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This paper presents a frame-level hybrid framework for modeling MPEG-4 and H.264 multi-layer variable bit rate (VBR) video traffic. To accurately capture long-range-dependent and short-range-dependent properties of VBR sequences, we use wavelets to model the distribution of I-frame sizes and a simple time-domain model for P/B frame sizes. However, unlike previous studies, we analyze and successfully model both inter-GOP (group of pictures) and intra-GOP correlation in VBR video and build an enhancement-layer model using cross-layer correlation. Simulation results demonstrate that our model effectively preserves the temporal burstiness and captures important statistical features (e.g., the autocorrelation function and the frame-size distribution) of original traffic. We also show that our model possesses lower complexity and has better performance than the previous methods in both single- and multi-layer sequences.

Arsenic is a human toxin and carcinogen commonly found as a contaminant in drinking water. Arsenite (As(III)) is the most toxic inorganic form, but recent evidence indicates that the metabolite monomethylarsonous acid (MMA(III)) is even more toxic. We have used a chemical genomics approach to identify the genes that modulate the cellular toxicity of MMA(III) and As(III) in the yeast Saccharomyces cerevisiae. Functional profiling using homozygous deletion mutants provided evidence of the requirement of highly conserved biological processes in the response against both arsenicals including tubulin folding, DNA double-strand break repair, and chromatin modification. At the equitoxic doses of 150 microM MMA(III) and 300 microM As(III), genes related to glutathione metabolism were essential only for resistance to the former, suggesting a higher potency of MMA(III) to disrupt glutathione metabolism than As(III). Treatments with MMA(III) induced a significant increase in glutathione levels in the wild-type strain, which correlated to the requirement of genes from the sulfur and methionine metabolic pathways and was consistent with the induction of oxidative stress. Based on the relative sensitivity of deletion strains deficient in GSH metabolism and tubulin folding processes, oxidative stress appeared to be the primary mechanism of MMA(III) toxicity whereas secondary to tubulin disruption in the case of As(III). Many of the identified yeast genes have orthologs in humans that could potentially modulate arsenic toxicity in a similar manner as their yeast counterparts.

Existing methods of measuring lifetimes in P2P systems usually rely on the so-called Create-Based Method (CBM), which divides a given observation window into two halves and samples users ldquocreatedrdquo in the first half every Delta time units until they die or the observation period ends. Despite its frequent use, this approach has no rigorous accuracy or overhead analysis in the literature. To shed more light on its performance, we first derive a model for CBM and show that small window size or large Delta may lead to highly inaccurate lifetime distributions. We then show that create-based sampling exhibits an inherent tradeoff between overhead and accuracy, which does not allow any fundamental improvement to the method. Instead, we propose a completely different approach for sampling user dynamics that keeps track of only residual lifetimes of peers and uses a simple renewal-process model to recover the actual lifetimes from the observed residuals. Our analysis indicates that for reasonably large systems, the proposed method can reduce bandwidth consumption by several orders of magnitude compared to prior approaches while simultaneously achieving higher accuracy. We finish the paper by implementing a two-tier Gnutella network crawler equipped with the proposed sampling method and obtain the distribution of ultrapeer lifetimes in a network of 6.4 million users and 60 million links. Our experimental results show that ultrapeer lifetimes are Pareto with shape alpha ap 1.1; however, link lifetimes exhibit much lighter tails with alpha ap 1.8.

Innovative efforts to provide a clean-slate design of congestion control for future high-speed heterogeneous networks have recently led to the development of explicit congestion control. These methods rely on multi-byte router feedback and aim to contribute to the design of a more scalable Internet of tomorrow. However, experimental evaluation and deployment experience with these approaches is still limited to either low bandwidth networks or simple topologies. In addition, all existing implementations are exclusively applicable to either rate- or window-based protocols and are unable to study performance of different protocols on a common platform. This paper aims to fill this void by designing a unified Linux implementation framework for both rate- and window-based methods that does not incur any interference with the system's network stack or applications. Using this implementation, we implement and reveal several key properties of four recent explicit congestion control protocols XCP, JetMax, RCP, and PIQI-RCP using Emulab's gigabit testbed with a ariety of simple yet representative network topologies. Our experiments not only confirm the known behavior of these methods, but also demonstrate their previously undocumented properties (e.g., RCP's transient overshoot under abrupt traffic load changes and JetMax's low utilization in the presence mice flows).

