Extraction and structure elucidation of natural compounds of some gorgonians of the Persian Gulf and also physiological and pharmacological effects of gorgonian extract on some selected organisms

In the present research, investigations were carried out for structure elucidation of natural compounds and also for studing biological and teratogenical effects of two Genus of soft corals named as " Echinogorgia cf. indica" and "Sinularia erecta" in Persian Gulf. First, 350 gr Echinogorgia was extracted by Acetone, then, the extract was separated by ether from aqueos phase to give 4.5 gr oil. The oil eluted with Petrol - ether Et2 o (9:1) which was recovered Linderazulene and it's derivative as purple Cristals (350 mg/ca 0.1 %). In order to determine molecular structure, the Samples were used for spectroscopic method as: H1- NMR , C13- NMR and 2D NMR. Also, for extraction and structure elucidation of natural compounds, the soft coral " sinularia erecta " were used 1187/37 gr and extracted by Aceton. The extract was concentrated and resulting aqueous suspension and extracted by using ether to give 8.41 gr oil. The oil , was Chromatographed on a column of silica gel and some different fractions were gathered. Initial fraction (1-11) which were nonpolar compounds were seprated by GC/MS. Mass spectrum were prepared and much compounds were recognized.

Parameter extraction and calorimetric validation for a silicon carbide jfet pspice model

This paper focuses on the PSpice model of SiC-JFET element inside a SiCED cascode device. The device model parameters are extracted from the I-V and C-V characterization curves. In order to validate the model, an inductive test rig circuit is designed and tested. The switching loss is estimated both using oscilloscope and calorimeter. These results are found to be in good agreement with the simulated results.

Treatment of Phaeodactylum tricornutum cells with papain facilitates lipid extraction.

Triacylglycerols (TAGs) from microalgae have the potential to be used for biodiesel, but several technical and economic hurdles have to be overcome. A major challenge is efficient extraction of intracellular TAGs from algae. Here we investigate the use of enzymes to deconstruct algal cell walls/membranes. We describe a rapid and simple assay that can assess the efficacy of different enzyme treatments on TAG-containing algae. By this means crude papain and bromelain were found to be effective in releasing TAGs from the diatom Phaeodactylum tricornutum, most likely because of their cysteine protease activity. Pre-treating algal biomass with crude papain enabled complete extraction of TAGs using heptane/isopropyl alcohol. Heptane as a single solvent was also effective, although complete recovery of TAG was not obtained. Economic implications of these findings are discussed, with the aim to reduce the complexity of, and energy needed in, TAG extraction.

Sparsity-Inducing Optimization Algorithm for the Extraction of Planar Structures in Noisy Point-Cloud Data

Most of the manual labor needed to create the geometric building information model (BIM) of an existing facility is spent converting raw point cloud data (PCD) to a BIM description. Automating this process would drastically reduce the modeling cost. Surface extraction from PCD is a fundamental step in this process. Compact modeling of redundant points in PCD as a set of planes leads to smaller file size and fast interactive visualization on cheap hardware. Traditional approaches for smooth surface reconstruction do not explicitly model the sparse scene structure or significantly exploit the redundancy. This paper proposes a method based on sparsity-inducing optimization to address the planar surface extraction problem. Through sparse optimization, points in PCD are segmented according to their embedded linear subspaces. Within each segmented part, plane models can be estimated. Experimental results on a typical noisy PCD demonstrate the effectiveness of the algorithm.

Treatment of Phaeodactylum tricornutum cells with papain facilitates lipid extraction

Triacylglycerols (TAGs) from microalgae have the potential to be used for biodiesel, but several technical and economic hurdles have to be overcome. A major challenge is efficient extraction of intracellular TAGs from algae. Here we investigate the use of enzymes to deconstruct algal cell walls/membranes. We describe a rapid and simple assay that can assess the efficacy of different enzyme treatments on TAG-containing algae. By this means crude papain and bromelain were found to be effective in releasing TAGs from the diatom Phaeodactylum tricornutum, most likely because of their cysteine protease activity. Pre-treating algal biomass with crude papain enabled complete extraction of TAGs using heptane/isopropyl alcohol. Heptane as a single solvent was also effective, although complete recovery of TAG was not obtained. Economic implications of these findings are discussed, with the aim to reduce the complexity of, and energy needed in, TAG extraction. © 2012 Elsevier B.V.

