GERWR: Identifying the Key Pathogenicity- Associated sRNAs of Magnaporthe Oryzae Infection in Rice Based on Graph Embedding and Random Walk With Restart.

Rice blast, caused by Magnaporthe oryzae(M.oryzae), is a destructive rice disease that reduces rice yield by 10% to 30% annually. It also affects other cereal crops such as barley, wheat, rye, millet, sorghum, and maize. Small RNAs (sRNAs) play an essential regulatory role in fungus-plant interaction during the fungal invasion, but studies on pathogenic sRNAs during the fungal invasion of plants based on multi-omics data integration are rare. This paper proposes a novel approach called Graph Embedding combined with Random Walk with Restart (GERWR) to identify pathogenic sRNAs based on multi-omics data integration during M.oryzae invasion. By constructing a multi-omics network (MRMO), we identified 29 pathogenic sRNAs of rice blast fungus. Further analysis revealed that these sRNAs regulate rice genes in a many-to-many relationship, playing a significant regulatory role in the pathogenesis of rice blast disease. This paper explores the pathogenic factors of rice blast disease from the perspective of multi-omics data analysis, revealing the inherent connection between pathogenic factors of different omics. It has essential scientific significance for studying the pathogenic mechanism of rice blast fungus, the rice blast fungus-rice model system, and the pathogen-host interaction in related fields.

USP11 exacerbates neuronal apoptosis after traumatic brain injury via PKM2-mediated PI3K/AKT signaling pathway.

Ubiquitin-specific protease 11 (USP11) is a ubiquitin-specific protease involved in the regulation of protein ubiquitination. However, its role in traumatic brain injury (TBI) remains unclear. This experiment suggests that USP11 is possibly involved in regulating neuronal apoptosis in TBI. Therefore, we use precision impactor device to established a TBI rat model and assayed the role of USP11 by overexpressing and inhibiting USP11. We found that Usp11 expression increased after TBI. In addition, we hypothesized that pyruvate kinase M2 (PKM2) is a potential USP11 target and experimentally confirmed that upregulation of Usp11 increased Pkm2 expression. Furthermore, elevated USP11 levels exacerbate blood-brain barrier damage, brain edema, and neurobehavioral impairment and cause apoptosis induction through Pkm2 upregulation. Moreover, we hypothesize that PKM2-induced neuronal apoptosis is mediated by the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway. Our findings were confirmed by changes in Pi3k and Akt expression with Usp11 upregulation and downregulation and PKM2 inhibition. In conclusion, our findings show that USP11 exacerbates injury in TBI through PKM2 and causes neurological impairment and neuronal apoptosis through the PI3K/AKT signaling pathway.

Cellulose photonic pigments.

When pursuing sustainable approaches to fabricate photonic structures, nature can be used as a source of inspiration for both the nanoarchitecture and the constituent materials. Although several biomaterials have been promised as suitable candidates for photonic materials and pigments, their fabrication processes have been limited to the small to medium-scale production of films. Here, by employing a substrate-free process, structurally coloured microparticles are produced via the confined self-assembly of a cholesteric cellulose nanocrystal (CNC) suspension within emulsified microdroplets. Upon drying, the droplets undergo multiple buckling events, which allow for greater contraction of the nanostructure than predicted for a spherical geometry. This buckling, combined with a solvent or thermal post-treatment, enables the production of dispersions of vibrant red, green, and blue cellulose photonic pigments. The hierarchical structure of these pigments enables the deposition of coatings with angular independent colour, offering a consistent visual appearance across a wide range of viewing angles.

A Multi-Level Iterative Bi-Clustering Method for Discovering miRNA Co-regulation Network of Abiotic Stress Tolerance in Soybeans.

Although growing evidence shows that microRNA (miRNA) regulates plant growth and development, miRNA regulatory networks in plants are not well understood. Current experimental studies cannot characterize miRNA regulatory networks on a large scale. This information gap provides an excellent opportunity to employ computational methods for global analysis and generate valuable models and hypotheses. To address this opportunity, we collected miRNA-target interactions (MTIs) and used MTIs from Arabidopsis thaliana and Medicago truncatula to predict homologous MTIs in soybeans, resulting in 80,235 soybean MTIs in total. A multi-level iterative bi-clustering method was developed to identify 483 soybean miRNA-target regulatory modules (MTRMs). Furthermore, we collected soybean miRNA expression data and corresponding gene expression data in response to abiotic stresses. By clustering these data, 37 MTRMs related to abiotic stresses were identified, including stress-specific MTRMs and shared MTRMs. These MTRMs have gene ontology (GO) enrichment in resistance response, iron transport, positive growth regulation, etc. Our study predicts soybean MTRMs and miRNA-GO networks under different stresses, and provides miRNA targeting hypotheses for experimental analyses. The method can be applied to other biological processes and other plants to elucidate miRNA co-regulation mechanisms.

