Large-scale Experiments Research Articles

Towards a RAG-based summarization for the Electron Ion Collider

The complexity and sheer volume of information — encompassing documents, papers, data, and other resources — from large-scale experiments demand significant time and effort to navigate, making the task of accessing and utilizing these varied forms of information daunting, particularly for new collaborators and early-career scientists.To tackle this issue, a Retrieval Augmented Generation (RAG)-based Summarization AI for EIC (RAGS4EIC) is under development. This AI-Agent not only condenses information but also effectively references relevant responses, offering substantial advantages for collaborators. Our project involves a two-step approach: first, querying a comprehensive vector database containing all pertinent experiment information; second, utilizing a Large Language Model (LLM) to generate concise summaries enriched with citations based on user queries and retrieved data. We describe the evaluation methods that use RAG assessments (RAGAs) scoring mechanisms to assess the effectiveness of responses. Furthermore, we describe the concept of prompt template based instruction-tuning which provides flexibility and accuracy in summarization. Importantly, the implementation relies on LangChain [1], which serves as the foundation of our entire workflow. This integration ensures efficiency and scalability, facilitating smooth deployment and accessibility for various user groups within the Electron Ion Collider (EIC) community. This innovative AI-driven framework not only simplifies the understanding of vast datasets but also encourages collaborative participation, thereby empowering researchers. As a demonstration, a web application has been developed to explain each stage of the RAG Agent development in detail. The application can be accessed at https://rags4eic-ai4eic.streamlit.app.[A tagged version of the source code can be found in https://github.com/ai4eic/EIC-RAG-Project/releases/tag/AI4EIC2023_PROCEEDING.]

Recognition of screening out hierarchical toxic contaminants tuned by quantified pseudo-components from complex engineering co-combustion

Co-combustion of industrial and municipal solid wastes has emerged as the most promising disposal technology, yet its effect on unknown contaminants generation remains rarely revealed due to waste complexity. Hence, six batches of large-scale engineering experiments were designed in an incinerator of 650 t/d, which overcame the inauthenticity and deviation of laboratory tests. 953–1772 non-targeted compounds were screened in fly ash. Targeting the impact of co-combustion, a pseudo-component matrix model was innovatively integrated to quantitatively extract nine components from complex wastes grouped into biomass and plastic. Thus, the influence was evaluated across eight dimensions, covering molecular characteristics and toxicity. The effect of co-combustion with biomass pseudo-components was insignificant. However, co-combustion with high ratios of plastic pseudo-components induced higher potential risks, significantly promoting the formation of unsaturated hydrocarbons, highly unsaturated compounds (DBE≥15), and cyclic compounds by 19 %− 49 %, 17 %− 31 %, and 7 %− 27 %, respectively. Especially, blending with high ratios of PET plastic pseudo-components produced more species of contaminants. Unique 2 Level I toxicants, bromomethyl benzene and benzofuran-2-carbaldehyde, as well as 4 Level II toxicants, were locked, receiving no concern in previous combustion. The results highlighted risks during high proportion plastics co-combustion, which can help pollution reduction by tuning source wastes to enable healthy co-combustion.

Studies of eco-friendly gas mixtures for RPC detectors

In the context of climate change, one of the main contributors to global warming is the greenhouse effect. Regulations have been implemented in different areas of society to reduce or ban the use of Greenhouse Gases (GHG), such as Tetrafluoroethane (TFE). Despite the fact that large-scale experiments (such as the ones held at CERN) have been excluded from these restrictions, it is necessary to look for alternatives more eco-friendly to the environment. In this work we present performance studies for RPC detectors using eco-friendly gas mixtures tested at the Gamma Irradiation Facility (GIF++) at CERN in the context of the Phase II upgrade of the CMS detector.

Post-blast histological changes to three animal bones exposed to close-range chemical detonation.

