The reaction of 2-amino benzoic acid with 1,2-dichloroethane under reflux in methanol and KOH as a base to gave the precursor [H4L]. The precursor under reflux and drops of CH3COOH which reacted with (2mole) from salicycaldehyde in methanol to gave a new type N2O4 ligand [H2L], this ligand was reacted with (MCl2) Where [M= Co (II), Ni(II), Cu(II) and Zn(II)] in (1:1) ratio at reflux in methanol using KOH as a base, to give complexes of the general formula [M(L)]. All compounds have been characterized by spectroscopic methods [1H NMR ( just to the ligand), FTIR, uv-vis, atomic absorption], melting point, conductivity, chloride content, as well as magnetic susceptibility measurements. From the above data, the proposed molecular structure of [Co(L)], [Ni(L)], [Cu(L)] and[Zn(L)] complexes adopting an octahedral about this metal ions. The synthesized ligand, along with their metal complexes were screened for their in vitro antibacterial activity against ten local strains of E. coli as gram-negative bacteria in addition to ten strains of Salmonella typhi and to ten strains of Acinetobacter baumannii and Ten gram- positive bacteria utilizing for locally strains of Staphylococcus aureus, were tested also using the agar diffusion technique.
Today’s modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by usin
... Show MoreRecalcitrant adventitious root (AR) development is a major hurdle in propagating commercially important woody plants. Although significant progress has been made to identify genes involved in subsequent steps of AR development, the molecular basis of differences in apparent recalcitrance to form AR between easy-to-root and difficult-to-root genotypes remains unknown. To address this, we generated cambium tissue-specific transcriptomic data from stem cuttings of hybrid aspen, T89 (difficult-to-root) and hybrid poplar OP42 (easy-to-root), and used transgenic approaches to verify the role of several transcription factors in the control of adventitious rooting. Increased peroxidase activity was positively correlated with better rooting. We foun
... Show MoreChemotherapy is one of the most efficient methods for treating cancer patients. Chemotherapy aims to eliminate cancer cells as thoroughly as possible. Delivering medications to patients’ bodies through various methods, either oral or intravenous is part of the chemotherapy process. Different cell-kill hypotheses take into account the interactions of the expansion of the tumor volume, external drugs, and the rate of their eradication. For the control of drug usage and tumor volume, a model based smooth super-twisting control (MBSSTC) is proposed in this paper. Firstly, three nonlinear cell-kill mathematical models are considered in this work, including the log-kill, Norton-Simon, and hypotheses subject to parametric uncertainties and exo
... Show MoreCOVID 19 has spread rapidly around the world due to the lack of a suitable vaccine; therefore the early prediction of those infected with this virus is extremely important attempting to control it by quarantining the infected people and giving them possible medical attention to limit its spread. This work suggests a model for predicting the COVID 19 virus using feature selection techniques. The proposed model consists of three stages which include the preprocessing stage, the features selection stage, and the classification stage. This work uses a data set consists of 8571 records, with forty features for patients from different countries. Two feature selection techniques are used in