On numerically accurate finite element

A general criterion for testing a mesh with topologically similar repeat units is given, and the analysis shows that only a few conventional element types and arrangements are, or can be made suitable for computations in the fully plastic range. Further, a new variational principle, which can easily and simply be incorporated into an existing finite element program, is presented. This allows accurate computations to be made even for element designs that would not normally be suitable. Numerical results are given for three plane strain problems, namely pure bending of a beam, a thick-walled tube under pressure, and a deep double edge cracked tensile specimen. The effects of various element designs and of the new variational procedure are illustrated. Elastic-plastic computation at finite strain are discussed

Designing to Debias: Measuring and Reducing Public Managers’ Anchoring Bias

Public managers’ decisions are affected by cognitive biases. For instance, employees’ previous year's performance ratings influence new ratings irrespective of actual performance. Nevertheless, experimental knowledge of public managers’ cognitive biases is limited, and debiasing techniques have rarely been studied. Using a survey experiment on 1,221 public managers and employees in the United Kingdom, this research (1) replicates two experiments on anchoring to establish empirical generalization across institutional contexts and (2) tests a consider-the-opposite debiasing technique. The results indicate that anchoring bias replicates in a different institutional context, although effect sizes differ. Furthermore, a low-cost, low-intensity consider-the-opposite technique mitigates anchoring bias in this survey experiment. An exploratory subgroup analysis indicates that the effect of the intervention depends on context. The next step is to test this strategy in real-world settings

Accuracy and repeatability of joint sparsity multi-component estimation in MR Fingerprinting

MR fingerprinting (MRF) is a promising method for quantitative characterization of tissues. Often, voxel-wise measurements are made, assuming a single tissue-type per voxel. Alternatively, the Sparsity Promoting Iterative Joint Non-negative least squares Multi-Component MRF method (SPIJN-MRF) facilitates tissue parameter estima-tion for identified components as well as partial volume segmentations. The aim of this paper was to evaluate the accuracy and repeatability of the SPIJN-MRF parameter estimations and partial volume segmentations. This was done (1) through numerical simulations based on the BrainWeb phantoms and (2) using in vivo acquired MRF data from 5 subjects that were scanned on the same week-day for 8 consecutive weeks. The partial volume segmen-tations of the SPIJN-MRF method were compared to those obtained by two conventional methods: SPM12 and FSL. SPIJN-MRF showed higher accuracy in simulations in comparison to FSL-and SPM12-based segmentations: Fuzzy Tanimoto Coefficients (FTC) comparing these segmentations and Brainweb references were higher than 0.95 for SPIJN-MRF in all the tissues and between 0.6 and 0.7 for SPM12 and FSL in white and gray matter and between 0.5 and 0.6 in CSF. For the in vivo MRF data, the estimated relaxation times were in line with literature and minimal variation was observed. Furthermore, the coefficient of variation (CoV) for estimated tissue volumes with SPIJN-MRF were 10.5% for the myelin water, 6.0% for the white matter, 5.6% for the gray matter, 4.6% for the CSF and 1.1% for the total brain volume. CoVs for CSF and total brain volume measured on the scanned data for SPIJN-MRF were in line with those obtained with SPM12 and FSL. The CoVs for white and gray mat-ter volumes were distinctively higher for SPIJN-MRF than those measured with SPM12 and FSL. In conclusion, the use of SPIJN-MRF provides accurate and precise tissue relaxation parameter estimations taking into account intrinsic partial volume effects. It facilitates obtaining tissue fraction maps of prevalent tissues including myelin water which can be relevant for evaluating diseases affecting the white matter.Radiolog

Pathologists' first opinions on barriers and facilitators of computational pathology adoption in oncological pathology: an international study.

Computational pathology (CPath) algorithms detect, segment or classify cancer in whole slide images, approaching or even exceeding the accuracy of pathologists. Challenges have to be overcome before these algorithms can be used in practice. We therefore aim to explore international perspectives on the future role of CPath in oncological pathology by focusing on opinions and first experiences regarding barriers and facilitators. We conducted an international explorative eSurvey and semi-structured interviews with pathologists utilizing an implementation framework to classify potential influencing factors. The eSurvey results showed remarkable variation in opinions regarding attitude, understandability and validation of CPath. Interview results showed that barriers focused on the quality of available evidence, while most facilitators concerned strengths of CPath. A lack of consensus was present for multiple factors, such as the determination of sufficient validation using CPath, the preferred function of CPath within the digital workflow and the timing of CPath introduction in pathology education. The diversity in opinions illustrates variety in influencing factors in CPath adoption. A next step would be to quantitatively determine important factors for adoption and initiate validation studies. Both should include clear case descriptions and be conducted among a more homogenous panel of pathologists based on sub specialization

