Chongqing Three Gorges University

Abstract Temperature sensitivity (Q 10 ) of soil organic matter (SOM) decomposition is a crucial parameter to predict the fate of soil carbon (C) under global warming. Nonetheless, the response pattern of Q 10 to continuous warming and the underlying mechanisms are still under debate, especially considering the complex interactions between Q 10 , SOM quality, and soil microorganisms. We examined the Q 10 of SOM decomposition across a mean annual temperature (MAT) gradient from −1.9 to 5.1°C in temperate mixed forest ecosystems in parallel with SOM quality and bioavailability, microbial taxonomic composition, and functional genes responsible for organic carbon decomposition. Within this temperature gradient of 7.0°C, the Q 10 values increased with MAT, but decreased with SOM bioavailability. The Q 10 values increased with the prevalence of K‐strategy of soil microbial community, which was characterized by: (i) high ratios of oligotrophic to copiotrophic taxa, (ii) ectomycorrhizal to saprotrophic fungi, (iii) functional genes responsible for degradation of recalcitrant to that of labile C, and (iv) low average 16S rRNA operon copy number. Because the recalcitrant organic matter was mainly utilized by the K‐strategists, these findings independently support the carbon quality‐temperature theory from the perspective of microbial taxonomic composition and functions. A year‐long incubation experiment was performed to determine the response of labile and recalcitrant C pools to warming based on the two‐pool model. The decomposition of recalcitrant SOM was more sensitive to increased temperature in southern warm regions, which might attribute to the dominance of K‐selected microbial communities. It implies that climate warming would mobilize the larger recalcitrant pools in warm regions, exacerbating the positive feedback between increased MAT and CO 2 efflux. This is the first attempt to link temperature sensitivity of SOM decomposition with microbial eco‐strategies by incorporating the genetic information and disentangling the complex relationship between Q 10 and soil microorganisms.

BACKGROUND: The inflammation and oxidative stress (OS) have been considered crucial components of the pathogenesis of depression. Edaravone (EDA), a free radical scavenger, processes strong biological activities including antioxidant, anti-inflammatory and neuroprotective properties. However, its role and potential molecular mechanisms in depression remain unclear. The present study aimed to investigate the antidepressant activity of EDA and its underlying mechanisms. METHODS: A chronic social defeat stress (CSDS) depression model was performed to explore whether EDA could produce antidepressant effects. Behaviors tests were carried out to examine depressive, anxiety-like and cognitive behaviors including social interaction (SI) test, sucrose preference test (SPT), open field test (OFT), elevated plus maze (EPM), novel object recognition (NOR), tail suspension test (TST) and forced swim test (FST). Hippocampal and medial prefrontal cortex (mPFC) tissues were collected for Nissl staining, immunofluorescence, targeted energy metabolomics analysis, enzyme-linked immunosorbent assay (ELISA), measurement of MDA, SOD, GSH, GSH-PX, T-AOC and transmission electron microscopy (TEM). Western blotting (WB) and quantitative real-time polymerase chain reaction (qRT-PCR) detected the Sirt1/Nrf2/HO-1/Gpx4 signaling pathway. EX527, a Sirt1 inhibitor and ML385, a Nrf2 inhibitor were injected intraperitoneally 30 min before EDA injection daily. Knockdown experiments were performed to determine the effects of Gpx4 on CSDS mice with EDA treatment by an adeno-associated virus (AAV) vector containing miRNAi (Gpx4)-EGFP infusion. RESULTS: The administrated of EDA dramatically ameliorated CSDS-induced depressive and anxiety-like behaviors. In addition, EDA notably attenuated neuronal loss, microglial activation, astrocyte dysfunction, oxidative stress damage, energy metabolism and pro-inflammatory cytokines activation in the hippocampus (Hip) and mPFC of CSDS-induced mice. Further examination indicated that the application of EDA after the CSDS model significantly increased the protein expressions of Sirt1, Nrf2, HO-1 and Gpx4 in the Hip. EX527 abolished the antidepressant effect of EDA as well as the protein levels of Nrf2, HO-1 and Gpx4. Similarly, ML385 reversed the antidepressant and anxiolytic effects of EDA via decreased expressions of HO-1 and Gpx4. In addition, Gpx4 knockdown in CSDS mice abolished EDA-generated efficacy on depressive and anxiety-like behaviors. CONCLUSION: These findings suggest that EDA possesses potent antidepressant and anxiolytic properties through Sirt1/Nrf2/HO-1/Gpx4 axis and Gpx4-mediated ferroptosis may play a key role in this effect.

