Shadowing practices: Ethnographic accounts of private eyes as entrepreneurs

In recent years, entrepreneurship studies scholars have begun studying entrepreneurship from a process-oriented philosophy and with an interest in the prosaic, everyday practices of entrepreneurs. In keeping with these new movement approaches, I have tried to catch entrepreneurship as it is happening within the field of private investigations. An in-depth, two-year field study of private investigators engaged in the entwined practices of investigating and entrepreneuring was conducted. Methodologically, I shadowed five private investigators and interviewed an additional 25. Because shadowing is an emergent methodology, an in-depth discussion of conducting and writing shadowing research is provided. As noted in this discussion, it is important that writing remain primarily descriptive yet linked to dominant contemporary discourses. Consequently, an overview of dominant narrative themes in popular and academic discourses about private investigating and entrepreneurship are included. Based on the framework of this methodology, dominant narrative themes, and field notes, various culturally-situated accounts of private investigator practices are offered. The findings of this research project suggest that private investigators use various rhetorical and practical strategies to successfully and simultaneously complete investigative and business-related tasks, such as planting suspicions, using gender and race to strategically position themselves in relation to others in opportunistic ways, and incorporating contemporary technology into their work routines. Drawing on actor-network-theory, I argue that opportunities are enacted through a series of taken-for-granted and everyday interactions among subjects and objects. This research privileges descriptive accounts over theory-building. However, the descriptive accounts of the practices of subjects and objects suggest pragmatic solutions for private investigators to create and manage entrepreneurial opportunities. For example, I propose that private investigators should collectively engage in practices that further professionalize their field. Such professionalizing activities would include, among other things, engaging in knowledge accumulation through academic and professional research activities and professional association public relations campaigns. Insights are also provided regarding the role of rhetoric and technology in opportunity creation and destruction. Readers interested in organization communication and theory will find many of the descriptions to be empirically rich examples of ethno-methods used by actors in highly institutionalized contexts. Similarly, these scholars may also find the descriptions to validate recent arguments regarding organizing as hybridized actions (or action nets) occurring in multiple spaces, places, and times. The examples herein demonstrate the usefulness of shadowing as an approach to understanding organizing practices, especially in fields where actors are always on the move. Readers interested in private investigating will find many of the examples rich in techniques that will enhance profitability. Finally, readers interested in entrepreneurship studies will undoubtedly find many novel potential research projects that are embedded in the various thick descriptions throughout the document

Explainable Semantic Medical Image Segmentation with Style

Semantic medical image segmentation using deep learning has recently achieved high accuracy, making it appealing to clinical problems such as radiation therapy. However, the lack of high-quality semantically labelled data remains a challenge leading to model brittleness to small shifts to input data. Most works require extra data for semi-supervised learning and lack the interpretability of the boundaries of the training data distribution during training, which is essential for model deployment in clinical practice. We propose a fully supervised generative framework that can achieve generalisable segmentation with only limited labelled data by simultaneously constructing an explorable manifold during training. The proposed approach creates medical image style paired with a segmentation task driven discriminator incorporating end-to-end adversarial training. The discriminator is generalised to small domain shifts as much as permissible by the training data, and the generator automatically diversifies the training samples using a manifold of input features learnt during segmentation. All the while, the discriminator guides the manifold learning by supervising the semantic content and fine-grained features separately during the image diversification. After training, visualisation of the learnt manifold from the generator is available to interpret the model limits. Experiments on a fully semantic, publicly available pelvis dataset demonstrated that our method is more generalisable to shifts than other state-of-the-art methods while being more explainable using an explorable manifold

Validity and reliability of computerized measurement of lumbar intervertebral disc height and volume from magnetic resonance images

BACKGROUND CONTEXT: Magnetic resonance (MR) examinations of morphologic characteristics of intervertebral discs (IVDs) have been used extensively for biomechanical studies and clinical investigations of the lumbar spine. Traditionally, the morphologic measurements have been performed using time- and expertise-intensive manual segmentation techniques not well suited for analyses of large-scale studies.

Automated 3D quantitative assessment and measurement of alpha angles from the femoral head-neck junction using MR imaging

To develop an automated approach for 3D quantitative assessment and measurement of alpha angles from the femoral head-neck (FHN) junction using bone models derived from magnetic resonance (MR) images of the hip joint

Comparison of Niskin vs. in situ approaches for analysis of gene expression in deep Mediterranean Sea water samples

