Studies of the renal effects of angiotensin II receptor blockade: the confounding factor of acute water loading on the action of vasoactive systems.

The acute renal tubular effects of two pharmacologically distinct angiotensin II receptor antagonists have been evaluated in normotensive volunteers on various salt diets. In the first study, the renal response to a single oral dose of losartan (100 mg) was assessed in subjects on a low (50 mmol Na/d) and on a high (200 mmol Na/d) salt intake. In a second protocol, the renal effects of 50 mg irbesartan were investigated in subjects receiving a 100 mmol Na/d diet. Both angiotensin II antagonists induced a significant increase in urinary sodium excretion. With losartan, a modest, transient increase in urinary potassium and a significant increase in uric acid excretion were found. In contrast, no change in potassium and uric acid excretions were observed with irbesartan, suggesting that the effects of losartan on potassium and uric acid are due to the intrinsic pharmacologic properties of losartan rather than to the specific blockade of renal angiotensin II receptors. Assessment of segmental sodium reabsorption using lithium as a marker of proximal tubular reabsorption demonstrated a decreased distal reabsorption of sodium with both antagonists. A direct proximal tubular natriuretic effect of the angiotensin II antagonist could be demonstrated only with irbesartan. This apparent discrepancy allowed us to reveal the importance of acute water loading as a possible confounding factor in renal studies. The results of the present analysis show that acute water loading per se may enhance renal sodium excretion and hence modify the level of activity of the renin-angiotensin system expected from a given sodium diet. Since acute water loading is a common practice in clinical renal studies, this confounding factor should be taken into account when investigating the renal effects of vasoactive systems.

hMMP9 as predictive factor for response and progression free survival in breast cancer patients treated with bevacizumab and pegylated liposomal doxorubicin (PLD)

Background: The anti-angiogenic drug, bevacizumab (Bv), is currently used in the treatment of different malignancies including breast cancer. Many angiogenesis-associated molecules are found in the circulation of cancer patients. Until now, there are no prognostic or predictive factors identified in breast cancer patients treated with Bv. We present here the first results of the prospective monitoring of 6 angiogenesis-related molecules in the peripheral blood of breast cancer patients treated with a combination of Bv and PLD in the phase II trial, SAKK 24/06. Methods: Patients were treated with PLD (20 mg/m2) and Bv (10 mg/kg) on days 1 and 15 of each 4-week cycle for a maximum of 6 cycles, followed by Bv monotherapy maintenance (10 mg/m2 q2 weeks) until progression or severe toxicity. Plasma and serum samples were collected at baseline, after 2 months of therapy, then every 3 months and at treatment discontinuation. Enzyme-linked immunosorbent assays (Quantikine, R&D Systems and Reliatech) were used to measure the expression levels of human vascular endothelial growth factor (hVEGF), placental growth factor (hPlGF), matrix metalloproteinase 9 (hMMP9) and soluble VEGF receptors hsVEGFR-1, hsVEGFR-2 and hsVEGFR-3. The log-transformed data (to reduce the skewness) for each marker was analyzed using an analysis of variance (ANOVA) model to determine if there was a difference between the mean of the subgroups of interest (where α = 0.05). The untransformed data was also analyzed in the same manner as a "sensitivity" check. Results: 132 blood samples were collected in 41 out of 43 enrolled patients. Baseline levels of the molecules were compared to disease status according to RECIST. There was a statistically significant difference in the mean of the log-transformed levels of hMMP9 between responders [CR+PR] versus the mean in patients with PD (p-value=0.0004, log fold change=0.7536), and between patients with disease control [CR+PR+SD] and those with PD (p-value=<0.0001, log fold change=0.81559), with the log-transformed level of hMMP9 being higher for the responder group. The mean of the log-transformed levels of hsVEGFR-1 was statistically significantly different between patients with disease control [CR+PR+SD] and those with PD (p-value=0.0068, log fold change=-0.6089), where the log-transformed level of hsVEGFR-1 was lower for the responder group. The log-transformed level of hMMP9 at baseline was identified as a significant prognostic factor in terms of progression free survival (PFS): p-value=0.0417, hazard ratio (HR)=0.574 with a corresponding 95% confidence interval (0.336 - 0.979)). No strong correlation was shown either between the log-transformed levels of hsVEGF, hPlGF, hsVEGFR-2 or hsVEGFR-3 and clinical response or the occurrence of severe toxicity, or between the levels of the different molecules. Conclusions: Our results suggest that baseline plasma level of the matrix metalloproteinase, hMMP9, could predict tumor response and PFS in patients treated with a combination of Bv and PLD. These data justify further investigation in breast cancer patients treated with anti-angiogenic therapy.

