We address the problem of realizing a given distance matrix by a planar phylogenetic network with a minimum number of faces. With the help of the popular software SplitsTree4, we start by approximating the distance matrix with a distance metric that is a linear combination of circular splits. The main results of this paper are the necessary and sufficient conditions for the existence of a network with a single face. We show how such a network can be constructed, and we present a heuristic for constructing a network with few faces using the first algorithm as the base case. Experimental results on biological data show that this heuristic algorithm can produce phylogenetic networks with far fewer faces than the ones computed by SplitsTree4, without affecting the approximation of the distance matrix.
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http://dx.doi.org/10.1109/TCBB.2011.109 | DOI Listing |
Acc Chem Res
January 2025
The Department of Chemistry, State University of New York at Binghamton, Binghamton, New York 13902, United States.
ConspectusIn the search for efficient and selective electrocatalysts capable of converting greenhouse gases to value-added products, enzymes found in naturally existing bacteria provide the basis for most approaches toward electrocatalyst design. Ni,Fe-carbon monoxide dehydrogenase (Ni,Fe-CODH) is one such enzyme, with a nickel-iron-sulfur cluster named the C-cluster, where CO binds and is converted to CO at high rates near the thermodynamic potential. In this Account, we divide the enzyme's catalytic contributions into three categories based on location and function.
View Article and Find Full Text PDFHeliyon
January 2025
Faculty of Mechanical Technology and Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Melaka, 76100, Malaysia.
This paper explores the electrical conductivity interphase of Ag/Epoxy composite using modified McLachlan theory and 3D finite element composite model through experimental verification. The model characteristic presents conductivity as a dynamic function influenced by particle content, particle electrical properties, electrical properties transition, and an exponent. This model was meticulously crafted, considering the intricate interplay between the polymer matrix and silver particles, the tunnelling distance between adjacent silver particles, and the interphase regions around particles.
View Article and Find Full Text PDFCancers (Basel)
January 2025
Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 2130 W Holcombe Blvd., Houston, TX 77030, USA.
Predicting the behavior of clear cell renal cell carcinoma (ccRCC) is challenging using standard-of-care histopathologic examination. Indeed, pathologic RCC tumor grading, based on nuclear morphology, performs poorly in predicting outcomes of patients with International Society of Urological Pathology/World Health Organization grade 2 and 3 tumors, which account for most ccRCCs. We applied spatial point process modeling of H&E-stained images of patients with grade 2 and grade 3 ccRCCs ( = 72) to find optimum separation into two groups.
View Article and Find Full Text PDFNeural Netw
January 2025
Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University (Qinhuangdao), Qinhuangdao 066004, China. Electronic address:
Entity alignment (EA) is a typical strategy for knowledge graph integration, aiming to identify and align different entity pairs representing the same real object from different knowledge graphs. Temporal Knowledge Graph (TKG) extends the static knowledge graph by introducing timestamps. However, since temporal knowledge graphs are constructed based on their own data sources, this usually leads to problems such as missing or redundant entity information in the temporal knowledge graph.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Guangdong Institute of Intelligence Science and Technology, 519031 Hengqin, Zhuhai, Guangdong, China.
Manifold learning techniques have emerged as crucial tools for uncovering latent patterns in high-dimensional single-cell data. However, most existing dimensionality reduction methods primarily rely on 2D visualization, which can distort true data relationships and fail to extract reliable biological information. Here, we present DTNE (diffusive topology neighbor embedding), a dimensionality reduction framework that faithfully approximates manifold distance to enhance cellular relationships and dynamics.
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