Chronic Suppurative Otitis Media in Cleft Palate: Microorganism Etiology and Susceptibilities

Objective: To investigate the microbial etiology of suppurative chronic otitis media (SCOM) in patients with complete cleft lip and palate and isolated cleft palate and to determine the sensitivity of isolated microorganisms to antibiotics by drug diffusion from impregnated discs in agar and the minimum inhibitory concentration of each drug to these microorganisms by drug dilution in agar. Design/Patients: Effusion samples of SCOM obtained from 40 patients with cleft lip and palate registered at the Hospital for Rehabilitation of Craniofacial Anomalies, University of Sao Paulo, at Bauru, Brazil, were bacteriologically analyzed by cultures. The isolated bacteria were submitted to an in vitro susceptibility test to clinically used drugs. Results: Positive cultures were obtained in 100% of studied cases. Among the 57 strains observed, the most frequent were Pseudomonas aeruginosa (35%), Staphylococcus aureus (15.5%), Enterococcus faecalis (14%), and Proteus mirabilis (12%). The frequency of Gram-negative bacilli (enterobacteriaceae and nonfermentative bacilli) was 67%. Pseudomonas aeruginosa presented the highest sensitivity to ciprofloxacin, and enterobacteriaceae exhibited the highest sensitivity to gentamicin. The strains of S. aureus and E. faecalis presented the highest sensitivity to imipenem and sulfamethoxazole/trimethoprim, respectively. Conclusion: Patients with cleft lip and palate presenting with SCOM exhibited 100% positive cultures, with the highest frequency of Pseudomonas and enterobacteriaceae. With regard to the action of antibiotics, imipenem was effective against the four species of isolated microorganisms, followed by ciprofloxacin, which was effective against 75% of isolated species.

Short-term effects of gamma ray bursts on Earth

The aim of the present work is to study the potential short-term atmospheric and biospheric influence of Gamma Ray Bursts on the Earth. We focus in the ultraviolet flash at planet`s surface, which occurs as a result of the retransmission of the gamma radiation through the atmosphere. This would be the only important short-term effect on life. We mostly consider Archean and Proterozoic eons, and for completeness we also comment on the Phanerozoic. Therefore, in our study we consider atmospheres with oxygen levels ranging from 10(-5) to 1 of the present atmospheric level, representing different moments in the oxygen rise history. Ecological consequences and some strategies to estimate their importance are outlined.

First-order-reversal-curve analysis of Pr-Fe-B-based exchange spring magnets

Ribbons of nominal composition (Pr(9.5)Fe(84.5)B(6))(0.96)Cr(0.01)(TiC)(0.03) were produced by arc-melting and melt-spinning the alloys on a Cu wheel. X-ray diffraction (XRD) reveals two main phases, one based upon alpha-Fe and the other upon Pr(2)Fe(14)B. The ribbons show exchange spring behavior with H (c) = 12.5 kOe and (BH)(max) = 13.6 MGOe when these two phases are well coupled. Transmission electron microscopy revealed the coupled behavior is observed when the microstructure consists predominantly of alpha-Fe grains (diameter similar to 100 nm.) surrounded by hard material containing Pr(2)Fe(14)B. The microstructure is discussed in terms of a calculation by Skomski and Coey. A first-order-reversal-curve (FORC) analysis was performed for both a well-coupled sample and a poorly coupled sample. The FORC diagrams show two strong peaks for both the poorly coupled sample and for the well-coupled material. In both cases, the localization of the FORC probability suggests magnetizing interactions between particles. Switching field distributions were calculated and are consistent with the sample microstructure.

