High Spatiotemporal Resolution Research Articles

Capturing the net ecosystem CO2 exchange dynamics of tidal wetlands with high spatiotemporal resolution by integrating process-based and machine learning estimations

Accurate estimation of the net ecosystem CO2 exchange (NEE) at regional scales is of great significance for studying the carbon sink potential of coastal wetland ecosystems and their responses to global climate change. However, current NEE estimation methods are mainly developed for terrestrial ecosystems and are therefore unsuitable for NEE estimation with high spatiotemporal resolution estimation in coastal wetlands subjected to sub-daily tidal flooding. In this study, we proposed a high spatiotemporal resolution NEE estimation method for coastal marsh wetlands that properly considered tidal influence by combining the advantages of process-based modeling and machine learning. This method was verified and applied in the Changjiang estuary and Liaohe estuary marsh wetlands based on eddy covariance and environmental measurements, climate reanalysis data, and satellite images. The proposed method had good performance in the NEE estimation of tidal marsh wetlands, with Phragmites australis, Spartina alterniflora, and Suaeda salsa having coefficients of determination (R2) of 0.850, 0.676, and 0.658, respectively, and root mean square error (RMSE) values of 7.211 μmol m−2 s−1, 8.105 μmol m−2 s−1, and 0.109 μmol m−2 s−1, respectively. By integrating the tide level and salinity, the NEE estimation accuracy for each vegetation type was improved. The total annual NEE values of the Changjiang estuary and Liaohe estuary marsh wetlands in 2022 were estimated to be −0.297 and −0.444 Tg C yr−1, respectively. This study demonstrated that integrating process-based model and machine learning estimation can reliably capture the NEE dynamics of coastal wetlands, providing a useful tool to quantify coastal blue carbon potential with high spatiotemporal resolution at large scales.

Sensitive, accurate, and high spatiotemporal resolution photonic thermometry

Real-time temperature monitoring with high accuracy and spatiotemporal resolution is critical for many biological applications, including disease diagnosis, drug delivery, and biomedical research. However, traditional methods for measuring temperature in biological systems present difficulties for a variety of reasons, such as slow response time, limited spatial resolution, low amplitude, and susceptibility to electromagnetic interference. Most importantly, in many cases, the thermal mass of temperature probes limits the accuracy and speed of measurement significantly. Here, we show that photonic microring resonators (MRRs) can be used for sensitive, precise, and high spatiotemporal resolution measurement of temperature in the biological milieu. The high refractive index of Si MRR and negligible thermal mass enable sensitive, ultrafast, and accurate temperature transients. By using a double resonator circuit, we demonstrate that MRR sensors can measure temperature with a 1 mm spatial resolution. We then show that MRR yields more accurate results than fiber optic probes for measuring temperature transients. Finally, we demonstrate the localized temperature measurement capability of MRRs in mouse brain tissue heated by superparamagnetic nanoparticles in an alternating magnetic field. This compact, lab-on-chip photonic temperature sensing platform holds great promise for continuous monitoring of temperature in critical biological and biomedical applications.

Nano-optogenetics for Disease Therapies.

Optogenetic, known as the method of 21 centuries, combines optic and genetic engineering to precisely control photosensitive proteins for manipulation of a broad range of cellular functions, such as flux of ions, protein oligomerization and dissociation, cellular intercommunication, and so on. In this technique, light is conventionally delivered to targeted cells through optical fibers or micro light-emitting diodes, always suffering from high invasiveness, wide-field illumination facula, strong absorption, and scattering by nontargeted endogenous substance. Light-transducing nanomaterials with advantages of high spatiotemporal resolution, abundant wireless-excitation manners, and easy functionalization for recognition of specific cells, recently have been widely explored in the field of optogenetics; however, there remain a few challenges to restrain its clinical applications. This review summarized recent progress on light-responsive genetically encoded proteins and the myriad of activation strategies by use of light-transducing nanomaterials and their disease-treatment applications, which is expected for sparking helpful thought to push forward its preclinical and translational uses.

