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Aerosol radiative forcing is an important but often poorly understood component of regional climate. While glacier ice contains the most detailed archives of past atmospheric aerosol composition and temperature, no well-preserved ice records extending into the last climatic transition have been reported for the historically important European region. Here, we use an Alpine ice core to document changes in European aerosols and climate from the end of the last glacial age (LGA) through the Holocene. The core was drilled on a glacier dome in the French Alps called the Dôme du Goûter (DDG), and it provides a stratigraphically intact record of aerosol and climate extending to at least 12 kyears (ky) before present. Although dating near the base of the glacier is not well constrained, the oldest DDG ice layers reflect glacial conditions in western Europe during the LGA. In addition to changes in atmospheric transport, increased sea-salt and dust deposition in western Europe recorded in the LGA ice suggest enhanced westerly winds and more active dust sources, possibly including North Africa. Deposition of terrestrial biogenic indicators during the cold LGA climate was lower, however, consistent with strongly reduced European vegetation. The DDG record of terrestrial biogenic emissions also suggests a decline of European forests throughout the Holocene, resulting from deterioration of climatic conditions and more recently from establishment of the first agricultural societies. The pronounced changes in atmospheric aerosol recorded in Alpine ice imply large variations in aerosol radiative forcing in western Europe during the last 12 ky. 

ABSTRACT Photoautotrophic diazotrophs, specifically the generaTrichodesmiumand UCYN-A, play a pivotal role in marine nitrogen cycling through their capacity for nitrogen fixation. Despite their global distribution, the microdiversity and environmental drivers of these diazotrophs remain underexplored. This study provides a comprehensive analysis of the global diversity and distribution ofTrichodesmiumand UCYN-A using the nitrogenase gene (nifH) as a genetic marker. We sequenced 954 samples from the Pacific, Atlantic, and Indian Oceans as part of the Bio-GO-SHIP project. Our results reveal significant phylogenetic and biogeographic differences between and within the two genera.Trichodesmiumexhibited greater microdiversity compared to UCYN-A, with clades showing region-specific distribution.Trichodesmiumclades were primarily influenced by temperature and nutrient availability. They were particularly frequent in regions of phosphorus stress. In contrast, UCYN-A was most frequently observed in regions experiencing iron stress. UCYN-A clades demonstrated more homogeneous distributions, with a single sequence variant within the UCYN-A1 clade dominating across varied environments. The biogeographic patterns and environmental correlations ofTrichodesmiumand UCYN-A highlight the role of microdiversity in their ecological adaptation and reflect their different ecological strategies. These findings underscore the importance of characterizing the global patterns of fine-scale genetic diversity to better understand the functional roles and distribution of marine nitrogen-fixing photoautotrophs.IMPORTANCEThis study provides insights into the global diversity and distribution of nitrogen-fixing photoautotrophs, specificallyTrichodesmiumand UCYN-A. We sequenced 954 oceanic samples of thenifHnitrogenase gene and uncovered significant differences in microdiversity and environmental associations between these genera.Trichodesmiumshowed high levels of sequence diversity and region-specific clades influenced by temperature and nutrient availability. In contrast, UCYN-A exhibited a more uniform distribution, thriving in iron-stressed regions. Quantifying these fine-scale genetic variations enhances our knowledge of their ecological roles and adaptations, emphasizing the need to characterize the genetic diversity of marine nitrogen-fixing prokaryotes. 

