Comparing treatment policies with assistance from the structural nested mean model

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142500/1/biom12391-sup-0001-SuppData.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142500/2/biom12391_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142500/3/biom12391.pd

Materials Swelling Revealed Through Automated Semantic Segmentation of Cavities in Electron Microscopy Images

Accurately quantifying swelling of alloys that have undergone irradiation is essential for understanding alloy performance in a nuclear reactor and critical for the safe and reliable operation of reactor facilities. However, typical practice is for radiation-induced defects in electron microscopy images of alloys to be manually quantified by domain-expert researchers. Here, we employ an end-to-end deep learning approach using the Mask Regional Convolutional Neural Network (Mask R-CNN) model to detect and quantify nanoscale cavities in irradiated alloys. We have assembled the largest database of labeled cavity images to date, which includes 400 images, >34k discrete cavities, and numerous alloy compositions and irradiation conditions. We have evaluated both statistical (precision, recall, and F1 scores) and materials property-centric (cavity size, density, and swelling) metrics of model performance, and performed in-depth analysis of materials swelling assessments. We find our model gives assessments of material swelling with an average (standard deviation) swelling mean absolute error based on random leave-out cross-validation of 0.30 (0.03) percent swelling. This result demonstrates our approach can accurately provide swelling metrics on a per-image and per-condition basis, which can provide helpful insight into material design (e.g., alloy refinement) and impact of service conditions (e.g., temperature, irradiation dose) on swelling. Finally, we find there are cases of test images with poor statistical metrics, but small errors in swelling, pointing to the need for moving beyond traditional classification-based metrics to evaluate object detection models in the context of materials domain applications

Pregnant Smokers Receiving Opioid Agonist Therapy Have an Elevated Nicotine Metabolite Ratio: A Replication Study.

INTRODUCTION: Pregnant women exposed chronically to opioids smoked more cigarettes per day (CPD) and had a higher nicotine metabolite ratio (NMR), 3-hydroxycotinine/cotinine, a biomarker of nicotine metabolism and clearance, than those not receiving opioids. We examined CPD and NMR in a group of pregnant smokers, a quarter of whom were receiving opioid agonist therapy (OAT). AIMS AND METHODS: Pregnant smokers recruited to participate in a placebo-controlled trial of bupropion for smoking cessation provided a blood sample for measurement of NMR. RESULTS: Half (52.4%) of the 124 women with NMR data were African American. OAT-treated women (n = 34, 27.4%; 27 receiving methadone and 7 buprenorphine) were more likely to be white (79% vs. 30%, p \u3c .001) and to have a lower mean PHQ-9 total score (2.91 [SD = 2.83] vs. 4.83 [SD = 3.82], p = .007). OAT-treated women reported smoking more CPD (9.50 [SD = 5.26] vs. 7.20 [SD = 3.65], p = .005) and had higher NMR (0.78 [SD = 0.36] vs. 0.56 [SD = 0.25], p = .001) than the non-OAT-treated group. In a linear regression analysis adjusting for race, depression severity, and CPD, NMR was greater in the OAT group (p = .025), among whom the daily methadone-equivalent dosage correlated with NMR (Spearman\u27s ρ = 0.49, p = .003). CONCLUSIONS: Consistent with the findings of Oncken et al. (2019), we found that OAT smokers smoked more and had higher NMR than non-OAT smokers. As higher NMR is associated with a reduced likelihood of smoking cessation, the effects on NMR of both pregnancy and OAT could contribute to a lower smoking cessation rate in pregnant smokers receiving chronic opioid therapy. IMPLICATIONS: We replicated the finding that the NMR is significantly greater among pregnant smokers receiving OAT than those not receiving this treatment for opioid use disorder. Furthermore, we found that the dosage of the OAT was significantly associated with the NMR level. These findings may contribute to a poorer response to smoking cessation treatment in pregnant women treated with OAT, particularly those receiving high-dose therapy, and raise the question of whether novel approaches are needed to treat smoking in this subgroup of pregnant smokers

Content Matters: Neuroimaging Investigation of Brain and Behavioral Impact of Televised Anti-Tobacco Public Service Announcements

Televised public service announcements are video ads that are a key component of public health campaigns against smoking. Understanding the neurophysiological correlates of anti-tobacco ads is an important step toward novel objective methods of their evaluation and design. In the present study, we used functional magnetic resonance imaging (fMRI) to investigate the brain and behavioral effects of the interaction between content ( argument strength, AS) and format ( message sensation value, MSV) of anti-smoking ads in humans. Seventy-one nontreatment-seeking smokers viewed a sequence of 16 high or 16 low AS ads during an fMRI scan. Dependent variables were brain fMRI signal, the immediate recall of the ads, the immediate change in intentions to quit smoking, and the urine levels of a major nicotine metabolite cotinine at a 1 month follow-up. Whole-brain ANOVA revealed that AS and MSV interacted in the inferior frontal, inferior parietal, and fusiform gyri; the precuneus; and the dorsomedial prefrontal cortex (dMPFC). Regression analysis showed that the activation in the dMPFC predicted the urine cotinine levels 1 month later. These results characterize the key brain regions engaged in the processing of persuasive communications and suggest that brain fMRI response to anti-smoking ads could predict subsequent smoking severity in nontreatment-seeking smokers. Our findings demonstrate the importance of the quality of content for objective ad outcomes and suggest that fMRI investigation may aid the prerelease evaluation of televised public health ads

Resonant and Non-Resonant Effects in Photon-Technipion Production at Lepton Colliders

Lepton collider experiments can search for light technipions in final states made striking by the presence of an energetic photon: e+e- \to \photon\technipion. To date, searches have focused on either production through anomalous coupling of the technipions to electroweak gauge bosons or on production through a technivector meson (\technirho, \techniomega) resonance. This paper creates a combined framework in which both contributions are included. This will allow stronger and more accurate limits on technipion production to be set using existing data from LEP or future data from a higher-energy linear collider. We provide explicit formulas and sample calculations (analytic and Pythia) in the framework of the Technicolor Straw Man Model, a model that includes light technihadrons.Comment: 11 pages, including title page, 3 figures; version 2: references adde

Composite Scalars at LEP: Constraining Technicolor Theories

LEPI and LEPII data can be used to constrain technicolor models with light, neutral pseudo-Nambu-Goldstone bosons, Pa. We use published limits on branching ratios and cross sections for final states with photons, large missing energy, jet pairs, and b bbar pairs to constrain the anomalous Pa Z0 Z0, Pa Z0 photon, and Pa photon photon couplings. From these results, we derive bounds on the size of the technicolor gauge group and the number of technifermion doublets in models such as Low-scale Technicolor.Comment: 27 pages (including title page), 15 figures, 6 tables. version 2: In addressing PRD referee comments, we have significantly expanded our manuscript, to include detailed discussion of limits from LEP II data, as well as expanding the number or specific models to which we apply our results. As a result, we have changed the title from "Z0 decays to composite scalars: constraining technicolor theories