Mind the gap: revealing the association between depression, marijuana use, and mental health care utilization

Depression is a widespread mental health condition affecting millions worldwide, with young adults being particularly vulnerable. The concurrent use of marijuana has become an increasingly relevant factor in treating depression, especially in this population. However, recent literature reviews have highlighted a significant gap in knowledge regarding treatment outcomes in adolescents who utilize both mental health treatments, including antidepressants (AD), and marijuana. This research seeks to better understand the relationship between mental health treatment utilization and marijuana together by exploring the association between depression, marijuana use, and mental health care utilization. Our findings suggest that depression and marijuana use tend to co-occur, and mental health treatment does not reduce this association. Moreover, mental health services may increase the association of marijuana use with depression significantly in adults aged 18 – 25 years old. Our study highlights the potential negative impact of marijuana use on mental health, specifically the increased odds of experiencing a major depressive episode (MDE) in individuals who use marijuana regardless of whether they are utilizing mental health treatment. These results have important implications for public health interventions and substance use prevention efforts, particularly among young adults, who have the highest prevalence of marijuana use and major depressive episodes

Mind the gap: revealing the association between depression, marijuana use, and mental health care utilization

Depression is a widespread mental health condition affecting millions worldwide, with young adults being particularly vulnerable. The concurrent use of marijuana has become an increasingly relevant factor in treating depression, especially in this population. However, recent literature reviews have highlighted a significant gap in knowledge regarding treatment outcomes in adolescents who utilize both mental health treatments, including antidepressants (AD), and marijuana. This research seeks to better understand the relationship between mental health treatment utilization and marijuana together by exploring the association between depression, marijuana use, and mental health care utilization. Our findings suggest that depression and marijuana use tend to co-occur, and mental health treatment does not reduce this association. Moreover, mental health services may increase the association of marijuana use with depression significantly in adults aged 18 – 25 years old. Our study highlights the potential negative impact of marijuana use on mental health, specifically the increased odds of experiencing a major depressive episode (MDE) in individuals who use marijuana regardless of whether they are utilizing mental health treatment. These results have important implications for public health interventions and substance use prevention efforts, particularly among young adults, who have the highest prevalence of marijuana use and major depressive episodes

The root of all evil? : the Mandrake myth in German literature from 1673 to 1913

The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Title from PDF of title page (University of Missouri--Columbia, viewed on month day, year).Thesis advisor: Dr. Sean Ireton.Text in English and German.M.A. University of Missouri--Columbia 2009.From its first appearance in the Ebers Papyrus to its reference in Goethe's Faust II and Hanns Heinz Ewers bestseller Alraune at the beginning of the 20th century, the plant-human mandrake has been notorious for its presence in folklore and superstitious beliefs, as well as in literary works and films among different epochs and cultures. Therefore, this study approaches mandrake and its myth in German literature and culture from 1673 to 1913. Central for my argument is the assumption that the potential of the mandrake as the anthropomorphic being par excellence allows insightful readings concerning the relations of myth, literature, and language. By combining a historico-anthropological background with an analysis of German literary texts from the late 17th to early 20th century, I generally aim to provide a closer look at both the content of the mandrake myth and its discursive aspects. On this meta-level, this study is predominantly concerned with the status of the mandrake myth: to what degree does the mandrake myth allow for readings of texts that differ from their conventional interpretations? What kind of myth is mandrake in literature?Includes bibliographical references

Implications of a temperature-dependent magnetic anisotropy for superparamagnetic switching

The macroscopic magnetic moment of a superparamagnetic system has to overcome an energy barrier in order to switch its direction. This barrier is formed by magnetic anisotropies in the material and may be surmounted typically after 10^9 to 10^12 attempts per second by thermal fluctuations. In a first step, the associated switching rate may be described by a Neel-Brown-Arrhenius law, in which the energy barrier is assumed as constant or a given temperature. Yet, magnetic anisotropies in general depend on temperature themselves which is known to modify the Neel-Brown-Arrhenius law. We illustrate quantitatively the implications of a temperature-dependent anisotropy on the switching rate and in particular for the interpretation of the prefactor as an attempt frequency. In particular, we show that realistic numbers for the attempt frequency are obtained when the temperature dependence of the anisotropy is taken into account.Comment: 15 pages, 5 figure

On Background Bias in Deep Metric Learning

Deep Metric Learning trains a neural network to map input images to a lower-dimensional embedding space such that similar images are closer together than dissimilar images. When used for item retrieval, a query image is embedded using the trained model and the closest items from a database storing their respective embeddings are returned as the most similar items for the query. Especially in product retrieval, where a user searches for a certain product by taking a photo of it, the image background is usually not important and thus should not influence the embedding process. Ideally, the retrieval process always returns fitting items for the photographed object, regardless of the environment the photo was taken in. In this paper, we analyze the influence of the image background on Deep Metric Learning models by utilizing five common loss functions and three common datasets. We find that Deep Metric Learning networks are prone to so-called background bias, which can lead to a severe decrease in retrieval performance when changing the image background during inference. We also show that replacing the background of images during training with random background images alleviates this issue. Since we use an automatic background removal method to do this background replacement, no additional manual labeling work and model changes are required while inference time stays the same. Qualitative and quantitative analyses, for which we introduce a new evaluation metric, confirm that models trained with replaced backgrounds attend more to the main object in the image, benefitting item retrieval systems.Comment: To be published at ICMV 202

Identifying and Correcting Step Losses in Single-Ended Fiber-Optic Distributed Temperature Sensing Data

Fiber-optic distributed temperature sensing (DTS) makes it possible to observe temperatures on spatial scales as fine as centimeters and at frequencies up to 1 Hz. Over the past decade, fiber-optic DTS instruments have increasingly been employed to monitor environmental temperatures, from oceans to atmospheric monitoring. Because of the nature of environmental deployments, optical fibers deployed for research purposes often encounter step losses in the Raman spectra signal. Whether these phenomena occur due to cable damage or impingements, sharp bends in the deployed cable, or connections and splices, the step losses are usually not adequately addressed by the calibration routines provided by instrument manufacturers and can be overlooked in postprocessing calibration routines as well. Here we provide a method to identify and correct for the effects of step losses in raw Raman spectra data. The utility of the correction is demonstrated with case studies, including synthetic and laboratory data sets