Abstract Restoring wetlands will reduce nitrogen contamination from excess fertilization but estimates of the efficacy of the strategy vary widely. The intervention is often described as effective for reducing nitrogen export from watersheds to mediate bottom-level hypoxia threatening marine ecosystems. Other research points to the necessity of applying a suite of interventions, including wetland restoration to mitigate meaningful quantities of nitrogen export. Here, we use process-based physical modeling to evaluate the effects of two hypothetical, but plausible large-scale wetland restoration programs intended to reduce nutrient export to the Gulf of Mexico. We show that full adoption of the two programs currently in place can meet as little as 10% to as much as 60% of nutrient reduction targets to reduce the Gulf of Mexico dead zone. These reductions are lower than prior estimates for three reasons. First, net storage of leachate in the subsurface precludes interception and thereby dampens the percent decline in nitrogen export caused by the policy. Unlike previous studies, we first constrained riverine fluxes to match observed fluxes throughout the basin. Second, the locations of many restorable lands are geographically disconnected from heavily fertilized croplands, limiting interception of runoff. Third, daily resolution of the model simulations captured the seasonal and stormflow dynamics that inhibit wetland nutrient removal because peak wetland effectiveness does not coincide with the timing of nutrient inputs. To improve the health of the Gulf of Mexico efforts to eliminate excess nutrient, loading should be implemented beyond the field-margin wetland strategies investigated here. 

The human auditory system can localize multiple sound sources using time, intensity, and frequency cues in the sound received by the two ears. Being able to spatially segregate the sources helps perception in a challenging condition when multiple sounds coexist. This study used model simulations to explore an algorithm for localizing multiple sources in azimuth with binaural (i.e., two) microphones. The algorithm relies on the “sparseness” property of daily signals in the time-frequency domain, and sound coming from different locations carrying unique spatial features will form clusters. Based on an interaural normalization procedure, the model generated spiral patterns for sound sources in the frontal hemifield. The model itself was created using broadband noise for better accuracy, because speech typically has sporadic energy at high frequencies. The model at an arbitrary frequency can be used to predict locations of speech and music that occurred alone or concurrently, and a classification algorithm was applied to measure the localization error. Under anechoic conditions, averaged errors in azimuth increased from 4.5° to 19° with RMS errors ranging from 6.4° to 26.7° as model frequency increased from 300 to 3000 Hz. The low-frequency model performance using short speech sound was notably better than the generalized cross-correlation model. Two types of room reverberations were then introduced to simulate difficult listening conditions. Model performance under reverberation was more resilient at low frequencies than at high frequencies. Overall, our study presented a spiral model for rapidly predicting horizontal locations of concurrent sound that is suitable for real-world scenarios. 

Introduction: Rapid advances in biotechnologies and transdisciplinary research are enhancing the ability to perform full-scale engineering of biology, contributing to worldwide efforts to create bioengineered plants, medicines, and commodities, which promise sustainability and innovative properties. Objective: This rapidly evolving biotechnology landscape is prompting focused scrutiny on biosecurity frameworks in place to mitigate harmful exploitation of biotechnology by state and non-state actors. Concerns about biosafety and biosecurity of engineering biology research have existed for decades as views about how advances in this and associated fields might provide new capabilities to malicious actors. This article considers biosecurity concerns using examples of research advances in engineering biology. Methods: The authors explore risk assessment and mitigation of transdisciplinary biotechnology research and development, using the framework developed in the National Academies' study on Biodefense in an Age of Synthetic Biology. Results: The Synthetic Biology Assessment Framework focuses on risks of using advanced approaches and technologies to enhance or create novel pathogens and toxins. The field of engineering biology continues to advance at a pace that challenges current risk assessment frameworks. Conclusions: This framework likely is sufficient to assess new science and technology advances affecting conventional biological agents. However, the risk assessment framework may have limited applicability for technologies that are not usable with conventional biological agents and result in economic or broader national security concerns. Finally, the vast majority of discourse has been focused primarily on risks rather than benefits, and analyzing both in future evaluations is critical to balancing scientific progress with risk reduction. 

Abstract The evolutionary histories of individual loci in a genome can be estimated independently, but this approach is error-prone due to the limited amount of sequence data available for each gene, which has led to the development of a diverse array of gene tree error correction methods which reduce the distance to the species tree. We investigate the performance of two representatives of these methods: TRACTION and TreeFix. We found that gene tree error correction frequently increases the level of error in gene tree topologies by “correcting” them to be closer to the species tree, even when the true gene and species trees are discordant. We confirm that full Bayesian inference of the gene trees under the multispecies coalescent model is more accurate than independent inference. Future gene tree correction approaches and methods should incorporate an adequately realistic model of evolution instead of relying on oversimplified heuristics. 

