Al aire...! : (hojas dispersas)

Indice.Ded. autógr. del aut.Copia digital : Diputación de Málaga. Biblioteca Canovas del Castillo, 201

Bacterial laccases: some recent advances and applications

Laccases belong to the large family of multi-copper oxidases (MCOs) that couple the one-electron oxidation of substrates with the four-electron reduction of molecular oxygen to water. Because of their high relative non-specific oxidation capacity particularly on phenols and aromatic amines as well as the lack of requirement for expensive organic cofactors, they have found application in a large number of biotechnological fields. The vast majority of studies and applications were performed using fungal laccases, but bacterial laccases show interesting properties such as optimal temperature above 50 °C, optimal pH at the neutral to alkaline range, thermal and chemical stability and increased salt tolerance. Additionally, bacterial systems benefit from a wide range of molecular biology tools that facilitates their engineering and achievement of high yields of protein production and set-up of cost-effective bioprocesses. In this review we will provide up-to-date information on the distribution and putative physiological role of bacterial laccases and highlight their distinctive structural and biochemical properties, discuss the key role of copper in the biochemical properties, discuss thermostability determinants and, finally, review biotechnological applications with a focus on catalytic mechanisms on phenolics and aromatic amines.info:eu-repo/semantics/publishedVersio

Prevalence, antimicrobial susceptibility and risk factors associated with non-typhoidal Salmonella on Ugandan layer hen farms

Abstract Background Non-typhoidal Salmonella (NTS) are among the leading global foodborne pathogens and a significant public health threat. Their occurrence in animal reservoirs and their susceptibilities to commonly used antimicrobials are poorly understood in developing countries. The aim of this study was to estimate the prevalence, determine antimicrobial susceptibility and identify risk factors associated with NTS presence in laying hen farms in Uganda through a cross-sectional study. Results Pooled faecal samples were collected from 237 laying hen farms and these were analysed for NTS following standard laboratory procedures. In total, 49 farms (20.7%; 95% Confidence interval (CI): 15.6–25.6%) were positive for NTS presence. Altogether, ten Salmonella serotypes were identified among the confirmed 78 isolates, and the predominant serotypes were Salmonella Newport (30.8%), S. Hadar (14.1%), S. Aberdeen (12.8%), S. Heidelberg (12.8%), and S. Bolton (12.8%). Phenotypic antimicrobial resistance was detected in 45(57.7%) of the isolates and the highest resistance was against ciprofloxacin (50.0%) followed by sulphonamides (26.9%) and sulphamethoxazole/trimethoprim (7.7%). Resistance was significantly associated with sampled districts (p = 0.034). Resistance to three or more drugs, multi-drug resistance (MDR) was detected in 12 (15.4%) of the isolates, 9 (75%) of these were from Wakiso district. A multivariable logistic model identified large farm size (OR = 7.0; 95% CI: 2.5–19.8) and the presence of other animal species on the farm (OR = 5.9; 95% CI: 2.1–16.1) as risk factors for NTS prevalence on farms. Having a separate house for birds newly brought to the farms was found to be protective (OR = 0,4; 95% CI: 0.2–0.8). Conclusion This study has highlighted a high prevalence and diversity of NTS species in laying hen farms in Uganda and identified associated risk factors. In addition, it has demonstrated high levels of antimicrobial resistance in isolates of NTS. This could be because of overuse or misuse of antimicrobials in poultry production. Also importantly, the insights provided in this study justifies a strong case for strengthening One Health practices and this will contribute to the development of NTS control strategies at local, national and international levels

Improving orbit prediction via thermospheric density calibration

The uncertainty on Thermospheric Mass Density (TMD), as derived from atmospheric models, can reach extremely high values. This effect is noteworthy in Low Earth Orbit (LEO), where atmospheric drag is the main perturbing force, as well as the most uncertain. LEO harbours almost 18,000 space objects at the end of 2021, around 60% of the total space debris population, and the rate of growth is increasing every year. Increasing the accuracy of TMD models, and thus the uncertainty characterisation, is important to ensure space environment sustainability in this congested and contested region. Accurate TMD modelling is a decisive factor in all space applications below the exopause, from LEO mission design to Space Situational Awareness (SSA) service provision: from conjunction assessment to re-entry and fragmentation analysis To enhance empirical TMD models, atmospheric density observations derived from satellite measurements are assimilated.This paper presents a novel approach for assimilating thermospheric density observations into atmospheric models to improve the accuracy of orbit predictions in short- to medium- term propagations. First, Global Navigation Satellite System (GNSS) derived density data from Swarm satellites are ingested from the publicly available Level 2 data products of the European Space Agency (ESA). In a second step, density data is assimilated into the empirical model NRMLSISE-00, using Principal Component Analysis (PCA) to decompose into the main temporal and spatial modes, providing useful physical insight into the main variables driving the model. Thirdly, the model is tested on several cases, whose data was not assimilated, such as LEO satellites that are well-tracked with GNSS-derived positions: Sentinel, and GRACE. The model is also tested with objects with less accurate reference trajectories, such as catalogued space debris in LEO. Finally, the orbits are propagated, using the improved drag model that includes the neutral density from the assimilation of the GNSS-derived observations into NLRMSISE-00. The accuracy of the method is assessed and compared to non-assimilated models. During the discussion of the results, other sources of uncertainty are analysed. To name a few, geomagnetic activity, solar radiation pressure coefficient, attitude knowledge, and spacecraft parameters such as mass, area, drag coefficient, and so on. The improvement on the state accuracy and uncertainty realism after a medium-term propagation is analysed and the application to catalogue maintenance discussed.Astrodynamics & Space Mission

