Quantitative isolation of mouse and human intestinal intraepithelial lymphocytes by elutriation centrifugation

Intestinal intraepithelial lymphocytes (IEL) are specialized subsets of T cells with distinct functional capacities. While some IEL subsets are circulating, others such as CD8alphaalpha TCRalphabeta IEL are believed to represent non-circulating resident T cell subsets [Sim, G.K., Intraepithelial lymphocytes and the immune system. Adv. Immunol., 1995. 58: 297-343.]. Current methods to obtain enriched preparations of intraepithelial lymphocytes are mostly based on Percoll density gradient or magnetic bead-based technologies [Lundqvist, C., et al., Isolation of functionally active intraepithelial lymphocytes and enterocytes from human small and large intestine. J. Immunol. Methods, 1992. 152(2): 253-263.]. However, these techniques are hampered by a generally low yield of isolated cells, and potential artifacts due to the interference of the isolation procedure with subsequent functional assays, in particular, when antibodies against cell surface markers are required. Here we describe a new method for obtaining relatively pure populations of intestinal IEL (55-75%) at a high yield (>85%) by elutriation centrifugation. This technique is equally suited for the isolation and enrichment of intraepithelial lymphocytes of both mouse and human origin. Time requirements for fractionating cell suspensions by elutriation centrifugation are comparable to Percoll-, or MACS-based isolation procedures. Hence, the substantially higher yield and the consistent robust enrichment for intraepithelial lymphocytes, together with the gentle treatment of the cells during elutriation that does not interfere with subsequent functional assays, are important aspects that are in favor of using this elegant technology to obtain unmanipulated, unbiased populations of intestinal intraepithelial lymphocytes, and, if desired, also of pure epithelial cells.

Screening of Indian Plants for Biological Activity: Part VI

Two hundred and ninety eight botanically identified plant materials have been tested as 50% ethanol extracts, for a wide variety of biological activities including antifertility, anticancer, chemotherapeutic and pharmacological activities. Biological activities been confirmed in fractions of 84 of these extracts.

Screeningof Indian Plants for Biological Activity :Part XII

Alcoholic extracts of 295 botanically identified plant materials from 267 plant species have been tested for a wide variety of biological activities including chemotherapeutic and pharmacological screenings. Biological activities have been confirmed in fractions of 64 of these extracts. Follow-up studies have been carried out in some plants with confirmed activity. The active principles and results of these studies are reported.

Screening of Indian Plants for Biological Activity : Part XIII

Alcoholic extracts of 300 botanically identified plant materials from 287 plant species have been tested for various biological activities including chemotherapeutic and pharmacological screenings. Biological activities have been confirmed in 51 fractions of the extracts. Follow-up studies have been carried out in some plants with confirmed activity. The active principles and results of these studies are reported.

Screening of Indian plants for biological activity: Part XV

Alcoholics extracts of 266 botanically identified plant materials from 222 plant species have been tested for various biological activities including chemotherapeutic and pharmacological screenings. Biological activities have been observed in 89 extracts. Follow-up studies have been carried out in some plants with confirmed activity. The active principles and results of these studies are reported.

The surface temperatures of Earth: steps towards integrated understanding of variability and change

Surface temperature is a key aspect of weather and climate, but the term may refer to different quantities that play interconnected roles and are observed by different means. In a community-based activity in June 2012, the EarthTemp Network brought together 55 researchers from five continents to improve the interaction between scientific communities who focus on surface temperature in particular domains, to exploit the strengths of different observing systems and to better meet the needs of different communities. The workshop identified key needs for progress towards meeting scientific and societal requirements for surface temperature understanding and information, which are presented in this community paper. A "whole-Earth" perspective is required with more integrated, collaborative approaches to observing and understanding Earth's various surface temperatures. It is necessary to build understanding of the relationships between different surface temperatures, where presently inadequate, and undertake large-scale systematic intercomparisons. Datasets need to be easier to obtain and exploit for a wide constituency of users, with the differences and complementarities communicated in readily understood terms, and realistic and consistent uncertainty information provided. Steps were also recommended to curate and make available data that are presently inaccessible, develop new observing systems and build capacities to accelerate progress in the accuracy and usability of surface temperature datasets.

Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.

Volume and shape in feature space on adaptive FCM in MRI segmentation.

Intensity non-uniformity (bias field) correction, contextual constraints over spatial intensity distribution and non-spherical cluster's shape in the feature space are incorporated into the fuzzy c-means (FCM) for segmentation of three-dimensional multi-spectral MR images. The bias field is modeled by a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of either intensity or membership are added into the FCM cost functions. Since the feature space is not isotropic, distance measures, other than the Euclidean distance, are used to account for the shape and volumetric effects of clusters in the feature space. The performance of segmentation is improved by combining the adaptive FCM scheme with the criteria used in Gustafson-Kessel (G-K) and Gath-Geva (G-G) algorithms through the inclusion of the cluster scatter measure. The performance of this integrated approach is quantitatively evaluated on normal MR brain images using the similarity measures. The improvement in the quality of segmentation obtained with our method is also demonstrated by comparing our results with those produced by FSL (FMRIB Software Library), a software package that is commonly used for tissue classification.