A range of investigative practices to aid explosive-related death investigations currently exist, although the use of histopathological bone samples to diagnose blast exposure and the distance of individuals from the blast source has not been previously reported. Forensic histopathology has been used effectively on soft tissue samples to define blast-related injuries effectively, analysing human organs such as the lungs, brain, liver, and skeletal muscles, providing important and useful forensic pathology interpretations. However, no studies currently exist examining the post-blast histological changes in human or animal bones subjected to blasts for forensic pathology practice, despite the opportunity that hard tissue bone samples present, given their significantly lower rate of decomposition over soft tissue. This study presents the first evidence-based findings on the post-blast histological changes in three animal bones when exposed to close-range chemical detonation (C4). The study's qualitative findings highlight critical changes in the tissue architecture of three different animal bone sources due to blast effects with range from the blast source. This emphasises the potential use of histopathological bone sample analysis in future blast-related death investigations, while providing ideas to further explore this work using larger-scale experiments and post-blast case studies in aid of applying this work to human samples and forensic pathology practice.

Evaluating computer science students reading comprehension of educational multimedia-enhanced text using scalable eye-tracking methodology

In this research, a mixed-method approach was employed to conduct large-scale eye-tracking measurements, traditionally associated with high costs and extensive time commitments. Utilizing consumer-grade webcams in conjunction with open-source software, data was collected from an expansive cohort of students, thereby demonstrating the scalability and cost-effectiveness of this innovative methodology. The primary objective of this research was to discern the disparities in reading behaviour when students were presented with standard text accompanied by illustrations, compared to the same text with highlighted key terms. The participants, comprised of first-year university students, completed a questionnaire and an introductory test to ascertain their knowledge level. Subsequently, they were segregated into two groups and participated in two reading sessions, during which their ocular movements were recorded. The amassed data underwent both qualitative analyses, facilitated by visualizations, and quantitative analysis, employing statistical measures on the data and test results. Notably, no significant difference was observed in the gaze patterns or test results between the experimental and control groups. However, a significant divergence in gaze patterns was identified between high-achieving students and those experiencing difficulties, as evidenced by the averaged composite heatmaps generated from the data. The findings underscore two pivotal points. Firstly, the feasibility of conducting large-scale eye-tracking experiments is demonstrated. Traditional studies in this field often employ small population samples due to the time and financial constraints associated with methods that utilize specialized eye-tracking hardware. In contrast, our methodology is scalable, relying on low-end hardware and enabling students to record data on their personal devices. Secondly, while eye-tracking may not provide substantial benefits for fine-tuning text already optimized for readability, it could serve as a valuable tool for identifying and assisting learners who are struggling. This mixed-method approach holds significant potential to revolutionize the conduct and interpretation of eye-tracking studies within educational settings.

Improving bio-conditioning dewatering performance of food waste anaerobic digestate at low ambient temperatures by heating treatment

ABSTRACT Food waste anaerobic digestate (FWAD) containing high concentrations of contaminants must be purified or recycled. Bio-conditioning dewatering followed by activated sludge process (BDAS) has emerged as a promising technology for treating FWAD. However, the bio-conditioning dewatering as a pivotal step of BDAS is often negatively affected by low ambient temperatures often occurred in winter. This study investigated the role of heating FWAD in improving the bio-conditioning dewatering performance of FWAD. Batch experiments demonstrated that the bio-conditioning dewatering efficiency increased with temperature rise. Notably, due to the low energy consumption, 50°C was considered to be the most appropriate heating treatment temperature, realizing a drastic reduction of specific resistance to filtration (SRF) of bio-conditioned FWAD from initial 1.24 × 1012 m/kg in the control at a ambient temperature of 10°C to 5.42 × 1011 m/kg and a saving of 25% in bio-conditioning reagents cost. The results of the pilot-scale and large-scale experiments revealed that heating treatment made the bio-conditioning dewatering more stable regardless of the fluctuation of ambient temperature in practical engineering. The decrease in the viscosity of bio-conditioned FWAD and the enhancement in microbial fermentation liquor flocculation capacity through heating treatment played pivotal roles in improving the bio-conditioning dewatering performance of FWAD. This work provides a cost-effective strategy to achieve efficient bio-conditioning dewatering at a relatively low ambient temperature, which was helpful in the engineering application of the novel BDAS process in wastewater treatment.

Sampling Strategies for Experimentally Mapping Molecular Fitness Landscapes Using High-Throughput Methods.

