Deciphering the role of particulate organic matter in soil nitrogen transformation in rice–rapeseed and rice–wheat rotation systems

Crop rotation affects the decomposition of soil organic matter (SOM) and thereby alter the composition of SOM fractions. It remains unclear how different SOM fractions impact soil nitrogen (N) transformation in various rotation systems. The aim of this study was to ascertain the role of particulate organic matter (POM)-a labile SOM fraction-in soil N transformation under various crop rotations. A paired plot experiment was conducted under two common cropping patterns, i.e., rice–rapeseed rotation (RR) vs. rice–wheat rotation (RW). Soil chemical composition and organic matter fraction before rice transplanting were compared between RR and RW systems after four years of crop rotations (2017–2021). With the same N inputs, the rice yield and N uptake under RR were 16.4 % and 13.2 % higher than those under RW, respectively. Compared with RW, RR resulted in higher carbon (C) and N contents in soil POM, despite minimal differences in total SOM. A larger potentially mineralizable N pool and a higher N mineralization rate occurred under RR than under RW, based on the results of soil net mineralization experiment. When POM was incubated alone, its contribution to potentially mineralizable N was 65.1 % and 61.3 % in RR and RW soils, respectively. Infrared spectroscopy revealed that in contrast with RW, RR promoted the accumulation of organic matter with high bioavailability (e.g., amides, carbohydrates, polysaccharides) in soil POM. This might be responsible for the higher gross mineralization and nitrification rates but lower gross immobilization rate under RR than under RW. Consequently, RR not only increased the contents of POMC and POMN but also improved the quality of POM fraction in soils. Findings of the present study demonstrate that POM plays a distinct role in soil N mineralization in various rotation systems. The discrepancy in POM content and composition resulting from various crop rotations leads to differences in soil N mineralization, which in turn affects the N supply and rice yield

Neighborhood garden\u27s age shapes phyllosphere microbiota associated with respiratory diseases in cold seasons

Neighborhood gardens serve as sensitive sites for human microbial encounters, with phyllosphere microbes directly impacting our respiratory health. Yet, our understanding remains limited on how factors like season, garden age, and land use shape the risk of respiratory diseases (RDs) tied to these garden microbes. Here we examined the microbial communities within the phyllosphere of 72 neighborhood gardens across Shanghai, spanning different seasons (warm and cold), garden ages (old and young), and locales (urban and rural). We found a reduced microbial diversity during the cold season, except for Gammaproteobacteria which exhibited an inverse trend. While land use influenced the microbial composition, urban and rural gardens had strikingly similar microbial profiles. Alarmingly, young gardens in the cold season hosted a substantial proportion of RDs-associated species, pointing towards increased respiratory inflammation risks. In essence, while newer gardens during colder periods show a decline in microbial diversity, they have an increased presence of RDs-associated microbes, potentially escalating respiratory disease prevalence. This underscores the pivotal role the garden age plays in enhancing both urban microbial diversity and respiratory health

The exploration of neuroinflammatory mechanism by which CRHR2 deficiency induced anxiety disorder

Inflammation stimulates the hypothalamic-pituitary adrenal (HPA) axis and triggers glial neuroinflammatory phenotypes, which reduces monoamine neurotransmitters by activating indoleamine 2,3-dioxygenase enzyme. These changes can induce psychiatric diseases, including anxiety. Corticotropin releasing hormone receptor 2 (CRHR2) in the HPA axis is involved in the etiology of anxiety. Omega(n)-3 polyunsaturated fatty acids (PUFAs) can attenuate anxiety through anti-inflammation and HPA axis modulation. However, the underlying molecular mechanism by CRHR2 modulates anxiety and its correlation with neuroinflammation remain unclear. Here, we first constructed a crhr2 zebrafish mutant line, and evaluated anxiety-like behaviors, gene expression associated with the HPA axis, neuroinflammatory response, neurotransmitters, and PUFAs profile in crhr2+/+ and crhr2−/− zebrafish. The crhr2 deficiency decreased cortisol levels and up-regulated crhr1 and down-regulated crhb, crhbp, ucn3l and proopiomelanocortin a (pomc a) in zebrafish. Interestingly, a significant increase in the neuroinflammatory markers, translocator protein (TSPO) and the activation of microglia M1 phenotype (CD11b) were found in crhr2−/− zebrafish. As a consequence, the expression of granulocyte-macrophage colony-stimulating factor, pro-inflammatory cytokines vascular endothelial growth factor, and astrocyte A1 phenotype c3 were up-regulated. While microglia anti-inflammatory phenotype (CD206), central anti-inflammatory cytokine interleukin-4, arginase-1, and transforming growth factor-β were downregulated. In parallel, crhr2-deficient zebrafish showed an upregulation of vdac1 protein, a TSPO ligand, and its downstream caspase-3. Furthermore, 5-HT/5-HIAA ratio was decreased and n-3 PUFAs deficiency was identified in crhr2−/− zebrafish. In conclusion, anxiety-like behavior displayed by crhr2-deficient zebrafish may be caused by the HPA axis dysfunction and enhanced neuroinflammation, which resulted in n-3 PUFAs and monoamine neurotransmitter reductions

