Psychosocial Effects of a Holistic Ayurvedic Approach to Well-being in Health and Wellness Courses.

BackgroundAs individuals are increasingly attending health and wellness courses outside of the conventional medical system, there is a need to obtain objective data on the effects of those programs on well-being.MethodsIn total, 154 men and women (mean age 54.7 years; range 25-83) participated in 3 different holistic wellness programs based on Ayurvedic Medicine principles (Seduction of Spirit, Journey into Healing, and Perfect Health) or a vacation control group. Psychosocial outcomes included spirituality (Delaney Spirituality Scale), mindful awareness (Mindful Attention Awareness Scale), psychological flexibility (Acceptance and Action Questionnaire), mood (Center for Epidemiology Studies-Depression), and anxiety (Patient-Reported Outcomes Measurement System Anxiety Scale).ResultsParticipants in the Seduction of Spirit (P < .004), Journey into Healing (P < .05), and Perfect Health (P < .004) courses showed significant increases in spirituality as compared to vacation controls. Participants in Seduction of Spirit (P < .007) also showed significant increases in mindfulness as compared to vacation controls. Participants in the Seduction of Spirit (P < .001) and Journey into Healing (P < .05) courses showed significant decreases in depressed mood as compared to those in the Perfect Health and vacation control groups. All study participants showed similar increases in psychological flexibility (P < .01) and decreases in anxiety (P < .01).ConclusionParticipation in wellness courses that incorporate a mind-body-spirit approach to health improves multiple domains of psychosocial well-being, which persists even after course participation

Predictive Modeling Using Shape Statistics for Interpretable and Robust Quality Assurance of Automated Contours in Radiation Treatment Planning

Deep learning methods for image segmentation and contouring are gaining prominence as an automated approach for delineating anatomical structures in medical images during radiation treatment planning. These contours are used to guide radiotherapy treatment planning, so it is important that contouring errors are flagged before they are used for planning. This creates a need for effective quality assurance methods to enable the clinical use of automated contours in radiotherapy. We propose a novel method for contour quality assurance that requires only shape features, making it independent of the platform used to obtain the images. Our method uses a random forest classifier to identify low-quality contours. On a dataset of 312 kidney contours, our method achieved a cross-validated area under the curve of 0.937 in identifying unacceptable contours. We applied our method to an unlabeled validation dataset of 36 kidney contours. We flagged 6 contours which were then reviewed by a cervix contour specialist, who found that 4 of the 6 contours contained errors. We used Shapley values to characterize the specific shape features that contributed to each contour being flagged, providing a starting point for characterizing the source of the contouring error. These promising results suggest our method is feasible for quality assurance of automated radiotherapy contours

Taking Control Using Academic SimplyE: Our Journey to an Open Reading Landscape

Libraries are part of a unique moment in history which unites broad technology adoption, readily-available technical training, widespread use of the internet, and support of openness and equitable access. We have the ability to change our own future in transforming the academic reading experience. Libraries have started down the road of providing open reading environments for everyone. We believe open source initiatives like SimplyE and open standards like Open Publication Distribution System (OPDS), Open and Distance Learning (ODL), Readium and Lightweight Content Protection (LCP) will allow all involved—commercial software vendors, publishers, libraries, consortia—to innovate. In this session, we will demonstrate progress in SimplyE for the academic reading landscape, and discuss what is yet to come. We will discuss our progress with these technologies, our services and content vendors, and ask for feedback as we consider how to incorporate additional academic functionality. Come with us on this journey. Be ready to engage, question, and consider

Analyzing the Relationship between Dose and Geometric Agreement Metrics for Auto-Contouring in Head and Neck Normal Tissues

