Patterns of spread of Tomato spotted wilt virus in field crops of lettuce and pepper: spatial dynamics and validation of control measures

Patterns of spread of Tomato spotted wilt virus (TSWV) were examined in lettuce and pepper plantings into which thrips vectors spread the virus from external virus sources. These plantings were: 1) seven separate field trials into which TSWV ‘infector’ plants of tomato were introduced alongside or near to plantings of lettuce or pepper, and 2) three commercial lettuce plantings into which spread from nearby external infection sources was occurring naturally. The vector thrips species were Frankliniella occidentalis, F. schnitzel and Thrips tabaci, at least two of which were always present. Spatial data for plants with TSWV infection collected at different stages in the growing period were assessed by plotting gradients of infection, and using Spatial Analysis by Distance IndicEs (SADIE) and maps of spatial pattern. Despite the persistent nature of TSWV transmission by thrips vectors, in both lettuce and pepper plantings there was a steep decline in TSWV incidence with distance from external infection sources that were alongside them. The extent of clustering increased over time and was greatest closest to the source. The relationship between percentage infection and assessment date suggested that spread was predominantly monocyclic with only limited polycyclic spread. Development of isolated clusters of infected plants distant from TSWV sources within both crops was consistent with only limited polycyclic spread. Spread to lettuce was greater downwind than upwind of virus source, with magnitude and proximity of source determining the amount of spread. When 15 m wide fallow or non-host (cabbage) barriers separated TSWV sources from lettuce plantings, spread was slower and there was much less clustering with the latter. In commercial lettuce plantings, spread was favoured by TSWV movement within successive side-by-side plantings. The spatial data from the diverse scenarios examined enabled recommendations to be made over ‘safe’ planting distances between external infection sources of different magnitudes and susceptible crops that were short-lived (e.g. lettuce) or long-lived (e.g. pepper). They also helped validate the inclusion of isolation and ‘safe’ planting distances, planting upwind, prompt removal of virus sources, avoidance of side-by-side plantings, and deploying intervening non-host barrier crops as control measures within an integrated disease management strategy for TSWV in field vegetable crops.     

Multivariate methods for clustered binary data with multiple subclasses, with application to binary longitudinal data.

Clustered binary data occur frequently in biostatistical work. Several approaches have been proposed for the analysis of clustered binary data. In Rosner (1984, Biometrics 40, 1025-1035), a polychotomous logistic regression model was proposed that is a generalization of the beta-binomial distribution and allows for unit- and subunit-specific covariates, while controlling for clustering effects. One assumption of this model is that all pairs of subunits within a cluster are equally correlated. This is appropriate for ophthalmologic work where clusters are generally of size 2, but may be inappropriate for larger cluster sizes. A beta-binomial mixture model is introduced to allow for multiple subclasses within a cluster and to estimate odds ratios relating outcomes for pairs of subunits within a subclass as well as in different subclasses. To include covariates, an extension of the polychotomous logistic regression model is proposed, which allows one to estimate effects of unit-, class-, and subunit-specific covariates, while controlling for clustering using the beta-binomial mixture model. This model is applied to the analysis of respiratory symptom data in children collected over a 14-year period in East Boston, Massachusetts, in relation to maternal and child smoking, where the unit is the child and symptom history is divided into early-adolescent and late-adolescent symptom experience.     

Inferring the dynamics of a spatial epidemic from time-series data

Spatial interactions are key determinants in the dynamics of many epidemiological and ecological systems; therefore it is important to use spatio-temporal models to estimate essential parameters. However, spatially-explicit data sets are rarely available; moreover, fitting spatially-explicit models to such data can be technically demanding and computationally intensive. Thus non-spatial models are often used to estimate parameters from temporal data. We introduce a method for fitting models to temporal data in order to estimate parameters which characterise spatial epidemics. The method uses semi-spatial models and pair approximation to take explicit account of spatial clustering of disease without requiring spatial data. The approach is demonstrated for data from experiments with plant populations invaded by a common soilborne fungus, Rhizoctonia solani. Model inferences concerning the number of sources of disease and primary and secondary infections are tested against independent measures from spatio-temporal data. The applicability of the method to a wide range of host-pathogen systems is discussed.     

