Applying a spread model to identify the entry points from which the pine wood nematode, the vector of pine wilt disease, would spread most rapidly across Europe

Pine wilt disease, which can rapidly kill pines, is caused by the pine wood nematode, Bursaphelenchus xylophilus. It is expanding its range in many countries in Asia and measures are being taken at the EU level to prevent its spread from Portugal. Due to the threat to European forests, it is important to prevent additional introductions and target surveillance to the points of entry that pose the greatest risk. In this study, we present a model to identify the European ports from which the nematode can spread most rapidly across Europe. This model describes: (1) the potential spread of the pine wood nematode based on short-distance spread (the active flight of the vector beetles) and long-distance spread (primarily due to human-mediated transportation), and (2) the development of pine wilt disease based on climate suitability and the potential spread of the nematode. Separate introductions at 200 European ports were simulated under various climate change scenarios. We found that the pine wood nematode could invade 19–60% of the study area (30°00 N–72°00 N, 25°00 W–40°00 E) by 2030, with the highest spread from ports located in Eastern and Northern Europe. Based on climate change scenarios, the disease could affect 8–34% of the study area by 2030, with the highest spread from ports located in South-Eastern Europe. This study illustrates how a spread model can be used to determine the critical points of entry for invasive species, so that surveillance can be targeted more accurately and control measures prioritised.     

Using the nematophagous fungus Esteya vermicola to control the disastrous pine wilt disease

The present study evaluated the protective effects of the nematophagous fungus Esteya vermicola on the large pine trees of Mt. Wora, Jinju, South Korea for six years. When pine trees were treated with E. vermicola 110 days before artificial normal pinewood nematode (PWN) infection, 30–50% of the trees survived for six years. When pine trees were treated with E. vermicola one week after artificial normal PWN infection, 40% of the trees were saved. In contrast, all of the control trees were killed by pine wilt disease in the first year. Although it has been more than six years since the beginning of this experiment, the existence of E. vermicola inside the treated pine trees was successfully detected using a PCR method with two pairs of specific primers for E. vermicola. These results suggest that E. vermicola possesses great potential as a biocontrol agent to combat the disastrous pine wilt disease. This is the first report of using nematophagous fungi to control pine wilt disease in the field for a duration of over five years.     

Pathogenic cellulase assay of pine wilt disease and immunological localization

The pine wilt disease caused by Bursaphelenchus xylophilus (BX), also known as the pine wood nematode (PWN), is the most devastating disease of pine trees. In this work, a high molecular weight B. xylophilus cellulase antigen (BXCa) was purified from total homogenates of nematodes. BXCa was found to be able to hydrolyze carboxymethyl cellulose (CMC) efficiently (155.65 U/mg) and to have an approximate molecular mass of 58.9 kDa. We harvested anti-BXCa antibodies and performed immunocytochemical assays, which revealed the localization of cellulase pools in the esophageal gland cells of the PWN. It was also discovered that cellulase was secreted from the stylet and was used to hydrolyze cellulose to facilitate the PWN entering host cells. These results are consistent with other plant parasitical nematodes. Interestingly, strong fluorescence signals from cellulase staining were observed in tracheid cells in naturally infected pine wood, in addition to ray cells and the resin canal zone. These results strongly suggest that the cellulase released by the PWN is one of the pathogenic substances of pine wilt disease and is responsible for the development of the early symptoms of the disease.     