Abstract Lithium–sulfur (Li–S) batteries are held great promise for next‐generation high‐energy‐density devices; however, polysulfide shuttle and Li‐dendrite growth severely hinders their commercial production. Herein, a sulfonate‐rich COF (SCOF‐2) is designed, synthesized, and used to modify the separator of Li–S batteries, providing a solution for the above challenges. It is found that the SCOF‐2 features stronger electronegativity and larger interlayer spacing than that of none/monosulfonate COFs, which can facilitate the Li + migration and alleviate the formation of Li‐dendrites. Density functional theory (DFT) calculations and in situ Raman analysis demonstrate that the SCOF‐2 possesses a narrow bandgap and strong interaction on sulfur species, thereby suppressing self‐discharge behavior. As a result, the modified batteries deliver an ultralow attenuation rate of 0.047% per cycle over 800 cycles at 1 C, and excellent anti‐self‐discharge performance by a low‐capacity attenuation of 6.0% over one week. Additionally, even with the high‐sulfur‐loading cathode (3.2–8.2 mg s cm ‐2 ) and lean electrolyte (5 µ L mg s ‐1 ), the batteries still exhibit ≈ 80% capacity retention over 100 cycles, showing great potential for practical application.

Cancer stem cells drive tumor initiation, progression, and recurrence, which compromise the effectiveness of anti-tumor drugs. Here, we report that demethylzeylasteral (DML), a triterpene anti-tumor compound, suppressed tumorigenesis of liver cancer stem cells (LCSCs) by interfering with lactylation of a metabolic stress-related histone. Using RNA sequencing (RNA-seq) and gas chromatography-mass spectrometric (GC-MS) analysis, we showed that the glycolysis metabolic pathway contributed to the anti-tumor effects of DML, and then focused on lactate downstream regulation as the molecular target. Mechanistically, DML opposed the progress of hepatocellular carcinoma (HCC), which was efficiently facilitated by the increase in H3 histone lactylation. Two histone modification sites: H3K9la and H3K56la, which were found to promote tumorigenesis, were inhibited by DML. In addition, we used a nude mouse tumor xenograft model to confirm that the anti-liver cancer effects of DML are mediated by regulating H3 lactylation in vivo. Our findings demonstrate that DML suppresses the tumorigenicity induced by LCSCs by inhibiting H3 histone lactylation, thus implicating DML as a potential candidate for the supplementary treatment of hepatocellular carcinoma.

Today East Asia harbors many "relict" plant species whose ranges were much larger during the Paleogene-Neogene and earlier. The ecological and climatic conditions suitable for these relict species have not been identified. Here, we map the abundance and distribution patterns of relict species, showing high abundance in the humid subtropical/warm-temperate forest regions. We further use Ecological Niche Modeling to show that these patterns align with maps of climate refugia, and we predict species' chances of persistence given the future climatic changes expected for East Asia. By 2070, potentially suitable areas with high richness of relict species will decrease, although the areas as a whole will probably expand. We identify areas in southwestern China and northern Vietnam as long-term climatically stable refugia likely to preserve ancient lineages, highlighting areas that could be prioritized for conservation of such species.

in the middle, and a bottom Au layer. The absorption sensor achieves the absorption efficiency of 99.41% and 99.22% at 5.664 THz and 8.062 THz, with the absorption bandwidths 0.0171 THz and 0.0152 THz, respectively. Compared with noble metal absorbers, our graphene absorber can achieve tunability by adjusting the Fermi level and relaxation time of the graphene layer with the geometry of the absorber unchanged, which greatly saves the manufacturing cost. The results show that the sensor has the properties of polarization-independence and large-angle insensitivity due to the symmetric structure. In addition, the practical application of testing the content of hemoglobin biomolecules was conducted, the frequency of first resonance mode shows a shift of 0.017 THz, and the second resonance mode has a shift of 0.016 THz, demonstrating the good frequency sensitivity of our sensor. The S (sensitivities) of the sensor were calculated at 875 GHz/RIU and 775 GHz/RIU, and quality factors FOM (Figure of Merit) are 26.51 and 18.90, respectively; and the minimum limit of detection is 0.04. By comparing with previous similar sensors, our sensor has better sensing performance, which can be applied to photon detection in the terahertz band, biochemical sensing, and other fields.

