IMRT QA using machine learning: A multi-institutional validation.

PurposeTo validate a machine learning approach to Virtual intensity-modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.MethodsA Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input.ResultsThe methodology predicted passing rates within 3% accuracy for all composite plans measured using diode-array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per-beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under-response in low-dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.ConclusionsWe have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process

Continuous Probabilistic Nearest-Neighbor Queries for Uncertain Trajectories

This work addresses the problem of processing continuous nearest neighbor (NN) queries for moving objects trajectories when the exact position of a given object at a particular time instant is not known, but is bounded by an uncertainty region. As has already been observed in the literature, the answers to continuous NN-queries in spatio-temporal settings are time parameterized in the sense that the objects in the answer vary over time. Incorporating uncertainty in the model yields additional attributes that affect the semantics of the answer to this type of queries. In this work, we formalize the impact of uncertainty on the answers to the continuous probabilistic NN-queries, provide a compact structure for their representation and efficient algorithms for constructing that structure. We also identify syntactic constructs for several qualitative variants of continuous probabilistic NN-queries for uncertain trajectories and present efficient algorithms for their processing. 1

Visualization of Barrier Tree Sequences Revisited

The increasing complexity of models for prediction of the native spatial structure of RNA molecules requires visualization methods that help to analyze and understand the models and their predictions. This paper improves the visualization method for sequences of barrier trees previously published by the authors. The barrier trees of these sequences are rough topological simplifications of changing folding landscapes – energy landscapes in which kinetic folding takes place. The folding landscapes themselves are generated for RNA molecules where the number of nucleotides increases. Successive landscapes are thus correlated and so are the corresponding barrier trees. The landscape sequence is visualized by an animation of a barrier tree that changes with time. The animation is created by an adaption of the foresight layout with tolerance algorithm for dynamic graph layout problems. Since it is very general, the main ideas for the adaption are presented: construction and layout of a supergraph, and how to build the final animation from its layout. Our previous suggestions for heuristics lead to visually unpleasing results for some datasets and, generally, suffered from a poor usage of available screen space. We will present some new heuristics that improve the readability of the final animation

An Evaluation of SmokeFree for Kansas Kids: An Intervention to Promote Tobacco Cessation in Pediatric Clinics

Introduction. Smokefree for Kansas Kids is a program designedto train pediatric clinic staff to assess for tobaccoexposure and provide brief smoking cessation interventionsto caregivers and patients. The purpose of this studywas to evaluate the impact of this program and improvefuture tobacco intervention efforts in pediatric clinics. Methods. Eighty-six pediatric physicians and staff attendedat least one of three training sessions. A randomsample of pediatric medical records was selected pre-intervention(n = 49) and post-intervention (n = 150). Electronicmedical records were reviewed to assess for documentationof tobacco use intervention implemented in the clinic. Results. Of the 199 pediatric clinic visits reviewed, 197 metthe study criteria. All but one visit documented an assessmentof tobacco exposure. Among children exposed to tobacco (n= 42), providers were more likely to discuss tobacco use withcaregivers post-intervention (35.7%) compared to pre-intervention(7.1%; p < 0.05). One in five caregivers in the postinterventiongroup were advised to quit (21.4%) compared tothe pre-intervention group (7.1%). In the post-interventiongroup, 14.3% were referred to the state quitline compared tono referrals in the pre-intervention group. The difference inrates for providing advice and referral between pre-interventionand post-intervention were not statistically significant. Conclusions. Implementation of the Smoke Free for Kansas Kidsintervention was associated with modest improvements in clinictobacco intervention efforts, but many patients still failed toreceive optimal assessments or interventions. Additional effortsmay be needed to enhance this program. KS J Med 2017;10(1):7-11

LncRNA RUS shapes the gene expression program towards neurogenesis

The evolution of brain complexity correlates with an increased expression of long, noncoding (lnc) RNAs in neural tissues. Although prominent examples illustrate the potential of lncRNAs to scaffold and target epigenetic regulators to chromatin loci, only few cases have been described to function during brain development. We present a first functional characterization of the lncRNA LINC01322, which we term RUS for RNA upstream of Slitrk3. The RUS gene is well conserved in mammals by sequence and synteny next to the neurodevelopmental gene Slitrk3. RUS is exclusively expressed in neural cells and its expression increases during neuronal differentiation of mouse embryonic cortical neural stem cells. Depletion of RUS locks neuronal precursors in an intermediate state towards neuronal differentiation resulting in arrested cell cycle and increased apoptosis. RUS associates with chromatin in the vicinity of genes involved in neurogenesis, most of which change their expression upon RUS depletion. The identification of a range of epigenetic regulators as specific RUS interactors suggests that the lncRNA may mediate gene activation and repression in a highly context-dependent manner

Effects of high-pressure on the structural, vibrational, and electronic properties of monazite-type PbCrO4

We have performed an experimental study of the crystal structure, lattice-dynamics, and optical properties of PbCrO4 (the mineral crocoite) at ambient and high pressures. In particular, the crystal structure, Raman-active phonons, and electronic band-gap have been accurately determined. X-ray-diffraction, Raman, and optical-absorption experiments have allowed us also to completely characterize two pressure-induced structural phase transitions. The first transition is isostructural, maintaining the monoclinic symmetry of the crystal, and having important consequences in the physical properties; among other a band-gap collapse is induced. The second one involves an increase of the symmetry of the crystal, a volume collapse, and probably the metallization of PbCrO4. The results are discussed in comparison with related compounds and the effects of pressure in the electronic structure explained. Finally, the room-temperature equation of state of the low-pressure phases is also obtained.Comment: 32 pages, 9 figures, 3 table