基于稳定氧同位素确定植物水分来源不同方法的比较

利用稳定同位素技术确定植物水分来源,对提高生态水文过程的认识和对干旱半干旱区的生态管理至关重要。目前基于稳定同位素技术确定植物水分来源的方法众多,但不同方法之间对比的研究较少。本研究基于原位样品采集,室内实验测试,利用直接对比法、多元线性混合模型(IsoSource)、贝叶斯混合模型(MixSIR、MixSIAR)和吸水深度模型分析植物水分来源,并对比各方法的优缺点。结果表明:相对于多元线性混合模型(IsoSource)而言,贝叶斯混合模型(MixSIR、MixSIAR)具有更好的水源区分性能,但对数据要求较高,且植物木质部水和潜在水源同位素组成的标准差越小,模型运行结果的可信度更高。本研究中贝叶斯混合模型(MixSIR)为最优解。在利用稳定氢氧同位素技术确定植物水分来源时,可先通过直接对比法定性判断植物可能利用的潜在水源,然后再用多元线性混合模型(IsoSource)、贝叶斯混合模型(MixSIR、MixSIAR)计算出各潜在水源对植物的贡献率和贡献范围,必要时可评估模型性能,选择出最优模型,定量分析植物的水分来源。若植物主要吸收利用不同土层深度的土壤水,可结合吸水深度模型计算出植物吸收土壤水的平均深度。本研究为干旱半干旱地区利用同位素技术确定植物水分来源方法的选择提供了理论依据。     

The clustering of regression models method with applications in gene expression data

Identification of differentially expressed genes and clustering of genes are two important and complementary objectives addressed with gene expression data. For the differential expression question, many "per-gene" analytic methods have been proposed. These methods can generally be characterized as using a regression function to independently model the observations for each gene; various adjustments for multiplicity are then used to interpret the statistical significance of these per-gene regression models over the collection of genes analyzed. Motivated by this common structure of per-gene models, we proposed a new model-based clustering method--the clustering of regression models method, which groups genes that share a similar relationship to the covariate(s). This method provides a unified approach for a family of clustering procedures and can be applied for data collected with various experimental designs. In addition, when combined with per-gene methods for assessing differential expression that employ the same regression modeling structure, an integrated framework for the analysis of microarray data is obtained. The proposed methodology was applied to two microarray data sets, one from a breast cancer study and the other from a yeast cell cycle study.     

Joint learning of gene functions--a Bayesian network model approach

In this paper, we develop a machine learning system for determining gene functions from heterogeneous data sources using a Weighted Naive Bayesian network (WNB). The knowledge of gene functions is crucial for understanding many fundamental biological mechanisms such as regulatory pathways, cell cycles and diseases. Our major goal is to accurately infer functions of putative genes or Open Reading Frames (ORFs) from existing databases using computational methods. However, this task is intrinsically difficult since the underlying biological processes represent complex interactions of multiple entities. Therefore, many functional links would be missing when only one or two sources of data are used in the prediction. Our hypothesis is that integrating evidence from multiple and complementary sources could significantly improve the prediction accuracy. In this paper, our experimental results not only suggest that the above hypothesis is valid, but also provide guidelines for using the WNB system for data collection, training and predictions. The combined training data sets contain information from gene annotations, gene expressions, clustering outputs, keyword annotations, and sequence homology from public databases. The current system is trained and tested on the genes of budding yeast Saccharomyces cerevisiae. Our WNB model can also be used to analyze the contribution of each source of information toward the prediction performance through the weight training process. The contribution analysis could potentially lead to significant scientific discovery by facilitating the interpretation and understanding of the complex relationships between biological entities.     

How consistent are trait data between sources? A quantitative assessment

The use of species’ traits is increasing in ecological research. Many studies obtain trait data from a single source, implicitly assuming the accuracy of these data. I critically evaluate this assumption by measuring agreement among sources for trait data. I evaluate inter‐source agreement for 22 traits (anatomical, behavioural, life‐history and niche‐related) among five authoritative data sources (two field guides, two atlases and one online resource) for 263 Canadian butterfly species. This represents the first quantitative comparison of trait data among field guides or atlases. Traits varied considerably in their agreement among sources. Some traits such as wingspan and overwinter stage were fairly consistent among sources, whereas other traits such as habitat breadth were remarkably inconsistent among sources. These findings call into question the reliability of research that relies on a single source for trait data. I offer several recommendations for how trait researchers can account for inter‐source variation in trait data.     

