Natural Occurrence of Maize Gibberella Ear Rot and Contamination of Grain with Mycotoxins in Association with Weather Variables.

Gibberella ear rot (GER) severity (percent area of the ear diseased) and associated grain contamination with mycotoxins were quantified in plots of 15 to 16 maize hybrids planted at 10 Ohio locations from 2015 to 2018. Deoxynivalenol (DON) was quantified in grain samples in all 4 years, whereas nivalenol, 3-acetyldeoxynivalenol, and 15-acetyldeoxynivalenol (15ADON) were quantified only in the last 2 years. Only DON and 15ADON were detected. The highest levels of GER and DON contamination were observed for 2018, followed by 2016 and 2017. No GER symptoms or DON were detected in 2015. Approximately 41% of the samples from asymptomatic ears had detectable levels of DON, and 7% of these samples from 2016 had DON > 5 ppm. Associations between DON contamination and 43 variables representing summaries of temperature (T), relative humidity (RH), rainfall (R), surface wetness, and T-RH combinations for different window lengths and positions relative to R1 growth stage were quantified with Spearman correlation coefficients (r). Fifteen-day window lengths tended to show the highest correlations. Most of the variables based on T, R, RH, and T-RH were significantly correlated with DON for the 15-day window, as well as other windows. For moisture-related variables, there generally was a negative correlation before R1, changing to a positive correlation after R1. Results showed that GER and DON can be frequently found in Ohio maize fields, with the risk of DON being associated with multiple weather variables, particularly those representing combinations of T between 15 and 30°C and RH > 80 summarized during the 3 weeks after R1.

Stability of Hybrid Maize Reaction to Gibberella Ear Rot and Deoxynivalenol Contamination of Grain.

Trials were conducted to quantify the stability (or lack of G × E interaction) of 15 maize hybrids to Gibberella ear rot (GER; caused by Fusarium graminearum) and deoxynivalenol (DON) contamination of grain across 30 Ohio environments (3 years × 10 locations). In each environment, one plot of each hybrid was planted and 10 ears per plot were inoculated via the silk channel. GER severity (proportion of ear area diseased) and DON contamination of grain (ppm) were quantified. Multiple rank-based methods, including Kendall's concordance coefficient (W) and Piepho's U, were used to quantify hybrid stability. The results found insufficient evidence to suggest crossover G × E interaction of ranks, with W greater than zero for GER (W = 0.28) and DON (W = 0.26), and U not statistically significant for either variable (P > 0.20). Linear mixed models (LMMs) were also used to quantify hybrid stability, accounting for crossover or noncrossover G × E interaction of transformed observed data. Based on information criteria and likelihood ratio tests for GER and DON response variables, the models with more complex variance-covariance structures-heterogeneous compound symmetry and factor-analytic-provided a better fit than the model with the simpler compound symmetry structure, indicating that one or more hybrids differed in stability. Overall, hybrids were stable based on rank-based methods, which indicated a lack of crossover G × E interaction, but the LMMs identified a few hybrids that were sensitive to environment. Resistant hybrids were generally more stable than susceptible hybrids.

The Future of Neurocritical Care Research: Proceedings and Recommendations from the Fifth Neurocritical Care Research Network Conference.

The Fifth Neurocritical Care Research Network (NCRN) Conference held in Boca Raton, Florida, in September of 2018 was devoted to challenging the current status quo and examining the role of the Neurocritical Care Society (NCS) in driving the science and research of neurocritical care. The aim of this in-person meeting was to set the agenda for the NCS's Neurocritical Care Research Central, which is the overall research arm of the society. Prior to the meeting, all 103 participants received educational content (book and seminar) on the 'Blue Ocean Strategy®,' a concept from the business world which aims to identify undiscovered and uncontested market space, and to brainstorm innovative ideas and methods with which to address current challenges in neurocritical care research. Three five-member working groups met at least four times by teleconference prior to the in-person meeting to prepare answers to a set of questions using the Blue Ocean Strategy concept as a platform. At the Fifth NCRN Conference, these groups presented to a five-member jury and all attendees for open discussion. The jury then developed a set of recommendations for NCS to consider in order to move neurocritical care research forward. We have summarized the topics discussed at the conference and put forward recommendations for the future direction of the NCRN and neurocritical care research in general.

