Secondary Level Screening of Chlamydomonas Reinhardtii Mutants Defective in Circadian Gene Expression

To elucidate the signal transduction chain mediating circadian clock control, this work focuses on the isolation of Chlamydomonas reinhardtii mutants which are defective in circadian gene expression. In a previous study, the reporter gene ARS2 encoding the arylsulfatase enzyme was fused to the promoter of the circadian-regulated CABII-1 gene and transformed into the Chlamydomonas nucleus. The ble marker was introduced into the genome of this transformant via insertional mutagenesis to generate mutants defective in circadian CABII-1 expression. Potential mutants were selected based on aberrant single-point accumulative arylsulfatase activity. In this study, the arylsulfatase activity over the entire growth cycle was further investigated in these mutants and the reliability of the single-point screen was assessed. Of the 16 strains whose accumulative arylsulfatase activity did not differ from the nonmutagenized control in the single-point screen, 12 still showed no significant difference in a multiple-point screen. Of the 9 potential mutants with significant difference to the control in the single-point screen, 3 showed no significant difference in the multiple-point screen. Subsequently, 8 of the candidate mutants with aberrant reporter enzyme activity in the multiple-point screen were characterized by the abundance of their mRNA. The peak-to-trough ratio of CABII-1 and ARS2 transcript abundance was significantly reduced in 4 of these mutants

A Bayesian solution to non-convergence of crossed random effects models

When observations (trials) are nested within combinations of subjects and stimuli, crossed random effects models simultaneously take into account both fixed effects and random effects of the subjects and stimuli; however, maximum likelihood estimation (MLE) and restricted maximum likelihood (REML) estimation often encounter convergence problems, which in turn lead to researchers fitting simpler models (e.g., only random intercepts). If the random effect structure is too simple, tests of fixed effects are not valid; if the random effect structure is too complex, tests of fixed effects are inefficient. This study examines issues of estimation, convergence and problems inherent with MLE and REML. We investigated whether Bayesian estimation can solve the convergence problem through a simulation study, which makes a case for adopting a Bayesian approach for estimation, especially when using crossed random effects models. In our simulation study were found that both MLE and REML encountered convergence problems even when trying to fit the correctly specified model and simpler versions of it. Bayesian estimation of models converged 100% of the time, and for the correctly specified model, the parameter estimates were accurate estimates for both fixed and random effects and were essentially unbiased. In sum, the Bayesian approach is a viable alternative to MLE/REML, because models fit by Bayesian estimation solves the non-convergence problem and yields valid and efficient estimates.LimitedAuthor requested closed access (OA after 2yrs) in Vireo ETD syste

Predictive Performance of Bayesian Stacking in Multilevel Education Data

The issue of model uncertainty has been gaining interest in education and the social sciences community over the years, and the dominant methods for handling model uncertainty are based on Bayesian inference, and particularly, Bayesian model averaging. However, Bayesian model averaging assumes that the true data-generating model is within the candidate model space over which averaging is taking place. Unlike Bayesian model averaging, the method of Bayesian stacking can account for model uncertainty without assuming that a true model exists. An issue with Bayesian stacking, however, is that it is an optimization technique that uses predictor-independent model weights and is, therefore, not fully Bayesian. Bayesian hierarchical stacking, proposed by \citeA{yao2021bayesian}, further incorporates uncertainty by applying a hyperprior to the stacking weights. Considering the importance of multilevel models commonly applied in educational settings, this paper investigates via a simulation study and a real data example the predictive performance of original Bayesian stacking and Bayesian hierarchical stacking along with two other readily available weighting methods, pseudo-BMA and pseudo-BMA bootstrap (PBMA and PBMA+). Predictive performance is measured by the Kullback-Leibler divergence score. Although the differences in predictive performance among these four weighting methods in Bayesian stacking are small, we still find that Bayesian hierarchical stacking performs as well as conventional stacking, PBMA, and PBMA+ in settings where a true model is not assumed to exist

A New Explication of Minimum Variable Sets (MVS) for Building Energy Prediction Based on Building Performance Database

Building energy simulation plays a significant role in buildings, with applications such as building performance evaluation, retrofit decisions and the optimization of building operations. However, the wide range of model inputs has limited much research into empirically customized case studies due to the insufficient availability of data inputs or the lack of systematic feature selection of key inputs. To address this gap, this study proposes the concept of minimum variable sets (MVSs) for building energy-prediction models to improve the general applicability of building energy prediction using forward simulation. An MVS, in this paper, refers to a variable set that contains the most indispensable energy-related variables/features for annual building energy prediction. This study developed MVSs for office buildings by applying feature engineering algorithms to a Building Performance Database (BPD), which was established by integrating the design of experiments (DoE) method with high-dimensional data-space metrics, as well as parallel simulation. Supervised feature dimension reduction methods and multiple statistical criteria were adopted to choose different numbers of indispensable variables from the primary 16 building variables. The hierarchical MVSs that consist of the selected variables are characterized by the most influential features for building energy prediction, with certain requirements for prediction accuracy. To further improve the feasibility of MVSs, this study utilized two separate office buildings located in Shanghai and California as validation cases and provided comparable prediction accuracies across different sizes of MVS. The results showed that the MVS that has 12 variables has higher prediction accuracy than that which has 9 variables, followed by that which has 7 variables. Finally, the quantitatively hierarchical correlations between different sizes of MVS with different prediction accuracies for annual building energy could provide potential support for reasonable decision-making regarding building energy model variables, especially when comprehensive consideration is needed of the limited cost and data availability, and the acceptable accuracy of building energy

