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In this research, the dopamine self-polymerization was used to coat polydopamine (PDA) on cellulose nanocrystal (CNC) surfaces, and we integrated the functionality and structural merits of the two components in poly(vinyl alcohol) (PVA) films at a nanometer scale. The results showed that coating PDA on CNCs led to a concurrent increase in strength and break elongation. With increasing PDA@CNC loading level, the Young’s modulus continuously increased, which could be ca. 3.1-fold over that of neat PVA film at a loading level of 15 wt%. Both tensile strength and breaking elongation of the nanocomposite reached the maximum values with 6 wt% PDA@CNC, which were 75.8% and 58.1% more than those of neat PVA, respectively. Besides, the maximum decomposition temperature shifted from 271.3 °C of neat PVA film to 278.5 °C of the nanocomposite containing 6 wt% PDA@CNC, and then was continuously elevated up to 328.2 °C when the PDA@CNC loading level reached 15 wt%. For packaging application, the PDA component contributed to the UV-shielding and radical-scavenging functions, and the PDA@CNC nanofiller reduced the permeability of oxygen and water–vapor into PVA-based composites. Overall, the integrated PDA@CNC nanofiller as an active ingredient enhanced the mechanical, thermal, and functional properties of the PVA-based materials, and hence intensified the potential of their packaging application.

Asparagine synthetase B (AS-B) is essential for nitrogen metabolism, but its broader physiological functions remain poorly understood. Here we show that the evolutionarily conserved Nicotiana benthamiana NbAS-B confers expression-dependent antiviral resistance and promotes plant growth. Multi-omics analyses indicate that NbAS-B-mediated antiviral immunity relies on glutamate-induced activation of Ca²⁺ signaling through the receptor GLR3.3, whereas its growth-promoting effect results from photosynthetic reprogramming. Building on these insights, we develop polyglutamate-loaded chitosan nanogels (PGANPs) to artificially manipulate this pathway. These nanogels efficiently enter plant tissues and enable sustained in situ release of glutamate, thereby mimicking and amplifying NbAS-B signaling outputs. PGANPs provide long-lasting systemic antiviral immunity while concurrently enhancing plant growth, without incurring metabolic costs. Our work identifies NbAS-B as a dual-function regulator linking metabolic status to immune activation and establishes PGANPs as an eco-friendly, controllable, and durable nanobiotechnology for managing viral diseases in crops. Controlling plant metabolism to improve growth and disease resistance has huge potential. Here, the authors find a conserved pathway which promotes antiviral resistance and promotes plant growth and develop polyglutamate-loaded chitosan nanogels to manipulate this pathway.

Ralstonia solanacearum (R. solanacearum)-induced bacterial wilt of the nightshade family causes a great loss in agricultural production annually. Although there has been some efficient pesticides against R. solanacearum, inaccurate pesticide releasing according to the onset time of bacterial wilt during the use of pesticides still hinders the disease management efficiency. Herein, on the basis of the soil pH change during R. solanacearum growth, and pH sensitivity of the Schiff base structure, a pH-sensitive oxidized alginate-based double-crosslinked gel was fabricated as a pesticide carrier. The gel was prepared by crosslinking oxidized sodium alginate (OSA) via adipic dihydrazide (ADH) and Ca2+. After loading tetramycin into the gel, it showed a pH-dependent pesticide releasing behavior and anti-bacterial activity against R. solanacearum. Further study also showed that the inhibition rate of the tetramycin-loaded gel was higher than that of industrial pesticide difenoconazole. This work aimed to reduce the difficulty of pesticide administration in the high incidence period of bacterial wilt and we believe it has a great application potential in nightshade production.

