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The Odds Don’t Lie: Mathematical Reasoning and Societal Ignorance in Don’t Look Up
No full textIn Adam McKay’s 2021 satirical sci-fi movie Don’t Look Up, two astronomers discover a comet heading directly toward Earth. Despite overwhelming evidence and near-certainty of global extinction, their warnings are ignored and ridiculed. This paper discusses the mathematical and scientific foundations of the movie’s social and political reception, and specifically focuses on orbital prediction and probabilistic modeling as they relate to public understanding of risk. This paper shows how data is often undermined by political and social dynamics, by connecting the fictional events of the movie with real-world crises like the COVID-19 pandemic and the climate emergency. In Don’t Look Up, when society repeatedly ignores clear scientific evidence, this demonstrates the gap between mathematical certainty and public response. This movie presents math as both a tool for discovery, and as a litmus test for collective rationality and the capacity to handle reality
Developing Middle School Students’ Systems Thinking in Earth Science Through Dynamic Simulations: The Case of the Rock Cycle
No full textIn this paper, we discuss the development of students’ systems thinking about the rock cycle as they interact with an instructional module that includes three interactive simulations and accompanying questioning. We present the reasoning of six students from a whole-class design experiment in a sixth-grade classroom to describe how students’ systems thinking may be constructed and reorganized through activity with our design. Our findings highlight a framework of students’ systems thinking about the rock cycle that builds and expands prior work to specific sub-components. We also discuss an emerging framework for supporting students’ systems thinking through careful design of simulation and questioning orchestrations. These two frameworks can be used to create other instructional modules that have the potential to develop students’ systems thinking in the context of earth science
Phase Separation Clustering of Poly Ubiquitin Cargos on Ternary Mixture Lipid Membranes by Synthetically Cross-Linked Ubiquitin Binder Peptides
No full textUbiquitylation is involved in various physiological processes, such as signaling and vesicle trafficking, whereas ubiquitin (UB) is considered an important clinical target. The polymeric addition of UB enables cargo molecules to be recognized specifically by multivalent binding interactions with UB-binding proteins, which results in various downstream processes. Recently, protein condensate formation by ubiquitylated proteins has been reported in many independent UB processes, suggesting its potential role in governing the spatial organization of ubiquitylated cargo proteins. We created modular polymeric UB binding motifs and polymeric UB cargos by synthetic bioconjugation and protein purification. Giant unilamellar vesicles with lipid raft composition were prepared to reconstitute the polymeric UB cargo organization on the membranes. Fluorescence imaging was used to observe the outcome. The polymeric UB cargos clustered on the membranes by forming a phase separation codomain during the interaction with the multivalent UB-binding conjugate. This phase separation was valence-dependent and strongly correlated with its potent ability to form protein condensate droplets in solution. Multivalent UB binding interactions exhibited a general trend toward the formation of phase-separated condensates and the resulting condensates were either in a liquid-like or solid-like state depending on the conditions and interactions. This suggests that the polymeric UB cargos on the plasma and endosomal membranes may use codomain phase separation to assist in the clustering of UB cargos on the membranes for cargo sorting. Our findings also indicate that such phase behavior model systems can be created by a modular synthetic approach that can potentially be used to further engineer biomimetic interactions in vitro
Versatile Imidazole Scaffold with Potent Activity against Multiple Apicomplexan Parasites
No full textMalaria, toxoplasmosis, and cryptosporidiosis are caused by apicomplexan parasites Plasmodium spp., Toxoplasma gondii, and Cryptosporidium parvum, respectively, and pose major health challenges. Their therapies are inadequate, ineffective or threatened by drug resistance. The development of novel drugs against them requires innovative and resource-efficient strategies. We exploited the kinome conservation of these parasites to determine the cellular targets and effects of two Plasmodium falciparum inhibitors in T. gondii and C. parvum. The imidazoles, (R)-RY-1-165 and (R)-RY-1-185, were developed to target the cGMP dependent protein kinase of P. falciparum (PfPKG), orthologs of which are present in T. gondii and C. parvum. Using structural and modeling approaches we determined that the molecules bind stereospecifically and interact with PfPKG in a manner unique among described inhibitors. We used enzymatic assays and mutant P. falciparum expressing PfPKG with a substituted “gatekeeper” residue to determine that cellular activity of the molecules is mediated through targets additional to PfPKG. These likely include P. falciparum calcium dependent protein kinase 1 and 4 (PfCDPK-1, -4), kinases that, like PfPKG, have small amino acids at the “gatekeeper” position. The molecules are active against T. gondii and C. parvum, with T. gondii tachyzoites being particularly sensitive. Using mutant parasites, enzyme assays and modeling studies we demonstrate that targets in T. gondii include TgPKG, TgCDPK1, TgCDPK4 and the mitogen activated kinase-like 1 (MAPKL-1). Our results suggest that this scaffold holds promise for the development of new toxoplasmosis drugs
The theory and practice of modifying soccer: Maximizing learning outcomes in elementary school physical education
