Long Noncoding RNA PSMB8-AS1 Mediates the Tobacco-Carcinogen-Induced Transformation of a Human Bronchial Epithelial Cell Line by Regulating Cell Cycle.

Lung cancer is the main cause of cancer deaths around the world. Nitrosamine 4-(methyl nitrosamine)-1-(3-pyridyl)-1-butanone (NNK) is a tobacco-specific carcinogen of lung cancer. Abundant evidence implicates long noncoding RNAs (lncRNAs) in tumorigenesis. Yet, the effects and mechanisms of lncRNAs in NNK-induced carcinogenesis are still unclear. In this study, we discovered that NNK-induced transformed Beas-2B cells (Beas-2B-NNK) showed increased cell migration and proliferation while decreasing rates of apoptosis. RNA sequencing and differentially expressed lncRNAs analyses showed that lncRNA PSMB8-AS1 was obviously upregulated. Interestingly, silencing the lncRNA PSMB8-AS1 in Beas-2B-NNK cells reduced cell proliferation and migration and produced cell cycle arrest in the G2/M phase along with a decrease in CDK1 expression. Conclusively, our results demonstrate that lncRNA PSMB8-AS1 could promote the malignant characteristics of Beas-2B-NNK cells by regulating CDK1 and affecting the cell cycle, suggesting that it may supply a new prospective epigenetic mechanism for lung cancer.

Identification of lung adenocarcinoma subtypes and a prognostic signature based on activity changes of the hallmark and immunologic gene sets.

Background: Lung adenocarcinoma (LUAD) has a complex tumor heterogeneity. Our research attempts to clearness LUAD subtypes and build a reliable prognostic signature according to the activity changes of the hallmark and immunologic gene sets. Methods: According to The Cancer Genome Atlas (TCGA) - LUAD dataset, changes in marker and immune gene activity were analyzed, followed by identification of prognosis-related differential gene sets (DGSs) and their related LUAD subtypes. Survival analysis, correlation with clinical characteristics, and immune microenvironment assessment for subtypes were performed. Moreover, the differentially expressed genes (DEGs) between different subtypes were identified, followed by the construction of a prognostic risk score (RS) model and nomogram model. The tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) of different risk groups were compared. Results: Two LUAD subtypes were determined according to the activity changes of the hallmark and immunologic gene sets. Cluster 2 had worse prognosis, more advanced tumor and clinical stages than cluster 1. Moreover, a prognostic RS signature was established using two LUAD subtype-related DEGs, which could stratify patients at different risk levels. Nomogram model incorporated RS and clinical stage exerted good prognostic performance in LUAD patients. A shorter survival time and higher TMB were observed in the high-risk patients. Conclusions: Our findings revealed that our constructed prognostic signature could exactly predict the survival status of LUAD cases, which was helpful in predicting the prognosis and guiding personalized therapeutic strategies for LUAD.

The Impact of China's Family Floating Population on the Participation of Medical Insurance in the Inflow Areas.

Background: With the transformation of China's economy and society, the floating population has also shown a new development trend, from individual migration to co-migration with family members. In 2020, among the 376 million floating population, the population flowing to cities and towns was 330 million, accounting for nearly 88.1%. The family mobility of the floating population is not just a simple personal gathering or geographical migration, but a profound adjustment of the living environment, social interaction and the interests of family members. Migrants no longer simply play the role of " urban passers-by", but gradually move with spouses, children, parents, and even settle in the city, which will inevitably produce different public service and social security needs. Objective: To explore the impact of floating population's familyization on the participation of medical insurance in the inflow areas. Methods: This study adopted the form of non-systematic literature review. The key words were floating population and medical insurance. The related analysis of PubMed, Embase, CNKI, Wanfang, and VIP databases were reviewed and summarized. Results: Due to the flow between domestic immigrants and regions, their medical insurance is difficult to be guaranteed. The domestic floating population's demand for health services is increasing, but the coverage of medical services provided by medical insurance is not comprehensive enough. Conclusion: It is necessary to integrate the medical insurance system and improve the adaptability of medical insurance to family mobility; protect the welfare needs of migrant families and increase their willingness to participate in medical insurance at the destination; pay attention to the interaction and integration of floating population families, understand and guide them to participate in the status quo of medical insurance, and improve the status quo.

