Impact of estradiol variability and progesterone on mood in perimenopausal women with depressive symptoms

OBJECTIVE: To determine whether estradiol variability, ovulatory levels of progesterone, and VMS burden are independently associated with perimenopausal depressive symptomatology. DESIGN AND INTERVENTION: Depressive symptoms, serum levels of estradiol and progesterone, and VMS frequency were assessed weekly in an 8-week observational study. Association of mood with estradiol variability, ovulatory levels of progesterone, and VMS frequency were estimated using generalized estimating equation models. SETTING: Academic medical center. PATIENTS: Fifty unmedicated perimenopausal women with mild-to-moderate depressive symptoms (mean Montgomery-Asberg Depression Rating Scale [MADRS] score 15.5 +/- 5.3). RESULTS: During the study, 90.0% of participants had varying estradiol levels, 51.1% had ovulatory progesterone levels, and 90% had VMS. Greater estradiol variability and absence of progesterone levels consistent with ovulation, but not VMS frequency, are associated with higher levels of depressive symptoms (beta= 0.11, 95% confidence interval [95%CI] [0.04 to 0.18, p=0.001]; beta= -2.62 [95%CI -4.52 to -0.71, p=0.007], respectively), after accounting for higher BMI, lifetime history of depression, and stressful life events. CONCLUSIONS: Increasing dysregulation of ovarian hormones, but not VMS, associates with more depressive symptom burden during perimenopause. These results suggest that perimenopausal mood instability is driven by the underlying hormonal dysregulation of the menopause transition involving changes in both estradiol and progesterone

Gravitational Redshift, Equivalence Principle, and Matter Waves

We review matter wave and clock comparison tests of the gravitational redshift. To elucidate their relationship to tests of the universality of free fall (UFF), we define scenarios wherein redshift violations are coupled to violations of UFF ("type II"), or independent of UFF violations ("type III"), respectively. Clock comparisons and atom interferometers are sensitive to similar effects in type II and precisely the same effects in type III scenarios, although type III violations remain poorly constrained. Finally, we describe the "Geodesic Explorer," a conceptual spaceborne atom interferometer that will test the gravitational redshift with an accuracy 5 orders of magnitude better than current terrestrial redshift experiments for type II scenarios and 12 orders of magnitude better for type III.Comment: Work in progress. 11 page

Medicaid crowd-out of long-term care insurance with endogenous Medicaid enrollment

With states facing tightening Medicaid budgets, the high cost of financing long-term care for the elderly through Medicaid has prompted proposals to make private long-term care insurance (LTCI) more affordable through tax incentives. The effectiveness of tax incentives for stimulating LTCI demand depends in part on the availability of Medicaid, since it is considered a substitute for LTCI. This paper examines the impact of tax subsidies and Medicaid financing on the demand for LTCI by developing and estimating a stochastic dynamic model of the decision to purchase private long-term care insurance. A key contribution of this paper is that the model also incorporates and accounts for endogenous decisions on Medicaid enrollment, nursing home use, and asset holdings, which reduces the estimate of the Medicaid crowd-out effect on LTCI demand. State-specific Medicaid enrollment criteria are explicitly accounted for in modeling the Medicaid enrollment decision. The parameters of the model are estimated using individual level data from the Health and Retirement Study for the years 1998 to 2002 by simulated maximum likelihood. Using the estimated parameters, counterfactual policy experiments are performed to investigate the effects of tax policy and Medicaid on LTCI demand. The main finding is that both effects are small. The estimated price elasticity of the LTCI demand is -0.08, implying that tax subsidies are expected to have only a limited effect in reducing the number of uninsured. Eliminating the Medicaid program increases LTCI holding by only 5.3%, implying that the demand for LTCI would remain small even without Medicaid

Brain tumor segmentation using deep fully convolutional neural networks

In this study, brain tumor substructures are segmented using 2D fully convolutional neural networks. A number of modifications such as double convolution layers, inception modules, and dense modules were added to a U-Net to achieve a deep architecture and test if the increased depth improves the performance. The experiments show that the deep architectures improve the performance. Also, the performance is enhanced from ensembling across the models trained on images in different orientations and ensembling across the models with different architectures. Even without any data augmentation, the ensembled model achieves a competitive performance and generalizes well on a new dataset. The resulting mean 3D Dice scores (ET/WT/TC) on the BRATS17 validation and test sets are 0.75/0.88/0.73 and 0.72/0.86/0.73

