Browsing by Author "Liu, Yan"

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    Bayesian-optimization-assisted discovery of stereoselective aluminum complexes for ring-opening polymerization of racemic lactide
    Wang, Xiaoqian; Huang, Yang; Xie, Xiaoyu; Liu, Yan; Huo, Ziyu; Lin, Maverick; Xin, Hongliang; Tong, Rong (Nature Research, 2023-06-20)
    Stereoselective ring-opening polymerization catalysts are used to produce degradable stereoregular poly(lactic acids) with thermal and mechanical properties that are superior to those of atactic polymers. However, the process of discovering highly stereoselective catalysts is still largely empirical.We aim to develop an integrated computational and experimental framework for efficient, predictive catalyst selection and optimization. As a proof of principle, we have developed a Bayesian optimization workflow on a subset of literature results for stereoselective lactide ring-opening polymerization, and using the algorithm, we identify multiple new Al complexes that catalyze either isoselective or heteroselective polymerization. In addition, feature attribution analysis uncovers mechanistically meaningful ligand descriptors, such as percent buried volume (%Vbur) and the highest occupied molecular orbital energy (Eₕₒₘₒ), that can access quantitative and predictivemodels for catalyst development.
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    GrOup based physical Activity for oLder adults (GOAL) randomized controlled trial: study protocol
    Beauchamp, Mark R.; Harden, Samantha M.; Wolf, Svenja A.; Rhodes, Ryan E.; Liu, Yan; Dunlop, William L.; Schmader, Toni; Sheel, Andrew W.; Zumbo, Bruno D.; Estabrooks, Paul A. (2015-06-27)
    Background Physical activity has health benefits across the lifespan, yet only 13 % of Canadian older adults are sufficiently active. Results from a number of observational studies indicate that adults display positive preferences for exercising with others of a similar age and same gender, and that intra-group age- and gender-similarity are associated with elevated exercise adherence. However, research has yet to experimentally examine the extent to which intra-group age- and gender-related similarity affect exercise adherence behaviors. Methods/design The GrOup-based physical Activity for oLder adults (GOAL) trial is a three-arm randomized control trial that will examine the efficacy of two different group-based exercise programs for older adults (informed by the tenets of self-categorization theory) in relation to a standard group-based exercise program. Within this manuscript we outline the design and proposed evaluation of the GOAL trial. The first arm is comprised of exercise groups made up of participants of a similar-age and of the same gender; the second arm consists of groups with similar-aged mixed gender participants; the control arm is comprised of mixed-aged mixed gender participants. We aim to compare the adherence rates of participants across conditions, as well as potential moderation effects and mediating mechanisms. Discussion Results from this trial will inform intervention designs to improve the exercise adherence behaviors of older adult. At a systems-level, should support be derived for the efficacy of the interventions tested in this trial, changing group composition (i.e., age, gender) represents a feasible program adaptation for physical activity centers. Trial registration ClinicalTrials.gov # NCT02023632 . Registered December 13, 2013.
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    Online-Delivered Group and Personal Exercise Programs to Support Low Active Older Adults' Mental Health During the COVID-19 Pandemic: Randomized Controlled Trial
    Beauchamp, Mark R.; Hulteen, Ryan M.; Ruissen, Geralyn R.; Liu, Yan; Rhodes, Ryan E.; Wierts, Colin M.; Waldhauser, Katrina J.; Harden, Samantha M.; Puterman, Eli (2021-07-30)
    Background: In response to the COVID-19 pandemic, experts in mental health science emphasized the importance of developing and evaluating approaches to support and maintain the mental health of older adults. Objective: The aim of this study was to assess whether a group-based exercise program relative to a personal exercise program (both delivered online) and waitlist control (WLC) can improve the psychological health of previously low active older adults during the early stages of the COVID-19 pandemic. Methods: The Seniors COVID-19 Pandemic and Exercise (SCOPE) trial was a 3-arm, parallel randomized controlled trial conducted between May and September 2020 in which low active older adults (aged >= 65 years) were recruited via media outlets and social media. After baseline assessments, consented participants were randomized to one of two 12-week exercise programs (delivered online by older adult instructors) or a WLC condition. A total of 241 older adults (n=187 women) provided baseline measures (via online questionnaires), were randomized (n(group)+80, n(personal)=82, n(control)=79), and completed measures every 2 , weeks for the duration of the trial. The trial's primary outcome was psychological flourishing. Secondary outcomes included global measures of mental and physical health, life satisfaction, and depression symptoms. Results: The results of latent growth modeling revealed no intervention effects for flourishing, life satisfaction, or depression symptoms (P>.05 for all). Participants in the group condition displayed improved mental health relative to WLC participants over the first 10 weeks (effect size [ES]=0.288-0.601), and although the week 12 effect (ES=0.375) was in the same direction the difference was not statistically significant (P=.089). Participants in the personal condition displayed improved mental health, when compared with WLC participants, in the same medium ES range (ES=0.293-0.565) over the first 8 weeks, and while the effects were of a similar magnitude at weeks 10 (ES=0.455, P=.069) and 12 (ES=0.258, P=.353), they were not statistically significant. In addition, participants in the group condition displayed improvements in physical health when compared with the WLC (ES=0.079-0.496) across all 12 weeks of the study following baseline. No differences were observed between the personal exercise condition and WLC for physical health (slope P=.271). Conclusions: There were no intervention effects for the trial's primary outcome (ie, psychological flourishing). It is possible that the high levels of psychological flourishing at baseline may have limited the extent to which those indicators could continue to improve further through intervention (ie, potential ceiling effects). However, the intervention effects for mental and physical health point to the potential capacity of low-cost and scalable at-home programs to support the mental and physical health of previously inactive adults in the COVID-19 pandemic.
