Endpoint Bias in Large Tn10-Catalyzed Inversions in Salmonella Typhimurium

A genetic strategy identified Salmonella typhimurium strains carrying large (>40 kb) Tn10-catalyzed inversions; the inverted segments were characterized by XbaI digestion and pulsed field gel electrophoresis. Two size classes of large inversions were found. More than half of the inversions extended 40-80 kb either clockwise or counterclockwise of the original Tn10 site. The remaining inversions extended up to 1620 kb (33% of the genome), but the distal endpoints of these inversions were not randomly scattered throughout the chromosome. Rather, each Tn10 repeatedly yielded similar (though not identical) inversions. The biased endpoint selection may reflect the limited search for target DNA sequences by the Tn10 transposase, and the spatial proximity of the donor and target regions in the folded S. typhimurium nucleoid. Using this interpretation, the data suggest that DNA sequences 40-80 kb clockwise and counterclockwise of the insertion site are in spatial proximity with the insertion, perhaps reflecting the organization of DNA into ~120-kb nucleoid domains. In addition, the data predict the spatial proximity of several distant DNA regions, including DNA sequences equidistant from the origin of DNA replication.     

Protective effect of hypothermia during ischemia in neural cell cultures

Hypothermia offers protection from the effects of ischemia in small animals. We have recently shown that similar to small animals, hypothermia may also be protective in an astrocytic model of simulated ischemia in cell culture. This study was designed to look at the protective effects of hypothermia in cultures of cerebellar granular (glutamatergic) and cortical (GABAergic) neurons. We used LDH release into the medium as an indicator for neuron damage. Experiments were all done in sister cultures, in groups of six cultures at two temperatures (37 and 32 degrees Celsius). The duration of ischemia was three hours in cerebellar granular neuronal cell cultures and six hours in cortical neurons. LDH release was measured immediately after the insult. Hypothermia protected both granular and cortical neurons. In granular cells, LDH release was 62+/–18 at 32 degrees and 212+/–15 at 37 degrees (p=0.02). Cortical neurons showed LDH release of 15+/–2 at 32 degrees and 32+/–2 at 37 degrees (p=0.005). Our study suggests that similar to astrocytes, the protective effects of hypothermia are evident in neuronal cell cultures from the cerebellum and the cerebral cortex. Cell culture systems should prove useful techniques in understanding mechanisms of hypothermic protection during simulated ischemia in neurons from different sites.     

Evolution of Socioeconomic Conditions and Its Relation to Spatial–Temporal Changes of Giardiasis and Helminthiasis in Amazonian Children

EcoHealth - This study analyzed the evolution of socioeconomic, sanitary, and personal factors as well as spatiotemporal changes in the prevalence of helminthiasis and giardiasis in urban Amazonian...     

Uncertainties regarding dengue modeling in Rio de Janeiro, Brazil

Dengue fever is currently the most important arthropod-borne viral disease in Brazil. Mathematical modeling of disease dynamics is a very useful tool for the evaluation of control measures. To be used in decision-making, however, a mathematical model must be carefully parameterized and validated with epidemiological and entomological data. In this work, we developed a simple dengue model to answer three questions: (i) which parameters are worth pursuing in the field in order to develop a dengue transmission model for Brazilian cities; (ii) how vector density spatial heterogeneity influences control efforts; (iii) with a degree of uncertainty, what is the invasion potential of dengue virus type 4 (DEN-4) in Rio de Janeiro city. Our model consists of an expression for the basic reproductive number (R0) that incorporates vector density spatial heterogeneity. To deal with the uncertainty regarding parameter values, we parameterized the model using a priori probability density functions covering a range of plausible values for each parameter. Using the Latin Hypercube Sampling procedure, values for the parameters were generated. We conclude that, even in the presence of vector spatial heterogeneity, the two most important entomological parameters to be estimated in the field are the mortality rate and the extrinsic incubation period. The spatial heterogeneity of the vector population increases the risk of epidemics and makes the control strategies more complex. At last, we conclude that Rio de Janeiro is at risk of a DEN-4 invasion. Finally, we stress the point that epidemiologists, mathematicians, and entomologists need to interact more to find better approaches to the measuring and interpretation of the transmission dynamics of arthropod-borne diseases.     

