******Michigan Indoor Corridor 2012 Dataset******
This dataset is made available for research purpose only.
Please contact Grace Tsai( firstname.lastname@example.org) for any questions or comments.
This dataset was used to produce the results in our IROS 2012 paper. If you use the data, please cite the following reference in your publications related to this work:
Grace Tsai and Benjamin Kuipers
Dynamic Visual Understanding of the Local Environment for an Indoor Navigating Robot
International Conference on Intelligent Robots and Systems (IROS'12)
The dataset contains 4 video sequences acquired with camera mounted on a wheeled vehicle. The camera was set-up so that there was zero tilt and roll angle with respect to the ground. The camera has a fixed height (0.47 m) with the ground throughout the video.
The intrinsic parameters of the cameras are:
Focal length fc = [ 1389.182714 1394.598277 ]
Principal point cc = [ 672.605430 387.235803 ]
The distortion of the camera has been corrected.
For each video sequences, an estimated camera pose in each frame of the video is provided in the file pose.txt. Each line in the file looks like:
<frame index> <x (pose)> <y (pose)> <theta (pose)>
Note the camera poses provided here are estimated by using an occupancy grid mapping algorithm with a laser range finder
to obtain the robot pose.
The dataset provides a ground truth labeling for all the pixels every 10 frames for each video. The labels of each frame is stored as a 2D matrix in a .mat file. The filename of each .mat file corresponds to the image frame. The labels can be interpreted as followed:
-2 -> ceiling plane
-1 -> ground plane
>0 -> walls
The labels of the walls are illustrated in a .pdf figure. Note the figure is only a illustration graph, not an actual floor plan.
Magnetic resonance angiography (MRA) of the aorta of a 30 yo healthy volunteer, segmented and discretized using the software CRIMSON ( www.crimson.software).
Additionally, models corresponding to virtually-aged aortic geometries at ages: 40, 60, and 75.
The ENVIREM dataset v1.0 is a set of 16 climatic and 2 topographic variables that can be used in modeling species' distributions. The strengths of this dataset include their close ties to ecological processes, and their availability at a global scale, at several spatial resolutions, and for several time periods. The underlying temperature and precipitation data that went into their construction comes from the WorldClim dataset ( www.worldclim.org), and the solar radiation data comes from the Consortium for Spatial Information ( www.cgiar-csi.org). The data are compatible with and expand the set of variables from WorldClim v1.4 ( www.worldclim.org).
For more information, please visit the project website: envirem.github.io
The food environment is: 1) The physical presence of food that affects a person’s diet; 2) A person’s proximity to food store locations; 3) The distribution of food stores, food service, and any physical entity by which food may be obtained; or 4) A connected system that allows access to food. (Source: https://www.cdc.gov/healthyplaces/healthtopics/healthyfood/general.htm) Data included here concern: 1) Food access; and 2) Liquor access. Spatial Coverage for most data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area, Michigan, USA. See exception for grocery store data below.
The rapid activation of the mechanistic target of rapamycin complex-1 (mTORC1) by growth factors is increased by extracellular amino acids through yet-undefined mechanisms of amino acid transfer into endolysosomes. Because the endocytic process of macropinocytosis concentrates extracellular solutes into endolysosomes and is increased in cells stimulated by growth factors or tumor-promoting phorbol esters, we analyzed its role in amino acid–dependent activation of mTORC1. Here, we show that growth factor-dependent activation of mTORC1 by amino acids, but not glucose, requires macropinocytosis. In murine bone marrow–derived macrophages and murine embryonic fibroblasts stimulated with their cognate growth factors or with phorbol myristate acetate, activation of mTORC1 required an Akt-independent vesicular pathway of amino acid delivery into endolysosomes, mediated by the actin cytoskeleton. Macropinocytosis delivered small, fluorescent fluid-phase solutes into endolysosomes sufficiently fast to explain growth factor–mediated signaling by amino acids. Therefore, the amino acid–laden macropinosome is an essential and discrete unit of growth factor receptor signaling to mTORC1
We provide the parameters used in Umbrella Sampling simulations reported in our study "Efficient Estimation of Binding Free Energies between Peptides and an MHC Class II Molecule Using Coarse-Grained Molecular Dynamics Simulations with a Weighted Histogram Analysis Method", namely the set positions and spring constants for each window in simulations. Two tables are provided. Table 1 lists the names of the peptides and their corresponding sequences. Table 2 lists the parameters. The abstract of our work is the following:
We estimate the binding free energy between peptides and an MHC class II molecule using molecular dynamics (MD) simulations with Weighted Histogram Analysis Method (WHAM). We show that, owing to its more thorough sampling in the available computational time, the binding free energy obtained by pulling the whole peptide using a coarse-grained (CG) force field (MARTINI) is less prone to significant error induced by biased-sampling than using an atomistic force field (AMBER). We further demonstrate that using CG MD to pull 3-4 residue peptide segments while leaving the remain-ing peptide segments in the binding groove and adding up the binding free energies of all peptide segments gives robust binding free energy estimations, which are in good agreement with the experimentally measured binding affinities for the peptide sequences studied. Our approach thus provides a promising and computationally efficient way to rapidly and relia-bly estimate the binding free energy between an arbitrary peptide and an MHC class II molecule.
The information and education environment refers to: 1) the presence of information infrastructures such as broadband Internet access and public libraries in a location; 2) a person’s proximity to information infrastructures and sources; 3) the distribution of information infrastructures, sources and in a specific location; and 4) exposure to specific messages (information content) within a specific location.
Coverage for all data: 10-county Detroit-Warren-Ann Arbor Combined Statistical Area.