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Obeticholic acid (OCA), a farnesoid X receptor (FXR) agonist with favorable effects on fatty and glucose metabolism, has been considered the leading candidate drug for nonalcoholic steatohepatitis (NASH) treatment. However, its limited effectiveness in resolving liver fibrosis and lipotoxicity-induced cell death remains a major drawback. Ferroptosis, a newly recognized form of cell death characterized by uncontrolled lipid peroxidation, is involved in the progression of NASH. Nitric oxide (NO) is a versatile biological molecule that can degrade extracellular matrix. In this study, we developed a PEGylated thiolated hollow mesoporous silica nanoparticles (MSN) loaded with OCA, as well as a ferroptosis inhibitor liproxsatin-1 and a NO donor S-nitrosothiol (ONL@MSN). Biochemical analyses, histology, multiplexed flow cytometry, bulk-tissue RNA sequencing, and fecal 16S ribosomal RNA sequencing were utilized to evaluate the effects of the combined nanoparticle (ONL@MSN) in a mouse NASH model. Compared with the OCA-loaded nanoparticles (O@MSN), ONL@MSN not only protected against hepatic steatosis but also greatly ameliorated fibrosis and ferroptosis. ONL@MSN also displayed enhanced therapeutic actions on the maintenance of intrahepatic macrophages/monocytes homeostasis, inhibition of immune response/lipid peroxidation, and correction of microbiota dysbiosis. These findings present a promising synergistic nanotherapeutic strategy for the treatment of NASH by simultaneously targeting FXR, ferroptosis, and fibrosis.

Study Region: A well-irrigated area along the Yellow River in the North China Plain. Study Focus: High nitrate concentrations in groundwater induced by soil nitrate leaching are found worldwide. In this research, we examined the main processes and mechanisms of nitrate leaching with 16 years of long-term observational data to evaluate the potential threat of nitrate pollution to the groundwater in a well-irrigated area. New Hydrological Insights for the Region: This study uncovered significant differences in the primary mechanisms of nitrate leaching in the region at different stages in response to the continuous groundwater table decline. In the first stage, groundwater tables were less than 5 m, nitrate leaching was mainly driven by irrigation or precipitation with average annual percolation of 116.8 mm. In the second stage, groundwater overexploitation led to a decline in the groundwater table, inducing the internal drainage and nitrate leaching of the lower root zone. In the third stage, a further decline of groundwater table almost cut off the hydrological connection of root zone to the groundwater, which hampered water percolation and thus nitrate leaching from the root zone during next irrigation or precipitation. However, one extreme precipitation as much as in 2021 washed out almost all the accumulated nitrate in the root zone. The findings provide a reference for nitrogen management and groundwater protection in similar regions.

Diamond is the hardest natural material, boasting a variety of remarkable properties. However, due to increased demand and technological advancements, natural diamond has struggled to meet the specialized requirements of various industries. This paper offers a comprehensively critical review of diamond synthesis, modification, and cutting tool in ultra-precision machining based on the remarkable properties of diamond. Firstly, the paper outlines the outstanding properties of diamonds and breakthroughs in the original performance achieved by doping elements. Secondly, the paper focuses on recent progress in high temperature high pressure (HTHP), specifically those involving solvent-catalysts and element catalysts. The advancements in chemical vapor deposition (CVD) for diamond synthesis, emphasizing improvements in growth rate and quality, are thoroughly reviewed and summarized. Thirdly, the mechanisms of boron doping and interface modification are analyzed. Finally, the paper presents the fabrication of diamond tools and their wear mechanisms during the machining of difficult-to-cut materials and explores assisted-machining methods. Notably, the future direction of diamond synthesis will emphasize producing large-sized diamond with high growth rates, as well as doping and the preparation of diamond/metal composite materials. This critical review provides a comprehensive review of diamond preparation, modification, and cutting tools in ultra-precision machining. It serves as a valuable reference for relevant research endeavors.

