Formation of organic chloride in the treatment of textile dyeing sludge by Fenton system.

In the oxidation treatment of textile dyeing sludge, the quantitative and transformation laws of organic chlorine are not clear enough. Thus, this study mainly evaluated the treatment of textile dyeing sludge by Fenton and Fenton-like system from the aspects of the influence of Cl-, the removal of polycyclic aromatic hydrocarbons (PAHs) and organic carbon, and the removal and formation mechanism of organic chlorine. The results showed that the organic halogen in sludge was mainly hydrophobic organic chlorine, and the content of adsorbable organic chlorine (AOCl) was 0.30 mg/g (dry sludge). In the Fenton system with pH=3, 500 mg/L Cl-, 30 mmol/L Fe2+ and 30 mmol/L H2O2, the removal of phenanthrene was promoted by chlorine radicals (•Cl), and the AOCl in sludge solid phase increased to 0.55 mg/g (dry sludge) at 30 min. According to spectral analysis, it was found that •Cl could chlorinate aromatic and aliphatic compounds (excluding PAHs) in solid phase at the same time, and eventually led to the accumulation of aromatic chlorides in solid phase. Strengthening the oxidation ability of Fenton system increased the formation of organic chlorines in liquid and solid phases. In weak acidity, the oxidation and desorption of superoxide anion promoted the removal and migration of PAHs and organic carbon in solid phase, and reduced the formation of total organic chlorine. The Fenton-like system dominated by non-hydroxyl radical could realize the mineralization of PAHs, organic carbon and organic chlorines instead of migration. This paper builds a basis for the selection of sludge conditioning methods.

Panicle Ratio Network: streamlining rice panicle measurement by deep learning with ultra-high-definition aerial images in the field.

The heading date and effective tiller percentage are important traits in rice, and they directly affect plant architecture and yield. Both traits are related to the ratio of the panicle number to the maximum tiller number, referred to as the panicle ratio (PR). In this study, an automatic PR estimation model (PRNet) based on a deep convolutional neural network was developed. Ultra-high-definition unmanned aerial vehicle (UAV) images were collected from cultivated rice varieties planted in 2384 experimental plots in 2019 and 2020 and in a large field in 2021. The determination coefficient between estimated PR and ground-measured PR reached 0.935, and the root mean square error values for the estimations of the heading date and effective tiller percentage were 0.687 d and 4.84%, respectively. Based on the analysis of the results, various factors affecting PR estimation and strategies for improving PR estimation accuracy were investigated. The satisfactory results obtained in this study demonstrate the feasibility of using UAVs and deep learning techniques to replace ground-based manual methods to accurately extract phenotypic information of crop micro targets (such as grains per panicle, panicle flowering, etc.) for rice and potentially for other cereal crops in future research.

Gut microbiota-derived butyrate regulates gut mucus barrier repair by activating the macrophage/WNT/ERK signaling pathway.

Ulcerative colitis (UC) is majorly associated with dysregulation of the dynamic cross-talk among microbial metabolites, intestinal epithelial cells, and macrophages. Several studies have reported the significant role of butyrate in host-microbiota communication. However, whether butyrate provides anti-inflammatory profiles in macrophages, thus contributing to UC intestinal mucus barrier protection, has currently remained elusive. In the current study, we found that butyrate increased mucin production and the proportion of mucin-secreting goblet cells in the colon crypt in a macrophage-dependent manner by using clodronate liposomes. Furthermore, in vivo and in vitro studies were conducted, validating that butyrate facilitates M2 macrophage polarization with the elevated expressions of CD206 and arginase-1 (Arg1). In macrophages/goblet-like LS174T cells co-culture systems, butyrate-primed M2 macrophages significantly enhanced the expression of mucin-2 (MUC2) and SPDEF (goblet cell marker genes) than butyrate alone, while blockade of WNTs secretion or ERK1/2 activation significantly decreased the beneficial effect of butyrate-primed macrophages on goblet cell function. Additionally, the adoptive transfer of butyrate-induced M2 macrophages facilitated the generation of goblet cells and mucus restoration following dextran sulfate sodium (DSS) insult. Taken together, our results revealed a novel mediator of macrophage-goblet cell cross-talk associated with the regulation of epithelial barrier integrity, implying that the microbial metabolite butyrate may serve as a candidate therapeutic target for UC.

