Characterization of a 320-kb region containing the HEXA gene on bovine chromosome 10 and analysis of its association with BSE susceptibility

Bovine spongiform encephalopathy (BSE) belongs to a group of neurodegenerative diseases known as transmissible prion diseases. Recently, variants in the promoter region of the prion protein ( PRNP ) gene have been shown to have a considerable effect on the susceptibility to BSE. However, a previous genome scan revealed other putative BSE-susceptibility loci. Here, we analysed such a region on BTA10, which contains the functional candidate gene HEXA . Three hundred and twenty kilobases that, besides HEXA , also contain ARIH1 , BRUNOL6 and PARP6 were characterized and screened for polymorphisms. Genotyping of 38 SNPs in Holstein–Friesian animals from the UK (350 diseased and 270 controls) revealed two intronic SNPs that were associated with BSE incidence, with experiment-wise P -values of 3.5 × 10⁻³ and 7.7 × 10⁻³ respectively. Both SNPs were in strong linkage disequilibrium and the rare alleles had a protective effect. These alleles were contained in a haplotype dubbed 'UK-protective' that was significantly overrepresented in the controls with a permuted P -value of 2 × 10⁻³. An association study in German Holstein animals (73 diseased and 627 controls) revealed an opposite effect of the 'UK-protective' haplotype in this population, i.e. it was overrepresented in the diseased animals, although not significant after correction for multiple testing. These findings indicate a causal variant for BSE susceptibility on BTA10 in linkage disequilibrium with the markers studied. Candidate gene analyses of the surrounding region and additional association studies will help to clarify the origin of the protective effects and to identify causal variants for BSE susceptibility on BTA10.     

Multi-test analysis and model-based estimation of the prevalence of Taenia saginata cysticercus infection in naturally infected dairy cows in the absence of a ‘gold standard’ reference test

The diagnostic values of seven serological tests (ELISAs) and of the obligatory European Union-approved routine visual meat inspection for the detection of Taenia saginata cysticercosis were investigated. A total of 793 slaughtered dairy cows were selected in three European Union approved abattoirs in Switzerland, an endemic area (apparent prevalence by enhanced meat inspection up to 4.5%) with typically low parasite burdens. ELISAs based on a somatic larval antigen, isoelectric focused somatic larval antigen, larval excretory/secretory antigens, peptide HP6-2, peptide Ts45S-10, pooled peptide solution and a monoclonal antibody antigen capture assay were initially screened. As there is no perfect diagnostic ‘gold standard’ reference test, the obligatory meat inspection and four selected serological tests were further analysed using Bayesian inference to estimate the “true” prevalence and the diagnostic test sensitivities and specificities. The ELISA for specific antibody detection based on excretory/secretory antigens showed highest sensitivity and specificity with 81.6% (95% credible interval: 70–92) and 96.3% (95% credible interval: 94–99), respectively. The Bayesian model estimated the specificity of the ELISA, based on the synthetic peptide Ts45S-10 as 55.2% (95% credible interval: 46–65) and sensitivity as 84.7% (95% credible interval: 82–88). The sensitivity of the ELISA based on mAbs, detecting circulating antigen, was 14.3% (95% credible interval: 9–23) with a specificity of 93.7% (95% credible interval: 92–96). The diagnostic sensitivity of the obligatory standard European Union meat inspection procedure for the detection of T. saginata cysticercus infection at the abattoir was estimated to be 15.6% (95% credible interval: 10–23). Based on these data, the modelled prevalence of cysticercosis in dairy cows presented at abattoirs in Switzerland was estimated to be 16.5% (95% credible interval: 13–21). These cattle also had a high prevalence of infection with Dicrocoelium dendriticum (60.8%) and Fasciola hepatica (13.5%).     

Improving estimates of the basic reproductive ratio: using both the mean and the dispersal of transition times

In both within-host and epidemiological models of pathogen dynamics, the basic reproductive ratio, R(0), is a powerful tool for gauging the risk associated with an emerging pathogen, or for estimating the magnitude of required control measures. Techniques for estimating R(0), either from incidence data or in-host clinical measures, often rely on estimates of mean transition times, that is, the mean time before recovery, death or quarantine occurs. In many cases, however, either data or intuition may provide additional information about the dispersal of these transition times about the mean, even if the precise form of the underlying probability distribution remains unknown. For example, we may know that recovery typically occurs within a few days of the mean recovery time. In this paper we elucidate common situations in which R(0) is sensitive to the dispersal of transition times about their respective means. We then provide simple correction factors that may be applied to improve estimates of R(0) when not only the mean but also the standard deviation of transition times out of the infectious state can be estimated.     

Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance,virologic testing,and mortality data: Four complementary approaches

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the usefulness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.3 to 4.8 million, with possibly as many as 7.6 million cases, up to 25 times greater than the cumulative confirmed cases of about 311,000. Extending our methods to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 4.9 to 10.1 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems.     

A framework for the joint modeling of longitudinal diagnostic outcome data and latent infection status: application to investigating the temporal relationship between infection and disease

For many diseases the infection status of individuals cannot be observed directly, but can only be inferred from biomarkers that are subject to measurement error. Diagnosis of infection based on observed symptoms can itself be regarded as an imperfect test of infection status. The temporal relationship between infection and marker outcomes may be complex, especially for recurrent diseases where individuals can experience multiple bouts of infection. We propose an approach that first models the unobserved longitudinal infection status of individuals conditional on relevant covariates, and then jointly models the longitudinal sequence of biomarker outcomes conditional on infection status and covariate information through time, thus resulting in a joint model for longitudinal infection and biomarker sequences. This model can be used to investigate the temporal dynamics of infection, and to evaluate the usefulness of biomarkers for monitoring purposes. Our work is motivated and illustrated by a longitudinal study of bovine digital dermatitis (BDD) on commercial dairy farms in North West England and North Wales, in which the infection of interest is Treponeme spp., and the biomarkers of interest are a continuous enzyme-linked immunosorbent assay test outcome and a dichotomous outcome, foot lesion status. BDD is known to be one of the possible causes of foot lesions in cows.     

Estimating survival of precocial chicks during the prefledging period using a catch-curve analysis and count-based age-class data

ABSTRACT.   Estimating reproductive success for birds with precocial young can be difficult because chicks leave nests soon after hatching and individuals or broods can be difficult to track. Researchers often turn to estimating survival during the prefledging period and, though effective, mark-recapture based approaches are not always feasible due to cost, time, and animal welfare concerns. Using a threatened population of Piping Plovers ( Charadrius melodus ) that breeds along the Missouri River, we present an approach for estimating chick survival during the prefledging period using long-term (1993–2005), count-based, age-class data. We used a modified catch-curve analysis, and data collected during three 5-day sampling periods near the middle of the breeding season. The approach has several ecological and statistical assumptions and our analyses were designed to minimize the probability of violating those assumptions. For example, limiting the sampling periods to only 5 days gave reasonable assurance that population size was stable during the sampling period. Annual daily survival estimates ranged from 0.825 (SD = 0.03) to 0.931 (0.02) depending on year and sampling period, with these estimates assuming constant survival during the prefledging period and no change in the age structure of the population. The average probability of survival to fledging ranged from 0.126 to 0.188. Our results are similar to other published estimates for this species in similar habitats. This method of estimating chick survival may be useful for a variety of precocial bird species when mark-recapture methods are not feasible and only count-based age class data are available.     

A novel approach to the discovery of survival biomarkers in glioblastoma using a joint analysis of DNA methylation and gene expression

Glioblastoma multiforme (GBM) is the most aggressive of all brain tumors, with a median survival of less than 1.5 years. Recently, epigenetic alterations were found to play key roles in both glioma genesis and clinical outcome, demonstrating the need to integrate genetic and epigenetic data in predictive models. To enhance current models through discovery of novel predictive biomarkers, we employed a genome-wide, agnostic strategy to specifically capture both methylation-directed changes in gene expression and alternative associations of DNA methylation with disease survival in glioma. Human GBM-associated DNA methylation, gene expression, IDH1 mutation status, and survival data were obtained from The Cancer Genome Atlas. DNA methylation loci and expression probes were paired by gene, and their subsequent association with survival was determined by applying an accelerated failure time model to previously published alternative and expression-based association equations. Significant associations were seen in 27 unique methylation/expression pairs with expression-based, alternative, and combinatorial associations observed (10, 13, and 4 pairs, respectively). The majority of the predictive DNA methylation loci were located within CpG islands, and all but three of the locus pairs were negatively correlated with survival. This finding suggests that for most loci, methylation/expression pairs are inversely related, consistent with methylation-associated gene regulatory action. Our results indicate that changes in DNA methylation are associated with altered survival outcome through both coordinated changes in gene expression and alternative mechanisms. Furthermore, our approach offers an alternative method of biomarker discovery using a priori gene pairing and precise targeting to identify novel sites for locus-specific therapeutic intervention.     

