Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm.

Aboveground biomass (AGB) is regarded as a critical variable in monitoring crop growth and yield. The use of hyperspectral remote sensing has emerged as a viable method for the rapid and precise monitoring of AGB. Due to the extensive dimensionality and volume of hyperspectral data, it is crucial to effectively reduce data dimensionality and select sensitive spectral features to enhance the accuracy of rice AGB estimation models. At present, derivative transform and feature selection algorithms have become important means to solve this problem. However, few studies have systematically evaluated the impact of derivative spectrum combined with feature selection algorithm on rice AGB estimation. To this end, at the Xiaogang Village (Chuzhou City, China) Experimental Base in 2020, this study used an ASD FieldSpec handheld 2 ground spectrometer (Analytical Spectroscopy Devices, Boulder, Colorado, USA) to obtain canopy spectral data at the critical growth stage (tillering, jointing, booting, heading, and maturity stages) of rice, and evaluated the performance of the recursive feature elimination (RFE) and Boruta feature selection algorithm through partial least squares regression (PLSR), principal component regression (PCR), support vector machine (SVM) and ridge regression (RR). Moreover, we analyzed the importance of the optimal derivative spectrum. The findings indicate that (1) as the growth stage progresses, the correlation between rice canopy spectrum and AGB shows a trend from high to low, among which the first derivative spectrum (FD) has the strongest correlation with AGB. (2) The number of feature bands selected by the Boruta algorithm is 19~35, which has a good dimensionality reduction effect. (3) The combination of FD-Boruta-PCR (FB-PCR) demonstrated the best performance in estimating rice AGB, with an increase in R² of approximately 10% ~ 20% and a decrease in RMSE of approximately 0.08% ~ 14%. (4) The best estimation stage is the booting stage, with R2 values between 0.60 and 0.74 and RMSE values between 1288.23 and 1554.82 kg/hm2. This study confirms the accuracy of hyperspectral remote sensing in estimating vegetation biomass and further explores the theoretical foundation and future direction for monitoring rice growth dynamics.

Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery.

The Soil Plant Analysis Development (SPAD) is a vital index for evaluating crop nutritional status and serves as an essential parameter characterizing the reproductive growth status of winter wheat. Non-destructive and accurate monitorin3g of winter wheat SPAD plays a crucial role in guiding precise management of crop nutrition. In recent years, the spectral saturation problem occurring in the later stage of crop growth has become a major factor restricting the accuracy of SPAD estimation. Therefore, the purpose of this study is to use features selection strategy to optimize sensitive remote sensing information, combined with features fusion strategy to integrate multiple characteristic features, in order to improve the accuracy of estimating wheat SPAD. This study conducted field experiments of winter wheat with different varieties and nitrogen treatments, utilized UAV multispectral sensors to obtain canopy images of winter wheat during the heading, flowering, and late filling stages, extracted spectral features and texture features from multispectral images, and employed features selection strategy (Boruta and Recursive Feature Elimination) to prioritize sensitive remote sensing features. The features fusion strategy and the Support Vector Machine Regression algorithm are applied to construct the SPAD estimation model for winter wheat. The results showed that the spectral features of NIR band combined with other bands can fully capture the spectral differences of winter wheat SPAD during the reproductive growth stage, and texture features of the red and NIR band are more sensitive to SPAD. During the heading, flowering, and late filling stages, the stability and estimation accuracy of the SPAD model constructed using both features selection strategy and features fusion strategy are superior to models using only a single feature strategy or no strategy. The enhancement of model accuracy by this method becomes more significant, with the greatest improvement observed during the late filling stage, with R2 increasing by 0.092-0.202, root mean squared error (RMSE) decreasing by 0.076-4.916, and ratio of performance to deviation (RPD) increasing by 0.237-0.960. In conclusion, this method has excellent application potential in estimating SPAD during the later stages of crop growth, providing theoretical basis and technical support for precision nutrient management of field crops.

Corrigendum: Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery.

[This corrects the article DOI: 10.3389/fpls.2023.1284235.].

Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery.

