PESSCARA: An Example Infrastructure for Big Data Research

Big data requires a flexible system for data management and curation which has to be intuitive, and it should also be able to execute non-linear analysis pipelines suitable to handle with the nature of big data. This is certainly true for medical images where the amount of data grows exponentially every year and the nature of images rapidly changes with technological advances and rapid genomic advances. In this chapter, we describe a system that provides flexible management for medical images plus a wide array of associated metadata, including clinical data, genomic data, and clinical trial information. The system consists of open-source Content Management System (CMS) that has a highly configurable workflow; has a single interface that can store, manage, enable curation, and retrieve imaging-based studies; and can handle the requirement for data auditing and project management. Furthermore, the system can be extended to interact with all the modern big data analysis technologies

Development and validation of a framework for the assessment of school curricula on the presence of evolutionary concepts (FACE)

Evolution is a key concept of biology, fundamental to understand the world and address important societal problems, but research studies show that it is still not widely understood and accepted. Several factors are known to influence evolution acceptance and understanding, but little information is available regarding the impacts of the curriculum on these aspects. Very few curricula have been examined to assess the coverage of biological evolution. The available studies do not allow comparative analyses, due to the different methodologies employed by the authors. However, such an analysis would be useful for research purposes and for the development of appropriate educational policies to address the problem of a lack of evolution acceptance in some countries. In this paper we describe the steps through which we developed a valid and reliable instrument for curricula analysis known as FACE: “Framework to Assess the Coverage of biological Evolution by school curricula.” This framework was developed based on the “Understanding Evolution Conceptual Framework” (UECF). After an initial pilot study, our framework was reformulated based on identified issues and experts’ opinions. To generate validity and reliability evidence in support of the framework, it was applied to four European countries’ curricula. For each country, a team of a minimum of two national and two foreign coders worked independently to assess the curriculum using this framework for content analysis. Reliability evidence was estimated using Krippendorf's alpha and resulted in appropriate values for coding the examined curricula. Some issues that coders faced during the analysis were discussed and, to ensure better reliability for future researchers, additional guidelines and one extra category were included in the framework. The final version of the framework includes six categories and 34 subcategories. FACE is a useful tool for the analysis and the comparison of curricula and school textbooks regarding the coverage of evolution, and such results can guide curricula development.info:eu-repo/semantics/publishedVersio

Flying to Quality: Cultural Influences on Online Reviews

Customers increasingly consult opinions expressed online before making their final decisions. However, inherent factors such as culture may moderate the criteria and the weights individuals use to form their expectations and evaluations. Therefore, not all opinions expressed online match customers’ personal preferences, neither can firms use this information to deduce general conclusions. Our study explores this issue in the context of airline services using Hofstede’s framework as a theoretical anchor. We gauge the effect of each dimension as well as that of cultural distance between the passenger and the airline on the overall satisfaction with the flight as well as specific service factors. Using topic modeling, we also capture the effect of culture on review text and identify factors that are not captured by conventional rating scales. Our results provide significant insights for airline managers about service factors that affect more passengers from specific cultures leading to higher satisfaction/dissatisfaction

Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning

We present a deep convolutional neural network application based on autoencoders aimed at segmentation of increased signal regions in fluid-attenuated inversion recovery magnetic resonance imaging images. The convolutional autoencoders were trained on the publicly available Brain Tumor Image Segmentation Benchmark (BRATS) data set, and the accuracy was evaluated on a data set where 3 expert segmentations were available. The simultaneous truth and performance level estimation (STAPLE) algorithm was used to provide the ground truth for comparison, and Dice coefficient, Jaccard coefficient, true positive fraction, and false negative fraction were calculated. The proposed technique was within the interobserver variability with respect to Dice, Jaccard, and true positive fraction. The developed method can be used to produce automatic segmentations of tumor regions corresponding to signal-increased fluid-attenuated inversion recovery regions

