Systematic Framework
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2021 ◽  
Vol 18 (6) ◽  
pp. 101-118
Renee Mazurek ◽  
Monna Arvinen-Barrow ◽  
Wendy Huddleston ◽  
Renee Reckelberg ◽  

This paper discusses how pedagogical theory can be used in conceptualizing a collaborative teaching development program in higher education. A theoretically driven teaching development program can be of benefit to both the reviewer and the reviewee by providing (a) a foundation for the reviewee to examine their educational content being reviewed; and (b) a systematic framework for the reviewee for evaluating the content under review. Appropriately used pedagogical theory enables the constructive alignment of teaching, learning, and assessment. This collaborative, self-reflective, and bi-directional teaching development process facilitates a sense of self-determination, which facilitates motivation and achievement of goals.

2021 ◽  
Vol 5 (4) ◽  
pp. 1-24
Siddharth Mysore ◽  
Bassel Mabsout ◽  
Kate Saenko ◽  
Renato Mancuso

We focus on the problem of reliably training Reinforcement Learning (RL) models (agents) for stable low-level control in embedded systems and test our methods on a high-performance, custom-built quadrotor platform. A common but often under-studied problem in developing RL agents for continuous control is that the control policies developed are not always smooth. This lack of smoothness can be a major problem when learning controllers as it can result in control instability and hardware failure. Issues of noisy control are further accentuated when training RL agents in simulation due to simulators ultimately being imperfect representations of reality—what is known as the reality gap . To combat issues of instability in RL agents, we propose a systematic framework, REinforcement-based transferable Agents through Learning (RE+AL), for designing simulated training environments that preserve the quality of trained agents when transferred to real platforms. RE+AL is an evolution of the Neuroflight infrastructure detailed in technical reports prepared by members of our research group. Neuroflight is a state-of-the-art framework for training RL agents for low-level attitude control. RE+AL improves and completes Neuroflight by solving a number of important limitations that hindered the deployment of Neuroflight to real hardware. We benchmark RE+AL on the NF1 racing quadrotor developed as part of Neuroflight. We demonstrate that RE+AL significantly mitigates the previously observed issues of smoothness in RL agents. Additionally, RE+AL is shown to consistently train agents that are flight capable and with minimal degradation in controller quality upon transfer. RE+AL agents also learn to perform better than a tuned PID controller, with better tracking errors, smoother control, and reduced power consumption. To the best of our knowledge, RE+AL agents are the first RL-based controllers trained in simulation to outperform a well-tuned PID controller on a real-world controls problem that is solvable with classical control.

2021 ◽  
Shengbo Wu ◽  
Shujuan Yang ◽  
Manman Wang ◽  
Hao Wu ◽  
Chunjiang Liu ◽  

Abstract Background:Various diseases and health are closely related to different gut microbes, which own complex interactions with diverse drugs, while the detailed targets for drug-microbe interactions are still limited and investigated separately. Quorum sensing (QS), a potential target for dealing with drug resistant, are yet to be further explored systematically for drug-microbe interactions. Furthermore, many existing studies have reported diverse casual associations among drugs, gut microbes, and diseases, which call for a systematic framework and repository to reveal their intricate interactions.Results:In this study, interactions between microbes and more than 8000 drugs have been systematically studied targeting on microbial quorum sensing receptors (LuxR, LasR, TraR, CviR, PqsR, QscR, YenR, SdiA, LsrB, LuxP, CckA) combined docking-based virtual screening technique and in vitro experimental validation. We have also illustrated the potential drug-microbe interaction network based on the predicted docking-based results to to have a more comprehensive illustration for the drug-microbe interactions, along with obtaining 14 possible potential broad-spectrum drugs for all of the 11 QS receptors. Furthermore, we have constructed a systematic framework including various connections for drugs, receptors, microbes, and diseases to form a comprehensive repository and network, which can give the QS-based underlying mechanisms for the reported causal associations between drugs and microbes at the phenotypic level. The framework, repository, and network will promote the understanding on personalized medicine and developing potential therapies for diverse diseases. Conclusions: Taken together, we curated and predicted various interactions carefully for drugs, receptors, microbes, and diseases to form a comprehensive framework, repository, and network for QS-based drug-microbe-disease interactions. This work contributes to the paradigm for the construction of the more comprehensive molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of the precision medicine in the future.

