Metal halide perovskite materials are emerging solution-processed semiconductors with considerable promise in optoelectronic devices 1,2 . Metal halide perovskite-based light-emitting devices (pLEDs) have received extensive interest for applications in flat-panel displays and solid-state lighting owing to their promise of low cost, tunable colors with narrow emission bandwidths, high photoluminescence quantum yield (PLQY), and facile solution processing [3][4][5][6][7] .However, the highest reported external quantum efficiency (EQE) of green-and red-emitting pLEDs are 14.36% 6,8 and 11.7% 7 , still far behind the performance of organic LEDs (OLEDs) [9][10][11] and inorganic quantum dot LEDs (QLEDs) 12 . Here we report visible perovskite LEDs that

SM2miR is freely available at http://bioinfo.hrbmu.edu.cn/SM2miR/.

Allostery is the most direct and efficient way for regulation of biological macromolecule function and is induced by the binding of a ligand at an allosteric site topographically distinct from the orthosteric site. AlloSteric Database (ASD, http://mdl.shsmu.edu.cn/ASD) has been developed to provide comprehensive information on allostery. Owing to the inherent high receptor selectivity and lower target-based toxicity, allosteric regulation is expected to assume a more prominent role in drug discovery and bioengineering, leading to the rapid growth of allosteric findings. In this updated version, ASD v2.0 has expanded to 1286 allosteric proteins, 565 allosteric diseases and 22 008 allosteric modulators. A total of 907 allosteric site-modulator structural complexes and >200 structural pairs of orthosteric/allosteric sites in the allosteric proteins were constructed for researchers to develop allosteric site and pathway tools in response to community demands. Up-to-date allosteric pathways were manually curated in the updated version. In addition, both the front-end and the back-end of ASD have been redesigned and enhanced to allow more efficient access. Taken together, these updates are useful for facilitating the investigation of allosteric mechanisms, allosteric target identification and allosteric drug discovery.

One of the world’s most important crops, barley, was domesticated in the Near East around 11,000 years ago. Barley is a highly resilient crop, able to grown in varied and marginal environments, such as in regions of high altitude and latitude. Archaeobotanical evidence shows that barley had spread throughout Eurasia by 2,000 BC. To further elucidate the routes by which barley cultivation was spread through Eurasia, simple sequence repeat (SSR) analysis was used to determine genetic diversity and population structure in three extant barley taxa: domesticated barley (Hordeum vulgare L. subsp. vulgare), wild barley (H. vulgare subsp. spontaneum) and a six-rowed brittle rachis form (H. vulgare subsp. vulgare f. agriocrithon (Åberg) Bowd.). Analysis of data using the Bayesian clustering algorithm InStruct suggests a model with three ancestral genepools, which captures a major split in the data, with substantial additional resolution provided under a model with eight genepools. Our results indicate that H. vulgare subsp. vulgare f. agriocrithon accessions and Tibetan Plateau H. vulgare subsp. spontaneum are closely related to the H. vulgare subsp. vulgare in their vicinity, and are therefore likely to be feral derivatives of H. vulgare subsp. vulgare. Under the eight genepool model, cultivated barley is split into six ancestral genepools, each of which has a distinct distribution through Eurasia, along with distinct morphological features and flowering time phenotypes. The distribution of these genepools and their phenotypic characteristics is discussed together with archaeological evidence for the spread of barley eastwards across Eurasia.

Background: Shotgun metagenomics based on untargeted sequencing can explore the taxonomic profile and the function of unknown microorganisms in samples, and complement the shortage of amplicon sequencing. Binning assembled sequences into individual groups, which represent microbial genomes, is the key step and a major challenge in metagenomic research. Both supervised and unsupervised machine learning methods have been employed in binning. Genome binning belonging to unsupervised method clusters contigs into individual genome bins by machine learning methods without the assistance of any reference databases. So far a lot of genome binning tools have emerged. Evaluating these genome tools is of great significance to microbiological research. In this study, we evaluate 15 genome binning tools containing 12 original binning tools and 3 refining binning tools by comparing the performance of these tools on chicken gut metagenomic datasets and the first CAMI challenge datasets. Results: For chicken gut metagenomic datasets, original genome binner MetaBat, Groopm2 and Autometa performed better than other original binner, and MetaWrap combined the binning results of them generated the most high-quality genome bins. For CAMI datasets, Groopm2 achieved the highest purity (> 0.9) with good completeness (> 0.8), and reconstructed the most high-quality genome bins among original genome binners. Compared with Groopm2, MetaBat2 had similar performance with higher completeness and lower purity. Genome refining binners DASTool predicated the most high-quality genome bins among all genomes binners. Most genome binner performed well for unique strains. Nonetheless, reconstructing common strains still is a substantial challenge for all genome binner. Conclusions: In conclusion, we tested a set of currently available, state-of-the-art metagenomics hybrid binning tools and provided a guide for selecting tools for metagenomic binning by comparing range of purity, completeness, adjusted rand index, and the number of high-quality reconstructed bins. Furthermore, available information for future binning strategy were concluded.