Nonlinear Color-Metallicity Relations of Globular Clusters. III. On the Discrepancy in Metallicity between Globular Cluster Systems and their Parent Elliptical Galaxies

One of the conundrums in extragalactic astronomy is the discrepancy in observed metallicity distribution functions (MDFs) between the two prime stellar components of early-type galaxies-globular clusters (GCs) and halo field stars. This is generally taken as evidence of highly decoupled evolutionary histories between GC systems and their parent galaxies. Here we show, however, that new developments in linking the observed GC colors to their intrinsic metallicities suggest nonlinear color-to-metallicity conversions, which translate observed color distributions into strongly-peaked, unimodal MDFs with broad metal-poor tails. Remarkably, the inferred GC MDFs are similar to the MDFs of resolved field stars in nearby elliptical galaxies and those produced by chemical evolution models of galaxies. The GC MDF shape, characterized by a sharp peak with a metal-poor tail, indicates a virtually continuous chemical enrichment with a relatively short timescale. The characteristic shape emerges across three orders of magnitude in the host galaxy mass, suggesting a universal process of chemical enrichment among various GC systems. Given that GCs are bluer than field stars within the same galaxy, it is plausible that the chemical enrichment processes of GCs ceased somewhat earlier than that of field stellar population, and if so, GCs preferentially trace the major, vigorous mode of star formation events in galactic formation. We further suggest a possible systematic age difference among GC systems, in that the GC systems in more luminous galaxies are older. This is consistent with the downsizing paradigm of galaxies and supports additionally the similar nature shared by GCs and field stars. Our findings suggest that GC systems and their parent galaxies have shared a more common origin than previously thought, and hence greatly simplify theories of galaxy formation.Comment: 55 pages, 7 figures, 5 tables; Accepted for publication in Ap

Association between use of hydrochlorothiazide and nonmelanoma skin cancer: Common data model cohort study in Asian population

Although hydrochlorothiazide (HCTZ) has been suggested to increase skin cancer risk in white Westerners, there is scant evidence for the same in Asians. We analyzed the association between the use of hydrochlorothiazide and non-melanoma in the Asian population using the common data model. METHODS: A retrospective multicenter observational study was conducted using a distributed research network to analyze the effect of HCTZ on skin cancer from 2004 to 2018. We performed Cox regression to evaluate the effects by comparing the use of HCTZ with other antihypertensive drugs. All analyses were re-evaluated using matched data using the propensity score matching (PSM). Then, the overall effects were evaluated by combining results with the meta-analysis. RESULTS: Positive associations were observed in the use of HCTZ with high cumulative dose for non-melanoma skin cancer (NMSC) in univariate analysis prior to the use of PSM. Some negative associations were observed in the use of low and medium cumulative doses. CONCLUSION: Although many findings in our study were inconclusive, there was a non-significant association of a dose-response pattern with estimates increasing in cumulative dose of HCTZ. In particular, a trend with a non-significant positive association was observed with the high cumulative dose of HCTZ

Impact of glycemic control on the progression of aortic stenosis: a single-center cohort study using a common data model

Background Diabetes mellitus (DM) is a well-established risk factor for the progression of degenerative aortic stenosis (AS). However, no study has investigated the impact of glycemic control on the rate of AS progression. We aimed to assess the association between the degree of glycemic control and the AS progression, using an electronic health record-based common data model (CDM). Methods We identified patients with mild AS (aortic valve [AV] maximal velocity [Vpeak] 2.0–3.0 m/sec) or moderate AS (Vpeak 3.0–4.0 m/sec) at baseline, and follow-up echocardiography performed at an interval of ≥ 6 months, using the CDM of a tertiary hospital database. Patients were divided into 3 groups: no DM (n = 1,027), well-controlled DM (mean glycated hemoglobin [HbA1c] < 7.0% during the study period; n = 193), and poorly controlled DM (mean HbA1c ≥ 7.0% during the study period; n = 144). The primary outcome was the AS progression rate, calculated as the annualized change in the Vpeak (△Vpeak/year). Results Among the total study population (n = 1,364), the median age was 74 (IQR 65–80) years, 47% were male, the median HbA1c was 6.1% (IQR 5.6–6.9), and the median Vpeak was 2.5 m/sec (IQR 2.2–2.9). During follow-up (median 18.4 months), 16.1% of the 1,031 patients with mild AS at baseline progressed to moderate AS, and 1.8% progressed to severe AS. Among the 333 patients with moderate AS, 36.3% progressed to severe AS. The mean HbA1c level during follow-up showed a positive relationship with the AS progression rate (β = 2.620; 95% confidence interval [CI] 0.732–4.507; p = 0.007); a 1%-unit increase in HbA1c was associated with a 27% higher risk of accelerated AS progression defined as △Vpeak/year values > 0.2 m/sec/year (adjusted OR = 1.267 per 1%-unit increase in HbA1c; 95% CI 1.106–1.453; p < 0.001), and HbA1c ≥ 7.0% was significantly associated with an accelerated AS progression (adjusted odds ratio = 1.524; 95% CI 1.010–2.285; p = 0.043). This association between the degree of glycemic control and AS progression rate was observed regardless of the baseline AS severity. Conclusion In patients with mild to moderate AS, the presence of DM, as well as the degree of glycemic control, is significantly associated with accelerated AS progression