Advancing air quality monitoring: A low-cost sensor network in motion – Part I

In urban areas, high levels of air pollution pose significant risks to human health, emphasising the need for detailed air quality (AQ) monitoring. However, traditional AQ monitoring relies on the data from Reference Monitoring Stations, which are sparsely distributed and provide only hourly or daily data, failing to capture the spatial and temporal variability of air pollutant concentrations. Addressing this challenge, we introduce in this article the ExpoLIS system, an all-weather mobile AQ monitoring system that integrates various AQ low-cost sensors (LCSs), providing high spatio-temporal resolution data. This study demonstrates that the inclusion of an extended sampling device may mitigate the effect of the meteorological parameters and other disturbances on readings. At the same time, it did not reduce the quality of the data, both in static conditions and in motion, as we were able to maintain a certain level of agreement between the LCSs. In conclusion, the ExpoLIS system proves its versatility by enabling the collection of large quantities of accurate data, allowing a deeper understanding of the AQ dynamics in urban environments.

Soft bioelectronics for diagnostic and therapeutic applications in neurological diseases

Physical and chemical signals in the central nervous system yield crucial information that is clinically relevant under both physiological and pathological conditions. The emerging field of bioelectronics focuses on the monitoring and manipulation of neurophysiological signals with high spatiotemporal resolution and minimal invasiveness. Significant advances have been realized through innovations in materials and structural design, which have markedly enhanced mechanical and electrical properties, biocompatibility, and overall device performance. The diagnostic and therapeutic potential of soft bioelectronics has been corroborated across a diverse array of pre-clinical settings. This review summarizes recent studies that underscore the developments and applications of soft bioelectronics in neurological disorders, including neuromonitoring, neuromodulation, tumor treatment, and biosensing. Limitations and outlooks of soft devices are also discussed in terms of power supply, wireless control, biocompatibility, and the integration of artificial intelligence. This review highlights the potential of soft bioelectronics as a future platform to promote deciphering brain functions and clinical outcomes of neurological diseases.

Human iPSC-derived neurons with reliable synapses and large presynaptic action potentials

Understanding the function of the human brain requires determining basic properties of synaptic transmission in human neurons. One of the most fundamental parameters controlling neurotransmitter release is the presynaptic action potential, but its amplitude and duration remain controversial. Presynaptic action potentials have so far been measured with high temporal resolution only in a limited number of vertebrate but not in human neurons. To uncover properties of human presynaptic action potentials, we exploited recently developed tools to generate human glutamatergic neurons by transient expression of Neurogenin 2 (Ngn2) in pluripotent stem cells. During maturation for 3 to 9 weeks of culturing in different established media, the proportion of cells with multiple axon initial segments decreased, while the amount of axonal tau protein and neuronal excitability increased. Super-resolution microscopy revealed the alignment of the pre- and postsynaptic proteins, Bassoon and Homer. Synaptic transmission was surprisingly reliable at frequencies of 20, 50, and 100 Hz. The synchronicity of synaptic transmission during high-frequency transmission increased during 9 weeks of neuronal maturation. To analyze the mechanisms of synchronous high-frequency glutamate release, we developed direct presynaptic patch-clamp recordings from human neurons. The presynaptic action potentials had large overshoots to ∼25 mV and short durations of ∼0.5 ms. Our findings show that Ngn2-induced neurons represent an elegant model system allowing for functional, structural, and molecular analyses of glutamatergic synaptic transmission with high spatio-temporal resolution in human neurons. Furthermore, our data predict that glutamatergic transmission is mediated by large and rapid presynaptic action potentials in the human brain.Significance statementPresynaptic physiology remains poorly understood despite its relevance to neurological and psychiatric diseases. Studying presynaptic functions in human iPSC-derived neurons offers the important advantage of characterizing molecular mechanisms of neurotransmitter release in neurons derived from diseased patients. As a first step towards this goal, we established direct presynaptic whole-cell patch-clamp recordings from human glutamatergic neurons induced by transient Neurogenin 2 overexpression. We furthermore analyzed the structure of the synapses with super-resolution light microscopy and the synaptic short-term plasticity during high-frequency transmission. Our findings show that synchronous high-frequency transmission is mediated by rapid and large presynaptic action potentials in human neurons, similar to small conventional nerve terminals of rodent neurons.

