High Spatial Resolution Mapping Research Articles

Mapping eye, arm, and reward information in frontal motor cortices using electrocorticography in non-human primates.

Goal-directed reaches give rise to dynamic neural activity across the brain as we move our eyes and arms, and process outcomes. High spatiotemporal resolution mapping of multiple cortical areas will improve our understanding of how these neural computations are spatially and temporally distributed across the brain. In this study, we used micro-electrocorticography (µECoG) recordings in two male monkeys performing visually guided reaches to map information related to eye movements, arm movements, and receiving rewards over primary motor cortex, premotor cortex, frontal eye field, and dorsolateral pre-frontal cortex. Time-frequency and decoding analyses revealed that eye and arm movement information shifts across brain regions during a reach, likely reflecting shifts from planning to execution. Although eye and arm movement temporally overlapped, phase clustering analyses enabled us to resolve differences in eye and arm information across brain regions. This analysis revealed that eye and arm information spatially overlapped in motor cortex, which we further confirmed by demonstrating that arm movement decoding performance from motor cortex activity was impacted by task-irrelevant eye movements. Phase clustering analyses also identified reward-related activity in the pre-frontal and premotor cortex. Our results demonstrate µECoG's strengths for functional mapping and provide further detail on the spatial distribution of eye, arm, and reward information processing distributed across frontal cortices during reaching. These insights advance our understanding of the overlapping neural computations underlying coordinated movements and reveal opportunities to leverage these signals to enhance future brain-computer interfaces.Significance statement Picking up your coffee mug requires coordinating movements of your eyes and hand and processing the outcomes of those movements. Mapping how neural activity relates to different functions helps us understand how the brain performs these computations. Many mapping techniques have limited spatial or temporal resolution, restricting our ability to dissect computations that overlap closely in space and time. We used micro-electrocorticography recordings to map neural activity across multiple cortical areas while monkeys made goal-directed reaches. These measurements revealed high spatial and temporal resolution maps of neural activity related to eye, arm, and reward information processing. These maps reveal overlapping neural computations underlying movement and open opportunities to use eye and reward information to improve therapies to restore motor function.

Low Field Magnetic Resonance Imaging to Detect Acute Kidney Injury.

Renal oxygenation is essential for maintaining kidney function. Disruptions in oxygen delivery can lead to renal hypoxia, which can exacerbate kidney injury through multiple pathways, including inflammation, oxidative stress, and ischemia-reperfusion injury. Despite the recognized importance of oxygenation in renal pathology, non-invasive and reliable methods for assessing kidney oxygen levels are limited. Current techniques either lack sensitivity or involve invasive procedures, restricting their use in routine monitoring. Therefore, there is a pressing need for innovative approaches to assess renal oxygenation, particularly in kidney injury. This study evaluated Electron Paramagnetic Resonance (EPR)-based oxygen imaging using the paramagnetic tracer Ox071 to assess kidney oxygen levels in mice with cyclophosphamide-induced kidney injury. Urine pO2 was also assessed as a potential surrogate marker. EPR oximetry accurately measured kidney oxygen distribution, revealing a temporary increase in pO2 post-injury. Urine oximetry, however, did not reliably reflect changes in kidney oxygenation. Furthermore, EPR oximetry provided high-resolution spatial mapping of oxygen levels within the kidney, allowing for a detailed understanding of the impact of hypoxia on renal tissue. EPR oximetry is a promising, non-invasive tool for monitoring renal oxygenation, offering high-resolution mapping and longitudinal assessment. Its ability to provide detailed information about oxygen distribution within the kidney makes it a valuable tool for studying the pathophysiology of renal diseases and for developing novel therapeutic strategies. Translational Statement: Quantitative spatially resolved measurement of renal oxygenation has the potential to guide clinical decision making in renal disorders such as acute kidney injury. In this study we demonstrate the utility of electron paramagnetic resonance imaging to provide non-invasive and quantitative high-resolution mapping of kidney oxygen concentrations.