No full textPhysical education (PE) has been affirmed over the past decade as a vital part of school curriculum. Drawing from Newell’s theory of constraints (1986) and a comprehensive review of evidence-based literature, this study meticulously investigates pedagogical practices, effective activity modifications, and the implementation of a game-like progression within elementary school PE, with a focus on augmenting learning outcomes across the psychomotor, cognitive, and affective domains. This paper places a primary emphasis on soccer—an open-skill team sport taught in PE programs globally—as its situational conditions make it an ideal option for maximizing students’ learning. The study equips readers with an extensive array of pedagogical resources, including a diversified range of activity types, innovative instructional strategies, and a detailed five-unit soccer lesson specifically designed for elementary school PE. The study underscores the significance of modifications to activities, rules, and equipment, all aimed at increasing learning opportunities, fostering developmentally appropriate motor-skill perception, and promoting moderate-to-vigorous physical activity. It posits that to achieve specific learning goals, a PE teacher should be adept at tailoring the task’s rules and equipment to align with contextual situations. Furthermore, the inclusion of multiple training phases in the curriculum is recommended. The study concludes by outlining prospective pathways for teacher education programs and professional development, emphasizing the critical need for inclusive adaptations. The aim of these modifications is to ensure an inclusive PE environment that accommodates students from all backgrounds and skill levels, providing them with the necessary support to maximize their potential, foster success, and encourage a physically active lifestyle
Pollution to solution: Harnessing the power of bioremediation for petroleum wastewater management
No full textPetroleum refinery effluents pose severe environmental and health risks due to their complex mixture of toxic pollutants, including polycyclic aromatic hydrocarbons (PAHs), heavy metals, and volatile organic compounds, which contaminate soil, water, and agricultural systems while threatening human health through carcinogenic and mutagenic effects. This review comprehensively evaluates sustainable bioremediation techniques for treating petroleum wastewater, analyzing their mechanisms, effectiveness, and potential for agricultural reuse. Major findings reveal that biological treatment methods, including bioaugmentation, biostimulation, composting, enzymatic bioremediation, and phytoremediation, achieve excellent removal efficiencies for various contaminants, with microbial consortia demonstrating superior performance compared to individual strains. Key bacterial genera (Pseudomonas, Rhodococcus, Acinetobacter) and factors affecting bioremediation success, including temperature, pH, nutrient availability, and contaminant bioavailability, were identified. Advanced approaches such as hybrid bioelectrochemical systems and genetically modified microorganisms show enhanced degradation capabilities, while integrated treatment strategies combining biological and physicochemical methods offer the most promising results for complex refinery effluents. The findings highlight that treated petroleum wastewater can serve as a valuable resource for agricultural irrigation, providing essential nutrients while reducing freshwater demand, though careful monitoring is required to prevent micropollutant accumulation. Despite challenges, including site-specific optimization requirements, long-term stability concerns, and scaling limitations, bioremediation represents a cost-effective, environmentally sustainable solution for petroleum wastewater management. Research should focus on developing novel microbial strains, optimizing integrated treatment systems, and establishing comprehensive monitoring protocols to ensure safe agricultural reuse while advancing toward circular economy principles in the petroleum industry
Zero Trust Architecture for Electric Transportation Systems: A Systematic Survey and Deep Learning Framework for Replay Attack Detection
No full textModern and autonomous hybrid electric vehicles (HEVs), as complex cyber-physical systems, represent a key innovation in the future of transportation. However, the increasing interconnectivity and reliance on digital components expose these vehicles to significant cybersecurity risks. To address these challenges, Zero Trust Architecture (ZTA) has emerged as a promising security framework. Operating on the principle of ‘never trust, always verify,’ ZTA offers a comprehensive approach to ensuring continuous trust verification in HEV systems. Despite its potential, the application of ZTA within cyber-physical vehicular systems remains underexplored, and its practical benefits and limitations are not yet fully understood by the engineering community. To bridge this gap, this article presents a detailed survey of ZTA tailored specifically to the needs of vehicular CPSs, highlighting existing technologies, security challenges, and the application of zero-trust principles in HEVs. Additionally, this work proposes a deep learning-based replay attack detection scheme for the battery management system (BMS) of HEVs. The approach leverages a deep learning model to estimate the battery\u27s State of Charge (SoC), analyzing the Error of Estimation using the Inter-Quartile Range (IQR) technique. The detection system analyzes the Error of Estimation using the IQR technique, demonstrating a 74.25% containment ratio and detecting deviations up to 2.39 units during attack scenarios. The system maintains a balanced detection sensitivity with 25.75% detection density. While the proposed method demonstrates high effectiveness in detecting stealth replay attacks through simulation results, it faces certain limitations including computational overhead for real-time processing, dependence on high-quality training data, and potential vulnerability to adversarial attacks on the underlying deep learning model. These challenges highlight the need for careful consideration in practical implementations while opening avenues for future research