2021 ◽  
Vol 12 ◽  
Jiye Wang ◽  
Lin Luo ◽  
Qiong Ding ◽  
Zengrui Wu ◽  
Yayuan Peng ◽  

Vitiligo is a complex disorder characterized by the loss of pigment in the skin. The current therapeutic strategies are limited. The identification of novel drug targets and candidates is highly challenging for vitiligo. Here we proposed a systematic framework to discover potential therapeutic targets, and further explore the underlying mechanism of kaempferide, one of major ingredients from Vernonia anthelmintica (L.) willd, for vitiligo. By collecting transcriptome and protein-protein interactome data, the combination of random forest (RF) and greedy articulation points removal (GAPR) methods was used to discover potential therapeutic targets for vitiligo. The results showed that the RF model performed well with AUC (area under the receiver operating characteristic curve) = 0.926, and led to prioritization of 722 important transcriptomic features. Then, network analysis revealed that 44 articulation proteins in vitiligo network were considered as potential therapeutic targets by the GAPR method. Finally, through integrating the above results and proteomic profiling of kaempferide, the multi-target strategy for vitiligo was dissected, including 1) the suppression of the p38 MAPK signaling pathway by inhibiting CDK1 and PBK, and 2) the modulation of cellular redox homeostasis, especially the TXN and GSH antioxidant systems, for the purpose of melanogenesis. Meanwhile, this strategy may offer a novel perspective to discover drug candidates for vitiligo. Thus, the framework would be a useful tool to discover potential therapeutic strategies and drug candidates for complex diseases.

2021 ◽  
Vol 300 ◽  
pp. 113673
Yuansheng Huang ◽  
Peng Li ◽  
Han Li ◽  
Bo Zhang ◽  
Yiliang He

2021 ◽  
Vol 17 (9) ◽  
pp. e1008913
Siyuan Ma ◽  
Boyu Ren ◽  
Himel Mallick ◽  
Yo Sup Moon ◽  
Emma Schwager ◽  

Many methods have been developed for statistical analysis of microbial community profiles, but due to the complex nature of typical microbiome measurements (e.g. sparsity, zero-inflation, non-independence, and compositionality) and of the associated underlying biology, it is difficult to compare or evaluate such methods within a single systematic framework. To address this challenge, we developed SparseDOSSA (Sparse Data Observations for the Simulation of Synthetic Abundances): a statistical model of microbial ecological population structure, which can be used to parameterize real-world microbial community profiles and to simulate new, realistic profiles of known structure for methods evaluation. Specifically, SparseDOSSA’s model captures marginal microbial feature abundances as a zero-inflated log-normal distribution, with additional model components for absolute cell counts and the sequence read generation process, microbe-microbe, and microbe-environment interactions. Together, these allow fully known covariance structure between synthetic features (i.e. “taxa”) or between features and “phenotypes” to be simulated for method benchmarking. Here, we demonstrate SparseDOSSA’s performance for 1) accurately modeling human-associated microbial population profiles; 2) generating synthetic communities with controlled population and ecological structures; 3) spiking-in true positive synthetic associations to benchmark analysis methods; and 4) recapitulating an end-to-end mouse microbiome feeding experiment. Together, these represent the most common analysis types in assessment of real microbial community environmental and epidemiological statistics, thus demonstrating SparseDOSSA’s utility as a general-purpose aid for modeling communities and evaluating quantitative methods. An open-source implementation is available at

2021 ◽  
Vol 11 (1) ◽  
Aliaksandr Mialdun ◽  
Mounir Bou-Ali ◽  
Valentina Shevtsova

