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Petal senescence is controlled by a complex regulatory network. Epigenetic regulation like histone modification influences chromatin state and gene expression. However, the involvement of histone methylation in regulating petal senescence remains poorly understood. Here, we found that the trimethylation of histone H3 at Lysine 4 (H3K4me3) is increased during ethylene-induced petal senescence in carnation (Dianthus caryophyllus L.). H3K4me3 levels were positively associated with the expression of transcription factor DcWRKY75, ethylene biosynthetic genes 1-aminocyclopropane-1-carboxylic acid (ACC) synthase (DcACS1), and ACC oxidase (DcACO1), and senescence associated genes (SAGs) DcSAG12 and DcSAG29. Further, we identified that carnation ARABIDOPSIS HOMOLOG OF TRITHORAX1 (DcATX1) encodes a histone lysine methyltransferase which can methylate H3K4. Knockdown of DcATX1 delayed ethylene-induced petal senescence in carnation, which was associated with the down-regulated expression of DcWRKY75, DcACO1, and DcSAG12, whereas overexpression of DcATX1 exhibited the opposite effects. DcATX1 promoted the transcription of DcWRKY75, DcACO1, and DcSAG12 by elevating the H3K4me3 levels within their promoters. Overall, our results demonstrate that DcATX1 is a H3K4 methyltransferase that promotes the expression of DcWRKY75, DcACO1, DcSAG12 and potentially other downstream target genes by regulating H3K4me3 levels, thereby accelerating ethylene-induced petal senescence in carnation. This study further indicates that epigenetic regulation is important for plant senescence processes.

Foxtail millet (Setaria italica), which was domesticated from the wild species green foxtail (Setaria viridis), is a rich source of phytonutrients for humans. To evaluate how breeding changed the metabolome of foxtail millet grains, we generated and analyzed the datasets encompassing the genomes, transcriptomes, metabolomes, and anti-inflammatory indices from 398 foxtail millet accessions. We identified hundreds of common variants that influence numerous secondary metabolites. We observed tremendous differences in natural variations of the metabolites and their underlying genetic architectures between distinct sub-groups of foxtail millet. Furthermore, we found that the selection of the gene alleles associated with yellow grains led to altered profiles of metabolites such as carotenoids and endogenous phytohormones. Using CRISPR-mediated genome editing we validated the function of PHYTOENE SYNTHASE 1 (PSY1) gene in affecting millet grain color and quality. Interestingly, our in vitro cell inflammation assays showed that 83 metabolites in millet grains have anti-inflammatory effects. Taken together, our multi-omics study illustrates how the breeding history of foxtail millet has shaped its metabolite profile. The datasets we generated in this study also provide important resources for further understanding how millet grain quality is affected by different metabolites, laying the foundations for future millet genetic research and metabolome-assisted improvement.

Physics-Informed Neural Networks (PINNs) have a wide range of applications as an alternative to traditional numerical methods in plasma simulation. However, in some specific cases of PINN-based modeling, a well-trained PINN may require tens of thousands of optimizing iterations during training stage for complex modeling and huge neural networks, which is sometimes very time-consuming. In this work, we propose a meta-learning method, namely Meta-PINN, to reduce the training time of PINN-based 1-D arc simulation. In Meta-PINN, the meta network is first trained by a two-loop optimization on various training tasks of plasma modeling, and then used to initialize the PINN-based network for new tasks. We demonstrate the power of Meta-PINN by four cases corresponding to 1-D arc models at different boundary temperatures, arc radii, arc pressures, and gas mixtures. We found that a well-trained meta network can produce good initial weights for PINN-based arc models even at conditions slightly outside of training range. The speed-up in terms of relative L2 error by Meta-PINN ranges from 1.1x to 6.9x in the cases we studied. The results indicate that Meta-PINN is an effective method for accelerating the PINN-based 1-D arc simulation., 11 pages, 6 figures, 4 tables