Author Posting. © The Author(s), 2014. This is the author's version of the work. It is posted here by permission of Elsevier for personal use, not for redistribution. The definitive version was published in Deep Sea Research Part II: Topical Studies in Oceanography 129 (2016): 213-222, doi:10.1016/j.dsr2.2014.10.020.Obtaining an accurate picture of microbial processes occurring in situ is essential for our understanding of marine biogeochemical cycles of global importance. Water samples are typically collected at depth and returned to the sea surface for processing and downstream experiments. Metatranscriptome analysis is one powerful approach for investigating metabolic activities of microorganisms in their habitat and which can be informative for determining responses of microbiota to disturbances such as the Deepwater Horizon oil spill. For studies of microbial processes occurring in the deep sea, however, sample handling, pressure, and other changes during sample recovery can subject microorganisms to physiological changes that alter the expression profile of labile messenger RNA. Here we report a comparison of gene expression profiles for whole microbial communities in a bathypelagic water column sample collected in the Eastern Mediterranean Sea using Niskin bottle sample collection and a new water column sampler for studies of marine microbial ecology, the Microbial Sampler – In Situ Incubation Device (MS-SID). For some taxa, gene expression profiles from samples collected and preserved 33 in situ were significantly different from potentially more stressful Niskin sampling and 34 preservation on deck. Some categories of transcribed genes also appear to be affected by sample 35 handling more than others. This suggests that for future studies of marine microbial ecology, 36 particularly targeting deep sea samples, an in situ sample collection and preservation approach 37 should be considered.This research was funded by NSF OCE-1061774 to VE and CT, NSF DBI-0424599 to CT and NSF OCE-0849578 to VE and colleague J. Bernhard. Cruise participation was partially supported by Deutsche Forschungsgemeinschaft (DFG) grant STO414/10-1 to T. Stoeck

Fabric Image Representation Encoding Networks for Large-scale 3D Medical Image Analysis

Deep neural networks are parameterised by weights that encode feature representations, whose performance is dictated through generalisation by using large-scale feature-rich datasets. The lack of large-scale labelled 3D medical imaging datasets restrict constructing such generalised networks. In this work, a novel 3D segmentation network, Fabric Image Representation Networks (FIRENet), is proposed to extract and encode generalisable feature representations from multiple medical image datasets in a large-scale manner. FIRENet learns image specific feature representations by way of 3D fabric network architecture that contains exponential number of sub-architectures to handle various protocols and coverage of anatomical regions and structures. The fabric network uses Atrous Spatial Pyramid Pooling (ASPP) extended to 3D to extract local and image-level features at a fine selection of scales. The fabric is constructed with weighted edges allowing the learnt features to dynamically adapt to the training data at an architecture level. Conditional padding modules, which are integrated into the network to reinsert voxels discarded by feature pooling, allow the network to inherently process different-size images at their original resolutions. FIRENet was trained for feature learning via automated semantic segmentation of pelvic structures and obtained a state-of-the-art median DSC score of 0.867. FIRENet was also simultaneously trained on MR (Magnatic Resonance) images acquired from 3D examinations of musculoskeletal elements in the (hip, knee, shoulder) joints and a public OAI knee dataset to perform automated segmentation of bone across anatomy. Transfer learning was used to show that the features learnt through the pelvic segmentation helped achieve improved mean DSC scores of 0.962, 0.963, 0.945 and 0.986 for automated segmentation of bone across datasets.Comment: 12 pages, 10 figure

Statistical shape model reconstruction with sparse anomalous deformations: application to intervertebral disc herniation

Many medical image processing techniques rely on accurate shape modeling of anatomical features. The presence of shape abnormalities challenges traditional processing algorithms based on strong morphological priors. In this work, a sparse shape reconstruction from a statistical shape model is presented. It combines the advantages of traditional statistical shape models (defining a ‘normal’ shape space) and previously presented sparse shape composition (providing localized descriptors of anomalies). The algorithm was incorporated into our image segmentation and classification software. Evaluation was performed on simulated and clinical MRI data from 22 sciatica patients with intervertebral disc herniation, containing 35 herniated and 97 normal discs. Moderate to high correlation (R = 0.73) was achieved between simulated and detected herniations. The sparse reconstruction provided novel quantitative features describing the herniation morphology and MRI signal appearance in three dimensions (3D). The proposed descriptors of local disc morphology resulted to the 3D segmentation accuracy of 1.07 ± 1.00 mm (mean absolute vertex-to-vertex mesh distance over the posterior disc region), and improved the intervertebral disc classification from 0.888 to 0.931 (area under receiver operating curve). The results show that the sparse shape reconstruction may improve computer-aided diagnosis of pathological conditions presenting local morphological alterations, as seen in intervertebral disc herniation

Are students reading my feedback? Using a feedback analytics capture system to understand how large cohorts of biomedical science students use feedback