Analysis of variance of primary data on plant growth analysis

Plant growth analysis presents difficulties related to statistical comparison of growth rates, and the analysis of variance of primary data could guide the interpretation of results. The objective of this work was to evaluate the analysis of variance of data from distinct harvests of an experiment, focusing especially on the homogeneity of variances and the choice of an adequate ANOVA model. Data from five experiments covering different crops and growth conditions were used. From the total number of variables, 19% were originally homoscedastic, 60% became homoscedastic after logarithmic transformation, and 21% remained heteroscedastic after transformation. Data transformation did not affect the F test in one experiment, whereas in the other experiments transformation modified the F test usually reducing the number of significant effects. Even when transformation has not altered the F test, mean comparisons led to divergent interpretations. The mixed ANOVA model, considering harvest as a random effect, reduced the number of significant effects of every factor which had the F test modified by this model. Examples illustrated that analysis of variance of primary variables provides a tool for identifying significant differences in growth rates. The analysis of variance imposes restrictions to experimental design thereby eliminating some advantages of the functional growth analysis.

Independent Central Banks: Low inflation at no cost?: A model with fiscal policy

In this article we extend the rational partisan model of Alesina and Gatti (1995) to include a second policy, fiscal policy, besides monetary policy. It is shown that, with this extension, the politically induced variance of output is not always eliminated nor reduced by delegating monetary policy to an independent and conservative central bank. Further, in flation and output stabilisation will be affected by the degree of conservativeness of the central bank and by the probability of the less in flation averse party gaining power. Keywords: rational partisan theory; fiscal policy; independent central bank JEL Classi fication: E58, E63.

Structural macro factors and the affine term structure of interest rates

This work consists of three essays investigating the ability of structural macroeconomic models to price zero coupon U.S. government bonds. 1. A small scale 3 factor DSGE model implying constant term premium is able to provide reasonable a fit for the term structure only at the expense of the persistence parameters of the structural shocks. The test of the structural model against one that has constant but unrestricted prices of risk parameters shows that the exogenous prices of risk-model is only weakly preferred. We provide an MLE based variance-covariance matrix of the Metropolis Proposal Density that improves convergence speeds in MCMC chains. 2. Affine in observable macro-variables, prices of risk specification is excessively flexible and provides term-structure fit without significantly altering the structural parameters. The exogenous component of the SDF is separating the macro part of the model from the term structure and the good term structure fit has as a driving force an extremely volatile SDF and an implied average short rate that is inexplicable. We conclude that the no arbitrage restrictions do not suffice to temper the SDF, thus there is need for more restrictions. We introduce a penalty-function methodology that proves useful in showing that affine prices of risk specifications are able to reconcile stable macro-dynamics with good term structure fit and a plausible SDF. 3. The level factor is reproduced most importantly by the preference shock to which it is strongly and positively related but technology and monetary shocks, with negative loadings, are also contributing to its replication. The slope factor is only related to the monetary policy shocks and it is poorly explained. We find that there are gains in in- and out-of-sample forecast of consumption and inflation if term structure information is used in a time varying hybrid prices of risk setting. In-sample yield forecast are better in models with non-stationary shocks for the period 1982-1988. After this period, time varying market price of risk models provide better in-sample forecasts. For the period 2005-2008, out of sample forecast of consumption and inflation are better if term structure information is incorporated in the DSGE model but yields are better forecasted by a pure macro DSGE model.

Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data?

We propose a multivariate approach to the study of geographic species distribution which does not require absence data. Building on Hutchinson's concept of the ecological niche, this factor analysis compares, in the multidimensional space of ecological variables, the distribution of the localities where the focal species was observed to a reference set describing the whole study area. The first factor extracted maximizes the marginality of the focal species, defined as the ecological distance between the species optimum and the mean habitat within the reference area. The other factors maximize the specialization of this focal species, defined as the ratio of the ecological variance in mean habitat to that observed for the focal species. Eigenvectors and eigenvalues are readily interpreted and can be used to build habitat-suitability maps. This approach is recommended in Situations where absence data are not available (many data banks), unreliable (most cryptic or rare species), or meaningless (invaders). We provide an illustration and validation of the method for the alpine ibex, a species reintroduced in Switzerland which presumably has not yet recolonized its entire range.

Hour of birth as a prognostic factor for perinatal death.

The analysis of the 220,540 births and 2152 perinatal deaths recorded in Switzerland between 1979 and 1981 showed a variation of perinatal mortality rates (PMR) according to the hour of birth. The PMR for babies born between 4 pm and 2 am was 12 per 1000, contrasting with a figure of 8.4 per 1000 for babies born between 2 am and 4 pm. This pattern, which was fairly constant throughout the week, was characterised by a slow and steady increase from the very early morning, reaching a maximum in the late evening. There was also an hour-to-hour variation in the proportion of babies born weighing less than 2500 g, with a maximum in the evening and a less pronounced peak in the morning: the mortality rates by birthweight were raised only in the evening. Since the availability of hospital staff and equipment also follows a circadian rhythm, the variation in PMR may be related to a circadian rhythm of quality of care or possibly to chronobiological or selection factors.

Three essays on the determinants of output, inflation and interest rates

Résumé: Output, inflation and interest rates are key macroeconomic variables, in particular for monetary policy. In modern macroeconomic models they are driven by random shocks which feed through the economy in various ways. Models differ in the nature of shocks and their transmission mechanisms. This is the common theme underlying the three essays of this thesis. Each essay takes a different perspective on the subject: First, the thesis shows empirically how different shocks lead to different behavior of interest rates over the business cycle. For commonly analyzed shocks (technology and monetary policy errors), the patterns square with standard models. The big unknown are sources of inflation persistence. Then the thesis presents a theory of monetary policy, when the central bank can better observe structural shocks than the public. The public will then seek to infer the bank's extra knowledge from its policy actions and expectation management becomes a key factor of optimal policy. In a simple New Keynesian model, monetary policy becomes more concerned with inflation persistence than otherwise. Finally, the thesis points to the huge uncertainties involved in estimating the responses to structural shocks with permanent effects.

Energetic efficiency of an agricultural tractor in function of tire inflation pressure

The tire inflation pressure, among other factors, determines the efficiency in which a tractor can exert traction. It was studied the effect of using two tire inflation pressures, 110.4 kPa in the front and rear wheels, 124.2 kPa in the front wheel and 138 kPa in the rear wheels, the energetic efficiency of an agricultural tractor of 147 kW of engine power, in the displacement speed of 6.0 km.h-1, on track with firm surface, with the tractor engine speed of 2000 rpm. For each condition of the tire pressure, the tested tractor was subjected to constant forces in the drawbar of 45 kN and 50 kN, covering 30 meters. It was used a randomized complete block with a 2x2 factorial arrangement (tire pressure and drawbar power) with four replications, totaling 16 experimental units. Data were subjected to analysis of variance, using the Tukey test at 5% probability for comparison averages. The lowest hourly and specific fuel consumption, the lowest slippage of the wheelsets and the highest efficiency in the drawbar was obtained with the tire inflation pressure of 110.4 kPa in the front and rear tires of the tractor, highlighting that lower pressures improve energetic and operational performance of the tractor.