Background We designed a meta-analysis to evaluate the clinical significance and efficacy of circulating noncoding RNAs (ncRNAs) in the early prediction of preeclampsia. Methods PubMed, Embase and the Cochrane Library were used to search for literature. The combined prediction performance was evaluated by calculating the area under the summary receiver operator characteristic (SROC) curve. The potential sources of heterogeneity were analysed by meta-regression analysis and subgroup analysis. All statistical analyses and mapping were performed by RevMan 5.3 and Stata 12.0. Results A total of 41 studies from 14 articles, including 557 preeclampsia patients and 842 controls, were included in our meta-analysis. All studies collected blood before onset. NcRNAs in blood performed relatively well in predicting preeclampsia. The combined sensitivity was 0.71, the specificity was 0.84, and the area under the SROC curve (AUC) was 0.86. Peripheral blood mononuclear cell (PBMC) samples showed the best diagnostic accuracy. The combined AUC was 0.93. Combined detection was better than single detection, and miRNA was better than circRNA. The heterogeneity of the study was determined by sample size, lncRNA characteristics, lncRNA source and race. Conclusion Circulating ncRNAs can be valuable biomarkers used as candidates for noninvasive early predictive biomarkers of preeclampsia and have great clinical application prospects. The clinical value of ncRNAs needs to be tested by further multicentre, comprehensive and prospective studies, and the test criteria should be established.

Carnation (Dianthus caryophyllus L.) is one of the most famous and ethylene-sensitive cut flowers worldwide, but how ethylene interacts with other plant hormones and factors to regulate petal senescence in carnation is largely unknown. Here we found that a gene encoding WRKY family transcription factor, DcWRKY33, was significantly upregulated upon ethylene treatment. Silencing and overexpression of DcWRKY33 could delay and accelerate the senescence of carnation petals, respectively. Abscisic acid (ABA) and H

In this study, nitrogen-doped carbon dots (N-CDs) from pine needles were obtained by one-step hydrothermal synthesis without any chemical reagents. The fluorescence quenching and absorbance enhancement of N-CDs occurred when Fe

Plasma simulation is an important, and sometimes the only, approach to investigating plasma behavior. In this work, we propose two general artificial-intelligence-driven frameworks for low-temperature plasma simulation: Coefficient-Subnet Physics-Informed Neural Network (CS-PINN) and Runge–Kutta Physics-Informed Neural Network (RK-PINN). CS-PINN uses either a neural network or an interpolation function (e.g., spline function) as the subnet to approximate solution-dependent coefficients (e.g., electron-impact cross sections, thermodynamic properties, transport coefficients, etc.) in plasma equations. Based on this, RK-PINN incorporates the implicit Runge–Kutta formalism in neural networks to achieve a large-time step prediction of transient plasmas. Both CS-PINN and RK-PINN learn the complex non-linear relationship mapping from spatiotemporal space to the equation's solution. Based on these two frameworks, we demonstrate preliminary applications in four cases covering plasma kinetic and fluid modeling. The results verify that both CS-PINN and RK-PINN have good performance in solving plasma equations. Moreover, RK-PINN has the ability to yield a good solution for transient plasma simulation with not only large time steps but also limited noisy sensing data.

SUMMARYLength is one of the key features of mRNA, varying from dozens to thousands of nucleotides. However, the contribution of length to proteome and its underlying mechanism are largely unknown. Here we show that the translation of long mRNA is regulated by the evolutionarily conserved mcm5s2U modification of tRNA. Loss of mcm5s2U modification results in decreased expression of long proteins in Arabidopsis, budding yeast, and humans. Mechanistically, the mcm5s2U modification facilitates ribosome loading on long mRNAs. Interestingly, the mcm5s2U modification is dynamically modulated to maintain long protein homeostasis during stress responses and cancer metastasis. Furthermore, gene ontology analysis reveals that long proteins are enriched in various conserved biological processes including immunity and DNA repair. Our study suggests that tRNA modification is a general mechanism for control the translation of long proteins and highlights the importance of mRNA length in shaping proteome.Highlightmcm5s2U of tRNA is required for immunity and DNA repairmcm5s2U of tRNA is required for long protein expressionmcm5s2U facilitate ribosome loading on long mRNAsLong proteins are involved in various conserved biological processes

