Lie Group Model Neuromorphic Geometric Engine for Real-time Terrain Reconstruction from Stereoscopic Aerial Photos

In the 1980's, neurobiologist suggested a simple mechanism in primate visual cortex for maintaining a stable and invariant representation of a moving object: The receptive field of visual neurons has real-time transforms in response to motion, to maintain a stable representation. When the visual stimulus is changed due to motion, the geometric transform of the stimulus triggers a dual transform of the receptive field. This dual transform in the receptive fields compensates geometric variation in the stimulus. This process can be modelled using a Lie group method. The massive array of affine parameter sensing circuits will function as a smart sensor tightly coupled to the passive imaging sensor (retina) . Neural geometric engine is a neuromorphic computing device simulating our Lie group model of spatial perception of primate's primal visual cortex. We have developed the computer simulation and experimented on realistic and synthetic image data, and performed a preliminary research of using analog VLSI technology for implementation of the neural geometric engine. We have benchmark tested on DMA's terrain data with their result and have built an analog integrated circuit to verify the computational structure of the engine. When fully implemented on ANALOG VLSI chip, we will be able to accurately reconstruct 3-D terrain surface in real-time from stereoscopic imagery

Comprehensive genomic characterization of squamous cell lung cancers

Lung squamous cell carcinoma is a common type of lung cancer, causing approximately 400,000 deaths per year worldwide. Genomic alterations in squamous cell lung cancers have not been comprehensively characterized, and no molecularly targeted agents have been specifically developed for its treatment. As part of The Cancer Genome Atlas, here we profile 178 lung squamous cell carcinomas to provide a comprehensive landscape of genomic and epigenomic alterations. We show that the tumour type is characterized by complex genomic alterations, with a mean of 360 exonic mutations, 165 genomic rearrangements, and 323 segments of copy number alteration per tumour. We find statistically recurrent mutations in 11 genes, including mutation of TP53 in nearly all specimens. Previously unreported loss-of-function mutations are seen in the HLA-A class I major histocompatibility gene. Significantly altered pathways included NFE2L2 and KEAP1 in 34%, squamous differentiation genes in 44%, phosphatidylinositol-3-OH kinase pathway genes in 47%, and CDKN2A and RB1 in 72% of tumours. We identified a potential therapeutic target in most tumours, offering new avenues of investigation for the treatment of squamous cell lung cancers.National Institutes of Health (U.S.) (Grant U24 CA126561)National Institutes of Health (U.S.) (Grant U24 CA126551)National Institutes of Health (U.S.) (Grant U24 CA126554)National Institutes of Health (U.S.) (Grant U24 CA126543)National Institutes of Health (U.S.) (Grant U24 CA126546)National Institutes of Health (U.S.) (Grant U24 CA126563)National Institutes of Health (U.S.) (Grant U24 CA126544)National Institutes of Health (U.S.) (Grant U24 CA143845)National Institutes of Health (U.S.) (Grant U24 CA143858)National Institutes of Health (U.S.) (Grant U24 CA144025)National Institutes of Health (U.S.) (Grant U24 CA143882)National Institutes of Health (U.S.) (Grant U24 CA143866)National Institutes of Health (U.S.) (Grant U24 CA143867)National Institutes of Health (U.S.) (Grant U24 CA143848)National Institutes of Health (U.S.) (Grant U24 CA143840)National Institutes of Health (U.S.) (Grant U24 CA143835)National Institutes of Health (U.S.) (Grant U24 CA143799)National Institutes of Health (U.S.) (Grant U24 CA143883)National Institutes of Health (U.S.) (Grant U24 CA143843)National Institutes of Health (U.S.) (Grant U54 HG003067)National Institutes of Health (U.S.) (Grant U54 HG003079)National Institutes of Health (U.S.) (Grant U54 HG003273