Despite their high abundance and wide distribution in ecosystems, most protists remain unknown to the public. Although science communication approaches were developed in historical times to raise public awareness of these 'enigmatic' taxa, many aspects have not been considered in the spotlight of modern techniques. We present selected ideas and activities on how to attract the public to unicellular eukaryotes. We give examples of how protists can be included in educational work. We explain that trained non-experts can understand and teach others how to recognize protists, where they live, in which habitats they can be found, what they look like and why they are important. Consequently, members of the public can learn how environmental threats impact not only the lives of protists but also ours, e.g., by the accumulation of microplastics through an aquatic food web, up to fish used for human consumption. We suggest age-appropriate methods for application in workshops on protist recognition.
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http://dx.doi.org/10.1016/j.ejop.2024.126094 | DOI Listing |
Sci Rep
December 2024
Shaanxi Key Laboratory of Complex System Control and Intelligent Informantion Processing, Xi'an University of Technology, Xi'an 710048, China.
In the integrated radar and communication system (IRCS), the design of signal that can simultaneously satisfy the radar detection and communication transmission is very important and difficult. Recently, some new properties of a class of solvable chaotic system have been studied for wireless applications, such as low bit error rate (BER) wireless communications and low cost target detection. In this paper, a novel IRCS based on the chaotic signal is proposed, and the performance of proposed scheme is analyzed.
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December 2024
Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, 03680, Kyiv, Ukraine.
The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Biomedical Data Science Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
Introduction: Alzheimer's disease (AD) shows significant sex differences in prevalence and clinical manifestations, but the underlying molecular mechanisms remain unclear.
Methods: This study used a large-scale, single-cell transcriptomic atlas of the human prefrontal cortex to investigate sex-dependent molecular changes in AD. Our approach combined cell type-specific and sex-specific differential gene expression analysis, pathway enrichment, gene regulatory network construction, and cell-cell communication analysis to identify sex-dependent changes.
J Pediatr Hematol Oncol
January 2025
Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center.
This quality improvement initiative aimed to reduce the no-show rate at a hospital-based tertiary sickle cell ophthalmology clinic. Missed appointments place a significant burden on the healthcare system, resulting in prolonged waiting times and underutilized clinical resources that impact the quality of care provided. Individuals with sickle cell disease commonly require multiple appointments to address the myriads of comorbidities associated with their disease.
View Article and Find Full Text PDFAutism Res
December 2024
Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder and its underlying neuroanatomical mechanisms still remain unclear. The scaled subprofile model of principal component analysis (SSM-PCA) is a data-driven multivariate technique for capturing stable disease-related spatial covariance pattern. Here, SSM-PCA is innovatively applied to obtain robust ASD-related gray matter volume pattern associated with clinical symptoms.
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