High-Spatial Resolution Maps of PM2.5 Using Mobile Sensors on Buses: A Case Study of Teltow City, Germany, in the Suburb of Berlin, 2023

Air quality monitoring networks regulated by law provide accurate but sparse measurements of PM2.5 mass concentrations. High-spatial resolution maps of the PM2.5 mass concentration values are necessary to better estimate the citizen exposure to outdoor air pollution and the sanitary consequences. To address this, a field campaign was conducted in Teltow, a midsize city southwest of Berlin, Germany, for the 2021–2023 period. A network of optical sensors deployed by Pollutrack included fixed monitoring stations as well as mobile sensors mounted on the roofs of buses and cars. This setup provides PM2.5 pollution maps with a spatial resolution down to 100 m on the main roads. The reliability of Pollutrack measurements was first established with comparison to measurements from the German Environment Agency (UBA) and modelling calculations based on high-resolution weather forecasts. Using these validated data, maps were generated for 2023, highlighting the mean PM2.5 mass concentrations and the number of days per year above the 15 µg.m−3 value (the daily maximum recommended by the World Health Organization (WHO) in 2021). The findings indicate that PM2.5 levels in Teltow are generally in the good-to-moderate range. The higher values (hot spots) are detected mainly along the highways and motorways, where traffic speeds are higher compared to inner-city roads. Also, the PM2.5 mass concentrations are higher on the street than on the sidewalks. The results were further compared to those in the city of Paris, France, obtained using the same methodology. The observed parallels between the two datasets underscore the strong correlation between traffic density and PM2.5 concentrations. Finally, the study discusses the advantages of integrating such high-resolution sensor networks with modelling approaches to enhance the understanding of localized PM2.5 variability and to better evaluate public exposure to air pollution.

Transcriptomic analysis of skeletal muscle regeneration across mouse lifespan identifies altered stem cell states

In aging, skeletal muscle regeneration declines due to alterations in both myogenic and non-myogenic cells and their interactions. This regenerative dysfunction is not understood comprehensively or with high spatiotemporal resolution. We collected an integrated atlas of 273,923 single-cell transcriptomes and high-resolution spatial transcriptomic maps from muscles of young, old and geriatric mice (~5, 20 and 26 months old) at multiple time points following myotoxin injury. We identified eight immune cell types that displayed accelerated or delayed dynamics by age. We observed muscle stem cell states and trajectories specific to old and geriatric muscles and evaluated their association with senescence by scoring experimentally derived and curated gene signatures in both single-cell and spatial transcriptomic data. This revealed an elevation of senescent-like muscle stem cell subsets within injury zones uniquely in aged muscles. This Resource provides a holistic portrait of the altered cellular states underlying muscle regenerative decline across mouse lifespan.

Laser-Treated Screen-Printed Carbon Electrodes for Electrochemiluminescence imaging.

Electrochemiluminescence (ECL) is nowadays a powerful technique widely used in biosensing and imaging, offering high sensitivity and specificity for detecting and mapping biomolecules. Screen-printed electrodes (SPEs) offer a versatile and cost-effective platform for ECL applications due to their ease of fabrication, disposability, and suitability for large-scale production. This research introduces a novel method for improving the ECL characteristics of screen-printed carbon electrodes (SPCEs) through the application of CO2 laser treatment following fabrication. Using advanced ECL microscopy, we analyze three distinct carbon paste-based electrodes and show that low-energy laser exposure (ranging from 7 to 12 mJ·cm-2) enhances the electrochemical performance of the electrodes. This enhancement results from the selective removal of surface binders and contaminants achieved by the laser treatment. We employed ECL microscopy to characterize the ECL emission using a bead-based system incorporating magnetic microbeads, like those used in commercial platforms. This approach enabled high-resolution spatial mapping of the electrode surface, offering valuable insights into its electrochemical performance. Through quantitative assessment using a photomultiplier tube (PMT), it was observed that GST electrodes could detect biomarkers with high sensitivity, achieving an approximate detection limit (LOD) of 11 antibodies per μm2. These findings emphasize the potential of laser-modified GST electrodes in enabling highly sensitive electrochemiluminescent immunoassays and various biosensing applications.