Empirical studies of genotype-phenotype-fitness maps of proteins are fundamental to understanding the evolutionary process, in elucidating the space of possible genotypes accessible through mutations in a landscape of phenotypes and fitness effects. Yet, comprehensively mapping molecular fitness landscapes remains challenging since all possible combinations of amino acid substitutions for even a few protein sites are encoded by an enormous genotype space. High-throughput mapping of genotype space can be achieved using large-scale screening experiments known as multiplexed assays of variant effect (MAVEs). However, to accommodate such multi-mutational studies, the size of MAVEs has grown to the point where a priori determination of sampling requirements is needed. To address this problem, we propose calculations and simulation methods to approximate minimum sampling requirements for multi-mutational MAVEs, which we combine with a new library construction protocol to experimentally validate our approximation approaches. Analysis of our simulated data reveals how sampling trajectories differ between simulations of nucleotide versus amino acid variants and among mutagenesis schemes. For this, we show quantitatively that marginal gains in sampling efficiency demand increasingly greater sampling effort when sampling for nucleotide sequences over their encoded amino acid equivalents. We present a new library construction protocol that efficiently maximizes sequence variation, and demonstrate using ultradeep sequencing that the library encodes virtually all possible combinations of mutations within the experimental design. Insights learned from our analyses together with the methodological advances reported herein are immediately applicable toward pooled experimental screens of arbitrary design, enabling further assay upscaling and expanded testing of genotype space.

Bar and berm dynamics during transition from dissipative to reflective beach profile

Bar and berm morphology characterize the seasonal beach evolution, and determine the protection against storm erosion as well as the touristic use of beaches. Thus, they are of particular interest for coastal management and engineering in the nearshore zone. This study used large-scale wave flume experiments to observe the transition from fully dissipative to fully reflective beach profile at a high level of detail. Starting from a post-storm profile generated under energetic waves, a very low energy wave condition caused dissipation of the outer and the inner bar, shoreline recovery, and berm accretion. Measurements revealed feedback between hydrodynamics and beach profile evolution with an onshore shift of the wave breaking location. As a result, the magnitude and cross-shore evolution of wave asymmetry-related bedload net onshore and suspended net offshore transport changed. The relative magnitudes of the two transport components and the way they shifted relative to each other caused the observed beach recovery. Additionally, a link between bar and berm morphology (surf-swash sand exchange) was observed. The shifting breakpoint enabled sustained, wave asymmetry-related onshore transport in the inner surf zone, feeding the berm accretion which occurred through advective swash zone processes including berm overwash.

Separation of uranium from lanthanides (La, Ce, Nd) and purification of waste salt via aluminum electrodes with different structures in LiCl-KCl eutectic

Aluminum is an ideal cathode material for the pyrochemical reprocessing of spent nuclear fuels. However, there is a lack of large-scale experiments to further assess its performance. Here we designed several Al electrodes with sheet, rod, and porous morphologies, and employed them in separating actinides (An) from lanthanides (Ln) and purifying the waste salts in about 500 g of LiCl-KCl eutectic melt at 773 K. By applying a constant potential of −1.2 V vs. Ag/AgCl, U can be separated from Ln (La, Ce, and Nd) by forming Al-U alloys consisting of Al3U and Al4U with high separation factors and current efficiency. Among all Al electrodes used in our experiments, the porous-shaped ones show the fastest electrochemical reaction rate, and hence only 56 h were required to achieve the separation. Subsequently, the purification of the waste salts from U-Ln separation was conducted via constant potential electrolysis at −1.5 V vs. Ag/AgCl on porous-shaped Al electrodes. About 99.9 % of Ln was extracted via forming Al-Ln alloys, leaving a purified electrolyte that can be reused. In all, about 17 g of U metals and 1 kg of waste salts were successfully reprocessed in our large-scale experiments, which envisions the feasibility of applying Al electrodes in engineering-scale pyrochemical reprocessing.

