Imagerie multimodale dans la maladie de Parkinson: PET FDOPA, IRM quantitative sensible au fer et à la neuromélanine

peer reviewedParkinson’s disease is a neurodegenerative synucleinopathy characterized by the degeneration of neuromelanin-containing dopaminergic neurons and deposition of iron in the substantia nigra (SN). How regional neuromelanin (NM) loss and iron accumulation within specific areas of SN relates to nigro-striatal dysfunction needs to be clarified. We measured dopaminergic function in pre- and post-commissural putamen by [18F]DOPA PET in twenty-three Parkinson’s disease (PD) patients and 23 healthy control (HC) participants in whom NM content and iron load was assessed in medial and lateral SN, respectively by neuromelanin-sensitive and quantitative R2* MRI. Data analysis consisted of voxelwise regressions testing the group effect and its interaction with NM or iron signals. In PD patients, R2* was selectively increased in left lateral SN as compared to healthy participants, suggesting a local accumulation of iron in Parkinson’s disease. By contrast, NM signal differed between PD and HC, without specific regional specificity within SN. Dopaminergic function in posterior putamen decreased as R2* increased in lateral SN, indicating that dopaminergic function impairment progresses with iron accumulation in the SN. Dopaminergic function was also positively correlated with NM signal in lateral SN, indicating that dopaminergic function impairment progresses with depigmentation in the SN. A complex relationship was detected between R2* in the lateral SN and NM signal in the medial substantia nigra. In conclusion, multimodal imaging reveals regionally-specific relationships between iron accumulation and depigmentation within the SN of Parkinson’s disease and provides in vivo insights in its neuropathology.Parkinson's disease multimodal imagin