Development of Land Use Regression (LUR) models and high-resolution spatial mapping of criteria air pollutants: Leveraging Delhi's continuous air monitoring network and remote sensing data

High-resolution air pollution maps can help researchers and governments better characterize and mitigate pollution levels in a given region. Land Use Regression (LUR) modeling is a statistical approach capable of predicting pollution levels at a high spatial resolution. In this study, we used pollution data (for the calendar year 2019) from a dense (compared to other Indian regions) regulatory monitoring network in Delhi, India, to develop simple linear and interpretable LUR models for various criteria pollutants. The observed annual mean PM2.5 and PM10 over Delhi were found to be ∼110 μg m−3 and 220 μg m−3, respectively. The PM concentration levels were 2.5–4 times higher than the prescribed national ambient standards, while the gaseous criteria pollutants were found to be within the standards (over most of the study area). The performance of the developed LUR models ranged from poor to moderate levels (adjusted-R2 values of the models were between 0.14 and 0.63). Land use and road-network related variables were found to be the most common predictors of the observed pollution levels. Moderately performing models (11 out of the developed 20) were then used to predict pollution levels at 50 m spatial intervals and to identify the most polluted districts. The advantages and limitations of using the existing regulatory network data for LUR development, and the other probable potential reasons responsible for the underperformance of the developed models are extensively discussed. To our knowledge, this is one of the few studies carried over Indian region to develop LUR models utilizing regulatory monitoring network data.

TMET-23. MASS SPECTROMETRY-BASED METABOLIC PROFILING AND IMAGING OF ASTROCYTE AND GLIOBLASTOMA INVASIVE MARGIN CELLS

Abstract Despite multimodal treatment, 90% of patients diagnosed with isocitrate dehydrogenase (IDH) wild-type glioblastoma experience tumour recurrence within 5 years. Poor prognosis is in considerable part attributed to the ability of cancer cells to infiltrate a healthy brain. We hypothesise that metabolic changes underpinning cellular crosstalk between astrocytes and glioblastoma cells isolated from the invasive margin within a physiologically representative in-vitro tumour microenvironment may reveal therapeutic targets to prevent or delay glioblastoma recurrence. This study used advanced analytical techniques to establish baseline metabolomic profiles for eGFP-tagged primary glioblastoma cells isolated from the invasive margin and healthy human cortical astrocytes. Liquid chromatography-mass spectrometry (LC-MS) enabled detailed analysis of biochemical pathways, and Atmospheric Pressure-Matrix Assisted Laser Desorption/Ionisation (AP-MALDI) imaging offered spatial localization of metabolites, critical for mapping their distribution within tumour microenvironments. Metabolites were extracted using a biphasic method (methanol:chloroform:water) for LC-MS analysis, and metabolic profiling was conducted using a ZIC-pHILIC column coupled with a Q-Exactive Orbitrap Mass Spectrometer. Additionally, AP-MALDI imaging facilitated the generation of high-resolution spatial distribution maps of selected metabolites, achieving a resolution of 10 µm. Both multivariate and univariate statistical analyses were applied to identify significant metabolic alterations. Metabolic profiling identified distinct pathways altered in glioblastoma cells compared to astrocytes, including alterations in aspartate, glycine and serine, and lipid metabolism. High-resolution imaging outlined the spatial distribution of metabolites involved in these pathways, revealing regions of metabolic heterogeneity. Furthermore, this methodology facilitated an expanded identification range of essential biomolecules, notably lipids and amino acids, thereby augmenting our understanding of their dynamic functions across diverse cellular contexts. By leveraging the complementary strengths of LC-MS and AP-MALDI imaging, this research enhances our understanding of spatial metabolite distribution in glioblastoma cells isolated from the invasive margin and suggests potential applications in patient tissue, offering a deeper exploration of disease mechanisms.

High-resolution spatio-temporal risk mapping for malaria in Namibia: a comprehensive analysis

BackgroundNamibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps.MethodsThis study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021.ResultsA fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically.ConclusionsWhile the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing to the achievement of national and sub-national elimination goals.

Mapping refugee populations at high resolution by unlocking humanitarian administrative data

BackgroundInforming local decision-making, improving service delivery and designing household surveys require having access to high-spatial resolution mapping of the targeted population. However, this detailed spatial information remains unavailable for specific population subgroups, such as refugees, a vulnerable group that would significantly benefit from focused interventions. Given the continuous increase in the number of refugees, reaching an all-time high of 35.3 million people in 2022, it is imperative to develop models that can accurately inform about their spatial locations, enabling better and more tailored assistance.MethodsWe leverage routinely collected registration data on refugees and combine it with high-resolution population maps, satellite imagery derived settlement maps and other spatial covariates to disaggregate observed refugee totals into 100-m grid cells. We suggest a deterministic grid cell allocation inside monitored refugee sites based on building count and a random-forest-derived grid cell allocation outside refugee sites based on geolocating the textual geographic information in the refugee register and on high-resolution population mapping. We test the method in Cameroon using the registration database monitored by the United Nations High Commissioner for Refugees.ResultsUsing OpenStreetMap, 83% of the manually inputted information in the registration database could be geolocated. The building footprint layer derived from satellite imagery by Ecopia AI offers extensive coverage within monitored refugee sites, although manual digitization was still required in rapidly evolving settings. The high-resolution mapping of refugees on a 100-m grid basis provides an unparalleled level of spatial detail, enabling valuable geospatial insights for informed local decision-making.ConclusionsGathering information on forcibly displaced persons in sparse data-setting environment can quickly become very costly. Therefore, it is critical to gain the most knowledge from operational data that is frequently collected, such as registration databases. Integrating it with ancillary information derived from satellite imagery paves the way for obtaining more timely and spatially precise information to better deliver services and enhance sampling frame for target data collection exercises that further improves the quality of information on people in need.