A novel approach to the discovery of survival biomarkers in glioblastoma using a joint analysis of DNA methylation and gene expression

Glioblastoma multiforme (GBM) is the most aggressive of all brain tumors, with a median survival of less than 1.5 years. Recently, epigenetic alterations were found to play key roles in both glioma genesis and clinical outcome, demonstrating the need to integrate genetic and epigenetic data in predictive models. To enhance current models through discovery of novel predictive biomarkers, we employed a genome-wide, agnostic strategy to specifically capture both methylation-directed changes in gene expression and alternative associations of DNA methylation with disease survival in glioma. Human GBM-associated DNA methylation, gene expression, IDH1 mutation status, and survival data were obtained from The Cancer Genome Atlas. DNA methylation loci and expression probes were paired by gene, and their subsequent association with survival was determined by applying an accelerated failure time model to previously published alternative and expression-based association equations. Significant associations were seen in 27 unique methylation/expression pairs with expression-based, alternative, and combinatorial associations observed (10, 13, and 4 pairs, respectively). The majority of the predictive DNA methylation loci were located within CpG islands, and all but three of the locus pairs were negatively correlated with survival. This finding suggests that for most loci, methylation/expression pairs are inversely related, consistent with methylation-associated gene regulatory action. Our results indicate that changes in DNA methylation are associated with altered survival outcome through both coordinated changes in gene expression and alternative mechanisms. Furthermore, our approach offers an alternative method of biomarker discovery using a priori gene pairing and precise targeting to identify novel sites for locus-specific therapeutic intervention.     

On bivalve phylogeny: a high-level analysis of the Bivalvia (Mollusca) based on combined morphology and DNA sequence data

Abstract. Bivalve classification has suffered in the past from the crossed-purpose discussions among paleontologists and neontologists, and many have based their proposals on single character systems. More recently, molecular biologists have investigated bivalve relationships by using only gene sequence data, ignoring paleontological and neontological data. In the present study we have compiled morphological and anatomical data with mostly new molecular evidence to provide a more stable and robust phylogenetic estimate for bivalve molluscs. The data here compiled consist of a morphological data set of 183 characters, and a molecular data set from 3 loci: 2 nuclear ribosomal genes (18S rRNA and 28S rRNA), and 1 mitochondrial coding gene (cytochrome c oxidase subunit I), totaling ∼3 Kb of sequence data for 76 molluscs (62 bivalves and 14 outgroup taxa). The data have been analyzed separately and in combination by using the direct optimization method of Wheeler (1996), and they have been evaluated under 12 analytical schemes. The combined analysis supports the monophyly of bivalves, paraphyly of protobranchiate bivalves, and monophyly of Autolamellibranchiata, Pteriomorphia, Heteroconchia, Palaeoheterodonta, and Heterodonta s.l., which includes the monophyletic taxon Anomalodesmata. These analyses strongly support the conclusion that Anomalodesmata should not receive a class status, and that the heterodont orders Myoida and Veneroida are not monophyletic. Among the most stable results of the analysis are the monophyly of Palaeoheterodonta, grouping the extant trigoniids with the freshwater unionids, and the sister-group relationship of the heterodont families Astartidae and Carditidae, which together constitute the sister taxon to the remaining heterodont bivalves. Internal relationships of the main bivalve groups are discussed on the basis of node support and clade stability.     

Identification and comparative analysis of the Eriocheir sinensis microRNA transcriptome response to Spiroplasma eriocheiris infection using a deep sequencing approach

