Understanding host-pathogen interactions requires analyses to address the multiplicity of scales in heterogeneous landscapes. Anthropogenic influence on plant communities, especially cultivation, is a major cause of environmental heterogeneity. We have approached the analysis of how environmental heterogeneity determines plant-virus interactions by studying virus infection in a wild plant currently undergoing incipient domestication, the wild pepper or chiltepin, across its geographical range in Mexico. We have shown previously that anthropogenic disturbance is associated with higher infection and disease risk, and with disrupted patterns of host and virus genetic spatial structure. We now show that anthropogenic factors, species richness, host genetic diversity and density in communities supporting chiltepin differentially affect infection risk according to the virus analysed. We also show that in addition to these factors, a broad range of abiotic and biotic variables meaningful to continental scales, have an important role on the risk of infection depending on the virus. Last, we show that natural virus infection of chiltepin plants in wild communities results in decreased survival and fecundity, hence negatively affecting fitness. This important finding paves the way for future studies on plant-virus co-evolution.
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http://dx.doi.org/10.1016/j.virusres.2017.05.015 | DOI Listing |
Int J Dev Disabil
May 2023
Department of Dietetics and Nutritional Sciences, Harokopio University, Athens, Greece.
The aetiology of autism spectrum disorder (ASD) is heterogeneous and is attributed to the concurrent interaction of a number of genetic and environmental factors. The steady increase in ASD rates in recent years makes the detection and study of environmental risk factors increasingly important. This systematic review identifies potential environmental factors associated with ASD focusing specifically on recent studies conducted in selected Southern European countries.
View Article and Find Full Text PDFFront Immunol
January 2025
Department of Neurosurgery, First Affiliated Hospital of Dalian Medical University, Dalian, China.
Background And Purpose: The characteristics and role of NOD-like receptor (NLR) signaling pathway in high-grade gliomas were still unclear. This study aimed to reveal the association of NLR with clinical heterogeneity of glioblastoma (GBM) patients, and to explore the role of NLR pathway hub genes in the occurrence and development of GBM.
Methods: Transcriptomic data from 496 GBM patients with complete prognostic information were obtained from the TCGA, GEO, and CGGA databases.
Intensive Crit Care Nurs
January 2025
Department of Environmental Engineering, Yildiz Technical University, İstanbul, Turkey.
Background: Surgical site infections (SSIs) are the most common postoperative complications after cesarean section (CS), with increased mortality, prolonged hospital stays, and increased healthcare costs.
Objective: To systematically estimate the global incidence and identify the risk factors associated with SSI, focusing on the variation between high- and low-income countries.
Search Strategy And Selection Criteria: Observational studies reporting on the incidence of SSI after CS were systematically searched in PubMed, Embase and SCOPUS.
Sci Rep
January 2025
Faculty of Social Sciences, University of Lodz, ul. Prez, prez. Gabriela Narutowicza 68, 90-136, Łódź, Poland.
Based on a balanced panel dataset of 272 prefecture-level cities from 2000 to 2022, this paper systematically investigates the impact of the carbon emissions trading system on green total factor productivity and its underlying mechanisms from an integrated perspective of overall, dynamic, and spatial dimensions. The findings reveal that (1) the carbon emissions trading system significantly enhances regional total factor productivity, primarily by optimizing resource allocation efficiency and strengthening regional competitiveness. (2) From a dynamic perspective, the policy effect exhibited a U-shaped relationship: from 2013 to 2018, green total factor productivity was suppressed due to underdeveloped market mechanisms and the policy environment; after 2018, with market maturation and policy stabilization, the policy effects improved significantly.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
Continuous and consistent access to quality medical imaging data stimulates innovations in artificial intelligence (AI) technologies for patient care. Breakthrough innovations in data-driven AI technologies are founded on seamless communication between data providers, data managers, data users and regulators or other evaluators to determine the standards for quality data. However, the complexity in imaging data quality and heterogeneous nature of AI-enabled medical devices and their intended uses presents several challenges limiting the clinical translation of novel AI technologies.
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