No abstract

All-inorganic perovskite quantum dots (PeQDs) have sparked extensive research focus on white-light-emitting diodes (WLEDs), but stability and photoluminescence efficiency issues are still remain obstacles impeding their practical application. Here, we reported a facile one-step method to synthesize CsPbBr 3 PeQDs at room temperature using branched didodecyldimethylammonium fluoride (DDAF) and short-chain-length octanoic acid as capping ligands. The obtained CsPbBr 3 PeQDs have a nearunity photoluminescence quantum yield of 97% due to the effective passivation of DDAF. More importantly, they exhibit much improved stability against air, heat, and polar solvents, maintaining >70% of initial PL intensity. Making use of these excellent optoelectronic properties, WLEDs based on CsPbBr 3 PeQDs, CsPbBr 1.2 I 1.8 PeQDs, and blue LEDs were fabricated, which show a color gamut of 122.7% of the National Television System Committee standard, a luminous efficacy of 17.1 lm/W, with a color temperature of 5890 K, and CIE coordinates of (0.32, 0.35). These results indicate that the CsPbBr 3 PeQDs have great practical potential in wide-color-gamut displays.

The accurate and rapid estimation of the aboveground biomass (AGB) of rice is crucial to food security. Unmanned aerial vehicles (UAVs) mounted with hyperspectral sensors can obtain images of high spectral and spatial resolution in a quick and effective manner. Integrating UAV-based spatial and spectral information has substantial potential for improving crop AGB estimation. Hyperspectral remote-sensing data with more continuous reflectance information on ground objects provide more possibilities for band selection. The use of band selection for the spectral vegetation index (VI) has been discussed in many studies, but few studies have paid attention to the band selection of texture features in rice AGB estimation. In this study, UAV-based hyperspectral images of four rice varieties in five nitrogen treatments (N0, N1, N2, N3, and N4) were obtained. First, multiple spectral bands were used to identify the optimal bands of the spectral vegetation indices, as well as the texture features; next, the vegetation index model (VI model), the vegetation index combined with the corresponding-band textures model (VI+CBT model), and the vegetation index combined with the full-band textures model (VI+FBT model) were established to compare their respective rice AGB estimation abilities. The results showed that the optimal bands of the spectral and textural information for AGB monitoring were inconsistent. The red-edge and near-infrared bands demonstrated a strong correlation with the rice AGB in the spectral dimension, while the green and red bands exhibited a high correlation with the rice AGB in the spatial dimension. The ranking of the monitoring accuracies of the three models, from highest to lowest, was: the VI+FBT model, then the VI+CBT model, and then the VI model. Compared with the VI model, the R2 of the VI+FBT model and the VI+CBT model increased by 1.319% and 9.763%, respectively. The RMSE decreased by 2.070% and 16.718%, respectively, while the rRMSE decreased by 2.166% and 16.606%, respectively. The results indicated that the integration of vegetation indices and textures can significantly improve the accuracy of rice AGB estimation. The full-band textures contained richer information that was highly related to rice AGB. The VI model at the tillering stage presented the greatest sensitivity to the integration of textures, and the models in the N3 treatment (1.5 times the normal nitrogen level) gave the best AGB estimation compared with the other nitrogen treatments. This research proposes a reliable modeling framework for monitoring rice AGB and provides scientific support for rice-field management.

Rice floret number per unit area as one of the key yield structure parameters is directly related to the final yield of rice. Previous studies paid little attention to the effect of the variations in vegetation indices (VIs) caused by rice flowering on rice yield estimation. Unmanned aerial vehicles (UAV) equipped with hyperspectral cameras can provide high spatial and temporal resolution remote sensing data about the rice canopy, providing possibilities for flowering monitoring. In this study, two consecutive years of rice field experiments were conducted to explore the performance of florescence spectral information in improving the accuracy of VIs-based models for yield estimates. First, the florescence ratio reflectance and florescence difference reflectance, as well as their first derivative reflectance, were defined and then their correlations with rice yield were evaluated. It was found that the florescence spectral information at the seventh day of rice flowering showed the highest correlation with the yield. The sensitive bands to yield were centered at 590 nm, 690 nm and 736 nm–748 nm, 760 nm–768 nm for the first derivative florescence difference reflectance, and 704 nm–760 nm for the first derivative florescence ratio reflectance. The florescence ratio index (FRI) and florescence difference index (FDI) were developed and their abilities to improve the estimation accuracy of models basing on vegetation indices at single-, two- and three-growth stages were tested. With the introduction of florescence spectral information, the single-growth VI-based model produced the most obvious improvement in estimation accuracy, with the coefficient of determination (R2) increasing from 0.748 to 0.799, and the mean absolute percentage error (MAPE) and the root mean squared error (RMSE) decreasing by 11.8% and 10.7%, respectively. Optimized by flowering information, the two-growth stage VIs-based model gave the best performance (R2 = 0.869, MAPE = 3.98%, RMSE = 396.02 kg/ha). These results showed that introducing florescence spectral information at the flowering stage into conventional VIs-based yield estimation models is helpful in improving rice yield estimation accuracy. The usefulness of florescence spectral information for yield estimation provides a new idea for the further development and improvement of the crop yield estimation method.

