Accelerating Radiation Dose Calculation with High Performance Computing and Machine Learning for Large-scale Radiotherapy Treatment Planning

Radiation therapy is powered by modern techniques in precise planning and executionof radiation delivery, which are being rapidly improved to maximize its benefit to cancerpatients. In the last decade, radiotherapy experienced the introduction of advanced methodsfor automatic beam orientation optimization, real-time tumor tracking, daily planadaptation, and many others, which improve the radiation delivery precision, planning easeand reproducibility, and treatment efficacy. However, such advanced paradigms necessitatethe calculation of orders of magnitude more causal dose deposition data, increasing the timerequirement of all pre-planning dose calculation. Principles of high-performance computingand machine learning were applied to address the insufficient speeds of widely-used dosecalculation algorithms to facilitate translation of these advanced treatment paradigms intoclinical practice.To accelerate CT-guided X-ray therapies, Collapsed-Cone Convolution-Superposition(CCCS), a state-of-the-art analytical dose calculation algorithm, was accelerated through itsnovel implementation on highly parallelized GPUs. This context-based GPU-CCCS approachtakes advantage of X-ray dose deposition compactness to parallelize calculation acrosshundreds of beamlets, reducing hardware-specific overheads, and enabling acceleration bytwo to three orders of magnitude compared to existing GPU-based beamlet-by-beamletapproaches. Near-linear increases in acceleration are achieved with a distributed, multi-GPUimplementation of context-based GPU-CCCS.Dose calculation for MR-guided treatment is complicated by electron return effects(EREs), exhibited by ionizing electrons in the strong magnetic field of the MRI scanner. EREsnecessitate the use of much slower Monte Carlo (MC) dose calculation, limiting the clinicalapplication of advanced treatment paradigms due to time restrictions. An automaticallydistributed framework for very-large-scale MC dose calculation was developed, grantinglinear scaling of dose calculation speed with the number of utilized computational cores. Itwas then harnessed to efficiently generate a large dataset of paired high- and low-noise MCdoses in a 1.5 tesla magnetic field, which were used to train a novel deep convolutionalneural network (CNN), DeepMC, to predict low-noise dose from faster high-noise MC-simulation. DeepMC enables 38-fold acceleration of MR-guided X-ray beamlet dosecalculation, while remaining synergistic with existing MC acceleration techniques to achievemultiplicative speed improvements.This work redefines the expectation of X-ray dose calculation speed, making it possibleto apply new highly-beneficial treatment paradigms to standard clinical practice for the firsttime

Performance Comparison of Knowledge-Based Dose Prediction Techniques Based on Limited Patient Data.

PurposeThe accuracy of dose prediction is essential for knowledge-based planning and automated planning techniques. We compare the dose prediction accuracy of 3 prediction methods including statistical voxel dose learning, spectral regression, and support vector regression based on limited patient training data.MethodsStatistical voxel dose learning, spectral regression, and support vector regression were used to predict the dose of noncoplanar intensity-modulated radiation therapy (4π) and volumetric-modulated arc therapy head and neck, 4π lung, and volumetric-modulated arc therapy prostate plans. Twenty cases of each site were used for k-fold cross-validation, with k = 4. Statistical voxel dose learning bins voxels according to their Euclidean distance to the planning target volume and uses the median to predict the dose of new voxels. Distance to the planning target volume, polynomial combinations of the distance components, planning target volume, and organ at risk volume were used as features for spectral regression and support vector regression. A total of 28 features were included. Principal component analysis was performed on the input features to test the effect of dimension reduction. For the coplanar volumetric-modulated arc therapy plans, separate models were trained for voxels within the same axial slice as planning target volume voxels and voxels outside the primary beam. The effect of training separate models for each organ at risk compared to all voxels collectively was also tested. The mean squared error was calculated to evaluate the voxel dose prediction accuracy.ResultsStatistical voxel dose learning using separate models for each organ at risk had the lowest root mean squared error for all sites and modalities: 3.91 Gy (head and neck 4π), 3.21 Gy (head and neck volumetric-modulated arc therapy), 2.49 Gy (lung 4π), and 2.35 Gy (prostate volumetric-modulated arc therapy). Compared to using the original features, principal component analysis reduced the 4π prediction error for head and neck spectral regression (-43.9%) and support vector regression (-42.8%) and lung support vector regression (-24.4%) predictions. Principal component analysis was more effective in using all/most of the possible principal components. Separate organ at risk models were more accurate than training on all organ at risk voxels in all cases.ConclusionCompared with more sophisticated parametric machine learning methods with dimension reduction, statistical voxel dose learning is more robust to patient variability and provides the most accurate dose prediction method

MicroFootPrinter: a tool for phylogenetic footprinting in prokaryotic genomes

Phylogenetic footprinting is a method for the discovery of regulatory elements in a set of homologous regulatory regions, usually collected from multiple species. It does so by identifying the most conserved motifs in those homologous regions. This note describes web software that has been designed specifically for this purpose in prokaryotic genomes, making use of the phylogenetic relationships among the homologous sequences in order to make more accurate predictions. The software is called MicroFootPrinter and is available at

A Pilot, Virtual Exercise Intervention Improves Health and Fitness during the COVID-19 Pandemic