Antibacterial Activity and Mechanism of Three Root Exudates from Mulberry Seedlings against Ralstonia pseudosolanacearum.

Bacterial wilt is a significant soil-borne disease that poses a threat to mulberry production yield and quality of agricultural production worldwide. However, the disease resistance mechanisms dependent on root exudates are not well understood. In this present study, we investigated the antibacterial mechanisms of the main active substances (erucamide, oleamide, and camphor bromide) present in mulberry root exudates (MRE) against Ralstonia pseudosolanacearum (Rp), the causal agent of bacterial wilt. Our findings revealed that these three active substances inhibited the growth activity of Rp by affecting the cell morphology and extracellular polysaccharide content, as well as triggering a burst of reactive oxygen species. The active substances induced oxidative stress, leading to a decrease in Rp growth. Additionally, the expression levels of key genes in the hrp gene cluster (hrpB, hrpX, and hrpF) and other virulence-related genes (such as ripAW, ripAE, Rs5-4819, Rs5-4374, ace, egl3, and pehB) were significantly reduced upon treatment with the active substances. Further pathogenicity experiments demonstrated that root exudates (at a concentration of 1.5 mg·mL-1) delayed or slowed down the occurrence of bacterial wilt in mulberry. These findings provide valuable insight into the antimicrobial mechanisms of MRE against Rp and lay a theoretical foundation for the development and application of biocontrol agents to control mulberry bacterial wilt.

Binding characteristics of pheromone-binding protein 1 in Glyphodes pyloalis to organophosphorus insecticides: Insights from computational and experimental approaches.

Glyphodes pyloalis (Lepidoptera: Pyralidae) is one of the major pests in mulberry production in China, which has developed resistance to various insecticides. Chemoreception is one of the most crucial physiological tactics in insects, playing a pivotal role in recognizing chemical stimuli in the environment, including noxious stimuli such as insecticides. Herein, we obtained recombinant pheromone-binding protein 1 (GpylPBP1) that exhibited antennae-biased expression in G. pyloalis. Ligand-binding assays indicated that GpylPBP1 had the binding affinities to two organophosphorus insecticides, with a higher binding affinity to chlorpyrifos than to phoxim. Computational simulations showed that a mass of nonpolar amino acid residues formed the binding pocket of GpylPBP1 and contributed to the hydrophobic interactions in the bindings of GpylPBP1 to both insecticides. Furthermore, the binding affinities of three GpylPBP1 mutants (F12A, I52A, and F118A) to both insecticides were all significantly reduced compared to those of the GpylPBP1-wild type, suggesting that Phe12, Ile52, and Phe118 residues were crucial binding sites and played crucial roles in the bindings of GpylPBP1 to both insecticides. Our findings can be instrumental in elucidating the effects of insecticides on olfactory recognition in moths and facilitating the development of novel pest management strategies using PBPs as targets based on insect olfaction.

The Effect of Temperature and Shear on the Gelation of Cellulose Nanocrystals in Deep Eutectic Solvents.

With the flourishing development of 3D printing technology, the demand for printing materials has been increasing rapidly in recent years. In particular, physical gels formed by cellulose nanocrystals (CNCs) exhibit suitable shear-thinning behavior, high storage moduli, and high yield stresses for extrusion-based printing. While most studies use water as the dispersing medium to form CNC percolated gels, the dispersing behavior of CNCs in alternative solvents, such as deep eutectic solvents (DESs), has not been fully explored. Especially, DESs have low volatility and good ionic conductivity to form functional ionogels. Precise control of the rheological properties and selection of suitable dispersion processes continue to pose significant challenges. In light of this, we have devised a novel dispersion process employing thermal and shear treatments to facilitate the gelation of CNCs within DESs. A crude dispersion of CNCs in the DES underwent thermal treatment to partially remove the surface sulfate ester on CNCs. As a result, the repulsive force between CNCs decreases. A second shear then significantly increases the strength of CNC/DES gels potentially because of the increased rod-rod contacts. This approach enables the formation of high-strength gels at low concentrations of CNCs. Both thermal treatment and a second shear are crucial to forming strong percolated CNC gels. In short, we showed a simple strategy to facilitate the dispersion and gelation of CNCs for direct ink writing.

Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction.

BACKGROUND: DNA methylation is a form of epigenetic modification that impacts gene expression without modifying the DNA sequence, thereby exerting control over gene function and cellular development. The prediction of DNA methylation is vital for understanding and exploring gene regulatory mechanisms. Currently, machine learning algorithms are primarily used for model construction. However, several challenges remain to be addressed, including limited prediction accuracy, constrained generalization capability, and insufficient learning capacity. RESULTS: In response to the aforementioned challenges, this paper leverages the similarities between DNA sequences and time series to introduce a time series-based hybrid ensemble learning model, called Multi2-Con-CAPSO-LSTM. The model utilizes multivariate and multidimensional encoding approach, combining three types of time series encodings with three kinds of genetic feature encodings, resulting in a total of nine types of feature encoding matrices. Convolutional Neural Networks are utilized to extract features from DNA sequences, including temporal, positional, physicochemical, and genetic information, thereby creating a comprehensive feature matrix. The Long Short-Term Memory model is then optimized using the Chaotic Accelerated Particle Swarm Optimization algorithm for predicting DNA methylation. CONCLUSIONS: Through cross-validation experiments conducted on 17 species involving three types of DNA methylation (6 mA, 5hmC, and 4mC), the results demonstrate the robust predictive capabilities of the Multi2-Con-CAPSO-LSTM model in DNA methylation prediction across various types and species. Compared with other benchmark models, the Multi2-Con-CAPSO-LSTM model demonstrates significant advantages in sensitivity, specificity, accuracy, and correlation. The model proposed in this paper provides valuable insights and inspiration across various disciplines, including sequence alignment, genetic evolution, time series analysis, and structure-activity relationships.

SaPt-CNN-LSTM-AR-EA: a hybrid ensemble learning framework for time series-based multivariate DNA sequence prediction.

Biological sequence data mining is hot spot in bioinformatics. A biological sequence can be regarded as a set of characters. Time series is similar to biological sequences in terms of both representation and mechanism. Therefore, in the article, biological sequences are represented with time series to obtain biological time sequence (BTS). Hybrid ensemble learning framework (SaPt-CNN-LSTM-AR-EA) for BTS is proposed. Single-sequence and multi-sequence models are respectively constructed with self-adaption pre-training one-dimensional convolutional recurrent neural network and autoregressive fractional integrated moving average fused evolutionary algorithm. In DNA sequence experiments with six viruses, SaPt-CNN-LSTM-AR-EA realized the good overall prediction performance and the prediction accuracy and correlation respectively reached 1.7073 and 0.9186. SaPt-CNN-LSTM-AR-EA was compared with other five benchmark models so as to verify its effectiveness and stability. SaPt-CNN-LSTM-AR-EA increased the average accuracy by about 30%. The framework proposed in this article is significant in biology, biomedicine, and computer science, and can be widely applied in sequence splicing, computational biology, bioinformation, and other fields.

Construction of an efficient artificial light-harvesting system based on hyperbranched polyethyleneimine and improvement of photocatalytic performance.

An artificial light-harvesting system (ALHS) was developed in aqueous solution by employing the electrostatic co-assembly of a tetraphenylethylene derivative modified with two sulfonate groups (TPE-BSBO) and hyperbranched polyethyleneimine (PEI) as the energy donors, and 4,7-bis(2-thienyl)-2,1,3-benzothiadiazole (DBT) as the energy acceptors. The ALHS exhibits not only high efficiency in energy transfer and conversion but also a significant enhancement in the generation of reactive oxygen species (ROS), especially superoxide anion radicals (O2˙-), facilitating its utilization in photocatalytic oxidation reactions.