Network feedback in a congestion-control system is subject to delay, which can significantly affect stability and performance of the entire system. While most existing stability conditions explicitly depend on delay Di of individual flow i, a recent study in Zhang, Y. et al, (2004) shows that the combination of a symmetric Jacobian A and condition rho(A) < 1 guarantees local stability of the system regardless of Di. However, the requirement of symmetry is very conservative and no further results have been obtained beyond this point. In this paper, we proceed in this direction and gain a better understanding of conditions under which congestion-control systems can achieve delay-independent stability. Towards this end, we first prove that if Jacobian matrix A satisfies parApar < 1 for any monotonic induced matrix norm parmiddotpar, the system is locally stable under arbitrary diagonal delay Di. We then derive a more generic result and prove that delay-independent stability is guaranteed as long as A is Schur diagonally stable in Kaszkurewicz, E. and Bhaya, A. (2000), which is also observed to be a necessary condition in simulations. Utilizing these results, we identify several classes of well-known matrices that are stable under diagonal delays if rho(A) < 1 and prove stability of MKC in Zhang, Y. et al, (2004) with arbitrary parameters alphai and betai

Previous analytical studies of unstructured P2P resilience have assumed exponential user lifetimes and only con-sidered age-independent neighbor replacement. In this paper, we overcome these limitations by introducing a general node-isolation model for heavy-tailed user lifetimes and arbitrary neighbor-se-lection algorithms. Using this model, we analyze two age-biased neighbor-selection strategies and show that they significantly improve the residual lifetimes of chosen users, which dramatically reduces the probability of user isolation and graph partitioning compared with uniform selection of neighbors. In fact, the second strategy based on random walks on age-proportional graphs demonstrates that, for lifetimes with infinite variance, the system monotonically increases its resilience as its age and size grow. Specifically, we show that the probability of isolation converges to zero as these two metrics tend to infinity. We finish the paper with simulations in finite-size graphs that demonstrate the effect of this result in practice.

In this paper, we analyze the problem of network disconnection in the context of large-scale P2P networks and understand how both static and dynamic patterns of node failure affect the resilience of such graphs. We start by applying classical results from random graph theory to show that a large variety of deterministic and random P2P graphs almost surely (i.e., with probability 1-o(1)) remain connected under random failure if and only if they have no isolated nodes. This simple, yet powerful, result subsequently allows us to derive in closed-form the probability that a P2P network develops isolated nodes, and therefore partitions, under both types of node failure. We finish the paper by demonstrating that our models match simulations very well and that dynamic P2P systems are extremely resilient under node churn as long as the neighbor replacement delay is much smaller than the average user lifetime.

Stability proofs of nonlinear congestion control systems under heterogeneous feedback delays are usually difficult and involve a fair amount of effort. In this paper, we show that there exist a class of congestion control methods that admit very simple proofs of asymptotic stability and allow control equations to be delay-independent. This is in contrast to most previous work, which requires that each flow (and sometimes each router) adapt its control-loop constants based on the feedback delay and/or the length of the corresponding end-to-end path. Our new congestion control method, which we call Max-Min Kelly Control (MKC), builds upon Kelly's original work in and allows end-flows to be stable and fair regardless of network feedback delays or the number of hops in their end-to-end paths. Using basic matrix algebra and discrete control theory, we show MKC's local asymptotic stability under heterogeneous, directional feedback delays. We also offer a simple proof of its global asymptotic stability assuming constant feedback delay.

Genomic technologies show great potential for classifying disease states and toxicological impacts from exposure to chemicals into functional categories. In environmental monitoring, the ability to classify field samples and predict the pollutants present in these samples could contribute to monitoring efforts and the diagnosis of contaminated sites. Using gene expression analysis, we challenged our custom Daphnia magna cDNA microarray to determine the presence of a specific metal toxicant in blinded field samples collected from two copper mines in California. We compared the gene expression profiles from our field samples to previously established expression profiles for Cu, Cd, and Zn. The expression profiles from the Cu-containing field samples clustered with the laboratory-exposed Cu-specific gene expression profiles and included genes previously identified as copper biomarkers, verifying that gene expression analysis can predict environmental exposure to a specific pollutant. In addition, our study revealed that upstream field samples containing undetectable levels of Cu caused the differential expression of only a few genes, lending support for the concept of a no observed transcriptional effect level (NOTEL). If confirmed by further studies, the NOTEL may play an important role in discriminating polluted and nonpolluted sites in future monitoring efforts.