Consistent land use and land cover classification across 20 years of various high-resolution images for detecting soil sealing in Murcia, Spain

Artificial human-induced soil sealing has numerous negative consequences. The extent of impervious surfaces is a key indicator of the location and intensity of human activity; however, it is also proof of damage to the natural environment as a result of the sealing and modification of ecosystems. Remote sensing techniques can help detect and monitor changes in land use and cover over an extended period. However, the limited availability of consistent satellite images with high spatiotemporal resolutions covering several decades poses major challenges for achieving high overall classification accuracy. An accurate methodology for the multitemporal detection of artificial land cover classes was developed and applied to a case study of the metropolitan area of Murcia (Spain) with its challenging landscape conditions due to the frequent presence of bare soil. For this purpose, a variety of high-resolution satellite images from SPOT 5, Rapid Eye, and PlanetScope covering a period of 20 years were used. To improve the automated detection of built-up areas, the reflectance values of the images, normalised difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), and a building surface digital model were used as inputs for the supervised classification model. We applied a random forest algorithm to non-public, high-resolution images in the Google Earth Engine (GEE) as a processing environment to identify eight target land cover classes. The results show that the proposed methodology leads to a substantial improvement, after including the indices and the digital building model, in the overall accuracy (from 93.16 to 95.97%) and in all classes. This improvement was significant for the artificial classes and was particularly noticeable for the built-up areas (from 91.1 to 95.64%) because their confusion with bare soil was considerably reduced. This work demonstrates the effectiveness of the building-surface digital model as a tool for training the classification model, as it reduces uncertainty in confusion with other spectrally similar classes and its applicability to multisource imagery.

Priority sites for coral aquaculture in Kume Island based on numerical simulation

Global and local stresses such as increased water temperature and terrestrial sediment runoff significantly affect coral reef ecosystems. Over the past three decades, the increase in the intensity and frequency of coral bleaching has been a significant problem in tropical and subtropical coastal seas, and maintaining healthy coral reef ecosystems has long been a challenge. Coral cover in Shimajiri Bay on Kume Island, located in the Nansei Islands (Okinawa Prefecture, southern Japan), has declined due to exposure to high water temperatures, an outbreak of coral predators, and sediment (red soil) inflow from the land. From such a background, coral aquaculture has been implemented at a few stations in the bay. Therefore, it is essential to conduct aquaculture in suitable locations for coral growth and survival. This study aimed to select suitable locations for coral culture with high spatial resolution in an enclosed bay that was greatly affected by red soil runoff, mainly from a sugarcane field. A freshwater and red soil runoff model for the watershed was merged with a coastal hydrodynamic, red soil dispersal, and accumulation model at a spatial resolution of 75 m for Shimajiri Bay. The simulation results were validated using in situ continuous and routine observations. Simulations were conducted over ten years. Locations not subjected to high water temperature in the bay or river loading were selected based on topography, simulated water temperature, salinity, and red soil content of sea sediment. This coupled model enables us to evaluate the growth and survival conditions of cultured corals with high spatiotemporal resolution and quantitatively consider the reduction target of red soil runoff to realize robust coral growth and survival.

Reconstructing long-term natural flows by ensemble machine learning

Large-scale human activities have caused significant disruption to the natural flow regime of rivers worldwide, leading to substantial deviations from river flows’ natural state. The natural hydrological regime of rivers holds great significance for the conservation and management of riverine ecosystems, which relies on the accurate reconstruction of natural river flows. However, the application of existing high spatiotemporal resolution large-scale natural flow datasets at the regional scale has not been adequately assessed, thereby limiting their regional-scale utility. In this study, utilizing global reanalysis datasets and observation data, in combination with ensemble machine learning models, we constructed long-term natural flow time series for inflows into the Pearl River Delta and evaluated the performance of these reconstructed inflows based on observed flow records during undisturbed periods. The reconstruction shows that the ensemble model, the AutoGluon model, indicates the best performance when compared with other machine learning models and outperforms the existing publicly available large-scale datasets in terms of evaluation metrics for the corresponding natural flows. The results of Indicator of Hydrologic Alteration and Range of Variability Analysis (IHA-RVA) indicate that climate variability, reservoir regulation and human activities have significantly altered the natural flow regime of the Pearl River Delta. Specifically, human activities generally tend to smooth natural flow variability in the Pearl River Delta. This research is expected to provide an efficient and reliable method for reconstructing natural flows and offers guidance for water resource management and ecosystem conservation.

Leveraging Temporal Information to Improve Machine Learning-Based Calibration Techniques for Low-Cost Air Quality Sensors.

Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory.