Commensal bacteria-derived extracellular vesicles suppress ulcerative colitis through regulating the macrophages polarization and remodeling the gut microbiota.

BACKGROUND: The extracellular vesicles (EVs) traffic constitutes an essential pathway of cellular communication. And the molecules in EVs produced by procaryotes help in maintaining homeostasis, addressing microbial imbalance and infections, and regulating the immune system. Despite the fact that Clostridium butyricum (C. butyricum) is commonly used for treating ulcerative colitis (UC), the potential role of C. butyricum-secreted EVs in commensals-host crosstalk remains unclear. RESULTS: Here, we performed flow cytometry, western blot, immunohistochemistry and 16S rRNA analysis to explore the role of C. butyricum-derived EVs on macrophage polarization and gut microbiota composition in a dextran sulfate sodium (DSS)-induced UC mouse model. The antibiotic cocktail-induced microbiome depletion and faecal transplantations were used to further investigate the mechanisms by which EVs regulate macrophage balance. Our findings showed that C. butyricum-derived EVs improved the remission of murine colitis and polarized the transformation of macrophages to the M2 type. Furthermore, C. butyricum-derived EVs restored gut dysbiosis and altered the relative abundance of Helicobacter, Escherichia-Shigella, Lactobacillus, Akkermansia and Bacteroides, which, in turn, faecal transplantations from EVs-treated mice relieved the symptoms of UC and improved the impact of EVs on the reprogramming of the M2 macrophages. CONCLUSION: C. butyricum-derived EVs could protect against DSS-induced colitis by regulating the repolarization of M2 macrophages and remodelling the composition of gut microbiota, suggesting the potential efficacy of EVs from commensal and probiotic Clostridium species against UC.

Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors.

A better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen. A Gaussian process regression (GPR) method with the sequential backward feature removal (SBBR) routine was used to identify the best combinations of vegetation indices (VIs) sensitive to wheat N indicators for different sensors. Wheat leaf N concentration (LNC), plant N concentration (PNC), and the nutrition index (NNI) were estimated by the VIs through parametric regression (PR), multivariable linear regression (MLR), and Gaussian process regression (GPR). The study results reveal that the optical fluorescence sensor provides more accurate estimates of winter wheat N status at a low-canopy coverage condition. The Dualex Nitrogen Balance Index (NBI) is the best leaf-level indicator for wheat LNC, PNC and NNI at the early wheat growth stage. At the early growth stage, Multiplex indices are the best canopy-level indicators for LNC, PNC, and NNI. At the late growth stage, ASD VIs provide accurate estimates for wheat N indicators. This study also reveals that the GPR with SBBR analysis method provides more accurate estimates of winter wheat LNC, PNC, and NNI, with the best VI combinations for these sensors across the different winter wheat growth stages, compared with the MLR and PR methods.

Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress.

Accurate and high-throughput phenotyping of the dynamic response of a large rice population to drought stress in the field is a bottleneck for genetic dissection and breeding of drought resistance. Here, high-efficiency and high-frequent image acquisition by an unmanned aerial vehicle (UAV) was utilized to quantify the dynamic drought response of a rice population under field conditions. Deep convolutional neural networks (DCNNs) and canopy height models were applied to extract highly correlated phenotypic traits including UAV-based leaf-rolling score (LRS_uav), plant water content (PWC_uav) and a new composite trait, drought resistance index by UAV (DRI_uav). The DCNNs achieved high accuracy (correlation coefficient R = 0.84 for modeling set and R = 0.86 for test set) to replace manual leaf-rolling rating. PWC_uav values were precisely estimated (correlation coefficient R = 0.88) and DRI_uav was modeled to monitor the drought resistance of rice accessions dynamically and comprehensively. A total of 111 significantly associated loci were detected by genome-wide association study for the three dynamic traits, and 30.6% of them were not detected in previous mapping studies using nondynamic drought response traits. Unmanned aerial vehicle and deep learning are confirmed effective phenotyping techniques for more complete genetic dissection of rice dynamic responses to drought and exploration of valuable alleles for drought resistance improvement.

