Temporal Snapshots Research Articles

PTNS: patent citation trajectory prediction based on temporal network snapshots

With the rapid development of science and technology, the pace of development in the knowledge economy is accelerating. Intellectual property, especially patents, is a strategic resource for technological innovation and a crucial support for building an innovative country. Therefore, it is particularly important to predict patents with high value and strong impact from the numerous and uneven-quality patents. However, patent citation behavior involves many uncertainties, and it is difficult to capture its temporal variations effectively. Therefore, this paper proposes a patent citation trajectory prediction model (PTNS) based on temporal network snapshots. It adopts relational graph convolutional networks (R-GCN) to learn the complex relationships among multiple attributes of patents and utilizes bidirectional long short-term memory networks (BiLSTM) to aggregate the temporal evolution differences of patents. Subsequently, principal component analysis (PCA) is used to explore the evolution characteristics of patent citations in depth, thereby capturing the aging effect and the ‘sleeping beauty’ phenomenon. Compared with other baselines, the PTNS performs well. In predicting new, grown, and random patents, the RMSLE decreases by approximately 0.04, 0.14, and 0.18 respectively, while the MALE decreases by approximately 0.04, 0.12, and 0.16 respectively.

Geographical distribution of the COVID‐19 pandemic and key determinants: Evolution across waves in Spain

AbstractAmidst the COVID‐19 pandemic, most research has examined specific temporal snapshots. This study diverges by offering a comprehensive analysis of COVID‐19 incidence across the Spanish provinces throughout six distinct waves of the pandemic. Using spatial exploratory techniques, we find no single pandemic; rather, there have been waves. Significant differences in the spatial distribution of cases and deaths across six waves show that each has unique characteristics. Homogeneous conclusions cannot be drawn at the national level. Notable regional differences in the pandemic’s spatial distribution suggest a need for subnational responses, reflecting variations in climate, economic dynamism, sectoral specialisation, and socio‐health resources. Spatial regression models show that the main determinants of COVID‐19 incidence depend on stage. Traditional factors commonly associated with epidemiological studies, such as temperature, exerted significant influence during the pandemic’s onset. However, as mobility restrictions were enforced and vaccination campaigns were rolled out, economic conditions, and especially levels of economic activity, emerged as increasingly significant determinants.

On controllability of temporal networks

Temporality has been recently identified as a useful feature to exploit when controlling a complex network. Empirical evidence has in fact shown that, with respect to their static counterparts, temporal networks (i) are often endowed with larger reachable sets and (ii) require less control energy when steered towards an arbitrary target state. However, to date, we lack conditions guaranteeing that the dimension of the controllable subspace of a temporal network is larger than that of its static counterpart. In this work, we consider the case in which a static network is input connected but not controllable. We show that when the structure of the graph underlying the temporal network remains the same throughout each temporal snapshot while the (nonvanishing) edge weights vary, then the temporal network will be completely controllable almost always, even when its static counterpart is not. An upper bound on the number of snapshots needed to achieve controllability is also provided.

Analysis of landscape pattern and ecological risk change characteristics in Bosten Lake basin based on optimal scale

Amidst swift socioeconomic progress, urbanization is intensifying, necessitating robust methodologies for environmental quality monitoring. Landscape Ecological Risk (LER) has emerged as a pivotal tool in this context. Extensive research has focused on the land utilization rates within watersheds, scrutinizing them through the lens of land use/land cover (LULC) dynamics. This study delves into the LULC attributes of the Bosten Lake Basin (BLB) across three temporal snapshots: 2000, 2010, and 2020. Grasslands and bare ground made up the vast majority area of the BLB. Selected twelve landscape pattern indices, analyzed their response curves with respect to grain size, determined the optimal grain size to be 150 m, and analysed its optimum amplitude of 10 km × 10 km on the basis of the semi-variance function, thus determined the optimal scale for analyzing the landscape pattern. Exploring scale effect facilitates a nuanced examination of the landscape patterns and land use efficacy within BLB. From 2000 to 2020, the landscape pattern showed less fragmented, less aggregated, and more spatially heterogeneous. The landscape ecological risk index model has been constructed for the investigation of the LER change characteristics of the BLB. Lowest-risk zones and lower-risk zones of LER predominate in BLB. In 2000, 2010 and 2020 the combined lower and lowest risk zones represent 62.02 %, 59.37 % and 58.44 % of the catchment area respectively. Finally, analyzed the spatial autocorrelation variation characteristics of LER. The global Moran' I index of BLB for the three periods from 2000 to 2020 exceeded 0.7. This study establishes theoretical and practical foundation for future multi-scale landscape ecological risk studies in eco-fragile regions.