Aboveground biomass (AGB) is a crucial physiological parameter for monitoring crop growth, assessing nutrient status, and predicting yield. Texture features (TFs) derived from remote sensing images have been proven to be crucial for estimating crops AGB, which can effectively address the issue of low accuracy in AGB estimation solely based on spectral information. TFs exhibit sensitivity to the size of the moving window and directional parameters, resulting in a substantial impact on AGB estimation. However, few studies systematically assessed the effects of moving window and directional parameters for TFs extraction on rice AGB estimation. To this end, this study used Unmanned aerial vehicles (UAVs) to acquire multispectral imagery during crucial growth stages of rice and evaluated the performance of TFs derived with different grey level co-occurrence matrix (GLCM) parameters by random forest (RF) regression model. Meanwhile, we analyzed the importance of TFs under the optimal parameter settings. The results indicated that: (1) the appropriate window size for extracting TFs varies with the growth stages of rice plant, wherein a small-scale window demonstrates advantages during the early growth stages, while the opposite holds during the later growth stages; (2) TFs derived from 45° direction represent the optimal choice for estimating rice AGB. During the four crucial growth stages, this selection improved performance in AGB estimation with R2 = 0.76 to 0.83 and rRMSE = 13.62% to 21.33%. Furthermore, the estimation accuracy for the entire growth season is R2 =0.84 and rRMSE =21.07%. However, there is no consensus regarding the selection of the worst TFs computation direction; (3) Correlation (Cor), Mean, and Homogeneity (Hom) from the first principal component image reflecting internal information of rice plant and Contrast (Con), Dissimilarity (Dis), and Second Moment (SM) from the second principal component image expressing edge texture are more important to estimate rice AGB among the whole growth stages; and (4) Considering the optimal parameters, the accuracy of texture-based AGB estimation slightly outperforms the estimation accuracy based on spectral reflectance alone. In summary, the present study can help researchers confident use of GLCM-based TFs to enhance the estimation accuracy of physiological and biochemical parameters of crops.

Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery.

Rapid and accurate assessment of yield and nitrogen use efficiency (NUE) is essential for growth monitoring, efficient utilization of fertilizer and precision management. This study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat by using the universal vegetation indices independent of growth period. Three vegetation indices having a strong correlation with yield or NUE during the entire growth season were determined through Pearson's correlational analysis, while multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods based on the aforementioned vegetation indices were adopted during different growth periods. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, and the late grain-filling stage was deemed as the optimal single stage with R2, root mean square error (RMSE), and mean absolute error (MAE) of 0.85, 793.96 kg/ha, and 656.31 kg/ha, respectively. MERIS terrestrial chlorophyll index (MTCI) performed better in the vegetative period and provided the best prediction results for the N partial factor productivity (NPFP) at the jointing stage, with R2, RMSE, and MAE of 0.65, 10.53 kg yield/kg N, and 8.90 kg yield/kg N, respectively. At the same time, the modified normalized difference blue index (mNDblue) was more accurate during the reproductive period, providing the best accuracy for agronomical NUE (aNUE) assessment at the late grain-filling stage, with R2, RMSE, and MAE of 0.61, 7.48 kg yield/kg N, and 6.05 kg yield/kg N, respectively. Furthermore, the findings indicated that model accuracy cannot be improved by increasing the number of input features. Overall, these results indicate that the consumer-grade P4M camera is suitable for early and efficient monitoring of important crop traits, providing a cost-effective choice for the development of the precision agricultural system.

Insights into the superior resistance of In-Co3O4-Ga2O3/H-Beta to SO2 and H2O in the selective catalytic reduction of NOx by CH4.

The existence of sulfur dioxide and water vapor in the flue gas generated from waste-to-energy stations could lead to catalyst deactivation, which has adverse effects on NOx removal. It is thus particularly important to study the reaction mechanism of catalyst resistance to poisoning. Herein, we report the mechanism of In-Co3O4-Ga2O3/H-Beta catalyst to SO2 and H2O resistance in the selective catalytic reduction (SCR) of NOx by CH4. The catalyst could achieve 74.6% NOx removal efficiency in the presence of 100 ppm SO2 and 5% H2O. In this catalyst, Co3O4 is attributed to enhancing the reversible poisoning of SO2 and CH4 activation and increasing the number of Brønsted acid sites by decomposing H2O. However, the InO+ active center was still eroded by a small amount of water vapor, leading to a reduction in NOx removal efficiency. The addition of Ga2O3 primarily provided an important intermediate NO2 for CH4-SCR reaction and reduced the aggregates of Co3O4 to increase the exposure of indium sites, and could reduce a part of SO2 to S2-. This study provides a good candidate for preparing catalysts with superior resistance towards SO2 and H2O for CH4-SCR.

Effect of Sensory Deprivation of Nasal Respiratory on Behavior of C57BL/6J Mice.

Nasal breathing is a dynamic cortical organizer involved in various behaviors and states, such as locomotion, exploration, memory, emotion, introspection. However, the effect of sensory deprivation of nasal respiratory breath (NRD) on behavior remain poorly understood. Herein, general locomotor activity, emotion, learning and memory, social interaction, and mechanical pain were evaluated using a zinc sulfate nasal irrigation induced nasal respiratory sensory deprivation animal model (ZnSO4-induced mouse model). In the open field test, the elevated O-maze test, and forced swim test, NRD mice exhibited depressive and anxiety-like behaviors. In memory-associated tests, NRD mice showed cognitive impairments in the hippocampal-dependent memory (Y maze, object recognition task, and contextual fear conditioning (CFC)) and amygdala-dependent memory (the tone-cued fear conditioning test (TFC)). Surprisingly, NRD mice did not display deficits in the acquisition of conditional fear in both CFC and TFC tests. Still, they showed significant memory retrieval impairment in TFC and enhanced memory retrieval in CFC. At the same time, in the social novelty test using a three-chamber setting, NRD mice showed impaired social and social novelty behavior. Lastly, in the von Frey filaments test, we found that the pain sensitivity of NRD mice was reduced. In conclusion, this NRD mouse model showed a variety of behavioral phenotypic changes, which could offer an important insight into the behavioral impacts of patients with anosmia or those with an impaired olfactory bulb (OB) (e.g., in COVID-19, Alzheimer's disease, Parkinson's disease, etc.).

