Effect of Pre-fermentative Treatments on Polysaccharide Composition of White and Rosé Musts and Wines.

This paper studied the effect of conventional pre-fermentative techniques (direct pressing "CP" and cold maceration "CM") and an innovate technique (high power ultrasounds "S"), applied to Viogner and Monastrell grapes on the polysaccharide content of the musts, white and rosé wines, and after six months of bottle aging. The results showed that the longer pre-fermentation maceration time applied with the CM technique compared to the short ultrasonic maceration was key in the extraction of polysaccharides from the grape to the must. CP treatment produced wines with the lowest content of total soluble polysaccharide families since it was the least intense pretreatment for the disruption of the grape berry cell wall polysaccharides. Ultrasonic pretreatment could be used as a new tool to increase the solubilization of polysaccharides in wines, positively affecting the wine colloidal properties. During bottle aging, there wasn't a clear effect of pretreatments on the evolution of polysaccharides.

Biodose Tools: an R shiny application for biological dosimetry.

INTRODUCTION: In the event of a radiological accident or incident, the aim of biological dosimetry is to convert the yield of a specific biomarker of exposure to ionizing radiation into an absorbed dose. Since the 1980s, various tools have been used to deal with the statistical procedures needed for biological dosimetry, and in general those who made several calculations for different biomarkers were based on closed source software. Here we present a new open source program, Biodose Tools, that has been developed under the umbrella of RENEB (Running the European Network of Biological and retrospective Physical dosimetry). MATERIALS AND METHODS: The application has been developed using the R programming language and the shiny package as a framework to create a user-friendly online solution. Since no unique method exists for the different mathematical processes, several meetings and periodic correspondence were held in order to reach a consensus on the solutions to be implemented. RESULTS: The current version 3.6.1 supports dose-effect fitting for dicentric and translocation assay. For dose estimation Biodose Tools implements those methods indicated in international guidelines and a specific method to assess heterogeneous exposures. The app can include information on the irradiation conditions to generate the calibration curve. Also, in the dose estimate, information about the accident can be included as well as the explanation of the results obtained. Because the app allows generating a report in various formats, it allows traceability of each biological dosimetry study carried out. The app has been used globally in different exercises and training, which has made it possible to find errors and improve the app itself. There are some features that still need consensus, such as curve fitting and dose estimation using micronucleus analysis. It is also planned to include a package dedicated to interlaboratory comparisons and the incorporation of Bayesian methods for dose estimation. CONCLUSION: Biodose Tools provides an open-source solution for biological dosimetry laboratories. The consensus reached helps to harmonize the way in which uncertainties are calculated. In addition, because each laboratory can download and customize the app's source code, it offers a platform to integrate new features.

Assessment methods for inter-laboratory comparisons of the dicentric assay.

PURPOSE: To test the performance of different algorithms that can be used in inter-laboratory comparisons based on dicentric chromosome analysis, and to evaluate the impact of considering a priori values different to calculate individual laboratory performance based on the ionizing radiation dose estimation. METHODS: Mean and standard deviation estimations in inter-laboratory comparisons are tested on simulated data and data from previously published inter-laboratory comparisons using three robust algorithms, Algorithm A, Algorithm B and Q/Hampel, all programmed in R-project language and implemented in a Shiny application. The simulated data were generated assuming three different probabilities to contaminate inter-laboratory comparisons samples with atypical dose values. Comparison between different algorithms was also done using published exercises where blood samples were irradiated at 0 and 0.7 Gy that represent a challenge for the assessment of an inter-laboratory comparison. RESULTS: The best performance was obtained with the Q/Hampel algorithm for the estimation of the dose mean and with the Algorithm B for the estimation of the dose standard deviation under the conditions tested in the simulations. The Q/Hampel algorithm showed the best performance when non-irradiated samples were evaluated and there was a high proportion of identical values. The presence identical values cause the Algorithm B to fail. Real examples illustrating the need to consider standard deviation priors, and the need to use algorithms resistant to a high proportion of identical values are presented. CONCLUSIONS: Q/Hampel algorithm is a serious candidate to estimate the dose mean in the inter-laboratory comparisons, and to estimate both parameters when the proportion of identical values equals or higher than the half of the results. When the proportion of identical values is less than the half of the results, the Algorithm B should be considered as a candidate to estimate the standard deviation in the inter-laboratory comparisons with small number of laboratories. We remark that special attention is needed to establish prior definitions of standard deviation in the assessment of inter-laboratory dicentric assay comparisons.

Optimal experimental design for cytogenetic dose-response calibration curves.