Effects of non-viable Lactobacillus reuteri combining with 14-day standard triple therapy on Helicobacter pylori eradication: A randomized double-blind placebo-controlled trial.

BACKGROUND: Viable probiotics have shown effects on the eradication of Helicobacter pylori, but the role of non-viable probiotics in H. pylori eradication is unclear. This study aimed to evaluate the effects of non-viable Lactobacillus reuteri DSM17648 combining with 14-day standard triple therapy on H. pylori eradication. MATERIALS AND METHODS: Two hundred treatment-naive H. pylori-positive adult patients were randomized equally to receive non-viable L. reuteri DSM17648 (LR group) or placebo for 4 weeks, with the latter 2 weeks treated together with triple therapy. The Gastrointestinal Symptom Rating Scale (GSRS) was completed before and after treatment. Stool samples were collected for 16S rRNA gene sequencing at week0, week2, and week8. RESULTS: Eradication rates in the LR group and the placebo group were 81.8% and 83.7% in ITT analysis (p = 0.730), 86.2% and 87.2% in PP analysis (p = 0.830), respectively. After treatment, the mean GSRS score decreased significantly in the LR group as compared with the placebo group (1.9 ± 0.2 vs. 2.7 ± 0.3; p = 0.030). Significantly less patients in the LR group as compared with the placebo group reported abdominal distention (5.1% vs. 16.3%; p = 0.010) and diarrhea (11.1% vs. 23.5%; p = 0.022). The relative abundance of Proteobacteria phylum and Escherichia-Shigella genus in the placebo group was about 4.0-fold and 8.1-fold of that in the LR group at wk2, respectively. Significant changes of diversity and enhancements of Fusicatenibacter, Subdoligranulum, and Faecalibacterium were observed in the LR group compared with the placebo group. CONCLUSIONS: Supplementation of non-viable L. reuteri DSM17648 with triple therapy did not improve the eradication rate of H. pylori, but it helped to build up a beneficial microbial profile and reduced the frequencies of abdominal distention, diarrhea, and the GSRS score.

The appropriate cutoff gastric pH value for Helicobacter pylori eradication with bismuth-based quadruple therapy.

BACKGROUND: This study aimed to investigate whether an increased proton pump inhibitor (PPI) dose enhanced the efficacy of Helicobacter pylori (H. pylori) eradication and determine the appropriate cutoff intragastric pH value that could predict H. pylori eradication with bismuth-based quadruple therapy. MATERIALS AND METHODS: A total of 207 H. pylori infected, treatment naive patients were enrolled in this prospective, open-label, randomized controlled trial. Patients were randomly allocated into Eso40-group (esomeprazole 40 mg bid) and Eso20-group (esomeprazole 20 mg bid), and their CYP2C19 genotyping status was assessed. The 24-h intragastric pH monitoring on day 7 was performed, and percentage of time gastric pH ≥ 3, ≥4, ≥5, and ≥6 (pH holding time ratios; HTRs) were measured. H. pylori eradication was evaluated using 13 C-urea breath test. RESULTS: No significant difference in the eradication rates was observed between two groups. The median 24-h intragastric pH value was not significant different between two groups but the Eso40 Group had a significant higher pH4 HTRs (91.11% [95%CI: 87.50%-95.83%] vs. 95.83% [95.83%-100%]; p = .002). Additionally, the median 24-h intragastric pH value showed significantly difference between two groups in EM genotype (Eso20 Group 6.00 [95%CI; 5.75-6.15] vs. Eso40 Group 6.30 [6.05-6.30]; p = .019). Similar results were observed in pH4 HTRs. There were significant differences in intragastric pH value (6.10 [95%CI: 4.40-7.00] vs. 5.65 [4.85-5.95], p = .038) and in pH4 HTRs (96% [95%CI: 92.00%-96.00%] vs. 87.5% [67.00%-100.0%], p = .019) between eradication-successful and eradication-failed patients. Statistical analysis suggested that the median intragastric pH = 5.7 could identify the success of H. pylori eradication. CONCLUSIONS: Bismuth-based quadruple therapy resulted in high H. pylori eradication rates either in PPI standard or double doses. Double dose of esomeprazole is associated with better intragastric acid suppression. A median 24-h intragastric pH of 5.7 could be appropriate cutoff value for predicting the successful H. pylori eradication.

