Low Spatiotemporal Resolution Research Articles

Multimodal identification of the mouse brain using simultaneous Ca 2+ imaging and fMRI.

Individual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment of various heterogeneous neurological conditions and diseases. Despite many advantages, the workhorse in this arena, BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI) suffers from low spatiotemporal resolution and specificity as well as a propensity for noise and spurious signal corruption. To better understand individual differences in BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, can be accessed. Here, we apply simultaneous wide-field fluorescence calcium imaging and BOLD-fMRI in mice to interrogate individual differences using a connectome-based identification framework adopted from the human fMRI literature. This approach yields high spatiotemporal resolution cell-type specific signals (here, from glia, excitatory, as well as inhibitory interneurons) from the whole cortex. We found mouse multimodal connectome- based identification to be successful and explored various features of these data.

Using magnetic resonance-guided laser interstitial thermal therapy corpus callosotomy to lateralize a seizure focus for staged surgical approach

New onset refractory status epilepticus (NORSE) is an etiologically heterogeneous condition that is associated with high morbidity and mortality. NORSE is often refractory to medical management prompting a workup for epilepsy surgery. Because NORSE remains etiologically elusive in most cases, surgical evaluations are challenging, especially when the epileptogenic zone (EZ) is not easy to lateralize as can be seen in frontal lobe seizures. Lateralizing a frontal lobe EZ may be challenging due to bilateral synchrony from commissural connections through the corpus callosum and low spatiotemporal resolution of the scalp electroencephalography (EEG). We report a pediatric patient with NORSE presenting with focal impaired awareness seizures clustering into super refractory status epilepticus (SRSE). She required surgical intervention for the treatment of her seizures after failing therapeutic doses of antiseizure medications, anesthetic drips, immunomodulation with methylprednisolone, intravenous immunoglobulin and anakinra, and the ketogenic diet. Despite her semiology being focal, the seizures were not well lateralized on scalp EEG and during phase 2 stereo-EEG (sEEG). Anterior magnetic resonance-guided laser interstitial thermal therapy corpus callosotomy (MRgLITT CC) was performed in a multistage surgical approach to successfully lateralize the EZ with a left-lateralized ictal pattern seen after reimplantation of sEEG electrodes. Our case suggests that minimally invasive MRgLITT CC can be successfully used to lateralize an EZ in frontal lobe epilepsy and that epilepsy surgery should be considered in patients with NORSE with SRSE. We also demonstrate that laser interstitial thermal therapy (LITT), while not always resulting in seizure freedom, can sufficiently disrupt a network to abort status epilepticus and lead to seizure improvements.

Acid-Triggered Degradation of Three-In-One Ag2S Quantum Dots for In Situ Ratiometric NIR-II Fluorescence Imaging-Guided Ion/Gas Combination Therapy.

Smart theranostic nanoprobes with the integration of multiple therapeutic modalities are preferred for precise diagnosis and efficient therapy of tumors. However, it remains a big challenge to arrange the imaging and two or more kinds of therapeutic agents without weakening the intended performances. In addition, most existing fluorescence (FL) imaging agents suffer from low spatiotemporal resolution due to the short emission wavelength (<900 nm). Here, novel three-in-one Ag2S quantum dot (QD)-based smart theranostic nanoprobes were proposed for in situ ratiometric NIR-II FL imaging-guided ion/gas combination therapy of tumors. Under the acidic tumor microenvironment, three-in-one Ag2S QDs underwent destructive degradation, generating toxic Ag+ and H2S. Meanwhile, their FL emission at 1270 nm was weakened. Upon introduction of a downconversion nanoparticle (DCNP) as the delivery carrier and NIR-II FL reference signal unit, the formed Ag2S QD-based theranostic nanoprobes could achieve precise diagnosis of tumors through ratiometric NIR-II FL signals. Also, the generated Ag+ and H2S enabled specific ion/gas combination therapy toward tumors. By combining the imaging and therapeutic functions, three-in-one Ag2S QDs may open a simple yet reliable avenue to design theranostic nanoprobes.

