A Mathematical Framework for Estimating Pathogen Transmission Fitness and Inoculum Size Using Data from a Competitive Mixtures Animal Model

We present a method to measure the relative transmissibility (“transmission fitness”) of one strain of a pathogen compared to another. The model is applied to data from “competitive mixtures” experiments in which animals are co-infected with a mixture of two strains. We observe the mixture in each animal over time and over multiple generations of transmission. We use data from influenza experiments in ferrets to demonstrate the approach. Assessment of the relative transmissibility between two strains of influenza is important in at least three contexts: 1) Within the human population antigenically novel strains of influenza arise and compete for susceptible hosts. 2) During a pandemic event, a novel sub-type of influenza competes with the existing seasonal strain(s). The unfolding epidemiological dynamics are dependent upon both the population's susceptibility profile and the inherent transmissibility of the novel strain compared to the existing strain(s). 3) Neuraminidase inhibitors (NAIs), while providing significant potential to reduce transmission of influenza, exert selective pressure on the virus and so promote the emergence of drug-resistant strains. Any adverse outcome due to selection and subsequent spread of an NAI-resistant strain is exquisitely dependent upon the transmission fitness of that strain. Measurement of the transmission fitness of two competing strains of influenza is thus of critical importance in determining the likely time-course and epidemiology of an influenza outbreak, or the potential impact of an intervention measure such as NAI distribution. The mathematical framework introduced here also provides an estimate for the size of the transmitted inoculum. We demonstrate the framework's behaviour using data from ferret transmission studies, and through simulation suggest how to optimise experimental design for assessment of transmissibility. The method introduced here for assessment of mixed transmission events has applicability beyond influenza, to other viral and bacterial pathogens

Dynamic Patterns of Circulating Seasonal and Pandemic A(H1N1)pdm09 Influenza Viruses From 2007–2010 in and around Delhi, India

Influenza surveillance was carried out in a subset of patients with influenza-like illness (ILI) presenting at an Employee Health Clinic (EHS) at All India Institute of Medical Sciences (AIIMS), New Delhi (urban) and pediatric out patients department of civil hospital at Ballabhgarh (peri-urban), under the Comprehensive Rural Health Services Project (CRHSP) of AIIMS, in Delhi region from January 2007 to December 2010. Of the 3264 samples tested, 541 (17%) were positive for influenza viruses, of which 221 (41%) were pandemic Influenza A(H1N1)pdm09, 168 (31%) were seasonal influenza A, and 152 (28%) were influenza B. While the Influenza viruses were detected year-round, their types/subtypes varied remarkably. While there was an equal distribution of seasonal A(H1N1) and influenza B in 2007, predominance of influenza B was observed in 2008. At the beginning of 2009, circulation of influenza A(H3N2) viruses was observed, followed later by emergence of Influenza A(H1N1)pdm09 with co-circulation of influenza B viruses. Influenza B was dominant subtype in early 2010, with second wave of Influenza A(H1N1)pdm09 in August-September, 2010. With the exception of pandemic H1N1 emergence in 2009, the peaks of influenza activity coincided primarily with monsoon season, followed by minor peak in winter at both urban and rural sites. Age group analysis of influenza positivity revealed that the percent positivity of Influenza A(H1N1)pdm09 influenza virus was highest in >5–18 years age groups (OR 2.5; CI = 1.2–5.0; p = 0.009) when compared to seasonal influenza. Phylogenetic analysis of Influenza A(H1N1)pdm09 from urban and rural sites did not reveal any major divergence from other Indian strains or viruses circulating worldwide. Continued surveillance globally will help define regional differences in influenza seasonality, as well as, to determine optimal periods to implement influenza vaccination programs among priority populations

Comparative Transcriptome Analysis of Bacillus subtilis Responding to Dissolved Oxygen in Adenosine Fermentation

