Time-Optimal Gate-Traversing Planner for Autonomous Drone Racing

In drone racing, the time-minimum trajectory is affected by the drone's capabilities, the layout of the race track, and the configurations of the gates (e.g., their shapes and sizes). However, previous studies neglect the configuration of the gates, simply rendering drone racing a waypoint-passing task. This formulation often leads to a conservative choice of paths through the gates, as the spatial potential of the gates is not fully utilized. To address this issue, we present a time-optimal planner that can faithfully model gate constraints with various configurations and thereby generate a more time-efficient trajectory while considering the single-rotor-thrust limits. Our approach excels in computational efficiency which only takes a few seconds to compute the full state and control trajectories of the drone through tracks with dozens of different gates. Extensive simulations and experiments confirm the effectiveness of the proposed methodology, showing that the lap time can be further reduced by taking into account the gate's configuration. We validate our planner in real-world flights and demonstrate super-extreme flight trajectory through race tracks

Genome-wide analysis of the GRAS gene family in Liriodendron chinense reveals the putative function in abiotic stress and plant development

IntroductionGRAS genes encode plant-specific transcription factors that play essential roles in plant growth and development. However, the members and the function of the GRAS gene family have not been reported in Liriodendron chinense. L. chinense, a tree species in the Magnolia family that produces excellent timber for daily life and industry. In addition, it is a good relict species for plant evolution research.MethodsTherefore, we conducted a genome-wide study of the LcGRAS gene family and identified 49 LcGRAS genes in L. chinense.ResultsWe found that LcGRAS could be divided into 13 sub-groups, among which there is a unique branch named HAM-t. We carried out RNA sequencing analysis of the somatic embryos from L. chinense and found that LcGRAS genes are mainly expressed after heart-stage embryo development, suggesting that LcGRAS may have a function during somatic embryogenesis. We also investigated whether GRAS genes are responsive to stress by carrying out RNA sequencing (RNA-seq) analysis, and we found that the genes in the PAT subfamily were activated upon stress treatment, suggesting that these genes may help plants survive stressful environments. We found that PIF was downregulated and COR was upregulated after the transient overexpression of PATs, suggesting that PAT may be upstream regulators of cold stress. DiscussionCollectively, LcGRAS genes are conserved and play essential roles in plant development and adaptation to abiotic stress

Biochar amendment alters root morphology of maize plant: Its implications in enhancing nutrient uptake and shoot growth under reduced irrigation regimes

IntroductionBiochar amendment provides multiple benefits in enhancing crop productivity and soil nutrient availability. However, whether biochar addition affects root morphology and alters plant nutrient uptake and shoot growth under different irrigation regimes remain largely unknown.MethodsA split-root pot experiment with maize (Zea mays L.) was conducted on clay loam soil mixed with 2% (w/w) of wheat-straw (WSP) and softwood (SWP) biochar. The plants were subjected to full (FI), deficit (DI), and alternate partial root-zone drying (PRD) irrigation from the fourth leaf to the grain-filling stage.Results and discussionThe results showed that, compared to plants grown in unamended soils, plants grown in the biochar-amended soils possessed greater total root length, area, diameter, volume, tips, forks, crossings, and root length density, which were further amplified by PRD. Despite a negative effect on soil available phosphorus (P) pool, WSP addition improved soil available nitrogen (N), potassium (K), and calcium (Ca) pool and cation exchange capacity under reduced irrigation. Even though biochar negatively affected nutrient concentrations in shoots as exemplified by lowered N, P, K (except leaf), and Ca concentration, it dramatically enhanced plant total N, P, K, Ca uptake, and biomass. Principal component analysis (PCA) revealed that the modified root morphology and increased soil available nutrient pools, and consequently, the higher plant total nutrient uptake might have facilitated the enhanced shoot growth and yield of maize plants in biochar-added soils. Biochar amendment further lowered specific leaf area but increased leaf N concentration per area-to-root N concentration per length ratio. All these effects were evident upon WSP amendment. Moreover, PRD outperformed DI in increasing root area-to-leaf area ratio. Overall, these findings suggest that WSP combined with PRD could be a promising strategy to improve the growth and nutrient uptake of maize plants

Resorcinol crystallization from the melt: a new ambient phase and new “riddles”

