A systematic study of the structural and magnetic properties of Mn-, Co-, and Ni-Doped Colloidal Fe3O4 nanoparticles

A series of colloidal MxFe3-xO4 (M = Mn, Co, Ni; x = 0–1) nanoparticles with diameters ranging from 6.8 to 11.6 nm was synthesized by hydrothermal reaction in aqueous medium at low temperature (200 °C). Energy-dispersive X-ray microa-nalysis and inductively coupled plasma spectrometry confirms that the actual elemental compositions agree well with the nominal ones. The structural properties of obtained nanoparticles were investigated by using powder X-ray diffraction, Raman scattering, Mössbauer spectroscopy, and electron microscopy. The results demonstrate that our synthesis technique leads to the formation of chemically uniform single-phase solid solution nanoparticles with cubic spinel structure, confirming the intrinsic doping. Magnetic studies showed that, in comparison to Fe3O4, the saturation magnetization of MxFe3-xO4 (M = Mn, Ni) decreases with increasing dopant concentration, while Co-doped samples showed similar saturation magnetizations. On other hand, whereas Mn- and Ni-doped nanoparticles exhibits superparamagnetic behavior at room temperature, ferromagnetism emerges for CoxFe3-xO4 nanoparticles, which can be tuned by the level of Co doping.

Upgrade of an industrial building: BIM model of the as-built situation and evaluation of modifications

Dissertação de mestrado integrado in Civil Engineering

Structural and enzymatic characterization of ABgp46, a novel phage endolysin with broad anti-Gram-negative bacterial activity

The present study demonstrates the antibacterial potential of a phage endolysin against Gram-negative pathogens, particularly against multidrug resistant strains of Acinetobacter baumannii. We have cloned, heterologously expressed and characterized a novel endolysin (ABgp46) from Acinetobacter phage vb_AbaP_CEB1 and tested its antibacterial activity against several multidrug-resistant A. baumannii strains. LC-MS revealed that ABgp46 is an N-acetylmuramidase, that is also active over a broad pH range (4.0-10.0) and temperatures up to 50°C. Interestingly, ABgp46 has intrinsic and specific anti-A. baumannii activity, reducing multidrug resistant strains by up to 2 logs within 2 hours. By combining ABgp46 with several organic acids that act as outer membrane permeabilizing agents, it is possible to increase and broaden antibacterial activity to include other Gram-negative bacterial pathogens. In the presence of citric and malic acid, ABgp46 reduces A. baumannii below the detection limit (> 5 log) and more than 4 logs P. aeruginosa and Salmonella Typhimurium strains. Overall, this globular endolysin exhibits a broad and high activity against Gram-negative pathogens, that can be enhanced in presence of citric and malic acid, and be used in human and veterinary medicine.

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.