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Databases of transitional states obtained from Direct Numerical simulations (DNS) of temporal, supercritical mixing layers for two species systems, O2/H2 and C7H16/N2, are analyzed to elucidate species-specific turbulence aspects and features of fluid disintegration.
A study of emerging turbulent scales entropy production is conducted for a supercritical shear layer as a precursor to the eventual modeling of Subgrid Scales (from a turbulent state) leading to Large Eddy Simulations.
Land-atmosphere carbon exchange is known to be extremely heterogeneous in arctic ice-wedge polygonal tundra regions. In this study, a Kalman filter-based method was developed to estimate the spatio-temporal dynamics of daytime average net ecosystem exchange (NEEday) at 0.5-m resolution over a 550 m by 700 m study site. We integrated multi-scale, multi-type datasets, including normalized difference vegetation indices (NDVIs) obtained from a novel automated mobile sensor system (or tram system) and a greenness index map obtained from airborne imagery. We took advantage of the significant correlations between NDVI and NEEday identified based on flux chamber measurements. The weighted average of the estimated NEEday within the flux-tower footprint agreed with the flux tower data in term of its seasonal dynamics. We then evaluated the spatial variability of the growing season average NEEday, as a function of polygon geomorphic classes; i.e., the combination of polygon types—which are known to present different degradation stages associated with permafrost thaw—and microtopographic features (i.e., troughs, centers and rims). Our study suggests the importance of considering microtopographic features and their spatial coverage in computing spatially aggregated carbon exchange.
With the significant increase in sources and volume of human mobility data through commercial data vendors as well as microsimulation of cities, the scale of geospatial-temporal data to analyze and assess for mobility characterization has grown to the level of Big Data. There are mobility related commercial organizations deploying scalable computing, but often the system architecture, workflow, and intermediate processing components are not fully disclosed in relevant scope. Current research literature has a notable lack of studies demonstrating architectures and workflows for human mobility analytics that are implemented on a TeraByte scale of geospatial-temporal data. In this context, this paper presents a hyperscale-level system solution named DICER (Data Intensive Computing Environment and Runtime) for processing and analytics of geospatial-temporal data at big data scale. Although the cluster computing architecture of DICER with Apache Spark job running on Kubernetes cluster is not new, there are innovations in the workflow, hierarchical processing logic, and a wide range of intermediate preprocessing and mobility metrics calculation. We have performed case studies to validate the effectiveness of DICER system solution by performing detailed analytics and assessment of human mobility microsimulation output at three different scopes and scale, including a usecase with 16.97 TeraByte and 259.2 Billion rows of data. In addition, we have presented another case study of utilizing DICER to perform the same mobility processing and comparative analytics on large-scale commercially available geospatial-temporal data. All these case studies validate the efficiency and usefulness of DICER in computing population mobility characteristics from geospatial-temporal trajectory data at an unprecedented scale (not only just data volume, but also combination of: number of user entities, temporal frequency, spatial resolution, data duration).
Abstract The complex structure of the catalytic active phase, and surface‐gas reaction networks have hindered understanding of the oxidative coupling of methane (OCM) reaction mechanism by supported Na 2 WO 4 /SiO 2 catalysts. The present study demonstrates, with the aid of in situ Raman spectroscopy and chemical probe (H 2 ‐TPR, TAP and steady‐state kinetics) experiments, that the long speculated crystalline Na 2 WO 4 active phase is unstable and melts under OCM reaction conditions, partially transforming to thermally stable surface Na‐WO x sites. Kinetic analysis via temporal analysis of products (TAP) and steady‐state OCM reaction studies demonstrate that ( i ) surface Na‐WO x sites are responsible for selectively activating CH 4 to C 2 H x and over‐oxidizing CH y to CO and ( ii ) molten Na 2 WO 4 phase is mainly responsible for over‐oxidation of CH 4 to CO 2 and also assists in oxidative dehydrogenation of C 2 H 6 to C 2 H 4 . These new insights reveal the nature of catalytic active sites and resolve the OCM reaction mechanism over supported Na 2 WO 4 /SiO 2 catalysts.