Dissolved oxygen (DO) is an important factor for adenosine fermentation. Our previous experiments have shown that low oxygen supply in the growth period was optimal for high adenosine yield. Herein, to better understand the link between oxygen supply and adenosine productivity in B. subtilis (ATCC21616), we sought to systematically explore the effect of DO on genetic regulation and metabolism through transcriptome analysis. The microarrays representing 4,106 genes were used to study temporal transcript profiles of B. subtilis fermentation in response to high oxygen supply (agitation 700 r/min) and low oxygen supply (agitation 450 r/min). The transcriptome data analysis revealed that low oxygen supply has three major effects on metabolism: enhance carbon metabolism (glucose metabolism, pyruvate metabolism and carbon overflow), inhibit degradation of nitrogen sources (glutamate family amino acids and xanthine) and purine synthesis. Inhibition of xanthine degradation was the reason that low oxygen supply enhanced adenosine production. These provide us with potential targets, which can be modified to achieve higher adenosine yield. Expression of genes involved in energy, cell type differentiation, protein synthesis was also influenced by oxygen supply. These results provided new insights into the relationship between oxygen supply and metabolism

Tracking of an electron beam through the solar corona with LOFAR

© ESO 2018. The Sun's activity leads to bursts of radio emission, among other phenomena. An example is type-III radio bursts. They occur frequently and appear as short-lived structures rapidly drifting from high to low frequencies in dynamic radio spectra. They are usually interpreted as signatures of beams of energetic electrons propagating along coronal magnetic field lines. Here we present novel interferometric LOFAR (LOw Frequency ARray) observations of three solar type-III radio bursts and their reverse bursts with high spectral, spatial, and temporal resolution. They are consistent with a propagation of the radio sources along the coronal magnetic field lines with nonuniform speed. Hence, the type-III radio bursts cannot be generated by a monoenergetic electron beam, but by an ensemble of energetic electrons with a spread distribution in velocity and energy. Additionally, the density profile along the propagation path is derived in the corona. It agrees well with three-fold coronal density model by (1961, ApJ, 133, 983)

Comparison of Luminex NxTAG Respiratory Pathogen Panel and xTAG Respiratory Viral Panel FAST Version 2 for the Detection of Respiratory Viruses.

Owing to advancements in molecular diagnostics, recent years have seen an increasing number of laboratories adopting respiratory viral panels to detect respiratory pathogens. In December 2015, the NxTAG respiratory pathogen panel (NxTAG RPP) was approved by the United States Food and Drug Administration. We compared the clinical performance of this new assay with that of the xTAG respiratory viral panel (xTAG RVP) FAST v2 using 142 clinical samples and 12 external quality assessment samples. Discordant results were resolved by using a laboratory-developed respiratory viral panel. The NxTAG RPP achieved 100% concordant negative results and 86.6% concordant positive results. It detected one coronavirus 229E and eight influenza A/H3N2 viruses that were missed by the xTAG RVP FAST v2. On the other hand, the NxTAG RPP missed one enterovirus/rhinovirus and one metapneumovirus that were detected by FAST v2. Both panels correctly identified all the pathogens in the 12 external quality assessment samples. Overall, the NxTAG RPP demonstrated good diagnostic performance. Of note, it was better able to subtype the influenza A/H3N2 viruses compared with the xTAG RVP FAST v2

Systematic phylogenetic analysis of influenza A virus reveals many novel mosaic genome segments

Recombination plays an important role in shaping the genetic diversity of a number of DNA and RNA viruses. Although some recent studies have reported bioinformatic evidence of mosaic sequences in a variety of influenza A viruses, it remains controversial as to whether these represent bona fide natural recombination events or laboratory artifacts. Importantly, mosaic genome structures can create significant topological incongruence during phylogenetic analyses, which can mislead additional phylogeny-based molecular evolutionary analyses such as molecular clock dating, the detection of selection pressures and phylogeographic inference. As a result, there is a strong need for systematic screenings for mosaic structures within the influenza virus genome database. We used a combination of sequence-based and phylogeny-based methods to identify 388 mosaic influenza genomic segments, of which 332 are previously unreported and are significantly supported by phylogenetic methods. It is impossible, however, to ascertain whether these represent natural recombinants. To facilitate the future identification of recombinants, reference sets of non-recombinant sequences were selected for use in an automatic screening protocol for detecting mosaic sequences. Tests using real and simulated mosaic sequences indicate that our screening protocol is both sensitive (average >90%) and accurate (average >77%) enough to identify a range of different mosaic patterns. The relatively high prevalence of mosaic influenza virus sequences implies that efficient systematic screens, such as that proposed here, should be performed routinely to detect natural recombinant strains, potential laboratory artifacts, and sequencing contaminants either prior to sequences being deposited in GenBank or before they are used for phylogenetic analyses. © 2013 Elsevier B.V