Anti-PI3 monoclonal antibody, clone FQS25742

The phosphoinositide 3-kinase (PI3-K)/Akt signaling pathway is important in the regulation of cell proliferation through its production of phosphatidylinositol 3,4,5-triphosphate (PIP3). Activation of this pathway is frequently observed in human cancers, including non-small cell lung carcinoma. The PI3-K/Akt pathway is negatively regulated by the dual-specificity phosphatase and tensin homolog (PTEN) protein. PTEN acts as a direct antagonist of PI3-K by dephosphorylating PIP3. Studies have shown that PTEN phosphatase activity is inhibited by PREX2, a guanine nucleotide exchanger factor (GEF). Multiple studies revealed that CELF2, an RNA binding protein, cooperates synergistically with PTEN as a tumor suppressor in multiple cancers. However, the underlying mechanism as to how CELF2 enhances PTEN activity remains unclear. Here, we report that CELF2 interacts with PREX2 and reduces the association of PREX2 with PTEN. Consistent with this observation, PTEN phosphatase activity is upregulated with CELF2 overexpression. In addition, overexpression of CELF2 represses both Akt phosphorylation and cell proliferation only in the presence of PTEN. In an ex vivo study, CELF2 gene delivery could significantly inhibit patient-derived xenografts (PDX) tumor growth. To further investigate the clinical relevance of this finding, we analyzed 87 paired clinical lung adenocarcinoma samples and the results showed that CELF2 protein expression is downregulated in tumor tissues and associated with poor prognosis. The CELF2 gene is located on the chromosome 10p arm, a region frequently lost in human cancers, including breast invasive carcinoma, low-grade glioma and glioblastoma. Analysis of TCGA datasets showed that CELF2 expression is also associated with shorter patient survival time in all these cancers. Overall, our work suggests that CELF2 plays a novel role in PI3-K signaling by antagonizing the oncogenic effect of PREX2.

A system to automatically recognize vehicle license plates is a growing need to improve safety and traffic control, specifically in major urban centers. However, the license plate recognition task is generally computationally intensive, where the entire input image frame is scanned, the found plates are segmented, and character recognition is then performed for each segmented character. This paper presents a methodology for engineering a system to detect and recognize Brazilian license plates using convolutional neural networks (CNN) that is suitable for embedded systems. The resulting system detects license plates in the captured image using Tiny YOLOv3 architecture and identifies its characters using a second convolutional network trained on synthetic images and fine-tuned with real license plate images. The proposed architecture has demonstrated to be robust to angle, lightning, and noise variations while requiring a single forward pass for each network, therefore allowing faster processing compared to other deep learning approaches. Our methodology was validated using real license plate images under different environmental conditions reached a detection rate of 99.37% and an overall recognition rate of 98.43% while showing an average time of 2.70 s to process 1024x768 images with a single license plate in a Raspberry Pi3 (ARM Cortex-A53 CPU). To improve the recognition accuracy, an ensemble of CNN models was tested instead of a single CNN model, which resulted in an increase in the average processing time to 4.88 s for each image while increasing the recognition rate to 99.53%. Finally, we discuss the impact of using an ensemble of CNNs considering the accuracy-performance trade-off when engineering embedded systems for license plate recognition.