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The Geostationary Operational Environmental Satellite R (GOES-R) series of four satellites are the next generation NOAA GOES satellites. Once on orbit and commissioned, they are renamed GOES 16–19, making critical terrestrial and space weather measurements through 2035. GOES 16 and 17 are currently on orbit, having been launched in 2016 and 2018, respectively. The GOES-R satellites include the Extreme Ultraviolet (EUV) and X-ray Irradiance Sensors (EXIS) instrument suite, which measures calibrated solar irradiance in eight lines or bands between 25 nm and 285 nm with the Extreme Ultraviolet Sensors (EUVS) instrument. EXIS also includes the X-Ray Sensor (XRS) instrument, which measures solar soft X-ray irradiance at the legacy GOES bands. The EUVS Measurements are used as inputs to the EUVS Model, a solar spectral irradiance model for space weather operations that predicts irradiance in twenty-two 5 nm wide intervals from 5 nm to 115 nm, and one 10 nm wide interval from 117 to 127 nm at 30 s cadence. Once fully operational, NOAA will distribute the EUVS Model irradiance with 1 min latency as a primary space weather data product, ushering in a new era of rapid dissemination and measurement continuity of EUV irradiance spectra. This paper describes the EUVS Model algorithms, data sources, calibration methods and associated uncertainties. Typical model (relative) uncertainties are less than ~5% for variability at time-scales longer than 6 h, and are ~25% for solar flare induced variability. The absolute uncertainties, originating from the instruments used to calibrate the EUVS Model, are ~10%. Examples of model results are presented at both sub-daily and multi-year timescales to demonstrate the model’s capabilities and limitations. Example solar flare irradiances are also modeled.

The Sun Coronal Ejection Tracker (SunCET) is an extreme ultraviolet imager and spectrograph instrument concept for tracking coronal mass ejections through the region where they experience the majority of their acceleration: the difficult-to-observe middle corona. It contains a wide field of view (0–4 R⊙) imager and a 1 Å spectral-resolution-irradiance spectrograph spanning 170–340 Å. It leverages new detector technology to read out different areas of the detector with different integration times, resulting in what we call “simultaneous high dynamic range”, as opposed to the traditional high dynamic range camera technique of subsequent full-frame images that are then combined in post-processing. This allows us to image the bright solar disk with short integration time, the middle corona with a long integration time, and the spectra with their own, independent integration time. Thus, SunCET does not require the use of an opaque or filtered occulter. SunCET is also compact – ~15 × 15 × 10 cm in volume – making it an ideal instrument for a CubeSat or a small, complementary addition to a larger mission. Indeed, SunCET is presently in a NASA-funded, competitive Phase A as a CubeSat and has also been proposed to NASA as an instrument onboard a 184 kg Mission of Opportunity.

Abstract Breast cancer is the most common malignancy affecting women worldwide and is notable for its morphologic and biologic diversity, with varying risks of recurrence following treatment. The Oncotype DX Breast Recurrence Score test is an important predictive and prognostic genomic assay for estrogen receptor positive/HER2 negative breast cancer that guides therapeutic strategies; however, such tests can be expensive, delay care, and are not widely available. The aim of this study was to develop a multi-model approach integrating the analysis of whole-slide images and clinicopathologic data to predict their associated breast cancer recurrence risks and categorize these patients into two risk groups according to the predicted score: low-risk and high-risk. The proposed novel methodology uses convolutional neural networks for feature extraction and vision transformers for contextual aggregation, complemented by a logistic regression model that analyzes clinicopathologic data for classification into two risk categories. This method was trained and tested on 956 hematoxylin and eosin-stained whole-slide images of 950 ER+/HER2− breast cancer patients with corresponding clinicopathological features that had prior Oncotype DX testing. The model’s performance was evaluated using an internal test set of 192 patients from Dartmouth Health and an external test set of 405 patients from the University of Chicago. The multi-model approach achieved an AUC of 0.91 (95% CI: 0.87–0.95) on the internal set and an AUC of 0.84 (95% CI: 0.78–0.89) on the external cohort for predicting low- and high-breast cancer recurrence risk categories based on the Oncotype DX recurrence score. With further validation, the proposed methodology could provide an alternative to assist clinicians in personalizing treatment for breast cancer patients and potentially improving their outcomes.

