bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pathologic Characteristics of Cancers Detected in the Prostate Cancer Prevention Trial: Implications for Prostate Cancer Detection and Chemoprevention
Authors: Lucia M. Scott , Darke Amy K. , Goodman Phyllis J. , La Rosa Francisco G. , Parnes Howard L. , Ford Leslie G. , Coltman Charles A. , Thompson Ian M. 
Published: May 2008

Abstract
The Prostate Cancer Prevention Trial (PCPT) showed a risk of prostate cancer at prostate-specific antigen (PSA) &lt;4.0 ng/mL and that prostate cancer risk is reduced by finasteride. A major concern about early detection by PSA and prevention by finasteride is that they may involve biologically inconsequential tumors. We reviewed the pathologic characteristics of prostate biopsies from men in the placebo and finasteride groups of the PCPT. We examined tumor pathology characteristics stratified by level of PSA for men in the placebo group who underwent radical prostatectomy. Seventy-five percent of all cancers and 62% of Gleason score ≤6 cancers in the PCPT met the biopsy criteria for clinically significant tumors. Surrogate measures for tumor volume (number of cores positive, percent cores positive, linear extent, and bilaterality) and risk of perineural invasion were lower in men who received finasteride. The PSA-associated risks of insignificant cancer were 51.7% (PSA, 0-1.0 ng/mL), 33.7% (1.1-2.5 ng/mL), 17.8% (2.6-4.0 ng/mL), and 11.7% (4.1-10 ng/mL). Conversely, the risks of high-grade (Gleason score ≥7) tumors for the same PSA strata were 15.6%, 37.9%, 49.1%, and 52.4%, respectively. These data highlight the dilemma of PSA when used for screening: Lower cutoff levels increase detection of insignificant disease, but cure is more likely, whereas higher cutoff levels make detection of significant cancer more likely, but cure is less likely. Therefore, the effectiveness of finasteride in preventing prostate cancer, including Gleason score ≤6 cancer, with meaningful rates of significant disease in the PCPT suggests that cutoff values for PSA screening should be individualized and that men undergoing screening should be informed of the opportunity to reduce their risk of disease with finasteride.
Fine-tuned: Phonology and Semantics Affect First- to Second-language Zooming In
Authors: Elston-Güttler Kerrie E. , Gunter Thomas C. 
Published: May 2008

Abstract
We investigate how L1 phonology and semantics affect processing of interlingual homographs by manipulating language context before, and auditory input during, a visual experiment in the L2. Three experiments contained German–English homograph primes (gift = German “poison”) in English sentences and was performed by German (L1) learners of English (L2). Both reaction times and event-related brain potentials were measured on targets reflecting the German meaning of the interlingual homograph. In Experiment 1, participants viewed a pre-experiment English film, then half of the participants (n = 16) heard noise and the other half (n = 16) heard German pseudowords during the experiment; in Experiment 2, participants (n = 16) viewed a pre-experiment German film then heard noise; and in Experiment 3, participants (n = 16) viewed the pre-experiment English film then heard real German words. Those who had viewed the English film then heard noise during Experiment 1 showed no L1 influence. Those who saw the English film but heard German pseudowords during Experiment 1, or viewed the German film before and heard noise during Experiment 2, showed L1 influence as indicated by N400 priming of L1-related targets in the first half of the experiment. This suggests that a pre-experiment film in the L1 or the presence of L1 phonology during the experiment slowed down adjustment to the L2 task. In Experiment 3 with real L1 words in the background, N400 priming of L1 meanings was observed throughout the entire experiment for lower-proficiency participants. We discuss our findings in terms of context types that affect L1-to-L2 adjustment.
Elevated Cloud and Aerosol Layer Retrievals from Micropulse Lidar Signal Profiles
Authors: Campbell James R. , Sassen Kenneth , Welton Ellsworth J. 
Published: May 2008

Abstract
A threshold-based detection algorithm for cloud and aerosol layer heights in elevated micropulse lidar data (0.523 μm) is described. Thresholds for differentiating cloud and aerosol signals from that of the molecular atmosphere are based on the signal uncertainties of the level 1.0 Micropulse Lidar Network (MPLNET) data product. To illustrate the algorithm, data from 1 to 10 June 2003 collected by an MPLNET instrument at the South Pole are discussed for polar stratospheric cloud-height retrievals. Additional tests are run for algorithm sensitivity relative to variable solar background scenes. The algorithm is run at multiple temporal resolutions. Results derived at a base resolution are used to screen attenuation-limited profiles from longer time averages to improve performance. A signal normalization step using a theoretical molecular scattering profile limits the application of the technique in the lower atmosphere for a ground-based instrument. This would not be the case for some nadir-viewing lidars, and the application of the algorithm to airborne and satellite datasets is speculated.
Microprocessor Field Impactometer Calibration: Do We Measure Drops’ Momentum or Their Kinetic Energy?
Authors: Licznar Paweł , Łomotowski Janusz , Błoński Sławomir , Ciach Grzegorz J. 
Published: May 2008

