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Classifying images is one of the most important tasks in computer vision. Recently, the best performance for image classification tasks has been shown by networks that are both deep and well-connected. These days, most datasets are made up of a fixed number of color images. The input images are taken in red green blue (RGB) format and classified without any changes being made to the original. It is observed that color spaces (basically changing original RGB images) have a major impact on classification accuracy, and we delve into the significance of color spaces. Moreover, datasets with a highly variable number of classes, such as the PlantVillage dataset utilizing a model that incorporates numerous color spaces inside the same model, achieve great levels of accuracy, and different classes of images are better represented in different color spaces. Furthermore, we demonstrate that this type of model, in which the input is preprocessed into many color spaces simultaneously, requires significantly fewer parameters to achieve high accuracy for classification. The proposed model basically takes an RGB image as input, turns it into seven separate color spaces at once, and then feeds each of those color spaces into its own Convolutional Neural Network (CNN) model. To lessen the load on the computer and the number of hyperparameters needed, we employ group convolutional layers in the proposed CNN model. We achieve substantial gains over the present state-of-the-art methods for the classification of crop disease. [ABSTRACT FROM AUTHOR]

The release of industrial effluents into fresh water is a major contributor of water contamination. Therefore, it is highly desirable to develop a viable solution to this alarming issue to improve the quality of clean water. To enhance the photocatalytic efficiency and hence utilization ratio of CuO, a series of CuO–FeSe2 composites with different varying contents of FeSe2 were prepared through facile synthetic methods. X-ray diffraction (XRD) and energy-dispersive X-rays (EDX) analysis verified that the samples were successfully prepared without any contaminating phases. The average crystallite sizes of CuO and FeSe2 were computed to be 29 and 21 nm respectively. For the CuO–FeSe2 composites, the crystallite sizes varied between 25 and 27 nm. The morphology of CuO was comprised of needle-shaped structures, whereas that of FeSe2 was observed to be flower-shaped structures comprising of irregular crystals. A red shift in the bandgap of CuO–FeSe2 composites was observed with the increase in FeSe2 concentration. The prepared photocatalysts were utilized to evaluate their efficiency under visible light against Rhodamine B (RhB). Among all the prepared composites, CuO–75% FeSe2 revealed a remarkable performance and degraded 96% RhB in 180 min. Rate constants for the degradation of the dye were determined by applying the Langmuir–Hinshelwood model. [ABSTRACT FROM AUTHOR]

In recent times, Internet of Medical Things (IoMT) gained much attention in medical services and healthcare management domain. Since healthcare sector generates massive volumes of data like personal details, historical medical data, hospitalization records, and discharging records, IoMT devices too evolved with potentials to handle such high quantities of data. Privacy and security of the data, gathered by IoMT gadgets, are major issues while transmitting or saving it in cloud. The advancements made in Artificial Intelligence (AI) and encryption techniques find a way to handle massive quantities of medical data and achieve security. In this view, the current study presents a new Optimal Privacy Preserving and Deep Learning (DL)-based DiseaseDiagnosis (OPPDL-DD) in IoMT environment. Initially, the proposed model enables IoMT devices to collect patient data which is then preprocessed to optimize quality. In order to decrease the computational difficulty during diagnosis, Radix Tree structure is employed. In addition, ElGamal public key cryptosystem with Rat Swarm Optimizer (EIG-RSO) is applied to encrypt the data. Upon the transmission of encrypted data to cloud, respective decryption process occurs and the actual data gets reconstructed. Finally, a hybridized methodology combining Gated Recurrent Unit (GRU) with Convolution Neural Network (CNN) is exploited as a classification model to diagnose the disease. Extensive sets of simulations were conducted to highlight the performance of the proposed model on benchmark dataset. The experimental outcomes ensure that the proposed model is superior to existing methods under different measures. [ABSTRACT FROM AUTHOR]

Solanaceae is one of the highly diverse plant families of which Solanum is the largest genera (1700 species) containing several pharmacological properties like anticancer and antimicrobial. This motivated us to explore the anticancer (against HepG2, HEK-293, and MCF-7 cells) and antimicrobial (against S. aureus, E. coli, P. aeruginosa, and C. albicans) properties of S. schimperianum, S. villosum, S. coagulans, S. glabratum, S. incanum, and S. nigrum along with rutin estimation by UPLC-PDA method. Of the studied Solanum extracts, S. nigrum exhibited significant cytotoxic property against HepG2 (IC50: 20.4 μg/mL) and MCF-7 (IC50: 30.1 μg/mL); S. coagulans showed toxicity against HepG2 (IC50: 28.4 μg/mL) and HEK-293 cells (IC50: 25.7 μg/mL) compared to 5-Fluorouracil (standard). Compared to these, extracts of S. coagulans and S. glabratum exhibited relatively high antimicrobial potency (MIC: 0.4-1.6 mg/mL). Nonetheless, all Solanum extracts significantly reduced the biofilm against PAO1-strain. Rutin was detected in all extracts with the highest content (53.79 μg/mg) in S. coagulans that supported its strong antimicrobial and anticancer properties. Molecular docking analysis showing strong binding of rutin with human DNA and proteins (DNA Topoisomerase IIα and E. coli DNA gyrase B) supported the anticancer and antimicrobial activities of Solanum species. [ABSTRACT FROM AUTHOR]

