The effects of intense fire on headwater streams of the Colville National Forest, WA

Thesis (M.S.) University of Alaska Fairbanks, 2006Forest fires play an important role in shaping ecosystems, and there has been growing concern on the effects of high intensity fires on forest and aquatic ecosystems. Headwater streams are highly connected to riparian and surrounding terrestrial systems, and to downstream aquatic systems, partly through prey and organic matter transfers via aquatic invertebrate drift and emergence. Because of their small size, headwater streams may experience the greatest initial impact from forest fire, but may also return to pre-fire conditions quicker than larger streams. In this study, headwater streams from replicated burned and control watersheds were sampled in the two years following an intense forest fire in northeastern Washington. Benthic, drift and emergence samples of aquatic invertebrates were taken and analyzed for differences in density, biomass and community composition between watershed types. There was significantly higher density of invertebrates in burned sites, but no difference in biomass except in invertebrate emergence which was greater at burned sites. There was lower diversity in the burned watersheds, and the invertebrate community was dominated by chironomids. These changes in invertebrate density and community composition could influence the food resources available to aquatic and riparian consumers

Continuum of vasodilator stress from rest to contrast medium to adenosine hyperemia for fractional flow reserve assessment

Objectives: This study compared the diagnostic performance with adenosine-derived fractional flow reserve (FFR) ≤0.8 of contrast-based FFR (cFFR), resting distal pressure (Pd)/aortic pressure (Pa), and the instantaneous wave-free ratio (iFR). Background: FFR objectively identifies lesions that benefit from medical therapy versus revascularization. However, FFR requires maximal vasodilation, usually achieved with adenosine. Radiographic contrast injection causes submaximal coronary hyperemia. Therefore, intracoronary contrast could provide an easy and inexpensive tool for predicting FFR. Methods: We recruited patients undergoing routine FFR assessment and made paired, repeated measurements of all physiology metrics (Pd/Pa, iFR, cFFR, and FFR). Contrast medium and dose were per local practice, as was the dose of intracoronary adenosine. Operators were encouraged to perform both intracoronary and intravenous adenosine assessments and a final drift check to assess wire calibration. A central core lab analyzed blinded pressure tracings in a standardized fashion. Results: A total of 763 subjects were enrolled from 12 international centers. Contrast volume was 8 ± 2 ml per measurement, and 8 different contrast media were used. Repeated measurements of each metric showed a bias <0.005, but a lower SD (less variability) for cFFR than resting indexes. Although Pd/Pa and iFR demonstrated equivalent performance against FFR ≤0.8 (78.5% vs. 79.9% accuracy; p = 0.78; area under the receiver-operating characteristic curve: 0.875 vs. 0.881; p = 0.35), cFFR improved both metrics (85.8% accuracy and 0.930 area; p < 0.001 for each) with an optimal binary threshold of 0.83. A hybrid decision-making strategy using cFFR required adenosine less often than when based on either Pd/Pa or iFR. Conclusions: cFFR provides diagnostic performance superior to that of Pd/Pa or iFR for predicting FFR. For clinical scenarios or health care systems in which adenosine is contraindicated or prohibitively expensive, cFFR offers a universal technique to simplify invasive coronary physiological assessments. Yet FFR remains the reference standard for diagnostic certainty as even cFFR reached only ∼85% agreement

ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks

Despite the fact that cyberattacks are constantly growing in complexity, the research community still lacks effective tools to easily monitor and understand them. In particular, there is a need for techniques that are able to not only track how prominently certain malicious actions, such as the exploitation of specific vulnerabilities, are exploited in the wild, but also (and more importantly) how these malicious actions factor in as attack steps in more complex cyberattacks. In this paper we present ATTACK2VEC, a system that uses temporal word embeddings to model how attack steps are exploited in the wild, and track how they evolve. We test ATTACK2VEC on a dataset of billions of security events collected from the customers of a commercial Intrusion Prevention System over a period of two years, and show that our approach is effective in monitoring the emergence of new attack strategies in the wild and in flagging which attack steps are often used together by attackers (e.g., vulnerabilities that are frequently exploited together). ATTACK2VEC provides a useful tool for researchers and practitioners to better understand cyberattacks and their evolution, and use this knowledge to improve situational awareness and develop proactive defenses

Constructing a no-reference H.264/AVC bitstream-based video quality metric using genetic programming-based symbolic regression

In order to ensure optimal quality of experience toward end users during video streaming, automatic video quality assessment becomes an important field-of-interest to video service providers. Objective video quality metrics try to estimate perceived quality with high accuracy and in an automated manner. In traditional approaches, these metrics model the complex properties of the human visual system. More recently, however, it has been shown that machine learning approaches can also yield competitive results. In this paper, we present a novel no-reference bitstream-based objective video quality metric that is constructed by genetic programming-based symbolic regression. A key benefit of this approach is that it calculates reliable white-box models that allow us to determine the importance of the parameters. Additionally, these models can provide human insight into the underlying principles of subjective video quality assessment. Numerical results show that perceived quality can be modeled with high accuracy using only parameters extracted from the received video bitstream