Super-resolution fluorescence microscopy is a widely employed technique in cell biology research, yet remains relatively unexplored in the field of histopathology. Here, we describe the sample preparation steps and acquisition parameters necessary to obtain fluorescent multicolor super-resolution structured illumination microscopy (SIM) images of both formalin-fixed paraffin-embedded and cryo-preserved ...

This work introduces a novel method that combines machine learning (ML) techniques with dual-polarimetric (dual-pol) synthetic aperture radar (SAR) observations for estimating quad-polarimetric (quad-pol) parameters, which are presumed to contain geophysical sea ice information. In the training phase, the output parameters are generated from quad-pol observations obtained by Radarsat-2 (RS2), and ...

Sea ice thickness is a critical variable, both as a climate indicator and for forecasting sea ice conditions on seasonal and longer time scales. The lack of snow depth and density information is a major source of uncertainty in current thickness retrievals from laser and radar altimetry. In response to this data gap, a new Lagrangian snow evolution model (SnowModel‐LG) was developed to simulate snow ...

In this study, we compare the retrieval results for the dielectric properties of verified oil slick, acquired using airborne multifrequency synthetic aperture radar. A polarimetric two-scale model was used to invert the radar imagery by first employing solely the co-polarization channels, and then by employing the co-polarization channels in conjunction with the cross-polarization channels, and ...

Artificial intelligent systems often model the solutions of typical machine learning problems, inspired by biological processes, because of the biological system is faster and much adaptive than deep learning. The utility of bio-inspired learning methods lie in its ability to discover unknown patterns, and its less dependence on mathematical modeling or exhaustive training. In this paper, we propose ...

The spatial and temporal characterization of trapped charged particle trajectories in magnetospheres has been extensively studied in dipole magnetic field structures. Such studies have allowed the calculation of spatial quantities, such as equatorial loss cone size as a function of radial distance, the location of the mirror points along particular field lines (<i>L</i>‐shells) as a function of the ...

In this paper, we test and evaluate policy gradient reinforcement learning for automated blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown that reinforcement learning is a promising approach to accommodate the need for individualized blood glucose level control algorithms. The motivation for using policy gradient algorithms comes from the fact that adaptively ...

Robust and reliable classification of sea ice types in synthetic aperture radar (SAR) images is needed for various operational and environmental applications. Previous studies have investigated the class-dependent decrease in SAR backscatter intensity with incident angle (IA); others have shown the potential of textural information to improve automated image classification. In this work, we investigate ...

Considering a coherent microscopy setup, influences of the substrate below the sample in the imaging performances are studied, with a focus on high refractive index substrate such as silicon. Analytical expression of 3D full-wave vectorial point spread function, i.e. the dyadic Green's function is derived for the optical setup together with the substrate. Numerical analysis are performed in order ...

Fram Strait, the deepest gateway to the Arctic Ocean, is strongly influenced by eddy dynamics. Here we analyse the output from two eddy-resolving models (ROMS – Regional Ocean Modeling System; FESOM – Finite-Element Sea-ice Ocean Model) with around 1 km mesh resolution in Fram Strait, with a focus on their representation of eddy properties and dynamics. A comparison with mooring observations shows ...