Closed-head concussive injury is one of the most common causes of traumatic brain injury (TBI). While single concussions result in short-term neurologic dysfunction, multiple concussions can result in cumulative damage and increased risk for neurodegenerative disease. Despite the prevalence of concussion, knowledge about what occurs in the brain following this injury is limited, in part due to the limited number of appropriate animal research models. To study clinically relevant concussion we recently developed a simple, non-invasive rodent model of closed-head projectile concussive impact (PCI) TBI. For this purpose, anesthetized rats were placed on a platform positioned above a torque-sealed microcentrifuge tube packed with fixed amounts of dry ice. Upon heating, rapid sublimation of the dry ice produced a build-up of compressed CO(2) that triggered an eruptive force causing the cap to launch as an intact projectile, resulting in a targeted PCI head injury. A stainless steel helmet was implemented to protect the head from bruising, yet allowing the brain to sustain a mild PCI event. Depending on the injury location and the application of the helmet, PCI-induced injuries ranged from severe (i.e., head injury with subdural hematomas, intracranial hemorrhage, and brain tissue damage), to mild (no head injury, intracranial hemorrhage, or gross morphological pathology). Although no gross pathology was evident in mild PCI-induced injury, the following protein changes and behavioral abnormalities were detected between 1 and 24 h after PCI injury: (1) upregulation of glial fibrillary acidic protein (GFAP) in hippocampal regions; (2) upregulation of ubiquitin carboxyl-terminal hydrolase L1 (UCHL-1) in cortical tissue; and (3) significant sensorimotor abnormalities. Overall, these results indicated that this PCI model was capable of replicating salient pathologies of a clinical concussion, and could generate reproducible and quantifiable outcome measures.
Damping Bragg scattering from the ocean surface is the basic underlying principle of synthetic aperture radar (SAR) oil slick detection, and they produce dark spots on SAR images. Dark spot detection is the first step in oil spill detection, which affects the accuracy of oil spill detection. However, some natural phenomena (such as waves, ocean currents, and low wind belts, as well as human factors) may change the backscatter intensity on the surface of the sea, resulting in uneven intensity, high noise, and blurred boundaries of oil slicks or lookalikes. In this paper, Segnet is used as a semantic segmentation model to detect dark spots in oil spill areas. The proposed method is applied to a data set of 4200 from five original SAR images of an oil spill. The effectiveness of the method is demonstrated through the comparison with fully convolutional networks (FCN), an initiator of semantic segmentation models, and some other segmentation methods. It is here observed that the proposed method can not only accurately identify the dark spots in SAR images, but also show a higher robustness under high noise and fuzzy boundary conditions.
Abstract-A bipartite concentrator is a single stage sparse crossbar switching device that can connect any m of its n ≥ m inputs to its m outputs, possibly without the ability to distinguish their order. Fat-andslim crossbars were introduced recently to show that bipartite concentrators can be constructed with a minimum number of crosspoints for any number of inputs and outputs. We generalize these graphs to obtain bipartite concentrators with nearly a fixed fanout without altering their (n -m + 1)m crosspoint complexity. We also present an O(log n)-time algorithm to route arbitrary concentration assignments on this new family of fat-and-slim crossbars.
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