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Nearly 30% of patients with relapsed breast cancer present activating mutations in estrogen receptor alpha (ERα) that confer partial resistance to existing endocrine-based therapies. We previously reported the development of H3B-5942, a covalent ERα antagonist that engages cysteine-530 (C530) to achieve potency against both wild-type (ERαWT) and mutant ERα (ERαMUT). Anticipating that the emergence of C530 mutations could promote resistance to H3B-5942, we applied structure-based drug design to improve the potency of the core scaffold to further enhance the antagonistic activity in addition to covalent engagement. This effort led to the development of the clinical candidate H3B-6545, a covalent antagonist that is potent against both ERαWT/MUT, and maintains potency even in the context of ERα C530 mutations. H3B-6545 demonstrates significant activity and superiority over standard-of-care fulvestrant across a panel of ERαWT and ERαMUT palbociclib sensitive and resistant models. In summary, the compelling preclinical activity of H3B-6545 supports its further development for the potential treatment of endocrine therapy–resistant ERα+ breast cancer harboring wild-type or mutant ESR1, as demonstrated by the ongoing clinical trials (NCT03250676, NCT04568902, NCT04288089). Summary: H3B-6545 is an ERα covalent antagonist that exhibits encouraging preclinical activity against CDK4/6i naïve and resistant ERαWT and ERαMUT tumors.

H3B-5942 in combination with palbocyclib shows synergy in vitro and in vivo

Additional methods provided.

Supplementary Figure from Covalent ERα Antagonist H3B-6545 Demonstrates Encouraging Preclinical Activity in Therapy-Resistant Breast Cancer

Supplementary Data from Covalent ERα Antagonist H3B-6545 Demonstrates Encouraging Preclinical Activity in Therapy-Resistant Breast Cancer

Enriched GSEA pathways

H3B-6527 response in 40 HCC cell lines and 625 cell lines.

Legends describing each of the supplementary figures and tables

RNA-seq analysis of 24 CCLE HCC cell lines.

Activation of the fibroblast growth factor receptor FGFR4 by FGF19 drives hepatocellular carcinoma (HCC), a disease with few, if any, effective treatment options. While a number of pan-FGFR inhibitors are being clinically evaluated, their application to FGF19-driven HCC may be limited by dose-limiting toxicities mediated by FGFR1–3 receptors. To evade the potential limitations of pan-FGFR inhibitors, we generated H3B-6527, a highly selective covalent FGFR4 inhibitor, through structure-guided drug design. Studies in a panel of 40 HCC cell lines and 30 HCC PDX models showed that FGF19 expression is a predictive biomarker for H3B-6527 response. Moreover, coadministration of the CDK4/6 inhibitor palbociclib in combination with H3B-6527 could effectively trigger tumor regression in a xenograft model of HCC. Overall, our results offer preclinical proof of concept for H3B-6527 as a candidate therapeutic agent for HCC cases that exhibit increased expression of FGF19. Cancer Res; 77(24); 6999–7013. ©2017 AACR.

Constructing ecological networks is known to be important and challenging in community ecology. In particular, to construct the holistic structure of ecological networks, identifying species interaction is essential but often costly and impalpable. Recent studies providing major challenges in assembling ecological networks have highlighted the need of new and more powerful approaches to reconstruct biological networks, including species interaction networks. In literature, there are no promising verifications in using machine leaning (ML) approaches to reconstruct ecological networks. In this work, we develop and employ a variety of ML methods, including penalized regression and graphical tools, to reconstruct ecological networks. For evaluation, we apply the methods to empirical time series data sets of 20 species abundances collected at Lake Constance in central Europe. We use resampled data to identify highly-ranked interactions among species and measure their consistency across 7 ML methods and 5,000 learning processes. We show that the best precision, recall, and F1 score were 0.48, 0.97, and 0.64, respectively, among all penalized regression methods under comparison. In summary, our study shows that machine learning methods offer promising data-driven and automated tools for reconstructing ecological networks and discovering underlying biological interactions among species.

