A Nearly Four-Year Longitudinal Study of Search-Engine Poisoning

We investigate the evolution of search-engine poisoning using data on over 5 million search results collected over nearly 4 years. We build on prior work investigating search-redirection attacks, where criminals compromise high-ranking websites and direct search traf-fic to the websites of paying customers, such as unlicensed phar-macies who lack access to traditional search-based advertisements. We overcome several obstacles to longitudinal studies by amalga-mating different resources and adapting our measurement infras-tructure to changes brought by adaptations by both legitimate op-erators and attackers. Our goal is to empirically characterize how strategies for carrying out and combating search poisoning have evolved over a relatively long time period. We investigate how the composition of search results themselves has changed. For in-stance, we find that search-redirection attacks have steadily grown to take over a larger share of results (rising from around 30 % in late 2010 to a peak of nearly 60 % in late 2012), despite efforts by search engines and browsers to combat their effectiveness. We also study the efforts of hosts to remedy search-redirection attacks. We find that the median time to clean up source infections has fallen from around 30 days in 2010 to around 15 days by late 2013, yet the number of distinct infections has increased considerably over the same period. Finally, we show that the concentration of traffic to the most successful brokers has persisted over time. Further, these brokers have been mostly hosted on a few autonomous systems, which indicates a possible intervention strategy. Categories and Subject Descriptors K.4.1 [Public Policy Issues]: Abuse and crime involving comput-er

Adversarial Matching of Dark Net Market Vendor Accounts

Many datasets feature seemingly disparate entries that actually refer to the same entity. Reconciling these entries, or matching, is challenging, especially in situations where there are errors in the data. In certain contexts, the situation is even more complicated: an active adversary may have a vested interest in having the matching process fail. By leveraging eight years of data, we investigate one such adversarial context: matching different online anonymous marketplace vendor handles to unique sellers. Using a combination of random forest classifiers and hierarchical clustering on a set of features that would be hard for an adversary to forge or mimic, we manage to obtain reasonable performance (over 75% precision and recall on labels generated using heuristics), despite generally lacking any ground truth for training. Our algorithm performs particularly well for the top 30% of accounts by sales volume, and hints that 22,163 accounts with at least one confirmed sale map to 15,652 distinct sellers---of which 12,155 operate only one account, and the remainder between 2 and 11 different accounts. Case study analysis further confirms that our algorithm manages to identify non-trivial matches, as well as impersonation attempts

Towards the reconstruction of the genome-scale metabolic model of Lactobacillus acidophilus La-14

Lactobacillus acidophilus is a probiotic lactic acid bacterium used in food and dietary supplements for many years. However, despite its importance for industrial development and recognized health-promoting effects, no genome-scale metabolic model has been reported. A GSM model for L. acidophilus La-14 was developed, accounting 494 genes and 783 reactions. A genome annotation was performed to identify the metabolic potential of the bacterium. The biomass composition was determined based on information available in literature and previously published models. The model was validated by comparing in silico simulations with experimental data, regarding the aerobic and anaerobic growth. The reconstruction of the metabolic model has confirmed the fastidious requirements of L. acidophilus for amino acids, fatty acids, and vitamins. This model can be used for a better understanding of the metabolism of this bacterium and identification of industrially desirable compounds.This study was performed under the scope of the project “BIODATA.PT – Portuguese Biological Data Network” (ref. LISBOA-01-0145-FEDER-022231), funded by FCT/MCTES, through national funds of PIDDAC, Fundo Europeu de Desenvolvimento Regional (FEDER), Programa Operacional de Competitividade e Internacionalização (POCI) and Programa Operacional Regional de Lisboa (Lisboa 2020).info:eu-repo/semantics/publishedVersio

Migalastat HCl Reduces Globotriaosylsphingosine (Lyso-Gb(3)) in Fabry Transgenic Mice and in the Plasma of Fabry Patients