Purpose: To introduce optimal experimental design techniques in the cytogenetic biological dosimetry practice. This includes the development of a new optimality criterion for the calibration of radiation doses.Materials and methods: The most typical optimal design criterion and the one developed in this research are introduced and applied in an example from the literature. In another example from the literature, a simulation study has been performed to compare the standard error of the dose estimation using different experimental designs. An RStudio project and a GitHub project have been developed to reproduce these results.Results: In the paper, it is observed that the application of optimal experimental design techniques can reduce the standard error of biodosimetric dose estimations.Conclusions: Optimal experimental design techniques jointly with practitioners' requirements may be applied. This practice would not involve an additional laboratory work.

Comparative study of micronucleus assays and dicentric plus ring chromosomes for dose assessment in particular cases of partial-body exposure.

Purpose: The goal was to compare the micronucleus (MN) and dicentric plus ring chromosomes (D + R) assays for dose assessment in cases of partial body irradiations (PBI). Materials and methods: We constructed calibration curves for each assay at doses ranging from 0 to 5 Gy of X-rays at dose rate of 0.275 Gy/min. To simulate partial-body exposures, blood samples from two donors were irradiated with 0.5, 1, 2 and 4 Gy and the ratios of irradiated to unirradiated blood were 25, 50, and 100%. Different tests were used to confirm if all samples were overdispersed or zero-inflated and for partial-body dose assessment we used the Qdr, Dolphin and Bayesian model. Results: In our samples for D + R calibration curve, practically all doses agreed with Poisson assumption, but MN exhibited overdispersed and zero-inflated cellular distributions. The exact Poisson tests and zero-inflated tests demonstrate that virtually all samples of D + R from PBI simulation fit the Poisson distribution and were not zero-inflated, but the MN samples were also overdispersed and zero-inflated. In the partial-body estimation, when Qdr and Dolphin methods were used the D + R results were better than MN, but the doses estimation defined by the Bayesian methodology were more accurate than the classical methods. Conclusions: Dicentric chromosomes continue to prove to be the best biological marker for dose assessment. However exposure scenarios of partial-body estimation, overdispersion and zero-inflation may not occur, it being a critical point not only for dose assessment, but also to confirm partial-body exposure. MN could be used as alternative assay for partial-body dose estimation, but in case of an accident without any information, the MN assay could not define whether the accident was a whole-body irradiation (WBI) or a PBI.

A statistical framework for radiation dose estimation with uncertainty quantification from the γ-H2AX assay.

Over the last decade, the γ-H2AX focus assay, which exploits the phosphorylation of the H2AX histone following DNA double-strand-breaks, has made considerable progress towards acceptance as a reliable biomarker for exposure to ionizing radiation. While the existing literature has convincingly demonstrated a dose-response effect, and also presented approaches to dose estimation based on appropriately defined calibration curves, a more widespread practical use is still hampered by a certain lack of discussion and agreement on the specific dose-response modelling and uncertainty quantification strategies, as well as by the unavailability of implementations. This manuscript intends to fill these gaps, by stating explicitly the statistical models and techniques required for calibration curve estimation and subsequent dose estimation. Accompanying this article, a web applet has been produced which implements the discussed methods.

Dicentric Dose Estimates for Patients Undergoing Radiotherapy in the RTGene Study to Assess Blood Dosimetric Models and the New Bayesian Method for Gradient Exposure.

The RTGene study was focused on the development and validation of new transcriptional biomarkers for prediction of individual radiotherapy patient responses to ionizing radiation. In parallel, for validation purposes, this study incorporated conventional biomarkers of radiation exposure, including the dicentric assay. Peripheral blood samples were taken with ethical approval and informed consent from a total of 20 patients undergoing external beam radiotherapy for breast, lung, gastrointestinal or genitourinary tumors. For the dicentric assay, two samples were taken from each patient: prior to radiotherapy and before the final fraction. Blood samples were set up using standard methods for the dicentric assay. All the baseline samples had dicentric frequencies consistent with the expected background for the normal population. For blood taken before the final fraction, all the samples displayed distributions of aberrations, which are indicative of partial-body exposures. Whole-body and partial-body cytogenetic doses were calculated with reference to a 250-kVp X-ray calibration curve and then compared to the dose to blood derived using two newly developed blood dosimetric models. Initial comparisons indicated that the relationship between these measures of dose appear very promising, with a correlation of 0.88 (P = 0.001). A new Bayesian zero-inflated Poisson finite mixture method was applied to the dicentric data, and partial-body dose estimates showed no significant difference (P > 0.999) from those calculated by the contaminated Poisson technique. The next step will be further development and validation in a larger patient group.

A note on Poisson goodness-of-fit tests for ionizing radiation induced chromosomal aberration samples.

PURPOSE: To present Poisson exact goodness-of-fit tests as alternatives and complements to the asymptotic u-test, which is the most widely used in cytogenetic biodosimetry, to decide whether a sample of chromosomal aberrations in blood cells comes from an homogeneous or inhomogeneous exposure. MATERIALS AND METHODS: Three Poisson exact goodness-of-fit test from the literature are introduced and implemented in the R environment. A Shiny R Studio application, named GOF Poisson, has been updated for the purpose of giving support to this work. The three exact tests and the u-test are applied in chromosomal aberration data from clinical and accidental radiation exposure patients. RESULTS: It is observed how the u-test is not an appropriate approximation in small samples with small yield of chromosomal aberrations. Tools are provided to compute the three exact tests, which is not as trivial as the implementation of the u-test. CONCLUSIONS: Poisson exact goodness-of-fit tests should be considered jointly to the u-test for detecting inhomogeneous exposures in the cytogenetic biodosimetry practice.

Cancer incidence among children and young adults who have undergone x-ray guided cardiac catheterization procedures.

Children and young adults with heart disease appear to be at increased risk of developing cancer, although the reasons for this are unclear. A cohort of 11,270 individuals, who underwent cardiac catheterizations while aged ≤ 22 years in the UK, was established from hospital records. Radiation doses from cardiac catheterizations and CT scans were estimated. The cohort was matched with the NHS Central Register and NHS Transplant Registry to determine cancer incidence and transplantation status. Standardized incidence ratios (SIR) with associated confidence intervals (CI) were calculated. The excess relative risk (ERR) of lymphohaematopoietic  neoplasia was also calculated using Poisson regression. The SIR was raised for all malignancies (2.32, 95% CI 1.65, 3.17), lymphoma (8.34, 95% CI 5.22, 12.61) and leukaemia (2.11, 95% CI 0.82, 4.42). After censoring transplant recipients, post-transplant, the SIR was reduced to 0.90 (95% CI 0.49, 1.49) for all malignancies. All lymphomas developed post-transplant. The SIR for all malignancies developing 5 years from the first cardiac catheterization (2 years for leukaemia/lymphoma) remained raised (3.01, 95% CI 2.09, 4.19) but was again reduced after censoring transplant recipients (0.98, 95% CI 0.48, 1.77). The ERR per mGy bone marrow dose for lymphohaematopoietic neoplasia was reduced from 0.541 (95% CI 0.104, 1.807) to 0.018 (95% CI - 0.002, 0.096) where transplantation status was accounted for as a time-dependent background risk factor. In conclusion, transplantation appears to be a large contributor to elevated cancer rates in this patient group. This is likely to be mainly due to associated immunosuppression, however, radiation exposure may also be a contributing factor.

AN EXACT GOODNESS-OF-FIT TEST BASED ON THE OCCUPANCY PROBLEMS TO STUDY ZERO-INFLATION AND ZERO-DEFLATION IN BIOLOGICAL DOSIMETRY DATA.

The goal in biological dosimetry is to estimate the dose of radiation that a suspected irradiated individual has received. For that, the analysis of aberrations (most commonly dicentric chromosome aberrations) in scored cells is performed and dose response calibration curves are built. In whole body irradiation (WBI) with X- and gamma-rays, the number of aberrations in samples is properly described by the Poisson distribution, although in partial body irradiation (PBI) the excess of zeros provided by the non-irradiated cells leads, for instance, to the Zero-Inflated Poisson distribution. Different methods are used to analyse the dosimetry data taking into account the distribution of the sample. In order to test the Poisson distribution against the Zero-Inflated Poisson distribution, several asymptotic and exact methods have been proposed which are focused on the dispersion of the data. In this work, we suggest an exact test for the Poisson distribution focused on the zero-inflation of the data developed by Rao and Chakravarti (Some small sample tests of significance for a Poisson distribution. Biometrics 1956; 12 : 264-82.), derived from the problems of occupancy. An approximation based on the standard Normal distribution is proposed in those cases where the computation of the exact test can be tedious. A Monte Carlo Simulation study was performed in order to estimate empirical confidence levels and powers of the exact test and other tests proposed in the literature. Different examples of applications based on in vitro data and also data recorded in several radiation accidents are presented and discussed. A Shiny application which computes the exact test and other interesting goodness-of-fit tests for the Poisson distribution is presented in order to provide them to all interested researchers.

UNCERTAINTY ON RADIATION DOSES ESTIMATED BY BIOLOGICAL AND RETROSPECTIVE PHYSICAL METHODS.

Biological and physical retrospective dosimetry are recognised as key techniques to provide individual estimates of dose following unplanned exposures to ionising radiation. Whilst there has been a relatively large amount of recent development in the biological and physical procedures, development of statistical analysis techniques has failed to keep pace. The aim of this paper is to review the current state of the art in uncertainty analysis techniques across the 'EURADOS Working Group 10-Retrospective dosimetry' members, to give concrete examples of implementation of the techniques recommended in the international standards, and to further promote the use of Monte Carlo techniques to support characterisation of uncertainties. It is concluded that sufficient techniques are available and in use by most laboratories for acute, whole body exposures to highly penetrating radiation, but further work will be required to ensure that statistical analysis is always wholly sufficient for the more complex exposure scenarios.

