Analyzing the Social Structure and Dynamics of E-mail and Spam in Massive Backbone Internet Traffic

E-mail is probably the most popular application on the Internet, with everyday business and personal communications dependent on it. Spam or unsolicited e-mail has been estimated to cost businesses significant amounts of money. However, our understanding of the network-level behavior of legitimate e-mail traffic and how it differs from spam traffic is limited. In this study, we have passively captured SMTP packets from a 10 Gbit/s Internet backbone link to construct a social network of e-mail users based on their exchanged e-mails. The focus of this paper is on the graph metrics indicating various structural properties of e-mail networks and how they evolve over time. This study also looks into the differences in the structural and temporal characteristics of spam and non-spam networks. Our analysis on the collected data allows us to show several differences between the behavior of spam and legitimate e-mail traffic, which can help us to understand the behavior of spammers and give us the knowledge to statistically model spam traffic on the network-level in order to complement current spam detection techniques.Comment: 15 pages, 20 figures, technical repor

Dual mechanisms of action of the RNA-binding protein human antigen R explains its regulatory effect on melanoma cell migration

Overexpression of wingless-type MMTV integration site family 5A (WNT5A) plays a significant role in melanoma cancer progression; however, the mechanism(s) involved remains unknown. In breast cancer, the human antigen R (HuR) has been implicated in the regulation of WNT5A expression. Here, we demonstrate that endogenous expression of WNT5A correlates with levels of active HuR in HTB63 and WM852 melanoma cells and that HuR binds to WNT5A messenger RNA in both cell lines. Although the HuR inhibitor MS-444 significantly impaired migration in both melanoma cell lines, it reduced WNT5A expression only in HTB63 cells, as did small interfering RNA knockdown of HuR. Consistent with this finding, MS-444-induced inhibition of HTB63 cell migration was restored by the addition of recombinant WNT5A, whereas MS-444-induced inhibition of WM852 cell migration was restored by the addition of recombinant matrix metalloproteinase-9, another HuR-regulated protein. Clearly, HuR positively regulates melanoma cell migration via at least 2 distinct mechanisms making HuR an attractive therapeutic target for halting melanoma dissemination

Measuring Iran’s success in achieving Millennium Development Goal 4: a systematic analysis of under-5 mortality at national and subnational levels from 1990 to 2015

Background Child mortality as one of the key Millennium Development Goals (MDG 4—to reduce child mortality by two-thirds from 1990 to 2015), is included in the Sustainable Development Goals (SDG 3, target 2—to reduce child mortality to fewer than 25 deaths per 1000 livebirths for all countries by 2030), and is a key indicator of the health system in every country. In this study, we aimed to estimate the level and trend of child mortality from 1990 to 2015 in Iran, to assess the progress of the country and its provinces toward these goals. Methods We used three different data sources: three censuses, a Demographic and Health Survey (DHS), and 5-year data from the death registration system. We used the summary birth history data from four data sources (the three censuses and DHS) and used maternal age cohort and maternal age period methods to estimate the trends in child mortality rates, combining the estimates of these two indirect methods using Loess regression. We also used the complete birth history method to estimate child mortality rate directly from DHS data. Finally, to synthesise different trends into a single trend and calculate uncertainty intervals (UI), we used Gaussian process regression. Findings Under-5 mortality rates (deaths per 1000 livebirths) at the national level in Iran in 1990, 2000, 2010, and 2015 were 63·6 (95% UI 63·1–64·0), 38·8 (38·5–39·2), 24·9 (24·3–25·4), and 19·4 (18·6–20·2), respectively. Between 1990 and 2015, the median annual reduction and total overall reduction in these rates were 4·9% and 70%, respectively. At the provincial level, the difference between the highest and lowest child mortality rates in 1990, 2000, and 2015 were 65·6, 40·4, and 38·1 per 1000 livebirths, respectively. Based on the MDG 4 goal, five provinces had not decreased child mortality by two-thirds by 2015. Furthermore, six provinces had not reached SDG 3 (target 2). Interpretation Iran and most of its provinces achieved MDG 4 and SDG 3 (target 2) goals by 2015. However, at the subnational level in some provinces, there is substantial inequity. Local policy makers should use effective strategies to accelerate the reduction of child mortality for these provinces by 2030. Possible recommendations for such strategies include enhancing the level of education and health literacy among women, tackling sex discrimination, and improving incomes for families

