Clinical implications of the anisotropic analytical algorithm for IMRT treatment planning and verification

PURPOSE: To determine the implications of the use of the Anisotropic Analytical Algorithm(AAA) for the production and dosimetric verification of IMRT plans for treatments of the prostate, parotid, nasopharynx and lung. METHODS: 72 IMRT treatment plans produced using the Pencil Beam Convolution (PBC)algorithm were recalculated using the AAA and the dose distributions compared. 24 of the plans were delivered to inhomogeneous phantoms and verification measurements made using a pinpoint ionisation chamber. The agreement between the AAA and measurement was determined. RESULTS: Small differences were seen in the prostate plans, with the AAA predicting slightly lower minimum PTV doses. In the parotid plans, there were small increases in the lens and contralateral parotid doses while the nasopharyngeal plans revealed a reduction in the volume of the PTV covered by the 95% isodose (the V95%) when the AAA was used. Large changes were seen in the lung plans, the AAA predicting reductions in the minimum PTV dose and large reductions in the V95%. The AAA also predicted small increases in the mean dose to the normal lung and the V20. In the verification measurements, all AAA calculations were within 3% or 3.5mm distance to agreement of the measured doses. Conclusions: The AAA should be used in preference to the PBC algorithm for treatments involving low density tissue but this may necessitate re-evaluation of plan acceptability criteria. Improvements to the Multi-Resolution Dose Calculation algorithm used in the inverse planning are required to reduce the convergence error in the presence of lung tissue. There was excellent agreement between the AAA and verification measurements for all sites

Supermassive Binaries and Extragalactic Jets

Some quasars show Doppler shifted broad emission line peaks. I give new statistics of the occurrence of these peaks and show that, while the most spectacular cases are in quasars with strong radio jets inclined to the line of sight, they are also almost as common in radio-quiet quasars. Theories of the origin of the peaks are reviewed and it is argued that the displaced peaks are most likely produced by the supermassive binary model. The separations of the peaks in the 3C 390.3-type objects are consistent with orientation-dependent "unified models" of quasar activity. If the supermassive binary model is correct, all members of "the jet set" (astrophysical objects showing jets) could be binaries.Comment: 31 pages, PostScript, missing figure is in ApJ 464, L105 (see http://www.aas.org/ApJ/v464n2/5736/5736.html

Valence-quark distributions in the pion

We calculate the pion's valence-quark momentum-fraction probability distribution using a Dyson-Schwinger equation model. Valence-quarks with an active mass of 0.30 GeV carry 71% of the pion's momentum at a resolving scale q_0=0.54 GeV = 1/(0.37 fm). The shape of the calculated distribution is characteristic of a strongly bound system and, evolved from q_0 to q=2 GeV, it yields first, second and third moments in agreement with lattice and phenomenological estimates, and valence-quarks carrying 49% of the pion's momentum. However, pointwise there is a discrepancy between our calculated distribution and that hitherto inferred from parametrisations of extant pion-nucleon Drell-Yan data.Comment: 8 pages, 3 figures, REVTEX, aps.sty, epsfig.sty, minor corrections, version to appear in PR

TP53 Pathway Function, Estrogen Receptor Status, and Breast Cancer Risk Factors in the Carolina Breast Cancer Study

Background: TP53 and estrogen receptor (ER) both play essential roles in breast cancer development and progression, with recent research revealing cross-talk between TP53 and ER signaling pathways. Although many studies have demonstrated heterogeneity of risk factor associations across ER subtypes, associations by TP53 status have been inconsistent. Methods: This case-case analysis included incident breast cancer cases (47% Black) from the Carolina Breast Cancer Study (1993- 2013). Formalin-fixed paraffin-embedded tumor samples were classified for TP53 functional status (mutant-like/wild-type-like) using a validated RNA signature. For IHC-based TP53 status, mutant-like was classified as at least 10% positivity. We used two-stage polytomous logistic regression to evaluate risk factor heterogeneity due to RNA-based TP53 and/or ER, adjusting for each other and for PR, HER2, and grade. We then compared this with the results when using IHC-based TP53 classification. Results: The RNA-based classifier identified 55% of tumors as TP53 wild-type-like and 45% as mutant-like. Several hormonerelated factors (oral contraceptive use, menopausal status, age at menopause, and pre- and postmenopausal body mass index) were associated with TP53 mutant-like status, whereas reproductive factors (age at first birth and parity) and smoking were associated with ER status. Multiparity was associated with both TP53 and ER. When classifying TP53 status using IHC methods, no associations were observed with TP53. Associations observed with RNA-based TP53 remained after accounting for basal-like subtype. Conclusions: This case-case study found breast cancer risk factors associated with RNA-based TP53 and ER

