Lost in PubMed. Factors influencing the success of medical information retrieval

With the explosion of information available on the Web, finding specific medical information in an efficient way has become a considerable challenge. PubMed/MEDLINE offers an alternative to free-text searching on the web, allowing searchers to do a keyword-based search using Medical Subject Headings. However, finding relevant information within a limited time frame remains a difficult task. The current study is based on an error analysis of data from a retrieval experiment conducted at the nursing departments of two Belgian universities and a British university. We identified the main difficulties in query formulation and relevance judgment and compared the profiles of the best and worst performers in the test.For the analysis, a query collection was built from the queries submitted by our test participants. The queries in this collection are all aimed at finding the same specific information in PubMed, which allowed us to identify what exactly went wrong in the query formulation step. Another crucial aspect for efficient information retrieval is relevance judgment. Differences between potential and actual recall of each query offered indications of the extent to which participants overlooked relevant citations.The test participants were divided into “worst”, “average” and “best” performers based on the number of relevant citations they selected: zero, one or two and three or more, respectively. We tried to find out what the differences in background and in search behavior were between these three groups.     

Optimal interpolation analysis of leaf area index using MODIS data

A simple data analysis technique for vegetation leaf area index (LAI) using Moderate Resolution Imaging Spectroradiometer (MODIS) data is presented. The objective is to generate LAI data that is appropriate for numerical weather prediction. A series of techniques and procedures which includes data quality control, time-series data smoothing, and simple data analysis is applied. The LAI analysis is an optimal combination of the MODIS observations and derived climatology, depending on their associated errors σ_o and σ_c. The “best estimate” LAI is derived from a simple three-point smoothing technique combined with a selection of maximum LAI (after data quality control) values to ensure a higher quality. The LAI climatology is a time smoothed mean value of the “best estimate” LAI during the years of 2002-2004. The observation error is obtained by comparing the MODIS observed LAI with the “best estimate” of the LAI, and the climatological error is obtained by comparing the “best estimate” of LAI with the climatological LAI value. The LAI analysis is the result of a weighting between these two errors. Demonstration of the method described in this paper is presented for the 15-km grid of Meteorological Service of Canada (MSC)'s regional version of the numerical weather prediction model. The final LAI analyses have a relatively smooth temporal evolution, which makes them more appropriate for environmental prediction than the original MODIS LAI observation data. They are also more realistic than the LAI data currently used operationally at the MSC which is based on land-cover databases.     

Establishment of rapid detection method of methamidophos in vegetables by surface enhanced Raman spectroscopy

Methamidophos (MAP) is a highly efficient and broad-spectrum organophosphate pesticide. In this study, a rapid method for detecting MAP in vegetables using surface enhanced Raman scattering (SERS) spectroscopy has been established. Density functional theory calculations were performed with Gaussian 03 at RB3LYP level and with the 6-311G (d) basis set. SERS, normal and theoretical Raman spectroscopy were compared to investigate the mechanism of Raman scattering enhancement. The SERS signal of MAP was improved in alkaline conditions with optimum scattering efficiency at pH of 13.46. Furthermore, MAP detection in vegetables by SERS method had a good linear relationship between 0.01 and 1,000?μg/mL. The concentration of MAP in vegetables was detected and chosen for recovery test with three levels: 4, 8, and 15?μg/mL. The results of three level tests were 86.7–96.6?% and their relative standard deviations were between 1.2 and 2.5?%, which shows the good repeatability of this method.