Prevention and treatment of venous thromboembolism in pregnancy in patients with hereditary antithrombin deficiency

Objective: The aims of the study reported here were to provide data from six pregnant subjects who were enrolled in a clinical trial of antithrombin (AT) concentrate, discuss other published case series and case reports, and provide general guidance for the use of AT concentrate for inherited AT deficiency in pregnancy. Methods: In the late 1980s, 31 AT-deficient subjects were enrolled in a prospective treatment trial of the plasma-derived AT concentrate Thrombate III®. Herein, newly available treatment data about the six pregnant subjects in the trial is tabulated and summarized. Results: All six experienced venous thromboembolism (VTE) during pregnancy, were dosed according to a weight-based protocol, and were treated concomitantly with anticoagulation. Loading doses of AT concentrate of 54–62 units/kg were followed by maintenance doses of 50%–100% of the loading dose for 3–10 days. At the time of labor, loading doses of 46–50 units/kg were followed by maintenance doses of 50%–75% of the loading dose for 5–7 days. None of the six experienced recurrent thrombosis while receiving treatment with AT concentrate. Conclusion: Currently we suggest that women with AT deficiency who are pregnant or postpartum and have a personal history of VTE or current VTE receive AT concentrates

Clustering Hyperspectral Imagery for Improved Adaptive Matched Filter Performance

This paper offers improvements to adaptive matched filter (AMF) performance by addressing correlation and non-homogeneity problems inherent to hyperspectral imagery (HSI). The estimation of the mean vector and covariance matrix of the background should be calculated using “target-free” data. This statement reflects the difficulty that including target data in estimates of the mean vector and covariance matrix of the background could entail. This data could act as statistical outliers and severely contaminate the estimators. This fact serves as the impetus for a 2-stage process: First, attempt to remove the target data from the background by way of the employment of anomaly detectors. Next, with remaining data being relatively “target-free” the way is cleared for signature matching. Relative to the first stage, we were able to test seven different anomaly detectors, some of which are designed specifically to deal with the spatial correlation of HSI data and/or the presence of anomalous pixels in local or global mean and covariance estimators. Relative to the second stage, we investigated the use of cluster analytic methods to boost AMF performance. The research shows that accounting for spatial correlation effects in the detector yields nearly “target-free” data for use in an AMF that is greatly benefitted through the use of cluster analysis methods

Exact field ionization rates in the barrier suppression-regime from numerical TDSE calculations

Numerically determined ionization rates for the field ionization of atomic hydrogen in strong and short laser pulses are presented. The laser pulse intensity reaches the so-called "barrier suppression ionization" regime where field ionization occurs within a few half laser cycles. Comparison of our numerical results with analytical theories frequently used shows poor agreement. An empirical formula for the "barrier suppression ionization"-rate is presented. This rate reproduces very well the course of the numerically determined ground state populations for laser pulses with different length, shape, amplitude, and frequency. Number(s): 32.80.RmComment: Enlarged and newly revised version, 22 pages (REVTeX) + 8 figures in ps-format, submitted for publication to Physical Review A, WWW: http://www.physik.tu-darmstadt.de/tqe

Cyber-Physical Security with RF Fingerprint Classification through Distance Measure Extensions of Generalized Relevance Learning Vector Quantization

Radio frequency (RF) fingerprinting extracts fingerprint features from RF signals to protect against masquerade attacks by enabling reliable authentication of communication devices at the “serial number” level. Facilitating the reliable authentication of communication devices are machine learning (ML) algorithms which find meaningful statistical differences between measured data. The Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is one ML algorithm which has shown efficacy for RF fingerprinting device discrimination. GRLVQI extends the Learning Vector Quantization (LVQ) family of “winner take all” classifiers that develop prototype vectors (PVs) which represent data. In LVQ algorithms, distances are computed between exemplars and PVs, and PVs are iteratively moved to accurately represent the data. GRLVQI extends LVQ with a sigmoidal cost function, relevance learning, and PV update logic improvements. However, both LVQ and GRLVQI are limited due to a reliance on squared Euclidean distance measures and a seemingly complex algorithm structure if changes are made to the underlying distance measure. Herein, the authors (1) develop GRLVQI-D (distance), an extension of GRLVQI to consider alternative distance measures and (2) present the Cosine GRLVQI classifier using this framework. To evaluate this framework, the authors consider experimentally collected Z -wave RF signals and develop RF fingerprints to identify devices. Z -wave devices are low-cost, low-power communication technologies seen increasingly in critical infrastructure. Both classification and verification, claimed identity, and performance comparisons are made with the new Cosine GRLVQI algorithm. The results show more robust performance when using the Cosine GRLVQI algorithm when compared with four algorithms in the literature. Additionally, the methodology used to create Cosine GRLVQI is generalizable to alternative measures

The Effectiveness of Using Diversity to Select Multiple Classifier Systems with Varying Classification Thresholds

