Sharif Digital Repository

People use social networks to share millions of stories every day, but these stories rarely become viral. Can we estimate the probability that a story becomes a viral cascade If so, can we find a set of users that are more likely to trigger viral cascades These estimation and maximization problems are very challenging since both rare-event nature of viral cascades and efficiency requirement should be considered. Unfortunately, this problem still remains largely unexplored to date. In this paper, given temporal dynamics of a network, we first develop an efficient viral cascade probability estimation method, VICE, that leverages an special importance sampling approximation to achieve high... 

People use social networks to share millions of stories every day, but these stories rarely become viral. Can we estimate the probability that a story becomes a viral cascade? If so, can we find a set of users that are more likely to trigger viral cascades? These estimation and maximization problems are very challenging since both rare-event nature of viral cascades and efficiency requirement should be considered. Unfortunately, this problem still remains largely unexplored to date. In this paper, given temporal dynamics of a network, we first develop an efficient viral cascade probability estimation method, ViCE, that leverages an special importance sampling approximation to achieve high... 

Social media usage is one of the most popular online activities and people shares millions of message in a short time; however this information rarely goes viral. The diffusion process begins with an initial set of source nodes and continues with other nodes. In addition, the viral cascade is triggered when the number of infected nodes exceeds a specific threshold. Then, we find an initial set of source nodes that maximizes the number of infected nodes given the source nodes. This study aims to answer the following questions: how does a spread like a viral cascade propagate in a network? Do the structural properties of the propagation pattern play an important role in virality? If so, can we... 

Wireless mesh networks (WMNs) are well-promising future networks especially for ubiquitous connection to Internet. In this paper, we propose an analytical model in order to evaluate the maximum stable throughput of a WMN based on a simplified version of IEEE 802.11s MAC protocol. Our modeling approach is based on a multi-class open queueing network. In this respect, we map some of the important features of IEEE 802.11s MAC protocol, esp., its contention-based and contention-free periods onto different parameters of the proposed queueing network. By solving the related traffic equations and applying the stability condition, we determine the maximum stable throughput, i.e., the maximum packet... 

Background: Focal segmental glomerulosclerosis (FSGS) is considered one of the most severe glomerular diseases and around 80% of cases are resistant to steroid treatment. Since a large proportion of steroid-resistant (SR) FSGS patients progress to end-stage renal disease, other therapeutic strategies may benefit this population. However, identification of non-invasive biomarkers to predict this high-risk population is needed. Objective: We aimed to identify the biomarker candidates to distinguish SR from steroid-sensitive (SS) patients using metabolomics approach and to identify the possible molecular mechanism of resistance. Methods: Urine was collected from biopsy-proven FSGS patients... 

Acute T cell-mediated rejection (TCMR)is a major complication after renal transplantation. TCMR diagnosis is very challenging and currently depends on invasive renal biopsy and nonspecific markers such as serum creatinine. A noninvasive metabolomics panel could allow early diagnosis and improved accuracy and specificity. We report, in this study, on urine metabolome changes in renal transplant recipients diagnosed with TCMR, with a view to future metabolomics-based diagnostics in transplant medicine. We performed urine metabolomic analyses in three study groups: (1) 7 kidney transplant recipients with acute TCMR, (2) 15 kidney transplant recipients without rejection but with impaired kidney... 

Contrast induced nephropathy (CIN) has been reported to be the third foremost cause of acute renal failure. Metabolomics is a robust technique that has been used to identify potential biomarkers for the prediction of renal damage. We aim to analyze the serum and urine metabolites changes, before and after using contrast for coronary angiography, to determine if metabolomics can predict early development of CIN. 66 patients undergoing elective coronary angiography were eligible for enrollment. Urine and serum samples were collected prior to administration of CM and 72 h post procedure and analyzed by nuclear magnetic resonance. The significant differential metabolites between patients who...