1,982 research outputs found

    RNA-binding protein CPEB1 remodels host and viral RNA landscapes.

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    Host and virus interactions occurring at the post-transcriptional level are critical for infection but remain poorly understood. Here, we performed comprehensive transcriptome-wide analyses revealing that human cytomegalovirus (HCMV) infection results in widespread alternative splicing (AS), shortening of 3' untranslated regions (3' UTRs) and lengthening of poly(A)-tails in host gene transcripts. We found that the host RNA-binding protein CPEB1 was highly induced after infection, and ectopic expression of CPEB1 in noninfected cells recapitulated infection-related post-transcriptional changes. CPEB1 was also required for poly(A)-tail lengthening of viral RNAs important for productive infection. Strikingly, depletion of CPEB1 reversed infection-related cytopathology and post-transcriptional changes, and decreased productive HCMV titers. Host RNA processing was also altered in herpes simplex virus-2 (HSV-2)-infected cells, thereby indicating that this phenomenon might be a common occurrence during herpesvirus infections. We anticipate that our work may serve as a starting point for therapeutic targeting of host RNA-binding proteins in herpesvirus infections

    Infrared video tracking of UAVs: Guided landing in the absence of GPS signals

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    Master's Project (M.S.) University of Alaska Fairbanks, 2019Unmanned Aerial Vehicles (UAVs) use Global Positioning System (GPS) signals to determine their position for automated flight. The GPS signals require an unobstructed view of the sky in order to obtain position information. When inside without a clear view of the sky, such as in a building or mine, other methods are necessary to obtain the relative position of the UAV. For obstacle avoidance a LIDAR/SONAR system is sufficient to ensure automated flight, but for precision landing the LIDAR/SONAR system is insufficient for effectively identifying the location of the landing platform and providing flight control inputs to guide the UAV to the landing platform. This project was developed in order to solve this problem by creating a guidance system utilizing an infrared (IR) camera to track an IR LED and blue LEDs mounted on the UAV from a RaspberryPI 3 Model B+. The RaspberryPI, using OpenCV libraries, can effectively track the position of the LED lights mounted on the UAV, determine rotational and lateral corrections based on this tracking, and, using Dronekit-Python libraries, command the UAV to position itself and land on the platform of the Husky UGV (Unmanned Ground Vehicle)

    Foxp2 controls synaptic wiring of corticostriatal circuits and vocal communication by opposing Mef2c

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    Cortico-basal ganglia circuits are critical for speech and language and are implicated in autism spectrum disorder, in which language function can be severely affected. We demonstrate that in the mouse striatum, the gene Foxp2 negatively interacts with the synapse suppressor gene Mef2c. We present causal evidence that Mef2c inhibition by Foxp2 in neonatal mouse striatum controls synaptogenesis of corticostriatal inputs and vocalization in neonates. Mef2c suppresses corticostriatal synapse formation and striatal spinogenesis, but can itself be repressed by Foxp2 through direct DNA binding. Foxp2 deletion de-represses Mef2c, and both intrastriatal and global decrease of Mef2c rescue vocalization and striatal spinogenesis defects of Foxp2-deletion mutants. These findings suggest that Foxp2-Mef2C signaling is critical to corticostriatal circuit formation. If found in humans, such signaling defects could contribute to a range of neurologic and neuropsychiatric disorders.National Institutes of Health (U.S.) (Grant R37 HD028341)Eunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) (Award R37 HD028341

    Early Forest Fire Detection via Principal Component Analysis of Spectral and Temporal Smoke Signature

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    The goal of this study is to develop a smoke detecting algorithm using digital image processing techniques on multi-spectral (visible & infrared) video. By utilizing principal component analysis (PCA) followed by spatial filtering of principal component images the location of smoke can be accurately identified over a period of exposure time with a given frame capture rate. This result can be further analyzed with consideration of wind factor and fire detection range to determine if a fire is present within a scene. Infrared spectral data is shown to contribute little information concerning the smoke signature. Moreover, finalized processing techniques are focused on the blue spectral band as it is furthest away from the infrared spectral bands and because it experimentally yields the largest footprint in the processed principal component images in comparison to other spectral bands. A frame rate of .5 images/sec (1 image every 2 seconds) is determined to be the maximum such that temporal variance of smoke can be captured. The study also shows eigenvectors corresponding to the principal components that best represent smoke and are valuable indications of smoke temporal signature. Raw video data is taken through rigorous pre-processing schemes to align frames from respective spectral band both spatially and temporally. A multi-paradigm numerical computing program, MATLAB, is used to match the field of view across five spectral bands: Red, Green, Blue, Long-Wave Infrared, and Mid-Wave Infrared. Extracted frames are aligned temporally from key frames throughout the data capture. This alignment allows for more accurate digital processing for smoke signature. v Clustering analysis on RGB and HSV value systems reveal that color alone is not helpful to segment smoke. The feature values of trees and other false positives are shown to be too closely related to features of smoke for in solely one instance in time. A temporal principal component transform on the blue spectral band eliminates static false positives and emphasizes the temporal variance of moving smoke in images with higher order. A threshold adjustment is applied to a blurred blue principal component of non-unity principal component order and smoke results can be finalized using median filtering. These same processing techniques are applied to difference images as a more simple and traditional technique for identifying temporal variance and results are compared
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