Spatial pattern of pine wilt disease caused byBursaphelenchus xylophilus (Nematoda: Aphelenchoididae) within aPinus thunbergii stand

To understand the mechanism of spread of pine wilt disease caused by the pinewood nematode, Bursaphelenchus xylophilus, which is vectored by a cerambycid, Monochamus alternatus, the spatial distribution of trees weakened by the nematode was examined within a Pinus thunbergii stand from June to October for 4 years. The weakened trees were distributed in a clumped pattern in 1980 and 1981, at an early stage of infestation. In many cases, they showed a double-clumped pattern. The degree of aggregation was higher in June or July than after August. They were uniformly distributed in June or July 1982 and in June 1983 whereas they showed a double-clumped pattern after August. The trees were frequently weakened in June or July when they were near the trees weakened during the previous year. At quadrat sizes of more than 25 m², spatial overlapping was pronounced between trees weakened during June–July of the current year and those weakened in the previous year. The seasonal changes in spatial distribution of weakened trees were explained by the interaction among M. alternatus, B. xylophilus and Pinus trees.     

Population density ofMonochamus alternatus adults (Coleoptera: Cerambycidae) and incidence of pine wilt disease caused byBursaphelenchus xylophilus (Nematoda: Aphelenchoididae)

Summary The seasonal occurrence ofMonochamus alternatus and newly weakened trees were investigated in aPinus thunbergii stand for 4 years. Adult beetles were present between June and September with a peak in their population occurring in early July followed by a decline then a period of about one month being in a steady number. The average number ofBursaphelenchus xylophilus (Nematoda), which is the causal agent of pine wilt disease, within beetles decreased as the season advanced. Pine trees newly weakened byB. xylophilus appeared between June and October, especially from August to October. The proportion of weakened or killed trees was directly proportional to the average beetle density per tree from June to August.     

Using a logical model to predict the growth of yeast

Background  

A logical model of the known metabolic processes in S. cerevisiae was constructed from iFF708, an existing Flux Balance Analysis (FBA) model, and augmented with information from the KEGG online pathway database. The use of predicate logic as the knowledge representation for modelling enables an explicit representation of the structure of the metabolic network, and enables logical inference techniques to be used for model identification/improvement.     

Trialling of an optimal health programme (OHP) across chronic disease

Population ageing is a worldwide phenomenon, most advanced in developed countries and expected to continue over the next few decades. As people are surviving longer with age-associated disease and disability, there is an imperative to identify innovative solutions for an already overburdened health care system. Such innovations need to be focused on disease management, taking into consideration the strong associations that have been established between psychosocial factors and pathophysiological mechanisms associated with chronic disease. Aside from personal and community costs, chronic diseases produce a significant economic burden due to the culmination of health care costs and lost productivity. This commentary reports on a programme of research, Translating Research, Integrated Public Health Outcomes and Delivery, which will evaluate an optimal health programme that adopts a person-centred approach and engages collaborative therapy to educate, support and improve the psychosocial health of those with chronic disease. The effectiveness of the optimal health programme will be evaluated across three of the most significant contributors to disease burden: diabetes mellitus, chronic kidney disease and stroke. Cost-effectiveness will also be evaluated. The findings derived from this series of randomised controlled trials will also provide evidence attesting to the potential applicability of the optimal health programme in other chronic conditions.     

Cis-vaccenic acid and the Framingham risk score predict chronic kidney disease: the multi-ethnic study of atherosclerosis (MESA)

IntroductionData on the associations of fatty acids with chronic kidney disease (CKD) are sparse.Materials and methodsWe performed a cross-sectional study of 2792 men and women from the MESA cohort of African–American, Caucasian, Chinese and Hispanic adults without known cardiovascular disease. Plasma phospholipid fatty acid proportions were associated with estimated glomerular filtration rate (eGFR) and the albumin/creatinine ratio.ResultsCis-vaccenic acid (18:1n?7), adjusted for other fatty acids using multivariate logistic regression (CI: 1.0–1.4), and step-wise logistic regression (CI: 1.02–1.42), was positively associated with reduced eGFR. The Framingham Risk Score, when adjusting for fatty acid proportions and demographic factors, was positively associated with CKD as measured by the eGFR and the albumin/creatinine ratio.Discussion and conclusionsPlasma phospholipid proportions of the 18 carbon monounsaturated cis-vaccenic acid {18:1n?7}) and the Framingham Risk Score are associated with kidney function. The potential role of 18:1n?7 in the development of CKD warrants further investigation.     