Mathematical learning has an important role and is often prioritized in education. In K-16 education, algebra is one of the most vital mathematical content domains: it represents one of the top barriers for students pursuing a postsecondary education. Game-based learning has been effective in fostering classroom math learning environments that are collaborative and focused on conceptual understanding. Sandbox games provide open-ended learning environments where players can set their own goals and level of effort. As part of the project "Math Snacks," the team designed Agrinautica, a sandbox game to enable constructivist-informed early algebra learning. This article identifies design recommendations for creating meaningful sandbox games for learning, considering students' and teachers' needs. Researchers discuss the decisions to create a sandbox game and describe challenges inherent in math learning through sandbox-type gameplay. This study provides impact results from a large-scale study of users of the game, and shares recommendations for developing future sandbox learning games.

Abstract Schistosomiasis is a neglected tropical disease caused by Schistosoma parasites. Schistosoma are obligate parasites of freshwater Biomphalaria and Bulinus snails, thus controlling snail populations is critical to reducing transmission risk. As snails are sensitive to environmental conditions, we expect their distribution is significantly impacted by global change. Here, we used machine learning, remote sensing, and 30 years of snail occurrence records to map the historical and current distribution of forward-transmitting Biomphalaria hosts throughout Brazil. We identified key features influencing the distribution of suitable habitat and determined how Biomphalaria habitat has changed with climate and urbanization over the last three decades. Our models show that climate change has driven broad shifts in snail host range, whereas expansion of urban and peri-urban areas has driven localized increases in habitat suitability. Elucidating change in Biomphalaria distribution—while accounting for non-linearities that are difficult to detect from local case studies—can help inform schistosomiasis control strategies.

Language learning classrooms are sites of discovery not only of language, but of identity, culture, and community. The pedagogical approach described here, critical media work (CMW), integrates language teaching and social issues by using media literacy as a bridge. CMW explores antiracist discourse in popular media (digital and analog) to dismantle simplified, binary thinking about race. CMW encourages semiotic analyses of antiracist discourse and examines messages along continuums that emphasize complexities and intersections of race, language, and culture. We share how language teachers at two urban colleges that serve immigrant populations integrate CMW into their classes, and we discuss the effectiveness of this pedagogy in terms of language learning and antiracist education. We maintain that language teaching and social justice education can support each other and that media literacy provides a meaningful and effective foundation for engaged antiracist pedagogy.

For teachers to provide students with meaningful instruction in area measurement, teachers need robust understandings of area. Here, I describe two cycles of a "lesson experiment" used to investigate prospective teachers' understandings of area units in an undergraduate mathematics class. For each cycle, I collected and analyzed the prospective teachers' written work and videotaped class discussions. The first cycle yielded a learning trajectory for helping prospective teachers better understand area units, which led to more teachers attaining the lesson goals in the second cycle. The paper articulates these classroom lessons and the lesson experiment process for fellow mathematics instructors and teacher educators.

BackgroundSchistosomiasis, a chronic parasitic disease, remains a public health issue in tropical and subtropical regions, especially in low and moderate-income countries lacking assured access to safe water and proper sanitation. A national prevalence survey carried out by the Brazilian Ministry of Health from 2011 to 2015 found a decrease in human infection rates to 1%, with 19 out of 26 states still classified as endemic areas. There is a risk of schistosomiasis reemerging as a public health concern in low-endemic regions. This study proposes an integrated landscape-based approach to aid surveillance and control strategies for schistosomiasis in low-endemic areas.Methodology/principal findingsIn the Middle Paranapanema river basin, specific landscapes linked to schistosomiasis were identified using a comprehensive methodology. This approach merged remote sensing, environmental, socioeconomic, epidemiological, and malacological data. A team of experts identified ten distinct landscape categories associated with varying levels of schistosomiasis transmission potential. These categories were used to train a supervised classification machine learning algorithm, resulting in a 92.5% overall accuracy and a 6.5% classification error. Evaluation revealed that 74.6% of collected snails from water collections in five key municipalities within the basin belonged to landscape types with higher potential for S. mansoni infection. Landscape connectivity metrics were also analysed.Conclusions/significanceThis study highlights the role of integrated landscape-based analyses in informing strategies for eliminating schistosomiasis. The methodology has produced new schistosomiasis risk maps covering the entire basin. The region's low endemicity can be partly explained by the limited connectivity among grouped landscape-units more prone to triggering schistosomiasis transmission. Nevertheless, changes in social, economic, and environmental landscapes, especially those linked to the rising pace of incomplete urbanization processes in the region, have the potential to increase risk of schistosomiasis transmission. This study will help target interventions to bring the region closer to schistosomiasis elimination.