In classification applications, the goal of fusion techniques is to exploit complementary approaches and merge the information provided by these methods to provide a solution superior than any single method. Associated with choosing a methodology to fuse pattern recognition algorithms is the choice of algorithm or algorithms to fuse. Historically, classifier ensemble accuracy has been used to select which pattern recognition algorithms are included in a multiple classifier system. More recently, research has focused on creating and evaluating diversity metrics to more effectively select ensemble members. Using a wide range of classification data sets, methodologies, and fusion techniques, current diversity research is extended by expanding classifier domains before employing fusion methodologies. The expansion is made possible with a unique classification score algorithm developed for this purpose. Correlation and linear regression techniques reveal that the relationship between diversity metrics and accuracy is tenuous and optimal ensemble selection should be based on ensemble accuracy. The strengths and weaknesses of popular diversity metrics are examined in the context of the information they provide with respect to changing classification thresholds and accuracies

Photometric observations of selected, optically bright quasars for Space Interferometry Mission and other future celestial reference frames

Photometric observations of 235 extragalactic objects that are potential targets for the Space Interferometry Mission (SIM) are presented. Mean B, V, R, I magnitudes at the 5% level are obtained at 1 - 4 epochs between 2005 and 2007 using the 1-m telescopes at Cerro Tololo Inter-American Observatory and Naval Observatory Flagstaff Station. Of the 134 sources which have V magnitudes in the Veron & Veron-Cetty catalog a difference of over 1.0 mag is found for the observed-catalog magnitudes for about 36% of the common sources, and 10 sources show over 3 mag difference. Our first set of observations presented here form the basis of a long-term photometric variability study of the selected reference frame sources to assist in mission target selection and to support in general QSO multi-color photometric variability studies.Comment: 40 pages, 13 figures, 4 table

FIRE Spectroscopy of Five Late-type T Dwarfs Discovered with the Wide-field Infrared Survey Explorer

We present the discovery of five late-type T dwarfs identified with the Wide-field Infrared Survey Explorer (WISE). Low-resolution near-infrared spectroscopy obtained with the Magellan Folded-port InfraRed Echellette (FIRE) reveal strong water and methane absorption in all five sources, and spectral indices and comparison to spectral templates indicate classifications ranging from T5.5 to T8.5:. The spectrum of the latest-type source, WISE J1812+2721, is an excellent match to that of the T8.5 companion brown dwarf Wolf 940B. WISE-based spectrophotometric distance estimates place these T dwarfs at 12-13 pc from the Sun, assuming they are single. Preliminary fits of the spectral data to the atmosphere models of Saumon & Marley indicate effective temperatures ranging from 600 K to 930 K, both cloudy and cloud-free atmospheres, and a broad range of ages and masses. In particular, two sources show evidence of both low surface gravity and cloudy atmospheres, tentatively supporting a trend noted in other young brown dwarfs and exoplanets. In contrast, the high proper motion T dwarf WISE J2018-7423 exhibits a suppressed K-band peak and blue spectrophotometric J-K colors indicative of an old, massive brown dwarf; however, it lacks the broadened Y-band peak seen in metal-poor counterparts. These results illustrate the broad diversity of low-temperature brown dwarfs that will be uncovered with WISE.Comment: 19 pages, 13 figures; accepted for publication to Ap

Malware Type Recognition and Cyber Situational Awareness

Current technologies for computer network and host defense do not provide suitable information to support strategic and tactical decision making processes. Although pattern-based malware detection is an active research area, the additional context of the type of malware can improve cyber situational awareness. This additional context is an indicator of threat capability thus allowing organizations to assess information losses and focus response actions appropriately. Malware Type Recognition (MaTR) is a research initiative extending detection technologies to provide the additional context of malware types using only static heuristics. Test results with MaTR demonstrate over a 99% accurate detection rate and 59% test accuracy in malware typing

Malware Target Recognition via Static Heuristics

Organizations increasingly rely on the confidentiality, integrity and availability of their information and communications technologies to conduct effective business operations while maintaining their competitive edge. Exploitation of these networks via the introduction of undetected malware ultimately degrades their competitive edge, while taking advantage of limited network visibility and the high cost of analyzing massive numbers of programs. This article introduces the novel Malware Target Recognition (MaTR) system which combines the decision tree machine learning algorithm with static heuristic features for malware detection. By focusing on contextually important static heuristic features, this research demonstrates superior detection results. Experimental results on large sample datasets demonstrate near ideal malware detection performance (99.9+% accuracy) with low false positive (8.73e-4) and false negative rates (8.03e-4) at the same point on the performance curve. Test results against a set of publicly unknown malware, including potential advanced competitor tools, show MaTR’s superior detection rate (99%) versus the union of detections from three commercial antivirus products (60%). The resulting model is a fine granularity sensor with potential to dramatically augment cyberspace situation awareness