A NEW LASER COOLING METHOD FOR LITHIUM ATOM INTERFEROMETRY

An atom interferometer offers means to measure physical constants and physical quantities with a high precision, with relatively low cost and convenience as a table-top experiment. A precision measurement of a gravitational acceleration can test fundamental physics concepts such as Einstein equivalence principle (EEP). We identified that the two lithium isotopes (${}^{7}$Li and ${}^{6}$Li) have an advantage for the test of EEP, according to the standard model extension (SME). We aim to build the world's first lithium atom interferometer and test the Einstein equivalence principle.\\We demonstrate a new laser cooling method suitable for a lithium atom interferometer. Although lithium is often used in ultra-cold atom experiments for its interesting physical properties and measurement feasibility, it is more difficult to laser cool lithium than other alkali atoms due to its unresolved hyperfine states, light mass (large recoil velocity) and high temperature from the oven. Typically, standard laser cooling techniques such as Zeeman slowers and magneto-optical traps are used to cool lithium atoms to about 1 mK, and the evaporative cooling method is used to cool lithium atoms to a few $\mu$K for Bose-Einstein condensate (BEC) experiments. However, for the atom interferometry purpose, the evaporative cooling method is not ideal for several reasons: First, its cooling efficiency is so low (0.01 \% or less) that typically only $10^4-10^5$ atoms are left after cooling when one begins with $10^9$ atoms. More atoms in an atom interferometer are needed to have a better signal to noise ratio. Second, an evaporative cooling is used to make a BEC, but we do not need a BEC to make an atom interferometer. In an atom interferometer, a high density of atoms as in a BEC should be avoided since it causes a phase shift due to atom interactions. Third, a setup for an evaporative cooling requires intricate RF generating coils or a high power laser. \\With a simple optical lattice and a moderate laser power (100 mW), we achieved a sub-Doppler cooling of lithium by a new laser cooling method despite the fact that lithium has un-resolved hyperfine structure. We identified that the Sisyphus cooling and the adiabatic cooling mechanisms cooperate and give both lower temperature and higher cooling efficiency than the result that can be achieved by each alone. We cooled ${}^{7}$Li atoms to $\sim \;50\;\mu$K (about 8 times the recoil temperature) in a one dimensional lattice with cooling efficiency of $50\%$. In three dimensions the cooling temperature was limited to $90\,\mu$K due to instability of our 3D lattice, however the same principle applies and potentially a lower temperature can be achieved in 3D as well

Medicaid crowd-out of long-term care insurance with endogenous Medicaid enrollment

With states facing tightening Medicaid budgets, the high cost of financing long-term care for the elderly through Medicaid has prompted proposals to make private long-term care insurance (LTCI) more affordable through tax incentives. The effectiveness of tax incentives for stimulating LTCI demand depends in part on the availability of Medicaid, since it is considered a substitute for LTCI. This paper examines the impact of tax subsidies and Medicaid financing on the demand for LTCI by developing and estimating a stochastic dynamic model of the decision to purchase private long-term care insurance. A key contribution of this paper is that the model also incorporates and accounts for endogenous decisions on Medicaid enrollment, nursing home use, and asset holdings, which reduces the estimate of the Medicaid crowd-out effect on LTCI demand. State-specific Medicaid enrollment criteria are explicitly accounted for in modeling the Medicaid enrollment decision. The parameters of the model are estimated using individual level data from the Health and Retirement Study for the years 1998 to 2002 by simulated maximum likelihood. Using the estimated parameters, counterfactual policy experiments are performed to investigate the effects of tax policy and Medicaid on LTCI demand. The main finding is that both effects are small. The estimated price elasticity of the LTCI demand is -0.08, implying that tax subsidies are expected to have only a limited effect in reducing the number of uninsured. Eliminating the Medicaid program increases LTCI holding by only 5.3%, implying that the demand for LTCI would remain small even without Medicaid

