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The proliferation of genomic sequencing approaches has significantly impacted the field of phylogenetics. Target capture approaches provide a cost-effective, fast and easily applied strategy for phylogenetic inference of non-model organisms. However, several existing target capture processing pipelines are incapable of incorporating whole genome sequencing (WGS). Here, we develop a new pipeline for capture and de novo assembly of the targeted regions using whole genome re-sequencing reads. This new pipeline captured targeted loci accurately, and given its unbiased nature, can be used with any target capture probe set. Moreover, due to its low computational demand, this new pipeline may be ideal for users with limited resources and when high-coverage sequencing outputs are required. We demonstrate the utility of our approach by incorporating WGS data into the first comprehensive phylogenomic reconstruction of the freshwater mussel family Margaritiferidae. We also provide a catalogue of well-curated functional annotations of these previously uncharacterized freshwater mussel-specific target regions, representing a complementary tool for scrutinizing phylogenetic inferences while expanding future applications of the probe set.
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http://dx.doi.org/10.1111/1755-0998.13802 | DOI Listing |
Rev Sci Instrum
September 2025
HUN-REN Centre for Energy Research, Budapest, Hungary.
A novel medium-current (up to 20 mA), low normalized beam emittance (<1 π mm mrad) electron cyclotron resonance microwave H+ ion source has been developed at the Center for Energy Research in Budapest, Hungary. This high-stability design targets an energy ripple below 1% while delivering a continuous or pulsed proton beam with adjustable pulse duration (0.1-10 ms) and frequency (0.
View Article and Find Full Text PDFJ Chem Phys
September 2025
Department of Chemistry, University of Rochester, Rochester, New York 14627, USA.
We introduce an efficient method, TTN-HEOM, for exactly calculating the open quantum dynamics for driven quantum systems interacting with highly structured bosonic baths by combining the tree tensor network (TTN) decomposition scheme with the bexcitonic generalization of the numerically exact hierarchical equations of motion (HEOM). The method yields a series of quantum master equations for all core tensors in the TTN that efficiently and accurately capture the open quantum dynamics for non-Markovian environments to all orders in the system-bath interaction. These master equations are constructed based on the time-dependent Dirac-Frenkel variational principle, which isolates the optimal dynamics for the core tensors given the TTN ansatz.
View Article and Find Full Text PDFAdv Healthc Mater
September 2025
School of Biological and Health Systems Engineering (SBHSE), Arizona State University, Tempe, AZ, 85287, USA.
The prognosis of glioblastoma multiforme (GBM) remains dismal, despite standard treatment regimens. A key challenge in treating GBM is the persistence of glioma stem cells (GSCs) within the perivascular niche (PVN) - a protective tumor microenvironment (TME) that is often associated with inadequate drug penetration. Current preclinical models do not capture complexity of the human TME, particularly the vasculature and niche-specific interactions that drive GBM progression.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
School of Information Science and Automation, Northeastern University, Shenyang, 110819 China.
Accurate prediction of drug-target interactions (DTIs) is crucial for improving the efficiency and success rate of drug development. Despite recent advancements, existing methods often fail to leverage interaction features at multiple granular levels, resulting in suboptimal data utilization and limited predictive performance. To address these challenges, we propose CF-DTI, a coarse-to-fine drug-target interaction model that integrates both coarse-grained and fine-grained features to enhance predictive accuracy.
View Article and Find Full Text PDFBiol Methods Protoc
September 2025
School of Information and Communications Technology, Hanoi University of Science and Technology, Hanoi 100000, Vietnam.
MicroRNAs (miRNAs) play a critical role in disease mechanisms, making the identification of disease-associated miRNAs essential for precision medicine. We propose a novel computational method, multiplex-heterogeneous network for MiRNA-disease associations (MHMDA), which integrates multiple miRNA functional similarity networks and a disease similarity network into a multiplex-heterogeneous network. This approach employs a tailored random walk with restart algorithm to predict disease-miRNA associations, leveraging the complementary information from experimentally validated and predicted miRNA-target interactions, as well as disease phenotypic similarities.
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