Transforming growth factor beta activity in urine of patients with type 2 diabetes and diabetic nephropathy

Diabetic nephropathy (DN) is characterized structurally by progressive mesangial deposition of extracellular matrix (ECM). Transforming growth factor-ß (TGF-ß) is considered to be one of the major cytokines involved in the regulation of ECM synthesis and degradation. Several studies suggest that an increase in urinary TGF-ß levels may reflect an enhanced production of this polypeptide by the kidney cells. We evaluated TGF-ß in occasional urine samples from 14 normal individuals and 23 patients with type 2 diabetes (13 with persistent proteinuria >500 mg/24 h, DN, 6 with microalbuminuria, DMMA, and 4 with normal urinary albumin excretion, DMN) by enzyme immunoassay. An increase in the rate of urinary TGF-ß excretion (pg/mg UCreat.) was observed in patients with DN (296.07 ± 330.77) (P<0.001) compared to normal individuals (17.04 ± 18.56) (Kruskal-Wallis nonparametric analysis of variance); however, this increase was not observed in patients with DMMA (25.13 ± 11.30) or in DMN (18.16 ± 11.82). There was a positive correlation between the rate of urinary TGF-ß excretion and proteinuria (r = 0.70, a = 0.05) (Pearson's analysis), one of the parameters of disease progression.

Social class as a factor for the construction of a musician’s identity : The Musician’s social background and identity questionnaire (MSBIQ) – a cross-sectional comparative quantitative online survey research study of music students at two higher education institutions in Finland

There is currently little empirical knowledge regarding the construction of a musician’s identity and social class. With a theoretical framework based on Bourdieu’s (1984) distinction theory, Bronfenbrenner’s (1979) theory of ecological systems, and the identity theories of Erikson (1950; 1968) and Marcia (1966), a survey called the Musician’s Social Background and Identity Questionnaire (MSBIQ) is developed to test three research hypotheses related to the construction of a musician’s identity, social class and ecological systems of development. The MSBIQ is administered to the music students at Sibelius Academy of the University of Arts Helsinki and Helsinki Metropolia University of Applied Sciences, representing the ’highbrow’ and the ’middlebrow’ samples in the field of music education in Finland. Acquired responses (N = 253) are analyzed and compared with quantitative methods including Pearson’s chi-square test, factor analysis and an adjusted analysis of variance (ANOVA). The study revealed that (1) the music students at Sibelius Academy and Metropolia construct their subjective musician’s identity differently, but (2) social class does not affect this identity construction process significantly. In turn, (3) the ecological systems of development, especially the individual’s residential location, do significantly affect the construction of a musician’s identity, as well as the age at which one starts to play one’s first musical instrument. Furthermore, a novel finding related to the structure of a musician’s identity was the tripartite model of musical identity consisting of the three dimensions of a musician’s identity: (I) ’the subjective dimension of a musician’s identity’, (II) ’the occupational dimension of a musician’s identity’ and, (III) ’the conservative-liberal dimension of a musician’s identity’. According to this finding, a musician’s identity is not a uniform, coherent entity, but a structure consisting of different elements continuously working in parallel within different dimensions. The results and limitations related to the study are discussed, as well as the objectives related to future studies using the MSBIQ to research the identity construction and social backgrounds of a musician or other performing artists.

The Inflation Bias When the Central Bank Targets, the Natural Rate of Unemployment

This paper studies the proposition that an inflation bias can arise in a setup where a central banker with asymmetric preferences targets the natural unemployment rate. Preferences are asymmetric in the sense that positive unemployment deviations from the natural rate are weighted more (or less) severely than negative deviations in the central banker's loss function. The bias is proportional to the conditional variance of unemployment. The time-series predictions of the model are evaluated using data from G7 countries. Econometric estimates support the prediction that the conditional variance of unemployment and the rate of inflation are positively related.