Predicting plant disease epidemics from functionally represented weather series.

Epidemics are often triggered by specific weather patterns favouring the pathogen on susceptible hosts. For plant diseases, models predicting epidemics have therefore often emphasized the identification of early season weather patterns that are correlated with a disease outcome at some later point. Toward that end, window-pane analysis is an exhaustive search algorithm traditionally used in plant pathology for mining correlations in a weather series with respect to a disease endpoint. Here we show, with reference to Fusarium head blight (FHB) of wheat, that a functional approach is a more principled analytical method for understanding the relationship between disease epidemics and environmental conditions over an extended time series. We used scalar-on-function regression to model a binary outcome (FHB epidemic or non-epidemic) relative to weather time series spanning 140 days relative to flowering (when FHB infection primarily occurs). The functional models overall fit the data better than previously described standard logistic regression (lr) models. Periods much earlier than heretofore realized were associated with FHB epidemics. The findings were used to create novel weather summary variables which, when incorporated into lr models, yielded a new set of models that performed as well as existing lr models for real-time predictions of disease risk. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

The role of scientific evidence in decisions to adopt complex innovations in cancer care settings: a multiple case study in Nova Scotia, Canada.

BACKGROUND: Health care delivery and outcomes can be improved by using innovations (i.e., new ideas, technologies, and practices) supported by scientific evidence. However, scientific evidence may not be the foremost factor in adoption decisions and is rarely sufficient. The objective of this study was to examine the role of scientific evidence in decisions to adopt complex innovations in cancer care. METHODS: Using an explanatory, multiple case study design, we examined the adoption of complex innovations in five purposively sampled cases in Nova Scotia, Canada. Data were collected via documents and key informant interviews. Data analysis involved an in-depth analysis of each case, followed by a cross-case analysis to develop theoretically informed, generalizable knowledge on the role of scientific evidence in innovation adoption that may be applied to similar settings and contexts. RESULTS: The analyses identified key concepts alongside important caveats and considerations. Key concepts were (1) scientific evidence underpinned the adoption process, (2) evidence from multiple sources informed decision-making, (3) decision-makers considered three key issues when making decisions, and (4) champions were essential to eventual adoption. Caveats and considerations related to the presence of urgent problems and short-term financial pressures and minimizing risk. CONCLUSIONS: The findings revealed the different types of issues decision-makers consider while making these decisions and why different sources of evidence are needed in these processes. Future research should examine how different types of evidence are legitimized and why some types are prioritized over others.

Functional Data Analysis of Weather Variables Linked to Fusarium Head Blight Epidemics in the United States.

In past efforts, input weather variables for Fusarium head blight (FHB) prediction models in the United States were identified after following some version of the window-pane algorithm, which discretizes a continuous weather time series into fixed-length windows before searching for summary variables associated with FHB risk. Functional data analysis, on the other hand, reconstructs the assumed continuous process (represented by a series of recorded weather data) by using smoothing functions, and is an alternative way of working with time series data with respect to FHB risk. Our objective was to functionally model weather-based time series data linked to 865 observations of FHB (covering 16 states and 31 years in total), classified as epidemics (FHB disease index ≥ 10%) and nonepidemics (FHB disease index < 10%). Altogether, 94 different time series variables were modeled by penalized cubic B-splines for the smoothing function, from 120 days pre-anthesis to 20 days post-anthesis. Functional mean curves, standard deviations, and first derivatives were plotted for FHB epidemics relative to nonepidemics. Function-on-scalar regressions assessed the temporal trends of the magnitude and significance of the mean difference between functionally represented weather time series associated with FHB epidemics and nonepidemics. The mean functional weather-variable curve for epidemics started to deviate, in general, from that for nonepidemics as early as 40 days pre-anthesis for several weather variables. The greatest deviations were often near anthesis, the period of maximum susceptibility of wheat to FHB-causing fungi. The most consistent separations between the mean functional curves were seen with the daily averages of moisture-related variables (such as average relative humidity) and with variables summarizing the daily variation in temperature (as opposed to the daily mean). Functional data analysis was useful for extending our knowledge of relationships between weather variables and FHB epidemics.

