f]pteridine derivatives as potential BRD4 inhibitors.

Recently, diverse kinase inhibitors were reported having interaction with BRD4. It provided a strategy for developing a new structural framework for the next-generation BRD4-selective inhibitors. Starting from PLK1 kinase inhibitor BI-2536, we designed 18 compounds by modifying dihydropteridine core. Compound 23 showed potent BRD4 inhibitory activities with IC50 of 79 nM and no inhibitory activities for PLK1. Cell antiproliferation assay was performed and potent inhibitory activity against MV4;11 with IC50 of 1.53 µM. Cell apoptosis and western blotting indicated compound 23 induced apoptosis by down-regulating c-Myc. These novel selective BRD4 inhibitors provided new lead compounds for further drug development.

Characterization of Sugar Contents and Sucrose Metabolizing Enzymes in Developing Leaves of Hevea brasiliensis.

Sucrose-metabolizing enzymes in plant leaves have hitherto been investigated mainly in temperate plants, and rarely conducted in tandem with gene expression and sugar analysis. Here, we investigated the sugar content, gene expression, and the activity of sucrose-metabolizing enzymes in the leaves of Hevea brasiliensis, a tropical tree widely cultivated for natural rubber. Sucrose, fructose and glucose were the major sugars detected in Hevea leaves at four developmental stages (I to IV), with starch and quebrachitol as minor saccharides. Fructose and glucose contents increased until stage III, but decreased strongly at stage IV (mature leaves). On the other hand, sucrose increased continuously throughout leaf development. Activities of all sucrose-cleaving enzymes decreased markedly at maturation, consistent with transcript decline for most of their encoding genes. Activity of sucrose phosphate synthase (SPS) was low in spite of its high transcript levels at maturation. Hence, the high sucrose content in mature leaves was not due to increased sucrose-synthesizing activity, but more to the decline in sucrose cleavage. Gene expression and activities of sucrose-metabolizing enzymes in Hevea leaves showed striking differences compared with other plants. Unlike in most other species where vacuolar invertase predominates in sucrose cleavage in developing leaves, cytoplasmic invertase and sucrose synthase (cleavage direction) also featured prominently in Hevea. Whereas SPS is normally responsible for sucrose synthesis in plant leaves, sucrose synthase (synthesis direction) was comparable or higher than that of SPS in Hevea leaves. Mature Hevea leaves had an unusually high sucrose:starch ratio of about 11, the highest reported to date in plants.