Spatial Agreement Research Articles

Exploring a uniform procedure to map Eucalyptus plantations based on fused medium–high spatial resolution satellite images

Accurate and up-to-date mapping of Eucalyptus plantations is essential for assessing their effects on soil, hydrology, and biodiversity. However, no proper approaches are available for extracting such data on Eucalyptus. This study proposes a uniform procedure for mapping Eucalyptus plantations based on fused medium-high spatial resolution satellite datasets. To develop this procedure, a total of 810 variables were extracted from the fused images of an experimental site, the Gaofeng Forest Farm in Guangxi, China. Six key variables were determined by using the Z-statistic and random forest methods. A total of 123 scenarios were then designed based on the key variables and three decision tree classifiers. Potential robust variables were determined through a comparative analysis of accuracy assessments from all scenarios. All the designed procedures were tested at a second site, Xingye County, Guangxi, using the same data processing methodology. The uniform procedure was finally determined through a comparative analysis of accuracy assessments from all the scenarios at both sites. The proposed procedure was then applied to a third study site, Yunxiao County, Fujian, China, to examine its transferability. Results from the Gaofeng site showed that the first short-wave infrared band (SWIR1) and the normalized difference index with near-infrared and green bands (NDNG) were two independent spectral-based variables for Eucalyptus delineation. Scenarios comprising mean texture with a window size of 15 (MEAN15), homogeneity with a window size of 5 (HOM5), or ratio of number of pixels where homogeneity based on window size 5 is greater than threshold T to the total number of pixels in the target segment polygon (HOM5RT) performed significantly greater than scenarios without these variables (α = 0.05). Scenarios with HOM5RT performed significantly greater than those with HOM5 (α = 0.05), and scenarios with random forest (RF) classifier performed greater than those with the other two classifiers. The results from the Xingye site were highly consistent with those from the Gaofeng site. Thus, the final uniform procedure containing these four optimal variables (SWIR1, NDNG, MEAN15, and HOM5RT) and RF classifier was determined. An additional independent test at the Yunxiao site showed an overall accuracy of 96.01% and a high spatial agreement between the delineated Eucalyptus and observed distributions, indicating that the proposed procedure has good transferability. This research implies that the proposed procedure is robust and can be used in other subtropical regions to map Eucalyptus distributions.

The environmental Risk Factors Significant to Anopheles Species Vector Mosquito Profusion, P.falciparum, P.vivax Parasite Development, and Malaria Transmission, Using Remote Sensing and Gis: Review Article

The prevalence of spatial distribution and the seasonal variation of malaria epidemics in India have beenmost significantly determined by the environmental variables including climate, landscape, and the manmade factors. The risk factors are acting as decisive factors on the development of Anopheles genusmosquitoes. The landscape environments (slope, altitude, land use / land covers), human settlementsproximity to permanent water bodies of mosquito breeding habitats (lake, pool, streams, rivers, tanks etc),agricultural wet rice cultivation land, land use dynamics, population density, urbanization, increase of manwater resource projects. The coefficient model of climate determinants (rainfall and temperature) with themosquito abundance are highly associated with the Normalized Difference Vegetation Index (NDVI) valuederived from multispectral satellite data, and is useful in the assess the ground situation of Anophelinemalaria vector mosquito larval abundance 7 days in advance in the wet irrigation rice fields using remotelysensed data. The result of logistic regression model provides the spatial agreement between the observedand predicted values of larval index within buffer zones 2.5 KM around the trap location in the wetcultivation rice fields much appropriate for Anopheline vector mosquito breeding. However, transmissionof Plasmodium vivax requires a minimum average temperature 15.0°C and transmission by Plasmodiumfalciparum, requires a minimum average temperature of 19.0°C. The P.vivax vector requires 15 to 25 daysto complete the parasite development cycle within the temperature range between 15°C to 20°C, the relativehumidity for both species requires range between 55% to 80% and its life cycle may be completed within 6to 10 days, if the temperature range remains within 25°C to 30°C. Multivariate analysis could be predictedaccurately the relative abundance of malaria vectors breeding habitats suitability and epidemics. The malariacases in the endemic districts and the relative abundance of the malaria vectors are directly controlled by theclimate variables with >85 % accuracy.