The Chinese mitten crab Eriocheir sinensis is one of the most important freshwater aquaculture crustacean species in China. MicroRNAs (miRNAs) are small non-coding RNAs that are important effectors in the intricate host-pathogen interaction network. To increase the repertoire of miRNAs characterized in crustaceans and to examine the relationship between host miRNA expression and pathogen infection, we used the Illumina/Solexa deep sequencing technology to sequence two small RNA libraries prepared from haemocytes of E. sinensis under normal conditions and during infection with Spiroplasma eriocheiris. The high-throughput sequencing resulted in approximately 30,975,151 and 30,826,277 raw reads corresponding to 12,077,088 and 16,271,545 high-quality mappable reads for the normal and infected haemocyte samples, respectively. Bioinformatic analyses identified 735 unique miRNAs, including 36 that are conserved in crustaceans, 134 that are novel to crabs but are present in other arthropods (PN-type), and 565 that are completely new (PC-type). Two hundred twenty-eight unique miRNAs displayed significant differential expression between the normal and infected haemocyte samples (p < 0.0001). Of these, 133 (58%) were significantly up-regulated and 95 (42%) were significantly down-regulated upon challenge with S. eriocheiris. Real-time quantitative PCR (RT-qPCR) experiments were preformed for 10 miRNAs of the two samples, and agreement was found between the sequencing and RT-qPCR data. To our knowledge, this is the first report of comprehensive identification of E. sinensis miRNAs and of expression analysis of E. sinensis miRNAs after exposure to S. eriocheiris. Many miRNAs were differentially regulated when exposed to the pathogen, and these findings support the hypothesis that certain miRNAs might be essential in host-pathogen interactions. Our results suggest that elucidation of the molecular mechanisms responsible for miRNA regulation of the host’s innate immune system should help with the development of new control strategies to prevent or treat S. eriocheiris infections in crustaceans.     

Genetic analysis of the Saimiri breeding colony of the Pasteur Institute (French Guiana): development of a molecular typing method using a combination of nuclear and mitochondrial DNA markers

Saimiri (Cebidae) groups a complex of species and subspecies, which present a large morphological plasticity. Genetic analysis is complicated by the absence of consensus on classification criteria and the paucity of molecular tools available for the genus. As the squirrel monkey is widely used in biomedical research, breeding centers have been established, but the genetic make up and diversity of many of the existing colonies is unknown precluding a rationale breeding policy. To develop a genetic typing strategy for the Saimiri breeding colony of Pasteur Institute of French Guiana, we have used Cytochrome b, a mitochondrial marker, and nuclear microsatellites. Cytochrome b sequences from wild-caught Saimiri boliviensis, Saimiri sciureus sciureus and S. s. collinsi reference specimens and captive animals identified 11 haplotypes, grouped into three distinct clades. An estimate of genetic variability within each captive morphotype, and of the extent of molecular divergence between the Bolivian, Guyanese and Brazilian breeds was obtained from the analysis of three nuclear microsatellites. Taxon-specific microsatellites enabled typing of F0-F3 animals, but did not differentiate Brazilian from Guyanese animals. Three locus microsatellite analysis of a representative sample from each generation showed no trend for loss of heterozygosity, and identified hybrid animals between Bolivian and the two others sub-species. These data provide novel evidence for taxonomic classification and a rationale strategy to further type the whole colony.     

Phylogenetic analysis of the insect order Odonata using 28S and 16S rDNA sequences: a comparison between data sets with different evolutionary rates

Molecular phylogenetic analyses were conducted for the insect order Odonata with a focus on testing the effectiveness of a slowly evolving gene to resolve deep branching and also to examine: (i) the monophyly of damselflies (the suborder Zygoptera); and (ii) the phylogenetic position of the relict dragonfly Epiophlebia superstes. Two independent molecular sources were used to reconstruct phylogeny: the 16S rRNA gene on the mitochondrial genome and the 28S rRNA gene on the nuclear genome. A comparison of the sequences showed that the obtained 28S rDNA sequences have evolved at a much slower rate than the 16S rDNA, and that the former is better than the latter for resolving deep branching in the Odonata. Both molecular sources indicated that the Zygoptera are paraphyletic, and when a reasonable weighting for among‐site rate variation was enforced for the 16S rDNA data set, E. superstes was placed between the two remaining major suborders, namely, Zygoptera and Anisoptera (dragonflies). Character reconstruction analysis suggests that multiple hits at the rapidly evolving sites in the 16S rDNA degenerated the phylogenetic signals of the data set.     