Factor models, VARMA processes and parameter instability with applications in macroeconomics

Avec les avancements de la technologie de l'information, les données temporelles économiques et financières sont de plus en plus disponibles. Par contre, si les techniques standard de l'analyse des séries temporelles sont utilisées, une grande quantité d'information est accompagnée du problème de dimensionnalité. Puisque la majorité des séries d'intérêt sont hautement corrélées, leur dimension peut être réduite en utilisant l'analyse factorielle. Cette technique est de plus en plus populaire en sciences économiques depuis les années 90. Étant donnée la disponibilité des données et des avancements computationnels, plusieurs nouvelles questions se posent. Quels sont les effets et la transmission des chocs structurels dans un environnement riche en données? Est-ce que l'information contenue dans un grand ensemble d'indicateurs économiques peut aider à mieux identifier les chocs de politique monétaire, à l'égard des problèmes rencontrés dans les applications utilisant des modèles standards? Peut-on identifier les chocs financiers et mesurer leurs effets sur l'économie réelle? Peut-on améliorer la méthode factorielle existante et y incorporer une autre technique de réduction de dimension comme l'analyse VARMA? Est-ce que cela produit de meilleures prévisions des grands agrégats macroéconomiques et aide au niveau de l'analyse par fonctions de réponse impulsionnelles? Finalement, est-ce qu'on peut appliquer l'analyse factorielle au niveau des paramètres aléatoires? Par exemple, est-ce qu'il existe seulement un petit nombre de sources de l'instabilité temporelle des coefficients dans les modèles macroéconomiques empiriques? Ma thèse, en utilisant l'analyse factorielle structurelle et la modélisation VARMA, répond à ces questions à travers cinq articles. Les deux premiers chapitres étudient les effets des chocs monétaire et financier dans un environnement riche en données. Le troisième article propose une nouvelle méthode en combinant les modèles à facteurs et VARMA. Cette approche est appliquée dans le quatrième article pour mesurer les effets des chocs de crédit au Canada. La contribution du dernier chapitre est d'imposer la structure à facteurs sur les paramètres variant dans le temps et de montrer qu'il existe un petit nombre de sources de cette instabilité. Le premier article analyse la transmission de la politique monétaire au Canada en utilisant le modèle vectoriel autorégressif augmenté par facteurs (FAVAR). Les études antérieures basées sur les modèles VAR ont trouvé plusieurs anomalies empiriques suite à un choc de la politique monétaire. Nous estimons le modèle FAVAR en utilisant un grand nombre de séries macroéconomiques mensuelles et trimestrielles. Nous trouvons que l'information contenue dans les facteurs est importante pour bien identifier la transmission de la politique monétaire et elle aide à corriger les anomalies empiriques standards. Finalement, le cadre d'analyse FAVAR permet d'obtenir les fonctions de réponse impulsionnelles pour tous les indicateurs dans l'ensemble de données, produisant ainsi l'analyse la plus complète à ce jour des effets de la politique monétaire au Canada. Motivée par la dernière crise économique, la recherche sur le rôle du secteur financier a repris de l'importance. Dans le deuxième article nous examinons les effets et la propagation des chocs de crédit sur l'économie réelle en utilisant un grand ensemble d'indicateurs économiques et financiers dans le cadre d'un modèle à facteurs structurel. Nous trouvons qu'un choc de crédit augmente immédiatement les diffusions de crédit (credit spreads), diminue la valeur des bons de Trésor et cause une récession. Ces chocs ont un effet important sur des mesures d'activité réelle, indices de prix, indicateurs avancés et financiers. Contrairement aux autres études, notre procédure d'identification du choc structurel ne requiert pas de restrictions temporelles entre facteurs financiers et macroéconomiques. De plus, elle donne une interprétation des facteurs sans restreindre l'estimation de ceux-ci. Dans le troisième article nous étudions la relation entre les représentations VARMA et factorielle des processus vectoriels stochastiques, et proposons