Genome-enabled insights into the biology of thrips as crop pests

Background The western flower thrips,Frankliniella occidentalis(Pergande), is a globally invasive pest and plant virus vector on a wide array of food, fiber, and ornamental crops. The underlying genetic mechanisms of the processes governing thrips pest and vector biology, feeding behaviors, ecology, and insecticide resistance are largely unknown. To address this gap, we present theF. occidentalisdraft genome assembly and official gene set.Results We report on the first genome sequence for any member of the insect order Thysanoptera. Benchmarking Universal Single-Copy Ortholog (BUSCO) assessments of the genome assembly (size = 415.8 Mb, scaffold N50 = 948.9 kb) revealed a relatively complete and well-annotated assembly in comparison to other insect genomes. The genome is unusually GC-rich (50%) compared to other insect genomes to date. The official gene set (OGS v1.0) contains 16,859 genes, of which similar to 10% were manually verified and corrected by our consortium. We focused on manual annotation, phylogenetic, and expression evidence analyses for gene sets centered on primary themes in the life histories and activities of plant-colonizing insects. Highlights include the following: (1) divergent clades and large expansions in genes associated with environmental sensing (chemosensory receptors) and detoxification (CYP4, CYP6, and CCE enzymes) of substances encountered in agricultural environments; (2) a comprehensive set of salivary gland genes supported by enriched expression; (3) apparent absence of members of the IMD innate immune defense pathway; and (4) developmental- and sex-specific expression analyses of genes associated with progression from larvae to adulthood through neometaboly, a distinct form of maturation differing from either incomplete or complete metamorphosis in the Insecta.Conclusions Analysis of theF. occidentalisgenome offers insights into the polyphagous behavior of this insect pest that finds, colonizes, and survives on a widely diverse array of plants. The genomic resources presented here enable a more complete analysis of insect evolution and biology, providing a missing taxon for contemporary insect genomics-based analyses. Our study also offers a genomic benchmark for molecular and evolutionary investigations of other Thysanoptera species.Animal science

Selective protein degradation by ligand-targeted enzymes: towards the creation of catalytic antagonists

Molecular angler fish: By precisely positioning different binding ligands (L) around the active site “mouth” of a degradative proteinase enzyme, target proteins (TP) can be plucked from solution, locked in position adjacent to the catalytic triad “jaws”, and in this way readily and specifically degraded (see scheme). In this strategy, the appropriate ligand acts as a homing device to confer and enhance selectivity, in the best case by more than 350-fold, in a generic process that exploits the intrinsic, ligand-recognition capabilities of the protein target to trigger its own destruction. The hunting strategy of the deep sea Angler Fish, which uses a lure above its mouth, illustrates this principle

SoftPOSIT: Simultaneous Pose and Correspondence Determination

The problem of pose estimation arises in many areas of computer vision, including object recognition, object tracking, site inspection and updating, and autonomous navigation when scene models are available. We present a new algorithm, called SoftPOSIT, for determining the pose of a 3D object from a single 2D image when correspondences between model points and image points are not known. The algorithm combines Gold's iterative softassign algorithm [Gold 1996, Gold 1998] for computing correspondences and DeMenthon's iterative POSIT algorithm [DeMenthon 1995] for computing object pose under a full-perspective camera model. Our algorithm, unlike most previous algorithms for pose determination, does not have to hypothesize small sets of matches and then verify the remaining image points. Instead, all possible matches are treated identically throughout the search for an optimal pose. The performance of the algorithm is extensively evaluated in Monte Carlo simulations on synthetic data The support of NSF grants EAR-99-05844 and IIS-00-86116 is gratefully acknowledged. under a variety of levels of clutter, occlusion, and image noise. These tests show that the algorithm performs well in a variety of difficult scenarios, and empirical evidence suggests that the algorithm has an asymptotic run-time complexity that is better than previous methods by a factor of the number of image points. The algorithm is being applied to a number of practical autonomous vehicle navigation problems including the registration of 3D architectural models of a city to images, and the docking of small robots onto larger robots.