Abstract Single‐atom catalysts have emerged as an efficient oxidant activator for eliminating organic pollutants in Fenton‐like systems. However, the complex preparation, single active site, lack of understanding of the fundamental mechanism, and harsh pH conditions currently limit their practical applications. In this work, single‐atom iron anchored nitrogen‐rich g‐C 3 N 4 nanotubes (FeCNs) are designed and synthesized by a facile approach, and eco‐friendly peracetic acid (PAA) is selected as the oxidant for Fenton‐like reactions. The constructed heterogenous system achieves an enhanced degradation of various organic contaminants over a wide pH range of 3.0–9.0, exhibiting an ultrahigh and stable catalytic activity, outperforming equivalent quantities of pristine g‐C 3 N 4 by 75 times. The 18 O isotope‐labeling technique, probe method, and theoretical calculations demonstrate that the efficient catalytic activity relies on the high‐valency iron‐oxo species coupled with organic radicals generated by PAA. An increase in electron transport from the contaminant to the formed “metastable PAA/FeCN catalyst surface complex” is detected. A double driving mechanism for the tubular g‐C 3 N 4 regulated by a single Fe site and PAA activation is proposed. This work opens an avenue for developing novel catalysts with the coexistence of multiple active units and providing opportunities for significantly improving catalytic efficiency.

This study aims at assessment the water quality condition in the rivers of Lake Chaohu Basin (LCB), and exploring the crucial parameters affecting its water quality. Four sampling activities were conducted seasonally from April to December in 2018 at 83 sites covering main rivers of LCB. We measured 15 physicochemical parameters, such as turbidity (tur), dissolved oxygen (DO), ammonium (NH4-N), nitrate (NO3-N), and permanganate index (CODMn). There were significant differences of the 15 environmental parameters (except T) on spatial level, and three significant groups (i.e., Group I, II, and III) were divided according to cluster analysis. Water quality was evaluated by a water quality index (WQI) based on these parameters, which are commonly used in WQI calculation. Generally, the water quality state was classified as “moderate” in LCB (mean value of 69.1). Meanwhile, the state varied obviously among the three groups, with “good” in Group I and “moderate” in Group II and III. Seasonally, WQI in autumn was highest, which was significantly different from that in the other seasons. The distribution of water quality was consistent with other studies regardless of some typical rivers and the whole basin. Moreover, a minimum water quality index (WQImin) consisted of 5 crucial parameters (i.e., tur, DO, NH4-N, NO3-N, and CODMn) was not significantly different with the WQI based on all the 15 parameters, and its percentage error was relatively low (6.02%). With the excellent application in Lake Taihu Basin (LTB) nearby LCB, this WQImin was robust and potentially universal in determining river water quality assessment in both basins. This finding is probably beneficial to low-cost and quick water quality evaluation in adjacent basins. In addition, TN maybe not suitable for WQImin development in rivers, especially in the area with relatively high concentration, which probably resulted in an underestimation of water quality condition.

Climate change, biodiversity loss, and chemical pollution are planetary-scale emergencies requiring urgent mitigation actions. As these "triple crises" are deeply interlinked, they need to be tackled in an integrative manner. However, while climate change and biodiversity are often studied together, chemical pollution as a global change factor contributing to worldwide biodiversity loss has received much less attention in biodiversity research so far. Here, we review evidence showing that the multifaceted effects of anthropogenic chemicals in the environment are posing a growing threat to biodiversity and ecosystems. Therefore, failure to account for pollution effects may significantly undermine the success of biodiversity protection efforts. We argue that progress in understanding and counteracting the negative impact of chemical pollution on biodiversity requires collective efforts of scientists from different disciplines, including but not limited to ecology, ecotoxicology, and environmental chemistry. Importantly, recent developments in these fields have now enabled comprehensive studies that could efficiently address the manifold interactions between chemicals and ecosystems. Based on their experience with intricate studies of biodiversity, ecologists are well equipped to embrace the additional challenge of chemical complexity through interdisciplinary collaborations. This offers a unique opportunity to jointly advance a seminal frontier in pollution ecology and facilitate the development of innovative solutions for environmental protection.

