907 resultados para imagined presence
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This is a report of a nine-year-old boy with both mitral stenosis and regurgitation and extensive endomyocardial fibrosis of the left ventricle. Focus is given to the singularity of the fibrotic process, with an emphasis on the etiopathogenic aspects.
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OBJECTIVE: To compare immediate and late results in patients with or without fenestration who underwent cavopulmonary anastomosis so that we could assess the efficiency of the technique. METHODS: Sixty-two patients underwent surgery between 1988 and 1999, 41 with fenestration (group I -G I) and 21 without fenestration (group II -G II). Tricuspid atresia was prevalent in group I (23-56%) and single ventricle was prevalent in group II (14-66%). Mean ages at the time of operation were 7.3 years in group I and 7.6 in group II. At late follow-up, mean ages were 10.6 years in group I and 12.8 years in group II. RESULTS: Immediate and late mortality were 7.3% in G-I and 4.7% in G-II. Significant pleural effusion occurred in 41.4% of G-I patients and in 23.8% of G-II patients. Significant pericardial effusion occurred in 29.2% and 14.2%, respectively, in groups I and II. Central venous pressure was greater in G-II, 17.7 cm in H2O, as opposed to 15 cm in G-I. Hospital stay was similar between the groups, 26.3 and 21.8 days, respectively. Cyanosis and arterial insaturation occurred in 5 patients, and 4 patients were in functional class II, all from G-I. At late follow-up, 58 (93.5%) were in functional class I. Sinus rhythm was present in 94%, and pulmonary perfusion was similar in both groups. Eleven patients who underwent spirometry had good tolerance to physical effort. CONCLUSION: Atrial fenestration did not improve the immediate or late follow-up of patients who underwent cavopulmonary anastomosis, and is, therefore, dispensable.
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სტატია მომზადდა ევროაკადემიის საერთაშორისო კონფერენციისთვის „აღმოსავლეთ ევროპის ხელახლა გამოგონება"
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Magdeburg, Univ., Fak. für Mathematik, Diss., 2014
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Psychodopygus wellcomei, a proven vector of (muco-)cutaneous leishmaniasis, has been found for the first time outside of the Amazon Basin, in Ceará State. Parasitological and entomological evidence suggests that the Leishmania braziliensis braziliensis/Ps. wellcomei zoonosis is widespread on the Brazilian Shield.
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Waveform tomographic imaging of crosshole georadar data is a powerful method to investigate the shallow subsurface because of its ability to provide images of pertinent petrophysical parameters with extremely high spatial resolution. All current crosshole georadar waveform inversion strategies are based on the assumption of frequency-independent electromagnetic constitutive parameters. However, in reality, these parameters are known to be frequency-dependent and complex and thus recorded georadar data may show significant dispersive behavior. In this paper, we evaluate synthetically the reconstruction limits of a recently published crosshole georadar waveform inversion scheme in the presence of varying degrees of dielectric dispersion. Our results indicate that, when combined with a source wavelet estimation procedure that provides a means of partially accounting for the frequency-dependent effects through an "effective" wavelet, the inversion algorithm performs remarkably well in weakly to moderately dispersive environments and has the ability to provide adequate tomographic reconstructions.
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1. Model-based approaches have been used increasingly in conservation biology over recent years. Species presence data used for predictive species distribution modelling are abundant in natural history collections, whereas reliable absence data are sparse, most notably for vagrant species such as butterflies and snakes. As predictive methods such as generalized linear models (GLM) require absence data, various strategies have been proposed to select pseudo-absence data. However, only a few studies exist that compare different approaches to generating these pseudo-absence data. 2. Natural history collection data are usually available for long periods of time (decades or even centuries), thus allowing historical considerations. However, this historical dimension has rarely been assessed in studies of species distribution, although there is great potential for understanding current patterns, i.e. the past is the key to the present. 3. We used GLM to model the distributions of three 'target' butterfly species, Melitaea didyma, Coenonympha tullia and Maculinea teleius, in Switzerland. We developed and compared four strategies for defining pools of pseudo-absence data and applied them to natural history collection data from the last 10, 30 and 100 years. Pools included: (i) sites without target species records; (ii) sites where butterfly species other than the target species were present; (iii) sites without butterfly species but with habitat characteristics similar to those required by the target species; and (iv) a combination of the second and third strategies. Models were evaluated and compared by the total deviance explained, the maximized Kappa and the area under the curve (AUC). 4. Among the four strategies, model performance was best for strategy 3. Contrary to expectations, strategy 2 resulted in even lower model performance compared with models with pseudo-absence data simulated totally at random (strategy 1). 5. Independent of the strategy model, performance was enhanced when sites with historical species presence data were not considered as pseudo-absence data. Therefore, the combination of strategy 3 with species records from the last 100 years achieved the highest model performance. 6. Synthesis and applications. The protection of suitable habitat for species survival or reintroduction in rapidly changing landscapes is a high priority among conservationists. Model-based approaches offer planning authorities the possibility of delimiting priority areas for species detection or habitat protection. The performance of these models can be enhanced by fitting them with pseudo-absence data relying on large archives of natural history collection species presence data rather than using randomly sampled pseudo-absence data.
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This study addresses the issue of the presence of a unit root on the growth rate estimation by the least-squares approach. We argue that when the log of a variable contains a unit root, i.e., it is not stationary then the growth rate estimate from the log-linear trend model is not a valid representation of the actual growth of the series. In fact, under such a situation, we show that the growth of the series is the cumulative impact of a stochastic process. As such the growth estimate from such a model is just a spurious representation of the actual growth of the series, which we refer to as a “pseudo growth rate”. Hence such an estimate should be interpreted with caution. On the other hand, we highlight that the statistical representation of a series as containing a unit root is not easy to separate from an alternative description which represents the series as fundamentally deterministic (no unit root) but containing a structural break. In search of a way around this, our study presents a survey of both the theoretical and empirical literature on unit root tests that takes into account possible structural breaks. We show that when a series is trendstationary with breaks, it is possible to use the log-linear trend model to obtain well defined estimates of growth rates for sub-periods which are valid representations of the actual growth of the series. Finally, to highlight the above issues, we carry out an empirical application whereby we estimate meaningful growth rates of real wages per worker for 51 industries from the organised manufacturing sector in India for the period 1973-2003, which are not only unbiased but also asymptotically efficient. We use these growth rate estimates to highlight the evolving inter-industry wage structure in India.
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The effects of structural breaks in dynamic panels are more complicated than in time series models as the bias can be either negative or positive. This paper focuses on the effects of mean shifts in otherwise stationary processes within an instrumental variable panel estimation framework. We show the sources of the bias and a Monte Carlo analysis calibrated on United States bank lending data demonstrates the size of the bias for a range of auto-regressive parameters. We also propose additional moment conditions that can be used to reduce the biases caused by shifts in the mean of the data.
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This paper proposes a model of choice that does not assume completeness of the decision maker’s preferences. The model explains in a natural way, and within a unified framework of choice when preference-incomparable options are present, four behavioural phenomena: the attraction effect, choice deferral, the strengthening of the attraction effect when deferral is per-missible, and status quo bias. The key element in the proposed decision rule is that an individual chooses an alternative from a menu if it is worse than no other alternative in that menu and is also better than at least one. Utility-maximising behaviour is included as a special case when preferences are complete. The relevance of the partial dominance idea underlying the proposed choice procedure is illustrated with an intuitive generalisation of weakly dominated strategies and their iterated deletion in games with vector payoffs.