Selective activation of photoactivatable fluorescent protein based on binary holography

The ability to deliver laser doses to different target locations with high spatial and temporal resolution has been a long-sought goal in photo-stimulation and optogenetics research via, for example, photoactivatable proteins. These light-sensitive proteins undergo conformational changes upon photoactivation, serving functions such as triggering fluorescence, modulating ion channel activities, or initiating biochemical reactions within cells. Conventionally, photo-stimulation on light-sensitive proteins is performed by serially scanning a laser focus or via 2D projection, which is limited by relatively low spatiotemporal resolution. In this work, we present a programmable two-photon stimulation method based on a digital micromirror device (DMD) and binary holography to perform the activation of photoactivatable green fluorescent protein (PAGFP) in live cells. This method achieved grayscale and 3D selective PAGFP activation with subcellular resolution. In the experiments, we demonstrated the 3D activation capability and investigated the diffusion dynamics of activated PAGFP on the cell membrane. A regional difference in cell membrane diffusivity was observed, indicating the great potential of our approach in interrogating the spatiotemporal dynamics of cellular processes inside living cells.

Julolidinyl aza-BODIPYs as NIR-II fluorophores for the bioimaging of nanocarriers

The aggregation-caused quenching (ACQ) rationale has been employed to improve the fluorescence imaging accuracy of nanocarriers by precluding free probe-derived interferences. However, its usefulness is undermined by limited penetration and low spatiotemporal resolution of NIR-I (700–900 nm) bioimaging owing to absorption and diffraction by biological tissues and tissue-derived autofluorescence. This study aimed to develop ACQ-based NIR-II (1000–1700 nm) probes to further improve the imaging resolution and accuracy. The strategy employed is to install highly planar and electron-rich julolidine into the 3,5-position of aza-BODIPY based on the larger substituent effects. The newly developed probes displayed remarkable photophysical properties, with intense absorption centered at approximately 850 nm and bright emission in the 950–1300 nm region. Compared with the NIR-I counterpart P2, the NIR-II probes demonstrated superior water sensitivity and quenching stability. ACQ1 and ACQ6 exhibited more promising ACQ effects with absolute fluorescence quenching at water fractions above 40% and higher quenching stability with less than 2.0% fluorescence reillumination in plasma after 24 h of incubation. Theoretical calculations verified that molecular planarity is more important than hydrophobicity for ACQ properties. Additionally, in vivo and ex vivo reillumination studies revealed less than 2.5% signal interference from prequenched ACQ1, in contrast to 15% for P2.

Assessing the Performance of Water Vapor Products from ERA5 and MERRA-2 during Heavy Rainfall in the Guangxi Region of China

Precipitable water vapor (PWV) is a crucial factor in regulating the Earth’s climate. Moreover, it demonstrates a robust correlation with precipitation. Situated in a region known for the generation and development of tropical cyclones, Guangxi in China is highly susceptible to floods triggered via intense rainfall. The atmospheric water vapor in this area displays prominent spatiotemporal features, thus posing challenges for precipitation forecasting. The water vapor products within the MERRA-2 and ERA5 reanalysis datasets present an opportunity to overcome constraints associated with low spatiotemporal resolution. In this study, the PWV data derived from GNSS and meteorological measurements in Guangxi from 2016 to 2018 were used to evaluate the accuracy of MERRA-2 and ERA5 water vapor products and their relationship with water vapor variations during extreme rainfall. Using GNSS PWV as a reference, the average bias of MERRA-2 PWV and ERA5 PWV for heavy rainfall was −0.22 mm and 1.84 mm, respectively, with average RMSE values of 3.72 mm and 3.31 mm. For severe rainfall, the average bias of MERRA-2 PWV and ERA5 PWV was −0.14 mm and 2.92 mm, respectively, with average RMSE values of 4.28 mm and 4.01 mm. During heavy rainfall days from Days 178 to 184 in 2017, the average bias of MERRA-2 PWV and ERA5 PWV was 0.92 mm and 2.42 mm, respectively, with average RMSE values of 4.04 mm and 3.40 mm. The accuracy was highest at the Guiping and Hechi stations and lowest at the Hezhou and Rongshui stations. Furthermore, when comparing MERRA-2/ERA5 PWV with GNSS PWV and actual precipitation, the trends in the variations of MERRA-2/ERA5 PWV were generally consistent with GNSS PWV and aligned with the increasing or decreasing trends of actual precipitation. In addition, ERA5 PWV exhibited high accuracy. Before the onset of heavy rainfall, PWV has a sharp surge. During heavy rainfall, PWV reaches its peak value. Subsequently, after the cessation of heavy rainfall, PWV tends to stabilize. Therefore, the reanalysis data of PWV can effectively reveal significant changes in water vapor and actual precipitation during periods of heavy rainfall in the Guangxi region.