Abstract The complex structure of the catalytic active phase, and surface‐gas reaction networks have hindered understanding of the oxidative coupling of methane (OCM) reaction mechanism by supported Na 2 WO 4 /SiO 2 catalysts. The present study demonstrates, with the aid of in situ Raman spectroscopy and chemical probe (H 2 ‐TPR, TAP and steady‐state kinetics) experiments, that the long speculated crystalline Na 2 WO 4 active phase is unstable and melts under OCM reaction conditions, partially transforming to thermally stable surface Na‐WO x sites. Kinetic analysis via temporal analysis of products (TAP) and steady‐state OCM reaction studies demonstrate that ( i ) surface Na‐WO x sites are responsible for selectively activating CH 4 to C 2 H x and over‐oxidizing CH y to CO and ( ii ) molten Na 2 WO 4 phase is mainly responsible for over‐oxidation of CH 4 to CO 2 and also assists in oxidative dehydrogenation of C 2 H 6 to C 2 H 4 . These new insights reveal the nature of catalytic active sites and resolve the OCM reaction mechanism over supported Na 2 WO 4 /SiO 2 catalysts.
The significance of oxygen isotope ratios in Archean chert has long been debated. Cherts from the c. 3.4 Ga Strelley Pool Formation (SPF) (Pilbara Craton, Western Australia) host some of the oldest stromatolite and microfossil evidence for life, but the genesis and timing of silica cements has been unclear. Field relations, petrography and a combination of laser fluorination and in-situ SIMS measurements of δ 18 O in quartz show that bedded cherts of the SPF were originally precipitated as carbonates and were later widely replaced by quartz. Three localities were studied and analyzed for δ 18 O(Qz) in chert: 1) Camel Creek: foliated, metamorphosed, bedded cherts and meter-scale black chert veins; 2) Unconformity Ridge and ABDP8 drill core: stromatolitic and bedded chert overlying basal detrital quartz sandstone; and 3) the Trendall locality: “bedded” stromatolitic chert replacing original dolomite, low temperature hydrothermal quartz, and mm- to decimeter-scale chert-quartz veins. Laser fluorination (mm-scale) values of δ18O(Qz) range from: 14.2 to 18.2‰ VSMOW at Camel Creek; 9.3 to 18.9‰ at Unconformity Ridge; and 13.7 to 25.7‰ at Trendall. Values of δ 18 O(Qz) in cm to decimeter-scale hydrothermal chert veins cutting bedded carbonates at Trendall range from ca. 15 to 16‰, whereas “bedded cherts” are 17 to 26‰. These laser data include the highest δ 18 O values reported for cherts in Paleoarchean sediments and are up to 4‰ higher than the upper limit of ~22‰ reported in other studies, in apparent contrast to the long-standing secular-temporal trend which shows such high δ 18 O only in younger chert. However, analysis by laser fluorination at the 1-mm scale cannot resolve microtextures seen petrographically. In contrast, in-situ SIMS analyses can resolve petrographic microtextures and show δ 18 O(Qz) at 10-μm scale have an even greater range of 7–31‰ in “bedded” cherts at the Trendall locality, up to 9‰ above the secular-temporal trend. Textures observed optically at the Trendall locality were classified as: microquartz, mesoquartz, chalcedony, megaquartz veins, and cavity megaquartz. SEM-CL imaging shows two generations of meso- and megaquartz; bright CL with well-developed growth zoning, and dark CL with massive or mottled texture. Microquartz is the earliest textural generation of quartz and has a maximum δ 18 O(Qz) of ~22‰ by SIMS. Dark-CL mesoquartz has similar δ 18 O to microquartz and is interpreted to also be early. Bright-zoned-CL mesoquartz, which formed post-Archean, has even higher δ 18 O, up to 29‰. Vein megaquartz crosscuts most quartz generations and has a restricted range of δ 18 O, mostly from 16 to 19‰. Chalcedony pseudomorphs rhombic cavities and fractures, lines the edges of veins, and has similar δ 18 O to veins (16–19‰). Late cavity megaquartz is bright and zoned by CL, grows into late open cavities, and has the highest δ 18 O(Qz) values reported from the Pilbara, up to 31.3‰. Thus, the highest-δ 18 O quartz cements at the Trendall locality are the youngest and may be related to weathering. Early silicification and the formation of microquartz, chalcedony and low δ 18 O mesoquartz occurred during low temperature hydrothermal activity in the Archean. None of the SPF quartz examined is interpreted to have formed as a direct precipitate from Paleoarchean seawater. Thus, values of δ 18 O(Qz) do not record either water chemistry or temperature of Archean oceans. In-situ SIMS analysis