Yield loss modélisation of wheat based on photosynthetic active area studies

Au Grand-Duché de Luxembourg, le développement de modèles opérationnels pour la prévisiondes rendements se heurte actuellement au problème de la non prise en compte de la diminution de lasurface verte utile et de sa relation avec des processus biotiques et abiotiques incriminés en situationde production. Pourtant, il apparaît que l’élaboration d’un modèle reliant la perte de surface verte à labaisse de rayonnement absorbé est la façon la plus adéquate d’améliorer les prévisions de perte derendement aux champs. De nombreuses manières d’estimer les rendements existent et se basent surdifférentes approches et méthodes. L’objectif de ce travail est de proposer un modèle d’estimation desrendements basé sur l’étude de la dégradation de la surface verte foliaire du blé et du rayonnementintercepté par cette surface tout au long de sa dégradation.L’approche envisagée consiste, dans un premier temps, à utiliser les deux principaux modèlesexistants qui décrivent l’interception du rayonnement par les surfaces foliaires vertes avec desdonnées issues d’expérimentations aux champs, au Grand-Duché de Luxembourg en 2006 et 2007.Différentes méthodes d’obtention des principales données d’entrée de ces modèles ont été comparéeset leurs avantages ont été mis en évidence. Ces données sont le LAI (Leaf Area Index) et lepourcentage de surface foliaire verte. Un LAI de référence, obtenu à l’aide d’une méthode d’analysed’images de feuilles a été validé et comparé à une méthode d’obtention du LAI basée sur la mesuredu rayonnement intercepté par le couvert ainsi qu’à une méthode fournissant la couverture verte dusol à partir d’images aériennes de courte distance. Il a été montré que le LAI issu de la mesure durayonnement intercepté et la couverture verte du sol sont obtenus plus rapidement et pour de plusgrandes surfaces, mais qu’ils ne sont pas suffisamment corrélés au LAI de référence pour être utilisésafin d’obtenir le LAI réel. Le pourcentage de la surface foliaire verte de référence a également étéobtenu à l’aide de la méthode d’analyse d’images de feuilles. La comparaison de celui-ci auxestimations visuelles du pourcentage de surface verte foliaire a montré que cette méthode est plusrapide, mais engendre une surestimation du pourcentage de surface foliaire verte. Une relationlinéaire significative entre la couverture verte du sol par prise d’images aériennes et le pourcentage desurface foliaire verte a été obtenue. Une amélioration de la prise d’images aériennes de courtedistance pourrait mener à une substitution du pourcentage de surface foliaire verte par la couvertureverte du sol sur de grandes surfaces à l’avenir.Les deux principaux modèles décrivant l’interception du rayonnement par les surfaces foliairesvertes ont été utilisés avec le LAI et le pourcentage de surface foliaire verte de référence. Unesimplification de ces modèles par l’utilisation de la dernière ou des deux dernières strates foliaires à laplace des trois dernières pour le pourcentage de surface verte a montré que la simplification ne menaitpas à une amélioration des résultats dans la plupart des cas. D’autre part, une estimation des biaisintroduits en utilisant les pourcentages de surface verte issus de l’estimation visuelle à la place desestimations par l’analyse d’images montre que l’estimation visuelle introduit un biais allant jusqu’à20%. La comparaison des deux modèles testés a mené à la sélection du modèle aux sorties fournissantla meilleure relation avec les rendements. C’est une relation linéaire simple entre les paramètres de lacourbe décrivant l’évolution des sorties du modèle dit du « calcul de la matière sèche » au cours de lasaison de culture et le rendement qui a été retenue.Dans un deuxième temps, le modèle sélectionné a été utilisé avec des données issuesd’expérimentations menées de l’année 2000 à 2005, afin d’obtenir une relation linéaire plus stableentre les rendements et les sorties de ce modèle. La relation obtenue montre des résultats significatifset expliquant plus de 66% des rendements si une variété au comportement atypique est exclue. Uneffet significatif de l’année, du précédent et de la variété sur cette relation a été mis en évidence.Dans un troisième temps, l’aspect prédictif du modèle d’estimation des rendements basé sur larelation linéaire simple retenue a été étudié sur deux années de données extérieures aux annéesutilisées pour la construction de celui-ci. Les données d’entrée nécessaires au fonctionnement dumodèle ont dû être obtenues de manière prédictive, afin de réaliser des estimations du rendement àvenir à partir de la floraison. Le modèle Proculture, basé sur la simulation de l’évolution dessymptômes de la septoriose, a permis d’obtenir des estimations en prévision des pourcentages desurface verte, et le LAI a été considéré comme constant par variété d’une année à l’autre. Le modèled’estimation utilisé a permis d’obtenir des prévisions de rendement ~20% supérieures aux rendementsréels./In the Grand Duchy of Luxembourg, the development of operational models for predictingyields currently runs against the failure to take into account the green leaf area decline and itsrelationship with biotic and non biotic processes involved in a situation of production. Yet itappears that the development of a model linking the loss of green leaf area to lower radiationabsorbed is the most adequate to improve prediction of yield loss in the fields. Many ways toestimate yields exist and are based on different approaches and methods. The objective ofthis work is to propose a model for estimating yields based on the study of the green leaf areadecline of wheat and radiation intercepted by this area throughout the season.The approach is, first, to use the two main existing models that describe the interception ofradiation by green leaf area with data from experiments in the field, in the Grand Duchy ofLuxembourg in 2006 and 2007. Different methods for obtaining key data entry of thesemodels were compared and their benefits have been identified. These data are LAI (LeafArea Index) and the percentage of green leaf area. The reference method, obtained usingimage analysis of leaves has been validated and compared to a method for obtaining LAIbased on the measurement of radiation intercepted by the canopy as well as a method basedon the green cover soil obtained from short distance aerial images. It was shown that the LAIobtained from the measurement of radiation intercepted and the green land cover obtainedfrom short distance aerial images are obtained faster and for larger surfaces, but they are notsufficiently correlated with the LAI from the reference method to be used in place ofreference LAI. The percentage of green leaf area of reference has also been obtained usingthe image analysis of leaves. Comparing it to visual estimates of the percentage of green leafarea has shown that this method is faster and creates an overestimation of the percentage ofgreen leaf area. A significant linear relationship between green land cover from shortdistance aerial images analysis and the percentage of green leaf area was obtained. Animproved short distance aerial image could lead to the substitution of the percentage of greenleaf area by the green land cover over large areas in the future. The two main modelsdescribing the interception of radiation by green leaf area were used with the LAI and thepercentage of green leaf area of reference. A simplification of these models by using only theupper leaf or the two last leaves to emerge in place of the last three leaves to emerge for thepercentage of green area has shown that simplification did not lead to improved results inmost cases. On the other hand, an estimate of bias using the percentage of green leaf areafrom the visual estimate in place of estimates by image analysis shows that visual estimateintroduce an approximate bias of 20%. A comparison of the two models tested led to theselection of the model outputs providing the best relationship with yields. It is a simple linearrelationship between parameters of the curve describing the evolution of model outputs socalled“calculation of dry matter” during the growing season and yield that was chosen.In a second time, the selected model was used with data from experiments conducted from2000 to 2005 to obtain a more stable linear relationship between yields and output of themodel. The relationship obtained shows significant results and explains over 66% yields ifdatas from an atypical variety are excluded. A significant effect of years, precedent andvariety on this relationship was highlighted.In a third time, the predictive aspect of the model to estimate yields based on the simplelinear relationship has been studied on two years of external data used for years to build it.The input data needed to run the model had to be obtained on a predictive way to makeestimates of future performance from flowering. The model Proculture, based on thesimulation of the progression of septoriose disease, allowed obtaining estimates inanticipation of the percentage of green area, and LAI was considered constant variety fromone year to another. The estimation model used resulted in expected future performance ~20% higher than actual yields

