SMASHing the LMC: A Tidally-induced Warp in the Outer LMC and a Large-scale Reddening Map

We present a study of the three-dimensional (3D) structure of the Large Magellanic Cloud (LMC) using ~2.2 million red clump (RC) stars selected from the Survey of the MAgellanic Stellar History. To correct for line-of-sight dust extinction, the intrinsic RC color and magnitude and their radial dependence are carefully measured by using internal nearly dust-free regions. These are then used to construct an accurate 2D reddening map (165 square degrees with ~10 arcmin resolution) of the LMC disk and the 3D spatial distribution of RC stars. An inclined disk model is fit to the 2D distance map yielding a best-fit inclination angle i = 25.86(+0.73,-1.39) degrees with random errors of +\-0.19 degrees and line-of-nodes position angle theta = 149.23(+6.43,-8.35) degrees with random errors of +/-0.49 degrees. These angles vary with galactic radius, indicating that the LMC disk is warped and twisted likely due to the repeated tidal interactions with the Small Magellanic Cloud (SMC). For the first time, our data reveal a significant warp in the southwestern part of the outer disk starting at rho ~ 7 degrees that departs from the defined LMC plane up to ~4 kpc toward the SMC, suggesting that it originated from a strong interaction with the SMC. In addition, the inner disk encompassing the off-centered bar appears to be tilted up to 5-15 degrees relative to the rest of the LMC disk. These findings on the outer warp and the tilted bar are consistent with the predictions from the Besla et al. simulation of a recent direct collision with the SMC.Comment: 25 pages, 15 figures, published in Ap

Genome Sequence of Striga asiatica Provides Insight into the Evolution of Plant Parasitism

Parasitic plants in the genus Striga, commonly known as witchweeds, cause major crop losses in sub-Saharan Africa and pose a threat to agriculture worldwide. An understanding of Striga parasite biology, which could lead to agricultural solutions, has been hampered by the lack of genome information. Here, we report the draft genome sequence of Striga asiatica with 34,577 predicted protein-coding genes, which reflects gene family contractions and expansions that are consistent with a three-phase model of parasitic plant genome evolution. Striga seeds germinate in response to host-derived strigolactones (SLs) and then develop a specialized penetration structure, the haustorium, to invade the host root. A family of SL receptors has undergone a striking expansion, suggesting a molecular basis for the evolution of broad host range among Striga spp. We found that genes involved in lateral root development in non-parasitic model species are coordinately induced during haustorium development in Striga, suggesting a pathway that was partly co-opted during the evolution of the haustorium. In addition, we found evidence for horizontal transfer of host genes as well as retrotransposons, indicating gene flow to S. asiatica from hosts. Our results provide valuable insights into the evolution of parasitism and a key resource for the future development of Striga control strategies.Peer reviewe

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

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