Cleavage of the natural lignin of cotton-plant stems with thioacetic acid in the presence of anthraquinone

1986 ◽  
Vol 22 (1) ◽  
pp. 93-95
Author(s):  
E. N. Yanishevskaya ◽  
Kh. A. Abduazimov
Keyword(s):  
Cotton Plant ◽  
Thioacetic Acid ◽  
Natural Lignin

Cleavage of dioxane lignin of the cotton plant with thioacetic acid

1978 ◽  
Vol 14 (4) ◽  
pp. 430-431 ◽  
Author(s):  
L. S. Smirnova ◽  
Kh. A. Abduazimov
Keyword(s):  
Cotton Plant ◽  
Thioacetic Acid ◽  
Dioxane Lignin

Effects of Some Insecticide Formulations on Fruiting of the Cotton Plant 1

Crop Science ◽  
1964 ◽  
Vol 4 (1) ◽  
pp. 97-98 ◽  
Author(s):  
Bruce Roark ◽  
T. R. Pfrimmer ◽  
M. E. Merkl
Keyword(s):  
Cotton Plant

Preparation of Chloro and Sulfanyl Derivatives of 1-(2-Deoxy-4-C-hydroxymethyl-α-L-threo-Pentofuranosyl)uracil

1997 ◽  
Vol 62 (7) ◽  
pp. 1114-1127 ◽  
Author(s):  
Hubert Hřebabecký ◽  
Jan Balzarini ◽  
Antonín Holý
Keyword(s):  
Nucleophilic Substitution ◽  
Hydrogen Chloride ◽  
Thionyl Chloride ◽  
Sodium Methoxide ◽  
Thioacetic Acid ◽  
Uracil Derivatives ◽  
Derivatives Of ◽  
Hiv 1

3'-Chloro and 3'-acetylsulfanyl derivatives of 1-(2-deoxy-4-C-hydroxymethyl-α-L-threo-pentofuranosyl)uracil were prepared by reaction of 2,3'-anhydro-1-{5'-O-benzoyl-4'-C-[(benzoyloxy)methyl]-2'-deoxy-α-L-erythro-pentofuranosyl}uracil (3) with hydrogen chloride and thioacetic acid, respectively. The reaction with hydrogen chloride gave a mixture of N-1 and N-3 substituted uracil derivatives 12 and 14. Reaction of 1-{3-O-benzoyl-4-C-[(benzoyloxy)methyl]-2-deoxy-α-L-threo-pentofuranosyl}uracil (7) with thionyl chloride and subsequent debenzoylation afforded 1-(4-C-chloromethyl-2-deoxy-β-D-erythro-pentofuranosyl)uracil (19). Nucleophilic substitution with lithium thioacetate, followed by deacylation, converted 1-{3-O-benzoyl-4-C-[(benzoyloxy)methyl]-2-deoxy-5-O-p-toluenesulfonyl-α-L-threo-pentofuranosyl}uracil (9) into 1-(2-deoxy-4-C-sulfanylmethyl-β-D-erythro-pentofuranosyl)uracil (21). The obtained thiols were oxidized with iodine or air to give 1,1'-[disulfandiylbis(2,3-dideoxy-4-hydroxymethyl-α-L-threo-pentofuranose-3,1-diyl]di(pyrimidine-2,4-(1H,3H)-dione) (17) and 1,1'-[disulfandiylbis(2,5-dideoxy-4-hydroxymethyl-α-L-threo-pentofuranose-5,1-diyl]di(pyrimidine-2,4(1H,3H)-dione) (22). Reaction of 1-{3-acetylsulfanyl-5-O-methanesulfonyl-4-C-[(benzoyloxy)methyl]-2,3-dideoxy-α-L-threo-pentofuranosyl)}uracil (24) with methanolic sodium methoxide afforded 1-(3,5-anhydro-2,3-dideoxy-4-C-hydroxymethyl-3-sulfanyl-α-L-threo-pentofuranosyl)uracil (25). The same reagent was used in the preparation of 1-(3,5-anhydro-2-deoxy-4-C-hydroxymethyl-α-L-threo-pentofuranosyl)uracil (26) from 1-{4-C-[(benzoyloxy)methyl]-2-deoxy-5-O-p-toluenesulfonyl-α-L-threo-pentofuranosyl}uracil (8). From the series of 4'-substituted 2'-deoxyuridine derivatives, synthesized in this study, solely the 4'-chloromethyl derivative 19 and the oxetane derivative 26 exhibited an appreciable activity against HIV-1 and HIV-2.

scholarly journals Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models

2021 ◽  
Vol 13 (14) ◽  
pp. 2822
Author(s):  
Zhe Lin ◽  
Wenxuan Guo
Keyword(s):  
Deep Learning ◽  
Cotton Plant ◽  
Unmanned Aerial System ◽  
Learning Models ◽  
Training Images ◽  
Testing Dataset ◽  
Cotton Plants ◽  
Detection And Counting ◽  
Different Dimensions ◽  
The Mean

An accurate stand count is a prerequisite to determining the emergence rate, assessing seedling vigor, and facilitating site-specific management for optimal crop production. Traditional manual counting methods in stand assessment are labor intensive and time consuming for large-scale breeding programs or production field operations. This study aimed to apply two deep learning models, the MobileNet and CenterNet, to detect and count cotton plants at the seedling stage with unmanned aerial system (UAS) images. These models were trained with two datasets containing 400 and 900 images with variations in plant size and soil background brightness. The performance of these models was assessed with two testing datasets of different dimensions, testing dataset 1 with 300 by 400 pixels and testing dataset 2 with 250 by 1200 pixels. The model validation results showed that the mean average precision (mAP) and average recall (AR) were 79% and 73% for the CenterNet model, and 86% and 72% for the MobileNet model with 900 training images. The accuracy of cotton plant detection and counting was higher with testing dataset 1 for both CenterNet and MobileNet models. The results showed that the CenterNet model had a better overall performance for cotton plant detection and counting with 900 training images. The results also indicated that more training images are required when applying object detection models on images with different dimensions from training datasets. The mean absolute percentage error (MAPE), coefficient of determination (R2), and the root mean squared error (RMSE) values of the cotton plant counting were 0.07%, 0.98 and 0.37, respectively, with testing dataset 1 for the CenterNet model with 900 training images. Both MobileNet and CenterNet models have the potential to accurately and timely detect and count cotton plants based on high-resolution UAS images at the seedling stage. This study provides valuable information for selecting the right deep learning tools and the appropriate number of training images for object detection projects in agricultural applications.

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