Inference of dynamic hypergraph representations in temporal interaction data.

A range of systems across the social and natural sciences generate data sets consisting of interactions between two distinct categories of items at various instances in time. Online shopping, for example, generates purchasing events of the form (user, product, time of purchase), and mutualistic interactions in plant-pollinator systems generate pollination events of the form (insect, plant, time of pollination). These data sets can be meaningfully modeled as temporal hypergraph snapshots in which multiple items within one category (i.e., online shoppers) share a hyperedge if they interacted with a common item in the other category (i.e., purchased the same product) within a given time window, allowing for the application of hypergraph analysis techniques. However, it is often unclear how to choose the number and duration of these temporal snapshots, which have a strong influence on the final hypergraph representations. Here we propose a principled nonparametric solution to this problem by extracting temporal hypergraph snapshots that optimally capture structural regularities in temporal event data according to the minimum description length principle. We demonstrate our methods on real and synthetic data sets, finding that they can recover planted artificial hypergraph structure in the presence of considerable noise and reveal meaningful activity fluctuations in human mobility data.

Data-driven models of nonautonomous systems

Nonautonomous dynamical systems are characterized by time-dependent inputs, which complicates the discovery of predictive models describing the spatiotemporal evolution of the state variables of quantities of interest from their temporal snapshots. When dynamic mode decomposition (DMD) is used to infer a linear model, this difficulty manifests itself in the need to approximate the time-dependent Koopman operators. Our approach is to approximate the original nonautonomous system with a modified system derived via a local parameterization of the time-dependent inputs. The modified system comprises a sequence of local parametric systems, which are subsequently approximated by a parametric surrogate model using the DRIPS (dimension reduction and interpolation in parameter space) framework. The offline step of DRIPS relies on DMD to build a linear surrogate model, endowed with reduced-order bases for the observables mapped from training data. The online step interpolates on suitable manifolds to construct a sequence of iterative parametric surrogate models; the target/test parameter points on these manifolds are specified by a local parameterization of the test time-dependent inputs. We use numerical experimentation to demonstrate the robustness of our method and compare its performance with that of deep neural networks.

SANe: Space adaptation network for temporal knowledge graph completion

Temporal Knowledge Graphs (TKGs) model time-dependent facts as relations between entities at specific timestamps, making them well-suited for real-world scenarios. However, TKGs are susceptible to incompleteness, necessitating Temporal Knowledge Graph Completion (TKGC) to predict missing facts. Prior methods often struggle to effectively handle two critical properties of TKGs, time-variability and time-stability, simultaneously, which hinders their performance. In this paper, we propose Space Adaptation Network (SANe), a novel approach for TKGC. SANe adapts facts at different timestamps to distinct latent spaces, effectively addressing time-variability. Our model introduces Parameter Generation Network to produce separate neural networks for each snapshot, which are then encoded into different latent spaces. A dynamic convolutional neural network processes entities and relations, utilizing different learned parameters generated by parameter generation network with respect to timestamps. By handling different temporal snapshots separately, TKGC is transformed into static KGC, enabling the modeling of time-variability. Dynamic convolutional neural network efficiently learns collective knowledge over large periods and supplements more specific knowledge gradually in smaller periods, facilitating time-stability. To strike a balance between learning time-variability and time-stability, we introduce a time-aware parameter generator to produce parameters hierarchically based on year, month, and day timestamps. Long-term knowledge is effectively shared across adjacent snapshots within the same year or month, while short-term knowledge within a day is preserved in specific parameters. However, in unbalanced TKGs, where many facts occur in small intervals, the large number of parameters generated by time-aware parameter generator may remain underutilized. To address this, we propose Adaptive Parameter Generation with a partition tree, ensuring parameter load balancing while maintaining time-stability. We conduct extensive experiments on five benchmark datasets, demonstrating the superiority of SANe over existing methods for TKGC, achieving state-of-the-art performance. Our contributions include pioneering TKGC from the perspective of space adaptation, achieving a balance between time-variability and time-stability through latent space overlap constraints, and substantiating the effectiveness of our model through comprehensive experiments on rich temporal datasets.

