From a large number of bacterial samples collected from different hospital in Iraq in central  health laboratory ,only ten isolates were identified primary as Vibrio. A number of  morphology and biochemical test were carried out to complete this identification that showed all bacterial isolates were related to Vibrio cholerae .In this study  all Vibrio isolates were investigated for Bio typing and the result showed that all (10) isolate were related to (Eltor biotypes) .Also, the susceptibility test towards eight antibiotics were carried  out .

Results shows that  ciprofloxacin , Norfloxacin, Erythromycin, Ampicillin,  ceftriaxone  and Amikacin were the most effective

Sol-gel method was use to prepare Ag-SiO2 nanoparticles. Crystal structure of the nanocomposite was investigated by means of X-ray diffraction patterns while the color intensity was evaluated by spectrophotometry. The morphology analysis using atomic force microscopy showed that the average grain sizes were in range (68.96-75.81 nm) for all samples. The characterization of Ag-SiO2 nanoparticles were investigated by using Scanning Electron Microscopy (SEM). Ag-SiO2 NPs are highly stable and have significant effect on both Gram positive and negative bacteria. Antibacterial properties of the nanocomposite were tested with the use of Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) bacteria. The results have shown antibacteri

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi

A new ligand complexes have been synthesis from reaction of metal ions of Mn(II), Co(II), Ni(II), Cu(II), Zn(II), Cd(II), Hg(II), Pd(II) and Pt(II) with schiff base LH. 5-[(2-Hydroxy-naphthalen-1-ylmethylene)-amino]-2-phenyl-2,4-dihydro-pyrazol-3-one, this ligand was characterized by Fourier transform infrared (FTIR), UV-vis, 1H, 13CNMR, and mass spectra. All complexes were characterized by techniques micro analysis C.H.N, UV-vis and FTIR spectral studies, atomic absorption, chloride content, molar conductivity measurements and magnetic susceptibility. The ligand acts as bidentate, coordination through nitrogen atom from azomethin group and deprotonated phenolic oxygen atom. The spectroscopic and analytical measurements showed that

The ligand [Potassium (E)-(4-(((2-((1-(3-aminophenyl) ethylidene) amino)-4-oxo-1, 4-dihydropteridin-6-yl) methyl) amino) benzoyl)-L-glutamate] was prepared from the condensation reaction of folic acid with (3-aminoacetophenone) through Schiff reaction to give a new Schiff base ligand [H2L]. The ligand [H2L] was characterized by elemental analysis CHN, atomic absorption (AA),(FT-IR),(UV-Vis), TLC, ES mass (for spectroscopes), molar conductance, and melting point. The new Schiff base ligand [H2L], reacts with Mn (II), Co (II), Ni (II), Cu (II), Cr (III) and Cd (II) metal ions and (2-aminophenol),(metal: derivative ligand: 2-aminophenol) to give a series of new mixed complexes in the general formula:-K3 [M2 (HL)(HA) 2],(where M= Mn (II) and Cd