A predictive model of avian natal dispersal distance provides prior information for investigating response to landscape change

1. Informative Bayesian priors can improve the precision of estimates in ecological studies or estimate parameters for which little or no information is available. While Bayesian analyses are becoming more popular in ecology, the use of strongly informative priors remains rare, perhaps because examples of informative priors are not readily available in the published literature.
2. Dispersal distance is an important ecological parameter, but is difficult to measure and estimates are scarce. General models that provide informative prior estimates of dispersal distances will therefore be valuable.
3. Using a world-wide data set on birds, we develop a predictive model of median natal dispersal distance that includes body mass, wingspan, sex and feeding guild. This model predicts median dispersal distance well when using the fitted data and an independent test data set, explaining up to 53% of the variation.
4. Using this model, we predict a priori estimates of median dispersal distance for 57 woodland-dependent bird species in northern Victoria, Australia. These estimates are then used to investigate the relationship between dispersal ability and vulnerability to landscape-scale changes in habitat cover and fragmentation.
5. We find evidence that woodland bird species with poor predicted dispersal ability are more vulnerable to habitat fragmentation than those species with longer predicted dispersal distances, thus improving the understanding of this important phenomenon.
6. The value of constructing informative priors from existing information is also demonstrated. When used as informative priors for four example species, predicted dispersal distances reduced the 95% credible intervals of posterior estimates of dispersal distance by 8-19%. Further, should we have wished to collect information on avian dispersal distances and relate it to species' responses to habitat loss and fragmentation, data from 221 individuals across 57 species would have been required to obtain estimates with the same precision as those provided by the general model.

A predictive model of avian natal dispersal distance provides prior information for investigating response to landscape change

1. Informative Bayesian priors can improve the precision of estimates in ecological studies or estimate parameters for which little or no information is available. While Bayesian analyses are becoming more popular in ecology, the use of strongly informative priors remains rare, perhaps because examples of informative priors are not readily available in the published literature.

2. Dispersal distance is an important ecological parameter, but is difficult to measure and estimates are scarce. General models that provide informative prior estimates of dispersal distances will therefore be valuable.

3. Using a world-wide data set on birds, we develop a predictive model of median natal dispersal distance that includes body mass, wingspan, sex and feeding guild. This model predicts median dispersal distance well when using the fitted data and an independent test data set, explaining up to 53% of the variation.

4. Using this model, we predict a priori estimates of median dispersal distance for 57 woodland-dependent bird species in northern Victoria, Australia. These estimates are then used to investigate the relationship between dispersal ability and vulnerability to landscape-scale changes in habitat cover and fragmentation.

5. We find evidence that woodland bird species with poor predicted dispersal ability are more vulnerable to habitat fragmentation than those species with longer predicted dispersal distances, thus improving the understanding of this important phenomenon.

6. The value of constructing informative priors from existing information is also demonstrated. When used as informative priors for four example species, predicted dispersal distances reduced the 95% credible intervals of posterior estimates of dispersal distance by 8-19%. Further, should we have wished to collect information on avian dispersal distances and relate it to species' responses to habitat loss and fragmentation, data from 221 individuals across 57 species would have been required to obtain estimates with the same precision as those provided by the general model.

Predicting brand preferences: an examination of the predictive power of consumer personality and values in the Australian fashion market

Purpose –
This paper aims to examine and compare the strength of personality and values in predicting brand preferences. It seeks to accomplish three main objectives. First, it will evaluate the strength of personality and values in predicting consumers' brand preferences. Second, it will examine whether values exercise a mediating role between personality and brand preferences. Finally, it will examine the mediating role of prestige sensitivity in influencing brand preferences.

Design/methodology/approach – 

A gene signature in histologically normal surgical margins is predictive of oral carcinoma recurrence