Machine learning methods have successfully been used to predict the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD), by classifying MCI converters (MCI-C) from MCI nonconverters (MCI-NC). However, most existing methods construct classifiers using data from one particular target domain (e.g., MCI), and ignore data in other related domains (e.g., AD and normal control (NC)) that may provide valuable information to improve MCI conversion prediction performance. To address is limitation, we develop a novel domain transfer learning method for MCI conversion prediction, which can use data from both the target domain (i.e., MCI) and auxiliary domains (i.e., AD and NC). Specifically, the proposed method consists of three key components: 1) a domain transfer feature selection component that selects the most informative feature-subset from both target domain and auxiliary domains from different imaging modalities; 2) a domain transfer sample selection component that selects the most informative sample-subset from the same target and auxiliary domains from different data modalities; and 3) a domain transfer support vector machine classification component that fuses the selected features and samples to separate MCI-C and MCI-NC patients. We evaluate our method on 202 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) that have MRI, FDG-PET, and CSF data. The experimental results show the proposed method can classify MCI-C patients from MCI-NC patients with an accuracy of 79.4%, with the aid of additional domain knowledge learned from AD and NC.

Electromagnetic waves generated by electronic equipment are widely present in all living and working spaces because of the rapid development of electronic products and frequent use of digital systems. Electromagnetic shielding is an effective method of protection against these waves. Therefore, the demand for materials with high electromagnetic shielding properties has remarkably increased. Magnesium (Mg) alloys, as potential electromagnetic shielding materials, have sparked great interest worldwide. This review highlights the effects of grain size, texture, alloying elements and second phase on the shielding properties of Mg alloys. Recent progress on the shielding properties of Mg–Zn, Mg–Al, Mg–RE and other new shielding Mg alloys is then summarised, and the successful design of Mg alloys with superior electromagnetic shielding properties, such as Mg–Zn–Y–Ce–Zr, Mg–Sn–Zn–Ca–Ce, Mg–Gd–Y–Zn–Zr and Mg-based composite materials, is described. Finally, this review provides insights into the future development and applications of Mg alloys with superior shielding properties.

Boosting is popular algorithm in the field of machine learning. Adaboost is the most typical Algorithm in the Boosting family. This paper introduces Boosting and its research status briefly, and introduces the typical algorithms of each series respectively.

Purpose The purpose of this paper is to investigate the impact of buyer-seller interpersonal interactions on the purchase intention of buyers, incorporating swift guanxi as a mediator. Design/methodology/approach Based on survey data obtained from 336 Taobao Live users, PLS techniques were used to test hypotheses. Findings Swift guanxi exists in buyer-seller interactions and matters, as it drives buyers' purchase intention in live stream shopping. Perceived expertise, perceived similarity and perceived likeability are found to be the three essential interpersonal interaction factors promoting the formation of swift guanxi. Perceived familiarity is also found to be significant but to a lesser extent. In addition, all these interpersonal interaction factors are found to significantly affect purchase intention through the mediation of swift guanxi. Originality/value Swift guanxi has been less explored in live stream shopping. This study takes the lead in empirically examining the mediating role of swift guanxi in the relationship between interpersonal interaction factors and purchase intention and offers a description of key buyer-seller interpersonal interaction factors (perceived expertise, perceived similarity and perceived likeability), thereby helping to extend the swift guanxi literature in social commerce.