Using a phytoplankton growth model to predict the fractionation of stable carbon isotopes

In the preceding paper in this issue, a phytoplankton growthmodel based on an analogy with chemical kinetics (the CR model)was re-derived, and a comparison made with the growth rate ofcultured phytoplankton assemblages extracted from temperatelakes. In this paper, further derivation of the CR model leadsto the same model of carbon isotope fractionation used by Rauet al. (Mar. Ecol. Prog. Ser., 133, 275–285, 1996). Boththe CR and Rau et al. models are compatible with the observationthat isotope fractionation during phytoplankton growth,     

Genetic differentiation of the pine wilt disease vector Monochamus alternatus (Coleoptera: Cerambycidae) over a mountain range - revealed from microsatellite DNA markers

To study the dispersal process of the pine sawyer Monochamus alternatus (Hope) in frontier populations, a microsatellite marker-based genetic analysis was performed on expanding populations at the northern limit of its range in Japan. In Asian countries, M. alternatus is the main vector of pine wilt disease, the most serious forest disease in Japan. Sawyers were collected from nine sites near the frontier of the pine wilt disease damage area. A mountain range divides the population into western and eastern sides. Five microsatellite loci were examined and a total of 188 individuals was genotyped from each locus with the number of alleles ranged from two to nine. The mean observed heterozygosity for all loci varied from 0.282 to 0.480 in the nine sites, with an overall mean of 0.364. None of the populations have experienced a significant bottleneck. Significant differentiation was found across the mountain range, but the genetic composition was similar amongst populations of each side. It is believed that the mountain range acts as a geographical barrier to dispersal and that gene flow without a geographical barrier is high. On the west side of the mountain range, a pattern of isolation by distance was detected. This was likely to be caused by secondary contact of different colonizing routes on a small spatial scale. Based on these data, a process linking genetic structure at local (kilometres) and regional spatial scales (hundreds of kilometres) was proposed.     

SurvJamda: an R package to predict patients' survival and risk assessment using joint analysis of microarray gene expression data

SurvJamda (Survival prediction by joint analysis of microarray data) is an R package that utilizes joint analysis of microarray gene expression data to predict patients' survival and risk assessment. Joint analysis can be performed by merging datasets or meta-analysis to increase the sample size and to improve survival prognosis. The prognosis performance derived from the combined datasets can be assessed to determine which feature selection approach, joint analysis method and bias estimation provide the most robust prognosis for a given set of datasets. AVAILABILITY: The survJamda package is available at the Comprehensive R Archive Network, http://cran.r-project.org. CONTACT: hyasrebi@yahoo.com.     

Pine wilt disease in Yunnan,China: Evidence of non-local pine sawyer Monochamus alternatus (Coleoptera: Cerambycidae) populations revealed by mitochondrial DNA

Abstract Monochamus alternatus (Hope) specimens were collected from nine geographical populations in China, where the pinewood nematode Bursaphelenchus xylophilus (Steiner et Buhrer) was present. There were seven populations in southwestern China in Yunnan Province (Ruili, Wanding, Lianghe, Pu’er, Huaning, Stone Forest and Yongsheng), one in central China in Hubei Province (Wuhan), and one in eastern China in Zhejiang Province (Hangzhou). Twenty-two polymorphic sites were recognized and 18 haplotypes were established by analyzing a 565 bp gene fragment of mitochondrial cytochrome oxidase subunit II (CO II). Kimura two-parameter distances demonstrated that M. alternatus populations in Ruili, Wanding and Lianghe (in southwestern Yunnan) differed from the other four Yunnan populations but were similar to the Zhejiang population. No close relationship was found between the M. alternatus populations in Yunnan and Hubei. Phylogenetic reconstruction established a neighbor-joining (NJ) tree, which divided haplotypes of southwestern Yunnan and the rest of Yunnan into different clades with considerable bootstrapping values. Analysis of molecular variance and spatial analysis of molecular variance also suggested significant genetic differentiation between M. alternatus populations in southwestern Yunnan and the rest of Yunnan. Our research suggests that non-local populations of M. alternates, possibly from eastern China, have become established in southwestern Yunnan.     