What is Liberalism? A Mixed-Method Study of Ideology and Representation in Latin American Party Systems

What is the concept of liberalism? Despite being a conceptually contested term, political parties still label themselves liberal and seek legitimacy by joining Liberal International (LI). In this paper, I adopt a mixed-methods strategy to assess what this ideology means in Latin America. First, I rely on economic and political theory to propose four potential components of liberalism: private property, liberal democracy, non-conformism, and social justice. Then, I search for these components in the declaration of principles of all the region’s LI members. Next, I assess liberals’ relative support for these components by comparing the attitudes of their elites and voters to those of conservatives and socialists in Paraguay, Honduras, and Nicaragua. This paper finds that liberal democracy is the only core component of liberalism in Latin America. Even though non-conformism and social justice are widely mentioned in political documents, their support among elites and voters is context-dependent. These results emphasize the contestability of liberalism while shedding light on what unites liberals in Latin America

Chemometric Modeling of Emerging Materials for the Removal of Environmental Pollutants

Widespread usage of pharmaceuticals, personal care products (PPCPs), and agrochemicals followed by the release of household waste, industrial and hospital wastes has affected the environment and ecosystems immensely. These toxic chemicals are primarily classified under contaminants of emerging concerns (CEC) and/or environmental pollutants (EPs). Due to their harmful effects, timely removal of these EPs is an utmost requirement under risk management of the environment. A series of traditional techniques are accepted by the environmental organization to remove these products from the environment. Adsorption is one of the low-budget, easy to perform, and efficient approaches. With the advancement of nanotechnology, materials like carbon nanotubes (CNTs), magnetic nanoparticles, modified activated carbons/biochar, clay polycations, polyamide nanofilters (PNF), etc. have emerged as the materials of interest at present time. Along with the existing hazardous chemicals in the ecosystems, every day thousands of new chemicals are introduced to the environment. As a result, there is a continuous requirement for efficient materials which are capable of adsorbing these contaminants from the environment. In this perspective, chemometric-based modeling and machine learning (ML) models are shown to be capable of predicting important structural and physicochemical features that are responsible for the efficient adsorption property of these emerging materials. Once these features are identified, further modification in the structure of these materials can be performed to make them much more efficient adsorbers than the existing materials. The present chapter discusses the CECs and EPs, emerging materials in the present time, along with details about the chemometric and ML models which can be employed for modeling of the adsorption of EPs. Finally, successful case studies for the prediction of adsorption of EPs onto different emerging materials are meticulously discussed with mechanistic interpretations

Do online review readers react differently when exposed to credible versus fake online reviews?

Marketing research on online reviews has attempted to understand the antecedents and consequences of review manipulation. Building on the elaboration likelihood model (ELM), this study deploys a rare dataset that allows distinguishing credible from less credible (and likely fake) online reviews by means of the online review posting policy adopted by the movie review website Naver.com. We use text analysis entailing word embedding and topic modelling techniques such as Latent Dirichlet Allocation, to capture content depth across different types of online reviews (credible vs manipulated). Furthermore, we explore how differences in the textual content of credible vs manipulated online reviews affect customer purchase decisions. Our results highlight that less credible reviews tend to contain more superficial information compared to more credible reviews, and that different levels of source credibility lead to distinctively different impacts of online reviews on box office revenue

