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We develop and assess centralized and decentralized signal control systems with short-term origin-destination (OD) demands as inputs. Considering each intersection turning movement as a virtual link, we assign traffic demand to paths based on minimal instantaneous travel time. Then, the optimal control is formulated using a G/G/n/FIFO open queueing network model (QNM). We also solve the issue of optimal control using a three-step naïve method for the centralized system with the new inputs. Because the optimization of large-scale network traffic signals can involve sizeable numbers of decision variables and nonlinear constraints, making it a nondeterministic polynomial time (NP) complete problem, we further decompose the centralized system into a decentralized system where the network is divided into subnetworks. Each subnetwork has a dedicated agent that optimizes signals within it. Furthermore, traffic demand for the entire network is decomposed into demands for subnetworks via path decomposition index (PDI). The proposed control systems are applied to test scenarios constructed using different demand profiles in grid networks. We also investigate the impact of network decomposition strategy on signal control system performance. Results show that network decomposition with smaller subnetworks results in less computational time (CT) but increased average travel time (ATT) and total travel delay (TTD).

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.

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.