shows that high-δ 18 O(Qz) values above 22‰ are only found in late-forming cavity megaquartz and bright-zoned-CL mesoquartz at the Trendall locality. The SPF results from our sample suite demonstrate the ability to resolve complex history using detailed petrography and SIMS analysis. Similar studies may show equal complexity of δ 18 O(Qz) data for other localities that are interpreted to show secular-temporal trends for chert. The apparent increase of δ 18 O(Qz) through time may reflect differences in diagenesis, and/or an inherent and previously unrecognized sampling bias that compares fundamentally different populations of quartz, such as Archean hydrothermal chert from volcanic greenstone belts, with unrelated Phanerozoic biogenic quartz.
The effective clinical application of atmospheric pressure plasma jet (APPJ) treatments requires a well-founded methodology that can describe the interactions between the plasma jet and a treated sample and the temporal and spatial changes that result from the treatment. In this study, we developed a large-scale image analysis method to identify the cell-cycle stage and quantify damage to nuclear DNA in single cells. The method was then tested and used to examine spatio-temporal distributions of nuclear DNA damage in two cell lines from the same anatomic location, namely the oral cavity, after treatment with a nitrogen APPJ. One cell line was malignant, and the other, nonmalignant. The results showed that DNA damage in cancer cells was maximized at the plasma jet treatment region, where the APPJ directly contacted the sample, and declined radially outward. As incubation continued, DNA damage in cancer cells decreased slightly over the first 4 h before rapidly decreasing by approximately 60% at 8 h post-treatment. In nonmalignant cells, no damage was observed within 1 h after treatment, but damage was detected 2 h after treatment. Notably, the damage was 5-fold less than that detected in irradiated cancer cells. Moreover, examining damage with respect to the cell cycle showed that S phase cells were more susceptible to DNA damage than either G1 or G2 phase cells. The proposed methodology for large-scale image analysis is not limited to APPJ post-treatment applications and can be utilized to evaluate biological samples affected by any type of radiation, and, more so, the cell-cycle classification can be used on any cell type with any nuclear DNA staining.
Soil microbial communities are highly spatially organized, shaped in part by the structure of soil itself. Understanding how spatially discrete microbial communities change across years and seasons in response to environmental factors, plant phenology and aggregate turnover, is key to understanding how varying management practices impact the ecology of soil microbial communities. We investigated both seasonal (within year) and annual (across sampling years) changes of discrete microbial communities in soil aggregate fractions, large macroaggregates (LM) and microaggregates (MICRO) in three different bioenergy management systems. We hypothesized that 1) seasonal changes due to plant phenology and aggregate turnover will be most pronounced within the MICRO aggregate soil microbial community; 2) inter-annual variability will lead to changes in microbial diversity across aggregate sizes and the magnitude of change will be mediated by management regime. We found that LM and MICRO aggregates have unique microbial communities within soil. MICRO aggregate microbial communities are more diverse and change more dynamically across the sampling season, peaking in diversity at peak plant growth and maximum biomass. The number of families indicative of specific MICRO aggregate habitats increases over the growing season for both bacteria (from 3 to 51) and fungi (from 8 to 14). The LM aggregates harbored less diverse, yet more stable, communities within a growing season. By contrast, between years the LM aggregates were the most responsive to inter-annual variability. Our study demonstrates the importance of including the spatio-temporal dynamics of soil microbes. We identified “hot spots” of microbial diversity within soil, with a greater diversity of microbes found under prairies, within the MICRO aggregates, and seasonally during peak plant biomass. Targeted analysis of the MICRO aggregates can contribute to deeper understanding of potential diversity and functioning of soil microbial communities for ecosystem maintenance as well as the response to climatic events and environmental change.
