An Octacarboxylate-Linked Sodium Metal-Organic Framework with High Porosity.

Alkali metal-based metal-organic frameworks (MOFs) with permanent porosity are scarce because of their high tendency to coordinate with solvents such as water. However, these MOFs are lightweight and bear gravimetric benefits for gas adsorption related applications. In this study, we present the successful construction of a microporous MOF, designated as HIAM-111, built solely on sodium ions by using an octacarboxylate linker. The structure of HIAM-111 is based on 8-connected Na4 clusters and exhibits a novel topology with an underlying 32,42,8-c net. Remarkably, HAM-111 possesses a robust and highly porous framework with a BET surface area of 1561 m2/g, significantly surpassing that of the previously reported Na-MOFs. Further investigations demonstrate that HIAM-111 is capable of separating C2H2/CO2 and purifying C2H4 directly from C2H4/C2H2/C2H6 with high adsorption capacities. The current work may shed light on the rational design of robust and porous MOFs based on alkali metals.

Structural Basis of Nucleic Acid Recognition and 6mA Demethylation by Caenorhabditis elegans NMAD-1A.

N6-methyladenine (6mA) of DNA is an emerging epigenetic mark in the genomes of Chlamydomonas, Caenorhabditis elegans, and mammals recently. Levels of 6mA undergo drastic fluctuation and thus affect fertility during meiosis and early embryogenesis. Here, we showed three complex structures of 6mA demethylase C. elegans NMAD-1A, a canonical isoform of NMAD-1 (F09F7.7). Biochemical results revealed that NMAD-1A prefers 6mA Bubble or Bulge DNAs. Structural studies of NMAD-1A revealed an unexpected "stretch-out" conformation of its Flip2 region, a conserved element that is usually bent over the catalytic center to facilitate substrate base flipping in other DNA demethylases. Moreover, the wide channel between the Flip1 and Flip2 of the NMAD-1A explained the observed preference of NMAD-1A for unpairing substrates, of which the flipped 6mA was primed for catalysis. Structural analysis and mutagenesis studies confirmed that key elements such as carboxy-terminal domain (CTD) and hypothetical zinc finger domain (ZFD) critically contributed to structural integrity, catalytic activity, and nucleosome binding. Collectively, our biochemical and structural studies suggest that NMAD-1A prefers to regulate 6mA in the unpairing regions and is thus possibly associated with dynamic chromosome regulation and meiosis regulation.

Study on the effect of enzymolysis combined fermentation on reducing the off-flavor of egg white powder.

BACKGROUND: The application of egg white powder (EWP) was subject to its off-flavor. In the present study, flavourzyme and lactic acid bacteria were used to treat egg white powder (EWP) and the mechanism effects of enzymolysis-fermentation were explored. RESULTS: Compared with the control group, enzymolysis combined with fermentation treatment group (EW-EF) reduced the four-representative off-flavor compounds (geranyl acetone, 1-octen-3-ol, octanal and nonanal) by more than 62.66%. Fermentation produced esters with good flavor, and enzymolysis produced fresh amino acids. Characterization of protein structure indicated that fermentation decreased both fluorescence intensity and surface negative charges, accelerating the aggregation of proteins; enzymolysis promoted aggregation and degradation, improving the stability of the egg white proteins. Meanwhile, enzymolysis broke down the hydrophobic cavities bound to off-flavor compounds, releasing protein-bound off-flavor compounds and removing them through fermentation. CONCLUSION: EW-EF had the best effect of off-flavor removal on EWP. The results of the present study could provide a green and effective method for improving the flavor of EWP. © 2024 Society of Chemical Industry.

Inhibition of water-diluted precipitate formation from egg whites by ultrasonic pretreatment: Insights from quantitative proteomics analysis.

The formation of the egg white precipitate (EWP) during dilution poses challenges in food processing. In this paper, the effects of 90 W and 360 W ultrasonic intensities on the inhibition of EWP formation were investigated. The findings revealed that 360 W sonication effectively disrupted protein aggregates, decreasing the dry matter of EWP by 5.24 %, particle size by 57.86 %, and viscosity by 82.28 %. Furthermore, the ultrasonic pretreatment unfolded protein structures and increased the content of ß-sheet structures. Combined with quantitative proteomics and intermolecular forces analysis, the mechanism by which ultrasonic pretreatment inhibited water-diluted EWP formation by altering protein interactions was proposed: ultrasonic pretreatment disrupted electrostatic interactions centered on lysozyme, as well as hydrogen-bonding interactions between ovomucin and water. In conclusion, our research provides valuable insights into the application of ultrasonic pretreatment as a means to control and improve the quality of egg white-based products.