A Fully-Automated Senescence Test (FAST) for the high-throughput quantification of senescence-associated markers

Cellular senescence is a major driver of aging and age-related diseases. Quantification of senescent cells remains challenging due to the lack of senescence-specific markers and generalist, unbiased methodology. Here, we describe the Fully-Automated Senescence Test (FAST), an image-based method for the high-throughput, single-cell assessment of senescence in cultured cells. FAST quantifies three of the most widely adopted senescence-associated markers for each cell imaged: senescence-associated β-galactosidase activity (SA-β-Gal) using X-Gal, proliferation arrest via lack of 5-ethynyl-2’-deoxyuridine (EdU) incorporation, and enlarged morphology via increased nuclear area. The presented workflow entails microplate image acquisition, image processing, data analysis, and graphing. Standardization was achieved by (i) quantifying colorimetric SA-β-Gal via optical density; (ii) implementing staining background controls; and (iii) automating image acquisition, image processing, and data analysis. In addition to the automated threshold-based scoring, a multivariate machine learning approach is provided. We show that FAST accurately quantifies senescence burden and is agnostic to cell type and microscope setup. Moreover, it effectively mitigates false-positive senescence marker staining, a common issue arising from culturing conditions. Using FAST, we compared X-Gal with fluorescent C12FDG live-cell SA-β-Gal staining on the single-cell level. We observed only a modest correlation between the two, indicating that those stains are not trivially interchangeable. Finally, we provide proof of concept that our method is suitable for screening compounds that modify senescence burden. This method will be broadly useful to the aging field by enabling rapid, unbiased, and user-friendly quantification of senescence burden in culture, as well as facilitating large-scale experiments that were previously impractical.

Spatial and temporal analysis on the impact of ultra-low volume indoor insecticide spraying on Aedes aegypti household density

BackgroundAedes aegypti is the primary mosquito vector for several arboviruses, such as dengue, chikungunya and Zika viruses, which cause frequent outbreaks of human disease in tropical and subtropical regions. Control of these outbreaks relies on vector control, commonly in the form of insecticide sprays that target adult female mosquitoes. However, the spatial coverage and frequency of sprays needed to optimize effectiveness are unclear. In this study, we characterize the effect of ultra-low-volume (ULV) indoor spraying of pyrethroid insecticides on Ae. aegypti abundance within households. We also evaluate the effects of spray events during recent time periods or in neighboring households. Improved understanding of the duration and distance of the impact of a spray intervention on Ae. aegypti populations can inform vector control interventions, in addition to modeling efforts that contrast vector control strategies.MethodsThis project analyzes data from two large-scale experiments that involved six cycles of indoor pyrethroid spray applications in 2 years in the Amazonian city of Iquitos, Peru. We developed spatial multi-level models to disentangle the reduction in Ae. aegypti abundance that resulted from (i) recent ULV treatment within households and (ii) ULV treatment of adjacent or nearby households. We compared fits of models across a range of candidate weighting schemes for the spray effect, based on different temporal and spatial decay functions to understand lagged ULV effects.ResultsOur results suggested that the reduction of Ae. aegypti in a household was mainly due to spray events occurring within the same household, with no additional effect of sprays that occurred in neighboring households. Effectiveness of a spray intervention should be measured based on time since the most recent spray event, as we found no cumulative effect of sequential sprays. Based on our model, we estimated the spray effect is reduced by 50% approximately 28 days after the spray event.ConclusionsThe reduction of Ae. aegypti in a household was mainly determined by the number of days since the last spray intervention in that same household, highlighting the importance of spray coverage in high-risk areas with a spray frequency determined by local viral transmission dynamics.Graphical abstract

Experimental Investigation of Water-Based Fire Suppression Systems on External Façade Fires

The use of external fire suppression systems can reduce the risk of fire spreading between buildings. This study investigated the effectiveness and efficiency of different externally placed water-based fire suppression systems on façade fire safety. A series of large-scale experiments comprising an SP Fire 105 setup equipped with sprinklers and high-pressure water mist nozzles have been performed. A combustible façade, consisting of 2.5 cm thick oriented strand board (OSB) plates, was installed to provide challenging conditions and allow a visual assessment of the post-fire damage. The temperature profile on the façade surface was measured with 34 thermocouples, while five heat flux gauges and two fast-response plate thermocouples were used to measure the heat flux on the façade surface and emitted to the ambient. The sprinklers and the high-pressure water mist system effectively suppressed the upwards flame migration and reduced the heat flux toward adjacent buildings. It was observed that the sprinklers acted as a water curtain and kept the façade wet during the fire, promoting minor damage (the burnt area is less than 1% of the total area). The temperature and heat flux measurements demonstrated that the sprinkler system was the most effective suppression system. However, the high-pressure water mist systems achieved similar effectiveness but a much higher efficiency concerning water consumption. The sprinkler nozzles used four times as much water as the high-pressure water mist nozzles.

