The antigenic index: a novel algorithm for predicting antigenic determinants

In this paper, we introduce a computer algorithm which can be used to predict the topological features of a protein directly from its primary amino acid sequence. The computer program generates values for surface accessibility parameters and combines these values with those obtained for regional backbone flexibility and predicted secondary structure. The output of this algorithm, the antigenic index, is used to create a linear surface contour profile of the protein. Because most, if not all, antigenic sites are located within surface exposed regions of a protein, the program offers a reliable means of predicting potential antigenic determinants. We have tested the ability of this program to generate accurate surface contour profiles and predict antigenic sites from the linear amino acid sequences of well-characterized proteins and found a strong correlation between the predictions of the antigenic index and known structural and biological data

Direct observations of coherent backscatter of radar waves in precipitation

In previous work, it was argued that a source of radar coherent scatter occurs in the direction perpendicular to the direction of wave propagation because of the presence of grids of enhanced particle concentrations with spatial periodicities in resonance with the radar wavelength. While convincing, the evidence thus far has been indirect. In this work the authors now present direct observations of radar coherent backscattered signals in precipitation in the direction of wave propagation. The theory is developed for the cross-correlation function of the complex amplitudes in the direction of propagation calculated for nearest neighbor range bins. Data are analyzed in snow and in rain. The results agree with the earlier conclusions in the previous work, namely that coherent scatter occurs in both rain and snow, that it is larger in snow than it is in rain, and that it can be significant at times

Partially coherent backscatter in radar observations of precipitation

Classical radar theory only considers incoherent backscatter from precipitation. Can precipitation generate coherent scatter as well? Until now, the accepted answer has been no, because hydrometeors are distributed sparsely in space (relative to radar wavelength) so that the continuum assumption used to explain coherent scatter in clear air and clouds does not hold. In this work, a theory for a different mechanism is presented. The apparent existence of the proposed mechanism is then illustrated in both rain and snow. A new power spectrum Z( f ), the Fourier transform of the time series of the radar backscattered reflectivities, reveals statistically significant frequencies f of periodic components that cannot be ascribed to incoherent scatter. It is shown that removing those significant fs from Z( f ) at lower frequencies greatly reduces the temporal coherency. These lower frequencies, then, are associated with the increased temporal coherency. It is also shown that these fs are also directly linked to the Doppler spectral peaks through integer multiples of one-half the radar wavelength, characteristic of Bragg scatter. Thus, the enhanced temporal coherency is directly related to the presence of coherent scatter in agreement with theory. Moreover, the normalized backscattered power spectrum Z( f ) permits the estimation of the fractional coherent power contribution to the total power, even for an incoherent radar. Analyses of approximately 26 000 one-second Z( f ) in both rain and snow reveal that the coherent scatter is pervasive in these data. These findings present a challenge to the usual assumption that the scatter of radar waves from precipitation is always incoherent and to interpretations of backscattered power based on this assumption

The effect of clustering on the uncertainty of differential reflectivity measurements

One of the most important avenues of recent meteorological radar research is the application of polarization techniques to improve radar rainfall estimation. A keystone in many of these methods is the so-called differential reflectivity ZDR, the ratio of the reflectivity factor ZH at horizontal polarization backscattered from a horizontally polarized transmission to that corresponding to a vertically polarized transmission ZV. For such quantitative applications, it is important to understand the statistical accuracy of observations of ZDR. The underlying assumption of all past estimations of meteorological radar uncertainties is that the signals obey Rayleigh statistics. It is now evident, however, that as a radar scans, the meteorological conditions no longer always satisfy the requirements for Rayleigh statistics. In this work, ZDR is reconsidered, but this time within the new framework of non-Rayleigh signal statistics. Using Monte Carlo experiments, it is found that clustering of the scatterers multiplies the standard deviation of ZDR beyond what is always calculated assuming Rayleigh statistics. The magnitude of this enhancement depends on the magnitudes of the clustering index and of the cross correlation between ZHand ZV. Also, it does not depend upon the number of independent samples in an ensemble estimate. An example using real radar data in convective showers suggests that non-Rayleigh signal statistics should be taken into account in future implementations of polarization radar rainfall estimation techniques using ZDR. At the very least, it is time to begin to document the prevalence and magnitude of the clustering index in a wide variety of meteorological conditions