Abstract
This study presents the construction and calibration of a low-cost piezoelectric microprocessor impactometer designed for the field measurements of the rainfall kinetic energy (KE) flux. Its precise calibration was performed in laboratory conditions using waterdrops of different sizes and fall velocities. High-speed photography was applied to measure the velocity of each waterdrop. Although the impactometer constructed for this study is not able to measure the momentum of waterdrops, its accuracy for measuring their KE is excellent. It was found that the processing of the piezoelectric signal might determine which physical quantity is measured by different impactometers. It was also found that the distance between the waterdrop impact position and the impactometer center has a significant effect on the sensor output. A scheme to account for this effect is developed in this study, and the calibration curve for field applications of the impactometer is derived. In addition, an example comparison of the concurrent field measurements of KE flux using the impactometer and rainfall rates using a weighing rain gauge is given.
Evaluation of Ultrasonic Snow Depth Sensors for U.S. Snow Measurements
Authors: Ryan Wendy A. , Doesken Nolan J. , Fassnacht Steven R. 
Published: May 2008

Abstract
Ultrasonic snow depth sensors are examined as a low cost, automated method to perform traditional snow measurements. In collaboration with the National Weather Service, nine sites across the United States were equipped with two manufacturers of ultrasonic depth sensors: the Campbell Scientific SR-50 and the Judd Communications sensor. Following standard observing protocol, manual measurements of 6-h snowfall and total snow depth on ground were also gathered. Results show that the sensors report the depth of snow directly beneath on average within ±1 cm of manual observations. However, the sensors tended to underestimate the traditional total depth of snow-on-ground measurement by approximately 2 cm. This is mainly attributed to spatial variability of the snow cover caused by factors such as wind scour and wind drift.
After assessing how well the sensors represented the depth of snow on the ground, two algorithms were created to estimate the traditional measurement of 6-h snowfall from the continuous snow depth reported by the sensors. A 5-min snowfall algorithm (5MSA) and a 60-min snowfall algorithm (60MSA) were created. These simple algorithms essentially sum changes in snow depth using 5- and 60-min intervals of change and sum positive changes over the traditional 6-h observation periods after compaction routines are applied. The algorithm results were compared to manual observations of snowfall. The results indicated that the 5MSA worked best with the Campbell Scientific sensor. The Campbell sensor appears to estimate snowfall more accurately than the Judd sensor due to the difference in sensor resolution. The Judd sensor results did improve with the 60-min snowfall algorithm. This technology does appear to have potential for collecting useful and timely information on snow accumulation, but determination of snowfall to the current requirement of 0.1 in. (0.25 cm) is a difficult task.
An In-Flight Calibration Method for Near-Real-Time Humidity Measurements with the Airborne MOZAIC Sensor
Authors: Smit Herman G. J. , Volz-Thomas Andreas , Helten Manfred , Paetz Werner , Kley Dieter 
Published: May 2008

Abstract
A new in-flight calibration (IFC) method is described for the humidity sensor flown routinely since 1994 on the Measurement of Ozone and Water Vapor by Airbus In-Service Aircraft (MOZAIC) program’s aircraft. The IFC method corrects the potential drift of the sensor offset at zero relative humidity, which is the critical parameter in determining the uncertainty of the measurements. The sensor offset is determined from the measurements themselves as obtained during periods when the aircraft is flying in the lower stratosphere at or above the hygropause, where the H2O mixing ratio reaches well-defined minimum values of about 5 ppmv and the contribution of atmospheric H2O to the sensor signal is minimal. The selection of stratospheric data is achieved with the help of potential temperature, which can be calculated in situ from measured temperature and pressure. The IFC method is capable of providing humidity measurements in near–real time with an uncertainty of ±8% RH at the surface and ±7% RH in the upper troposphere.
For validation, the IFC method was applied to 5 yr of archived raw signals from the MOZAIC aircraft. The resulting humidity data are in good agreement (within 2% RH) with the original MOZAIC data that used monthly pre- and postflight calibrations of the sensor. The standard deviation of the differences varies with altitude between ±4% and ±6% RH, which is comparable to the accuracy of the MOZAIC laboratory calibrations. Compared to MOZAIC operation based on monthly calibrations in the laboratory, the use of IFC will substantially reduce the efforts for maintenance and thus will enable operation of the sensor on a large fleet of in-service aircraft for near-real-time measurements of humidity in the troposphere. Because the IFC method will not work on aircraft that never enter the lower stratosphere, for example, aircraft that fly exclusively regional routes or in the tropics, regular offline calibrations will remain important for such aircraft.
Validation of the Global Relocatable Tide/Surge Model PCTides
Authors: Posey Pamela G. , Allard Richard A. , Preller Ruth H. , Dawson Gretchen M. 
Published: May 2008

