Sublimation Pressures of Refractory Fluorides

Vapor species identification, absolute vapor pressures and heats of sublimation of refractory metal fluoride

Validation of a 22-Gene Genomic Classifier in Patients With Recurrent Prostate Cancer An Ancillary Study of the NRG/RTOG 9601 Randomized Clinical Trial

IMPORTANCE: Decipher (Decipher Biosciences Inc) is a genomic classifier (GC) developed to estimate the risk of distant metastasis (DM) after radical prostatectomy (RP) in patients with prostate cancer. OBJECTIVE: To validate the GC in the context of a randomized phase 3 trial. DESIGN, SETTING, AND PARTICIPANTS: This ancillary study used RP specimens from the phase 3 placebo-controlled NRG/RTOG 9601 randomized clinical trial conducted from March 1998 to March 2003. The specimens were centrally reviewed, and RNA was extracted from the highest-grade tumor available in 2019 with a median follow-up of 13 years. Clinical-grade whole transcriptomes from samples passing quality control were assigned GC scores (scale, 0-1). A National Clinical Trials Network–approved prespecified statistical plan included the primary objective of validating the independent prognostic ability of GC for DM, with secondary end points of prostate cancer–specific mortality (PCSM) and overall survival (OS). Data were analyzed from September 2019 to December 2019. INTERVENTIONS: Salvage radiotherapy (sRT) with or without 2 years of bicalutamide. MAIN OUTCOMES AND MEASURES: The preplanned primary end point of this study was the independent association of the GC with the development of DM. RESULTS: In this ancillary study of specimens from a phase 3 randomized clinical trial, GC scores were generated from 486 of 760 randomized patients with a median follow-up of 13 years; samples from a total of 352 men (median [interquartile range] age, 64.5 (60-70) years; 314 White [89.2%] participants) passed microarray quality control and comprised the final cohort for analysis. On multivariable analysis, the GC (continuous variable, per 0.1 unit) was independently associated with DM (hazard ratio [HR], 1.17; 95% CI, 1.05-1.32; P = .006), PCSM (HR, 1.39; 95% CI, 1.20-1.63; P < .001), and OS (HR, 1.17; 95% CI, 1.06-1.29; P = .002) after adjusting for age, race/ethnicity, Gleason score, T stage, margin status, entry prostate-specific antigen, and treatment arm. Although the original planned analysis was not powered to detect a treatment effect interaction by GC score, the estimated absolute effect of bicalutamide on 12-year OS was less when comparing patients with lower vs higher GC scores (2.4% vs 8.9%), which was further demonstrated in men receiving early sRT at a prostate-specific antigen level lower than 0.7 ng/mL (−7.8% vs 4.6%). CONCLUSIONS AND RELEVANCE: This ancillary validation study of the Decipher GC in a randomized trial cohort demonstrated association of the GC with DM, PCSM, and OS independent of standard clinicopathologic variables. These results suggest that not all men with biochemically recurrent prostate cancer after surgery benefit equally from the addition of hormone therapy to sRT. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT0000287

Signal Processing for NDE

Nowadays, testing and evaluating of industrial equipment using nondestructive tests, is a fundamental step in the manufacturing process. The complexity and high costs of manufacturing industrial components, require examinations in some way about the quality and reliability of the specimens. However, it should be noted, that in order to accurately perform the nondestructive test, in addition to theoretical knowledge, it is also essential to have the experience and carefulness, which requires special courses and experience with theoretical education. Therefore, in the traditional methods, which are based on manual testing techniques and the test results depend on the operator, there is the possibility of an invalid inference from the test data. In other words, the accuracy of conclusion from the obtained data is dependent on the skill and experience of the operator. Thus, using the signal processing techniques for nondestructive evaluation (NDE), it is possible to optimize the methods of nondestructive inspection, and in other words, to improve the overall system performance, in terms of reliability and system implementation costs. In recent years, intelligent signal processing techniques have had a significant impact on the progress of nondestructive assessment. In other words, by automating the processing of nondestructive data and signals, and using the artificial intelligence methods, it is possible to optimize nondestructive inspection methods. Hence, improve overall system performance in terms of reliability and Implementation costs of the system. This chapter reviews the issues of intelligent processing of nondestructive testing (NDT) signals

Estate planning Law and tax

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