A New Explication of Minimum Variable Sets (MVS) for Building Energy Prediction Based on Building Performance Database

Building energy simulation plays a significant role in buildings, with applications such as building performance evaluation, retrofit decisions and the optimization of building operations. However, the wide range of model inputs has limited much research into empirically customized case studies due to the insufficient availability of data inputs or the lack of systematic feature selection of key inputs. To address this gap, this study proposes the concept of minimum variable sets (MVSs) for building energy-prediction models to improve the general applicability of building energy prediction using forward simulation. An MVS, in this paper, refers to a variable set that contains the most indispensable energy-related variables/features for annual building energy prediction. This study developed MVSs for office buildings by applying feature engineering algorithms to a Building Performance Database (BPD), which was established by integrating the design of experiments (DoE) method with high-dimensional data-space metrics, as well as parallel simulation. Supervised feature dimension reduction methods and multiple statistical criteria were adopted to choose different numbers of indispensable variables from the primary 16 building variables. The hierarchical MVSs that consist of the selected variables are characterized by the most influential features for building energy prediction, with certain requirements for prediction accuracy. To further improve the feasibility of MVSs, this study utilized two separate office buildings located in Shanghai and California as validation cases and provided comparable prediction accuracies across different sizes of MVS. The results showed that the MVS that has 12 variables has higher prediction accuracy than that which has 9 variables, followed by that which has 7 variables. Finally, the quantitatively hierarchical correlations between different sizes of MVS with different prediction accuracies for annual building energy could provide potential support for reasonable decision-making regarding building energy model variables, especially when comprehensive consideration is needed of the limited cost and data availability, and the acceptable accuracy of building energy

Microstructure evolution and corrosion behavior of 316L stainless steel subjected to torsion

The microstructure evolution of 316L stainless steels subjected to torsion deformation and its corrosion resistance in 1 M H _2 SO _4 solutions were studied. Microstructure evolution of the annealed and torsion-processed samples was characterized by x-ray diffraction and electron backscatter diffraction techniques. The results showed that no martensitic transformation occurred during torsion deformation, while dynamic recrystallization occurred within the samples slowing down the tendency of increasing dislocation density and storage energy. Electrochemical tests including potentiodynamic polarization tests and electrochemical impedance spectroscopy (EIS) were used in the 1 M H _2 SO _4 solution to evaluate the corrosion resistance of the annealed and torsion-processed samples. The results illustrated that small deformation (torsion for 1 turn) could enhance the corrosion resistance of the 316L stainless steels by increasing the stability of the passive film, the medium deformation (torsion for 3 turns) will deteriorate the corrosion resistance due to high-density dislocations formed during torsion deformation, while large deformation (torsion for 5 turns) could improve the corrosion resistance compared with the medium deformation due to the occurrence of dynamic recrystallization and the high-density deformation twins formed

An occupant-centric air-conditioning system for occupant thermal preference recognition control in personal micro-environment

Thermal comfort is one of the most important factors of indoor environment quality, affecting occupants’ well-being and work efficiency. With the advent of smart control technology, personalized and intelligent air conditioners have been promoted for occupant-centric intelligent air-conditioning control. Based on commonly used air-conditioning (AC), this paper quantitatively describes the method for occupant thermal preference adaptation, and proposes a rule-based classification method of occupant thermal preference recognition. With the quantitative description and classification of the occupant thermal preference, this paper proposes a multi-step input control method for an occupant-centric fan-coil system. This method provides an indoor thermal environment that fulfills the demands of different preferences and is easy to implement with existing air-conditioning control systems without additional sensors. To perform an application-oriented, closed-loop research of the proposed control method, two predictionmodels of occupant thermal preferences are developed based on an occupant behavior dataset and they could be used as the well initialized models for future online tunning by continually accumulated dataset. Moreover, aiming for a practical operation guide for conventional occupant-centric air-conditioning systems, this paper validates the effectiveness and accuracy of the proposed multi-step input control method, integrated with occupant thermal preference recognition. This was done by using Programmable Logic Controller (PLC) control experiments and Simulink simulations of an actual personal office room, equipped with a fan-coil unit (FCU) in Shanghai. The research results indicate that dynamic indoor air temperature response with different air-conditioning control modes can meet the control needs of different occupant thermal preference patterns.Peer reviewe

A comprehensive evaluation method for air-conditioning system plants based on building performance simulation and experiment information

Funding Information: Funding: This research was funded by the China National Science Foundation: Methods of minimum variables set construction for building energy prediction based on high-dimensional space theory, grant number 51978481. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.During the design stage of an HVAC (heating, ventilation, and air conditioning) system in a construction project, designers must decide on the most workable design scheme for the plant room in the building based on the evaluation of multiple aspects related to system performance that need to be considered, such as energy efficiency, economic effectiveness, etc. To solve this problem, this paper proposes a comprehensive evaluation method for the plant rooms of centralized air-conditioning systems in commercial buildings. This new method consists of two analyses used in tandem: Building Performance Simulation (BPS) models and a collection of real HVAC design cases (the carried-out design solutions). The BPS models and a knowledge of the reduction approach based on Rough Set (RS) theory are used to generate data and weight factors for the indices of energy efficiency; and the real design cases are employed with a heuristic algorithm to extract the compiled empirical information for other evaluation items of the centralized HVAC system. In addition, this paper also demonstrates an application in an actual case of a building construction project. By comparing the expert decision-making process and the evaluation results, it is found that they are basically consistent, which verifies the reasonability of the comprehensive evaluation method.Peer reviewe