Abstract
The Naval Research Laboratory (NRL) has developed a global, relocatable, tide/surge forecast system called PCTides. This system was designed in response to a U.S. Navy requirement to rapidly produce tidal predictions anywhere in the world. The system is composed of a two-dimensional barotropic ocean model driven by tidal forcing only or in conjunction with surface wind and pressure forcing. PCTides is unique in its ability to forecast tidal parameters for a user-specified latitude/longitude domain easily and quickly, and is especially useful in areas where observations are nonexistent. PCTides provides short-term (daily to weekly) predictions of water-level elevation and depth-averaged ocean currents. The system has been tested in numerous regions and validated against observations collected in conjunction with several navy exercises.
Objective Array Design: Application to the Tropical Indian Ocean
Authors: Sakov Pavel , Oke Peter R. 
Published: May 2008

Abstract
A simple, versatile, computationally efficient ensemble-based method for objectively designing an observation array is described. The method seeks to compute the observation array that minimizes the analysis error variance, according to Kalman filter theory. While most elements of the method have been described elsewhere, this paper attempts to present a simple, yet comprehensive, recipe for array design based on an ensemble of anomalies that represents the background error covariance. The versatility of the method is demonstrated through a series of applications to the tropical Indian Ocean (TIO). The first application uses model-generated fields of high-pass-filtered mixed layer depth to design an array to monitor intraseasonal variability. The second uses gridded observations of sea level anomaly to design an array to monitor intraseasonal-to-interannual variability. For both applications, the objectively designed arrays are compared to an array that will soon be implemented under the auspices of the Climate Variability and Predictability–Global Ocean Observing System (CLIVAR–GOOS) Indian Ocean Panel (CG-IOP). The authors conclude that the CG-IOP array produces results that compare well to the objectively designed arrays for intraseasonal variability, and observations to the east and northeast of the TIO and south of India are most important for resolving intraseasonal variability. The authors also find that observations near 9°S, where seasonal Rossby waves dominate, are important for observing seasonal-to-interannual variability. The described method for objective array design can be applied to a wide range of geophysical applications where time series of gridded modeled or observed fields are available.
Complex EOF Analysis as a Method to Separate Barotropic and Baroclinic Velocity Structure in Shallow Water
Authors: Edwards Catherine R. , Seim Harvey E. 
Published: May 2008

Abstract
Defining the vertical depth average of measured currents to be barotropic is a widely used method of separating barotropic and baroclinic tidal currents in the ocean. Away from the surface and bottom boundary layers, depth-averaging measured velocity is an excellent estimate of barotropic tidal flow, and internal tidal dynamics can be well represented by the difference between the measured currents and their depth average in the vertical. However, in shallow and/or energetic tidal environments such as the shelf of the South Atlantic Bight (SAB), bottom boundary layers can occupy a significant fraction of the water column, and depth averaging through the bottom boundary layer can overestimate the barotropic current by several tens of centimeters per second near bottom. The depth-averaged current fails to capture the bottom boundary layer structure associated with the barotropic tidal signal, and the resultant estimate of baroclinic tidal currents can mimic a bottom-trapped internal tide.
Complex empirical orthogonal function (CEOF) analysis is proposed as a method to retain frictional effects in the estimate of the barotropic tidal currents and allow an improved determination of the baroclinic currents. The method is applied to a midshelf region of the SAB dominated by tides and friction to quantify the effectiveness of CEOF analysis to represent internal structure underlying a strong barotropic signal in shallow water. Using examples of synthesized and measured data, EOF estimates of the barotropic and baroclinic modes of motion are compared to those made using depth averaging. The estimates of barotropic tidal motion using depth-averaging and CEOF methods produce conflicting predictions of the frequencies at which there is meaningful baroclinic variability. The CEOF method preserves the frictional boundary layer as part of the barotropic tidal current structure in the gravest mode, providing a more accurate representation of internal structure in higher modes. The application of CEOF techniques to isolate internal structure co-occurring with highly energetic tidal dynamics in shallow water is a significant test of the method. Successful separation of barotropic and baroclinic modes of motion suggests that, by fully capturing the effects of friction associated with the barotropic tide, CEOF analysis is a viable technique to facilitate examination of the internal tide in similar environments.
Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part I: Sensitivity Analysis and Parameter Identifiability
Authors: Tong Mingjing , Xue Ming 
Published: May 2008