AbstractThe Soret effect describes the transport of constituent species in multicomponent mixtures that occurs due to a temperature gradient. This cross-coupling effect of heat and mass transfer has been successfully examined in binary liquid mixtures, while experiments with ternary mixtures are rare as they impose significant difficulties. We introduce a new and innovative concept, the Soret vector, for the characterization of Soret driven separation in ternary mixtures. The presentation of the component separation in the vector form offers several advantages: (i) to predict the Soret sign of a ternary mixture from knowledge of the Soret coefficients in binary subsystems; (ii) to control consistency of measured coefficients, this is especially important when results are obtained using different instruments and methods; (iii) to determine in which regions and which components cause the greatest separation; (iv) to identify the regions where the Soret separation is inaccessible for optical techniques or gravitationally unstable. We demonstrate these features by exploring ternary mixtures of different origins: (a) nearly ideal mixture composed by THN–IBB–nC12 when Soret coefficients in binary subsystems ($$S_{T}^{bin}$$ S T bin ) are positive, (b) non-ideal mixture containing water and ethanol TEG–Wat–EtOH when $$S_{T}^{bin}$$ S T bin are positive and negative and (c) Tol–MeOH–Ch mixture containing demixing zone with positive and negative $$S_{T}^{bin}$$ S T bin . Our approach provides a promising systematic framework for the future research of an important and challenging problem of thermodiffusion in multicomponent liquids.

2021 ◽  
Vol 2021 (9) ◽  
Borna Salehian ◽  
Hong-Yi Zhang ◽  
Mustafa A. Amin ◽  
David I. Kaiser ◽  
Mohammad Hossein Namjoo

Abstract Massive scalar fields provide excellent dark matter candidates, whose dynamics are often explored analytically and numerically using nonrelativistic Schrödinger-Poisson (SP) equations in a cosmological context. In this paper, starting from the nonlinear and fully relativistic Klein-Gordon-Einstein (KGE) equations in an expanding universe, we provide a systematic framework for deriving the SP equations, as well as relativistic corrections to them, by integrating out ‘fast modes’ and including nonlinear metric and matter contributions. We provide explicit equations for the leading-order relativistic corrections, which provide insight into deviations from the SP equations as the system approaches the relativistic regime. Upon including the leading-order corrections, our equations are applicable beyond the domain of validity of the SP system, and are simpler to use than the full KGE case in some contexts. As a concrete application, we calculate the mass-radius relationship of solitons in scalar dark matter and accurately capture the deviations of this relationship from the SP system towards the KGE one.

Global Policy ◽  
2021 ◽  
Keman Huang ◽  
Stuart Madnick ◽  
Nazli Choucri ◽  
Fang Zhang

Prof. Dr. Nazem Malkawi ◽  
Dr.Ahmad MALKAWI ◽  

Motivation/Background: The purpose of this paper is to shed light on leadership and decision-making in situations of uncertainty and risk-the case of the emerging coronavirus (COVID-19) crisis, as well as to give a systematic framework as a guide for leaders to deal with the COVID-19 and other sudden crises. Method: This paper is based on theoretical analytical methodology, were questions of the study are built, and then data were collected from previous research about study concepts. This helps in extracting lessons and principles that help researchers to answer study questions and build a methodological the framework. Results: The current COVID-19 crisis is a global sudden crisis that differs from previous crises in terms of its strength, effects, and speed, it strucks all health, economic, social and psychological aspects of life. It causes a challenge of supply chains pose to governments and organizations, accelerated transformation to virtual work, and brought cultural change at all levels. All this forced leaders to take quick and bold decisions in the absence of complete information and lack of transparency. Conclusions: The originality of this study stems from studying the new COVID-19 crisis that suddenly struck the world and confused the most powerful countries and institutions, as leaders stood unable to deal with this crisis and its destructive effects in various aspects of life. Research on dealing with this crisis is still incomplete and subject to modification and change. Therefore, studying this and coming up with a systematic framework increases originality and novelty of this study.

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