Background Emerging transcriptome-wide high-throughput screenings reveal the landscape and functions of RNAs, such as circular RNAs (circRNAs), in human cancer. In addition, the post-transcriptional RNA internal modifications, especially N 6 -methyladenosine (m 6 A), greatly enrich the variety of RNAs metabolism. However, the m 6 A modification on circRNAs has yet to be addressed. Results Here, we report an epitranscriptome-wide mapping of m 6 A-modified circRNAs (m 6 A-circRNA) in oral squamous cell carcinoma (OSCC). Utilizing the data of m 6 A methylated RNA immunoprecipitation sequencing (MeRIP-seq) and m 6 A-circRNAs microarray, we found that m 6 A-circRNAs exhibited particular modification styles in OSCC, which was independent of m 6 A-mRNA. Besides, m 6 A modification on circRNAs frequently occurred on the long exons in the front part of the coding sequence (CDS), which was distinct from m 6 A-mRNA that in 3’-UTR or stop codon. Conclusion In conclusion, our work preliminarily demonstrates the traits of m 6 A-circRNAs, which may bring enlighten for the roles of m 6 A-circRNAs in OSCC. Highlights 1. m 6 A-circRNAs exhibited their particular modification style in OSCC, which was independent of m 6 A-mRNA. 2. m 6 A on circRNAs frequently occurred on the long exons in the front part of CDS, which was distinct from m 6 A-mRNA that in 3’-UTR or stop codon.

At present, the management of Phytophthora capsici (P. capsici) mainly relies on chemical pesticides. However, along with the resistance generated by P. capsici to these chemical pesticides, the toxicity and non-degradability of this chemical molecule may also cause serious environmental problems. Herein, a new bio-based nano-antifungal material (CNC@CTAB) was made with coating hexadecyl trimethyl ammonium bromide (CTAB) on the surface of a cellulose nanocrystal (CNC). This material was then applied to the prevention of P. capcisi. This particle was facilely fabricated by mixing CTAB and sulfuric group modified CNC in an aqueous solvent. Compared to pure CTAB, the enrichment of CTAB on the CNC surface showed a better anti-oomycete activity both in vitro and in vivo. When CNC@CTAB was applied on P. capsici in vitro, the inhibition rate reached as high as 100%, while on the pepper leaf, the particle could also efficiently prevent the infection of P. capsici, and achieve a disease index as low as zero Thus, considering the high safety of CNC@CTAB in agricultural applications, and its high anti-oomycete activity against P. capsici, we believe that this CNC@CTAB has great application potential as a new green nano-fungicide in P. capsici management during the production of peppers or other vegetables.

Head-on collisions between two solitary waves in the framework of the nonlinear Schrödinger (NLS) equation were investigated using the Fourier spectral method. When solitary waves undergo collision, the peak value of surface elevation (hereafter referred to as ζmax) exhibits fluctuations with increasing relative water depths k0h (where k0 is the wave number and h is the water depth). ζmax is approximately equal to the sum of the peak values of the two solitary waves with smaller wave steepness ε0 (ε0 = k0a0, a0 is the free background amplitude parameter), and it exhibits fluctuations for ε0 > 0.10. Similar results have been observed in the study of head-on collisions for four solitary waves. These results show that the water depth and wave steepness play important roles in the collision of solitary waves, and the effects of the interactions of intense wave groups are important in studies of the mechanisms and manifestations of freak oceanic waves.

Silver (Ag)-containing agents or materials are widely used today in plant protection for their antimicrobial activity. In view of the superior inhibitory ability of biosynthesized (aldehyde-modified sodium alginate based) silver nanoparticles (AgNPs) against plant pathogenic fungi in our previous research, here we explored the antagonistic effect of biosynthesized AgNPs on plant pathogenic bacteria and the underlying mechanism. We selected Pseudomonas syringae pv. tabaci , the causal agent of tobacco wildfire disease, as the target and found that 1.2 μg/mL biosynthesized AgNPs completely inhibited the growth of P. syringae pv. tabaci in vitro and in vivo by partly destroying the cell membrane structure of the pathogen, resulting in cytoplasmic leakage. Moreover, Nicotiana benthamiana treated with 1.2 μg/mL biosynthesized AgNPs exhibited a significant upregulation of nonexpressor of pathogenesis-related genes 1 ( NPR1 ) and pathogenesis-related gene 2 ( PR2 ), the typical markers of the salicylic acid (SA)-mediated defense system, and an increase in peroxidase (POD) and polyphenol oxidase (PPO) activities as well as the production of reactive oxygen species (ROS). Furthermore, biosynthesized AgNPs treatment increased the chlorophyll content and dry weight of N. benthamiana . Overall, we demonstrated that biosynthesized AgNPs at a low concentration have high inhibitory effect on the pathogen causing tobacco wildfire disease by destroying bacterial cell membrane and inducing defense resistance in host plant. These results lay a theoretical foundation for further application of biosynthesized AgNPs in the control of plant bacterial diseases.