Linking High-Resolution UAV-Based Remote Sensing Data to Long-Term Vegetation Sampling—A Novel Workflow to Study Slow Ecotone Dynamics

Unravelling slow ecosystem migration patterns requires a fundamental understanding of the broad-scale climatic drivers, which are further modulated by fine-scale heterogeneities just outside established ecosystem boundaries. While modern Unoccupied Aerial Vehicle (UAV) remote sensing approaches enable us to monitor local scale ecotone dynamics in unprecedented detail, they are often underutilised as a temporal snapshot of the conditions on site. In this study in the Southern Alps of New Zealand, we demonstrate how the combination of multispectral and thermal data, as well as LiDAR data (2019), supplemented by three decades (1991–2021) of treeline transect data can add great value to field monitoring campaigns by putting seedling regeneration patterns at treeline into a spatially explicit context. Orthorectification and mosaicking of RGB and multispectral imagery produced spatially extensive maps of the subalpine area (~4 ha) with low spatial offset (Craigieburn: 6.14 ± 4.03 cm; Mt Faust: 5.11 ± 2.88 cm, mean ± standard error). The seven multispectral bands enabled a highly detailed delineation of six ground cover classes at treeline. Subalpine shrubs were detected with high accuracy (up to 90%), and a clear identification of the closed forest canopy (Fuscospora cliffortioides, >95%) was achieved. Two thermal imaging flights revealed the effect of existing vegetation classes on ground-level thermal conditions. UAV LiDAR data acquisition at the Craigieburn site allowed us to model vegetation height profiles for ~6000 previously classified objects and calculate annual fine-scale variation in the local solar radiation budget (20 cm resolution). At the heart of the proposed framework, an easy-to-use extrapolation procedure was used for the vegetation monitoring datasets with minimal georeferencing effort. The proposed method can satisfy the rapidly increasing demand for high spatiotemporal resolution mapping and shed further light on current treeline recruitment bottlenecks. This low-budget framework can readily be expanded to other ecotones, allowing us to gain further insights into slow ecotone dynamics in a drastically changing climate.

Real-Time Cell Cycle Imaging in a 3D Cell Culture Model of Melanoma, Quantitative Analysis, Optical Clearing, and Mathematical Modeling.

Aberrant cell cycle progression is a hallmark of solid tumors. Therefore, cell cycle analysis is an invaluable technique to study cancer cell biology. However, cell cycle progression has been most commonly assessed by methods that are limited to temporal snapshots or that lack spatial information. In this chapter, we describe a technique that allows spatiotemporal real-time tracking of cell cycle progression of individual cells in a multicellular context. The power of this system lies in the use of 3D melanoma spheroids generated from melanoma cells engineered with the fluorescent ubiquitination-based cell cycle indicator (FUCCI). This technique, combined with mathematical modeling, allows us to gain further and more detailed insight into several relevant aspects of solid cancer cell biology, such as tumor growth, proliferation, invasion, and drug sensitivity.

Resurrecting ancestral antibiotics: unveiling the origins of modern lipid II targeting glycopeptides

Antibiotics are central to modern medicine, and yet they are mainly the products of intra and inter-kingdom evolutionary warfare. To understand how nature evolves antibiotics around a common mechanism of action, we investigated the origins of an extremely valuable class of compounds, lipid II targeting glycopeptide antibiotics (GPAs, exemplified by teicoplanin and vancomycin), which are used as last resort for the treatment of antibiotic resistant bacterial infections. Using a molecule-centred approach and computational techniques, we first predicted the nonribosomal peptide synthetase assembly line of paleomycin, the ancestral parent of lipid II targeting GPAs. Subsequently, we employed synthetic biology techniques to produce the predicted peptide and validated its antibiotic activity. We revealed the structure of paleomycin, which enabled us to address how nature morphs a peptide antibiotic scaffold through evolution. In doing so, we obtained temporal snapshots of key selection domains in nonribosomal peptide synthesis during the biosynthetic journey from ancestral, teicoplanin-like GPAs to modern GPAs such as vancomycin. Our study demonstrates the synergy of computational techniques and synthetic biology approaches enabling us to journey back in time, trace the temporal evolution of antibiotics, and revive these ancestral molecules. It also reveals the optimisation strategies nature has applied to evolve modern GPAs, laying the foundation for future efforts to engineer this important class of antimicrobial agents.

