Multiple Spatiotemporal Scales Research Articles

Spatial transition tensor of single cells.

Spatial transcriptomics and messenger RNA splicing encode extensive spatiotemporal information for cell states and transitions. The current lineage-inference methods either lack spatial dynamics for state transition or cannot capture different dynamics associated with multiple cell states and transition paths. Here we present spatial transition tensor (STT), a method that uses messenger RNA splicing and spatial transcriptomes through a multiscale dynamical model to characterize multistability in space. By learning a four-dimensional transition tensor and spatial-constrained random walk, STT reconstructs cell-state-specific dynamics and spatial state transitions via both short-time local tensor streamlines between cells and long-time transition paths among attractors. Benchmarking and applications of STT on several transcriptome datasets via multiple technologies on epithelial-mesenchymal transitions, blood development, spatially resolved mouse brain and chicken heart development, indicate STT's capability in recovering cell-state-specific dynamics and their associated genes not seen using existing methods. Overall, STT provides a consistent multiscale description of single-cell transcriptome data across multiple spatiotemporal scales.

Ecology of fear alters behavior of grizzly bears exposed to bear-viewing ecotourism.

Humans are perceived as predators by many species and may generate landscapes of fear, influencing spatiotemporal activity of wildlife. Additionally, wildlife might seek out human activity when faced with predation risks (human shield hypothesis). We used the anthropause, a decrease in human activity resulting from the COVID-19 pandemic, to test ecology of fear and human shield hypotheses and quantify the effects of bear-viewing ecotourism on grizzly bear (Ursus arctos) activity. We deployed camera traps in the Khutze watershed in Kitasoo Xai'xais Territory in the absence of humans in 2020 and with experimental treatments of variable human activity when ecotourism resumed in 2021. Daily bear detection rates decreased with more people present and increased with days since people were present. Human activity was also associated with more bear detections at forested sheltered sites and less at exposed sites, likely due to the influence of habitat on bear perception of safety. The number of people negatively influenced adult male detection rates, but we found no influence on female with young detections, providing no evidence that females responded behaviorally to a human shield effect from reduced male activity. We also observed apparent trade-offs of risk avoidance and foraging. When salmon levels were moderate to high, detected bears were more likely to be females with young than adult males on days with more people present. Should managers want to minimize human impacts on bear activity and maintain baseline age-sex class composition at ecotourism sites, multiday closures and daily occupancy limits may be effective. More broadly, this work revealed that antipredator responses can vary with intensity of risk cues, habitat structure, and forage trade-offs and manifest as altered age-sex class composition of individuals using human-influenced areas, highlighting that wildlife avoid people across multiple spatiotemporal scales.

What drives wild pig (Sus scrofa) movement in bottomland and upland forests?

BackgroundThe wild pig (Sus scrofa) is an exotic species that has been present in the southeastern United States for centuries yet continues to expand into new areas dominated by bottomland and upland forests, the latter of which are less commonly associated with wild pigs. Here, we aimed to investigate wild pig movement and space use attributes typically used to guide wild pig management among multiple spatiotemporal scales. Our investigation focused on a newly invaded landscape dominated by bottomland and upland forests.MethodsWe examined (1) core and total space use using an autocorrelated kernel density estimator; (2) resource selection patterns and hot spots of space use in relation to various landscape features using step-selection analysis; and (3) daily and hourly differences in movement patterns between non-hunting and hunting seasons using generalized additive mixed models.ResultsEstimates of total space use among wild pigs (n = 9) were smaller at calculated core (1.2 ± 0.3 km2) and 90% (5.2 ± 1.5 km2) isopleths than estimates reported in other landscapes in the southeastern United States, suggesting that wild pigs were able to meet foraging, cover, and thermoregulatory needs within smaller areas. Generally, wild pigs selected areas closer to herbaceous, woody wetlands, fields, and perennial streams, creating corridors of use along these features. However, selection strength varied among individuals, reinforcing the generalist, adaptive nature of wild pigs. Wild pigs also showed a tendency to increase movement from fall to winter, possibly paralleling increases in hard mast availability. During this time, there were also increases in anthropogenic pressures (e.g. hunting), causing movements to become less diurnal as pressure increased.ConclusionsOur work demonstrates that movement patterns by exotic generalists must be understood across individuals, the breadth of landscapes they can invade, and multiple spatiotemporal scales. This improved understanding will better inform management strategies focused on curbing emerging invasions in novel landscapes, while also protecting native natural resources.