Fabry disease (FD) results from mutations in the gene (GLA) that encodes the lysosomal enzyme α-galactosidase A (α-Gal A), and involves pathological accumulation of globotriaosylceramide (GL-3) and globotriaosylsphingosine (lyso-Gb3). Migalastat hydrochloride (GR181413A) is a pharmacological chaperone that selectively binds, stabilizes, and increases cellular levels of α-Gal A. Oral administration of migalastat HCl reduces tissue GL-3 in Fabry transgenic mice, and in urine and kidneys of some FD patients. A liquid chromatography-tandem mass spectrometry method was developed to measure lyso-Gb3 in mouse tissues and human plasma. Oral administration of migalastat HCl to transgenic mice reduced elevated lyso-Gb3 levels up to 64%, 59%, and 81% in kidney, heart, and skin, respectively, generally equal to or greater than observed for GL-3. Furthermore, baseline plasma lyso-Gb3 levels were markedly elevated in six male FD patients enrolled in Phase 2 studies. Oral administration of migalastat HCl (150 mg QOD) reduced urine GL-3 and plasma lyso-Gb3 in three subjects (range: 15% to 46% within 48 weeks of treatment). In contrast, three showed no reductions in either substrate. These results suggest that measurement of tissue and/or plasma lyso-Gb3 is feasible and may be warranted in future studies of migalastat HCl or other new potential therapies for FD

4-(4-nitrobenzyl)pyridine tests for alkylating agents following chemical oxidative activation

A chemical activation system (CAS) designed to mimic the mammalian mixed-function oxidase enzymes was found to activate target compounds to reactive electrophiles. Activated compounds were assayed by reaction with 4-(4-nitrobenzyl)pyridine (NBP). A model nucleophile of 7-alkylguanine of nucleic acids, NBP produces a violet color following alkylation. Twenty compounds from several chemical classes were tested. The test generally gave positive and negative responses where expected. Two compounds, trichloroethylene and diethylnitrosamine, exhibited a linear Beer's law relationship in the concentration range tested. A high degree of linear correlation (r>0.97) was obtained for these compounds. Other compounds showed varying degrees of linear correlation from high correlation (r=0.94) to weak correlation (r=0.44). The CAS-NBP assay results were compared to bacterial mutagenicity and animal carcinogenicity test results when information was available. A good correlation (r=0.80) existed between direct alkylating activity and direct mutagenicity. Similar correlations existed between NBP alkylation following activation and mutagenicity following microsomal activation (r=0.73). Also, different correlations were observed between carcinogenicity and NBP alkylation following activation (r=0.69) and without activation (r=0.38).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48076/1/244_2004_Article_BF00213289.pd

Sex- and age-related differences in the management and outcomes of chronic heart failure: an analysis of patients from the ESC HFA EORP Heart Failure Long-Term Registry

Aims: This study aimed to assess age- and sex-related differences in management and 1-year risk for all-cause mortality and hospitalization in chronic heart failure (HF) patients. Methods and results: Of 16 354 patients included in the European Society of Cardiology Heart Failure Long-Term Registry, 9428 chronic HF patients were analysed [median age: 66 years; 28.5% women; mean left ventricular ejection fraction (LVEF) 37%]. Rates of use of guideline-directed medical therapy (GDMT) were high (angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, beta-blockers and mineralocorticoid receptor antagonists: 85.7%, 88.7% and 58.8%, respectively). Crude GDMT utilization rates were lower in women than in men (all differences: P\ua0 64 0.001), and GDMT use became lower with ageing in both sexes, at baseline and at 1-year follow-up. Sex was not an independent predictor of GDMT prescription; however, age >75 years was a significant predictor of GDMT underutilization. Rates of all-cause mortality were lower in women than in men (7.1% vs. 8.7%; P\ua0=\ua00.015), as were rates of all-cause hospitalization (21.9% vs. 27.3%; P\ua075 years. Conclusions: There was a decline in GDMT use with advanced age in both sexes. Sex was not an independent predictor of GDMT or adverse outcomes. However, age >75 years independently predicted lower GDMT use and higher all-cause mortality in patients with LVEF 6445%

Analysing wireless EEG based functional connectivity measures with respect to change in environmental factors

In this paper we present a systematic exploration to formulate a predictive model of the human cognitive process with the changing environmental conditions at workplace. We select six different environmental conditions with small change in temperature/ventilation representative of realistic work environment having manual control. EEG data were acquired through 19-channel wireless system from three participants and CO2, Temperature, Relative humidity were recorded throughout the six conditions. The EEG data was pre-processed using an artifact reduction algorithm and 129 neurophysiological features were extracted from functional connectivity measures using complex network analysis. The environmental data were processed to generate 15 time/frequency domain features. Five best features selected through a ranking algorithm for all the variables across the six conditions were processed to formulate a model (environmental parameters as predictors) using retrospective 10-fold cross-validation in conjunction with multiple linear regression. The model was prospectively evaluated over 10 runs on a test set to predict the EEG variable across the six conditions and parameters corresponding to the run producing least root mean square error were reported. Our exploration shows that the condition having no modulation of the ambient environmental parameters reflects the optimum condition for predicting the EEG features using the examined environmental parameters