Multi-dimensional evaluation of four monthly long-term and high-resolution gridded precipitation datasets on the Qinghai-Tibet Plateau

The reliability assessment of gridded precipitation datasets (GPDs) is vital for various precipitation-related studies, particularly in poorly gauged regions as the Qinghai-Tibet Plateau (QTP). However, existing studies predominantly focuses more on mainstream GPDs with low spatiotemporal resolution, making it challenging to meet the requirements for widespread applications. There is an urgent need for a systematic assessment of long-term and high-resolution GPDs. In this study, we considered four GPDs (HRLT, TPHiPr, PENG, and CHIRPS) with a spatial resolution above 0.05° during 1981–2019, evaluating their monthly performance across three dimensions: spatial accuracy, temporal accuracy, and precipitation event detection ability. The results revealed the following: (1) TPHiPr exhibited the highest spatial accuracy (R2 = 0.81), and the four GPDs consistently performed the worst in arid areas (R2 = 0.59 ± 0.15) and the best in semi-humid areas (R2 = 0.80 ± 0.02). (2) PENG attained the highest temporal accuracy (NSE = −0.56), and the four GPDs performed the worst in winter (NSE = -1.06 ± 0.9) and the best in autumn (NSE = 0.08 ± 0.16). (3) HRLT and TPHiPr demonstrated superior precipitation event detection abilities compared to others. The four GPDs consistently exhibited high detection abilities for light monthly precipitation events (0–10 mm) and low detection abilities for heavy monthly precipitation events (150–200 mm). This study underscored the potential advantages of GPDs in the three dimensions, and also highlighted their limitations in arid areas, winter, and heavy precipitation events, thus providing directions for further GPDs' accuracy improvement.

Objective scanning-based fluorescence cross-correlation spectroscopy (Scan-FCCS) for studying the fusion dynamics of protein phase separation.

Protein phase separation plays a very important role in many biological processes and is closely related to the occurrence and development of some serious diseases. So far, the fluorescence imaging method and fluorescence correlation spectroscopy (FCS) have been frequently used to study the phase separation behavior of proteins. Due to the wide size distribution of protein condensates in phase separation from nano-scale to micro-scale in solution and living cells, it is difficult for the fluorescence imaging method and conventional FCS to fully reflect the real state of protein phase separation in the solution due to the low spatio-temporal resolution of the conventional fluorescence imaging method and the limited detection area of FCS. Here, we proposed a novel method for studying the protein phase separation process by objective scanning-based fluorescence cross-correlation spectroscopy (Scan-FCCS). In this study, CRDBP proteins were used as a model and respectively fused with fluorescent proteins (EGFP and mCherry). We first compared conventional FCS and Scan-FCS methods for characterizing the CRDBP protein phase separation behaviors and found that the reproducibility of Scan-FCS is significantly improved by the scanning mode. We studied the self-fusion process of mCherry-CRDBP and EGFP-CRDBP and observed that the phase change concentration of CRDBP was 25 nM and the fusion of mCherry-CRDBP and EGFP-CRDBP at 500 nM was completed within 70 min. We studied the effects of salt concentration and molecular crowding agents on the phase separation of CRDBP and found that salt can prevent the self-fusion of CRDBP and molecular crowding agents can improve the self-fusion of CRDBP. Furthermore, we found the recruitment behavior of CRDBP to β-catenin proteins and studied their recruitment dynamics. Compared to conventional FCS, Scan-FCCS can significantly improve the reproducibility of measurements due to the dramatic increase of detection zone, and more importantly, this method can provide information about self-fusion and recruitment dynamics in protein phase separation.