Multi-level modeling and robustness evaluation of disturbances in intelligent workshop with temporal snapshot network

Disturbances occurred in the workshop production process disrupt the original production order, thus affecting production efficiency and production stability. Previous research focused on addressing status perception and operation control problem of intelligent workshop, but pays little attention on the formation process of disturbances and the evaluation of disturbances disruption effect on process completion time or manufacturing system robustness. It leads to difficulty in quantifying the destruction degree of disturbances and ensuring production robustness stability after being disrupted. To this end, a multi-level modeling and robustness evaluation method of disturbances are proposed. Firstly, a multi-level modeling method which quantifies disturbances disruption degree is presented by the fluctuation between the ideal level and the actual level of process completion time. Secondly, the evolution models of gradual disturbance and abrupt disturbance are constructed to analyze the correlation between disturbance evolution process and production robustness fluctuation. Then, a critical vertex identification algorithm is also presented to reveal the influence of machine importance on the disturbance destruction range. Finally, the production process of a typical manufacturing system is taken as a case study to illustrate the effectiveness of the proposed methods in quantifying the disturbance disruption degree and evaluating the robustness fluctuation of the manufacturing system.

Improving temporal smoothness and snapshot quality in dynamic network community discovery using NOME algorithm.

The goal of dynamic community discovery is to quickly and accurately mine the network structure for individuals with similar attributes for classification. Correct classification can effectively help us screen out more desired results, and it also reveals the laws of dynamic network changes. We propose a dynamic community discovery algorithm, NOME, based on node occupancy assignment and multi-objective evolutionary clustering. NOME adopts the multi-objective evolutionary algorithm MOEA/D framework based on decomposition, which can simultaneously decompose the two objective functions of modularization and normalized mutual information into multiple single-objective problems. In this algorithm, we use a Physarum-based network model to initialize populations, and each population represents a group of community-divided solutions. The evolution of the population uses the crossover and mutation operations of the genome matrix. To make the population in the evolution process closer to a better community division result, we develop a new strategy for node occupancy assignment and cooperate with mutation operators, aiming at the boundary nodes in the connection between the community and the connection between communities, by calculating the comparison node. The occupancy rate of the community with the neighbor node, the node is assigned to the community with the highest occupancy rate, and the authenticity of the community division is improved. In addition, to select high-quality final solutions from candidate solutions, we use a rationalized selection strategy from the external population size to obtain better time costs through smaller snapshot quality loss. Finally, comparative experiments with other representative dynamic community detection algorithms on synthetic and real datasets show that our proposed method has a better balance between snapshot quality and time cost.

Synthesizing Spectra from 3D Radiation Hydrodynamic Models of Massive Stars Using Monte Carlo Radiation Transport

Observations indicate that turbulent motions are present on most massive star surfaces. Starting from the observed phenomena of spectral lines with widths that are much larger than their thermal broadening (e.g., micro- and macroturbulence), and considering the detection of stochastic low-frequency variability (SLFV) in the Transiting Exoplanet Survey Satellite photometry, these stars clearly have large-scale turbulent motions on their surfaces. The cause of this turbulence is debated, with near-surface convection zones, core internal gravity waves, and wind variability being proposed. Our 3D gray radiation hydrodynamic (RHD) models previously characterized the convective dynamics of the surfaces, driven by near-surface convection zones, and provided reasonable matches to the observed SLFV of the most luminous massive stars. We now explore the complex emitting surfaces of these 3D RHD models, which strongly violate the 1D assumption of a plane-parallel atmosphere. By post-processing the gray RHD models with the Monte Carlo radiation transport code Sedona, we synthesize stellar spectra and extract information from the broadening of individual photospheric lines. The use of Sedona enables the calculation of the viewing angle and temporal dependence of spectral absorption line profiles. By combining uncorrelated temporal snapshots together, we compare the turbulent broadening from the 3D RHD models to the thermal broadening of the extended emitting region, showing that our synthesized spectral lines closely resemble the observed macroturbulent broadening from similarly luminous stars. More generally, the new techniques that we have developed will allow for systematic studies of the origins of turbulent velocity broadening from any future 3D simulations.