Given the tradeoffs between spatial and temporal resolution, questions about resolution optimality are fundamental to the study of global snow. Answers to these questions will inform future scientific priorities and mission specifications. Heterogeneity of mountain snowpacks drives a need for daily snow cover mapping at the slope scale (≤30 m) that is unmet for a variety of scientific users, ranging from hydrologists to the military to wildlife biologists. But finer spatial resolution usually requires coarser temporal or spectral resolution. Thus, no single sensor can meet all these needs. Recently, constellations of satellites and fusion techniques have made noteworthy progress. The efficacy of two such recent advances is examined: (1) a fused MODIS–Landsat product with daily 30 m spatial resolution and (2) a harmonized Landsat 8 and Sentinel 2A and B (HLS) product with 3–4 d temporal and 30 m spatial resolution. State-of-the-art spectral unmixing techniques are applied to surface reflectance products from 1 and 2 to create snow cover and albedo maps. Then an energy balance model was run to reconstruct snow water equivalent (SWE). For validation, lidar-based Airborne Snow Observatory SWE estimates were used. Results show that reconstructed SWE forced with 30 m resolution snow cover has lower bias, a measure of basin-wide accuracy, than the baseline case using MODIS (463 m cell size) but greater mean absolute error, a measure of per-pixel accuracy. However, the differences in errors may be within uncertainties from scaling artifacts, e.g., basin boundary delineation. Other explanations are (1) the importance of daily acquisitions and (2) the limitations of downscaled forcings for reconstruction. Conclusions are as follows: (1) spectrally unmixed snow cover and snow albedo from MODIS continue to provide accurate forcings for snow models and (2) finer spatial and temporal resolution through sensor design, fusion techniques, and satellite constellations are the future for Earth observations, but existing moderate-resolution sensors still offer value.
Tree bark has proved to be a useful bioindicator for trace elements in the atmosphere, however it reflects an exposure occurring during an unidentified period of time, so it provides spatial information about the distribution of contaminants in a certain area, but it cannot be used to detect temporal changes or trends, which is an important achievement in environmental studies. In order to obtain information about a known period of time, the bark collected from the annual segments of tree branches can be used, allowing analyses going back 10–15 years with annual resolution. In the present study, the concentrations of As, Cd, Co, Cu, Fe, Mn, Ni, Pb, V and Zn were measured by atomic emission spectrometry in a series of samples covering the period from 2001 to 2013 in an urban environment. Downward time trends were significant for Cd, Pb and Zn. The only trace element showing an upward time trend was V. The concentrations of the remaining six trace elements were constant over time, showing that their presence in bark is not simply proportional to the duration of exposure. This approach, which is simple, reliable and widely applicable at a low cost, allows the “a posteriori” reconstruction of atmospheric trace element deposition when or where no monitoring programme is in progress and no other natural archives are available. - Highlights: • Branch bark allows the historical reconstruction of atmospheric trace elements. • This approach is simple, reliable, widely applicable and “a posteriori”. • Downward time trends were found for Cd, Pb and Zn; upward trend for V.
Until recently, human +Gz acceleration tolerance has relied solely on subjective criteria relating to loss of vision. By use of newly developed noninvasive instrumentation using a transcutaneous Doppler flow system, objective end point criteria have been developed based on measured blood flow to the head. The system consists of miniature 8 MHz Doppler sensors (2 x 1 x 0.5 cm) placed on the forehead over both frontal branches of the temporal arteries to detect blood flow velocity from back scattered ultrasound. Its use has allowed for correlation of altered, decreased and actual reversal of eye level blood flow with subsequent central light loss. Over 100 subjects have now been studied during more than 2,000 centrifuge runs. Objective changes in temporal artery flow velocity consistently preceded visual degradation for each subject during all acceleration profiles. No subject has gone unconscious without first exhibiting a minimum 6 sec of total flow cessation. Retrograde flow followed by complete flow cessation always preceded central light loss. Results indicate that this method can be successfully used with a wide variety of tasks during exposure to +Gz acceleration. It is recommended for use during evaluation of protective maneuvers or devices on the centrifuge or during actual flight in high performance aircraft. It may also serve as a potential safety monitor during space Shuttle re-entry if there is doubt about a passenger's cardiovascular status.
