IQ-motif selectivity in human IQGAP2 and IQGAP3: binding of calmodulin and myosin essential light chain

The IQGAP [IQ-motif-containing GAP (GTPase-activating protein)] family members are eukaryotic proteins that act at the interface between cellular signalling and the cytoskeleton. As such they collect numerous inputs from a variety of signalling pathways. A key binding partner is the calcium-sensing protein CaM (calmodulin). This protein binds mainly through a series of IQ-motifs which are located towards the middle of the primary sequence of the IQGAPs. In some IQGAPs, these motifs also provide binding sites for CaM-like proteins such as myosin essential light chain and S100B. Using synthetic peptides and native gel electrophoresis, the binding properties of the IQ-motifs from human IQGAP2 and IQGAP3 have been mapped. The second and third IQ-motifs in IQGAP2 and all four of the IQ-motifs of IQGAP3 interacted with CaM in the presence of calcium ions. However, there were differences in the type of interaction: while some IQ-motifs were able to form complexes with CaM which were stable under the conditions of the experiment, others formed more transient interactions. The first IQ-motifs from IQGAP2 and IQGAP3 formed transient interactions with CaM in the absence of calcium and the first motif from IQGAP3 formed a transient interaction with the myosin essential light chain MIc1sa. None of these IQ-motifs interacted with S100B. Molecular modelling suggested that all of the IQ-motifs, except the first one from IQGAP2 formed alpha-helices in solution. These results extend our knowledge of the selectivity of IQ-motifs for CaM and related proteins.

Changing Climate, Human Evolution, and the Revival of Environmental Determinism

The role of climatic change in determining the shape of human evolution,
a theme that came to prominence during the early years of the twentieth
century, has resurfaced with renewed vigor. The author examines the rise and
resurgence of the modern history of the idea that hominid evolutionary pathways
have been triggered by climatic causes to illustrate the continuing vitality of environmental determinism and to highlight some continuities between early-twentieth century and contemporary archaeoanthropology.

Rao-blackwellized particle filter for human appearance and position tracking

In human motion analysis, the joint estimation of appearance, body pose and location parameters is not always tractable due to its huge computational cost. In this paper, we propose a Rao-Blackwellized Particle Filter for addressing the problem of human pose estimation and tracking. The advantage of the proposed approach is that Rao-Blackwellization allows the state variables to be splitted into two sets, being one of them analytically calculated from the posterior probability of the remaining ones. This procedure reduces the dimensionality of the Particle Filter, thus requiring fewer particles to achieve a similar tracking performance. In this manner, location and size over the image are obtained stochastically using colour and motion clues, whereas body pose is solved analytically applying learned human Point Distribution Models.

Tracking Human Position and Lower Body Parts Using Kalman and Particle Filters Constrained by Human Biomechanics

In this paper, a novel framework for visual tracking of human body parts is introduced. The approach presented demonstrates the feasibility of recovering human poses with data from a single uncalibrated camera by using a limb-tracking system based on a 2-D articulated model and a double-tracking strategy. Its key contribution is that the 2-D model is only constrained by biomechanical knowledge about human bipedal motion, instead of relying on constraints that are linked to a specific activity or camera view. These characteristics make our approach suitable for real visual surveillance applications. Experiments on a set of indoor and outdoor sequences demonstrate the effectiveness of our method on tracking human lower body parts. Moreover, a detail comparison with current tracking methods is presented.

2D silhouette and 3D skeletal models for human detection and tracking

In this paper we propose a statistical model for detection and tracking of human silhouette and the corresponding 3D skeletal structure in gait sequences. We follow a point distribution model (PDM) approach using a Principal Component Analysis (PCA). The problem of non-lineal PCA is partially resolved by applying a different PDM depending of pose estimation; frontal, lateral and diagonal, estimated by Fisher's linear discriminant. Additionally, the fitting is carried out by selecting the closest allowable shape from the training set by means of a nearest neighbor classifier. To improve the performance of the model we develop a human gait analysis to take into account temporal dynamic to track the human body. The incorporation of temporal constraints on the model increase reliability and robustness.