Optical absorption parameters of amorphous carbon films from Forouhi–Bloomer and Tauc–Lorentz models: a comparative study

International audienceParametrization models of optical constants, namely Tauc-Lorentz (TL), Forouhi-Bloomer (FB) and modified FB models, were applied to the interband absorption of amorphous carbon films. The optical constants were determined by means of transmittance and reflectance measurements in the visible range. The studied films were prepared by rf sputtering and characterized for their chemical properties. The analytical models were also applied to other optical data published in the literature pertaining to films produced by various deposition techniques. The different approaches used to determine important physical parameters of the interband transition yielded different results. A figure-of-merit was introduced to check the applicability of the models and the results showed that FB modified for an energy dependence of the dipole matrix element adequately represents the interband transition in the amorphous carbons. Further, the modified FB model shows a relative superiority over the TL ones for concerning the determination of the band gap energy, as it is the only one to be validated by an independent, though indirect, gap measurement by x-ray photoelectron spectroscopy. Finally, the application of the modified FB model allowed us to establish some important correlations between film structure and optical absorption properties

Mast cell activation by group A streptococcal polysaccharide in the rat and its role in experimental arthritis.

Acute edematous responses were induced in Sprague-Dawley rats by the intravenous injection of group-specific polysaccharide (PS) isolated from group A streptococci. Thirty minutes after the intravenous injection of PS there was marked degranulation of subcutaneous and periarticular mast cells in all 4 feet, carbon particle labeling of adjacent venules, and an 8-fold increase in Evans blue dye content of the extremities. This acute reaction to PS was completely blocked by pretreatment with compound 48/80, but the polyarticular relapsing arthritis following the systemic injection of an arthropathic dose of streptococcal cell wall fragments containing large, covalently bound peptidoglycan-polysaccharide (PG-PS) was not blocked

Prospects for a Statistical Theory of LC/TOFMS Data

The critical importance of employing sound statistical arguments when seeking to draw inferences from inexact measurements is well-established throughout the sciences. Yet fundamental statistical methods such as hypothesis testing can currently be applied to only a small subset of the data analytical problems encountered in LC/MS experiments. The means of inference that are more generally employed are based on a variety of heuristic techniques and a largely qualitative understanding of their behavior. In this article, we attempt to move towards a more formalized approach to the analysis of LC/TOFMS data by establishing some of the core concepts required for a detailed mathematical description of the data. Using arguments that are based on the fundamental workings of the instrument, we derive and validate a probability distribution that approximates that of the empirically obtained data and on the basis of which formal statistical tests can be constructed. Unlike many existing statistical models for MS data, the one presented here aims for rigor rather than generality. Consequently, the model is closely tailored to a particular type of TOF mass spectrometer although the general approach carries over to other instrument designs. Looking ahead, we argue that further improvements in our ability to characterize the data mathematically could enable us to address a wide range of data analytical problems in a statistically rigorous manner

Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics

Timm W, Scherbart A, Boecker S, Kohlbacher O, Nattkemper TW. Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics. BMC Bioinformatics. 2008;9(1):443.Background: Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides and proteins, however, is the fact that absolute quantification is severely hampered by the unclear relationship between the observed peak intensity and the peptide concentration in the sample. While there are numerous approaches to circumvent this problem experimentally (e. g. labeling techniques), reliable prediction of the peak intensities from peptide sequences could provide a peptide-specific correction factor. Thus, it would be a valuable tool towards label-free absolute quantification. Results: In this work we present machine learning techniques for peak intensity prediction for MALDI mass spectra. Features encoding the peptides' physico-chemical properties as well as string-based features were extracted. A feature subset was obtained from multiple forward feature selections on the extracted features. Based on these features, two advanced machine learning methods (support vector regression and local linear maps) are shown to yield good results for this problem (Pearson correlation of 0.68 in a ten-fold cross validation). Conclusion: The techniques presented here are a useful first step going beyond the binary prediction of proteotypic peptides towards a more quantitative prediction of peak intensities. These predictions in turn will turn out to be beneficial for mass spectrometry-based quantitative proteomics

A Multi-disciplinary Commentary on Preclinical Research to investigate Vascular Contributions to Dementia

Although dementia research has been dominated by Alzheimer's disease (AD), most dementia in older people is now recognised to be due to mixed pathologies, usually combining vascular and AD brain pathology. Vascular cognitive impairment (VCI), which encompasses vascular dementia (VaD) is the second most common type of dementia. Models of VCI have been delayed by limited understanding of the underlying aetiology and pathogenesis. This review by a multidisciplinary, diverse (in terms of sex, geography and career stage), cross-institute team provides a perspective on limitations to current VCI models and recommendations for improving translation and reproducibility. We discuss reproducibility, clinical features of VCI and corresponding assessments in models, human pathology, bioinformatics approaches, and data sharing. We offer recommendations for future research, particularly focusing on small vessel disease as a main underpinning disorder