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Recent studies have shown that sub-lethal doses of herbicides may affect plant flowering, however, no study has established a direct relationship between the concentrations of deposited herbicide and plant flowering. Here the aim was to investigate the relationship between herbicide spray drift deposited on non-target plants and plant flowering in a realistic agro-ecosystem setting. The concentrations of the herbicide glyphosate deposited on plants were estimated by measuring the concentration of a dye tracer applied together with the herbicide. The estimated maximal and average deposition of glyphosate within the experimental area corresponded to 30 g glyphosate/ha (2.08% of the label rate of 1440 g a.i./ha) and 2.4 g glyphosate/ha (0.15% label rate), respectively, and the concentrations decreased rapidly with increasing distance from the spraying track. However, there were not a unique relation between distance and deposition, which indicate that heterogeneities of turbulence, wind speed and/or direction can strongly influence the deposition from 1 min to another during spraying. The effects of glyphosate on cumulative flower numbers and flowering time were modelled using Gompertz growth models on four non-target species. Glyphosate had a significantly negative effect on the cumulative number of flowers on Trifolium pratense and Lotus corniculatus, whereas there were no significant effects on Trifolium repens, and a positive, but non-significant, effect on number of flowers on Cichorium intybus. Glyphosate did not affect the flowering time of any of the four species significantly. Lack of floral resources is known to be of major importance for pollinator declines. The implications of the presented results for pesticide risk assessment are discussed.

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Conscious handling of uncertainty is critical in the assessment of chemicals, where the demand of knowledge is high compared to the available amount of resources. Consequently weighing between the use of resources and the acceptance of uncertainty has to be a transparent process and this sets up a challenge in relation to the application of mathematical methods. The maximum entropy principle is a useful paradigm for setting up prior information that is as non-informative as possible. It is shown how the use of partial ordering for deriving ranking results can be considered as a maximum entropy strategy in relation to the model structure uncertainty. In this way it seems possible to solve central parts of the problem of resource optimising risk assessment by combining the precautionary principle and screening before more detailed risk assessment is undertaken. The non-parametric approach of partial order technique further appears to be more transparent than most other ranking methodologies for complex systems. Because in the partial order techniques, no more or less known uncertainty will be hidden in the application of specific weighting factors and functional relationships among the criteria.

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The development of Quantitative Structure Activity Relationships (QSAR's) often relies heavily on the application of statistical methods such as multi-linear regression (MLR) or principal component analysis/partial least square (PCA/PLS). Partial order ranking (POR), which from a mathematical point of view is based on elementary methods of Discrete Mathematics, appears as an attractive and operationally simple and more general alternative since the method does not require specific functional relationships between the single descriptors or the end-points. The POR method allows ranking of a series of compounds, based on selected descriptors characterizing their structural and/or electronic nature (model diagram). The ranking of the compounds based on their end-points (experimental ranking) can then be compared to the model diagram. If the model diagram resembles the experimental ranking of the end-points under investigation, other compounds, not being experimentally investigated, can be assigned a rank in the model and hereby obtain an identity based on the known compounds. The present study elucidates the applicability of POR as a simple tool for QSAR modeling. Based on illustrative examples the POR approach to QSAR modeling will be presented with special focus on the precision and the uncertainties of the method, which will be discussed in terms of the number of descriptors and compounds involved. The advantageous interplay between POR and PCA, the latter being applied in order to reduce a possible large number of descriptors into a limited number of latent descriptors will be discussed.

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Quantitative structure-activity relationships are often based on standard multidimensional statistical analyses and sophisticated local and global molecular descriptors. Here, the aim is to develop a tool helpful to define a molecule or a class of molecules which fulfills pre-described properties, i.e., an Inverse QSAR approach. If highly sophisticated descriptors are used in QSAR, the structure and then the synthesis recipe may be hard to derive. Thus, descriptors, from which the synthesis recipe can be easily derived, seem appropriate to be included within this study. However, if descriptors simple enough to be useful for defining syntheses recipes of chemicals were used, the accuracy of a numeric expression may fail. This paper suggests a method, based on very simple elements of the theory of partially ordered sets, to find a qualitative basis for the relationship between such fairly simple descriptors on the one side and a series of ecotoxicological properties, on the other side. The partial order ranking method assumes neither linearity nor certain statistical distribution properties. Therefore the method may be more general compared to many standard statistical techniques. A series of chlorinated aliphatic compounds has been used as an illustrative example and a comparison with more sophisticated descriptors derived from quantum chemistry and graph theory is given. Among the results, it was disclosed that only for algae lethal concentration, as one of the four ecotoxicological properties, the synthesis specific predictors seem to be good estimators. For all other ecotoxicological properties quantum chemical descriptors appear as the more suitable estimators.

