Body Fat is Associated with Decreased Endocrine and Cognitive Resilience to Acute Emotional Stress

*Objective:* Cortisol is elevated both in individuals with increased emotional stress as well as with higher percentages of body fat. Cortisol is also known to affect cognitive performance, particularly spatial processing, selective attention, and working memory. We hypothesized that increased body fat might therefore be associated with decreased performance on a spatial processing task, in response to an acute real-world stressor. 

*Design:* We tested two separate samples of subjects undergoing their first (tandem) skydive. In the first sample (N=78), subjects were tested for salivary cortisol and state-anxiety (Spielberger State Anxiety Scale) during the plane's fifteen-minute ascent to altitude in immediate anticipation of the jump. In a second sample (N=20), subjects were tested for salivary cortisol, as well as cardiac variables (heart rate, autonomic regulation via heart rate variability) and performance on a cognitive task of spatial processing, selective attention, and working memory. 

*Results:* In response to the skydive, individuals with greater body fat percentages showed significantly increased reactivity for both cortisol (on both samples) and cognition, including decreased accuracy of our task of spatial processing, selective attention, and working memory. These cognitive effects were restricted to the stress response and were not found under baseline conditions. There were no body fat interactions with cardiac changes in response to the stressor, suggesting that the cognitive effects were specifically hormone-mediated rather than secondary to general activation of the autonomic nervous system. 

*Conclusions:* Our results indicate that, under real-world stress, increased body fat may be associated with endocrine stress-vulnerability, with consequences for deleterious cognitive performance

Sub-millimeter nuclear medical imaging with high sensitivity in positron emission tomography using beta-gamma coincidences

We present a nuclear medical imaging technique, employing triple-gamma trajectory intersections from beta^+ - gamma coincidences, able to reach sub-millimeter spatial resolution in 3 dimensions with a reduced requirement of reconstructed intersections per voxel compared to a conventional PET reconstruction analysis. This '$\gamma$-PET' technique draws on specific beta^+ - decaying isotopes, simultaneously emitting an additional photon. Exploiting the triple coincidence between the positron annihilation and the third photon, it is possible to separate the reconstructed 'true' events from background. In order to characterize this technique, Monte-Carlo simulations and image reconstructions have been performed. The achievable spatial resolution has been found to reach ca. 0.4 mm (FWHM) in each direction for the visualization of a 22Na point source. Only 40 intersections are sufficient for a reliable sub-millimeter image reconstruction of a point source embedded in a scattering volume of water inside a voxel volume of about 1 mm^3 ('high-resolution mode'). Moreover, starting with an injected activity of 400 MBq for ^76Br, the same number of only about 40 reconstructed intersections are needed in case of a larger voxel volume of 2 x 2 x 3~mm^3 ('high-sensitivity mode'). Requiring such a low number of reconstructed events significantly reduces the required acquisition time for image reconstruction (in the above case to about 140 s) and thus may open up the perspective for a quasi real-time imaging.Comment: 17 pages, 5 figutes, 3 table

Novel imaging and quality assurance techniques for ion beam therapy: a Monte Carlo study

Ion beams exhibit a finite and well defined range in matter together with an “inverted” depth-dose profile, the so-called Bragg peak. These favourable physical properties may enable superior tumour-dose conformality for high precision radiation therapy. On the other hand, they introduce the issue of sensitivity to range uncertainties in ion beam therapy. Although these uncertainties are typically taken into account when planning the treatment, correct delivery of the intended ion beam range has to be assured to prevent undesired underdosage of the tumour or overdosage of critical structures outside the target volume. Therefore, it is necessary to define dedicated Quality Assurance procedures to enable in-vivo range verification before or during therapeutic irradiation. For these purposes, Monte Carlo transport codes are very useful tools to support the development of novel imaging modalities for ion beam therapy. In the present work, we present calculations performed with the FLUKA Monte Carlo code and preliminary experimental studies