Rigosertib elicits potent anti-tumor responses in colorectal cancer by inhibiting Ras signaling pathway

Background: The therapeutic potency of Rigosertib (RGS) in the treatment of the myelodysplastic syndrome has been investigated previously, but little is known about its mechanisms of action. Methods: The present study integrates systems and molecular biology approaches to investigate the mechanisms of the anti-tumor effects of RGS, either alone or in combination with 5-FU in cellular and animal models of colorectal cancer (CRC). Results: The effects of RGS were more pronounced in dedifferentiated CRC cell types, compared to cell types that were epithelial-like. RGS inhibited cell proliferation and cell cycle progression in a cell-type specific manner, and that was dependent on the presence of mutations in KRAS, or its down-stream effectors. RGS increased both early and late apoptosis, by regulating the expression of p53, BAX and MDM2 in tumor model. We also found that RGS induced cell senescence in tumor tissues by increasing ROS generation, and impairing oxidant/anti-oxidant balance. RGS also inhibited angiogenesis and metastatic behavior of CRC cells, by regulating the expression of CD31, E-cadherin, and matrix metalloproteinases-2 and 9. Conclusion: Our findings support the therapeutic potential of this potent RAS signaling inhibitor either alone or in combination with standard regimens for the management of patients with CRC.Peer reviewe

Angiotensin receptor blocker losartan inhibits tumor growth of colorectal cancer

The renin-angiotensin system (RAS) is up-regulated in patients with colorectal cancer (CRC) and is reported to be associated with poor prognosis and chemo-resistance. Here we explored the therapeutic potential of targeting RAS in CRC using Losartan, an angiotensin receptor blocker. An integrative-systems biology approach was used to explore a proteome-level dataset of a gene signature that is modulated by Losartan. The anti-proliferative activity of Losartan was evaluated using 2- and 3-dimensional cell culture models. A xenograft model of colon cancer was used to investigate tumor growth with Losartan alone and in combination with 5-FU followed by histological staining (Hematoxylin & Eosin and Masson trichrome staining), biochemical analyses, gene expression analyses by RT-PCR, western blot/IHC, or MMP Gelatin Zymography studies. Effects on cell cycle and cell death were assessed by flow cytometry. Losartan inhibited cell growth and suppressed cell cycle progression, causing an increase in CRC cells in the G1 phase. Losartan significantly reduced tumor growth and enhanced tumor cell necrosis. An impact on the inflammatory response, including up-regulation of pro-inflammatory cytokines and chemokines in CRC cells are potential mechanisms that could partially explain Losartan’s anti-proliferative effects. Moreover, metastasis and angiogenesis were reduced in Losartan-treated mice as observed by inhibited matrix metalloproteinase-2 and -9 activities and decreased tumor vasculature. These data demonstrate the therapeutic potential of combining chemotherapeutic regimens with Losartan to synergistically enhance its activity and target the renin-angiotensin system as a new approach in colorectal cancer treatment

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Towards Understanding the Social Structure of Email and Spam Traffic

Email is a pervasive means of communication on the Internet. Email exchanges between individuals can be seen as social interactions between email sender(s) and receiver(s), thus can be represented as a network. Networks of human interactions such as friendship relations, research collaborations, and phone calls have been widely studied before to allow understanding of the characteristics, as well as the structure and dynamics of such social interactions. In this thesis, we look into the social network properties of email networks generated from real traffic, and investigate how a vast amount of unsolicited email traffic (spam) affect these properties.Current advances in Internet data collection and processing has facilitated the study of the characteristics of email traffic observed on the Internet. In our study, we have collected large-scale email datasets from traffic traversing a high-speed Internet backbone link and have generated email networks from the observed communications to analyze the structure and dynamics of these social interactions. Moreover, we aim at unveiling the distinguishingcharacteristics of legitimate and unsolicited email communications.We show that the networks of legitimate email traffic has the same structural and temporal properties that other social networks exhibit, and therefore can be modeled as small-world scale-free networks. However, the unsolicited email communications cause deviations and anomalies in the structure of email networks, and this deviation from the expected socialstructural properties can be used to find the sources of spam email.We also show that email networks, similar to other social networks, have a community structure which can be found using different community detection algorithms. However,not all community detection algorithms can identify structural communities that coincide with the true logical communities of email networks, i.e., distinct communities of legitimateand unsolicited email. Our study shows that a link-based community detection algorithm is more suitable for this purpose than more widely used node-based algorithms.The possibility of merely using the social structure of email traffic to identify the source of spam and separate the unsolicited email from legitimate email, can potentially be used to improve the protection against spam and other types of malicious activities on the Internet