Integrated Effects of Genetic Resistance and Prothioconazole + Tebuconazole Application Timing on Fusarium Head Blight in Wheat.

Integrated Fusarium head blight (FHB) management programs consisting of different combinations of cultivar resistance class and an application of the fungicide prothioconazole + tebuconazole at or after 50% early anthesis were evaluated for efficacy against FHB incidence (INC; percentage of diseased spikes), index (IND; percentage of diseased spikelets per spike), Fusarium damaged kernel (FDK), deoxynivalenol (DON) toxin contamination, grain yield, and test weight (TW) in inoculated field trials conducted in 11 U.S. states in 2014 and 2015. Mean log response ratios and corresponding percent control values for INC, IND, FDK, and DON, and mean differences in yield and TW relative to a nontreated, inoculated susceptible check (S_CK), were estimated through network meta-analyses as measures of efficacy. Results from the analyses were then used to estimate the economic benefit of each management program for a range of grain prices and fungicide applications costs. Management programs consisting of a moderately resistant (MR) cultivar treated with the fungicide were the most efficacious, reducing INC by 60 to 69%, IND by 71 to 76%, FDK by 66 to 72%, and DON by 60 to 64% relative to S_CK, compared with 56 to 62% for INC, 68 to 72% for IND, 66 to 68% for FDK, and 58 to 61% for DON for programs with a moderately susceptible (MS) cultivar. The least efficacious programs were those with a fungicide application to a susceptible (S) cultivar, with less than a 45% reduction of INC, IND, FDK, or DON. All programs were more efficacious under conditions favorable for FHB compared with less favorable conditions, with applications made at 50% early anthesis being of comparable efficacy to those made 2 to 7 days later. Programs with an MS cultivar resulted in the highest mean yield increases relative to S_CK (541 to 753 kg/ha), followed by programs with an S cultivar (386 to 498 kg/ha) and programs with an MR cultivar (250 to 337 kg/ha). Integrated management programs with an MS or MR cultivar treated with the fungicide at or after 50% early anthesis were the most likely to result in a 50 or 75% control of IND, FDK, or DON in a future trial. At a fixed fungicide application cost, these programs were $4 to $319/MT more economically beneficial than corresponding fungicide-only programs, depending on the cultivar and grain price. These findings demonstrate the benefits of combining genetic resistance with a prothioconazole + tebuconazole treatment to manage FHB, even if that treatment is applied a few days after 50% early anthesis.

Meta-Analysis of the Effects of QoI and DMI Fungicide Combinations on Fusarium Head Blight and Deoxynivalenol in Wheat.

Field trials were conducted in 17 U.S. states to evaluate the effects of quinone outside inhibitor (QoI) and demethylation inhibitor (DMI) fungicide programs on Fusarium head blight index (IND) and deoxynivalenol (DON) toxin in wheat. Four DMI-only treatments applied at Feekes 10.5.1, five QoI-only treatments applied between Feekes 9 or Feekes 10.5, three QoI+DMI mixtures applied at Feekes 10.5, and three treatments consisting of a QoI at Feekes 9 followed by a DMI at Feekes 10.5.1 were evaluated. Network meta-analytical models were fitted to log-transformed mean IND and DON data and estimated contrasts of log means were used to obtain estimates of mean percent controls relative to the nontreated check as measures of efficacy. Results from the meta-analyses were also used to assess the risk of DON increase in future trials. DMI at Feekes 10.5.1 were the most effective programs against IND and DON and the least likely to increase DON in future trials. QoI-only programs increased mean DON over the nontreated checks and were the most likely to do so in future trials, particularly when applied at Feekes 10.5. The effects of QoI+DMI combinations depended on the active ingredients and whether the two were applied as a mixture at heading or sequentially. Following a Feekes 9 QoI application with a Feekes 10.5.1 application of a DMI reduced the negative effect of the QoI on DON but was not sufficient to achieve the efficacy of the Feekes 10.5.1 DMI-only treatments. Our results suggest that one must be prudent when using QoI treatments under moderate to high risk of FHB, particularly where the QoI is used without an effective DMI applied in combination or in sequence.

