Incorporation of nitric oxide-releasing crosslinked polyethyleneimine microspheres into vascular grafts

Over the years, many attempts have been made to increase the patency of small- to medium-sized prosthetic vascular grafts. However, none of them has greatly affected long-term rates. Recently, nitric oxide (NO) has been shown to inhibit thrombus formation in such grafts, suggesting that local delivery of NO may help to increase graft patency. This study describes the site-specific delivery of NO by entrapping NO-releasing microspheres in the pores of a vascular graft. NO-releasing polyethyleneimine microspheres (PEIX) were developed using a novel water-in-oil emulsion technique involving chemical crosslinking with a bis-epoxide. The PEIX microspheres were then derivatized with NO forming the [N(O)NO]- moiety of the diazeniumdiolates formerly known as NONOates. These polymeric NO-releasing particles were found to spontaneously release 194 nmol NO/mg with a half-life of over 66 h under physiologic conditions. Fluorescein isothiocyanate-labeled microspheres were then embedded into the pores of a 60-micron nonreinforced Gore-tex vascular graft using a simple evacuation technique and evaluated for microsphere placement and NO release. Scanning electron microscopic analysis showed the microspheres entrapped in the pores of the vascular graft releasing 10 nmol NO/mg with a half-life of 51 h. The microspheres remained entrapped in the graft even after immersion and NO release, as confirmed by fluorescence of the medium. These results suggest that NO-releasing particles can be incorporated into the pores of a vascular graft to deliver therapeutic amounts of NO for the prevention of thrombosis in small-diameter prosthetic grafts.     

Optical Splitting Trees for High-Precision Monocular Imaging

In this article, we consider the design of monocular multiview optical systems that form optical splitting trees, where the optical path topology takes the shape of a tree because of recursive beam splitting. Designing optical splitting trees is challenging when it requires many views with specific spectral properties. We introduce a manual design paradigm for optical splitting trees and a computer-assisted design tool to create efficient splitting-tree cameras. The tool accepts as input a specification for each view and a set of weights describing the user's relative affinity for efficiency, measurement accuracy, and economy. An optimizer then searches for a design that maximizes these weighted priorities. Our tool's output is a splitting-tree design that implements the input specification and an analysis of the efficiency of each root-to-leaf path. Automatically designed trees appear comparable to those designed by hand; we even show some cases where they are superior. With the help of the optimizer, the system demonstrates high dynamic range, focusing, matting, and hybrid imaging implemented on a single, reconfigurable camera containing eight sensors     

Stereo and Specular Reflection

The problem of accurate depth estimation using stereo in the presence of specular reflection is addressed. Specular reflection, a fundamental and ubiquitous reflection mechanism, is viewpoint dependent and can cause large intensity differences at corresponding points, resulting in significant depth errors. We analyze the physics of specular reflection and the geometry of stereopsis which lead to a relationship between stereo vergence, surface roughness, and the likelihood of a correct match. Given a lower bound on surface roughness, an optimal binocular stereo configuration can be determined which maximizes precision in depth estimation despite specular reflection. However, surface roughness is difficult to estimate in unstructured environments. Therefore, trinocular configurations, independent of surface roughness are determined such that at each scene point visible to all sensors, at least one stereo pair can produce correct depth. We have developed a simple algorithm to reconstruct depth from the multiple stereo pairs.     

Corneal Imaging System: Environment from Eyes

This paper provides a comprehensive analysis of exactly what visual information about the world is embedded within a single image of an eye. It turns out that the cornea of an eye and a camera viewing the eye form a catadioptric imaging system. We refer to this as a corneal imaging system. Unlike a typical catadioptric system, a corneal one is flexible in that the reflector (cornea) is not rigidly attached to the camera. Using a geometric model of the cornea based on anatomical studies, its 3D location and orientation can be estimated from a single image of the eye. Once this is done, a wide-angle view of the environment of the person can be obtained from the image. In addition, we can compute the projection of the environment onto the retina with its center aligned with the gaze direction. This foveated retinal image reveals what the person is looking at. We present a detailed analysis of the characteristics of the corneal imaging system including field of view, resolution and locus of viewpoints. When both eyes of a person are captured in an image, we have a stereo corneal imaging system. We analyze the epipolar geometry of this stereo system and show how it can be used to compute 3D structure. The framework we present in this paper for interpreting eye images is passive and non-invasive. It has direct implications for several fields including visual recognition, human-machine interfaces, computer graphics and human affect studies. This research was conducted while the first author was affiliated with Columbia University. A shorter version of this paper appeared in (Nishino and Nayar, 2004).     

A provably secure and efficient two‐party password‐based explicit authenticated key exchange protocol resistance to password guessing attacks

Password‐based two‐party authenticated key exchange (2PAKE) protocol enables two or more entities, who only share a low‐entropy password between them, to authenticate each other and establish a high‐entropy secret session key. Recently, Zheng et al. proposed a password‐based 2PAKE protocol based on bilinear pairings and claimed that their protocol is secure against the known security attacks. However, in this paper, we indicate that the protocol of Zheng et al. is insecure against the off‐line password guessing attack, which is a serious threat to such protocols. Consequently, we show that an attacker who obtained the users' password by applying the off‐line password guessing attack can easily obtain the secret session key. In addition, the protocol of Zheng et al. does not provide the forward secrecy of the session key. As a remedy, we also improve the protocol of Zheng et al. and prove the security of our enhanced protocol in the random oracle model. The simulation result shows that the execution time of our 2PAKE protocol is less compared with other existing protocols. Copyright © 2015 John Wiley & Sons, Ltd.     