CII] emission properties of the massive star-forming region RCW 36 in a filamentary molecular cloud

Aims. We investigate the properties of [C II] 158 μm emission of RCW 36 in a dense filamentary cloud. Methods. [C II] observations of RCW 36, covering an area of ~30′ × 30′, were carried out with a Fabry-Pérot spectrometer on board a 100-cm balloon-borne far-infrared (IR) telescope with an angular resolution of 90′′. Using AKARI and Herschel images, we compared the spatial distribution of the [C II] intensity with the emission from the large grains and polycyclic aromatic hydrocarbon (PAH). Results. The [C II] emission is in good spatial agreement with shell-like structures of a bipolar lobe observed in IR images, which extend along the direction perpendicular to the direction of cold dense filament. We found that the [C II]–160 μm relation for RCW 36 shows a higher brightness ratio of [C II]/160 μm than that for RCW 38, while the [C II]–9 μm relation for RCW 36 is in good agreement with that for RCW 38. Conclusions. Via a spectral decomposition analysis on a pixel-by-pixel basis using IR images, the [C II] emission is spatially well correlated with PAH and cold dust emissions. This means that the observed [C II] emission predominantly comes from photo-dissociation regions. Moreover, the L[C II]∕LFIR ratio shows large variation (10−2–10−3), as compared with the L[C II]/LPAH ratio. In view of the observed tight correlation between L[C II]∕LFIR and the optical depth at λ = 160 μm, the large variation in L[C II]∕LFIR can be simply explained by the geometrical effect, that is, LFIR has contributions from the entire dust-cloud column along the line of sight, while L[C II] has contributions from far-UV illuminated cloud surfaces. Based on the picture of the geometry effect, the enhanced brightness ratio of [C II]/160 μm is attributed to the difference in gas structures where massive stars are formed: filamentary (RCW 36) and clumpy (RCW 38) molecular clouds; thus suggesting that RCW 36 is dominated by far-UV illuminated cloud surfaces, as compared with RCW 38.

The emergence of systematic argument distinctions in artificial sign languages

AbstractWord order is a key property by which languages indicate the relationship between a predicate and its arguments. However, sign languages use a number of other modality-specific tools in addition to word order such as spatial agreement, which has been likened to verbal agreement in spoken languages, and role shift, where the signer takes on characteristics of propositional agents. In particular, data from emerging sign languages suggest that, though some use of a conventional word order can appear within a few generations, systematic spatial modulation as a grammatical feature takes time to develop. We experimentally examine the emergence of systematic argument marking beyond word order, investigating how artificial gestural systems evolve over generations of participants in the lab. We find that participants converge on different strategies to disambiguate clause arguments, which become more consistent through the use and transmission of gestures; in some cases, this leads to conventionalized iconic spatial contrasts, comparable to those found in natural sign languages. We discuss how our results connect with theoretical issues surrounding the analysis of spatial agreement and role shift in established and newly emerging sign languages, and the possible mechanisms behind its evolution.

Comparison of dual-energy computer tomography and dynamic contrast-enhanced MRI for evaluating lung perfusion defects in chronic thromboembolic pulmonary hypertension.

ObjectivesTo evaluate the agreement in detecting pulmonary perfusion defects in patients with chronic thromboembolic pulmonary hypertension using dual-energy CT and dynamic contrast-enhanced MRI. Second, to compare both imaging modalities in monitoring lung perfusion changes in these patients after undergoing pulmonary endarterectomy.Methods20 patients were examined with CT and MRI before and/or after pulmonary endarterectomy. Estimated perfusion defect percentage from both modalities was compared in a lobe-based analysis. Spatial agreement of perfusion defect maps was also assessed.ResultsA significant correlation between CT and MRI based perfusion defect percentage was calculated in all lung lobes (r > 0.78; p < 0.001). In addition, a good spatial agreement between perfusion defect maps was found (mean spatial overlap for the whole lung was 68.2%; SD = 6.9). Both CT and MRI detected improvements in pulmonary perfusion after pulmonary endarterectomy: 8% and 7% decrease in whole lung perfusion defect percentage (p = 0.007 and 0.004), respectively. In a lobe-wise analysis, improvements were statistically significant only in lower lobes using both modalities (reduction in defect percentage ranged from 16–29%; p < 0.02).ConclusionsDual-energy CT is an alternative to MRI in monitoring chronic thromboembolic pulmonary hypertension. Both imaging modalities provided comparable estimations of perfusion defects and could detect similar improvement in lung perfusion after pulmonary endarterectomy.

