Strong coordination interaction in amorphous Sn-Ti-ethylene glycol compound for stable Li-ion storage

Sn has been considered one of the most promising metallic anode materials for lithium-ion batteries (LIBs) because of its high specific capacity. Herein, we report a novel amorphous tin-titanium-ethylene glycol (Sn-Ti-EG) bimetal organic compound as an anode for LIBs. The Sn-Ti-EG electrode exhibits exceptional cyclic stability with high Li-ion storage capacity. Even after 700 cycles at a current density of 1.0 A g−1, the anode maintains a capacity of 345 mAh g−1. The unique bimetal organic structure of the Sn-Ti-EG anode and the strong coordination interaction between Sn/Ti and O within the framework effectively suppress the aggregation of Sn atoms, eliminating the usual pulverization of bulk Sn through volume expansion. Furthermore, the Sn M-edge of the X-ray absorption near-edge structure spectra obtained using soft X-ray absorption spectroscopy signifies the conversion of Sn2+ ions into Sn0 during the initial lithiation process, which is reversible upon delithiation. These findings reveal that Sn is one of the most active components that account for the excellent electrochemical performance of the Sn-Ti-EG electrode, whereas Ti has no practical contribution to the capacity of the electrode. The reversible formation of organic functional groups on the solid electrolyte interphase is also partly responsible for its cyclic stability

Sentiment Analysis of Movie Reviews Based on CNN-BLSTM

Part 3: Big Data Analysis and Machine LearningInternational audienceSentiment analysis has been a hot area in the research field of language understanding, but complex deep neural network used in it is still lacked. In this study, we combine convolutional neural networks (CNNs) and BLSTM (bidirectional Long Short-Term Memory) as a complex model to analyze the sentiment orientation of text. First, we design an appropriate structure to combine CNN and BLSTM to find out the most optimal one layer, and then conduct six experiments, including single CNN and single LSTM, for the test and accuracy comparison. Specially, we pre-process the data to transform the words into word vectors to improve the accuracy of the classification result. The classification accuracy of 89.7% resulted from CNN-BLSTM is much better than single CNN or single LSTM. Moreover, CNN with one convolution layer and one pooling layer also performs better than CNN with more layers

Milletdb: A multi-omics database to accelerate the research of functional genomics and molecular breeding of millets

Millets are a class of nutrient-rich coarse cereals with high resistance to abiotic stress; thus, they guarantee food security for people living in areas with extreme climatic conditions and provide stress-related genetic resources for other crops. However, no platform is available to provide a comprehensive and systematic multi-omics analysis for millets, which seriously hinders the mining of stress-related genes and the molecular breeding of millets. Here, a free, web-accessible, user-friendly millets multi-omics database platform (Milletdb, http://milletdb.novogene.com) has been developed. The Milletdb contains six millets and their one related species genomes, graph-based pan-genomics of pearl millet, and stress-related multi-omics data, which enable Milletdb to be the most complete millets multi-omics database available. We stored GWAS (genome-wide association study) results of 20 yield-related trait data obtained under three environmental conditions [field (no stress), early drought and late drought] for 2 years in the database, allowing users to identify stress-related genes that support yield improvement. Milletdb can simplify the functional genomics analysis of millets by providing users with 20 different tools (e.g., ‘Gene mapping’, ‘Co-expression’, ‘KEGG/GO Enrichment’ analysis, etc.). On the Milletdb platform, a gene PMA1G03779.1 was identified through ‘GWAS’, which has the potential to modulate yield and respond to different environmental stresses. Using the tools provided by Milletdb, we found that the stress-related PLATZs TFs (transcription factors) family expands in 87.5% of millet accessions and contributes to vegetative growth and abiotic stress responses. Milletdb can effectively serve researchers in the mining of key genes, genome editing and molecular breeding of millets

Additional file 2: Table S1. of A de novo silencer causes elimination of MITF-M expression and profound hearing loss in pigs

The goodness of fit test for Mendelian ratios of the hearing loss. Table S2. Re-annotation of porcine MITF gene in Genome of Tibet pig. Table S3. Co-segregated mutations detected in mutation screening. Table S4. Summary and mapping statistics of the pig genome re-sequencing data. (DOCX 58 kb

Additional file 4: Table S8. of A de novo silencer causes elimination of MITF-M expression and profound hearing loss in pigs

Co-segregated variants detected in re-sequencing and mutation screening. Table S9. Differential expressed genes between MITF  R/r and MITF  r/r stria vascularis (SVs). Table S10. Expression levels of melanocyte marker genes in porcine SVs. Table S11. Primer pairs used for screening the MITF gene, for qPCR and for mice genotyping. Table S12. Expression levels of SOX family members in porcine SVs. Table S13. Distribution of hearing loss phenotype and genotype in a large Rongchang pig population. (DOCX 55 kb

Additional file 1: Figure S1. of A de novo silencer causes elimination of MITF-M expression and profound hearing loss in pigs

Eye morphology defects of albino pigs. Figure S2. Three family pedigrees of mapping population. Figure S3. Images showing presence of intermediate cells in the stria vascularis of albino pigs at the embryo stage. Figure S4. Results of EMSA using probe R1 and r1. Figure S5. Genotyping of Rongchang pigs for causative mutation. Figure S6. The human orthologous of the causative mutant region found in MITF  r/r pigs are formerly lack of regulatory activity. (DOCX 6667 kb

Additional file 3: Table S5. of A de novo silencer causes elimination of MITF-M expression and profound hearing loss in pigs

SNPs detected by re-sequencing in the associated region of Rongchang pigs. Table S6. SNPs co-segregated with hearing loss phenotype in three MITF  r/r pigs and three MITF  R/R Rongchang pigs. Table S7. SNPs uniquely detected in Rongchang pigs, and homozygous in MITF  r/r . (XLSX 465 kb