A task-based parallelism and vectorized approach to 3D Method of Characteristics (MOC) reactor simulation for high performance computing architectures

In this study we present and analyze a formulation of the 3D Method of Characteristics (MOC) technique applied to the simulation of full core nuclear reactors. Key features of the algorithm include a task-based parallelism model that allows independent MOC tracks to be assigned to threads dynamically, ensuring load balancing, and a wide vectorizable inner loop that takes advantage of modern SIMD computer architectures. The algorithm is implemented in a set of highly optimized proxy applications in order to investigate its performance characteristics on CPU, GPU, and Intel Xeon Phi architectures. Speed, power, and hardware cost efficiencies are compared. Additionally, performance bottlenecks are identified for each architecture in order to determine the prospects for continued scalability of the algorithm on next generation HPC architectures. Keywords: Method of Characteristics; Neutron transport; Reactor simulation; High performance computingUnited States. Department of Energy (Contract DE-AC02-06CH11357

Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome

Background: Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods: Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering. Results: Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. Conclusions: The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.Peer reviewe

Equipoise across the patient population: Optimising recruitment to a randomised controlled trial

© 2016 The Author(s). Background: This paper proposes a novel perspective on the value of qualitative research for improving trial design and optimising recruitment. We report findings from a qualitative study set within the OPEN trial, a surgical randomised controlled trial (RCT) comparing two interventions for recurrent bulbar urethral stricture, a common cause of urinary problems in men. Methods: Interviews were conducted with men meeting trial eligibility criteria (n = 19) to explore reasons for accepting or declining participation and with operating urologists (n = 15) to explore trial acceptability. Results: Patients expressed various preferences and understood these in the context of relative severity and tolerability of their symptoms. Accounts suggest a common trajectory of worsening symptoms with a particular window within which either treatment arm would be considered acceptable. Interviews with clinician recruiters found that uncertainty varied between general and specialist sites, which reflect clinicians' relative exposure to different proportions of the patient population. Conclusion: Recruitment post referral, at specialist sites, was challenging due to patient (and clinician) expectations. Trial design, particularly where there are fixed points for recruitment along the care pathway, can enable or constrain the possibilities for effective accrual depending on how it aligns with the optimum point of patient equipoise. Qualitative recruitment investigations, often focussed on information provision and patient engagement, may also look to better understand the target patient population in order to optimise the point at which patients are approached. Trial registration: ISRCTN Registry, ISRCTN98009168. Registered on 29 November 2012

Echium oil is not protective against weight loss in head and neck cancer patients undergoing curative radio(chemo)therapy: a randomised-controlled trial

Background: Therapy-induced mucositis and dysphagia puts head and neck (H&N) cancer patients at increased risk for developing cachexia. Omega-3 fatty acids (n-3 FA) have been suggested to protect against cachexia. We aimed to examine if echium oil, a plant source of n-3 FA, could reduce weight loss in H&N cancer patients undergoing radio(chemo)therapy with curative intent. Methods: In a double-blind trial, patients were randomly assigned to echium oil (intervention (I) group; 7.5 ml bis in die (b.i.d.), 235 mg/ml α-linolenic acid (ALA) + 95 mg/ml stearidonic acid (SDA) + 79 mg/ml γ-linolenic acid (GLA)) or n-3 FA deficient sunflower oil high oleic (control (C) group; 7.5 ml b.i.d.) additional to standard nutritional support during treatment. Differences in percentage weight loss between both groups were analysed according to the intention-to-treat principle. Erythrocyte FA profile, body composition, nutritional status and quality of life were collected. Results: Ninety-one eligible patients were randomised, of whom 83 were evaluable. Dietary supplement adherence was comparable in both groups (median, I: 87%, C: 81%). At week 4, the I group showed significantly increased values of erythrocyte n-3 eicosapentanoic acid (EPA, 14% vs −5%) and n-6 GLA (42% vs −20%) compared to the C group, without a significant change in n-6 arachidonic acid (AA, 2% vs −1%). Intention-to-treat analysis could not reveal a significant reduction in weight loss related to echium oil consumption (median weight loss, I: 8.9%, C: 7.6%). Also, no significant improvement was observed in the other evaluated anthropometric parameters. Conclusions: Echium oil effectively increased erythrocyte EPA and GLA FAs in H&N cancer patients. It failed however to protect against weight loss, or improve nutritional parameters. Trial registration: ClinicalTrials.gov Identifier NCT01596933

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

Memory Bottlenecks and Memory Contention in Multi-Core Monte Carlo Transport Codes

We have extracted a kernel that executes only the most computationally expensive steps of the Monte Carlo particle transport algorithm - the calculation of macroscopic cross sections - in an effort to expose bottlenecks within multi-core, shared memory architectures

The Random Ray Method versus Multigroup Monte Carlo: The Method of Characteristics in OpenMC and SCONE

The Random Ray Method (TRRM) is a recently developed approach to solving neutral particle transport problems based on the method of characteristics. While the method previously has been implemented only in closed-source or limited-functionality codes, this work describes its implementation in two open-source Monte Carlo codes, OpenMC and SCONE. The random ray implementations required small modifications to the existing multigroup Monte Carlo (MGMC) solvers, offering a rare venue for redundant, fine-grained, “apples-to-apples” speed and accuracy comparisons between transport methods. To this end, TRRM and MGMC solvers are evaluated against each other using each code’s native capabilities on reactor eigenvalue problems with different degrees of energy discretization. On the C5G7 benchmark (featuring only seven energy groups), TRRM achieves a maximum pin power error comparable to or lower than that of MGMC for a given runtime. On a problem with 69 energy groups, MGMC is found to scale more efficiently, obtaining a lower pin power error for a given runtime. However, the defining difference between the two transport methods is found to be their vastly different uncertainty distributions. Specifically, TRRM is found to maintain similar levels of accuracy and uncertainty throughout the simulation domain, whereas MGMC can exhibit orders of magnitude greater errors in areas of the problem that feature low neutron flux. For instance, TRRM provided up to a 373× speed advantage compared with MGMC for computing the flux in low-flux regions in the moderator surrounding the C5G7 core