Structures of the alpha and beta phases of resorcinol, a major commodity chemical in the pharmaceutical, agrichemical, and polymer industries, were the first polymorphic pair of molecular crystals solved by X-ray analysis. It was recently stated that "no additional phases can be found under atmospheric conditions" (Druzbicki, K. et al. J. Phys. Chem. B 2015, 119, 1681.). Herein, is described the growth and structure of a new ambient pressure phase, epsilon, through a combination of optical and X-ray crystallography evaluated by computational crystal structure prediction algorithms. alpha-Resorcinol has long been a model for mechanistic crystal growth studies from solution and the vapor because prisms extended along the polar axis grow much faster in one direction than in the opposite direction. Research has focused on identifying the absolute sense of the fast direction – the so-called ‘resorcinol riddle’ – with the aim of identifying how solvent controls crystal growth. Here, the growth velocity dissymmetry in the melt is analyzed for the ? phase. The epsilon phase only grows from the melt, concomitant with the beta phase, as polycrystalline, radially growing spherulites. If the radii are polar, the sense of the polar axis is an essential feature of the form. Here, this determination is made for spherulites of beta resorcinol (epsilon, point symmetry 222, does not have a polar axis) with additives that stereoselectively modify growth velocities. Both beta and epsilon have the additional feature that individual radial lamellae may adopt helicoidal morphologies. We correlate the appearance of twisting in beta and epsilon with the symmetry of twist-inducing additives

A heterozygous moth genome provides insights into herbivory and detoxification

How an insect evolves to become a successful herbivore is of profound biological and practical importance. Herbivores are often adapted to feed on a specific group of evolutionarily and biochemically related host plants1, but the genetic and molecular bases for adaptation to plant defense compounds remain poorly understood2. We report the first whole-genome sequence of a basal lepidopteran species, Plutella xylostella, which contains 18,071 protein-coding and 1,412 unique genes with an expansion of gene families associated with perception and the detoxification of plant defense compounds. A recent expansion of retrotransposons near detoxification-related genes and a wider system used in the metabolism of plant defense compounds are shown to also be involved in the development of insecticide resistance. This work shows the genetic and molecular bases for the evolutionary success of this worldwide herbivore and offers wider insights into insect adaptation to plant feeding, as well as opening avenues for more sustainable pest management.Minsheng You … Simon W Baxter … et al

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Machine Learning Techniques for Heterogeneous Data Sets

Over the past few decades, machine learning tools are under rapid development in various application fields to support statistical decision making. In this dissertation, we aim at investigating new supervised machine learning techniques which can contribute to analysis of complex datasets. First, we discuss a new learning method under Reproducing Kernel Hilbert Spaces (RKHS) to achieve variable selection and data extraction simultaneously. In particular, we propose a unified RKHS learning method, namely, DOuble Sparsity Kernel (DOSK) learning, to overcome this challenge. We prove that under certain conditions, our new method can asymptotically achieve variable selection consistency. Numerical study results demonstrate that DOSK is highly competitive among existing approaches for RKHS learning. Second, we study on how machine learning can be applied to heterogeneous data analysis by detecting an optimal individual treatment rule for the ordinal treatment case. One of the primary goals in precision medicine is to obtain an optimal individual treatment rule (ITR). Recently, outcome weighted learning (OWL) has been proposed to estimate such an optimal ITR in a binary treatment setting by maximizing the expected clinical outcome. However, for the ordinal treatment settings such as dose level finding, it is unclear how to use OWL. We propose a new technique for estimating ITR with ordinal treatments. Simulated examples and an application to a type-2 diabetes study demonstrate the highly competitive performance of the proposed method. Third, we also focus on analyzing the heterogeneous data but in a different point of view. In particular, we develop a new exploratory machine learning tool to identify the heterogeneous subpopulations without much prior knowledge. To achieve this goal, we formulate a regression problem with subject specific regression coefficients and use adaptive fusion to cluster the coefficients into subpopulations. This method has two main advantages. First, it relies on little prior knowledge on the underlying subpopulation structure. Second, it makes use of the outcome-predictor relationship and hence can have competitive estimation and prediction accuracy. To estimate the parameters, we design a highly efficient accelerated proximal gradient algorithm. Numerical studies show that the proposed method has competitive estimation and prediction accuracy.Doctor of Philosoph