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Legumes are able to form a symbiotic relationship with nitrogen-fixing soil bacteria called rhizobia. The result of this symbiosis is to form nodules on the plant root, within which the bacteria can convert atmospheric nitrogen into ammonia that can be used by the plant. Establishment of a successful symbiosis requires the two symbiotic partners to be compatible with each other throughout the process of symbiotic development. However, incompatibility frequently occurs, such that a bacterial strain is unable to nodulate a particular host plant or forms nodules that are incapable of fixing nitrogen. Genetic and molecular mechanisms that regulate symbiotic specificity are diverse, involving a wide range of host and bacterial genes/signals with various modes of action. In this review, we will provide an update on our current knowledge of how the recognition specificity has evolved in the context of symbiosis signaling and plant immunity.

Controlled self-assembly of colloidal particles into predetermined organization facilitates the bottom-up manufacture of artificial materials with designated hierarchies and synergistically integrated functionalities. However, it remains a major challenge to assemble individual nanoparticles with minimal building instructions in a programmable fashion due to the lack of directional interactions. Here, we develop a general paradigm for controlled co-assembly of soft block copolymer micelles and simple unvarnished hard nanoparticles through variable noncovalent interactions, including hydrogen bonding and coordination interactions. Upon association, the hairy micelle corona binds with the hard nanoparticles with a specific valence depending exactly on their relative size and feeding ratio. This permits the integration of block copolymer micelles with a diverse array of hard nanoparticles with tunable chemistry into multidimensional colloidal molecules and polymers. Secondary co-assembly of the resulting colloidal molecules further leads to the formation of more complex hierarchical colloidal superstructures. Notably, such colloidal assembly is processible on surface either through initiating the alternating co-assembly from a micelle immobilized on a substrate or directly grafting a colloidal oligomer onto the micellar anchor. Colloidal self-assembly enables bottom-up manufacture of materials with designed hierarchies and functions. Here the authors develop a facile method to construct multidimensional colloidal architectures via the association of soft block copolymer micelles with simple unvarnished hard nanoparticles.

We investigated the feasibility and potentiality of presymptomatic detection of tobacco disease using hyperspectral imaging, combined with the variable selection method and machine-learning classifiers. Images from healthy and TMV-infected leaves with 2, 4, and 6 days post infection were acquired by a pushbroom hyperspectral reflectance imaging system covering the spectral range of 380–1023 nm. Successive projections algorithm was evaluated for effective wavelengths (EWs) selection. Four texture features, including contrast, correlation, entropy, and homogeneity were extracted according to grey-level co-occurrence matrix (GLCM). Additionally, different machine-learning algorithms were developed and compared to detect and classify disease stages with EWs, texture features and data fusion respectively. The performance of chemometric models with data fusion manifested better results with classification accuracies of calibration and prediction all above 80% than those only using EWs or texture features; the accuracies were up to 95% employing back propagation neural network (BPNN), extreme learning machine (ELM), and least squares support vector machine (LS-SVM) models. Hence, hyperspectral imaging has the potential as a fast and non-invasive method to identify infected leaves in a short period of time (i.e. 48 h) in comparison to the reference images (5 days for visible symptoms of infection, 11 days for typical symptoms).

The conversion of tumor-promoting M2 macrophage phenotype to tumor-suppressing M1 macrophages is a promising therapeutic approach for cancer treatment. However, the tumor normally provides an abundance of M2 macrophage stimuli, which creates an M2 macrophage-dominant immunosuppressive microenvironment. In our study, docetaxel (DTX) as chemotherapeutic modularity was loaded into M1 macrophage-derived exosomes (M1-Exo) with M1 proinflammatory nature to establish DTX-M1-Exo drug delivery system. We found that DTX-M1-Exo induced naïve M0 macrophages to polarize to M1 phenotype, while failed to repolarize to M2 macrophages upon Interleukin 4 restimulation due to impaired mitochondrial function. This suggests that DTX-M1-Exo can achieve long-term robust M1 activation in immunosuppressive tumor microenvironment. The in vivo results further confirmed that DTX-M1-Exo has a beneficial effect on macrophage infiltration and activation in the tumor tissues. Thus, DTX-M1-Exo is a novel macrophage polarization strategy via combined chemotherapy and immunotherapy to achieve great antitumor therapeutic efficacy.

Pancreatic cancer is a malignant disease with high mortality and poor prognosis due to lack of early diagnosis and low treatment efficiency after diagnosis. Although Gemcitabine (GEM) is used as the first-line chemotherapeutic drug, chemoresistance is still the major problem that limits its therapeutic efficacy. Here in this study, we developed a specific M1 macrophage-derived exosome (M1Exo)-based drug delivery system against GEM resistance in pancreatic cancer. In addition to GEM, Deferasirox (DFX) was also loaded into drug carrier, M1Exo, in order to inhibit ribonucleotide reductase regulatory subunit M2 (RRM2) expression via depleting iron, and thus increase chemosensitivity of GEM. The M1Exo nanoformulations combining both GEM and DFX significantly enhanced the therapeutic efficacy on the GEM-resistant PANC-1/GEM cells and 3D tumor spheroids by inhibiting cancer cell proliferation, cell attachment and migration, and chemoresistance to GEM. These data demonstrated that M1Exo loaded with GEM and DFX offered an efficient therapeutic strategy for drug-resistant pancreatic cancer.

