Contrast Sensitivity of Cats and Humans in Scotopic and Mesopic Conditions

Human contrast sensitivity in low scotopic conditions is regulated according to the deVries–Rose law. Previous cat behavioral data, as well as cat and mice electrophysiological data, have not confirmed this relationship. To resolve this discrepancy at the behavioral level, we compared sensitivity in dim light for cats and humans in parallel experiments using the same visual stimuli and similar behavioral paradigms. Both species had to detect Gabor functions (SD = 1.5°, spatial frequencies from 0 to 4 cpd, temporal frequency 4 Hz) presented 8° to the right or left of a central fixation point over an 8 log-unit range of adaptation levels spanning scotopic vision and extending well into the mesopic range. Cats had 0.74 log unit greater absolute sensitivity than that of humans for spatial frequencies ≤1/8 cpd. Cats had better contrast sensitivity overall for spatial frequencies <1/2 cpd, whereas humans were more sensitive for spatial frequencies above this. However, most of the cat's sensitivity advantage for low spatial frequencies could be accounted for by the greater light-concentrating abilities of its optics. Contrast sensitivity to 4 cpd was poor or absent in the scotopic range for both species. For both, scotopic increment thresholds were proportional to the square root of retinal illuminance, in accordance with the deVries–Rose law. Overall, cat and human visual systems appear to operate under very similar constraints for rod vision, including the regulation of contrast sensitivity across adaptation levels. A companion paper compares sensitivity of neurons in the lateral geniculate nucleus to these behavioral data

Spectrally resolved neurophotonics: a case report of hemodynamics and vascular components in the mammalian brain.

We developed a spectral technique that is independent of the light transport modality (diffusive or nondiffusive) to separate optical changes in scattering and absorption in the cat's brain due to the hemodynamic signal following visual stimulation. We observe changes in oxyhemoglobin and deoxyhemoglobin concentration signals during visual stimulation reminiscent of the functional magnetic resonance imaging (fMRI) blood oxygenation level dependence (BOLD) effect. Repeated measurements at different locations show that the observed changes are local rather than global. We also determine that there is an apparent large decrease in the water concentration and scattering coefficient during stimulation. We model the apparent change in water concentration on the separation of the optical signal from two tissue compartments. One opaque compartment is featureless (black), due to relatively large blood vessels. The other compartment is the rest of the tissue. When blood flow increases due to stimulation, the opaque compartment increases in volume, resulting in an overall decrease of tissue transmission. This increase in baseline absorption changes the apparent relative proportion of all tissue components. However, due to physiological effects, the deoxyhemoglobin is exchanged with oxyhemoglobin resulting in an overall increase in the oxyhemoglobin signal, which is the only component that shows an apparent increase during stimulation

Spectrally resolved neurophotonics: a case report of hemodynamics and vascular components in the mammalian brain.

We developed a spectral technique that is independent of the light transport modality (diffusive or nondiffusive) to separate optical changes in scattering and absorption in the cat's brain due to the hemodynamic signal following visual stimulation. We observe changes in oxyhemoglobin and deoxyhemoglobin concentration signals during visual stimulation reminiscent of the functional magnetic resonance imaging (fMRI) blood oxygenation level dependence (BOLD) effect. Repeated measurements at different locations show that the observed changes are local rather than global. We also determine that there is an apparent large decrease in the water concentration and scattering coefficient during stimulation. We model the apparent change in water concentration on the separation of the optical signal from two tissue compartments. One opaque compartment is featureless (black), due to relatively large blood vessels. The other compartment is the rest of the tissue. When blood flow increases due to stimulation, the opaque compartment increases in volume, resulting in an overall decrease of tissue transmission. This increase in baseline absorption changes the apparent relative proportion of all tissue components. However, due to physiological effects, the deoxyhemoglobin is exchanged with oxyhemoglobin resulting in an overall increase in the oxyhemoglobin signal, which is the only component that shows an apparent increase during stimulation

Integrated Computational and Experimental Structure Refinement for Nanoparticles

Determining the three-dimensional (3D) atomic structure of nanoparticles is critical to identifying the structures controlling their properties. Here, we demonstrate an integrated genetic algorithm (GA) optimization tool that refines the 3D structure of a nanoparticle by matching forward modeling to experimental scanning transmission electron microscopy (STEM) data and simultaneously minimizing the particle energy. We use the tool to create a refined 3D structural model of an experimentally observed ∼6000 atom Au nanoparticle

Integrated Computational and Experimental Structure Refinement for Nanoparticles

Determining the three-dimensional (3D) atomic structure of nanoparticles is critical to identifying the structures controlling their properties. Here, we demonstrate an integrated genetic algorithm (GA) optimization tool that refines the 3D structure of a nanoparticle by matching forward modeling to experimental scanning transmission electron microscopy (STEM) data and simultaneously minimizing the particle energy. We use the tool to create a refined 3D structural model of an experimentally observed ∼6000 atom Au nanoparticle

Integrated Computational and Experimental Structure Refinement for Nanoparticles

Determining the three-dimensional (3D) atomic structure of nanoparticles is critical to identifying the structures controlling their properties. Here, we demonstrate an integrated genetic algorithm (GA) optimization tool that refines the 3D structure of a nanoparticle by matching forward modeling to experimental scanning transmission electron microscopy (STEM) data and simultaneously minimizing the particle energy. We use the tool to create a refined 3D structural model of an experimentally observed ∼6000 atom Au nanoparticle

Integrated Computational and Experimental Structure Refinement for Nanoparticles

Determining the three-dimensional (3D) atomic structure of nanoparticles is critical to identifying the structures controlling their properties. Here, we demonstrate an integrated genetic algorithm (GA) optimization tool that refines the 3D structure of a nanoparticle by matching forward modeling to experimental scanning transmission electron microscopy (STEM) data and simultaneously minimizing the particle energy. We use the tool to create a refined 3D structural model of an experimentally observed ∼6000 atom Au nanoparticle

Magnetic Resonance Imaging-Based Radiomic Profiles Predict Patient Prognosis in Newly Diagnosed Glioblastoma Before Therapy

Magnetic resonance imaging (MRI) is used to diagnose and monitor brain tumors. Extracting additional information from medical imaging and relating it to a clinical variable of interest is broadly defined as radiomics. Here, multiparametric MRI radiomic profiles (RPs) of de novo glioblastoma (GBM) brain tumors is related with patient prognosis. Clinical imaging from 81 patients with GBM before surgery was analyzed. Four MRI contrasts were aligned, masked by margins defined by gadolinium contrast enhancement and T2/fluid attenuated inversion recovery hyperintensity, and contoured based on image intensity. These segmentations were combined for visualization and quantification by assigning a 4-digit numerical code to each voxel to indicate the segmented RP. Each RP volume was then compared with overall survival. A combined classifier was then generated on the basis of significant RPs and optimized volume thresholds. Five RPs were predictive of overall survival before therapy. Combining the RP classifiers into a single prognostic score predicted patient survival better than each alone (P &lt; .005). Voxels coded with 1 RP associated with poor prognosis were pathologically confirmed to contain hypercellular tumor. This study applies radiomic profiling to de novo patients with GBM to determine imaging signatures associated with poor prognosis at tumor diagnosis. This tool may be useful for planning surgical resection or radiation treatment margins