une nouvelle classe de modèles VARMA augmentés par facteurs (FAVARMA). Notre point de départ est de constater qu'en général les séries multivariées et facteurs associés ne peuvent simultanément suivre un processus VAR d'ordre fini. Nous montrons que le processus dynamique des facteurs, extraits comme combinaison linéaire des variables observées, est en général un VARMA et non pas un VAR comme c'est supposé ailleurs dans la littérature. Deuxièmement, nous montrons que même si les facteurs suivent un VAR d'ordre fini, cela implique une représentation VARMA pour les séries observées. Alors, nous proposons le cadre d'analyse FAVARMA combinant ces deux méthodes de réduction du nombre de paramètres. Le modèle est appliqué dans deux exercices de prévision en utilisant des données américaines et canadiennes de Boivin, Giannoni et Stevanovic (2010, 2009) respectivement. Les résultats montrent que la partie VARMA aide à mieux prévoir les importants agrégats macroéconomiques relativement aux modèles standards. Finalement, nous estimons les effets de choc monétaire en utilisant les données et le schéma d'identification de Bernanke, Boivin et Eliasz (2005). Notre modèle FAVARMA(2,1) avec six facteurs donne les résultats cohérents et précis des effets et de la transmission monétaire aux États-Unis. Contrairement au modèle FAVAR employé dans l'étude ultérieure où 510 coefficients VAR devaient être estimés, nous produisons les résultats semblables avec seulement 84 paramètres du processus dynamique des facteurs. L'objectif du quatrième article est d'identifier et mesurer les effets des chocs de crédit au Canada dans un environnement riche en données et en utilisant le modèle FAVARMA structurel. Dans le cadre théorique de l'accélérateur financier développé par Bernanke, Gertler et Gilchrist (1999), nous approximons la prime de financement extérieur par les credit spreads. D'un côté, nous trouvons qu'une augmentation non-anticipée de la prime de financement extérieur aux États-Unis génère une récession significative et persistante au Canada, accompagnée d'une hausse immédiate des credit spreads et taux d'intérêt canadiens. La composante commune semble capturer les dimensions importantes des fluctuations cycliques de l'économie canadienne. L'analyse par décomposition de la variance révèle que ce choc de crédit a un effet important sur différents secteurs d'activité réelle, indices de prix, indicateurs avancés et credit spreads. De l'autre côté, une hausse inattendue de la prime canadienne de financement extérieur ne cause pas d'effet significatif au Canada. Nous montrons que les effets des chocs de crédit au Canada sont essentiellement causés par les conditions globales, approximées ici par le marché américain. Finalement, étant donnée la procédure d'identification des chocs structurels, nous trouvons des facteurs interprétables économiquement. Le comportement des agents et de l'environnement économiques peut varier à travers le temps (ex. changements de stratégies de la politique monétaire, volatilité de chocs) induisant de l'instabilité des paramètres dans les modèles en forme réduite. Les modèles à paramètres variant dans le temps (TVP) standards supposent traditionnellement les processus stochastiques indépendants pour tous les TVPs. Dans cet article nous montrons que le nombre de sources de variabilité temporelle des coefficients est probablement très petit, et nous produisons la première évidence empirique connue dans les modèles macroéconomiques empiriques. L'approche Factor-TVP, proposée dans Stevanovic (2010), est appliquée dans le cadre d'un modèle VAR standard avec coefficients aléatoires (TVP-VAR). Nous trouvons qu'un seul facteur explique la majorité de la variabilité des coefficients VAR, tandis que les paramètres de la volatilité des chocs varient d'une façon indépendante. Le facteur commun est positivement corrélé avec le taux de chômage. La même analyse est faite avec les données incluant la récente crise financière. La procédure suggère maintenant deux facteurs et le comportement des coefficients présente un changement important depuis 2007. Finalement, la méthode est appliquée à un modèle TVP-FAVAR. Nous trouvons que seulement 5 facteurs dynamiques gouvernent l'instabilité temporelle dans presque 700 coefficients.