Background: Oral Squamous Cell Carcinoma (OSCC) is a major cause of cancer death worldwide, which is mainly due to recurrence leading to treatment failure and patient death. Histological status of surgical margins is a currently available assessment for recurrence risk in OSCC; however histological status does not predict recurrence, even in patients with histologically negative margins. Therefore, molecular analysis of histologically normal resection margins and the corresponding OSCC may aid in identifying a gene signature predictive of recurrence.Methods: We used a meta-analysis of 199 samples (OSCCs and normal oral tissues) from five public microarray datasets, in addition to our microarray analysis of 96 OSCCs and histologically normal margins from 24 patients, to train a gene signature for recurrence. Validation was performed by quantitative real-time PCR using 136 samples from an independent cohort of 30 patients.Results: We identified 138 significantly over-expressed genes (> 2-fold, false discovery rate of 0.01) in OSCC. By penalized likelihood Cox regression, we identified a 4-gene signature with prognostic value for recurrence in our training set. This signature comprised the invasion-related genes MMP1, COL4A1, P4HA2, and THBS2. Overexpression of this 4-gene signature in histologically normal margins was associated with recurrence in our training cohort (p = 0.0003, logrank test) and in our independent validation cohort (p = 0.04, HR = 6.8, logrank test).Conclusion: Gene expression alterations occur in histologically normal margins in OSCC. Over-expression of the 4-gene signature in histologically normal surgical margins was validated and highly predictive of recurrence in an independent patient cohort. Our findings may be applied to develop a molecular test, which would be clinically useful to help predict which patients are at a higher risk of local recurrence.

Prognostic value of canine frozen-thawed semen parameters on in vitro sperm-oocyte interactions

Combining the data from conventional semen analysis with oocyte penetration assays should improve the assessment of the fertilizing ability of a semen sample. Thus, the objective of the present study was to evaluate the prognostic value of various semen parameters on the in vitro interactions between frozen-thawed canine sperm and homologous oocytes. Ten ejaculates from five stud dogs (two ejaculates/dog) were collected by digital manipulation. Semen samples were evaluated, extended in Tris-egg yolk-glycerol, frozen and stored in liquid nitrogen, and thawed several weeks later. Samples were evaluated for motility and sperm populations by computer-aided semen analysis (CASA), plasma membrane integrity (carboxy-fluorescein diacetate and propidium iodide), and sperm morphology (Bengal Rose). Thawed spermatozoa were also incubated with homologous oocytes for 18 h in an atmosphere of 5% CO2 and 95% air at 38 degrees C and sperm-oocyte interactions were evaluated. Simple linear regression models were calculated, with sperm parameters as independent variables and sperm-oocyte interactions as the dependent variable. There were significant associations between: percentage of oocytes bound to spermatozoa and beat cross frequency (BCF; R-2 = 63%); percentage of oocytes that interacted with spermatozoa and BCF (R-2 = 73%); and number of penetrated spermatozoa and velocity average pathway (VAP; R-2 = 64%) and velocity straight line (VSL; R-2 = 64%). Although plasma membrane integrity and sperm morphology had little prognostic value for in vitro interactions between canine frozen-thawed sperm and homologous oocytes, some motility patterns (evaluated by CASA) were predictive of in vitro sperm-oocyte interactions. (c) 2005 Elsevier B.V. All rights reserved.

Prevalence of the hepatitis C virus among university employees in São Paulo, southeastern Brazil: predictive factors and geoprocessing spatial analysis

Background There are limited studies on the prevalence and risk factors associated with hepatitis C virus (HCV) infection. Objective Identify the prevalence and risk factors for HCV infection in university employees of the state of São Paulo, Brazil. Methods Digital serological tests for anti-HCV have been performed in 3153 volunteers. For the application of digital testing was necessary to withdraw a drop of blood through a needlestick. The positive cases were performed for genotyping and RNA. Chi-square and Fisher’s exact test were used, with P-value <0.05 indicating statistical significance. Univariate and multivariate logistic regression were also used. Results Prevalence of anti-HCV was 0.7%. The risk factors associated with HCV infection were: age >40 years, blood transfusion, injectable drugs, inhalable drugs (InDU), injectable Gluconergam®, glass syringes, tattoos, hemodialysis and sexual promiscuity. Age (P=0.01, OR 5.6, CI 1.4 to 22.8), InDU (P<0.0001, OR=96.8, CI 24.1 to 388.2), Gluconergam® (P=0.0009, OR=44.4, CI 4.7 to 412.7) and hemodialysis (P=0.0004, OR=90.1, CI 7.5 – 407.1) were independent predictors. Spatial analysis of the prevalence with socioeconomic indices, Gross Domestic Product and Human Development Index by the geoprocessing technique showed no positive correlation. Conclusions The prevalence of HCV infection was 0.7%. The independent risk factors for HCV infection were age, InDU, Gluconergan® and hemodialysis. There was no spatial correlation of HCV prevalence with local economic factors.

