Grazing Effects on Soil Seed Banks: A Global Synthesis

Livestock grazing is a major disturbance affecting plant diversity and abundance in terrestrial ecosystems. The intermediate disturbance hypothesis (IDH) predicts that moderate-intensity grazing should produce the highest species diversity, while the Milchunas-Sala-Lauenroth (MSL) model posits that the IDH is valid only for mesic areas. However, it remains unclear how grazing affects soil seed bank and whether or not the IDH or MSL models are valid for soil seed bank communities. Here, we presented a global meta-analysis synthesizing 483 observations: we found that grazing had a negative effect on soil seed bank abundance, but did not alter seed bank richness. Further refining the analysis, light-intensity grazing was found to increase seed bank richness, while moderate-intensity grazing had no effect, and heavy-intensity grazing had a negative effect. Additionally, for both arid and mesic areas, soil seed bank richness declined with grazing intensity increased. Overall, grazing effects on soil seed banks differed from expectations set by studies of aboveground vegetation. Our study provides key insights for policy-makers managing livestock grazing and grassland conservation

Grazing Effects on Soil Seed Banks: A Global Synthesis

Livestock grazing is a major disturbance affecting plant diversity and abundance in terrestrial ecosystems. The intermediate disturbance hypothesis (IDH) predicts that moderate-intensity grazing should produce the highest species diversity, while the Milchunas-Sala-Lauenroth (MSL) model posits that the IDH is valid only for mesic areas. However, it remains unclear how grazing affects soil seed bank and whether or not the IDH or MSL models are valid for soil seed bank communities. Here, we presented a global meta-analysis synthesizing 483 observations: we found that grazing had a negative effect on soil seed bank abundance, but did not alter seed bank richness. Further refining the analysis, light-intensity grazing was found to increase seed bank richness, while moderate-intensity grazing had no effect, and heavy-intensity grazing had a negative effect. Additionally, for both arid and mesic areas, soil seed bank richness declined with grazing intensity increased. Overall, grazing effects on soil seed banks differed from expectations set by studies of aboveground vegetation. Our study provides key insights for policy-makers managing livestock grazing and grassland conservation

Collective Flow Distributions and Nuclear Stopping in Heavy-ion Collisions at AGS, SPS and RHIC

We study the production of proton, antiproton and net-proton at \AGS, \SPS and \RHIC within the framework non-uniform flow model(NUFM) in this paper. It is found that the system of RHIC has stronger longitudinally non-uniform feature than AGS and SPS, which means that nuclei at RHIC energy region is much more transparent. The NUFM model provides a very good description of all proton rapidity at whole AGS, SPS and RHIC. It is shown that our analysis relates closely to the study of nuclear stopping and longitudinally non-uniform flow distribution of experiment. This comparison with AGS and SPS help us to understand the feature of particle stopping of thermal freeze-out at RHIC experiment.Comment: 16 pages,7 figure

Phycocyanin relieves myocardial ischemia-reperfusion injury in rats by inhibiting oxidative stress

Purpose: To investigate the effect of phycocyanin on myocardial ischemia-reperfusion injury, and the possible mechanisms involved. Methods: Twenty-four Sprague-Dawley (SD) rats were randomly divided into Sham group (only threading without ligation), IRI group (myocardial ischemia-reperfusion injury group) and phycocyanin group (phycocyanin pretreatment + myocardial ischemia-reperfusion injury group). The heart was harvested and cardiomyocytes were isolated. Colorimetry was used to determine the contents of cardiomyocyte serum creatine phospho-MB (CK-MB), lactate dehydrogenase (LDH) and malondialdehyde (MDA), and the activities of total antioxidant capacity (T-AOC), catalase (CAT), glutathione (GSH), total superoxide dismutase (SOD) and other related oxidative stress indicators. Furthermore, apoptosis was evaluated using TUNEL staining. Protein levels of cardiac factor E2 related factor 2 (Nrf2), heme oxygenase-1 (HO-1), human NADPH dehydrogenase 1 (NQO1) and nuclear factor-κB (NF-κB) were evaluated by Western blot and immunohistochemistry. Results: Compared with the myocardial IRI group, the contents of CK-MB, LDH, MAD and ROS in the treated group were significantly decreased (p < 0.05), but the activities of SOD, GSH, SOD, CAT, and T-AOC in the myocardial tissues were significantly enhanced (p < 0.05). Moreover, the pathological changes in myocardial tissue were significantly reduced. In addition, the expression levels of Nrf2, HO-1 and NQO-1 were significantly up-regulated after phycocyanin pretreatment, while expression of NF-κB was significantly down-regulated (p < 0.05). Conclusion: Phycocyanin improves myocardial anti-oxidative stress via activation of Nrf2 signaling pathway, and also protects rats from myocardial ischemia-reperfusion injury by reducing inflammatory response via inhibition of NF-κB signaling pathway

