Throughout their lifetimes, plants must coordinate the regulation of various facets of growth and development. Previous evidence has suggested that the Arabidopsis thaliana R2R3-MYB, AtMYB61, might function as a coordinate regulator of multiple aspects of plant resource allocation. Using a combination of cell biology, transcriptome analysis and biochemistry, in conjunction with gain-of-function and loss-of-function genetics, the role of AtMYB61 in conditioning resource allocation throughout the plant life cycle was explored. In keeping with its role as a regulator of resource allocation, AtMYB61 is expressed in sink tissues, notably xylem, roots and developing seeds. Loss of AtMYB61 function decreases xylem formation, induces qualitative changes in xylem cell structure and decreases lateral root formation; in contrast, gain of AtMYB61 function has the opposite effect on these traits. AtMYB61 coordinates a small network of downstream target genes, which contain a motif in their upstream regulatory regions that is bound by AtMYB61, and AtMYB61 activates transcription from this same motif. Loss-of-function analysis supports the hypothesis that AtMYB61 targets play roles in shaping subsets of AtMYB61-related phenotypes. Taken together, these findings suggest that AtMYB61 links the transcriptional control of multiple aspects of plant resource allocation.
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http://dx.doi.org/10.1111/j.1469-8137.2012.04201.x | DOI Listing |
Sensors (Basel)
December 2024
Xuejiawan Power Supply Company, Ordos 010300, China.
Recently, massive intelligent applications have emerged for the smart grid (SG), such as inspection and sensing. To support these applications, there have been high requirements on wireless communication for the SG, especially in remote areas. To tackle these challenges, a UAV-assisted heterogeneous wireless network is proposed in this paper for the SG, where multiple UAVs and a macro base station collaboratively provide a wide range of communication services.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Computer Science and Engineering, Northeastern University, Shenyang 110000, China.
Natural disasters cause significant losses. Unmanned aerial vehicles (UAVs) are valuable in rescue missions but need to offload tasks to edge servers due to their limited computing power and battery life. This study proposes a task offloading decision algorithm called the multi-agent deep deterministic policy gradient with cooperation and experience replay (CER-MADDPG), which is based on multi-agent reinforcement learning for UAV computation offloading.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Qualcomm, San Jose, CA 95110, USA.
With the development of Internet of Vehicles (IoV) technology, the need for real-time data processing and communication in vehicles is increasing. Traditional request-based methods face challenges in terms of latency and bandwidth limitations. Mode 4 in cellular vehicle-to-everything (C-V2X), also known as autonomous resource selection, aims to address latency and overhead issues by dynamically selecting communication resources based on real-time conditions.
View Article and Find Full Text PDFPlants (Basel)
December 2024
Neotropical Biodiversity Graduate Program, Federal University of Latin American Integration, Foz do Iguaçu 85866-000, PR, Brazil.
Communities with high native species diversity tend to be less susceptible to the establishment of invasive species, especially in studies that test their local impact. This study investigated the impact of competition between native submerged aquatic macrophytes (SAMs) ( and ) and the exotic , recognized for its invasive potential in aquatic ecosystems, through a mesocosm experiment conducted over six months. Two treatments were evaluated: the intraspecific competition of and an interspecific competition involving all three species.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany.
Background: The COVID-19 pandemic severely impacted healthcare systems, affecting patient outcomes and resource allocation. This study applied automated machine learning (AutoML) to analyze key health outputs, such as discharge conditions, mortality, and COVID-19 cases, with the goal of improving responses to future crises.
Methods: AutoML was used to train and validate models on an ICD-10 dataset covering the first wave of COVID-19 in Romania (January-September 2020).
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