Strongly heterogeneous patterns of groundwater depletion in Northwestern India

Northwestern India has been identified as a significant hotspot of groundwater depletion, with major implications for groundwater sustainability caused by excessive abstraction. We know relatively little about the detailed spatial and temporal changes in groundwater storage in this region, nor do we understand the interplay of factors controlling these changes. Groundwater managers and policymakers in India require such information to monitor groundwater development and make strategic decisions for the sustainable management of groundwater. Here, we characterise high-resolution spatio-temporal variability in groundwater levels and storage change across northwestern India through analysis of in situ measurements of historical groundwater level data. We note a slow gain in groundwater storage of + 0.58 ± 0.35 km3 for the pre-monsoon and + 0.40 ± 0.35 km3 for the post-monsoon period between 1974 and 2001. However, from 2002 to 2010, groundwater storage was rapidly depleted by −32.30 ± 0.34 km3 in the pre-monsoon and −24.42 ± 0.34 km3 in the post-monsoon period. Importantly, we observe marked spatial heterogeneity in groundwater levels and storage change and distinct hotspots of groundwater depletion with lateral length scales of tens of kilometers. Spatial variability in groundwater abstraction partially explains the depletion pattern, but we also find that the sedimentological heterogeneity of the aquifer system correlates broadly with long-term patterns of groundwater-level change. This correlation, along with the spatial agreement between groundwater level change and water quality, provides a framework for anticipating future depletion patterns and guiding groundwater monitoring and domain-specific management strategies.

Lipiodol as an intra-procedural imaging biomarker for liver tumor response to transarterial chemoembolization: Post-hoc analysis of a prospective clinical trial

BackgroundThe use of the ethiodized oil- Lipiodol in conventional trans-arterial chemoembolization (cTACE) ensures radiopacity to visualize drug delivery in the process of providing selective drug targeting to hepatic cancers and arterial embolization. Lipiodol functions as a carrier of chemo drugs for targeted therapy, as an embolic agent, augmenting the drug effect by efflux into the portal veins as well as a predictor for the tumor response and survival. PurposeTo prospectively evaluate the role of 3D quantitative assessment of intra-procedural Lipiodol deposition in liver tumors on CBCT immediately after cTACE as a predictive biomarker for the outcome of cTACE. Materials & methodsThis was a post-hoc analysis of data from an IRB-approved prospective clinical trial. Thirty-two patients with hepatocellular carcinoma or liver metastases underwent contrast enhanced CBCT obtained immediately after cTACE, unenhanced MDCT at 24 h after cTACE, and follow-up imaging 30-, 90- and 180-days post-procedure. Lipiodol deposition was quantified on CBCT after cTACE and was characterized by 4 ordinal levels: ≤25%, >25–50%, >50–75%, >75%. Tumor response was assessed on follow-up MRI. Lipiodol deposition on imaging, correlation between Lipiodol deposition and tumor response criteria, and correlation between Lipiodol coverage and median overall survival (MOS) were evaluated. ResultsImage analysis demonstrated a high degree of agreement between the Lipiodol deposition on CBCT and the 24 h post-TACE CT, with a Bland-Altman plot of Lipiodol deposition on imaging demonstrated a bias of 2.75, with 95%-limits-of-agreement: −16.6 to 22.1%. An inverse relationship between Lipiodol deposition in responders versus non-responders for two-dimensional EASL reached statistical significance at 30 days (p = 0.02) and 90 days (p = 0.05). Comparing the Lipiodol deposition in Modified Response Evaluation Criteria in Solid Tumors (mRECIST) responders versus non-responders showed a statistically significant higher volumetric deposition in responders for European Association for the Study of the Liver (EASL)-30d, EASL-90d, and quantitative EASL-180d. The correlation between the relative Lipiodol deposition and the change in enhancing tumor volume showed a negative association post-cTACE (30-day: p < 0.001; rho = −0.63). A Kaplan-Meier analysis for patients with high vs. low Lipiodol deposition showed a MOS of 46 vs. 33 months (p = 0.05). Conclusion3D quantification of Lipiodol deposition on intra-procedural CBCT is a predictive biomarker of outcome in patients with primary or metastatic liver cancer undergoing cTACE. There are spatial and volumetric agreements between 3D quantification of Lipiodol deposition on intra-procedural CBCT and 24 h post-cTACE MDCT. The spatial and volumetric agreement between Lipiodol deposition on intra-procedural CBCT and 24 h post-cTACE MDCT could suggest that acquiring MDCT 24 h after cTACE is redundant. Importantly, the demonstrated relationship between levels of tumor coverage with Lipiodol and degree and timeline of tumor response after cTACE underline the role of Lipiodol as an intra-procedural surrogate for tumor response, with potential implications for the prediction of survival.