ABSTRACT Introduction Head and neck cancer (HNC) is a significant global health burden, with late presentation leading to complex treatment. While human papillomavirus (HPV) infection has been implicated in HNC, data from low‐ and middle‐income countries (LMICs) are limited. In this study, we investigated the prevalence and role of HPV in head and neck cancers diagnosed in Rwanda. Methods A retrospective cross‐sectional study was conducted using Rwanda Cancer Registry from January 2011 through December 2020. p16 immunohistochemistry as a surrogate for HPV was performed on a randomly selected case. p16‐positive cases were genotyped. Results A total of 1001 patients with HNC were identified; 82% (n = 819) had squamous cell carcinoma. The mean age at diagnosis was 51.1 years, with a majority being males (58%). Oral cavity and lip (27%) were the most common primary cancer sites. Stage was unknown in most cases (75%, n = 747). HIV status was known in 33% (n = 334) of patients with 10% (n = 33) HIV‐positive; 22% of 202 randomly selected cases were p16‐positive; 34% of the p16‐positive cases were oropharynx. PCR analysis of p16‐positive cases showed 19% HPV positivity, and HPV16 was the most common high‐risk HPV strain, and 55.5% were recorded HPV‐positive by PCR. Conclusions HNC cases in Rwanda have been increasing from 2011 to 2020, with a significant portion being HPV‐positive. Strategies to implement routine testing for p16, especially in oropharynx cancer patients, improved preservation of tissue samples, collection of comprehensive information including cancer risk factors, staging, and treatment are needed in Rwanda.

The school food environment is a key intervention point for influencing children’s and adolescents’ diets. As more countries establish school meal programs to provide critical nourishment to students, establishing standards for the foods served can increase the consumption of key nutrients and limit the consumption of foods that do not build health. This global scoping review explores the prevalence and basic characteristics of national policies that regulate food served through school meals across 193 countries, particularly by restricting the provision of categories, nutrients, or ingredients of nutritional concern. We gathered evidence from policy databases, grey literature, peer-reviewed literature, and primary policy documents. We included nationally mandated policies that included restrictions on categories, nutrients, or ingredients of concern served in school meals. Policies that were sub-national, voluntary, and/or did not include restrictive language were excluded from this review. Data was collected in research electronic data capture then extracted into Microsoft Excel and analyzed for policy frequency, prevalence by world region or country income group, and prevalence of certain policy characteristics. Globally, only 15% of countries were found to have a national-level policy restricting foods served through school meals in some capacity, including either nutritional or categorical restrictions. The majority of these policies were found in high-income countries, and no low-income countries had a policy meeting inclusion criteria. Policies in Latin-American and Caribbean countries limited the content of more nutrients of concern than in other regions. Although many policies included explicit guidelines to monitor implementation, few outlined mechanisms for policy enforcement. Future research should evaluate the impact of various school meal regulatory approaches, including implementation of similar policies at sub-national levels, and other elements that affect the impact of school meal programs, such as procurement, infrastructure, costs to school and to students and their families, and acceptability and consumption of foods provided.

Although research has explored how in-class pedagogical practices and narrative feedback affect student engagement and motivation, questions remain on the impact of grading systems (i.e. multi-interval grades vs pass/fail and narrative evaluation) on academic motivation. Here, we compared the motivation of students who received multi-interval grades to students who were evaluated with a pass/fail and end of course narrative evaluation. In addition, we compared academic motivation at institutions with different grading systems. Grades did not enhance academic motivation. Instead, grades enhanced anxiety and avoidance of challenging courses. In contrast, narrative evaluations supported basic psychological needs and enhanced motivation by providing actionable feedback, promoting trust between instructors and students and cooperation amongst students. Even when accounting for potential confounding factors, students in universities that used narrative evaluations experienced higher intrinsic and autonomous motivation compared to students who received multi-interval grades. Given the potential for grades to thwart basic psychological needs and academic motivation, institutions should re-evaluate when and in which programs grades may be appropriate or necessary.

