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Mapping maize water stress status and monitoring its spatial variability at a farm scale are a prerequisite for precision irrigation. High-resolution multispectral images acquired from an unmanned aerial vehicle (UAV) were used to evaluate the applicability of the data in mapping water stress status of maize under different levels of deficit irrigation at the late vegetative, reproductive and maturation growth stages. Canopy temperature, field air temperature and relative humidity obtained by a handheld infrared thermometer and a portable air temperature/relative humidity meter were used to establish a crop water stress index (CWSI) empirical model under the weather conditions in Ordos, Inner Mongolia, China. Nine vegetation indices (VIs) related to crop water stress were derived from the UAV multispectral imagery and used to establish CWSI inversion models. The results showed that non-water-stressed baseline had significant difference in the reproductive and maturation stages with an increase of 2.1 °C, however, the non-transpiring baseline did not change significantly with an increase of 0.1 °C. The ratio of transformed chlorophyll absorption in reflectance index (TCARI) and renormalized difference vegetation index (RDVI), and the TCARI and soil-adjusted vegetation index (SAVI) had the best correlations with CWSI. R2 values were 0.47 and 0.50 for TCARI/RDVI and TCARI/SAVI at the reproductive and maturation stages, respectively; and 0.81 and 0.80 for TCARI/RDVI and TCARI/SAVI at the late reproductive and maturation stages, respectively. Compared to CWSI calculated by on-site measurements, CWSI values retrieved by VI-CWSI regression models established in this study had more abilities to assess the field variability of crop and soil. This study demonstrates the potentiality of using high-resolution UAV multispectral imagery to map maize water stress.

The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB point clouds. To search the optimal estimation method, the estimation performances of the models based on vegetation indices alone, based on plant height alone, and based on both vegetation indices and plant height were compared. The results showed that plant height directly derived from UAV RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an RMSE value of 0.12 m. The above-ground biomass exponential regression models based on plant height alone had higher correlations for both fresh and dry above-ground biomass with R2 values of 0.77 and 0.76, respectively, compared to the linear regression model (both R2 values were 0.59). The vegetation indices derived from UAV RGB imagery had great potential to estimate maize above-ground biomass with R2 values ranging from 0.63 to 0.73. When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82. There was no significant improvement of the estimation performance when plant height derived from UAV RGB imagery was added into the multivariable linear regression model based on vegetation indices. When estimating crop above-ground biomass based on UAV RGB remote-sensing system alone, looking for optimized vegetation indices and establishing estimation models with high performance based on advanced algorithms (e.g., machine learning technology) may be a better way.

To identify drought-tolerant crop cultivars or achieve a balance between water use and yield, accurate measurements of crop water stress are needed. In this study, the canopy temperature (Tc) of maize at the late vegetative stage was extracted from high-resolution red–green–blue (RGB, 1.25 cm) and thermal (7.8 cm) images taken by an unmanned aerial vehicle (UAV). To reduce the number of parameters for crop water stress monitoring, four simple methods that require only Tc were identified: Tc, degrees above non-stress, standard deviation of Tc, and variation coefficient of Tc. The ground-truth temperatures obtained using a handheld infrared thermometer were used to calibrate the temperature obtained from the UAV thermal images and to evaluate the Tc extraction results. Measured leaf stomatal conductance values were used to evaluate the performance of the four Tc-based crop water stress indicators. The results showed a strong correlation between ground-truth Tc and Tc extracted by the red–green ratio index (RGRI)-Otsu method proposed in this study, with a coefficient of determination of 0.94 ( n = 15) and root mean square error value of 0.7°C. The RGRI-Otsu method was most accurate for estimating temperatures around 32.9°C, but the magnitude of residuals increased above and below this value. This phenomenon may be attributable to changes in canopy cover (leaf curling) under water stress, resulting in changes in the proportion of exposed sunlit soil in UAV thermal orthophotographs. Therefore, to improve the accuracy of maize canopy detection and extraction, optimal methods and better strategies for eliminating mixed pixels are needed. This study demonstrates the potential of using high-resolution UAV RGB images to supplement UAV thermal images for the accurate extraction of maize Tc.