Links between atmospheric blocking and North American winter cold spells in two generations of Canadian Earth System Model large ensembles

Due to the significant negative consequences of winter cold extremes, there is need to better understand and simulate the mechanisms driving their occurrence. The impact of atmospheric blocking on winter cold spells over North America is investigated using ERA-Interim and NCEP-DOE-R2 reanalyses for 1981–2010. Initial-condition large-ensembles of two generations of Canadian Earth System Models (CanESM5 and its predecessor, CanESM2) are evaluated in terms of their ability to represent the blocking-cold spell linkage and the associated internal-variability. The reanalysis datasets show that 72 and 58% of cold spells in southern and northern North America coincide with blocking occurring in the high-latitude Pacific-North America. Compared to the two reanalyses, CanESM2 and CanESM5 ensembles underestimate by 19.9 and 14.3% cold spell events coincident with blocking, due to significant under-representation of blocking frequency over the North Pacific (− 47.1 and − 29.0%), whereas biases in cold spell frequency are relatively small (6.6 and − 4.7%). In the reanalyses, regions with statistically significant above-normal cold spell frequency relative to climatology lie on the east and/or south flanks of blocking events, whereas those with below-normal frequency lie along the core or surrounding the blocking. The two ensembles reproduce the observed blocking-cold spell linkage over North America, despite underestimating the magnitude of blocking frequency. The two ensembles also reproduce the physical drivers that underpin the blocking-cold spell linkage. Spatial agreement with the reanalyses is found in the simulated patterns of temperature advection and surface heat flux forcing anomalies during blocking events. While CanESM5 shows an improved representation of the blocking climatology relative to CanESM2, both yield similar results in terms of the blocking-cold spell linkage and associated internal-variability.

Multitask Learning Based Three-Dimensional Striatal Segmentation of MRI: fMRI and PET Objective Assessments.

Recent studies have established a clear topographical and functional organization of projections to and from complex subdivisions of the striatum. Manual segmentation of these functional subdivisions is labor-intensive and time-consuming, and automated methods are not as reliable as manual segmentation. To utilize multitask learning (MTL) as a method to segment subregions of the striatum consisting of pre-commissural putamen (prePU), pre-commissural caudate (preCA), post-commissural putamen (postPU), post-commissural caudate (postCA), and ventral striatum (VST). Retrospective. Eighty-seven total data sets from patients with schizophrenia and matched controls. 1.5 T and 3.0 T, T1 -weighted (SPGR SENSE, 3D BRAVO). MTL-generated segmentations were compared to the Imperial College London Clinical Imaging Center (CIC) atlas. Dice similarity coefficient (DSC) was used to compare the automated methods to manual segmentations. Positron emission tomography (PET) imaging: 60 minutes of emission data were acquired using [11 C]raclopride. Data were reconstructed by filtered back projection (FBP) with computed tomography (CT) used for attenuation correction. Binding potential values, BPND , and region of interest (ROI) time series and whole-brain connectivity using functional magnetic resonance imaging (fMRI) images were compared between manual and both automated segmentations. Pearson correlation and paired t-test. MTL-generated segmentations showed excellent spatial agreement with manual (DSC ≥0.72 across all striatal subregions). BPND values from MTL-generated segmentations were shown to correlate well with manual segmentations with R2 ≥ 0.91 in all caudate and putamen subregions, and R2 =0.69 in VST. Mean Pearson correlation coefficients of the fMRI data between MTL-generated and manual segmentations were also high in time series (≥0.86) and whole-brain connectivity (≥0.89) across all subregions. Across both PET and fMRI task-based assessments, results from MTL-generated segmentations more closely corresponded to results from manually drawn ROIs than CIC-generated segmentations did. Therefore, the proposed MTL approach is a fast and reliable method for three-dimensional striatal subregion segmentation with results comparable to manually segmented ROIs. 2 TECHNICAL EFFICACY STAGE: 1.

High-frequency stable isotope signals in uneven-aged forests as proxy for physiological responses to climate in Central Europe.

