The implementation of liquid chromatography tandem mass spectrometry for the official control of lipophilic toxins in seafood: Single-laboratory validation under four chromatographic conditions

We performed a comprehensive study to assess the fit for purpose of four chromatographic conditions for the determination of six groups of marine lipophilic toxins (okadaic acid and dinophysistoxins, pectenotoxins, azaspiracids, yessotoxins, gymnodimine and spirolides) by LC-MS/MS to select the most suitable conditions as stated by the European Union Reference Laboratory for Marine Biotoxins (EURLMB). For every case, the elution gradient has been optimized to achieve a total run-time cycle of 12 min. We performed a single-laboratory validation for the analysis of three relevant matrices for the seafood aquaculture industry (mussels, pacific oysters and clams), and for sea urchins for which no data about lipophilic toxins have been reported before. Moreover, we have compared the method performance under alkaline conditions using two quantification strategies: the external standard calibration (EXS) and the matrix-matched standard calibration (MMS). Alkaline conditions were the only scenario that allowed detection windows with polarity switching in a 3200 QTrap mass spectrometer, thus the analysis of all toxins can be accomplished in a single run, increasing sample throughput. The limits of quantification under alkaline conditions met the validation requirements established by the EURLMB for all toxins and matrices, while the remaining conditions failed in some cases. The accuracy of the method and the matrix effects where generally dependent on the mobile phases and the seafood species. The MMS had a moderate positive impact on method accuracy for crude extracts, but it showed poor trueness for seafood species other than mussels when analyzing hydrolyzed extracts. Alkaline conditions with EXS and recovery correction for OA were selected as the most proper conditions in the context of our laboratory. This comparative study can help other laboratories to choose the best conditions for the implementation of LC-MS/MS according to their own necessities.

Official Directory of the Legislature

This publication list all State of Iowa Senate and House of Represeative staffs' office information. Along with their hometown, district and mailing information.

Vital Statistics of Iowa, 2001

This publication is an historical recording of the most requested statistics on vital events and is a source of information that can be used in further analysis.

Iowa Official Register, 2000

The Iowa Official Register, commonly known as the "Redbook," serves as a biographical and historical record of Iowa's leaders, government and people.

Iowa Prison Population Forecast FY 2003-2013

CJJP takes a look at the forecast of inmates population in the state of Iowa in a ten year period. Information was produced by Division of Criminal and Juvenile Justice Planning. This report was made possible partially through funding from the U.S. Department of Justice, Bureau of Justice Statistics and its program for State Statistical Analysis Centers. Points of view or opinions expressed in this report are those of the Division of Criminal and Juvenile Justice Planning (CJJP), and do not necessarily reflect official positions of the U.S. Department of Justice.

Iowa Prison Population Forecast FY 2002-2012

CJJP takes a look at the forecast of inmates population in the state of Iowa in a ten year period. Information was produced by Division of Criminal and Juvenile Justice Planning. This report was made possible partially through funding from the U.S. Department of Justice, Bureau of Justice Statistics and its program for State Statistical Analysis Centers. Points of view or opinions expressed in this report are those of the Division of Criminal and Juvenile Justice Planning (CJJP), and do not necessarily reflect official positions of the U.S. Department of Justice.

Iowa Prison Population Forecast, FY 2002-2011

CJJP takes a look at the forecast of inmates population in the state of Iowa in a ten year period. Information was produced by Division of Criminal and Juvenile Justice Planning. This report was made possible partially through funding from the U.S. Department of Justice, Bureau of Justice Statistics and its program for State Statistical Analysis Centers. Points of view or opinions expressed in this report are those of the Division of Criminal and Juvenile Justice Planning (CJJP), and do not necessarily reflect official positions of the U.S. Department of Justice.

Iowa Prison Population Forecast FY 2001-2010

CJJP takes a look at the forecast of inmates population in the state of Iowa in a ten year period. Information was produced by Division of Criminal and Juvenile Justice Planning. This report was made possible partially through funding from the U.S. Department of Justice, Bureau of Justice Statistics and its program for State Statistical Analysis Centers. Points of view or opinions expressed in this report are those of the Division of Criminal and Juvenile Justice Planning (CJJP), and do not necessarily reflect official positions of the U.S. Department of Justice.

Iowa Prison Population Forecast FY 2000-2009

CJJP takes a look at the forecast of inmates population in the state of Iowa in a ten year period. Information was produced by Division of Criminal and Juvenile Justice Planning. This report was made possible partially through funding from the U.S. Department of Justice, Bureau of Justice Statistics and its program for State Statistical Analysis Centers. Points of view or opinions expressed in this report are those of the Division of Criminal and Juvenile Justice Planning (CJJP), and do not necessarily reflect official positions of the U.S. Department of Justice.

Reliable detection of directional couplings using rank statistics

To detect directional couplings from time series various measures based on distances in reconstructed state spaces were introduced. These measures can, however, be biased by asymmetries in the dynamics' structure, noise color, or noise level, which are ubiquitous in experimental signals. Using theoretical reasoning and results from model systems we identify the various sources of bias and show that most of them can be eliminated by an appropriate normalization. We furthermore diminish the remaining biases by introducing a measure based on ranks of distances. This rank-based measure outperforms existing distance-based measures concerning both sensitivity and specificity for directional couplings. Therefore, our findings are relevant for a reliable detection of directional couplings from experimental signals.

Vital Statistics of Iowa, 2000

This publication is an historical recording of the most requested statistics on vital events and is a source of information that can be used in further analysis.

Vital Statistics of Iowa, 1999

This publication is an historical recording of the most requested statistics on vital events and is a source of information that can be used in further analysis.

Vital Statistics of Iowa, 2003

This publication is an historical recording of the most requested statistics on vital events and is a source of information that can be used in further analysis.

Vital Statistics of Iowa, 2002

This publication is an historical recording of the most requested statistics on vital events and is a source of information that can be used in further analysis.