O 1s excitation and ionization processes in the CO2 molecule studied via detection of low-energy fluorescence emission

Oxygen 1s excitation and ionization processes in the CO2 molecule have been studied with dispersed and non-dispersed fluorescence spectroscopy as well as with the vacuum ultraviolet (VUV) photon?photoion coincidence technique. The intensity of the neutral O emission line at 845 nm shows particular sensitivity to core-to-Rydberg excitations and core?valence double excitations, while shape resonances are suppressed. In contrast, the partial fluorescence yield in the wavelength window 300?650 nm and the excitation functions of selected O+ and C+ emission lines in the wavelength range 400?500 nm display all of the absorption features. The relative intensity of ionic emission in the visible range increases towards higher photon energies, which is attributed to O 1s shake-off photoionization. VUV photon?photoion coincidence spectra reveal major contributions from the C+ and O+ ions and a minor contribution from C2+. No conclusive changes in the intensity ratios among the different ions are observed above the O 1s threshold. The line shape of the VUV?O+ coincidence peak in the mass spectrum carries some information on the initial core excitation

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

Mass spectrometry (MS) data provide a promising strategy for biomarker discovery. For this purpose, the detection of relevant peakbins in MS data is currently under intense research. Data from mass spectrometry are challenging to analyze because of their high dimensionality and the generally low number of samples available. To tackle this problem, the scientific community is becoming increasingly interested in applying feature subset selection techniques based on specialized machine learning algorithms. In this paper, we present a performance comparison of some metaheuristics: best first (BF), genetic algorithm (GA), scatter search (SS) and variable neighborhood search (VNS). Up to now, all the algorithms, except for GA, have been first applied to detect relevant peakbins in MS data. All these metaheuristic searches are embedded in two different filter and wrapper schemes coupled with Naive Bayes and SVM classifiers.

Experimental comparison of FBARs and SMRs responsitivities to mass loadings

The utilisation of thin film technology to develop film bulk acoustic resonators (FBARs) and solidly mounted resonators (SMRs), offers great potential to outperform the sensitivity and minimum detection limit of gravimetric sensors. Up to now, the choice between FBARs and SMRs depends primarily on the users' ability to design and fabricate Bragg reflectors and/or membranes, because neither of these two types of resonators has been demonstrated to be superior to the other. In the work reported here, it is shown that identically designed FBARs and SMRs resonating at the same frequency exhibit different responsitivities, Rm, to mass loadings, being the FBARs more responsive than the SMRs. For the specific device design and resonant frequency (~2 GHz) of the resonators presented, FBARs' mass responsitivity is ~20% greater than that of SMRs, and although this value should not be taken as universal for all possible device designs, it clearly indicates that FBAR devices should be favoured over SMRs in gravimetric sensing applications.

On-line breath analysis of volatile organic compounds as a method for colorectal cancer detection

Background: Analysis of exhaled volatile organic compounds (VOCs) in breath is an emerging approach for cancer diagnosis, but little is known about its potential use as a biomarker for colorectal cancer (CRC). We investigated whether a combination of VOCs could distinct CRC patients from healthy volunteers. Methods: In a pilot study, we prospectively analyzed breath exhalations of 38 CRC patient and 43 healthy controls all scheduled for colonoscopy, older than 50 in the average-risk category. The samples were ionized and analyzed using a Secondary ElectroSpray Ionization (SESI) coupled with a Time-of-Flight Mass Spectrometer (SESI-MS). After a minimum of 2 hours fasting, volunteers deeply exhaled into the system. Each test requires three soft exhalations and takes less than ten minutes. No breath condensate or collection are required and VOCs masses are detected in real time, also allowing for a spirometric profile to be analyzed along with the VOCs. A new sampling system precludes ambient air from entering the system, so background contamination is reduced by an overall factor of ten. Potential confounding variables from the patient or the environment that could interfere with results were analyzed. Results: 255 VOCs, with masses ranging from 30 to 431 Dalton have been identified in the exhaled breath. Using a classification technique based on the ROC curve for each VOC, a set of 9 biomarkers discriminating the presence of CRC from healthy volunteers was obtained, showing an average recognition rate of 81.94%, a sensitivity of 87.04% and specificity of 76.85%. Conclusions: A combination of cualitative and cuantitative analysis of VOCs in the exhaled breath could be a powerful diagnostic tool for average-risk CRC population. These results should be taken with precaution, as many endogenous or exogenous contaminants could interfere as confounding variables. On-line analysis with SESI-MS is less time-consuming and doesn’t need sample preparation. We are recruiting in a new pilot study including breath cleaning procedures and spirometric analysis incorporated into the postprocessing algorithms, to better control for confounding variables.