IHUP: An Integrated High-Throughput Universal Phenotyping Software Platform to Accelerate Unmanned-Aerial-Vehicle-Based Field Plant Phenotypic Data Extraction and Analysis.

With the threshold for crop growth data collection having been markedly decreased by sensor miniaturization and cost reduction, unmanned aerial vehicle (UAV)-based low-altitude remote sensing has shown remarkable advantages in field phenotyping experiments. However, the requirement of interdisciplinary knowledge and the complexity of the workflow have seriously hindered researchers from extracting plot-level phenotypic data from multisource and multitemporal UAV images. To address these challenges, we developed the Integrated High-Throughput Universal Phenotyping (IHUP) software as a data producer and study accelerator that included 4 functional modules: preprocessing, data extraction, data management, and data analysis. Data extraction and analysis requiring complex and multidisciplinary knowledge were simplified through integrated and automated processing. Within a graphical user interface, users can compute image feature information, structural traits, and vegetation indices (VIs), which are indicators of morphological and biochemical traits, in an integrated and high-throughput manner. To fulfill data requirements for different crops, extraction methods such as VI calculation formulae can be customized. To demonstrate and test the composition and performance of the software, we conducted case-related rice drought phenotype monitoring experiments. In combination with a rice leaf rolling score predictive model, leaf rolling score, plant height, VIs, fresh weight, and drought weight were efficiently extracted from multiphase continuous monitoring data. Despite the significant impact of image processing during plot clipping on processing efficiency, the software can extract traits from approximately 500 plots/min in most application cases. The software offers a user-friendly graphical user interface and interfaces for customizing or integrating various feature extraction algorithms, thereby significantly reducing barriers for nonexperts. It holds the promise of significantly accelerating data production in UAV phenotyping experiments.

Machine learning-based solution reveals cuproptosis features in inflammatory bowel disease.