Towards Understanding the Social Structure of Email and Spam Traffic

Email is a pervasive means of communication on the Internet. Email exchanges between individuals can be seen as social interactions between email sender(s) and receiver(s), thus can be represented as a network. Networks of human interactions such as friendship relations, research collaborations, and phone calls have been widely studied before to allow understanding of the characteristics, as well as the structure and dynamics of such social interactions. In this thesis, we look into the social network properties of email networks generated from real traffic, and investigate how a vast amount of unsolicited email traffic (spam) affect these properties.Current advances in Internet data collection and processing has facilitated the study of the characteristics of email traffic observed on the Internet. In our study, we have collected large-scale email datasets from traffic traversing a high-speed Internet backbone link and have generated email networks from the observed communications to analyze the structure and dynamics of these social interactions. Moreover, we aim at unveiling the distinguishingcharacteristics of legitimate and unsolicited email communications.We show that the networks of legitimate email traffic has the same structural and temporal properties that other social networks exhibit, and therefore can be modeled as small-world scale-free networks. However, the unsolicited email communications cause deviations and anomalies in the structure of email networks, and this deviation from the expected socialstructural properties can be used to find the sources of spam email.We also show that email networks, similar to other social networks, have a community structure which can be found using different community detection algorithms. However,not all community detection algorithms can identify structural communities that coincide with the true logical communities of email networks, i.e., distinct communities of legitimateand unsolicited email. Our study shows that a link-based community detection algorithm is more suitable for this purpose than more widely used node-based algorithms.The possibility of merely using the social structure of email traffic to identify the source of spam and separate the unsolicited email from legitimate email, can potentially be used to improve the protection against spam and other types of malicious activities on the Internet

Improving Community Detection Methods for Network Data Analysis

Empirical analysis of network data has been widely conducted for understanding and predicting the structure and function of real systems and identifying interesting patterns and anomalies. One of the most widely studied structural properties of networks is their community structure. In this thesis we investigate some of the challenges and applications of community detection for analysis of network data and propose different approaches for improving community detection methods.One of the challenges in using community detection for network data analysis is that there is no consensus on a definition for a community despite excessive studies which have been performed on the community structure of real networks. Therefore, evaluating the quality of the communities identified by different community detection algorithms is problematic. In this thesis, we perform an empirical comparison and evaluation of the quality of the communities identified by a variety of community detection algorithms which use different definitions for communities for different applications of network data analysis. Another challenge in using community detection for analysis of network data is the scalability of the existing algorithms. Parallelizing community detection algorithms is one way to improve the scalability of community detection. Local community detection algorithms are by nature suitable for parallelization. One of the most successful approaches to local community detection is local expansion of seed nodes into overlapping communities. However, the communities identified by a local algorithm might cover only a subset of the nodes in a network if the seeds are not selected carefully. The selection of good seeds that are well distributed over a network using only the local structure of a network is therefore crucial. In this thesis, we propose a novel local seeding algorithm, which is based on link prediction and graph coloring, for selecting good seeds for local community detection in large-scale networks.Overall, mining network data has many applications. The focus of this thesis is on analyzing network data obtained from backbone Internet traffic, social networks, and search query log files. We show that mining the structural and temporal properties of email networks generated from Internet backbone traffic can be used to identify unsolicited email from the mixture of email traffic. We also show that a link based community detection algorithm can separate legitimate and unsolicited email into distinct communities. Moreover, we show that, in contrast to previous studies, community detection algorithms can be used for network anomaly detection. We also propose a method for enhancing community detection algorithms and present a framework for using community detection as a basis for network misbehavior detection. Finally, we show that network analysis of query log files obtained from a health care portal can complement the existing methods for semantic analysis of health related queries