The Value of Immunohistochemical Expression of BAX in Formulating a Prognosis for Canine Cutaneous Mast Cell Tumours

Irnmunohistochcmical expression of BAX was evaluated in 24 canine cutaneous mast cell tumours in order to verify the relationship of this expression to the histopathological grade of the lesions and its prognostic value for clinical outcome. BAX expression increased with higher histopathological grades (P = 0.0148; P < 0.05 between grades I and III). Animals with high levels of BAX expression were 4.25 times more likely to die from the disease and had shorter post-surgical survival times (P = 0.0009). These results suggest that alterations in BAX expression may be related to the aggressiveness of canine cutaneous mast cell tumours, indicating that immunohistochemical detection of BAX may be predictive of clinical outcome. (C) 2011 Elsevier Ltd. All rights reserved.

The diagnostic value of a treadmill test in predicting lumbar spinal stenosis

Lumbar spinal stenosis is a frequent indication for spinal surgery. The predictive quality of treadmill testing and MRI for diagnostic verification is not yet clearly defined. Aim of the current study was to assess correlations between treadmill testing and MRI findings in the lumbar spine. Twenty-five patients with lumbar spinal stenosis were prospectively examined. Treadmill tests were performed and the area of the dural sac and neuroforamina was examined with MRI for the narrowest spinal segment. VAS and ODI were used for clinical assessment. The median age of the patients was 67 years. In the narrowest spinal segment the median area of the dural sac was 91 mm(2). The median ODI was 66 per cent. The median walking distance in the treadmill test was 70 m. The distance reached in the treadmill test correlated with the area of the dural sac (Spearman's rho = 0.53) and ODI (rho = -0.51), but not with the area of the neuroforamina and VAS. The distance reached in the treadmill test predicts the grade of stenosis in MRI but has a limited diagnostic importance for the level of clinical symptoms in lumbar spinal stenosis.

The Costs and Predictive Factors of Bovine Respiratory Disease in Standardized Steer Tests

A retrospective study of 2,146 feedlot cattle in 17 feedlot tests from 1988 to 1997 was conducted to determine the impact of bovine respiratory disease (BRD) on veterinary treatment costs, average daily gain, carcass traits, mortality, and net profit. Morbidity caused by BRD was 20.6%. The average cost to treat each case of BRD was $12.39. Mortality rate of calves diagnosed and treated for BRD was 5.9% vs. .35% for those not diagnosed with BRD. Average daily gain differed between treated and non-treated steers during the first 28 days on feed but did not differ from 28 days to harvest. Net profit was $57.48 lower for treated steers. Eighty-two percent of this difference was due to a combination of mortality and treatment costs. Eighteen percent of the net profit difference was due to improved performance and carcass value of the non-treated steers. Data from 496 steers and heifers in nine feedlot tests were used to determine the effects of age, weaning, and use of modified live virus or killed vaccines prior to the test to predict BRD. Younger calves, non-weaned calves, and calves vaccinated with killed vaccines prior to the test had higher BRD morbidity than those that were older, weaned, or vaccinated with modified live virus vaccines, respectively. Treatment regimes that precluded relapse resulting in re-treatment prevented reduced performance and loss of carcass value. Using modified live virus vaccines and weaning calves 30 days prior to shipment reduced the incidence of BRD.

The additional facet of immunoscore: immunoprofiling as a possible predictive tool for cancer treatment

Recent investigations of the tumor microenvironment have shown that many tumors are infiltrated by inflammatory and lymphocytic cells. Increasing evidence suggests that the number, type and location of these tumor-infiltrating lymphocytes in primary tumors has prognostic value, and this has led to the development of an 'immunoscore. As well as providing useful prognostic information, the immunoscore concept also has the potential to help predict response to treatment, thereby improving decision- making with regard to choice of therapy. This predictive aspect of the tumor microenvironment forms the basis for the concept of immunoprofiling, which can be described as 'using an individual's immune system signature (or profile) to predict that patient's response to therapy' The immunoprofile of an individual can be genetically determined or tumor-induced (and therefore dynamic). Ipilimumab is the first in a series of immunomodulating antibodies and has been shown to be associated with improved overall survival in patients with advanced melanoma. Other immunotherapies in development include anti-programmed death 1 protein (nivolumab), anti-PD-ligand 1, anti-CD137 (urelumab), and anti-OX40. Biomarkers that can be used as predictive factors for these treatments have not yet been clinically validated. However, there is already evidence that the tumor microenvironment can have a predictive role, with clinical activity of ipilimumab related to high baseline expression of the immune-related genes FoxP3 and indoleamine 2,3-dioxygenase and an increase in tumor-infiltrating lymphocytes. These biomarkers could represent the first potential proposal for an immunoprofiling panel in patients for whom anti-CTLA-4 therapy is being considered, although prospective data are required. In conclusion, the evaluation of systemic and local immunological biomarkers could offer useful prognostic information and facilitate clinical decision making. The challenge will be to identify the individual immunoprofile of each patient and the consequent choice of optimal therapy or combination of therapies to be used.