Hedge and safe-haven properties of FAANA against gold, US Treasury, bitcoin, and US Dollar/CHF during the pandemic period

The sudden market crash around 20 February 2020 on the dawn of the COVID-19 pandemic has accelerated the digitalization of all human communication and revived the interest for risk mitigation during stress periods. Interestingly, FAANA (Facebook, Apple, Amazon, Netflix, and Alphabet) stocks exhibited positive returns with remarkable resilience throughout the pandemic period, suggesting a change in their investing risk. In this paper, we take a different step from the existing literature and examine the hedging, diversifying, and safe haven properties of FAANA stocks against four alternative assets, namely gold, U.S. Treasury bonds, Bitcoin, and U.S. Dollar/CHF. Our analysis covers an extended sample period comprising the heightened uncertainty during the recent pandemic period. It involves conditional correlations, optimal weights, hedge ratios, and hedging effectiveness for the pairs of FAANA stock and alternative asset during the full sample period and the COVID-19 pandemic period. The results show that the majority of FAANA stocks serve as weak/strong safe havens against gold, Treasury bonds, Bitcoin, and Dollar/CHF in the full sample period. Further, few FAANA stocks serve as strong safe havens against the U.S. Treasury and Dollar/CHF during the pandemic. Our findings suggest that FAANA, once thought as risky high growth tech stocks, have gained maturity and became a safe blanket during the latest turbulent period

FEM computations and Taguchi optimization in nonlinear radiative MHD MWCNT-MgO/EG hybrid nanoliquid ?ow and heat transfer over a 3D wedge surface

The problems concerning to heat transport in hybrid nanofluids on a wedge surfaces are of great interest due to their relevance in applications such as solar panels, drying processes, cooling of electronic equipment, heat exchangers, and air heaters. Therefore, the present study explores the nonlinear thermal radiative heat transport features and three-dimensional rheological characteristics of MWCNT-MgO/EG hybrid nanofluid flowing over a wedge considering the variable magnetic field, and multiple slip boundary conditions. The single-phase model comprising of the experimental data with their dependency on temperature and nanoparticle weight fraction is used for thermal conductivity and viscosity of composite nanomaterial. Rosseland and Boussinesq approximations are accounted. The wedge surface is maintained at thermal jump condition, whereas the ambient state is considered at a constant temperature. The governing conservation laws are transformed into a system of ordinary differential equations via space type similarity transformations, and then are solved using the Galerkin finite element method. The L16 orthogonal array-based Taguchi method is applied to determine the optimal setting of governing parameters are determined for maximum heat transport rate. The contribution of physical parameters in the enhancement of heat transport rate is also estimated. The temperature jump condition has the maximum contribution (51.72%), among the selected four physical parameters whereas the nanoparticle weight fraction has the least (10.02%). The nonlinear thermal radiation has the maximum heat transfer rate compared to the linear one

TextFace: Text-to-Style Mapping Based Face Generation and Manipulation

As a subtopic of text-to-image synthesis, text-to-face generation has great potential in face-related applications. In this paper, we propose a generic text-to-face framework, namely, TextFace, to achieve diverse and high-quality face image generation from text descriptions. We introduce text-to-style mapping, a novel method where the text description can be directly encoded into the latent space of a pretrained StyleGAN. Guided by our text-image similarity matching and face captioning-based text alignment, the textual latent code can be fed into the generator of a well-trained StyleGAN to produce diverse face images with high resolution (1024×1024). Furthermore, our model inherently supports semantic face editing using text descriptions. Finally, experimental results quantitatively and qualitatively demonstrate the superior performance of our model

Transmission effects of oil prices, Ruble, and Euro in the outbreak of the Russian-Ukrainian war: evidence from the VAR-DCC-GARCH approach

Using the VAR-DCC-GARCH model, the paper studies the dynamic correlation between Ruble closing price and WTI\u27s oil price, and between Euro and WTI\u27s oil price during the Russo-Ukrainian war. The results show that before the Russia Ukraine war, there was a strong correlation between the Ruble and WTI oil prices, and between the Euro and WTI oil prices. Their connection deteriorates sharply and became negative during the Russia Ukraine conflict. We speculate that European stock market investors will flee risky assets and turn to safe haven assets, and China may use Euros in oil trade settlement in the future