The regularities of the world render an intricate interplay between past and present. Even across independent trials, current-trial perception can be automatically shifted by preceding trials, namely the “serial bias.” Meanwhile, the neural implementation of the spontaneous shift of present by past that operates on multiple features remains unknown. In two auditory categorization experiments with human electrophysiological recordings, we demonstrate that serial bias arises from the co-occurrence of past-trial neural reactivation and the neural encoding of current-trial features. The meeting of past and present shifts the neural representation of current-trial features and modulates serial bias behavior. Critically, past-trial features (i.e., pitch, category choice, motor response) keep their respective identities in memory and are only reactivated by the corresponding features in the current trial, giving rise to dissociated feature-specific serial biases. The feature-specific automatic reactivation might constitute a fundamental mechanism for adaptive past-to-present generalizations over multiple features.

Plants are capable of assembling beneficial rhizomicrobiomes through a “cry for help” mechanism upon pathogen infestation; however, it remains unknown whether we can use nonpathogenic strains to induce plants to assemble a rhizomicrobiome against pathogen invasion. Here, we used a series of derivatives of Pseudomonas syringae pv. tomato DC3000 to elicit different levels of the immune response to Arabidopsis and revealed that two nonpathogenic DC3000 derivatives induced the beneficial soil-borne legacy, demonstrating a similar “cry for help” triggering effect as the wild-type DC3000. In addition, an increase in the abundance of Devosia in the rhizosphere induced by the decreased root exudation of myristic acid was confirmed to be responsible for growth promotion and disease suppression of the soil-borne legacy. Furthermore, the “cry for help” response could be induced by heat-killed DC3000 and flg22 and blocked by an effector triggered immunity (ETI) -eliciting derivative of DC3000. In conclusion, we demonstrate the potential of nonpathogenic bacteria and bacterial elicitors to promote the generation of disease-suppressive soils. Upon pathogen attack, plants can trigger the “cry for help” response and assemble beneficial rhizobacteria. Here, the authors use nonpathogenic Pseudomonas syringae DC3000 derivatives to elicit a similar “cry for help” response as the wild-type pathogenic DC3000 in Arabidopsis.

Artemisinin and its derivatives have shown broad-spectrum antitumor activities and . Furthermore, outcomes from a limited number of clinical trials provide encouraging evidence for their excellent antitumor activities. However, some problems such as poor solubility, toxicity and controversial mechanisms of action hamper their use as effective antitumor agents in the clinic. In order to accelerate the use of ARTs in the clinic, researchers have recently developed novel therapeutic approaches including developing novel derivatives, manufacturing novel nano-formulations, and combining ARTs with other drugs for cancer therapy. The related mechanisms of action were explored. This review describes ARTs used to induce non-apoptotic cell death containing oncosis, autophagy, and ferroptosis. Moreover, it highlights the ARTs-caused effects on cancer metabolism, immunosuppression and cancer stem cells and discusses clinical trials of ARTs used to treat cancer. The review provides additional insight into the molecular mechanism of action of ARTs and their considerable clinical potential.

The implementation of low-cost transition-metal complexes in CO 2 reduction reaction (CO 2 RR) is hampered by poor mechanistic understanding. Herein, a carbon-supported copper bis-(terpyridine) complex enabling facile kilogram-scale production of the catalyst is developed. We directly observe an intriguing baton-relay-like mechanism of active sites transfer by employing a widely accessible operando Raman/Fourier-transform infrared spectroscopy analysis coupled with density functional theory computations. Our analyses reveal that the first protonation step involves Cu-N bond breakage before the *COOH intermediate forms exclusively at the central N site, followed by an N-to-Cu active site transfer. This unique active site transfer features energetically favorable *CO formation on Cu sites, low-barrier CO desorption and reversible catalyst regeneration, endowing the catalyst with a CO selectively of 99.5 %, 80 h stability, and a turn-over efficiency of 9.4 s −1 at −0.6 V vs. the reversible hydrogen electrode in an H-type cell configuration. We expect that the approach and findings presented here may accelerate future mechanistic studies of next-generation CO 2 RR electrocatalysts. Transition metal complexes are potential low-cost electrocatalysts for CO 2 reduction. Here, using accessible spectrochemistry, the authors observe an intriguing baton relay-like mechanism of active site transfer during CO 2 reduction to CO on a carbon-supported copper bis-(terpyridine) complex.

