Topological measures of large-scale complex networks are applied to a specific artificial regulatory network model created through a whole genome duplication and divergence mechanism. This class of networks share topological features with natural transcriptional regulatory networks. Specifically, these networks display scale-free and small-world topology and possess subgraph distributions similar to those of natural networks. Thus, the topologies inherent in natural networks may be in part due to their method of creation rather than being exclusively shaped by subsequent evolution under selection. The evolvability of the dynamics of these networks is also examined by evolving networks in simulation to obtain three simple types of output dynamics. The networks obtained from this process show a wide variety of topologies and numbers of genes indicating that it is relatively easy to evolve these classes of dynamics in this model.
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http://dx.doi.org/10.1016/j.biosystems.2006.01.004 | DOI Listing |
Ecotoxicol Environ Saf
January 2025
College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China; Yuelushan Laboratory, Changsha 410125, China. Electronic address:
Soil heavy metal pollution presents substantial risks to food security and human health. This study focused on the efficiency of plant growth-promoting fungus-Beauveria bassiana FE14 and Miscanthus floridulus on the synergistic remediation of soil Cd contamination. Results revealed that B.
View Article and Find Full Text PDFEcotoxicol Environ Saf
January 2025
College of Resource and Environment, Henan Polytechnic University, Jiaozuo 454003, China.
Identifying and quantifying the dominant factors influencing heavy metal (HM) pollution sources are essential for maintaining soil ecological health and implementing effective pollution control measures. This study analyzed soil HM samples from 53 different land use types in Jiaozuo City, Henan Province, China. Pollution sources were identified using Absolute Principal Component Score (APCS), with 8 anthropogenic factors, 9 natural factors, and 4 soil physicochemical properties mapped using Geographic Information System (GIS) kernel density estimation.
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January 2025
College of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China. Electronic address:
This study aimed to investigate the potential protective properties of a traditional Chinese medicine (TCM) herbal product, Siraitia grosvenorii granules (SGG) against PM2.5-induced lung injury, as well as their active constituents and underlying mechanisms. The chemical composition of SGG, such as wogonin (MOL000173), luteolin (MOL000006), nobiletin (MOL005828), naringenin (MOL004328), acacetin (MOL001689), were identified via ultra-high-performance liquid chromatography-Q Exactive (UHPLC-QE) Orbitrap/MS.
View Article and Find Full Text PDFDrug Alcohol Depend
January 2025
RAND, Boston, MA, United States. Electronic address:
Importance: States have implemented multiple policies likely to influence opioid prescribing; few national general population studies examine those policies' effects on per-capita opioid morphine milligram equivalents (MME) dispensed.
Objective: To examine state policies' effects on opioids per-capita MMEs dispensed at retail pharmacies.
Design: A longitudinal study of associations between MME per capita and implementation of policy interventions at different times across states.
Comput Med Imaging Graph
January 2025
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; National Key Laboratory of Kidney Diseases, Beijing 100853, China. Electronic address:
In clinical optical molecular imaging, the need for real-time high frame rates and low excitation doses to ensure patient safety inherently increases susceptibility to detection noise. Faced with the challenge of image degradation caused by severe noise, image denoising is essential for mitigating the trade-off between acquisition cost and image quality. However, prevailing deep learning methods exhibit uncontrollable and suboptimal performance with limited interpretability, primarily due to neglecting underlying physical model and frequency information.
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