The current study characterizes brain activation in response to two modes of vestibular stimulation: skull tap and auditory tone burst. The auditory tone burst has been used in previous studies to elicit either the vestibulo-spinal reflex (saccular-mediated colic Vestibular Evoked Myogenic Potentials (cVEMP)), or the ocular muscle response (utricle-mediated ocular VEMP (oVEMP)). Some researchers have reported that air-conducted skull tap elicits both saccular and utricle-mediated VEMPs, while being faster and less irritating for the subjects. However, it is not clear whether the skull tap and auditory tone burst elicit the same pattern of cortical activity. Both forms of stimulation target the otolith response, which provides a measurement of vestibular function independent from semicircular canals. This is of high importance for studying otolith-specific deficits, including gait and balance problems that astronauts experience upon returning to earth. Previous imaging studies have documented activity in the anterior and posterior insula, superior temporal gyrus, inferior parietal lobule, inferior frontal gyrus, and the anterior cingulate cortex in response to different modes of vestibular stimulation. Here we hypothesized that skull taps elicit similar patterns of cortical activity as the auditory tone bursts, and previous vestibular imaging studies. Subjects wore bilateral MR compatible skull tappers and headphones inside the 3T GE scanner, while lying in the supine position, with eyes closed. Subjects received both forms of the stimulation in a counterbalanced fashion. Pneumatically powered skull tappers were placed bilaterally on the cheekbones. The vibration of the cheekbone was transmitted to the vestibular system, resulting in the vestibular cortical response. Auditory tone bursts were also delivered for comparison. To validate our stimulation method, we measured the ocular VEMP outside of the scanner. This measurement showed that both skull tap and auditory tone burst elicited vestibular evoked myogenic potentials, indicated by eye muscle responses. We further assessed subjects' postural control and its correlation with vestibular cortical activity. Our results provide the first evidence of using skull taps to elicit vestibular activity inside the MRI scanner. By conducting conjunction analyses we showed that skull taps elicit the same activation pattern as auditory tone bursts (superior temporal gyrus), and both modes of stimulation activate previously identified vestibular cortical regions. Additionally, we found that skull taps elicit more robust vestibular activity compared to auditory tone bursts, with less reported aversive effects. This further supports that the skull tap could replace auditory tone burst stimulation in clinical interventions and basic science research. Moreover, we observed that greater vestibular activation is associated with better balance control. We showed that not only the quality of balance (indicated by the amount of body sway) but also the ability to maintain balance for a longer time (indicated by the balance time) was associated with individuals' vestibular cortical excitability. Our findings support an association between vestibular cortical activity and individual differences in balance. In sum, we found that the skull tap stimulation results in activation of canonical vestibular cortex, suggesting an equally valid, but more tolerable stimulation method compared to auditory tone bursts. This is of high importance in longitudinal vestibular assessments, in which minimizing aversive effects may contribute to higher protocol adherence.
Socio-demographic trends and recent economic development patterns have resulted in travel behavior changes that call for more flexible and accessible public transit options. Because flexible transit services vary in scope, size, and service type, new data-informed methods are useful to optimize services based on the specific needs of local communities and riders. In this study, real-world demand and vehicle trajectory data were used to evaluate and optimize system performance for an existing first-mile–last-mile (FMLM) service in Robinson Township, PA. A general FMLM model for arbitrary demand and service supply was then developed to quantify system performance—both travel time costs and day-to-day reliability—for various operational polices considering spatio-temporal demand variation and transportation network dynamics. Heuristics were used for optimal real-time vehicle routing in sizable real-world networks accommodating various service types and scopes. In this case study, total user costs were reduced by 18.6% when rides were coordinated with mainline fixed-route transit. Predictive routing strategies were shown to marginally improve system performance under sparse and variable spatio-temporal demand. The case study also highlights potentially large travel time and user reliability improvements—reductions of 51% and 53.8%, respectively—when trip requests were made in advance of their desired pickup time. Finally, we show that travel time reliability can be improved for time-inflexible trips with trip prioritization without increasing total user costs. These results were stable to changes in demand density.