First-order-reversal-curve analysis of Pr-Fe-B-based nanocomposites

Ribbons of nominal composition (Pr(9.5)Fe(84.5)B(6))(0.96)Cr(0.01)(TiC)(0.03) were produced by arc-melting and melt-spinning the alloys on a Cu wheel. X-ray diffraction reveals two main phases, one based upon alpha-Fe and the other upon Pr(2)Fe(14)B. The ribbons show exchange spring behavior with H(c)=12.5 kOe and (BH)(max)= 13.6 MGOe when these two phases are well coupled. Transmission electron microscopy revealed that the coupled behavior is observed when the microstructure consists predominantly of alpha-Fe grains(diameter similar to 100 nm.) surrounded by hard material containing Pr(2)Fe(14)B. A first-order-reversal-curve (FORC) analysis was performed for both a well-coupled sample and a partially-coupled sample. The FORC diagrams show two strong peaks for both the partially-coupled sample and for the well coupled material. In both cases, the localization of the FORC probability suggests demagnetizing interactions between particles. Switching field distributions were calculated and are consistent with the sample microstructure. (C) 2009 Elsevier B.V. All rights reserved.

Effect of Ti-C and Cr Additions on Magnetic Properties of Nanocrystalline (Pr,Nd)-Fe-B Alloys

The addition of both Ti-C and Cr as grain refiners in Nd-Fe-B nanocomposites substantially increases the coercive field Hc. This motived our investigation of the effect of Ti-C and Cr on Pr-Fe-B nanocomposites. Melt-spun ribbons of composition (Pr(9.5)Fe(84.5)B(6))(0.97-x)Cr(x)(TiC)(0.03)(x = 0; 0.25; 0.5; 0.75; 1) and (Nd(9.5)Fe(84.5)B(6))(0.97-x)Cr(x)(TiC)(0.03)(x = 0.5 and 1) were produced for study. For a Pr nanocomposite with 1% Cr, Hc = 12.5 kOe. However, the energy product was limited to 13.6 MGOe by the remanence value. Rietveld analysis of X-ray spectra showed the ribbons to consist of predominantly hard (similar to 70 wt%) R(2)Fe(14)B, the soft phase being (similar to 30 wt%) alpha-Fe. Mossbauer measurements at 300 K are consistent with a reduced hyperfine field for the hard magnetic phase due to the Cr addition. Analysis of transmission electron microscopy images showed the Pr nanocomposite with 1% Cr to have an increased average grain size.

MODELING THE EVOLUTION OF COMPLEX NETWORKS THROUGH THE PATH-STAR TRANSFORMATION AND OPTIMAL MULTIVARIATE METHODS

The topology of real-world complex networks, such as in transportation and communication, is always changing with time. Such changes can arise not only as a natural consequence of their growth, but also due to major modi. cations in their intrinsic organization. For instance, the network of transportation routes between cities and towns ( hence locations) of a given country undergo a major change with the progressive implementation of commercial air transportation. While the locations could be originally interconnected through highways ( paths, giving rise to geographical networks), transportation between those sites progressively shifted or was complemented by air transportation, with scale free characteristics. In the present work we introduce the path-star transformation ( in its uniform and preferential versions) as a means to model such network transformations where paths give rise to stars of connectivity. It is also shown, through optimal multivariate statistical methods (i.e. canonical projections and maximum likelihood classification) that while the US highways network adheres closely to a geographical network model, its path-star transformation yields a network whose topological properties closely resembles those of the respective airport transportation network.

Sensitivity of complex networks measurements

Complex networks obtained from real-world networks are often characterized by incompleteness and noise, consequences of imperfect sampling as well as artifacts in the acquisition process. Because the characterization, analysis and modeling of complex systems underlain by complex networks are critically affected by the quality and completeness of the respective initial structures, it becomes imperative to devise methodologies for identifying and quantifying the effects of the sampling on the network structure. One way to evaluate these effects is through an analysis of the sensitivity of complex network measurements to perturbations in the topology of the network. In this paper, measurement sensibility is quantified in terms of the relative entropy of the respective distributions. Three particularly important kinds of progressive perturbations to the network are considered, namely, edge suppression, addition and rewiring. The measurements allowing the best balance of stability (smaller sensitivity to perturbations) and discriminability (separation between different network topologies) are identified with respect to each type of perturbation. Such an analysis includes eight different measurements applied on six different complex networks models and three real-world networks. This approach allows one to choose the appropriate measurements in order to obtain accurate results for networks where sampling bias cannot be avoided-a very frequent situation in research on complex networks.