Network-level encoding of local neurotransmitters in cortical astrocytes

Astrocytes, the most abundant non-neuronal cell type in the mammalian brain, are crucial circuit components that respond to and modulate neuronal activity through calcium (Ca2+) signalling1–7. Astrocyte Ca2+ activity is highly heterogeneous and occurs across multiple spatiotemporal scales—from fast, subcellular activity3,4 to slow, synchronized activity across connected astrocyte networks8–10—to influence many processes5,7,11. However, the inputs that drive astrocyte network dynamics remain unclear. Here we used ex vivo and in vivo two-photon astrocyte imaging while mimicking neuronal neurotransmitter inputs at multiple spatiotemporal scales. We find that brief, subcellular inputs of GABA and glutamate lead to widespread, long-lasting astrocyte Ca2+ responses beyond an individual stimulated cell. Further, we find that a key subset of Ca2+ activity—propagative activity—differentiates astrocyte network responses to these two main neurotransmitters, and may influence responses to future inputs. Together, our results demonstrate that local, transient neurotransmitter inputs are encoded by broad cortical astrocyte networks over a minutes-long time course, contributing to accumulating evidence that substantial astrocyte–neuron communication occurs across slow, network-level spatiotemporal scales12–14. These findings will enable future studies to investigate the link between specific astrocyte Ca2+ activity and specific functional outputs, which could build a consistent framework for astrocytic modulation of neuronal activity.

IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods

IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB.

Computation of Real-Fluid Thermophysical Properties Using a Neural Network Approach Implemented in OpenFOAM

Machine learning based on neural networks facilitates data-driven techniques for handling large amounts of data, either obtained through experiments or simulations at multiple spatio-temporal scales, thereby finding the hidden patterns underlying these data and promoting efficient research methods. The main purpose of this paper is to extend the capabilities of a new solver called realFluidReactingNNFoam, under development at the University of Perugia, in OpenFOAM with a neural network algorithm for replacing complex real-fluid thermophysical property evaluations, using the approach of coupling OpenFOAM and Python-trained neural network models. Currently, neural network models are trained against data generated using the Peng–Robinson equation of state assuming a mixture’s frozen temperature. The OpenFOAM solver, where needed, calls the neural network models in each grid cell with appropriate inputs, and the returned results are used and stored in suitable OpenFOAM data structures. Such inference for thermophysical properties is achieved via the “Neural Network Inference in C made Easy (NNICE)” library, which proved to be very efficient and robust. The overall model is validated considering a liquid-rocket benchmark comprised of liquid-oxygen (LOX) and gaseous-hydrogen (GH2) streams. The model accounts for real-fluid thermodynamics and transport properties, making use of the Peng–Robinson equation of state and the Chung transport model. First, the development of a real-fluid model with an artificial neural network is described in detail. Then, the numerical results of the transcritical mixing layer (LOX/GH2) benchmark are presented and analyzed in terms of accuracy and computational efficiency. The results of the overall implementation indicate that the combined OpenFOAM and machine learning approach provides a speed-up factor higher than seven, while preserving the original solver accuracy.