Ecotoxicogenomic approaches to environmental monitoring provide holistic information, offer insight into modes of action, and help to assess the causal agents and potential toxicity of effluents beyond the traditional end points of death and reproduction. Recent investigations of toxicant exposure indicate dose-dependent changes are a key issue in interpreting genomic studies. Additionally, there is interest in developing methods to integrate gene expression studies in environmental monitoring and regulation, and the No Observed Transcriptional Effect Level (NOTEL) has been proposed as a means for screening effluents and unknown chemicals fortoxicity. However, computational methods to determine the NOTEL have yet to be established. Therefore, we examined effects on gene expression in Daphnia magna following exposure to Cu, Cd, and Zn over a range of concentrations including a tolerated, a sublethal, and a nearly acutely toxic concentration. Each concentration produced a distinct gene expression profile. We observed differential expression of a very few genes at tolerated concentrations that were distinct from the expression profiles observed at concentrations associated with toxicity. These results suggest that gene expression analysis may offer a strategy for distinguishing toxic and nontoxic concentrations of metals in the environment and provide support for a NOTEL for metal exposure in D. magna. Mechanistic insights could be inferred from the concentration-dependent gene expression profiles including metal specific effects on disparate metabolic processes such as digestion, immune response, development and reproduction, and less specific stress responses at higher concentrations.

Recent surge of interest towards congestion control that relies on single-link feedback (e.g., XCP, RCP, MaxNet, EMKC, VCP), suggests that such systems may offer certain benefits over traditional models of additive packet loss. Besides topology-independent stability and faster convergence to efficiency/fairness, it was recently shown that any stable single-link system with a symmetric Jacobian tolerates arbitrary fixed, as well as time-varying, feedback delays. Although delay-independence is an appealing characteristic, the EMKC system developed in exhibits undesirable equilibrium properties and slow convergence behavior. To overcome these drawbacks, we propose a new method called JetMax and show that it admits a low-overhead implementation inside routers (three additions per packet), overshoot-free transient and steady state, tunable link utilization, and delay-insensitive flow dynamics. The proposed framework also provides capacity-independent convergence time, where fairness and utilization are reached in the same number of RTT steps for a link of any bandwidth. Given a 1mb/s, 10gb/s, or googol (10^1^0^0) bps link, the method converges to within 1% of the stationary state in six RTTs. We finish the paper by comparing JetMax's performance to that of existing methods in ns2 simulations and discussing its Linux implementation.

This paper analyzes the asymptotic behavior of packet-train probing over a multi-hop network path P carrying arbitrarily routed bursty cross-traffic flows. We examine the statistical mean of the packet-train output dispersions and its relationship to the input dispersion. We call this relationship the response curve of path P. We show that the real response curve Z is tightly lower-bounded by its multi-hop fluid counterpart F, obtained when every cross-traffic flow on P is hypothetically replaced with a constant-rate fluid flow of the same average intensity and routing pattern. The real curve Z asymptotically approaches its fluid counterpart F as probing packet size or packet train length increases. Most existing measurement techniques are based upon the single-hop fluid curve S associated with the bottleneck link in P. We note that the curve S coincides with F in a certain large-dispersion input range, but falls below F in the remaining small-dispersion input ranges. As an implication of these findings, we show that bursty cross-traffic in multi-hop paths causes negative bias (asymptotic underestimation) to most existing techniques. This bias can be mitigated by reducing the deviation of Z from S using large packet size or long packet-trains. However, the bias is not completely removable for the techniques that use the portion of S that falls below F.

In this paper, we show that end-host based congestion prediction is more accurate than previously characterized. However, it may not be possible to entirely eliminate the uncertainties in congestion prediction. To address these uncertainties, we propose Probabilistic Early Response TCP (PERT). PERT emulates the behavior of AQM/ECN, in the congestion response function of end-hosts. We present fluid-flow analysis of PERT/RED and PERT/PI, versions of PERT that emulate router-based RED and PI controllers. Our analysis shows that PERT/RED has better stability behavior than router-based RED. We also present results from ns-2 simulations to show the practical feasibility of PERT. The scheme presented here is general and can be used for emulating other AQM algorithms.