Colorectal cancer risk after removal of polyps in fecal immunochemical test based screening

Background: Colonoscopy surveillance intervals are based on the predicted risk of metachronous colorectal cancer (CRC) after polyp removal. However, risk estimation per polyp subtype is difficult due to the fact that many patients have multiple polyps. To enable risk estimation per polyp subtypes we examined the metachronous CRC risk of subgroups based on presence or absence of co-occurring findings. Methods: Using high-quality screening colonoscopies performed after a positive fecal immunochemical test between 2014 and 2020 within the Dutch CRC screening program, we applied Cox regression analysis to evaluate the association between findings at baseline colonoscopy and metachronous CRCs. For our primary outcome, we appointed each patient to unique subgroups based on removed polyp subtypes that were present or absent at baseline colonoscopy and used the groups without polyps as reference. High-risk subgroups were individuals with high-risk serrated polyps, defined as serrated polyp ≥10 mm, sessile serrated lesions with dysplasia, or traditional serrated adenomas, as well as high-risk adenomas, defined as adenoma ≥10 mm or containing high-grade dysplasia. Findings: In total 253,833 colonoscopies were included. Over a median follow-up of 36 months (IQR, 21–57), we identified 504 metachronous CRCs. Hazard ratios for metachronous CRC was 1.70 (95% CI, 1.07–2.69) for individuals with high-risk serrated polyps without high-risk adenomas, 1.22 (0.96–1.55) for individuals with high-risk adenomas without high-risk serrated polyps, and 2.00 (1.19–3.39) for individuals with high-risk serrated polyps and high-risk adenomas, compared to patients without polyps. Interpretation: Accounting for co-occurring findings, we observed an increased metachronous CRC risk for individuals that had high-risk serrated polyps with the presence of high-risk adenomas, or individuals with high-risk serrated polyps without high-risk adenomas. These findings could provide more evidence to support post-polypectomy surveillance guidelines. Funding: None.</p

Application profiling and resource management for MapReduce

Scale of data generated and processed is exponential growth in the Big Data ear. It poses a challenge that is far beyond the goal of a single computing system. Processing such vast amount of data on a single machine is impracticable in term of time or cost. Hence, distributed systems, which can harness very large clusters of commodity computers and processing data within restrictive time deadlines, are imperative. In this thesis, we target two aspects of distributed systems: application profiling and resource management. We study a MapReduce system in detail, which is a programming paradigm for large scale distributed computing, and presents solutions to tackle three key problems. Firstly, this thesis analyzes the characteristics of jobs running on the MapReduce system to reveal the problem—the Application scope of MapReduce has been extended beyond the original design goal that was large-scale data processing. This problem enables us to present a Workload Characteristic Oriented Scheduler (WCO), which strives for co-locating tasks of possibly different MapReduce jobs with complementing resource usage characteristics. Secondly, this thesis studies the current job priority mechanism focusing on resource management. In the MapReduce system, job priority only exists at scheduling level. High priority jobs are placed at the front of the scheduling queue and dispatched first. Resource, however, is fairly shared among jobs running at the same worker node without any consideration for their priorities. In order to resolve this, this thesis presents a non-intrusive slot layering solution, which dynamically allocates resource between running jobs based on their priority and efficiently reduces the execution time of high priority jobs while improves overall throughput. Last, based on the fact of underutilization of resource at each individual worker node, this thesis propose a new way, Local Resource Shaper (LRS), to smooth resource consumption of each individual job by automatically tuning the execution of concurrent jobs to maximize resource utilization while minimizing resource contention

Gastrointestinal tissue‐based molecular biomarkers: A practical categorization based on the 2019 WHO Classification of Epithelial Digestive Tumours

Molecular biomarkers have come to constitute one of the cornerstones of oncological pathology. The method of classification not only directly affects the manner in which patients are diagnosed and treated, but also guides the development of drugs and of artificial intelligence tools. The aim of this article is to organise and update gastrointestinal molecular biomarkers in order to produce an easy-to-use guide for routine diagnostics. For this purpose, we have extracted and reorganised the molecular information on epithelial neoplasms included in the 2019 World Health Organization classification of tumours. Digestive system tumours, 5th edn