A computational methodology to account for the liquid film thickness evolution in Direct Numerical Simulation of prefilming airblast atomization

Prefilming airblast atomization is becoming widely used in current aero engines. Fundamental studies on the actual annular configuration of airblast atomizers are difficult to realize. For this reason, researchers have also focused on planar configurations. In this regard, the Karlsruhe Institute of Technology (KIT) developed a test rig to conduct experimental activities, conforming a large database with results for different working fluids and operating conditions. Such data allow two-phase flow modelers to validate their calculations concerning primary atomization on these devices. The present investigation proposes a Direct Numerical Simulation (DNS) study on the KIT planar configuration through the Volume of Fluid (VOF) method within the PARIS Simulator code. The novelty compared to DNS works reported in the literature resides in the use of a boundary condition that allows accounting not only for the gas inflow turbulence but also for the spatio-temporal evolution of the liquid film thickness at the DNS inlet and its related effect on turbulence. The proposed methodology requires computing precursor single-phase and two-phase flow Large-Eddy Simulations on the prefilmer flow, with the assumption that the flow between computational domains is one-way coupled. Results are compared to DNS that only account for a constant (both timewise and spanwise) liquid film thickness at the domain inlet, validating the full methodology workflow. The proposed methodology is shown to improve the qualitative description of the atomization mechanism, as the different stages of breakup (liquid accumulation behind the prefilmer edge, bag formation, bag breakup, ligament formation and ligament breakup) coexist spanwise for a given temporal snapshot. This implies a more continuous atomization than the one predicted by the constant film thickness case, which showed the same breakup stage to be present along the prefilmer span for a given instant and led to a more discretized set of atomization events. The proposed workflow allows quantifying the influence of the liquid film flow evolution above the prefilmer surface on primary breakup frequency and relevant atomization features.

DGTR: Dynamic graph transformer for rumor detection.

Social media rumors have the capacity to harm the public perception and the social progress. The news propagation pattern is a key clue for detecting rumors. Existing propagation-based rumor detection methods represent propagation patterns as a static graph structure. They simply consider the structure information of news distribution in social networks and disregard the temporal information. The dynamic graph is an effective modeling tool for both the structural and temporal information involved in the process of news dissemination. Existing dynamic graph representation learning approaches struggle to capture the long-range dependence of the structure and temporal sequence as well as the rich semantic association between full graph features and individual parts. We build a transformer-based dynamic graph representation learning approach for rumor identification DGTR to address the aforementioned challenges. We design a position embedding format for the graph data such that the original transformer model can be utilized for learning dynamic graph representations. The model can describe the structural long-range reliance between the dynamic graph nodes and the temporal long-range dependence between the temporal snapshots by employing a self-attention mechanism. In addition, the CLS token in transformer may model the rich semantic relationships between the complete graph and each subpart. Extensive experiments demonstrate the superiority of our model when compared to the state of the art.

The Parable of the Peanut Butter Sandwich: An Exercise in Artificial Intelligence and (Pseudo-) Bible Translation

The public release of artificial intelligence tools such as Microsoft’s Bing Chat built on OpenAI’s generative pre-trained transformer (GPT) continues to spark extensive interest—attracting headlines, prompting important interdisciplinary questions, and posing dilemmas both ethical and methodological. The aim of this paper is to provide a sampling and temporal snapshot of AI-powered Bing’s abilities through the creation, translation, and adaptation of pseudo-biblical content. We adopt as our source text an AI-generated parable in the style of the King James Bible—here entitled “The Parable of the Peanut Butter Sandwich”—originally prompted by software developer Thomas Ptacek. For the purposes of this paper, Bing adapted the original parable into multiple versions (Nida, poem, acrostic, and French), supplied it with paratext (introduction, illustrations, and glossary), and analyzed it from the perspectives of sociologist Pierre Bourdieu and the Documentary Hypothesis. Lastly, Bing generated a sermon outline in the style of renowned orator St. John Chrysostom. We hope that subsequent studies at the nexus of artificial intelligence and Bible translation will continue to build on the Parable of the Peanut Butter Sandwich to explore both the potential and the implications of emergent technology.

