The alternative uses of disused dairy factories in Taranaki : a thesis presented in partial fulfilment of the requirements for the degree of Master of Arts in Geography at Massey University

Small local dairy factories have long been a part of New Zealand's dairying heritage. No longer profitable in their original use, subsequent redundancy and abandonment has seen the appearance of the "disused dairy factory" in the rural landscape. In disuse these buildings find their greatest asset for potential reuse. As existing capital stock, these disused dairy factories manifest potential opportunities for enterprises other than dairying, to establish alternative uses. As a product of the past, the phenomenon of dairy factory reuse represents a change in use to meet the demands of the present. The extent to which this has been achieved, and how this pattern can be explained, evidences the interaction of past and present forces in effecting a potential future for these buildings

The Massive and Distant Clusters of WISE Survey V: Extended Radio Sources in Massive Galaxy Clusters at z~1

We present the results from a pilot study with the Karl G. Jansky Very Large Array (JVLA) to determine the radio morphologies of extended radio sources and the properties of their host-galaxies in 10 massive galaxy clusters at z~1, an epoch in which clusters are assembling rapidly. These clusters are drawn from a parent sample of WISE-selected galaxy clusters that were cross-correlated with the VLA Faint Images of the Radio Sky at Twenty-Centimeters survey (FIRST) to identify extended radio sources within 1$^{\prime}$ of the cluster centers. Out of the ten targeted sources, six are FR II sources, one is an FR I source, and three sources have undetermined morphologies. Eight radio sources have associated Spitzer data, 75% presenting infrared counterparts. A majority of these counterparts are consistent with being massive galaxies. The angular extent of the FR sources exhibits a strong correlation with the cluster-centric radius, which warrants further investigation with a larger sample.Comment: accepted to Ap

Open-source Web Portal for Managing Self-reported Data and Real-world Data Donation in Diabetes Research: Platform Feasibility Study

Background: People with diabetes and their support networks have developed open-source automated insulin delivery systems to help manage their diabetes therapy, as well as to improve their quality of life and glycemic outcomes. Under the hashtag #WeAreNotWaiting, a wealth of knowledge and real-world data have been generated by users of these systems but have been left largely untapped by research; opportunities for such multimodal studies remain open. Objective: We aimed to evaluate the feasibility of several aspects of open-source automated insulin delivery systems including challenges related to data management and security across multiple disparate web-based platforms and challenges related to implementing follow-up studies. Methods: We developed a mixed methods study to collect questionnaire responses and anonymized diabetes data donated by participants-which included adults and children with diabetes and their partners or caregivers recruited through multiple diabetes online communities. We managed both front-end participant interactions and back-end data management with our web portal (called the Gateway). Participant questionnaire data from electronic data capture (REDCap) and personal device data aggregation (Open Humans) platforms were pseudonymously and securely linked and stored within a custom-built database that used both open-source and commercial software. Participants were later given the option to include their health care providers in the study to validate their questionnaire responses; the database architecture was designed specifically with this kind of extensibility in mind. Results: Of 1052 visitors to the study landing page, 930 participated and completed at least one questionnaire. After the implementation of health care professional validation of self-reported clinical outcomes to the study, an additional 164 individuals visited the landing page, with 142 completing at least one questionnaire. Of the optional study elements, 7 participant-health care professional dyads participated in the survey, and 97 participants who completed the survey donated their anonymized medical device data. Conclusions: The platform was accessible to participants while maintaining compliance with data regulations. The Gateway formalized a system of automated data matching between multiple data sets, which was a major benefit to researchers. Scalability of the platform was demonstrated with the later addition of self-reported data validation. This study demonstrated the feasibility of custom software solutions in addressing complex study designs. The Gateway portal code has been made available open-source and can be leveraged by other research groups

Emotional and Physical Health Impact in Children and Adolescents and Their Caregivers Using Open-source Automated Insulin Delivery: Qualitative Analysis of Lived Experiences

