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Abstract Transient transformation assays using protoplasts have become a widely employed technique in plant research. Positive fluorescent selection was subsequently developed to assess the effect of transient effector gene expression in only successfully transfected cells using flow cytometry. This process, though effective, often requires considerable manual effort and subjective judgment to quantify reporter gene expression in the intended cell populations. To address this, we introduce a new, open-source workflow based on the R programming language. This method enhances the reproducibility and scalability of such experiments, which enable rapid study of gene regulation and signal transduction in plants. This workflow is available athttps://github.com/PlantSynBioLab/positive-fluorescence-selection. 

The vast and rapidly growing amount of science education research makes it challenging for researchers to navigate and synthesize developments across the field, particularly concerning broad concepts evolving along divergent paths. To address this issue, a novel review methodology employing bibliometrics and network analysis was tested to identify and characterize clusters of research focused on the relationship between school‐based science learning and contexts where that science is applied, experienced, observable, or otherwise relevant (e.g., socio‐scientific inquiry, place‐based learning, culturally‐responsive pedagogy). Using a sample of 935 academic papers, the bibliometric network analysis revealed the landscape of contextualized science learning research, identifying 13 distinct clusters of scholarship. Bibliometric and qualitative data were used to describe the research trends within clusters and confirm they were conceptually meaningful and distinct. This methodology facilitated greater understanding of how research can become clustered into “invisible colleges” over time, offering a synthesis approach to grasp interrelated lines of research within an evolving landscape. The methodology has potential to identify other schools of thought or overarching themes in science education, enhancing researchers’ ability to perceive the field as a coherent landscape of interconnected ideas or to identify specific research trajectories within a broad concept. 

Reporter-gene activation studies using transient transformation of protoplasts are a powerful tool for the investigation of transcriptional regulation in plants. Here, we perform a comparative analysis of reporter-gene activation sensitivity using an integrated versus a co-transfected reporter-gene construct in Arabidopsis seedling mesophyll protoplasts. The DR5 synthetic auxin-responsive promoter was used to assay the response to auxin treatment and over-expression of activator Auxin Response Factors. We show that sensitivity, as measured by the fold-change in fluorescent-protein reporter-gene expression, is significantly increased by using a co-transfected reporter-gene construct. 

Abstract Auxin is a central regulator of plant growth, development, and responses to environmental cues. How a single phytohormone mediates such a diverse array of developmental responses has remained a longstanding question in plant biology. Somehow, perception of the same auxin signal can lead to divergent responses in different organs, tissues, and cell types. These responses are primarily mediated by the nuclear auxin signaling pathway, composed of ARF transcription factors, Aux/IAA repressors, and TIR1/AFB auxin receptors, which act together to regulate auxin‐dependent transcriptional changes. Transcriptional specificity likely arises through the functional diversity within these signaling components, forming many coordinated regulatory layers to generate unique transcriptional outputs. These layers include differential binding affinities forcis‐regulatory elements, protein–protein interaction‐specificity, subcellular localization, co‐expression patterns, and protein turnover. In this review, we explore the experimental evidence of functional diversity within auxin signaling machinery and discuss how these differences could contribute to transcriptional output specificity. 

IntroductionThroughout domestication, crop plants have gone through strong genetic bottlenecks, dramatically reducing the genetic diversity in today’s available germplasm. This has also reduced the diversity in traits necessary for breeders to develop improved varieties. Many strategies have been developed to improve both genetic and trait diversity in crops, from backcrossing with wild relatives, to chemical/radiation mutagenesis, to genetic engineering. However, even with recent advances in genetic engineering we still face the rate limiting step of identifying which genes and mutations we should target to generate diversity in specific traits. MethodsHere, we apply a comparative evolutionary approach, pairing phylogenetic and expression analyses to identify potential candidate genes for diversifying soybean (Glycine max) canopy cover development via the nuclear auxin signaling gene families, while minimizing pleiotropic effects in other tissues. In soybean, rapid canopy cover development is correlated with yield and also suppresses weeds in organic cultivation. Results and discussionWe identified genes most specifically expressed during early canopy development from the TIR1/AFB auxin receptor, Aux/IAA auxin co-receptor, and ARF auxin response factor gene families in soybean, using principal component analysis. We defined Arabidopsis thaliana and model legume species orthologs for each soybean gene in these families allowing us to speculate potential soybean phenotypes based on well-characterized mutants in these model species. In future work, we aim to connect genetic and functional diversity in these candidate genes with phenotypic diversity in planta allowing for improvements in soybean rapid canopy cover, yield, and weed suppression. Further development of this and similar algorithms for defining and quantifying tissue- and phenotype-specificity in gene expression may allow expansion of diversity in valuable phenotypes in important crops. 