State Schooling Policies and Cognitive Performance Trajectories: A Natural Experiment in a National US Cohort of Black and White Adults
No full textBackground: Education is strongly associated with cognitive outcomes at older ages, yet the extent to which these associations reflect causal effects remains uncertain due to potential confounding. Methods: Leveraging changes in historical measures of state-level education policies as natural experiments, we estimated the effects of educational attainment on cognitive performance over 10 years in 20,248 non-Hispanic Black and non-Hispanic White participants, aged 45+ in the Reasons for Geographic and Racial Disparities in Stroke cohort (2003-2020) by (1) using state- and year-specific compulsory schooling laws, school-term length, attendance rate, and student-teacher ratio policies to predict educational attainment for US Census microsample data from 1980 and 1990, and (2) applying policy-predicted years of education (PPYEd) to predict memory, verbal fluency, and a cognitive composite. We estimated overall and race- and sex-specific effects of PPYEd on level and change in each cognitive outcome using random intercept and slope models, adjusting for age, year of first cognitive assessment, and indicators for state of residence at age 6. Results: Each year of PPYEd was associated with higher baseline cognition (0.11 standard deviation [SD] increase in composite measure for each year of PPYEd, 95% confidence interval [CI] = 0.07, 0.15). Subanalyses focusing on individual cognitive domains estimate the largest effects of PPYEd on memory. PPYEd was not associated with the rate of change in cognitive scores. Estimates were similar across Black and White participants and across sex. Conclusions: Historical policies shaping educational attainment are associated with better later-life memory, a major determinant of dementia risk
Development and evaluation of RhizoQOL, a quality-of-life caregiver-reported survey for rhizomelic chondrodysplasia punctata, a rare peroxisomal disorder
No full textBackground: Rhizomelic chondrodysplasia punctata (RCDP) is a rare genetic disorder characterized by symptoms such as respiratory dysfunction, seizures, orthopedic issues, and neurodevelopmental delay. Potential therapeutics for RCDP warrant the development of clinical outcome assessments to assess the efficacy of treatment and the well-being of patients. Our study aimed to develop a valid quality-of-life (QOL) caregiver-reported survey instrument, RhizoQOL, to be used as a supportive endpoint in RCDP clinical trials. Methods: Development of the RhizoQOL survey tool included three RCDP caregiver focus groups to elicit concepts to serve as potential domains in a QOL survey instrument for RCDP, pilot survey development and initial testing, cognitive interviewing of revised survey drafts to determine content validity, as well as a three-month longitudinal study for reliability and internal consistency of the survey instrument. Results: Twenty-eight caregivers participated in the focus groups, reporting that concepts that could be appropriate domains of QOL in RCDP include psychosocial behavior, feeding symptoms, mobility symptoms, respiratory symptoms, seizures and related activity, and impact of treatment. Following pilot survey testing (n = 22) and stakeholder feedback, a revised pilot survey instrument was administered to five caregivers for cognitive interviewing. This resulted in a revised survey instrument with 31 question items, six domains, and a 1–5 Likert scale item response assessing frequency or severity of event in the question item. Longitudinal testing (n = 18) of the revised survey instrument found the average response score was 1.98 ± 0.97 for all question items, and a Cronbach’s alpha value of 0.856, suggesting strong intra-survey question reliability. Using individual question item results from reliability testing, linear regression modeling, and testing for required magnitude of significant treatment effects, eight question items were removed from the survey instrument, resulting in a total of 23 question items within 6 discrete domains. Conclusions: The final RhizoQOL survey instrument, consisting of 23 questions, assesses the symptoms and experiences of RCDP patients as observed by caregivers and serves as a novel clinical outcome assessment for RCDP therapeutic clinical trials to assess the impacts of RCDP and support the overall effectiveness of treatments
When Positive Sentiment is not so Positive: Textual Analytics and Bank Failures
No full textWe examine U.S. publicly traded bank holding companies (BHCs) that failed during the 2007–2009 global financial crisis. Using consolidated data at the BHC level and 10-K filings, we investigate the determinants of bank failures during this period using nonlinear machine learning (ML). The in-sample analysis demonstrates that 90% of the failed banks can be classified during 2007–2009. In addition, our sensitivity analysis for interpretable ML shows that net tone is among the top five important features. However, the power of tone/text is less evident when we consider predictive (out-of-sample) analysis. While nonlinear ML models such as random forest and support vector regressions benefit from textual data in forming predictions, linear models that rely on actuarial data attain a similar or even better performance. Overall, our paper demonstrates that the least complex linear models use conventional financial ratios efficiently in predicting the failure of publicly traded banks, deeming more complex ML algorithms with 10-K textual data redundant. Our findings remain robust, even when incorporating large language models such as FinBERT (Huang et al. in Contemp Account Res 40(2):806–841, 2023)