High-resolution Annual Dynamic dataset of Curve Number from 2008 to 2021 over Conterminous United States

The spatial distribution and data quality of curve number (CN) values determine the performance of hydrological estimations. However, existing CN datasets are constrained by universal-applicability hypothesis, medium resolution, and imbalance between specificity CN tables to generalized land use/land cover (LULC) maps, which hinder their applicability and predictive accuracy. A new annual CN dataset named CUSCN30, featuring an enhanced resolution of 30 meters and accounting for temporal variations in climate and LULC in the continental United States (CONUS) between 2008 and 2021, was developed in this study. CUSCN30 demonstrated good performance in surface runoff estimation using CN method when compared to observed surface runoff for the selected watersheds. Compared with existing CN datasets, CUSCN30 exhibits the highest accuracy in runoff estimation for both normal and extreme rainfall events. In addition, CUSCN30, with its high spatial resolution, better captures the spatial heterogeneity of watersheds. This developed CN dataset can be used as input for hydrological models or machine learning algorithms to simulate rainfall-runoff across multiple spatiotemporal scales.

Impacts of microplastics on terrestrial plants: A critical review

AbstractMicroplastic (MP) pollution is an important environmental problem owing to its widespread use, long residence time, and overall persistence. MPs threaten the health of humans, animals, and plants. However, studies on the effects of MPs on terrestrial plants are less common compared to those conducted in aquatic systems. This review discusses the sources of MPs in terrestrial ecosystems, their effects on C and N cycling in soils, and the impact of MPs on terrestrial plants, and focuses on plant growth and the potential risks to human health. MPs affect plants and their performance by altering soil structure, microbial activity, nutrient immobilization, transporting contaminants, and causing direct toxicity. Chemicals, such as plasticizers, additives, and colorants, in MPs may negatively affect ecosystems and their inhabitants, and MPs may interact with a wide array of pollutants, including pesticides, heavy metals, and antibiotics. These impacts vary as a function of soil type, plant species, and MP type. Future research efforts should focus on interaction complexity, uptake mechanisms, and impacts on plants at multiple spatiotemporal scales, while concurrently considering their effects on food chains and human health.

Modeling a domain wall network in BiFeO3 with stochastic geometry and entropy-based similarity measure

A compact and tractable two-dimensional model to generate the topological network structure of domain walls in BiFeO3 thin films is presented in this study. Our method combines the stochastic geometry parametric model of the centroidal Voronoi tessellation optimized using the von Neumann entropy, a novel information-theoretic tool for networks. The former permits the generation of image-based stochastic artificial samples of domain wall networks, from which the network structure is subsequently extracted and converted to the graph-based representation. The von Neumann entropy, which reflects information diffusion across multiple spatiotemporal scales in heterogeneous networks, plays a central role in defining a fitness function. It allows the use of the network as a whole rather than using a subset of network descriptors to search for optimal model parameters. The optimization of the parameters is carried out by a genetic algorithm through the maximization of the fitness function and results in the desired graph-based network connectivity structure. Ground truth empirical networks are defined, and a dataset of network connectivity structures of domain walls in BiFeO3 thin films is undertaken through manual annotation. Both a versatile tool for manual network annotation of noisy images and a new automatic network extraction method for high-quality images are developed.

Estimating the temporal scale of lagged responses in species abundance and occurrence

AbstractUnderstanding drivers of species distributions is a central theme in ecology, and species are known to respond to drivers at multiple spatiotemporal scales. Yet, little attention has been paid to the temporal dimensions of predictors of species abundance or occurrence. Studies considering lagged responses typically contrast few points in time, rather than integrating multiple temporal scales. Here, I transfer an approach developed for the integration of predictors across spatial scales into the temporal realm. In this approach, a weighted mean predictor is used in a generalized linear model for abundance or occurrence; weights are estimated for each temporal scale based on a parametric function, and the parameters defining the function are estimated within the model. In a simulation study using a half‐normal declining weight function, I show that the scale parameter σ (measuring temporal scale of response) and effect strength of the predictor, , can be estimated accurately in abundance models with and without imperfect detection for a range of sample sizes and input values of σ and . Accuracy for both parameters was lower in occurrence‐based models. Estimates of σ improved in accuracy with increasing , and precision of both estimates tended to be lower in models with imperfect detection. A higher resolution time series of predictor values improved estimates of both parameters, and lower detection probability most strongly increased bias in in occurrence models. The approach can be viewed as a more parsimonious but less flexible case of memory models or distributed lag models. It is easily incorporated into standard methods used to model animal abundance and occurrence and therefore hopefully will open up avenues for future research into lagged responses in wildlife distribution to environmental conditions.