V-NeuroStack: Open-source 3D time stack software for identifying patterns in neuronal data.

Understanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large-scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant visualization challenges. We developed V-NeuroStack, a novel network visualization tool to visualize data obtained using calcium imaging of spontaneous activity of neurons in a mouse brain slice as well as in vivo using two-photon imaging. V-NeuroStack creates 3D time stacks by stacking 2D time frames for a time-series dataset. It provides a web interface to explore and analyze data using both 3D and 2D visualization techniques. Previous attempts to analyze such data have been limited by the tools available to visualize large numbers of correlated activity traces. V-NeuroStack's 3D view is used to explore patterns in dynamic large-scale correlations between neurons over time. The 2D view is used to examine any timestep of interest in greater detail. Furthermore, a dual-line graph provides the ability to explore the raw and first-derivative values of activity from an individual or a functional cluster of neurons. V-NeuroStack can scale to datasets with at least a few thousand temporal snapshots. It can potentially support future advancements in in vitro and in vivo data capturing techniques to bring forth novel hypotheses by allowing unambiguous visualization of massive patterns in neuronal activity data.

The weighted Bitcoin Lightning Network

The Bitcoin Lightning Network (BLN) was launched in 2018 to scale up the number of transactions between Bitcoin owners. Although several contributions concerning the analysis of the BLN binary structure have recently appeared in the literature, the properties of its weighted counterpart are still largely unknown. The present contribution aims at filling this gap, by considering the Bitcoin Lightning Network over a period of 18 months, ranging from 12th January 2018 to 17th July 2019, and focusing on its weighted, undirected, daily snapshot representation - each weight representing the total capacity of the channels the two involved nodes have established on a given temporal snapshot. As the study of the BLN weighted structural properties reveals, it is becoming increasingly ‘centralized’ at different levels, just as its binary counterpart: (1) the Nakamoto coefficient shows that the percentage of nodes whose degrees/strengths ‘enclose’ the 51% of the total number of links/total weight is rapidly decreasing; (2) the Gini coefficient confirms that several weighted centrality measures are becoming increasingly unevenly distributed; (3) the weighted BLN topology is becoming increasingly compatible with a core–periphery structure, with the largest nodes ‘by strength’ constituting the core of such a network, whose size keeps shrinking as the BLN evolves. Further inspection of the resilience of the weighted BLN shows that removing such hubs leads to the network fragmentation into many components, an evidence indicating potential security threats — as the ones represented by the so called ‘split attacks’.

Growth Substrate and Prophage Induction Collectively Influence Metabolite and Lipid Profiles in a Marine Bacterium.

ABSTRACTBacterial growth substrates influence a variety of biological functions, including the biosynthesis and regulation of lipid intermediates. The extent of this rewiring is not well understood nor has it been considered in the context of virally infected cells. Here, we used a one-host-two-temperate phage model system to probe the combined influence of growth substrate and phage infection on host carbon and lipid metabolism. Using untargeted metabolomics and lipidomics, we reported the detection of a suite of metabolites and lipid classes for two Sulfitobacter lysogens provided with three growth substrates of differing complexity and nutrient composition (yeast extract/tryptone [complex], glutamate and acetate). The growth medium led to dramatic differences in the detectable intracellular metabolites, with only 15% of 175 measured metabolites showing overlap across the three growth substrates. Between-strain differences were most evident in the cultures grown on acetate, followed by glutamate then complex medium. Lipid distribution profiles were also distinct between cultures grown on different substrates as well as between the two lysogens grown in the same medium. Five phospholipids, three aminolipid, and one class of unknown lipid-like features were identified. Most (≥94%) of these 75 lipids were quantifiable in all samples. Metabolite and lipid profiles were strongly determined by growth medium composition and modestly by strain type. Because fluctuations in availability and form of carbon substrates and nutrients, as well as virus pressure, are common features of natural systems, the influence of these intersecting factors will undoubtedly be imprinted in the metabolome and lipidome of resident bacteria.IMPORTANCE Community-level metabolomics approaches are increasingly used to characterize natural microbial populations. These approaches typically depend upon temporal snapshots from which the status and function of communities are often inferred. Such inferences are typically drawn from lab-based studies of select model organisms raised under limited growth conditions. To better interpret community-level data, the extent to which ecologically relevant bacteria demonstrate metabolic flexibility requires elucidation. Herein, we used an environmentally relevant model heterotrophic marine bacterium to assess the relationship between growth determinants and metabolome. We also aimed to assess the contribution of phage activity to the host metabolome. Striking differences in primary metabolite and lipid profiles appeared to be driven primarily by growth regime and, secondarily, by phage type. These findings demonstrated the malleable nature of metabolomes and lipidomes and lay the foundation for future studies that relate cellular composition with function in complex environmental microbial communities.