Twenty-Five Years of the Binary Power Law for Characterizing Heterogeneity of Disease Incidence.

Spatial pattern, an important epidemiological property of plant diseases, can be quantified at different scales using a range of methods. The spatial heterogeneity (or overdispersion) of disease incidence among sampling units is an especially important measure of small-scale pattern. As an alternative to Taylor's power law for the heterogeneity of counts with no upper bound, the binary power law (BPL) was proposed in 1992 as a model to represent the heterogeneity of disease incidence (number of plant units diseased out of n observed in each sampling unit, or the proportion diseased in each sampling unit). With the BPL, the log of the observed variance is a linear function of the log of the variance for a binomial (i.e., random) distribution. Over the last quarter century, the BPL has contributed to both theory and multiple applications in the study of heterogeneity of disease incidence. In this article, we discuss properties of the BPL and use it to develop a general conceptualization of the dynamics of spatial heterogeneity in epidemics; review the use of the BPL in empirical and theoretical studies; present a synthesis of parameter estimates from over 200 published BPL analyses from a wide range of diseases and crops; discuss model fitting methods, and applications in sampling, data analysis, and prediction; and make recommendations on reporting results to improve interpretation. In a review of the literature, the BPL provided a very good fit to heterogeneity data in most publications. Eighty percent of estimated slope (b) values from field studies were between 1.06 and 1.51, with b positively correlated with the BPL intercept parameter. Stochastic simulations show that the BPL is generally consistent with spatiotemporal epidemiological processes and holds whenever there is a positive correlation of disease status of individuals composing sampling units.

Upregulation of epidermal gap junctional proteins in patients with venous disease.

BACKGROUND: Leg ulceration is a feared complication of venous insufficiency. It is not known whether varicose veins predispose skin to poor wound healing. The expression pattern of gap junctional protein connexin, a known marker of poor wound healing, was investigated across various stages of venous disease. METHODS: Patients undergoing intervention for varicose veins were assessed according to the Clinical Etiologic Anatomic Pathophysiologic (CEAP) classification of varicose veins. Paired 4-mm punch biopsies were taken from above the ankle (pathological) and above the knee (control). Tissues were stained with haematoxylin and eosin, and for connexin 43, connexin 30 and connexin 26. RESULTS: Forty-eight paired biopsies were taken (12 each for CEAP class C0, C2, C4 and C6). The pathological skin showed progressive epithelial hyperthickening, an increase in the number and depth of rete ridges, increased inflammation and loss of dermal architecture with disease progression from C4 onwards. The overall absolute connexin expression and mean connexin expression per cell in the pathological skin similarly increased across the CEAP classes from as early as C2. Increasing levels of connexin in control skin were also noted, indicating progression of the disease proximally. Connexin 43 expression showed the strongest positive correlation between pathological and control skin. CONCLUSION: Connexins were overexpressed in patients with simple varicose veins, with a stepwise increased expression through venous eczema to ulceration. Connexin 43 is a potential biomarker for venous disease. This finding suggests that varicose veins predispose skin to poor wound healing. Surgical relevance The overexpression of connexins, a family of gap junctional proteins, is known to cause poor healing in venous leg ulceration. It is not known whether there is any association with superficial venous disease. Here, connexin proteins were overexpressed in patients with uncomplicated varicose veins, before histological skin changes. Connexin could be a biomarker of venous disease progression.

Plant Virus Epidemiology: Applications and Prospects for Mathematical Modeling and Analysis to Improve Understanding and Disease Control.