Conformational and static properties of tagged chains in solvents: effect of chain connectivity in solvent molecules.

Polymer chains immersed in different solvent molecules exhibit diverse properties due to multiple spatiotemporal scales and complex interactions. Using molecular dynamics simulations, we study the conformational and static properties of tagged chains in different solvent molecules. Two types of solvent molecules were examined: one type consisted of chain molecules connected by bonds, while the other type consisted of individual bead molecules without any bonds. The only difference between the two solvent molecules lies in the chain connectivity. Our results show a compression of the tagged chains with the addition of bead or chain molecules. Chain molecule confinement induces a stronger compression compared to bead molecule confinement. In chain solvent molecules, the tagged chain's radius of gyration reached a minimum at a monomer volume fraction of ∼0.3. Notably, the probability distributions of chain size remain unchanged at different solvent densities, irrespective of whether the solvent consists of beads or polymers. Furthermore, as solvent density increases, a crossover from a unimodal to a bimodal distribution of bond angles is observed, indicating the presence of both compressed and expanded regions within the chain. The effective monomer-solvent interaction is obtained by calculating the partial radial distribution function and the potential of the mean force. In chain solvents, the correlation hole effect results in a reduced number of nearest neighbors around tagged monomers compared to bead solvents. The calculation of pore size distribution reveals that the solvent nonhomogeneity induced by chain connectivity leads to a broader distribution of pore sizes and larger pore dimensions at low volume fractions. These findings provide a deeper understanding of the conformational behavior of polymer chains in different solvent environments.

Diagnosing Uncertainty Embedded in South Korean Population Projections at Multiple Spatio-temporal Scales through Measuring Future Exposure to Extreme Heat

Diagnosing Uncertainty Embedded in South Korean Population Projections at Multiple Spatio-temporal Scales through Measuring Future Exposure to Extreme Heat

Issues and perspective of hydrological processes research in multiple spatiotemporal scales: A personal view

Issues and perspective of hydrological processes research in multiple spatiotemporal scales: A personal view

Cross-scale environmental impacts across persistent and dynamic aggregations within a complex population: implications for fisheries management

Accounting for marine stocks spatiotemporal complexity has become one of the most pressing improvements that should be added to the new generation of stock assessment. Disentangling persistent and dynamic population subcomponents and understanding their main drivers of variation are still stock-specific challenges. Here, we hypothesized that the spatiotemporal variability of density in two adjacent fish stocks is associated with spatially structured environmental processes across multiple spatiotemporal scales. To test this, we applied a generalized empirical orthogonal function and dynamic factor analysis to fishery-independent and -dependent data of red mullet, a highly commercial species, in the Western Mediterranean Sea. Areas with persistent and dynamic high aggregations were detected for both stock units. A large-scale climatic index and local open-ocean convection were associated with both stocks, while other variables exhibited stock-specific effects. We also revealed spatially structured density dynamics within the examined management units. This suggests a metapopulation structure and supports the future implementation of a spatial stock assessment. Considering the common—generally unrealistic—assumptions of panmictic structure and absence of connectivity with neighbouring stock units, our methodology can be applied to other species and systems with putative spatial complexity to inform more accurate population dynamics and structure.