Climate and the inter‐annual variability of fire in southern Africa: a meta‐analysis using long‐term field data and satellite‐derived burnt area data

Aim This study investigates inter‐annual variability in burnt area in southern Africa and the extent to which climate is responsible for this variation. We compare data from long‐term field sites across the region with remotely sensed burnt area data to test whether it is possible to develop a general model. Location Africa south of the equator. Methods Linear mixed effects models were used to determine the effect of rainfall, seasonality and fire weather in driving variation in fire extent between years, and to test whether the effect of these variables changes across the subcontinent and in areas more and less impacted by human activities. Results A simple model including rainfall and seasonality explained 40% of the variance in burnt area between years across 10 different protected areas on the subcontinent, but this model, when applied regionally, indicated that climate had less impact on year‐to‐year variation in burnt area than would be expected. It was possible to demonstrate that the relative importance of rainfall and seasonality changed as one moved from dry to wetter systems, but most noticeable was the reduction in climatically driven variability of fire outside protected areas. Inter‐annual variability is associated with the occurrence of large fires, and large fires are only found in areas with low human impact. Main conclusions This research gives the first data‐driven analysis of fire–climate interactions in southern Africa. The regional analysis shows that human impact on fire regimes is substantial and acts to limit the effect of climate in driving variation between years. This is in contrast to patterns in protected areas, where variation in accumulated rainfall and the length of the dry season influence the annual area burnt. Global models which assume strong links between fire and climate need to be re‐assessed in systems with high human impact.     

Evolution of covered stents in the contemporary era: clinical application,materials and manufacturing strategies using nanotechnology

Endovascular stents have revolutionised the field of interventional cardiology. Despite their excellent clinical outcome complications associated with percutaneous stent implantation following the procedure have remained a major drawback in their widespread use. To overcome such limitations, a number of novel endovascular stents have emerged including a covered stent wrapped in a thin membrane sleeve. As well as prevention of complications associated with stenting, covered stents owing to their physical barrier are used as the treatment option of choice for trauma devices during emergency situations and to treat a number of pathological disease states. The aim of this review is to provide the reader with an overall objective outlook in the use of covered stents as a treatment option in a number of vascular complications and addresses their design and materials used in the manufacturing process. In addition, new strategies are highlighted and future prospects with the emergence of novel smart alloys for 3D scaffolds and the use of nanotechnology in the development of nanocomposite materials.     

Metabolomic fingerprinting using nuclear magnetic resonance and multivariate data analysis as a tool for biodiversity informatics: A case study on the classification of Rosa x damascena

Metabolomics is the comprehensive and simultaneous identification and quantification of metabolites in living cells. The term metabolome is used to describe the observable chemical profile or fingerprint of the metabolites in a whole tissue. Although being a new approach to study natural compounds, metabolomics uses traditional analytical techniques, including extraction methods, which can be followed by nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis. Although metabolomics has been successfully applied to quality control issues, the examples of its use for chemosystematics are few. Thus, the analysis of four taxa of Rosa x damascena (R. damascena Mill., R. damascenasemperflorens, R. damascenatrigintipetala and R. duchesse of Portland) was carried out by NMR spectroscopy as a tool for their classification. A principal component analysis of the ¹H NMR spectra, based on the metabolites found in organic and aqueous fractions, showed a clear similarity of the samples. In particular, the major contributions from the aqueous fraction, preliminarily considered as a biomarker of R. x damascena group, are the flavonoids kaempferol and quercetin, glycosilated with glucose and rhamnose units. Our analysis demonstrated a close chemotaxonomic correlation among the four taxa, making this method a reliable tool for chemosystematics.     

A possible mechanism of metabolic regulation in Gibberella fujikuroi using a mixed carbon source of glucose and corn oil inferred from analysis of the kinetics data obtained in a stirrer tank bioreactor

A nonstructured model was used to study the dynamics of gibberellic acid production in a stirred tank bioreactor. Experimental data were obtained from submerged batch cultures of Gibberella fujikuroi (CDBB H‐984) grown in varying ratios of glucose‐corn oil as the carbon source. The nitrogen depletion effect was included in mathematical model by considering the specific kinetic constants as a linear function of the normalized nitrogen consumption rate. The kinetics of biomass growth and consumption of phosphate and nitrogen were based on the logistic model. The traditional first‐order kinetic model was used to describe the specific consumption of glucose and corn oil. The nitrogen effect was solely included in the phosphate and corn oil consumption and biomass growth. The model fit was satisfactory, revealing the dependence of the kinetics with respect to the nitrogen assimilation rate. Through simulations, it was possible to make diagrams of specific growth rate and specific rate of substrate consumptions, which was a powerful tool for understanding the metabolic interactions that occurred during the various stages of fermentation process. This kinetic analysis provided the proposal of a possible mechanism of regulation on growth, substrate consumptions, and production of gibberellic acid (GA₃) in G. fujikuroi. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 29:1169–1180, 2013