Prognostic significance of RNA-based TP53 pathway function among estrogen receptor positive and negative breast cancer cases

TP53 and estrogen receptor (ER) are essential in breast cancer development and progression, but TP53 status (by DNA sequencing or protein expression) has been inconsistently associated with survival. We evaluated whether RNA-based TP53 classifiers are related to survival. Participants included 3213 women in the Carolina Breast Cancer Study (CBCS) with invasive breast cancer (stages I–III). Tumors were classified for TP53 status (mutant-like/wildtype-like) using an RNA signature. We used Cox proportional hazards models to estimate covariate-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for breast cancer-specific survival (BCSS) among ER- and TP53-defined subtypes. RNA-based results were compared to DNA- and IHC-based TP53 classification, as well as Basal-like versus non-Basal-like subtype. Findings from the diverse (50% Black), population-based CBCS were compared to those from the largely white METABRIC study. RNA-based TP53 mutant-like was associated with BCSS among both ER-negatives and ER-positives (HR (95% CI) = 5.38 (1.84–15.78) and 4.66 (1.79–12.15), respectively). Associations were attenuated when using DNA- or IHC-based TP53 classification. In METABRIC, few ER-negative tumors were TP53-wildtype-like, but TP53 status was a strong predictor of BCSS among ER-positives. In both populations, the effect of TP53 mutant-like status was similar to that for Basal-like subtype. RNA-based measures of TP53 status are strongly associated with BCSS and may have value among ER-negative cancers where few prognostic markers have been robustly validated. Given the role of TP53 in chemotherapeutic response, RNA-based TP53 as a prognostic biomarker could address an unmet need in breast cancer

Under What Conditions Can Recursion Be Learned? Effects of Starting Small in Artificial Grammar Learning of Center-Embedded Structure

It has been suggested that external and/or internal limitations paradoxically may lead to superior learning, that is, the concepts of starting small and less is more (Elman,; Newport,). In this paper, we explore the type of incremental ordering during training that might help learning, and what mechanism explains this facilitation. We report four artificial grammar learning experiments with human participants. In Experiments 1a and 1b we found a beneficial effect of starting small using two types of simple recursive grammars: right-branching and center-embedding, with recursive embedded clauses in fixed positions and fixed length. This effect was replicated in Experiment 2 (N = 100). In Experiment 3 and 4, we used a more complex center-embedded grammar with recursive loops in variable positions, producing strings of variable length. When participants were presented an incremental ordering of training stimuli, as in natural language, they were better able to generalize their knowledge of simple units to more complex units when the training input “grew” according to structural complexity, compared to when it “grew” according to string length. Overall, the results suggest that starting small confers an advantage for learning complex center-embedded structures when the input is organized according to structural complexity

A New Framework for Music Education Knowledge and Skill

This study investigates perceptions of secondary school band and orchestra teachers regarding the relative importance of knowledge and skill categories to professional success, using a framework modeled after Schulman (1986, 1987). Band and orchestra teachers in secondary schools (N = 214) complete an anonymous, online survey ranking the relative importance of various knowledge and skill categories. Participants rank pedagogical content knowledge, content knowledge, and general pedagogical knowledge highest. There are no significant differences in the rankings of the categories among various subgroups at the p < .05 level. Results confirm the applicability of Schulman's model to music education. This framework has implications for undergraduate, graduate, and continuing professional education. Analysis of categories' interaction provides insight into effective classroom instruction.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Shrinking a large dataset to identify variables associated with increased risk of Plasmodium falciparum infection in Western Kenya

Large datasets are often not amenable to analysis using traditional single-step approaches. Here, our general objective was to apply imputation techniques, principal component analysis (PCA), elastic net and generalized linear models to a large dataset in a systematic approach to extract the most meaningful predictors for a health outcome. We extracted predictors for Plasmodium falciparum infection, from a large covariate dataset while facing limited numbers of observations, using data from the People, Animals, and their Zoonoses (PAZ) project to demonstrate these techniques: data collected from 415 homesteads in western Kenya, contained over 1500 variables that describe the health, environment, and social factors of the humans, livestock, and the homesteads in which they reside. The wide, sparse dataset was simplified to 42 predictors of P. falciparum malaria infection and wealth rankings were produced for all homesteads. The 42 predictors make biological sense and are supported by previous studies. This systematic data-mining approach we used would make many large datasets more manageable and informative for decision-making processes and health policy prioritization