Barnyardgrass (Echinochloa crus-galli) is a pernicious weed in agricultural fields worldwide. The molecular mechanisms underlying its success in the absence of human intervention are presently unknown. Here we report a draft genome sequence of the hexaploid species E. crus-galli, i.e., a 1.27 Gb assembly representing 90.7% of the predicted genome size. An extremely large repertoire of genes encoding cytochrome P450 monooxygenases and glutathione S-transferases associated with detoxification are found. Two gene clusters involved in the biosynthesis of an allelochemical 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA) and a phytoalexin momilactone A are found in the E. crus-galli genome, respectively. The allelochemical DIMBOA gene cluster is activated in response to co-cultivation with rice, while the phytoalexin momilactone A gene cluster specifically to infection by pathogenic Pyricularia oryzae. Our results provide a new understanding of the molecular mechanisms underlying the extreme adaptation of the weed.

De-domestication is a unique evolutionary process by which domesticated crops are converted into ‘wild predecessor like' forms. Weedy rice (Oryza sativa f. spontanea) is an excellent model to dissect the molecular processes underlying de-domestication. Here, we analyse the genomes of 155 weedy and 76 locally cultivated rice accessions from four representative regions in China that were sequenced to an average 18.2 × coverage. Phylogenetic and demographic analyses indicate that Chinese weedy rice was de-domesticated independently from cultivated rice and experienced a strong genetic bottleneck. Although evolving from multiple origins, critical genes underlying convergent evolution of different weedy types can be found. Allele frequency analyses suggest that standing variations and new mutations contribute differently to japonica and indica weedy rice. We identify a Mb-scale genomic region present in weedy rice but not cultivated rice genomes that shows evidence of balancing selection, thereby suggesting that there might be more complexity inherent to the process of de-domestication.

The target of rapamycin (TOR) plays a central role in eukaryotic cell growth control1. With prevalent hyper-activation of the mTOR pathway in human cancers2, novel strategies to enhance TOR pathway inhibition are highly desirable. We used a yeast-based platform to identify small-molecule enhancers of rapamycin (SMERs) and discovered an inhibitor of the SCFMet30 ubiquitin ligase (SMER3). The large SCF (Skp1-Cullin-F-box) family of ubiquitin ligases performs important functions in diverse cellular processes including transcription, cell-cycle control, and immune response3. Accordingly, there would be great value in developing SCF ligase inhibitors that act by a defined mechanism to specifically inactivate ligase activity. We show here that SMER3 selectively inhibits SCFMet30 in vivo and in vitro, but not the closely related SCFCdc4. Our results demonstrate that there is no fundamental barrier to obtaining specific inhibitors to modulate function of individual SCF complexes, and suggest new strategies for combination therapy with rapamycin.

Causal networks are graphically represented by directed acyclic graphs (DAGs). Learning causal networks from data is a challenging problem due to the size of the space of DAGs, the acyclicity constraint placed on the graphical structures, and the presence of equivalence classes. In this article, we develop an L 1 -penalized likelihood approach to estimate the structure of causal Gaussian networks. A blockwise coordinate descent algorithm, which takes advantage of the acyclicity constraint, is proposed for seeking a local maximizer of the penalized likelihood. We establish that model selection consistency for causal Gaussian networks can be achieved with the adaptive lasso penalty and sufficient experimental interventions. Simulation and real data examples are used to demonstrate the effectiveness of our method. In particular, our method shows satisfactory performance for DAGs with 200 nodes, which have about 20,000 free parameters. Supplementary materials for this article are available online.

A facile method was presented to synthesize three-dimensional carbon nanotubes/graphene-sulfur (3DCGS) sponge with high sulfur loading of 80.1%. In the well-designed 3D architecture, the two-dimensional graphene nanosheets functions as the 3D porous backbone and the one-dimensional (1D) highly conductive carbon nanotubes (CNT) can not only significantly enhance the conductivity, but also effectively tune the mesopore structure. Compared to the three-dimensional graphene-sulfur (3DGS) sponge without CNT, the conductivity of 3DCGS is enhanced by 324.7%; most importantly, compared to the monomodal mesopores (with a size of 3.5 nm) formed in the 3DG, the bimodal mesopores (with sizes of 3.5 and 32.1 nm) were formed in 3DCG; the bimodal mesopores, especially the newly formed 32.1-nm mesopores, provide abundant electrochemical nanoreactors, accommodate plenty of sulfur and polysulfides, and facilitate the charge transportation and electrolyte penetration. The significantly enhanced conductivity and the unique bimodal-mesopore structure in 3DCGS, result in its superior electrochemical performance. The reversible discharge capacity for sulfur is 1217 mAh g -1 ; corresponding capacity for the whole electrode (including the 3DCGS, the conductive additive and the binder) is 877.4 mAh g ିଵ , which is the highest ever reported. In addition, the capacity decay is as low as 0.08% per cycle, and the high-rate capacity up to 4 C is as large as 653.4 mAh g -1 . The 3DCGS sponge with high sulfur loading is promising as superior-capacity cathode for lithium-sulfur batteries.