Endurance training increases FFA oxidation and reduces triacylglycerol utilization in contracting rat soleus

We examined the effects of 8 wk of intense endurance training on free fatty acid (FFA) transporters and metabolism in resting and contracting soleus muscle using pulse-chase procedures. Endurance training increased maximal citrate synthase activity in red muscles (+54 to +91%; P 相似文献   

Risk of acquiring Creutzfeldt-Jakob disease from blood transfusions: systematic review of case-control studies

ObjectiveTo determine the strength of association between history of blood transfusion and development of Creutzfeldt-Jakob disease.ConclusionsCase-control studies do not suggest a risk of developing Creutzfeldt-Jakob disease from blood transfusion. Rather, a trend seems to exist towards a lower frequency of previous blood transfusion in patients with Creutzfeldt-Jakob disease than in controls. However, it is important to be aware of these studies'' methodological limitations—primarily the choice of control population and reliability of recall of transfusion status.     

A Bayesian Hierarchical Model for Estimation of Abundance and Spatial Density of Aedes aegypti

Strategies to minimize dengue transmission commonly rely on vector control, which aims to maintain Ae. aegypti density below a theoretical threshold. Mosquito abundance is traditionally estimated from mark-release-recapture (MRR) experiments, which lack proper analysis regarding accurate vector spatial distribution and population density. Recently proposed strategies to control vector-borne diseases involve replacing the susceptible wild population by genetically modified individuals’ refractory to the infection by the pathogen. Accurate measurements of mosquito abundance in time and space are required to optimize the success of such interventions. In this paper, we present a hierarchical probabilistic model for the estimation of population abundance and spatial distribution from typical mosquito MRR experiments, with direct application to the planning of these new control strategies. We perform a Bayesian analysis using the model and data from two MRR experiments performed in a neighborhood of Rio de Janeiro, Brazil, during both low- and high-dengue transmission seasons. The hierarchical model indicates that mosquito spatial distribution is clustered during the winter (0.99 mosquitoes/premise 95% CI: 0.80–1.23) and more homogeneous during the high abundance period (5.2 mosquitoes/premise 95% CI: 4.3–5.9). The hierarchical model also performed better than the commonly used Fisher-Ford’s method, when using simulated data. The proposed model provides a formal treatment of the sources of uncertainty associated with the estimation of mosquito abundance imposed by the sampling design. Our approach is useful in strategies such as population suppression or the displacement of wild vector populations by refractory Wolbachia-infected mosquitoes, since the invasion dynamics have been shown to follow threshold conditions dictated by mosquito abundance. The presence of spatially distributed abundance hotspots is also formally addressed under this modeling framework and its knowledge deemed crucial to predict the fate of transmission control strategies based on the replacement of vector populations.     

Antidepressant-like profile of action of two 4-amine derivatives of 10,11-dihydro-5H-dibenzo [a,d] cycloheptane in mice evaluated in the forced swimming test

This study investigated the antidepressant-like effect of 4-amine derivatives of 10,11-dihydro-5H-dibenzo-alkylamine-cycloheptane, 4-amine (3-N,N-dimethylpropylamine)-10,11-dihydro-5H-dibenzo[a,d]cycloheptane-5-one (ADDCH1) and 1,2,3,4,8,9-hexahydro-dibenzocyclohepta[4,4a,5-ef]1,4-diazepin (ADDCH2), in a validated experimental model of depression, the forced swimming test (FST) in mice. Female adult mice were sub-chronically (three doses in 24 h) or repeatedly (once a day for 10 days) treated with either of the compounds and evaluated in the FST. The sub-chronic treatment promoted a dose-dependent reduction in the immobility time in the FST with the doses of 50 mg/kg (ADDCH1) and 30 mg/kg (ADDCH2) ip being the most effective (33% and 37% of reduction, respectively). A similar profile of action was observed in the animals repeatedly treated with ADDCH1 50 mg/kg or ADDCH2 30 mg/kg ip (for 10 days) and there was no sign of motor impairment or locomotor activation as evaluated in the rota-rod and open-field tests, respectively. These findings suggest that these amine derivatives of the system dibenzocycloheptane have an antidepressant-like action which could be of clinical interest and, therefore, deserves further investigation. In addition, putative underlying mechanisms of action are discussed.     

A Bayesian framework for parameter estimation in dynamical models

Mathematical models in biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real system. Proper handling of such uncertainties is key to the successful usage of models to predict experimental or field observations. This problem has been addressed over the years by many tools for model calibration and parameter estimation. In this article we present a general framework for uncertainty analysis and parameter estimation that is designed to handle uncertainties associated with the modeling of dynamic biological systems while remaining agnostic as to the type of model used. We apply the framework to fit an SIR-like influenza transmission model to 7 years of incidence data in three European countries: Belgium, the Netherlands and Portugal.