A Factor analysis of hidrochemical composition of Llobregat river basin

Hydrogeological research usually includes some statistical studies devised to elucidate mean background state, characterise relationships among different hydrochemical parameters, and show the influence of human activities. These goals are achieved either by means of a statistical approach or by mixing models between end-members. Compositional data analysis has proved to be effective with the first approach, but there is no commonly accepted solution to the end-member problem in a compositional framework. We present here a possible solution based on factor analysis of compositions illustrated with a case study. We find two factors on the compositional bi-plot fitting two non-centered orthogonal axes to the most representative variables. Each one of these axes defines a subcomposition, grouping those variables that lay nearest to it. With each subcomposition a log-contrast is computed and rewritten as an equilibrium equation. These two factors can be interpreted as the isometric log-ratio coordinates (ilr) of three hidden components, that can be plotted in a ternary diagram. These hidden components might be interpreted as end-members. We have analysed 14 molarities in 31 sampling stations all along the Llobregat River and its tributaries, with a monthly measure during two years. We have obtained a bi-plot with a 57% of explained total variance, from which we have extracted two factors: factor G, reflecting geological background enhanced by potash mining; and factor A, essentially controlled by urban and/or farming wastewater. Graphical representation of these two factors allows us to identify three extreme samples, corresponding to pristine waters, potash mining influence and urban sewage influence. To confirm this, we have available analysis of diffused and widespread point sources identified in the area: springs, potash mining lixiviates, sewage, and fertilisers. Each one of these sources shows a clear link with one of the extreme samples, except fertilisers due to the heterogeneity of their composition. This approach is a useful tool to distinguish end-members, and characterise them, an issue generally difficult to solve. It is worth note that the end-member composition cannot be fully estimated but only characterised through log-ratio relationships among components. Moreover, the influence of each endmember in a given sample must be evaluated in relative terms of the other samples. These limitations are intrinsic to the relative nature of compositional data

The retrieval of ice water content from radar reflectivity factor and temperature and its use in evaluating a mesoscale model

Ice clouds are an important yet largely unvalidated component of weather forecasting and climate models, but radar offers the potential to provide the necessary data to evaluate them. First in this paper, coordinated aircraft in situ measurements and scans by a 3-GHz radar are presented, demonstrating that, for stratiform midlatitude ice clouds, radar reflectivity in the Rayleigh-scattering regime may be reliably calculated from aircraft size spectra if the "Brown and Francis" mass-size relationship is used. The comparisons spanned radar reflectivity values from -15 to +20 dBZ, ice water contents (IWCs) from 0.01 to 0.4 g m(-3), and median volumetric diameters between 0.2 and 3 mm. In mixed-phase conditions the agreement is much poorer because of the higher-density ice particles present. A large midlatitude aircraft dataset is then used to derive expressions that relate radar reflectivity and temperature to ice water content and visible extinction coefficient. The analysis is an advance over previous work in several ways: the retrievals vary smoothly with both input parameters, different relationships are derived for the common radar frequencies of 3, 35, and 94 GHz, and the problem of retrieving the long-term mean and the horizontal variance of ice cloud parameters is considered separately. It is shown that the dependence on temperature arises because of the temperature dependence of the number concentration "intercept parameter" rather than mean particle size. A comparison is presented of ice water content derived from scanning 3-GHz radar with the values held in the Met Office mesoscale forecast model, for eight precipitating cases spanning 39 h over Southern England. It is found that the model predicted mean I WC to within 10% of the observations at temperatures between -30 degrees and - 10 degrees C but tended to underestimate it by around a factor of 2 at colder temperatures.