Volume Component Analysis for Classification of LiDAR Data

One of the most difficult challenges of working with LiDAR data is the large amount of data points that are produced. Analysing these large data sets is an extremely time consuming process. For this reason, automatic perception of LiDAR scenes is a growing area of research. Currently, most LiDAR feature extraction relies on geometrical features specific to the point cloud of interest. These geometrical features are scene-specific, and often rely on the scale and orientation of the object for classification. This paper proposes a robust method for reduced dimensionality feature extraction of 3D objects using a volume component analysis (VCA) approach. This VCA approach is based on principal component analysis (PCA). PCA is a method of reduced feature extraction that computes a covariance matrix from the original input vector. The eigenvectors corresponding to the largest eigenvalues of the covariance matrix are used to describe an image. Block-based PCA is an adapted method for feature extraction in facial images because PCA, when performed in local areas of the image, can extract more significant features than can be extracted when the entire image is considered. The image space is split into several of these blocks, and PCA is computed individually for each block. This VCA proposes that a LiDAR point cloud can be represented as a series of voxels whose values correspond to the point density within that relative location. From this voxelized space, block-based PCA is used to analyze sections of the space where the sections, when combined, will represent features of the entire 3-D object. These features are then used as the input to a support vector machine which is trained to identify four classes of objects, vegetation, vehicles, buildings and barriers with an overall accuracy of 93.8%

The Peptide–Drug Conjugate Melflufen Modulates the Unfolded Protein Response of Multiple Myeloma and Amyloidogenic Plasma Cells and Induces Cell Death

Immunoglobulin light-chain (AL) amyloidosis is a rare disease caused by clonal plasma cell secretion of misfolded light chains that assemble as toxic amyloid fibrils, depositing in vital organs including the heart and kidneys, causing organ dysfunction. Plasma cell–directed therapeutics are expected to reduce production of toxic light chain by eliminating amyloidogenic cells in bone marrow, thereby diminishing amyloid fibril deposition and providing the potential for organ recovery. Melphalan flufenamide (melflufen) is a first-in-class peptide–drug conjugate that targets aminopeptidases and rapidly releases alkylating agents inside tumor cells. Melflufen is highly lipophilic, permitting rapid uptake by cells, where it is enzymatically hydrolyzed by aminopeptidases, resulting in intracellular accumulation of the alkylating agents, including melphalan. Previous data demonstrating sensitivity of myeloma cells to melflufen suggest that the drug might be useful in AL amyloidosis. We describe the effects of melflufen on amyloidogenic plasma cells in vitro and ex vivo, demonstrating enhanced cytotoxic effects in comparison to melphalan, as well as novel mechanisms of action through the unfolded protein response (UPR) pathway. These findings provide evidence that melflufen-mediated cytotoxicity extends to amyloidogenic plasma cells, and support the rationale for the evaluation of melflufen in patients with AL amyloidosis.Peer reviewe

3D Object Classification in Uncalibrated Structure from Motion Models

We present a 3D object classification technique that is robust to noise and inaccuracies of 3D Structure from Motion models. We illustrate our classification results on 3D models reconstructed from real-world aerial imagery