Retention efficiency for microplastic in a landscape estimated from empirically validated dynamic model predictions

Soils are recipients of microplastic that can be subsequently transferred to the sea. Land sources dominate inputs to the ocean, but knowledge gaps about microplastic retention by land hinder assessments of input rates. Here we present the first empirical evaluation of a dynamic microplastic fate model operating at landscape level. This mechanistic model accounts for hydrology, soil and sediment erosion, particle characteristics and behavior. We predict microplastic concentrations in water and sediments of the Henares river (Spain) within the measurement uncertainty boundaries (error factors below 2 and 10, respectively). Microplastic export from land and discharge by river fluctuates in a non-linear manner with precipitation and runoff variability. This indicates the need of accurate dynamic descriptions of soil and stream hydrology even when modeling microplastic fate and transport in generic scenarios and at low spatio-temporal resolution. A time-averaged landscape retention efficiency was calculated showing 20–50% of the microplastics added to the catchment over a multiannual period were retained. While the analysis reveals persistent uncertainties and knowledge gaps on microplastic sources to the catchment, these results contribute to the quantitative understanding of the role of terrestrial environments in accumulating microplastics, delaying their transport to the sea.

High-resolution assessment of the carrying capacity and utilization intensity in mountain rangelands with remote sensing and field data

Dry rangelands provide resources for half of the world's livestock, but degradation due to overgrazing is a major threat to system sustainability. Existing carrying capacity assessments are limited by low spatiotemporal resolution and high generalization, which hampers applied ecological management decisions. This paper provides an example for deriving the carrying capacity and utilization levels for cold drylands at a new level of detail by including major parts of the transhumance system. We combined field data on vegetation biomass and communities, forage quality, productivity, livestock species and quantities, grazing areas and their spatiotemporal variations with Sentinel-2 and MODIS snow cover satellite imagery to develop maps of forage requirements and availability. These products were used to calculate carrying capacity and grazing potential in the Pamir-Hindukush Mountains. Results showed high spatial variability of utilization rates between 5% and 77%. About 30% of the area showed unsustainable grazing above the carrying capacity. Utilization rates displayed strong spatial differences with unsustainable grazing in winter pastures and at lower elevations, and low rates at higher altitudes. The forage requirements of wild herbivores (ungulates and marmots) were estimated to be negligible compared to livestock, with one tenth of the biomass consumption and no increase in unsustainably grazed pastures due to the wider distribution of animals. The assessment was sensitive to model parameterization of forage requirements and demand, whereby conservative scenarios, i.e. lower fodder availability or higher fodder requirements of livestock due to climate and altitude effects, increased the area with unsustainable grazing practices to 50%. The presented approach enables an in-depth evaluation of the carrying capacity and corresponding management actions. It includes new variables relevant for transhumance systems, such as the combination of forage quantity and quality or accessibility restrictions due to snow, and shows utilization patterns at high spatial resolutions. Regional maps allow the identification of unsustainable utilization areas, such as winter pastures in this study.

Root Walker: an automated pipeline for large scale quantification of early root growth responses at high spatial and temporal resolution.

Plants are sessile organisms that constantly adapt to their changing environment. The root is exposed to numerous environmental signals ranging from nutrients and water to microbial molecular patterns. These signals can trigger distinct responses including the rapid increase or decrease of root growth. Consequently, using root growth as a readout for signal perception can help decipher which external cues are perceived by roots, and how these signals are integrated. To date, studies measuring root growth responses using large numbers of roots have been limited by a lack of high-throughput image acquisition, poor scalability of analytical methods, or low spatiotemporal resolution. Here, we developed the Root Walker pipeline, which uses automated microscopes to acquire time-series images of many roots exposed to controlled treatments with high spatiotemporal resolution, in conjunction with fast and automated image analysis software. We demonstrate the power of Root Walker by quantifying root growth rate responses at different time and throughput scales upon treatment with natural auxin and two mitogen-associated protein kinase cascade inhibitors. We find a concentration-dependent root growth response to auxin and reveal the specificity of one MAPK inhibitor. We further demonstrate the ability of Root Walker to conduct genetic screens by performing a genome-wide association study on 260 accessions in under 2 weeks, revealingknown and unknown root growth regulators. Root Walker promises to be a useful toolkit for the plant science community, allowing large-scale screening of root growth dynamics for a variety of purposes, including genetic screens for root sensing and root growth response mechanisms.