Uncertainty of fast biological radiation dose assessment for emergency response scenarios.

PURPOSE: Reliable dose estimation is an important factor in appropriate dosimetric triage categorization of exposed individuals to support radiation emergency response. MATERIALS AND METHODS: Following work done under the EU FP7 MULTIBIODOSE and RENEB projects, formal methods for defining uncertainties on biological dose estimates are compared using simulated and real data from recent exercises. RESULTS: The results demonstrate that a Bayesian method of uncertainty assessment is the most appropriate, even in the absence of detailed prior information. The relative accuracy and relevance of techniques for calculating uncertainty and combining assay results to produce single dose and uncertainty estimates is further discussed. CONCLUSIONS: Finally, it is demonstrated that whatever uncertainty estimation method is employed, ignoring the uncertainty on fast dose assessments can have an important impact on rapid biodosimetric categorization.

Zero-inflated regression models for radiation-induced chromosome aberration data: A comparative study.

Within the field of cytogenetic biodosimetry, Poisson regression is the classical approach for modeling the number of chromosome aberrations as a function of radiation dose. However, it is common to find data that exhibit overdispersion. In practice, the assumption of equidispersion may be violated due to unobserved heterogeneity in the cell population, which will render the variance of observed aberration counts larger than their mean, and/or the frequency of zero counts greater than expected for the Poisson distribution. This phenomenon is observable for both full- and partial-body exposure, but more pronounced for the latter. In this work, different methodologies for analyzing cytogenetic chromosomal aberrations datasets are compared, with special focus on zero-inflated Poisson and zero-inflated negative binomial models. A score test for testing for zero inflation in Poisson regression models under the identity link is also developed.

A new Bayesian model applied to cytogenetic partial body irradiation estimation.

A new zero-inflated Poisson model is introduced for the estimation of partial body irradiation dose and fraction of body irradiated. The Bayes factors are introduced as tools to help determine whether a data set of chromosomal aberrations obtained from a blood sample reflects partial or whole body irradiation. Two examples of simulated cytogenetic radiation exposure data are presented to demonstrate the usefulness of this methodology in cytogenetic biological dosimetry.

radir package: an R implementation for cytogenetic biodosimetry dose estimation.

The Bayesian framework has been shown to be very useful in cytogenetic dose estimation. This approach allows description of the probability of an event in terms of previous knowledge, e.g. its expectation and/or its uncertainty. A new R package entitled radir (radiation inverse regression) has been implemented with the aim of reproducing a recent Bayesian-type dose estimation methodology. radir adopts the method of dose estimation under the Poisson assumption of the responses (the chromosomal aberrations counts) for the required dose-response curve (typically linear or quadratic). The individual commands are described in detail and relevant examples of the use of the methods and the corresponding radir software tools are given. The suitability of this methodology is highlighted and its application encouraged by providing a user-friendly command-type software interface within the R statistical software (version 3.1.1 or higher), which includes a complete manual.

A new inverse regression model applied to radiation biodosimetry.

Biological dosimetry based on chromosome aberration scoring in peripheral blood lymphocytes enables timely assessment of the ionizing radiation dose absorbed by an individual. Here, new Bayesian-type count data inverse regression methods are introduced for situations where responses are Poisson or two-parameter compound Poisson distributed. Our Poisson models are calculated in a closed form, by means of Hermite and negative binomial (NB) distributions. For compound Poisson responses, complete and simplified models are provided. The simplified models are also expressible in a closed form and involve the use of compound Hermite and compound NB distributions. Three examples of applications are given that demonstrate the usefulness of these methodologies in cytogenetic radiation biodosimetry and in radiotherapy. We provide R and SAS codes which reproduce these examples.

Review of Bayesian statistical analysis methods for cytogenetic radiation biodosimetry, with a practical example.

Classical methods of assessing the uncertainty associated with radiation doses estimated using cytogenetic techniques are now extremely well defined. However, several authors have suggested that a Bayesian approach to uncertainty estimation may be more suitable for cytogenetic data, which are inherently stochastic in nature. The Bayesian analysis framework focuses on identification of probability distributions (for yield of aberrations or estimated dose), which also means that uncertainty is an intrinsic part of the analysis, rather than an 'afterthought'. In this paper Bayesian, as well as some more advanced classical, data analysis methods for radiation cytogenetics are reviewed that have been proposed in the literature. A practical overview of Bayesian cytogenetic dose estimation is also presented, with worked examples from the literature.