The Transcription Factor CsgD Contributes to Engineered Escherichia coli Resistance by Regulating Biofilm Formation and Stress Responses.

The high cell density, immobilization and stability of biofilms are ideal characteristics for bacteria in resisting antibiotic therapy. CsgD is a transcription activating factor that regulates the synthesis of curly fimbriae and cellulose in Escherichia coli, thereby enhancing bacterial adhesion and promoting biofilm formation. To investigate the role of CsgD in biofilm formation and stress resistance in bacteria, the csgD deletion mutant ΔcsgD was successfully constructed from the engineered strain E. coli BL21(DE3) using the CRISPR/Cas9 gene-editing system. The results demonstrated that the biofilm of ΔcsgD decreased by 70.07% (p < 0.05). Additionally, the mobility and adhesion of ΔcsgD were inhibited due to the decrease in curly fimbriae and extracellular polymeric substances. Furthermore, ΔcsgD exhibited a significantly decreased resistance to acid, alkali and osmotic stress conditions (p < 0.05). RNA-Seq results revealed 491 differentially expressed genes between the parent strain and ΔcsgD, with enrichment primarily observed in metabolism-related processes as well as cell membrane structure and catalytic activity categories. Moreover, CsgD influenced the expression of biofilm and stress response genes pgaA, motB, fimA, fimC, iraP, ompA, osmC, sufE and elaB, indicating that the CsgD participated in the resistance of E. coli by regulating the expression of biofilm and stress response. In brief, the transcription factor CsgD plays a key role in the stress resistance of E. coli, and is a potential target for treating and controlling biofilm.

Bioinspired simultaneous regulation in fluorescence of AIEgen-embedded hydrogels.

The development of stimuli-responsive functional fluorescent hydrogels is of great significance for the realization of artificial intelligence. In the present work, we design and synthesize a stimulus-responsive hydrogel embedded with an aggregation-induced emission (AIE) monomer, in which the fluorescence brightness and intensity can be tuned. The hydrogel embedded with tetraphenylethene-grafted-poly[3-sulfopropyl methacrylate potassium salt] (TPE-PSPMA) as the functional element is prepared by the radical polymerization method. Among them, the TPE core exhibits adaptive fluorescence ability through the AIE effect, while the PSPMA chain provides tunable hydrophilic properties under an external stimulus. The effect of different cationic surfactants with different lengths of hydrophobic tails on the fluorescence properties of TPE-PSPMA in solution is systematically investigated. With cationic surfactants, such as cetyltrimethylammonium bromide (CTAB), the fluorescence intensity is gradually tuned from 1059 to 4623. And the fluorescence intensities increase with the growth of hydrophobic tails of surfactants, which results from hydrophobicity-induced electrostatic interactions among surfactants and polymer chains. Furthermore, an obvious tunable fluorescence feature of hydrogel copolymerized TPE-PSPMA is realized, resulting from the change of brightness and the dynamic increase of fluorescence intensity (from 1031 to 3138) for the hydrogel immersed in CTAB solution with different soaking times. Such a typical fluorescence-regulated behavior can be attributed to the AIE of the TPE-PSPMA chain and the electrostatic interaction between the surfactant and the anionic polymer chain. The designed TPE-PSPMA-based hydrogel is responsive to stimuli, inspiring the development of intelligent systems such as soft robots and smart wearables.

Host trehalose metabolism disruption by validamycin A results in reduced fitness of parasitoid offspring.

