Predicting bacterial transport through saturated porous media using an automated machine learning model

Escherichia coli, as an indicator of fecal contamination, can move from manure-amended soil to groundwater under rainfall or irrigation events. Predicting its vertical transport in the subsurface is essential for the development of engineering solutions to reduce the risk of microbiological contamination. In this study, we collected 377 datasets from 61 published papers addressing E. coli transport through saturated porous media and trained six types of machine learning algorithms to predict bacterial transport. Eight variables, including bacterial concentration, porous medium type, median grain size, ionic strength, pore water velocity, column length, saturated hydraulic conductivity, and organic matter content were used as input variables while the first-order attachment coefficient and spatial removal rate were set as target variables. The eight input variables have low correlations with the target variables, namely, they cannot predict target variables independently. However, using the predictive models, input variables can effectively predict the target variables. For scenarios with higher bacterial retention, such as smaller median grain size, the predictive models showed better performance. Among six types of machine learning algorithms, Gradient Boosting Machine and Extreme Gradient Boosting outperformed other algorithms. In most predictive models, pore water velocity, ionic strength, median grain size, and column length showed higher importance than other input variables. This study provided a valuable tool to evaluate the transport risk of E.coli in the subsurface under saturated water flow conditions. It also proved the feasibility of data-driven methods that could be used for predicting other contaminants’ transport in the environment

Thieno [2, 3-d] pyrimidine inhibits gastric cancer cell proliferation via the down-regulation of bcl-2 and survivin expressions

Purpose: To investigate the effect of thieno [2, 3-d] pyrimidine on gastric cancer (GC) cell proliferation, and elucidate the mechanism of action involved. Methods: Human GC cells (MKN1, MKN28 and SGC 7901) were cultured in RPMI-1640 medium supplemented with 10 % fetal bovine serum (FBS) and 1 % penicillin/ streptomycin solution at 37 °C for 24 h in a humidified atmosphere of 5 % CO2 and 95 % air. After attaining 60 - 70 % confluency, the cells were treated with serum-free medium and graded concentrations of thieno [2, 3-d] pyrimidine (0 – 12 µM) for 24 h. Normal cell culture without thieno [2, 3-d] pyrimidine served as control group. The cells were used in logarithmic growth phase. Cell viability and apoptosis were assessed using 3 (4,5 dimethyl thiazol 2 yl) 2,5 diphenyl 2H tetrazolium bromide (MTT), and flow cytometric assays, respectively. The levels of expression of ZNF139, B cell lymphoma 2 (bcl-2) and survivin in MKN1 cells and orthotopically transplanted mice were determined using Western blotting and real-time quantitative polymerase chain reaction (qRT-PCR). Results: Treatment of MKN1, MKN28 and SGC 7901 cells with thieno [2, 3-d] pyrimidine for 72 h led to significant and dose-dependent reductions in their viabilities, as well as significant and dose-dependent increases in the number of apoptotic cells (p < 0.05). The results of qRT-PCR and Western blotting showed that ZNF139 mRNA and protein expressions in MKN1 cells were significantly down-regulated by thieno [2, 3-d] pyrimidine treatment (p < 0.05). Thieno [2, 3-d] pyrimidine treatment significantly and dose-dependently down-regulated the expressions of bcl 2 and survivin proteins in MKN1 cells and orthotopically transplanted mice (p < 0.05). It also significantly and dose-dependently inhibited the proliferation of GC cells in orthotopic mouse model of GC after 31 days of treatment (p < 0.05). Conclusion: These results suggest that thieno [2, 3-d] pyrimidine suppresses the proliferation of GC cells via down-regulation of the expressions of ZNF139, bcl 2 and sur¬vivin. Thus, it has potentials for development for the management of gastric cancer

Map-based Channel Modeling and Generation for U2V mmWave Communication

Unmanned aerial vehicle (UAV) aided millimeter wave (mmWave) technologies have a promising prospect in the future communication networks. By considering the factors of three-dimensional (3D) scattering space, 3D trajectory, and 3D antenna array, a non-stationary channel model for UAV-to-vehicle (U2V) mmWave communications is proposed. The computation and generation methods of channel parameters including interpath and intra-path are analyzed in detail. The inter-path parameters are calculated in a deterministic way, while the parameters of intra-path rays are generated in a stochastic way. The statistical properties are obtained by using a Gaussian mixture model (GMM) on the massive ray tracing (RT) data. Then, a modified method of equal areas (MMEA) is developed to generate the random intra-path variables. Meanwhile, to reduce the complexity of RT method, the 3D propagation space is reconstructed based on the user-defined digital map. The simulated and analyzed results show that the proposed model and generation method can reproduce non-stationary U2V channels in accord with U2V scenarios. The generated statistical properties are consistent with the theoretical and measured ones as well

Fluorescent gold nanoparticles-based fluorescence sensor for Cu2+ ions

A new fluorescence sensor for the highly selective detection of Cu2+ ion with a detection limit of 3.6 nM based on the aggregation-induced fluorescence quenching of the highly fluorescent glutathione-capped gold nanoparticles is reported.National Natural Science Foundation of China [20675068, 20835005