International Journal of Exercise Science 15(7): 1395-1417, 2022. Physical activity levels are low in individuals with chronic disease (e.g., obesity) and have worsened during the COVID-19 pandemic. Purpose: Our pilot study tested a virtual exercise intervention for rural-dwelling adults with chronic disease from January-April 2021 for changes in mental health, physical fitness, and physical activity and for intervention fidelity. Methods: Participants (n = 8 [7 female]; age = 57.5 ± 13.8 years, body mass index = 38.2 ± 8.0 kg/m2) completed an exercise intervention led virtually by collegiate health science majors. Participants attended two 60-minute sessions/week for 12 weeks, completing individually-tailored and progressed aerobic and muscle-strengthening training. A non-randomized control group matched on gender and age continued normal activity during the 12 weeks. Changes in mental health, physical fitness, and physical activity measures were evaluated using a 2x2 (group x time) analysis of covariance. Results: Both groups improved mental health, but only intervention participants lost weight (3.1 ± 1.0 kg; no change in controls). Step test, arm curls, and chair stands improved by 16.1-20.6% in the intervention and 7.8-12.1% in the control groups. Intervention participants did not increase overall physical activity during or after the intervention. Intervention fidelity was high; participants attended ~73% of sessions and rated the sessions 4.7 ± 0.6 (out of 5). Researcher observations rated exercise sessions as meeting 12.7 ± 0.6 of 16 goals. Conclusions: Our virtual exercise program was associated with positive mental health and physical fitness changes. Such programs may provide a method, even beyond the pandemic, to improve fitness in adults with chronic disease

A Computational Pipeline for High- Throughput Discovery of cis-Regulatory Noncoding RNA in Prokaryotes

Noncoding RNAs (ncRNAs) are important functional RNAs that do not code for proteins. We present a highly efficient computational pipeline for discovering cis-regulatory ncRNA motifs de novo. The pipeline differs from previous methods in that it is structure-oriented, does not require a multiple-sequence alignment as input, and is capable of detecting RNA motifs with low sequence conservation. We also integrate RNA motif prediction with RNA homolog search, which improves the quality of the RNA motifs significantly. Here, we report the results of applying this pipeline to Firmicute bacteria. Our top-ranking motifs include most known Firmicute elements found in the RNA family database (Rfam). Comparing our motif models with Rfam's hand-curated motif models, we achieve high accuracy in both membership prediction and base-pair–level secondary structure prediction (at least 75% average sensitivity and specificity on both tasks). Of the ncRNA candidates not in Rfam, we find compelling evidence that some of them are functional, and analyze several potential ribosomal protein leaders in depth

Associations of physical activity levels with fatigue in people with inflammatory rheumatic disease in the LIFT trial

Objectives: The overall aim of the current study was to quantify physical activity levels in inflammatory rheumatic diseases (IRDs) and to explore its role in fatigue.Methods: Secondary analysis of data from the Lessening the Impact of Fatigue in IRDs (LIFT) trial of the personalized exercise programme (PEP) intervention for fatigue. Participants with IRDs were recruited from 2017–2019 and the current analysis used the fatigue, measured by the chalder fatigue scale (CFS) and the fatigue severity scale (FSS), and accelerometer measured physical activity data collected at baseline and at 6 months follow up. Physical activity levels were quantified, associations with fatigue and effects of PEP investigated.Results: Of the 337 included participants, 195 (68.4%) did not meet the current recommendations for moderate-vigorous physical activity (MVPA). In baseline cross-sectional analysis, many dimensions of physical activity were associated with fatigue. After mutual adjustment, overall physical activity (vector magnitude) was associated with CFS (-0.88(-0.12, -1.64)) and distribution of time spent at different activity intensity was associated with FSS (-1.16 (-2.01, -0.31)). Relative to usual care, PEP resulted in an increase in upright time, with trends for increases in step count and overall physical activity. People who increased overall physical activity (vector magnitude) more had greater improvements in CFS and FSS, whilst those that increased step count and MVPA more had greater improvements in FSS.Conclusion: Increasing physical activity is important for fatigue management in people with IRDs and further work is needed to optimize PEP to target the symptoms and impact of fatigue.Trial registrationClinicalTrials.Gov, NCT0324851

The Human Mitochondrial Transcriptome

SummaryThe human mitochondrial genome comprises a distinct genetic system transcribed as precursor polycistronic transcripts that are subsequently cleaved to generate individual mRNAs, tRNAs, and rRNAs. Here, we provide a comprehensive analysis of the human mitochondrial transcriptome across multiple cell lines and tissues. Using directional deep sequencing and parallel analysis of RNA ends, we demonstrate wide variation in mitochondrial transcript abundance and precisely resolve transcript processing and maturation events. We identify previously undescribed transcripts, including small RNAs, and observe the enrichment of several nuclear RNAs in mitochondria. Using high-throughput in vivo DNaseI footprinting, we establish the global profile of DNA-binding protein occupancy across the mitochondrial genome at single-nucleotide resolution, revealing regulatory features at mitochondrial transcription initiation sites and functional insights into disease-associated variants. This integrated analysis of the mitochondrial transcriptome reveals unexpected complexity in the regulation, expression, and processing of mitochondrial RNA and provides a resource for future studies of mitochondrial function (accessed at http://mitochondria.matticklab.com)