From photons to big-data applications: terminating terabits.

Computer architectures have entered a watershed as the quantity of network data generated by user applications exceeds the data-processing capacity of any individual computer end-system. It will become impossible to scale existing computer systems while a gap grows between the quantity of networked data and the capacity for per system data processing. Despite this, the growth in demand in both task variety and task complexity continues unabated. Networked computer systems provide a fertile environment in which new applications develop. As networked computer systems become akin to infrastructure, any limitation upon the growth in capacity and capabilities becomes an important constraint of concern to all computer users. Considering a networked computer system capable of processing terabits per second, as a benchmark for scalability, we critique the state of the art in commodity computing, and propose a wholesale reconsideration in the design of computer architectures and their attendant ecosystem. Our proposal seeks to reduce costs, save power and increase performance in a multi-scale approach that has potential application from nanoscale to data-centre-scale computers.

Hydrocodone in postoperative personalized pain management: pro-drug or drug?

BACKGROUND: Genetic variations in enzymes that produce active metabolites from pro-drugs are well known. Such variability could account for some of the clinically observed differences in analgesia and side effects seen in postoperative patients. Using genotyping and quantitation of serum concentrations of hydrocodone and its metabolites, we sought to demonstrate the clinical effects of the metabolites of hydrocodone on pain relief. The objective of the current study was to determine whether CYP2D6 genotype and serum hydromorphone levels account for some of the variability in pain relief seen with hydrocodone in a cohort of women post-Cesarean section. METHODS: In 156 post-Cesarean section patients who received hydrocodone, we assessed serum opioid concentrations and CYP2D6 genotypes. Blood samples were collected at that time for genotyping and determination of concentrations of hydrocodone and metabolites by LC-MS/MS. Multivariate analysis was used to determine the relationship between CYP2D6 genotypes, pain relief, side effects, and serum concentrations of hydrocodone and hydromorphone. RESULTS: The CYP2D6 genotyping results indicated that 60% of subjects were extensive, 30% intermediate, 3% poor, and 7% ultra-rapid metabolizers. In the poor metabolizers, the mean plasma hydromorphone concentration was 8-fold lower when compared to that of ultra-rapid metabolizers. Hydromorphone, and not hydrocodone concentrations correlated with pain relief. CONCLUSIONS: This study shows that hydromorphone is generated at substantially different rates, dependent on CYP2D6 genotype. Pain relief correlated with plasma concentrations of hydromorphone, and not with hydrocodone. This suggests that pain relief will vary with CYP2D6 genotype. Inability to metabolize hydrocodone to hydromorphone as seen in the poor metabolizers should alert the clinician to consider alternative medications for managing pain postoperatively.

Bayesian neural networks for internet traffic classification.

Internet traffic identification is an important tool for network management. It allows operators to better predict future traffic matrices and demands, security personnel to detect anomalous behavior, and researchers to develop more realistic traffic models. We present here a traffic classifier that can achieve a high accuracy across a range of application types without any source or destination host-address or port information. We use supervised machine learning based on a Bayesian trained neural network. Though our technique uses training data with categories derived from packet content, training and testing were done using features derived from packet streams consisting of one or more packet headers. By providing classification without access to the contents of packets, our technique offers wider application than methods that require full packet/payloads for classification. This is a powerful advantage, using samples of classified traffic to permit the categorization of traffic based only upon commonly available information.

Algorithms for rapid outbreak detection: a research synthesis.

The threat of bioterrorism has stimulated interest in enhancing public health surveillance to detect disease outbreaks more rapidly than is currently possible. To advance research on improving the timeliness of outbreak detection, the Defense Advanced Research Project Agency sponsored the Bio-event Advanced Leading Indicator Recognition Technology (BioALIRT) project beginning in 2001. The purpose of this paper is to provide a synthesis of research on outbreak detection algorithms conducted by academic and industrial partners in the BioALIRT project. We first suggest a practical classification for outbreak detection algorithms that considers the types of information encountered in surveillance analysis. We then present a synthesis of our research according to this classification. The research conducted for this project has examined how to use spatial and other covariate information from disparate sources to improve the timeliness of outbreak detection. Our results suggest that use of spatial and other covariate information can improve outbreak detection performance. We also identified, however, methodological challenges that limited our ability to determine the benefit of using outbreak detection algorithms that operate on large volumes of data. Future research must address challenges such as forecasting expected values in high-dimensional data and generating spatial and multivariate test data sets.

Automated syndromic surveillance for the 2002 Winter Olympics.

The 2002 Olympic Winter Games were held in Utah from February 8 to March 16, 2002. Following the terrorist attacks on September 11, 2001, and the anthrax release in October 2001, the need for bioterrorism surveillance during the Games was paramount. A team of informaticists and public health specialists from Utah and Pittsburgh implemented the Real-time Outbreak and Disease Surveillance (RODS) system in Utah for the Games in just seven weeks. The strategies and challenges of implementing such a system in such a short time are discussed. The motivation and cooperation inspired by the 2002 Olympic Winter Games were a powerful driver in overcoming the organizational issues. Over 114,000 acute care encounters were monitored between February 8 and March 31, 2002. No outbreaks of public health significance were detected. The system was implemented successfully and operational for the 2002 Olympic Winter Games and remains operational today.