The application of non-linear curve fitting routines to the analysis of mid-infrared images obtained from single polymeric microparticles

For the first time, we report a series of time resolved images of a single PLGA microparticle undergoing hydrolysis at 70 °C that have been obtained using attenuated total reflectance-Fourier transform infrared spectroscopic (ATR-FTIR) imaging. A novel partially supervised non-linear curve fitting (NLCF) tool was developed to identify and fit peaks to the infrared spectrum obtained from each pixel within the 64 × 64 array. The output from the NLCF was evaluated by comparison with a traditional peak height (PH) data analysis approach and multivariate curve resolution alternating least squares (MCR-ALS) analysis for the same images, in order to understand the limitations and advantages of the NLCF methodology. The NLCF method was shown to facilitate consistent spatial resolution enhancement as defined using the step-edge approach on dry microparticle images when compared to images derived from both PH measurements and MCR-ALS. The NLCF method was shown to improve both the S/N and sharpness of images obtained during an evolving experiment, providing a better insight into the magnitude of hydration layers and particle dimension changes during hydrolysis. The NLCF approach facilitated the calculation of hydrolysis rate constants for both the glycolic (kG) and lactic (kL) acid segments of the PLGA copolymer. This represents a real advantage over MCR-ALS which could not distinguish between the two segments due to colinearity within the data. The NLCF approach made it possible to calculate the hydrolysis rate constants from a single pixel, unlike the peak height data analysis approach which suffered from poor S/N at each pixel. These findings show the potential value of applying NLCF to the study of real-time chemical processes at the micron scale, assisting in the understanding of the mechanisms of chemical processes that occur within microparticles and enhancing the value of the mid-IR ATR analysis

Wellcome to English

130 tr. ; 21 cm

Utilization of Polygraph in Order to Qualify Subjective Aspect of Perpetrator of a Prohibited Act

Artykuł 199a kodeksu postępowania karnego określa przesłanki wykorzystania badania wariograficznego na oskarżonym sensu largo. W referacie skupiono się na przedstawieniu możliwości wykorzystania badania wariograficznego, któremu podlega oskarżony, w celu określenia jego strony podmiotowej wobec popełnionego przez niego przedmiotowo pojmowanego zachowania się opisanego w przepisie określąjacym znamiona tego czynu. Określono warunki przystąpienia do wymienionego wyżej badania oraz jego ewentualne efekty w odniesieniu do strony podmiotowej badanego.Article 199a of polish Code of Criminal Procedure describes premises of utilization of polygraph examination of defendant sensu largo. This article focuses on presenting possibilities of utilization polygraph examination on defendant in order to qualify his subjective aspect towards committed action described in regulation which defines characteristics of the crime it consists. Terms of acceding to previously mentioned examination and its eventual effects in reference to subjective aspect of examined shall be defined

Local rank analysis for exploratory spectroscopic image analysis. Fixed Size Image Window-Evolving Factor Analysis

11 pages, 8 figures.-- Printed version published May 28, 2005.-- Issue title: "Festschrift honouring professor D.L. Massart" (Edited by P. Hopke and C. Spiegelman).Many pharmaceutical and consumer products are composed of emulsion-based formulations that act as delivery systems for active ingredients or other benefit agents. The efficacy and performance of these products often rely on how the actives interact and are stabilized by the base emulsion formulation. It is therefore of great significance to the development and processing of these formulations to be able to accurately characterize the structure and chemical distribution of components in these systems. Recent advances in spectroscopic imaging technology have played an important role in bringing these methods into acceptance and perhaps even prominence in both the consumer product and pharmaceutical industries as standard methods of analyzing these kinds of chemical systems. Concurrently, this has kindled the interest in methodology capable of analyzing the large data sets generated by these multivariate imaging experiments.Efficient exploratory analysis of spectroscopic images is therefore crucial, the desired goal being to resolve the chemical information in the data into its pure component parts. An approximate description of the compositional complexity of images is not yet available through the usual exploratory procedures.Methods used for this purpose should be local and be able to take into account the complex spatial structure of the image. Fixed Size Moving Window-Evolving Factor Analysis has been a powerful approach to locally define the complexity of a process through the subsequent PCA analyses of data subsets built by moving a fixed size window along a unique process direction (e.g., time, pH). Spectroscopic images have two or three spatial directions (in surface or multilayer images, respectively). Algorithms based on local data analysis should be adapted to preserve this higher dimensionality in order to provide a representative description of the image complexity. Fixed Size Image Window-Evolving Factor Analysis (FSIW-EFA) modifies the parent local rank algorithm to achieve this purpose.Peer reviewe

Two-photon 3-D mapping of ex vivo human skin endogenous fluorescence species based on fluorescence emission spectra

Spectral resolved tissue imaging has a broad range of biomedical applications such as the minimally invasive diagnosis of diseases and the study of wound healing and tissue engineering processes. Two-photon microscopy imaging of endogenous fluorescence has been shown to be a powerful method for the quantification of tissue structure and biochemistry. While two-photon excited autofluorescence is observed ubiquitously, the identities and distributions of endogenous fluorophores have not been completely characterized in most tissues. We develop an image-guided spectral analysis method to analyze the distribution of fluorophores in human skin from 3-D resolved two-photon images. We identify five factors that contribute to most of the luminescence signals from human skin. Luminescence species identified include tryptophan, NAD(P)H, melanin, and elastin, which are autofluorescent, and collagen that contributes to a second harmonic signal.Unilever (Firm)American Cancer Society (RPG-98-058-01-CCE)National Institutes of Health (U.S.) (R33 CA091354-01A1