Dual-Mode Gold Nanocluster-Based Nanoprobe Platform for Two-Photon Fluorescence Imaging and Fluorescence Lifetime Imaging of Intracellular Endogenous miRNA.

Bioimaging is widely used in various fields of modern medicine. Fluorescence imaging has the advantages of high sensitivity, high selectivity, noninvasiveness, in situ imaging, and so on. However, one-photon (OP) fluorescence imaging has problems, such as low tissue penetration depth and low spatiotemporal resolution. These disadvantages can be solved by two-photon (TP) fluorescence imaging. However, TP imaging still uses fluorescence intensity as a signal. The complexity of organisms will inevitably affect the change of fluorescence intensity, cause false-positive signals, and affect the accuracy of the results obtained. Fluorescence lifetime imaging (FLIM) is different from other kinds of fluorescence imaging, which is an intrinsic property of the material and independent of the material concentration and fluorescence intensity. FLIM can effectively avoid the fluctuation of TP imaging based on fluorescence intensity and the interference of autofluorescence. Therefore, based on silica-coated gold nanoclusters (AuNCs@SiO2) combined with nucleic acid probes, the dual-mode nanoprobe platform was constructed for TP and FLIM imaging of intracellular endogenous miRNA-21 for the first time. First, the dual-mode nanoprobe used a dual fluorescence quencher of BHQ2 and graphene oxide (GO), which has a high signal-to-noise ratio and anti-interference. Second, the dual-mode nanoprobe can detect miR-21 with high sensitivity and selectivity in vitro, with a detection limit of 0.91 nM. Finally, the dual-mode nanoprobes performed satisfactory TP fluorescence imaging (330.0 μm penetration depth) and FLIM (τave = 50.0 ns) of endogenous miR-21 in living cells and tissues. The dual-mode platforms have promising applications in miRNA-based early detection and therapy and hold much promise for improving clinical efficacy.

Deep Spatial Prediction via Heterogeneous Multi-source Self-supervision

Spatial prediction is to predict the values of the targeted variable, such as PM2.5 values and temperature, at arbitrary locations based on the collected geospatial data. It greatly affects the key research topics in geoscience in terms of obtaining heterogeneous spatial information (e.g., soil conditions, precipitation rates, wheat yields) for geographic modeling and decision-making at local, regional, and global scales. In situ data, collected by ground-level in situ sensors, and remote sensing data, collected by satellite or aircraft, are two important data sources for this task. In situ data are relatively accurate while sparse and unevenly distributed. Remote sensing data cover large spatial areas, but are coarse with low spatiotemporal resolution and prone to interference. How to synergize the complementary strength of these two data types is still a grand challenge. Moreover, it is difficult to model the unknown spatial predictive mapping while handling the tradeoff between spatial autocorrelation and heterogeneity. Third, representing spatial relations without substantial information loss is also a critical issue. To address these challenges, we propose a novel Heterogeneous Self-supervised Spatial Prediction (HSSP) framework that synergizes multi-source data by minimizing the inconsistency between in situ and remote sensing observations. We propose a new deep geometric spatial interpolation model as the prediction backbone that automatically interpolates the values of the targeted variable at unknown locations based on existing observations by taking into account both distance and orientation information. Our proposed interpolator is proven to both be the general form of popular interpolation methods and preserve spatial information. The spatial prediction is enhanced by a novel error-compensation framework to capture the prediction inconsistency due to spatial heterogeneity. Extensive experiments have been conducted on real-world datasets and demonstrated our model’s superiority in performance over state-of-the-art models.

Melding wildlife surveys to improve conservation inference.

Integrated models are a popular tool for analyzing species of conservation concern. Species of conservation concern are often monitored by multiple entities that generate several datasets. Individually, these datasets may be insufficient for guiding management due to low spatio-temporal resolution, biased sampling, or large observational uncertainty. Integrated models provide an approach for assimilating multiple datasets in a coherent framework that can compensate for these deficiencies. While conventional integrated models have been used to assimilate count data with surveys of survival, fecundity, and harvest, they can also assimilate ecological surveys that have differing spatio-temporal regions and observational uncertainties. Motivated by independent aerial and ground surveys of lesser prairie-chicken, we developed an integrated modeling approach that assimilates density estimates derived from surveys with distinct sources of observational error into a joint framework that provides shared inference on spatio-temporal trends. We model these data using a Bayesian Markov melding approach and apply several data augmentation strategies for efficient sampling. In a simulation study, we show that our integrated model improved predictive performance relative to models for analyzing the surveys independently. We use the integrated model to facilitate prediction of lesser prairie-chicken density at unsampled regions and perform a sensitivity analysis to quantify the inferential cost associated with reduced surveyeffort.