Prognostic and predictive roles of MGMT protein expression and promoter methylation in sporadic pancreatic neuroendocrine neoplasms.

BACKGROUND/AIMS O(6)-methylguanine-methyltransferase (MGMT) is an important enzyme of DNA repair. MGMT promoter methylation is detectable in a subset of pancreatic neuroendocrine neoplasms (pNEN). A subset of pNEN responds to the alkylating agent temozolomide (TMZ). We wanted to correlate MGMT promoter methylation with MGMT protein loss in pNEN, correlate the findings with clinico-pathological data and determine the role of MGMT to predict response to TMZ chemotherapy. METHODS We analysed a well-characterized collective of 141 resected pNEN with median follow-up of 83 months for MGMT protein expression and promoter methylation using methylation-specific PCR (MSP). A second collective of 10 metastasized, pretreated and progressive patients receiving TMZ was used to examine the predictive role of MGMT by determining protein expression and promoter methylation using primer extension-based quantitative PCR. RESULTS In both collectives there was no correlation between MGMT protein expression and promoter methylation. Loss of MGMT protein was associated with an adverse outcome, this prognostic value, however, was not independent from grade and stage in multivariate analysis. Promoter hypermethylation was significantly associated with response to TMZ. CONCLUSION Loss of MGMT protein expression is associated with adverse outcome in a surgical series of pNET. MGMT promoter methylation could be a predictive marker for TMZ chemotherapy in pNEN, but further, favourably prospective studies will be needed to confirm this result and before this observation can influence clinical routine.

Using Bayes' rule to define the value of evidence from syndromic surveillance

In this work we propose the adoption of a statistical framework used in the evaluation of forensic evidence as a tool for evaluating and presenting circumstantial "evidence" of a disease outbreak from syndromic surveillance. The basic idea is to exploit the predicted distributions of reported cases to calculate the ratio of the likelihood of observing n cases given an ongoing outbreak over the likelihood of observing n cases given no outbreak. The likelihood ratio defines the Value of Evidence (V). Using Bayes' rule, the prior odds for an ongoing outbreak are multiplied by V to obtain the posterior odds. This approach was applied to time series on the number of horses showing clinical respiratory symptoms or neurological symptoms. The separation between prior beliefs about the probability of an outbreak and the strength of evidence from syndromic surveillance offers a transparent reasoning process suitable for supporting decision makers. The value of evidence can be translated into a verbal statement, as often done in forensics or used for the production of risk maps. Furthermore, a Bayesian approach offers seamless integration of data from syndromic surveillance with results from predictive modeling and with information from other sources such as disease introduction risk assessments.

Prognostic value of matrix metalloproteinases in oral squamous cell carcinoma

The aim of this study was to investigate whether there is a correlation between the expressions of four matrix metalloproteinases (MMPs): MMP-2, MMP-7, MMP-9 and MMP-13, and the TNM (tumour-node-metastasis) stages of oral squamous cell carcinoma (OSCC); and to explore the implication of these MMPs in OSCC dissemination. Samples from 61 patients diagnosed with oropharyngeal tumour were studied by immunohistochemistry against MMP-2, MMP-7, MMP-9 and MMP-13. The assessment of immunoreactivity was semi-quantitative. The results showed that MMP-2 and MMP-9 had similar expression patterns in the tumour cells with no changes in the immunoreactivity during tumour progression. MMP-9 always had the highest expression, whereas that of MMP-2 was moderate. MMP-7 showed a significant decrease in expression levels during tumour evolution. MMP-13 had constant expression levels within stage T2 and T3, but showed a remarkable decline in immunoreactivity in stage T4. No significant differences in the MMPs immunoreactivity between tumour cells and stroma were observed. Although strong evidence for the application of MMPs as reliable predictive markers for node metastasis was not acquired, we believe that combining patients' MMPs expression intensity and clinical features may improve the diagnosis and prognosis. Strong evidence for the application of MMPs as reliable predictive markers for node metastasis was not acquired. Application of MMPs as prognostic indicators for the malignancy potential of OSCC might be considered in every case of tumour examination. We believe that combining patients' MMPs expression intensity and clinical features may improve the process of making diagnosis and prognosis.

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20–30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow–Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.