Analysis of H5N8 influenza virus infection in chicken with mApple reporter genes in vivo and in vitro.

H5N8 highly pathogenic avian influenza virus (HPAIV) has caused huge losses to the global poultry industry and critically threatens public health. Chickens are the important host for the transmission. However, the distribution of H5N8 avian influenza virus (AIV) in chicken and the infected cell types are limitedly studied. Therefore, in this study, we detected viral replication and infection by generating recombinant H5N8 AIV expressing an easily tracked mApple fluorescent reporter. The results showed that recombinant viruses passaged four times in chicken embryos successfully expressed mApple proteins detected by fluorescence microscopy and WB, which verified that the constructed recombinant viruses were stable. Compared to parental virus, although recombinant virus attenuated for replication in MDCK cells, it can still replicate effectively, and form visible plaques. Importantly, the experiments on infection of chicken PBMCs in vitro showed a strong correlation between mApple positivity rate and NP positivity rate (r = 0.7594, P =0.0176), demonstrating that mApple reporter could be used as an indicator to accurately reflect AIV infection. Then we infected monocytes/macrophages in PBMCs in vitro and detected the mApple positive percentage was 55.1%-80.4%, which confirmed the chicken primary monocytic/macrophages are important target cells for avian influenza virus infection. In chicken, compared with parental virus, the recombinant virus-infected chickens had lower viral titers in oropharyngeal cloacal and organs, but it can cause significant pathogenicity in chicken and the mortality rate was approximately 66%. In addition, the results of bioluminescent imaging showed that the fluorescence in the lungs was strongest at 5 days post-infection (DPI). Finally, we discovered the mApple positive expression in chicken lung immune cells (CD45+ cells), especially some T cells (CD4 and CD8 T cells) also carrying mApple, which indicates that the H5N8 AIV showed a tropism for immune cells including chicken T cells causing potentially aggressive against cellular immunity. We have provided a simple visualization for further exploration of H5N8 AIV infected chicken immune cells, which contributes to further understanding pathogenic mechanism of H5N8 AIV infection in chicken.

Chain reactions of temperature-induced egg white protein amorphous aggregates: Formation, structure and material composition of thermal gels.

Egg white protein is widely used in food, chemical, medical and other fields due to its excellent thermal gel properties. However, the regularity of egg white thermal gel (EWTG) by temperature influence is still unknown. In this study, we investigated the potential mechanism of temperature (75-95 °C, 15 min) gradient changes inducing thermal aggregation and gel formation of EWTG. The results showed that changes in textural characteristics and water holding capacity (WHC) of EWTGs depended on switching in protein aggregation morphology (spherical shape - chain shape - regiment shape) and gel network structure differences ("irregular bead-like" - "regular lamellar structure"). In addition, proteomics indicated that the generation of amorphous protein aggregates at 95 °C might be related to Mucin 5B as the aggregation core. The research revealed the EWTG formation from "whole egg white protein" to "single molecules", aiming to provide a reference for quality control in gel food processing.

The fusion of vegetation indices increases the accuracy of cotton leaf area prediction.

Introduction: Rapid and accurate estimation of leaf area index (LAI) is of great significance for the precision agriculture because LAI is an important parameter to evaluate crop canopy structure and growth status. Methods: In this study, 20 vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods extreme learning machine (ELM), random forest (RF), back propagation (BP), multivariable linear regression (MLR), support vector machine (SVM)], and the optimal modeling strategy (RF) was selected. Finally, the vegetation indices with a high correlation with LAI were fused to construct the VI-fusion RF model, to explore the potential of multi-vegetation index fusion in the estimation of cotton LAI. Results: The RF model had the highest estimation accuracy among the LAI estimation models, and the estimation accuracy of models constructed by fusing multiple VIs was higher than that of models constructed based on single VIs. Among the multi-VI fusion models, the RF model constructed based on the fusion of seven vegetation indices (MNDSI, SRI, GRVI, REP, CIred-edge, MSR, and NVI) had the highest estimation accuracy, with coefficient of determination (R2), rootmean square error (RMSE), normalized rootmean square error (NRMSE), and mean absolute error (MAE) of 0.90, 0.50, 0.14, and 0.26, respectively. Discussion: Appropriate fusion of vegetation indices can include more spectral features in modeling and significantly improve the cotton LAI estimation accuracy. This study will provide a technical reference for improving the cotton LAI estimation accuracy, and the proposed method has great potential for crop growth monitoring applications.

Effect of zearalenone on the jejunum of weaned gilts through the Epac1/Rap1/JNK pathway.