Context:Premature ovarian insufficiency (POI) is highly heterogeneous, both in phenotype and etiology. They are not yet clearly stated and correlated.Objective:To characterize clinical presentations of a large, well-phenotyped cohort of women with POI, and correlate phenotypes with etiologies to draw a comprehensive clinical picture of POI.Design, Patients, Interventions, and Main Outcome Measures:In this retrospective study, a total of 955 Chinese women with overt POI between 2006 and 2015 were systemically evaluated and analyzed. The phenotypic features, including menstrual characteristics, hormone profiles, ovarian ultrasonography/biopsy, pregnancy/family history, and genetic/autoimmune/iatrogenic etiologies were assessed and further compared within different subgroups.Results:Among 955 women with POI, 85.97% presented with secondary amenorrhea (SA) and 14.03% with primary amenorrhea (PA). PA represented the most severe ovarian dysfunction and more chromosomal aberrations than SA. The decline of ovarian function in patients with SA progressed quickly. They had shortened reproductive periods (approximately 10 years) and developed amenorrhea within 1 to 2 years after menstrual irregularity. The ovaries were invisible or small, and the presence of follicles (28.43%) was correlated with other good reproductive indicators. Familial patients (12.25%) manifested better ovarian status and fewer chromosomal aberrations than sporadic patients. The etiologies consisted of genetic (13.15%), autoimmune (12.04%), and iatrogenic (7.29%), approximately 68% remaining idiopathic. There were significant differences among different etiologies, with the genetic group representing the most severe phenotype.Conclusion:Our results regarding distinct phenotypic characteristics and association with different etiologies further confirmed the high heterogeneity of POI. Additional longitudinal clinical studies and pathogenesis research are warranted.There is high heterogeneity of phenotype and etiology in women with overt POI. Phenotypic variabilities exist among patients, even when stratified by different etiological diagnosis.

Aroma and taste are the most important attributes of alcoholic beverages. In the study, the self-developed electronic tongue (e-tongue) and electronic nose (e-nose) were used for evaluating the marked ages of rice wines. Six types of feature data sets (e-tongue data set, e-nose data set, direct-fusion data set, weighted-fusion data set, optimized direct-fusion data set, and optimized weighted-fusion data set) were used for identifying rice wines with different wine ages. Pearson coefficient analysis and variance inflation factor (VIF) analysis were used to optimize the fusion matrixes by removing the multicollinear information. Two types of discrimination methods (principal component analysis (PCA) and locality preserving projections (LPP)) were used for classifying rice wines, and LPP performed better than PCA in the discrimination work. The best result was obtained by LPP based on the weighted-fusion data set, and all the samples could be classified clearly in the LPP plot. Therefore, the weighted-fusion data were used as independent variables of partial least squares regression, extreme learning machine, and support vector machines (LIBSVM) for evaluating wine ages, respectively. All the methods performed well with good prediction results, and LIBSVM presented the best correlation coefficient (R2 ≥ 0.9998).

With the steady progress of China’s agricultural modernization, the demand for agricultural machinery for production is widely growing. Agricultural unmanned aerial vehicles (UAVs) are becoming a new force in the field of precision agricultural aviation in China. In those agricultural areas where ground-based machinery have difficulties in executing farming operations, agricultural UAVs have shown obvious advantages. With the development of precision agricultural aviation technology, one of the inevitable trends is to realize autonomous identification of obstacles and real-time obstacle avoidance (OA) for agricultural UAVs. However, the complex farmland environment and changing obstacles both increase the complexity of OA research. The objective of this paper is to introduce the development of agricultural UAV OA technology in China. It classifies the farmland obstacles in two ways and puts forward the OA zones and related avoidance tactics, which helps to improve the safety of aviation operations. This paper presents a comparative analysis of domestic applications of agricultural UAV OA technology, features, hotspot and future research directions. The agricultural UAV OA technology of China is still at an early development stage and many barriers still need to be overcome.