55 research outputs found

    Genome-wide analysis of Cushion willow provides insights into alpine plant divergence in a biodiversity hotspot

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    Funding: Strategic Priority Research Program of Chinese Academy of Sciences (XDA 20050203 (H.S.); Major Program of the NSFC 31590823 (H.S.); National Key R & D Program of China 2017YF0505200 (H.S.); NSFC (31670198 to J.C., 31560062 to Y.H.,31871271 to W.Z.); Science and Technology Research Program of KIB (NO.KIB2016005 to J.C.); Youth Innovation Promotion Association, CAS (J.C.), and Peking-Tsinghua Center for Life Science, the State Key Laboratory of Protein and PlantGene Research and Qidong-SLS Innovation Fund (W.Z.).The Hengduan Mountains (HDM) biodiversity hotspot exhibits exceptional alpine plant diversity. Here, we investigate factors driving intraspecific divergence within a HDM alpine species Salix brachista (Cushion willow), a common component of subnival assemblages. We produce a high-quality genome assembly for this species and characterize its genetic diversity, population structure and pattern of evolution by resequencing individuals collected across its distribution. We detect population divergence that has been shaped by a landscape of isolated sky island-like habitats displaying strong environmental heterogeneity across elevational gradients, combined with population size fluctuations that have occurred since approximately the late Miocene. These factors are likely important drivers of intraspecific divergence within Cushion willow and possibly other alpine plants with a similar distribution. Since intraspecific divergence is often the first step toward speciation, the same factors can be important contributors to the high alpine species diversity in the HDM.Publisher PDFPeer reviewe

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

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    : 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

    Consistency and General Solutions to Some Sylvester-like Quaternion Matrix Equations

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    This article makes use of simultaneous decomposition of four quaternion matrixes to investigate some Sylvester-like quaternion matrix equation systems. We present some useful necessary and sufficient conditions for the consistency of the system of quaternion matrix equations in terms of the equivalence form and block matrixes. We also derive the general solution to the system according to the partition of the coefficient matrixes. As an application of the system, we present some practical necessary and sufficient conditions for the consistency of a ϕ-Hermitian solution to the system of quaternion matrix equations in terms of the equivalence form and block matrixes. We also provide the general ϕ-Hermitian solution to the system when the equation system is consistent. Moreover, we present some numerical examples to illustrate the availability of the results of this paper

    Unitary Diagonalization of the Generalized Complementary Covariance Quaternion Matrices with Application in Signal Processing

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    Let H denote the quaternion algebra. This paper investigates the generalized complementary covariance, which is the ϕ-Hermitian quaternion matrix. We give the properties of the generalized complementary covariance matrices. In addition, we explore the unitary diagonalization of the covariance and generalized complementary covariance. Moreover, we give the generalized quaternion unitary transform algorithm and test the performance by numerical simulation

    Consistency and General Solutions to Some Sylvester-like Quaternion Matrix Equations

    No full text
    This article makes use of simultaneous decomposition of four quaternion matrixes to investigate some Sylvester-like quaternion matrix equation systems. We present some useful necessary and sufficient conditions for the consistency of the system of quaternion matrix equations in terms of the equivalence form and block matrixes. We also derive the general solution to the system according to the partition of the coefficient matrixes. As an application of the system, we present some practical necessary and sufficient conditions for the consistency of a ϕ-Hermitian solution to the system of quaternion matrix equations in terms of the equivalence form and block matrixes. We also provide the general ϕ-Hermitian solution to the system when the equation system is consistent. Moreover, we present some numerical examples to illustrate the availability of the results of this paper

    The Solvability of a System of Quaternion Matrix Equations Involving <i>ϕ</i>-Skew-Hermicity

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    Let H be the real quaternion algebra and Hm×n denote the set of all m×n matrices over H. For A∈Hm×n, we denote by Aϕ the n×m matrix obtained by applying ϕ entrywise to the transposed matrix AT, where ϕ is a non-standard involution of H. A∈Hn×n is said to be ϕ-skew-Hermicity if A=−Aϕ. In this paper, we provide some necessary and sufficient conditions for the existence of a ϕ-skew-Hermitian solution to the system of quaternion matrix equations with four unknowns AiXi(Ai)ϕ+BiXi+1(Bi)ϕ=Ci,(i=1,2,3),A4X4(A4)ϕ=C4
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