Estimating survival in patients with operable skeletal metastases: an application of a bayesian belief network

Background

Accurate estimations of life expectancy are important in the management of patients with metastatic cancer affecting the extremities, and help set patient, family, and physician expectations. Clinically, the decision whether to operate on patients with skeletal metastases, as well as the choice of surgical procedure, are predicated on an individual patient''s estimated survival. Currently, there are no reliable methods for estimating survival in this patient population. Bayesian classification, which includes Bayesian belief network (BBN) modeling, is a statistical method that explores conditional, probabilistic relationships between variables to estimate the likelihood of an outcome using observed data. Thus, BBN models are being used with increasing frequency in a variety of diagnoses to codify complex clinical data into prognostic models. The purpose of this study was to determine the feasibility of developing Bayesian classifiers to estimate survival in patients undergoing surgery for metastases of the axial and appendicular skeleton.

Methods

We searched an institution-owned patient management database for all patients who underwent surgery for skeletal metastases between 1999 and 2003. We then developed and trained a machine-learned BBN model to estimate survival in months using candidate features based on historical data. Ten-fold cross-validation and receiver operating characteristic (ROC) curve analysis were performed to evaluate the BNN model''s accuracy and robustness.

Results

A total of 189 consecutive patients were included. First-degree predictors of survival differed between the 3-month and 12-month models. Following cross validation, the area under the ROC curve was 0.85 (95% CI: 0.80–0.93) for 3-month probability of survival and 0.83 (95% CI: 0.77–0.90) for 12-month probability of survival.

Conclusions

A robust, accurate, probabilistic naïve BBN model was successfully developed using observed clinical data to estimate individualized survival in patients with operable skeletal metastases. This method warrants further development and must be externally validated in other patient populations.     

Internet information on urology

Keeping up with all the current information in the field of urology is not as difficult anymore, with the many sources available on the World Wide Web. But which sites will give you the most and best data? Where can you go for continuing medical education? For sources of patient education information? For a list of upcoming meetings? Check below for recommendations on general medical, urology-specific, and reference resources.     

Combining sources in stable isotope mixing models: alternative methods

Stable isotope mixing models are often used to quantify source contributions to a mixture. Examples include pollution source identification; trophic web studies; analysis of water sources for soils, plants; or water bodies, and many others. A common problem is having too many sources to allow a unique solution. We discuss two alternative procedures for addressing this problem. One option is a priori to combine sources with similar signatures so the number of sources is small enough to provide a unique solution. Aggregation should be considered only when isotopic signatures of clustered sources are not significantly different, and sources are related so the combined source group has some functional significance. For example, in a food web analysis, lumping several species within a trophic guild allows more interpretable results than lumping disparate food sources, even if they have similar isotopic signatures. One result of combining mixing model sources is increased uncertainty of the combined end-member isotopic signatures and consequently the source contribution estimates; this effect can be quantified using the IsoError model (). As an alternative to lumping sources before a mixing analysis, the IsoSource mixing model () can be used to find all feasible solutions of source contributions consistent with isotopic mass balance. While ranges of feasible contributions for each individual source can often be quite broad, contributions from functionally related groups of sources can be summed a posteriori, producing a range of solutions for the aggregate source that may be considerably narrower. A paleohuman dietary analysis example illustrates this method, which involves a terrestrial meat food source, a combination of three terrestrial plant foods, and a combination of three marine foods. In this case, a posteriori aggregation of sources allowed strong conclusions about temporal shifts in marine versus terrestrial diets that would not have otherwise been discerned.     

Modeling of protein binary complexes using structural mass spectrometry data

In this article, we describe a general approach to modeling the structure of binary protein complexes using structural mass spectrometry data combined with molecular docking. In the first step, hydroxyl radical mediated oxidative protein footprinting is used to identify residues that experience conformational reorganization due to binding or participate in the binding interface. In the second step, a three-dimensional atomic structure of the complex is derived by computational modeling. Homology modeling approaches are used to define the structures of the individual proteins if footprinting detects significant conformational reorganization as a function of complex formation. A three-dimensional model of the complex is constructed from these binary partners using the ClusPro program, which is composed of docking, energy filtering, and clustering steps. Footprinting data are used to incorporate constraints-positive and/or negative-in the docking step and are also used to decide the type of energy filter-electrostatics or desolvation-in the successive energy-filtering step. By using this approach, we examine the structure of a number of binary complexes of monomeric actin and compare the results to crystallographic data. Based on docking alone, a number of competing models with widely varying structures are observed, one of which is likely to agree with crystallographic data. When the docking steps are guided by footprinting data, accurate models emerge as top scoring. We demonstrate this method with the actin/gelsolin segment-1 complex. We also provide a structural model for the actin/cofilin complex using this approach which does not have a crystal or NMR structure.     