Increased accuracy and signal-to-noise ratio through recent improvements in infra-red video bolometer fabrication and calibration.

The infra-red video bolometer (IRVB) is a diagnostic equipped with an infra-red camera that measures the total radiated power in thousands of lines of sight within a large field of view. Recently validated in MAST-U [Fderici et al., Rev. Sci. Instrum. 94, 033502 (2023)], it offers a high spatial resolution map of the radiated power in the divertor region, where large gradients are expected. The IRVB's sensing element comprises a thin layer of high Z absorbing material, typically platinum, usually coated with carbon to reduce reflections [Peterson et al., Rev. Sci. Instrum. 79, 10E301 (2008)].Here, the possibility of using a relatively inert material such as titanium, is explored that can be produced in layers up to 1 μm compared to 2.5 μm for Pt and then coat it with Pt of the desired thickness (0.3 μm per side here) and carbon. This leads to a higher temperature signal (about 3 times) and better spatial resolution (about 4 times), resulting in higher accuracy in the measured power [Peterson et al., Rev. Sci. Instrum. 79, 10E301 (2008)]. This assembly is also expected to improve foil uniformity, as the Pt layer is obtained via deposition rather than mechanical processes [Mukai et al., Rev. Sci. Instrum. 87, 2014 (2016)].Given its multi-material composition, measuring the thermal properties of the foil assembly is vital. Various methods using a calibrated laser as a heat source have been developed, analyzing the temperature profile shape [Sano et al., Plasma and Fusion Res. 7, 2405039 (2012)] and [Mukai et al., Rev. Sci. Instrum. 89, 10E114 (2018)] or fitting the calculated laser power for different intensities and frequencies [Fderici et al., Rev. Sci. Instrum. 94, 033502 (2023)]. Here, a simpler approach is presented, which relies on analyzing the separate components of the foil heat equation for a single laser exposure in a given area. This can then be iterated over the entire foil to capture local deviations.

Detection of sex-specific glutamate changes in subregions of hippocampus in an early-stage Alzheimer's disease mouse model using GluCEST MRI.

Regional glucose hypometabolism resulting in glutamate loss has been shown as one of the characteristics of Alzheimer's disease (AD). Because the impact of AD varies between the sexes, we utilized glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI) for high-resolution spatial mapping of cerebral glutamate and investigated subregional changes in a sex-specific manner. Eight-month-old male and female AD mice harboring mutant amyloid precursor protein (APPNL-F/NL-F: n = 36) and wild-type (WT: n = 39) mice underwent GluCEST MRI, followed by proton magnetic resonance spectroscopy (1H-MRS) in hippocampus and thalamus/hypothalamus using 9.4T preclinical MR scanner. GluCEST measurements revealed significant (p≤0.02) glutamate loss in the entorhinal cortex (% change ± standard error: 8.73±2.12%), hippocampus (11.29±2.41%), and hippocampal fimbriae (19.15±2.95%) of male AD mice. A similar loss of hippocampal glutamate in male AD mice (11.22±2.33%; p = 0.01) was also observed in 1H-MRS. GluCEST MRI detected glutamate reductions in the fimbria and entorhinal cortex of male AD mice, which was not reported previously. Resilience in female AD mice against these changes indicates an intact status of cerebral energy metabolism. Glutamate levels were monitored in different brain regions of early-stage Alzheimer's disease (AD) and wild-type male and female mice using glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI). Male AD mice exhibited significant glutamate loss in the hippocampus, entorhinal cortex, and the fimbriae of the hippocampus. Interestingly, female AD mice did not have any glutamate loss in any brain region and should be investigated further to find the probable cause. These findings demonstrate previously unreported sex-specific glutamate changes in hippocampal sub-regions using high-resolution GluCEST MRI.

