With documented global declines in insects, including wild bees, there has been increasing interest in developing and expanding insect monitoring programs. Our objective here was to organize, validate, and share an analysis-ready version of one of the few existing long-term monitoring datasets for wild bees in the United States. Since 1999, the Native Bee Inventory and Monitoring Lab (BIML) of the United States Geological Survey has sampled wild-bee communities in the Mid-Atlantic U.S., but samples were collected in multiple studies and the datasets are not fully integrated. Furthermore, critical information about sampling methodology was often lacking, though these factors can significantly influence collection outcomes and must be considered in analyses. We cleaned and verified BIML data from Maryland, Delaware, and Washington DC, USA, and generated sampling methodology for over 84% of the 99,053 pan-trapped occurrences in this region. We enthusiastically invite creative analyses of this rich dataset to advance understanding of the biology and ecology of wild bees, inform conservation efforts, and perhaps help design a nationwide bee monitoring program.
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http://dx.doi.org/10.1038/s41597-020-00577-0 | DOI Listing |
Plant Physiol Biochem
January 2025
Botany Department, Federal University of Pelotas, Capão Do Leão, RS, 96160-000, Brazil.
Waterlogging is a significant stressor for crops, particularly in lowland regions where soil conditions exacerbate the problem. Waterlogged roots experience hypoxia, disrupting oxidative phosphorylation and triggering metabolic reorganization to sustain energy production. Here, we investigated the metabolic aspects that differentiate two soybean sister lines contrasting for waterlogging tolerance.
View Article and Find Full Text PDFBiomed Phys Eng Express
January 2025
F. Joseph Halcomb III, MD, Department of Biomedical Engineering, University of Kentucky, 143 Graham Ave., Lexington, Kentucky, 40506, UNITED STATES.
Brain-computer interfaces (BCIs) offer disabled individuals the means to interact with devices by decoding the electroencephalogram (EEG). However, decoding intent in fine motor tasks can be challenging, especially in stroke survivors with cortical lesions. Here, we attempt to decode graded finger extension from the EEG in stroke patients with left-hand paresis and healthy controls.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
School of Computer Science, University of Technology Sydney, Sydney, Australia.
The integration of artificial intelligence (AI) into health communication systems has introduced a transformative approach to public health management, particularly during public health emergencies, capable of reaching billions through familiar digital channels. This paper explores the utility and implications of generalist conversational artificial intelligence (CAI) advanced AI systems trained on extensive datasets to handle a wide range of conversational tasks across various domains with human-like responsiveness. The specific focus is on the application of generalist CAI within messaging services, emphasizing its potential to enhance public health communication.
View Article and Find Full Text PDFJ Particip Med
January 2025
Division of Allergy & Pulmonary Medicine, Washington University School of Medicine, St Louis, MO, United States.
Background: Adolescents and young adults (AYA) with cystic fibrosis (CF) are at risk for deviating from their daily treatment regimen due to significant time burden, complicated daily therapies, and life stressors. Developing patient-centric, effective, engaging, and practical behavioral interventions is vital to help sustain therapeutically meaningful self-management.
Objective: This study aimed to devise and refine a patient-centered telecoaching intervention to foster self-management in AYA with CF using a combination of intervention development approaches, including an evidence- and theory-based approach (ie, applying existing theories and research evidence for behavior change) and a target population-centered approach (ie, intervention refinement based on the perspectives and actions of those individuals who will use it).
JMIR Form Res
January 2025
Department of Computer Science, Purdue University, West Lafayett, IN, United States.
Background: Patient engagement is a critical but challenging public health priority in behavioral health care. During telehealth sessions, health care providers need to rely predominantly on verbal strategies rather than typical nonverbal cues to effectively engage patients. Hence, the typical patient engagement behaviors are now different, and health care provider training on telehealth patient engagement is unavailable or quite limited.
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