Improving orbit prediction via thermospheric density calibration

The uncertainty on Thermospheric Mass Density (TMD), as derived from atmospheric models, can reach extremely high values. This effect is noteworthy in Low Earth Orbit (LEO), where atmospheric drag is the main perturbing force, as well as the most uncertain. LEO harbours almost 18,000 space objects at the end of 2021, around 60% of the total space debris population, and the rate of growth is increasing every year. Increasing the accuracy of TMD models, and thus the uncertainty characterisation, is important to ensure space environment sustainability in this congested and contested region. Accurate TMD modelling is a decisive factor in all space applications below the exopause, from LEO mission design to Space Situational Awareness (SSA) service provision: from conjunction assessment to re-entry and fragmentation analysis To enhance empirical TMD models, atmospheric density observations derived from satellite measurements are assimilated.This paper presents a novel approach for assimilating thermospheric density observations into atmospheric models to improve the accuracy of orbit predictions in short- to medium- term propagations. First, Global Navigation Satellite System (GNSS) derived density data from Swarm satellites are ingested from the publicly available Level 2 data products of the European Space Agency (ESA). In a second step, density data is assimilated into the empirical model NRMLSISE-00, using Principal Component Analysis (PCA) to decompose into the main temporal and spatial modes, providing useful physical insight into the main variables driving the model. Thirdly, the model is tested on several cases, whose data was not assimilated, such as LEO satellites that are well-tracked with GNSS-derived positions: Sentinel, and GRACE. The model is also tested with objects with less accurate reference trajectories, such as catalogued space debris in LEO. Finally, the orbits are propagated, using the improved drag model that includes the neutral density from the assimilation of the GNSS-derived observations into NLRMSISE-00. The accuracy of the method is assessed and compared to non-assimilated models. During the discussion of the results, other sources of uncertainty are analysed. To name a few, geomagnetic activity, solar radiation pressure coefficient, attitude knowledge, and spacecraft parameters such as mass, area, drag coefficient, and so on. The improvement on the state accuracy and uncertainty realism after a medium-term propagation is analysed and the application to catalogue maintenance discussed.<br/

Effect of storage time at low temperature on the volatile compound composition of Sevillana and Maravilla raspberries

In this study, the effect of storage time at low temperature on volatile compounds in two cultivars of raspberry, Rubus idaeus L. cv. Sevillana and Maravilla, was determined. A total of 28 compounds were identified in both cultivars and showed quantitative differences between the cultivars. The Sevillana cultivar was richer in volatile compounds than the Maravilla cultivar. beta-Ionone had the highest concentration in both cultivars. We observed opposing trends in the volatile compound composition for the cultivars during storage at low temperature, in which 'Sevillana' lost compounds and 'Maravilla' was enriched. Therefore, storage at low temperature causes important changes in the volatile compound profile of raspberry, particularly the Sevillana cultivar, with significant decreases in C-13-norisoprenoids and increases in terpenes. These changes are most likely responsible for the aromatic differences between the cultivars because of the presence of terpenes in 'Sevillana' and C-13-norisoprenoids in 'Maravilla'. (C) 2014 Elsevier B.V. All rights reserved

Internalisation of hybrid titanium dioxide/para-amino benzoic acid nanoparticles in human dendritic cells did not induce toxicity and changes in their functions

Migdal, Camille Rahal, Raed Rubod, Alain Callejon, Sylvie Colomb, Evelyne Atrux-Tallau, Nicolas Haftek, Marek Vincent, Claude Serres, Mireille Daniele, StephaneInternational audienceNanoparticles (NPs) have been reported to penetrate into human skin through lesional skin or follicular structures. Therefore, their ability to interact with dendritic cell (DC) was investigated using DCs generated from monocytes (mono-DCs). Hybrid titanium dioxide/para-amino benzoic acid (TiO2/PABA) NPs did not induce any cell toxicity. NPs were internalised into DCs through macropinocytosis and not by a receptor-mediated mechanism. Confocal microscopy showed that NPs were not detected in the nucleus. These data are confirmed by electronic microscopy which demonstrated that hybrid NPs were rapidly in contact with cellular membrane and localised into cytoplasmic vesicles without colocalisation with clathrin-coated vesicles. Hybrid NPs did not induce CD86 or HLA-DR overexpression or cytokine secretion (IL-8 and TNF-alpha) indicating no DC activation. Internalisation of hybrid NPs did not modify DC response towards sensitisers such as nickel and thimerosal or LPS used as positive controls. Moreover, hybrid NPs did not induce any oxidative stress implicated in DC activation process. After mono-DC irradiation by ultraviolet A (UVA), hybrid NP-treated cells did not produce UVA-induced reactive oxygen species (ROS) and exhibited a better cell viability compared with UVA-irradiated control cells, suggesting a protecting effect of hybrid TiO2/PABA NPs against UVA-induced ROS. (C) 2010 Elsevier Ireland Ltd. All rights reserved