Interpreting Distributional Reinforcement Learning: A Regularization Perspective

Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation. Despite the remarkable performance of distributional RL, a theoretical understanding of its advantages over expectation-based RL remains elusive. In this paper, we attribute the superiority of distributional RL to its regularization effect in terms of the value distribution information regardless of its expectation. Firstly, by leverage of a variant of the gross error model in robust statistics, we decompose the value distribution into its expectation and the remaining distribution part. As such, the extra benefit of distributional RL compared with expectation-based RL is mainly interpreted as the impact of a \textit{risk-sensitive entropy regularization} within the Neural Fitted Z-Iteration framework. Meanwhile, we establish a bridge between the risk-sensitive entropy regularization of distributional RL and the vanilla entropy in maximum entropy RL, focusing specifically on actor-critic algorithms. It reveals that distributional RL induces a corrected reward function and thus promotes a risk-sensitive exploration against the intrinsic uncertainty of the environment. Finally, extensive experiments corroborate the role of the regularization effect of distributional RL and uncover mutual impacts of different entropy regularization. Our research paves a way towards better interpreting the efficacy of distributional RL algorithms, especially through the lens of regularization

Functionalized self-assembled monolayers on mesoporous silica nanoparticles with high surface coverage

This paper proposes three content-based image classification techniques based on fusing various low-level MPEG-7 visual descriptors. Fusion is necessary as descriptors would be otherwise incompatible and inappropriate to directly include e.g. in a Euclidean distance. Three approaches are described: A “merging” fusion combined with an SVM classifier, a back-propagation fusion combined with a KNN classifier and a Fuzzy-ART neurofuzzy network. In the latter case, fuzzy rules can be extracted in an effort to bridge the “semantic gap” between the low-level descriptors and the high-level semantics of an image. All networks were evaluated using content from the repository of the aceMedia project1 and more specifically in a beach/urban scene classification problem

TREEasy: An automated workflow to infer gene trees, species trees, and phylogenetic networks from multilocus data

Multilocus genomic data sets can be used to infer a rich set of information about the evolutionary history of a lineage, including gene trees, species trees, and phylogenetic networks. However, user-friendly tools to run such integrated analyses are lacking, and workflows often require tedious reformatting and handling time to shepherd data through a series of individual programs. Here, we present a tool written in Python-TREEasy-that performs automated sequence alignment (with MAFFT), gene tree inference (with IQ-Tree), species inference from concatenated data (with IQ-Tree and RaxML-NG), species tree inference from gene trees (with ASTRAL, MP-EST, and STELLS2), and phylogenetic network inference (with SNaQ and PhyloNet). The tool only requires FASTA files and nine parameters as inputs. The tool can be run as command line or through a Graphical User Interface (GUI). As examples, we reproduced a recent analysis of staghorn coral evolution, and performed a new analysis on the evolution of the "WGD clade" of yeast. The latter revealed novel patterns that were not identified by previous analyses. TREEasy represents a reliable and simple tool to accelerate research in systematic biology (https://github.com/MaoYafei/TREEasy)

Functionalized self-assembled monolayers on mesoporous silica nanoparticles with high surface coverage

Mesoporous silica nanoparticles (MSNs) containing vinyl-, propyl-, isobutyl- and phenyl functionalized monolayers were reported. These functionalized MSNs were prepared via molecular self-assembly of organosilanes on the mesoporous supports. The relative surface coverage of the organic monolayers can reach up to 100% (about 5.06 silanes/nm(2)). These monolayer functionalize MSNs were analyzed by a number of techniques including transmission electron microscope, fourier transform infrared spectroscopy, X-ray diffraction pattern, cross-polarized Si(29) MAS NMR spectroscopy, and nitrogen sorption measurement. The main elements (i.e., the number of absorbed water, the reactivity of organosilanes, and the stereochemistry of organosilane) that greatly affected the surface coverage and the quality of the organic functionalized monolayers on MSNs were fully discussed. The results show that the proper amount of physically absorbed water, the use of high active trichlorosilanes, and the functional groups with less steric hindrance are essential to generate MSNs with high surface coverage of monolayers