Isocyanurate transformation induced healing of isocyanurate–oxazolidone polymers

Isocyanurate–oxazolidone (ISOX) polymers have been reported as a novel, intrinsically self‐healable thermoset, and their healing mechanism under the effect of nucleophiles, such as tertiary amines and pyridines during polymerization, is thoroughly investigated in this study. This work provides evidence that the healing behavior of the polymers results part from the transformation of isocyanurate to oxazolidone on the fracture surfaces of the ISOX polymers at elevated temperatures. The isocyanurate transformation is characterized by chemical composition of the ISOX polymers before and after a predetermined healing procedure, through a combination characterization of Fourier transform infrared spectroscopy and carbon nuclear magnetic resonance spectroscopy. From the chemical composition of the ISOX polymers, an increased oxazolidone fraction is observed after the healing event, which verifies the hypothesized healing mechanism. By correlating the change in oxazolidone fraction in the polymers during the healing event, with the corresponding healing performance of the polymers, healing efficiencies of the polymers are shown to be inversely proportional to the ratio of oxazolidone to isocyanurate in the polymers. The transformation to oxazolidone is also shown to be dependent on two variables, nucleophilicity of the polymerization catalyst and duration of the postcure. The isocyanate and epoxide polymerization mechanism in the presence of nucleophiles is also investigated to explain the effect of the catalyst nucleophilicity on the chemical composition as well as the healing performance of the ISOX polymers. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 137, 48698.Isocyanurate‐to‐oxazolidone transformation within the polymers for healing.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154270/1/app48695-sup-0001-FigureS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154270/2/app48698_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154270/3/app48698.pd

Hermitian quark mass matrices with four texture zeros

We provide a complete and systematic analysis of hermitian, hierarchical quark mass matrices with four texture zeros. Using triangular mass matrices, each pattern of texture zeros is readily shown to lead to a definite relation between the CKM parameters and the quark masses. Nineteen pairs are found to be consistent with present data, and one other is marginally acceptable. In particular, no parallel structure between the up and down mass matrices is found to be favorable with data.Comment: 18 pages, no figure, references [8] and [10] adde

A filtering approach for PET and PG predictions in a proton treatment planning system

Positron emission tomography (PET) and prompt gamma (PG) detection are promising proton therapy monitoring modalities. Fast calculation of the expected distributions is desirable for comparison to measurements and to develop/train algorithms for automatic treatment error detection. A filtering formalism was used for positron-emitter predictions and adapted to allow for its use for the beamline of any proton therapy centre. A novel approach based on a filtering formalism was developed for the prediction of energy-resolved PG distributions for arbitrary tissues. The method estimates PG yields and their energy spectra in the entire treatment field. Both approaches were implemented in a research version of the RayStation treatment planning system. The method was validated against PET monitoring data and Monte Carlo simulations for four patients treated with scanned proton beams. Longitudinal shifts between profiles from analytical and Monte Carlo calculations were within -1.7 and 0.9 mm, with maximum standard deviation of 0.9 mm and 1.1 mm, for positron-emitters and PG shifts, respectively. Normalized mean absolute errors were within 1.2 and 5.3%. When comparing measured and predicted PET data, the same more complex case yielded an average shift of 3 mm, while all other cases were below absolute average shifts of 1.1 mm. Normalized mean absolute errors were below 7.2% for all cases. A novel solution to predict positron-emitter and PG distributions in a treatment planning system is proposed, enabling calculation times of only a few seconds to minutes for entire patient cases, which is suitable for integration in daily clinical routine

Observational Comparison of Star Formation in Different Galaxy Types

Galaxies cover a wide range of masses and star formation histories. In this review, I summarize some of the evolutionary key features of common galaxy types. At the high-mass end, very rapid, efficient early star formation is observed, accompanied by strong enrichment and later quiescence, well-described by downsizing scenarios. In the intermediate-mass regime, early-type galaxies may still show activity in low-mass environments or when being rejuvenated by wet mergers. In late-type galaxies, we find continuous, though variable star formation over a Hubble time. In the dwarf regime, a wide range of properties from bursty activity to quiescence is observed. Generally, stochasticity dominates here, and star formation rates and efficiencies tend to be low. Morphological types and their star formation properties correlate with environment.Comment: Invited review to appear in IAU Symposium 270 on "Computational Star Formation". Editors: J. Alves, B.G. Elmegreen, J.M. Girart, and V. Trimble (Cambridge University Press). 12 pages, no figure

A large language model-assisted education tool to provide feedback on open-ended responses

Open-ended questions are a favored tool among instructors for assessing student understanding and encouraging critical exploration of course material. Providing feedback for such responses is a time-consuming task that can lead to overwhelmed instructors and decreased feedback quality. Many instructors resort to simpler question formats, like multiple-choice questions, which provide immediate feedback but at the expense of personalized and insightful comments. Here, we present a tool that uses large language models (LLMs), guided by instructor-defined criteria, to automate responses to open-ended questions. Our tool delivers rapid personalized feedback, enabling students to quickly test their knowledge and identify areas for improvement. We provide open-source reference implementations both as a web application and as a Jupyter Notebook widget that can be used with instructional coding or math notebooks. With instructor guidance, LLMs hold promise to enhance student learning outcomes and elevate instructional methodologies