A Comparison of Agricultural Productivity in the European Union Regions

This paper is concerned with the estimation of productivity and technical progress based on DEA applied to complete panel data (intertemporal-DEA). Instead of assuming unchanged technology, this paper presents a formulation of technical change that allows the decomposition of productivity scores obtained using intertemporal-DEA. The assumption here is that the technology level in period t for each country is the maximum productivity index obtained until this period. The model assumes that improvements over earlier productivity levels are due to technical progress and that productivity scores below the earlier maximum productivity level are due to inefficiency. The methodology is applied to the analysis of agricultural productivity in the European Union regions in the 1985-97 period. The major source of data is Cronos in Eurostat. This database is used to obtain the disaggregated outputs, intermediate inputs, and depreciation, in current and constant 1990 prices, and labor in annual work units. Capital is measured by depreciation. Land is agricultural area in hectares. Outputs are aggregated in two categories: crops and animal products. Intermediate inputs are grouped into two major categories: feedstuffs and other materials. Aggregation uses national price indices and regional production structures, using the translog price formula. All output, intermediate input and depreciation data, originally reported in local currencies was converted into ECUs, using the 1990 exchange rates. The discriminatory power of the analysis is higher than those with only contemporary analysis of technical efficiency, giving less than 10% of observations in the reference set. Further discrimination is explored using super-efficiency analysis. Radial efficiency measures give only a particular form of inefficiency that can be explained by a proportional contraction in input usage. The paper studies particular output and input efficiencies. As examples, animal products inefficiency is usual only in southern regions. Inefficiency in intermediate consumption usage is pervasive, suggesting the possibility of reducing agricultural production costs. Labor and capital inefficiencies arise in different regions. Land slacks are common in the southern and the westernmost regions.

A Comparison of Agricultural Productivity in the European Union Regions

This paper is concerned with the estimation of productivity and technical progress based on DEA applied to complete panel data (intertemporal-DEA). Instead of assuming unchanged technology, this paper presents a formulation of technical change that allows the decomposition of productivity scores obtained using intertemporal-DEA. The assumption here is that the technology level in period t for each country is the maximum productivity index obtained until this period. The model assumes that improvements over earlier productivity levels are due to technical progress and that productivity scores below the earlier maximum productivity level are due to inefficiency. The methodology is applied to the analysis of agricultural productivity in the European Union regions in the 1985-97 period. The major source of data is Cronos in Eurostat. This database is used to obtain the disaggregated outputs, intermediate inputs, and depreciation, in current and constant 1990 prices, and labor in annual work units. Capital is measured by depreciation. Land is agricultural area in hectares. Outputs are aggregated in two categories: crops and animal products. Intermediate inputs are grouped into two major categories: feedstuffs and other materials. Aggregation uses national price indices and regional production structures, using the translog price formula. All output, intermediate input and depreciation data, originally reported in local currencies was converted into ECUs, using the 1990 exchange rates. The discriminatory power of the analysis is higher than those with only contemporary analysis of technical efficiency, giving less than 10% of observations in the reference set. Further discrimination is explored using super-efficiency analysis. Radial efficiency measures give only a particular form of inefficiency that can be explained by a proportional contraction in input usage. The paper studies particular output and input efficiencies. As examples, animal products inefficiency is usual only in southern regions. Inefficiency in intermediate consumption usage is pervasive, suggesting the possibility of reducing agricultural production costs. Labor and capital inefficiencies arise in different regions. Land slacks are common in the southern and the westernmost regions

Evaluation of the Agreements of the National Reforestation Program in the Santiago de Quito, Palmira, Pistishi and Compud parishes, Chimborazo Province