Disease Severity Estimates – Effects of Rater Accuracy and Assessment Methods for Comparing Treatments

Assessment of disease severity is required for several purposes in plant pathology; most often the estimates are made visually. It is established that visual estimates can be inaccurate and unreliable. The ramifications of biased or imprecise estimates by raters have not been fully explored using empirical data; partly because of the logistical difficulties involved in different raters assessing the same leaves for which actual disease has been measured in a replicated experiment with multiple treatments. In this study nearest percent estimates (NPEs) of Septoria leaf blotch (SLB) on leaves of winter wheat from non-treated and fungicide treated plots were assessed in both 2006 and 2007 by four raters and compared to assumed true values measured using image analysis. Lin’s concordance correlation (LCC, ρc) was used to assess agreement between the two approaches. NPEs were converted to Horsfall-Barratt (HB) mid-points and again compared for agreement with true values. The estimates of SLB severity from fungicide-treated and non-treated plots were analyzed using generalized linear mixed modeling to ascertain effects of rater using both the NPE and HB values. Rater 1 showed good agreement with image analysis (ρc = 0.986 to 0.999), while raters 3 and 4 had less good agreement (ρc = 0.205 to 0.936). Conversion to the HB scale had little effect on bias or accuracy, but reduced both precision and agreement for most raters on most assessment dates (precision, r = -0.001 to -0.132; and agreement, ρc = -0.003 to -0.468). Inter-rater reliability was also reduced slightly by conversion of estimates to HB midpoint values. Estimates of mean SLB severity were significantly different between image analysis and raters 2, 3 and 4, and there were frequently significant differences among raters (F=151 to 1260, P=0.001 to <0.0001). Conversion to the HB scale changed the means separation ranking of rater estimates on 26 June 2007. Nonetheless, image analysis and all raters were able to differentiate control and treated plots treatments (F=116 to 1952, P=0.002 to <0.0001, depending on date and rater). Conversion of NPEs to the HB scale tended to reduce F-values slightly (2006: NPEs, F=116 to 276, P=0.002 to 0.0005, and for the HB converted values F=101 to 270, P=0.002 to 0.0005, and in 2007, NPEs, F=164 to 1952 P=0.001 to <0.0001, and for HB converted values F=126 to 1633 P=0.002 to <0.0001). The results demonstrated the need for accurate and reliable disease assessment to minimize over or underestimates compared to actual disease, and where multiple raters are deployed, they should be assigned in a manner to reduce any potential effect of rater differences on the analysis

Disease severity assessment in epidemiological studies: accuracy and reliability of visual estimates of Septoria leaf blotch (SLB) in winter wheat