Front- and back-end employee satisfaction during service transition

Purpose Scholars studying servitization argue that manufacturers moving into services need to develop new job roles or modify existing ones, which must be enacted by employees with the right mentality, skill sets, attitudes and capabilities. However, there is a paucity of empirical research on how such changes affect employee-level outcomes. Design/methodology/approach The authors theorize that job enrichment and role stress act as countervailing forces during the manufacturer's service transition, with implications for employee satisfaction. The authors test the hypotheses using a sample of 21,869 employees from 201 American manufacturers that declared revenues from services over a 10-year period. Findings The authors find an inverted U-shaped relationship between the firm's level of service infusion and individual employee satisfaction, which is flatter for front-end staff. This relationship differs in shape and/or magnitude between firms, highlighting the role of unobserved firm-level idiosyncratic factors. Practical implications Servitized manufacturers, especially those in the later stage of their transition (i.e. when services start to account for more than 50% of annual revenues), should try to ameliorate their employees' role-induced stress to counter a drop in satisfaction. Originality/value This is one of the first studies to examine systematically the relationship between servitization and individual employee satisfaction. It shows that back-end employees in manufacturing firms are considerably affected by an increasing emphasis on services, while past literature has almost exclusively been concerned with front-end staff

Increased signal intensity within glioblastoma resection cavities on fluid-attenuated inversion recovery imaging to detect early progressive disease in patients receiving radiotherapy with concomitant temozolomide therapy

PURPOSE: Our study tested the diagnostic accuracy of increased signal intensity (SI) within FLAIR MR images of resection cavities in differentiating early progressive disease (ePD) from pseudoprogression (PsP) in patients with glioblastoma treated with radiotherapy with concomitant temozolomide therapy. METHODS: In this retrospective study approved by our Institutional Review Board, we evaluated the records of 122 consecutive patients with partially or totally resected glioblastoma. Region of interest (ROI) analysis assessed 33 MR examinations from 11 subjects with histologically confirmed ePD and 37 MR examinations from 14 subjects with PsP (5 histologically confirmed, 9 clinically diagnosed). After applying an N4 bias correction algorithm to remove B0 field distortion and to standardize image intensities and then normalizing the intensities based on an ROI of uninvolved white matter from the contralateral hemisphere, the mean intensities of the ROI from within the resection cavities were calculated. Measures of diagnostic performance were calculated from the receiver operating characteristic (ROC) curve using the threshold intensity that maximized differentiation. Subgroup analysis explored differences between the patients with biopsy-confirmed disease. RESULTS: At an optimal threshold intensity of 2.9, the area under the ROC curve (AUROC) for FLAIR to differentiate ePD from PsP was 0.79 (95% confidence interval 0.686-0.873) with a sensitivity of 0.818 and specificity of 0.694. The AUROC increased to 0.86 when only the patients with biopsy-confirmed PsP were considered. CONCLUSIONS: Increased SI within the resection cavity of FLAIR images is not a highly specific sign of ePD in glioblastoma patients treated with the Stupp protocol

AI in the Loop -- Functionalizing Fold Performance Disagreement to Monitor Automated Medical Image Segmentation Pipelines

Methods for automatically flag poor performing-predictions are essential for safely implementing machine learning workflows into clinical practice and for identifying difficult cases during model training. We present a readily adoptable method using sub-models trained on different dataset folds, where their disagreement serves as a surrogate for model confidence. Thresholds informed by human interobserver values were used to determine whether a final ensemble model prediction would require manual review. In two different datasets (abdominal CT and MR predicting kidney tumors), our framework effectively identified low performing automated segmentations. Flagging images with a minimum Interfold test Dice score below human interobserver variability maximized the number of flagged images while ensuring maximum ensemble test Dice. When our internally trained model was applied to an external publicly available dataset (KiTS21), flagged images included smaller tumors than those observed in our internally trained dataset, demonstrating the methods robustness to flagging poor performing out-of-distribution input data. Comparing interfold sub-model disagreement against human interobserver values is an efficient way to approximate a model's epistemic uncertainty - its lack of knowledge due to insufficient relevant training data - a key functionality for adopting these applications in clinical practice.Comment: 16 pages, 6 figure