Knowing the fishery to know the bycatch: bias-corrected estimates of harbour porpoise bycatch in gillnet fisheries.

Incidental captures (bycatch) remain a key global conservation threat for cetaceans. Bycatch of harbour porpoise Phocoena phocoena in set gillnets is routinely monitored in European Union fisheries, but generally relies on data collected at low spatio-temporal resolution or over short periods. In Denmark, a long-term monitoring programme started in 2010 using electronic monitoring to collect data on porpoise bycatch and gillnet fishing effort at a fine spatial and temporal scale, including time and position of each fishing operation, together with every associated bycatch event. We used these observations to model bycatch rates, given the operational and ecological characteristics of each haul observed in Danish waters. Data on fishing effort from the Danish and Swedish gillnet fleets were collected to predict fleet-wide porpoise bycatch in gillnets at regional level. Between 2010 and 2020, yearly total bycatch averaged 2088 animals (95% Cl: 667-6798). For the Western Baltic assessment unit, bycatch levels were above sustainability thresholds. These results demonstrate that fishing characteristics are key determinants of porpoise bycatch and that classical approaches ignoring these features would produce biased estimates. It emphasizes the need for efficient and informative monitoring methods to understand possible conservation impacts of marine mammal bycatch and to implement tailored mitigation techniques.

Protein@cyanine-based NIR-II lymphography enables the supersensitive visualization of lymphedema and tumor lymphatic metastasis.

Visualization of the lymphatic system is clinically indispensable for the diagnosis and/or treatment of lymphatic diseases. Although indocyanine green (ICG) lymphography becomes an alternate imaging modality compared to traditional lymphoscintigraphy, it's still far from ideal due to the insufficient detection depth and low spatiotemporal resolution. Herein, w e rationally develop protein@cyanine probes to solve the limitations of the current NIR-I lymphography. The protein@cyanine probes are synthesized following a chlorine-containing dye-labeling strategy based on structure-selectivity (facile covalent binding between the dye and protein with a 1:1 molar ratio). As expected, ou r probes display exceptional NIR-II imaging ability with much-improved imaging contrast/resolution and controllable pharmacokinetics, superior to the clinical ICG. Ou r protein@cyanine probes locate lymph nodes (LNs) and delineate lymphatic vessels (LVs) with super-high sensitivity and signal-to-background ratio, enabling real-time diagnosing lymphatic diseases such as lymphedema and tumor lymphatic metastasis. In particular, the NIR-II lymphography provides us an opportunity to discover the disparate morbidity rate of primary lymphedema in different types of mice. Given the fact of lacking clinically transferable NIR-II probes, this work not only provides a promising strategy for enriching of the current library of NIR-II probes, but also promotes the clinical translation of NIR-II lymphography technology. This article is protected by copyright. All rights reserved.

Particle Smoother to Assimilate Asynchronous Movie Data of High-Speed AFM with MD Simulations.

High-speed (HS) atomic force microscopy (AFM) can be used to observe structural dynamics of biomolecules under near-physiological conditions. In the AFM measurement, the probe tip scans an area of interest and acquires height data pixel by pixel so that the obtained AFM image contains a measurement time difference. In this study, to integrate molecular dynamics simulations with asynchronous HS-AFM movie data, we developed a particle smoother (PS) method for Bayesian data assimilation, one of the machine learning approaches, by extending the previous particle filter method. With a twin experiment with an asynchronous pseudo HS-AFM movie of a nucleosome, we found that the PS method with the pixel-by-pixel data acquisition reproduced the dynamic behavior of a nucleosome better than the previous particle filter method that ignored the data asynchronicity. We examined several frequencies of particle resampling in the PS method, and found that resampling once per one frame was optimal for reproducing the dynamic behavior. Thus, we found that the PS method with an appropriate resampling frequency is a powerful method for estimating the dynamic behavior of a target molecule from HS-AFM data with low spatiotemporal resolution.