Quantitative analysis of NDVI driving factors based on the geographical detector model in the Chengdu-Chongqing region, China

In the circumstance of global change, studying the dynamic changes of vegetation and the factors that influence it holds immense practical significance to monitor the quality of the regional ecological environment and improve the structure and function of ecosystems. Therefore, based on NDVI data, combined with natural and anthropogenic activities, this study focused on the spatiotemporal evolution characteristics and stability of NDVI in the Chengdu-Chongqing region from 2001 to 2020 at multiple spatiotemporal scales, and quantitatively investigates the main influencing factors driving vegetation NDVI spatial differentiation using Theil-Sen Medium slope estimation, Mann-Kendall significance, stability analysis and geographical detector. The findings indicated that: (1) The overall NDVI exhibited favorable levels over the 2001–2020 period; (2) The upward trends observed in the NDVI values indicated an enhancement in the ecological environment of the investigation area; (3) The spatial coefficient of variation for NDVI in the investigation region was computed as 0.048, suggesting a high degree of stability in NDVI values across the region; (4) NDVI is primarily affected by a range of variances in natural phenomena and anthropogenic activities. The collective influence of environmental factors and anthropogenic activities holds a more substantial sway over NDVI. The research results aim to offer guidance for the execution of vegetation restoration initiatives and the establishment of policies for the preservation of the ecological environment in the upstream region of the Yangtze River.

The Antibiocene – towards an eco-social analysis of humanity’s antimicrobial footprint

Over eight decades of mass antimicrobial exposures have changed microbial populations and genes at a global level. This thought piece argues that adequately responding to the anthropogenic transformation of the microbial commons requires reframing antimicrobial resistance (AMR) as the pheno- and genotypic signal of a new geological era – an Antibiocene. Thinking through the multiple spatiotemporal, biological, and social scales of this Antibiocene opens important perspectives on the long-term goals of (anti)microbial stewardship, the injustices connected with unequal antimicrobial exposures, and the scale of necessary changes to health, food production, and waste disposal systems. Conceptualising AMR within an Antibiocene also necessitates shifting the political gaze from the clinic and sites of food production to the hitherto neglected environmental domain of One Health, whose terrestrial and aquatic milieus act as crucial reservoirs for antimicrobial substances, antimicrobial resistance genes, and resistant organisms. Finally, disentangling the human and non-human dimensions of the Antibiocene requires working across the humanities, social-, environmental, and biomedical sciences. The thought piece ends by proposing comparative research on particularly contaminated sites as a promising way of reconstructing the microbial, biochemical, and societal fallouts of chronic antimicrobial exposures as well as already existing societal adaptations. Resulting findings will provide orientation for emerging debates on the preservation of our microbial commons and the development of new forms of ‘eubiotic governance’.

Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks

Battery-powered electric vehicles (EVs) are poised to accelerate decarbonization in nearly every aspect of transportation. However, safety issues of commercial lithium-ion batteries related to the faults and failures in real-world applications are still serious concern. Even a small increase in risk during the battery's operational lifetime may evolve into a safety hazard-fire and explosion, named as thermal runaway, after long-term incubation. Modelling and predicting the evolution of nonlinear multiscale electrochemical systems is challenging due to uncertainties in materials and manufacturing processes, dynamic environmental and operating conditions, as well as a lack of high-quality datasets. This challenge is further complicated when solving real-life physical problems with missing and noisy data and uncertain boundary conditions. In this study, we address these challenges by developing a specialized Transformer network architecture called BERTtery (Bidirectional Encoder Representations from Transformers for batteries) based on field data of EVs. By using charging voltage and temperature curves from early cycles before exhibiting symptoms of battery, the two-tower Transformer with temporal-wise encoder and channel-wise encoder is demonstrated as a powerful tool to capture early-warning signals across multiple spatio-temporal scales under a wide range of operating conditions. The method reliably predicts the evolution of faults in battery systems using only data provided by the onboard sensor measurements of battery performance.