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    Registration of two rice mapping populations using weedy rice ecotypes as a novel germplasm resource
    Singh, Vijay; Jia, Yulin; Gealy, David; Liu, Yan; Ma, Jiabing; Thurber, Carrie; Burgos, Nilda; Olsen, Kenneth; Caicedo, Anna (2021-12-07)
    Two mapping populations were developed from crosses of the Asian indica rice (Oryza sativa L.) cultivar ‘Dee GeoWoo Gen’ (DGWG; PI 699210 Parent, PI 699212 Parent) and two weedy rice ecotypes, an early-flowering straw hull (SH) biotype AR-2000-1135-01 (PI 699209 Parent) collected in Arkansas and a late-flowering black hull (BHA) biotype MS-1996-9 (PI 699211 Parent) collected in Mississippi. The weed and crop-based rice recombinant inbred line (RIL) mapping populations have been used to identify genomic regions associated with weedy traits as well as resistance to sheath blight and rice blast diseases. The mapping population consists of 185 (DGWG/SH; Reg. no. MP-9, NSL 541035 MAP) and 234 (BHA/DGWG; Reg. no. MP-10, NSL 541036 MAP) F8 RILs, of which 175 (DGWG/SH) and 224 (BHA/DGWG) were used to construct two linkage maps using single nucleotide polymorphic markers to identify weedy traits, sheath blight, and blast resistance loci. These mapping populations and related datasets represent a valuable resource for basic rice evolutionary genomic research and applied marker-assisted breeding efforts in disease resistance.
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    Segmenting, Summarizing and Predicting Data Sequences
    Chen, Liangzhe (Virginia Tech, 2018-06-19)
    Temporal data is ubiquitous nowadays and can be easily found in many applications. Consider the extensively studied social media website Twitter. All the information can be associated with time stamps, and thus form different types of data sequences: a sequence of feature values of users who retweet a message, a sequence of tweets from a certain user, or a sequence of the evolving friendship networks. Mining these data sequences is an important task, which reveals patterns in the sequences, and it is a very challenging task as it usually requires different techniques for different sequences. The problem becomes even more complicated when the sequences are correlated. In this dissertation, we study the following two types of data sequences, and we show how to carefully exploit within-sequence and across-sequence correlations to develop more effective and scalable algorithms. 1. Multi-dimensional value sequences: We study sequences of multi-dimensional values, where each value is associated with a time stamp. Such value sequences arise in many domains such as epidemiology (medical records), social media (keyword trends), etc. Our goals are: for individual sequences, to find a segmentation of the sequence to capture where the pattern changes; for multiple correlated sequences, to use the correlations between sequences to further improve our segmentation; and to automatically find explanations of the segmentation results. 2. Social media post sequences: Driven by applications from popular social media websites such as Twitter and Weibo, we study the modeling of social media post sequences. Our goal is to understand how the posts (like tweets) are generated and how we can gain understanding of the users behind these posts. For individual social media post sequences, we study a prediction problem to find the users' latent state changes over the sequence. For dependent post sequences, we analyze the social influence among users, and how it affects users in generating posts and links. Our models and algorithms lead to useful discoveries, and they solve real problems in Epidemiology, Social Media and Critical Infrastructure Systems. Further, most of the algorithms and frameworks we propose can be extended to solve sequence mining problems in other domains as well.
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    SMART: Situationally-Aware Multi-Agent Reinforcement Learning-Based Transmissions
    Jiang, Zhiyuan; Liu, Yan; Hribar, Jernej; DaSilva, Luiz A.; Zhou, Sheng; Niu, Zhisheng (IEEE, 2021-12-01)
    In future wireless systems, latency of information needs to be minimized to satisfy the requirements of many mission-critical applications. Meanwhile, not all terminals carry equally-urgent packets given their distinct situations, e.g., status freshness. Leveraging this feature, we propose an on-demand Medium Access Control (MAC) scheme, whereby each terminal transmits with dynamically adjusted aggressiveness based on its situations which are modeled as Markov states. A Multi-Agent Reinforcement Learning (MARL) framework is utilized and each agent is trained with a Deep Deterministic Policy Gradient (DDPG) network. A notorious issue for MARL is slow and non-scalable convergence – to address this, a new Situationally-aware MARL-based Transmissions (SMART) scheme is proposed. It is shown that SMART can significantly shorten the convergence time and the converged performance is also dramatically improved compared with state-of-the-art DDPG-based MARL schemes, at the expense of an additional offline training stage. SMART also outperforms conventional MAC schemes significantly, e.g., Carrier Sensing and Multiple Access (CSMA), in terms of average and peak Age of Information (AoI). In addition, SMART also has the advantage of versatility – different Quality-of-Service (QoS) metrics and hence various state space definitions are tested in extensive simulations, where SMART shows robustness and scalability in all considered scenarios.