Feedback is one of the most potent teaching strategies known to produce student learning gains (Hattie, 2009). However, the provision of feedback has been identified as one of the weakest elements of university practices (Graduate Careers Australia, 2012). Although there are many theoretical frameworks for improving feedback provision (Hattie & Timperley, 2007; Nicol & Macfarlane Dick, 2006; Sadler, 2010), little is known about how students actually use feedback (Jonsson, 2013). Many authors contend that students commonly ignore feedback (Boud & Molloy, 2013), with some empirical evidence that students do not collect or read written feedback (Sinclair & Cleland, 2007), or ignore it when they do not understand what it means (Still & Koerber, 2010). The increasingly widespread adoption of online marking and feedback tools facilitates students’ access to their feedback, but until now there has been no systematic characterise the patterns of student access of this feedback, nor how this impacts on their subsequent performance (Ellis, 2013). We have developed, and extensively trialled, a Feedback Analytics Capture System (FACS, previously called UQMarkUP) which synthesises large-scale data on digital feedback provision, how students access feedback, and changes in students’ academic performance (Zimbardi et al., 2013). Specifically, FACS captures detailed information about the audio, typed and hand-drawn annotations markers insert in situ in electronic assessment submissions, and the marks awarded across a variety of systems, including detailed criteria-standards rubrics. FACS also collects detailed information about how students access this feedback, logging the timing and nature of every mouse click a student uses to interact with the feedback-embedded document. In this exploratory study, we investigated the frequency, timing, and patterns in how students access their feedback. Analyses of FACS data from laboratory reports submitted for summative assessment in two biomedical science courses in level 1 (n=1781 students) and level 2 (n=389), in Semesters 1 and 2, 2013, revealed that the vast majority of students opened their feedback. In the level 1 course 93% students opened Report 1, 92% opened Report 2, 87% opened Report 3 and 85% opened Report 4. In contrast, far fewer students in the level 2 course opened their feedback, and fewer students opened Report 1 (68%) than Report 2 (82%). Although a similar pattern existed for how long students had their feedback open (level 1 Report 1: 12±8 hours; Report 2: 3.4±1.6 hours; Report 3: 2.1±1.4 hours; Report 4: 43±7 minutes), the level 2 reports now reverted to greater duration of interaction with Report 1 (5.6±0.6 hours) than Report 2 (1.2±0.3 hours). The number of students accessing feedback surges 1-2 days after feedback release, followed by a persistent tail of students accessing the feedback for the subsequent two months. In this context of undergraduate biomedical science laboratory assessments, students are not only collecting and reading their feedback, but they are interacting with it extensively. There may also be potential maturational, course-specific, and interaction effects that shape feedback use, and require further exploration as we expand this feedback analytics approach across a broader range of educational contexts

Characterization and performance of nucleic acid nanoparticles combined with protamine and gold

Macromolecular nucleic acids such as DNA vaccines, siRNA, and splice-site switching oligomers (SSO) have vast chemotherapeutic potential. Nanoparticulate biomaterials hold promise for DNA and RNA delivery when a means for binding is identified that retains structure-function and provides stabilization by the nanoparticles. In order to provide these benefits of binding, we combined DNA and RNA with protamine— demonstrating association to gold microparticles by electrophoretic, gel shot, fluorescence, and dynamic laser light spectroscopy (DLLS). A pivotal finding in these studies is that the Au-protamine-DNA conjugates greatly stabilize the DNA; and DNA structure and vaccine activity are maintained even after exposure to physical, chemical, and temperature-accelerated degradation. Specifically, protamine formed nanoparticles when complexed to RNA. These complexes could be detected by gel shift and were probed by high throughput absorbance difference spectroscopy (HTADS). Biological activity of these RNA nanoparticles (RNPs) was demonstrated also by a human tumor cell splice-site switching assay and by siRNA delivery against B-Raf—a key cancer target. Finally, RNA:protamine particles inhibited growth of cultured human tumor cells and bacteria. These data provide new insights into DNA and RNA nanoparticles and prospects for their delivery and chemotherapeutic activity

A lightweight rapid application development framework for biomedical image analysis

Biomedical imaging analysis typically comprises a variety of complex tasks requiring sophisticated algorithms and visualising high dimensional data. The successful integration and deployment of the enabling software to clinical (research) partners, for rigorous evaluation and testing, is a crucial step to facilitate adoption of research innovations within medical settings. In this paper, we introduce the Simple Medical Imaging Library Interface (SMILI), an object oriented open-source framework with a compact suite of objects geared for rapid biomedical imaging (cross-platform) application development and deployment. SMILI supports the development of both command-line (shell and Python scripting) and graphical applications utilising the same set of processing algorithms. It provides a substantial subset of features when compared to more complex packages, yet it is small enough to ship with clinical applications with limited overhead and has a license suitable for commercial use. After describing where SMILI fits within the existing biomedical imaging software ecosystem, by comparing it to other state-of-the-art offerings, we demonstrate its capabilities in creating a clinical application for manual measurement of cam-type lesions of the femoral head-neck region for the investigation of femoro-acetabular impingement (FAI) from three dimensional (3D) magnetic resonance (MR) images of the hip. This application for the investigation of FAI proved to be convenient for radiological analyses and resulted in high intra (ICC=0.97) and inter-observer (ICC=0.95) reliabilities for measurement of α-angles of the femoral head-neck region. We believe that SMILI is particularly well suited for prototyping biomedical imaging applications requiring user interaction and/or visualisation of 3D mesh, scalar, vector or tensor data