PubMed · 15014517
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Caitlin Smith. 2004-03-11. Getting up speed.. https://doi.org/10.1038/428229a
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OBJECTIVE: The objective of the study is to validate self-reported cellular phone use information by comparing it with the cumulative emitted power and duration of calls measured by software-modified cellular phones (SMP). The information was obtained using a questionnaire developed for the international case-control study on the risk of the use of mobile phones in tumours of the brain or salivary gland (INTERPHONE-study). METHOD: The study was conducted in Bielefeld, Germany. Volunteers were asked to use SMPs instead of their own cellular phones for a period of 1 month. The SMP recorded the power emitted by the mobile phone handset during each base station contact. Information on cellular phone use for the same time period from traffic records of the network providers and from face-to-face interviews with the participants 3 months after the SMP use was assessed. Pearson's correlation coefficients and linear regression models were used to analyse the association between information from the interview and from the SMP. RESULTS: In total, 1757 personal mobile phone calls were recorded for 45 persons by SMP and traffic records. The correlation between the self-reported information about the number and the duration of calls with the cumulative power of calls was 0.50 (P<0.01) and 0.48 (P<0.01), respectively. Almost 23% of the variance of the cumulative power was explained by either the number or the cumulative duration of calls. After inclusion of possible confounding factors in the regression model, the variance increased to 26%. Minor confounding factors were "network provider", "contract form", and "cellular phone model". DISCUSSION: The number of calls alone is a sufficient parameter to estimate the cumulative power emitted by the handset of a cellular telephone. The cumulative power emitted by these phones is only associated with number of calls but not with possible confounding factors. Using the mobile phone while driving, mainly in cities, or mainly in rural areas is not associated with the recorded cumulative power in the SMP.
We have developed a new scoring function, the template modeling score (TM-score), to assess the quality of protein structure templates and predicted full-length models by extending the approaches used in Global Distance Test (GDT)1 and MaxSub.2 First, a protein size-dependent scale is exploited to eliminate the inherent protein size dependence of the previous scores and appropriately account for random protein structure pairs. Second, rather than setting specific distance cutoffs and calculating only the fractions with errors below the cutoff, all residue pairs in alignment/modeling are evaluated in the proposed score. For comparison of various scoring functions, we have constructed a large-scale benchmark set of structure templates for 1489 small to medium size proteins using the threading program PROSPECTOR_3 and built the full-length models using MODELLER and TASSER. The TM-score of the initial threading alignments, compared to the GDT and MaxSub scoring functions, shows a much stronger correlation to the quality of the final full-length models. The TM-score is further exploited as an assessment of all 'new fold' targets in the recent CASP5 experiment and shows a close coincidence with the results of human-expert visual assessment. These data suggest that the TM-score is a useful complement to the fully automated assessment of protein structure predictions. The executable program of TM-score is freely downloadable at http://bioinformatics.buffalo.edu/TM-score.
BACKGROUND: Mass spectrometry based proteomics result in huge amounts of data that has to be processed in real time in order to efficiently feed identification algorithms and to easily integrate in automated environments. We present wiff2dta, a tool created to convert MS/MS data obtained using Applied Biosystem's QStar and QTrap 2000 and 4000 series. RESULTS: Comparing the performance of wiff2dta with the standard tools, we find wiff2dta being the fastest solution for extracting spectrum data from ABIs raw file format. wiff2dta is at least 10% faster than the standard tools. It is also capable of batch processing and can be easily integrated in high throughput environments. The program is freely available via http://www.protein-ms.de, http://sourceforge.net/projects/protms/ and is also available from Applied Biosystems. CONCLUSIONS: wiff2dta offers the possibility to run as stand-alone application or within a batch process as command-line tool integrated in automation and high-throughput environments. It is more efficient than the state-of-the-art tools provided.