The total phenolic and flavonoid contents in the fruit tissues (peels, pulp residues, seeds, and juices) of 19 citrus genotypes belonged to Citrus reticulata Blanco were evaluated and their antioxidant capacity was tested by 2,2-diphenyl-1-picrylhydrazyl radicals (DPPH) method and 2,2′-azino-bis(3-ethylbenzthiozoline-6)-sulphonic acid (ABTS) method. The total phenolic and flavonoid contents, and their antioxidant capacity varied in different citrus fruit tissues. Generally, the peel had both the highest average of total phenolics (27.18 mg gallic acid equivalent (GAE) g−1 DW) and total flavonoids (38.97 mg rutin equivalent (RE) g−1 DW). The highest antioxidant capacity was also the average of DPPH value (21.92 mg vitamin C equivalent antioxidant capacity (VCEAC) g−1 DW) and average of ABTS value (78.70 mg VCEAC g−1 DW) in peel. The correlation coefficient between the total phenolics and their antioxidant capacity of different citrus fruits tissues ranged from 0.079 to 0.792, and from −0.150 to 0.664 for the total flavonoids. The antioxidant capacity of fruit tissues were correlated with the total phenoilc content and flavonoid content except in case of the peel. In addition, the total phenolic content and antioxidant capacity varied in different citrus genotypes. Manju and Karamandarin were better genotypes with higher antioxidation and the phenolic content, however Shagan was the poorest genotype with lower antioxidation and the phenolic content.

Forest fire recognition is important to the protection of forest resources. To effectively monitor forest fires, it is necessary to deploy multiple monitors from different angles. However, most of the traditional recognition models can only recognize single-source images. The neglection of multi-view images leads to a high false positive/negative rate. To improve the accuracy of forest fire recognition, this paper proposes a graph neural network (GNN) model based on the feature similarity of multi-view images. Specifically, the correlations (nodes) between multi-view images and library images were established to convert the input features of graph nodes into the correlation features between different images. Based on feature relationships, the image features in the library were updated to estimate the node similarity in the GNN model, improving the image recognition rate of our model. Furthermore, a fire area feature extraction method was designed based on image segmentation, aiming to simplify the complex preprocessing of images, and effectively extract the key features from images. By setting the threshold in the hue-saturation-value (HSV) color space, the fire area was extracted from the images, and the dynamic features were extracted from the continuous frames of the fire area. Experimental results show that our method recognized forest fires more effectively than the baselines, improving the recognition accuracy by 4%. In addition, the multi-source forest fire data experiment also confirms that our method could adapt to different forest fire scenes, and boast a strong generalization ability and anti-interference ability.

Chronic wounds are characterized by delayed and dysregulated healing processes. As such, they have emerged as an increasingly significant threat. The associated morbidity and socioeconomic toll are clinically and financially challenging, necessitating novel approaches in the management of chronic wounds. Metal-organic frameworks (MOFs) are an innovative type of porous coordination polymers, with low toxicity and high eco-friendliness. Documented anti-bacterial effects and pro-angiogenic activity predestine these nanomaterials as promising systems for the treatment of chronic wounds. In this context, the therapeutic applicability and efficacy of MOFs remain to be elucidated. It is, therefore, reviewed the structural-functional properties of MOFs and their composite materials and discusses how their multifunctionality and customizability can be leveraged as a clinical therapy for chronic wounds.

Ginger (Zingiber officinale), the type species of Zingiberaceae, is one of the most widespread medicinal plants and spices. Here, we report a high-quality, chromosome-scale reference genome of ginger 'Zhugen', a traditionally cultivated ginger in Southwest China used as a fresh vegetable, assembled from PacBio long reads, Illumina short reads, and high-throughput chromosome conformation capture (Hi-C) reads. The ginger genome was phased into two haplotypes, haplotype 1 (1.53 Gb with a contig N50 of 4.68 M) and haplotype 0 (1.51 Gb with a contig N50 of 5.28 M). Homologous ginger chromosomes maintained excellent gene pair collinearity. In 17,226 pairs of allelic genes, 11.9% exhibited differential expression between alleles. Based on the results of ginger genome sequencing, transcriptome analysis, and metabolomic analysis, we proposed a backbone biosynthetic pathway of gingerol analogs, which consists of 12 enzymatic gene families, PAL, C4H, 4CL, CST, C3'H, C3OMT, CCOMT, CSE, PKS, AOR, DHN, and DHT. These analyses also identified the likely transcription factor networks that regulate the synthesis of gingerol analogs. Overall, this study serves as an excellent resource for further research on ginger biology and breeding, lays a foundation for a better understanding of ginger evolution, and presents an intact biosynthetic pathway for species-specific gingerol biosynthesis.