Fish oils rank among the world’s most popular nutritional supplements and are purported to have numerous health benefits. Previous work suggested that fish oils increase collagen production; however, the effect of fish oils on musculoskeletal health is poorly understood. Further, the divergent effects of omega-3 (Ω3FA) and saturated fatty acids (SFA) remains poorly understood. We tested the effects of Ω3FA and SFAs on in vitro-engineered human ligament (EHL) function. EHLs were treated with bovine serum albumin (BSA)-conjugated eicosapentaenoic acid (EPA, 20:5(n-3)), palmitic acid (PA, 16:0), or a BSA control for 6 days. EPA did not significantly alter, whereas PA significantly decreased EHL function and collagen content. To determine whether this was an in vitro artifact, mice were fed a control or high-lard diet for 14 weeks and musculoskeletal mass, insulin sensitivity, and the collagen content, and mechanics of tendon and bone were determined. Body weight was 40 % higher on a HFD, but muscle, tendon, and bone mass did not keep up with body weight resulting in relative losses in muscle mass, tendon, and bone collagen, as well as mechanical properties. Importantly, we show that PA acutely decreases collagen synthesis in vitro to a similar extent as the decrease in collagen content with chronic treatment. These data suggest that Ω3FAs have a limited effect on EHLs, whereas SFA exert a negative effect on collagen synthesis resulting in smaller and weaker musculoskeletal tissues both in vitro and in vivo.

Background and purpose: Daily target re-delineation in online adaptive radiotherapy (oART) introduces uncertainty. The aim of this study was to evaluate artificial intelligence (AI) generated contours and inter-observer target variation among radiotherapy technicians in cone-beam CT (CBCT) guided oART of bladder cancer. Materials and methods: For each of 10 consecutive patients treated with oART for bladder cancer, one CBCT was randomly selected and retrospectively included. The bladder (CTV-T) was AI-segmented (CTV-TAI). Seven radiotherapy technicians independently reviewed and edited CTV-TAI, generating CTV-TADP. Contours were benchmarked against a ground truth contour (CTV-TGT) delineated blindly from scratch. CTV-TADP and CTV-TAI were compared to CTV-TGT using volume, dice similarity coefficient, and bidirectional local distance. Dose coverage (D99%>95 %) of CTV-TGT was evaluated for treatment plans optimized for CTV-TAI and CTV-TADP with clinical margins. Inter-observer variation among CTV-TADP was assessed using coefficient of variation and generalized conformity index. Results: CTV-TGT ranged from 48.7 cm3 to 211.6 cm3. The median [range] volume difference was 4.5 [−17.8, 42.4] cm3 for CTV-TADP and −15.5 [−54.2, 4.3] cm3 for CTV-TAI, compared to CTV-TGT. Corresponding dice similarity coefficients were 0.87 [0.71, 0.95] and 0.84 [0.64, 0.95]. CTV-TGT was adequately covered in 68/70 plans optimized on CTV-TADP and in 6/10 plans optimized on CTV-TAI with clinical margins. The median [range] coefficient of variation was 0.08 [0.05, 0.11] and generalized conformity index was 0.78 [0.71, 0.88] among CTV-TADP. Conclusions: Target re-delineation in CBCT-guided oART of bladder cancer demonstrated non-isotropic inter-observer variation. Manual adjustment of AI-generated contours was necessary to cover ground truth targets.

Abstract Habitability at the surface of a planet depends on having an atmosphere long enough for life to develop. The loss of atmosphere to space is an important component in assessing planetary surface habitability. Current models of atmospheric escape from exoplanets are not well constrained by observations. Atmospheric escape observations from the terrestrial planets are available in public data archives. We recast oxygen escape rates from Earth derived from an instrument on Dynamics Explorer‐1 as function of solar wind and compare them to similar data from Mars. Analysis demonstrates that oxygen escape rates from Mars are not as sensitive to variations in solar power components as those from Earth. Available data from Venus can confirm or refute the assertion that oxygen escape from magnetized planets is more sensitive than that from unmagnetized planets.