We propose a novel framework that leverages Visual Question Answering (VQA) models to automate the evaluation of LLM-generated data visualizations. Traditional evaluation methods often rely on human judgment, which is costly and unscalable, or focus solely on data accuracy, neglecting the effectiveness of visual communication. By employing VQA models, we assess data representation quality and the general communicative clarity of charts. Experiments were conducted using two leading VQA benchmark datasets, ChartQA and PlotQA, with visualizations generated by OpenAI’s GPT-3.5 Turbo and Meta’s Llama 3.1 70B-Instruct models. Our results indicate that LLM-generated charts do not match the accuracy of the original non-LLM-generated charts based on VQA performance measures. Moreover, while our results demonstrate that few-shot prompting significantly boosts the accuracy of chart generation, considerable progress remains to be made before LLMs can fully match the precision of human-generated graphs. This underscores the importance of our work, which expedites the research process by enabling rapid iteration without the need for human annotation, thus accelerating advancements in this field. 

Urban areas often experience higher air temperatures than their surrounding rural counterparts, a phenomenon known as the urban heat island (UHI) effect. This significant human-induced alteration of urban microclimates has notable consequences, especially on urban energy consumption and resulting economic implications. This study presents an in-depth analysis of the UHI effect on urban building energy consumption in a US Midwest neighbourhood. Utilizing a three-phase methodology, the research first simulated UHI intensities with current and future Typical Meteorological Year (TMY) data, integrated with the Local Climate Zone (LCZ) classification system and the Urban Weather Generator (UWG) model. The second phase employed the urban modelling interface (umi) for building energy simulation, capturing the UHI impact on both residential and commercial buildings. The third phase demonstrates that UHI effects lead to reduced heating demand but increased cooling requirements in the future, with residential areas being more affected. The study's findings reveal critical challenges for urban planners and policymakers, emphasizing the need for sustainable designs to address fluctuating heating and cooling demands in changing climates. 

Course-based undergraduate research experiences (CUREs) are laboratory courses that integrate broadly relevant problems, discovery, use of the scientific process, collaboration, and iteration to provide more students with research experiences than is possible in individually mentored faculty laboratories. Members of the national Malate dehydrogenase CUREs Community (MCC) investigated the differences in student impacts between traditional laboratory courses (control), a short module CURE within traditional laboratory courses (mCURE), and CUREs lasting the entire course (cCURE). The sample included approximately 1,500 students taught by 22 faculty at 19 institutions. We investigated course structures for elements of a CURE and student outcomes including student knowledge, student learning, student attitudes, interest in future research, overall experience, future GPA, and retention in STEM. We also disaggregated the data to investigate whether underrepresented minority (URM) outcomes were different from White and Asian students. We found that the less time students spent in the CURE the less the course was reported to contain experiences indicative of a CURE. The cCURE imparted the largest impacts for experimental design, career interests, and plans to conduct future research, while the remaining outcomes were similar between the three conditions. The mCURE student outcomes were similar to control courses for most outcomes measured in this study. However, for experimental design, the mCURE was not significantly different than either the control or cCURE. Comparing URM and White/Asian student outcomes indicated no difference for condition, except for interest in future research. Notably, the URM students in the mCURE condition had significantly higher interest in conducting research in the future than White/Asian students. 

Human tutoring interventions play a crucial role in supporting student learning, improving academic performance, and promoting personal growth. This paper focuses on analyzing mathematics tutoring discourse using talk moves—a framework of dialogue acts grounded in Accountable Talk theory. However, scaling the collection, annotation, and analysis of extensive tutoring dialogues to develop machine learning models is a challenging and resource-intensive task. To address this, we present SAGA22, a compact dataset, and explore various modeling strategies, including dialogue context, speaker information, pretraining datasets, and further fine-tuning. By leveraging existing datasets and models designed for classroom teaching, our results demonstrate that supplementary pretraining on classroom data enhances model performance in tutoring settings, particularly when incorporating longer context and speaker information. Additionally, we conduct extensive ablation studies to underscore the challenges in talk move modeling.