Tuning the Metal Ions of Prussian Blue Analogues in Separators to Enable High-Power Lithium Metal Batteries.

The Li dendrite issue is the major barrier that limits the implement of Li metal anode practically, especially at high current density. From the perspective of the nucleation and growth mechanism of the Li dendrite, we rationally develop a novel Prussian blue analogues (PBA)-derived separator, where tuning the metal ions bestows the PBAs with open metal site to confine anion movement and thereby afford a high Li+ transference number (0.78), and PBA with ordered micropores could act as an ionic sieve to selectively extract Li+ and thereby homogenize Li+ flux. This demonstrates a highly reversible Li plating/stripping cycling for 3000 h at a practically high current density (5.0 mA cm-2). Consequently, a high loading Li||LiFeO4 battery (∼10.0 mg cm-2) demonstrates ultralong cycling life at high current densities (∼5.1 mA cm-2). This work highlights the prospect of optimizing PBAs in regulating ion transport behavior to enable high-power Li metal batteries.

PAHs Accumulations in Plant Leaves Around Coal-Fired Power Plant and Identification of their Potential Use as Bioindicators.

The purpose of this study was to investigate polycyclic aromatic hydrocarbons (PAHs) accumulation in leaves of different plant species growing in the neighborhood of coal-fired power plant (CPP) and to identify potential bioindicators for PAHs pollution monitoring. The study was performed in 8 sites in the surrounding areas of CPP. PAHs concentrations in leaves of 21 plant species growing within 1 km of CPP ranged from 0.043 to 4.52 µg g-1. A higher mean concentration of PAHs was found in leaves of perennial herbs and shrubs compared with annual herbs and trees. Herbaceous plants had the highest concentrations of 5-6 rings PAHs, and 4-ring PAHs mainly existed in shrubs. For 2- to 3-rings PAHs, there was no significant difference among herbaceous plants trees and shrubs. Then, four representative plants were further chosen for investigating the effect of CPP on the spatial distribution patterns of PAH compounds. No distinct difference in the level of 2- to 3-rings PAHs was observed on Broussonetia kaempferi Sieb, whereas 4 rings, 5-6 rings, and Σ16PAHs had regional statistical differences. PAHs in Kalimeris indica (L.) Sch.-Bip had significant regional statistical differences. With the change of distance, the concentration of PAHs showed a significant decrease. Taraxacum mongolicum tended to capture the largest amount of both total PAHs and 5- to 6-ring PAHs, especially to BaP. These results could improve scientific evidence for the screening of bioindicators, in particular, T. mongolicum could be a priority.

A novel approach for drug response prediction in cancer cell lines via network representation learning.

MOTIVATION: Prediction of cancer patient's response to therapeutic agent is important for personalized treatment. Because experimental verification of reactions between large cohort of patients and drugs is time-intensive, expensive and impractical, preclinical prediction model based on large-scale pharmacogenomic of cancer cell line is highly expected. However, most of the existing computational studies are primarily based on genomic profiles of cancer cell lines while ignoring relationships among genes and failing to capture functional similarity of cell lines. RESULTS: In this study, we present a novel approach named NRL2DRP, which integrates protein-protein interactions and captures similarity of cell lines' functional contexts, to predict drug responses. Through integrating genomic aberrations and drug responses information with protein-protein interactions, we construct a large response-related network, where the neighborhood structure of cell line provides a functional context to its therapeutic responses. Representation vectors of cell lines are extracted through network representation learning method, which could preserve vertices' neighborhood similarity and serve as features to build predictor for drug responses. The predictive performance of NRL2DRP is verified by cross-validation on GDSC dataset and methods comparison, where NRL2DRP achieves AUC > 79% for half drugs and outperforms previous methods. The validity of NRL2DRP is also supported by its effectiveness on uncovering accurate novel relationships between cell lines and drugs. Lots of newly predicted drug responses are confirmed by reported experimental evidences. AVAILABILITY AND IMPLEMENTATION: The code and documentation are available on https://github.com/USTC-HIlab/NRL2DRP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A novel heterogeneous network-based method for drug response prediction in cancer cell lines.

