Effect of variation in argon content of calibration gases on determination of atmospheric carbon dioxide.

Carbon dioxide (CO(2)) is a greenhouse gas that makes by far the largest contribution to the global warming of the Earth's atmosphere. For the measurements of atmospheric CO(2) a non-dispersive infrared analyzer (NDIR) and gas chromatography are conventionally being used. We explored whether and to what degree argon content can influence the determination of atmospheric CO(2) using the comparison of CO(2) concentrations between the sample gas mixtures with varying Ar amounts at 0 and 18.6 mmol mol(-1) and the calibration gas mixtures with Ar at 8.4, 9.1, and 9.3 mmol mol(-1). We newly discovered that variation of Ar content in calibration gas mixtures could undermine accuracy for precise and accurate determination of atmospheric CO(2) in background air. The differences in CO(2) concentration due to the variation of Ar content in the calibration gas mixtures were negligible (<+/-0.03 micromol mol(-1)) for NDIR systems whereas they noticeably increased (<+/-1.09 micromol mol(-1)) especially for the modified GC systems to enhance instrumental sensitivity. We found that the thermal mass flow controller is the main source of the differences although such differences appeared only in the presence of a flow restrictor in GC systems. For reliable monitoring of real atmospheric CO(2) samples, one should use calibration gas mixtures that contain Ar content close to the level (9.332 mmol mol(-1)) in the ambient air as possible. Practical guidelines were highlighted relating to selection of appropriate analytical approaches for the accurate and precise measurements of atmospheric CO(2). In addition, theoretical implications from the findings were addressed.

Direct determination of arsine in gases by inductively coupled plasma-dynamic reaction cell-mass spectrometry.

Reliable determination of arsine (AsH(3)) in gases is of great importance due to stringent regulations associated with health, safety and environmental issues. It is, however, challenging for an analyst to determine trace airborne arsine concentrations without specifically designed collection procedures using adsorption, desorption, dissolution or impinging techniques. To circumvent such technical barrier, we have newly developed a direct analytical method, characterized by introduction of an arsine gas sample into stable plasma stream, followed by gas-phase oxidation of arsine with molecular oxygen in a dynamic reaction cell (DRC) equipped within the inductively coupled plasma-mass spectrometry (ICP/MS) system, followed by subsequent detection of AsO(+) ion. This preliminary work used trace arsine concentrations (161 microg m(-3), 322 microg m(-3), and 645 microg m(-3)) gravimetrically prepared in N(2) balance. The proposed method was optimized for the important experimental parameters such as the flow rates of the reaction gas, the arsine sample, and the carrier gas. This method was then validated by demonstrating good figure-of-merits including the low limit of detection (0.10 microg m(-3)), good linearity (r(2)>0.9915), low measurement uncertainty (0.66%), and high speed of analysis (<6 min). The proposed method is expected to be potentially applicable to the determination of arsine in real workplace air after appropriate modifications are made.

Automatic extension of Gene Ontology with flexible identification of candidate terms.

MOTIVATION: Gene Ontology (GO) has been manually developed to provide a controlled vocabulary for gene product attributes. It continues to evolve with new concepts that are compiled mostly from existing concepts in a compositional way. If we consider the relatively slow growth rate of GO in the face of the fast accumulation of the biological data, it is much desirable to provide an automatic means for predicting new concepts from the existing ones. RESULTS: We present a novel method that predicts more detailed concepts by utilizing syntactic relations among the existing concepts. We propose a validation measure for the automatically predicted concepts by matching the concepts to biomedical articles. We also suggest how to find a suitable direction for the extension of a constantly growing ontology such as GO. AVAILABILITY: http://autogo.biopathway.org SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.