Empowering pandemic pivots: the inclusive power of remote work and school

ABSTRACT We examine the transitions to remote work and education as unplanned large-scale social experiments in critical institutions that were rapidly digitized in response to the COVID-19 pandemic. As the long tail of the pandemic continues, going remote continues to impact marginalized populations in unexpectedly positive ways and meet needs that are invisible to dominant populations. Therefore, we make the case for remote work and school as new frontiers in digital inclusion with particular benefit to traditionally underrepresented groups. We use Symbolic Interactionist approaches drawing on Hochschild’s emotion work and Goffman’s impression management and stigma to reveal interactional mechanisms through which digital communication modalities offer a way to circumvent some of the deleterious aspects of face-to-face social interaction that occur in these institutional spheres. Digital interaction tools facilitate flexibility and greater control of impression management to avoid stigma among workers and students who are most at risk of being marginalized or harmed in face-to-face institutionally embedded interactions. Thus, both remote work and school can supply new avenues for institutional inclusion of particular value for those who suffer the most from bias, stigma, and/or discrimination in face-to-face institutional settings governed by norms that were built by those with privilege to serve and replicate their privilege. Finally, we call attention to the ways that going remote is being resisted as older forms of social control reemerge, as well as to how remote workers and students are increasingly subjected to digital surveillance tools that extend Foucault’s concept of the panopticon into the digital realm.

Data-driven learning of structure augments quantitative prediction of biological responses.

Multi-factor screenings are commonly used in diverse applications in medicine and bioengineering, including optimizing combination drug treatments and microbiome engineering. Despite the advances in high-throughput technologies, large-scale experiments typically remain prohibitively expensive. Here we introduce a machine learning platform, structure-augmented regression (SAR), that exploits the intrinsic structure of each biological system to learn a high-accuracy model with minimal data requirement. Under different environmental perturbations, each biological system exhibits a unique, structured phenotypic response. This structure can be learned based on limited data and once learned, can constrain subsequent quantitative predictions. We demonstrate that SAR requires significantly fewer data comparing to other existing machine-learning methods to achieve a high prediction accuracy, first on simulated data, then on experimental data of various systems and input dimensions. We then show how a learned structure can guide effective design of new experiments. Our approach has implications for predictive control of biological systems and an integration of machine learning prediction and experimental design.

Predicting the penetration depth and towing resistance of beam trawl fishing gears in sand

Accurate characterization of mechanical perturbations on the seabed is essential for developing models assessing the environmental impacts from physical disturbances. Furthermore, understanding the relationship between (1) seabed resistance and (2) penetration depth, can also facilitate the development of more efficient and less impactful fishing gears. This study examines these two aspects of tickler chain rigged beam trawling via large-scale physical experiments. Three scaled down models (“light,” “medium,” and “heavy” designs) were developed to represent the impacts from typical beam trawl configurations used in the North Sea and were towed at various speeds on a saturated sand bed. Results reveal that increasing the towing speed reduces the mean penetration depth and the steady-state towing resistance of the gears. Smaller scale physical model tests incorporating tickler chains in sand, demonstrate that the towing resistance is significantly influenced by the soil compaction and particle sizes. Moreover, our study offers a simple and efficient method to estimate the penetration depth and towing resistance of prototype beam trawl gears in sand. This approach, along with the associated research, may be valuable for marine scientists assessing trawling impacts and demersal fishing gear designers seeking to optimize efficiency while minimizing seabed disturbance.

Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge.

Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to the analysis of transcriptomic data. Nevertheless, relying on the potential space of these generative models alone is insufficient to generate biological explanations. In addition, most of the previous work based on generative models is limited to shallow neural networks with one to three layers of latent variables, which may limit the capabilities of the models. Here, we propose a deep interpretable generative model called d-scIGM for single-cell data analysis. d-scIGM combines sawtooth connectivity techniques and residual networks, thereby constructing a deep generative framework. In addition, d-scIGM incorporates hierarchical prior knowledge of biological domains to enhance the interpretability of the model. We show that d-scIGM achieves excellent performance in a variety of fundamental tasks, including clustering, visualization, and pseudo-temporal inference. Through topic pathway studies, we found that d-scIGM-learned topics are better enriched for biologically meaningful pathways compared to the baseline models. Furthermore, the analysis of drug response data shows that d-scIGM can capture drug response patterns in large-scale experiments, which provides a promising way to elucidate the underlying biological mechanisms. Lastly, in the melanoma dataset, d-scIGM accurately identified different cell types and revealed multiple melanin-related driver genes and key pathways, which are critical for understanding disease mechanisms and drug development.

Meta-Selective Copper-Catalyzed C-H Arylation of Pyridines and Isoquinolines through Dearomatized Intermediates.

C(sp2)-H functionalization offers an efficient strategy for the synthesis of various elaborated N-containing heteroarenes. Along these lines, oxazino pyridines that can be readily prepared from pyridines, have been introduced as powerful substrates in radical- and ionic-mediated meta-C-H functionalization. However, the regioselective meta-C-H arylation of pyridines remains a great challenge. Herein, a copper-catalyzed meta-selective C-H arylation of pyridines and isoquinolines through bench-stable dearomatized intermediates is reported. Electrophilic aryl-Cu(III) species, generated from readily accessible aryl I(III) reagents, enable the efficient meta-arylation of a broad range of pyridines and isoquinolines. The method also allows the meta-selective alkenylation of these heteroarenes using the corresponding alkenyl I(III)-reagents. Late-stage arylation of drug-derived pyridines and larger-scale experiments demonstrate the potential of this synthetic methodology.

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing.

Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.

Dynamic resource matching in manufacturing using deep reinforcement learning

Matching plays an important role in the logical allocation of resources across a wide range of industries. The benefits of matching have been increasingly recognized in manufacturing industries. In particular, capacity sharing has received much attention recently. In this paper, we consider the problem of dynamically matching demand-capacity types of manufacturing resources. We formulate the multi-period, many-to-many manufacturing resource-matching problem as a sequential decision process. The formulated manufacturing resource-matching problem involves large state and action spaces, and it is not practical to accurately model the joint distribution of various types of demands. To address the curse of dimensionality and the difficulty of explicitly modeling the transition dynamics, we use a model-free deep reinforcement learning approach to find optimal matching policies. Moreover, to tackle the issue of infeasible actions and slow convergence due to initial biased estimates caused by the maximum operator in Q-learning, we introduce two penalties to the traditional Q-learning algorithm: a domain knowledge-based penalty based on a prior policy and an infeasibility penalty that conforms to the demand–supply constraints. We establish theoretical results on the convergence of our domain knowledge-informed Q-learning providing performance guarantee for small-size problems. For large-size problems, we further inject our modified approach into the deep deterministic policy gradient (DDPG) algorithm, which we refer to as domain knowledge-informed DDPG (DKDDPG). In our computational study, including small- and large-scale experiments, DKDDPG consistently outperformed traditional DDPG and other RL algorithms, yielding higher rewards and demonstrating greater efficiency in time and episodes.

BIFROST: a method for registering diverse imaging datasets of the Drosophila brain.

Large-scale functional imaging experiments in Drosophila have given us new insights into neural activity in various sensory and behavioral contexts. However, precisely registering volumetric images from different studies has proven challenging, limiting quantitative comparisons of data across experiments. Here, we address this limitation by developing BIFROST, a registration pipeline robust to differences across experimental setups and datasets. We benchmark this pipeline by genetically labeling cell types in the fly brain and demonstrate sub-10 micron registration precision, both across specimens and across laboratories. We further demonstrate accurate registration between in-vivo brain volumes and ultrastructural connectomes, enabling direct structure-function comparisons in future experiments.