A global dataset of terrestrial evapotranspiration and soil moisture dynamics from 1982 to 2020

Quantifying terrestrial evapotranspiration (ET) and soil moisture dynamics accurately is crucial for understanding the global water cycle and surface energy balance. We present a novel, long-term dataset of global ET and soil moisture derived from the newly developed Simple Terrestrial Hydrosphere model, version 2 (SiTHv2). This ecohydrological model, driven by multi-source satellite observations and hydrometeorological variables from reanalysis data, provides daily global ET-related estimates (e.g., total ET, plant transpiration, soil evaporation, intercepted evaporation) and three-layer soil moisture dynamics at a 0.1° spatial resolution. Validation with in-situ measurements and comparisons with mainstream global ET and soil moisture products demonstrate robust performance of SiTHv2 in both magnitude and temporal dynamics of ET and soil moisture at multiple scales. The comprehensive water path characterization in the SiTHv2 model makes this seamless dataset particularly valuable for studies requiring synchronized water budget and vegetation response to water constraints. With its long-term coverage and high spatiotemporal resolution, the SiTHv2-derived ET and soil moisture product will be suitable to support analyses related to the hydrologic cycle, drought assessment, and ecosystem health.

Insulin resistance and its relationship with long-term exposure to ozone: Data based on a national population cohort

The relationship of ozone (O3), particularly the long-term exposure, with impacting metabolic homeostasis in population was understudied and under-recognised. Here, we used data from ChinaHEART, a nationwide, population-based cohort study, combined with O3 and PM2.5 concentration data with high spatiotemporal resolution, to explore the independent association of exposure to O3 with the prevalence of insulin resistance (IR). Among the 271 540 participants included, the crude prevalence of IR was 39.1%, while the age and sex standardized prevalence stood at 33.0%. Higher IR prevalence was observed with each increase of 10.0 μg/m3 in long-term O3 exposure, yielding adjusted odds ratios (OR) of 1.084 (95% CI: 1.079–1.089) in the one-pollutant model and 1.073 (95% CI: 1.067–1.079) in the two-pollutant model. Notably, a significant additive interaction between O3 and PM2.5 on the prevalence of IR was observed (P for additive interaction < 0.001). Our main findings remained consistent and robust in the sensitivity analyses. Our study suggests long-term exposure to O3 was independently and positively associated with prevalence of IR. It emphasized the benefits of policy interventions to reduce O3 and PM2.5 exposure jointly, which could ultimately alleviate the health and economic burden related to DM.

Mobility census for monitoring rapid urban development.

Monitoring urban structure and development requires high-quality data at high spatio-temporal resolution. While traditional censuses have provided foundational insights into demographic and socio-economic aspects of urban life, their pace may not always align with the pace of urban development. To complement these traditional methods, we explore the potential of analysing alternative big-data sources, such as human mobility data. However, these often noisy and unstructured big data pose new challenges. Here, we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which are produced as a by-product of mobile communication, we show that meaningful features can be extracted, revealing, for example, the emergence and absorption of subcentres. This method allows the analysis of urban dynamics at a high-spatial resolution (here 500 m) and near real-time frequency, and high computational efficiency, which is especially suitable for tracing event-driven mobility changes and their impact on urban structures.

CAVE: Cerebral artery–vein segmentation in digital subtraction angiography

Cerebral X-ray digital subtraction angiography (DSA) is a widely used imaging technique in patients with neurovascular disease, allowing for vessel and flow visualization with high spatio-temporal resolution. Automatic artery–vein segmentation in DSA plays a fundamental role in vascular analysis with quantitative biomarker extraction, facilitating a wide range of clinical applications. The widely adopted U-Net applied on static DSA frames often struggles with disentangling vessels from subtraction artifacts. Further, it falls short in effectively separating arteries and veins as it disregards the temporal perspectives inherent in DSA. To address these limitations, we propose to simultaneously leverage spatial vasculature and temporal cerebral flow characteristics to segment arteries and veins in DSA. The proposed network, coined CAVE, encodes a 2D+time DSA series using spatial modules, aggregates all the features using temporal modules, and decodes it into 2D segmentation maps. On a large multi-center clinical dataset, CAVE achieves a vessel segmentation Dice of 0.84 (±0.04) and an artery–vein segmentation Dice of 0.79 (±0.06). CAVE surpasses traditional Frangi-based k-means clustering (P < 0.001) and U-Net (P < 0.001) by a significant margin, demonstrating the advantages of harvesting spatio-temporal features. This study represents the first investigation into automatic artery–vein segmentation in DSA using deep learning. The code is publicly available at https://github.com/RuishengSu/CAVE_DSA.