Abstract
The possibility of estimating fundamental parameters common in single-moment ice microphysics schemes using radar observations is investigated for a model-simulated supercell storm by examining parameter sensitivity and identifiability. These parameters include the intercept parameters for rain, snow, and hail/graupel, and the bulk densities of snow and hail/graupel. These parameters are closely involved in the definition of drop/particle size distributions of microphysical species but often assume highly uncertain specified values.
The sensitivity of model forecast within data assimilation cycles to the parameter values, and the issue of solution uniqueness of the estimation problem, are examined. The ensemble square root filter (EnSRF) is employed for model state estimation. Sensitivity experiments show that the errors in the microphysical parameters have a larger impact on model microphysical fields than on wind fields; radar reflectivity observations are therefore preferred over those of radial velocity for microphysical parameter estimation. The model response time to errors in individual parameters are also investigated. The results suggest that radar data should be used at about 5-min intervals for parameter estimation. The response functions calculated from ensemble mean forecasts for all five individual parameters show concave shapes, with unique minima occurring at or very close to the true values; therefore, true values of these parameters can be retrieved at least in those cases where only one parameter contains error.
The identifiability of multiple parameters together is evaluated from their correlations with forecast reflectivity. Significant levels of correlation are found that can be interpreted physically. As the number of uncertain parameters increases, both the level and the area coverage of significant correlations decrease, implying increased difficulties with multiple-parameter estimation. The details of the estimation procedure and the results of a complete set of estimation experiments are presented in Part II of this paper.
Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part II: Parameter Estimation Experiments
Authors: Tong Mingjing , Xue Ming 
Published: May 2008

Abstract
The ensemble Kalman filter method is applied to correct errors in five fundamental microphysical parameters that are closely involved in the definition of drop/particle size distributions of microphysical species in a commonly used single-moment ice microphysics scheme, for a model-simulated supercell storm, using radar data. The five parameters include the intercept parameters for rain, snow, and hail/graupel and the bulk densities of hail/graupel and snow. The ensemble square root Kalman filter (EnSRF) is employed for simultaneous state and parameter estimation.
The five microphysical parameters are estimated individually or in different combinations starting from different initial guesses. A data selection procedure based on correlation information is introduced, which combined with variance inflation, effectively avoids the collapse of the spread of parameter ensemble, hence filter divergence. Parameter estimation results demonstrate, for the first time, that the ensemble-based method can be used to correct model errors in microphysical parameters through simultaneous state and parameter estimation, using radar reflectivity observations. When error exists in only one of the microphysical parameters, the parameter can be successfully estimated without exception. The estimation of multiple parameters is less reliable, mainly because the identifiability of the parameters becomes weaker and the problem might have no unique solution. The parameter estimation results are found to be very sensitive to the realization of the initial parameter ensemble, which is mainly related to the use of relatively small ensemble sizes. Increasing ensemble size generally improves the parameter estimation. The quality of parameter estimation also depends on the quality of observation data. It is also found that the results of state estimation are generally improved when simultaneous parameter estimation is performed, even when the estimated parameter values are not very accurate.
Potential Vorticity Diagnosis of the Severe Convective Regime. Part I: Methodology
Authors: Gold David A. , Nielsen-Gammon John W. 
Published: May 2008

AbstractObservational and modeling studies have shown that shear and instability are powerful predictors of the likelihood of severe weather and tornadoes. To the extent that upper-tropospheric forecast errors can be described as potential vorticity (PV) anomalies on the forecasted PV field, knowing (and being able to quantify) the effects of such errors on shear and instability would allow forecasters to anticipate the effects of those errors on the likely mode of severe weather. To test the sensitivity of the severe convective environment to PV fluctuations, a PV inversion framework is adopted that utilizes nonlinear balance. The observed PV field is modified in a way that mimics realistic perturbations of trough intensity, location, or shape. Soundings, including moisture profiles, are reconstructed from the balanced geopotential height field assuming that air parcels conserve mixing ratio while their isentropic surfaces are displaced upward or downward by the addition of anomalous PV. Unperturbed balanced soundings agree reasonably well with full, unbalanced soundings, and differences are attributable to departures from nonlinear balance in areas of strong vorticity or acceleration. Balanced vertical wind profiles do not include the effects of friction, so the vertical shear of the balanced wind departs unacceptably from total shear within the lowest 1 km of the troposphere. The balanced wind perturbations are added to the total analyzed shear profile to estimate the effect of PV perturbations on shear and storm-relative helicity. By this process, the importance of typical or hypothesized upper-tropospheric forecast errors may be addressed in an idealized, case-study, or operational context.