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8. Bayesian Inference of a Finite Population Mean Under Length-Biased Sampling

Author

Xu, Zhiqing, Nandram, Balgobin, and Manandhar, Binod

Subjects
Statistics - Applications
Abstract

We present a robust Bayesian method to analyze forestry data when samples are selected with probability proportional to length from a finite population of unknown size. Specifically, we use Bayesian predictive inference to estimate the finite population mean of shrub widths in a limestone quarry dominated by re-growth of mountain mahogany. The data on shrub widths are collected using transect sampling and it is assumed that the probability that a shrub is selected is proportional to its width; this is length-biased sampling. In this type of sampling, the population size is also unknown and this creates an additional challenge. The quantity of interest is average finite population shrub width and the total shrub area of the quarry can be estimated. Our method is assisted by using the three-parameter generalized gamma distribution, thereby robustifying our procedure against a possible model failure. Using conditional predictive ordinates, we show that the model, which accommodates length bias, performs better than the model that does not. In the Bayesian computation, we overcome a technical problem associated with Gibbs sampling by using a random sampler.

Published
2019

14. Bayesian Inference of a Finite Population Mean Under Length-Biased Sampling

Author

Xu, Zhiqing, Nandram, Balgobin, Manandhar, Binod, Subrahmanyam, P. V., Editor-in-Chief, Chaubey, Yogendra Prasad, Editorial Board Member, Cuellar, Jorge, Editorial Board Member, Matkowski, Janusz, Editorial Board Member, Parthasarathy, Thiruvenkatachari, Editorial Board Member, Sikirić, Mathieu Dutour, Editorial Board Member, Sharma, Bhu Dev, Editorial Board Member, Chandra, Girish, editor, Nautiyal, Raman, editor, and Chandra, Hukum, editor

Published
2020
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27. Incorporating peer feedback in writing instruction: examining its effects on Chinese English-as-a-foreign-language (EFL) learners' writing performance.

Author

Xu, Zhiqing, Zhang, Lawrence Jun, and Parr, Judy M.

Subjects
*ENGLISH as a foreign language, *PSYCHOLOGICAL feedback, *COMPOSITION (Language arts), *EDUCATIONAL intervention, *COLLEGE students
Abstract

Peer feedback has been widely employed in writing instruction in various contexts. However, studies into its effects on improving students' writing performance, particularly its long-term effects, are under-represented. To fill the research gap, we adopted a pre-, post-, and delayed post-test quasi-experimental design to investigate the long-term effects of a peer feedback intervention. Two intact classes of EFL students from a Chinese university were recruited, with one class participating in peer feedback activities and the other class receiving collective feedback from their teacher for one semester. Their compositions for the pre-, post-, delayed post-tests were compared in terms of the overall text quality, content, organization, complexity, accuracy, and fluency (CAF). The results show that the experimental group participants made significant improvements in the overall text quality, content, organization, and accuracy in the post-test and they retained the improvements 12 weeks after the intervention. In addition, they outperformed their counterparts receiving collective feedback in the overall text quality and organization in the post- and delayed post-tests, and in accuracy only in the delayed post-test. Results suggest that peer feedback could help Chinese EFL learners make progress in writing. [ABSTRACT FROM AUTHOR]

Published
2023
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31. A Randomized, Double-Blind, Placebo-Controlled, Phase 1 Trial of Radiopaque Islatravir-Eluting Subdermal Implants for Pre-exposure Prophylaxis Against HIV-1 Infection

Author

Matthews, Randolph P., primary, Zang, Xiaowei, additional, Barrett, Stephanie E., additional, Koynov, Athanas, additional, Goodey, Adrian, additional, Heimbach, Tycho, additional, Weissler, Vanessa L., additional, Leyssens, Carlien, additional, Reynders, Tom, additional, Xu, Zhiqing, additional, Rottey, Sylvie, additional, Vargo, Ryan, additional, Robertson, Michael N., additional, Stoch, S. Aubrey, additional, and Iwamoto, Marian, additional

Published
2022
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35. Enzyme Activity Prediction of Sequence Variants on Novel Substrates using Improved Substrate Encodings and Convolutional Pooling

Author

Xu, Zhiqing, Wu, Jinghao, Song, Yun S., and Mahadevan, Radhakrishnan

Subjects
Article
Abstract

Protein engineering is currently being revolutionized by deep learning applications, especially through natural language processing (NLP) techniques. It has been shown that state-of-the-art self-supervised language models trained on entire protein databases capture hidden contextual and structural information in amino acid sequences and are capable of improving sequence-to-function predictions. Yet, recent studies have reported that current compound-protein modeling approaches perform poorly on learning interactions between enzymes and substrates of interest within one protein family. We attribute this to low-grade substrate encoding methods and over-compressed sequence representations received by downstream predictive models. In this study, we propose a new substrate-encoding based on Extended Connectivity Fingerprints (ECFPs) and a convolutional-pooling of the sequence embeddings. Through testing on an activity profiling dataset of haloalkanoate dehalogenase superfamily that measures activities of 218 phosphatases against 168 substrates, we show substantial improvements in predictive performances of compound-protein interaction modeling. In addition, we also test the workflow on three other datasets from the halogenase, kinase and aminotransferase families and show that our pipeline achieves good performance on these datasets as well. We further demonstrate the utility of this downstream model architecture by showing that it achieves good performance with six different protein embeddings, including ESM-1b (Rives et al., 2021), TAPE (Rao et al., 2019), ProtBert, ProtAlbert, ProtT5, and ProtXLNet (Elnaggar et al., 2021). This study provides a new workflow for activity prediction on novel substrates that can be used to engineer new enzymes for sustainability applications.

Published
2022

39. Reamed or unreamed intramedullary nailing for tibial fractures: a meta-analysis

Author

Yu Guangshu, Lin Yanbin, Wang Yu, and Xu Zhiqing

Subjects
Medicine (General), R5-920
Abstract

【Abstract】Objective: To compare the treating effects of different intramedullary nailing methods on tibial fractures in adults. Methods: Literature reports in both Chinese and English languages were retrieved (from the earliest available records to October 1, 2013) from the PubMed, FMJS, CNKI, Wanfang Data using randomized controlled trials (RCTs) to compare reamed and unreamed intramedullary nailing for treatment of tibial fractures. Methodological quality of the trials was critically assessed, and relevant data were extracted. Statistical software Revman 5.0 was used for data-analysis. Results:A total of 12 randomized controlled trials, comprising 985 patients (475 in the unreamed group and 510 in the reamed group), were eligible for inclusion in this meta-analysis. The results of metaanalysis showed that there were no statistically significant differences between the two methods in the reported outcomes of infection (RR=0.64; 95%CI, 0.39 to 1.07; P=0.09), compartment syndrome (RR=1.44; 95%CI, 0.8 to 2.41; P=0.16), thrombosis (RR=1.29; 95%CI, 0.43 to 3.87; P=0.64), time to union (WMD=5.01; 95%CI, -1.78 to 11.80; P=0.15), delayed union (nonunion) (RR=1.56; 95%CI, 0.97 to 2.49; P=0.06), malunion (RR=1.75; 95%CI, 1.00 to 3.08; P=0.05) and knee pain (RR=0.94; 95%CI, 0.73 to 1.22; P=0.66). But there was a significantly higher fixation failure rate in the unreamed group than in the reamed group (RR=4.29; 95%CI, 2.58 to 7.14; P

Published
2014

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