County Snowfall Estimates, December 2007

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. The Iowa Crops and Weather report released by the USDA National Agricultural Statistical Service.

County Snowfall Estimates, January 2008

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. The Iowa Crops and Weather report released by the USDA National Agricultural Statistical Service.

County Snowfall Estimates, February 2008

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. The Iowa Crops and Weather report released by the USDA National Agricultural Statistical Service.

County Snowfall Estimates, March 2008

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. The Iowa Crops and Weather report released by the USDA National Agricultural Statistical Service.

County Snowfall Estimates, November 2008

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. The Iowa Crops and Weather report released by the USDA National Agricultural Statistical Service.

County Snowfall Estimates, November 2008

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. The Iowa Crops and Weather report released by the USDA National Agricultural Statistical Service.

County Snowfall Estimates, January 2009

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. The Iowa Crops and Weather report released by the USDA National Agricultural Statistical Service.

County Snowfall Estimates, February 2009

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. The Iowa Crops and Weather report released by the USDA National Agricultural Statistical Service.

County Snowfall Estimates, March 2009

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. The Iowa Crops and Weather report released by the USDA National Agricultural Statistical Service.

County Snowfall Estimates, April 2009

Report produced by the The Department of Agriculture and Land Stewardship, Climatology Bureau. The Iowa Crops and Weather report released by the USDA National Agricultural Statistical Service.

Reliable tidal volume estimates at the airway opening with an infant monitor during high-frequency oscillatory ventilation.

OBJECTIVE: To assess the suitability of a hot-wire anemometer infant monitoring system (Florian, Acutronic Medical Systems AG, Hirzel, Switzerland) for measuring flow and tidal volume (Vt) proximal to the endotracheal tube during high-frequency oscillatory ventilation. DESIGN: In vitro model study. SETTING: Respiratory research laboratory. SUBJECT: In vitro lung model simulating moderate to severe respiratory distress. INTERVENTION: The lung model was ventilated with a SensorMedics 3100A ventilator. Vt was recorded from the monitor display (Vt-disp) and compared with the gold standard (Vt-adiab), which was calculated using the adiabatic gas equation from pressure changes inside the model. MEASUREMENTS AND MAIN RESULTS: A range of Vt (1-10 mL), frequencies (5-15 Hz), pressure amplitudes (10-90 cm H2O), inspiratory times (30% to 50%), and Fio2 (0.21-1.0) was used. Accuracy was determined by using modified Bland-Altman plots (95% limits of agreement). An exponential decrease in Vt was observed with increasing oscillatory frequency. Mean DeltaVt-disp was 0.6 mL (limits of agreement, -1.0 to 2.1) with a linear frequency dependence. Mean DeltaVt-disp was -0.2 mL (limits of agreement, -0.5 to 0.1) with increasing pressure amplitude and -0.2 mL (limits of agreement, -0.3 to -0.1) with increasing inspiratory time. Humidity and heating did not affect error, whereas increasing Fio2 from 0.21 to 1.0 increased mean error by 6.3% (+/-2.5%). CONCLUSIONS: The Florian infant hot-wire flowmeter and monitoring system provides reliable measurements of Vt at the airway opening during high-frequency oscillatory ventilation when employed at frequencies of 8-13 Hz. The bedside application could improve monitoring of patients receiving high-frequency oscillatory ventilation, favor a better understanding of the physiologic consequences of different high-frequency oscillatory ventilation strategies, and therefore optimize treatment.

Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.

BACKGROUND: With the large amount of biological data that is currently publicly available, many investigators combine multiple data sets to increase the sample size and potentially also the power of their analyses. However, technical differences ("batch effects") as well as differences in sample composition between the data sets may significantly affect the ability to draw generalizable conclusions from such studies. FOCUS: The current study focuses on the construction of classifiers, and the use of cross-validation to estimate their performance. In particular, we investigate the impact of batch effects and differences in sample composition between batches on the accuracy of the classification performance estimate obtained via cross-validation. The focus on estimation bias is a main difference compared to previous studies, which have mostly focused on the predictive performance and how it relates to the presence of batch effects. DATA: We work on simulated data sets. To have realistic intensity distributions, we use real gene expression data as the basis for our simulation. Random samples from this expression matrix are selected and assigned to group 1 (e.g., 'control') or group 2 (e.g., 'treated'). We introduce batch effects and select some features to be differentially expressed between the two groups. We consider several scenarios for our study, most importantly different levels of confounding between groups and batch effects. METHODS: We focus on well-known classifiers: logistic regression, Support Vector Machines (SVM), k-nearest neighbors (kNN) and Random Forests (RF). Feature selection is performed with the Wilcoxon test or the lasso. Parameter tuning and feature selection, as well as the estimation of the prediction performance of each classifier, is performed within a nested cross-validation scheme. The estimated classification performance is then compared to what is obtained when applying the classifier to independent data.