Ultra-high-resolution mapping of myelin and g-ratio in a panel of Mbp enhancer-edited mouse strains using microstructural MRI

Non-invasive myelin water fraction (MWF) and g-ratio mapping using microstructural MRI have the potential to offer critical insights into brain microstructure and our understanding of neuroplasticity and neuroinflammation. By leveraging a unique panel of variably hypomyelinating mouse strains, we validated a high-resolution, model-free image reconstruction method for whole-brain MWF mapping. Further, by employing a bipolar gradient echo MRI sequence, we achieved high spatial resolution and robust mapping of MWF and g-ratio across the whole mouse brain. Our regional white matter-tract specific analyses demonstrated a graded decrease in MWF in white matter tracts which correlated strongly with myelin basic protein gene (Mbp) mRNA levels. Using these measures, we derived the first sensitive calibrations between MWF and Mbp mRNA in the mouse. Minimal changes in axonal density supported our hypothesis that observed MWF alterations stem from hypomyelination. Overall, our work strongly emphasizes the potential of non-invasive, MRI-derived MWF and g-ratio modeling for both preclinical model validation and ultimately translation to humans.

Generating high-resolution land use and land cover maps for the greater Mariño watershed in 2019 with machine learning

Land Use and Land Cover (LULC) maps are important tools for environmental planning and social-ecological modeling, as they provide critical information for evaluating risks, managing natural resources, and facilitating effective decision-making. This study aimed to generate a very high spatial resolution (0.5 m) and detailed (21 classes) LULC map for the greater Mariño watershed (Peru) in 2019, using the MORINGA processing chain. This new method for LULC mapping consisted in a supervised object-based LULC classification, using the random forest algorithm along with multi-sensor satellite imagery from which spectral and textural predictors were derived (a very high spatial resolution Pléiades image and a time serie of high spatial resolution Sentinel-2 images). The random forest classifier showed a very good performance and the LULC map was further improved through additional post-treatment steps that included cross-checking with external GIS data sources and manual correction using photointerpretation, resulting in a more accurate and reliable map. The final LULC provides new information for environmental management and monitoring in the greater Mariño watershed. With this study we contribute to the efforts to develop standardized and replicable methodologies for high-resolution and high-accuracy LULC mapping, which is crucial for informed decision-making and conservation strategies.

Mapping eye, arm, and reward information in frontal motor cortices using electrocorticography in non-human primates.

Goal-directed reaches give rise to dynamic neural activity across the brain as we move our eyes and arms, and process outcomes. High spatiotemporal resolution mapping of multiple cortical areas will improve our understanding of how these neural computations are spatially and temporally distributed across the brain. In this study, we used micro-electrocorticography (µECoG) recordings in two male monkeys performing visually guided reaches to map information related to eye movements, arm movements, and receiving rewards over a 1.37 cm 2 area of frontal motor cortices (primary motor cortex, premotor cortex, frontal eye field, and dorsolateral pre-frontal cortex). Time-frequency and decoding analyses revealed that eye and arm movement information shifts across brain regions during a reach, likely reflecting shifts from planning to execution. We then used phase-based analyses to reveal potential overlaps of eye and arm information. We found that arm movement decoding performance was impacted by task-irrelevant eye movements, consistent with the presence of intermixed eye and arm information across much of motor cortices. Phase-based analyses also identified reward-related activity primarily around the principal sulcus in the pre-frontal cortex as well as near the arcuate sulcus in the premotor cortex. Our results demonstrate µECoG's strengths for functional mapping and provide further detail on the spatial distribution of eye, arm, and reward information processing distributed across frontal cortices during reaching. These insights advance our understanding of the overlapping neural computations underlying coordinated movements and reveal opportunities to leverage these signals to enhance future brain-computer interfaces. Significance statement Picking up your coffee mug requires coordinating movements of your eyes and hand and processing the outcomes of those movements. Mapping how neural activity relates to different functions helps us understand how the brain performs these computations. Many mapping techniques have limited spatial or temporal resolution, restricting our ability to dissect computations that overlap closely in space and time. We used micro-electrocorticography recordings to map neural activity across multiple cortical areas while monkeys made goal-directed reaches. These measurements revealed high spatial and temporal resolution maps of neural activity related to eye, arm, and reward information processing. These maps reveal overlapping neural computations underlying movement and open opportunities to use eye and reward information to improve therapies to restore motor function.