Coronavirus (CoV) cause considerable morbidity and mortality in humans and other mammals, as evidenced by the emergence of Severe Acute Respiratory CoV (SARS-CoV) in 2003, Middle East Respiratory CoV (MERS-CoV) in 2012, and SARS-CoV-2 in 2019. Although poorly characterized, natural genetic variation in human and other mammals modulate virus pathogenesis, as reflected by the spectrum of clinical outcomes ranging from asymptomatic infections to lethal disease. Using multiple human epidemic and zoonotic Sarbecoviruses, coupled with murine Collaborative Cross genetic reference populations, we identify several dozen quantitative trait loci that regulate SARS-like group-2B CoV pathogenesis and replication. Under a Chr4 QTL, we deleted a candidate interferon stimulated gene, Trim14 which resulted in enhanced SARS-CoV titers and clinical disease, suggesting an antiviral role during infection. Importantly, about 60 % of the murine QTL encode susceptibility genes identified as priority candidates from human genome-wide association studies (GWAS) studies after SARS-CoV-2 infection, suggesting that similar selective forces have targeted analogous genes and pathways to regulate Sarbecovirus disease across diverse mammalian hosts. These studies provide an experimental platform in rodents to investigate the molecular-genetic mechanisms by which potential cross mammalian susceptibility loci and genes regulate type-specific and cross-SARS-like group 2B CoV replication, immunity, and pathogenesis in rodent models. Our study also provides a paradigm for identifying susceptibility loci for other highly heterogeneous and virulent viruses that sporadically emerge from zoonotic reservoirs to plague human and animal populations.

The geographical range of schistosomiasis is affected by the ecology of schistosome parasites and their obligate host snails, including their response to temperature. Previous models predicted schistosomiasis' thermal optimum at 21.7°C, which is not compatible with the temperature in sub-Saharan Africa (SSA) regions where schistosomiasis is hyperendemic. We performed an extensive literature search for empirical data on the effect of temperature on physiological and epidemiological parameters regulating the free-living stages of S. mansoni and S. haematobium and their obligate host snails, i.e., Biomphalaria spp. and Bulinus spp., respectively. We derived nonlinear thermal responses fitted on these data to parameterize a mechanistic, process-based model of schistosomiasis. We then re-cast the basic reproduction number and the prevalence of schistosome infection as functions of temperature. We found that the thermal optima for transmission of S. mansoni and S. haematobium range between 23.1-27.3°C and 23.6-27.9°C (95% CI) respectively. We also found that the thermal optimum shifts toward higher temperatures as the human water contact rate increases with temperature. Our findings align with an extensive dataset of schistosomiasis prevalence in SSA. The refined nonlinear thermal-response model developed here suggests a more suitable current climate and a greater risk of increased transmission with future warming for more than half of the schistosomiasis suitable regions with mean annual temperature below the thermal optimum.

Background Olfactory impairment is common in older adults and may be associated with adverse cardiovascular health; however, empirical evidence is sparse. We examined olfaction in relation to the risk of coronary heart disease (CHD), stroke, and congestive heart failure (CHF). Methods and Results This study included 2537 older adults (aged 75.6±2.8 years) from the Health ABC (Health, Aging, and Body Composition) study with olfaction assessed by the 12‐item Brief Smell Identification Test in 1999 to 2000, defined as poor (score ≤8), moderate (9–10), or good (11–12). The outcomes were incident CHD, stroke, and CHF. During up to a 12‐year follow‐up, 353 incident CHD, 258 stroke, and 477 CHF events were identified. Olfaction was statistically significantly associated with incident CHF, but not with CHD or stroke. After adjusting for demographics, risk factors, and biomarkers of CHF, the cause‐specific hazard ratio (HR) of CHF was 1.32 (95% CI, 1.05–1.66) for moderate and 1.28 (95% CI, 1.01–1.64) for poor olfaction. These associations were robust in preplanned subgroup analyses by age, sex, race, and prevalent CHD/stroke. While the subgroup results were not statistically significantly different, the association of olfaction with CHF appeared to be evident among participants who reported very good to excellent health (HR, 1.47 [95% CI, 1.01–2.14] for moderate; and 1.76 [95% CI, 1.20–2.58] for poor olfaction), but not among those with fair to poor self‐reported health (HR, 1.04 [95% CI, 0.64–1.70] for moderate; and 0.92 [95% CI, 0.58–1.47] for poor olfaction). Conclusions In community‐dwelling older adults, a single olfaction test was associated with a long‐term risk for incident CHF, particularly among those reporting very good to excellent health.