Infragravity (IG) waves significantly affect the operational efficiency of ports. Therefore, an accurate prediction of IG waves inside a harbor is necessary. In this study, the accuracy of the wave-group-resolving model XBeach Surfbeat (XB-SB, Delft University of Technology, Delft, The Netherlands) in predicting the IG waves inside a harbor was assessed by comparing its results with field measurements. Field measurements were performed at Hambantota Port in southern Sri Lanka. Three acoustic waves and current sensors were used to observe the wave characteristics inside and outside the harbor. First, the model was validated against observations outside the port. Next, the performance accuracy of XB-SB in modeling the hydrodynamics in the harbor was evaluated by comparing its results with the values measured inside the port. The results of the numerical simulations indicated that both the nearshore short and IG wave heights can be accurately reproduced by XB-SB in an open domain without many obstacles. However, the short wave heights in the harbor are severely underestimated by XB-SB. The IG waves inside the harbor are overestimated most of the time. Moreover, the natural periods of Hambantota Port are well calculated by XB-SB. In general, XB-SB is a reliable tool for predicting nearshore IG waves. However, it requires further improvement to reproduce the hydrodynamics in a well-sheltered harbor, such as Hambantota Port.

Numerical simulations were performed to study the long-distance evolution of irregular waves in deep water. It was observed that some solitons, which are the theoretical solutions of the nonlinear Schrödinger equation, emerged spontaneously as irregular wave trains propagated in deep water. The solitons propagated approximately at a speed of the linear group velocity. All the solitons had a relatively large amplitude and one detected soliton’s height was two times larger than the significant wave height of the wave train, therefore satisfying the rogue wave definition. The numerical results showed that solitons can persist for a long distance, reaching about 65 times the peak wavelength. By analyzing the spatial variations of these solitons in both time and spectral domains, it is found that the third-and higher-order resonant interactions and dispersion effects played significant roles in the formation of solitons.

In this paper, a 2-stage cascaded deep learning framework, Port Wave Prediction Network (PWPNet), is proposed for real-time prediction of significant wave height (SWH) distribution in a port. The PWP-out model of the first stage, predicting port-entrance wave parameters, utilizes three branches, the first branch using a Long Short Term Memory (LSTM) module to learn the temporal dependencies of time sequences of port-entrance wave parameters, the second branch using Wave and Wind field Feature Extraction (WWFE) modules, composed of a residual network with spatial and channel attention, to capture spatiotemporal characteristics of outside-port 2D wave and wind field data, the third branch using multi-scale time encoding to capture the periodic characteristics of waves and wind. The PWP-in model of the second stage, estimating the in-port SWH distribution, uses port-entrance wave parameters based on a customized Artificial Neural Network (ANN) and takes PWP-out’s output as its input. A comparison of the performance of PWP-out and mainstream machine learning models including LSTM, GRU, BPNN, SVR, ELM, and RF at Hambantota Port shows that PWP-out outperforms all other models regarding medium-term (25–48 h), med–long-term (49–72 h), and long-term (73–96 h) predictions, and ablation experiments proved the effectiveness of the three branches. Furthermore, the performance comparison of our PWPNet and other 2-stage models of LSTM, GRU, BPNN, SVR, ELM, and RF cascaded with PWP-in shows that PWPNet outperforms those cascaded models for medium-term to long-term predictions of SWH distribution in a port.