History and future of water footprint in the Yangtze River Delta of China.

Quantifying the drivers of water footprint evolution in the Yangtze River Delta is vital for the optimization of China's total water consumption. The article aims to decompose and predict the water footprint of the Yangtze River Delta and provide policy recommendations for optimizing water use in the Yangtze River Delta. The paper applies the LMDI method to decompose the water footprint of the Yangtze River Delta and its provinces into five major drivers: water footprint structure, water use intensity, R&D scale, R&D efficiency, and population size. Furthermore, this paper combines scenario analysis and Monte Carlo simulation methods to predict the potential evolution trends of water footprint under the basic, general, and enhanced water conservation scenario, respectively. The results show that (1) the expansion of R&D scale is the main factor promoting the growth of water footprint, the improvement of R&D efficiency, and the reduction of water intensity are the main factors inhibiting the increase of water footprint, and the water footprint structure and population size have less influence on water footprint. (2) The evolution trend of water footprint of each province under three scenarios is different. Compared to the basic scenario, the water footprint decreases more in Shanghai, Zhejiang, and Anhui under the general and enhanced water conservation scenario. The increase in water footprint in Jiangsu under the enhanced scenario is smaller than that of the general water conservation scenario.

The price behavior characteristics of China and Europe carbon emission trading market based on the perspective of time scaling and expected returns.

China has the world's largest carbon market in terms of greenhouse gas emissions, but its system needs to be improved and enhanced. In comparison, the European carbon market stands as the most mature and well-developed carbon market globally. Carbon trading prices, serving as a barometer for the carbon market, are significantly influenced by investor behavior. Therefore, it is necessary to analyze the characteristics of carbon trading prices in both China and Europe, considering the impact of investor trading intervals and psychological expected returns. This study utilizes the Zipf method to characterize the dynamic behavior of carbon trading prices between China and Europe, conducting a comparative analysis. The results show distinctive asymmetry in the behavior of carbon trading prices in both markets. In the Chinese market, when τ < 277, the absolute deviation da(τ, ε) value gradually changes but consistently indicates a bullish trend. However, when τ ≥ 277, the da(τ, ε) value surges rapidly, reflecting a pronounced bullish sentiment among investors toward carbon trading prices in China. In the European market, within the sample period, regardless of variations in τ and ε, the da(τ, ε) value shows a linear upward trend, indicating a significant overall bullishness in prices. This suggests a higher probability of long-term bullishness in carbon trading prices. Investors' investment time scale (τ) and expected returns (ε) both influence the behavior of carbon trading prices in both China and Europe. Generally, a longer τ implies a higher probability of bullishness. As for ε, higher values lead to more extreme judgments on price movements, resulting in greater distortion in carbon trading prices. Short-term investors (τ<1 month) anticipate extreme fluctuations, exhibiting random behavior when ε < 0.15 and converging rapidly to extreme values of 1 or 0 when ε ≥ 0.15. Long-term investors (τ>quarter) are less biased, expressing a bullish outlook on both Chinese and European carbon prices. With increasing ε, the probability of bullishness either increases or decreases rapidly until reaching the saturation point. Once saturated, there is no further distortion in carbon price behavior. Furthermore, the Chinese carbon market displays a positive trend in carbon trading prices and a higher probability of long-term bullishness. For the European market, lower expected returns contribute to considerable carbon trading price fluctuations, exacerbating risk and uncertainty. The results of this study contribute to understanding the diverse trading behaviors in Chinese and European carbon markets and provide guidance for avoiding extreme volatility in carbon trading prices.

Low-Cost, Unsinkable, and Highly Efficient Solar Evaporators Based on Coating MWCNTs on Nonwovens with Unidirectional Water-Transfer.

