Kinetics of acute hepatitis B virus infection in humans

Using patient data from a unique single source outbreak of hepatitis B virus (HBV) infection, we have characterized the kinetics of acute HBV infection by monitoring viral turnover in the serum during the late incubation and clinical phases of the disease in humans. HBV replicates rapidly with minimally estimated doubling times ranging between 2.2 and 5.8 d (mean 3.7 ± 1.5 d). After a peak viral load in serum of nearly 1010 HBV DNA copies/ml is attained, clearance of HBV DNA follows a two or three phase decay pattern with an initial rapid decline characterized by mean half-life (t1/2) of 3.7 ± 1.2 d, similar to the t1/2 observed in the noncytolytic clearance of covalently closed circular DNA for other hepadnaviruses. The final phase of virion clearance occurs at a variable rate (t1/2 of 4.8 to 284 d) and may relate to the rate of loss of infected hepatocytes. Free virus has a mean t1/2 of at most 1.2 ± 0.6 d. We estimate a peak HBV production rate of at least 1013 virions/day and a maximum production rate of an infected hepatocyte of 200–1,000 virions/day, on average. At this peak rate of virion production we estimate that every possible single and most double mutations would be created each day

Statistics of extinction and survival in Lotka-Volterra systems

We analyze purely competitive many-species Lotka-Volterra systems with random interaction matrices, focusing the attention on statistical properties of their asymptotic states. Generic features of the evolution are outlined from a semiquantitative analysis of the phase-space structure, and extensive numerical simulations are performed to study the statistics of the extinctions. We find that the number of surviving species depends strongly on the statistical properties of the interaction matrix, and that the probability of survival is weakly correlated to specific initial conditions.Comment: Previous version had error in authors. 11 pages, including 5 figure

Ecological model of extinctions

We present numerical results based on a simplified ecological system in evolution, showing features of extinction similar to that claimed for the biosystem on Earth. In the model each species consists of a population in interaction with the others, that reproduces and evolves in time. Each species is simultaneously a predator and a prey in a food chain. Mutations that change the interactions are supposed to occur randomly at a low rate. Extinctions of populations result naturally from the predator-prey dynamics. The model is not pinned in a fitness variable, and natural selection arises from the dynamics.Comment: 16 pages (LaTeX type, RevTeX style), including 6 figures in gif format. To be published in Phys. Rev. E (prob. Dic. 96

Classification using distance nearest neighbours

This paper proposes a new probabilistic classification algorithm using a Markov random field approach. The joint distribution of class labels is explicitly modelled using the distances between feature vectors. Intuitively, a class label should depend more on class labels which are closer in the feature space, than those which are further away. Our approach builds on previous work by Holmes and Adams (2002, 2003) and Cucala et al. (2008). Our work shares many of the advantages of these approaches in providing a probabilistic basis for the statistical inference. In comparison to previous work, we present a more efficient computational algorithm to overcome the intractability of the Markov random field model. The results of our algorithm are encouraging in comparison to the k-nearest neighbour algorithm.Comment: 12 pages, 2 figures. To appear in Statistics and Computin

Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach

In non-linear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of 'hold-out' or 'split-sample' data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. First, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance

Gene expression time delays & Turing pattern formation systems

The incorporation of time delays can greatly affect the behaviour of partial differential equations and dynamical systems. In addition, there is evidence that time delays in gene expression due to transcription and translation play an important role in the dynamics of cellular systems. In this paper, we investigate the effects of incorporating gene expression time delays into a one-dimensional putative reaction diffusion pattern formation mechanism on both stationary domains and domains with spatially uniform exponential growth. While oscillatory behaviour is rare, we find that the time taken to initiate and stabilise patterns increases dramatically as the time delay is increased. In addition, we observe that on rapidly growing domains the time delay can induce a failure of the Turing instability which cannot be predicted by a naive linear analysis of the underlying equations about the homogeneous steady state. The dramatic lag in the induction of patterning, or even its complete absence on occasions, highlights the importance of considering explicit gene expression time delays in models for cellular reaction diffusion patterning

Chasing the identification of ASCA Galactic Objects (ChIcAGO): An X-ray survey of unidentified sources in the galactic plane. I : Source sample and initial results