Responses in the start of Betula (birch) pollen seasons to recent changes in spring temperatures across Europe

A shift in the timing of birch pollen seasons is important because it is well known to be a significant aeroallergen, especially in NW Europe where it is a notable cause of hay fever and pollen-related asthma. The research reported in this paper aims to investigate temporal patterns in the start dates of Betula (birch) pollen seasons at selected sites across Europe. In particular it investigates relationships between the changes in start dates and changes in spring temperatures over approximately the last 20 years. Daily birch pollen counts were used from Kevo, Turku, London, Brussels, Zurich and Vienna, for the core period from 1982 to 1999 and, in some cases, from 1970 to 2000. The sites represent a range of biogeographical situations from just within the Arctic Circle through to North West Maritime and Continental Europe. Pollen samples were taken with Hirst-type volumetric spore traps. Weather data were obtained from the sites nearest to the pollen traps. The timing of birch pollen seasons is known to depend mostly on a non-linear balance between the winter chilling required to break dormancy, and spring temperatures. Pollen start dates and monthly mean temperatures for January through to May were compiled to 5-year running means to examine trends. The start dates for the next 10 years were calculated from regression equations for each site, on the speculative basis that the current trends would continue. The analyses show regional contrasts. Kevo shows a marked trend towards cooler springs and later starts. If this continues the mean start date will become about 6 days later over the next 10 years. Turku exhibits cyclic patterns in start dates. A current trend towards earlier starts is expected to continue until 2007, followed by another fluctuation. London, Brussels, Zurich and Vienna show very similar patterns in the trends towards earlier start dates. If the trend continues the mean start dates at these sites will advance by about 6 days over the next 10 years. Following this work, amendments will be needed to pollen calendars and local predictive models. It will also be important to assess the implications of earlier seasons for allergy sufferers.     

Classification and regression tree (CART) model to predict pulmonary tuberculosis in hospitalized patients

ABSTRACT: BACKGROUND: Tuberculosis (TB) remains a public health issue worldwide. The lack of specific clinical symptoms to diagnose TB makes the correct decision to admit patients to respiratory isolation a difficult task for the clinician. Isolation of patients without the disease is common and increases health costs. Decision models for the diagnosis of TB in patients attending hospitals can increase the quality of care and decrease costs, without the risk of hospital transmission. We present a predictive model for predicting pulmonary TB in hospitalized patients in a high prevalence area in order to contribute to a more rational use of isolation rooms without increasing the risk of transmission. METHODS: Cross sectional study of patients admitted to CFFH from March 2003 to December 2004. A classification and regression tree (CART) model was generated and validated. The area under the ROC curve (AUC), sensitivity, specificity, positive and negative predictive values were used to evaluate the performance of model. Validation of the model was performed with a different sample of patients admitted to the same hospital from January to December 2005. RESULTS: We studied 290 patients admitted with clinical suspicion of TB. Diagnosis was confirmed in 26.5% of them. Pulmonary TB was present in 83.7% of the patients with TB (62.3% with positive sputum smear) and HIV/AIDS was present in 56.9% of patients. The validated CART model showed sensitivity, specificity, positive predictive value and negative predictive value of 60.00%, 76.16%, 33.33%, and 90.55%, respectively. The AUC was 79.70%. CONCLUSIONS: The CART model developed for these hospitalized patients with clinical suspicion of TB had fair to good predictive performance for pulmonary TB. The most important variable for prediction of TB diagnosis was chest radiograph results. Prospective validation is still necessary, but our model offer an alternative for decision making in whether to isolate patients with clinical suspicion of TB in tertiary health facilities in countries with limited resources.