Background: Cuproptosis, a new cell death mode, is majorly modulated by mitochondrial metabolism and protein lipoylation. Nonetheless, cuproptosis-related genes (CRGs) have not yet been thoroughly studied for their clinical significance and relationship with the immune microenvironment in inflammatory bowel disease (IBD). Methods: We screened CRGs that had a significant correlation with immune status, which was determined utilizing single-sample GSEA (ssGSEA) and Gene Expression Omnibus datasets (GSE75214). Furthermore, utilizing the R package "CensusClusterPlus", these CRGs' expression was used to obtain different patient clusters. Subsequently, gene-set enrichment analysis (GSEA), gene set variation analysis (GSVA), and CIBERSORT assessed the variations in the enrichment of gene function and the abundance of immune cell infiltration and immune functions across these clusters. Additionally, weighted gene co-expression network analysis (WGCNA) and analysis of differentially expressed genes (DEGs) were executed, and for the purpose of identifying hub genes between these clusters, the construction of protein-protein interaction (PPI) network was done. Lastly, we used the GSE36807 and GSE10616 datasets as external validation cohorts to validate the immune profiles linked to the expression of CRG. ScRNA-seq profiling was then carried out using the publicly available dataset to examine the CRGs expression in various cell clusters and under various conditions. Results: Three CRGs, PDHA1, DLD, and FDX1, had a significant association with different immune profiles in IBD. Patients were subsequently classified into two clusters: low expression levels of DLD and PDHA1, and high expression levels of FDX1 were observed in Cluster 1 compared to Cluster 2. According to GSEA, Cluster 2 had a close association with the RNA processes and protein synthesis whereas Cluster 1 was substantially linked to environmental stress response and metabolism regulations. Furthermore, Cluster 2 had more immune cell types, which were characterized by abundant memory B cells, CD4+ T memory activated cells, and follicular helper T cells, and higher levels of immune-related molecules (CD44, CD276,CTLA4 and ICOS) than Cluster 1. During the analysis, the PPI network was divided into three significant MCODEs using the Molecular Complex Detection (MCODE) algorithm. The three MCODEs containing four genes respectively were linked to mitochondrial metabolism, cell development, ion and amino acid transport. Finally, external validation cohorts validated these findings, and scRNA-seq profiling demonstrated diverse intestinal cellular compositions with a wide variation in CRGs expression in the gut of IBD patients. Conclusions: Cuproptosis has been implicated in IBD, with PDHA1, DLD, and FDX1 having the potential as immune biomarkers and therapeutic targets. These results offer a better understanding of the development of precise, dependable, and cutting-edge diagnosis and treatment of IBD.

Extracellular vesicles of Fusobacterium nucleatum compromise intestinal barrier through targeting RIPK1-mediated cell death pathway.

Microbial factors that mediate microbes-host interaction in ulcerative colitis (UC), a chronic disease seriously affecting human health, are not fully known. The emerging oncobacterium Fusobacterium nucleatum (Fn) secretes extracellular vesicles carrying several types of harmful molecules in the intestine which can alter microbes-host interaction, especially the epithelial homeostasis in UC. However, the mechanism is not yet clear. Previously, we isolated EVs by the ultracentrifugation of Fn culture media and characterized them as the potent inducer of pro-inflammatory cytokines. Here, we examined the mechanism in detail. We found that in macrophage/Caco-2 co-cultures, FnEVs significantly promoted epithelial barrier loss and oxidative stress damage, which are related to epithelial necroptosis caused by the activation of receptor-interacting protein kinase 1 (RIPK1) and receptor-interacting protein kinase 3 (RIPK3). Furthermore, FnEVs promoted the migration of RIPK1 and RIPK3 into necrosome in Caco2 cells. Notably, these effects were reversed by TNF-α neutralizing antibody or Necrostatin-1 (Nec-1), a RIPK1 inhibitor. This suggested that FADD-RIPK1-caspase-3 signaling is involved in the process. Moreover, the observed effects were verified in the murine colitis model treated with FnEVs or by adoptive transfer of FnEVs-trained macrophages. In conclusion, we propose that RIPK1-mediated epithelial cell death promotes FnEVs-induced gut barrier disruption in UC and the findings can be used as the basis to further investigate this disease.

Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks.