Background: Given the limitations in the access and license status of commercially developed automated insulin delivery (AID) systems, open-source AID systems are becoming increasingly popular among people with diabetes, including children and adolescents. Objective: This study aimed to investigate the lived experiences and physical and emotional health implications of children and their caregivers following the initiation of open-source AID, their perceived challenges, and sources of support, which have not been explored in the existing literature. Methods: Data were collected through 2 sets of open-ended questions from a web-based multinational survey of 60 families from 16 countries. The narratives were thematically analyzed, and a coding framework was identified through iterative alignment. Results: A range of emotions and improvements in quality of life and physical health were reported, as open-source AID enabled families to shift their focus away from diabetes therapy. Caregivers were less worried about hypoglycemia at night and outside their family homes, leading to increased autonomy for the child. Simultaneously, the glycemic outcomes and sleep quality of both the children and caregivers improved. Nonetheless, the acquisition of suitable hardware and technical setup could be challenging. The #WeAreNotWaiting community was the primary source of practical and emotional support. Conclusions: Our findings show the benefits and transformative impact of open-source AID and peer support on children with diabetes and their caregivers and families, where commercial AID systems are not available or suitable. Further efforts are required to improve the effectiveness and usability and facilitate access for children with diabetes, worldwide, to benefit from this innovative treatment

How big is the elephant in the room? Estimated and actual IT costs in an online behaviour change trial

The practical and methodological challenges inherent in online behaviour change studies are both novel and complex. We relate our experiences of estimating and managing information technology (IT) research and intervention costs in an ongoing internet trial in the hope that others will find this information useful

Predictive Modeling Techniques in Prostate Cancer

A number of new predictive modeling techniques have emerged in the past several years. These methods can be used independently or in combination with traditional modeling techniques to produce useful tools for the management of prostate cancer. Investigators should be aware of these techniques and avail themselves of their potentially useful properties. This review outlines selected predictive methods that can be used to develop models that may be useful to patients and clinicians for prostate cancer management.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63147/1/10915360152745812.pd

Constraints on the Thermal Contents of the X-ray Cavities of Cluster MS 0735.6+7421 with Sunyaev-Zel'dovich Effect Observations

Outbursts from active galactic nuclei (AGN) can inflate cavities in the intracluster medium (ICM) of galaxy clusters and are believed to play the primary role in offsetting radiative cooling in the ICM. However, the details of how the energy from AGN feedback thermalizes to heat the ICM is not well understood, partly due to the unknown composition and energetics of the cavities. The Sunyaev-Zel'dovich (SZ) effect, a measure of the integrated pressure along the line of sight, provides a means of measuring the thermal contents of the cavities, to discriminate between thermal, nonthermal, and other sources of pressure support. Here we report measurements of the SZ effect at 30 GHz toward the galaxy cluster MS 0735.6+7421 (MS0735), using the Combined Array for Research in Millimeter-wave Astronomy (CARMA). MS0735 hosts the most energetic AGN outburst known and lobes of radio synchrotron emission coincident with a pair of giant X-ray cavities $\sim 200$ across. Our CARMA maps show a clear deficit in the SZ signal coincident with the X-ray identified cavities, when compared to a smooth X-ray derived pressure model. We find that the cavities have very little SZ-contributing material, suggesting that they are either supported by very diffuse thermal plasma with temperature in excess of hundreds of keV, or are not supported thermally. Our results represent the first detection (with $4.4 \sigma$ significance) of this phenomenon with the SZ effect.Comment: 15 pages, 9 figures, submitted to ApJ Jun 2018, Accepted Dec 2018, Published Jan 2019. This is the version of the article before editing, as submitted by an author to ApJ. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.3847/1538-4357/aaf88

Automated Grading of Radiographic Knee Osteoarthritis Severity Combined with Joint Space Narrowing

The assessment of knee osteoarthritis (KOA) severity on knee X-rays is a central criteria for the use of total knee arthroplasty. However, this assessment suffers from imprecise standards and a remarkably high inter-reader variability. An algorithmic, automated assessment of KOA severity could improve overall outcomes of knee replacement procedures by increasing the appropriateness of its use. We propose a novel deep learning-based five-step algorithm to automatically grade KOA from posterior-anterior (PA) views of radiographs: (1) image preprocessing (2) localization of knees joints in the image using the YOLO v3-Tiny model, (3) initial assessment of the severity of osteoarthritis using a convolutional neural network-based classifier, (4) segmentation of the joints and calculation of the joint space narrowing (JSN), and (5), a combination of the JSN and the initial assessment to determine a final Kellgren-Lawrence (KL) score. Furthermore, by displaying the segmentation masks used to make the assessment, our algorithm demonstrates a higher degree of transparency compared to typical "black box" deep learning classifiers. We perform a comprehensive evaluation using two public datasets and one dataset from our institution, and show that our algorithm reaches state-of-the art performance. Moreover, we also collected ratings from multiple radiologists at our institution and showed that our algorithm performs at the radiologist level. The software has been made publicly available at https://github.com/MaciejMazurowski/osteoarthritis-classification