Recent research efforts to design better Internet transport protocols combined with scalable active queue management (AQM) have led to significant advances in congestion control. One of the hottest topics in this area is the design of discrete congestion control algorithms that are asymptotically stable under heterogeneous feedback delay and whose control equations do not explicitly depend on the RTTs of end-flows. In this paper, we first prove that single-link congestion control methods with a stable radial Jacobian remain stable under arbitrary feedback delay (including heterogeneous directional delays) and that the stability condition of such methods does not involve any of the delays. We then extend this result to generic networks with fixed consistent bottleneck assignments and max-min network feedback. To demonstrate the practicality of the obtained result, we change the original controller in Kelly's work [ldquoRate Control for communication networks: Shadow prices, proportional fairness and stability,rdquo Journal of the Operational Research Society, vol. 49, no. 3, pp. 237-252, March 1998] to become robust under random feedback delay and fixed constants of the control equation. We call the resulting framework max-min Kelly control (MKC) and show that it offers smooth sending rate, exponential convergence to efficiency, and fast convergence to fairness, all of which make it appealing for future high-speed networks.

Most existing available-bandwidth measurement techniques are justified using a constant-rate fluid cross-traffic model. To achieve a better understanding of the performance of current bandwidth measurement techniques in general traffic conditions, this paper presents a queueing-theoretic foundation of single-hop packet-train bandwidth estimation under bursty arrivals of discrete cross-traffic packets. We analyze the statistical mean of the packet-train output dispersion and its mathematical relationship to the input dispersion, which we call the probing-response curve. This analysis allows us to prove that the single-hop response curve in bursty cross-traffic deviates from that obtained under fluid cross traffic of the same average intensity and to demonstrate that this may lead to significant measurement bias in certain estimation techniques based on fluid models. We conclude the paper by showing, both analytically and experimentally, that the response-curve deviation vanishes as the packet-train length or probing packet size increases, where the vanishing rate is decided by the burstiness of cross-traffic.

Balancing of structured peer-to-peer graphs, including their zone sizes, has recently become an important topic of distributed hash table (DHT) research. To bring analytical understanding into the various peer-join mechanisms based on consistent hashing, we study how zone-balancing decisions made during the initial sampling of the peer space affect the resulting zone sizes and derive several asymptotic bounds for the maximum and minimum zone sizes that hold with high probability. Several of our results contradict those of prior work and shed new light on the theoretical performance limitations of consistent hashing. We use simulations to verify our models and compare the performance of the various methods using the example of recently proposed de Bruijn DHTs.

The use of chemical flame-retardants (FR) in consumer products has steadily increased over the last 30 years. Toxicity data exist for legacy FRs such as pentabromodiphenyl ether (pentaBDE), but less is known about effects of new formulations. To address this issue, the toxicity of seven FR chemicals and formulations was assessed on the freshwater crustacean Daphnia magna. Acute 48-h nominal LC50 values for penta- and octabromodiphenyl ether (pentaBDE, octaBDE), Firemaster 550 (FM550), Firemaster BZ-54 (BZ54), bis(2-ethylhexyl) tetrabromophthalate (BEH-TEBP), triphenyl phosphate (TPhP), and nonbrominated BEH-TEBP analog bis(2-ethylhexyl) phthalate (BEHP) ranged from 0.058 mg/L (pentaBDE) to 3.96 mg/L (octaBDE). mRNA expression, (1)H NMR-based metabolomic and lipidomic profiling at 1/10 LC50 revealed distinct patterns of molecular response for each exposure, suggesting pentaPBDE affects transcription and translation, octaBDE and BEH-TEBP affect glycosphingolipid biosynthesis and BZ54 affects Wnt and Hedgehog signal pathways as well as glycosaminoglycan degradation. Brominated components of FM550 (i.e., BZ54) were significantly higher in Daphnia after 48 h following 1/10 LC50 exposure. FM550 elicited significant mRNA changes at five concentrations across a range from 1/10(6) LC50 to 1/2 LC50. Analyses suggest FM550 impairs nutrient utilization or uptake in Daphnia.

