Speed invariant gait recognition-The enhanced mutual subspace method.

This paper introduces an enhanced MSM (Mutual Subspace Method) methodology for gait recognition, to provide robustness to variations in walking speed. The enhanced MSM (eMSM) methodology expands and adapts the MSM, commonly used for face recognition, which is a static/physiological biometric, to gait recognition, which is a dynamic/behavioral biometrics. To address the loss of accuracy during calculation of the covariance matrix in the PCA step of MSM, we use a 2D PCA-based mutual subspace. Furhtermore, to enhance the discrimination capability, we rotate images over a number of angles, which enables us to extract richer gait features to then be fused by a boosting method. The eMSM methodology is evaluated on existing data sets which provide variable walking speed, i.e. CASIA-C and OU-ISIR gait databases, and it is shown to outperform state-of-the art methods. While the enhancement to MSM discussed in this paper uses combinations of 2D-PCA, rotation, boosting, other combinations of operations may also be advantageous.

Safety and efficacy of an anti-claudin-5 monoclonal antibody to increase blood-brain barrier permeability for drug delivery to the brain in a non-human primate.

Claudin-5 (CLDN-5) is an essential component of the tight junction seal in the blood-brain barrier. Previously, we showed that CLDN-5 modulation in vitro via an anti-CLDN-5 monoclonal antibody (mAb) may be useful for increasing the permeability of the blood-brain barrier for drug delivery to the brain. Based on these findings, here we examined the safety and efficacy of the anti-CLDN-5 mAb in a non-human primate. Cynomolgus monkeys were intravenously administered the anti-CLDN-5 mAb followed by fluorescein dye (376 Da), and the concentrations of the dye in the cerebrospinal fluid was examined. When the mAb was administered at 3.0 mg/kg, the concentration of dye in the cerebrospinal fluid was increased, and no behavioral changes or changes in plasma biomarkers for inflammation or liver or kidney injury were observed. However, a monkey that received the mAb at 6 mg/kg experienced convulsions, and subsequent histopathological examination of this animal revealed vasodilation in the liver, lung, and kidney; hemorrhage in the lung; and edema in the brain. Together, our data indicate that CLDN-5 might be a potential target for enhancing drug delivery to the brain, but also that the therapeutic window of the anti-CLDN-5 mAb may be narrow for separating efficacy and toxicity.

Tight Junction Modulating Bioprobes for Drug Delivery System to the Brain: A Review.

The blood-brain barrier (BBB), which is composed of endothelial cells, pericytes, astrocytes, and neurons, separates the brain extracellular fluid from the circulating blood, and maintains the homeostasis of the central nervous system (CNS). The BBB endothelial cells have well-developed tight junctions (TJs) and express specific polarized transport systems to tightly control the paracellular movements of solutes, ions, and water. There are two types of TJs: bicellular TJs (bTJs), which is a structure at the contact of two cells, and tricellular TJs (tTJs), which is a structure at the contact of three cells. Claudin-5 and angulin-1 are important components of bTJs and tTJs in the brain, respectively. Here, we review TJ-modulating bioprobes that enable drug delivery to the brain across the BBB, focusing on claudin-5 and angulin-1.

Gait-based person identification robust to changes in appearance.

The identification of a person from gait images is generally sensitive to appearance changes, such as variations of clothes and belongings. One possibility to deal with this problem is to collect possible subjects' appearance changes in a database. However, it is almost impossible to predict all appearance changes in advance. In this paper, we propose a novel method, which allows robustly identifying people in spite of changes in appearance, without using a database of predicted appearance changes. In the proposed method, firstly, the human body image is divided into multiple areas, and features for each area are extracted. Next, a matching weight for each area is estimated based on the similarity between the extracted features and those in the database for standard clothes. Finally, the subject is identified by weighted integration of similarities in all areas. Experiments using the gait database CASIA show the best correct classification rate compared with conventional methods experiments.

Real-time nonlinear FEM with neural network for simulating soft organ model deformation.

This paper presents a new method for simulating the deformation of organ models by using a neural network. The proposed method is based on the idea proposed by Chen et al. that a deformed model can be estimated from the superposition of basic deformation modes. The neural network finds a relationship between external forces and the models deformed by the forces. The experimental results show that the trained network can achieve a real-time simulation while keeping the acceptable accuracy compared with the nonlinear FEM computation.