Picea abies (L.) Karst. and Fagus sylvatica (L.) are important tree species in Europe, and the foreseen increase in temperature and vapour pressure deficit (VPD) could increase the vulnerability of these species. However, their physiological performance under climate change at temperate and productive sites is not yet fully understood, especially in uneven-aged stands. Therefore, we investigated tree-ring width and stable isotope chronologies (δ13C/δ18O) of these two species at 10 sites along a climate gradient in Central Europe. In these uneven-aged stands, we compared the year-to-year variability of dominant and suppressed trees for the last 80years in relation to the sites' spatial distribution and climate. δ18O and δ13C were generally consistent across sites and species, showing high sensitivity to summer VPD, whereas climate correlations with radial growth varied much more and depended on mean local climate. We found no significant differences between dominant and suppressed trees in the response of stable isotope ratios to climate variability, especially within the annual high-frequency signals. In addition, we observed a strikingly high coherence of the high-frequency δ18O variations across long distances with significant correlations above 1500km, whereas the spatial agreement of δ13C variations was weaker (~700km). We applied a dual-isotope approach that is based on known theoretical understanding of isotope fractionations to translate the observed changes into physiological components, mainly photosynthetic assimilation rate and stomatal conductance. When separating the chronologies in two time windows and investigating the shifts in isotopes ratios, a significant enrichment of either or both isotope ratios over the last decades can be observed. These results, translated by the dual-isotope approach, indicate a general climate-driven decrease in stomatal conductance. This improved understanding of the physiological mechanisms controlling the short-term variation of the isotopic signature will help to define the performance of these tree species under future climate.

Quantifying Contributions of Local Emissions and Regional Transport to NOX in Beijing Using TROPOMI Constrained WRF-Chem Simulation

Air quality is strongly influenced by both local emissions and regional transport. Atmospheric chemical transport models can distinguish between emissions and regional transport sources in air pollutant concentrations. However, quantifying model inventories is challenging due to emission changes caused by the recent strict control measures taken by the Chinese government. In this study, we use NO2 column observations from the Tropospheric Monitoring Instrument to retrieve top-down nitrogen oxide (NOX) emissions and quantify the contributions of local emissions and regional transport to NOx in Beijing (BJ), from 1 November 2018 to 28 February 2019 (W_2018) and 1 November 2019 to 29 February 2020 (W_2019). In W_2018 and W_2019, the BJ bottom-up NOX emissions from the multi-resolution emission inventory for China in 2017 were overestimated by 11.8% and 40.5%, respectively, and the input of NOX from other cities to BJ was overestimated by 10.9% and 51.6%, respectively. The simulation using our adjusted inventory exhibited a much higher spatial agreement (slope = 1.0, R2 = 0.79) and reduced a mean relative error by 45% compared to those of bottom-up NOX emissions. The top-down inventory indicated that (1) city boundary transport contributes approximately 40% of the NOX concentration in BJ; (2) in W_2019, NOX emissions and transport in BJ decreased by 20.4% and 17.2%, respectively, compared to those of W_2018; (3) in W_2019, NOX influx substantially decreased (−699 g/s) in BJ compared to that of W_2018 despite negative meteorological conditions that should have increased NOx influx by +503 g/s. Overall, the contribution of intercity input to NOx in BJ has declined with decreasing emissions in the surrounding cities due to regional cooperative control measures, and the role of local emissions in BJ NOx levels was more prominent. Our findings indicate that local emissions may play vital roles in regional center city air quality.

Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India

Landslide susceptibility modeling is complex as it involves different types of landslides and diverse interests of the end-user. Developing mitigation strategies for the landslides depends on their typology. Therefore, a landslide susceptibility based on different types should be more appealing than a susceptibility model based on a single inventory set. In this research, susceptibility models are generated considering the different types of landslides. Prior to the development of the model, we analyzed landslide inventory for understanding the complexity and scope of alternative landslide susceptibility mapping. We conducted this work by examining a case study of Kalimpong region (Himalayas), characterized by different types of landslides. The landslide inventory was analyzed based on its differential attributes, such as movement types, state of activity, material type, distribution, style, and failure mechanism. From the landslide inventory, debris slides, rockslides, and rockfalls were identified to generate two landslide susceptibility models using deep learning algorithms. The findings showed high accuracy for both models (above 0.90), although the spatial agreement is highly varied among the models.

