Goat Anti-Dog TF Polyclonal Antibody

Phishing is the easiest way to use cybercrime with the aim of enticing people to give accurate information such as account IDs, bank details, and passwords. This type of cyberattack is usually triggered by emails, instant messages, or phone calls. The existing anti-phishing techniques are mainly based on source code features, which require to scrape the content of web pages, and on third-party services which retard the classification process of phishing URLs. Although the machine learning techniques have lately been used to detect phishing, they require essential manual feature engineering and are not an expert at detecting emerging phishing offenses. Due to the recent rapid development of deep learning techniques, many deep learning-based methods have also been introduced to enhance the classification performance. In this paper, a fast deep learning-based solution model, which uses character-level convolutional neural network (CNN) for phishing detection based on the URL of the website, is proposed. The proposed model does not require the retrieval of target website content or the use of any third-party services. It captures information and sequential patterns of URL strings without requiring a prior knowledge about phishing, and then uses the sequential pattern features for fast classification of the actual URL. For evaluations, comparisons are provided between different traditional machine learning models and deep learning models using various feature sets such as hand-crafted, character embedding, character level TF-IDF, and character level count vectors features. According to the experiments, the proposed model achieved an accuracy of 95.02% on our dataset and an accuracy of 98.58%, 95.46%, and 95.22% on benchmark datasets which outperform the existing phishing URL models.

T issue factor (TF) is highly expressed in sub-endothelial tissue. The extracellular allosteric disulfide bond Cys186-Cys209 of human TF shows high evolutionary conservation and in vitro evidence suggests that it significantly contributes to TF procoagulant activity. To investigate the role of this allosteric disulfide bond in vivo, we generated a C213G mutant Tf mouse by replacing Cys213 of the corresponding disulfide Cys190-Cys213 in murine Tf A bleeding phenotype was prominent in homozygous C213G Tf mice. Pre-natal lethality of one third of homozygous offspring was observed between embryonic (E) day E9.5 and E14.5 associated with placental hemorrhages. After birth, homozygous mice suffered from bleedings in different organs and reduced survival. Homozygous C213G Tf male mice showed higher incidence of lung bleedings and lower survival rates than females. In both sexes, C213G mutation evoked a reduced protein expression (about 10-fold) and severely reduced pro-coagulant activity (at least 100-fold). Protein glycosylation was impaired and cell membrane exposure decreased in macrophages in vivo. Single housing of homozygous C213G Tf males reduced the occurrence of severe bleeding and significantly improved survival, suggesting that inter-male aggressiveness might significantly account for the sex differences. These experiments show that the TF allosteric disulfide bond is of crucial importance for normal in vivo expression, post-translational processing and activity of murine TF. Although C213G Tf mice do not display the severe embryonic lethality of Tf knock-out mice, their postnatal bleeding phenotype emphasizes the importance of fully functional TF for hemostasis.