A Nacre-Like Carbon Nanotube Sheet for High Performance Li-Polysulfide Batteries with High Sulfur Loading

© 2018 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Lithium-sulfur (Li-S) batteries are considered as one of the most promising energy storage systems for next-generation electric vehicles because of their high-energy density. However, the poor cyclic stability, especially at a high sulfur loading, is the major obstacles retarding their practical use. Inspired by the nacre structure of an abalone, a similar configuration consisting of layered carbon nanotube (CNT) matrix and compactly embedded sulfur is designed as the cathode for Li-S batteries, which are realized by a well-designed unidirectional freeze-drying approach. The compact and lamellar configuration with closely contacted neighboring CNT layers and the strong interaction between the highly conductive network and polysulfides have realized a high sulfur loading with significantly restrained polysulfide shuttling, resulting in a superior cyclic stability and an excellent rate performance for the produced Li-S batteries. Typically, with a sulfur loading of 5 mg cm−2, the assembled batteries demonstrate discharge capacities of 1236 mAh g−1 at 0.1 C, 498 mAh g−1 at 2 C and moreover, when the sulfur loading is further increased to 10 mg cm−2 coupling with a carbon-coated separator, a superhigh areal capacity of 11.0 mAh cm−2 is achieved

Construction and Application of an Electronic Spatiotemporal Expression Profile and Gene Ontology Analysis Platform Based on the EST Database of the Silkworm, Bombyx mori

An Expressed Sequence Tag (EST) is a short sub-sequence of a transcribed cDNA sequence. ESTs represent gene expression and give good clues for gene expression analysis. Based on EST data obtained from NCBI, an EST analysis package was developed (apEST). This tool was programmed for electronic expression, protein annotation and Gene Ontology (GO) category analysis in Bombyx mori (L.) (Lepidoptera: Bombycidae). A total of 245,761 ESTs (as of 01 July 2009) were searched and downloaded in FASTA format, from which information for tissue type, development stage, sex and strain were extracted, classified and summed by running apEST. Then, corresponding distribution profiles were formed after redundant parts had been removed. Gene expression profiles for one tissue of different developmental stages and from one development stage of the different tissues were attained. A housekeeping gene and tissue-and-stage-specific genes were selected by running apEST, contrasting with two other online analysis approaches, microarray-based gene expression profile on SilkDB (BmMDB) and EST profile on NCBI. A spatio-temporal expression profile of catalase run by apEST was then presented as a three-dimensional graph for the intuitive visualization of patterns. A total of 37 query genes confirmed from microarray data and RT—PCR experiments were selected as queries to test apEST. The results had great conformity among three approaches. Nevertheless, there were minor differences between apEST and BmMDB because of the unique items investigated. Therefore, complementary analysis was proposed. Application of apEST also led to the acquisition of corresponding protein annotations for EST datasets and eventually for their functions. The results were presented according to statistical information on protein annotation and Gene Ontology (GO) category. These all verified the reliability of apEST and the operability of this platform. The apEST can also be applied in other species by modifying some parameters and serves as a model for gene expression study for Lepidoptera

Akt1 in Osteoblasts and Osteoclasts Controls Bone Remodeling

Bone mass and turnover are maintained by the coordinated balance between bone formation by osteoblasts and bone resorption by osteoclasts, under regulation of many systemic and local factors. Phosphoinositide-dependent serine-threonine protein kinase Akt is one of the key players in the signaling of potent bone anabolic factors. This study initially showed that the disruption of Akt1, a major Akt in osteoblasts and osteoclasts, in mice led to low-turnover osteopenia through dysfunctions of both cells. Ex vivo cell culture analyses revealed that the osteoblast dysfunction was traced to the increased susceptibility to the mitochondria-dependent apoptosis and the decreased transcriptional activity of runt-related transcription factor 2 (Runx2), a master regulator of osteoblast differentiation. Notably, our findings revealed a novel role of Akt1/forkhead box class O (FoxO) 3a/Bim axis in the apoptosis of osteoblasts: Akt1 phosphorylates the transcription factor FoxO3a to prevent its nuclear localization, leading to impaired transactivation of its target gene Bim which was also shown to be a potent proapoptotic molecule in osteoblasts. The osteoclast dysfunction was attributed to the cell autonomous defects of differentiation and survival in osteoclasts and the decreased expression of receptor activator of nuclear factor-κB ligand (RANKL), a major determinant of osteoclastogenesis, in osteoblasts. Akt1 was established as a crucial regulator of osteoblasts and osteoclasts by promoting their differentiation and survival to maintain bone mass and turnover. The molecular network found in this study will provide a basis for rational therapeutic targets for bone disorders

A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature

The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein–protein interactions (PPIs) reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study shows that three kernels are clearly superior to the other methods

Soybean Trihelix Transcription Factors GmGT-2A and GmGT-2B Improve Plant Tolerance to Abiotic Stresses in Transgenic Arabidopsis

BACKGROUND:Trihelix transcription factors play important roles in light-regulated responses and other developmental processes. However, their functions in abiotic stress response are largely unclear. In this study, we identified two trihelix transcription factor genes GmGT-2A and GmGT-2B from soybean and further characterized their roles in abiotic stress tolerance. FINDINGS:Both genes can be induced by various abiotic stresses, and the encoded proteins were localized in nuclear region. In yeast assay, GmGT-2B but not GmGT-2A exhibits ability of transcriptional activation and dimerization. The N-terminal peptide of 153 residues in GmGT-2B was the minimal activation domain and the middle region between the two trihelices mediated the dimerization of the GmGT-2B. Transactivation activity of the GmGT-2B was also confirmed in plant cells. DNA binding analysis using yeast one-hybrid assay revealed that GmGT-2A could bind to GT-1bx, GT-2bx, mGT-2bx-2 and D1 whereas GmGT-2B could bind to the latter three elements. Overexpression of the GmGT-2A and GmGT-2B improved plant tolerance to salt, freezing and drought stress in transgenic Arabidopsis plants. Moreover, GmGT-2B-transgenic plants had more green seedlings compared to Col-0 under ABA treatment. Many stress-responsive genes were altered in GmGT-2A- and GmGT-2B-transgenic plants. CONCLUSION:These results indicate that GmGT-2A and GmGT-2B confer stress tolerance through regulation of a common set of genes and specific sets of genes. GmGT-2B also affects ABA sensitivity

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe