Phalangeal curvature and positional behavior in extinct sloth lemurs (Primates, Palaeopropithecidae)

Recent paleontological discoveries in Madagascar document the existence of a diverse clade of palaeopropithecids or “sloth lemurs”: Mesopropithecus (three species), Babakotia (one species), Palaeopropithecus (three species), and Archaeoindris (one species). This mini-radiation of now extinct (“subfossil”) lemurs is most closely related to the living indrids (Indri, Propithecus, and Avahi). Whereas the extant indrids are known for their leaping acrobatics, the palaeopropithecids (except perhaps for the poorly known giant Archaeoindris) exhibit numerous skeletal design features for antipronograde or suspensory positional behaviors (e.g., high intermembral indices and mobile joints). Here we analyze the curvature of the proximal phalanges of the hands and feet. Computed as the included angle (θ), phalangeal curvature develops in response to mechanical use and is known to be correlated in primates with hand and foot function in different habitats; terrestrial species have straighter phalanges than their arboreal counterparts, and highly suspensory forms such as the orangutan possess the most curved phalanges. Sloth lemurs as a group are characterized by very curved proximal phalanges, exceeding those seen in spider monkeys and siamangs, and approaching that of orangutans. Indrids have curvatures roughly half that of sloth lemurs, and the more terrestrial, subfossil Archaeolemur possesses the least curved phalanges of all the indroids. Taken together with many other derived aspects of their postcranial anatomy, phalangeal curvature indicates that the sloth lemurs are one of the most suspensory clades of mammals ever to evolve.

Analytical method to measure bending deformations in prismatic optical films

The aim of this work is to provide an analytical method based on experimental measurements in order to obtain the prismatic film deformation for different curvatures of Hollow Cylindrical Prismatic Light Guides (CPLG). To conform cylindrical guides is necessary bend the film to guide the light, changes induced by curving the film give rise to deformation shifts. Light losses affected by deformation has been experimentally evaluated and numerically analyzed. The effect of deformation in prism angle is specially increased for CPLG of curvatures higher than 20 m-1. An experimental method for accurate transmittance measurements related to bending is presented.

Intrasubject repeatability in keratoconus-eye measurements obtained with a new Scheimpflug photography–based system

Purpose: To evaluate in keratoconus eyes the intrasubject repeatability of anterior and posterior corneal curvature and of other anterior segment anatomic measurements obtained with a new topography system combining Scheimpflug-photography and Placido-disk technology. Setting: Vissum Corporation, Alicante, Spain. Design: Evaluation of technology. Methods: All keratoconus eyes had a comprehensive ophthalmologic examination including analysis with the Sirius system. Three consecutive measurements were obtained to assess the intrasubject repeatability of the following parameters: anterior and posterior corneal curvature and shape factor, white-to-white (WTW) corneal diameter, central and minimum corneal thickness, and anterior chamber depth (ACD). The within-subject standard deviation (Sw) and intraclass correlation coefficient (ICC) were calculated. Results: This study comprised 61 eyes of 61 patients ranging in age from 14 to 64 years. For anterior and posterior corneal curvatures and power vector components, the Sw was 0.29 mm or less in all cases. The ICC was above 0.990 in all cases except the flattest curvature of the posterior corneal surface at 3.0 mm, which was 0.840 (moderate agreement), and the posterior power vector J0, which was 0.665 (poor agreement), 0.752, and 0.758 (moderate agreement) for 3.0 mm, 5.0 mm, and 7.0 mm, respectively. In shape factor measurements, the Sw was 0.12 or less in all cases and the ICC ranged between 0.989 and 0.999. Pachymetry, ACD, and WTW had ICC values very close to 1. Conclusion: The new topography system provided repeatable measurements of corneal shape and other anatomic parameters in eyes with keratoconus.

Orbscan Topography in Primary Open-Angle Glaucoma

Purpose: To compare anterior and posterior corneal curvatures between eyes with primary open-angle glaucoma (POAG) and healthy eyes. Methods: This is a prospective, cross-sectional, observer-masked study. A total of 138 white subjects (one eye per patient) were consecutively recruited; 69 eyes had POAG (study group), and the other 69 comprised a group of healthy control eyes matched for age and central corneal pachymetry with the study ones. Exclusion criteria included any corneal or ocular inflammatory disease, previous ocular surgery, or treatment with carbonic anhydrase inhibitors. The same masked observer performed Goldmann applanation tonometry, ultrasound pachymetry, and Orbscan II topography in all cases. Central corneal thickness, intraocular pressure, and anterior and posterior topographic elevation maps were analyzed and compared between both groups. Results: Patients with POAG had greater forward shifting of the posterior corneal surface than that in healthy control eyes (p < 0.01). Significant differences in anterior corneal elevation between controls and POAG eyes were also found (p < 0.01). Conclusions: Primary open-angle glaucoma eyes have a higher elevation of the posterior corneal surface than that in central corneal thickness–matched nonglaucomatous eyes.

Investigating the Role of cAMP-Signaling in the Regulation of Vascular Endothelial Cell Adaptive Responses to Fluid Shear Stress

The circulating blood exerts a force on the vascular endothelium, termed fluid shear stress (FSS), which directly impacts numerous vascular endothelial cell (VEC) functions. For example, high rates of linear and undisturbed (i.e. laminar) blood flow maintains a protective and quiescent VEC phenotype. Meanwhile, deviations in blood flow, which can occur at vascular branchpoints and large curvatures, create areas of low, and/or oscillatory FSS, and promote a pro-inflammatory, pro-thrombotic and hyperpermeable phenotype. Indeed, it is known that these areas are prone to the development of atherosclerotic lesions. Herein, we show that cyclic nucleotide phosphodiesterase (PDE) 4D (PDE4D) activity is increased by FSS in human arterial endothelial cells (HAECs) and that this activation regulates the activity of cAMP-effector protein, Exchange Protein-activated by cAMP-1 (EPAC1), in these cells. Importantly, we also show that these events directly and critically impact HAEC responses to FSS, especially when FSS levels are low. Both morphological events induced by FSS, as measured by changes in cell alignment and elongation in the direction of FSS, and the expression of critical FSS-regulated genes, including Krüppel-like factor 2 (KLF2), endothelial nitric oxide synthase (eNOS) and thrombomodlin (TM), are mediated by EPAC1/PDE4D signaling. At a mechanistic level, we show that EPAC1/PDE4D acts through the vascular endothelial-cadherin (VECAD)/ platelet-cell adhesion molecule-1 (PECAM1)/vascular endothelial growth factor receptor 2 (VEGFR2) mechanosensor to activate downstream signaling though Akt. Given the critical role of PDE4D in mediating these effects, we also investigated the impact of various patterns of FSS on the expression of individual PDE genes in HAECs. Notably, PDE2A was significantly upregulated in response to high, laminar FSS, while PDE3A was upregulated under low, oscillatory FSS conditions only. These data may provide novel therapeutic targets to limit FSS-dependent endothelial cell dysfunction (ECD) and atherosclerotic development.

Data visualization during the early stages of drug discovery

Multidimensional compound optimization is a new paradigm in the drug discovery process, yielding efficiencies during early stages and reducing attrition in the later stages of drug development. The success of this strategy relies heavily on understanding this multidimensional data and extracting useful information from it. This paper demonstrates how principled visualization algorithms can be used to understand and explore a large data set created in the early stages of drug discovery. The experiments presented are performed on a real-world data set comprising biological activity data and some whole-molecular physicochemical properties. Data visualization is a popular way of presenting complex data in a simpler form. We have applied powerful principled visualization methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), to help the domain experts (screening scientists, chemists, biologists, etc.) understand and draw meaningful decisions. We also benchmark these principled methods against relatively better known visualization approaches, principal component analysis (PCA), Sammon's mapping, and self-organizing maps (SOMs), to demonstrate their enhanced power to help the user visualize the large multidimensional data sets one has to deal with during the early stages of the drug discovery process. The results reported clearly show that the GTM and HGTM algorithms allow the user to cluster active compounds for different targets and understand them better than the benchmarks. An interactive software tool supporting these visualization algorithms was provided to the domain experts. The tool facilitates the domain experts by exploration of the projection obtained from the visualization algorithms providing facilities such as parallel coordinate plots, magnification factors, directional curvatures, and integration with industry standard software. © 2006 American Chemical Society.

Changes of corneal biomechanics with keratoconus

PURPOSE: To perform advanced analysis of the corneal deformation response to air pressure in keratoconics compared with age- and sex-matched controls. METHODS: The ocular response analyzer was used to measure the air pressure-corneal deformation relationship of 37 patients with keratoconus and 37 age (mean 36 ± 10 years)- and sex-matched controls with healthy corneas. Four repeat air pressure-corneal deformation profiles were averaged, and 42 separate parameters relating to each element of the profiles were extracted. Corneal topography and pachymetry were performed with the Orbscan II. The severity of the keratoconus was graded based on a single metric derived from anterior corneal curvatures, difference in astigmatism in each meridian, anterior best-fit sphere, and posterior best-fit sphere. RESULTS: Most of the biomechanical characteristics of keratoconic eyes were significantly different from normal eyes (P <0.001), especially during the initial corneal applanation. With increasing keratoconus severity, the cornea was thinner (r = -0.407, P <0.001), the speed of corneal concave deformation past applanation was quicker (dive; r = -0.314, P = 0.01), and the tear film index was lower (r = -0.319, P = 0.01). The variance in keratoconus severity could be accounted for by the corneal curvature and central corneal thickness (r = 0.80) with biomechanical characteristics contributing an additional 4% (total r = 0.84). The area under the receiver operating characteristic curve was 0.919 ± 0.025 for keratometry alone, 0.965 ± 0.014 with the addition of pachymetry, and 0.972 ± 0.012 combined with ocular response analyzer biomechanical parameters. CONCLUSIONS: Characteristics of the air pressure-corneal deformation profile are more affected by keratoconus than the traditionally extracted corneal hysteresis and corneal resistance factors. These biomechanical metrics slightly improved the detection and severity prediction of keratoconus above traditional keratometric and pachymetric assessment of corneal shape.

Hierarchical GTM: Constructing localized non-linear projection manifolds in a principled way

It has been argued that a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets, and therefore a hierarchical visualization system is desirable. In this paper we extend an existing locally linear hierarchical visualization system PhiVis ¸iteBishop98a in several directions: bf(1) We allow for em non-linear projection manifolds. The basic building block is the Generative Topographic Mapping. bf(2) We introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree. General training equations are derived, regardless of the position of the model in the tree. bf(3) Using tools from differential geometry we derive expressions for local directional curvatures of the projection manifold. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. It enables the user to interactively highlight those data in the parent visualization plot which are captured by a child model. We also incorporate into our system a hierarchical, locally selective representation of magnification factors and directional curvatures of the projection manifolds. Such information is important for further refinement of the hierarchical visualization plot, as well as for controlling the amount of regularization imposed on the local models. We demonstrate the principle of the approach on a toy data set and apply our system to two more complex 12- and 19-dimensional data sets.

Hierarchical GTM: constructing localized non-linear projection manifolds in a principled way

It has been argued that a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets, and therefore a hierarchical visualization system is desirable. In this paper we extend an existing locally linear hierarchical visualization system PhiVis ¸iteBishop98a in several directions: bf(1) We allow for em non-linear projection manifolds. The basic building block is the Generative Topographic Mapping (GTM). bf(2) We introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree. General training equations are derived, regardless of the position of the model in the tree. bf(3) Using tools from differential geometry we derive expressions for local directional curvatures of the projection manifold. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. It enables the user to interactively highlight those data in the ancestor visualization plots which are captured by a child model. We also incorporate into our system a hierarchical, locally selective representation of magnification factors and directional curvatures of the projection manifolds. Such information is important for further refinement of the hierarchical visualization plot, as well as for controlling the amount of regularization imposed on the local models. We demonstrate the principle of the approach on a toy data set and apply our system to two more complex 12- and 18-dimensional data sets.

A principled approach to interactive hierarchical non-linear visualization of high-dimensional data

Hierarchical visualization systems are desirable because a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex high-dimensional data sets. We extend an existing locally linear hierarchical visualization system PhiVis [1] in several directions: bf(1) we allow for em non-linear projection manifolds (the basic building block is the Generative Topographic Mapping -- GTM), bf(2) we introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree, bf(3) we describe folding patterns of low-dimensional projection manifold in high-dimensional data space by computing and visualizing the manifold's local directional curvatures. Quantities such as magnification factors [3] and directional curvatures are helpful for understanding the layout of the nonlinear projection manifold in the data space and for further refinement of the hierarchical visualization plot. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. We demonstrate the visualization system principle of the approach on a complex 12-dimensional data set and mention possible applications in the pharmaceutical industry.

miniDVMS v1.8 : A user manual(The data visualization and modeling system)

Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. miniDVMS v1.8 provides a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualisation domain. The advantage of this interface is that the user is directly involved in the data mining process. Principled projection methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), are integrated with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, and user interaction facilities, to provide this integrated visual data mining framework. The software also supports conventional visualisation techniques such as principal component analysis (PCA), Neuroscale, and PhiVis. This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install and use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.