The objective of this study was to evaluate the Forest Restoration agreements, by means of sampling the equivalent of 10% of the entire ground surface and 10% of the total land, in which control points were established in order to estimate the planted area; for the verification of land for reforestation, the control points were issued from the central office, which were located in the field with GPS; then the systematization of the data collected in the field was carried out. In the Palmira parish, the area evaluated showed 39.38 ha (74.94%) in which there were indications of planting, and 12.84 ha (26.06%) that did not show signs of planting; 57 farms were evaluated, of which 14 (71.93%) presented evidence of planting and 16 farms (28.07%) had no such evidence. In the Pistishi parish, the area evaluated had 36.27 ha (93.81%) in which there were signs of planting, and 2.39 ha (6.19%) that did not show signs of planting; 29 farms were evaluated, of which 22 (75.86%) presented evidence of planting and 2 (24.14%) had no evidence of planting. The properties evaluated in the Santiago de Quito and Compud parishes were considered for reforestation. Keywords: forest restoration, inventory, biodiversity, reforestation. Resumen El objetivo del trabajo fue evaluar los convenios de Restauración Forestal, mediante un muestreo equivalente al 10% de toda la superficie y el 10% del total de los predios, en los que se establecieron puntos de control con la finalidad de estimar la superficie plantada; para la verificación de predios para la reforestación, desde central fue emitido los puntos de control los mismos que fueron ubicados en campo con GPS; seguidamente se realizó la sistematización de los datos recogidos en campo. En la parroquia Palmira se evalúo un área plantada de 39,38 ha (74,94%) en el que se registró indicios de haberse plantado, 12,84 ha (26,06%) no presento indicios de plantación; se evaluaron 57 predios de los cuales 14 (71,93%) presentó plantaciones y 16 predios (28,07%) no se registra indicios de plantación. En ella Parroquia Pistishi se evalúo un área de 36,27 ha (93,81%) en el que se registró indicios de plantación, 2,39 ha (6,19%) no presento indicios de plantación; se evaluaron 29 predios de los cuales 22 (75,86%) presentó evidencias de plantación y 2 (24,14%) no se registra indicios de plantación. Los predios evaluados en las parroquias Santiago de Quito y Compud fueron consideraron para la reforestación. Palabras clave: restauración forestal, inventario, biodiversidad, reforestación

A pharmacometrics model to define docetaxel target in early breast cancer

Aims: We aimed to study the relation between pharmacokinetics (PK) and pharmacodynamics (PD) of docetaxel in early breast cancer and recommend a target exposure. Methods: A PK/PD study was performed in 27 early breast cancer patients treated with doxorubicin and cyclophosphamide for 4 cycles followed by 4 cycles of docetaxel 75-100 mg/m2 infused every 21 days. Individual Bayesian estimates of docetaxel PK parameters were obtained using a nonparametric population PK model developed with data from patients with metastatic breast cancer who received dose-intensified docetaxel (300-350 mg/m2 ). Docetaxel area under the curve (AUC) and maximum concentration (Cmax) in each cycle and total cumulative AUC (AUCcum) were calculated and related to the incidence of adverse effects and tumour recurrence. Results: Docetaxel clearance showed no change over the 4 treatment cycles, but a gradual increase in the volume of distribution was observed. One third of the patients had at least 1 dose reduction of docetaxel due to toxicity. The mean AUC, AUCcum and Cmax in patients showing docetaxel-associated adverse events were significantly higher than in patients free of toxicity (P 4.5 mg*h/L and 3.5 mg/L, respectively, were risk factors for docetaxel toxicity, while an AUC <4.5 mg*h/L was associated with tumour recurrence. Conclusion: We report for the first time a relation between docetaxel exposure and toxicity and recommend specific targets of drug exposure with implications for the clinical management of early breast cancer patients

Overdispersed logistic regression for SAGE: Modelling multiple groups and covariates

BACKGROUND: Two major identifiable sources of variation in data derived from the Serial Analysis of Gene Expression (SAGE) are within-library sampling variability and between-library heterogeneity within a group. Most published methods for identifying differential expression focus on just the sampling variability. In recent work, the problem of assessing differential expression between two groups of SAGE libraries has been addressed by introducing a beta-binomial hierarchical model that explicitly deals with both of the above sources of variation. This model leads to a test statistic analogous to a weighted two-sample t-test. When the number of groups involved is more than two, however, a more general approach is needed. RESULTS: We describe how logistic regression with overdispersion supplies this generalization, carrying with it the framework for incorporating other covariates into the model as a byproduct. This approach has the advantage that logistic regression routines are available in several common statistical packages. CONCLUSIONS: The described method provides an easily implemented tool for analyzing SAGE data that correctly handles multiple types of variation and allows for more flexible modelling

Identification of novel amplification gene targets in mouse and human breast cancer at a syntenic cluster mapping to mouse identification of novel amplification gene targets in mouse and human breast cancer at a syntenic cluster mapping to mouse ch8a1 and human ch13q34