We conducted a comprehensive, systematic review and meta-analysis on the effects of the BSCS 5E Instructional Model—and its related variants—on science, math, and motivation outcomes. The 5E Instructional Model is a framework for delivering STEM instruction that is based on constructivist learning theory; it has been used throughout the U.S. and other countries, particularly in Turkey. Despite its wide usage, no comprehensive systematic review and meta-analysis on the effects of 5E and related models has yet been conducted. Our search and screening procedures yielded 61 randomized controlled trial studies, estimating 156 effect sizes; 70% of studies met WWC standards with or without reservations. We found that the 5E instructional model resulted in improved science outcomes ( g = 0.82, 95% CI [0.67, 0.97]), but a large amount of heterogeneity requires some caution ( t = 0.56). We explored numerous explanations for the effect heterogeneity and provided practical recommendations. 

Geometric fluctuations of the density mode in a fractional quantum Hall (FQH) state can give rise to a nematic FQH phase, a topological state with a spontaneously broken rotational symmetry. While experiments on FQH states in the second Landau level have reported signatures of putative FQH nematics in anisotropic transport, a realistic model for this state has been lacking. We show that the standard model of particles in the lowest Landau level interacting via the Coulomb potential realizes the FQH nematic transition, which is reached by a progressive reduction of the strength of the shortest-range Haldane pseudopotential. Using exact diagonalization and variational wave functions, we demonstrate that the FQH nematic transition occurs when the system’s neutral gap closes in the long-wavelength limit while the charge gap remains open. We confirm the symmetry-breaking nature of the transition by demonstrating the existence of a “circular moat” potential in the manifold of states with broken rotational symmetry, while its geometric character is revealed through the strong fluctuations of the nematic susceptibility and Hall viscosity. Published by the American Physical Society2024 

Machine learning (ML) can be an appropriate approach to overcoming common problems associated with sensors for low-cost, point-of-care diagnostics, such as non-linearity, multidimensionality, sensor-to-sensor variations, presence of anomalies, and ambiguity in key features. This study proposes a novel approach based on ML algorithms (neural nets, Gaussian Process Regression, among others) to model the electrochemiluminescence (ECL) quenching mechanism of the [Ru(bpy)3]2+/TPrA system by phenolic compounds, thus allowing their detection and quantification. The relationships between the concentration of phenolic compounds and their effect on the ECL intensity and current data measured using a mobile phone-based ECL sensor is investigated. The ML regression tasks with a tri-layer neural net using minimally processed time series data showed better or comparable detection performance compared to the performance using extracted key features without extra preprocessing. Combined multimodal characteristics produced an 80% more enhanced performance with multilayer neural net algorithms than a single feature based-regression analysis. The results demonstrated that the ML could provide a robust analysis framework for sensor data with noises and variability. It demonstrates that ML strategies can play a crucial role in chemical or biosensor data analysis, providing a robust model by maximizing all the obtained information and integrating nonlinearity and sensor-to-sensor variations. 

This study used nationally representative data from the 2011 National Assessment of Educational Progress (8th-grade Science) and the 2018 National Survey of Science and Mathematics Education toward two primary purposes: (a) to examine the association between teachers’ formal (university) content preparation in science and student outcomes in science, and (b) to document the prevalence and locality of Out-of-Field (OoF) science teaching in the US. The relationship between teachers’ formal science preparation and students’ 8th-grade science outcomes was mixed across science disciplines with a statistically significant association being observed for students’ earth science outcomes. Teachers’ experience teaching science and access to science instructional materials/kits were more strongly associated with student outcomes than was their formal content preparation, with statistically significant associations observed for all student outcomes (physical science, life science, and earth science). The prevalence of OoF science teaching was higher in middle schools than in high schools, as well as more frequently occurring in historically lower achieving and impoverished educational contexts.