An Architectural Review of Privacy Value in Traditional Indonesian Housings: Framework of Locality-Based on Islamic Architecture Design

This study aims to provide an understanding of the locality-based privacy of traditional Indonesian houses, which is in line with Islamic architecture values. This study reviews scientific papers about traditional Indonesian houses and summarizes the privacy establishment of each house. The result of the summary found that privacy in traditional Indonesian houses consists of physical and non-physical. Physical privacy comprises separator or divider; height adjustment for building elements; build layers of rooms as transitional space; clear division of zoning; selected wall material; and distinction entrance. In comparison, non-physical privacy involves spatial agreement and gender spatial that requires agreement space between men and women. This study is expectedly fruitful for the development of Islamic-based concept residence in the Indonesian real estate realm.

Reconciling humans and birds when designing ecological corridors and parks within urban landscapes.

Considering the necessity of interdisciplinary approaches for planning and managing the expansion of urban landscapes worldwide, this study aimed to (1) assess landscape permeability for birds and people inhabiting a Neotropical city and (2) propose priority streets and areas for the implementation of a green infrastructure project that could benefit both. To reach these goals, we generated resistance surfaces using expert knowledge to simulate multiple least-cost corridors (MLCC) between parks and green spaces within an urban landscape for people and seven bird species. We compared the solutions using a corridors' spatial agreement analysis, which allow us to identify the overlap between modeled corridors for all organisms or functional groups of interest. We also identified the streets most selected by the simulated MLCC and then identified a green space which is a convergence point of corridors modeled for both people and bird species. Finally, we suggested priority streets for planting trees and proposed interventions to turn the green space into a multifunctional park, conciliating social and ecological perspectives.

Surface Flux Equilibrium Estimates of Evapotranspiration at Large Spatial Scales

AbstractA recent theory proposes that inland continental regions are in a state of surface flux equilibrium (SFE), in which tight coupling between the land and atmosphere allow estimation of the Bowen ratio at daily to monthly time scales solely from atmospheric measurements, without calibration, even when the land surface strongly constrains the surface energy budget. However, since the theory has only been evaluated at quasi-point spatial scales using eddy covariance measurements with limited global coverage, it is unclear if it is applicable to the larger spatial scales relevant to studies of global climate. In this study, SFE estimates of the Bowen ratio are combined with satellite observations of surface net radiation to obtain large-scale estimates of latent heat flux λE. When evaluated against multiyear mean annual λE obtained from catchment water balance estimates from 221 catchments across the United States, the resulting error statistics are comparable to those in the catchment water balance estimates themselves. The theory is then used to diagnostically estimate λE using historical simulations from 26 CMIP6 models. The resulting SFE estimates are typically at least as accurate as the CMIP6 model’s simulated λE, when compared with catchment water balance estimates. Globally, there is broad spatial and temporal agreement between CMIP6 model SFE estimates and the CMIP6 model’s simulated λE, although SFE likely overestimates λE in some arid regions. We conclude that SFE applies reasonably at large spatial scales relevant to climate studies, and is broadly reproduced in climate models.

Cross-validation study between the HRRT and the PET component of the SIGNA PET/MRI system with focus on neuroimaging

BackgroundThe Siemens high-resolution research tomograph (HRRT - a dedicated brain PET scanner) is to this day one of the highest resolution PET scanners; thus, it can serve as useful benchmark when evaluating performance of newer scanners. Here, we report results from a cross-validation study between the HRRT and the whole-body GE SIGNA PET/MR focusing on brain imaging.Phantom data were acquired to determine recovery coefficients (RCs), % background variability (%BG), and image voxel noise (%). Cross-validation studies were performed with six healthy volunteers using [11C]DTBZ, [11C]raclopride, and [18F]FDG. Line profiles, regional time-activity curves, regional non-displaceable binding potentials (BPND) for [11C]DTBZ and [11C]raclopride scans, and radioactivity ratios for [18F]FDG scans were calculated and compared between the HRRT and the SIGNA PET/MR.ResultsPhantom data showed that the PET/MR images reconstructed with an ordered subset expectation maximization (OSEM) algorithm with time-of-flight (TOF) and TOF + point spread function (PSF) + filter revealed similar RCs for the hot spheres compared to those obtained on the HRRT reconstructed with an ordinary Poisson-OSEM algorithm with PSF and PSF + filter. The PET/MR TOF + PSF reconstruction revealed the highest RCs for all hot spheres. Image voxel noise of the PET/MR system was significantly lower. Line profiles revealed excellent spatial agreement between the two systems. BPND values revealed variability of less than 10% for the [11C]DTBZ scans and 19% for [11C]raclopride (based on one subject only). Mean [18F]FDG ratios to pons showed less than 12% differences.ConclusionsThese results demonstrated comparable performances of the two systems in terms of RCs with lower voxel-level noise (%) present in the PET/MR system. Comparison of in vivo human data confirmed the comparability of the two systems. The whole-body GE SIGNA PET/MR system is well suited for high-resolution brain imaging as no significant performance degradation was found compared to that of the reference standard HRRT.