Alternative splicing (AS) occurs widely in plants and can provide the main source of transcriptome and proteome diversity in an organism. AS functions in a range of physiological processes, including plant disease resistance, but its biological roles and functional mechanisms remain poorly understood. Many plant disease resistance (R) genes undergo AS, and several R genes require alternatively spliced transcripts to produce R proteins that can specifically recognize pathogen invasion. In the finely-tuned process of R protein activation, the truncated isoforms generated by AS may participate in plant disease resistance either by suppressing the negative regulation of initiation of immunity, or by directly engaging in effector-triggered signaling. Although emerging research has shown the functional significance of AS in plant biotic stress responses, many aspects of this topic remain to be understood. Several interesting issues surrounding the AS of R genes, especially regarding its functional roles and regulation, will require innovative techniques and additional research to unravel.

We investigated the feasibility and potentiality of determining firmness, soluble solids content (SSC), and pH in kiwifruits using hyperspectral imaging, combined with variable selection methods and calibration models. The images were acquired by a push-broom hyperspectral reflectance imaging system covering two spectral ranges. Weighted regression coefficients (BW), successive projections algorithm (SPA) and genetic algorithm–partial least square (GAPLS) were compared and evaluated for the selection of effective wavelengths. Moreover, multiple linear regression (MLR), partial least squares regression and least squares support vector machine (LS-SVM) were developed to predict quality attributes quantitatively using effective wavelengths. The established models, particularly SPA-MLR, SPA-LS-SVM and GAPLS-LS-SVM, performed well. The SPA-MLR models for firmness ( R pre  = 0.9812, RPD = 5.17) and SSC ( R pre  = 0.9523, RPD = 3.26) at 380–1023 nm showed excellent performance, whereas GAPLS-LS-SVM was the optimal model at 874–1734 nm for predicting pH ( R pre  = 0.9070, RPD = 2.60). Image processing algorithms were developed to transfer the predictive model in every pixel to generate prediction maps that visualize the spatial distribution of firmness and SSC. Hence, the results clearly demonstrated that hyperspectral imaging has the potential as a fast and non-invasive method to predict the quality attributes of kiwifruits.

Near-infrared (874-1734 nm) hyperspectral imaging (NIR-HSI) technique combined with chemometric methods was used to trace origins of 1200 Chinese wolfberry samples, which from Ningxia, Inner Mongolia, Sinkiang and Qinghai in China. Two approaches, named pixel-wise and object-wise, were investigated to discriminative the origin of these Chinese wolfberries. The pixel-wise classification assigned a class to each pixel from individual Chinese wolfberries, and with this approach, the differences in the Chinese wolfberries from four origins were reflected intuitively. Object-wise classification was performed using mean spectra. The average spectral information of all pixels of each sample in the hyperspectral image was extracted as the representative spectrum of a sample, and then discriminant analysis models of the origins of Chinese wolfberries were established based on these average spectra. Specifically, the spectral curves of all samples were collected, and after removal of obvious noise, the spectra of 972-1609 nm were viewed as the spectra of wolfberry. Then, the spectral curves were pretreated with moving average smoothing (MA), and discriminant analysis models including support vector machine (SVM), neural network with radial basis function (NN-RBF) and extreme learning machine (ELM) were established based on the full-band spectra, the extracted characteristic wavelengths from loadings of principal component analysis (PCA) and 2nd derivative spectra, respectively. Among these models, the recognition accuracies of the calibration set and prediction set of the ELM model based on extracted characteristic wavelengths from loadings of PCA were higher than 90%. The model not only ensured a high recognition rate but also simplified the model and was conducive to future rapid on-line testing. The results revealed that NIR-HSI combined with PCA loadings-ELM could rapidly trace the origins of Chinese wolfberries.

Controlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in crop yield and quality caused by these problems. In recent years, the remote sensing (RS) areas has been prevailed over by unmanned aerial vehicle (UAV)-based applications. Herein, by using methods such as keyword co-contribution analysis and author co-occurrence analysis in bibliometrics, we found out the hot-spots of this field. UAV platforms equipped with various types of cameras and other advanced sensors, combined with artificial intelligence (AI) algorithms, especially for deep learning (DL) were reviewed. Acknowledging the critical role of comprehending crop diseases and pests, along with their defining traits, we provided a concise overview as indispensable foundational knowledge. Additionally, some widely used traditional machine learning (ML) algorithms were presented and the performance results were tabulated to form a comparison. Furthermore, we summarized crop diseases and pests monitoring techniques using DL and introduced the application for prediction and classification. Take it a step further, the newest and the most concerned applications of large language model (LLM) and large vision model (LVM) in agriculture were also mentioned herein. At the end of this review, we comprehensively discussed some deficiencies in the existing research and some challenges to be solved, as well as some practical solutions and suggestions in the near future.

Leguminous plants can enter into root nodule symbioses with nitrogen-fixing soil bacteria known as rhizobia. An intriguing but still poorly understood property of the symbiosis is its host specificity, which is controlled at multiple levels involving both rhizobial and host genes. It is widely believed that the host specificity is determined by specific recognition of bacterially derived Nod factors by the cognate host receptor(s). Here we describe the positional cloning of two soybean genes Rj2 and Rfg1 that restrict nodulation with specific strains of Bradyrhizobium japonicum and Sinorhizobium fredii, respectively. We show that Rj2 and Rfg1 are allelic genes encoding a member of the Toll-interleukin receptor/nucleotide-binding site/leucine-rich repeat (TIR-NBS-LRR) class of plant resistance (R) proteins. The involvement of host R genes in the control of genotype-specific infection and nodulation reveals a common recognition mechanism underlying symbiotic and pathogenic host-bacteria interactions and suggests the existence of their cognate avirulence genes derived from rhizobia. This study suggests that establishment of a root nodule symbiosis requires the evasion of plant immune responses triggered by rhizobial effectors.