The general cutworm, Spodoptera litura (Lepidoptera: Noctuidae) is a worldwide destructive omnivorous pest and the endoparasitoid wasp Meteorus pulchricornis (Hymenoptera: Braconidae) is the dominant endoparasitoid of S. litura larvae. Trehalase is a key enzyme in insect trehalose metabolism and plays an important role in the growth and development of insects. However, the specific function of trehalase in parasitoid and host associations has been less reported. In this study, we obtained two trehalase genes (SlTre1 and SlTre2) from our previously constructed S. litura transcriptome database; they were highly expressed in 3rd instar larvae. SlTre1 was mainly expressed in the midgut, and SlTre2 was expressed highest in the head. SlTre1 and SlTre2 were highly expressed 5 days after parasitization by M. pulchricornis. Treatment with the trehalase inhibitor validamycin A significantly inhibited the expression levels of SlTre1 and SlTre2, and the trehalase activity. Besides, the content of trehalose was increased but the content of glucose was decreased 24 h after validamycin A treatment in parasitized S. litura larvae. In addition, the immune-related genes in phenoloxidase (PO) pathway and fatty acid synthesis-related genes in lipid metabolism were upregulated in parasitized host larvae after validamycin A treatment. Importantly, the emergence rate, proportion of normal adults, and body size of parasitoid offspring was decreased in parasitized S. litura larvae after validamycin A treatment, indicating that validamycin A disrupts the trehalose metabolism of parasitized host and thus reduces the fitness of parasitoid offspring. The present study provides a novel perspective for coordinating the application of biocontrol and antibiotics in agroecosystem.

Identification and functional study of detoxification-related genes in response to tolfenpyrad stress in Glyphodes pyloalis Walker (Lepidoptera: Pyralidae).

Glyphodes pyloalis Walker (G. pyloalis) is a common destructive mulberry pest. Due to the long-term and frequent use of insecticides, it has developed tolerance to commonly used insecticides. Tolfenpyrad (TFP) is a novel pyrazole heterocyclic insecticide. In order to understand the TFP detoxification mechanism of G. pyloalis larvae, we first estimated the LC30 dose of TFP for 3rd instar G. pyloalis larvae. Next, we identified genes that were differentially expressed in 3rd instar G. pyloalis larvae treated with TFP compared to the control group by transcriptome sequencing. In total, 86,949,569 and 67,442,028 clean reads were obtained from TFP-treated and control G. pyloalis larvae, respectively. A total of 5588 differentially expressed genes (DEGs) were identified in TFP-treated and control G. pyloalis larvae, of which 3084 genes were upregulated and 2504 genes were downregulated. We analyzed the expression of 43 candidate detoxification enzyme genes associated with insecticide tolerance using qPCR. According to the spatiotemporal expression pattern of DEGs, we found that CYP6ABE1, CYP333A36 and GST-epsilon8 were highly expressed in the midgut, while CarEs14 was strongly expressed in haemolymph. Furthermore, we successfully knocked down these genes by RNA interference. After silencing CYP6ABE1 and CYP333A36, bioassay showed that the mortality rate of TFP-treated G. pyloalis larvae was significantly higher compared to the control group. This study provides a theoretical foundation for understanding the sensitivity of G. pyloalis to TFP and establish the basis for the effective and green management of this pest.

Lipopolysaccharide-induced Trigeminal Ganglion Nerve Fiber Damage is Associated with Autophagy Inhibition.

OBJECTIVE: This study aimed to determine whether lipopolysaccharide (LPS) induces the loss of corneal nerve fibers in cultured trigeminal ganglion (TG) cells, and the underlying mechanism of LPS-induced TG neurite damage. METHODS: TG neurons were isolated from C57BL/6 mice, and the cell viability and purity were maintained for up to 7 days. Then, they were treated with LPS (1 µg/mL) or the autophagy regulator (autophibib and rapamycin) alone or in combination for 48 h, and the length of neurites in TG cells was examined by the immunofluorescence staining of the neuron-specific protein ß3-tubulin. Afterwards, the molecular mechanisms by which LPS induces TG neuron damage were explored. RESULTS: The immunofluorescence staining revealed that the average length of neurites in TG cells significantly decreased after LPS treatment. Importantly, LPS induced the impairment of autophagic flux in TG cells, which was evidenced by the increase in the accumulation of LC3 and p62 proteins. The pharmacological inhibition of autophagy by autophinib dramatically reduced the length of TG neurites. However, the rapamycin-induced activation of autophagy significantly lessened the effect of LPS on the degeneration of TG neurites. CONCLUSION: LPS-induced autophagy inhibition contributes to the loss of TG neurites.