Ductal carcinoma in situ develops within clonal fields of mutant cells in morphologically normal ducts.

Mutations are abundantly present in tissues of healthy individuals, including the breast epithelium. Yet it remains unknown whether mutant cells directly induce lesion formation or first spread, leading to a field of mutant cells that is predisposed towards lesion formation. To study the clonal and spatial relationships between morphologically normal breast epithelium adjacent to pre-cancerous lesions, we developed a three-dimensional (3D) imaging pipeline combined with spatially resolved genomics on archival, formalin-fixed breast tissue with the non-obligate breast cancer precursor ductal carcinoma in situ (DCIS). Using this 3D image-guided characterization method, we built high-resolution spatial maps of DNA copy number aberration (CNA) profiles within the DCIS lesion and the surrounding normal mammary ducts. We show that the local heterogeneity within a DCIS lesion is limited. However, by mapping the CNA profiles back onto the 3D reconstructed ductal subtree, we find that in eight out of 16 cases the healthy epithelium adjacent to the DCIS lesions has overlapping structural variations with the CNA profile of the DCIS. Together, our study indicates that pre-malignant breast transformations frequently develop within mutant clonal fields of morphologically normal-looking ducts. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

High Spatiotemporal Resolution Radial Encoding Single-Vessel fMRI.

High-field preclinical functional MRI (fMRI) is enabled the high spatial resolution mapping of vessel-specific hemodynamic responses, that is single-vessel fMRI. In contrast to investigating the neuronal sources of the fMRI signal, single-vessel fMRI focuses on elucidating its vascular origin, which can be readily implemented to identify vascular changes relevant to vascular dementia or cognitive impairment. However, the limited spatial and temporal resolution of fMRI is hindered hemodynamic mapping of intracortical microvessels. Here, the radial encoding MRI scheme is implemented to measure BOLD signals of individual vessels penetrating the rat somatosensory cortex. Radial encoding MRI is employed to map cortical activation with a focal field of view (FOV), allowing vessel-specific functional mapping with 50×50 µm2 in-plane resolution at a 1 to 2 Hz sampling rate. Besides detecting refined hemodynamic responses of intracortical micro-venules, the radial encoding-based single-vessel fMRI enables the distinction of fMRI signals from vessel and peri-vessel voxels due to the different contribution of intravascular and extravascular effects.

Mapping dynamic human sentiments of heat exposure with location-based social media data

Understanding urban heat exposure dynamics is critical for public health, urban management, and climate change resilience. Near real-time analysis of urban heat enables quick decision-making and timely resource allocation, thereby enhancing the well-being of urban residents, especially during heatwaves or electricity shortages. To serve this purpose, we develop a cyberGIS framework to analyze and visualize human sentiments of heat exposure dynamically based on near real-time location-based social media (LBSM) data. Large volumes and low-cost LBSM data, together with a content analysis algorithm based on natural language processing are used effectively to generate near real-time heat exposure maps from human sentiments on social media at both city and national scales with km spatial resolution and census tract spatial unit. We conducted a case study to visualize and analyze human sentiments of heat exposure in Chicago and the United States in September 2021. Enabled with high-performance computing, dynamic visualization of heat exposure is achieved with fine spatiotemporal scales while heat exposure detected from social media data can be used to understand heat exposure from a human perspective and allow timely responses to extreme heat. HIGHLIGHTS Near real-time and high spatial resolution mapping of human sentiments of heat exposure with Twitter data An integrated cyberGIS and machine learning framework for visualizing heat exposure with Twitter data Human sentiment of heat exposure mapping in the City of Chicago and the United States

Mapping almond stem water potential using machine learning and multispectral imagery