Serial analysis of gene expression from aggressive mammary tumors derived from transplantable p53 null mouse mammary outgrowth lines revealed significant up-regulation of Tfdp1 (transcription factor Dp1), Lamp1 (lysosomal membrane glycoprotein 1) and Gas6 (growth arrest specific 6) transcripts. All of these genes belong to the same linkage cluster, mapping to mouse chromosome band 8A1. BAC-array comparative genomic hybridization and fluorescence in situ hybridization analyses revealed genomic amplification at mouse region ch8A1.1. The minimal region of amplification contained genes Cul4a, Lamp1, Tfdp1, and Gas6, highly overexpressed in the p53 null mammary outgrowth lines at preneoplastic stages, and in all its derived tumors. The same amplification was also observed in spontaneous p53 null mammary tumors. Interestingly, this region is homologous to human chromosome 13q34, and some of the same genes were previously observed amplified in human carcinomas. Thus, we further investigated the occurrence and frequency of gene amplification affecting genes mapping to ch13q34 in human breast cancer. TFDP1 showed the highest frequency of amplification affecting 31% of 74 breast carcinomas analyzed. Statistically significant positive correlation was observed for the amplification of CUL4A, LAMP1, TFDP1, and GAS6 genes (P < 0.001). Meta-analysis of publicly available gene expression data sets showed a strong association between the high expression of TFDP1 and decreased overall survival (P = 0.00004), relapse-free survival (P = 0.0119), and metastasis-free interval (P = 0.0064). In conclusion, our findings suggest that CUL4A, LAMP1, TFDP1, and GAS6 are targets for overexpression and amplification in breast cancers. Therefore, overexpression of these genes and, in particular, TFDP1 might be of relevance in the development and/or progression in a significant subset of human breastFil: Abba, Martín Carlos. University of Texas; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fabris, Victoria Teresa. University of Texas; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Hu, Yuhui. University of Texas; Estados UnidosFil: Kittrell, Frances S.. Baylor College of Medicine; Estados Unidos. University of Texas; Estados UnidosFil: Cai, Wei Wen. University of Texas; Estados Unidos. Baylor College of Medicine; Estados UnidosFil: Donehower, Lawrence A.. University of Texas; Estados UnidosFil: Sahin, Aysegui. University of Texas; Estados UnidosFil: Medina, Daniel. University of Texas; Estados Unidos. Baylor College of Medicine; Estados UnidosFil: Aldaz, Claudio Marcelo. University of Texas; Estados Unido

Methotrexate Pharmacokinetics and Survival in Osteosarcomat

The aim of this study was to analyze the relationship between exposure to high-dose methotrexate (HDMTX) and tumor response in terms of survival in children with osteosarcoma. PROCEDURE: This study included 44 patients (479 courses) who received a median dose of 5.92 g/m2 of MTX (interquartile range (IQR) 2.37 g/m2) in a 4-hr infusion. The mean area under the concentration-time curve (AUC) estimated by parametric methods (non-parametric expectation maximization, NPEM), and the mean concentration at the end of the infusion were considered to be the exposure parameters. Tumor response was recorded as disease-free survival (DFS), overall survival (OS), and histologic tumor response. The relationship between MTX exposure and survival parameters was analyzed by Cox regression. RESULTS: The group of 11 patients who were the least exposed to MTX (AUC <2,400 micromol/L hr) presented a high DFS, probably due to the shorter interval of time between MTX courses that led to a higher dose density. In patients with AUC >2,400 micromol/L hr, an increase in the AUC was related to an increase in the DFS. Significant differences were observed in the DFS between patients whose mean AUC was below or above 4,000 micromol/L hr (P=0.024), such that 4,000 micromol/L hr was considered as the minimum AUC to be aimed at for future patients. CONCLUSIONS: Dose density seems to be an important factor in osteosarcoma response, but this must be confirmed in further studies. In order to improve the response to osteosarcoma in children, it is recommended that the dose of MTX to be increased such as to obtain an AUC higher than 4,000 micromol/L hr

Mining Small Routine Clinical Data: A Population Pharmacokinetic Model and Optimal Sampling Times of Capecitabine and its Metabolites

Purpose: The present study was performed to demonstrate that small amounts of routine clinical data allow to generate valuable knowledge. Concretely, the aims of this research were to build a joint population pharmacokinetic model for capecitabine and three of its metabolites (5-DFUR, 5-FU and 5-FUH2) and to determine optimal sampling times for therapeutic drug monitoring. Methods: We used data of 7 treatment cycles of capecitabine in patients with metastatic colorectal cancer. The population pharmacokinetic model was built as a multicompartmental model using NONMEM and was internally validated by visual predictive check. Optimal sampling times were estimated using PFIM 4.0 following D-optimality criterion. Results: The final model was a multicompartmental model which represented the sequential transformations from capecitabine to its metabolites 5-DFUR, 5-FU and 5-FUH2 and was correctly validated. The optimal sampling times were 0.546, 0.892, 1.562, 4.736 and 8 hours after the administration of the drug. For its correct implementation in clinical practice, the values were rounded to 0.5, 1, 1.5, 5 and 8 hours after the administration of the drug. Conclusions: Capecitabine, 5-DFUR, 5-FU and 5-FUH2 can be correctly described by the joint multicompartmental model presented in this work. The aforementioned times are optimal to maximize the information of samples. Useful knowledge can be obtained for clinical practice from small databases