The genus Nata Watson, 1934 is a southern African endemic belonging to the Gondwanan family of carnivorous snails, Rhytididae. We present a molecular phylogeny of the genus based on two mitochondrial (16S and COI) and two nuclear genes (ITS2 and 28S RNA), and complement this with an appraisal of morphological characters relating to both the shell and soft parts. We identify four reciprocally monophyletic lineages for which valid names are already available, plus two undescribed species restricted to the Albany Thicket Biome. We show that Nata sensu lato may not be monophyletic. Rather there exist two deep lineages within Nata s.l., one lineage potentially sister to a clade dominated by the Australian and New Zealand radiation, and the other occupying a basal position within Rhytididae. Accordingly we recommend a revision recognising two genera, namely Nata s.s. and Natella respectively. Despite deep molecular divergences within Nata s.s., phenotypic evolution has been remarkably conserved, and contrasts greatly with that exhibited across other major lineages within the Rhytididae.
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http://dx.doi.org/10.1016/j.ympev.2015.11.003 | DOI Listing |
Brief Bioinform
November 2024
Departamento de Genética del Desarrollo y Fisiología Molecular, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México.
This study addresses the challenging task of identifying viruses within metagenomic data, which encompasses a broad array of biological samples, including animal reservoirs, environmental sources, and the human body. Traditional methods for virus identification often face limitations due to the diversity and rapid evolution of viral genomes. In response, recent efforts have focused on leveraging artificial intelligence (AI) techniques to enhance accuracy and efficiency in virus detection.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
Department of Chemistry, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra, 400076, India.
Stabilizing large easy-axis type magnetic anisotropy in molecular complexes is a challenging task, yet it is crucial for the development of information storage devices and applications in molecular spintronics. Achieving this requires a deep understanding of electronic structure and the relationships between structure and properties to develop magneto-structural correlations that are currently unexplored in the literature. Herein, a series of five-coordinate distorted square pyramidal Co complexes [Co(L)(X)].
View Article and Find Full Text PDFMater Horiz
January 2025
Institute of Biomedical Engineering, College of Medicine, Southwest Jiaotong University, Chengdu 610031, China.
Flexible hydrogel sensors have found extensive applications. However, the insufficient sensing sensitivity and the propensity to freeze at low temperatures restrict their use, particularly in frigid conditions. Herein, a multifunctional eutectogel with high transparency, anti-freezing, anti-swelling, adhesive, and self-healing properties is prepared by a one-step photopolymerization of acrylic acid and lauryl methacrylate in a binary solvent comprising water and deep eutectic solvent (DES).
View Article and Find Full Text PDFBMC Biol
January 2025
CAS Key Laboratory of Marine Ecology and Environmental Sciences, and Center of Deep Sea Research, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China.
Background: Lindaspio polybranchiata, a member of the Spionidae family, has been reported at the Lingshui Cold Seep, where it formed a dense population around this nascent methane vent. We sequenced and assembled the genome of L. polybranchiata and performed comparative genomic analyses to investigate the genetic basis of adaptation to the deep sea.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital of Capital Medical University, Beijing, 101100, China.
Background: MicroRNAs (miRNAs) are pivotal in the initiation and progression of complex human diseases and have been identified as targets for small molecule (SM) drugs. However, the expensive and time-intensive characteristics of conventional experimental techniques for identifying SM-miRNA associations highlight the necessity for efficient computational methodologies in this field.
Results: In this study, we proposed a deep learning method called Multi-source Data Fusion and Graph Neural Networks for Small Molecule-MiRNA Association (MDFGNN-SMMA) to predict potential SM-miRNA associations.
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