Rapeseed is an important oil crop in China. Timely estimation of rapeseed stand count at early growth stages provides useful information for precision fertilization, irrigation, and yield prediction. Based on the nature of rapeseed, the number of tillering leaves is strongly related to its growth stages. However, no field study has been reported on estimating rapeseed stand count by the number of leaves recognized with convolutional neural networks (CNNs) in unmanned aerial vehicle (UAV) imagery. The objectives of this study were to provide a case for rapeseed stand counting with reference to the existing knowledge of the number of leaves per plant and to determine the optimal timing for counting after rapeseed emergence at leaf development stages with one to seven leaves. A CNN model was developed to recognize leaves in UAV-based imagery, and rapeseed stand count was estimated with the number of recognized leaves. The performance of leaf detection was compared using sample sizes of 16, 24, 32, 40, and 48 pixels. Leaf overcounting occurred when a leaf was much bigger than others as this bigger leaf was recognized as several smaller leaves. Results showed CNN-based leaf count achieved the best performance at the four- to six-leaf stage with F-scores greater than 90% after calibration with overcounting rate. On average, 806 out of 812 plants were correctly estimated on 53 days after planting (DAP) at the four- to six-leaf stage, which was considered as the optimal observation timing. For the 32-pixel patch size, root mean square error (RMSE) was 9 plants with relative RMSE (rRMSE) of 2.22% on 53 DAP, while the mean RMSE was 12 with mean rRMSE of 2.89% for all patch sizes. A sample size of 32 pixels was suggested to be optimal accounting for balancing performance and efficiency. The results of this study confirmed that it was feasible to estimate rapeseed stand count in field automatically, rapidly, and accurately. This study provided a special perspective in phenotyping and cultivation management for estimating seedling count for crops that have recognizable leaves at their early growth stage, such as soybean and potato.

An index of non-sampling error in area frame sampling based on remote sensing data.

Agricultural areas are often surveyed using area frame sampling. Using non-updated area sampling frame causes significant non-sampling errors when land cover and usage changes between updates. To address this problem, a novel method is proposed to estimate non-sampling errors in crop area statistics. Three parameters used in stratified sampling that are affected by land use changes were monitored using satellite remote sensing imagery: (1) the total number of sampling units; (2) the number of sampling units in each stratum; and (3) the mean value of selected sampling units in each stratum. A new index, called the non-sampling error by land use change index (NELUCI), was defined to estimate non-sampling errors. Using this method, the sizes of cropping areas in Bole, Xinjiang, China, were estimated with a coefficient of variation of 0.0237 and NELUCI of 0.0379. These are 0.0474 and 0.0994 lower, respectively, than errors calculated by traditional methods based on non-updated area sampling frame and selected sampling units.

Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion.

To better understand the progression of cotton root rot within the season, time series monitoring is required. In this study, an improved spatial and temporal data fusion approach (ISTDFA) was employed to combine 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Different Vegetation Index (NDVI) and 10-m Sentinetl-2 NDVI data to generate a synthetic Sentinel-2 NDVI time series for monitoring this disease. Then, the phenology of healthy cotton and infected cotton was modeled using a logistic model. Finally, several phenology parameters, including the onset day of greenness minimum (OGM), growing season length (GLS), onset of greenness increase (OGI), max NDVI value, and integral area of the phenology curve, were calculated. The results showed that ISTDFA could be used to combine time series MODIS and Sentinel-2 NDVI data with a correlation coefficient of 0.893. The logistic model could describe the phenology curves with R-squared values from 0.791 to 0.969. Moreover, the phenology curve of infected cotton showed a significant difference from that of healthy cotton. The max NDVI value, OGM, GSL and the integral area of the phenology curve for infected cotton were reduced by 0.045, 30 days, 22 days, and 18.54%, respectively, compared with those for healthy cotton.

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery.

The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%. Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages.

Chemical cotton stalk destruction for maintenance of host-free periods for the control of overwintering boll weevil in tropical and subtropical climates.

