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Purpose: Thrombus permeability has been related to clot composition and treatment outcomes in stroke patients undergoing reperfusion therapies. The aim of this study was to evaluate whether thrombus perviousness, evaluated by multiphase computed tomography angiography (mCTA), is associated with distal embolization risk. Methods: We interrogated our dataset of acute ischemic stroke (AIS) patients involving the M1 segment of the middle cerebral artery (MCA) who had undergone mechanical thrombectomy, and we calculated thrombus average attenuation measurement (dHU) on non-contrast CT (NCCT) and clot perviousness on mCTA. dHU was calculated as the difference between the thrombus HU average value (tHU) and the HU average value on the contralateral side (cHU), while perviousness was calculated as the difference in mean clot density on mCTA and NCCT both in arterial (Perviousness pre-post-1) and delayed (Perviousness pre-post 2) phases. Results: A total of 100 patients (53 females (53%), mean age 72.74 [± 2.31]) with M1 occlusion were available for analysis. Perviousness, calculated between baseline and arterial phase of mCTA (Perviousness pre-post1), was lower in patients with distal embolization (p = 0.05), revealing an association between reduced perviousness and distal embolization risk. Logistic regression showed that thrombus perviousness calculated on the arterial phase of mCTA (OR, 0.66; 95% CI, 0.44–0.99] (p = 0.04)) and the contact aspiration technique (OR, 0.39; 95% CI, 0.15–1.02] (p = 0.05)) were protecting factors against distal embolization. Conclusion: Our study showed an association between reduced perviousness and distal embolization, suggesting that perviousness evaluation may be a useful neuroimaging biomarker in predicting distal embolization risk during mechanical thrombectomy.

Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) for plant-N-related traits was assessed on a diverse real-world dataset including multiple crops, field sites and years. The plant N traits included the mass-based N measure, N concentration in the biomass (Nconc), and an area-based N measure approximating the plant N uptake (NUP). Spectral indices such as normalized ratio indices (NRIs) performed well, but the RFR and GPR methods outperformed the NRIs. Key spectral bands for each trait were identified using the RFR variable importance measure and the Gaussian processes regression band analysis tool (GPR-BAT), highlighting the importance of the short-wave infrared (SWIR) region for estimation of plant Nconc—and to a lesser extent the NUP. The red edge (RE) region was also important. The GPR-BAT showed that five bands were sufficient for plant N trait and leaf area index (LAI) estimation and that a surplus of bands effectively reduced prediction performance. A global sensitivity analysis (GSA) was performed on all traits simultaneously, showing the dominance of the LAI in the mixed remote sensing signal. To delineate the plant-N-related traits from this signal, regional and/or national data collection campaigns producing large crop spectral libraries (CSL) are needed. An improved database will likely enable the mapping of N at the agro-ecosystem level or for use in precision farming by farmers in the future.