Petal senescence is controlled by a complex regulatory network. Epigenetic regulation like histone modification influences chromatin state and gene expression. However, involvement of histone methylation in regulating petal senescence is still largely unknown. Here, we found that the trimethylation of histone H3 at Lysine 4 (H3K4me3) is increased during the ethylene induced petal senescence in carnation (Dianthus caryophyllus L.). The H3K4me3 levels are positively associated with the expression of transcription factor DcWRKY75, ethylene biosynthetic genes DcACS1 and DcACO1, and senescence associated genes (SAGs) DcSAG12 and DcSAG29. Further, we identified that carnation DcATX1 (ARABIDOPSIS HOMOLOG OF TRITHORAX1) encodes a histone lysine methyltransferase which can methylate H3K4. Knockdown of DcATX1 delays ethylene induced petal senescence in carnation, which is associated with the downregulated expression of DcWRKY75, DcACO1 and DcSAG12. While overexpression of DcATX1 exhibits the opposite effects. DcATX1 promotes the transcription of DcWRKY75, DcACO1 and DcSAG12 by targeting to their promoters to elevate the H3K4me3 levels. Overall, our results demonstrate that DcATX1 is a H3K4 methyltransferase that promotes the expression of DcWRKY75, DcACO1 and DcSAG12 by regulating H3K4me3 levels, thereby accelerating ethylene induced petal senescence in carnation. This study further indicates that epigenetic regulation is important for plant senescence process.One sentence summaryA histone methyltransferase promotes ethylene induced petal senescence in cut flower

Cervical cancer is one of common diseases among women. There are limited therapies for patients with metastatic or recurrent cervical cancer. This study sought to explore the role of monoacylglycerol lipase (MAGL), an important metabolic enzyme, in cervical cancer progression.In in vitro experiments, MAGL expression was inhibited by si-MAGL or JZL184 in cervical cancer cells. Quantitative real-time polymerase chain reaction and western blotting were performed to measure the expression of target molecules. Proliferation of cervical cancer cells was assessed by CCK-8 and colony formation assays. Apoptosis and cell cycle progression were evaluated by flow cytometry. The migration and invasion were detected by transwell assay. The in vivo tumor growth was detected in nude mice. TUNEL was utilized to observe apoptotic cells in tumor tissues.MAGL was upregulated in cervical cancer tissues and cells. Further, MAGL inhibition suppressed the growth of cervical cancer cells in vitro and in vivo. In addition, apoptosis and G1-phase cell cycle arrest were induced by MAGL knockdown. MAGL silencing-mediated upregulation of Bax and cleaved caspase-3, and downregulation of Bcl-2 was responsible for triggering apoptosis. More importantly, the migration and invasion of cervical cancer cells were restrained by MAGL depletion.MAGL drives the progression of cervical cancer, which can be a promising candidate to identify effective therapy for cervical cancer.

Lipids are important nutrient components in walnuts. However, there has been a lack of comprehensive analysis of lipid composition in walnuts. Here, UHPLC-Q-Exactive Orbitrap/MS was employed to qualify and quantify the lipid compounds in seven walnut varieties from Xinjiang. A total of 390 lipids were tentatively identified, which were classified into six categories and 30 subcategories. GL and GP were the two most abundant lipids in all walnuts. Compared with fresh walnuts (FW), dried walnuts (DW) showed significantly higher lipids; in the 30 subcategories, DW showed decreases in some MGDG and PC while increases in LPC and MePC, all of which were related to the moisture content of walnuts. Totally, 128 lipids can be used as potential markers to distinguish FW from DW. Besides, the lipid profile varied among different varieties of walnut kernels. These findings may facilitate walnut variety selection and processing based on the characteristic lipids.

Federated Learning (FL) enables plenty of edge computing devices to jointly learn a model without data sharing. However, in real-world federated datasets, traditional FL algorithms perform poorly with non-IID data. To better learn from non-IID data, we propose DMFL (Dynamic Margin for Federated Learning) as a strategy to improve the performance of federated learning with highly skewed imbalanced dataset. Accordingly, we perform a theoretical analysis for dynamic margin loss in FL, demonstrating that it is applicable to yield a lower generalization error bound. DMFL encourages clients to have a large margin for minority classes by adding a dynamic term into margins. With a proper margin shift on the fixed margin, the imbalance between clients can be alleviated. Our extensive experiments indicate that DMFL outperforms the other baselines on test accuracy. The results also indicate that each client in DMFL not only performs well locally but also has a good generalization on global test data.

Fresh walnut kernel (FW) and dry walnut kernel (DW) are significantly different in antioxidant activity. However, the metabolomic mechanism underlying these differences remains to be explored. Here, we evaluated the antioxidant activities of FW and DW of four walnut varieties, and characterized their metabolites by GC-MS and LC-MS/MS. The influence of different metabolites on antioxidant activity of walnut kernel was investigated through a multivariate analysis. DW showed significantly higher antioxidant activities than FW. A total of 144 metabolites were detected in all samples. Pearson correlation analysis demonstrated that phenolic metabolites and polyunsaturated fatty acids significantly affect the antioxidant activity. A further network analysis of 21 target differential metabolites indicated that the higher antioxidant activity of DW may be mainly attributed to the higher content of phenolic acid polymers. This study reveals the potential mechanism for improving of antioxidant activity of walnut kernels by drying.