Effect of preparation and reaction conditions on the performance of In/H-Beta for selective catalytic reduction of NOx with CH4.

In/H-Beta catalyst was prepared by optimizing the support, concentration of ion exchange liquid and calcination temperature to investigate the effects of synthesis conditions on catalytic activity of selective catalytic reduction of NOx with CH4. The results showed that the In/H-Beta catalyst exhibited the superior activity when concentration of exchange liquid was 0.033 M and calcination temperature was 500 °C, the NOx removal ratio could reach 97.6%. In addition, reaction conditions could affect the catalytic performance. When O2 concentration was 10%, CH4:NO ratio was no less than one, space velocity was lower than 23600 h-1 and NO initial concentration was no more than 700 ppm, In/H-Beta could exhibit superior catalytic activity. Moreover, the catalytic performances of In/H-Beta catalysts were discussed after enduring H2O or/and SO2. This novel strategy could open the door for selective catalytic reduction of NOx with CH4.

1α,25-Dihydroxyvitamin D3 up-regulates IL-34 expression in SH-SY5Y neural cells.

Vitamin D supplementation is regarded as a novel approach to treat Alzheimer's disease, but the underlying mechanism remains elusive. The cytokine IL-34 provides strong neuroprotective and survival signals in brain injury and neurodegeneration and could be an immunological mediator for the vitamin D-induced protection. The aim of this study was to investigate whether human IL-34 is up-regulated in neuronal cells by the hormonally active form of vitamin D, 1α,25-dihydroxyvitamin D3 [1α,25(OH)2D3]. We found that IL-34 was detectable in a variety of cell lines and its expression was strongly induced in SH-SY5Y neural cells in a dose- and time-dependent manner by 1α,25(OH)2D3 through the vitamin D receptor (VDR). Furthermore, we identified the core promoter of IL-34 gene and a VDR binding site (CGCCCT) that was required for 1α,25(OH)2D3-induced IL-34 expression. These findings suggest that the induction of IL-34 expression by 1α,25(OH)2D3 may constitute a mechanism that explains the protective function of vitamin D in Alzheimer's disease.

Interferon-stimulated gene factor 3 complex is required for the induction of sterile α motif and HD domain-containing protein 1 expression by interferon-α in SMMC-7721 cells.

Sterile α motif and HD domain-containing protein 1 (SAMHD1) is a novel intrinsic restriction factor that inhibits the replication of certain retroviruses and DNA viruses through its deoxynucleoside triphosphate triphosphohydrolase activity. A previous study by our group showed that SAMHD1 restrained hepatitis B virus replication and interferon (IFN)­α induced SAMHD1 expression in liver cells. However the mechanisms of SAMHD1 upregulation by IFN­α in liver cells have remained elusive. The present study demonstrated that IFN­α treatment increased SAMHD1 mRNA levels in SMMC­7721 cells in a time­dependent manner. Knockdown of STAT1 inhibited the induction of SAMHD1 expression by IFN­α in SMMC­7721 cells. STAT2 silencing also suppressed the induction of SAMHD1 expression by IFN­α in SMMC­7721 cells. Furthermore, the induction of SAMHD1 expression in SMMC­7721 cells by IFN­α was found to be dependent on IFN­regulatory factor 9 (IRF9). In conclusion, these results suggested that the interferon­stimulated gene factor 3 complex, which consists of STAT1, STAT2 and IRF9, is required for the induction of SAMHD1 expression by IFN-α in SMMC-7721 cells.

Inhibition of Hepatitis B virus replication by SAMHD1.

Sterile alpha motif and HD-domain containing protein 1 (SAMHD1) is a newly identified intracellular antiviral factor. By depleting the dNTPs pool of host cells to a low level that cannot support the efficient synthesis of viral cDNA, it restricts replication of some retroviruses. As a DNA virus, Hepatitis B virus (HBV) experiences a process of reverse transcription in its life cycle akin to that of retroviruses. However, whether SAMHD1 can restrict HBV replication in liver cells is unknown. Here, we reported that SAMHD1 expression was detectable in four liver cell lines. Exogenous expression of SAMHD1 in SMMC-7721 cells restrained HBV replication. Similarly, SAMHD1 impeded HBV replication in another liver cell line, BEL-7402. Remarkably, the catalytically inactive mutant, SAMHD1 HD/AA also hampered HBV replication. Additionally, HBV replication reduced SAMHD1 expression in HepG2 cells. Moreover, it was found that IFN-α induced expression of SAMHD1 in liver cells. Together, these findings suggested that IFN-α-inducible SAMHD1 inhibited HBV replication in liver cells.