Exploring a multi-output temporal convolutional network driven encoder-decoder framework for ammonia nitrogen forecasting.

Artificial neural networks exhibit significant advantages in terms of learning capability and generalizability, and have been increasingly applied in water quality prediction. Through learning a compressed representation of the input data, the Encoder-Decoder (ED) structure not only could remove noise and redundancies, but also could efficiently capture the complex nonlinear relationships of meteorological and water quality factors. The novelty of this study lies in proposing a multi-output Temporal Convolutional Network based ED model (TCN-ED) to make ammonia nitrogen forecasts for the first time. The contribution of our study is indebted to systematically assessing the significance of combining the ED structure with advanced neural networks for making accurate and reliable water quality forecasts. The water quality gauge station located at Haihong village of an island in Shanghai City of China constituted the case study. The model input contained one hourly water quality factor and hourly meteorological factors of 32 observed stations, where each factor was traced back to the previous 24 h and each meteorological factor of 32 gauge stations was aggregated into one areal average factor. A total of 13,128 hourly water quality and meteorological data were divided into two datasets corresponding to model training and testing stages. The Long Short-Term Memory based ED (LSTM-ED), LSTM and TCN models were constructed for comparison purposes. The results demonstrated that the developed TCN-ED model can succeed in mimicking the complex dependence between ammonia nitrogen and water quality and meteorological factors, and provide more accurate ammonia nitrogen forecasts (1- up to 6-h-ahead) than the LSTM-ED, LSTM and TCN models. The TCN-ED model, in general, achieved higher accuracy, stability and reliability compared with the other models. Consequently, the improvement can facilitate river water quality forecasting and early warning, as well as benefit water pollution prevention in the interest of river environmental restoration and sustainability.

Exploring a similarity search-based data-driven framework for multi-step-ahead flood forecasting.

Due to a small proportion of observations, reliable and accurate flood forecasts for large floods present a fundamental challenge to artificial neural network models, especially when the forecast horizons exceed the flood concentration time of a river basin. This study proposed for the first time a Similarity search-based data-driven framework, and takes the advanced Temporal Convolutional Network based Encoder-Decoder model (S-TCNED) as an example for multi-step-ahead flood forecasting. A total of 5232 hourly hydrological data were divided into two datasets for model training and testing. The input sequence of the model included hourly flood flows of a hydrological station and rainfall data (traced back to the previous 32 h) of 15 gauge stations, and the output sequence stepped into 1- up to 16-hour-ahead flood forecasts. A conventional TCNED model was also built for comparison purposes. The results demonstrated that both TCNED and S-TCNED could make suitable multi-step-ahead flood forecasts, while the proposed S-TCNED model not only could effectively mimic the long-term rainfall-runoff relationship but also could provide more reliable and accurate forecasts of large floods than the TCNED model even in extreme weather conditions. There is a significant positive correlation between the mean sample label density improvement and the mean Nash-Sutcliffe Efficiency (NSE) improvement of the S-TCNED over the TCNED at the long forecast horizons (13 h up to 16 h). Based on the analysis of the sample label density, it is found that the similarity search largely improves the model performance by enabling the S-TCNED model to learn the development process of similar historical floods in a targeted manner. We conclude that the proposed S-TCNED model that converts and associates the previous rainfall-runoff sequence with the forecasting runoff sequence under a similar scenario can enhance the reliability and accuracy of flood forecasts while extending the length of forecast horizons.

Photocatalytic reduction of Cr(VI) using newly synthesized black phosphorus/ZnO nanocomposites.