The world of online learning has exploded over the past 15 years, causing professors to quickly determine the best methods for teaching in an online format. At this point there is much knowledge and experience to be explored to help inform both faculty and academic institutions. The purpose of this empirical research study was to seek additional knowledge from successful faculty members on the teaching strategies that contribute to student success in an online CACREP counselor education program. A discussion of previous research is provided with a focus on the best practices for teaching students in an online format. Secondly, the results from a faculty survey and faculty focus group are provided along with descriptions of helpful strategies for teaching online students. The findings indicate that empathic faculty engagement with students was a strong predictor of success. Another key factor was prompt constructive feedback along with intentionality in looking for ways to help students succeed. Overall, finding ways to connect with students personally increased retention rates as well as provided overall faculty and student satisfaction. A discussion follows with implications as well as suggestions for future research.

This paper describes a critical media analysis of antiracist messages from both teaching and research perspectives. Antiracist discourse of public media (yard signs and websites) was collected in two communities in the Northeastern United States in 2020 and are discussed here, first as a site of social construction of antiracism, and second as a model for pedagogy. As a critical media analysis, this study reveals antiracist messages on continuums from passive to active, low-risk to high risk, self-oriented to other-oriented, and detached "not racist" postures to actively antiracist stances. These continuums encourage interrogation of what it means to be antiracist as an individual and as a community. As a model for pedagogy, this study provides a flexible yet structured activity that asks students to observe messages where they live, interrogate the complexities of antiracism within their communities, and reflect on their own participation in antiracist work.

In this paper, we argue that Indigenous data sovereignty (IDS) is vital for addressing threats to ecosystems, as well as for Indigenous Peoples re-establishing and maintaining sovereignty over their territories. Indigenous knowledge-holders face pressure from non-Indigenous scientists to collaborate to address environmental problems, while the open data movement is pressuring them to make their data public. We examine the role of IDS in the context of cumulative effects and climate change that threaten salmon-bearing ecosystems in British Columbia, guided by content from an online workshop in June 2022 and attended exclusively by a Tier-1 audience (First Nations knowledge-holders and/or technical staff working for Nations). Attention to data is required for fruitful collaborations between Indigenous communities and non-Indigenous researchers to address the impacts of climate change and the cumulative effects affecting salmon-bearing watersheds in BC. In addition, we provide steps that Indigenous governments can take to assert sovereignty over data, recommendations that external researchers can use to ensure they respect IDS, and questions that external researchers and Indigenous partners can discuss to guide decision-making about data management. Finally, we reflect on what we learned during the process of co-creating materials.

The lack of timely, high-resolution data on agricultural production is a major challenge in developing countries where such information can guide the allocation of scarce resources for food security, agricultural investment, and other objectives. While much research has suggested that remote sensing can potentially help address these gaps, few studies have indicated the immediate potential for large-scale estimations over both time and space. In this study we described a machine learning approach to estimate smallholder maize yield in Ethiopia, using well-measured and broadly distributed ground truth data and freely available spatiotemporal covariates from remote sensing. A neural networks model outperformed other algorithms in our study. Importantly, our work indicates that a model developed and calibrated on a previous year's data could be used to reasonably estimate maize yield in the subsequent year. Our study suggests the feasibility of developing national programs for the routine generation of broad-scale and high-resolution estimates of smallholder maize yield, including seasonal forecasts, on the basis of machine learning algorithms, well-measured ground control data, and currently existing time series satellite data.

Remote sensing offers a low-cost method for estimating yields at large spatio-temporal scales. Here, we examined the ability of Sentinel-2 satellite imagery to map field-level maize yields across smallholder farms in two regions in Oromia district, Ethiopia. We evaluated how effectively different indices, the MTCI, GCVI, and NDVI, and different models, linear regression and random forest regression, can be used to map field-level yields. We also examined if models improved by adding weather and soil data and how generalizable our models were if trained in one region and applied to another region, where no data were used for model calibration. We found that random forest regression models that used monthly MTCI composites led to the highest yield prediction accuracies (R2 up to 0.63), particularly when using only localized data for training the model. These models were not very generalizable, especially when applied to regions that had significant haze remaining in the imagery. We also found that adding soil and weather data did little to improve model fit. Our results highlight the ability of Sentinel-2 imagery to map field-level yields in smallholder systems, though accuracies are limited in regions with high cloud cover and haze.