Bias correction of bounded location error in binary data

Binary regression models for spatial data are commonly used in disciplines such as epidemiology and ecology. Many spatially referenced binary data sets suffer from location error, which occurs when the recorded location of an observation differs from its true location. When location error occurs, values of the covariates associated with the true spatial locations of the observations cannot be obtained. We show how a change of support (COS) can be applied to regression models for binary data to provide coefficient estimates when the true values of the covariates are unavailable, but the unknown location of the observations are contained within nonoverlapping arbitrarily shaped polygons. The COS accommodates spatial and nonspatial covariates and preserves the convenient interpretation of methods such as logistic and probit regression. Using a simulation experiment, we compare binary regression models with a COS to naive approaches that ignore location error. We illustrate the flexibility of the COS by modeling individual-level disease risk in a population using a binary data set where the locations of the observations are unknown but contained within administrative units. Our simulation experiment and data illustration corroborate that conventional regression models for binary data that ignore location error are unreliable, but that the COS can be used to eliminate bias while preserving model choice.     

Perinatal care in British Columbia: Diagnosis and management of hemolytic disease of the newborn

We undertook to measure standards of perinatal care in British Columbia by studying the management of hemolytic disease of the newborn as the sample situation.Our data show that many isoimmunized pregnant women are delivered in hospitals that have infrequent experience with this problem, and by physicians who have little experience with this disease.The physician referral pattern, in regard to maternal isoimmunization, indicated that the more severely affected patients were managed by specialists, particularly those attached to teaching hospitals. However, 25% of the infants treated by exchange transfusion were managed by nonspecialists in nonteaching hospitals.Hospital record search, used as a method of medical audit and as a source of data for comparison with physician reports, did not result in dependable or complete information. Rates of disagreement between items from two data sources, physician report and hospital record, were frequently very high. Our experience suggests that comparison of these two data sources is not an ideal method of assessment of quality of care.A smaller caseload of isoimmunized pregnant women will result from the present prevention program. Nevertheless, cases will continue to occur. Our work supports the conclusion that a program of continuing education covering the diagnosis and management of hemolytic disease of the newborn is still necessary.     

Prevalence dependence in model goodness measures with special emphasis on true skill statistics

It has long been a concern that performance measures of species distribution models react to attributes of the modeled entity arising from the input data structure rather than to model performance. Thus, the study of Allouche et al. (Journal of Applied Ecology, 43, 1223, 2006) identifying the true skill statistics (TSS) as being independent of prevalence had a great impact. However, empirical experience questioned the validity of the statement. We searched for technical reasons behind these observations. We explored possible sources of prevalence dependence in TSS including sampling constraints and species characteristics, which influence the calculation of TSS. We also examined whether the widespread solution of using the maximum of TSS for comparison among species introduces a prevalence effect. We found that the design of Allouche et al. (Journal of Applied Ecology, 43, 1223, 2006) was flawed, but TSS is indeed independent of prevalence if model predictions are binary and under the strict set of assumptions methodological studies usually apply. However, if we take realistic sources of prevalence dependence, effects appear even in binary calculations. Furthermore, in the widespread approach of using maximum TSS for continuous predictions, the use of the maximum alone induces prevalence dependence for small, but realistic samples. Thus, prevalence differences need to be taken into account when model comparisons are carried out based on discrimination capacity. The sources we identified can serve as a checklist to safely control comparisons, so that true discrimination capacity is compared as opposed to artefacts arising from data structure, species characteristics, or the calculation of the comparison measure (here TSS).     

基于稳定同位素的消费者营养来源溯源的方法和过程

为降低利用稳定同位素分析对消费者营养来源进行溯源所造成的一定程度误读, 文章基于实测同位素数据集(浮游动物同位素数据集), 通过原始数据的简单统计检验、营养来源先验信息的矫正, 构建系列贝叶斯模型; 通过同位素空间的构建与检验, 及先验信息和后验分布差异等多种分析方法与步骤, 来描述消费者营养来源溯源的方法和过程。以此为应用稳定性同位素技术开展消费者营养溯源研究提供指导。     

