RESUMEN

The rapid advancements in Agriculture 4.0 have led to the development of the continuous monitoring of the soil parameters and recommend crops based on soil fertility to improve crop yield. Accordingly, the soil parameters, such as pH, nitrogen, phosphorous, potassium, and soil moisture are exploited for irrigation control, followed by the crop recommendation of the agricultural field. The smart irrigation control is performed utilizing the Interactive guide optimizer-Deep Convolutional Neural Network (Interactive guide optimizer-DCNN), which supports the decision-making regarding the soil nutrients. Specifically, the Interactive guide optimizer-DCNN classifier is designed to replace the standard ADAM algorithm through the modeled interactive guide optimizer, which exhibits alertness and guiding characters from the nature-inspired dog and cat population. In addition, the data is down-sampled to reduce redundancy and preserve important information to improve computing performance. The designed model attains an accuracy of 93.11 % in predicting the minerals, pH value, and soil moisture thereby, exhibiting a higher recommendation accuracy of 97.12% when the model training is fixed at 90%. Further, the developed model attained the F-score, specificity, sensitivity, and accuracy values of 90.30%, 92.12%, 89.56%, and 86.36% with k-fold 10 in predicting the minerals that revealed the efficacy of the model.

RESUMEN

Continuous monitoring of glucose and sugar sensing plays a vital role in diabetes control. The drawbacks of the present enzyme-based sugar sensors have encouraged the investigation into alternate approaches to design new sensors. The popularity of fluorescence sensors is due to their ability to bind reversibly to compounds containing diol. In this study we investigated the binding ability of phenyl boronic acid P1 for monosaccharides and disaccharides (sugars) in aqueous medium at physiological pH 7.4 using steady-state fluorescence and absorbance. P1 fluorescence was quenched due to formation of esters with sugars. Absorbance and fluorescence measurements led to results that indicated that the sugars studied could be ordered in terms of their affinity to P1, as stated: sucrose > lactose > galactose > xylose > ribose > arabinose. In each case, the slope of modified Stern-Volmer plots was nearly 1, indicating the presence of only a single binding site in boronic acids for sugars. Docking studies were carried out using Schrodinger Maestro v.11.2 software. The binding affinity of phenyl boronic acid P1 with periplasmic protein (PDB ID 2IPM and 2IPL) was estimated using GlideScore.

Asunto(s)

Ácidos Borónicos , Azúcares , Simulación del Acoplamiento Molecular , Monosacáridos , Espectrometría de Fluorescencia

RESUMEN

A series of novel 2-(4-(acridin-9-ylamino)phenyl)isoindoline-1,3-dione derivatives were designed and synthesized namely 2-(4-(4-methoxyacridin-9-ylamino)phenyl)isoindoline-1,3-dione [S1], 2-(4-(3-chloroacridin-9ylamino)phenyl)isoindoline-1,3-dione [S2], 2-(4-(2-fluor oacridin-9ylamino)phenyl)isoindoline-1,3-dione [S3], 2-(4-(1,4-dichloroacridin-9ylamino) phenyl)isoindoline-1,3-dione [S4]. The photophysical, thermal properties of these compounds were characterized by the spectroscopic and thermographic method. The absorbance and fluorescence spectra of these derivatives were recorded in different solvents to understand the role of solute and solvent interaction. The compounds showed high thermal stability with thermal decomposition temperatures at 5% weight loss in a range of 250-287 °C. Compared with these compounds, donor groups containing derivatives exhibited excellent properties as fluorescent compounds. Computational studies were done using DFT (Density Functional Theory) G09 software (B3LYP/6-311G++V(d,p)) basis sets in order to calculate the optical band gap and FMO (Frontier Molecular Orbital) energies. The chemical stability of the four derivatives was determined by means of chemical hardness (η) using HOMO-LUMO energies.