In recent years, mathematical modeling has increasingly been used to complement experimental and observational studies of biological phenomena across different levels of organization. In this article, we consider the contribution of mathematical models developed using a wide range of techniques and uses to the study of plant virus disease epidemics. Our emphasis is on the extent to which models have contributed to answering biological questions and indeed raised questions related to the epidemiology and ecology of plant viruses and the diseases caused. In some cases, models have led to direct applications in disease control, but arguably their impact is better judged through their influence in guiding research direction and improving understanding across the characteristic spatiotemporal scales of plant virus epidemics. We restrict this article to plant virus diseases for reasons of length and to maintain focus even though we recognize that modeling has played a major and perhaps greater part in the epidemiology of other plant pathogen taxa, including vector-borne bacteria and phytoplasmas.

Epidemiology: Past, Present, and Future Impacts on Understanding Disease Dynamics and Improving Plant Disease Management-A Summary of Focus Issue Articles.

Epidemiology has made significant contributions to plant pathology by elucidating the general principles underlying the development of disease epidemics. This has resulted in a greatly improved theoretical and empirical understanding of the dynamics of disease epidemics in time and space, predictions of disease outbreaks or the need for disease control in real-time basis, and tactical and strategic solutions to disease problems. Availability of high-resolution experimental data at multiple temporal and spatial scales has now provided a platform to test and validate theories on the spread of diseases at a wide range of spatial scales ranging from the local to the landscape level. Relatively new approaches in plant disease epidemiology, ranging from network to information theory, coupled with the availability of large-scale datasets and the rapid development of computer technology, are leading to revolutionary thinking about epidemics that can result in considerable improvement of strategic and tactical decision making in the control and management of plant diseases. Methods that were previously restricted to topics such as population biology or evolution are now being employed in epidemiology to enable a better understanding of the forces that drive the development of plant disease epidemics in space and time. This Focus Issue of Phytopathology features research articles that address broad themes in epidemiology including social and political consequences of disease epidemics, decision theory and support, pathogen dispersal and disease spread, disease assessment and pathogen biology and disease resistance. It is important to emphasize that these articles are just a sample of the types of research projects that are relevant to epidemiology. Below, we provide a succinct summary of the articles that are published in this Focus Issue .

A White Paper on Global Wheat Health Based on Scenario Development and Analysis.

Scenario analysis constitutes a useful approach to synthesize knowledge and derive hypotheses in the case of complex systems that are documented with mainly qualitative or very diverse information. In this article, a framework for scenario analysis is designed and then, applied to global wheat health within a timeframe from today to 2050. Scenario analysis entails the choice of settings, the definition of scenarios of change, and the analysis of outcomes of these scenarios in the chosen settings. Three idealized agrosystems, representing a large fraction of the global diversity of wheat-based agrosystems, are considered, which represent the settings of the analysis. Several components of global changes are considered in their consequences on global wheat health: climate change and climate variability, nitrogen fertilizer use, tillage, crop rotation, pesticide use, and the deployment of host plant resistances. Each idealized agrosystem is associated with a scenario of change that considers first, a production situation and its dynamics, and second, the impacts of the evolving production situation on the evolution of crop health. Crop health is represented by six functional groups of wheat pathogens: the pathogens associated with Fusarium head blight; biotrophic fungi, Septoria-like fungi, necrotrophic fungi, soilborne pathogens, and insect-transmitted viruses. The analysis of scenario outcomes is conducted along a risk-analytical pattern, which involves risk probabilities represented by categorized probability levels of disease epidemics, and risk magnitudes represented by categorized levels of crop losses resulting from these levels of epidemics within each production situation. The results from this scenario analysis suggest an overall increase of risk probabilities and magnitudes in the three idealized agrosystems. Changes in risk probability or magnitude however vary with the agrosystem and the functional groups of pathogens. We discuss the effects of global changes on the six functional groups, in terms of their epidemiology and of the crop losses they cause. Scenario analysis enables qualitative analysis of complex systems, such as plant pathosystems that are evolving in response to global changes, including climate change and technology shifts. It also provides a useful framework for quantitative simulation modeling analysis for plant disease epidemiology.

Questioning assent: how are children's views included as families make decisions about clinical trials?

