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ABSTRACT Plants exhibit extensive environment-dependent intraspecific metabolic variation, which likely plays a role in determining variation in whole plant phenotypes. However, much of the work seeking to use natural variation to link genes and transcript’s impacts on plant metabolism has employed data from controlled environments. Here we generate and employ data on variation in the abundance of twenty-six metabolites across 660 maize inbred lines under field conditions. We employ these data and previously published transcript and whole plant phenotype data reported for the same field experiment to identify both genomic intervals (through genome-wide association studies) and transcripts (through both transcriptome-wide association studies and an explainable AI approach based on the random forest) associated with variation in metabolite abundance. Both genome-wide association and random forest-based methods identified substantial numbers of significant associations including genes with plausible links to the metabolites they are associated with. In contrast, the transcriptome-wide association identified only six significant associations. In three cases, genetic markers associated with metabolic variation in our study colocalized with markers linked to variation in non-metabolic traits scored in the same experiment. We speculate that the poor performance of transcriptome-wide association studies in identifying transcript-metabolite associations may reflect a high prevalence of non-linear interactions between transcripts and metabolites and/or a bias towards rare transcripts playing a large role in determining intraspecific metabolic variation. 

The inference stage of diffusion models involves running a reverse-time diffusion stochastic differential equation, transforming samples from a Gaussian latent distribution into samples from a target distribution on a low-dimensional manifold. The intermediate values can be interpreted as noisy images, with the amount of noise determined by the forward diffusion process noise schedule. Boomerang is an approach for local sampling of image manifolds, which involves adding noise to an input image, moving it closer to the latent space, and mapping it back to the image manifold through a partial reverse diffusion process. Boomerang can be used with any pretrained diffusion model without adjustments to the reverse diffusion process, and we present three applications: constructing privacy-preserving datasets with controllable anonymity, increasing generalization performance with Boomerang for data augmentation, and enhancing resolution with a perceptual image enhancement framework. 

Aerial imagery is a powerful tool when it comes to analyzing temporal changes in ecosystems and extracting valuable information from the observed scene. It allows us to identify and assess various elements such as objects, structures, textures, waterways, and shadows. To extract meaningful information, multispectral cameras capture data across different wavelength bands of the electromagnetic spectrum. In this study, the collected multispectral aerial images were subjected to principal component analysis (PCA) to identify independent and uncorrelated components or features that extend beyond the visible spectrum captured in standard RGB images. The results demonstrate that these principal components contain unique characteristics specific to certain wavebands, enabling effective object identification and image segmentation. 

The HSI (Hispanic Serving Institution) ATE (Advanced Technological Education) Hub 2 is a three-year collaborative research project funded by the National Science Foundation (NSF) that continues the partnership between two successful programs and involves a third partner in piloting professional development that draws upon findings from the initial program. The goal of HSI ATE Hub 2 is to improve outcomes for Latinx students in technician education programs through design, development, pilot delivery, and dissemination of a 3-tier professional development (PD) model for culturally responsive technician education at 2-year Hispanic Serving Institutions (HSIs). The project seeks to do this by developing the awareness and ability of faculty to appreciate, engage, and affirm the unique cultural identities of the students in their classes and use this connection to deepen students’ belonging and emerging identities as STEM learners and future STEM technicians. This paper shares the research foundations shaping this approach and the methods by which faculty professional development is being provided to develop this important and sensitive instructional capability in participating faculty. The tiered PD model features a scaffolded series of reflective and activity-oriented modules to incrementally enrich the instructional practices and mindset of HSI STEM educators and strengthen their repertoire of strategies for engaging culturally diverse students. Scaffolding that translates culturally responsive theory to practice spans each of the four distinct topic modules in each tier. Each topic module in a tier then scaffolds to a more advanced topic module in the next tier. Tier 1, Bienvenidos, welcomes HSI STEM educators who recognize the need to better serve their Latinx students, and want guidance for small practical activities to try with their students. Tier 2, Transformation through Action, immerses HSI STEM educators in additional activities that bring culturally responsive practices into their technician training while building capacity to collect evidence about impacts and outcomes for students. Tier 3, Engaging Community, strengthens leadership as HSI STEM educators disseminate results from activities completed in Tiers 1 and 2 at conferences that attract technician educators. Sharing the evidence-based practices and their outcomes contributes to achieving broader impacts in the Advanced Technological Education or ATE Community of NSF grantees. Westchester Community College (WCC), the first 2-year HSI in the State University of New York (SUNY) 64 campus system, is piloting the 3-tier PD model using virtual learning methods mastered through previous NSF ATE work and the COVID-19 context. During the pilot, over 20 WCC technician educators in three cohorts will develop leadership skills and practice culturally responsive methods. The pilot will build capacity within WCC STEM technician programs to better support the diversity of students, industry demand for a diverse workforce, and WCC’s capacity for future development of technician education programs. This first paper in a three part series describes the program goals and objectives, the 3-Tier PD model, and reports initial results for Cohort A’s engagement in the first three modules of Tier 1. 

Abstract There is a long-standing discrepancy between the observed Galactic classical nova rate of ∼10 yr −1 and the predicted rate from Galactic models of ∼30–50 yr −1 . One explanation for this discrepancy is that many novae are hidden by interstellar extinction, but the degree to which dust can obscure novae is poorly constrained. We use newly available all-sky three-dimensional dust maps to compare the brightness and spatial distribution of known novae to that predicted from relatively simple models in which novae trace Galactic stellar mass. We find that only half (53%) of the novae are expected to be easily detectable ( g ≲ 15) with current all-sky optical surveys such as the All-Sky Automated Survey for Supernovae (ASAS-SN). This fraction is much lower than previously estimated, showing that dust does substantially affect nova detection in the optical. By comparing complementary survey results from the ASAS-SN, OGLE-IV, and Palomar Gattini IR surveys using our modeling, we find a tentative Galactic nova rate of ∼30 yr −1 , though this could be as high as ∼40 yr −1 , depending on the assumed distribution of novae within the Galaxy. These preliminary estimates will be improved in future work through more sophisticated modeling of nova detection in ASAS-SN and other surveys. 

ABSTRACT A wide variety of Galactic sources show transient emission at soft and hard X-ray energies: low- and high-mass X-ray binaries containing compact objects, isolated neutron stars exhibiting extreme variability as magnetars as well as pulsar-wind nebulae. Although most of them can show emission up to MeV and/or GeV energies, many have not yet been detected in the TeV domain by Imaging Atmospheric Cherenkov Telescopes. In this paper, we explore the feasibility of detecting new Galactic transients with the Cherenkov Telescope Array Observatory (CTAO) and the prospects for studying them with Target of Opportunity observations. We show that CTAO will likely detect new sources in the TeV regime, such as the massive microquasars in the Cygnus region, low-mass X-ray binaries with low-viewing angle, flaring emission from the Crab pulsar-wind nebula or other novae explosions, among others. Since some of these sources could also exhibit emission at larger time-scales, we additionally test their detectability at longer exposures. We finally discuss the multiwavelength synergies with other instruments and large astronomical facilities.