Self-assessment of clinical competence is an important tool for effective learning and training for some educational programs. The New York University (NYU) Langone Hospital's Advanced Education in General Dentistry (AEGD) Program has had its residents complete self-assessment of clinical competency evaluations for many years. The evaluation is used to understand the residents' perception of their own clinical skill upon beginning the program and to determine the necessary resources to provide to the residents for them to meet program standards. The same evaluation is completed by the residents 6 months later to determine if they perceived advancement in their clinical performance while in the program. Dental education, along with other fields of education was disrupted by the coronavirus disease 2019 (COVID-19) pandemic. The purpose of this study was to examine the impact of COVID-19 on clinical competency self-assessments among the NYU Langone AEGD residents before and during the pandemic.In this cross-sectional study, data was collected from two AEGD cohorts representing 2019-2020 and 2020-2021 academic years; from July 2019 (n = 196) to January 2020 (n = 189) and July 2020 (n = 202) to January 2021 (n = 184). The self-assessment evaluations were administered via an online residency management platform on the first days of July and January of the academic year. The survey consisted of 48 questions on "clinical skills and performance" as established by CODA standards for postdoctoral general dentistry programs.Survey response rate was 100% for both cohorts. When comparing results, the findings indicate the COVID-19 pandemic had interrupted clinical learning during dental school. However, training through the AEGD program led to improvements in perceived clinical competence by the residents in mid-program evaluation.The self-assessment evaluation can be used as a tool to enhance training as part of the AEGD program's performance improvement plan.

Following airline mergers and network reorganizations aimed at reducing operational costs, consolidated air services at large hub airports have encouraged air travelers to forego use of their smaller local airports to access large hub airports offering superior air services farther away. This study investigates airport leakage in areas of Wisconsin and Michigan served by small airports, where air travelers may leak to neighboring large hubs. Using a proximity-based service area definition, three airports experiencing leakage are identified, and a hierarchical logit airport choice model is applied that accounts for air service characteristics and access distance for travelers coming from these airports’ service areas. Results show that a similar mean number of flight legs at both the local and substitute (large hub) airports will encourage leakage at Dane County Regional and Gerald R. Ford International airports, indicating that adding direct flights alone will not be sufficient to combat leakage. Comparable access distances to local and substitute airports have opposite effects on the local markets of Gerald R. Ford International and Milwaukee Mitchell International airports—promoting leakage at the former but discouraging it at the latter. Furthermore, proportional increases in airfares at local airports lead to uneven losses of markets in investigated service areas. Overall, the study provides empirical evidence of long-distance airport leakage in parts of the U.S. Midwest, and how its implications can be used by small airports seeking to further understand and respond to travelers’ airport choices within their local markets.

The Covid-19 pandemic was a reminder of the importance of increasing connectivity amidst the accelerated rate of changes and disruptive events of our era. The need and the rationale for global citizenship education (GCED) were ever more emphasized by many educational organizations, including UNESCO. This article reviews the GCED discourses conceptualizing global competence as instrumental action and a binary view of global-local relations. In turn, the article proposes the idea of curriculum-as-relations for GCED. Curriculum-as-relations conceptualizes competence as situated praxis and focuses on providing authentic critical-translocal learning. Authentic critical-translocal learning through the strategy of comparison offers an alternative view of global-local relations as "articulated moments created by situated praxis". This new understanding of global-local relations may help different stakeholders to imagine GCED curricula beyond a Tylerian instrumentalist, ends-means orientation of curriculum.

The introductory graphic represents the identity conflict that the author experiences daily. The terms represent labels assigned to the author in the past, the barriers that Autistic people must constantly overcome, and some of the social rules the author has very consciously and intentionally adopted to "fit in" with society. The accompanying experience piece outlines some of the difficulties the author has experienced that suggest that there needs to be more awareness in Canadian society regarding autism and intersectionality. Anecdotally, there appears to be a need for increased access to diagnostic and clinical services across Canada for women across the spectrum, and further investigation into how disability, race, and gender interact.