Adequate laser focusing and signal acquisition conditions for 3D measurement of electric-field distribution by the E-FISHG method

Electric field measurement using electric-field-induced second-harmonic generation (E-FISHG) has attracted attention because of its non-invasiveness and high spatiotemporal resolution. In the electric field measurement by the E-FISHG method, the applied electric-field profile along the laser path outside the focal spot affects the second-harmonic generation (SHG) signal. We have proposed a method of calibrating and inferring the applied electric-field profile from the SHG distribution along the laser path. In our previous research, the successful inference of a relatively simple electric-field profile from a series of SHG signals was demonstrated. To measure more complex electric-field profiles, we apply our method to three cases of electric-field profiles: (1) the profile with different sharpness, (2) the profile with two peaks, and (3) the profile with noise superimposed on the SHG signal. The applied electric-field distribution can be inferred within 10% error by adequately choosing the confocal parameter. We also provide guidelines for the required signal acquisition region and measurement pitch when the approximate shape of the applied electric field is known, which are important for actual measurement.

Validation of ultrasound velocimetry and computational fluid dynamics for flow assessment in femoral artery stenotic disease.

To investigate the accuracy of high-framerate echo particle image velocimetry (ePIV) and computational fluid dynamics (CFD) for determining velocity vectors in femoral bifurcation models through comparison with optical particle image velocimetry (oPIV). Separate femoral bifurcation models were built for oPIV and ePIV measurements of a non-stenosed (control) and a 75%-area stenosed common femoral artery. A flow loop was used to create triphasic pulsatile flow. In-plane velocity vectors were measured with oPIV and ePIV. Flow was simulated with CFD using boundary conditions from ePIV and additional duplex-ultrasound (DUS) measurements. Mean differences and 95%-limits of agreement (1.96*SD) of the velocity magnitudes in space and time were compared, and the similarity of vector complexity (VC) and time-averaged wall shear stress (TAWSS) was assessed. Similar flow features were observed between modalities with velocities up to 110 and in the control and the stenosed model, respectively. Relative to oPIV, ePIV and CFD-ePIV showed negligible mean differences in velocity (), with limits of agreement of (control) and (stenosed). CFD-DUS overestimated velocities with limits of agreements of and for the control and stenosed model, respectively. VC showed good agreement, whereas TAWSS showed similar trends but with higher values for ePIV, CFD-DUS, and CFD-ePIV compared to oPIV. EPIV and CFD-ePIV can accurately measure complex flow features in the femoral bifurcation and around a stenosis. CFD-DUS showed larger deviations in velocities making it a less robust technique for hemodynamical assessment. The applied ePIV and CFD techniques enable two- and three-dimensional assessment of local hemodynamics with high spatiotemporal resolution and thereby overcome key limitations of current clinical modalities making them an attractive and cost-effective alternative for hemodynamical assessment in clinical practice.

Joint estimation of PM2.5 and O3 concentrations using a hybrid model in Beijing-Tianjin-Hebei, China

The distribution data of fine particulate matter (PM2.5) and ozone (O3) concentrations at high spatiotemporal resolutions play a pivotal role in monitoring and collectively combating atmospheric PM2.5 and O3 pollution, thereby enhancing the quality of human habitation environments. This study integrates reanalysis, satellite observations, and ground-level monitoring data to develop a GLMM-DF (generalized linear mixed effects model-deep forest) hybrid model for joint estimation of PM2.5 and O3 concentrations in the Beijing-Tianjin-Hebei region. The model effectively considers the spatiotemporal heterogeneity inherent in PM2.5 and O3 and their nonlinear relationships with the estimating variables. The cross-validation results indicate that the GLMM-DF model exhibits high estimation accuracy. For PM2.5 estimation, the coefficient of determination (R2), mean absolute error (MAE), and root mean square Error (RMSE) are 0.949, 5.81 μg·m-3, and 9.42 μg·m-3, respectively. Regarding O3 estimation, the R2, MAE, and RMSE are 0.966, 7.77 μg·m-3, and 11.82 μg·m-3, respectively. Furthermore, the model demonstrates favorable spatiotemporal performance, with the PM2.5-O3 interaction contributing to enhancing model performance. Utilizing this model for mapping, we analyzed the spatiotemporal trends of PM2.5 and O3 in the Beijing-Tianjin-Hebei region, uncovering several intriguing phenomena.