Characterizing Liquid Water Distribution in Polymer Electrolyte Fuel Cells Using Operando 2D and 3D X-Ray Imaging

The potential to reduce the cost of Polymer Electrolyte Fuel Cells (PEFCs) through high power density operation is limited by the attending water management challenges at such operating conditions [1]. Addressing PEFC water management requires detailed understanding of liquid water distribution characteristics of an operating fuel cell, to refine the design of membrane electrode assemblies (MEAs) for improved performance.Recent advances in X-ray operando imaging [2, 3] and custom hardware development [4], have enabled the visualization of liquid water in an operating PEFC, through the analysis of water phase segmented 3-dimensional (3D) images [2, 3] and 2-dimensional (2D) radiographs [5]. 2D image datasets may be acquired in a shorter time than 3D tomographic datasets but only provide grayscale information averaged through the thickness of the sample. 3D image datasets obtained from a reconstruction of many angular radiographs yield a 3D representation of the sample in addition to the grayscale information but take a long collection time. However, time dependent liquid water distribution characteristics such as channel water breakthrough, observable in the 2D image datasets, tend to be elusive to 3D imaging methods. Although efforts have been made to reduce the image acquisition time for 3D X-ray imaging by using Synchrotron sources and improved image processing techniques [2], these efforts tend to compromise image quality and are restricted to a brief temporal snapshot of liquid water distribution. Also, the brightness of the Synchrotron sources necessitates short exposure duration tests due to the likelihood of cell degradation caused by the sample’s exposure to radiation [4]. Given the unique capabilities of 2D and 3D imaging modes, we therefore focus on combining information accessible in both modes to further the understanding of PEFC liquid water distribution.In this work, an improved understanding of liquid water distribution in the PEFC is presented by integrating operando 2D and 3D image datasets acquired under the same cell operating conditions and using laboratory scale X-ray computed tomography (XCT) equipment which was interfaced with fuel cell testing and diagnostic tools. Analysis of 3D image datasets is extended beyond binary information to include the grayscale value (GSV) of liquid water to reveal a change in the through-plane values. These results, when combined with observed 2D liquid water breakthrough characteristics suggest that the change in GSV is indicative of the differences in the intermittency of liquid water presence in the different regions of the GDL; see Figure 1. These results therefore present a more complete interpretation of PEFC operando liquid water distribution, highlighting distinct characteristics from the microporous (MPL) region to the GDL-land interface. Unique findings and insight obtained using this methodology will be discussed. Acknowledgments Funding for this research was provided by the Natural Sciences and Engineering Research Council of Canada, Ballard Power Systems, Canada Foundation for Innovation, British Columbia Knowledge Development Fund, Western Economic Diversification Canada, and Canada Research Chairs. References Jiao and X. Li, Progress in energy and combustion Science, vol. 37, no. 3, pp. 221–291, 2011.Xu, S. Nagashima, H. P. Nguyen, K. Kishita, F. Marone, F. N. Büchi, and J. Eller, Journal of Power Sources, vol. 490, p. 229492, 2021.Nagai, J. Eller, T. Hatanaka, S. Yamaguchi, S. Kato, A. Kato, F. Marone, H. Xu, and F. N. Büchi, Journal of Power Sources, vol. 435, p. 226809, 2019.T. White, F. P. Orfino, M. El Hannach, O. Luo, M. Dutta, A. P. Young, and E. Kjeang, Journal of The Electrochemical Society, vol. 163, no. 13, pp. F1337–F1343, 2016.R. Banerjee, N. Ge, J. Lee, M. G. George, S. Chevalier, H. Liu, P. Shrestha, D. Muirhead, and A. Bazylak, Journal of The Electrochemical Society, vol. 164, no. 2, p. F154, 2017. Figure 1