ChESS - Quick and Robust Detection of Chess-board Features

Localization of chess-board vertices is a common task in computer vision, underpinning many applications, but relatively little work focusses on designing a specific feature detector that is fast, accurate and robust. In this paper the `Chess-board Extraction by Subtraction and Summation' (ChESS) feature detector, designed to exclusively respond to chess-board vertices, is presented. The method proposed is robust against noise, poor lighting and poor contrast, requires no prior knowledge of the extent of the chess-board pattern, is computationally very efficient, and provides a strength measure of detected features. Such a detector has significant application both in the key field of camera calibration, as well as in Structured Light 3D reconstruction. Evidence is presented showing its robustness, accuracy, and efficiency in comparison to other commonly used detectors both under simulation and in experimental 3D reconstruction of flat plate and cylindrical objects

A neurophysiologically plausible population code model for feature integration explains visual crowding.

An object in the peripheral visual field is more difficult to recognize when surrounded by other objects. This phenomenon is called "crowding". Crowding places a fundamental constraint on human vision that limits performance on numerous tasks. It has been suggested that crowding results from spatial feature integration necessary for object recognition. However, in the absence of convincing models, this theory has remained controversial. Here, we present a quantitative and physiologically plausible model for spatial integration of orientation signals, based on the principles of population coding. Using simulations, we demonstrate that this model coherently accounts for fundamental properties of crowding, including critical spacing, "compulsory averaging", and a foveal-peripheral anisotropy. Moreover, we show that the model predicts increased responses to correlated visual stimuli. Altogether, these results suggest that crowding has little immediate bearing on object recognition but is a by-product of a general, elementary integration mechanism in early vision aimed at improving signal quality.

Analytical field-effect method for extraction of subgap states in thin-film transistors

We present an analytical field-effect method to extract the density of subgap states (subgap DOS) in amorphous semiconductor thin-film transistors (TFTs), using a closed-form relationship between surface potential and gate voltage. By accounting the interface states in the subthreshold characteristics, the subgap DOS is retrieved, leading to a reasonably accurate description of field-effect mobility and its gate voltage dependence. The method proposed here is very useful not only in extracting device performance but also in physically based compact TFT modeling for circuit simulation. © 2012 IEEE.

On the relation of slow feature analysis and Laplacian eigenmaps

The past decade has seen a rise of interest in Laplacian eigenmaps (LEMs) for nonlinear dimensionality reduction. LEMs have been used in spectral clustering, in semisupervised learning, and for providing efficient state representations for reinforcement learning. Here, we show that LEMs are closely related to slow feature analysis (SFA), a biologically inspired, unsupervised learning algorithm originally designed for learning invariant visual representations. We show that SFA can be interpreted as a function approximation of LEMs, where the topological neighborhoods required for LEMs are implicitly defined by the temporal structure of the data. Based on this relation, we propose a generalization of SFA to arbitrary neighborhood relations and demonstrate its applicability for spectral clustering. Finally, we review previous work with the goal of providing a unifying view on SFA and LEMs. © 2011 Massachusetts Institute of Technology.

A theory of slow feature analysis for transformation-based input signals with an application to complex cells

We develop a group-theoretical analysis of slow feature analysis for the case where the input data are generated by applying a set of continuous transformations to static templates. As an application of the theory, we analytically derive nonlinear visual receptive fields and show that their optimal stimuli, as well as the orientation and frequency tuning, are in good agreement with previous simulations of complex cells in primary visual cortex (Berkes and Wiskott, 2005). The theory suggests that side and end stopping can be interpreted as a weak breaking of translation invariance. Direction selectivity is also discussed. © 2011 Massachusetts Institute of Technology.

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models

We propose a probabilistic model to infer supervised latent variables in the Hamming space from observed data. Our model allows simultaneous inference of the number of binary latent variables, and their values. The latent variables preserve neighbourhood structure of the data in a sense that objects in the same semantic concept have similar latent values, and objects in different concepts have dissimilar latent values. We formulate the supervised infinite latent variable problem based on an intuitive principle of pulling objects together if they are of the same type, and pushing them apart if they are not. We then combine this principle with a flexible Indian Buffet Process prior on the latent variables. We show that the inferred supervised latent variables can be directly used to perform a nearest neighbour search for the purpose of retrieval. We introduce a new application of dynamically extending hash codes, and show how to effectively couple the structure of the hash codes with continuously growing structure of the neighbourhood preserving infinite latent feature space.