BACKGROUND: Assent is used to take children's wishes into account when they are invited into clinical trials, but the concept has attracted considerable criticism. We investigated children's accounts of decision-making with the aim of informing practice in supporting children when invited to join a clinical trial. METHODS: We audio-recorded qualitative, semi-structured interviews with 22 children aged 8-16 years about being invited to take part in a clinical trial. Most children were interviewed with their parents. Analysis of the transcribed interviews examined the content of participants' accounts thematically, whilst also drawing on principles of discourse analysis, which examines how individuals use talk to achieve certain effects or social practices. RESULTS: It was not possible to separate children's knowledge of the clinical trial, or their decision-making processes from that of their parents, with parents taking a substantial mediating role in producing their children's decisions. Decision-making gradually unfolded across time and events and was interwoven within the family context, rather than happening in one moment or in the clinical setting. Whilst children valued their parents' role, a case study of child-parent disagreement indicated how children can struggle to be heard. CONCLUSIONS: Decisions happen within a process of family dynamics, in contrast to ideas of assent that isolate it from this context. Parents have a substantial role in children's decisions, and thus how families come to provide consent. Reflecting this we argue that assent practices need to focus on supporting parents to support their children in learning and deliberating about trials. However, this needs to be accompanied by practitioners being alert to the possibility of divergence in child and parent views and enabling children's perspectives to be heard.

PO-27 - Thrombin generation in pancreatic cancer and multiple myeloma with use of calibrated automated thrombography.

INTRODUCTION: The calibrated automated thrombography (CAT) assay is emerging as a reliable tool for real time estimation of thrombin generation (TG) potential. As a time-dependent colorimetric assessment of thrombin quantity generated per sample, it measures the amount of thrombin-cleaved fluorogenic substrate produced, and so is regarded as a better overall indicator of the clotting efficiency and function of the haemostatic process than one stage clotting-time based assays. AIM: We already recognise that the pathways underlying the thrombotic phenotype for different malignancies may be driven by different factors of the coagulation cascade with TG has a common denominator. Two such malignancies with high venous thromboembolism (VTE) incidence are Multiple myeloma (MM) and Pancreatic cancer (PC). Understanding the underlying variations in these two distinct cancer models using patient samples and cell lines might potentially allow individual approaches to identifying thrombotic risk and relevant prevention strategies. MATERIALS AND METHODS: Citrated blood samples were taken from healthy controls, pre-surgical pancreatic cancer and pre-chemotherapy multiple myeloma patients enrolled into ongoing clinical trials. The clotting ability was tested using platelet free plasma (PPP) on a one-step clotting time-based (CT) assay and the TG profiles were evaluated on a Thrombinoscope™ software (Thrombinoscope BV, Maastricht, Netherlands). Solid tumour cells of pancreatic cancer and malignant haematological cell lines were used at various cell concentrations for the CAT assay, which was performed with the addition of platelet-free control plasma or control plasma deficient in coagulation factors VII and XII. RESULTS: At TF concentration conditions of 1 pmol/L, the peak height of thrombin generated on thrombogram curves strongly correlated with CT of patient samples. Compared to healthy controls, pancreatic cancer had higher thrombin peaks (P), shorter lag times (LG), and an overall stronger TG profile than MM. Pancreatic cancer cell lines exhibited higher concentration-dependent TG profiles in control plasma than haematological cell lines, with higher peaks, endogenous thrombin potential (ETP), shorter lag times (LG) and faster times-to-peak (ttPeak). CONCLUSIONS: This study demonstrates that the CAT assay is a useful predictor of the thrombotic phenotype in cancer patients as it gives a more comprehensive overall coagulation profile than one stage CT-based assays. It shows that for patient samples and cell lines, the similarities and differences that exists in the TG potential, significantly depends on specific coagulation factors present in the intrinsic or extrinsic arms of the clotting cascade.

PO-30 - Changes in soluble CD106 and CD54 serum levels during chemotherapy treatment for multiple myeloma.