The issue of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has created enormous threat to global health. In an effort to contain the spread of COVID-19, a huge amount of disinfectants and antibiotics have been utilized on public health. Accordingly, the concentration of disinfectants and antibiotics is increasing rapidly in various environments, including wastewater, surface waters, soils and sediments. The aims of this study were to analyze the potential ecological environment impacts of disinfectants and antibiotics by summarizing their utilization, environmental occurrence, distribution and toxicity. The paper highlights the promoting effects of disinfectants and antibiotics on antibiotic resistance genes (ARGs) and even antibiotic resistant bacteria (ARB). The scientific evidences indicate that the high concentration and high dose of disinfectants and antibiotics promote the evolution toward antimicrobial resistance through horizontal gene transformation and vertical gene transformation, which threaten human health. Further concerns should be focused more on the enrichment, bioaccumulation and biomagnification of disinfectants, antibiotics, antibiotic resistance genes (ARGs) and even antibiotic resistant bacteria (ARB) in human bodies.

Antibiotic resistance genes (ARGs), environmental pollutants of emerging concern, have posed a potential threat to the public health. Soil is one of the huge reservoirs and propagation hotspot of ARGs. To alleviate the potential risk of ARGs, it is necessary to figure out the source and fate of ARGs in the soil. This paper mainly reviewed recent studies on the association of ARGs with the microbiome and the transmission mechanism of ARGs in soil. The compositions and abundance of ARGs can be changed by modulating microbiome, soil physicochemical properties, such as pH and moisture. The relationships of ARGs with antibiotics, heavy metals, polycyclic aromatic hydrocarbons and pesticides were discussed in this review. Among the various factors mentioned above, microbial community structure, mobile genetic elements, pH and heavy metals have a relatively more important impact on ARGs profiles. Moreover, human health could be impacted by soil ARGs through plants and animals. Understanding the dynamic changes of ARGs with influencing factors promotes us to develop strategies for mitigating the occurrence and dissemination of ARGs to reduce health risks.

BACKGROUND: Anaerobic digesters become unstable when operated at a high organi c loading rate (OLR). Investigating the microbial community response to OLR disturbance is helpful for achieving efficient and stable process operation. However, previous studies have only focused on community succession during different process stages. How does community succession influence process stability? Is this kind of succession resilient? Are any key microbial indicator closely related to process stability? Such relationships between microbial communities and process stability are poorly understood. RESULTS: In this study, a mesophilic anaerobic digester for treating food waste (FW) was operated to study the microbial diversity and dynamicity due to OLR disturbance. Overloading resulted in proliferation of acidogenic bacteria, and the resulting high volatile fatty acid (VFA) yield triggered an abundance of acetogenic bacteria. However, the abundance and metabolic efficiency of hydrogenotrophic methanogens decreased after disturbance, and as a consequence, methanogens and acetogenic bacteria could not efficiently complete the syntrophy. This stress induced the proliferation of homoacetogens as alternative hydrogenotrophs for converting excessive H2 to acetate. However, the susceptible Methanothrix species also failed to degrade the excessive acetate. This metabolic imbalance finally led to process deterioration. After process recovery, the digester gradually returned to its original operational conditions, reached close to its original performance, and the microbial community profile achieved a new steady-state. Interestingly, the abundance of Syntrophomonas and Treponema increased during the deteriorative stage and rebounded after disturbance, suggesting they were resilient groups. CONCLUSIONS: Acidogenic bacteria showed high functional redundancy, rapidly adapted to the increased OLR, and shaped new microbial community profiles. The genera Syntrophomonas and Treponema were resilient groups. This observation provides insight into the key microbial indicator that are closely related to process stability. Moreover, the succession of methanogens during the disturbance phase was unsuitable for the metabolic function needed at high OLR. This contradiction resulted in process deterioration. Thus, methanogenesis is the main step that interferes with the stable operation of digesters at high OLR. Further studies on identifying and breeding high-efficiency methanogens may be helpful for breaking the technical bottleneck of process instability and achieving stable operation under high OLR.