EEG and implanted sources in the brain

Localisation procedures are based on models of the EEG that are relatively simple. The models are based on assumptions and choices of parameters that can be mistaken. Thus, it is crucial to validate the localisation procedures used in EEG. One of the options is to use the data obtained with electrodes that are implanted within the brain of an epileptic patient as part of the pre-surgical evaluation. When one of two neighbouring electrodes is used as a current source and the other as a current sink this can be regarded as a current dipole. The current injected has to be below the threshold for activation of cells. The position of this dipole can be deduced from magnetic resonance or X-ray images. The current dipole gives rise to a potential distribution at the scalp that can be measured by EEG. The measurements can be compared with the potential distribution that is calculated in a forward computation. Another method is to use the measured potential at the scalp to localize the source and to compare the result with the actual position of the dipole. In this paper the measured potential distributions at the scalp due to implanted dipoles were used to evaluate different volume conductor models. Since intracerebral and subdural electrodes were introduced through trephine holes over the fronto-central areas, and the diameter of the holes was rather large, approximately 23 mm, special effort was put into modelling the skull. Two important assumptions could be validated in this study: the electric currents within the head are Ohmic and a dipole can be used to model the induced electric activity of pairs of contacts on subdural electrodes or intra cerebral electrodes.     

How important is the statistical approach for analyzing categorical data? A critique using artificial nests

Ecologists rely heavily upon statistics to make inferences concerning ecological phenomena and to make management recommendations. It is therefore important to use statistical tests that are most appropriate for a given data-set. However, inappropriate statistical tests are often used in the analysis of studies with categorical data (i.e. count data or binary data). Since many types of statistical tests have been used in artificial nests studies, a review and comparison of these tests provides an opportunity to demonstrate the importance of choosing the most appropriate statistical approach for conceptual reasons as well as type I and type II errors.
Artificial nests have routinely been used to study the influences of habitat fragmentation, and habitat edges on nest predation. I review the variety of statistical tests used to analyze artificial nest data within the framework of the generalized linear model and argue that logistic regression is the most appropriate and flexible statistical test for analyzing binary data-sets. Using artificial nest data from my own studies and an independent data set from the medical literature as examples, I tested equivalent data using a variety of statistical methods. I then compared the p-values and the statistical power of these tests. Results vary greatly among statistical methods. Methods inappropriate for analyzing binary data often fail to yield significant results even when differences between study groups appear large, while logistic regression finds these differences statistically significant. Statistical power is is 2–3 times higher for logistic regression than for other tests. I recommend that logistic regression be used to analyze artificial nest data and other data-sets with binary data.     

Trust and responsibility in molecular tumour boards

Molecular tumour boards (MTBs) offer recommendations for potentially effective, but potentially burdensome, molecularly targeted treatments to a patient's treating physician. In this paper, we discuss the question of who is responsible for ensuring that there is an adequate evidence base for any treatments recommended to a patient. We argue that, given that treating oncologists cannot usually offer a robust evaluation of the evidence underlying an MTB's recommendation, members of the MTB are responsible for ensuring that the evidence level is adequate. We explore two models for how to share responsibility between MTB members. According to the first model, each MTB member, as well as the treating physician, should be held maximally and equally responsible for the recommendations. We argue that this insufficiently accounts for differences in roles and expertise of MTB members. We propose instead that responsibility is delegated via relationships of trust. We argue if these relationships of trust are to be instances of reasonable trust, (a) MTBs should offer a clinical representative to whom a treating physician may delegate the responsibility of ensuring there is sufficient evidence for treatment recommendations, (b) the relationships of trust between the representative and the other MTB members should be clearly defined, and (c) MTB members should be carefully selected. Treating oncologists retain a responsibility to consider general limitations of the evidence for targeted treatments in assessing whether the treatment recommendation offered by an MTB's representative is adequate for a given clinical situation.     

Robustness of prevalence estimates derived from misclassified data from administrative databases

Because primary data collection can be expensive, researchers are increasingly using information collected in medical administrative databases for scientific purposes. This information, however, is typically collected for reasons other than research, and many such databases have been shown to contain substantial proportions of misclassification errors. For example, many administrative databases contain fields for patient diagnostic codes, but these are often missing or inaccurate, in part because physician reimbursement schemes depend on medical acts performed rather than any diagnosis. Errors in ascertaining which individuals have a given disease bias not only prevalence estimates, but also estimates of associations between the disease and other variables, such as medication use. We attempt to estimate the prevalence of osteoarthritis (OA) among elderly Quebeckers using a government administrative database. We compare a naive estimate relying solely on the physician diagnoses of OA listed in the database to estimates from several different Bayesian latent class models which adjust for misclassified physician diagnostic codes via use of other available diagnostic clues. We find that the prevalence estimates vary widely, depending on the model used and assumptions made. We conclude that any inferences from these databases need to be interpreted with great caution, until further work estimating the reliability of database items is carried out.