Nav1.7 is an extensively investigated target for pain with a strong genetic link in humans, yet in spite of this effort, it remains challenging to identify efficacious, selective, and safe inhibitors. Here, we disclose the discovery and preclinical profile of GDC-0276 (1) and GDC-0310 (2), selective Nav1.7 inhibitors that have completed Phase 1 trials. Our initial search focused on close-in analogues to early compound 3. This resulted in the discovery of GDC-0276 (1), which possessed improved metabolic stability and an acceptable overall pharmacokinetics profile. To further derisk the predicted human pharmacokinetics and enable QD dosing, additional optimization of the scaffold was conducted, resulting in the discovery of a novel series of N-benzyl piperidine Nav1.7 inhibitors. Improvement of the metabolic stability by blocking the labile benzylic position led to the discovery of GDC-0310 (2), which possesses improved Nav selectivity and pharmacokinetic profile over 1.

Although the exposure to PM2.5 has serious health implications, indoor PM2.5 monitoring is not a widely applied practice. Regulations on the indoor PM2.5 level and measurement schemes are not well established. Compared to other indoor settings, PM2.5 prediction models for large office buildings are particularly lacking. In response to these challenges, statistical models were developed in this paper to predict the PM2.5 concentration in well-mixed indoor air in a commercial office building. The performances of different modeling methods, including multiple linear regression (MLR), partial least squares regression (PLS), distributed lag model (DLM), least absolute shrinkage selector operator (LASSO), simple artificial neural networks (ANN), and long-short term memory (LSTM), were compared. Various combinations of environmental and meteorological parameters were used as predictors. The root-mean-square error (RMSE) of the predicted hourly PM2.5 was 1.73 μg/m3 for the LSTM model and in the range of 2.20-4.71 μg/m3 for the other models when regulatory ambient PM2.5 data were used as predictors. The LSTM models outperformed other modeling approaches across the performance metrics used by learning the predictors' temporal patterns. Even without any ambient PM2.5 information, the developed models still demonstrated relatively high skill in predicting the PM2.5 levels in well-mixed indoor air.

The Four Types of Cyclists is a widely adopted typology developed by Portland’s Bicycle Coordinator, Roger Geller. No Way No How, Interested but Concerned, Enthused and Confident, and Strong and Fearless have become ubiquitous in academic literature and practice. However, the classification was subjectively developed and contains several known contradictions. This research aims to develop a data-driven typology using near-identical explanatory variables to those of the Four Types of Cyclists. The objective is to develop a typology with a similar functional purpose, but derived using statistical methods. An online survey was distributed to a panel of Edmonton, Canada, residents to this effect, and the use of video clips rather than descriptions is tested as a means to assess comfort on different types of cycling infrastructure. Cluster Correspondence Analysis is used to carry out the segmentations, including variables of comfort, cycling intent, and cycling in the previous summer. The survey sample tends to segment into three categories, as opposed to the four suggested by Geller: Uncomfortable or Uninterested, Cautious Majority, and Very Comfortable Cyclists. The Four Types of Cyclists typology is also shown to generate heterogeneous comfort patterns within each cyclist type, a limitation our empirically-derived segmentations overcome.

