Degenerative Pathways of Lumbar Motion Segments--A Comparison in Two Samples of Patients with Persistent Low Back Pain

Background: Magnetic resonance imaging (MRI) is used to identify spinal pathoanatomy in people with persistent low back pain. However, the clinical relevance of spinal degenerative MRI findings remains uncertain. Although multiple MRI findings are almost always present at the same time, research into the association with clinical outcomes (such as pain) has predominantly focused on individual MRI findings. This study aimed to: (i) investigate how multiple MRI lumbar spine findings cluster together within two different samples of patients with low back pain, (ii) classify these clusters into hypothetical pathways of degeneration based on scientific knowledge of disco-vertebral degeneration, and (iii) compare these clusters and degenerative pathways between samples. Methods: We performed a secondary cross-sectional analysis on two dissimilar MRI samples collected in a hospital department: (1) data from the spinal MRI reports of 4,162 low back pain patients and (2) data from an MRI research protocol of 631 low back pain patients. Latent Class Analysis was used in both samples to cluster MRI findings from lumbar motion segments. Using content analysis, each cluster was then categorised into hypothetical pathways of degeneration. Results: Six clusters of MRI findings were identified in each of the two samples. The content of the clusters in the two samples displayed some differences but had the same overall pattern of MRI findings. Although the hypothetical degenerative pathways identified in the two samples were not identical, the overall pattern of increasing degeneration within the pathways was the same. Conclusions: It was expected that different clusters could emerge from different samples, however, when organised into hypothetical pathways of degeneration, the overall pattern of increasing degeneration was similar and biologically plausible. This evidence of reproducibility suggests that Latent Class Analysis may provide a new approach to investigating the relationship between MRI findings and clinically important characteristics such as pain and activity limitation

Data Science for All: Apache Spark & Jupyter Notebooks

The Nation\u27s research enterprise faces a shortage of data scientists. Expanding the pipeline of data science students, particularly from underrepresented populations, requires educational institutions to increase awareness of data science and inspire a passion for data in students as they begin their academic careers. In this tutorial we discuss the development and delivery of a free seminar designed to provide hands-on lessons in the use of both Apache Spark and Jupyter notebooks to students from any academic background in an approachable, no-risk environment. An explanation of the seminar resources, exercises, and implementation guidelines are included, as are lessons learned from several successful seminars held both in-person and virtually at two institutions of high education

Bayesian integration of sensor information and a multivariate dynamic linear model for prediction of dairy cow mastitis

AbstractRapid detection of dairy cow mastitis is important so corrective action can be taken as soon as possible. Automatically collected sensor data used to monitor the performance and the health state of the cow could be useful for rapid detection of mastitis while reducing the labor needs for monitoring. The state of the art in combining sensor data to predict clinical mastitis still does not perform well enough to be applied in practice. Our objective was to combine a multivariate dynamic linear model (DLM) with a naïve Bayesian classifier (NBC) in a novel method using sensor and nonsensor data to detect clinical cases of mastitis. We also evaluated reductions in the number of sensors for detecting mastitis. With the DLM, we co-modeled 7 sources of sensor data (milk yield, fat, protein, lactose, conductivity, blood, body weight) collected at each milking for individual cows to produce one-step-ahead forecasts for each sensor. The observations were subsequently categorized according to the errors of the forecasted values and the estimated forecast variance. The categorized sensor data were combined with other data pertaining to the cow (week in milk, parity, mastitis history, somatic cell count category, and season) using Bayes’ theorem, which produced a combined probability of the cow having clinical mastitis. If this probability was above a set threshold, the cow was classified as mastitis positive. To illustrate the performance of our method, we used sensor data from 1,003,207 milkings from the University of Florida Dairy Unit collected from 2008 to 2014. Of these, 2,907 milkings were associated with recorded cases of clinical mastitis. Using the DLM/NBC method, we reached an area under the receiver operating characteristic curve of 0.89, with a specificity of 0.81 when the sensitivity was set at 0.80. Specificities with omissions of sensor data ranged from 0.58 to 0.81. These results are comparable to other studies, but differences in data quality, definitions of clinical mastitis, and time windows make comparisons across studies difficult. We found the DLM/NBC method to be a flexible method for combining multiple sensor and nonsensor data sources to predict clinical mastitis and accommodate missing observations. Further research is needed before practical implementation is possible. In particular, the performance of our method needs to be improved in the first 2 wk of lactation. The DLM method produces forecasts that are based on continuously estimated multivariate normal distributions, which makes forecasts and forecast errors easy to interpret, and new sensors can easily be added

Online informationssøgning i en overgangstid: - med særligt fokus på det historiske forløb i et større dansk forskningsbibliotek

Over the centuries, searching for information took place through searches in printed works. computers enabled the onset of electronic data processing. In the wake of this, an information industry emerged that partly digitized information in large databases and made the contents of the databases searchable and accessible. This article describes the brief but hectic development where online information search was established as a service in documentation centres. This is exemplified by illustrating the conditions at the then Odense University Library. In the period up to the turn of the millennium, end users of online information gradually took over the search process itself, which led to a reduction in the search activity at the documentation centres, while the total number of searches increased. Gradually, the information search service was adapted to the users’ new needs. The users' use of paid databases remains an area of ​​concern for the library sector, as users often prefer the free bases of the Internet, but they do not necessarily find the best references. Thus, there is still a task in promoting the scientific databases and educating the users. Concepts such as “information literacy” and “digital education” are therefore central to the work of research libraries.&nbsp

The impact of market use of consumer generated content on a brand community

Many studies have demonstrated that members of brand communities are capable of extensive, and increasingly professional, creation of brand content. However, little work has examined how the use of such community-created content impacts the community or its members. We conducted a netnographic study of the Jones Soda brand community. Jones Soda relies heavily upon its community of loyal users for the creation of branding content, including product innovations, packaging, promotions and advertising. We found a brand community that possesses all three of the markers of brand community and allows for personal transformation and consumer empowerment, yet is largely inorganic in nature. These findings have implications for our conceptualizations of brand communities. [to cite]