The paper presents a rainfall estimation technique based on algorithms that couple, along a radar ray, profiles of horizontal polarization reflectivity (Z H), differential reflectivity (Z DR), and differential propagation phase shift (⌽ DP) from X-band polarimetric radar measurements. Based on in situ raindrop size distribution (DSD) data and using a three-parameter ''normalized'' gamma DSD model, relationships are derived that correct X-band reflectivity profiles for specific and differential attenuation, while simultaneously retrieving variations of the normalized intercept DSD parameter (N w). The algorithm employs an iterative scheme to intrinsically account for raindrop oblateness variations from equilibrium condition. The study is facilitated from a field experiment conducted in the period October-November 2001 in Iowa City, Iowa, where observations from X-band dualpolarization mobile radar (XPOL) were collected simultaneously with high-resolution in situ disdrometer and rain-gauge rainfall measurements. The observed rainfall events ranged in intensity from moderate stratiform precipitation to high-intensity (Ͼ50 mm h Ϫ1) convective rain cells. The XPOL measurements were tested for calibration, noise, and physical consistency using corresponding radar parameters derived from coincidentally measured raindrop spectra. Retrievals of N w from the attenuation correction scheme are shown to be unbiased and consistent with N w values calculated from independent raindrop spectra. The attenuation correction based only on profiles of reflectivity measurements is shown to diverge significantly from the corresponding polarimetric-based corrections. Several rain retrieval algorithms were investigated using matched pairs of instantaneous high-resolution XPOL observations with rain rates from 3-min-averaged raindrop spectra at close range (ϳ5 km) and rain-gauge measurements from further ranges (ϳ10 km). It is shown that combining along-a-ray (corrected Z H , Z DR , and specific differential phase shift) values gets the best performance in rainfall estimation with about 40% (53%) relative standard deviation in the radar-disdrometer (radar-gauge) differences. The casetuned reflectivity-rainfall rate (Z-R) relationship gives about 65% (73%) relative standard deviation for the same differences. The systematic error is shown to be low (ϳ3% overestimation) and nearly independent of rainfall intensity for the multiparameter algorithm, while for the standard Z-R it varied from 10% underestimation to 3% overestimation.

Rapidly accumulating clinical information can support cancer care and discovery. Future success depends on information management, access, use, and reuse. Electronic health records (EHRs) are highlighted as a critical component of evidence development and implementation, but to fully harness the potential of EHRs, they need to be more than electronic renderings of the traditional paper medical chart. Clinical informatics and structured accessible secure data captured through EHR systems provide mechanisms through which EHRs can facilitate comparative effectiveness research (CER). Use of large linked administrative databases to answer comparative questions is an early version of informatics-enabled CER familiar to oncologists. An updated version of informatics-enabled CER relies on EHR-derived structured data linked with supplemental information to provide patient-level information that can be aggregated and analyzed to support hypothesis generation, comparative assessment, and personalized care. As implementation of EHRs continues to expand, electronic databases containing information collected via EHRs will continuously aggregate; aggregating data enhanced with real-time analytics can provide point-of-care evidence to oncologists, tailored to patient-level characteristics. The system learns when clinical care informs research, and insights derived from research are reinvested in care. Challenges must be overcome, including interoperability, standardization, access, and development of real-time analytics.

Objective To examine associations of anti-cyclic citrullinated peptide antibody (aCCP) and rheumatoid factor (RF) concentrations with future disease burden in patients with rheumatoid arthritis (RA). Methods Outcome measures were examined in U.S. veterans with RA and included: 1) proportion of observation in remission (Disease Activity Score [DAS]28 ≤ 2.6), 2) remission for ≥ 3 consecutive months, and 3) area under the curve [AUC] for DAS28. Associations of autoantibody concentration (per 100 unit increments) with outcomes were examined using multivariate regression. Results Patients (n = 855) were predominantly men (91%) with mean (SD) age of 66 (11) years and 2.3 (1.2) years of follow-up. Most were aCCP (75%) and RF (80%) positive. After multivariate adjustment, aCCP (OR 0.93; 95% CI 0.91-0.96) and RF concentrations (OR 0.92; 95% CI 0.90-0.95) were associated with a lower odds of remission, a lower proportion of observation in remission (p = 0.054 and p = 0.014, respectively), and greater AUC DAS28 (p = 0.05 and p = 0.002, respectively). In aCCP+ / RF- patients, higher aCCP concentrations were associated with an increased likelihood of remission (OR 1.10; 95% CI 1.00-1.20). Among aCCP- / RF+ patients, higher RF concentrations trended towards an inverse association with remission (OR 0.81; 95% CI 0.58-1.13). Conclusions aCCP concentrations (particularly in RF positive patients) are associated with poor prognosis in U.S. veterans with RA. Analyses of autoantibody discordant patients suggest that RF concentrations may be a stronger predictor of disease burden than aCCP concentration.

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