Laboratory studies show that bisphenol A (BPA) leaches from bisphenol A-glycidyl methacrylate (bisGMA)-based dental materials. We aimed to quantify the extent to which children are exposed to BPA from dental treatment with bisGMA materials, by amount of treatment and type of sedation. We hypothesized that posttreatment urinary BPA (uBPA) concentrations would be higher among patients with more surfaces treated with bisGMA-based materials and among patients receiving general anesthesia compared with pretreatment concentrations. We conducted a prospective cohort study in 211 children, 4 to 12 y old, who had no prior resin-based dental treatment. We measured uBPA concentrations twice before treatment and at 2 d and 1, 4, and 16 wk posttreatment. We abstracted treatment data (surfaces treated) from the chart. We generated descriptive statistics and compared pre- and posttreatment uBPA concentrations using generalized estimating equations. Participants were 51% female, 46% non-White, and 74% publicly insured. The median age was 6 y. The mean number of tooth surfaces exposed to BisGMA materials (composites/sealants) was 7.5 (SD 5.3). Overall, uBPA concentrations were 86% higher (95% confidence interval [CI] 42% to 143%, P < 0.001) at 2 d posttreatment compared with pretreatment concentrations. The uBPA concentrations 2 d posttreatment versus pretreatment tended to be higher (112%, 95% CI 53% to 194%) among those receiving treatment on >4 surfaces than those receiving treatment on ≤4 surfaces (50%, 95% CI −2% to 130%). Two days after treatment, uBPA was significantly higher than pretreatment concentrations in children receiving nitrous oxide but not in those receiving general anesthesia. Among all findings, uBPA concentrations returned to baseline by 4 wk. Children experience short-term increases in BPA from dental treatment. The impact of relatively high, short-term BPA exposure on child health is unknown. Given the widespread use of BisGMA-based dental materials and that chronic low-dose BPA exposure may adversely affect child health, strategies that minimize BPA exposure could potentially improve child health.

This paper explores the application of count models to represent the relationship between flight disruptions and weather. Throughout the world, flights are regularly disrupted by delays at airports and in the terminal airspace, and less frequently by diversions and cancelations. Many delay studies have been conducted for large American and European airports, in part due to the availability of high-quality data. However, such high-quality data is not as readily available for other airports throughout the world. In this study, excess-zero count models are built using a publicly available dataset for Iqaluit Airport (YFB) in Northern Canada, to determine the influence of different weather components on disruption counts. Visibility and crosswind speeds are shown to have the largest influence on flight disruptions. The models are also applied using Aviation System Performance Metrics (ASPM) flight data for Anchorage Airport (ANC) in Alaska; the data is systematically degraded to match completeness of the Iqaluit data to test the models. The results verify that an excess-zero model using incomplete data yields results similar to that of a count model with complete data, demonstrating that an excess-zero model can overcome data incompleteness to yield acceptable results. Although count models have been applied extensively in the transportation literature, the authors believe this to be the first application to flight disruptions, and the first quantitative model of operations at a northern Canadian airport. This paper demonstrates that challenges in data availability—the case for most airports throughout the world—can be addressed with novel statistical modeling applications, and thus, delay studies can be conducted for almost any airport.

Background The conventional guidelines to obtain a safe proximal resection margin (PRM) of 5-6 cm during advanced gastric cancer (AGC) surgery are still applied by many surgeons across the world. Several recent studies have raised questions regarding the need for such extensive resection, but without reaching consensus. This study was designed to prove that the PRM distance does not affect the prognosis of patients who undergo gastrectomy for AGC. Aim To investigate the influence of the PRM distance on the prognosis of patients who underwent gastrectomy for AGC. Methods Electronic medical records of 1518 patients who underwent curative gastrectomy for AGC between June 2004 and December 2007 at Asan Medical Center, a tertiary care center in Korea, were reviewed retrospectively for the study. The demographics and clinicopathologic outcomes were compared between patients who underwent surgery with different PRM distances using one-way ANOVA and Fisher's exact test for continuous and categorical variables, respectively. The influence of PRM on recurrence-free survival and overall survival were analyzed using Kaplan-Meier survival analysis and Cox proportional hazard analysis. Results The median PRM distance was 4.8 cm and 3.5 cm in the distal gastrectomy (DG) and total gastrectomy (TG) groups, respectively. Patient cohorts in the DG and TG groups were subdivided into different groups according to the PRM distance; ≤ 1.0 cm, 1.1-3.0 cm, 3.1-5.0 cm and > 5.0 cm. The DG and TG groups showed no statistical difference in recurrence rate (23.5% vs 30.6% vs 24.0% vs 24.7%, P = 0.765) or local recurrence rate (5.9% vs 6.5% vs 8.4% vs 6.2%, P = 0.727) according to the distance of PRM. In both groups, Kalpan-Meier analysis showed no statistical difference in recurrence-free survival (P = 0.467 in DG group; P = 0.155 in TG group) or overall survival (P = 0.503 in DG group; P = 0.155 in TG group) according to the PRM distance. Multivariate analysis using Cox proportional hazard model revealed that in both groups, there was no significant difference in recurrence-free survival according to the PRM distance. Conclusion The distance of PRM is not a prognostic factor for patients who undergo curative gastrectomy for AGC.