Estimation on the hourly near-surface temperature lapse rate and its time-varying characteristics

Since much of the current researches have focused on daily, monthly or annual near-surface (2 m) temperature lapse rate (NSTLR), there is little guidance on best estimation practices and analyses of time-varying characteristics for the hourly NSTLR. To estimate hourly NSTLR and identify its time-varying characteristics accurately and objectively, this study proposed a robust estimation strategy based on IGGIII equivalent weight using multiple linear regression models. The accuracy and reliability of the proposed method was verified. The results show that the robust estimation strategy can further improve the hourly NSTLR solution accuracy relative to the least square (LSQ) method, especially in the time period of relatively high temperature. The hourly NSTLR was positively correlated with temperature, with a 24-hour average maximum of 0.604°C/100 m at universal time coordinated (UTC) 7.2 h and minimum of 0.284°C/100 m at UTC 20.5 h, respectively. Throughout the year, the NSTLR was the largest from June to August, with an average median of around 0.492°C/100 m. However, from November to the following January, the NSTLR value was the smallest, with a mean median of about 0.323°C/100 m. In addition, the hourly NSTLR values were essentially less than the constant value of 0.65°C/100 m. When the hourly NSTLR estimated based on the proposed method was applied to the temperature interpolation, the interpolation accuracies at the highest altitude (1545 m) and other meteorological stations (below 310 m) can increase by 22.4% and 8.1%, respectively, relative to the hourly NSTLR calculated by the LSQ method, and increased by 55.6% and 13.0%, respectively, relative to the no-NSTLR correction. The results are important for the fine establishment of high spatiotemporal resolution temperature fields and for the study of climatic phenomena characterized with rapid spatiotemporal variation.

Intrusive mechanism of the 2018 Sierra Negra eruption, constrained by 4D tomographic images

Understanding the mechanisms of magma intrusion underpins our ability to interpret geophysical monitoring signals at volcanoes and thus issue reliable forecasts of future activity. The basaltic caldera volcanoes of the western Galápagos Islands, Ecuador, exhibit exceptionally high rates of deformation driven by shallow magma accumulation and migration. However, the nature and evolution of magma storage at these volcanoes is poorly constrained by earthquake hypocentre locations or geodetic inversions. Here we show that transient variations in seismic velocity before, during, and after the 2018 eruption of the Sierra Negra volcano track the accumulation of magma in a shallow sill complex and the emplacement of a lateral flank intrusion. A four-dimensional tomographic technique applied to the P-wave arrivals of local earthquakes provides high spatiotemporal resolution of changes in the physical properties of the shallow volcanic system. In the month before the eruption, the expansion of a low-velocity zone above the sub-caldera sill complex coincides with caldera uplift and near-surface fracturing, driven by persistent shallow magma accumulation. A new low-velocity anomaly appeared progressively in the western flank of the volcano in the days after the onset of the eruption, coinciding with the opening of a curved sill that supplied magma to secondary eruptive fissures. The anomaly disappeared as the curved sill deflated after the initial opening, despite it remaining the conduit for magma from the caldera complex to the flank fissures. Low velocities across the shallow caldera after the end of the eruption result from rapid inflation due to recharge with fresh magma from depth. These results indicate a previously unknown complexity to the magmatic plumbing system at Sierra Negra and suggest velocity changes resulting from an interplay of thermal and stress perturbations.

Direct Salmonella injection into enteroid cells allows the study of host-pathogen interactions in the cytosol with high spatiotemporal resolution.

Intestinal epithelial cells (IECs) play pivotal roles in nutrient uptake and in the protection against gut microorganisms. However, certain enteric pathogens, such as Salmonella enterica serovar Typhimurium (S. Tm), can invade IECs by employing flagella and type III secretion systems (T3SSs) with cognate effector proteins and exploit IECs as a replicative niche. Detection of flagella or T3SS proteins by IECs results in rapid host cell responses, i.e., the activation of inflammasomes. Here, we introduce a single-cell manipulation technology based on fluidic force microscopy (FluidFM) that enables direct bacteria delivery into the cytosol of single IECs within a murine enteroid monolayer. This approach allows to specifically study pathogen-host cell interactions in the cytosol uncoupled from preceding events such as docking, initiation of uptake, or vacuole escape. Consistent with current understanding, we show using a live-cell inflammasome reporter that exposure of the IEC cytosol to S. Tm induces NAIP/NLRC4 inflammasomes via its known ligands flagellin and T3SS rod and needle. Injected S. Tm mutants devoid of these invasion-relevant ligands were able to grow in the cytosol of IECs despite the absence of T3SS functions, suggesting that, in the absence of NAIP/NLRC4 inflammasome activation and the ensuing cell death, no effector-mediated host cell manipulation is required to render the epithelial cytosol growth-permissive for S. Tm. Overall, the experimental system to introduce S. Tm into single enteroid cells enables investigations into the molecular basis governing host-pathogen interactions in the cytosol with high spatiotemporal resolution.