Solar vapor generation technology is promising in seawater desalination, sewage purification, and other fields. However, wide application of this technology is still largely confined due to its high cost and difficulties for scalable production. In this study, an ever-floating solar evaporator is fabricated by coating multiwall carbon nanotubes on a bicomponent nonwoven composed of polypropylene/polyethylene core-sheath fibers. This all-fiber structure is highly porous and ultralight, with large specific area (for efficient water evaporation), interconnected channels (for easy vapor escape), and low thermal conductivity (to avoid heat loss). The unique unidirectional water-transfer behavior of the nonwoven enables it to spontaneously pump an adjustable amount of water for interfacial solar heating and a delicate balance between water supply and loss may accelerate the evaporation speed of water. These distinct benefits endow the solar evaporator with excellent evaporation rates of 1.44 kg m-2  h-1 under the simulated irradiation of 1 sun and 12.81 kg m-2  d-1 under natural sunlight. Moreover, the evaporator can be fabricated by using low-cost materials and industrialized methods (overall cost ≈2.4 USD m-2 ), making one believe its practical significance for commercial solar steam evaporation.

Relationship and integrated development of low-carbon economy, food safety, and agricultural mechanization.

The organic integration of food security and agricultural mechanization has become a challenge to realize a low-carbon economy, which helps promote carbon peaking and carbon neutralization. In this work, a simultaneous equation model has developed to analyze the relationship between food security, agricultural automation, and agricultural carbon emissions in China. The ordinary least square method was used to verify the method. The logarithmic mean Divisia index decomposition was used to decompose further the influencing factors of agricultural carbon emissions. Results show that the organic coupling of a low-carbon economy, food security, and agricultural mechanization positively affects environmental protection. In which, unit fertilizer usage and crop sown area have the greatest impact on carbon emission intensity, followed by agricultural diesel fuel and agricultural plastic film. It is worth noting that the bottom line of the grain sown area cannot be touched. It is a prerequisite for ensuring grain production. Finally, this paper presents suggestions based on China's achievements, where the top-level design is crucial.

Resistance risk assessment for a novel succinate dehydrogenase inhibitor pydiflumetofen in Fusarium asiaticum.

BACKGROUND: Fusarium asiaticum is one of predominant pathogens of Fusarium head blight (FHB) in China. Pydiflumetofen (Pyd) is a novel succinate dehydrogenase inhibitor (SDHI) which has been commercialized in China for the controlling of wheat FHB since 2019. In the current study, a risk assessment of the pydiflumetofen-resistance selected in Fusarium asiaticum was investigated. RESULTS: One PydMR mutant [resistance factor (RF) < 80] and four PydHR mutants (RF > 3000) were generated by fungicide-taming from 1000 mycelial discs of the wild-type strain 2021. Nucleotide sequences alignment results of FaSdh from the wild-type strain and resistant mutants showed that all the mutations were categorized into three genotypes, i.e. FaSdhBH248Y from PydMR mutant, both FaSdhC1 A64V and FaSdhC1 R67K from PydHR mutants. All the resistant mutants possessed no fitness penalty based on the data of mycelial linear growth, conidiation and virulence. In addition, the FaSdhC1 A64V mutants showed positive cross-resistance between pydiflumetofen and boscalid or thifluzamide, but no cross-resistance between pydiflumetofen and Y13149 or Y12196, while the FaSdhC1 R67K mutants exhibited positive cross-resistance between pydiflumetofen and boscalid, thifluzamide or Y12196, and no cross-resistance between pydiflumetofen and Y13149. Furthermore, positive cross-resistance between the five tested SDHIs was detected in the FaSdhBH248Y mutants. CONCLUSION: The results suggest a moderate to high resistance risk of F. asiaticum to pydiflumetofen, and provide essential data for monitoring the emergence of resistance and resistance management strategies for pydiflumetofen, which will be useful for scientific application of this fungicide in China.

Simulation of Proton-Induced DNA Damage Patterns Using an Improved Clustering Algorithm.