In the Lower Rio Grande Valley (LRGV) of Texas, cotton regrows and produces fruit from undestroyed stalks throughout the winter, and in spring weevils from such locations become a serious threat. The success of the boll weevil eradication program, which was reintroduced in the LRGV in 2005, will be dependent on thorough stalk destruction following harvest. However, adverse weather conditions and conservation tillage often impede immediate and complete stalk destruction using typical tool implements, and alternative stalk control methods are needed. This study provides an examination of the efficacy for cotton stalk destruction of different herbicides (thifensulfuron-methyl + tribenuron-methyl, dicamba-diolamine, 2,4-D-dimethylammonium, flumioxazin, 2,4-DB-dimethylammonium and carfentrazone-ethyl) and their rates, spray volumes and application timings on shredded or standing cotton stalks after stripper or picker harvest. None of the tested herbicides, except 2,4-D-dimethylammonium, stopped post-harvest cotton regrowth and fruiting. 2,4-D-dimethylammonium sprayed once (0 or 7 days) after cotton was harvested at 1 lb AE acre(-1) (1.12 kg ha(-1)), in a spray volume of 10 gal water acre(-1) (93.5 L ha(-1)) with 5 mL L(-1) surfactant, was highly effective in stalk destruction (72-90%). The best results were achieved when the herbicide was applied immediately after the cotton was shredded, followed by standing stripper-harvested and standing picker-harvested cotton. 2,4-D-dimethylammonium applied twice, 0 and 14 (or 21) days after cotton harvest, was 100% effective in killing stalks, regardless of whether they were shredded or standing, or whether harvest was by stripper or picker. These findings showed that 2,4-D-dimethylammonium cotton stalk destruction eliminated food and reproductive opportunities for managing overwintering boll weevils [Anthonomus grandis grandis Boheman (Coleoptera: Curculionidae)].

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research.

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1-the summer 2015 and winter 2016 growing seasons-of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.

Multimodal cues drive host-plant assessment in Asian citrus psyllid (Diaphorina citri).

Asian citrus psyllid (Diaphorina citri) transmits the causal agent of Huanglongbing, a devastating disease of citrus trees. In this study we measured behavioral responses of D. citri to combinations of visual, olfactory, and gustatory stimuli in test arenas. Stimuli were presented to the psyllids in droplets or lines of an emulsified wax formulation in two different arena types in no-choice tests. First, when placed on a colored ring situated halfway between the center and perimeter of a petri dish, D. citri spent more time on yellow versus gray rings; however, this response disappeared when either gray or yellow wax droplets were applied. When the psyllids were presented with droplets scented with terpenes, the response to both scent and color was increased. The addition of a dilute (≍0.1 M) sucrose solution to the wax droplets increased the magnitude of D. citri responses. Next, groups of D. citri were placed on plastic laboratory film covering a sucrose solution, to mimic a leaf surface. Test stimuli were presented via two 'midribs' made from lines of emulsified wax formulation. Probing levels were measured as a function of color saturation and scent composition, and concentration. The test scents were based on qualitatively major volatiles emitted by Murraya paniculata (L.) Jack, Citrus aurantifolia (Christm.) Swingle, and C. sinensis (L.) Osbeck. The highest probing response was observed on the middle concentration (20-µl scent/10 ml wax formulation) of the C. aurantifolia-scented wax lines. Results indicate that there are interactive effects between the different sensory modalities in directing host-plant assessment behavior.

Combination of multispectral remote sensing, variable rate technology and environmental modeling for citrus pest management.

The Lower Rio Grande Valley (LRGV) of south Texas is an agriculturally rich area supporting intensive production of vegetables, fruits, grain sorghum, and cotton. Modern agricultural practices involve the combined use of irrigation with the application of large amounts of agrochemicals to maximize crop yields. Intensive agricultural activities in past decades might have caused potential contamination of soil, surface water, and groundwater due to leaching of pesticides in the vadose zone. In an effort to promote precision farming in citrus production, this paper aims at developing an airborne multispectral technique for identifying tree health problems in a citrus grove that can be combined with variable rate technology (VRT) for required pesticide application and environmental modeling for assessment of pollution prevention. An unsupervised linear unmixing method was applied to classify the image for the grove and quantify the symptom severity for appropriate infection control. The PRZM-3 model was used to estimate environmental impacts that contribute to nonpoint source pollution with and without the use of multispectral remote sensing and VRT. Research findings using site-specific environmental assessment clearly indicate that combination of remote sensing and VRT may result in benefit to the environment by reducing the nonpoint source pollution by 92.15%. Overall, this study demonstrates the potential of precision farming for citrus production in the nexus of industrial ecology and agricultural sustainability.