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Partial order ranking appears as an attractive alternative to conventional Quantitative Structure Activity Relationships (QSAR) methods, the latter typically relying on the application of statistical methods. The method seems attractive as a priori knowledge of specific functional relationships is not required. In the present study, it is demonstrated that QSAR models based on a partial order ranking approach can be used satisfactorily to predict solubilities and octanol-water partitioning for a selection of organic compounds exhibiting different structural and electronic characteristics. The uncertainty is validated using well-established LSER descriptors. Two requirements to the model with regard to precision prevail, i.e., the model must be able to rank the single compounds in the basis set correctly compared to the experimental data, and the model should be based on a basis set of compounds large enough to secure a satisfactorily fine-meshed net, taking the number of descriptors into account. In the present study, the model was able to rank 318 out of 319 comparisons correctly in the case of solubilities. The corresponding figures for the octanol-water partitioning were 407 out of 408. The precision and the uncertainties of the method which, were found closely related to the mutual interplay between the number of compounds and the number of descriptors is discussed in terms of the number of descriptors and compounds involved. The limitations of the method are discussed.

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The method of partial order ranking has been used within the environmental area for a variety of purposes as an attractive way of handling complex information. However, the environmental data are often associated with a significant degree of uncertainty. In this investigation the general nature of the influence from data uncertainty on the partial order ranking is analyzed. A Monte Carlo type analysis is performed in which a series of randomly formed data are used to test the influence of data uncertainty. The partial order ranking is interpreted, where the results are transferred to a one-dimensional ranking scale taking into account that not all elements are ranked with the same certainty. A simple general robustness parameter (E) in form of the expected number of comparisons for each ranking element is defined and correlated to the uncertainty analysis results. A simple equation relates E to the number of elements and the number of parameters, respectively. The magnitude of the ranking uncertainty is shown to increase rapidly when the E value decreases below 4-5 comparisons per element. When the E value exceeds 5 the ranking uncertainty becomes nearly constant and independent on the actual E value.

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The helper T lymphocyte precursor (HTLp) assay is of value for predicting graft-vs.-host disease after allogeneic bone marrow transplantation. The assay is based on limiting dilution analysis and requires detection of the amount of interleukin 2 (IL-2) produced by one activated T cell. IL-2 is detected by 3H-thymidine (3H-TdR) incorporation into the IL-2 dependent cell line, CTLL-2. Detection of IL-2 in the whole culture volume of the wells in the microtiter plates increases sensitivity, but requires elimination of 3H-TdR incorporation into the responder cells in the HTLp assay. We have compared the ability of X-radiation and ultraviolet B (UV-B) radiation to eliminate 3H-TdR incorporation. X-irradiation of the cells reduced 3H-TdR incorporation by 90% with doses up to 400 Gy, but the incorporation was still 10 times higher than in wells with stimulator cells only. UV-B irradiation (with Philips TL 12 tubes) of the cells with > or = 2 J/cm2 decreased 3H-TdR incorporation to the level of wells with stimulator cells only. Neither X-irradiation nor UV-B irradiation of the cultures affected IL-2 produced in the assay or human recombinant IL-2 measured by 3H-TdR incorporation into the IL-2 dependent cell line, CTLL-2. HTLp frequencies determined after 25 Gy X-irradiation were higher (mean 1.5, range 1.02-2.2 times higher) than HTLp frequencies determined after UV-B irradiation with 2 J/cm2.

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We have compared the effect of tangential and direct irradiation on regional lung function in 22 consecutive patients with breast cancer, who had been treated by post-operative irradiation 3 months prior to the examination. The tangential technique (total dose 32-36 Gy, less than 5 Gy of the lung) was employed in eight of the patients, while the direct technique (total dose 40 Gy, 5-20 Gy to the lung) was used in the other 14. In the group that had been treated by the tangential technique, there was no impairment of the ventilation or perfusion of the irradiated lung compared with the contralateral lung. The radiographs of the thorax were all normal. In the group that had been treated by the direct technique, there was a reduction in both the ventilation and the perfusion in 12 of the patients (p less than 0.01). The radiographs of the thorax were abnormal in only seven of these 12 patients. In both groups the regional leakiness of the lungs was evaluated as the pulmonary clearance of inhaled nebulised 99Tcm-DTPA. Due to differences in smoking habits between and within the groups, the results were not conclusive. It is concluded that the regional lung function was not significantly affected by the tangential technique, in contrast to a pronounced and harmful effect of the direct technique.

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