We present the Chasing the Identification of ASCA Galactic Objects (ChIcAGO) survey, which is designed to identify the unknown X-ray sources discovered during the ASCA Galactic Plane Survey (AGPS). Little is known about most of the AGPS sources, especially those that emit primarily in hard X-rays (2-10 keV) within the Fx 10-13 to 10-11 erg cm -2 s-1 X-ray flux range. In ChIcAGO, the subarcsecond localization capabilities of Chandra have been combined with a detailed multiwavelength follow-up program, with the ultimate goal of classifying the >100 unidentified sources in the AGPS. Overall to date, 93 unidentified AGPS sources have been observed with Chandra as part of the ChIcAGO survey. A total of 253 X-ray point sources have been detected in these Chandra observations within 3′ of the original ASCA positions. We have identified infrared and optical counterparts to the majority of these sources, using both new observations and catalogs from existing Galactic plane surveys. X-ray and infrared population statistics for the X-ray point sources detected in the Chandra observations reveal that the primary populations of Galactic plane X-ray sources that emit in the Fx 10-13 to 10-11 erg cm -2 s-1 flux range are active stellar coronae, massive stars with strong stellar winds that are possibly in colliding wind binaries, X-ray binaries, and magnetars. There is also another primary population that is still unidentified but, on the basis of its X-ray and infrared properties, likely comprises partly Galactic sources and partly active galactic nuclei.Peer reviewedSubmitted Versio

Polyketide synthesis genes associated with toxin production in two species of Gambierdiscus (Dinophyceae)

Background Marine microbial protists, in particular, dinoflagellates, produce polyketide toxins with ecosystem-wide and human health impacts. Species of Gambierdiscus produce the polyether ladder compounds ciguatoxins and maitotoxins, which can lead to ciguatera fish poisoning, a serious human illness associated with reef fish consumption. Genes associated with the biosynthesis of polyether ladder compounds are yet to be elucidated, however, stable isotope feeding studies of such compounds consistently support their polyketide origin indicating that polyketide synthases are involved in their biosynthesis. Results Here, we report the toxicity, genome size, gene content and transcriptome of Gambierdiscus australes and G. belizeanus. G. australes produced maitotoxin-1 and maitotoxin-3, while G. belizeanus produced maitotoxin-3, for which cell extracts were toxic to mice by IP injection (LD50 = 3.8 mg kg-1). The gene catalogues comprised 83,353 and 84,870 unique contigs, with genome sizes of 32.5 ± 3.7 Gbp and 35 ± 0.88 Gbp, respectively, and are amongst the most comprehensive yet reported from a dinoflagellate. We found three hundred and six genes involved in polyketide biosynthesis, including one hundred and ninty-two ketoacyl synthase transcripts, which formed five unique phylogenetic clusters. Conclusions Two clusters were unique to these maitotoxin-producing dinoflagellate species, suggesting that they may be associated with maitotoxin biosynthesis. This work represents a significant step forward in our understanding of the genetic basis of polyketide production in dinoflagellates, in particular, species responsible for ciguatera fish poisoning.Postprin

Higher-Dimensional Twistor Transforms using Pure Spinors

Hughston has shown that projective pure spinors can be used to construct massless solutions in higher dimensions, generalizing the four-dimensional twistor transform of Penrose. In any even (Euclidean) dimension d=2n, projective pure spinors parameterize the coset space SO(2n)/U(n), which is the space of all complex structures on R^{2n}. For d=4 and d=6, these spaces are CP^1 and CP^3, and the appropriate twistor transforms can easily be constructed. In this paper, we show how to construct the twistor transform for d>6 when the pure spinor satisfies nonlinear constraints, and present explicit formulas for solutions of the massless field equations.Comment: 17 pages harvmac tex. Modified title, abstract, introduction and references to acknowledge earlier papers by Hughston and other

The freeze-out mechanism and phase-space density in ultrarelativistic heavy-ion collisions

We explore the consequences of a freeze-out criterion for heavy-ion collisions, based on pion escape probabilities from the hot and dense but rapidly expanding collision region. The influence of the expansion and the scattering rate on the escape probability is studied. The temperature dependence of this scattering rate favors a low freeze-out temperature of ~100 MeV. In general, our results support freeze-out along finite four-volumes rather than sharp three-dimensional hypersurfaces, with high-pt particles decoupling earlier from smaller volumes. We compare our approach to the proposed universal freeze-out criteria using the pion phase-space density and its mean free path.Comment: 8 pages, 2 figures, although conclusions are unchanged, the paper has been re-written and the title has been changed for the sake of better presentatio