Rainfall retrieval algorithms for passive microwave radiometers often exploit the brightness temperature depression due to ice scattering at high-frequency channels (≥85 GHz) over land. This study presents an alternate method to estimate the daily rainfall amount using the emissivity temporal variation (i.e., Δe) under rain-free conditions at low-frequency channels (19, 24, and 37 GHz). Emissivity is derived from 10 passive microwave radiometers, including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), three Special Sensor Microwave Imager/Sounders (SSMIS), the Advanced Technology Microwave Sounder (ATMS), and four Advanced Microwave Sounding Units-A (AMSU-A). Four different satellite combination schemes are used to derive the Δe for daily rainfall estimates. They are all 10 satellites, 5 imagers, 6 satellites with very different equator crossing times, and GMI only. Results show that Δe from all 10 satellites has the best performance with a correlation of 0.60 and RMSE of 6.52 mm, compared with the Integrated Multisatellite Retrievals for GPM (IMERG) Final run product. The 6-satellites scheme has comparable performance with the all-10-satellites scheme. The 5-imagers scheme performs noticeably worse with a correlation of 0.49 and RMSE of 7.28 mm, while the GMI-only scheme performs the worst with a correlation of 0.25 and RMSE of 11.36 mm. The inferior performance from the 5-imagers and GMI-only schemes can be explained by the much longer revisit time, which cannot accurately capture the emissivity temporal variation.
The goal of this research project was to further our understanding of biological electron transfer through the development and application of new methods for studying proteins and their complexes with high spatial and temporal detail. The approach was based on incorporation of reporter bonds with distinct vibrational frequencies at specific locations in proteins and their characterization through the interaction of the bonds with pulses of infrared light. The project focused on the proteins plastocyanin (Pc) and cytochrome (cyt) f, two electron transfer partners in photosynthesis. The first thrust of the project was the incorporation of carbon deuterium bonds as non-perturbative probes at a ligand to the copper ion of Pc and their use to detect changes in the bonding due to the number of electrons at the metal and how the bonding changes upon association with cyt f. Toward developing more advanced approaches to characterize proteins, we incorporated a stronger reporter, the cyano group of cyanophenylalanine, at several locations in Pc and measured the frequency dynamics in the absence and presence of cyt f. The acquired data improved the current model for the protein complex. Notably, the data indicated that the majority of the population is involved in a loosely bound complex, providing insight into the nature of the ensembles formed by electron transfer protein complexes that is not available via other approaches. In addition to learning about the protein complex, we made strides toward developing and improving the new experimental approach to study highly dynamic behaviors of proteins.
A significant contribution to the temporal resolution of an ultrafast electron diffraction (UED) instrument is arrival time jitter caused by amplitude and phase variation of radio-frequency (rf) cavities. In this paper, we present a semianalytical approach for calculating rf-induced temporal jitter from klystron and rf cavity parameters. Our approach allows fast estimation of temporal jitter for MeV-UED beamlines and can serve as a virtual timing tool when shot-to-shot measurements of rf amplitude and phase jitters are available. A simulation study for the SLAC MeV-UED instrument is presented, and the temporal resolution of several beamline configurations is compared.
ABSTRACT Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we presentMetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach—encompassing microenvironmental conditions, agent information, and metabolic pathways—we simulated different communities to showcase the potential of the model. Using ourin-silicoplatform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases.MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology, as it offers new insights into predicting and analyzing microbial communities. IMPORTANCE Our study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include the following: (i) prediction of metabolic cross-feeding and spatial organization in multi-species communities, (ii) insights into how oxygen gradients and nutrient availability shape community composition in different gut regions, and (iii) identification of spatiallyregulated metabolic pathways and enzymes inE. coli. We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.