Identifying the dominant local factors of 2000–2019 changes in dust loading over East Asia

East Asian dust aerosols play a vital role in the local and regional climate through its direct, indirect, and semidirect effects, but the dominant factors affecting the interannual variation of dust aerosols over East Asia and their regional differences remain unclear. This study verified the accuracy of MEERA-2 dust data in East Asia, analyzed the interannual trends of dust in East Asia from 2000 to 2019 using the MERRA-2 dust column mass density (DCMD) and identified the dominant factors affecting the interannual variation during the dusty season (March–July) by developing the regional multiple linear regression models, combined with correlation and partial correlation analysis. The comparison with the dust index (DI) calculated from ground-based observations of dust events frequency indicated that MERRA-2 DCMD exhibited high spatial agreement ( R > 0.8) with ground-based observations in most regions (especially in the dust source region of North China). The trend analysis revealed that DCMD in East Asia decreased significantly after 2000, particularly in the dusty season (March–July). These significant decreases were generally highly correlated with increases in normalized differential vegetation index (NDVI), volumetric soil moisture (VSM), and precipitation (PPT) and with decreases in wind speed (WS). Furthermore, WS dominated the interannual variation in the dust concentration over the East Asian dust source regions and their downstream. By contrast, PPT, through its wet deposition effect, dominated the variation in the rest of the regions away from the dust source regions. The study findings may help clarify the associations between local meteorological and surface factors and long-term variations in dust aerosols over East Asia. • MERRA-2 DCMD has a high accuracy in East Asia in 2000-2018. • Significant increases in NDVI, SM and PPT and significant decrease in WS all contributed to the decrease in DCMD. • WS dominated the variation of dust over the East Asian dust source regions and their downstream. • PPT dominated the variation of dust in the rest of the regions away from the dust source regions.

Use of a convolutional neural network to identify infarct core using computed tomography perfusion parameters.

Computed tomography perfusion (CTP) is used to diagnose ischemic strokes through contralateral hemisphere comparisons of various perfusion parameters. Various perfusion parameter thresholds have been utilized to segment infarct tissue due to differences in CTP software and patient baseline hemodynamics. This study utilized a convolutional neural network (CNN) to eliminate the need for non-universal parameter thresholds to segment infarct tissue. CTP data from 63 ischemic stroke patients was retrospectively collected and perfusion parameter maps were generated using Vitrea CTP software. Infarct ground truth labels were segmented from diffusion-weighted imaging (DWI) and CTP and DWI volumes were registered. A U-net based CNN was trained and tested five separate times using each CTP parameter (cerebral blood flow (CBF), cerebral blood volume (CBV), time-to-peak (TTP), mean-transit-time (MTT), delay time). 8,352 infarct slices were utilized with a 60:30:10 training:testing:validation split and Monte Carlo cross-validation was conducted using 20 iterations. Infarct volumes were reconstructed following segmentation from each CTP slice. Infarct spatial and volumetric agreement was compared between each CTP parameter and DWI. Spatial agreement metrics (Dice coefficient, positive predictive value) for each CTP parameter in predicting infarct volumes are: CBF=(0.67, 0.76), CBV=(0.44, 0.62), TTP=(0.60, 0.67), MTT=(0.58, 0.62), delay time=(0.57, 0.60). 95% confidence intervals for volume differences with DWI infarct are: CBF=14.3±11.5 mL, CBV=29.6±21.2 mL, TTP=7.7±15.2 mL, MTT=-10.7±18.6 mL, delay time=-5.7±23.6 mL. CBF is the most accurate CTP parameter in segmenting infarct tissue. Segmentation of infarct using a CNN has the potential to eliminate non-universal CTP contralateral hemisphere comparison thresholds.

Evaluation of polar stratospheric clouds in the global chemistry–climate model SOCOLv3.1 by comparison with CALIPSO spaceborne lidar measurements

Abstract. Polar stratospheric clouds (PSCs) contribute to catalytic ozone destruction by providing surfaces for the conversion of inert chlorine species into active forms and by denitrification. The latter describes the removal of HNO3 from the stratosphere by sedimenting PSC particles, which hinders chlorine deactivation by the formation of reservoir species. Therefore, an accurate representation of PSCs in chemistry–climate models (CCMs) is of great importance to correctly simulate polar ozone concentrations. Here, we evaluate PSCs as simulated by the CCM SOCOLv3.1 for the Antarctic winters 2006, 2007 and 2010 by comparison with backscatter measurements by CALIOP on board the CALIPSO satellite. The year 2007 represents a typical Antarctic winter, while 2006 and 2010 are characterized by above- and below-average PSC occurrence. The model considers supercooled ternary solution (STS) droplets, nitric acid trihydrate (NAT) particles, water ice particles and mixtures thereof. PSCs are parameterized in terms of temperature and partial pressures of HNO3 and H2O, assuming equilibrium between the gas and particulate phase. The PSC scheme involves a set of prescribed microphysical parameters, namely ice number density, NAT particle radius and maximum NAT number density. In this study, we test and optimize the parameter settings through several sensitivity simulations. The choice of the value for the ice number density affects simulated optical properties and dehydration, while modifying the NAT parameters impacts stratospheric composition via HNO3 uptake and denitrification. Depending on the NAT parameters, reasonable denitrification can be modeled. However, its impact on ozone loss is minor. The best agreement with the CALIOP optical properties and observed denitrification was for this case study found with the ice number density increased from the hitherto used value of 0.01 to 0.05 cm−3 and the maximum NAT number density from 5×10-4 to 1×10-3 cm−3. The NAT radius was kept at the original value of 5 µm. The new parameterization reflects the higher importance attributed to heterogeneous nucleation of ice and NAT particles following recent new data evaluations of the state-of-the-art CALIOP measurements. A cold temperature bias in the polar lower stratosphere results in an overestimated PSC areal coverage in SOCOLv3.1 by up to 40 %. Offsetting this cold bias by +3 K delays the onset of ozone depletion by about 2 weeks, which improves the agreement with observations. Furthermore, the occurrence of mountain-wave-induced ice, as observed mainly over the Antarctic Peninsula, is continuously underestimated in the model due to the coarse model resolution (T42L39) and the fixed ice number density. Nevertheless, we find overall good temporal and spatial agreement between modeled and observed PSC occurrence and composition. This work confirms previous studies indicating that simplified PSC schemes, which avoid nucleation and growth calculations in sophisticated but time-consuming microphysical process models, may also achieve good approximations of the fundamental properties of PSCs needed in CCMs.

Investigation of convolutional neural networks using multiple computed tomography perfusion maps to identify infarct core in acute ischemic stroke patients.

Purpose: To assess acute ischemic stroke (AIS) severity, infarct is segmented using computed tomography perfusion (CTP) software, such as RAPID, Sphere, and Vitrea, relying on contralateral hemisphere thresholds. Since this approach is potentially patient dependent, we investigated whether convolutional neural networks (CNNs) could achieve better performances without the need for contralateral hemisphere thresholds. Approach: CTP and diffusion-weighted imaging (DWI) data were retrospectively collected for 63 AIS patients. Cerebral blood flow (CBF), cerebral blood volume (CBV), time-to-peak, mean-transit-time (MTT), and delay time maps were generated using Vitrea CTP software. U-net shaped CNNs were developed, trained, and tested for 26 different input CTP parameter combinations. Infarct labels were segmented from DWI volumes registered with CTP volumes. Infarct volumes were reconstructed from two-dimensional CTP infarct segmentations. To remove erroneous segmentations, conditional random field (CRF) postprocessing was applied and compared with prior results. Spatial and volumetric infarct agreement was assessed between DWI and CTP (CNNs and commercial software) using median infarct difference, median absolute error, dice coefficient, positive predictive value. Results: The most accurate combination of parameters for CNN segmenting infarct using CRF postprocessing was CBF, CBV, and MTT (4.83mL, 10.14mL, 0.66, 0.73). Commercial software results are: RAPID = (2.25mL, 21.48mL, 0.63, 0.70), Sphere = (7.57mL, 17.74mL, 0.64, 0.70), Vitrea = (6.79mL, 15.28mL, 0.63, 0.72). Conclusions: Use of CNNs with multiple input perfusion parameters has shown to be accurate in segmenting infarcts and has the ability to improve clinical workflow by eliminating the need for contralateral hemisphere comparisons.