Exponential growth of the web continues to present challenges to the design and scalability of web crawlers. Our previous work on a high-performance platform called IRLbot [28] led to the development of new algorithms for realtime URL manipulation, domain ranking, and budgeting, which were tested in a 6.3B-page crawl. Since very little is known about the crawl itself, our goal in this paper is to undertake an extensive measurement study of the collected dataset and document its crawl dynamics. We also propose a framework for modeling the scaling rate of various data structures as crawl size goes to infinity and offer a methodology for comparing crawl coverage to that of commercial search engines.

Many BigData applications (e.g., MapReduce, web caching, search in large graphs) process streams of random key-value records that follow highly skewed frequency distributions. In this work, we first develop stochastic models for the probability to encounter unique keys during exploration of such streams and their growth rate over time. We then apply these models to the analysis of LRU caching, MapReduce overhead, and various crawl properties (e.g., node-degree bias, frontier size) in random graphs.

We analyze synchronization issues arising between two stochastic point processes, one of which models data churn at an information source and the other periodic downloads from its replica (e.g., search engine, web cache, distributed database). Due to lazy (pull-based) synchronization, the replica experiences recurrent staleness, which translates into some form of penalty stemming from its reduced ability to perform consistent computation and/or provide up-to-date responses to customer requests. We model this system under non-Poisson update/refresh processes and obtain sample-path averages of various metrics of staleness cost, generalizing previous results and exposing novel problems in this field.

Transcriptomic analysis can complement traditional ecotoxicology data by providing mechanistic insight, and by identifying sub-lethal organismal responses and contaminant classes underlying observed toxicity. Before transcriptomic information can be used in monitoring and risk assessment, it is necessary to determine its reproducibility and detect key steps impacting the reliable identification of differentially expressed genes. A custom 15K-probe microarray was used to conduct transcriptomics analyses across six laboratories with estuarine amphipods exposed to cyfluthrin-spiked or control sediments (10 days). Two sample types were generated, one consisted of total RNA extracts (Ex) from exposed and control samples (extracted by one laboratory) and the other consisted of exposed and control whole body amphipods (WB) from which each laboratory extracted RNA. Our findings indicate that gene expression microarray results are repeatable. Differentially expressed data had a higher degree of repeatability across all laboratories in samples with similar RNA quality (Ex) when compared to WB samples with more variable RNA quality. Despite such variability a subset of genes were consistently identified as differentially expressed across all laboratories and sample types. We found that the differences among the individual laboratory results can be attributed to several factors including RNA quality and technical expertise, but the overall results can be improved by following consistent protocols and with appropriate training.

End-to-end packet-pair probing has been used as one of the primary mechanisms to measure bottleneck capacity, cross-traffic intensity, and available bandwidth of end-to-end Internet paths. However, there has been lacking an understanding of what types of information about the path are sampled by packet-pairs and how they are encoded in the corresponding output dispersions. In this paper, we answer these questions by deriving the expression of individual output spacings (i.e., dispersions) in the context of multihop paths and general bursty cross-traffic arrivals. Based on these results, we examine stochastic properties of the sampled signals and derive the statistical mean of the output interpacket spacings as a recursive function of the input spacing. We show that the result is different from what has been obtained in prior work using fluid cross-traffic models and that this discrepancy has a significant adverse impact on the accuracy of packet-pair bandwidth measurement techniques

Rate-distortion (R-D) modeling of video coders has always been an important issue in video streaming; however, few of the traditional R-D models and their performance have been closely examined in the context of scalable (FGS-like) video. To overcome this shortcoming, the first half of the paper models rate-distortion of DCT-based fine-granular scalable coders and derives a simple operational R-D model for Internet streaming applications. Experimental results demonstrate that this R-D result, an extension of the classical R-D formula, is very accurate within the domain of scalable coding methods exemplified by MPEG-4 FGS and H.264 progressive FGS. In the second half of the paper, we examine congestion control and dynamic rate-scaling algorithms that achieve smooth visual quality during streaming using the proposed R-D model. In constant bitrate (CBR) channels, our R-D based quality-control algorithm dramatically reduces PSNR variation between adjacent frames (to less than 0.1 dB in sample sequences). Since the Internet is a changing environment shared by many sources, even R-D based quality control often cannot guarantee nonfluctuating PSNR in variable-bitrate (VBR) channels without the help from an appropriate congestion controller. Thus, we apply recent utility-based congestion control methods to our problem and show how a combination of this approach and our R-D model can benefit future streaming applications