INTRODUCTION: IMiD-based regimens are now widely considered standard of care in the treatment of Multiple myeloma (MM) patients (Kumar et al., 2008). One of the major adverse events noted in many MM clinical studies in patients treated with combination regimens including Thalidomide (Thal), Lenanlidomide (Len) and dexamethasone or chemotherapy was the development of hrombosis (Carrier et al., 2011). AIM: We have postulated that one mechanism for venous thromboembolism (VTE) occurrence may be through chemotherapy damage to endothelium (Date et al., 2013) and much recent work has centred on the study of soluble dysfunction markers to predict this event. In states of endothelial dysfunction soluble antigen concentrations of circulating endothelial activation markers sCD106 and sCD54 have been shown to increase (Burger and Touyz, 2012), and sCD106 was recently associated with inferior survival in newly diagnosed MM patients treated with Thal- or Len-based therapies (Terpos et al., 2013). MATERIALS AND METHODS: Serum from newly diagnosed and relapsed MM patients were collected before, during and after prescribed chemotherapy courses (minimum of 4 cycles). Levels of endothelial activation markers sCD106 and sCD54 were evaluated by quantitative ELISA (Platinum ELISA kits; eBioscience, Hatfield, UK). RESULTS: The percentage mean±SD change in the serum concentration of sCD106 increased by 25.8% after the first cycle of chemotherapy (T2; n=10) relative to T1 prior to chemotherapy administration. These levels subsequently decreased after the second cycle of chemotherapy (T3; n=9) and after completion (T4; n=5), but were still higher than baseline levels (15.5 and 15.3% increase in comparison to baseline, respectively). In contrast, the mean±SD percentage change in sCD54 relative to T1 were similar after the first cycle (T2; n=10) and the second cycle (T3; n=9), but increased by 15.0% after completion of chemotherapy (T4; n=5). Additionally, a statistical correlation was found to exist between the serum concentration of sCD106 and sCD54 (Pearson's correlation coefficient r=0.84, p <0.0005) for all analysed samples (n=39). CONCLUSIONS: In this study, the increase in serum levels of both sCD106 and sCD54 after IMiD-based chemotherapy implies a disruptive effect of these combination regimens on the vascular endothelium. This agrees with previous studies where serum levels of sCD106 were shown to be significantly elevated in chronic lymphocytic leukaemia patients on Len indicating Len-induced endothelial dysfunction, and associated with subsequent DVT development (Aue et al., 2011). Our study confirms the potential significance of these biomarkers in demonstrating chemotherapy-induced endothelial damage. Correlation with VTE however is difficult as most MM patients on chemotherapy receive thromboprophylaxis as per international guidelines.

PO-48 - Assessment of the procoagulant potential state of tumour-MP in cancer patients.

INTRODUCTION: The venous thromboembolism is considered one of the highest risk factor in cancer patients for instance ovarian and pancreas. This hypercoagulability state is believed to be caused by tumour cells that can produce a variety of procaogulant factors including tissue factor (TF) bearing microparticles (MP). Chemotherapy is an independent risk factor for venous thromboembolism (VTE) in patients and it can leads to coagulation activation and may increase microparticles that can increase risk of thrombosis. AIM: Therefore, our current hypothesis is that this increased risk of VTE is due to release of tumour MP into the blood. To further investigate this mechanism an ex-vivo microfluidic model system was developed wherein tumour spheroids were grown and transferred onto a microfluidic chip and assessed, under flow conditions, for procoagulant activity (PCA). MATERIALS AND METHODS: Tumour spheroids from pancreatic cancer cell line AsPC1 and human glioblastoma cell line U87 were generated using the liquid overlay method in a 96-well plate, then transferred to a microfluidic chip, designed with a trap within the device to immobilise the spheroid. The procoagulant potential of cell-free supernatant was measured using a prothrombin time clotting assay. Procoagulant activity was assessed under flow rate of 3.0 µL min-1 for 6 hours. RESULTS: Several tumour cell lines (A2780, SKOV3, MIA Paca2, AsPC1 and U87) were assessed for PCA of media (MP associated) and then subsequently assessed for spheroid formation. Prothrombin time of cell-free media was A2780: 794s, SKOV3: 203.4s, MIA Paca2: 412s, AsPC1: 69s and U87: 50.3s. The pancreatic cell line AsPC-1 and glioblastoma cell line U87 were selected for further study on the basis of relatively high PCA and ability to form stable spheroids. When transferred to a microfluidic chip, AsPC1 tumour spheroids showed a slowing of PCA of media over a 6-hour period from 36.6 to 309s. U87 tumour spheroids showed a reduction in PCA of media over a 6-hour period from 51.3 to 108.9s. CONCLUSIONS: This is the first report of tumour spheroids maintained in a microfluidic device and then subsequently assessed for PCA. Tumour spheroids of AsPC1 were shown to produce continuous procaogulant activity and this is presumably due to tumour microparticle release. This new model system offers a way to assess tumour associated PCA under flow conditions.