AbstractAlmonds are a major crop in California which produces 80% of all the world’s almonds. Widespread drought and strict groundwater regulations pose significant challenges to growers. Irrigation regimes based on observed crop water status can help to optimize water use efficiency, but consistent and accurate measurement of water status can prove challenging. In almonds, crop water status is best represented by midday stem water potential measured using a pressure chamber, which despite its accuracy is impractical for growers to measure on a regular basis. This study aimed to use machine learning (ML) models to predict stem water potential in an almond orchard based on canopy spectral reflectance, soil moisture, and daily evapotranspiration. Both artificial neural network and random forest models were trained and used to produce high-resolution spatial maps of stem water potential covering the entire orchard. Also, for each ML model type, one model was trained to predict raw stem water potential values, while another was trained to predict baseline-adjusted values. Together, all models resulted in an average coefficient of correlation of R2 = 0.73 and an average root mean squared error (RMSE) of 2.5 bars. Prediction accuracy decreased significantly when models were expanded to spatial maps (R2 = 0.33, RMSE = 3.31 [avg]). These results indicate that both artificial neural networks and random forest frameworks can be used to predict stem water potential, but both approaches were unable to fully account for the spatial variability observed throughout the orchard. Overall, the most accurate maps were produced by the random forest model (raw stem water potential R2 = 0.47, RMSE = 2.71). The ability to predict stem water potential spatially can aid in the implementation of variable rate irrigation. Future studies should attempt to train similar models with larger datasets and develop a simpler faster workflow for producing stress predictions from field measurements.

Ecotoxicity of 2,4-Dichlorophenol to Microsorium pteropus by High Spatial Resolution Mapping of Stoma Oxygen Emission

The toxicity of emerging organic pollutants to photosystems of aquatic plants is still not well clarified. This study aimed to develop a novel ecotoxicological experimental protocol based on nanoscale electrochemical mapping of photosynthetic oxygen evolution of aquatic plants by scanning electrochemical microscopy (SECM). The protocol was also checked by confocal laser scanning microscopy (CLSM), the traditional Clark oxygen electrode method, and the chlorophyll fluorescence technique. The typical persistent organic pollutant 2,4-dichlorophenol (2,4-DCP) in a water environment and the common aquatic Microsorium pteropus (M. pteropus) were chosen as the model organic pollutant and tested plant, respectively. It was found that the SECM method could discriminate the responses of stoma micromorphology and spatial pattens of photosynthetic oxygen evolution on single stoma well. The shape of stoma blurred with increasing 2,4-DCP concentration, which was in good agreement with the CLSM images. The dose–response curves and IC50 values obtained from the SECM data were verified by the data measured by the traditional Clark oxygen electrode method and chlorophyll fluorescence test. The IC50 value of single-stoma oxygen emission of plant leaves exposed for 24 h, which was derived from the SECM current data (32,535 μg L−1), was close to those calculated from the maximum photosynthetic efficiency (Fv/Fm) measured by the chlorophyll fluorescence test (33,963 μg L−1) and the Clark oxygen electrode method photosynthetic oxygen evolution rate (32,375 μg L−1). The 72 h and 96 h 2,4-DCP exposure data further confirmed the reliability of the nanoscale stoma oxygen emission mapping methodology for ecotoxicological assessment. In this protocol, the procedures for how to collect effective electrochemical data and how to extract useful information from the single-stoma oxygen emission pattern were well established. This study showed that SECM is a feasible and reliable ecotoxicological tool for evaluation of toxicity of organic pollutants to higher plants with a unique nanoscale visualization advantage over the conventional methods.

The digital climate atlas of the Canary Islands: A tool to improve knowledge of climate and temperature and precipitation trends in the Atlantic islands

This paper presents the Interactive Digital Climate Atlas of the Canary Islands, a web portal based on geographic information systems for the dissemination of climate information. It provides citizens and public and private administrations with high spatial resolution maps (100 m) of different climate variables such as temperature, rainfall, relative humidity, cloudiness frequency and wind velocity. Also available are Köppen climate classification maps and decadal trends of temperature and precipitation in a context of climate change. This spatial resolution is suitable for the detailed climatic description of small and orographically complex islands. For this purpose, climate series from databases of different public institutions as well as data from remote sensing and reanalysis systems were employed. A statistical process involving data filtering and the detection and correction of inhomogeneous segments was carried out as a preparatory step. Then, a spatial interpolation model was developed using a multiple linear regression method for the generation of the high spatial resolution climate cartography. The results are annual and monthly maps of the climate variables in Canary Islands at 100 m spatial resolution and with different time periods depending on the availability of data on the different variables, but in most cases between 1991 and 2020. Following the European Directive INSPIRE (Directive 2007/2/CE, Infrastructure for Spatial Information in Europe), both map visualisation and downloading in GeoTIFF and NetCDF format are permitted, as well as consultation of the data and metadata of the climatic series used.