Direct Itaconate Production from Brown Macroalgae Using Engineered Vibrio sp. dhg.

Itaconate is a promising platform chemical with broad applicability, including the synthesis of poly(methyl methacrylate). Most studies on microbial itaconate production entail the use of crop-based feedstock, which imposes constraints due to its limited supply. Brown macroalgae have recently gained attention as next-generation biomass owing to their high biomass productivity and carbohydrate content and amenability to mass production. Therefore, the use of macroalgae for itaconate production warrants exploration. In this study, the direct production of itaconate from brown macroalgae was demonstrated using engineered Vibrio sp. dhg, which has emerged as an efficient platform host for brown macroalgal biorefineries. Specifically, to enhance production, cis-aconitate decarboxylase (Cad) from Aspergillus terreus was heterologously expressed and isocitrate dehydrogenase (icd) was deleted. Notably, the resulting strain, VIC, achieved itaconate titers of 2.5 and 1.5 g/L from a mixture of alginate and mannitol (10 g/L of each) and 40 g/L of raw Saccharina japonica (S. japonica), respectively. Overall, this study highlights the utility of brown macroalgae as feedstock, as well as that of Vibrio sp. dhg as a platform strain for improving itaconate bioproduction.

Accounting for careless and insufficient effort responding in large-scale survey data-development, evaluation, and application of a screen-time-based weighting procedure.

Careless and insufficient effort responding (C/IER) poses a major threat to the quality of large-scale survey data. Traditional indicator-based procedures for its detection are limited in that they are only sensitive to specific types of C/IER behavior, such as straight lining or rapid responding, rely on arbitrary threshold settings, and do not allow taking the uncertainty of C/IER classification into account. Overcoming these limitations, we develop a two-step screen-time-based weighting procedure for computer-administered surveys. The procedure allows considering the uncertainty in C/IER identification, is agnostic towards the specific types of C/IE response patterns, and can feasibly be integrated with common analysis workflows for large-scale survey data. In Step 1, we draw on mixture modeling to identify subcomponents of log screen time distributions presumably stemming from C/IER. In Step 2, the analysis model of choice is applied to item response data, with respondents' posterior class probabilities being employed to downweigh response patterns according to their probability of stemming from C/IER. We illustrate the approach on a sample of more than 400,000 respondents being administered 48 scales of the PISA 2018 background questionnaire. We gather supporting validity evidence by investigating relationships between C/IER proportions and screen characteristics that entail higher cognitive burden, such as screen position and text length, relating identified C/IER proportions to other indicators of C/IER as well as by investigating rank-order consistency in C/IER behavior across screens. Finally, in a re-analysis of the PISA 2018 background questionnaire data, we investigate the impact of the C/IER adjustments on country-level comparisons.

Adjusted Residuals for Evaluating Conditional Independence in IRT Models for Multistage Adaptive Testing.

The key assumption of conditional independence of item responses given latent ability in item response theory (IRT) models is addressed for multistage adaptive testing (MST) designs. Routing decisions in MST designs can cause patterns in the data that are not accounted for by the IRT model. This phenomenon relates to quasi-independence in log-linear models for incomplete contingency tables and impacts certain types of statistical inference based on assumptions on observed and missing data. We demonstrate that generalized residuals for item pair frequencies under IRT models as discussed by Haberman and Sinharay (J Am Stat Assoc 108:1435-1444, 2013. https://doi.org/10.1080/01621459.2013.835660 ) are inappropriate for MST data without adjustments. The adjustments are dependent on the MST design, and can quickly become nontrivial as the complexity of the routing increases. However, the adjusted residuals are found to have satisfactory Type I errors in a simulation and illustrated by an application to real MST data from the Programme for International Student Assessment (PISA). Implications and suggestions for statistical inference with MST designs are discussed.

Molecular characterization of wheat floret development-related F-box protein (TaF-box2): Possible involvement in regulation of Arabidopsis flowering.

In wheat (Triticum aestivum L.), the floret development stage is an important step in determining grain yield per spike; however, the molecular mechanisms underlying floret development remain unclear. In this study, we elucidated the role of TaF-box2, a member of the F-box-containing E3 ubiquitin protein ligases, which is involved in floret development and anthesis of wheat. TaF-box2 was transiently expressed in the plasma membrane and cytoplasm of both tobacco and wheat. We also found that the SCFF-box2 (Skp1-Cul1-Rbx1-TaF-box2) ubiquitin ligase complex mediated self-ubiquitination activity. Transgenic Arabidopsis plants that constitutively overexpressed TaF-box2 showed markedly greater hypocotyl and root length than wild-type plants, and produced early flowering phenotypes. Flowering-related genes were significantly upregulated in TaF-box2-overexpressing Arabidopsis plants. Further protein interaction analyses such as yeast two-hybrid, in vitro pull-down, and bimolecular fluorescence complementation assays confirmed that TaF-box2 physically interacted with TaCYCL1 (Triticum aestivum cyclin-L1-1). Ubiquitination and degradation assays demonstrated that TaCYCL1 was ubiquitinated by SCFF-box2 and degraded through the 26S proteasome complex. The physiological functions of the TaF-box2 protein remain unclear; however, we discuss several potential routes of involvement in various physiological mechanisms which counteract flowering in transgenic Arabidopsis plants.

Trifactor Models for Multiple-Ratings Data.

In this study we extend and assess the trifactor model for multiple-ratings data in which two different raters give independent scores for the same responses (e.g., in the GRE essay or to subset of PISA constructed-responses). The trifactor model was extended to incorporate a cross-classified data structure (e.g., items and raters) instead of a strictly hierarchical structure. we present a set of simulations to reflect the incompleteness and imbalance in real-world assessments. The effects of the rate of missingness in the data and of ignoring differences among raters are investigated using two sets of simulations. The use of the trifactor model is also illustrated with empirical data analysis using a well-known international large-scale assessment.

The Effects of Vignette Scoring on Reliability and Validity of Self-Reports.

The research presented in this article combines mathematical derivations and empirical results to investigate effects of the nonparametric anchoring vignette approach proposed by King, Murray, Salomon, and Tandon on the reliability and validity of rating data. The anchoring vignette approach aims to correct rating data for response styles to improve comparability across individuals and groups. Vignettes are used to adjust self-assessment responses on the respondent level but entail significant assumptions: They are supposed to be invariant across respondents, and the responses to vignette prompts are supposed to be without error and strictly ordered. This article shows that these assumptions are not always met and that the anchoring vignette approach leads to higher Cronbach's alpha values and increased correlations among adjusted variables regardless of whether the assumptions of the approach are met or violated. Results suggest that the underlying assumptions and effects of the anchoring vignette approach should be carefully examined as the increased correlations and reliability estimates can be observed even for response variables that are independent random draws and uncorrelated with any other variable.

A genetic approach for microbial electrosynthesis system as biocommodities production platform.

Microbial electrosynthesis is a process that can produce biocommodities from the reduction of substrates with microbial catalysts and an external electron supply. This process is expected to become a new application of a cell factory for novel chemical production, wastewater treatment, and carbon capture and utilization. However, microbial electrosynthesis is still subject to several problems that need to be overcome for commercialization, so continuous development such as metabolic engineering is essential. The development of microbial electrosynthesis can open up new opportunities for sustainable biocommodities production platforms. This review provides significant information on the current state of MES development, focusing on extracellularly electron transfer and metabolic engineering.