Characteristics of gate-all-around silicon nanowire field effect transistors with asymmetric channel width and source/drain doping concentration

We performed 3D simulations to demonstrate structural effects in sub-20 nm gate-all-around silicon nanowire field effect transistors having asymmetric channel width along the channel direction. We analyzed the differences in the electrical and physical properties for various slopes of the channel width in asymmetric silicon nanowire field effect transistors (SNWFETs) and compared them to symmetrical SNWFETs with uniform channel width. In the same manner, the effects of the individual doping concentration at the source and drain also have been investigated. For various structural conditions, the current and switching characteristics are seriously affected. The differences attributed to the doping levels and geometric conditions are due to the electric field and electron density profile. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4745858]ope

Optimal Operating Schedule for Energy Storage System: Focusing on Efficient Energy Management for Microgrid

A microgrid is a group of many small-scale distributed energy resources, such as solar/wind energy sources, diesel generators, energy storage units, and electric loads. As a small-scale power grid, it can be operated independently or within an existing power grid(s). The microgrid energy management system is a system that controls these components to achieve optimized operation in terms of price by reducing costs and maximizing efficiency in energy consumption. A post-Industry-4.0 consumer requires an optimal design and control of energy storage based on a demand forecast, using big data to stably supply clean, new, and renewable energy when necessary while maintaining a consistent level of quality. Thus, this study focused on software technology through which an optimized operation schedule for energy storage in a microgrid is derived. This energy storage operation schedule minimizes the costs involved in electricity use. For this, an optimization technique is used that sets an objective function representing the information and costs pertaining to electricity use, while minimizing its value by using Mixed Integer Linear Programming or a Genetic Algorithm. The main feature of the software is that an optimal operation schedule derivation function has been implemented with MATLAB for the following circumstances: when the basic operation rules are applied, when operating with another grid, when the external operating conditions are applied, and when the internal operating conditions are applied

Incident Type 2 Diabetes Risk of Selective Estrogen Receptor Modulators in Female Patients with Breast Cancer

Accumulating evidence indicates a link between diabetes and cancer. Selective estrogen receptor modulators (SERMs) may increase diabetes risk via antiestrogen effects. This study investigated incident diabetes risk of SERM treatment and its effects on metastatic cancer and death prevention in breast cancer survivors. This retrospective cohort study included female patients with early-stage breast cancer, treated with or without SERMs, between 2008 and 2020 in a tertiary care hospital in Korea. Four propensity score-matched comparison pairs were designed: SERM use versus non-use, long-term use (≥1500 days) versus non-use, tamoxifen use versus non-use, and toremifene use versus non-use; then, logistic regression analysis was performed for risk analysis. SERMs in general were not associated with an elevated risk of diabetes; however, when used for ≥1500 days, SERMs—especially toremifene—substantially increased diabetes risk in breast cancer patients (OR 1.63, p = 0.048). Meanwhile, long-term SERM treatment was effective at preventing metastatic cancer (OR 0.20, p &lt; 0.001) and death (OR 0.13, p &lt; 0.001). SERM treatment, albeit generally safe and effective, may increase diabetes risk with its long-term use in women with breast cancer. Further studies are required to verify the association between toremifene treatment and incident diabetes

Development and Validation of a Prognostic Classification Model Predicting Postoperative Adverse Outcomes in Older Surgical Patients Using a Machine Learning Algorithm: Retrospective Observational Network Study

BackgroundOlder adults are at an increased risk of postoperative morbidity. Numerous risk stratification tools exist, but effort and manpower are required. ObjectiveThis study aimed to develop a predictive model of postoperative adverse outcomes in older patients following general surgery with an open-source, patient-level prediction from the Observational Health Data Sciences and Informatics for internal and external validation. MethodsWe used the Observational Medical Outcomes Partnership common data model and machine learning algorithms. The primary outcome was a composite of 90-day postoperative all-cause mortality and emergency department visits. Secondary outcomes were postoperative delirium, prolonged postoperative stay (≥75th percentile), and prolonged hospital stay (≥21 days). An 80% versus 20% split of the data from the Seoul National University Bundang Hospital (SNUBH) and Seoul National University Hospital (SNUH) common data model was used for model training and testing versus external validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) with a 95% CI. ResultsData from 27,197 (SNUBH) and 32,857 (SNUH) patients were analyzed. Compared to the random forest, Adaboost, and decision tree models, the least absolute shrinkage and selection operator logistic regression model showed good internal discriminative accuracy (internal AUC 0.723, 95% CI 0.701-0.744) and transportability (external AUC 0.703, 95% CI 0.692-0.714) for the primary outcome. The model also possessed good internal and external AUCs for postoperative delirium (internal AUC 0.754, 95% CI 0.713-0.794; external AUC 0.750, 95% CI 0.727-0.772), prolonged postoperative stay (internal AUC 0.813, 95% CI 0.800-0.825; external AUC 0.747, 95% CI 0.741-0.753), and prolonged hospital stay (internal AUC 0.770, 95% CI 0.749-0.792; external AUC 0.707, 95% CI 0.696-0.718). Compared with age or the Charlson comorbidity index, the model showed better prediction performance. ConclusionsThe derived model shall assist clinicians and patients in understanding the individualized risks and benefits of surgery