Toxicogenomics, the genome-wide analysis of gene expression to study the effect of toxicants, has great potential for use in environmental toxicology. Applied to standard test organisms, it has possible applications in aquatic toxicology as a sensitive monitoring tool to detect the presence of contaminants while providing information on the mechanisms of action of these pollutants. We describe the use of a complementary DNA (cDNA) microarray of the fathead minnow (Pimephales promelas) a standard sentinel organism in aquatic toxicology, to better understand the mechanisms of toxicity of 2,4-dinitrotoluene (2,4-DNT) which is released in the environment through military and industrial use. We have constructed a fathead minnow microarray containing 5000 randomly picked anonymous cDNAs from a whole fish cDNA library. Expression profiles were analyzed in fish exposed to 2,4-DNT for 10 days at three concentrations (11, 22, and 44 microM, respectively) below the measured median lethal concentration (58 microM). Sequence analysis of cDNAs corresponding to differentially expressed genes affected by exposure revealed that lipid metabolism and oxygen transport genes were prominently affected in a dose-specific manner. We measured liver lipids and demonstrate that lipid metabolism is indeed perturbed following exposure. These observations correlate well with available toxicological data on 2,4-DNT. We present possible modes of action of 2,4-DNT toxicity and suggest that fathead minnow cDNA microarrays can be useful to identify mechanisms of toxicity in fish and as a predictive tool for toxicity in mammals.

This paper examines graph-theoretic properties of existing peer-to-peer networks and proposes a new infrastructure based on optimal-diameter de Bruijn graphs. Since generalized de Bruijn graphs exhibit very short average distances and high resilience to node failure, they are well suited for distributed hash tables (DHTs). Using the example of Chord, CAN, and de Bruijn, we study the routing performance, graph expansion, clustering properties, and bisection width of each graph. Having confirmed that de Bruijn graphs offer the best diameter and highest connectivity among the existing peer-to-peer structures, we offer a very simple incremental building process that preserves optimal properties of de Bruijn graphs under uniform user joins/departures. We call the combined peer-to-peer architecture optimal diameter routing infrastructure.

In this paper, we present a wavelet-based despeckling method for synthetic aperture radar images and derive a Bayesian wavelet shrinkage factor to estimate noise-free wavelet coefficients. To preserve edges during despeckling, we apply a modified ratio edge detector to the original image and use the obtained edge information in our despeckling framework. Experimental results demonstrate that our method compares favorably to several other despeckling methods on test images.

Exploratory outlier identification methods and confirmatory gene expression studies showed induction of the iron regulon in Saccharomyces cerevisiae lacking Mac1p, a copper-responsive transcription factor. The Aft1p/Aft2p binding motif was the most discriminating motif between up- and down-regulated genes, and we identified new genes potentially regulated by Aft1p/Aft2p. In addition, multiple genes encoding proteins containing Fe-S clusters were down-regulated suggesting metabolic reorganization to conserve iron in mac1Delta. Null mutants of each of the differentially expressed genes were characterized for copper- or iron-related phenotypes. New or additional support for a role in copper and iron homeostasis is provided in this study for the gene products of AKR1, MRS4, PCA1, SSU1, TIS11, YBR047W, YHL035C, YHR045W, YLR047C, YLR126C, and YTP1.

In this paper, we study several properties of binary-feedback congestion control in rate-based applications. We first derive necessary conditions for generic binary-feedback congestion control to converge to fairness monotonically (which guarantees asymptotic stability of the fairness point) and show that AIMD is the only TCP-friendly binomial control with monotonic convergence to fairness. We then study steady-state behavior of binomial controls with n competing flows on a single bottleneck. Our main result here shows that combined probing for new bandwidth by all flows results in significant overshoot of the available bandwidth and rapid (often super-linear as a function of n) increase in packet loss. We also show that AIMD has the best scalability and lowest packet-loss increase among all TCP-friendly binomial schemes. We conclude the paper by deriving the conditions necessary to achieve constant packet loss regardless of the number of competing flows n and examine one new scheme with such constant packet loss called ideally scalable congestion control in both simulation and streaming experiments.