Enhanced radar precipitation estimates using a combined clutter and beam blockage correction technique

Weather radar observations are currently the most reliable method for remote sensing of precipitation. However, a number of factors affect the quality of radar observations and may limit seriously automated quantitative applications of radar precipitation estimates such as those required in Numerical Weather Prediction (NWP) data assimilation or in hydrological models. In this paper, a technique to correct two different problems typically present in radar data is presented and evaluated. The aspects dealt with are non-precipitating echoes - caused either by permanent ground clutter or by anomalous propagation of the radar beam (anaprop echoes) - and also topographical beam blockage. The correction technique is based in the computation of realistic beam propagation trajectories based upon recent radiosonde observations instead of assuming standard radio propagation conditions. The correction consists of three different steps: 1) calculation of a Dynamic Elevation Map which provides the minimum clutter-free antenna elevation for each pixel within the radar coverage; 2) correction for residual anaprop, checking the vertical reflectivity gradients within the radar volume; and 3) topographical beam blockage estimation and correction using a geometric optics approach. The technique is evaluated with four case studies in the region of the Po Valley (N Italy) using a C-band Doppler radar and a network of raingauges providing hourly precipitation measurements. The case studies cover different seasons, different radio propagation conditions and also stratiform and convective precipitation type events. After applying the proposed correction, a comparison of the radar precipitation estimates with raingauges indicates a general reduction in both the root mean squared error and the fractional error variance indicating the efficiency and robustness of the procedure. Moreover, the technique presented is not computationally expensive so it seems well suited to be implemented in an operational environment.

Estimates of genetic parameters of trotting performance traits for repeated annual records

Selostus: Ravihevosten jalostettavia ominaisuuksia kuvaavien kilpailumittojen perinnölliset tunnusluvut

Physicians' estimates of the 10-year cardiovascular risk in hypertensive patients

RESUME L'objectif de cette étude est d'évaluer comment de jeunes médecins en formation perçoivent le risque cardiovasculaire de leurs patients hypertendus en se basant sur les recommandations médicales (« guidelines ») et sur leur jugement clinique. Il s'agit d'une étude transversale observationnelle effectuée à la Policlinique Médicale Universitaire de Lausanne (PMU). 200 patients hypertendus ont été inclus dans l'étude ainsi qu'un groupe contrôle de 50 patients non hypertendus présentant au moins un facteur de risque cardiovasculaire. Nous avons comparé le risque cardiovasculaire à 10 ans calculé par un programme informatique basé sur l'équation de Framingham. L'équation a été adaptée pour les médecins par l'OMS-ISH au risque perçu, estimé cliniquement par les médecins. Les résultats de notre étude ont montrés que les médecins sous-estiment le risque cardiovasculaire à 10 ans de leurs patients, comparé au risque calculé selon l'équation de Framingham. La concordance entre les deux méthodes était de 39% pour les patients hypertendus et de 30% pour le groupe contrôle de patients non hypertendus. La sous-estimation du risque. cardiovasculaire pour les patients hypertendus était corrélée au fait qu'ils avaient une tension artérielle systolique stabilisée inférieure a 140 mmHg (OR=2.1 [1.1 ;4.1]). En conclusion, les résultats de cette étude montrent que les jeunes médecins en formation ont souvent une perception incorrecte du risque cardiovasculaire de leurs patients, avec une tendance à sous-estimer ce risque. Toutefois le risque calculé pourrait aussi être légèrement surestimé lorsqu'on applique l'équation de Framingham à la population suisse. Pour mettre en pratique une évaluation systématique des facteurs de risque en médecine de premier recours, un accent plus grand devrait être mis sur l'enseignement de l'évaluation du risque cardiovasculaire ainsi que sur la mise en oeuvre de programme pour l'amélioration de la qualité.