An enduring challenge in personalized medicine lies in selecting a suitable drug for each individual patient. Here we concentrate on predicting drug responses based on a cohort of genomic, chemical structure, and target information. Therefore, a recently study such as GDSC has provided an unprecedented opportunity to infer the potential relationships between cell line and drug. While existing approach rely primarily on regression, classification or multiple kernel learning to predict drug responses. Synthetic approach indicates drug target and protein-protein interaction could have the potential to improve the prediction performance of drug response. In this study, we propose a novel heterogeneous network-based method, named as HNMDRP, to accurately predict cell line-drug associations through incorporating heterogeneity relationship among cell line, drug and target. Compared to previous study, HNMDRP can make good use of above heterogeneous information to predict drug responses. The validity of our method is verified not only by plotting the ROC curve, but also by predicting novel cell line-drug sensitive associations which have dependable literature evidences. This allows us possibly to suggest potential sensitive associations among cell lines and drugs. Matlab and R codes of HNMDRP can be found at following https://github.com/USTC-HIlab/HNMDRP .

New application of an old drug: Antitumor activity and mechanisms of doxycycline in small cell lung cancer.

Small cell lung cancer (SCLC) remains one of the most aggressive tumors with a poor prognosis. The clinical outcome of SCLC patients has reached its plateau with the existing standard treatment and thus new therapies are urgently required. Accumulating evidences have indicated that doxycycline, a commonly used antibiotic, has antitumor activity against several malignancies. However, whether doxycycline has antitumor activity in SCLC and its underlying mechanisms remain unclear. Our investigation demonstrated that doxycycline could significantly inhibit the proliferation and colony formulation of SCLC cells (p<0.05). Furthermore, both Hoechst 33258 dye staining and TUNEL assays indicated that doxycycline could induce remarkable apoptosis of H446 cells in a concentration-dependent manner. RT-PCR and western blot assays proved that apoptosis induction effect of doxycycline was achieved via inducing the expression of caspase-3 and bax, as well as attenuating the expression of survivin and bcl-2. Moreover, the wound healing assay and Transwell assay indicated that doxycycline could significantly suppress the migration and invasion of H446 cells in a concentration-dependent manner (p<0.05). ELISA assay proved that the inhibitory effect of doxycycline on the migration and invasion of H446 cells was achieved via decreasing the secretion of MMP-2, MMP-9 and VEGF, as well as increasing the secretion of TIMP-2. Taken together, doxycycline dose-dependently suppressed the proliferation, colony formulation, migration and invasion of SCLC cells, as well as induced apoptosis. These findings encourage further investigations on the potential of doxycycline as a candidate drug for the treatment of SCLC.

Redefining the modular organization of the core Mediator complex.

The Mediator complex plays an essential role in the regulation of eukaryotic transcription. The Saccharomyces cerevisiae core Mediator comprises 21 subunits, which are organized into Head, Middle and Tail modules. Previously, the Head module was assigned to a distinct dense domain at the base, and the Middle and Tail modules were identified to form a tight structure above the Head module, which apparently contradicted findings from many biochemical and functional studies. Here, we compared the structures of the core Mediator and its subcomplexes, especially the first 3D structure of the Head + Middle modules, which permitted an unambiguous assignment of the three modules. Furthermore, nanogold labeling pinpointing four Mediator subunits from different modules conclusively validated the modular assignment, in which the Head and Middle modules fold back on one another and form the upper portion of the core Mediator, while the Tail module forms a distinct dense domain at the base. The new modular model of the core Mediator has reconciled the previous inconsistencies between the structurally and functionally defined Mediator modules. Collectively, these analyses completely redefine the modular organization of the core Mediator, which allow us to integrate the structural and functional information into a coherent mechanism for the Mediator's modularity and regulation in transcription initiation.