This paper presents a trace-driven simulation study of two classes of retransmission timeout (RTO) estimators in the context of real-time streaming over the Internet. We explore the viability of employing retransmission timeouts in NACK-based (i.e., rate-based) streaming applications to support multiple retransmission attempts per lost packet. The first part of our simulation is based on trace data collected during a number of real-time streaming tests between dialup clients in all 50 states in the U.S. (including 653 major U.S. cities) and a backbone video server. The second part of the study is based on streaming tests over DSL and ISDN access links. First, we define a generic performance measure for assessing the accuracy of hypothetical RTO estimators based on the samples of the round-trip delay (RTT) recorded in the trace data. Second, using this performance measure, we evaluate the class of TCP-like estimators and find the optimal estimator given our performance measure. Third, we introduce a new class of estimators based on delay jitter and show that they significantly outperform TCP-like estimators in NACK-based applications with low-frequency RTT sampling. Finally, we show that high-frequency sampling of the RTT completely changes the situation and makes the class of TCP-like estimators as accurate as the class of delay-jitter estimators.

In this paper, we analyze the results of a seven-month real-time streaming experiment, which was conducted between a number of unicast dialup clients, connecting to the Internet through access points in more than 600 major U.S. cities, and a backbone video server. During the experiment, the clients streamed low-bitrate MPEG-4 video sequences from the server over paths with more than 5,000 distinct Internet routers. We describe the methodology of the experiment, the architecture of our NACK-based streaming application, study end-to-end dynamics of 16 thousand ten-minute sessions (85 million packets), and analyze the behavior of the following network parameters: packet loss, round-trip delay, oneway delay jitter, packet reordering, and path asymmetry. We also study the impact of these parameters on the quality of real-time streaming.

Hereditary hemochromatosis is an iron overload disorder most commonly caused by a defect in the HFE gene. While the genetic defect is highly prevalent, the majority of individuals do not develop clinically significant iron overload, suggesting the importance of genetic modifiers. Murine hfe knockout models have demonstrated that strain background has a strong effect on the severity of iron loading. We noted that hepatic iron loading in hfe−/− mice occurs primarily over the first postnatal weeks (loading phase) followed by a timeframe of relatively static iron concentrations (plateau phase). We thus evaluated the effects of background strain and of age on hepatic gene expression in Hfe knockout mice (hfe−/−). Hepatic gene expression profiles were examined using cDNA microarrays in 4- and 8-week-old hfe−/− and wild-type mice on two different genetic backgrounds, C57BL/6J (C57) and AKR/J (AKR). Genes differentially regulated in all hfe−/− mice groups, compared with wild-type mice, including those involved in cell survival, stress and damage responses and lipid metabolism. AKR strain-specific changes in lipid metabolism genes and C57 strain-specific changes in cell adhesion and extracellular matrix protein genes were detected in hfe−/− mice. Mouse strain and age are each significantly associated with hepatic gene expression profiles in hfe−/− mice. These affects may underlie or reflect differences in iron loading in these mice.

We consider pull-based data synchronization issues between a source and its replicas in P2P networks. Under continuous information change and lazy synchronization, these systems are highly susceptible to serving outdated content, which negatively affects their performance and user satisfaction. To understand these scenarios, we first introduce a novel model of interaction between two stochastic point processes — updates at the source and downloads at the replica — and derive the probability that a random query against the replica retrieves fresh content. Unlike prior work, we assume non-Poisson dynamics and determine statistical properties of the replication process that make it perform better for a given download rate. The second half of the paper applies these results to several more difficult algorithms — cascaded replication, cooperative caching, and redundant querying from the clients. Surprisingly, we discover that optimal cooperation involves just a single peer and that redundant querying can hurt the ability of the system to handle load (i.e., may lead to lower scalability).