Simulations of deoxyribonucleic acid (DNA) molecular damage use the traversal algorithm that has the disadvantages of being time-consuming, slowly converging, and requiring high-performance computer clusters. This work presents an improved version of the algorithm, "density-based spatial clustering of applications with noise" (DBSCAN), using a KD-tree approach to find neighbors of each point for calculating clustered DNA damage. The resulting algorithm considers the spatial distributions for sites of energy deposition and hydroxyl radical attack, yielding the statistical probability of (single and double) DNA strand breaks. This work achieves high accuracy and high speed at calculating clustered DNA damage that has been induced by proton treatment at the molecular level while running on an i7 quad-core CPU. The simulations focus on the indirect effect generated by hydroxyl radical attack on DNA. The obtained results are consistent with those of other published experiments and simulations. Due to the array of chemical processes triggered by proton treatment, it is possible to predict the effects that different track structures of various energy protons produce on eliciting direct and indirect damage of DNA.

α-Ketoglutarate Modulates Macrophage Polarization Through Regulation of PPARγ Transcription and mTORC1/p70S6K Pathway to Ameliorate ALI/ARDS.

As tissue-resident cells in the lung, alveolar macrophages display remarkable heterogeneity and play a crucial role in the development and control of septic acute lung injury/acute respiratory distress syndrome (ALI/ARDS). Recent evidence suggests that α-ketoglutarate (α-KG) plays an important role in alternative activation of macrophage (M2) through metabolic and epigenetic reprogramming, and thus possesses anti-inflammatory properties. However, the underlying mechanisms of α-KG's effect on alveolar macrophage polarization and the potential effects of α-KG in ALI/ARDS remain unclear. Here, we examined the effects and mechanisms of α-KG on alveolar macrophage polarization, and investigated the possible effects of α-KG on lipopolysaccharide (LPS)-induced ALI/ARDS in a mouse model. We found that α-KG inhibited M1 macrophage polarization and promoted IL-4-induced M2 macrophage polarization in MH-S cells (a murine alveolar macrophage cell line). Further experiments showed that α-KG down-regulated the expression of M1-polarized marker genes and inhibited the activities of mammalian target of rapamycin complex 1 (mTORC1)/p70 ribosomal protein S6 kinase (p70S6K) signaling pathway in M1-polarized MH-S cells. Moreover, our results showed that α-KG promoted IL-4-induced M2 polarization of MH-S cells by augmenting nuclear translocation of peroxisome proliferator-activated receptor γ (PPARγ) and increasing expression of relevant fatty acid metabolic genes. Finally, using an LPS-induced ALI/ARDS mouse model, we found that α-KG ameliorated the LPS-induced inflammation and lung pathological damage, as well as α-KG pretreated mice had better clinical scores compared with the LPS group. These findings reveal new mechanisms of α-KG in regulating macrophage polarization which may provide novel strategies for the prevention and treatment of inflammatory diseases, including sepsis and septic ALI/ARDS.

[Study of clustered damage in DNA after proton irradiation based on density-based spatial clustering of applications with noise algorithm].

The deoxyribonucleic acid (DNA) molecule damage simulations with an atom level geometric model use the traversal algorithm that has the disadvantages of quite time-consuming, slow convergence and high-performance computer requirement. Therefore, this work presents a density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm based on the spatial distributions of energy depositions and hydroxyl radicals (·OH). The algorithm with probability and statistics can quickly get the DNA strand break yields and help to study the variation pattern of the clustered DNA damage. Firstly, we simulated the transportation of protons and secondary particles through the nucleus, as well as the ionization and excitation of water molecules by using Geant4-DNA that is the Monte Carlo simulation toolkit for radiobiology, and got the distributions of energy depositions and hydroxyl radicals. Then we used the damage probability functions to get the spatial distribution dataset of DNA damage points in a simplified geometric model. The DBSCAN clustering algorithm based on damage points density was used to determine the single-strand break (SSB) yield and double-strand break (DSB) yield. Finally, we analyzed the DNA strand break yield variation trend with particle linear energy transfer (LET) and summarized the variation pattern of damage clusters. The simulation results show that the new algorithm has a faster simulation speed than the traversal algorithm and a good precision result. The simulation results have consistency when compared to other experiments and simulations. This work achieves more precise information on clustered DNA damage induced by proton radiation at the molecular level with high speed, so that it provides an essential and powerful research method for the study of radiation biological damage mechanism.