Modern ancient Chinese

Abstract This article adopts the notions of chronotope and scalar intimacy to discuss collective identification, in particular Hanfu identity among Chinese youth in the context of contemporary Chinese nationalism. Drawing upon ethnographic interviews and observations of self-identified Hanfu fans in Beijing, China, this paper analyzes how they, through invoking and shifting back and forth across multiple spatiotemporal scales, discursively enact collective and intimate identification among individuals, among Hanfu fan groups (on a local scale), and the general Chinese population (on a larger, national scale). This paper suggests that the great ancient China chronotope (GACC) has played a crucial role in establishing continuity between the present (or other time-spaces) and the ‘past,’ ultimately legitimizing the inseparable link between Hanfu groups and collectives and solidifying great China and collective identity ideologies. It also demonstrates that modern Chinese youth are internalizing the Chinese nationalist ideology in order to establish a sociocultural relationship of belonging to and sharing with Chinese collectives as a way to empower themselves to cope with uncertainty.

Fluid migration pathways to groundwater in mature oil fields: Exploring the roles of water injection/production and oil-well integrity in California, USA

Mature oil fields potentially contain multiple fluid migration pathways toward protected groundwater (total dissolved solids, TDS, in nonexempted aquifer <10,000 mg/L) because of their extensive development histories. Time-series data for water use, fluid pressures, oil-well construction, and geochemistry from the South Belridge and Lost Hills mature oil fields in California are used to explore the roles of injection/production of oil-field water and well-integrity issues in fluid migration. Injection/production of oil-field water modified hydraulic gradients in both oil fields, resulting in chemical transport from deeper groundwater and hydrocarbon-reservoir systems to aquifers in the oil fields. Those aquifers are used for water supply outside the oil-field boundaries. Oil wells drilled before 1976 can be fluid migration pathways because a relatively large percentage of them have >10 m of uncemented annulus that straddles oil-well casing damage and/or the base of groundwater with TDS <10,000 mg/L. The risk of groundwater-quality degradation is higher when wells with those risk factors occur in areas with upward hydraulic gradients created by positive net injection, groundwater withdrawals, or combinations of these variables. The complex changes in hydrologic conditions and groundwater chemistry likely would not have been discovered in the absence of years to decades of monitoring data for groundwater elevations and chemistry, and installation of monitoring wells in areas with overlapping risk factors. Important monitoring concepts based on results from this and other studies include monitoring hydrocarbon-reservoir and groundwater systems at multiple spatiotemporal scales and maintaining transparency and accessibility of data and analyses. This analysis focuses on two California oil fields, but the methods used and processes affecting fluid migration could be relevant in other oil fields where substantial injection/production of oil-field water occurs and oil-well integrity is of concern.

Phylogenomic and population genomic analyses reveal the spatial–temporal dynamics of diversification of the Nigella arvensis complex (Ranunculaceae) in the Aegean archipelago

The continental-shelf islands of the Aegean Sea provide an ideal geographical setting for evolutionary-biogeographical studies but disentangling the relationships between palaeogeographical history and the times, orders of modes of taxon divergence is not straightforward. Here, we used phylogenomic and population genomic approaches, based on orthologous gene sequences and transcriptome-derived SNP data, to reconstruct the spatial–temporal evolution of the Aegean Nigella arvensis complex (Ranunculaceae; 11 out of 12 taxa). The group’s early diversification in the Early/Mid-Pliocene (c. 3.77 Mya) resulted in three main lineages (Greek mainland vs. central Aegean + Turkish mainland/eastern Aegean islands), while all extant taxa are of Late Plio-/Early Pleistocene origin (c. 3.30–1.59 Mya). Demographic modelling of the outcrossing taxa uncovered disparate modes of (sub)speciation, including divergence with gene flow on the Greek mainland, para- or peripatric diversification across eastern Aegean islands, and a ‘mixing–isolation–mixing (MIM)’ mode of subspeciation in the Cyclades. The two selfing species (N. stricta, N. doerfleri) evolved independently from the outcrossers. Present-day island configurations are clearly insufficient to explain the spatial–temporal history of lineage diversification and modes of (sub)speciation in Aegean Nigella. Moreover, our identification of positively selected genes in almost all taxa calls into question that this plant group represents a case of ‘non-adaptive’ radiation. Our study revealed an episodic diversification history of the N. arvensis complex, giving new insight into the modes and drivers of island speciation and adaption across multiple spatiotemporal scales.