Nanowires (NWs), high-aspect-ratio nanomaterials, are increasingly used in technological materials and consumer products and may have toxicological characteristics distinct from nanoparticles. We carried out a comprehensive evaluation of the physico-chemical stability of four silver nanowires (AgNWs) of two sizes and coatings and their toxicity to Daphnia magna. Inorganic aluminum-doped silica coatings were less effective than organic poly(vinyl pyrrolidone) coatings at preventing silver oxidation or Ag+ release and underwent a significant morphological transformation within one-hour following addition to low ionic strength Daphnia growth media. All AgNWs were highly toxic to D. magna but less toxic than ionic silver. Toxicity varied as a function of AgNW dimension, coating and solution chemistry. Ag+ release in the media could not account for observed AgNW toxicity. Single-particle inductively coupled plasma mass spectrometry (spICPMS) distinguished and quantified dissolved and nanoparticulate silver in microliter-scale volumes of Daphnia magna hemolymph with a limit of detection of approximately 10 ppb. The silver levels within the hemolymph of Daphnia exposed to both Ag+ and AgNW met or exceeded the initial concentration in the growth medium, indicating effective accumulation during filter feeding. Silver-rich particles were the predominant form of silver in hemolymph following exposure to both AgNWs and Ag+. Scanning electron microscopy (SEM) imaging of dried hemolymph found both AgNWs and silver precipitates that were not present in the AgNW stock or the growth medium. Both organic and inorganic coatings on the AgNW were transformed during ingestion or absorption. Pathway, gene ontology and clustering analyses of gene expression response indicated effects of AgNWs distinct from ionic silver on Daphnia magna.

Dimethyl sulfoxide (DMSO) is frequently utilized as a solvent in toxicological and pharmaceutical investigations. It is therefore important to establish the cellular and molecular targets of DMSO in order to differentiate its intrinsic effects from those elicited by a compound of interest. We performed a genome-wide functional screen in Saccharomyces cerevisiae to identify deletion mutants exhibiting sensitivity to 1% DMSO, a concentration standard to yeast chemical profiling studies. We report that mutants defective in Golgi/ER transport are sensitive to DMSO, including those lacking components of the conserved oligomeric Golgi (COG) complex. Moreover, strains deleted for members of the SWR1 histone exchange complex are hypersensitive to DMSO, with additional chromatin remodeling mutants displaying a range of growth defects. We also identify DNA repair genes important for DMSO tolerance. Finally, we demonstrate that overexpression of histone H2A.Z, which replaces chromatin-associated histone H2A in a SWR1-catalyzed reaction, confers resistance to DMSO. Many yeast genes described in this study have homologs in more complex organisms, and the data provided is applicable to future investigations into the cellular and molecular mechanisms of DMSO toxicity.

Exposure to toxaphene, an environmentally persistent mixture of chlorinated terpenes previously utilized as an insecticide, has been associated with various cancers and diseases such as amyotrophic lateral sclerosis. Nevertheless, the cellular and molecular mechanisms responsible for these toxic effects have not been established. In this study, we used a functional approach in the model eukaryote Saccharomyces cerevisiae to demonstrate that toxaphene affects yeast mutants defective in (1) processes associated with transcription elongation and (2) nutrient utilization. Synergistic growth defects are observed upon exposure to both toxaphene and the known transcription elongation inhibitor mycophenolic acid (MPA). However, unlike MPA, toxaphene does not deplete nucleotides and additionally has no detectable effect on transcription elongation. Many of the yeast genes identified in this study have human homologs, warranting further investigations into the potentially conserved mechanisms of toxaphene toxicity.

Daphnia magna is a bioindicator organism accepted by several international water quality regulatory agencies. Current approaches for assessment of water quality rely on acute and chronic toxicity that provide no insight into the cause of toxicity. Recently, molecular approaches, such as genome wide gene expression responses, are enabling an alternative mechanism based approach to toxicity assessment. While these genomic methods are providing important mechanistic insight into toxicity, statistically robust prediction systems that allow the identification of chemical contaminants from the molecular response to exposure are needed. Here we apply advanced machine learning approaches to develop predictive models of contaminant exposure using a D. magna gene expression data set for 36 chemical exposures. We demonstrate here that we can discriminate between chemicals belonging to different chemical classes including endocrine disruptors and inorganic and organic chemicals based on gene expression. We also show that predictive models based on indices of whole pathway transcriptional activity can achieve comparable results while facilitating biological interpretability.