In this work, Eu2O3/Au/SnO2 ternary nanofibers were prepared via a synergetic approach including electrospinning and calcination. The sensor based on Eu2O3/Au/SnO2 nanofibers exhibited a remarkable response (~65) to 100 ppm acetone at a relatively low operating temperature (200 °C), furthermore, the fast response-recovery kinetics and selective acetone detection had been observed simultaneously. The enhancement of acetone sensing properties can be attributed to the construction of Eu2O3/SnO2 heterostructures and the chemical sensitization of Au nanocatalysts, suggesting a pathway to develop high-efficient acetone sensors for health security.

The sensing application of cellulose nanocrystals (CNCs) is fascinating. CNCs can’t produce electrical or optical signals directly. Different from published sensing methods, a novel and simple electrochemical sensor for sensitive and selective detection of ascorbic acid (AA) was constructed based on three-dimensional nitrogen-doped hierarchical porous carbons (NHPC) by carbonizing CNCs and urea. Compared with bare glassy carbon electrode (GCE), the presence of porous carbons effectively increases the electron transport between analytes and the electrode, and significantly reduces the overpotential of AA oxidation. Under the optimum conditions, the electrode comprised of NHPC exhibits an extremely wide linear range from 8 × 10−5 to 1 × 10−2 M (R2 = 0.9996) and 1 × 10−6 to 5 × 10−5 M (R2 = 0.9867) to recognize the AA molecules, with a low detection limit of 2.45 × 10−7 M (S/N = 3) at a sensitivity of 0.1138 μA μM−1 cm−2. In particular, the sensing system for AA detection was further verified by the successful monitoring of AA in some spiked real samples. The fabricated nitrogen-doped CNC-based porous carbon material shows broad potential in the electrochemical application.

Electron-impact ionization cross sections are essential parameters required in various plasma models. To provide self-consistent ionization cross sections in plasma modeling, an open database with tabular fitting parameters and an online web system are established in this work. There are 88 species composed of H, C, N, O, and F in this database. The Binary-Encounter-Bethe (BEB) model, the Deutsch-Mark (DM) model, and the combined model which consolidates the BEB and DM models are considered to obtain the atomic and molecular ionization cross sections. The input parameters of the above models for all the species are calculated according to the quantum chemical computation with the hybrid density functional theory method with the Austin–Petersson–Frisch functional including dispersion. The same methods and basis set ensure consistent ionization cross sections in the database. The average relative error between the calculated and experimental ionization potential is 1.84%. The average root-mean-square error of the fitted ionization cross sections is 1.81% with respect to the exact values predicted by the combined model. The online database can be accessed from the website http://plasma.mathboylinlin.com/db/cs/ion/.

In this study, an environmentally friendly and water-soluble nitrogen-doped carbon quantum dots (N-CQDs) with quantum yield (QY) of 8.59% were prepared by one-step hydrothermal synthesis without any chemical reagent using the leaves of prunus lannesiana as precursors. The properties and quality of N-CQDs were investigated by Ultraviolet–visible absorption spectroscopy, infrared spectroscopy, X-ray photoelectron spectroscopy, zeta potential, high-resolution transmission electron microscopy and fluorescence spectroscopy. The fluorescence of the prepared N-CQDs can be quenched by Fe3+ through the synergistic effect of the formation of non-fluorescent complex and internal filtration effect (IFE) between Fe3+ and N-CQDs. And the quenched fluorescence can be “turned on” after adding ascorbic acid (AA) because Fe3+ can be released from the surface of N-CQDs through the redox reaction between AA and Fe3+. While the restored fluorescence can be “turned off” again by hydrogen peroxide (H2O2) due to the re-oxidation of Fe2+ to Fe3+. So, the three inputs “logic gate” is achieved and the “on-off-on-off” continuous response fluorescence sensor is formed, which can be applied for the continuous detection of Fe3+, AA and H2O2 with the linear range of 40–260 μM, 10–200 μM and 40–140 μM, respectively. Finally, the sensor was successfully applied to determine Fe3+, AA and H2O2 in real samples with the satisfactory recoveries (95.35%-104.10%) and repeatability (relative standard deviation (RSD) ≤ 1.68%). The continuous response fluorescence sensor prepared by simple green synthesis route has the characteristics of fast response, acceptable sensitivity and good selectivity.