This study aimed to determine the relationship between geometric and dosimetric agreement metrics in head and neck (H&N) cancer radiotherapy plans. A total 287 plans were retrospectively analyzed, comparing auto-contoured and clinically used contours using a Dice similarity coefficient (DSC), surface DSC (sDSC), and Hausdorff distance (HD). Organs-at-risk (OARs) with ≥200 cGy dose differences from the clinical contour in terms of Dmax (D0.01cc) and Dmean were further examined against proximity to the planning target volume (PTV). A secondary set of 91 plans from multiple institutions validated these findings. For 4995 contour pairs across 19 OARs, 90% had a DSC, sDSC, and HD of at least 0.75, 0.86, and less than 7.65 mm, respectively. Dosimetrically, the absolute difference between the two contour sets was \u3c200 cGy for 95% of OARs in terms of Dmax and 96% in terms of Dmean. In total, 97% of OARs exhibiting significant dose differences between the clinically edited contour and auto-contour were within 2.5 cm PTV regardless of geometric agreement. There was an approximately linear trend between geometric agreement and identifying at least 200 cGy dose differences, with higher geometric agreement corresponding to a lower fraction of cases being identified. Analysis of the secondary dataset validated these findings. Geometric indices are approximate indicators of contour quality and identify contours exhibiting significant dosimetric discordance. For a small subset of OARs within 2.5 cm of the PTV, geometric agreement metrics can be misleading in terms of contour quality

Creative and Stylistic Devices Employed by Children During a Storybook Narrative Task: A Cross-Cultural Study

Purpose: The purpose of this study was to analyze the effects of culture on the creative and stylistic features children employ when producing narratives based on wordless picture books. Method: Participants included 60 first- and second-grade African American, Latino American, and Caucasian children. A subset of narratives based on wordless picture books collected as part of a larger study was coded and analyzed for the following creative and stylistic conventions: organizational style (topic centered, linear, cyclical), dialogue (direct, indirect), reference to character relationships (nature, naming, conduct), embellishment (fantasy, suspense, conflict), and paralinguistic devices (expressive sounds, exclamatory utterances). Results: Many similarities and differences between ethnic groups were found. No significant differences were found between ethnic groups in organizational style or use of paralinguistic devices. African American children included more fantasy in their stories, Latino children named their characters more often, and Caucasian children made more references to the nature of character relationships. Conclusion: Even within the context of a highly structured narrative task based on wordless picture books, culture influences children’s production of narratives. Enhanced understanding of narrative structure, creativity, and style is necessary to provide ecologically valid narrative assessment and intervention for children from diverse cultural backgrounds

Leveraging Deep Learning-Based Segmentation and Contours-Driven Deformable Registration for Dose Accumulation in Abdominal Structures

PURPOSE: Discrepancies between planned and delivered dose to GI structures during radiation therapy (RT) of liver cancer may hamper the prediction of treatment outcomes. The purpose of this study is to develop a streamlined workflow for dose accumulation in a treatment planning system (TPS) during liver image-guided RT and to assess its accuracy when using different deformable image registration (DIR) algorithms. MATERIALS AND METHODS: Fifty-six patients with primary and metastatic liver cancer treated with external beam radiotherapy guided by daily CT-on-rails (CTOR) were retrospectively analyzed. The liver, stomach and duodenum contours were auto-segmented on all planning CTs and daily CTORs using deep-learning methods. Dose accumulation was performed for each patient using scripting functionalities of the TPS and considering three available DIR algorithms based on: (i) image intensities only; (ii) intensities + contours; (iii) a biomechanical model (contours only). Planned and accumulated doses were converted to equivalent dose in 2Gy (EQD2) and normal tissue complication probabilities (NTCP) were calculated for the stomach and duodenum. Dosimetric indexes for the normal liver, GTV, stomach and duodenum and the NTCP values were exported from the TPS for analysis of the discrepancies between planned and the different accumulated doses. RESULTS: Deep learning segmentation of the stomach and duodenum enabled considerable acceleration of the dose accumulation process for the 56 patients. Differences between accumulated and planned doses were analyzed considering the 3 DIR methods. For the normal liver, stomach and duodenum, the distribution of the 56 differences in maximum doses (D2%) presented a significantly higher variance when a contour-driven DIR method was used instead of the intensity only-based method. Comparing the two contour-driven DIR methods, differences in accumulated minimum doses (D98%) in the GTV were \u3e2Gy for 15 (27%) of the patients. Considering accumulated dose instead of planned dose in standard NTCP models of the duodenum demonstrated a high sensitivity of the duodenum toxicity risk to these dose discrepancies, whereas smaller variations were observed for the stomach. CONCLUSION: This study demonstrated a successful implementation of an automatic workflow for dose accumulation during liver cancer RT in a commercial TPS. The use of contour-driven DIR methods led to larger discrepancies between planned and accumulated doses in comparison to using an intensity only based DIR method, suggesting a better capability of these approaches in estimating complex deformations of the GI organs