Exploring the Common Mechanism of Fungal sRNA Transboundary Regulation of Plants Based on Ensemble Learning Methods.

Studies have found that pathogenic fungi and plants have sRNA transboundary regulation mechanisms. However, no researchers have used computer methods to carry out comprehensive studies on whether there is a more remarkable similarity in the transboundary regulation of plants by pathogenic fungi. In this direction, high-throughput non-coding sRNA data of three types of fungi and fungi-infected plants for 72 h were obtained. These include the Magnaporthe, Magnaporthe oryzae infecting Oryza sativa, Botrytis cinerea, Botrytis cinerea infecting Solanum lycopersicum, Phytophthora infestans and Phytophthora infestans infecting Solanum tuberosum. Research on these data to explore the commonness of fungal sRNA transboundary regulation of plants. First, using the big data statistical analysis method, the sRNA whose expression level increased significantly after infection was found as the key sRNA for pathogenicity, including 355 species of Magnaporthe oryzae, 399 species of Botrytis cinerea, and 426 species of Phytophthora infestans. Secondly, the target prediction was performed on the key sRNAs of the above three fungi, and 96, 197, and 112 core nodes were screened out, respectively. After functional enrichment analysis, multiple GO and KEGG_Pathway were obtained. It is found that there are multiple identical GO and KEGG_Pathway that can participate in plant gene expression regulation, metabolism, and other life processes, thereby affecting plant growth, development, reproduction, and response to the external environment. Finally, the characteristics of key pathogenic sRNAs and some non-pathogenic sRNAs are mined and extracted. Five Ensemble learning algorithms of Gradient Boosting Decision Tree, Random Forest, Adaboost, XGBoost, and Light Gradient Boosting Machine are used to construct a binary classification prediction model on the data set. The five indicators of accuracy, recall, precision, F1 score, and AUC were used to compare and analyze the models with the best parameters obtained by training, and it was found that each model performed well. Among them, XGBoost performed very well in the five models, and the AUC of the validation set was 0.86, 0.93, and 0.90. Therefore, this model has a reference value for predicting other fungi's key sRNAs that transboundary regulation of plants.

Construction of sRNA Regulatory Network for Magnaporthe oryzae Infecting Rice Based on Multi-Omics Data.

Studies have shown that fungi cause plant diseases through cross-species RNA interference mechanism (RNAi) and secreted protein infection mechanism. The small RNAs (sRNAs) of Magnaporthe oryzae use the RNAi mechanism of rice to realize the infection process, and different effector proteins can increase the autotoxicity by inhibiting pathogen-associated molecular patterns triggered immunity (PTI) to achieve the purpose of infection. However, the coordination of sRNAs and proteins in the process of M. oryzae infecting rice is still poorly understood. Therefore, the combination of transcriptomics and proteomics to study the mechanism of M. oryzae infecting rice has important theoretical significance and practical value for controlling rice diseases and improving rice yields. In this paper, we used the high-throughput data of various omics before and after the M. oryzae infecting rice to screen differentially expressed genes and sRNAs and predict protein interaction pairs based on the interolog and the domain-domain methods. We were then used to construct a prediction model of the M. oryzae-rice interaction proteins according to the obtained proteins in the proteomic network. Finally, for the differentially expressed genes, differentially expressed sRNAs, the corresponding mRNAs of rice and M. oryzae, and the interacting protein molecules, the M. oryzae-rice sRNA regulatory network was built and analyzed, the core nodes were selected. The functional enrichment analysis was conducted to explore the potential effect pathways and the critical infection factors of M. oryzae sRNAs and proteins were mined and analyzed. The results showed that 22 sRNAs of M. oryzae, 77 secretory proteins of M. oryzae were used as effect factors to participate in the infection process of M. oryzae. And many significantly enriched GO modules were discovered, which were related to the infection mechanism of M. oryzae.

Angular-Independent Photonic Pigments via the Controlled Micellization of Amphiphilic Bottlebrush Block Copolymers.

Photonic materials with angular-independent structural color are highly desirable because they offer the broad viewing angles required for application as colorants in paints, cosmetics, textiles, or displays. However, they are challenging to fabricate as they require isotropic nanoscale architectures with only short-range correlation. Here, porous microparticles with such a structure are produced in a single, scalable step from an amphiphilic bottlebrush block copolymer. This is achieved by exploiting a novel "controlled micellization" self-assembly mechanism within an emulsified toluene-in-water droplet. By restricting water permeation through the droplet interface, the size of the pores can be precisely addressed, resulting in structurally colored pigments. Furthermore, the reflected color can be tuned to reflect across the full visible spectrum using only a single polymer (Mn  = 290 kDa) by altering the initial emulsification conditions. Such "photonic pigments" have several key advantages over their crystalline analogues, as they provide isotropic structural coloration that suppresses iridescence and improves color purity without the need for either refractive index matching or the inclusion of a broadband absorber.

Transboundary Pathogenic microRNA Analysis Framework for Crop Fungi Driven by Biological Big Data and Artificial Intelligence Model.

Plant fungal diseases have been affecting the world's agricultural production and economic levels for a long time, such as rice blast, gray tomato mold, potato late blight etc. Recent studies have shown that fungal pathogens transmit microRNA as an effector to host plants for infection. However, bioassay-based verification analysis is time-consuming and challenging, and it is difficult to analyze from a global perspective. With the accumulation of fungal and plant-related data, data analysis methods can be used to analyze pathogenic fungal microRNA further. Based on the microRNA expression data of fungal pathogens infecting plants before and after, this paper discusses the selection strategy of sample data, the extraction strategy of pathogenic fungal microRNA, the prediction strategy of a fungal pathogenic microRNA target gene, the bicluster-based fungal pathogenic microRNA functional analysis strategy and experimental verification methods. A general analysis pipeline based on machine learning and bicluster-based function module was proposed for plant-fungal pathogenic microRNA.The pipeline proposed in this paper is applied to the infection process of Magnaporthe oryzae and the infection process of potato late blight. It has been verified to prove the feasibility of the pipeline. It can be extended to other relevant crop pathogen research, providing a new idea for fungal research on plant diseases. It can be used as a reference for understanding the interaction between fungi and plants.

Shape Memory Polyurethane Microcapsules with Active Deformation.

From smart self-tightening sutures and expandable stents to morphing airplane wings, shape memory structures are increasingly present in our daily life. The lack of methods for synthesizing intricate structures from them on the micron and submicron level, however, is stopping the field from developing. In particular, the methods for the synthesis of shape memory polymers (SMPs) and structures at this scale and the effect of new geometries remain unexplored. Here, we describe the synthesis of shape memory polyurethane (PU) capsules accomplished by interfacial polymerization of emulsified droplets. The emulsified droplets contain the monomers for the hard segments, while the continuous aqueous phase contains the soft segments. A trifunctional chemical cross-linker for shape memory PU synthesis was utilized to eliminate creep and improve the recovery ratios of the final capsules. We observe an anomalous dependence of the recovery ratio with the amount of programmed strain compared to previous SMPs. We develop quantitative characterization methods and theory to show that when dealing with thin-shell objects, alternative parameters to quantify recovery ratios are needed. We show that while achieving 94-99% area recovery ratios, the linear capsule recovery ratios can be as low as 70%. This quantification method allows us to convert from observed linear aspect ratios in capsules to find out unrecovered area strain and stress. The hollow structure of the capsules grants high internal volume for some applications (e.g., drug delivery), which benefit from much higher loading of active ingredients than polymeric particles. The methods we developed for capsule synthesis and programming could be easily scaled up for larger volume applications.

Hierarchical Photonic Pigments via the Confined Self-Assembly of Bottlebrush Block Copolymers.

Hierarchical, structurally colored materials offer a wide variety of visual effects that cannot be achieved with standard pigments or dyes. However, their fabrication requires simultaneous control over multiple length-scales. Here we introduce a robust strategy for the fabrication of hierarchical photonic pigments via the confined self-assembly of bottlebrush block copolymers within emulsified microdroplets. The bottlebrush block copolymer self-assembles into highly ordered concentric lamellae, giving rise to a near perfect photonic multilayer in the solid state, with reflectivity up to 100%. The reflected color can be readily tuned across the whole visible spectrum by either altering the molecular weight or by blending the bottlebrush block copolymers. Furthermore, the developed photonic pigments are responsive, with a selective and reversible color change observed upon swelling in different solvents. Our system is particularly suited for the scalable production of photonic pigments, arising from their rapid self-assembly mechanism and size-independent color.

The Self-Assembly of Cellulose Nanocrystals: Hierarchical Design of Visual Appearance.

By controlling the interaction of biological building blocks at the nanoscale, natural photonic nanostructures have been optimized to produce intense coloration. Inspired by such biological nanostructures, the possibility to design the visual appearance of a material by guiding the hierarchical self-assembly of its constituent components, ideally using natural materials, is an attractive route for rationally designed, sustainable manufacturing. Within the large variety of biological building blocks, cellulose nanocrystals are one of the most promising biosourced materials, primarily for their abundance, biocompatibility, and ability to readily organize into photonic structures. Here, the mechanisms underlying the formation of iridescent, vividly colored materials from colloidal liquid crystal suspensions of cellulose nanocrystals are reviewed and recent advances in structural control over the hierarchical assembly process are reported as a toolbox for the design of sophisticated optical materials.

Rapid Self-Assembly of Shaped Microtiles into Large, Close-Packed Crystalline Monolayers on Solid Surfaces.

The rapid self-assembly of photolithographic microtiles into large crystalline monolayers is achieved. Crystalline monolayers get trapped at the liquid-liquid interface and re-emerge at the air-liquid interface by mixing a cosolvent, which then deposits on the solid surface in seconds. This method has the potential to assemble different shapes and sizes of microtiles into complex architectures.