Modeling worldwide highway networks

This Letter addresses the problem of modeling the highway systems of different countries by using complex networks formalism. More specifically, we compare two traditional geographical models with a modified geometrical network model where paths, rather than edges, are incorporated at each step between the origin and the destination vertices. Optimal configurations of parameters are obtained for each model and used for the comparison. The highway networks of Australia, Brazil, India, and Romania are considered and shown to be properly modeled by the modified geographical model. (C) 2009 Elsevier B.V. All rights reserved.

Border trees of complex networks

The comprehensive characterization of the structure of complex networks is essential to understand the dynamical processes which guide their evolution. The discovery of the scale-free distribution and the small-world properties of real networks were fundamental to stimulate more realistic models and to understand important dynamical processes related to network growth. However, the properties of the network borders (nodes with degree equal to 1), one of its most fragile parts, remained little investigated and understood. The border nodes may be involved in the evolution of structures such as geographical networks. Here we analyze the border trees of complex networks, which are defined as the subgraphs without cycles connected to the remainder of the network (containing cycles) and terminating into border nodes. In addition to describing an algorithm for identification of such tree subgraphs, we also consider how their topological properties can be quantified in terms of their depth and number of leaves. We investigate the properties of border trees for several theoretical models as well as real-world networks. Among the obtained results, we found that more than half of the nodes of some real-world networks belong to the border trees. A power-law with cut-off was observed for the distribution of the depth and number of leaves of the border trees. An analysis of the local role of the nodes in the border trees was also performed.

Switching off the reservoir through nonstationary quantum systems

In this paper, we demonstrate that the inevitable action of the environment can be substantially weakened when considering appropriate nonstationary quantum systems. Beyond protecting quantum states against decoherence, an oscillating frequency can be engineered to make the system-reservoir coupling almost negligible. Differently from the program for engineering reservoir and similarly to the schemes for dynamical decoupling of open quantum systems, our technique does not require previous knowledge of the state to be protected. However, differently from the previously-reported schemes for dynamical decoupling, our technique does not rely on the availability of tailored external pulses acting faster than the shortest timescale accessible to the reservoir degree of freedom.

A pattern recognition approach to complex networks

Complex networks exist in many areas of science such as biology, neuroscience, engineering, and sociology. The growing development of this area has led to the introduction of several topological and dynamical measurements, which describe and quantify the structure of networks. Such characterization is essential not only for the modeling of real systems but also for the study of dynamic processes that may take place in them. However, it is not easy to use several measurements for the analysis of complex networks, due to the correlation between them and the difficulty of their visualization. To overcome these limitations, we propose an effective and comprehensive approach for the analysis of complex networks, which allows the visualization of several measurements in a few projections that contain the largest data variance and the classification of networks into three levels of detail, vertices, communities, and the global topology. We also demonstrate the efficiency and the universality of the proposed methods in a series of real-world networks in the three levels.

Spontaneous recoherence of quantum states after decoherence

In this work, we identify the set of time-dependent pure states building the statistical mixture to which a system, initially in a pure state, is driven by the reservoir. This set of time-dependent pure states, composing what we term a pure basis, are those that diagonalize the reduced density operator of the system. Next, we show that the evolution of the pure-basis states reveals an interesting phenomenon as the system, after decoherence, evolves toward the equilibrium: the spontaneous recoherence of quantum states. Around our defined recoherence time, the statistical mixture associated with a special kind of initial states termed even-symmetric, spontaneously undergoes a recoherence process, by which the initial state of the system emerges from the mixture except for its reduced excitation drained into the reservoir. This phenomenon reveals that the reservoir only shuffle the original information carried out by the initial state of the system instead of erasing it. Moreover, as the spontaneously recohered state occurs only for asymptotic time, we also present a protocol to extract it from the mixture through specific projective measurements. The password to retrieve the original information stems is the knowledge of both the initial state itself and the associated pure basis. A definition of the decoherence time of an N-state superposition is also presented.