Zearalenone (ZEN) is a non-steroidal estrogenic mycotoxin produced by Fusarium strains that is harmful to the intestinal health of animals and is widely present in contaminated crops. The objective of this study was to investigate the potential therapeutic target of ZEN-induced jejunal damage in weaned gilts. Sixteen weaned gilts either received a basal diet or a basal diet supplemented with 3.0 mg/kg ZEN in a 32-day experiment. The results showed that ZEN at the concentration of 3.0 mg/kg diet activated the inflammatory response and caused oxidative stress of gilts (P < 0.05). ZEN exposure resulted in the up-regulation (P < 0.05) of the Exchange protein directly activated by the cAMP 1/Ras-related protein1/c-Jun N-terminal kinase (Epac1/Rap1/JNK signaling pathway in the jejunum of gilts in vivo and in the intestinal porcine epithelial cells in vitro. The cell viability, EdU-positive cells, and the mRNA expression of B-cell lymphoma-2 (Bcl-2) were decreased, whereas the reactive oxygen species production and the mRNA expressions of Bcl-2-associated X (Bax) and Cysteine-aspartic acid protease 3 (Caspase3) were increased (P < 0.05) by ZEN. However, ZEN increased the mRNA expression of Bcl-2 and decreased the mRNA expressions of Bax and caspase3 (P < 0.05) after the Epac1 was blocked. These results collectively indicated that 3.0 mg ZEN /kg diet induced jejunal damage via the Epac1/Rap1/JNK signaling pathway.

The impact of palliative care on the physical and mental status and quality of life of patients with chronic heart failure: A randomized controlled trial.

BACKGROUND: Chronic heart failure (CHF) is the terminal stage of several diseases. The present study aimed to investigate the impact of palliative care on the physical and mental status and quality of life of patients with CHF. METHODS: This single-center randomized controlled clinical trial was conducted at Xiangtan Central Hospital. A total of 103 cases were included and divided into a study group (n = 54) and a control group (n = 49). The control group received usual care, whereas the study group received usual care plus palliative care. Statistical analyses were conducted on Simplified Coping Style Questionnaire, negative emotions, Minnesota Living with Heart Failure Questionnaire scores, and nursing satisfaction before and after intervention in the 2 groups. RESULTS: After the intervention, the positive coping style score in the research group was higher than that in the control group, while the negative coping style score was lower than that of the control group (P < .05). After the intervention, the Beck Anxiety Inventory and Beck Depression Inventory-II scores of the 2 groups decreased compared to before the intervention, and the study group had lower scores than the control group (P < .05). After the intervention, the Minnesota Living with Heart Failure Questionnaire scores of the 2 groups decreased compared to those before the intervention, and the study group had lower scores than the control group (P < .05). Nursing satisfaction of the research group (94.44%) was higher than that of the control group (81.63%) (P < .05). CONCLUSIONS: Adopting palliative care to intervene in CHF patients can effectively regulate their physical and mental state, alleviate negative emotions, transform coping styles towards the disease, and improve their quality of life, with high patient satisfaction.

Cotton Fusarium wilt diagnosis based on generative adversarial networks in small samples.

This study aimed to explore the feasibility of applying Generative Adversarial Networks (GANs) for the diagnosis of Verticillium wilt disease in cotton and compared it with traditional data augmentation methods and transfer learning. By designing a model based on small-sample learning, we proposed an innovative cotton Verticillium wilt disease diagnosis system. The system uses Convolutional Neural Networks (CNNs) as feature extractors and applies trained GAN models for sample augmentation to improve classification accuracy. This study collected and processed a dataset of cotton Verticillium wilt disease images, including samples from normal and infected plants. Data augmentation techniques were used to expand the dataset and train the CNNs. Transfer learning using InceptionV3 was applied to train the CNNs on the dataset. The dataset was augmented using GAN algorithms and used to train CNNs. The performances of the data augmentation, transfer learning, and GANs were compared and analyzed. The results have demonstrated that augmenting the cotton Verticillium wilt disease image dataset using GAN algorithms enhanced the diagnostic accuracy and recall rate of the CNNs. Compared to traditional data augmentation methods, GANs exhibit better performance and generated more representative and diverse samples. Unlike transfer learning, GANs ensured an adequate sample size. By visualizing the images generated, GANs were found to generate realistic cotton images of Verticillium wilt disease, highlighting their potential applications in agricultural disease diagnosis. This study has demonstrated the potential of GANs in the diagnosis of cotton Verticillium wilt disease diagnosis, offering an effective approach for agricultural disease detection and providing insights into disease detection in other crops.