Tracing the Mathematical Modeling Phases of Pre-Service Teachers: A MAD Analysis

Mathematical modeling has been receiving increased focus with researchers identifying its benefits for students, such as using mathematics in real world applications and affecting change in attitudes towards mathematics. Mathematical modeling also provides the opportunity to support equitable learning, promoting cooperation among students from various mathematical backgrounds with the use of low-floor and high-ceiling tasks. With numerous benefits, it is important to also examine how groups of pre-service teachers negotiate the modeling process in order to better support those who look to include modeling activities in their future classrooms. While there have been several theoretical modeling cycles published that describe the various phases, these tend to provide a simplified view of the complex nature of these tasks. To gain insight into this phenomena, we created, taught, and observed two mathematical modeling sessions for pre-service teachers. This work enabled us to explore how pre-service teachers move between phases of the modeling cycle and if these shifts mirror what is being represented in current modeling cycle diagrams. Our analysis focuses on a holistic task that was completed after pre-service teachers had learned about mathematical modeling and had attempted an atomistic task. We used a modified version of the Modeling Activity Diagram (MAD) framework to create a visual representation of group movements through the modeling cycle phases. While our analysis is ongoing, our emerging findings show that pre-service teachers are able to adapt to the complex nature of modeling activities to varying degrees as they work together to build their mathematical models

Designing & Implementing Professional Development on Mathematical Modeling to Address Evolving Teacher Challenges

Mathematical modeling is one of the Common Core State Standards for Mathematical Practice (MP 4) that can be used as a vehicle for learning mathematical content standards. However, there are many challenges faced by both teachers implementing mathematical modeling and students engaging with mathematical modeling tasks. While many of these challenges are already known, it is less clear how teacher challenges implementing mathematical modeling evolve over time. In this action research study, a team of five doctoral students (including a teacher-researcher) responsively designed professional development (PD) on mathematical modeling during the 2021-2022 school year for high school Algebra 1 and 2 teachers implementing mathematical modeling for the first time. In this study, we report on how teacher challenges evolved over time and how we as researchers addressed those challenges through professional development. Data was collected through surveys, interviews, PD sessions, check-ins, and observations. The constant comparative method was used to analyze the data as it was collected to inform the design of the PD sessions. Scaffolding, student engagement, and student modeling mindset were mostly early challenges. Teachers indicated student engagement as an early challenge, but towards the end of the year frequently indicated that it was a positive of modeling. By mid-year (January), fewer challenges related to students’ modeling mindset were reported. Both the nature and duration of teacher challenges related to mathematical modeling have implications for how to support school districts and their teachers as they engage in mathematical modeling with their students

Risk factors for the occurrence and persistence of coronary aneurysms in Kawasaki disease

Purpose Prognostic factors of coronary aneurysms in Kawasaki disease have been investigated in many studies. The aim of this study was to identify risk factors associated with early and late coronary artery outcomes in treated patients with Kawasaki disease. Methods A total of 392 patients diagnosed with Kawasaki disease from January 2012 to December 2015 in Pusan National University Children’s Hospital were retrospectively selected as subjects of the present study to determine risk factors for coronary aneurysms and persistence of coronary aneurysms after a 1-year follow-up. Results Coronary aneurysms were detected in 30 of 392 patients within 1 month after the occurrence of Kawasaki disease. Coronary aneurysms persisted in 5 of 30 patients after a 1-year follow-up. A long duration of fever (adjusted odds ratio [OR], 1.47; 95% confidence interval [CI], 1.06–2.02; P=0.018) and high platelet count (adjusted OR, 1.00; 95% CI, 1.00–1.01; P=0.009) were found to be independent factors to predict the development of coronary aneurysms in the early phase. Initial coronary severity (adjusted OR, 46.0; 95% CI, 2.01–1047.80; P=0.016) and a high white blood cell count (adjusted OR, 1.17; 95% CI, 1.01–1.36; P=0.028) were found to be significant factors for the persistence of late coronary aneurysms in univariate analysis. However, no significant factors were found in multivariate analysis. Conclusion These data are from early and late follow-up of coronary aneurysms in our unit. Further studies are needed to determine the mechanisms involved in the disappearance of coronary aneurysms and related factors