Rational Filters for Passive Depth from Defocus

A fundamental problem in depth from defocus is the measurement of relative defocus between images. The performance of previously proposed focus operators are inevitably sensitive to the frequency spectra of local scene textures. As a result, focus operators such as the Laplacian of Gaussian result in poor depth estimates. An alternative is to use large filter banks that densely sample the frequency space. Though this approach can result in better depth accuracy, it sacrifices the computational efficiency that depth from defocus offers over stereo and structure from motion. We propose a class of broadband operators that, when used together, provide invariance to scene texture and produce accurate and dense depth maps. Since the operators are broadband, a small number of them are sufficient for depth estimation of scenes with complex textural properties. In addition, a depth confidence measure is derived that can be computed from the outputs of the operators. This confidence measure permits further refinement of computed depth maps. Experiments are conducted on both synthetic and real scenes to evaluate the performance of the proposed operators. The depth detection gain error is less than 1%, irrespective of texture frequency. Depth accuracy is found to be 0.51.2% of the distance of the object from the imaging optics.     

Evidence for inhibition of monoamine oxidase in vivo in rat brain by 7,8-dichloro-1,2,3,4-tetrahydroisoquinoline

7,8-Dichloro-1,2,3,4-tetrahydroisoquinoline (DCTQ) (50 mg/kg i.p.) antagonized the inactivation of MAO by pargyline as measured by direct enzyme assays in brain homogenates and by accumulation of hypothalamic catecholamines. These findings lend credence to the earlier interpretation that changes in brain concentrations of biogenic amines and their metabolites in DCTQ-treated rats were due at least in part to MAO inhibition.     

A more efficient and secure ID-based remote mutual authentication with key agreement scheme for mobile devices on elliptic curve cryptosystem

Recently, Yang and Chang proposed an identity-based remote login scheme using elliptic curve cryptography for the users of mobile devices. We have analyzed the security aspects of the Yang and Chang's scheme and identified some security flaws. Also two improvements of the Yang and Chang's scheme have been proposed recently, however, it has been found that the schemes have similar security flaws as in the Yang and Chang's scheme. In order to remove the security pitfalls of the Yang and Chang and the subsequent schemes, we proposed an enhanced remote user mutual authentication scheme that uses elliptic curve cryptography and identity-based cryptosystem with three-way challenge-response handshake technique. It supports flawless mutual authentication of participants, agreement of session key and the leaked key revocation capability. In addition, the proposed scheme possesses low power consumption, low computation cost and better security attributes. As a result, the proposed scheme seems to be more practical and suitable for mobile users for secure Internet banking, online shopping, online voting, etc.     

Generalization of the Lambertian model and implications for machine vision

Lambert's model for diffuse reflection is extensively used in computational vision. It is used explicitly by methods such as shape from shading and photometric stereo, and implicitly by methods such as binocular stereo and motion detection. For several real-world objects, the Lambertian model can prove to be a very inaccurate approximation to the diffuse component. While the brightness of a Lambertian surface is independent of viewing direction, the brightness of a rough diffuse surface increases as the viewer approaches the source direction. A comprehensive model is developed that predicts reflectance from rough diffuse surfaces. The model accounts for complex geometric and radiometric phenomena such as masking, shadowing, and interreflections between points on the surface. Experiments have been conducted on real samples, such as, plaster, clay, sand, and cloth. All these surfaces demonstrate significant deviation from Lambertian behavior. The reflectance measurements obtained are in strong agreement with the reflectance predicted by the proposed model. The paper is concluded with a discussion on the implications of these results for machine vision.     

Visual learning and recognition of 3-d objects from appearance

The problem of automatically learning object models for recognition and pose estimation is addressed. In contrast to the traditional approach, the recognition problem is formulated as one of matching appearance rather than shape. The appearance of an object in a two-dimensional image depends on its shape, reflectance properties, pose in the scene, and the illumination conditions. While shape and reflectance are intrinsic properties and constant for a rigid object, pose and illumination vary from scene to scene. A compact representation of object appearance is proposed that is parametrized by pose and illumination. For each object of interest, a large set of images is obtained by automatically varying pose and illumination. This image set is compressed to obtain a low-dimensional subspace, called the eigenspace, in which the object is represented as a manifold. Given an unknown input image, the recognition system projects the image to eigenspace. The object is recognized based on the manifold it lies on. The exact position of the projection on the manifold determines the object's pose in the image.A variety of experiments are conducted using objects with complex appearance characteristics. The performance of the recognition and pose estimation algorithms is studied using over a thousand input images of sample objects. Sensitivity of recognition to the number of eigenspace dimensions and the number of learning samples is analyzed. For the objects used, appearance representation in eigenspaces with less than 20 dimensions produces accurate recognition results with an average pose estimation error of about 1.0 degree. A near real-time recognition system with 20 complex objects in the database has been developed. The paper is concluded with a discussion on various issues related to the proposed learning and recognition methodology.