Rooted phylogenetic networks provide an explicit representation of the evolutionary history of a set X of sampled species. In contrast to phylogenetic trees which show only speciation events, networks can also accommodate reticulate processes (for example, hybrid evolution, endosymbiosis, and lateral gene transfer). A major goal in systematic biology is to infer evolutionary relationships, and while phylogenetic trees can be uniquely determined from various simple combinatorial data on X, for networks the reconstruction question is much more subtle. Here we ask when can a network be uniquely reconstructed from its 'ancestral profile' (the number of paths from each ancestral vertex to each element in X). We show that reconstruction holds (even within the class of all networks) for a class of networks we call 'orchard networks', and we provide a polynomial-time algorithm for reconstructing any orchard network from its ancestral profile. Our approach relies on establishing a structural theorem for orchard networks, which also provides for a fast (polynomial-time) algorithm to test if any given network is of orchard type. Since the class of orchard networks includes tree-sibling tree-consistent networks and tree-child networks, our result generalise reconstruction results from 2008 and 2009. Orchard networks allow for an unbounded number k of reticulation vertices, in contrast to tree-sibling tree-consistent networks and tree-child networks for which k is at most 2|X|-4 and |X|-1, respectively.
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http://dx.doi.org/10.1016/j.mbs.2019.04.009 | DOI Listing |
Acc Chem Res
January 2025
School of Engineering, Westlake University, Hangzhou 310024, Zhejiang Province, China.
ConspectusCovalent triazine frameworks (CTFs) are a novel class of nitrogen-rich conjugated porous organic materials constructed by robust and functional triazine linkages, which possess unique structures and excellent physicochemical properties. They have demonstrated broad application prospects in gas/molecular adsorption and separation, catalysis, energy conversion and storage, etc. In particular, crystalline CTFs with well-defined periodic molecular network structures and regular pore channels can maximize the utilization of the features of CTFs and promote a deep understanding of the structure-property relationship.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Engineering, Westlake University, No. 600 Dunyu Road, 310030 Zhejiang, P.R. China.
Single-cell RNA sequencing (scRNA-seq) offers remarkable insights into cellular development and differentiation by capturing the gene expression profiles of individual cells. The role of dimensionality reduction and visualization in the interpretation of scRNA-seq data has gained widely acceptance. However, current methods face several challenges, including incomplete structure-preserving strategies and high distortion in embeddings, which fail to effectively model complex cell trajectories with multiple branches.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Guangdong Provincial Key Laboratory of Mathematical and Neural Dynamical Systems, Great Bay University, No. 16 Daxue Rd, Songshanhu District, Dongguan, Guangdong, 523000, China.
Multimodal omics provide deeper insight into the biological processes and cellular functions, especially transcriptomics and proteomics. Computational methods have been proposed for the integration of single-cell multimodal omics of transcriptomics and proteomics. However, existing methods primarily concentrate on the alignment of different omics, overlooking the unique information inherent in each omics type.
View Article and Find Full Text PDFNeuro Oncol
January 2025
Department of Breast Oncology, Moffitt Cancer Center.
Background: Screening of asymptomatic stage IV breast cancer with brain MRIs is currently not recommended by National Comprehensive Cancer Network (NCCN) Guidelines. The incidence of asymptomatic brain metastasis is not well documented.
Methods: The study is designed as a single arm, phase II trial, with the goal of investigating surveillance brain MRIs in neurologically asymptomatic patients with metastatic breast cancer.
Int J Neural Syst
January 2025
Alibaba Cloud, Hangzhou, P. R. China.
Multi-label zero-shot learning (ML-ZSL) strives to recognize all objects in an image, regardless of whether they are present in the training data. Recent methods incorporate an attention mechanism to locate labels in the image and generate class-specific semantic information. However, the attention mechanism built on visual features treats label embeddings equally in the prediction score, leading to severe semantic ambiguity.
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