Bending and orientational characteristics of long period gratings written in D-shaped optical fiber

Long period gratings (LPGs) were written into a D-shaped single-mode fiber. These LPGs were subjected to a range of curvatures, and it was found that as curvature increased, there was increasingly strong coupling to certain higher order cladding modes without the usual splitting of the LPGs stopbands. A bend-induced stopband yielded a spectral sensitivity of 12.55 nm·m for curvature and 2.2×10-2 nm°C-1 for temperature. It was also found that the wavelength separation between adjacent bend-induced stopbands varied linearly as a function of curvature. Blue and red wavelength shifts of the stopbands were observed as the sensor was rotated around a fixed axis for a given curvature; thus, in principle, this sensor could be used to obtain bending and orientational information. The behavior of the stopbands was successfully modeled using a finite element approach.

Constructing localized non-linear projection manifolds in a principled way:hierarchical generative topographic mapping

It has been argued that a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets, and therefore a hierarchical visualization system is desirable. In this paper we extend an existing locally linear hierarchical visualization system PhiVis (Bishop98a) in several directions: 1. We allow for em non-linear projection manifolds. The basic building block is the Generative Topographic Mapping. 2. We introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree. General training equations are derived, regardless of the position of the model in the tree. 3. Using tools from differential geometry we derive expressions for local directionalcurvatures of the projection manifold. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. It enables the user to interactively highlight those data in the parent visualization plot which are captured by a child model.We also incorporate into our system a hierarchical, locally selective representation of magnification factors and directional curvatures of the projection manifolds. Such information is important for further refinement of the hierarchical visualization plot, as well as for controlling the amount of regularization imposed on the local models. We demonstrate the principle of the approach on a toy data set andapply our system to two more complex 12- and 19-dimensional data sets.

Fabrication and applications of fibre Bragg gratings

The consequences of fabricating Bragg gratings in various fibres, with or without hydrogen loading, and with varying laser power levels are explored. Three new techniques for fabricating chirped gratings are presented. Beams with dissimilar wavefront curvatures are interfered to give chirped gratings. With the same aim techniques of writing gratings on tapered fibres and on deformed fibres are also covered. With these techniques, a wide variety of gratings has been fabricated from the 'superbroad' (with bandwidths of up to 180 nm), small to medium bandwidth gratings with linear chirp profiles and quadratic chirped gratings. It is demonstrated that chirped grating can be concatenated to form all-fibre Fabry-Perot and Moiré resonators. These are further concatenated with chirped gratings to produce filters with narrow passbands and very broad stopbands. A number of other applications are also addressed. The use of chirped fibre gratings for dispersion compensation and femtosecond chirped pulse amplification is demonstrated. Chirped gratings are used as dispersive elements in modelocked fibre lasers producing ultrashort pulses. A chirped fibre grating Fabry-Perot transmission filter is used in a continuous wave laser that exhibits eleven simultaneously lasing wavelengths. Finally, the use of grating-coupler devices as variable reflectivity mirrors for laser optimisation and gain clamping is considered.

Visual data mining: integrating machine learning with information visualization

Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. Most existing systems concentrate either on mining algorithms or on visualization techniques. Though visual methods developed in information visualization have been helpful, for improved understanding of a complex large high-dimensional dataset, there is a need for an effective projection of such a dataset onto a lower-dimension (2D or 3D) manifold. This paper introduces a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualization domain. The framework follows Shneiderman’s mantra to provide an effective user interface. The advantage of such an interface is that the user is directly involved in the data mining process. We integrate principled projection methods, such as Generative Topographic Mapping (GTM) and Hierarchical GTM (HGTM), with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, billboarding, and user interaction facilities, to provide an integrated visual data mining framework. Results on a real life high-dimensional dataset from the chemoinformatics domain are also reported and discussed. Projection results of GTM are analytically compared with the projection results from other traditional projection methods, and it is also shown that the HGTM algorithm provides additional value for large datasets. The computational complexity of these algorithms is discussed to demonstrate their suitability for the visual data mining framework.