Super-resolution reconstruction in ultrahigh-field MRI

Magnetic resonance imaging (MRI) is a highly significant imaging platform for a variety of medical and research applications. However, the low spatiotemporal resolution of conventional MRI limits its applicability toward rapid acquisition of ultrahigh-resolution scans. Current aims at high-resolution MRI focus on increasing the accuracy of tissue delineation, assessments of structural integrity, and early identification of malignancies. Unfortunately, high-resolution imaging often leads to decreased signal/noise (SNR) and contrast/noise (CNR) ratios and increased time cost, which are unfeasible in many clinical and academic settings, offsetting any potential benefits. In this study, we apply and assess the efficacy of super-resolution reconstruction (SRR) through iterative back-projection utilizing through-plane voxel offsets. SRR allows for high-resolution imaging in condensed time frames. Rat skulls and archerfish samples, typical models in academic settings, were used to demonstrate the impact of SRR on varying sample sizes and applicability for translational and comparative neuroscience. The SNR and CNR increased in samples that did not fully occupy the imaging probe and in instances where the low-resolution data were acquired in three dimensions, while the CNR was found to increase with both 3D and 2D low-resolution data reconstructions when compared with directly acquired high-resolution images. Limitations to the applied SRR algorithm were investigated to determine the maximum ratios between low-resolution inputs and high-resolution reconstructions and the overall cost effectivity of the strategy. Overall, the study revealed that SRR could be used to decrease image acquisition time, increase the CNR in nearly all instances, and increase the SNR in small samples.

Modeling Soil CO2 Efflux in a Subtropical Forest by Combining Fused Remote Sensing Images with Linear Mixed Effect Models

Monitoring tropical and subtropical forest soil CO2 emission efflux (FSCO2) is crucial for understanding the global carbon cycle and terrestrial ecosystem respiration. In this study, we addressed the challenge of low spatiotemporal resolution in FSCO2 monitoring by combining data fusion and model methods to improve the accuracy of quantitative inversion. We used time series Landsat 8 LST and MODIS LST fusion images and a linear mixed effect model to estimate FSCO2 at watershed scale. Our results show that modeling without random factors, and the use of Fusion LST as the fixed predictor, resulted in 47% (marginal R2 = 0.47) of FSCO2 variability in the Monthly random effect model, while it only accounted for 19% of FSCO2 variability in the Daily random effect model and 7% in the Seasonally random effect model. However, the inclusion of random effects in the model’s parameterization improved the performance of both models. The Monthly random effect model that performed optimally had an explanation rate of 55.3% (conditional R2 = 0.55 and t value &gt; 1.9) for FSCO2 variability and yielded the smallest deviation from observed FSCO2. Our study highlights the importance of incorporating random effects and using Fusion LST as a fixed predictor to improve the accuracy of FSCO2 monitoring in tropical and subtropical forests.

Understanding Subsurface Fracture Evolution Dynamics Using Time‐Lapse Full Waveform Inversion of Continuous Active‐Source Seismic Monitoring Data

AbstractPredicting the behavior, geometry, and flow properties of subsurface fractures remains a challenging problem. Seismic models that can characterize fractures usually suffer from low spatiotemporal resolution. Here, we develop a correlative double‐difference time‐lapse full waveform inversion of continuous active source seismic monitoring data for determining high‐spatiotemporal‐resolution time‐lapse Vp models of in‐situ fracture evolution at a shallow contamination site in Wyoming, USA. Assisted by rock physics modeling, we find that (a) rapidly increasing pore pressure initializes and grows the fracture, increasing the porosity slightly (from ∼13.7% to ∼14.6%) in the tight clay formation, thus decreasing Vp (∼50 m/s); (b) the fluid injection continues decreasing Vp, likely through the introduction of gas bubbles in the injectate; and (c) final Vp reductions reach over ∼150 m/s due to a posited ∼4.5% gas saturation. Our results demonstrate that high‐resolution Vp changes are indicative of mechanical and fluid changes within the fracture zone during hydrofracturing.