Several methods are available to measure the severity of Septoria leaf blotch (SLB) of winter wheat but differ in accuracy, reliability, ease of use and rapidity. The accuracy and reliability of visual assessments of SLB severity by raters was determined by comparison with assumed actual values obtained by digital image analysis. Raters included one plant pathologist with extensive experience of visual disease assessment, and three other raters who were trained prior to field observations using standard area diagrams and the software DISTRAIN. Initially analyses were performed using SLB severity over the full 0-100% range; subsequently, to explore error over short ranges of the 0-100% scale, the scale was divided into sequential 10%-increments (i.e., 0-10%, 10-20%,…90-100%) based on the actual values. Lin’s concordance correlation (LCC) analysis demonstrated that all raters were accurate when compared over the whole severity range (LCC coefficient (ρc) = 0.92-0.99). However, agreement between actual SLB severities and the estimates by raters was less good when compared over the short intervals of the 10×10% classes (ρc = -0.12-0.99, depending on the percentage class and the experiment), demonstrating that agreement will vary depending on the actual disease range over which it is compared. Inter-rater reliability over the full 0-100% range measured using correlation analysis was high between each pair of raters (r = 0.970 to 0.992, P<0.0001), which was confirmed by the inter-class correlation coefficient (ρ ≥ 0.927). This study provides new insight into using a full range of actual disease severity versus limited ranges to ensure a realistic measure of rater accuracy and reliability, in addition to contributing to the ongoing debate on the use of visual disease estimates based on the 0-100% ratio scale for epidemiological research

A comparison of raters and disease assessment methods for estimating disease severity for purposes of hypothesis testing.

Assessment of disease severity is most often made visually, and estimates can be inaccurate. Nearest percent estimates (NPEs) of Septoria leaf blotch on leaves of winter wheat by four raters (R1-R4) assessing non-treated (NT) and fungicide-treated (FT) plots were compared to true values using Lin’s concordance correlation coefficient (ρc) on two dates in 2006 and 2007. Estimates were converted to Horsfall-Barratt (HB) mid-points and again compared for accuracy and precision. Estimates of severity from FT and NT plots were analyzed to ascertain effects of rater using both the NPE and HB values. Regardless of method, all raters showed a range of agreement with true values on FT and NT plots (ρc = 0 to 1). Use of the HB scale most often reduced agreement (84.4% of the time), and did not improve rater-associated bias of treatment mean severity estimates. Consequently, estimates of mean severity differed significantly among raters and from true values (F=126 to 1260, P=0.002 to<0.0001). However, a comparison of treatment effects showed that the true values and R1 to R4 all demonstrated significant effects of fungicide (F=101 to 1952, P=0.002 to <0.00001). Ranking of raters differed on one occasion when HB values were used. These results demonstrate the effect of the HB scale, and the need for accurate disease assessment to minimize over or underestimates compared to true severity so as to minimize the potential for type II errors

A comparison between visual estimates and image analysis measurements to determine Septoria leaf blotch severity in winter wheat

Methods to estimate disease severity vary in accuracy, reliability, ease of use and cost. Severity of Septoria leaf blotch (SLB, caused by Zymoseptoria graminicola) was estimated by four raters and by image analysis (assumed actual values) on individual leaves of winter wheat in order to explore accuracy and reliability of estimates, and to ascertain whether there were any general characteristics of error. Specifically, (i) we determined the accuracy and reliability of visual assessments of SLB over the full range of severity from 0 to 100%, and we investigated (ii) whether certain 10% ranges in actual disease severity between 0 and 100% were more prone to estimation error compared with others, and (iii) whether leaf position affected accuracy within those ranges. Lin's concordance correlation analysis of all severities (0 to 100%) demonstrated that all raters had estimates close to the actual values (agreement: ρc = 0.92-0.99). However, agreement between actual SLB severities and estimates by raters was less good when compared over short 10% subdivisions within the 0-100% range (ρc = -0.12 to 0.99). Despite common rater imprecision at estimating low and high SLB severities, individual raters differed considerably in their accuracy over the short 10% subdivisions. There was no effect of leaf position on accuracy or precision of severity estimate on separate leaves (L1-L3). Pursuing efforts in understanding error in disease estimation should aid in improving the accuracy of assessments, making visual estimates of disease severity more useful for research and applied purposes