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Exceptionally preserved fossil sites have allowed specimen-based identification of trophic interactions to which network analyses have been applied. However, network analyses of the fossil record suffer from incomplete and indirect data, time averaging that obscures species coexistence, and biases in preservation. Here, we present a high-resolution fossil data set from Raymond Quarry member of the mid-Cambrian Burgess Shale (7,549 specimens, 61 taxa, ∼510 Mya) and formulate a measure of "preservation bias" that aids identification of assemblage subsets to which network analyses can be reliably applied. For these sections, abundance correlation network analyses predicted longitudinally consistent trophic and competitive interactions. Our analyses predicted previously postulated trophic interactions with 83.5% accuracy and demonstrated a shift from specialist interaction-dominated assemblages to ones dominated by generalist and competitive interactions. This approach provides a robust, taphonomically corrected framework to explore and predict in detail the existence and ecological character of putative interactions in fossil data sets.
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http://dx.doi.org/10.1016/j.isci.2021.102271 | DOI Listing |
BMC Public Health
September 2025
Heidelberg Institute of Global Health, Heidelberg University, Bergheimer Str. 20, Zimmer 317, 69115, Heidelberg, Germany.
Background: People living in prison face exceptionally high prevalence rates of tooth decay, periodontal disease, and poor oral health-related quality of life. Despite its importance, various aspects of oral healthcare in prison settings remain understudied. The present study investigates the barriers and facilitators associated with providing and utilizing oral health services in prison settings, drawing on insights from prison health experts, managerial and custodial staff, healthcare providers, and individuals with lived experience of imprisonment.
View Article and Find Full Text PDFSci China Life Sci
September 2025
State Key Laboratory of Plant Environmental Resilience, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.
Diurnal floret opening and closure (DFOC) is essential for rice reproductive development and hybrid breeding, yet transcriptional dynamics and underlying regulatory networks remain poorly characterized. Here, we conducted high-temporal-resolution transcriptomic analyses of lodicules to dissect DFOC regulatory networks in two japonica rice cultivars. Analysis of differentially expressed genes (DEGs) uncovered core genes shared by both cultivars, primarily associated with jasmonic acid (JA) signaling and cell wall remodeling.
View Article and Find Full Text PDFMetabolomics
September 2025
Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France.
Introduction: Initially developed for transcriptomics data, pathway analysis (PA) methods can introduce biases when applied to metabolomics data, especially if input parameters are not chosen with care. This is particularly true for exometabolomics data, where there can be many metabolic steps between the measured exported metabolites in the profile and internal disruptions in the organism. However, evaluating PA methods experimentally is practically impossible when the sample's "true" metabolic disruption is unknown.
View Article and Find Full Text PDFGeroscience
September 2025
Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.
Cognitive decline is common in multiple sclerosis (MS), although neural mechanisms are not fully understood. The objective was to investigate the impact of mild cognitive impairment (MCI) on the relationship between resting state functional connectivity (RSFC) and cognitive function in older adults with multiple sclerosis (OAMS) and age matched healthy controls. Participants underwent magnetic resonance imaging (MRI) scans and cognitive assessments.
View Article and Find Full Text PDFNat Biomed Eng
September 2025
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Phenotype-driven approaches identify disease-counteracting compounds by analysing the phenotypic signatures that distinguish diseased from healthy states. Here we introduce PDGrapher, a causally inspired graph neural network model that predicts combinatorial perturbagens (sets of therapeutic targets) capable of reversing disease phenotypes. Unlike methods that learn how perturbations alter phenotypes, PDGrapher solves the inverse problem and predicts the perturbagens needed to achieve a desired response by embedding disease cell states into networks, learning a latent representation of these states, and identifying optimal combinatorial perturbations.
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