Removal of high toxic Cr(VI) with solar plays an important role in improving water pollution, but is facing a dilemma of developing excellent photocatalysts with high conversion efficiency and low cost. Different from traditional nano-structuring, this work focuses on the interfacial hybridization by considering the intrinsic difference in bonding interaction. Herein, we intentionally make some layered black phosphorus (BP) sheets with Van der Waals interaction to bond with ZnO surfaces, in which some additional electron channels can be formed by this multilevel atomic hybridization to accelerate carrier transfer and separation. Compared to the pristine ZnO and BP nanosheets, the light absorption and carrier separation efficiency can be sharply enhanced by this particular electronic structure, which makes the Cr reduction performance enhanced about 7.1 times. Our findings suggest a new insight into accelerating Cr(VI) reduction by designing interfacial atom hybridization.

Response of gene abundance of ammonia-oxidizing microorganisms and denitrifying microorganisms to nitrogen and phosphorus addition in subtropical forest.

We conducted a nitrogen (N) and phosphorus (P) addition experiment in Qianjiangyuan National Park in 2015, to investigate the response of ammonia-oxidizing microorganisms and denitrifying microorganisms. There were four treatments, including N addition (N), P addition (P), NP, and control (CK). Soil samples were collected in April (wet season) and November (dry season) of 2021. The abundance of amoA gene of ammonia-oxidizing microorganisms (i.e., ammonia-oxidizing archaea, AOA; ammonia-oxidizing bacteria, AOB; comammox) and denitrifying microbial genes (i.e., nirS, nirK, and nosZ) were determined using quantitative PCR approach. The results showed that soil pH was significantly decreased by long-term N addition, while soil ammonium and nitrate contents were significantly increased. Soil available P and total P contents were significantly increased with the long-term P addition. The addition of N (N and NP treatments) significantly increased the abundance of AOB-amoA gene in both seasons, and reached the highest in the N treatment around 8.30×107 copies·g-1 dry soil. The abundance of AOA-amoA gene was significantly higher in the NP treatment than that in CK, with the highest value around 1.17×109 copies·g-1 dry soil. There was no significant difference in N-related gene abundances between two seasons except for the abundance of comammox-amoA. Nitrogen addition exerted significant effect on the abundance of AOB-amoA, nirK and nosZ genes, especially in wet season. Phosphorus addition exerted significant effect on the abundance of AOA-amoA and AOB-amoA genes in both seasons, but did not affect denitrifying gene abundances. Soil pH, ammonium, nitrate, available P, and soil water contents were the main factors affecting the abundance of soil N-related functional genes. In summary, the response of soil ammonia-oxidizing microorganisms and denitrifying microorganisms was more sensitive to N addition than to P addition. These findings shed new light for evaluating soil nutrient availability as well as their response mechanism to global change in subtropical forests.

Odorant-Binding Protein 6 Contributes High Binding Affinity to Insecticides in a Parasitic Wasp Meteorus pulchricornis (Hymenoptera: Braconidae).

Meteorus pulchricornis is a preponderant parasitic wasp of various lepidopteran pests. The extensive application of broad-spectrum insecticides usually causes serious threats to the olfactory recognition of nontarget insects such as parasitoid wasps. However, the binding mechanism of odorant-binding proteins (OBPs) to insecticides in parasitoid wasps remains unknown. Herein, we find that the MpulOBP6 protein had a strong binding affinity to three insecticides (phoxim, chlorpyrifos, and chlorfenapyr). Results of computational simulations revealed that the hydrophobic interaction contributed by a mass of nonpolar amino acid residues was the primary driving force in the formation and stabilization of MpulOBP6-insecticide complexes. Among them, four residues (Met75, Val84, Phe121, and Pro122) and two residues (Val84 and Phe111) play an essential role in the binding of MpulOBP6 to phoxim and chlorfenapyr, respectively. Our findings could be instrumental to elucidate the effects of insecticide application toward the olfactory recognition of nontarget insects in the processes of agricultural production.

Correction: Fluorido-bridged robust metal-organic frameworks for efficient C2H2/CO2 separation under moist conditions.

[This corrects the article DOI: 10.1039/D2SC06699H.].