Soil microbial diversity is extremely vulnerable to fertilization, which is one of the main anthropogenic activities associated with global changes. Yet we know little about how and why soil microbial diversity responds to fertilization across contrasting local ecological contexts. This knowledge is fundamental for predicting changes in soil microbial diversity in response to ongoing global changes. We analyzed soils from ten 20-year field fertilization (organic and/or inorganic) experiments across China and found that the national-scale responses of soil bacterial diversity to fertilization are dependent on ecological context. In acidic soils from regions with high precipitation and soil fertility, inorganic fertilization can result in further acidification, resulting in negative impacts on soil bacterial diversity. In comparison, organic fertilization causes a smaller disturbance to soil bacterial diversity. Despite the overall role of environmental contexts in driving soil microbial diversity, a small group of bacterial taxa were found to respond to fertilization in a consistent way across contrasting regions throughout China. Taxa such as Nitrosospira and Nitrososphaera, which benefit from nitrogen fertilizer addition, as well as Chitinophagaceae, Bacilli, and phototrophic bacteria, which respond positively to organic fertilization, could be used as bioindicators for soil fertility in response to fertilization at the national scale. Overall, our work provides new insights into the importance of local environmental context in determining the responses of soil microbial diversity to fertilization, and identifies regions with acidic soils wherein soil microbial diversity is more vulnerable to fertilization at the national scale.

Summary: Atmospheric emission sources of persistent organic pollutants (POPs) in China’s eastern seaboard regions cause heavy POP contamination in the Bohai Sea (BS), China. Because many rivers are emptying into the BS, terrestrial runoff has been considered a dominant pathway of POPs onto the BS. Here, we explored the contribution of atmospheric transport and terrestrial runoff to organophosphorus flame retardants (OPFRs) to the BS by using an atmospheric transport model and a terrestrial runoff model. We examined the sensitivity and response of OPFR in the BS seawater to its atmospheric transport, deposition, and riverine discharge via terrestrial runoff. Both terrestrial runoff and atmospheric transport model simulations reveal that the atmospheric transport and deposition, including dry, wet, and diffusive gaseous deposition, dominate OPFR input into the BS. The total OPFR fluxes entering the BS via the atmospheric pathway and riverine input were 70.4 and 2.8 t/yr in 2013, respectively.

We study CP violation in b-baryon decays of Ξb−→Ξ−D with D=D0,D¯0 and Di(i=1,2). We find that these baryonic decay processes provide an ideal opportunity to measure the weak phase due to the absence of the relative strong phase. Explicitly, we relate ρ¯ and η¯ the CKM elements with the decay rate ratios of Ri=Γ(Ξb−→Ξ−Di)/Γ(Ξb−→Ξ−D0) without the charge conjugate states. As a complementary, we also examine the decay distributions of Λb→Λ(→pπ−)D. There are in total 32 decay observables, which can be parameterized by 9 real parameters, allowing the experiments to extract the angle γ≡arg⁡(−VudVub⁎/VcdVcb⁎) in the CKM unitarity triangle. In addition, the feasibilities of the experimental measurements are discussed. We find that ρ¯ and η¯ can be extracted at LHCb Run3 from Ξb−→Ξ−D, and a full analysis of Λb→Λ(→pπ−)D is available at LHCb Run4.

The dynamic architecture of the human brain has been consistently observed. However, there is still limited modeling work to elucidate how neuronal circuits are hierarchically and flexibly organized in functional systems. Here we proposed a reachable probability approach based on non-homogeneous Markov chains, to characterize all possible connectivity flows and the hierarchical structure of brain functional systems at the dynamic level. We proved at the theoretical level the convergence of the functional brain network system, and demonstrated that this approach is able to detect network steady states across connectivity structure, particularly in areas of the default mode network. We further explored the dynamically hierarchical functional organization centered at the primary sensory cortices. We observed smaller optimal reachable steps to their local functional regions, and differentiated patterns in larger optimal reachable steps for primary perceptual modalities. The reachable paths with the largest and second largest transition probabilities between primary sensory seeds via multisensory integration regions were also tracked to explore the flexibility and plasticity of the multisensory integration. The present work provides a novel approach to depict both the stable and flexible hierarchical connectivity organization of the human brain.

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences. Public summary: • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on. • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc. • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks.