How do Humans Determine Reflectance Properties under Unknown Illumination?

Under normal viewing conditions, humans find it easy to distinguish between objects made out of different materials such as plastic, metal, or paper. Untextured materials such as these have different surface reflectance properties, including lightness and gloss. With single isolated images and unknown illumination conditions, the task of estimating surface reflectance is highly underconstrained, because many combinations of reflection and illumination are consistent with a given image. In order to work out how humans estimate surface reflectance properties, we asked subjects to match the appearance of isolated spheres taken out of their original contexts. We found that subjects were able to perform the task accurately and reliably without contextual information to specify the illumination. The spheres were rendered under a variety of artificial illuminations, such as a single point light source, and a number of photographically-captured real-world illuminations from both indoor and outdoor scenes. Subjects performed more accurately for stimuli viewed under real-world patterns of illumination than under artificial illuminations, suggesting that subjects use stored assumptions about the regularities of real-world illuminations to solve the ill-posed problem.

A Perturbation Scheme for Spherical Arrangements with Application to Molecular Modeling

We describe a software package for computing and manipulating the subdivision of a sphere by a collection of (not necessarily great) circles and for computing the boundary surface of the union of spheres. We present problems that arise in the implementation of the software and the solutions that we have found for them. At the core of the paper is a novel perturbation scheme to overcome degeneracies and precision problems in computing spherical arrangements while using floating point arithmetic. The scheme is relatively simple, it balances between the efficiency of computation and the magnitude of the perturbation, and it performs well in practice. In one O(n) time pass through the data, it perturbs the inputs necessary to insure no potential degeneracies and then passes the perturbed inputs on to the geometric algorithm. We report and discuss experimental results. Our package is a major component in a larger package aimed to support geometric queries on molecular models; it is currently employed by chemists working in "rational drug design." The spherical subdivisions are used to construct a geometric model of a molecule where each sphere represents an atom. We also give an overview of the molecular modeling package and detail additional features and implementation issues.

C-Kit(+) Cells Isolated from Developing Kidneys Are a Novel Population of Stem Cells with Regenerative Potential

The presence of tissue specific precursor cells is an emerging concept in organ formation and tissue homeostasis. Several progenitors are described in the kidneys. However, their identity as a true stem cell remains elusive. Here, we identify a neonatal kidney-derived c-kit(+) cell population that fulfills all of the criteria as a stem cell. These cells were found in the thick ascending limb of Henle's loop and exhibited clonogenicity, self-renewal, and multipotentiality with differentiation capacity into mesoderm and ectoderm progeny. Additionally, c-kit(+) cells formed spheres in nonadherent conditions when plated at clonal density and expressed markers of stem cells, progenitors, and differentiated cells. Ex vivo expanded c-kit(+) cells integrated into several compartments of the kidney, including tubules, vessels, and glomeruli, and contributed to functional and morphological improvement of the kidney following acute ischemia-reperfusion injury in rats. Together, these findings document a novel neonatal rat kidney c-kit(+) stem cell population that can be isolated, expanded, cloned, differentiated, and used for kidney repair following acute kidney injury. These cells have important biological and therapeutic implications. STEM Cells 2013;31:1644-1656