Statistical Models and Methods for Network Meta-Analysis.

Meta-analysis, the methodology for analyzing the results from multiple independent studies, has grown tremendously in popularity over the last four decades. Although most meta-analyses involve a single effect size (summary result, such as a treatment difference) from each study, there are often multiple treatments of interest across the network of studies in the analysis. Multi-treatment (or network) meta-analysis can be used for simultaneously analyzing the results from all the treatments. However, the methodology is considerably more complicated than for the analysis of a single effect size, and there have not been adequate explanations of the approach for agricultural investigations. We review the methods and models for conducting a network meta-analysis based on frequentist statistical principles, and demonstrate the procedures using a published multi-treatment plant pathology data set. A major advantage of network meta-analysis is that correlations of estimated treatment effects are automatically taken into account when an appropriate model is used. Moreover, treatment comparisons may be possible in a network meta-analysis that are not possible in a single study because all treatments of interest may not be included in any given study. We review several models that consider the study effect as either fixed or random, and show how to interpret model-fitting output. We further show how to model the effect of moderator variables (study-level characteristics) on treatment effects, and present one approach to test for the consistency of treatment effects across the network. Online supplemental files give explanations on fitting the network meta-analytical models using SAS.

Does the P Value Have a Future in Plant Pathology?

The P value (significance level) is possibly the mostly widely used, and also misused, quantity in data analysis. P has been heavily criticized on philosophical and theoretical grounds, especially from a Bayesian perspective. In contrast, a properly interpreted P has been strongly defended as a measure of evidence against the null hypothesis, H0. We discuss the meaning of P and null-hypothesis statistical testing, and present some key arguments concerning their use. P is the probability of observing data as extreme as, or more extreme than, the data actually observed, conditional on H0 being true. However, P is often mistakenly equated with the posterior probability that H0 is true conditional on the data, which can lead to exaggerated claims about the effect of a treatment, experimental factor or interaction. Fortunately, a lower bound for the posterior probability of H0 can be approximated using P and the prior probability that H0 is true. When one is completely uncertain about the truth of H0 before an experiment (i.e., when the prior probability of H0 is 0.5), the posterior probability of H0 is much higher than P, which means that one needs P values lower than typically accepted for statistical significance (e.g., P = 0.05) for strong evidence against H0. When properly interpreted, we support the continued use of P as one component of a data analysis that emphasizes data visualization and estimation of effect sizes (treatment effects).

Abnormal connexin expression in human chronic wounds.

BACKGROUND: Regulated alteration of connexin expression has been shown to be integral to acute wound repair. Downregulation of the gap-junction protein connexin 43 at the wound edge has been correlated with keratinocyte and fibroblast migration, while abnormal overexpression of connexin 43 significantly perturbs healing, as shown in the streptozotocin diabetic rodent impaired healing model. OBJECTIVES: To examine the protein expression levels of connexin 43, in addition to connexins 26 and 30, in a variety of human chronic wounds. METHODS: Wound-edge punch biopsies and a matched control from the arm were taken from a cohort of patients with venous leg, diabetic foot or pressure ulcers. Wound connexin expression in each patient was compared with that in a matched, nonwounded arm punch. Tissue was sectioned, stained and imaged by confocal microscopy using identical parameters per patient to permit quantification. RESULTS: Epidermal connexin 43, connexin 26 and connexin 30, and dermal connexin 43 were discovered to be strikingly upregulated in every ulcer from all three wound types, pointing to connexin upregulation as a common feature between chronic wounds. CONCLUSIONS: This result supports efforts to target connexin 43 to promote cell migration and wound healing in chronic ulcers.