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Objectives: The objective of this data set was to identify transcriptional networks that control elongation of seedling leaf sheaths in the C4 grass Sorghum bicolor. One motivation was that leaf sheaths are a primary constituent of stems in grass seedlings; therefore, genes that control growth of this organ are important contributors to successful transition from the seedling stage to the mature plant stage and, ultimately, crop success. Since diurnal rhythms contribute to regulation of signaling networks responsible for growth, a time course representing the late afternoon and early evening was anticipated to pinpoint important control genes for stem growth. Ultimately, the expected outcome was discovery of transcript networks that integrate internal and external signals to fine tune leaf sheath growth and, consequently, plant height.
Data Description: The data set is RNAseq profiling of upper leaf sheaths collected from wild type Sorghum bicolor (BTx623 line) plants at four-hour intervals from 12.5 h after dawn to 20 h after dawn. Global transcript levels in leaves were determined by deep sequencing of mRNA from four individual seedlings at each time point. This data set contains sequences representing the spectrum of mRNAs from individual genes. This data set enables detection of significant changes in gene-level expression caused by the progression of the day from late afternoon to the middle of the night. This data set is useful to identify gene expression networks regulating growth in the leaf sheath, an organ that is a major contributor to the sorghum seedling stem and defines seedling height.
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http://dx.doi.org/10.1186/s13104-023-06653-z | DOI Listing |
Chaos
September 2025
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
Although many real-world time series are complex, developing methods that can learn from their behavior effectively enough to enable reliable forecasting remains challenging. Recently, several machine-learning approaches have shown promise in addressing this problem. In particular, the echo state network (ESN) architecture, a type of recurrent neural network where neurons are randomly connected and only the read-out layer is trained, has been proposed as suitable for many-step-ahead forecasting tasks.
View Article and Find Full Text PDFAust J Rural Health
October 2025
AgHealth Australia, School of Rural Health, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
Objective: To describe the pattern and estimated direct economic burdens associated with unintentional deaths and injuries on Australian farms over the past 11 years (2013-2023).
Design: Descriptive retrospective epidemiological study of National Coronial Information System (NCIS) data for persons fatally injured on a farm and workers' compensation injuries data from the National Data Set.
Setting: Australia.
Alzheimers Dement
September 2025
Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.
Introduction: We compared and measured alignment between the Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) standard used by electronic health records (EHRs), the Clinical Data Interchange Standards Consortium (CDISC) standards used by industry, and the Uniform Data Set (UDS) used by the Alzheimer's Disease Research Centers (ADRCs).
Methods: The ADRC UDS, consisting of 5959 data elements across eleven packets, was mapped to FHIR and CDISC standards by two independent mappers, with discrepancies adjudicated by experts.
Results: Forty-five percent of the 5959 UDS data elements mapped to the FHIR standard, indicating possible electronic obtainment from EHRs.
Most of the United States (US) population resides in cities, where they are subjected to the urban heat island effect. In this study, we develop a method to estimate hourly air temperatures at resolution, improving exposure assessment of US population when compared to existing gridded products. We use an extensive network of personal weather stations to capture the intra-urban variability.
View Article and Find Full Text PDFArch Toxicol
September 2025
Norwegian Scientific Committee for Food and Environment, Norwegian Institute of Public Health, Oslo, Norway.
The transition from traditional animal-based approaches and assessments to New Approach Methodologies (NAMs) marks a scientific revolution in regulatory toxicology, with the potential of enhancing human and environmental protection. However, implementing the effective use of NAMs in regulatory toxicology has proven to be challenging, and so far, efforts to facilitate this change frequently focus on singular technical, psychological or economic inhibitors. This article takes a system-thinking approach to these challenges, a holistic framework for describing interactive relationships between the components of a system of interest.
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