Heart failure subtypes and thromboembolic risk in patients with atrial fibrillation::The PREFER in AF - HF substudy

BACKGROUND AND OBJECTIVES: To assess thromboembolic and bleeding risks in patients with heart failure (HF) and atrial fibrillation (AF) according to HF type. METHODS: We analyzed 6170 AF patients from the Prevention of thromboembolic events - European Registry in Atrial Fibrillation (PREFER in AF), and categorized patients into: HF with reduced left-ventricular ejection fraction (HFrEF; LVEF60%), and no HF. Outcomes were ischemic stroke, major adverse cardiovascular and cerebral events (MACCE) and major bleeding occurring within 1-year. RESULTS: The annual incidence of stroke was linearly and inversely related to LVEF, increasing by 0.054% per each 1% of LVEF decrease (95% CI: 0.013%-0.096%; p=0.031). Patients with HFHpEF had the highest CHA2DS2-VASc score, but significantly lower stroke incidence than other HF groups (0.65%, compared to HFLpEF 1.30%; HFmrEF 1.71%; HFrEF 1.75%; trend p=0.014). The incidence of MACCE was also lower in HFHpEF (2.0%) compared to other HF groups (range: 3.8-4.4%; p=0.001). Age, HF type, and NYHA class were independent predictors of thromboembolic events. Conversely, major bleeding did not significantly differ between groups (p=0.168). CONCLUSION: Our study in predominantly anticoagulated patients with AF shows that, reduction in LVEF is associated with higher thromboembolic, but not higher bleeding risk. HFHpEF is a distinct and puzzling group, featuring the highest CHA2DS2-VASc score but the lowest residual risk of thromboembolic events, which warrants further investigation

Genomic Diversity of Mycobacterium tuberculosis Complex Strains in Cantabria (Spain), a Moderate TB Incidence Setting

Background Tuberculosis (TB) control strategies are focused mainly on prevention, early diagnosis, compliance to treatment and contact tracing. The objectives of this study were to explore the frequency and risk factors of recent transmission of clinical isolates of Mycobacterium tuberculosis complex (MTBC) in Cantabria in Northern Spain from 2012 through 2013 and to analyze their clonal complexity for better understanding of the transmission dynamics in a moderate TB incidence setting. Methods DNA from 85 out of 87 isolates from bacteriologically confirmed cases of MTBC infection were extracted directly from frozen stocks and genotyped using the mycobacterial interspersed repetitive units-variable number tandem repeat (MIRU-VNTR) method. The MIRUVNTRplus database tool was used to identify clusters and lineages and to build a neighbor joining (NJ) phylogenetic tree. In addition, data were compared to the SITVIT2 database at the Pasteur Institute of Guadeloupe. Results The rate of recent transmission was calculated to 24%. Clustering was associated with being Spanish-born. A high prevalence of isolates of the Euro-American lineage was found. In addition, MIRU-VNTR profiles of the studied isolates corresponded to previously found MIRU-VNTR types in other countries, including Spain, Belgium, Great Britain, USA, Croatia, South Africa and The Netherlands. Six of the strains analyzed represented clonal variants. Conclusion Transmission of MTBC is well controlled in Cantabria. The majority of TB patients were born in Spain. The population structure of MTBC in Cantabria has a low diversity of major clonal lineages with the Euro-American lineage predominating

Using per-Host Measurements for Fast Internet Traffic Classification

Accurate classification of Internet traffic is of fundamental importance for network management applications such as security monitoring, accounting, Quality-of-Service (QoS) provisioning, and for providing operators with useful information for network planning. Classical port-based or payload-based classification techniques are becoming less effective, because of the increasing presence of protocol obfuscation and payload encryption in today’s internet traffic. Therefore, there is growing interest in classification algorithms that only look at the IP and transport packet headers, along with other information which are difficult to obfuscate, such as the packet lengths and the interarrival times. Several recent papers have identified machine learning techniques as a viable technique for designing a classifier capable of dealing with the wide variety of protocols and implementations. In the real-time scenario, a traffic flow has to be classified by looking only at the first packets of the flow. In this context, measuring the activity of internet hosts can provide useful information about the applications that are generating the traffic coming from that host. In particular, we assume that the sequence of TCP connection requests (or, for UDP traffic, the sequence of new flows) generated by a given host using a given transport protocol towards a given transport port can be modeled as a random process with a power spectral density decaying according to a power law. Computation of the power law exponent for a given host/port pair requires some computational effort, but is available at the beginning of each flow, with no additional delay. In this paper, we show that using such information makes it possible to achieve a good classification accuracy by looking at very few packets, therefore yielding very quick response

Using per-Source Measurements to Improve Performance of Internet Traffic Classification

Obfuscated and encrypted protocols hinder traffic classification by classical techniques such as port analysis or deep packet inspection. Therefore, there is growing interest for classification algorithms based on statistical analysis of the length of the first packets of flows. Most classifiers proposed in literature are based on machine learning techniques and consider each flow independently of previous source activity (per-flow analysis). In this paper, we propose to use specific per-source information to improve classification accuracy: the sequence of starting times of flows generated by single sources may be analyzed along time to estimate peculiar statistical parameters, in our case the exponent α of the power law f -α that approximates the PSD of their counting process. In our method, this measurement is used to train a classifier in addition to the lengths of the first packets of the flows. In our experiments, considering this additional per-source information yielded the same accuracy as using only per-flow data, but observing fewer packets in each flow and thus allowing a quicker response. For the proposed classifier, we report performance evaluation results obtained on sets of Internet traffic traces collected in three sites

On the Efficiency of a game theoretic approach to sparse regenerator placement in WDM networks

In this paper we provide a mathematical ILP model for the Regeneration Placement Problem (RPP) which minimizes the total number of regeneration nodes allocated in a translucent optical network ensuring that all the node pairs can always reach one another via two link-disjoint lightpaths under physical impairment constraints. Since RPP is NP-complete, large-site design problem can not be solved relying upon exact approaches. We then propose a game-theoretic approach to model RPP as a non-cooperative game and solve it applying the best response dynamic concept. Finally, we evaluate the performance of the proposed approach in terms of closeness of the obtained results to these provided by ILP: a MILP formulation is given in order to study the quality of the Nash equilibria by comparison to Price-of-Anarchy and Price-of-Stability bounds