RELICS: Strong Lens Models for Five Galaxy Clusters From the Reionization Lensing Cluster Survey

Strong gravitational lensing by galaxy clusters magnifies background galaxies, enhancing our ability to discover statistically significant samples of galaxies at z>6, in order to constrain the high-redshift galaxy luminosity functions. Here, we present the first five lens models out of the Reionization Lensing Cluster Survey (RELICS) Hubble Treasury Program, based on new HST WFC3/IR and ACS imaging of the clusters RXC J0142.9+4438, Abell 2537, Abell 2163, RXC J2211.7-0349, and ACT-CLJ0102-49151. The derived lensing magnification is essential for estimating the intrinsic properties of high-redshift galaxy candidates, and properly accounting for the survey volume. We report on new spectroscopic redshifts of multiply imaged lensed galaxies behind these clusters, which are used as constraints, and detail our strategy to reduce systematic uncertainties due to lack of spectroscopic information. In addition, we quantify the uncertainty on the lensing magnification due to statistical and systematic errors related to the lens modeling process, and find that in all but one cluster, the magnification is constrained to better than 20% in at least 80% of the field of view, including statistical and systematic uncertainties. The five clusters presented in this paper span the range of masses and redshifts of the clusters in the RELICS program. We find that they exhibit similar strong lensing efficiencies to the clusters targeted by the Hubble Frontier Fields within the WFC3/IR field of view. Outputs of the lens models are made available to the community through the Mikulski Archive for Space TelescopesComment: Accepted to Ap

RELICS: High-Resolution Constraints on the Inner Mass Distribution of the z=0.83 Merging Cluster RXJ0152.7-1357 from strong lensing

Strong gravitational lensing (SL) is a powerful means to map the distribution of dark matter. In this work, we perform a SL analysis of the prominent X-ray cluster RXJ0152.7-1357 (z=0.83, also known as CL 0152.7-1357) in \textit{Hubble Space Telescope} images, taken in the framework of the Reionization Lensing Cluster Survey (RELICS). On top of a previously known $z=3.93$ galaxy multiply imaged by RXJ0152.7-1357, for which we identify an additional multiple image, guided by a light-traces-mass approach we identify seven new sets of multiply imaged background sources lensed by this cluster, spanning the redshift range [1.79-3.93]. A total of 25 multiple images are seen over a small area of ~0.4 $arcmin^2$, allowing us to put relatively high-resolution constraints on the inner matter distribution. Although modestly massive, the high degree of substructure together with its very elongated shape make RXJ0152.7-1357 a very efficient lens for its size. This cluster also comprises the third-largest sample of z~6-7 candidates in the RELICS survey. Finally, we present a comparison of our resulting mass distribution and magnification estimates with those from a Lenstool model. These models are made publicly available through the MAST archive.Comment: 15 Pages, 7 Figures, 4 Tables Accepted for publication in Ap

Light-quark connected intermediate-window contributions to the muon g−2g-2 hadronic vacuum polarization from lattice QCD

We present a lattice-QCD calculation of the light-quark connected contribution to window observables associated with the leading-order hadronic vacuum polarization contribution to the anomalous magnetic moment of the muon, $a_\mu^{\mathrm{HVP,LO}}$. We employ the MILC Collaboration's isospin-symmetric QCD gauge-field ensembles, which contain four flavors of dynamical highly-improved-staggered quarks with four lattice spacings between $a\approx 0.06$-$0.15$~fm and close-to-physical quark masses. We consider several effective-field-theory-based schemes for finite-volume and other lattice corrections and combine the results via Bayesian model averaging to obtain robust estimates of the associated systematic uncertainties. After unblinding, our final results for the intermediate and ``W2'' windows are $a^{ll,{\mathrm W}}_{\mu}(\mathrm{conn.})=206.6(1.0) \times 10^{-10}$ and $a^{ll,\mathrm {W2}}_{\mu}(\mathrm{conn.}) = 100.7(3.2)\times 10^{-10}$, respectively

Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans

PURPOSE: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. METHODS AND MATERIALS: A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model\u27s performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared with manual flags by 3 HN radiation oncologists. RESULTS: The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D CONCLUSIONS: Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality