Inhibitory effect of Turkish Rosmarinus officinalis L. on acetylcholinesterase and butyrylcholinesterase enzymes

In the current study, we have tested acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitory activity of the petroleum ether, ethyl acetate, chloroform, and methanol extracts, rosmarinic acid as well as the essential oil obtained from Rosmarinus officinalis L. growing in Turkey by a spectrophotometric method of Ellman using ELISA microplate-reader at 0.2, 0.5, and 1.0 mg/mL concentrations. In addition, quantification of rosmarinic acid, a common phenolic acid found in rosemary, was carried out by reversed-phase HPLC in the methanolic extract of the plant, which was found to have 12.21 ± 0.95% (122.1 ± 9.5 mg/g extract) of rosmarinic acid. Rosmarinic acid was also tested for its AChE and BChE inhibitory effect and found to cause 85.8% of inhibition against AChE at only 1.0 mg/mL. Besides, the essential oil was analyzed by GC–MS technique, which was shown to be dominated by 1,8-cineol (44.42%) and followed by α-pinene (12.57%).     

Fractionation of metals in street sediment samples by using the BCR sequential extraction procedure and multivariate statistical elucidation of the data

The concentrations of metals (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) in street sediment samples were determined by flame atomic absorption spectrometry (FAAS) using the modified BCR (the European Community Bureau of Reference) sequential extraction procedure. According to the BCR protocol for extracting the metals from the relevant target phases, 1.0 g of specimen of the sample was treated with 0.11 M acetic acid (exchangeable and bound to carbonates), 0.5M hydroxylamine hydrochloride (bound to iron- and manganese-oxides), and 8.8M hydrogen peroxide plus 1M ammonium acetate (bound to sulphides and organics), sequentially. The residue was treated with aqua regia solution for recovery studies, although this step is not part of the BCR procedure. The mobility sequence based on the sum of the BCR sequential extraction stages was: Cd approximately Zn ( approximately 90%)>Pb ( approximately 84%)>Cu ( approximately 75%)>Mn ( approximately 70%)>Co ( approximately 57%)>Ni ( approximately 43%)>Cr ( approximately 40%)>Fe ( approximately 17%). Enrichment factors as the criteria for examining the impact of the anthropogenic emission sources of heavy metals were calculated, and it was observed that the highest enriched elements were Cd, Pb, and Zn in the dust samples, average 190, 111, and 20, respectively. Correlation analysis (CA) and principal component analysis (PCA) were applied to the data matrix to evaluate the analytical results and to identify the possible pollution sources of metals. PCA revealed that the sampling area was mainly influenced from three pollution sources, namely; traffic, industrial, and natural sources. The results show that chemical sequential extraction is a precious operational tool. Validation of the analytical results was checked by both recovery studies and analysis of the standard reference material (NIST SRM 2711 Montana Soil).     

Synthesis of in situ N‐, S‐, and B‐doped few‐layer graphene by chemical vapor deposition technique and their superior glucose electrooxidation activity

At present, N‐, S‐, and B‐doped grapheme‐modified indium tin oxide (ITO) electrodes are produced and doping method effect on the glucose electrooxidation is investigated. Firstly, few‐layer graphene is produced by chemical vapor deposition (CVD) method. Then, N, S, and B doping is carried out after graphene produced by CVD to prepare N‐doped, B‐doped, and S‐doped few‐layer graphene. N, S, and B doping is carried out by two different ways as (a) doping after synthesis of few‐layer graphene and (b) in situ doping during few‐layer graphene production. These materials are characterized by X‐ray diffraction, scanning electron microscopy‐energy (SEM), Raman spectroscopy, and X‐ray photoelectron spectroscopy (XPS). One could note that graphene and nitrogen networks are clearly visible from SEM images. Raman spectra show that B, N, and S are doped on few‐layer graphene/ITO successfully. XPS results of graphene, N‐doped graphene, and in situ N‐doped graphene reveal that graphene and nitrogen atoms used in the preparation of the electrodes obtain mainly in their elemental state. Then, these N‐, S‐, B‐doped and in situ N‐, S‐, B‐doped few‐layer graphene materials are coated onto indium tin oxide (ITO) to obtain N‐, S‐, B‐doped and in situ N‐, S‐, B‐doped ITO electrodes for glucose (C₆H₁₂O₆) electrooxidation. C₆H₁₂O₆ electrooxidation measurements are investigated with cyclic voltammetry, chronoamperometry, and electrochemical impedance spectroscopy measurements. As a result, in situ N‐doped few‐layer graphene/ITO electrode displays the best C₆H₁₂O₆ electrooxidation activity with 9.12 mA.cm^?2 current density compared with other N‐, S‐, B‐doped graphene and in situ doped S and B grapheme‐modified ITO electrodes. Furthermore, this current density value for in situ N‐doped few‐layer graphene/ITO is highly above the values reported in the literature. In situ N‐doped few‐layer graphene/ITO electrode is a promising electrode for C₆H₁₂O₆ electrooxidation because it exhibits the best electrocatalytic activity, stability, and resistance compared with other electrodes.     

Specific localization of the respiratory alternative oxidase in meristematic and xylematic tissues from developing soybean roots and hypocotyls

We used tissue printing and specific immunostaining to examine the localization of the alternative oxidase (AOX) protein in correlation with measurements of AOX capacity. Selected root and hypocotyl regions were analyzed during the first 14 d of growth. It is shown that AOX protein is localized in the apical meristem and in developing xylem. The temporal pattern of expression is coincident with the evolution of AOX capacity. Data suggest that AOX expression is linked to xylem differentiation. Since heat is a major product of the alternative pathway, we speculate that thermogenesis is implicated in morphogenesis.     

Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

Object detection (OD) in remote sensing images (RSI) acts as a vital part in numerous civilian and military application areas, like urban planning, geographic information system (GIS), and search and rescue functions. Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions. The latest advancements in deep learning (DL) approaches permit the design of effectual OD approaches. This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection (AEODCNN-VD) model on Remote Sensing Images. The proposed AEODCNN-VD model focuses on the identification of vehicles accurately and rapidly. To detect vehicles, the presented AEODCNN-VD model employs single shot detector (SSD) with Inception network as a baseline model. In addition, Multiway Feature Pyramid Network (MFPN) is used for handling objects of varying sizes in RSIs. The features from the Inception model are passed into the MFPN for multiway and multiscale feature fusion. Finally, the fused features are passed into bounding box and class prediction networks. For enhancing the detection efficiency of the AEODCNN-VD approach, AEO based hyperparameter optimizer is used, which is stimulated by the energy transfer strategies such as production, consumption, and decomposition in an ecosystem. The performance validation of the presented method on benchmark datasets showed promising performance over recent DL models.