Electrochemical properties and facile preparation of hollow porous V2O5 microspheres for lithium-ion batteries.

Vanadium pentoxide (V2O5) has shown great potential to be used in lithium-ion batteries (LIBs), but it has limited applications because it has cycle instability and poor rate capability, and its lithiation mechanism is not well understood. In this work, hollow porous V2O5 microspheres (HPVOM) were obtained by a facile poly(vinylpyrrolidone) and ethylene glycol-assisted soft-template solvothermal method. Half cells with HPVOM exhibited good capacity, rate capability, and stability, delivering 407.9 mAh g-1 at 1.0 A g-1 after 700 cycles. Furthermore, a LiFePO4/HPVOM full cell had a discharge capacity of 109.9 mAh g-1 after 150 cycles at 0.1 A g-1. Using an equivalent circuit model (ECM) and distribution of relaxation times (DRT), we found that the charge-transfer (including the solid-state interface resistance) and bulk resistances varied regularly with the charge/discharge state, while the electrolyte resistance was largely maintained. The bulk resistance finally vanished, indicative of dynamic activation. The method used to prepare the hollow microspheres, as well as the display of electrode kinetics via ECM and DRT, could be broadly applied in developing efficient electrodes for LIBs.

Nonflammable, robust and flexible electrolytes enabled by phosphate coupled polymer-polymer for Li-metal batteries.

Liquid organic electrolytes commonly employed in commercial Li-ion batteries suffer from safety issues such as flammability and explosions. Replacing liquid electrolytes with nonflammable electrolytes has become increasingly attractive in the development of safe, high-energy Li-metal batteries (LMBs). In this work, nonflammable, robust, and flexible composite polymer-polymer electrolytes (PPEs) were successfully fabricated by flame-retardant solution casting with polyimide (PI) and polyvinylidene fluoride (PVDF). The optimized nonflammable PPEs (e.g., PPE-50) demonstrate not only good mechanical properties (i.e., a high tensile strength of 29.6 MPa with an elongation at break of 87.2%), but also high Li salts dissolubility, the former of which ensures the suppression of Li dendrites, while the latter further improves the ionic conductivity (∼1.86 × 10-4 S cm-1 at 30 °C). The resulting symmetric cells (Li|PPE-50|Li) offer excellent Li stripping and plating stability for 1000 h at 0.5 mA cm-2/0.25 mAh cm-2 and 600 h at 2.0 mA cm-2/1.0 mAh cm-2. In addition, the LiFePO4|PPE-50|Li half cells show high cycling performance (e.g., a reversible discharge capacity of 135.9 mAh g-1 after 300 cycles at 1C) and rate capability (e.g., 117.2 mAh g-1 at 4C). The PPE-50 is also compatible with a high-voltage cathode (e.g., LiNi0.5Mn0.3Co0.2O2), and the resulting batteries demonstrate long-term cycling stability with a high cut-off voltage of 4.5 V vs. Li/Li+. Because of the incorporation of a mechanically robust and thermally stable PI, a polar PVDF, and flame-retardant trimethyl phosphate (TMP) within PPEs, as well as the coordination between Li salts and TMP, and the interaction between Li salts and polymers (especially between Li bis(oxalato)borate) and PI, as well as the bis(oxalato)borate anion and PI), PPEs show great potential for practical and high-energy LMBs without safety concerns.

Transcriptome-derived stromal and immune scores infer clinical outcomes of patients with cancer.

The stromal and immune cells that form the tumor microenvironment serve a key role in the aggressiveness of tumors. Current tumor-centric interpretations of cancer transcriptome data ignore the roles of stromal and immune cells. The aim of the present study was to investigate the clinical utility of stromal and immune cells in tissue-based transcriptome data. The 'Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data' (ESTIMATE) algorithm was used to probe diverse cancer datasets and the fraction of stromal and immune cells in tumor tissues was scored. The association between the ESTIMATE scores and patient survival data was asessed; it was indicated that the two scores have implications for patient survival, metastasis and recurrence. Analysis of a colorectal cancer progression dataset revealed that decreased levels immune cells could serve an important role in cancer progression. The results of the present study indicated that trasncriptome-derived stromal and immune scores may be a useful indicator of cancer prognosis.

Impact of SNPs on Protein Phosphorylation Status in Rice (Oryza sativa L.).

Single nucleotide polymorphisms (SNPs) are widely used in functional genomics and genetics research work. The high-quality sequence of rice genome has provided a genome-wide SNP and proteome resource. However, the impact of SNPs on protein phosphorylation status in rice is not fully understood. In this paper, we firstly updated rice SNP resource based on the new rice genome Ver. 7.0, then systematically analyzed the potential impact of Non-synonymous SNPs (nsSNPs) on the protein phosphorylation status. There were 3,897,312 SNPs in Ver. 7.0 rice genome, among which 9.9% was nsSNPs. Whilst, a total 2,508,261 phosphorylated sites were predicted in rice proteome. Interestingly, we observed that 150,197 (39.1%) nsSNPs could influence protein phosphorylation status, among which 52.2% might induce changes of protein kinase (PK) types for adjacent phosphorylation sites. We constructed a database, SNP_rice, to deposit the updated rice SNP resource and phosSNPs information. It was freely available to academic researchers at http://bioinformatics.fafu.edu.cn. As a case study, we detected five nsSNPs that potentially influenced heterotrimeric G proteins phosphorylation status in rice, indicating that genetic polymorphisms showed impact on the signal transduction by influencing the phosphorylation status of heterotrimeric G proteins. The results in this work could be a useful resource for future experimental identification and provide interesting information for better rice breeding.

Classification of the fragrant styles and evaluation of the aromatic quality of flue-cured tobacco leaves by machine-learning methods.

During commercial transactions, the quality of flue-cured tobacco leaves must be characterized efficiently, and the evaluation system should be easily transferable across different traders. However, there are over 3000 chemical compounds in flue-cured tobacco leaves; thus, it is impossible to evaluate the quality of flue-cured tobacco leaves using all the chemical compounds. In this paper, we used Support Vector Machine (SVM) algorithm together with 22 chemical compounds selected by ReliefF-Particle Swarm Optimization (R-PSO) to classify the fragrant style of flue-cured tobacco leaves, where the Accuracy (ACC) and Matthews Correlation Coefficient (MCC) were 90.95% and 0.80, respectively. SVM algorithm combined with 19 chemical compounds selected by R-PSO achieved the best assessment performance of the aromatic quality of tobacco leaves, where the PCC and MSE were 0.594 and 0.263, respectively. Finally, we constructed two online tools to classify the fragrant style and evaluate the aromatic quality of flue-cured tobacco leaf samples. These tools can be accessed at http://bioinformatics.fafu.edu.cn/tobacco .