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4,404 results on '"Yao, B."'

102. E2F7/8 is involved in cardiomyocyte polyploidy but does not affect myocardial reperfusion injury recovery

Author

Fang, J, primary, Van Der Geest, JSA, additional, Yao, B, additional, Yang, Q, additional, Chirico, N, additional, Brans, MA, additional, Roefs, MT, additional, Vader, P, additional, De Jager, SCA, additional, De Bruin, A, additional, Vink, A, additional, Van Mil, A, additional, Schiffelers, RM, additional, Lei, Z, additional, and Sluijter, JPG, additional

Published
2022
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111. Subjective endpoints in clinical trials: the case for blinded independent central review

Author

Walovitch R, Yao B, Chokron P, Le H, and Bubley G

Subjects
Medicine
Abstract

Richard Walovitch,1 Bin Yao,2 Patrick Chokron,1 Helen Le,1 Glenn Bubley3 1WorldCare Clinical, LLC, Boston, MA, USA; 2Amgen, Inc, Thousand Oaks, CA, USA; 3Director of Genitourinary Medical Oncology, Beth Israel Deaconess Medical Center, Boston, MA, USA Abstract: Primary efficacy and safety endpoints in clinical trials are often subjective assessments made by site personnel. For international confirmatory trials conducted over broad geographic regions and different clinical practice settings, variability in these subjective assessments can be substantial. Centralized endpoint assessment committees (EACs) offer a mechanism through which to reduce assessment bias and potentially increase assessment precision and accuracy, particularly in open-label trials. An overview of regulatory agencies' rationales for an EAC is reviewed. In addition, the two main types of EACs, the blinded independent central review, and the consensus panel are compared. Selection of endpoints for EAC evaluation and design of EAC process to maximize EAC value proposition are also discussed. Keywords: endpoint assessment committee, FDA, central review, BICR, adjudication, consensus panel

Published
2013

120. Graphene-loaded and Mn-doped SrCoO3perovskite oxide as a cathode catalyst for aluminum–air battery

Author

Qin, S.P., Wang, Y., Li, Y.C., Zhang, W.W., Yao, B., and Zhang, X.Y.

Abstract

In this study, a SrCoO3perovskite catalyst is prepared by sol–gel method, and modified by loading graphene and doping Mn. The effects of 20% graphene loading and 50% Mn doping on the performance of SrCoO3perovskite as a cathode catalyst for Al–air battery are studied by morphology observation, electrochemical performance analysis, and full battery test. The results show that 20% graphene loading alone and 50% Mn doping alone do not significantly improve the oxygen reduction reaction (ORR) catalytic performance. However, 50% Mn-doped SrCoO3perovskite exhibits a fine grain size, cubic hexagonal mixed structure, and low lattice stability. The 20% graphene-loaded SrCoO3perovskite reduces the oxygen absorption capacity and elevates the peak potential of ORR reaction. After 20% graphene-loaded SrCo0.5Mn0.5O3effectively improves the catalytic performance of ORR. The interfacial interaction between graphene and SrCoO3elevates the peak potential of ORR reaction, but no interaction occurs between graphene and SrCo0.5Mn0.5O3interface. The catalyst containing the graphene-loaded SrCo0.5Mn0.5O3perovskite exhibits outstanding electrocatalytic ORR activity and is suitable for use as cathode catalyst in Al–air battery.

Published
2023
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146. Nanopore callers for epigenetics from limited supervised data

Author

Jennifer Listgarten, Yael Michaeli, Goldner G, Chloe Ching-Yun Hsu, Yuval Ebenstein, and Yao B

Subjects
Nanopore, Training set, Computer science, business.industry, Deep neural networks, Artificial intelligence, Nanopore sequencing, Hidden Markov model, business, Machine learning, computer.software_genre, computer
Abstract

Nanopore sequencing platforms combined with supervised machine learning (ML) have been effective at detecting base modifications in DNA such as 5mC and 6mA. These ML-based nanopore callers have typically been trained on data that span all modifications on all possible DNA k-mer backgrounds—a complete training dataset. However, as nanopore technology is pushed to more and more epigenetic modifications, such complete training data will not be feasible to obtain. Nanopore calling has historically been performed with Hidden Markov Models (HMMs) that cannot make successful calls for k-mer contexts not seen during training because of their independent emission distributions. However, deep neural networks (DNNs), which share parameters across contexts, are increasingly being used as callers, often outperforming their HMM cousins. It stands to reason that a DNN approach should be able to better generalize to unseen k-mer contexts. Indeed, herein we demonstrate that a common DNN approach (DeepSignal) outperforms a common HMM approach (Nanopolish) in the incomplete data setting. Furthermore, we propose a novel hybrid HMM-DNN approach, Amortized-HMM, that outperforms both the pure HMM and DNN approaches on 5mC calling when the training data are incomplete. Such an approach is expected to be useful for calling 5hmC and combinations of cytosine modifications, where complete training data are not likely to be available.

Published
2021

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