Biomarker rutin was analyzed in methanol extracts of leaves of five different species of genus Ficus (Ficus carica, Ficus nitida, Ficus ingens, Ficus palmata and Ficus vasta) by NP-HPTLC (Method I) and RP- HPTLC methods (Method II). The development and validation for method I was carried out with silica gel 60F254 plates using EA: GAA: FA: H20 (10:1:1:2.5, v/v/v/v) as developing system. Method II was carried out on silica gel 60F254 RP-18 plates using mobile phase ACN: H20 (4:6 v/v). Both analyses were scanned at 305 nm and were found to give well resolved peak of rutin at Rf 0.28±0.01 and 0.68±0.03 for Method I and Method II, respectively. The percentage of rutin was found to be 0.51% & 0.66% in F. ingens, 0.24% & 0.54% in F. palmata and 0.14% & 0.17% in F. vasta by Method I & Method II, respectively. Method II (RP-HPTLC) was found to be more accurate, precise and sensitive than Method I. Method II can be used as an important tool for standardization and quality control of bulk drugs and in-process formulations of rutin. [ABSTRACT FROM AUTHOR]

Biomarker (3-amyrin was analyzed in the leaves of four different Acacia species (A. salicina, A. loreta, A. hamulosa and A. tortilis) grown in Kingdom of Saudi Arabia by a validated HPTLC method. The chromatography was performed on glass-backed silica gel 60 F254 HPTLC plates using solvents toluene: methanol (9:1, v/v) as mobile phase. The developed TLC plate was derivatized with anisaldehyde and scanned at 520nm. A sharp peak of (3-amyrin was found at Rf=0.58±0.01. The r2 and the linear regression equation for [3-amyrin was found to be 0.991 and 19.913X+107.803, respectively in the concentration range of 100-800ng. The percentage of P-amyrin was found to be maximum 2.70% w/w in A. tortilis, 1.85% w/w in A. loreta and 1.80% w/w in A. hamulosa while it was totally absent in A. salicina. This study conceives maiden reporting of quantification of p-amyrin in four different species of Acacia by validated HPTLC method. The developed method for the analysis of p-amyrin was proved to be reproducible by statistical analysis hence it can be employed for further analysis of P-amyrin in plasma, other biological fluids and in finished products available in the market. [ABSTRACT FROM AUTHOR]

A new bioactive oxygenated homoditerpenic compound along with one known compound from the antimicrobial active ethanol extract of leaves of an endemic plant Centaurothamnus maximus was isolated. The n- hexane, dichloromethane, ethyl acetate and ethanol fractions of C. maximus leaves were evaluated for their antimicrobial potential by using standard agar well diffusion method against various microorganisms viz. B. subtilis, S. aureus, E. coli, P. aeruginosa, C. albicans and M. smegmatis. The results revealed that only ethanol extract was active against all microbes except the fungus C. albicans. A new compound 2α, 3α -dihydroxy-8α -methoxy-15-hydroxy- methylene- pimar-5,9 (11)-diene (CM-1) was isolated along with a known compound α -D-xylose (CM-2) from ethanol extract by reverse phase (RP-18) column chromatography and 1D and 2D NMR (DEPT, COSY, HMBC and HSQC) aided by EIMS mass and IR spectra were used to establish the structure. CM-1 was found to be active against B. subtilis, S. aureus and M. smegmatis (P>0.005) at MIC 20 µg/ml. Findings of this study may provide a lead for synthesis of more potent antimicrobial agents to serve the humanity against multidrug-resistant bacterial infections. [ABSTRACT FROM AUTHOR]

Biomarker β-amyrin was analyzed in the leaves of four different Acacia species (A. salicina, A. loreta, A. hamulosa and A. tortilis) grown in Kingdom of Saudi Arabia by a validated HPTLC method. The chromatography was performed on glass-backed silica gel 60 F254 HPTLC plates using solvents toluene: methanol (9:1, v/v) as mobile phase. The developed TLC plate was derivatized with anisaldehyde and scanned at 520 nm. A sharp peak of β-amyrin was found at Rf=0.58±0.01. The r2 and the linear regression equation for β-amyrin was found to be 0.991 and 19.913X+107.803, respectively in the concentration range of 100-800 ng. The percentage of β-amyrin was found to be maximum 2.70% w/w in A. tortilis, 1.85% w/w in A. loreta and 1.80% w/w in A. hamulosa while it was totally absent in A. salicina. This study conceives maiden reporting of quantification of β-amyrin in four different species of Acacia by validated HPTLC method. The developed method for the analysis of β-amyrin was proved to be reproducible by statistical analysis hence it can be employed for further analysis of β-amyrin in plasma, other biological fluids and in finished products available in the market.