With encouraging antitumor effects, immunotherapy represented by immune checkpoint blockade has developed into a mainstream cancer therapeutic modality. However, only a minority of ovarian cancer (OC) patients could benefit from immunotherapy. The main reason is that most OC harbor a suppressive tumor immune microenvironment (TIME). Emerging studies suggest that M2 tumor-associated macrophages (TAMs), T regulatory cells (Tregs), myeloid-derived suppressor cells (MDSCs), and cancer-associated fibroblasts (CAFs) are enriched in OC. Thus, reversing the suppressive TIME is considered an ideal candidate for improving the efficiency of immunotherapy. Nanoparticles encapsulating immunoregulatory agents can regulate immunocytes and improve the TIME to boost the antitumor immune response. In addition, some nanoparticle-mediated photodynamic and photothermal therapy can directly kill tumor cells and induce tumor immunogenic cell death to activate antigen-presenting cells and promote T cell infiltration. These advantages make nanoparticles promising candidates for modulating the TIME and improving OC immunotherapy. In this review, we analyzed the composition and function of the TIME in OC and summarized the current clinical progress of OC immunotherapy. Then, we expounded on the promising advances in nanomaterial-mediated immunotherapy for modulating the TIME in OC. Finally, we discussed the obstacles and challenges in the clinical translation of this novel combination treatment regimen. We believe this resourceful strategy will open the door to effective immunotherapy of OC and benefit numerous patients.

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Providencia rettgeri has been recognized as a zoonotic pathogen of humans and aquaculture animals and has become a global public health concern. However, scarce information is available on the characterization of pathogenic P. rettgeri from kuruma shrimp Marsupenaeus japonicus. In the present study, a P. rettgeri isolate (KM4) was confirmed as a causative agent of red leg disease in cultured M. japonicus, which showed a median lethal dose (LD50) value of 5.01 × 105 CFU·ml−1 and had multiple resistances to aminoglycosides, sulfonamides, and tetracycline antimicrobials used in aquaculture. In addition, the whole genome of isolate KM4 was sequenced and found to consist of a single circular chromosome of 4,378,712 bp and a circular plasmid of 171,394 bp. The genome sequence analysis further revealed the presence of potential virulence and antibiotic resistance genes in isolate KM4, which probably rendered this isolate particularly virulent. To our knowledge, this is the first study to characterize P. rettgeri pathogens from kuruma shrimp infected with red leg disease. The findings of this study can provide novel insights into the presence and distribution of pathogenicity‐associated genes in shrimp‐pathogenic P. rettgeri.

Rice is one of the most important food crops around the world. Remote sensing technology, as an effective and rapidly developing method, has been widely applied to precise rice management. To observe the current research status in the field of rice remote sensing (RRS), a bibliometric analysis was carried out based on 2680 papers of RRS published during 1980–2021, which were collected from the core collection of the Web of Science database. Quantitative analysis of the number of publications, top countries and institutions, popular keywords, etc. was conducted through the knowledge mapping software CiteSpace, and comprehensive discussions were carried out from the aspects of specific research objects, methods, spectral variables, and sensor platforms. The results revealed that an increasing number of countries and institutions have conducted research on RRS and a great number of articles have been published annually, among which, China, the United States of America, and Japan were the top three and the Chinese Academy of Sciences, Zhejiang University, and Nanjing Agricultural University were the first three research institutions with the largest publications. Abundant interest was paid to “reflectance”, followed by “vegetation index” and “yield” and the specific objects mainly focused on growth, yield, area, stress, and quality. From the perspective of spectral variables, reflectance, vegetation index, and back-scattering coefficient appeared the most frequently in the frontiers. In addition to satellite remote sensing data and empirical models, unmanned air vehicle (UAV) platforms and artificial intelligence models have gradually become hot topics. This study enriches the readers’ understanding and highlights the potential future research directions in RRS.