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The production of prompt photons providing high photon time densities is a promising avenue to reach ultrahigh coincidence time resolution (CTR) in time-of-flight PET. Detectors producing prompt photons are receiving high interest experimentally, ignited by past exploratory theoretical studies that have anchored some guiding principles. Here, we aim to consolidate and extend the foundations for the analytical modeling of prompt generating detectors. We extend the current models to a larger range of prompt emission kinetics where more stringent requirements on the prompt photon yield rapidly emerge as a limiting factor. Lower bound and estimator evaluations are investigated with different underlying models, notably by merging or keeping separate the prompt and scintillation photon populations. We further show the potential benefits of knowing the proportion of prompt photons within a detection set to improve the CTR by mitigating the detrimental effect of population (prompt vs scintillation) mixing. Taking into account the fluctuations on the average number of detected prompt photons in the model reveals a limited influence when prompt photons are accompanied by fast scintillation (e.g., LSO:Ce:Ca) but a more significant effect when accompanied by slower scintillation (e.g., BGO). Establishing performance characteristics and limitations of prompt generating detectors is paramount to gauging and targeting the best possible timing capabilities they can offer.

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Dietary data collected from 24-hour dietary recalls are observed with significant measurement errors. In the nonparametric curve estimation literature, much of the effort has been devoted to designing methods that are consistent under contamination by noise, and which have been traditionally applied for analyzing those data. However, some foods such as alcohol or fruits are consumed only episodically, and may not be consumed during the day when the 24-hour recall is administered. These so-called excess zeros make existing nonparametric estimators break down, and new techniques need to be developed for such data. We develop two new consistent semiparametric estimators of the distribution of such episodically consumed food data, making parametric assumptions only on some less important parts of the model. We establish its theoretical properties and illustrate the good performance of our fully data-driven method in simulated and real data. Supplementary materials for this article are available online.

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BACKGROUND: The integration of a personal recovery-oriented practice in mental health services is an emerging principle in policy planning. Self-management support (SMS) is an intervention promoting recovery that aims at educating patients on the nature of their mental disorder, improving their strategies to manage their day-to-day symptoms, fostering self-efficacy and empowerment, preventing relapse, and promoting well-being. While SMS is well established for chronic physical conditions, there is a lack of evidence to support the implementation of structured SMS programs for common mental disorders, and particularly for anxiety disorders. This study aims to examine the effectiveness of a group-based self-management support program for anxiety disorders as an add-on to treatment-as-usual in community-based care settings. METHODS/DESIGN: We will conduct a multicentre pragmatic randomized controlled trial with a pre-treatment, post-treatment (4-month post-randomization), and follow-ups at 8, 12 and 24-months. TREATMENT AND CONTROL GROUPS: a) group self-management support (10 weekly 2.5-h group web-based sessions with 10-15 patients with two trained facilitators); b) treatment-as-usual. Participants will include adults meeting DSM-5 criteria for Panic Disorder, Agoraphobia, Social Anxiety Disorder, and/or Generalized Anxiety Disorder. The primary outcome measure will be the Beck Anxiety Inventory; secondary outcome measures will comprise self-reported instruments for anxiety and depressive symptoms, recovery, self-management, quality of life, and service utilisation. STATISTICAL ANALYSIS: Data will be analysed based on intention-to-treat with a mixed effects regression model accounting for between and within-subject variations in the effects of the intervention. DISCUSSION: This study will contribute to the limited knowledge base regarding the effectiveness of structured group self-management support for anxiety disorders. It is expected that changes in patients' self-management behaviour will lead to better anxiety management and, consequently, to improved patient outcomes. TRIAL REGISTRATION: ClinicalTrials.gov: NCT05124639 . Prospectively registered 18 November 2021.

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OBJECTIVE: This retrospective study aimed to characterize trimester-specific and total gestational weight gain (GWG) over the course of two consecutive pregnancies, as well as maternal determinants associated with interpregnancy weight change (IPWC) and excessive GWG in the second pregnancy. METHODS: We analyzed the electronic medical records of women who delivered their first two consecutive infants at term between 2001 and 2017. RESULTS: Weight gain trajectories differed between the first and second pregnancy for the 1497 women included in this study, with lower second- and third-trimester weight gain in the second pregnancy. Respectively, 53% and 41% of women had excessive GWG in the first and second pregnancies, with a higher proportion of excessive GWG found in women with a higher body mass index (BMI). Most women (55%) experienced interpregnancy weight gain. Maternal determinants of IPWC were BMI before first pregnancy, first-trimester and total GWG in the first pregnancy, and interpregnancy interval (P < 0.0001). Maternal risk factors associated with excessive GWG in the second pregnancy were excessive total GWG in the first pregnancy (OR 6.23; 95% CI 4.67-8.32), interpregnancy weight gain (OR 1.58; 95% CI 1.19-2.09), and interpregnancy interval (OR 1.18; 95% CI 1.07-1.29) as well as BMI before the second pregnancy (OR 1.04, 95% CI 1.02-1.07). CONCLUSION: Weight gain trajectories differ between consecutive pregnancies. GWG in the first pregnancy is a key determinant for IPWC and GWG in the second pregnancy.

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