On the enhanced temporal coherency of radar observations in precipitation

In this work, the authors present observations of enhanced temporal coherency beyond that expected using the observations of the standard deviation of the Doppler velocities and the assumption of a family of exponentially decaying autocorrelation functions. The purpose of this paper is to interpret these observations by developing the complex amplitude autocorrelation function when both incoherent and coherent backscatter are present. Using this expression, it is then shown that when coherent scatter is present, the temporal coherency increases as observed. Data are analyzed in snow and in rain. The results agree with the theoretical expectations, and the authors interpret this agreement as an indication that coherent scatter is the likely explanation for the observed enhanced temporal coherency. This finding does not affect decorrelation times measured using time series. However, when the time series is not available (as in theoretical studies), the times to decorrelation are often computed based upon the assumptions that the autocorrelation function is a member of the family of exponentially decaying autocorrelation functions and that the signal decorrelation is due solely to the Doppler velocity fluctuations associated with incoherent scatter. Such an approach, at times, may significantly underestimate the true required times to decorrelation thus leading to overestimates of statistical reliability of parameters

An example of persistent microstructure in a long rain event

A 2D video disdrometer (2DVD) probe was used to gather detailed drop measurements over a 770-min rain event. Accumulated totals of the rainfall and of the number of drops for each square centimeter showed persistent, significant correlated structures across the approximately 11 cm × 11 cm grid of the 2DVD. This is surprising because larger-scale studies suggest that the values in each square centimeter should be highly correlated with very little variation. Nevertheless, this correlation remains strikingly similar to what is observed at a coarser resolution, suggesting that it somehow scales with spatial resolution. However, because the correlation functions are not power laws, the origin of this scaling must be due to a factor other than fractal geometry. Analysis reveals that this occurs because of a filtering effect such that as the domain size (or resolution of a remote sensor) becomes finer, it is only the smaller wavelengths that contribute most to the variance so that the correlation function also scales. Consequently, correlated finescale structures can apparently occur even over 10 cm. This fine structure was also found for the kinetic energy and impact power of the rain, important for understanding the initiation of soil erosion. The patterns in the integrated parameters appeared to arise almost exclusively from patterns in the total number of drops with patterns in the drop sizes playing an insignificant role. Therefore, in future studies of rain it is recommended that the total number of drops be retained as a crucial variable

On the variability of drop size distributions over areas

Past studies of the variability of drop size distributions (DSDs) have used moments of the distribution such as the mass-weighted mean drop size as proxies for the entire size distribution. In this study, however, the authors separate the total number of drops Nt from the DSD leaving the probability size distributions (PSDs); that is, DSD = Nt × PSD. The variability of the PSDs are then considered using the frequencies of size [P(D)] values at each different drop diameter P(PD | D) over an ensemble of observations collected using a network of 21 optical disdrometers. The relative dispersions RD of P(PD | D) over all the drop diameters are used as a measure of PSD variability. An intrinsic PSD is defined as an average over one or more instruments excluding zero drop counts. It is found that variability associated with an intrinsic PSD fails to characterize its true variability over an area. It is also shown that this variability is not due to sampling limitations but rather originates for physical reasons. Furthermore, this variability increases with the expansion of the network size and with increasing drop diameter. A physical explanation is that the network acts to integrate the Fourier transform of the spatial correlation function from smaller toward larger wavelengths as the network size increases so that the contributions to the variance by all spatial wavelengths being sampled also increases. Consequently, RDand, hence, PSD variability will increase as the size of the area increases

Ideologies of time: How elite corporate actors engage the future

Our paper deals with how elite corporate actors in a Western capitalist-democratic society conceive of and prepare for the future. Paying attention to how senior officers of ten important Danish companies make sense of the future will help us to identify how particular temporal narratives are ideologically marked. This ideological dimension offers a common sense frame that is structured around a perceived inevitability of capitalism, a market economy as the basic organizational structure of the social and economic order, and an assumption of confident access to the future. Managers envisage their organization?s future and make plans for organizational action in a space where ?business as usual? reigns, and there is little engagement with the future as fundamentally open; as a time-yet-to-come. In using a conceptual lens inspired by the work of Fredric Jameson, we first explore the details of this presentism and a particular colonization of the future, and then linger over small disruptions in the narratives of our interviewees which point to what escapes or jars their common sense frame, explore the implicit meanings they assign to their agency, and also find clues and traces of temporal actions and strategies in their narratives that point to a subtly different engagement with time