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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. 

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. 

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. 

Agricultural simplification continues to expand at the expense of more diverse forms of agriculture. This simplification, for example, in the form of intensively managed monocultures, poses a risk to keeping the world within safe and just Earth system boundaries. Here, we estimated how agricultural diversification simultaneously affects social and environmental outcomes. Drawing from 24 studies in 11 countries across 2655 farms, we show how five diversification strategies focusing on livestock, crops, soils, noncrop plantings, and water conservation benefit social (e.g., human well-being, yields, and food security) and environmental (e.g., biodiversity, ecosystem services, and reduced environmental externalities) outcomes. We found that applying multiple diversification strategies creates more positive outcomes than individual management strategies alone. To realize these benefits, well-designed policies are needed to incentivize the adoption of multiple diversification strategies in unison. 

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. 

Green building applications need to efficiently communicate fine-grained power consumption patterns of a wide variety of consumer-grade appliances for an effective adaptation and percolation of demand response models in the home environment. A key hurdle to the widespread adoption of such demand response policies in these appliances is the lack of efficient connectivity to a local area network. One solution is delivering telemetry data over existing electrical infrastructure to which the devices are already connected. The use of existing wiring produces a simple and cost-effective solution, avoiding many issues observed with wireless mesh networks (such as islands and bottlenecks), while helping to vacate increasingly congested spectrum. In this paper we explore the feasibility and efficacy of Power-line Communications (PLC) as a backbone of wireless communications in a home environment. We evaluate the behavior of several state-of-the art PLC modems using end-to-end measurements to establish their performance and throughput characteristics. Our preliminary results suggest that PLC is a promising technology for low-bandwidth hungry green building applications but more in depth study is required before making large-scale smart grid deployment. 

Abstract Recent foodborne illness outbreaks have heightened pressures on growers to deter wildlife from farms, jeopardizing conservation efforts. However, it remains unclear which species, particularly birds, pose the greatest risk to food safety. Using >11,000 pathogen tests and 1565 bird surveys covering 139 bird species from across the western United States, we examined the importance of 11 traits in mediating wild bird risk to food safety. We tested whether traits associated with pathogen exposure (e.g., habitat associations, movement, and foraging strategy) and pace‐of‐life (clutch size and generation length) mediated foodborne pathogen prevalence and proclivities to enter farm fields and defecate on crops.Campylobacterspp. were the most prevalent enteric pathogen (8.0%), whileSalmonellaand Shiga‐toxin producingEscherichia coli(STEC) were rare (0.46% and 0.22% prevalence, respectively). We found that several traits related to pathogen exposure predicted pathogen prevalence. Specifically,Campylobacterand STEC‐associated virulence genes were more often detected in species associated with cattle feedlots and bird feeders, respectively.Campylobacterwas also more prevalent in species that consumed plants and had longer generation lengths. We found that species associated with feedlots were more likely to enter fields and defecate on crops. Our results indicated that canopy‐foraging insectivores were less likely to deposit foodborne pathogens on crops, suggesting growers may be able to promote pest‐eating birds and birds of conservation concern (e.g., via nest boxes) without necessarily compromising food safety. As such, promoting insectivorous birds may represent a win‐win‐win for bird conservation, crop production, and food safety. Collectively, our results suggest that separating crop production from livestock farming may be the best way to lower food safety risks from birds. More broadly, our trait‐based framework suggests a path forward for co‐managing wildlife conservation and food safety risks in farmlands by providing a strategy for holistically evaluating the food safety risks of wild animals, including under‐studied species.