Suchergebnisse

The main application of helmet detection is in traffic roads where accidents are more. Even though various measures are taken by government, it is not followed correctly by the motorcyclists, so several smart techniques should be employed. In developing countries like India, the two-wheeler is the most common means of transportation. Though it is convenient to ride, negligence of Helmet compulsion law by the riders is leading to many accidents. According to the statistics provided in the Road Accident Report, at least 98 riders, who were not wearing a helmet, died daily in 2017. Deaths caused by not wearing helmet rose to 36,000 in 2017 which was 10,135 in 2016. WHO has declared that the negligence of riders in using safety devices as one of the causes for the rise in road accidents. Government has implemented many methods to catch the violators. But those methods require human assistance, which decreases the performance and reliability of the system. In this project, an automatic helmet detection system is designed using Faster RCNN machine learning algorithm. Faster RCNN algorithm is used to detect the helmet.

The main application of helmet detection is in traffic roads where accidents are more. Even though various measures are taken by government, it is not followed correctly by the motorcyclists, so several smart techniques should be employed. In developing countries like India, the two-wheeler is the most common means of transportation. Though it is convenient to ride, negligence of Helmet compulsion law by the riders is leading to many accidents. According to the statistics provided in the Road Accident Report, at least 98 riders, who were not wearing a helmet, died daily in 2017. Deaths caused by not wearing helmet rose to 36,000 in 2017 which was 10,135 in 2016. WHO has declared that the negligence of riders in using safety devices as one of the causes for the rise in road accidents. Government has implemented many methods to catch the violators. But those methods require human assistance, which decreases the performance and reliability of the system. In this project, an automatic helmet detection system is designed using Faster RCNN machine learning algorithm. Faster RCNN algorithm is used to detect the helmet.

Abstract
A small population of self-renewing stem cells initiate tumors and maintain therapeutic resistance in glioblastoma (GBM). Given the limited treatment options and dismal prognosis for this disease, there is urgent need to identify drivers of stem cells that could be druggable targets. Previous work showed that the endosomal pH regulator NHE9 is upregulated in GBM and correlates with worse survival prognosis. Here, we probed for aberrant signaling pathways in patient-derived GBM cells and found that NHE9 increases cell surface expression and phosphorylation of multiple receptor tyrosine kinases (RTKs) by promoting their escape from lysosomal degradation. Downstream of NHE9-mediated receptor activation, oncogenic signaling pathways converged on the JAK2-STAT3 transduction axis to induce pluripotency genes Oct4 and Nanog and suppress markers of glial differentiation. We used both genetic and chemical approaches to query the role of endosomal pH in GBM phenotypes. Loss-of-function mutations in NHE9 that failed to alkalinize endosomal lumen did not increase self-renewal capacity of gliomaspheres in vitro. However, monensin, a chemical mimetic of Na+/H+ exchanger activity, and the H+ pump inhibitor bafilomycin bypassed NHE9 to directly alkalinize the endosomal lumen resulting in stabilization of RTKs and induction of Oct4 and Nanog. Using orthotopic models of primary GBM cells we found that NHE9 increased tumor initiation in vivo. We propose that NHE9 initiates inside-out signaling from the endosomal lumen, distinct from the established effects of cytosolic and extracellular pH on tumorigenesis. Endosomal pH may be an attractive therapeutic target that diminishes stemness in GBM, agnostic of specific receptor subtype.