As a prospective mathematics teacher, the most inspiring observation I, Audrey, the first author, made about Terry Wood, from those who spoke at the May 2021 Memorial Event, was her passion for mathematics education as evidenced by the intermingling of her work and personal life. From the colleagues whom she invited to live in her home to the teachers she collaborated with to her son speaking of the ways his children's grandma watched them become mathematics learners, it was clear that both her personal and professional life was dedicated to the field. The community that Terry created, inspired, and impacted is one that boldly and critically reflects on traditional methods, contributing to practical and lasting changes in the field. In particular, her work with social norms in the mathematics classroom and processes of argumentation inspires new teachers like me to embrace her ideas and vision and move them over the horizon, to see the field shift in positive directions and turn theories into practice.

The study was a comparison of general students of promise affect and mathematical students of promise affect after doing a mathematical modeling activity. Participants? gender (n=160), in grades 7-8, were nearly equal in number (81 girls & 79 boys). After completing a Model-eliciting Activity (MEA) in groups of three, participants completed the 31-item Chamberlin Affective Instrument for Mathematical Problem Solving, hereafter referred to as CAIMPS (Chamberlin, Moore, & Parks, 2017). Using four subconstructs, it was determined that the only statistically significant difference in student affect among the groups was self-esteem and self-efficacy (SS) with the general students of promise group having a mean of 3.43 and the mathematical students of promise group having a mean of 3.76. Implications are that the difference in SS may have surfaced because of the mathematical demands of the problems that ultimately influenced participants? ratings. Three subconstructs (Attitude Value Interest [AVI], Anxiety [ANX], and Aspiration [ASP]) may not have realized a statistically significant difference because they were not as contingent upon mathematical content knowledge as was SS. The final implication is that similar affective ratings may be an indication that MEAs are similarly suitable for use with groups containing individuals with varying talents.

BackgroundMechanistic understanding of transient exposures that lead to adverse health outcomes will enhance our ability to recognize biological signatures of disease. Here, we measured the transcriptomic and epigenomic alterations due to exposure to the metabolic reprogramming agent, dichloroacetic acid (DCA). Previously, we showed that exposure to DCA increased liver tumor incidence in B6C3F1 mice after continuous or early life exposures significantly over background level.MethodsUsing archived formalin-fixed liver samples, we utilized modern methodologies to measure gene expression and DNA methylation levels to link to previously generated phenotypic measures. Gene expression was measured by targeted RNA sequencing (TempO-seq 1500+ toxicity panel: 2754 total genes) in liver samples collected from 10-, 32-, 57-, and 78-week old mice exposed to deionized water (controls), 3.5 g/L DCA continuously in drinking water (“Direct” group), or DCA for 10-, 32-, or 57-weeks followed by deionized water until sample collection (“Stop” groups). Genome-scaled alterations in DNA methylation were measured by Reduced Representation Bisulfite Sequencing (RRBS) in 78-week liver samples for control, Direct, 10-week Stop DCA exposed mice.ResultsTranscriptomic changes were most robust with concurrent or adjacent timepoints after exposure was withdrawn. We observed a similar pattern with DNA methylation alterations where we noted attenuated differentially methylated regions (DMRs) in the 10-week Stop DCA exposure groups compared to the Direct group at 78-weeks. Gene pathway analysis indicated cellular effects linked to increased oxidative metabolism, a primary mechanism of action for DCA, closer to exposure windows especially early in life. Conversely, many gene signatures and pathways reversed patterns later in life and reflected more pro-tumorigenic patterns for both current and prior DCA exposures. DNA methylation patterns correlated to early gene pathway perturbations, such as cellular signaling, regulation and metabolism, suggesting persistence in the epigenome and possible regulatory effects.ConclusionLiver metabolic reprogramming effects of DCA interacted with normal age mechanisms, increasing tumor burden with both continuous and prior DCA exposure in the male B6C3F1 rodent model.

Abstract Mathematical models of complex systems rely on parameter values to produce a desired behavior. As mathematical and computational models increase in complexity, it becomes correspondingly difficult to find parameter values that satisfy system constraints. We propose a Markov Chain Monte Carlo (MCMC) approach for the problem of constrained model parameter generation by designing a Markov chain that efficiently explores a model’s parameter space. We demonstrate the use of our proposed methodology to analyze responses of a newly constructed bistability-constrained model of protein phosphorylation to perturbations in the underlying protein network. Our results suggest that parameter generation for constrained models using MCMC provides powerful tools for modeling-aided analysis of complex natural processes.