Jamespedersen5536

or surgical biopsy. This article is protected by copyright. All rights reserved.PURPOSE To evaluate the performance of the first clinical real-time motion tracking and compensation system using multi-leaf collimator (MLC) and jaws during helical tomotherapy delivery. METHODS Appropriate mechanical and dosimetry tests were performed on the first clinical real-time motion tracking system (Synchrony on Radixact, Accuray Inc) recently installed in our institution. kV radiography dose was measured by CTDIw using a pencil chamber. Changes of beam characteristics with jaw offset and MLC leaf shift were evaluated. Various dosimeters and phantoms including A1SL ion chamber (Standard Imaging), Gafchromic EBT3 films (Ashland), TomoPhantom (Med Cal), ArcCheck (Sun Nuclear), Delta4 (ScandiDos), with fiducial or high contrast inserts, placed on two dynamical motion platforms (CIRS dynamic motion-CIRS, Hexamotion-ScandiDos), were used to assess the dosimetric accuracy of the available Synchrony modalities fiducial tracking with non-respiratory motion (FNR), fiducial tracking with respiratory modeling (ony was found to be within 2%. The gamma passing rates of 3D dose measurements for a variety of Synchrony plans were well within the acceptable level. CONCLUSIONS The motion tracking and compensation using kV radiography, MLC shifting and jaw swing during helical tomotherapy delivery was tested to be mechanically and dosimetrically accurate for clinical use. This article is protected by copyright. All rights reserved.The discovery of rare genetic variants through Next Generation Sequencing is a very challenging issue in the field of human genetics. We propose a novel region-based statistical approach based on a Bayes Factor (BF) to assess evidence of association between a set of rare variants (RVs) located on the same genomic region and a disease outcome in the context of case-control design. Marginal likelihoods are computed under the null and alternative hypotheses assuming a binomial distribution for the RV count in the region and a beta or mixture of Dirac and beta prior distribution for the probability of RV. We derive the theoretical null distribution of the BF under our prior setting and show that a Bayesian control of the False Discovery Rate (BFDR) can be obtained for genome-wide inference. Informative priors are introduced using prior evidence of association from a Kolmogorov-Smirnov test statistic. We use our simulation program, sim1000G, to generate RV data similar to the 1,000 genomes sequencing project. Our simulation studies showed that the new BF statistic outperforms standard methods (SKAT, SKAT-O, Burden test) in case-control studies with moderate sample sizes and is equivalent to them under large sample size scenarios. Our real data application to a lung cancer case-control study found enrichment for RVs in known and novel cancer genes. It also suggests that using the BF with informative prior improves the overall gene discovery compared to the BF with non-informative prior. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.Alzheimer's disease (AD) is a progressive neurodegenerative disorder with no approved disease-modifying therapy (DMT). In this review, we summarize the various past approaches taken in an attempt to find treatments capable of altering the long-term course for individuals with AD, including translating epidemiological observations into potential treatment options; seeking a single treatment approach across the continuum of AD severity; utilizing biomarkers for assessing target engagement; using biomarkers as early surrogates of clinical efficacy; and enriching study populations to demonstrate adequate placebo decline during the limited duration of clinical trials. Although targeting the amyloid-β (Aβ) pathway has been central to the search for an effective DMT, to date, trials of anti-Aβ monoclonal antibodies have failed to consistently demonstrate significant clinical efficacy. Key learnings from these anti-Aβ trials, as well as the trials that came before them, have shifted the focus within clinical development programs to identifying target populations thought most likely to benefit from treatments (i.e. individuals at an earlier stage of disease). Other learnings include strategies to increase the likelihood of showing measurable improvements within the clinical trial setting by better predicting decline in placebo participants, as well as developing measures to quantify the needed treatment exposure (e.g. higher doses). Given the complexity associated with AD pathology and progression, treatments targeting non-amyloid AD pathologies in combination with anti-amyloid therapies may offer an alternative for the successful development of DMTs. This article is protected by copyright. All rights reserved.PURPOSE This work introduces a new lattice geometry library, egs_lattice, into the EGSnrc Monte Carlo code, which can be used for both modeling very large (previously unfeasible) quantities of geometries (e.g., cells or gold nanoparticles) and establishing recursive boundary conditions. Eganelisib molecular weight The reliability of egs_lattice, as well as EGSnrc in general, is cross-validated and tested at short length scales and low energies. METHODS New Bravais, cubic, and hexagonal lattice geometries are defined in egs_lattice and their transport algorithms are described. Simulations of cells and Gold NanoParticle (GNP) containing cavities are implemented to compare to independent, published Geant4-DNA and PENELOPE results. Recursive boundary conditions, implemented through a cubic lattice, are used to perform electron Fano cavity tests. The Fano test is performed on three different-sized cells containing GNPs in the region around the nucleus for three source energies. RESULTS Lattices are successfully implemented in EGSnrc, and areand score in microscopic cavities. This article is protected by copyright. All rights reserved.In many multiple testing applications in genetics, the signs of the test statistics provide useful directional information, such as whether genes are potentially up- or down-regulated between two experimental conditions. However, most existing procedures that control the false discovery rate (FDR) are p-value based and ignore such directional information. We introduce a novel procedure, the signed-knockoff procedure, to utilize the directional information and control the FDR in finite samples. We demonstrate the power advantage of our procedure through simulation studies and two real applications. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.