Historically an important transportation corridor in the Northwest Territories, climate change has shortened the duration of the Mackenzie River’s navigational season. Communities rely increasingly on airlift as the growing volatility affects barging operations, leading to higher overall freight costs. Using an options approach, we present a methodological framework that supports flexible infrastructure decision making, accounting for the impacts of climate change uncertainty. We apply this method to the decision of whether to continue barging on the Mackenzie River, or connect the entire corridor by extending the all-weather Mackenzie Valley Highway, explicitly considering uncertainties in river barging conditions. We first model river open season days as a stochastic process; barging is dependent on the number of open season days, which in turn is affected by climate change. Second, we evaluate the expected cost of barging and airlift each season using a modified Black-Scholes model. Finally, we use real options to determine how long construction of the all-weather highway may be deferred. The results indicate that it is advisable to defer construction nearly a decade, in balancing the costs of construction against climate change uncertainty. This paper demonstrates that when we explicitly incorporate the impact of climate change on project valuations, particularly those in northern and Arctic Canada where these impacts are considerable, project valuations can change significantly such that all-weather road construction is supported, even if it is deferred to future years. This method can assist federal and territorial governments in communicating the impacts of climate change on communities, and provide another tool to support multi-layered, complex transportation infrastructure investment decisions that address these rapidly changing environments.

Barge transport operations on the Mackenzie River, a major transportation corridor in the Northwest Territories, are impacted by multiple sources of uncertainties. In particular, the impacts of climate change on this important corridor have led to summer shipping seasons that are growing more volatile in terms of length and quality. This change can lead to a growing reliance on costly airlifts for delivering essential freight that cannot be delivered by barge during seasons that end early due to low water. The Government of Northwest Territories has been planning the construction of the Mackenzie Valley Highway (MVH) for decades to provide cheaper, more reliable transportation for communities. However, the costs of constructing the MVH are prohibitive, and traditional benefit-cost analyses are unable to consider flexible investment actions in response to uncertainties. Therefore, we apply a real options modeling framework to determine if and when to construct the different segments of the MVH, considering climate change and freight demand uncertainties. We first model climate and freight demand uncertainties as geometric Brownian motion processes. Next, a benefit-cost model is developed. Finally, we use the least-squares Monte Carlo method to solve for extended project values and optimal investment times for each segment. The results indicate that Segment 2 has the largest value with an optimal seven-year delay in investment time, followed by Segment 1, Segment 3, and Segment 4 in the last year of the planning period (or possibly beyond). Freight demand volatility appears to have the greatest impact on project values and investment years. The results show that, although the benefits of construction may not outweigh the costs now, they may at some future date; in between, decision makers have opportunities to change their minds as conditions change. This is particularly important in northern Canada, where highly costly infrastructure investment decisions are subject to massive uncertainties. Overall, we see such an approach as a tool to communicate the value of uncertainty in infrastructure benefit-cost analyses and as one tool in a larger decision-support toolbox that is required for major transportation investments in northern Canada.